From 8a5ec3d824799d28c3a5fbd8ed6f5a04fb355de6 Mon Sep 17 00:00:00 2001 From: Hamza Tahir Date: Mon, 22 Apr 2024 18:08:20 +0200 Subject: [PATCH 001/121] Addded CV project --- end-to-end-computer-vision/.dockerignore | 2 + end-to-end-computer-vision/.gitignore | 3 + end-to-end-computer-vision/ls_export.py | 67 +++++++++++++++ .../pipelines/__init__.py | 20 +++++ .../pipelines/training.py | 82 +++++++++++++++++++ end-to-end-computer-vision/requirements.txt | 11 +++ end-to-end-computer-vision/run.py | 57 +++++++++++++ 7 files changed, 242 insertions(+) create mode 100644 end-to-end-computer-vision/.dockerignore create mode 100644 end-to-end-computer-vision/.gitignore create mode 100644 end-to-end-computer-vision/ls_export.py create mode 100644 end-to-end-computer-vision/pipelines/__init__.py create mode 100644 end-to-end-computer-vision/pipelines/training.py create mode 100644 end-to-end-computer-vision/requirements.txt create mode 100644 end-to-end-computer-vision/run.py diff --git a/end-to-end-computer-vision/.dockerignore b/end-to-end-computer-vision/.dockerignore new file mode 100644 index 00000000..455f4d7a --- /dev/null +++ b/end-to-end-computer-vision/.dockerignore @@ -0,0 +1,2 @@ +.venv* +.requirements* \ No newline at end of file diff --git a/end-to-end-computer-vision/.gitignore b/end-to-end-computer-vision/.gitignore new file mode 100644 index 00000000..181d4d7a --- /dev/null +++ b/end-to-end-computer-vision/.gitignore @@ -0,0 +1,3 @@ +*.pt +!data/.gitkeep +data/ \ No newline at end of file diff --git a/end-to-end-computer-vision/ls_export.py b/end-to-end-computer-vision/ls_export.py new file mode 100644 index 00000000..d44c17ec --- /dev/null +++ b/end-to-end-computer-vision/ls_export.py @@ -0,0 +1,67 @@ +# Apache Software License 2.0 +# +# Copyright (c) ZenML GmbH 2024. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# + +import os +import time +from label_studio_sdk import Client +from label_studio_converter import Converter + +LABEL_STUDIO_URL = os.getenv('LABEL_STUDIO_URL', default='http://localhost:8080') +# API_KEY = "xxxxx" +PROJECT_ID = int("1") +VIEW_ID = False # or:int("18") + +# connect to Label Studio +ls = Client(url=LABEL_STUDIO_URL, api_key="78afe31bd58c157723501a0351e3716e9c52a714") +ls.check_connection() + +# get existing project +project = ls.get_project(PROJECT_ID) + +# get the first tab +views = project.get_views() + + +project.export_tasks(export_type="YOLO", export_location="/tmp/whatever.zip", download_resources=True) + + +breakpoint() + +for view in views: + if VIEW_ID and VIEW_ID != view['id']: + continue + + task_filter_options = {'view': view['id']} if views else {} + view_name = view["data"]["title"] + + # create new export snapshot + export_result = project.export_snapshot_create( + title='Export SDK Snapshot', task_filter_options=task_filter_options + ) + assert 'id' in export_result + export_id = export_result['id'] + + # wait until snapshot is ready + while project.export_snapshot_status(export_id).is_in_progress(): + time.sleep(1.0) + + # download snapshot file + status, file_name = project.export_snapshot_download(export_id) + assert status == 200 + assert file_name is not None + os.rename(file_name, view_name + ".json") + print(f"Status of the export is {status}.\nFile name is {view_name}.json") \ No newline at end of file diff --git a/end-to-end-computer-vision/pipelines/__init__.py b/end-to-end-computer-vision/pipelines/__init__.py new file mode 100644 index 00000000..6d8015e2 --- /dev/null +++ b/end-to-end-computer-vision/pipelines/__init__.py @@ -0,0 +1,20 @@ +# Apache Software License 2.0 +# +# Copyright (c) ZenML GmbH 2024. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# + +from .feature_engineering import feature_engineering +from .inference import inference +from .training import training diff --git a/end-to-end-computer-vision/pipelines/training.py b/end-to-end-computer-vision/pipelines/training.py new file mode 100644 index 00000000..be95df32 --- /dev/null +++ b/end-to-end-computer-vision/pipelines/training.py @@ -0,0 +1,82 @@ +# Apache Software License 2.0 +# +# Copyright (c) ZenML GmbH 2024. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# + +from typing import Optional +from uuid import UUID + +from steps import model_evaluator, model_promoter, model_trainer + +from pipelines import ( + feature_engineering, +) +from zenml import pipeline +from zenml.client import Client +from zenml.logger import get_logger + +logger = get_logger(__name__) + + +@pipeline +def training( + train_dataset_id: Optional[UUID] = None, + test_dataset_id: Optional[UUID] = None, + target: Optional[str] = "target", + model_type: Optional[str] = "sgd", + random_state: int = None, +): + """ + Model training pipeline. + + This is a pipeline that loads the data from a preprocessing pipeline, + trains a model on it and evaluates the model. If it is the first model + to be trained, it will be promoted to production. If not, it will be + promoted only if it has a higher accuracy than the current production + model version. + + Args: + train_dataset_id: ID of the train dataset produced by feature engineering. + test_dataset_id: ID of the test dataset produced by feature engineering. + target: Name of target column in dataset. + model_type: The type of model to train. + """ + # Link all the steps together by calling them and passing the output + # of one step as the input of the next step. + + # Execute Feature Engineering Pipeline + if train_dataset_id is None or test_dataset_id is None: + dataset_trn, dataset_tst = feature_engineering(random_state=random_state) + else: + client = Client() + dataset_trn = client.get_artifact_version( + name_id_or_prefix=train_dataset_id + ) + dataset_tst = client.get_artifact_version( + name_id_or_prefix=test_dataset_id + ) + + model = model_trainer( + dataset_trn=dataset_trn, target=target, model_type=model_type + ) + + acc = model_evaluator( + model=model, + dataset_trn=dataset_trn, + dataset_tst=dataset_tst, + target=target, + ) + + model_promoter(accuracy=acc) diff --git a/end-to-end-computer-vision/requirements.txt b/end-to-end-computer-vision/requirements.txt new file mode 100644 index 00000000..c3fd2c34 --- /dev/null +++ b/end-to-end-computer-vision/requirements.txt @@ -0,0 +1,11 @@ +zenml[server]>=0.55.2 +notebook +scikit-learn<1.3 +s3fs>2022.3.0,<=2023.4.0 +boto3<=1.26.76 +aws-profile-manager +sagemaker==2.117.0 +pyarrow +wandb +seaborn +xgboost \ No newline at end of file diff --git a/end-to-end-computer-vision/run.py b/end-to-end-computer-vision/run.py new file mode 100644 index 00000000..195e43f2 --- /dev/null +++ b/end-to-end-computer-vision/run.py @@ -0,0 +1,57 @@ +# Apache Software License 2.0 +# +# Copyright (c) ZenML GmbH 2024. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# + +from zenml import step, pipeline, Model, ArtifactConfig +from ultralytics import YOLO +from typing_extensions import Annotated +from zenml.logger import get_logger +from typing import Tuple + +logger = get_logger(__name__) + + +@step +def load_model(model_checkpoint: str = "yolov8l.pt") -> Annotated[YOLO, ArtifactConfig(name="Raw_YOLO", is_model_artifact=True)]: + return YOLO(model_checkpoint) + + +@step +def train_model( + model: YOLO, + dataset_name: str = "quickstart", +) -> Tuple[ + Annotated[YOLO, ArtifactConfig(name="Trained_YOLO", is_model_artifact=True)], + Annotated[dict, "metrics"], + Annotated[dict, "result"], +]: + + + model.train(data='data.yaml', epochs=1) + + # model.train(data="coco8.yaml", epochs=3) # train the model + metrics = model.val() # evaluate model performance on the validation set + results = model("https://ultralytics.com/images/bus.jpg") # predict on an image + return model, metrics, results + + +@pipeline(enable_cache=False, model=Model(name="Yolo_Object_Detection")) +def my_pipeline(): + model = load_model() + train_model(model=model) + +if __name__ == "__main__": + my_pipeline() From 95c2dc8aa02b508d88c7a2a824dc710337af22af Mon Sep 17 00:00:00 2001 From: Hamza Tahir Date: Mon, 22 Apr 2024 18:43:27 +0200 Subject: [PATCH 002/121] Added all the logic for splitting --- end-to-end-computer-vision/run.py | 44 +++++++++++-- .../utils/split_data.py | 61 +++++++++++++++++++ 2 files changed, 100 insertions(+), 5 deletions(-) create mode 100644 end-to-end-computer-vision/utils/split_data.py diff --git a/end-to-end-computer-vision/run.py b/end-to-end-computer-vision/run.py index 195e43f2..db1069fa 100644 --- a/end-to-end-computer-vision/run.py +++ b/end-to-end-computer-vision/run.py @@ -14,33 +14,67 @@ # See the License for the specific language governing permissions and # limitations under the License. # - +import os +import tempfile from zenml import step, pipeline, Model, ArtifactConfig +from zenml.client import Client from ultralytics import YOLO from typing_extensions import Annotated from zenml.logger import get_logger from typing import Tuple +from utils.split_data import unzip_dataset, split_dataset, generate_yaml logger = get_logger(__name__) +def load_and_split_data(dataset_name: str) -> str: + + annotator = Client().active_stack.annotator + from zenml.integrations.label_studio.annotators.label_studio_annotator import ( + LabelStudioAnnotator, + ) + + if not isinstance(annotator, LabelStudioAnnotator): + raise TypeError( + "This step can only be used with the Label Studio annotator." + ) + + if annotator and annotator._connection_available(): + for dataset in annotator.get_datasets(): + if dataset.get_params()["title"] == dataset_name: + with tempfile.TemporaryDirectory() as tmpdirname: + export_location = os.path.join(tmpdirname, "data.zip") + extract_location = os.path.join(tmpdirname, "data") + dataset.export_tasks( + export_type="YOLO", + export_location=export_location, + download_resources=True, + ) + unzip_dataset(export_location, extract_location) + split_dataset(extract_location, ratio=(0.7, 0.15, 0.15), seed=42) + return generate_yaml(extract_location) + + raise ValueError(f"Dataset {dataset_name} not found in Label Studio.") + @step def load_model(model_checkpoint: str = "yolov8l.pt") -> Annotated[YOLO, ArtifactConfig(name="Raw_YOLO", is_model_artifact=True)]: + logger.info(f"Loading YOLO checkpoint {model_checkpoint}") return YOLO(model_checkpoint) @step def train_model( model: YOLO, - dataset_name: str = "quickstart", + dataset_name: str = "zenml_test_project", ) -> Tuple[ Annotated[YOLO, ArtifactConfig(name="Trained_YOLO", is_model_artifact=True)], Annotated[dict, "metrics"], Annotated[dict, "result"], ]: - - - model.train(data='data.yaml', epochs=1) + + data_path = load_and_split_data(dataset_name) + + model.train(data=data_path, epochs=1) # model.train(data="coco8.yaml", epochs=3) # train the model metrics = model.val() # evaluate model performance on the validation set diff --git a/end-to-end-computer-vision/utils/split_data.py b/end-to-end-computer-vision/utils/split_data.py new file mode 100644 index 00000000..3e9eb85b --- /dev/null +++ b/end-to-end-computer-vision/utils/split_data.py @@ -0,0 +1,61 @@ +import os +import random +import shutil +import zipfile +import yaml + +def unzip_dataset(zip_path: str, extract_dir: str): + with zipfile.ZipFile(zip_path, 'r') as zip_ref: + zip_ref.extractall(extract_dir) + +def split_dataset(data_path: str, ratio: tuple = (0.7, 0.2, 0.1), seed: int = None): + # Ensure the ratio is correct + assert sum(ratio) == 1.0 + + # Ensure the paths exist + for subdir in ['images', 'labels']: + for split in ['train', 'test', 'val']: + os.makedirs(os.path.join(data_path, subdir, split), exist_ok=True) + + # Get a list of all files + files = next(os.walk(os.path.join(data_path, 'images')))[2] + + # Seed to get consistent results + if seed is not None: + random.seed(seed) + + # Shuffle the list of files + random.shuffle(files) + + # Calculate the indices to split at + split_indices = [0] + [round(x * len(files)) for x in ratio] + [None] + + for split, start, end in zip(['train', 'test', 'val'], split_indices[:-1], split_indices[1:]): + for file in files[start:end]: + for subdir in ['images', 'labels']: + # Move each file to the proper split + shutil.move(os.path.join(data_path, subdir, file), + os.path.join(data_path, subdir, split, file)) + + +def generate_yaml(data_path: str, yaml_path: str = None): + if yaml_path is None: + yaml_path = data_path + # Read classes from classes.txt + with open(os.path.join(data_path, 'classes.txt')) as f: + classes = [line.strip() for line in f] + + # Define yaml data + data = { + 'path': os.path.abspath(data_path), + 'train': 'images/train', + 'val': 'images/val', + 'test': 'images/test', + 'names': {i: name for i, name in enumerate(classes)} + } + + # Write to yaml file + yaml_path = os.path.join(data_path, 'data.yaml') + with open(yaml_path, 'w') as f: + yaml.dump(data, f) + return yaml_path From c52b8eb0e99eede47d5d50970ed35b1a7ccae8a5 Mon Sep 17 00:00:00 2001 From: Hamza Tahir Date: Mon, 22 Apr 2024 18:47:30 +0200 Subject: [PATCH 003/121] Added more logic and formatted --- end-to-end-computer-vision/ls_export.py | 30 ++++++++----- .../pipelines/training.py | 4 +- end-to-end-computer-vision/run.py | 28 ++++++++---- .../utils/split_data.py | 44 +++++++++++-------- 4 files changed, 67 insertions(+), 39 deletions(-) diff --git a/end-to-end-computer-vision/ls_export.py b/end-to-end-computer-vision/ls_export.py index d44c17ec..50e58f17 100644 --- a/end-to-end-computer-vision/ls_export.py +++ b/end-to-end-computer-vision/ls_export.py @@ -20,13 +20,17 @@ from label_studio_sdk import Client from label_studio_converter import Converter -LABEL_STUDIO_URL = os.getenv('LABEL_STUDIO_URL', default='http://localhost:8080') +LABEL_STUDIO_URL = os.getenv( + "LABEL_STUDIO_URL", default="http://localhost:8080" +) # API_KEY = "xxxxx" PROJECT_ID = int("1") -VIEW_ID = False # or:int("18") +VIEW_ID = False # or:int("18") # connect to Label Studio -ls = Client(url=LABEL_STUDIO_URL, api_key="78afe31bd58c157723501a0351e3716e9c52a714") +ls = Client( + url=LABEL_STUDIO_URL, api_key="78afe31bd58c157723501a0351e3716e9c52a714" +) ls.check_connection() # get existing project @@ -36,24 +40,28 @@ views = project.get_views() -project.export_tasks(export_type="YOLO", export_location="/tmp/whatever.zip", download_resources=True) +project.export_tasks( + export_type="YOLO", + export_location="/tmp/whatever.zip", + download_resources=True, +) breakpoint() for view in views: - if VIEW_ID and VIEW_ID != view['id']: + if VIEW_ID and VIEW_ID != view["id"]: continue - - task_filter_options = {'view': view['id']} if views else {} + + task_filter_options = {"view": view["id"]} if views else {} view_name = view["data"]["title"] # create new export snapshot export_result = project.export_snapshot_create( - title='Export SDK Snapshot', task_filter_options=task_filter_options + title="Export SDK Snapshot", task_filter_options=task_filter_options ) - assert 'id' in export_result - export_id = export_result['id'] + assert "id" in export_result + export_id = export_result["id"] # wait until snapshot is ready while project.export_snapshot_status(export_id).is_in_progress(): @@ -64,4 +72,4 @@ assert status == 200 assert file_name is not None os.rename(file_name, view_name + ".json") - print(f"Status of the export is {status}.\nFile name is {view_name}.json") \ No newline at end of file + print(f"Status of the export is {status}.\nFile name is {view_name}.json") diff --git a/end-to-end-computer-vision/pipelines/training.py b/end-to-end-computer-vision/pipelines/training.py index be95df32..1af79755 100644 --- a/end-to-end-computer-vision/pipelines/training.py +++ b/end-to-end-computer-vision/pipelines/training.py @@ -58,7 +58,9 @@ def training( # Execute Feature Engineering Pipeline if train_dataset_id is None or test_dataset_id is None: - dataset_trn, dataset_tst = feature_engineering(random_state=random_state) + dataset_trn, dataset_tst = feature_engineering( + random_state=random_state + ) else: client = Client() dataset_trn = client.get_artifact_version( diff --git a/end-to-end-computer-vision/run.py b/end-to-end-computer-vision/run.py index db1069fa..96616eea 100644 --- a/end-to-end-computer-vision/run.py +++ b/end-to-end-computer-vision/run.py @@ -14,7 +14,7 @@ # See the License for the specific language governing permissions and # limitations under the License. # -import os +import os import tempfile from zenml import step, pipeline, Model, ArtifactConfig from zenml.client import Client @@ -28,7 +28,7 @@ def load_and_split_data(dataset_name: str) -> str: - + annotator = Client().active_stack.annotator from zenml.integrations.label_studio.annotators.label_studio_annotator import ( LabelStudioAnnotator, @@ -51,15 +51,20 @@ def load_and_split_data(dataset_name: str) -> str: download_resources=True, ) unzip_dataset(export_location, extract_location) - split_dataset(extract_location, ratio=(0.7, 0.15, 0.15), seed=42) + split_dataset( + extract_location, ratio=(0.7, 0.15, 0.15), seed=42 + ) return generate_yaml(extract_location) raise ValueError(f"Dataset {dataset_name} not found in Label Studio.") + @step -def load_model(model_checkpoint: str = "yolov8l.pt") -> Annotated[YOLO, ArtifactConfig(name="Raw_YOLO", is_model_artifact=True)]: +def load_model( + model_checkpoint: str = "yolov8l.pt", +) -> Annotated[YOLO, ArtifactConfig(name="Raw_YOLO", is_model_artifact=True)]: logger.info(f"Loading YOLO checkpoint {model_checkpoint}") - return YOLO(model_checkpoint) + return YOLO() @step @@ -67,18 +72,22 @@ def train_model( model: YOLO, dataset_name: str = "zenml_test_project", ) -> Tuple[ - Annotated[YOLO, ArtifactConfig(name="Trained_YOLO", is_model_artifact=True)], + Annotated[ + YOLO, ArtifactConfig(name="Trained_YOLO", is_model_artifact=True) + ], Annotated[dict, "metrics"], Annotated[dict, "result"], ]: - + data_path = load_and_split_data(dataset_name) - + model.train(data=data_path, epochs=1) # model.train(data="coco8.yaml", epochs=3) # train the model metrics = model.val() # evaluate model performance on the validation set - results = model("https://ultralytics.com/images/bus.jpg") # predict on an image + results = model( + "https://ultralytics.com/images/bus.jpg" + ) # predict on an image return model, metrics, results @@ -87,5 +96,6 @@ def my_pipeline(): model = load_model() train_model(model=model) + if __name__ == "__main__": my_pipeline() diff --git a/end-to-end-computer-vision/utils/split_data.py b/end-to-end-computer-vision/utils/split_data.py index 3e9eb85b..e4bb935c 100644 --- a/end-to-end-computer-vision/utils/split_data.py +++ b/end-to-end-computer-vision/utils/split_data.py @@ -4,21 +4,25 @@ import zipfile import yaml + def unzip_dataset(zip_path: str, extract_dir: str): - with zipfile.ZipFile(zip_path, 'r') as zip_ref: + with zipfile.ZipFile(zip_path, "r") as zip_ref: zip_ref.extractall(extract_dir) -def split_dataset(data_path: str, ratio: tuple = (0.7, 0.2, 0.1), seed: int = None): + +def split_dataset( + data_path: str, ratio: tuple = (0.7, 0.2, 0.1), seed: int = None +): # Ensure the ratio is correct assert sum(ratio) == 1.0 # Ensure the paths exist - for subdir in ['images', 'labels']: - for split in ['train', 'test', 'val']: + for subdir in ["images", "labels"]: + for split in ["train", "test", "val"]: os.makedirs(os.path.join(data_path, subdir, split), exist_ok=True) # Get a list of all files - files = next(os.walk(os.path.join(data_path, 'images')))[2] + files = next(os.walk(os.path.join(data_path, "images")))[2] # Seed to get consistent results if seed is not None: @@ -30,32 +34,36 @@ def split_dataset(data_path: str, ratio: tuple = (0.7, 0.2, 0.1), seed: int = No # Calculate the indices to split at split_indices = [0] + [round(x * len(files)) for x in ratio] + [None] - for split, start, end in zip(['train', 'test', 'val'], split_indices[:-1], split_indices[1:]): + for split, start, end in zip( + ["train", "test", "val"], split_indices[:-1], split_indices[1:] + ): for file in files[start:end]: - for subdir in ['images', 'labels']: + for subdir in ["images", "labels"]: # Move each file to the proper split - shutil.move(os.path.join(data_path, subdir, file), - os.path.join(data_path, subdir, split, file)) + shutil.move( + os.path.join(data_path, subdir, file), + os.path.join(data_path, subdir, split, file), + ) def generate_yaml(data_path: str, yaml_path: str = None): if yaml_path is None: yaml_path = data_path # Read classes from classes.txt - with open(os.path.join(data_path, 'classes.txt')) as f: + with open(os.path.join(data_path, "classes.txt")) as f: classes = [line.strip() for line in f] # Define yaml data data = { - 'path': os.path.abspath(data_path), - 'train': 'images/train', - 'val': 'images/val', - 'test': 'images/test', - 'names': {i: name for i, name in enumerate(classes)} + "path": os.path.abspath(data_path), + "train": "images/train", + "val": "images/val", + "test": "images/test", + "names": {i: name for i, name in enumerate(classes)}, } # Write to yaml file - yaml_path = os.path.join(data_path, 'data.yaml') - with open(yaml_path, 'w') as f: + yaml_path = os.path.join(data_path, "data.yaml") + with open(yaml_path, "w") as f: yaml.dump(data, f) - return yaml_path + return yaml_path From 852445b2842203a161b91046e80757e4c5f713f5 Mon Sep 17 00:00:00 2001 From: Hamza Tahir Date: Mon, 22 Apr 2024 19:22:27 +0200 Subject: [PATCH 004/121] latest --- end-to-end-computer-vision/.gitignore | 3 +- end-to-end-computer-vision/run.py | 31 +++++++------ .../utils/split_data.py | 46 ++++++++++--------- 3 files changed, 42 insertions(+), 38 deletions(-) diff --git a/end-to-end-computer-vision/.gitignore b/end-to-end-computer-vision/.gitignore index 181d4d7a..39e38347 100644 --- a/end-to-end-computer-vision/.gitignore +++ b/end-to-end-computer-vision/.gitignore @@ -1,3 +1,4 @@ *.pt !data/.gitkeep -data/ \ No newline at end of file +data/ +runs/ \ No newline at end of file diff --git a/end-to-end-computer-vision/run.py b/end-to-end-computer-vision/run.py index 96616eea..4cb0359f 100644 --- a/end-to-end-computer-vision/run.py +++ b/end-to-end-computer-vision/run.py @@ -42,19 +42,20 @@ def load_and_split_data(dataset_name: str) -> str: if annotator and annotator._connection_available(): for dataset in annotator.get_datasets(): if dataset.get_params()["title"] == dataset_name: - with tempfile.TemporaryDirectory() as tmpdirname: - export_location = os.path.join(tmpdirname, "data.zip") - extract_location = os.path.join(tmpdirname, "data") - dataset.export_tasks( - export_type="YOLO", - export_location=export_location, - download_resources=True, - ) - unzip_dataset(export_location, extract_location) - split_dataset( - extract_location, ratio=(0.7, 0.15, 0.15), seed=42 - ) - return generate_yaml(extract_location) + tmpdirname = tempfile.TemporaryDirectory() + export_location = os.path.join(tmpdirname.name, "data.zip") + extract_location = os.path.join(tmpdirname.name, "data") + dataset.export_tasks( + export_type="YOLO", + export_location=export_location, + download_resources=True, + ) + unzip_dataset(export_location, extract_location) + split_dataset( + extract_location, ratio=(0.7, 0.15, 0.15), seed=42 + ) + breakpoint() + return generate_yaml(extract_location) raise ValueError(f"Dataset {dataset_name} not found in Label Studio.") @@ -64,9 +65,9 @@ def load_model( model_checkpoint: str = "yolov8l.pt", ) -> Annotated[YOLO, ArtifactConfig(name="Raw_YOLO", is_model_artifact=True)]: logger.info(f"Loading YOLO checkpoint {model_checkpoint}") + # return YOLO(model_checkpoint) return YOLO() - @step def train_model( model: YOLO, @@ -91,7 +92,7 @@ def train_model( return model, metrics, results -@pipeline(enable_cache=False, model=Model(name="Yolo_Object_Detection")) +@pipeline(enable_cache=True, model=Model(name="Yolo_Object_Detection")) def my_pipeline(): model = load_model() train_model(model=model) diff --git a/end-to-end-computer-vision/utils/split_data.py b/end-to-end-computer-vision/utils/split_data.py index e4bb935c..74d4ded8 100644 --- a/end-to-end-computer-vision/utils/split_data.py +++ b/end-to-end-computer-vision/utils/split_data.py @@ -3,7 +3,9 @@ import shutil import zipfile import yaml +from zenml.logger import get_logger +logger = get_logger(__name__) def unzip_dataset(zip_path: str, extract_dir: str): with zipfile.ZipFile(zip_path, "r") as zip_ref: @@ -11,39 +13,39 @@ def unzip_dataset(zip_path: str, extract_dir: str): def split_dataset( - data_path: str, ratio: tuple = (0.7, 0.2, 0.1), seed: int = None + data_path: str, img_subdir: str = "images", lbl_subdir: str = "labels", ratio: tuple = (0.7, 0.2, 0.1), seed: int = None ): # Ensure the ratio is correct assert sum(ratio) == 1.0 - # Ensure the paths exist - for subdir in ["images", "labels"]: - for split in ["train", "test", "val"]: - os.makedirs(os.path.join(data_path, subdir, split), exist_ok=True) - - # Get a list of all files - files = next(os.walk(os.path.join(data_path, "images")))[2] - # Seed to get consistent results if seed is not None: random.seed(seed) - # Shuffle the list of files + # Get a list of all files + img_files = [f for f in next(os.walk(os.path.join(data_path, img_subdir)))[2] if f.endswith(".jpg")] + lbl_files = [f for f in next(os.walk(os.path.join(data_path, lbl_subdir)))[2] if f.endswith(".txt")] + + # Zip together image and label files, shuffle in unison + files = list(zip(img_files, lbl_files)) random.shuffle(files) - # Calculate the indices to split at + # Make directories for each split in images and labels + for split in ['train', 'test', 'val']: + for subdir in [img_subdir, lbl_subdir]: + os.makedirs(os.path.join(data_path, subdir, split), exist_ok=True) + + # Unpack the files and split the list based on the ratios provided split_indices = [0] + [round(x * len(files)) for x in ratio] + [None] + splits = [files[start:end] for start, end in zip(split_indices[:-1], split_indices[1:])] - for split, start, end in zip( - ["train", "test", "val"], split_indices[:-1], split_indices[1:] - ): - for file in files[start:end]: - for subdir in ["images", "labels"]: - # Move each file to the proper split - shutil.move( - os.path.join(data_path, subdir, file), - os.path.join(data_path, subdir, split, file), - ) + # Iterate over each file split and move each pair of image and label files to the proper split + for split_name, split_files in zip(['train', 'test', 'val'], splits): + for img_file, lbl_file in split_files: + shutil.move(os.path.join(data_path, img_subdir, img_file), + os.path.join(data_path, img_subdir, split_name, img_file)) + shutil.move(os.path.join(data_path, lbl_subdir, lbl_file), + os.path.join(data_path, lbl_subdir, split_name, lbl_file)) def generate_yaml(data_path: str, yaml_path: str = None): @@ -61,7 +63,7 @@ def generate_yaml(data_path: str, yaml_path: str = None): "test": "images/test", "names": {i: name for i, name in enumerate(classes)}, } - + logger.info("Generated yaml data: %s", data) # Write to yaml file yaml_path = os.path.join(data_path, "data.yaml") with open(yaml_path, "w") as f: From d3b8995b9069d89fd8920c1d06c6091ba2715901 Mon Sep 17 00:00:00 2001 From: Hamza Tahir Date: Mon, 22 Apr 2024 19:37:22 +0200 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-54,7 +55,6 @@ def load_and_split_data(dataset_name: str) -> str: split_dataset( extract_location, ratio=(0.7, 0.15, 0.15), seed=42 ) - breakpoint() return generate_yaml(extract_location) raise ValueError(f"Dataset {dataset_name} not found in Label Studio.") @@ -68,6 +68,7 @@ def load_model( # return YOLO(model_checkpoint) return YOLO() + @step def train_model( model: YOLO, @@ -77,19 +78,16 @@ def train_model( YOLO, ArtifactConfig(name="Trained_YOLO", is_model_artifact=True) ], Annotated[dict, "metrics"], - Annotated[dict, "result"], ]: data_path = load_and_split_data(dataset_name) - model.train(data=data_path, epochs=1) # model.train(data="coco8.yaml", epochs=3) # train the model metrics = model.val() # evaluate model performance on the validation set - results = model( - "https://ultralytics.com/images/bus.jpg" - ) # predict on an image - return model, metrics, results + + breakpoint() + return model, metrics @pipeline(enable_cache=True, model=Model(name="Yolo_Object_Detection")) diff --git a/end-to-end-computer-vision/utils/split_data.py b/end-to-end-computer-vision/utils/split_data.py index 74d4ded8..277a4299 100644 --- a/end-to-end-computer-vision/utils/split_data.py +++ b/end-to-end-computer-vision/utils/split_data.py @@ -7,13 +7,18 @@ logger = get_logger(__name__) + def unzip_dataset(zip_path: str, extract_dir: str): with zipfile.ZipFile(zip_path, "r") as zip_ref: zip_ref.extractall(extract_dir) def split_dataset( - data_path: str, img_subdir: str = "images", lbl_subdir: str = "labels", ratio: tuple = (0.7, 0.2, 0.1), seed: int = None + data_path: str, + img_subdir: str = "images", + lbl_subdir: str = "labels", + ratio: tuple = (0.7, 0.2, 0.1), + seed: int = None, ): # Ensure the ratio is correct assert sum(ratio) == 1.0 @@ -23,29 +28,48 @@ def split_dataset( random.seed(seed) # Get a list of all files - img_files = [f for f in next(os.walk(os.path.join(data_path, img_subdir)))[2] if f.endswith(".jpg")] - lbl_files = [f for f in next(os.walk(os.path.join(data_path, lbl_subdir)))[2] if f.endswith(".txt")] + img_files = [ + f + for f in next(os.walk(os.path.join(data_path, img_subdir)))[2] + if (f.endswith(".jpg") or f.endswith(".png") or f.endswith(".jpeg")) + ] + lbl_files = [ + f + for f in next(os.walk(os.path.join(data_path, lbl_subdir)))[2] + if f.endswith(".txt") + ] # Zip together image and label files, shuffle in unison files = list(zip(img_files, lbl_files)) random.shuffle(files) # Make directories for each split in images and labels - for split in ['train', 'test', 'val']: + for split in ["train", "test", "val"]: for subdir in [img_subdir, lbl_subdir]: os.makedirs(os.path.join(data_path, subdir, split), exist_ok=True) # Unpack the files and split the list based on the ratios provided - split_indices = [0] + [round(x * len(files)) for x in ratio] + [None] - splits = [files[start:end] for start, end in zip(split_indices[:-1], split_indices[1:])] + train_idx_end = round(ratio[0] * len(files)) + test_idx_end = train_idx_end + round(ratio[1] * len(files)) + splits = [ + files[:train_idx_end], + files[train_idx_end:test_idx_end], + files[test_idx_end:], + ] + + breakpoint() # Iterate over each file split and move each pair of image and label files to the proper split - for split_name, split_files in zip(['train', 'test', 'val'], splits): + for split_name, split_files in zip(["train", "test", "val"], splits): for img_file, lbl_file in split_files: - shutil.move(os.path.join(data_path, img_subdir, img_file), - os.path.join(data_path, img_subdir, split_name, img_file)) - shutil.move(os.path.join(data_path, lbl_subdir, lbl_file), - os.path.join(data_path, lbl_subdir, split_name, lbl_file)) + shutil.move( + os.path.join(data_path, img_subdir, img_file), + os.path.join(data_path, img_subdir, split_name, img_file), + ) + shutil.move( + os.path.join(data_path, lbl_subdir, lbl_file), + os.path.join(data_path, lbl_subdir, split_name, lbl_file), + ) def generate_yaml(data_path: str, yaml_path: str = None): From 5581aaaca0db7d6fe93c66205b4bc46ad53517d5 Mon Sep 17 00:00:00 2001 From: Hamza Tahir Date: Mon, 22 Apr 2024 19:51:57 +0200 Subject: [PATCH 006/121] Added matrix: --- end-to-end-computer-vision/run.py | 32 ++++++++++++++++--- .../utils/split_data.py | 2 -- 2 files changed, 27 insertions(+), 7 deletions(-) diff --git a/end-to-end-computer-vision/run.py b/end-to-end-computer-vision/run.py index 8d8feb5a..88f94400 100644 --- a/end-to-end-computer-vision/run.py +++ b/end-to-end-computer-vision/run.py @@ -16,17 +16,33 @@ # import os import tempfile -from zenml import step, pipeline, Model, ArtifactConfig +from zenml import step, pipeline, Model, ArtifactConfig, log_artifact_metadata from zenml.client import Client from ultralytics import YOLO from typing_extensions import Annotated from zenml.logger import get_logger from typing import Tuple from utils.split_data import unzip_dataset, split_dataset, generate_yaml +from PIL import Image +import os logger = get_logger(__name__) +def load_images_from_folder(folder): + images = [] + for filename in os.listdir(folder): + if ( + filename.endswith(".png") + or filename.endswith(".jpg") + or filename.endswith(".jpeg") + ): + with open(os.path.join(folder, filename), "rb") as f: + img = Image.open(f) + images.append(img) + return images + + def load_and_split_data(dataset_name: str) -> str: annotator = Client().active_stack.annotator @@ -42,7 +58,9 @@ def load_and_split_data(dataset_name: str) -> str: if annotator and annotator._connection_available(): for dataset in annotator.get_datasets(): if dataset.get_params()["title"] == dataset_name: - tmpfile_ = tempfile.NamedTemporaryFile(delete=False) + tmpfile_ = tempfile.NamedTemporaryFile( + dir="data", delete=False + ) tmpdirname = os.path.basename(tmpfile_.name) export_location = os.path.join(tmpdirname, "data.zip") extract_location = os.path.join(tmpdirname, "data") @@ -77,7 +95,7 @@ def train_model( Annotated[ YOLO, ArtifactConfig(name="Trained_YOLO", is_model_artifact=True) ], - Annotated[dict, "metrics"], + Annotated[Image.Image, "confusion_matrix"], ]: data_path = load_and_split_data(dataset_name) @@ -86,8 +104,12 @@ def train_model( # model.train(data="coco8.yaml", epochs=3) # train the model metrics = model.val() # evaluate model performance on the validation set - breakpoint() - return model, metrics + log_artifact_metadata(metadata={"metrics": metrics.results_dict}) + + # Read images as PIL images from directory metrics.save_dir for all png and jpg files + images = load_images_from_folder(metrics.save_dir) + + return model, images[0] @pipeline(enable_cache=True, model=Model(name="Yolo_Object_Detection")) diff --git a/end-to-end-computer-vision/utils/split_data.py b/end-to-end-computer-vision/utils/split_data.py index 277a4299..7e977edb 100644 --- a/end-to-end-computer-vision/utils/split_data.py +++ b/end-to-end-computer-vision/utils/split_data.py @@ -57,8 +57,6 @@ def split_dataset( files[test_idx_end:], ] - breakpoint() - # Iterate over each file split and move each pair of image and label files to the proper split for split_name, split_files in zip(["train", "test", "val"], splits): for img_file, lbl_file in split_files: From f62459f5450ac9faa49df78bec2cde01f5e0b941 Mon Sep 17 00:00:00 2001 From: Hamza Tahir Date: Mon, 22 Apr 2024 19:53:51 +0200 Subject: [PATCH 007/121] More --- end-to-end-computer-vision/run.py | 5 ++++- .../tmp0r8tlddd/data.zip | Bin 0 -> 6707193 bytes .../tmp7eex2m8l/data.zip | Bin 0 -> 6707193 bytes 3 files changed, 4 insertions(+), 1 deletion(-) create mode 100644 end-to-end-computer-vision/tmp0r8tlddd/data.zip create mode 100644 end-to-end-computer-vision/tmp7eex2m8l/data.zip diff --git a/end-to-end-computer-vision/run.py b/end-to-end-computer-vision/run.py index 88f94400..7ee61e23 100644 --- a/end-to-end-computer-vision/run.py +++ b/end-to-end-computer-vision/run.py @@ -104,7 +104,10 @@ def train_model( # model.train(data="coco8.yaml", 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6707193 -> 0 bytes end-to-end-computer-vision/utils/__init__.py | 0 6 files changed, 72 insertions(+), 1 deletion(-) create mode 100644 end-to-end-computer-vision/materializers/__init__.py create mode 100644 end-to-end-computer-vision/materializers/yolo_materializer.py delete mode 100644 end-to-end-computer-vision/tmp0r8tlddd/data.zip delete mode 100644 end-to-end-computer-vision/tmp7eex2m8l/data.zip create mode 100644 end-to-end-computer-vision/utils/__init__.py diff --git a/end-to-end-computer-vision/materializers/__init__.py b/end-to-end-computer-vision/materializers/__init__.py new file mode 100644 index 00000000..e69de29b diff --git a/end-to-end-computer-vision/materializers/yolo_materializer.py b/end-to-end-computer-vision/materializers/yolo_materializer.py new file mode 100644 index 00000000..32f3903b --- /dev/null +++ b/end-to-end-computer-vision/materializers/yolo_materializer.py @@ -0,0 +1,70 @@ +# Copyright (c) ZenML GmbH 2022. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at: +# +# https://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express +# or implied. See the License for the specific language governing +# permissions and limitations under the License. +"""Implementation of the PyTorch DataLoader materializer.""" + +import os +from typing import Any, ClassVar, Type +import tempfile + +from ultralytics import YOLO + +from zenml.io import fileio +from zenml.integrations.pytorch.materializers.pytorch_module_materializer import ( + PyTorchModuleMaterializer, +) + +DEFAULT_FILENAME = "obj.pt" + + +class UltralyticsMaterializer(PyTorchModuleMaterializer): + """Base class for ultralytics YOLO models.""" + + FILENAME: ClassVar[str] = DEFAULT_FILENAME + SKIP_REGISTRATION: ClassVar[bool] = True + ASSOCIATED_TYPES = (YOLO,) + + def load(self, data_type: Type[Any]) -> YOLO: + """Reads a ultralytics YOLO model from a serialized JSON file. + + Args: + data_type: A ultralytics YOLO type. + + Returns: + A ultralytics YOLO object. + """ + filepath = os.path.join(self.uri, DEFAULT_FILENAME) + + # Create a temporary folder + with tempfile.TemporaryDirectory(prefix="zenml-temp-") as temp_dir: + temp_file = os.path.join(str(temp_dir), DEFAULT_FILENAME) + + # Copy from artifact store to temporary file + fileio.copy(filepath, temp_file) + model = YOLO(temp_file) + + return model + + def save(self, model: YOLO) -> None: + """Creates a JSON serialization for a ultralytics YOLO model. + + Args: + model: A ultralytics YOLO model. + """ + filepath = os.path.join(self.uri, DEFAULT_FILENAME) + + # Make a temporary phantom artifact + with tempfile.NamedTemporaryFile(mode="w", suffix=".json") as f: + model.save(f.name) + # Copy it into artifact store + fileio.copy(f.name, filepath) diff --git a/end-to-end-computer-vision/run.py b/end-to-end-computer-vision/run.py index 7ee61e23..5427e7a1 100644 --- a/end-to-end-computer-vision/run.py +++ b/end-to-end-computer-vision/run.py @@ -25,6 +25,7 @@ from utils.split_data import unzip_dataset, split_dataset, generate_yaml from PIL import Image import os +from materializers.yolo_materializer import UltralyticsMaterializer logger = get_logger(__name__) @@ -87,7 +88,7 @@ def load_model( return YOLO() -@step +@step(output_materializers={"Trained_YOLO": UltralyticsMaterializer}) def train_model( model: YOLO, dataset_name: str = "zenml_test_project", diff --git a/end-to-end-computer-vision/tmp0r8tlddd/data.zip b/end-to-end-computer-vision/tmp0r8tlddd/data.zip deleted file mode 100644 index e69a949d4d8ee8d6b9bf5af8380f69ec3d228da6..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 6707193 zcmaI7V~j3L&@DQ)ZQIym+qP}nwr%aPXZF~(ZQJHEJn#9=xw*N2ZYQg|epFRfS0|lL zb(NwFC>R>h{~j&z>6-r|{ND-kztzgl$imEpQStv*$=1l&%=Z6Pg8Cm%9mn0rk^gyE z{|_+#!QR2u%!R?)#lhY#jv01P5H9TPCpxiXyoJ*ZRf9vqsl8PRge~bxEz$XRM^}eZ zkKowvj?@J>FfYM%t^bcPD5ee2DS4ceLmq8k`8%Xm5G2Cf0PF#XGC-orzoZy(Yxgmm zxp8}W-uf`dc0usw@|)%;W^kNlqs|?ntyLu|Xp7o9|7fD^Pd}GDpN)TF^fU1Pv#I~X zSM}j$9q~V&@c#k!KbY7Wxw!nt*wxe3Wtxs=Txwe8#95y15CrJ|Ye%^fR_;*&0;Dhl 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zMLRy>blnC=(B}uMZ$(f0OwhX@&GfkTtH0xJc)P8gI$nWUasTDwD-AyL;RF{M9@$|T zUB4yhEqA8ge;%1S(67NNpZRovtF3#u%X+#+nxKyqE$ylp4VbDg0XnNA z3wK*xw;mJpsiC%xM{z&Os|Y zkLelBw*OVfuDD_2Y!vh{=(>~1TZ^&InJ4JAnXY!-N4KDJmI?Z6=ySco1&C=o~zP{w6`!#*p}t=0Fkj#w=I6ZbBf`&N(6Iap<~(M$kF>1HFBvt6lf*6m$;j zKwqBibm=N0SRBEDbrn`}*Ct3Dtbw#3$5r+Jd#0dr7zXM9)TH)QfH=GYD3t5U(_P^O zoii!WpC{(sYS$fug3fUd=yB+} z*@&QXiUa!5)vk8kbWYGYodNwj^yDF>AaR}oQqLM!r*1MQ=$w&&o|f-)-8e?jIs5>< z54!F`D(D<(fS$3|)vg;l2s#H9pbtWq=c|Io=>uq)1+G@z2t&|0QUHA@K~D-8@JA7c z0Kk+Mx*~PcGoeV{?L!}huA6TOIxpj)A7AHc*EPn1&O3JK)6jK`0zseA5*O#tJH3Al z_HSpT8!-zyuhF58Lzf06g2kI}SgjjerMfAipz}f-`cLS(&P338O%44_k*l3rc!A-? zGMLnjj?oQ)1)Vp+(1)SxnruPm1uyjSO|Ev`_(9NlaSMG6x^8+js|QkMgnIA$qPOR( JD{l6F`wN Date: Mon, 22 Apr 2024 20:20:15 +0200 Subject: [PATCH 009/121] Latest --- end-to-end-computer-vision/run.py | 32 +++++++++++++++++++++++-------- 1 file changed, 24 insertions(+), 8 deletions(-) diff --git a/end-to-end-computer-vision/run.py b/end-to-end-computer-vision/run.py index 5427e7a1..e78c9dd3 100644 --- a/end-to-end-computer-vision/run.py +++ b/end-to-end-computer-vision/run.py @@ -88,17 +88,25 @@ def load_model( return YOLO() +# @step(output_materializers={"Trained_YOLO": UltralyticsMaterializer}) +# def train_model( +# model: YOLO, +# dataset_name: str = "zenml_test_project", +# ) -> Tuple[ +# Annotated[ +# YOLO, ArtifactConfig(name="Trained_YOLO", is_model_artifact=True) +# ], +# Annotated[Image.Image, "confusion_matrix"], +# ]: + + @step(output_materializers={"Trained_YOLO": UltralyticsMaterializer}) def train_model( model: YOLO, dataset_name: str = "zenml_test_project", -) -> Tuple[ - Annotated[ - YOLO, ArtifactConfig(name="Trained_YOLO", is_model_artifact=True) - ], - Annotated[Image.Image, "confusion_matrix"], +) -> Annotated[ + YOLO, ArtifactConfig(name="Trained_YOLO", is_model_artifact=True) ]: - data_path = load_and_split_data(dataset_name) model.train(data=data_path, epochs=1) @@ -113,13 +121,21 @@ def train_model( # Read images as PIL images from directory metrics.save_dir for all png and jpg files images = load_images_from_folder(metrics.save_dir) - return model, images[0] + return model + # return model, images[0] + + +@step +def predict_image(model: YOLO): + results = model("https://ultralytics.com/images/bus.jpg") + print(results) @pipeline(enable_cache=True, model=Model(name="Yolo_Object_Detection")) def my_pipeline(): model = load_model() - train_model(model=model) + trained_model = train_model(model=model) + predict_image(trained_model) if __name__ == "__main__": From 69e76395b4224cc1dc35146973f7ef6fafd72fcc Mon Sep 17 00:00:00 2001 From: Hamza Tahir Date: Mon, 22 Apr 2024 20:20:47 +0200 Subject: [PATCH 010/121] Latest --- end-to-end-computer-vision/run.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/end-to-end-computer-vision/run.py b/end-to-end-computer-vision/run.py index e78c9dd3..2f6302c1 100644 --- a/end-to-end-computer-vision/run.py +++ b/end-to-end-computer-vision/run.py @@ -84,8 +84,8 @@ def load_model( model_checkpoint: str = "yolov8l.pt", ) -> Annotated[YOLO, ArtifactConfig(name="Raw_YOLO", is_model_artifact=True)]: logger.info(f"Loading YOLO checkpoint {model_checkpoint}") - # return YOLO(model_checkpoint) - return YOLO() + return YOLO(model_checkpoint) + # return YOLO() # @step(output_materializers={"Trained_YOLO": UltralyticsMaterializer}) From b523d6c79b8f0eee56a0abb63ee14aa3cf8ba412 Mon Sep 17 00:00:00 2001 From: Alexej Penner Date: Mon, 22 Apr 2024 21:26:37 +0200 Subject: [PATCH 011/121] Initial implementation for separate data loader pipeline --- end-to-end-computer-vision/README.md | 5 ++ .../configs/data_export_alexej.yaml | 10 +++ .../label_studio_yolo_dataset_materializer.py | 90 +++++++++++++++++++ .../pipelines/__init__.py | 5 +- .../pipelines/data_export.py | 55 ++++++++++++ end-to-end-computer-vision/run.py | 7 +- end-to-end-computer-vision/steps/__init__.py | 0 .../steps/export_label_studio.py | 0 8 files changed, 168 insertions(+), 4 deletions(-) create mode 100644 end-to-end-computer-vision/README.md create mode 100644 end-to-end-computer-vision/configs/data_export_alexej.yaml create mode 100644 end-to-end-computer-vision/materializers/label_studio_yolo_dataset_materializer.py create mode 100644 end-to-end-computer-vision/pipelines/data_export.py create mode 100644 end-to-end-computer-vision/steps/__init__.py create mode 100644 end-to-end-computer-vision/steps/export_label_studio.py diff --git a/end-to-end-computer-vision/README.md b/end-to-end-computer-vision/README.md new file mode 100644 index 00000000..c108c838 --- /dev/null +++ b/end-to-end-computer-vision/README.md @@ -0,0 +1,5 @@ + +```commandline +zenml integration install label_studio torch gcp +pip install -r requirements.txt +``` \ No newline at end of file diff --git a/end-to-end-computer-vision/configs/data_export_alexej.yaml b/end-to-end-computer-vision/configs/data_export_alexej.yaml new file mode 100644 index 00000000..275e5126 --- /dev/null +++ b/end-to-end-computer-vision/configs/data_export_alexej.yaml @@ -0,0 +1,10 @@ +# pipeline configuration +parameters: + dataset_name: polution + +# configuration of the Model Control Plane +model: + name: YOLO + license: Apache 2.0 + description: Object Detection Model. + tags: ["object dete", "customer_satisfaction"] \ No newline at end of file diff --git a/end-to-end-computer-vision/materializers/label_studio_yolo_dataset_materializer.py b/end-to-end-computer-vision/materializers/label_studio_yolo_dataset_materializer.py new file mode 100644 index 00000000..60559b67 --- /dev/null +++ b/end-to-end-computer-vision/materializers/label_studio_yolo_dataset_materializer.py @@ -0,0 +1,90 @@ +# Copyright (c) ZenML GmbH 2022. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at: +# +# https://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express +# or implied. See the License for the specific language governing +# permissions and limitations under the License. +"""Implementation of the PyTorch DataLoader materializer.""" + +import os +from typing import Any, ClassVar, Type, Optional +import tempfile + +from pydantic import BaseModel +from ultralytics import YOLO + +from zenml.io import fileio +from zenml.integrations.pytorch.materializers.pytorch_module_materializer import ( + PyTorchModuleMaterializer, +) +from zenml.materializers.base_materializer import BaseMaterializer + +DEFAULT_FILENAME = "data.zip" + + +class LabelStudioYOLODataset: + dataset: Optional[Any] + filepath: Optional[str] + + def download_dataset(self): + tmpfile_ = tempfile.NamedTemporaryFile( + dir="data", delete=False + ) + tmpdirname = os.path.basename(tmpfile_.name) + self.filepath = os.path.join(tmpdirname, DEFAULT_FILENAME) + self.dataset.export_tasks( + export_type="YOLO", + export_location=self.filepath, + download_resources=True, + ) + + +class LabelStudioYOLODatasetMaterializer(BaseMaterializer): + """Base class for Label Studio YOLO dataset models.""" + + FILENAME: ClassVar[str] = DEFAULT_FILENAME + SKIP_REGISTRATION: ClassVar[bool] = True + ASSOCIATED_TYPES = (LabelStudioYOLODataset,) + + def load(self, data_type: Type[Any]) -> LabelStudioYOLODataset: + """Reads a ultralytics YOLO model from a serialized JSON file. + + Args: + data_type: A ultralytics YOLO type. + + Returns: + A ultralytics YOLO object. + """ + filepath = os.path.join(self.uri, DEFAULT_FILENAME) + + # Create a temporary folder + with tempfile.TemporaryDirectory(prefix="zenml-temp-") as temp_dir: + temp_file = os.path.join(str(temp_dir), DEFAULT_FILENAME) + + # Copy from artifact store to temporary file + fileio.copy(filepath, temp_file) + dataset = LabelStudioYOLODataset(filepath=temp_file) + + return dataset + + def save(self, dataset: LabelStudioYOLODataset) -> None: + """Creates a JSON serialization for a label studio YOLO dataset model. + + Args: + dataset: A label studio YOLO dataset model. + """ + dataset.download_dataset() + + filepath = os.path.join(self.uri, DEFAULT_FILENAME) + + # Make a temporary phantom artifact + with tempfile.NamedTemporaryFile(mode="w", suffix=".json") as f: + # Copy it into artifact store + fileio.copy(dataset.filepath, filepath) diff --git a/end-to-end-computer-vision/pipelines/__init__.py b/end-to-end-computer-vision/pipelines/__init__.py index 6d8015e2..e5046710 100644 --- a/end-to-end-computer-vision/pipelines/__init__.py +++ b/end-to-end-computer-vision/pipelines/__init__.py @@ -15,6 +15,5 @@ # limitations under the License. # -from .feature_engineering import feature_engineering -from .inference import inference -from .training import training +from .data_export import data_export +#from .training import training diff --git a/end-to-end-computer-vision/pipelines/data_export.py b/end-to-end-computer-vision/pipelines/data_export.py new file mode 100644 index 00000000..95fd7f61 --- /dev/null +++ b/end-to-end-computer-vision/pipelines/data_export.py @@ -0,0 +1,55 @@ +# Apache Software License 2.0 +# +# Copyright (c) ZenML GmbH 2024. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# +import os +import tempfile +from typing import Any, Annotated + +from zenml import pipeline, step +from zenml.client import Client +from zenml.logger import get_logger + +from materializers.label_studio_yolo_dataset_materializer import \ + LabelStudioYOLODataset + +logger = get_logger(__name__) + + + +@step +def load_data_from_label_studio(dataset_name: str) -> LabelStudioYOLODataset: + annotator = Client().active_stack.annotator + from zenml.integrations.label_studio.annotators.label_studio_annotator import ( + LabelStudioAnnotator, + ) + + if not isinstance(annotator, LabelStudioAnnotator): + raise TypeError( + "This step can only be used with the Label Studio annotator." + ) + + if annotator and annotator._connection_available(): + for dataset in annotator.get_datasets(): + if dataset.get_params()["title"] == dataset_name: + return LabelStudioYOLODataset(dataset=dataset) + else: + raise TypeError( + "This step can only be used with an active Label Studio annotator." + ) + +@pipeline +def data_export(dataset_name: str = 'polution'): + load_data_from_label_studio(dataset_name=dataset_name) diff --git a/end-to-end-computer-vision/run.py b/end-to-end-computer-vision/run.py index 2f6302c1..8ec23e04 100644 --- a/end-to-end-computer-vision/run.py +++ b/end-to-end-computer-vision/run.py @@ -22,6 +22,8 @@ from typing_extensions import Annotated from zenml.logger import get_logger from typing import Tuple + +from pipelines.data_export import data_export from utils.split_data import unzip_dataset, split_dataset, generate_yaml from PIL import Image import os @@ -70,6 +72,8 @@ def load_and_split_data(dataset_name: str) -> str: export_location=export_location, download_resources=True, ) + extract_location = os.path.join(tmpdirname, "data") + unzip_dataset(export_location, extract_location) split_dataset( extract_location, ratio=(0.7, 0.15, 0.15), seed=42 @@ -139,4 +143,5 @@ def my_pipeline(): if __name__ == "__main__": - my_pipeline() + data_export() #.with_options(config_path="configs/data_export_alexej.yaml")() + #my_pipeline() diff --git a/end-to-end-computer-vision/steps/__init__.py b/end-to-end-computer-vision/steps/__init__.py new file mode 100644 index 00000000..e69de29b diff --git a/end-to-end-computer-vision/steps/export_label_studio.py b/end-to-end-computer-vision/steps/export_label_studio.py new file mode 100644 index 00000000..e69de29b From 2c4f2805de976317dd2e76b7609b8d9480efe528 Mon Sep 17 00:00:00 2001 From: Alex Strick van Linschoten Date: Mon, 22 Apr 2024 21:32:39 +0200 Subject: [PATCH 012/121] add ultralytics to requirements --- end-to-end-computer-vision/requirements.txt | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/end-to-end-computer-vision/requirements.txt b/end-to-end-computer-vision/requirements.txt index c3fd2c34..97507e73 100644 --- a/end-to-end-computer-vision/requirements.txt +++ b/end-to-end-computer-vision/requirements.txt @@ -8,4 +8,5 @@ sagemaker==2.117.0 pyarrow wandb seaborn -xgboost \ No newline at end of file +xgboost +ultralytics From feb070c3f12e800d6d47e952c64c15db39c28811 Mon Sep 17 00:00:00 2001 From: Alex Strick van Linschoten Date: Mon, 22 Apr 2024 21:49:45 +0200 Subject: [PATCH 013/121] fix --- end-to-end-computer-vision/pipelines/data_export.py | 4 +++- 1 file changed, 3 insertions(+), 1 deletion(-) diff --git a/end-to-end-computer-vision/pipelines/data_export.py b/end-to-end-computer-vision/pipelines/data_export.py index 95fd7f61..6babf4b8 100644 --- a/end-to-end-computer-vision/pipelines/data_export.py +++ b/end-to-end-computer-vision/pipelines/data_export.py @@ -44,7 +44,9 @@ def load_data_from_label_studio(dataset_name: str) -> LabelStudioYOLODataset: if annotator and annotator._connection_available(): for dataset in annotator.get_datasets(): if dataset.get_params()["title"] == dataset_name: - return LabelStudioYOLODataset(dataset=dataset) + ls_dataset = LabelStudioYOLODataset() + ls_dataset.dataset = dataset + return ls_dataset else: raise TypeError( "This step can only be used with an active Label Studio annotator." From ac21d9150fd443c4cf490c8674c9821d801988a2 Mon Sep 17 00:00:00 2001 From: Alex Strick van Linschoten Date: Mon, 22 Apr 2024 22:01:33 +0200 Subject: [PATCH 014/121] fix data export and materialization --- end-to-end-computer-vision/pipelines/data_export.py | 9 +++------ 1 file changed, 3 insertions(+), 6 deletions(-) diff --git a/end-to-end-computer-vision/pipelines/data_export.py b/end-to-end-computer-vision/pipelines/data_export.py index 6babf4b8..09c157d8 100644 --- a/end-to-end-computer-vision/pipelines/data_export.py +++ b/end-to-end-computer-vision/pipelines/data_export.py @@ -17,20 +17,17 @@ import os import tempfile from typing import Any, Annotated - from zenml import pipeline, step from zenml.client import Client from zenml.logger import get_logger -from materializers.label_studio_yolo_dataset_materializer import \ - LabelStudioYOLODataset - +from materializers.label_studio_yolo_dataset_materializer import LabelStudioYOLODataset, LabelStudioYOLODatasetMaterializer logger = get_logger(__name__) -@step -def load_data_from_label_studio(dataset_name: str) -> LabelStudioYOLODataset: +@step(output_materializers=LabelStudioYOLODatasetMaterializer) +def load_data_from_label_studio(dataset_name: str) -> Annotated[LabelStudioYOLODataset, "yolo_dataset"]: annotator = Client().active_stack.annotator from zenml.integrations.label_studio.annotators.label_studio_annotator import ( LabelStudioAnnotator, From acd1aebb56499fd33e6f1697e48b429c2602ca8f Mon Sep 17 00:00:00 2001 From: Alexej Penner Date: Mon, 22 Apr 2024 22:28:47 +0200 Subject: [PATCH 015/121] Split training pipeline up into steps --- .../configs/data_export_alexej.yaml | 2 +- .../configs/training.yaml | 14 ++++ .../pipelines/training.py | 63 ++-------------- end-to-end-computer-vision/run.py | 62 +--------------- .../steps/load_model.py | 30 ++++++++ .../steps/predict_image.py | 24 ++++++ .../steps/train_model.py | 47 ++++++++++++ end-to-end-computer-vision/utils.py | 74 +++++++++++++++++++ 8 files changed, 200 insertions(+), 116 deletions(-) create mode 100644 end-to-end-computer-vision/configs/training.yaml create mode 100644 end-to-end-computer-vision/steps/load_model.py create mode 100644 end-to-end-computer-vision/steps/predict_image.py create mode 100644 end-to-end-computer-vision/steps/train_model.py create mode 100644 end-to-end-computer-vision/utils.py diff --git a/end-to-end-computer-vision/configs/data_export_alexej.yaml b/end-to-end-computer-vision/configs/data_export_alexej.yaml index 275e5126..dd38bd00 100644 --- a/end-to-end-computer-vision/configs/data_export_alexej.yaml +++ b/end-to-end-computer-vision/configs/data_export_alexej.yaml @@ -7,4 +7,4 @@ model: name: YOLO license: Apache 2.0 description: Object Detection Model. - tags: ["object dete", "customer_satisfaction"] \ No newline at end of file + tags: ["object detection", "customer_satisfaction"] \ No newline at end of file diff --git a/end-to-end-computer-vision/configs/training.yaml b/end-to-end-computer-vision/configs/training.yaml new file mode 100644 index 00000000..651e1995 --- /dev/null +++ b/end-to-end-computer-vision/configs/training.yaml @@ -0,0 +1,14 @@ +enable_cache: False + +settings: + docker: + required_integrations: + - gcp + - torch + +# configuration of the Model Control Plane +model: + name: YOLO + license: Apache 2.0 + description: Object Detection Model. + tags: ["object detection", "customer_satisfaction"] \ No newline at end of file diff --git a/end-to-end-computer-vision/pipelines/training.py b/end-to-end-computer-vision/pipelines/training.py index 1af79755..bb8fda06 100644 --- a/end-to-end-computer-vision/pipelines/training.py +++ b/end-to-end-computer-vision/pipelines/training.py @@ -18,67 +18,18 @@ from typing import Optional from uuid import UUID -from steps import model_evaluator, model_promoter, model_trainer - -from pipelines import ( - feature_engineering, -) from zenml import pipeline from zenml.client import Client from zenml.logger import get_logger +from run import train_model, predict_image +from steps.load_model import load_model + logger = get_logger(__name__) @pipeline -def training( - train_dataset_id: Optional[UUID] = None, - test_dataset_id: Optional[UUID] = None, - target: Optional[str] = "target", - model_type: Optional[str] = "sgd", - random_state: int = None, -): - """ - Model training pipeline. - - This is a pipeline that loads the data from a preprocessing pipeline, - trains a model on it and evaluates the model. If it is the first model - to be trained, it will be promoted to production. If not, it will be - promoted only if it has a higher accuracy than the current production - model version. - - Args: - train_dataset_id: ID of the train dataset produced by feature engineering. - test_dataset_id: ID of the test dataset produced by feature engineering. - target: Name of target column in dataset. - model_type: The type of model to train. - """ - # Link all the steps together by calling them and passing the output - # of one step as the input of the next step. - - # Execute Feature Engineering Pipeline - if train_dataset_id is None or test_dataset_id is None: - dataset_trn, dataset_tst = feature_engineering( - random_state=random_state - ) - else: - client = Client() - dataset_trn = client.get_artifact_version( - name_id_or_prefix=train_dataset_id - ) - dataset_tst = client.get_artifact_version( - name_id_or_prefix=test_dataset_id - ) - - model = model_trainer( - dataset_trn=dataset_trn, target=target, model_type=model_type - ) - - acc = model_evaluator( - model=model, - dataset_trn=dataset_trn, - dataset_tst=dataset_tst, - target=target, - ) - - model_promoter(accuracy=acc) +def training(model_checkpoint: str = "yolov8l.pt"): + model = load_model(model_checkpoint) + trained_model = train_model(model=model) + predict_image(trained_model) diff --git a/end-to-end-computer-vision/run.py b/end-to-end-computer-vision/run.py index 8ec23e04..f8ac21ef 100644 --- a/end-to-end-computer-vision/run.py +++ b/end-to-end-computer-vision/run.py @@ -14,75 +14,19 @@ # See the License for the specific language governing permissions and # limitations under the License. # -import os -import tempfile from zenml import step, pipeline, Model, ArtifactConfig, log_artifact_metadata -from zenml.client import Client from ultralytics import YOLO from typing_extensions import Annotated from zenml.logger import get_logger -from typing import Tuple from pipelines.data_export import data_export -from utils.split_data import unzip_dataset, split_dataset, generate_yaml -from PIL import Image -import os +from pipelines.training import training +from utils import load_images_from_folder, load_and_split_data from materializers.yolo_materializer import UltralyticsMaterializer logger = get_logger(__name__) -def load_images_from_folder(folder): - images = [] - for filename in os.listdir(folder): - if ( - filename.endswith(".png") - or filename.endswith(".jpg") - or filename.endswith(".jpeg") - ): - with open(os.path.join(folder, filename), "rb") as f: - img = Image.open(f) - images.append(img) - return images - - -def load_and_split_data(dataset_name: str) -> str: - - annotator = Client().active_stack.annotator - from zenml.integrations.label_studio.annotators.label_studio_annotator import ( - LabelStudioAnnotator, - ) - - if not isinstance(annotator, LabelStudioAnnotator): - raise TypeError( - "This step can only be used with the Label Studio annotator." - ) - - if annotator and annotator._connection_available(): - for dataset in annotator.get_datasets(): - if dataset.get_params()["title"] == dataset_name: - tmpfile_ = tempfile.NamedTemporaryFile( - dir="data", delete=False - ) - tmpdirname = os.path.basename(tmpfile_.name) - export_location = os.path.join(tmpdirname, "data.zip") - extract_location = os.path.join(tmpdirname, "data") - dataset.export_tasks( - export_type="YOLO", - export_location=export_location, - download_resources=True, - ) - extract_location = os.path.join(tmpdirname, "data") - - unzip_dataset(export_location, extract_location) - split_dataset( - extract_location, ratio=(0.7, 0.15, 0.15), seed=42 - ) - return generate_yaml(extract_location) - - raise ValueError(f"Dataset {dataset_name} not found in Label Studio.") - - @step def load_model( model_checkpoint: str = "yolov8l.pt", @@ -144,4 +88,4 @@ def my_pipeline(): if __name__ == "__main__": data_export() #.with_options(config_path="configs/data_export_alexej.yaml")() - #my_pipeline() + training()#.with diff --git a/end-to-end-computer-vision/steps/load_model.py b/end-to-end-computer-vision/steps/load_model.py new file mode 100644 index 00000000..a1eb1bec --- /dev/null +++ b/end-to-end-computer-vision/steps/load_model.py @@ -0,0 +1,30 @@ +# Apache Software License 2.0 +# +# Copyright (c) ZenML GmbH 2024. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# +from typing import Annotated + +from ultralytics import YOLO +from zenml import step, ArtifactConfig +from zenml.logger import get_logger + +logger = get_logger(__name__) + +@step +def load_model( + model_checkpoint: str, +) -> Annotated[YOLO, ArtifactConfig(name="Raw_YOLO", is_model_artifact=True)]: + logger.info(f"Loading YOLO checkpoint {model_checkpoint}") + return YOLO(model_checkpoint) \ No newline at end of file diff --git a/end-to-end-computer-vision/steps/predict_image.py b/end-to-end-computer-vision/steps/predict_image.py new file mode 100644 index 00000000..9239db5c --- /dev/null +++ b/end-to-end-computer-vision/steps/predict_image.py @@ -0,0 +1,24 @@ +# Apache Software License 2.0 +# +# Copyright (c) ZenML GmbH 2024. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# +from ultralytics import YOLO +from zenml import step + + +@step +def predict_image(model: YOLO): + results = model("https://ultralytics.com/images/bus.jpg") + print(results) \ No newline at end of file diff --git a/end-to-end-computer-vision/steps/train_model.py b/end-to-end-computer-vision/steps/train_model.py new file mode 100644 index 00000000..c68cd6a6 --- /dev/null +++ b/end-to-end-computer-vision/steps/train_model.py @@ -0,0 +1,47 @@ +# Apache Software License 2.0 +# +# Copyright (c) ZenML GmbH 2024. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# +from typing import Annotated + +from ultralytics import YOLO +from zenml import step, ArtifactConfig, log_artifact_metadata + +from materializers.yolo_materializer import UltralyticsMaterializer +from utils import load_images_from_folder, load_and_split_data + + +@step(output_materializers={"Trained_YOLO": UltralyticsMaterializer}) +def train_model( + model: YOLO, + dataset_name: str = "zenml_test_project", +) -> Annotated[ + YOLO, ArtifactConfig(name="Trained_YOLO", is_model_artifact=True) +]: + data_path = load_and_split_data(dataset_name) + model.train(data=data_path, epochs=1) + + # model.train(data="coco8.yaml", epochs=3) # train the model + metrics = model.val() # evaluate model performance on the validation set + + log_artifact_metadata( + artifact_name="Trained_YOLO", + metadata={"metrics": metrics.results_dict}, + ) + + # Read images as PIL images from directory metrics.save_dir for all png and jpg files + images = load_images_from_folder(metrics.save_dir) + + return model \ No newline at end of file diff --git a/end-to-end-computer-vision/utils.py b/end-to-end-computer-vision/utils.py new file mode 100644 index 00000000..fa9aadfa --- /dev/null +++ b/end-to-end-computer-vision/utils.py @@ -0,0 +1,74 @@ +# Apache Software License 2.0 +# +# Copyright (c) ZenML GmbH 2024. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# +import os +import tempfile + +from PIL import Image +from zenml.client import Client + +from utils.split_data import unzip_dataset, split_dataset, generate_yaml + + +def load_images_from_folder(folder): + images = [] + for filename in os.listdir(folder): + if ( + filename.endswith(".png") + or filename.endswith(".jpg") + or filename.endswith(".jpeg") + ): + with open(os.path.join(folder, filename), "rb") as f: + img = Image.open(f) + images.append(img) + return images + + +def load_and_split_data(dataset_name: str) -> str: + + annotator = Client().active_stack.annotator + from zenml.integrations.label_studio.annotators.label_studio_annotator import ( + LabelStudioAnnotator, + ) + + if not isinstance(annotator, LabelStudioAnnotator): + raise TypeError( + "This step can only be used with the Label Studio annotator." + ) + + if annotator and annotator._connection_available(): + for dataset in annotator.get_datasets(): + if dataset.get_params()["title"] == dataset_name: + tmpfile_ = tempfile.NamedTemporaryFile( + dir="data", delete=False + ) + tmpdirname = os.path.basename(tmpfile_.name) + export_location = os.path.join(tmpdirname, "data.zip") + extract_location = os.path.join(tmpdirname, "data") + dataset.export_tasks( + export_type="YOLO", + export_location=export_location, + download_resources=True, + ) + extract_location = os.path.join(tmpdirname, "data") + + unzip_dataset(export_location, extract_location) + split_dataset( + extract_location, ratio=(0.7, 0.15, 0.15), seed=42 + ) + return generate_yaml(extract_location) + + raise ValueError(f"Dataset {dataset_name} not found in Label Studio.") From ae505b5a73bc8dfa74b7f5d3151a970b6acc658f Mon Sep 17 00:00:00 2001 From: Alex Strick van Linschoten Date: Mon, 22 Apr 2024 22:32:10 +0200 Subject: [PATCH 016/121] extra cleaning --- .../label_studio_yolo_dataset_materializer.py | 21 ++++++++++++------- .../pipelines/data_export.py | 18 ++++++++++++---- end-to-end-computer-vision/run.py | 2 +- 3 files changed, 28 insertions(+), 13 deletions(-) diff --git a/end-to-end-computer-vision/materializers/label_studio_yolo_dataset_materializer.py b/end-to-end-computer-vision/materializers/label_studio_yolo_dataset_materializer.py index 60559b67..09cca7a6 100644 --- a/end-to-end-computer-vision/materializers/label_studio_yolo_dataset_materializer.py +++ b/end-to-end-computer-vision/materializers/label_studio_yolo_dataset_materializer.py @@ -14,7 +14,7 @@ """Implementation of the PyTorch DataLoader materializer.""" import os -from typing import Any, ClassVar, Type, Optional +from typing import Any, ClassVar, List, Type, Optional import tempfile from pydantic import BaseModel @@ -32,6 +32,7 @@ class LabelStudioYOLODataset: dataset: Optional[Any] filepath: Optional[str] + task_ids: Optional[List[int]] def download_dataset(self): tmpfile_ = tempfile.NamedTemporaryFile( @@ -43,6 +44,7 @@ def download_dataset(self): export_type="YOLO", export_location=self.filepath, download_resources=True, + ids=self.task_ids, ) @@ -65,14 +67,17 @@ def load(self, data_type: Type[Any]) -> LabelStudioYOLODataset: filepath = os.path.join(self.uri, DEFAULT_FILENAME) # Create a temporary folder - with tempfile.TemporaryDirectory(prefix="zenml-temp-") as temp_dir: - temp_file = os.path.join(str(temp_dir), DEFAULT_FILENAME) + tmpfile_ = tempfile.NamedTemporaryFile( + dir="data", delete=False + ) + temp_dir = os.path.basename(tmpfile_.name) + temp_file = os.path.join(str(temp_dir), DEFAULT_FILENAME) - # Copy from artifact store to temporary file - fileio.copy(filepath, temp_file) - dataset = LabelStudioYOLODataset(filepath=temp_file) + # Copy from artifact store to temporary file + fileio.copy(filepath, temp_file) + dataset = LabelStudioYOLODataset(filepath=temp_file) - return dataset + return dataset def save(self, dataset: LabelStudioYOLODataset) -> None: """Creates a JSON serialization for a label studio YOLO dataset model. @@ -85,6 +90,6 @@ def save(self, dataset: LabelStudioYOLODataset) -> None: filepath = os.path.join(self.uri, DEFAULT_FILENAME) # Make a temporary phantom artifact - with tempfile.NamedTemporaryFile(mode="w", suffix=".json") as f: + with tempfile.NamedTemporaryFile(mode="w", suffix=".zip") as f: # Copy it into artifact store fileio.copy(dataset.filepath, filepath) diff --git a/end-to-end-computer-vision/pipelines/data_export.py b/end-to-end-computer-vision/pipelines/data_export.py index 09c157d8..3e3776f5 100644 --- a/end-to-end-computer-vision/pipelines/data_export.py +++ b/end-to-end-computer-vision/pipelines/data_export.py @@ -16,18 +16,19 @@ # import os import tempfile -from typing import Any, Annotated +from typing import Any, Annotated, List, Tuple from zenml import pipeline, step from zenml.client import Client from zenml.logger import get_logger - +from zenml import log_artifact_metadata +from zenml import get_step_context from materializers.label_studio_yolo_dataset_materializer import LabelStudioYOLODataset, LabelStudioYOLODatasetMaterializer logger = get_logger(__name__) @step(output_materializers=LabelStudioYOLODatasetMaterializer) -def load_data_from_label_studio(dataset_name: str) -> Annotated[LabelStudioYOLODataset, "yolo_dataset"]: +def load_data_from_label_studio(dataset_name: str) -> Tuple[Annotated[LabelStudioYOLODataset, "yolo_dataset"], Annotated[List[int], "task_ids"]]: annotator = Client().active_stack.annotator from zenml.integrations.label_studio.annotators.label_studio_annotator import ( LabelStudioAnnotator, @@ -43,7 +44,16 @@ def load_data_from_label_studio(dataset_name: str) -> Annotated[LabelStudioYOLOD if dataset.get_params()["title"] == dataset_name: ls_dataset = LabelStudioYOLODataset() ls_dataset.dataset = dataset - return ls_dataset + + c = Client() + step_context = get_step_context() + + last_run = c.get_pipeline().last_successful_run + last_task_ids = last_run.steps[-1].outputs['task_ids'].read() + latest_task_ids = dataset.get_task_ids() + new_task_ids = list(set(latest_task_ids) - set(last_task_ids)) + + return ls_dataset, new_task_ids else: raise TypeError( "This step can only be used with an active Label Studio annotator." diff --git a/end-to-end-computer-vision/run.py b/end-to-end-computer-vision/run.py index 8ec23e04..0419640c 100644 --- a/end-to-end-computer-vision/run.py +++ b/end-to-end-computer-vision/run.py @@ -143,5 +143,5 @@ def my_pipeline(): if __name__ == "__main__": - data_export() #.with_options(config_path="configs/data_export_alexej.yaml")() + data_export(dataset_name="cv_proj") #.with_options(config_path="configs/data_export_alexej.yaml")() #my_pipeline() From 84241d7e74d9c172c60471786b75b6fc365f2dcc Mon Sep 17 00:00:00 2001 From: Alexej Penner Date: Mon, 22 Apr 2024 23:19:34 +0200 Subject: [PATCH 017/121] Working data loader pipeline --- .../configs/training.yaml | 2 + .../label_studio_yolo_dataset_materializer.py | 16 ++---- .../pipelines/data_export.py | 44 ++-------------- end-to-end-computer-vision/run.py | 5 +- .../steps/export_label_studio.py | 52 +++++++++++++++++++ .../{utils.py => utils/dataset_utils.py} | 1 - 6 files changed, 64 insertions(+), 56 deletions(-) rename end-to-end-computer-vision/{utils.py => utils/dataset_utils.py} (97%) diff --git a/end-to-end-computer-vision/configs/training.yaml b/end-to-end-computer-vision/configs/training.yaml index 651e1995..476aaeae 100644 --- a/end-to-end-computer-vision/configs/training.yaml +++ b/end-to-end-computer-vision/configs/training.yaml @@ -5,6 +5,8 @@ settings: required_integrations: - gcp - torch + requirements: + - ultralytics # configuration of the Model Control Plane model: diff --git a/end-to-end-computer-vision/materializers/label_studio_yolo_dataset_materializer.py b/end-to-end-computer-vision/materializers/label_studio_yolo_dataset_materializer.py index 09cca7a6..91ef3dcb 100644 --- a/end-to-end-computer-vision/materializers/label_studio_yolo_dataset_materializer.py +++ b/end-to-end-computer-vision/materializers/label_studio_yolo_dataset_materializer.py @@ -17,13 +17,8 @@ from typing import Any, ClassVar, List, Type, Optional import tempfile -from pydantic import BaseModel -from ultralytics import YOLO - from zenml.io import fileio -from zenml.integrations.pytorch.materializers.pytorch_module_materializer import ( - PyTorchModuleMaterializer, -) + from zenml.materializers.base_materializer import BaseMaterializer DEFAULT_FILENAME = "data.zip" @@ -68,14 +63,13 @@ def load(self, data_type: Type[Any]) -> LabelStudioYOLODataset: # Create a temporary folder tmpfile_ = tempfile.NamedTemporaryFile( - dir="data", delete=False + dir="data", delete=False, suffix='.zip' ) - temp_dir = os.path.basename(tmpfile_.name) - temp_file = os.path.join(str(temp_dir), DEFAULT_FILENAME) # Copy from artifact store to temporary file - fileio.copy(filepath, temp_file) - dataset = LabelStudioYOLODataset(filepath=temp_file) + fileio.copy(filepath, tmpfile_.name, overwrite=True) + dataset = LabelStudioYOLODataset() + dataset.filepath = tmpfile_.name return dataset diff --git a/end-to-end-computer-vision/pipelines/data_export.py b/end-to-end-computer-vision/pipelines/data_export.py index 3e3776f5..8e844f41 100644 --- a/end-to-end-computer-vision/pipelines/data_export.py +++ b/end-to-end-computer-vision/pipelines/data_export.py @@ -14,50 +14,12 @@ # See the License for the specific language governing permissions and # limitations under the License. # -import os -import tempfile -from typing import Any, Annotated, List, Tuple -from zenml import pipeline, step -from zenml.client import Client +from zenml import pipeline from zenml.logger import get_logger -from zenml import log_artifact_metadata -from zenml import get_step_context -from materializers.label_studio_yolo_dataset_materializer import LabelStudioYOLODataset, LabelStudioYOLODatasetMaterializer -logger = get_logger(__name__) - - - -@step(output_materializers=LabelStudioYOLODatasetMaterializer) -def load_data_from_label_studio(dataset_name: str) -> Tuple[Annotated[LabelStudioYOLODataset, "yolo_dataset"], Annotated[List[int], "task_ids"]]: - annotator = Client().active_stack.annotator - from zenml.integrations.label_studio.annotators.label_studio_annotator import ( - LabelStudioAnnotator, - ) - if not isinstance(annotator, LabelStudioAnnotator): - raise TypeError( - "This step can only be used with the Label Studio annotator." - ) +from steps.export_label_studio import load_data_from_label_studio - if annotator and annotator._connection_available(): - for dataset in annotator.get_datasets(): - if dataset.get_params()["title"] == dataset_name: - ls_dataset = LabelStudioYOLODataset() - ls_dataset.dataset = dataset - - c = Client() - step_context = get_step_context() - - last_run = c.get_pipeline().last_successful_run - last_task_ids = last_run.steps[-1].outputs['task_ids'].read() - latest_task_ids = dataset.get_task_ids() - new_task_ids = list(set(latest_task_ids) - set(last_task_ids)) - - return ls_dataset, new_task_ids - else: - raise TypeError( - "This step can only be used with an active Label Studio annotator." - ) +logger = get_logger(__name__) @pipeline def data_export(dataset_name: str = 'polution'): diff --git a/end-to-end-computer-vision/run.py b/end-to-end-computer-vision/run.py index 63bc7fd0..dbafca84 100644 --- a/end-to-end-computer-vision/run.py +++ b/end-to-end-computer-vision/run.py @@ -20,8 +20,7 @@ from zenml.logger import get_logger from pipelines.data_export import data_export -from pipelines.training import training -from utils import load_images_from_folder, load_and_split_data +from utils.dataset_utils import load_images_from_folder, load_and_split_data from materializers.yolo_materializer import UltralyticsMaterializer logger = get_logger(__name__) @@ -90,4 +89,4 @@ def my_pipeline(): # data_export(dataset_name="cv_proj") #.with_options(config_path="configs/data_export_alexej.yaml")() #my_pipeline() data_export() #.with_options(config_path="configs/data_export_alexej.yaml")() - training()#.with + # training()#.with_options(config_path="configs/training.yaml")() diff --git a/end-to-end-computer-vision/steps/export_label_studio.py b/end-to-end-computer-vision/steps/export_label_studio.py index e69de29b..feb16205 100644 --- a/end-to-end-computer-vision/steps/export_label_studio.py +++ b/end-to-end-computer-vision/steps/export_label_studio.py @@ -0,0 +1,52 @@ +from typing import Annotated, Tuple, List + +from zenml import step, get_step_context +from zenml.client import Client + +from materializers.label_studio_yolo_dataset_materializer import \ + LabelStudioYOLODatasetMaterializer, LabelStudioYOLODataset + + +@step(output_materializers={"yolo_dataset": LabelStudioYOLODatasetMaterializer}) +def load_data_from_label_studio( + dataset_name: str +) -> Tuple[ + Annotated[LabelStudioYOLODataset, "yolo_dataset"], + Annotated[List[int], "task_ids"] +]: + annotator = Client().active_stack.annotator + from zenml.integrations.label_studio.annotators.label_studio_annotator import ( + LabelStudioAnnotator, + ) + + if not isinstance(annotator, LabelStudioAnnotator): + raise TypeError( + "This step can only be used with the Label Studio annotator." + ) + + if annotator and annotator._connection_available(): + for dataset in annotator.get_datasets(): + if dataset.get_params()["title"] == dataset_name: + ls_dataset = LabelStudioYOLODataset() + ls_dataset.dataset = dataset + + c = Client() + step_context = get_step_context() + cur_pipeline_name = step_context.pipeline.name + cur_step_name = step_context.step_name + + try: + last_run = c.get_pipeline(cur_pipeline_name).last_successful_run + last_task_ids = last_run.steps[cur_step_name].outputs[ + 'task_ids'].read() + except (RuntimeError, KeyError): + last_task_ids = [] + + cur_task_ids = dataset.get_tasks_ids() + new_task_ids = list(set(cur_task_ids) - set(last_task_ids)) + ls_dataset.task_ids = new_task_ids + return ls_dataset, new_task_ids + else: + raise TypeError( + "This step can only be used with an active Label Studio annotator." + ) diff --git a/end-to-end-computer-vision/utils.py b/end-to-end-computer-vision/utils/dataset_utils.py similarity index 97% rename from end-to-end-computer-vision/utils.py rename to end-to-end-computer-vision/utils/dataset_utils.py index fa9aadfa..a3e65cc3 100644 --- a/end-to-end-computer-vision/utils.py +++ b/end-to-end-computer-vision/utils/dataset_utils.py @@ -57,7 +57,6 @@ def load_and_split_data(dataset_name: str) -> str: ) tmpdirname = os.path.basename(tmpfile_.name) export_location = os.path.join(tmpdirname, "data.zip") - extract_location = os.path.join(tmpdirname, "data") dataset.export_tasks( export_type="YOLO", export_location=export_location, From 58f1014d7407fce1fddc34a71842cafb21907544 Mon Sep 17 00:00:00 2001 From: Alexej Penner Date: Mon, 22 Apr 2024 23:57:55 +0200 Subject: [PATCH 018/121] Training works --- .../configs/data_export_alexej.yaml | 4 +- .../configs/training.yaml | 5 +- .../pipelines/training.py | 14 ++-- end-to-end-computer-vision/run.py | 70 ++----------------- .../steps/export_label_studio.py | 8 +++ .../steps/load_model.py | 5 +- .../steps/train_model.py | 8 ++- .../utils/dataset_utils.py | 35 ++++------ 8 files changed, 45 insertions(+), 104 deletions(-) diff --git a/end-to-end-computer-vision/configs/data_export_alexej.yaml b/end-to-end-computer-vision/configs/data_export_alexej.yaml index dd38bd00..767e1b27 100644 --- a/end-to-end-computer-vision/configs/data_export_alexej.yaml +++ b/end-to-end-computer-vision/configs/data_export_alexej.yaml @@ -1,10 +1,12 @@ +enable_cache: False + # pipeline configuration parameters: dataset_name: polution # configuration of the Model Control Plane model: - name: YOLO + name: Yolo_Object_Detection license: Apache 2.0 description: Object Detection Model. tags: ["object detection", "customer_satisfaction"] \ No newline at end of file diff --git a/end-to-end-computer-vision/configs/training.yaml b/end-to-end-computer-vision/configs/training.yaml index 476aaeae..17fb6022 100644 --- a/end-to-end-computer-vision/configs/training.yaml +++ b/end-to-end-computer-vision/configs/training.yaml @@ -10,7 +10,8 @@ settings: # configuration of the Model Control Plane model: - name: YOLO + name: Yolo_Object_Detection license: Apache 2.0 description: Object Detection Model. - tags: ["object detection", "customer_satisfaction"] \ No newline at end of file + tags: ["object detection", "customer_satisfaction"] + version: staging \ No newline at end of file diff --git a/end-to-end-computer-vision/pipelines/training.py b/end-to-end-computer-vision/pipelines/training.py index bb8fda06..e069a3d1 100644 --- a/end-to-end-computer-vision/pipelines/training.py +++ b/end-to-end-computer-vision/pipelines/training.py @@ -14,15 +14,11 @@ # See the License for the specific language governing permissions and # limitations under the License. # - -from typing import Optional -from uuid import UUID - -from zenml import pipeline -from zenml.client import Client +from zenml import pipeline, get_pipeline_context from zenml.logger import get_logger -from run import train_model, predict_image +from steps.train_model import train_model +from steps.predict_image import predict_image from steps.load_model import load_model logger = get_logger(__name__) @@ -31,5 +27,7 @@ @pipeline def training(model_checkpoint: str = "yolov8l.pt"): model = load_model(model_checkpoint) - trained_model = train_model(model=model) + mv = get_pipeline_context().model + dataset = mv.get_artifact("yolo_dataset") + trained_model = train_model(model=model, dataset=dataset) predict_image(trained_model) diff --git a/end-to-end-computer-vision/run.py b/end-to-end-computer-vision/run.py index dbafca84..6904363f 100644 --- a/end-to-end-computer-vision/run.py +++ b/end-to-end-computer-vision/run.py @@ -14,79 +14,17 @@ # See the License for the specific language governing permissions and # limitations under the License. # -from zenml import step, pipeline, Model, ArtifactConfig, log_artifact_metadata -from ultralytics import YOLO -from typing_extensions import Annotated from zenml.logger import get_logger -from pipelines.data_export import data_export -from utils.dataset_utils import load_images_from_folder, load_and_split_data -from materializers.yolo_materializer import UltralyticsMaterializer +from pipelines import data_export +from pipelines.training import training logger = get_logger(__name__) -@step -def load_model( - model_checkpoint: str = "yolov8l.pt", -) -> Annotated[YOLO, ArtifactConfig(name="Raw_YOLO", is_model_artifact=True)]: - logger.info(f"Loading YOLO checkpoint {model_checkpoint}") - return YOLO(model_checkpoint) - # return YOLO() - - -# @step(output_materializers={"Trained_YOLO": UltralyticsMaterializer}) -# def train_model( -# model: YOLO, -# dataset_name: str = "zenml_test_project", -# ) -> Tuple[ -# Annotated[ -# YOLO, ArtifactConfig(name="Trained_YOLO", is_model_artifact=True) -# ], -# Annotated[Image.Image, "confusion_matrix"], -# ]: - - -@step(output_materializers={"Trained_YOLO": UltralyticsMaterializer}) -def train_model( - model: YOLO, - dataset_name: str = "zenml_test_project", -) -> Annotated[ - YOLO, ArtifactConfig(name="Trained_YOLO", is_model_artifact=True) -]: - data_path = load_and_split_data(dataset_name) - model.train(data=data_path, epochs=1) - - # model.train(data="coco8.yaml", epochs=3) # train the model - metrics = model.val() # evaluate model performance on the validation set - - log_artifact_metadata( - artifact_name="Trained_YOLO", - metadata={"metrics": metrics.results_dict}, - ) - - # Read images as PIL images from directory metrics.save_dir for all png and jpg files - images = load_images_from_folder(metrics.save_dir) - - return model - # return model, images[0] - - -@step -def predict_image(model: YOLO): - results = model("https://ultralytics.com/images/bus.jpg") - print(results) - - -@pipeline(enable_cache=True, model=Model(name="Yolo_Object_Detection")) -def my_pipeline(): - model = load_model() - trained_model = train_model(model=model) - predict_image(trained_model) - if __name__ == "__main__": # data_export(dataset_name="cv_proj") #.with_options(config_path="configs/data_export_alexej.yaml")() #my_pipeline() - data_export() #.with_options(config_path="configs/data_export_alexej.yaml")() - # training()#.with_options(config_path="configs/training.yaml")() + # data_export.with_options(config_path="configs/data_export_alexej.yaml")() + training.with_options(config_path="configs/training.yaml")() diff --git a/end-to-end-computer-vision/steps/export_label_studio.py b/end-to-end-computer-vision/steps/export_label_studio.py index feb16205..7e3ae982 100644 --- a/end-to-end-computer-vision/steps/export_label_studio.py +++ b/end-to-end-computer-vision/steps/export_label_studio.py @@ -2,10 +2,13 @@ from zenml import step, get_step_context from zenml.client import Client +from zenml.logger import get_logger from materializers.label_studio_yolo_dataset_materializer import \ LabelStudioYOLODatasetMaterializer, LabelStudioYOLODataset +logger = get_logger(__name__) + @step(output_materializers={"yolo_dataset": LabelStudioYOLODatasetMaterializer}) def load_data_from_label_studio( @@ -43,7 +46,12 @@ def load_data_from_label_studio( last_task_ids = [] cur_task_ids = dataset.get_tasks_ids() + logger.info(f"{len(cur_task_ids)} total labels found.") + new_task_ids = list(set(cur_task_ids) - set(last_task_ids)) + logger.info(f"{len(new_task_ids)} new labels are being beamed " + f"straight to you.") + ls_dataset.task_ids = new_task_ids return ls_dataset, new_task_ids else: diff --git a/end-to-end-computer-vision/steps/load_model.py b/end-to-end-computer-vision/steps/load_model.py index a1eb1bec..e7b7531b 100644 --- a/end-to-end-computer-vision/steps/load_model.py +++ b/end-to-end-computer-vision/steps/load_model.py @@ -22,9 +22,10 @@ logger = get_logger(__name__) -@step + +@step(enable_cache=True) def load_model( model_checkpoint: str, ) -> Annotated[YOLO, ArtifactConfig(name="Raw_YOLO", is_model_artifact=True)]: logger.info(f"Loading YOLO checkpoint {model_checkpoint}") - return YOLO(model_checkpoint) \ No newline at end of file + return YOLO() #model_checkpoint) \ No newline at end of file diff --git a/end-to-end-computer-vision/steps/train_model.py b/end-to-end-computer-vision/steps/train_model.py index c68cd6a6..d9438d40 100644 --- a/end-to-end-computer-vision/steps/train_model.py +++ b/end-to-end-computer-vision/steps/train_model.py @@ -19,18 +19,20 @@ from ultralytics import YOLO from zenml import step, ArtifactConfig, log_artifact_metadata +from materializers.label_studio_yolo_dataset_materializer import \ + LabelStudioYOLODataset from materializers.yolo_materializer import UltralyticsMaterializer -from utils import load_images_from_folder, load_and_split_data +from utils.dataset_utils import load_images_from_folder, load_and_split_data @step(output_materializers={"Trained_YOLO": UltralyticsMaterializer}) def train_model( model: YOLO, - dataset_name: str = "zenml_test_project", + dataset: LabelStudioYOLODataset, ) -> Annotated[ YOLO, ArtifactConfig(name="Trained_YOLO", is_model_artifact=True) ]: - data_path = load_and_split_data(dataset_name) + data_path = load_and_split_data(dataset=dataset) model.train(data=data_path, epochs=1) # model.train(data="coco8.yaml", epochs=3) # train the model diff --git a/end-to-end-computer-vision/utils/dataset_utils.py b/end-to-end-computer-vision/utils/dataset_utils.py index a3e65cc3..6e2b1b0e 100644 --- a/end-to-end-computer-vision/utils/dataset_utils.py +++ b/end-to-end-computer-vision/utils/dataset_utils.py @@ -20,6 +20,8 @@ from PIL import Image from zenml.client import Client +from materializers.label_studio_yolo_dataset_materializer import \ + LabelStudioYOLODataset from utils.split_data import unzip_dataset, split_dataset, generate_yaml @@ -37,7 +39,7 @@ def load_images_from_folder(folder): return images -def load_and_split_data(dataset_name: str) -> str: +def load_and_split_data(dataset: LabelStudioYOLODataset) -> str: annotator = Client().active_stack.annotator from zenml.integrations.label_studio.annotators.label_studio_annotator import ( @@ -48,26 +50,15 @@ def load_and_split_data(dataset_name: str) -> str: raise TypeError( "This step can only be used with the Label Studio annotator." ) + tmpfile_ = tempfile.NamedTemporaryFile( + dir="data", delete=False + ) + tmpdirname = os.path.basename(tmpfile_.name) - if annotator and annotator._connection_available(): - for dataset in annotator.get_datasets(): - if dataset.get_params()["title"] == dataset_name: - tmpfile_ = tempfile.NamedTemporaryFile( - dir="data", delete=False - ) - tmpdirname = os.path.basename(tmpfile_.name) - export_location = os.path.join(tmpdirname, "data.zip") - dataset.export_tasks( - export_type="YOLO", - export_location=export_location, - download_resources=True, - ) - extract_location = os.path.join(tmpdirname, "data") - - unzip_dataset(export_location, extract_location) - split_dataset( - extract_location, ratio=(0.7, 0.15, 0.15), seed=42 - ) - return generate_yaml(extract_location) + extract_location = os.path.join(tmpdirname, "data") - raise ValueError(f"Dataset {dataset_name} not found in Label Studio.") + unzip_dataset(dataset.filepath, extract_location) + split_dataset( + extract_location, ratio=(0.7, 0.15, 0.15), seed=42 + ) + return generate_yaml(extract_location) From 5e4bb28ee13d16017a06ef0d203c1c6eb827b408 Mon Sep 17 00:00:00 2001 From: Alexej Penner Date: Mon, 22 Apr 2024 23:59:54 +0200 Subject: [PATCH 019/121] Fixed config files --- end-to-end-computer-vision/configs/data_export_alexej.yaml | 2 +- end-to-end-computer-vision/configs/training.yaml | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/end-to-end-computer-vision/configs/data_export_alexej.yaml b/end-to-end-computer-vision/configs/data_export_alexej.yaml index 767e1b27..161f76d5 100644 --- a/end-to-end-computer-vision/configs/data_export_alexej.yaml +++ b/end-to-end-computer-vision/configs/data_export_alexej.yaml @@ -9,4 +9,4 @@ model: name: Yolo_Object_Detection license: Apache 2.0 description: Object Detection Model. - tags: ["object detection", "customer_satisfaction"] \ No newline at end of file + tags: ["object detection"] \ No newline at end of file diff --git a/end-to-end-computer-vision/configs/training.yaml b/end-to-end-computer-vision/configs/training.yaml index 17fb6022..1badbd73 100644 --- a/end-to-end-computer-vision/configs/training.yaml +++ b/end-to-end-computer-vision/configs/training.yaml @@ -13,5 +13,5 @@ model: name: Yolo_Object_Detection license: Apache 2.0 description: Object Detection Model. - tags: ["object detection", "customer_satisfaction"] + tags: ["object detection"] version: staging \ No newline at end of file From 424f87789991b42467b309fc56c27b20f31e15c0 Mon Sep 17 00:00:00 2001 From: Alex Strick van Linschoten Date: Tue, 23 Apr 2024 08:48:24 +0200 Subject: [PATCH 020/121] add notebooks --- .../notebooks/huggingface.ipynb | 112 ++++++ .../notebooks/ultralytics.ipynb | 372 ++++++++++++++++++ end-to-end-computer-vision/requirements.txt | 12 +- 3 files changed, 492 insertions(+), 4 deletions(-) create mode 100644 end-to-end-computer-vision/notebooks/huggingface.ipynb create mode 100644 end-to-end-computer-vision/notebooks/ultralytics.ipynb diff --git a/end-to-end-computer-vision/notebooks/huggingface.ipynb b/end-to-end-computer-vision/notebooks/huggingface.ipynb new file mode 100644 index 00000000..b8f5e049 --- /dev/null +++ b/end-to-end-computer-vision/notebooks/huggingface.ipynb @@ -0,0 +1,112 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import fiftyone as fo\n", + "import fiftyone.zoo as foz" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Downloading config file fiftyone.yml from Voxel51/mvtec-ad\n", + "Loading dataset\n" + ] + }, + { + "ename": "ValueError", + "evalue": "Dataset name 'Voxel51/mvtec-ad' is not available", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[0;32mIn[3], line 6\u001b[0m\n\u001b[1;32m 2\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mfiftyone\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mutils\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mhuggingface\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m \u001b[38;5;21;01mfouh\u001b[39;00m\n\u001b[1;32m 4\u001b[0m \u001b[38;5;66;03m# Load the dataset\u001b[39;00m\n\u001b[1;32m 5\u001b[0m \u001b[38;5;66;03m# Note: other available arguments include 'max_samples', etc\u001b[39;00m\n\u001b[0;32m----> 6\u001b[0m dataset \u001b[38;5;241m=\u001b[39m \u001b[43mfouh\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mload_from_hub\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mVoxel51/mvtec-ad\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmax_samples\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m5\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[1;32m 8\u001b[0m \u001b[38;5;66;03m# Launch the App\u001b[39;00m\n\u001b[1;32m 9\u001b[0m session \u001b[38;5;241m=\u001b[39m fo\u001b[38;5;241m.\u001b[39mlaunch_app(dataset)\n", + "File \u001b[0;32m~/.pyenv/versions/3.10.12/envs/ls51/lib/python3.10/site-packages/fiftyone/utils/huggingface.py:273\u001b[0m, in \u001b[0;36mload_from_hub\u001b[0;34m(repo_id, revision, split, splits, subset, subsets, max_samples, batch_size, num_workers, overwrite, persistent, name, token, config_file, **kwargs)\u001b[0m\n\u001b[1;32m 270\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m config \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m 271\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mCould not find fiftyone metadata for \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mrepo_id\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m--> 273\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43m_load_dataset_from_config\u001b[49m\u001b[43m(\u001b[49m\u001b[43mconfig\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n", + "File \u001b[0;32m~/.pyenv/versions/3.10.12/envs/ls51/lib/python3.10/site-packages/fiftyone/utils/huggingface.py:665\u001b[0m, in \u001b[0;36m_load_dataset_from_config\u001b[0;34m(config, **kwargs)\u001b[0m\n\u001b[1;32m 663\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m _load_parquet_files_dataset_from_config(config, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[1;32m 664\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m--> 665\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43m_load_fiftyone_dataset_from_config\u001b[49m\u001b[43m(\u001b[49m\u001b[43mconfig\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n", + "File \u001b[0;32m~/.pyenv/versions/3.10.12/envs/ls51/lib/python3.10/site-packages/fiftyone/utils/huggingface.py:1144\u001b[0m, in \u001b[0;36m_load_fiftyone_dataset_from_config\u001b[0;34m(config, **kwargs)\u001b[0m\n\u001b[1;32m 1141\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m name \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m 1142\u001b[0m dataset_kwargs[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mname\u001b[39m\u001b[38;5;124m\"\u001b[39m] \u001b[38;5;241m=\u001b[39m name\n\u001b[0;32m-> 1144\u001b[0m dataset \u001b[38;5;241m=\u001b[39m \u001b[43mfod\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mDataset\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfrom_dir\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdownload_dir\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mdataset_kwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 1146\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m dataset_type_name \u001b[38;5;241m!=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mFiftyOneDataset\u001b[39m\u001b[38;5;124m\"\u001b[39m:\n\u001b[1;32m 1147\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m dataset\n", + "File \u001b[0;32m~/.pyenv/versions/3.10.12/envs/ls51/lib/python3.10/site-packages/fiftyone/core/dataset.py:5557\u001b[0m, in \u001b[0;36mDataset.from_dir\u001b[0;34m(cls, dataset_dir, dataset_type, data_path, labels_path, name, persistent, overwrite, label_field, tags, dynamic, progress, **kwargs)\u001b[0m\n\u001b[1;32m 5445\u001b[0m \u001b[38;5;129m@classmethod\u001b[39m\n\u001b[1;32m 5446\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mfrom_dir\u001b[39m(\n\u001b[1;32m 5447\u001b[0m \u001b[38;5;28mcls\u001b[39m,\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 5459\u001b[0m \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs,\n\u001b[1;32m 5460\u001b[0m ):\n\u001b[1;32m 5461\u001b[0m \u001b[38;5;250m \u001b[39m\u001b[38;5;124;03m\"\"\"Creates a :class:`Dataset` from the contents of the given directory.\u001b[39;00m\n\u001b[1;32m 5462\u001b[0m \n\u001b[1;32m 5463\u001b[0m \u001b[38;5;124;03m You can create datasets with this method via the following basic\u001b[39;00m\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 5555\u001b[0m \u001b[38;5;124;03m a :class:`Dataset`\u001b[39;00m\n\u001b[1;32m 5556\u001b[0m \u001b[38;5;124;03m \"\"\"\u001b[39;00m\n\u001b[0;32m-> 5557\u001b[0m dataset \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mcls\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mname\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mpersistent\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mpersistent\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43moverwrite\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43moverwrite\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 5558\u001b[0m dataset\u001b[38;5;241m.\u001b[39madd_dir(\n\u001b[1;32m 5559\u001b[0m dataset_dir\u001b[38;5;241m=\u001b[39mdataset_dir,\n\u001b[1;32m 5560\u001b[0m dataset_type\u001b[38;5;241m=\u001b[39mdataset_type,\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 5567\u001b[0m \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs,\n\u001b[1;32m 5568\u001b[0m )\n\u001b[1;32m 5569\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m dataset\n", + "File \u001b[0;32m~/.pyenv/versions/3.10.12/envs/ls51/lib/python3.10/site-packages/fiftyone/core/singletons.py:36\u001b[0m, in \u001b[0;36mDatasetSingleton.__call__\u001b[0;34m(cls, name, _create, *args, **kwargs)\u001b[0m\n\u001b[1;32m 29\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m (\n\u001b[1;32m 30\u001b[0m _create\n\u001b[1;32m 31\u001b[0m \u001b[38;5;129;01mor\u001b[39;00m instance \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[1;32m 32\u001b[0m \u001b[38;5;129;01mor\u001b[39;00m instance\u001b[38;5;241m.\u001b[39mdeleted\n\u001b[1;32m 33\u001b[0m \u001b[38;5;129;01mor\u001b[39;00m instance\u001b[38;5;241m.\u001b[39mname \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[1;32m 34\u001b[0m ):\n\u001b[1;32m 35\u001b[0m instance \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mcls\u001b[39m\u001b[38;5;241m.\u001b[39m\u001b[38;5;21m__new__\u001b[39m(\u001b[38;5;28mcls\u001b[39m)\n\u001b[0;32m---> 36\u001b[0m \u001b[43minstance\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[38;5;21;43m__init__\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mname\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mname\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m_create\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m_create\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 37\u001b[0m name \u001b[38;5;241m=\u001b[39m instance\u001b[38;5;241m.\u001b[39mname \u001b[38;5;66;03m# `__init__` may have changed `name`\u001b[39;00m\n\u001b[1;32m 38\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n", + "File \u001b[0;32m~/.pyenv/versions/3.10.12/envs/ls51/lib/python3.10/site-packages/fiftyone/core/dataset.py:285\u001b[0m, in \u001b[0;36mDataset.__init__\u001b[0;34m(self, name, persistent, overwrite, _create, _virtual, **kwargs)\u001b[0m\n\u001b[1;32m 282\u001b[0m delete_dataset(name)\n\u001b[1;32m 284\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m _create:\n\u001b[0;32m--> 285\u001b[0m doc, sample_doc_cls, frame_doc_cls \u001b[38;5;241m=\u001b[39m \u001b[43m_create_dataset\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 286\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mname\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mpersistent\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mpersistent\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\n\u001b[1;32m 287\u001b[0m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 288\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m 289\u001b[0m doc, sample_doc_cls, frame_doc_cls \u001b[38;5;241m=\u001b[39m _load_dataset(\n\u001b[1;32m 290\u001b[0m \u001b[38;5;28mself\u001b[39m, name, virtual\u001b[38;5;241m=\u001b[39m_virtual\n\u001b[1;32m 291\u001b[0m )\n", + "File \u001b[0;32m~/.pyenv/versions/3.10.12/envs/ls51/lib/python3.10/site-packages/fiftyone/core/dataset.py:6934\u001b[0m, in \u001b[0;36m_create_dataset\u001b[0;34m(obj, name, persistent, _patches, _frames, _clips, _src_collection)\u001b[0m\n\u001b[1;32m 6925\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m_create_dataset\u001b[39m(\n\u001b[1;32m 6926\u001b[0m obj,\n\u001b[1;32m 6927\u001b[0m name,\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 6932\u001b[0m _src_collection\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m,\n\u001b[1;32m 6933\u001b[0m ):\n\u001b[0;32m-> 6934\u001b[0m slug \u001b[38;5;241m=\u001b[39m \u001b[43m_validate_dataset_name\u001b[49m\u001b[43m(\u001b[49m\u001b[43mname\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 6936\u001b[0m _id \u001b[38;5;241m=\u001b[39m ObjectId()\n\u001b[1;32m 6938\u001b[0m sample_collection_name \u001b[38;5;241m=\u001b[39m _make_sample_collection_name(\n\u001b[1;32m 6939\u001b[0m _id, patches\u001b[38;5;241m=\u001b[39m_patches, frames\u001b[38;5;241m=\u001b[39m_frames, clips\u001b[38;5;241m=\u001b[39m_clips\n\u001b[1;32m 6940\u001b[0m )\n", + "File \u001b[0;32m~/.pyenv/versions/3.10.12/envs/ls51/lib/python3.10/site-packages/fiftyone/core/dataset.py:115\u001b[0m, in \u001b[0;36m_validate_dataset_name\u001b[0;34m(name, skip)\u001b[0m\n\u001b[1;32m 113\u001b[0m clashing_name \u001b[38;5;241m=\u001b[39m clashing_name_doc[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mname\u001b[39m\u001b[38;5;124m\"\u001b[39m]\n\u001b[1;32m 114\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m clashing_name \u001b[38;5;241m==\u001b[39m name:\n\u001b[0;32m--> 115\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mDataset name \u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mname\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m is not available\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m 116\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m 117\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\n\u001b[1;32m 118\u001b[0m \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mDataset name \u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mname\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m is not available: slug \u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mslug\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 119\u001b[0m \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124min use by dataset \u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mclashing_name\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 120\u001b[0m )\n", + "\u001b[0;31mValueError\u001b[0m: Dataset name 'Voxel51/mvtec-ad' is not available" + ] + } + ], + "source": [ + "import fiftyone as fo\n", + "import fiftyone.utils.huggingface as fouh\n", + "\n", + "# Load the dataset\n", + "# Note: other available arguments include 'max_samples', etc\n", + "dataset = fouh.load_from_hub(\"Voxel51/mvtec-ad\", max_samples=5)\n", + "\n", + "# Launch the App\n", + "session = fo.launch_app(dataset)\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Suppress Ultralytics logging\n", + "import os; os.environ[\"YOLO_VERBOSE\"] = \"False\"\n", + "\n", + "import fiftyone as fo\n", + "import fiftyone.zoo as foz\n", + "import fiftyone.utils.ultralytics as fou\n", + "\n", + "from ultralytics import YOLO\n", + "\n", + "# YOLOv8\n", + "model = YOLO(\"yolov8s.pt\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "\n", + "\n", + "dataset.apply_model(model, label_field=\"boxes\")\n", + "\n", + "session = fo.launch_app(dataset)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "ls51", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.12" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/end-to-end-computer-vision/notebooks/ultralytics.ipynb b/end-to-end-computer-vision/notebooks/ultralytics.ipynb new file mode 100644 index 00000000..149f0eae --- /dev/null +++ b/end-to-end-computer-vision/notebooks/ultralytics.ipynb @@ -0,0 +1,372 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Ultralytics & Fiftyone: YOLOv5 Training and Inference" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Migrating database to v0.23.8\n", + "Downloading dataset to '/home/strickvl/fiftyone/quickstart'\n", + "Downloading dataset...\n", + " 100% |████| 187.5Mb/187.5Mb [443.0ms elapsed, 0s remaining, 423.3Mb/s] \n", + "Extracting dataset...\n", + "Parsing dataset metadata\n", + "Found 200 samples\n", + "Dataset info written to '/home/strickvl/fiftyone/quickstart/info.json'\n", + "Loading 'quickstart'\n", + " 100% |███████████████████| 25/25 [132.0ms elapsed, 0s remaining, 190.8 samples/s] \n", + "Dataset 'quickstart-25' created\n" + ] + } + ], + "source": [ + "# Suppress Ultralytics logging\n", + "import os; os.environ[\"YOLO_VERBOSE\"] = \"False\"\n", + "\n", + "import fiftyone as fo\n", + "import fiftyone.zoo as foz\n", + "import fiftyone.utils.ultralytics as fou\n", + "\n", + "from ultralytics import YOLO\n", + "\n", + "# Load an example dataset\n", + "dataset = foz.load_zoo_dataset(\"quickstart\", max_samples=25)\n", + "dataset.select_fields().keep_fields()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# YOLOv8\n", + "model = YOLO(\"yolov8s.pt\")\n", + "# model = YOLO(\"yolov8m.pt\")\n", + "# model = YOLO(\"yolov8l.pt\")\n", + "# model = YOLO(\"yolov8x.pt\")\n", + "\n", + "# YOLOv5\n", + "# model = YOLO(\"yolov5s.pt\")\n", + "# model = YOLO(\"yolov5m.pt\")\n", + "# model = YOLO(\"yolov5l.pt\")\n", + "# model = YOLO(\"yolov5x.pt\")\n", + "\n", + "# YOLOv9\n", + "# model = YOLO(\"yolov9c.pt\")\n", + "# model = YOLO(\"yolov9e.pt\")\n", + "\n", + "dataset.apply_model(model, label_field=\"boxes\")\n", + "\n", + "session = fo.launch_app(dataset)" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " 100% |███████████████████| 25/25 [246.4ms elapsed, 0s remaining, 101.5 samples/s] \n" + ] + } + ], + "source": [ + "for sample in dataset.iter_samples(progress=True):\n", + " result = model(sample.filepath)[0]\n", + " sample[\"boxes\"] = fou.to_detections(result)\n", + " sample.save()" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + ",\n", + " ,\n", + " ,\n", + " ],\n", + " }>,\n", + "}>" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "sample" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "dataset = foz.load_zoo_dataset(\n", + " \"coco-2017\",\n", + " split=\"validation\",\n", + " label_types=[\"detections\"],\n", + " classes=[\"cat\", \"dog\"],\n", + " max_samples=25,\n", + " dataset_name=\"coco-2017-validation-cats-dogs-detections\",\n", + ")\n", + "\n", + "session = fo.launch_app(dataset)" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " 100% |███████████████████| 25/25 [381.8ms elapsed, 0s remaining, 65.5 samples/s] \n" + ] + } + ], + "source": [ + "dataset.apply_model(model, label_field=\"boxes\")" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + " " + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "session = fo.launch_app(dataset)" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " 100% |███████████████████| 25/25 [281.6ms elapsed, 0s remaining, 88.8 samples/s] \n" + ] + } + ], + "source": [ + "# convert the detections to FiftyOne format\n", + "\n", + "for sample in dataset.iter_samples(progress=True):\n", + " result = model(sample.filepath)[0]\n", + " sample[\"boxes\"] = fou.to_detections(result)\n", + " sample.save()" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "

[\n",
+       "    'yolo-nas-torch',\n",
+       "    'yolov5l-coco-torch',\n",
+       "    'yolov5m-coco-torch',\n",
+       "    'yolov5n-coco-torch',\n",
+       "    'yolov5s-coco-torch',\n",
+       "    'yolov5x-coco-torch',\n",
+       "    'yolov8l-coco-torch',\n",
+       "    'yolov8l-obb-dotav1-torch',\n",
+       "    'yolov8l-seg-coco-torch',\n",
+       "    'yolov8l-world-torch',\n",
+       "    'yolov8m-coco-torch',\n",
+       "    'yolov8m-obb-dotav1-torch',\n",
+       "    'yolov8m-seg-coco-torch',\n",
+       "    'yolov8m-world-torch',\n",
+       "    'yolov8n-coco-torch',\n",
+       "    'yolov8n-obb-dotav1-torch',\n",
+       "    'yolov8n-seg-coco-torch',\n",
+       "    'yolov8s-coco-torch',\n",
+       "    'yolov8s-obb-dotav1-torch',\n",
+       "    'yolov8s-seg-coco-torch',\n",
+       "    'yolov8s-world-torch',\n",
+       "    'yolov8x-coco-torch',\n",
+       "    'yolov8x-obb-dotav1-torch',\n",
+       "    'yolov8x-seg-coco-torch',\n",
+       "    'yolov8x-world-torch',\n",
+       "    'yolov9c-coco-torch',\n",
+       "    'yolov9c-seg-coco-torch',\n",
+       "    'yolov9e-coco-torch',\n",
+       "    'yolov9e-seg-coco-torch'\n",
+       "]\n",
+       "
\n" + ], + "text/plain": [ + "\u001b[1m[\u001b[0m\n", + " \u001b[32m'yolo-nas-torch'\u001b[0m,\n", + " \u001b[32m'yolov5l-coco-torch'\u001b[0m,\n", + " \u001b[32m'yolov5m-coco-torch'\u001b[0m,\n", + " \u001b[32m'yolov5n-coco-torch'\u001b[0m,\n", + " \u001b[32m'yolov5s-coco-torch'\u001b[0m,\n", + " \u001b[32m'yolov5x-coco-torch'\u001b[0m,\n", + " \u001b[32m'yolov8l-coco-torch'\u001b[0m,\n", + " \u001b[32m'yolov8l-obb-dotav1-torch'\u001b[0m,\n", + " \u001b[32m'yolov8l-seg-coco-torch'\u001b[0m,\n", + " \u001b[32m'yolov8l-world-torch'\u001b[0m,\n", + " \u001b[32m'yolov8m-coco-torch'\u001b[0m,\n", + " \u001b[32m'yolov8m-obb-dotav1-torch'\u001b[0m,\n", + " \u001b[32m'yolov8m-seg-coco-torch'\u001b[0m,\n", + " \u001b[32m'yolov8m-world-torch'\u001b[0m,\n", + " \u001b[32m'yolov8n-coco-torch'\u001b[0m,\n", + " \u001b[32m'yolov8n-obb-dotav1-torch'\u001b[0m,\n", + " \u001b[32m'yolov8n-seg-coco-torch'\u001b[0m,\n", + " \u001b[32m'yolov8s-coco-torch'\u001b[0m,\n", + " \u001b[32m'yolov8s-obb-dotav1-torch'\u001b[0m,\n", + " \u001b[32m'yolov8s-seg-coco-torch'\u001b[0m,\n", + " \u001b[32m'yolov8s-world-torch'\u001b[0m,\n", + " \u001b[32m'yolov8x-coco-torch'\u001b[0m,\n", + " \u001b[32m'yolov8x-obb-dotav1-torch'\u001b[0m,\n", + " \u001b[32m'yolov8x-seg-coco-torch'\u001b[0m,\n", + " \u001b[32m'yolov8x-world-torch'\u001b[0m,\n", + " \u001b[32m'yolov9c-coco-torch'\u001b[0m,\n", + " \u001b[32m'yolov9c-seg-coco-torch'\u001b[0m,\n", + " \u001b[32m'yolov9e-coco-torch'\u001b[0m,\n", + " \u001b[32m'yolov9e-seg-coco-torch'\u001b[0m\n", + "\u001b[1m]\u001b[0m\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# see supported ultralytics models\n", + "from rich import print\n", + "print(foz.list_zoo_models(tags=\"yolo\"))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "ls51", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.12" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/end-to-end-computer-vision/requirements.txt b/end-to-end-computer-vision/requirements.txt index 97507e73..d05a2217 100644 --- a/end-to-end-computer-vision/requirements.txt +++ b/end-to-end-computer-vision/requirements.txt @@ -1,12 +1,16 @@ zenml[server]>=0.55.2 notebook scikit-learn<1.3 -s3fs>2022.3.0,<=2023.4.0 -boto3<=1.26.76 -aws-profile-manager -sagemaker==2.117.0 +# s3fs>2022.3.0,<=2023.4.0 +# boto3<=1.26.76 +# aws-profile-manager +# sagemaker==2.117.0 pyarrow wandb seaborn xgboost ultralytics +torch +huggingface_hub>=0.20.0 # needed for fiftyone imports of HF datasets +# fiftyone @ git+https://github.com/voxel51/fiftyone.git@develop # fiftyone v0.24.0 +fiftyone From 111ff947279451e543cd3ec35d7fe6d65b9555bd Mon Sep 17 00:00:00 2001 From: Alex Strick van Linschoten Date: Tue, 23 Apr 2024 09:50:24 +0200 Subject: [PATCH 021/121] remove aws requirements --- end-to-end-computer-vision/requirements.txt | 4 ---- 1 file changed, 4 deletions(-) diff --git a/end-to-end-computer-vision/requirements.txt b/end-to-end-computer-vision/requirements.txt index d05a2217..009650b2 100644 --- a/end-to-end-computer-vision/requirements.txt +++ b/end-to-end-computer-vision/requirements.txt @@ -1,10 +1,6 @@ zenml[server]>=0.55.2 notebook scikit-learn<1.3 -# s3fs>2022.3.0,<=2023.4.0 -# boto3<=1.26.76 -# aws-profile-manager -# sagemaker==2.117.0 pyarrow wandb seaborn From 881cd4ae44a14a725b19263f8db2e711b72b55e2 Mon Sep 17 00:00:00 2001 From: Alex Strick van Linschoten Date: Tue, 23 Apr 2024 09:50:34 +0200 Subject: [PATCH 022/121] update notebooks --- .../notebooks/label_studio_annotation.ipynb | 418 ++++++++++++++++++ .../notebooks/ultralytics.ipynb | 86 +++- 2 files changed, 494 insertions(+), 10 deletions(-) create mode 100644 end-to-end-computer-vision/notebooks/label_studio_annotation.ipynb diff --git a/end-to-end-computer-vision/notebooks/label_studio_annotation.ipynb b/end-to-end-computer-vision/notebooks/label_studio_annotation.ipynb new file mode 100644 index 00000000..b0e0ede9 --- /dev/null +++ b/end-to-end-computer-vision/notebooks/label_studio_annotation.ipynb @@ -0,0 +1,418 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Dataset already downloaded\n", + "Loading existing dataset 'coco-2014'. To reload from disk, either delete the existing dataset or provide a custom `dataset_name` to use\n", + "Connected to FiftyOne on port 5151 at localhost.\n", + "If you are not connecting to a remote session, you may need to start a new session and specify a port\n" + ] + }, + { + "data": { + "text/html": [ + "\n", + "\n", + "\n", + "
\n", + "
\n", + " \n", + "
\n", + " \n", + "
\n", + "\n", + "" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import fiftyone as fo\n", + "import fiftyone.zoo as foz\n", + "from fiftyone import ViewField as F\n", + "\n", + "# Step 1: Load your data into FiftyOne\n", + "\n", + "coco_dataset2 = foz.load_zoo_dataset(\n", + " \"quickstart\", dataset_name=\"coco-2014\"\n", + ")\n", + "# coco_dataset.persistent = True\n", + "\n", + "session = fo.launch_app(coco_dataset2)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Evaluating detections...\n", + " 100% |█████████████████| 200/200 [11.3s elapsed, 0s remaining, 12.8 samples/s] \n" + ] + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "coco_dataset2.evaluate_detections(\n", + " \"predictions\", gt_field=\"ground_truth\", eval_key=\"eval\"\n", + ")\n" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + " " + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "\n", + "# Step 2: Locate a subset of your data requiring annotation\n", + "\n", + "# Create a view that contains only high confidence false positive model\n", + "# predictions, with samples containing the most false positives first\n", + "most_fp_view = (\n", + " coco_dataset2\n", + " .filter_labels(\"predictions\", (F(\"confidence\") > 0.8) & (F(\"eval\") == \"fp\"))\n", + " .sort_by(F(\"predictions.detections\").length(), reverse=True)\n", + ")\n", + "\n", + "# Retrieve the sample with the most high confidence false positives\n", + "sample_id = most_fp_view.first().id\n", + "view = coco_dataset2.select(sample_id)\n", + "\n", + "session = fo.launch_app(view)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The backend 'labelstudio' does not support attributes. Provided attributes will be ignored.\n", + "Please enter your API key.\n", + "You can avoid this in the future by setting your `FIFTYONE_LABELSTUDIO_API_KEY` environment variable\n", + "Uploading media to Label Studio...\n", + "Computing metadata...\n", + " 100% |█████████████████████| 1/1 [18.0ms elapsed, 0s remaining, 55.5 samples/s] \n", + "Upload complete\n", + "Launching editor at 'http://localhost:8080/projects/2'...\n", + "{\n", + " \"key\": \"labelstudio_basic_recipe2\",\n", + " \"version\": \"0.23.8\",\n", + " \"timestamp\": \"2024-04-23T06:57:44.772629\",\n", + " \"config\": {\n", + " \"cls\": \"fiftyone.utils.labelstudio.LabelStudioBackendConfig\",\n", + " \"type\": \"annotation\",\n", + " \"method\": \"labelstudio\",\n", + " \"name\": \"labelstudio\",\n", + " \"label_schema\": {\n", + " \"new_ground_truth\": {\n", + " \"type\": \"detections\",\n", + " \"classes\": [\n", + " \"airplane\",\n", + " \"apple\",\n", + " \"backpack\",\n", + " \"banana\",\n", + " \"baseball glove\",\n", + " \"bear\",\n", + " \"bed\",\n", + " \"bench\",\n", + " \"bird\",\n", + " \"boat\",\n", + " \"book\",\n", + " \"bottle\",\n", + " \"bowl\",\n", + " \"broccoli\",\n", + " \"bus\",\n", + " \"cake\",\n", + " \"car\",\n", + " \"carrot\",\n", + " \"cat\",\n", + " \"cell phone\",\n", + " \"chair\",\n", + " \"clock\",\n", + " \"couch\",\n", + " \"cow\",\n", + " \"cup\",\n", + " \"dining table\",\n", + " \"dog\",\n", + " \"donut\",\n", + " \"elephant\",\n", + " \"fire hydrant\",\n", + " \"fork\",\n", + " \"frisbee\",\n", + " \"giraffe\",\n", + " \"handbag\",\n", + " \"horse\",\n", + " \"hot dog\",\n", + " \"kite\",\n", + " \"knife\",\n", + " \"laptop\",\n", + " \"microwave\",\n", + " \"motorcycle\",\n", + " \"orange\",\n", + " \"oven\",\n", + " \"parking meter\",\n", + " \"person\",\n", + " \"pizza\",\n", + " \"refrigerator\",\n", + " \"sandwich\",\n", + " \"scissors\",\n", + " \"sheep\",\n", + " \"sink\",\n", + " \"skateboard\",\n", + " \"skis\",\n", + " \"snowboard\",\n", + " \"spoon\",\n", + " \"sports ball\",\n", + " \"stop sign\",\n", + " \"suitcase\",\n", + " \"surfboard\",\n", + " \"teddy bear\",\n", + " \"tennis racket\",\n", + " \"tie\",\n", + " \"toothbrush\",\n", + " \"traffic light\",\n", + " \"train\",\n", + " \"truck\",\n", + " \"tv\",\n", + " \"umbrella\",\n", + " \"vase\",\n", + " \"wine glass\",\n", + " \"zebra\"\n", + " ],\n", + " \"attributes\": {},\n", + " \"existing_field\": false\n", + " }\n", + " },\n", + " \"media_field\": \"filepath\",\n", + " \"url\": \"http://localhost:8080\",\n", + " \"project_name\": null\n", + " }\n", + "}\n" + ] + } + ], + "source": [ + "\n", + "# Step 3: Send samples to Label Studio\n", + "\n", + "# A unique identifier for this run\n", + "anno_key = \"labelstudio_basic_recipe2\"\n", + "\n", + "label_schema = {\n", + " \"new_ground_truth\": {\n", + " \"type\": \"detections\",\n", + " \"classes\": coco_dataset2.distinct(\"ground_truth.detections.label\"),\n", + " },\n", + "}\n", + "\n", + "view.annotate(\n", + " anno_key,\n", + " backend=\"labelstudio\",\n", + " label_schema=label_schema,\n", + " launch_editor=True,\n", + " url=\"http://localhost:8080\",\n", + ")\n", + "print(coco_dataset2.get_annotation_info(anno_key))\n", + "\n", + "# Step 4: Perform annotation in Label Studio and save the tasks" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "ename": "ValueError", + "evalue": "Dataset 'quickstart' not found", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[0;32mIn[10], line 7\u001b[0m\n\u001b[1;32m 3\u001b[0m anno_key \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mlabelstudio_basic_recipe2\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 5\u001b[0m \u001b[38;5;66;03m# Step 5: Merge annotations back into FiftyOne dataset\u001b[39;00m\n\u001b[0;32m----> 7\u001b[0m dataset \u001b[38;5;241m=\u001b[39m \u001b[43mfo\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mload_dataset\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mquickstart\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[1;32m 8\u001b[0m dataset\u001b[38;5;241m.\u001b[39mload_annotations(anno_key)\n\u001b[1;32m 10\u001b[0m \u001b[38;5;66;03m# Load the view that was annotated in the App\u001b[39;00m\n", + "File \u001b[0;32m~/.pyenv/versions/3.10.12/envs/ls51/lib/python3.10/site-packages/fiftyone/core/dataset.py:142\u001b[0m, in \u001b[0;36mload_dataset\u001b[0;34m(name)\u001b[0m\n\u001b[1;32m 125\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mload_dataset\u001b[39m(name):\n\u001b[1;32m 126\u001b[0m \u001b[38;5;250m \u001b[39m\u001b[38;5;124;03m\"\"\"Loads the FiftyOne dataset with the given name.\u001b[39;00m\n\u001b[1;32m 127\u001b[0m \n\u001b[1;32m 128\u001b[0m \u001b[38;5;124;03m To create a new dataset, use the :class:`Dataset` constructor.\u001b[39;00m\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 140\u001b[0m \u001b[38;5;124;03m a :class:`Dataset`\u001b[39;00m\n\u001b[1;32m 141\u001b[0m \u001b[38;5;124;03m \"\"\"\u001b[39;00m\n\u001b[0;32m--> 142\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mDataset\u001b[49m\u001b[43m(\u001b[49m\u001b[43mname\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m_create\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mFalse\u001b[39;49;00m\u001b[43m)\u001b[49m\n", + "File \u001b[0;32m~/.pyenv/versions/3.10.12/envs/ls51/lib/python3.10/site-packages/fiftyone/core/singletons.py:36\u001b[0m, in \u001b[0;36mDatasetSingleton.__call__\u001b[0;34m(cls, name, _create, *args, **kwargs)\u001b[0m\n\u001b[1;32m 29\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m (\n\u001b[1;32m 30\u001b[0m _create\n\u001b[1;32m 31\u001b[0m \u001b[38;5;129;01mor\u001b[39;00m instance \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[1;32m 32\u001b[0m \u001b[38;5;129;01mor\u001b[39;00m instance\u001b[38;5;241m.\u001b[39mdeleted\n\u001b[1;32m 33\u001b[0m \u001b[38;5;129;01mor\u001b[39;00m instance\u001b[38;5;241m.\u001b[39mname \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[1;32m 34\u001b[0m ):\n\u001b[1;32m 35\u001b[0m instance \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mcls\u001b[39m\u001b[38;5;241m.\u001b[39m\u001b[38;5;21m__new__\u001b[39m(\u001b[38;5;28mcls\u001b[39m)\n\u001b[0;32m---> 36\u001b[0m \u001b[43minstance\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[38;5;21;43m__init__\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mname\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mname\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m_create\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m_create\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 37\u001b[0m name \u001b[38;5;241m=\u001b[39m instance\u001b[38;5;241m.\u001b[39mname \u001b[38;5;66;03m# `__init__` may have changed `name`\u001b[39;00m\n\u001b[1;32m 38\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n", + "File \u001b[0;32m~/.pyenv/versions/3.10.12/envs/ls51/lib/python3.10/site-packages/fiftyone/core/dataset.py:289\u001b[0m, in \u001b[0;36mDataset.__init__\u001b[0;34m(self, name, persistent, overwrite, _create, _virtual, **kwargs)\u001b[0m\n\u001b[1;32m 285\u001b[0m doc, sample_doc_cls, frame_doc_cls \u001b[38;5;241m=\u001b[39m _create_dataset(\n\u001b[1;32m 286\u001b[0m \u001b[38;5;28mself\u001b[39m, name, persistent\u001b[38;5;241m=\u001b[39mpersistent, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs\n\u001b[1;32m 287\u001b[0m )\n\u001b[1;32m 288\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m--> 289\u001b[0m doc, sample_doc_cls, frame_doc_cls \u001b[38;5;241m=\u001b[39m \u001b[43m_load_dataset\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 290\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mname\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mvirtual\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m_virtual\u001b[49m\n\u001b[1;32m 291\u001b[0m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 293\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_doc \u001b[38;5;241m=\u001b[39m doc\n\u001b[1;32m 294\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_sample_doc_cls \u001b[38;5;241m=\u001b[39m sample_doc_cls\n", + "File \u001b[0;32m~/.pyenv/versions/3.10.12/envs/ls51/lib/python3.10/site-packages/fiftyone/core/dataset.py:7168\u001b[0m, in \u001b[0;36m_load_dataset\u001b[0;34m(obj, name, virtual)\u001b[0m\n\u001b[1;32m 7166\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m_load_dataset\u001b[39m(obj, name, virtual\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mFalse\u001b[39;00m):\n\u001b[1;32m 7167\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m virtual:\n\u001b[0;32m-> 7168\u001b[0m \u001b[43mfomi\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mmigrate_dataset_if_necessary\u001b[49m\u001b[43m(\u001b[49m\u001b[43mname\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 7170\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m 7171\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m _do_load_dataset(obj, name)\n", + "File \u001b[0;32m~/.pyenv/versions/3.10.12/envs/ls51/lib/python3.10/site-packages/fiftyone/migrations/runner.py:222\u001b[0m, in \u001b[0;36mmigrate_dataset_if_necessary\u001b[0;34m(name, destination, error_level, verbose)\u001b[0m\n\u001b[1;32m 220\u001b[0m _migrate_dataset_if_necessary(name, destination, verbose)\n\u001b[1;32m 221\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mException\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m e:\n\u001b[0;32m--> 222\u001b[0m \u001b[43mfou\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mhandle_error\u001b[49m\u001b[43m(\u001b[49m\u001b[43me\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43merror_level\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43merror_level\u001b[49m\u001b[43m)\u001b[49m\n", + "File \u001b[0;32m~/.pyenv/versions/3.10.12/envs/ls51/lib/python3.10/site-packages/fiftyone/core/utils.py:681\u001b[0m, in \u001b[0;36mhandle_error\u001b[0;34m(error, error_level, base_error)\u001b[0m\n\u001b[1;32m 668\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mhandle_error\u001b[39m(error, error_level, base_error\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m):\n\u001b[1;32m 669\u001b[0m \u001b[38;5;250m \u001b[39m\u001b[38;5;124;03m\"\"\"Handles the error at the specified error level.\u001b[39;00m\n\u001b[1;32m 670\u001b[0m \n\u001b[1;32m 671\u001b[0m \u001b[38;5;124;03m Args:\u001b[39;00m\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 679\u001b[0m \u001b[38;5;124;03m base_error: (optional) a base Exception from which to raise ``error``\u001b[39;00m\n\u001b[1;32m 680\u001b[0m \u001b[38;5;124;03m \"\"\"\u001b[39;00m\n\u001b[0;32m--> 681\u001b[0m \u001b[43metau\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mhandle_error\u001b[49m\u001b[43m(\u001b[49m\u001b[43merror\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43merror_level\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mbase_error\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mbase_error\u001b[49m\u001b[43m)\u001b[49m\n", + "File \u001b[0;32m~/.pyenv/versions/3.10.12/envs/ls51/lib/python3.10/site-packages/eta/core/utils.py:1016\u001b[0m, in \u001b[0;36mhandle_error\u001b[0;34m(error, error_level, base_error)\u001b[0m\n\u001b[1;32m 1014\u001b[0m six\u001b[38;5;241m.\u001b[39mraise_from(error, base_error)\n\u001b[1;32m 1015\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m-> 1016\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m error\n\u001b[1;32m 1018\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m error_level \u001b[38;5;241m==\u001b[39m \u001b[38;5;241m1\u001b[39m:\n\u001b[1;32m 1019\u001b[0m logger\u001b[38;5;241m.\u001b[39mwarning(error)\n", + "File \u001b[0;32m~/.pyenv/versions/3.10.12/envs/ls51/lib/python3.10/site-packages/fiftyone/migrations/runner.py:220\u001b[0m, in \u001b[0;36mmigrate_dataset_if_necessary\u001b[0;34m(name, destination, error_level, verbose)\u001b[0m\n\u001b[1;32m 201\u001b[0m \u001b[38;5;250m\u001b[39m\u001b[38;5;124;03m\"\"\"Migrates the dataset from its current revision to the specified\u001b[39;00m\n\u001b[1;32m 202\u001b[0m \u001b[38;5;124;03mdestination revision.\u001b[39;00m\n\u001b[1;32m 203\u001b[0m \n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 217\u001b[0m \u001b[38;5;124;03m verbose (False): whether to log incremental migrations that are run\u001b[39;00m\n\u001b[1;32m 218\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m 219\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m--> 220\u001b[0m \u001b[43m_migrate_dataset_if_necessary\u001b[49m\u001b[43m(\u001b[49m\u001b[43mname\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdestination\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mverbose\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 221\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mException\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m e:\n\u001b[1;32m 222\u001b[0m fou\u001b[38;5;241m.\u001b[39mhandle_error(e, error_level\u001b[38;5;241m=\u001b[39merror_level)\n", + "File \u001b[0;32m~/.pyenv/versions/3.10.12/envs/ls51/lib/python3.10/site-packages/fiftyone/migrations/runner.py:229\u001b[0m, in \u001b[0;36m_migrate_dataset_if_necessary\u001b[0;34m(name, destination, verbose)\u001b[0m\n\u001b[1;32m 226\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m _migrations_disabled():\n\u001b[1;32m 227\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m\n\u001b[0;32m--> 229\u001b[0m head \u001b[38;5;241m=\u001b[39m \u001b[43mget_dataset_revision\u001b[49m\u001b[43m(\u001b[49m\u001b[43mname\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 230\u001b[0m db_version \u001b[38;5;241m=\u001b[39m get_database_revision()\n\u001b[1;32m 232\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m head \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n", + "File \u001b[0;32m~/.pyenv/versions/3.10.12/envs/ls51/lib/python3.10/site-packages/fiftyone/migrations/runner.py:62\u001b[0m, in \u001b[0;36mget_dataset_revision\u001b[0;34m(name)\u001b[0m\n\u001b[1;32m 60\u001b[0m dataset_doc \u001b[38;5;241m=\u001b[39m conn\u001b[38;5;241m.\u001b[39mdatasets\u001b[38;5;241m.\u001b[39mfind_one({\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mname\u001b[39m\u001b[38;5;124m\"\u001b[39m: name}, {\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mversion\u001b[39m\u001b[38;5;124m\"\u001b[39m: \u001b[38;5;241m1\u001b[39m})\n\u001b[1;32m 61\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m dataset_doc \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m---> 62\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mDataset \u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;132;01m%s\u001b[39;00m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m not found\u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;241m%\u001b[39m name)\n\u001b[1;32m 64\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m dataset_doc\u001b[38;5;241m.\u001b[39mget(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mversion\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;28;01mNone\u001b[39;00m)\n", + "\u001b[0;31mValueError\u001b[0m: Dataset 'quickstart' not found" + ] + } + ], + "source": [ + "import fiftyone as fo\n", + "\n", + "anno_key = \"labelstudio_basic_recipe2\"\n", + "\n", + "# Step 5: Merge annotations back into FiftyOne dataset\n", + "\n", + "dataset = fo.load_dataset(\"quickstart\")\n", + "dataset.load_annotations(anno_key)\n", + "\n", + "# Load the view that was annotated in the App\n", + "view = dataset.load_annotation_view(anno_key)\n", + "session = fo.launch_app(view=view)\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "\n", + "# Step 6: Cleanup\n", + "\n", + "# Delete tasks from Label Studio\n", + "results = dataset.load_annotation_results(anno_key)\n", + "results.cleanup()\n", + "\n", + "# Delete run record (not the labels) from FiftyOne\n", + "dataset.delete_annotation_run(anno_key)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "ls51", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.12" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/end-to-end-computer-vision/notebooks/ultralytics.ipynb b/end-to-end-computer-vision/notebooks/ultralytics.ipynb index 149f0eae..f58bd6c8 100644 --- a/end-to-end-computer-vision/notebooks/ultralytics.ipynb +++ b/end-to-end-computer-vision/notebooks/ultralytics.ipynb @@ -211,18 +211,84 @@ "data": { "text/html": [ "\n", - " \n", - " " + "\n", + "\n", + "
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\n", + "\n", + "" ], "text/plain": [ - "" + "" ] }, "metadata": {}, From 009bc74ea5cb64e098856de540f83602ed125249 Mon Sep 17 00:00:00 2001 From: Alex Strick van Linschoten Date: Tue, 23 Apr 2024 10:51:48 +0200 Subject: [PATCH 023/121] add env vars --- end-to-end-computer-vision/README.md | 8 +++++++- 1 file changed, 7 insertions(+), 1 deletion(-) diff --git a/end-to-end-computer-vision/README.md b/end-to-end-computer-vision/README.md index c108c838..10fb6ab8 100644 --- a/end-to-end-computer-vision/README.md +++ b/end-to-end-computer-vision/README.md @@ -2,4 +2,10 @@ ```commandline zenml integration install label_studio torch gcp pip install -r requirements.txt -``` \ No newline at end of file +``` + +# Env variables + +```bash +DATA_UPLOAD_MAX_NUMBER_FILES = 1000000 +``` From e66a23e4707e8ef10d2a334c3cdb4c436506ff67 Mon Sep 17 00:00:00 2001 From: Alex Strick van Linschoten Date: Tue, 23 Apr 2024 11:05:20 +0200 Subject: [PATCH 024/121] updates --- .../notebooks/fiftyone_prelabelling.ipynb | 413 ++++++++++++++++++ end-to-end-computer-vision/requirements.txt | 1 + 2 files changed, 414 insertions(+) create mode 100644 end-to-end-computer-vision/notebooks/fiftyone_prelabelling.ipynb diff --git a/end-to-end-computer-vision/notebooks/fiftyone_prelabelling.ipynb b/end-to-end-computer-vision/notebooks/fiftyone_prelabelling.ipynb new file mode 100644 index 00000000..5a509ea6 --- /dev/null +++ b/end-to-end-computer-vision/notebooks/fiftyone_prelabelling.ipynb @@ -0,0 +1,413 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Download the data" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "1da63562d69241818c06d6192a3c741f", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "Resolving data files: 0%| | 0/257 [00:00, , ]\n" + ] + } + ], + "source": [ + "import fiftyone as fo\n", + "\n", + "name = \"ships\"\n", + "\n", + "# Create the dataset\n", + "dataset = fo.Dataset.from_dir(\n", + " dataset_dir=output_dir,\n", + " dataset_type=fo.types.ImageDirectory,\n", + " name=name,\n", + ")\n", + "\n", + "# View summary info about the dataset\n", + "print(dataset)\n", + "\n", + "# Print the first few samples in the dataset\n", + "print(dataset.head())" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Make initial predictions using pre-trained Yolov8 model" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [], + "source": [ + "# Suppress Ultralytics logging\n", + "import os; os.environ[\"YOLO_VERBOSE\"] = \"False\"\n", + "\n", + "import fiftyone as fo\n", + "import fiftyone.zoo as foz\n", + "import fiftyone.utils.ultralytics as fou\n", + "\n", + "from ultralytics import YOLO" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# YOLOv8\n", + "model = YOLO(\"yolov8s.pt\")\n", + "\n", + "dataset.apply_model(model, label_field=\"boxes\")\n", + "\n", + "session = fo.launch_app(dataset)" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The backend 'labelstudio' does not support attributes. Provided attributes will be ignored.\n", + "Uploading media to Label Studio...\n", + "Upload complete\n", + "Launching editor at 'http://localhost:8080/projects/8'...\n", + "{\n", + " \"key\": \"shipdet1\",\n", + " \"version\": \"0.23.8\",\n", + " \"timestamp\": \"2024-04-23T08:51:56.926945\",\n", + " \"config\": {\n", + " \"cls\": \"fiftyone.utils.labelstudio.LabelStudioBackendConfig\",\n", + " \"type\": \"annotation\",\n", + " \"method\": \"labelstudio\",\n", + " \"name\": \"labelstudio\",\n", + " \"label_schema\": {\n", + " \"new_ground_truth\": {\n", + " \"type\": \"detections\",\n", + " \"classes\": [\n", + " \"ship\"\n", + " ],\n", + " \"attributes\": {},\n", + " \"existing_field\": false\n", + " }\n", + " },\n", + " \"media_field\": \"filepath\",\n", + " \"url\": \"http://localhost:8080\",\n", + " \"project_name\": null\n", + " }\n", + "}\n" + ] + } + ], + "source": [ + "\n", + "# Step 3: Send samples to Label Studio\n", + "\n", + "# A unique identifier for this run\n", + "anno_key = \"shipdet1\"\n", + "\n", + "label_schema = {\n", + " \"new_ground_truth\": {\n", + " \"type\": \"detections\",\n", + " \"classes\": [\"ship\"],\n", + " },\n", + "}\n", + "\n", + "dataset.annotate(\n", + " anno_key,\n", + " backend=\"labelstudio\",\n", + " label_schema=label_schema,\n", + " launch_editor=True,\n", + " url=\"http://localhost:8080\",\n", + " api_key=\"9896fa7abd15277ab9046842469fe3c2ec17e0bb\"\n", + ")\n", + "print(dataset.get_annotation_info(anno_key))\n", + "\n", + "# Step 4: Perform annotation in Label Studio and save the tasks" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Downloading labels from Label Studio...\n", + "Download complete\n", + "Loading labels for field 'new_ground_truth'...\n", + " 100% |███████████████████| 15/15 [12.7ms elapsed, 0s remaining, 1.2K samples/s] \n" + ] + }, + { + "data": { + "text/html": [ + "\n", + " \n", + " " + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import fiftyone as fo\n", + "\n", + "# Step 5: Merge annotations back into FiftyOne dataset\n", + "\n", + "dataset = fo.load_dataset(name)\n", + "dataset.load_annotations(anno_key)\n", + "\n", + "# Load the view that was annotated in the App\n", + "view = dataset.load_annotation_view(anno_key)\n", + "session = fo.launch_app(view=view)\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "ls51", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.12" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/end-to-end-computer-vision/requirements.txt b/end-to-end-computer-vision/requirements.txt index 009650b2..3e6b7019 100644 --- a/end-to-end-computer-vision/requirements.txt +++ b/end-to-end-computer-vision/requirements.txt @@ -10,3 +10,4 @@ torch huggingface_hub>=0.20.0 # needed for fiftyone imports of HF datasets # fiftyone @ git+https://github.com/voxel51/fiftyone.git@develop # fiftyone v0.24.0 fiftyone +datasets From c2abecedbfef4e88cd520e1af1e1bfd7ce963315 Mon Sep 17 00:00:00 2001 From: Alex Strick van Linschoten Date: Tue, 23 Apr 2024 11:53:54 +0200 Subject: [PATCH 025/121] Update environment variables and add new model configuration --- end-to-end-computer-vision/README.md | 3 ++- .../configs/data_export_alex.yaml | 12 ++++++++++++ .../configs/data_export_alexej.yaml | 4 ++-- 3 files changed, 16 insertions(+), 3 deletions(-) create mode 100644 end-to-end-computer-vision/configs/data_export_alex.yaml diff --git a/end-to-end-computer-vision/README.md b/end-to-end-computer-vision/README.md index 10fb6ab8..ea41b02e 100644 --- a/end-to-end-computer-vision/README.md +++ b/end-to-end-computer-vision/README.md @@ -7,5 +7,6 @@ pip install -r requirements.txt # Env variables ```bash -DATA_UPLOAD_MAX_NUMBER_FILES = 1000000 +export DATA_UPLOAD_MAX_NUMBER_FILES=1000000 +export WANDB_DISABLED=True ``` diff --git a/end-to-end-computer-vision/configs/data_export_alex.yaml b/end-to-end-computer-vision/configs/data_export_alex.yaml new file mode 100644 index 00000000..ea84cf1d --- /dev/null +++ b/end-to-end-computer-vision/configs/data_export_alex.yaml @@ -0,0 +1,12 @@ +enable_cache: False + +# pipeline configuration +parameters: + dataset_name: "FiftyOne_ships_1713862317" + +# configuration of the Model Control Plane +model: + name: Yolo_Object_Detection + license: Apache 2.0 + description: Object Detection Model. + tags: ["object detection"] diff --git a/end-to-end-computer-vision/configs/data_export_alexej.yaml b/end-to-end-computer-vision/configs/data_export_alexej.yaml index 161f76d5..5d25c82e 100644 --- a/end-to-end-computer-vision/configs/data_export_alexej.yaml +++ b/end-to-end-computer-vision/configs/data_export_alexej.yaml @@ -2,11 +2,11 @@ enable_cache: False # pipeline configuration parameters: - dataset_name: polution + dataset_name: "polution" # configuration of the Model Control Plane model: name: Yolo_Object_Detection license: Apache 2.0 description: Object Detection Model. - tags: ["object detection"] \ No newline at end of file + tags: ["object detection"] From e7a11013ba66650ffb37e09b9c886e59664944d5 Mon Sep 17 00:00:00 2001 From: Alex Strick van Linschoten Date: Tue, 23 Apr 2024 12:26:25 +0200 Subject: [PATCH 026/121] add inference and add to README --- end-to-end-computer-vision/README.md | 7 +++ .../pipelines/inference.py | 46 +++++++++++++++++++ 2 files changed, 53 insertions(+) create mode 100644 end-to-end-computer-vision/pipelines/inference.py diff --git a/end-to-end-computer-vision/README.md b/end-to-end-computer-vision/README.md index ea41b02e..21c3a52f 100644 --- a/end-to-end-computer-vision/README.md +++ b/end-to-end-computer-vision/README.md @@ -2,6 +2,13 @@ ```commandline zenml integration install label_studio torch gcp pip install -r requirements.txt +pip uninstall wandb +``` + +## Maybe + +```shell +zenml integration install mlflow -y ``` # Env variables diff --git a/end-to-end-computer-vision/pipelines/inference.py b/end-to-end-computer-vision/pipelines/inference.py new file mode 100644 index 00000000..0f4e9771 --- /dev/null +++ b/end-to-end-computer-vision/pipelines/inference.py @@ -0,0 +1,46 @@ +# Apache Software License 2.0 +# +# Copyright (c) ZenML GmbH 2024. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# +from zenml import pipeline, get_pipeline_context, step +from zenml.logger import get_logger + +from steps.train_model import train_model +from steps.predict_image import predict_image +from steps.load_model import load_model +from zenml import Model +logger = get_logger(__name__) +from zenml.client import Client + + + +@step +def predict_over_images(): + images_dir_path = "data/ships/subset" + # model = Model( + # name="Yolo_Object_Detection", + # version="staging", + # ) + # model.get_model_artifact(name="staging").load() + # model_artifact = model_version.get_model_artifact(name="Raw_YOLO") + artifact = Client().get_artifact_version('105c768c-8c86-465a-b018-b1a800ad4e19') + yolo_model = artifact.load() + results = yolo_model(images_dir_path, half=True, conf=0.6) + breakpoint() + + +@pipeline +def inference(): + predict_over_images() From 4fc5c6f6603e1beca72172dd5888b9ad08d27752 Mon Sep 17 00:00:00 2001 From: Alex Strick van Linschoten Date: Tue, 23 Apr 2024 12:42:42 +0200 Subject: [PATCH 027/121] update inference --- .../notebooks/ultralytics.ipynb | 319 ++++++++++++++++++ .../pipelines/inference.py | 39 ++- 2 files changed, 352 insertions(+), 6 deletions(-) diff --git a/end-to-end-computer-vision/notebooks/ultralytics.ipynb b/end-to-end-computer-vision/notebooks/ultralytics.ipynb index f58bd6c8..5c053e47 100644 --- a/end-to-end-computer-vision/notebooks/ultralytics.ipynb +++ b/end-to-end-computer-vision/notebooks/ultralytics.ipynb @@ -406,6 +406,325 @@ "print(foz.list_zoo_models(tags=\"yolo\"))" ] }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\u001b[33mYour artifact was materialized under Python version 'unknown' but you are currently using '3.10.12'. This might cause unexpected behavior since pickle is not reproducible across Python versions. Attempting to load anyway...\u001b[0m\n" + ] + }, + { + "data": { + "text/html": [ + "
╭─────────────────────────────── Traceback (most recent call last) ────────────────────────────────╮\n",
+       " /home/strickvl/.pyenv/versions/3.10.12/envs/ls51/lib/python3.10/site-packages/fiftyone/core/data \n",
+       " set.py:7174 in _load_dataset                                                                     \n",
+       "                                                                                                  \n",
+       "   7171 │   │   return _do_load_dataset(obj, name)                                                \n",
+       "   7172 except Exception as e:                                                                \n",
+       "   7173 │   │   try:                                                                              \n",
+       " 7174 │   │   │   version = fomi.get_dataset_revision(name)                                     \n",
+       "   7175 │   │   except:                                                                           \n",
+       "   7176 │   │   │   raise e                                                                       \n",
+       "   7177                                                                                           \n",
+       "                                                                                                  \n",
+       " /home/strickvl/.pyenv/versions/3.10.12/envs/ls51/lib/python3.10/site-packages/fiftyone/migration \n",
+       " s/runner.py:62 in get_dataset_revision                                                           \n",
+       "                                                                                                  \n",
+       "    59 conn = foo.get_db_conn()                                                               \n",
+       "    60 dataset_doc = conn.datasets.find_one({\"name\": name}, {\"version\": 1})                   \n",
+       "    61 if dataset_doc is None:                                                                \n",
+       "  62 │   │   raise ValueError(\"Dataset '%s' not found\" % name)                                  \n",
+       "    63                                                                                        \n",
+       "    64 return dataset_doc.get(\"version\", None)                                                \n",
+       "    65                                                                                            \n",
+       "╰──────────────────────────────────────────────────────────────────────────────────────────────────╯\n",
+       "ValueError: Dataset 'ships' not found\n",
+       "\n",
+       "During handling of the above exception, another exception occurred:\n",
+       "\n",
+       "╭─────────────────────────────── Traceback (most recent call last) ────────────────────────────────╮\n",
+       " in <module>:6                                                                                    \n",
+       "                                                                                                  \n",
+       "   3                                                                                              \n",
+       "   4 artifact = Client().get_artifact_version('40e81c44-bfb0-4f84-9c18-78571f6c1577')             \n",
+       "   5 dataset = artifact.load()                                                                    \n",
+       " 6 session = fo.launch_app(dataset)                                                             \n",
+       "   7                                                                                              \n",
+       "   8                                                                                              \n",
+       "                                                                                                  \n",
+       " /home/strickvl/.pyenv/versions/3.10.12/envs/ls51/lib/python3.10/site-packages/fiftyone/core/sess \n",
+       " ion/session.py:196 in launch_app                                                                 \n",
+       "                                                                                                  \n",
+       "    193 │   │   a :class:`Session`                                                                \n",
+       "    194 \"\"\"                                                                                   \n",
+       "    195 global _session  # pylint: disable=global-statement                                   \n",
+       "  196 _session = Session(                                                                   \n",
+       "    197 │   │   dataset=dataset,                                                                  \n",
+       "    198 │   │   view=view,                                                                        \n",
+       "    199 │   │   spaces=spaces,                                                                    \n",
+       "                                                                                                  \n",
+       " /home/strickvl/.pyenv/versions/3.10.12/envs/ls51/lib/python3.10/site-packages/fiftyone/core/sess \n",
+       " ion/session.py:413 in __init__                                                                   \n",
+       "                                                                                                  \n",
+       "    410 │   │   if spaces is None:                                                                \n",
+       "    411 │   │   │   spaces = default_spaces.copy()                                                \n",
+       "    412 │   │                                                                                     \n",
+       "  413 │   │   self._state = StateDescription(                                                   \n",
+       "    414 │   │   │   config=config,                                                                \n",
+       "    415 │   │   │   dataset=view._root_dataset if view is not None else dataset,                  \n",
+       "    416 │   │   │   view=view,                                                                    \n",
+       "                                                                                                  \n",
+       " /home/strickvl/.pyenv/versions/3.10.12/envs/ls51/lib/python3.10/site-packages/fiftyone/core/stat \n",
+       " e.py:70 in __init__                                                                              \n",
+       "                                                                                                  \n",
+       "    67 │   │   self.selected = selected or []                                                     \n",
+       "    68 │   │   self.selected_labels = selected_labels or []                                       \n",
+       "    69 │   │   if dataset is not None:                                                            \n",
+       "  70 │   │   │   dataset.reload()                                                               \n",
+       "    71 │   │   self.view = (                                                                      \n",
+       "    72 │   │   │   dataset.load_saved_view(view_name)                                             \n",
+       "    73 │   │   │   if dataset is not None and view_name                                           \n",
+       "                                                                                                  \n",
+       " /home/strickvl/.pyenv/versions/3.10.12/envs/ls51/lib/python3.10/site-packages/fiftyone/core/data \n",
+       " set.py:6808 in reload                                                                            \n",
+       "                                                                                                  \n",
+       "   6805                                                                                       \n",
+       "   6806 def reload(self):                                                                     \n",
+       "   6807 │   │   \"\"\"Reloads the dataset and any in-memory samples from the database.\"\"\"            \n",
+       " 6808 │   │   self._reload(hard=True)                                                           \n",
+       "   6809 │   │   self._reload_docs(hard=True)                                                      \n",
+       "   6810                                                                                       \n",
+       "   6811 def clear_cache(self):                                                                \n",
+       "                                                                                                  \n",
+       " /home/strickvl/.pyenv/versions/3.10.12/envs/ls51/lib/python3.10/site-packages/fiftyone/core/data \n",
+       " set.py:6831 in _reload                                                                           \n",
+       "                                                                                                  \n",
+       "   6828 │   │   │   self._doc.reload()                                                            \n",
+       "   6829 │   │   │   return                                                                        \n",
+       "   6830 │   │                                                                                     \n",
+       " 6831 │   │   doc, sample_doc_cls, frame_doc_cls = _load_dataset(                               \n",
+       "   6832 │   │   │   self, self.name, virtual=True                                                 \n",
+       "   6833 │   │   )                                                                                 \n",
+       "   6834                                                                                           \n",
+       "                                                                                                  \n",
+       " /home/strickvl/.pyenv/versions/3.10.12/envs/ls51/lib/python3.10/site-packages/fiftyone/core/data \n",
+       " set.py:7176 in _load_dataset                                                                     \n",
+       "                                                                                                  \n",
+       "   7173 │   │   try:                                                                              \n",
+       "   7174 │   │   │   version = fomi.get_dataset_revision(name)                                     \n",
+       "   7175 │   │   except:                                                                           \n",
+       " 7176 │   │   │   raise e                                                                       \n",
+       "   7177 │   │                                                                                     \n",
+       "   7178 │   │   if version != focn.VERSION:                                                       \n",
+       "   7179 │   │   │   raise ValueError(                                                             \n",
+       "                                                                                                  \n",
+       " /home/strickvl/.pyenv/versions/3.10.12/envs/ls51/lib/python3.10/site-packages/fiftyone/core/data \n",
+       " set.py:7171 in _load_dataset                                                                     \n",
+       "                                                                                                  \n",
+       "   7168 │   │   fomi.migrate_dataset_if_necessary(name)                                           \n",
+       "   7169                                                                                       \n",
+       "   7170 try:                                                                                  \n",
+       " 7171 │   │   return _do_load_dataset(obj, name)                                                \n",
+       "   7172 except Exception as e:                                                                \n",
+       "   7173 │   │   try:                                                                              \n",
+       "   7174 │   │   │   version = fomi.get_dataset_revision(name)                                     \n",
+       "                                                                                                  \n",
+       " /home/strickvl/.pyenv/versions/3.10.12/envs/ls51/lib/python3.10/site-packages/fiftyone/core/data \n",
+       " set.py:7193 in _do_load_dataset                                                                  \n",
+       "                                                                                                  \n",
+       "   7190 db = foo.get_db_conn()                                                                \n",
+       "   7191 res = db.datasets.find_one({\"name\": name})                                            \n",
+       "   7192 if not res:                                                                           \n",
+       " 7193 │   │   raise ValueError(\"Dataset '%s' not found\" % name)                                 \n",
+       "   7194 dataset_doc = foo.DatasetDocument.from_dict(res)                                      \n",
+       "   7195                                                                                       \n",
+       "   7196 sample_collection_name = dataset_doc.sample_collection_name                           \n",
+       "╰──────────────────────────────────────────────────────────────────────────────────────────────────╯\n",
+       "ValueError: Dataset 'ships' not found\n",
+       "
\n" + ], + "text/plain": [ + "\u001b[31m╭─\u001b[0m\u001b[31m──────────────────────────────\u001b[0m\u001b[31m \u001b[0m\u001b[1;31mTraceback \u001b[0m\u001b[1;2;31m(most recent call last)\u001b[0m\u001b[31m \u001b[0m\u001b[31m───────────────────────────────\u001b[0m\u001b[31m─╮\u001b[0m\n", + "\u001b[31m│\u001b[0m \u001b[2;33m/home/strickvl/.pyenv/versions/3.10.12/envs/ls51/lib/python3.10/site-packages/fiftyone/core/\u001b[0m\u001b[1;33mdata\u001b[0m \u001b[31m│\u001b[0m\n", + "\u001b[31m│\u001b[0m \u001b[1;33mset.py\u001b[0m:\u001b[94m7174\u001b[0m in \u001b[92m_load_dataset\u001b[0m \u001b[31m│\u001b[0m\n", + "\u001b[31m│\u001b[0m \u001b[31m│\u001b[0m\n", + "\u001b[31m│\u001b[0m \u001b[2m7171 \u001b[0m\u001b[2m│ │ \u001b[0m\u001b[94mreturn\u001b[0m _do_load_dataset(obj, name) \u001b[31m│\u001b[0m\n", + "\u001b[31m│\u001b[0m \u001b[2m7172 \u001b[0m\u001b[2m│ \u001b[0m\u001b[94mexcept\u001b[0m \u001b[96mException\u001b[0m \u001b[94mas\u001b[0m e: \u001b[31m│\u001b[0m\n", + "\u001b[31m│\u001b[0m \u001b[2m7173 \u001b[0m\u001b[2m│ │ \u001b[0m\u001b[94mtry\u001b[0m: \u001b[31m│\u001b[0m\n", + "\u001b[31m│\u001b[0m \u001b[31m❱ \u001b[0m7174 \u001b[2m│ │ │ \u001b[0mversion = fomi.get_dataset_revision(name) \u001b[31m│\u001b[0m\n", + "\u001b[31m│\u001b[0m \u001b[2m7175 \u001b[0m\u001b[2m│ │ \u001b[0m\u001b[94mexcept\u001b[0m: \u001b[31m│\u001b[0m\n", + "\u001b[31m│\u001b[0m \u001b[2m7176 \u001b[0m\u001b[2m│ │ │ \u001b[0m\u001b[94mraise\u001b[0m e \u001b[31m│\u001b[0m\n", + "\u001b[31m│\u001b[0m \u001b[2m7177 \u001b[0m \u001b[31m│\u001b[0m\n", + "\u001b[31m│\u001b[0m \u001b[31m│\u001b[0m\n", + "\u001b[31m│\u001b[0m \u001b[2;33m/home/strickvl/.pyenv/versions/3.10.12/envs/ls51/lib/python3.10/site-packages/fiftyone/migration\u001b[0m \u001b[31m│\u001b[0m\n", + "\u001b[31m│\u001b[0m \u001b[2;33ms/\u001b[0m\u001b[1;33mrunner.py\u001b[0m:\u001b[94m62\u001b[0m in \u001b[92mget_dataset_revision\u001b[0m \u001b[31m│\u001b[0m\n", + "\u001b[31m│\u001b[0m \u001b[31m│\u001b[0m\n", + "\u001b[31m│\u001b[0m \u001b[2m 59 \u001b[0m\u001b[2m│ \u001b[0mconn = foo.get_db_conn() \u001b[31m│\u001b[0m\n", + "\u001b[31m│\u001b[0m \u001b[2m 60 \u001b[0m\u001b[2m│ \u001b[0mdataset_doc = conn.datasets.find_one({\u001b[33m\"\u001b[0m\u001b[33mname\u001b[0m\u001b[33m\"\u001b[0m: name}, {\u001b[33m\"\u001b[0m\u001b[33mversion\u001b[0m\u001b[33m\"\u001b[0m: \u001b[94m1\u001b[0m}) \u001b[31m│\u001b[0m\n", + "\u001b[31m│\u001b[0m \u001b[2m 61 \u001b[0m\u001b[2m│ \u001b[0m\u001b[94mif\u001b[0m dataset_doc \u001b[95mis\u001b[0m \u001b[94mNone\u001b[0m: \u001b[31m│\u001b[0m\n", + "\u001b[31m│\u001b[0m \u001b[31m❱ \u001b[0m 62 \u001b[2m│ │ \u001b[0m\u001b[94mraise\u001b[0m \u001b[96mValueError\u001b[0m(\u001b[33m\"\u001b[0m\u001b[33mDataset \u001b[0m\u001b[33m'\u001b[0m\u001b[33m%s\u001b[0m\u001b[33m'\u001b[0m\u001b[33m not found\u001b[0m\u001b[33m\"\u001b[0m % name) \u001b[31m│\u001b[0m\n", + "\u001b[31m│\u001b[0m \u001b[2m 63 \u001b[0m\u001b[2m│ \u001b[0m \u001b[31m│\u001b[0m\n", + "\u001b[31m│\u001b[0m \u001b[2m 64 \u001b[0m\u001b[2m│ \u001b[0m\u001b[94mreturn\u001b[0m dataset_doc.get(\u001b[33m\"\u001b[0m\u001b[33mversion\u001b[0m\u001b[33m\"\u001b[0m, \u001b[94mNone\u001b[0m) \u001b[31m│\u001b[0m\n", + "\u001b[31m│\u001b[0m \u001b[2m 65 \u001b[0m \u001b[31m│\u001b[0m\n", + "\u001b[31m╰──────────────────────────────────────────────────────────────────────────────────────────────────╯\u001b[0m\n", + "\u001b[1;91mValueError: \u001b[0mDataset \u001b[32m'ships'\u001b[0m not found\n", + "\n", + "\u001b[3mDuring handling of the above exception, another exception occurred:\u001b[0m\n", + "\n", + "\u001b[31m╭─\u001b[0m\u001b[31m──────────────────────────────\u001b[0m\u001b[31m \u001b[0m\u001b[1;31mTraceback \u001b[0m\u001b[1;2;31m(most recent call last)\u001b[0m\u001b[31m \u001b[0m\u001b[31m───────────────────────────────\u001b[0m\u001b[31m─╮\u001b[0m\n", + "\u001b[31m│\u001b[0m in \u001b[92m\u001b[0m:\u001b[94m6\u001b[0m \u001b[31m│\u001b[0m\n", + "\u001b[31m│\u001b[0m \u001b[31m│\u001b[0m\n", + "\u001b[31m│\u001b[0m \u001b[2m3 \u001b[0m \u001b[31m│\u001b[0m\n", + "\u001b[31m│\u001b[0m \u001b[2m4 \u001b[0martifact = Client().get_artifact_version(\u001b[33m'\u001b[0m\u001b[33m40e81c44-bfb0-4f84-9c18-78571f6c1577\u001b[0m\u001b[33m'\u001b[0m) \u001b[31m│\u001b[0m\n", + "\u001b[31m│\u001b[0m \u001b[2m5 \u001b[0mdataset = artifact.load() \u001b[31m│\u001b[0m\n", + "\u001b[31m│\u001b[0m \u001b[31m❱ \u001b[0m6 session = fo.launch_app(dataset) \u001b[31m│\u001b[0m\n", + "\u001b[31m│\u001b[0m \u001b[2m7 \u001b[0m \u001b[31m│\u001b[0m\n", + "\u001b[31m│\u001b[0m \u001b[2m8 \u001b[0m \u001b[31m│\u001b[0m\n", + "\u001b[31m│\u001b[0m \u001b[31m│\u001b[0m\n", + "\u001b[31m│\u001b[0m \u001b[2;33m/home/strickvl/.pyenv/versions/3.10.12/envs/ls51/lib/python3.10/site-packages/fiftyone/core/sess\u001b[0m \u001b[31m│\u001b[0m\n", + "\u001b[31m│\u001b[0m \u001b[2;33mion/\u001b[0m\u001b[1;33msession.py\u001b[0m:\u001b[94m196\u001b[0m in \u001b[92mlaunch_app\u001b[0m \u001b[31m│\u001b[0m\n", + "\u001b[31m│\u001b[0m \u001b[31m│\u001b[0m\n", + "\u001b[31m│\u001b[0m \u001b[2m 193 \u001b[0m\u001b[2;33m│ │ \u001b[0m\u001b[33ma :class:`Session`\u001b[0m \u001b[31m│\u001b[0m\n", + "\u001b[31m│\u001b[0m \u001b[2m 194 \u001b[0m\u001b[2;33m│ \u001b[0m\u001b[33m\"\"\"\u001b[0m \u001b[31m│\u001b[0m\n", + "\u001b[31m│\u001b[0m \u001b[2m 195 \u001b[0m\u001b[2m│ \u001b[0m\u001b[94mglobal\u001b[0m _session \u001b[2m# pylint: disable=global-statement\u001b[0m \u001b[31m│\u001b[0m\n", + "\u001b[31m│\u001b[0m \u001b[31m❱ \u001b[0m 196 \u001b[2m│ \u001b[0m_session = Session( \u001b[31m│\u001b[0m\n", + "\u001b[31m│\u001b[0m \u001b[2m 197 \u001b[0m\u001b[2m│ │ \u001b[0mdataset=dataset, \u001b[31m│\u001b[0m\n", + "\u001b[31m│\u001b[0m \u001b[2m 198 \u001b[0m\u001b[2m│ │ \u001b[0mview=view, \u001b[31m│\u001b[0m\n", + "\u001b[31m│\u001b[0m \u001b[2m 199 \u001b[0m\u001b[2m│ │ \u001b[0mspaces=spaces, \u001b[31m│\u001b[0m\n", + "\u001b[31m│\u001b[0m \u001b[31m│\u001b[0m\n", + "\u001b[31m│\u001b[0m \u001b[2;33m/home/strickvl/.pyenv/versions/3.10.12/envs/ls51/lib/python3.10/site-packages/fiftyone/core/sess\u001b[0m \u001b[31m│\u001b[0m\n", + "\u001b[31m│\u001b[0m \u001b[2;33mion/\u001b[0m\u001b[1;33msession.py\u001b[0m:\u001b[94m413\u001b[0m in \u001b[92m__init__\u001b[0m \u001b[31m│\u001b[0m\n", + "\u001b[31m│\u001b[0m \u001b[31m│\u001b[0m\n", + "\u001b[31m│\u001b[0m \u001b[2m 410 \u001b[0m\u001b[2m│ │ \u001b[0m\u001b[94mif\u001b[0m spaces \u001b[95mis\u001b[0m \u001b[94mNone\u001b[0m: \u001b[31m│\u001b[0m\n", + "\u001b[31m│\u001b[0m \u001b[2m 411 \u001b[0m\u001b[2m│ │ │ \u001b[0mspaces = default_spaces.copy() \u001b[31m│\u001b[0m\n", + "\u001b[31m│\u001b[0m \u001b[2m 412 \u001b[0m\u001b[2m│ │ \u001b[0m \u001b[31m│\u001b[0m\n", + "\u001b[31m│\u001b[0m \u001b[31m❱ \u001b[0m 413 \u001b[2m│ │ \u001b[0m\u001b[96mself\u001b[0m._state = StateDescription( \u001b[31m│\u001b[0m\n", + "\u001b[31m│\u001b[0m \u001b[2m 414 \u001b[0m\u001b[2m│ │ │ \u001b[0mconfig=config, \u001b[31m│\u001b[0m\n", + "\u001b[31m│\u001b[0m \u001b[2m 415 \u001b[0m\u001b[2m│ │ │ \u001b[0mdataset=view._root_dataset \u001b[94mif\u001b[0m view \u001b[95mis\u001b[0m \u001b[95mnot\u001b[0m \u001b[94mNone\u001b[0m \u001b[94melse\u001b[0m dataset, \u001b[31m│\u001b[0m\n", + "\u001b[31m│\u001b[0m \u001b[2m 416 \u001b[0m\u001b[2m│ │ │ \u001b[0mview=view, \u001b[31m│\u001b[0m\n", + "\u001b[31m│\u001b[0m \u001b[31m│\u001b[0m\n", + "\u001b[31m│\u001b[0m \u001b[2;33m/home/strickvl/.pyenv/versions/3.10.12/envs/ls51/lib/python3.10/site-packages/fiftyone/core/\u001b[0m\u001b[1;33mstat\u001b[0m \u001b[31m│\u001b[0m\n", + "\u001b[31m│\u001b[0m \u001b[1;33me.py\u001b[0m:\u001b[94m70\u001b[0m in \u001b[92m__init__\u001b[0m \u001b[31m│\u001b[0m\n", + "\u001b[31m│\u001b[0m \u001b[31m│\u001b[0m\n", + "\u001b[31m│\u001b[0m \u001b[2m 67 \u001b[0m\u001b[2m│ │ \u001b[0m\u001b[96mself\u001b[0m.selected = selected \u001b[95mor\u001b[0m [] \u001b[31m│\u001b[0m\n", + "\u001b[31m│\u001b[0m \u001b[2m 68 \u001b[0m\u001b[2m│ │ \u001b[0m\u001b[96mself\u001b[0m.selected_labels = selected_labels \u001b[95mor\u001b[0m [] \u001b[31m│\u001b[0m\n", + "\u001b[31m│\u001b[0m \u001b[2m 69 \u001b[0m\u001b[2m│ │ \u001b[0m\u001b[94mif\u001b[0m dataset \u001b[95mis\u001b[0m \u001b[95mnot\u001b[0m \u001b[94mNone\u001b[0m: \u001b[31m│\u001b[0m\n", + "\u001b[31m│\u001b[0m \u001b[31m❱ \u001b[0m 70 \u001b[2m│ │ │ \u001b[0mdataset.reload() \u001b[31m│\u001b[0m\n", + "\u001b[31m│\u001b[0m \u001b[2m 71 \u001b[0m\u001b[2m│ │ \u001b[0m\u001b[96mself\u001b[0m.view = ( \u001b[31m│\u001b[0m\n", + "\u001b[31m│\u001b[0m \u001b[2m 72 \u001b[0m\u001b[2m│ │ │ \u001b[0mdataset.load_saved_view(view_name) \u001b[31m│\u001b[0m\n", + "\u001b[31m│\u001b[0m \u001b[2m 73 \u001b[0m\u001b[2m│ │ │ \u001b[0m\u001b[94mif\u001b[0m dataset \u001b[95mis\u001b[0m \u001b[95mnot\u001b[0m \u001b[94mNone\u001b[0m \u001b[95mand\u001b[0m view_name \u001b[31m│\u001b[0m\n", + "\u001b[31m│\u001b[0m \u001b[31m│\u001b[0m\n", + "\u001b[31m│\u001b[0m \u001b[2;33m/home/strickvl/.pyenv/versions/3.10.12/envs/ls51/lib/python3.10/site-packages/fiftyone/core/\u001b[0m\u001b[1;33mdata\u001b[0m \u001b[31m│\u001b[0m\n", + "\u001b[31m│\u001b[0m \u001b[1;33mset.py\u001b[0m:\u001b[94m6808\u001b[0m in \u001b[92mreload\u001b[0m \u001b[31m│\u001b[0m\n", + "\u001b[31m│\u001b[0m \u001b[31m│\u001b[0m\n", + "\u001b[31m│\u001b[0m \u001b[2m6805 \u001b[0m\u001b[2m│ \u001b[0m \u001b[31m│\u001b[0m\n", + "\u001b[31m│\u001b[0m \u001b[2m6806 \u001b[0m\u001b[2m│ \u001b[0m\u001b[94mdef\u001b[0m \u001b[92mreload\u001b[0m(\u001b[96mself\u001b[0m): \u001b[31m│\u001b[0m\n", + "\u001b[31m│\u001b[0m \u001b[2m6807 \u001b[0m\u001b[2;90m│ │ \u001b[0m\u001b[33m\"\"\"Reloads the dataset and any in-memory samples from the database.\"\"\"\u001b[0m \u001b[31m│\u001b[0m\n", + "\u001b[31m│\u001b[0m \u001b[31m❱ \u001b[0m6808 \u001b[2m│ │ \u001b[0m\u001b[96mself\u001b[0m._reload(hard=\u001b[94mTrue\u001b[0m) \u001b[31m│\u001b[0m\n", + "\u001b[31m│\u001b[0m \u001b[2m6809 \u001b[0m\u001b[2m│ │ \u001b[0m\u001b[96mself\u001b[0m._reload_docs(hard=\u001b[94mTrue\u001b[0m) \u001b[31m│\u001b[0m\n", + "\u001b[31m│\u001b[0m \u001b[2m6810 \u001b[0m\u001b[2m│ \u001b[0m \u001b[31m│\u001b[0m\n", + "\u001b[31m│\u001b[0m \u001b[2m6811 \u001b[0m\u001b[2m│ \u001b[0m\u001b[94mdef\u001b[0m \u001b[92mclear_cache\u001b[0m(\u001b[96mself\u001b[0m): \u001b[31m│\u001b[0m\n", + "\u001b[31m│\u001b[0m \u001b[31m│\u001b[0m\n", + "\u001b[31m│\u001b[0m \u001b[2;33m/home/strickvl/.pyenv/versions/3.10.12/envs/ls51/lib/python3.10/site-packages/fiftyone/core/\u001b[0m\u001b[1;33mdata\u001b[0m \u001b[31m│\u001b[0m\n", + "\u001b[31m│\u001b[0m \u001b[1;33mset.py\u001b[0m:\u001b[94m6831\u001b[0m in \u001b[92m_reload\u001b[0m \u001b[31m│\u001b[0m\n", + "\u001b[31m│\u001b[0m \u001b[31m│\u001b[0m\n", + "\u001b[31m│\u001b[0m \u001b[2m6828 \u001b[0m\u001b[2m│ │ │ \u001b[0m\u001b[96mself\u001b[0m._doc.reload() \u001b[31m│\u001b[0m\n", + "\u001b[31m│\u001b[0m \u001b[2m6829 \u001b[0m\u001b[2m│ │ │ \u001b[0m\u001b[94mreturn\u001b[0m \u001b[31m│\u001b[0m\n", + "\u001b[31m│\u001b[0m \u001b[2m6830 \u001b[0m\u001b[2m│ │ \u001b[0m \u001b[31m│\u001b[0m\n", + "\u001b[31m│\u001b[0m \u001b[31m❱ \u001b[0m6831 \u001b[2m│ │ \u001b[0mdoc, sample_doc_cls, frame_doc_cls = _load_dataset( \u001b[31m│\u001b[0m\n", + "\u001b[31m│\u001b[0m \u001b[2m6832 \u001b[0m\u001b[2m│ │ │ \u001b[0m\u001b[96mself\u001b[0m, \u001b[96mself\u001b[0m.name, virtual=\u001b[94mTrue\u001b[0m \u001b[31m│\u001b[0m\n", + "\u001b[31m│\u001b[0m \u001b[2m6833 \u001b[0m\u001b[2m│ │ \u001b[0m) \u001b[31m│\u001b[0m\n", + "\u001b[31m│\u001b[0m \u001b[2m6834 \u001b[0m \u001b[31m│\u001b[0m\n", + "\u001b[31m│\u001b[0m \u001b[31m│\u001b[0m\n", + "\u001b[31m│\u001b[0m \u001b[2;33m/home/strickvl/.pyenv/versions/3.10.12/envs/ls51/lib/python3.10/site-packages/fiftyone/core/\u001b[0m\u001b[1;33mdata\u001b[0m \u001b[31m│\u001b[0m\n", + "\u001b[31m│\u001b[0m \u001b[1;33mset.py\u001b[0m:\u001b[94m7176\u001b[0m in \u001b[92m_load_dataset\u001b[0m \u001b[31m│\u001b[0m\n", + "\u001b[31m│\u001b[0m \u001b[31m│\u001b[0m\n", + "\u001b[31m│\u001b[0m \u001b[2m7173 \u001b[0m\u001b[2m│ │ \u001b[0m\u001b[94mtry\u001b[0m: \u001b[31m│\u001b[0m\n", + "\u001b[31m│\u001b[0m \u001b[2m7174 \u001b[0m\u001b[2m│ │ │ \u001b[0mversion = fomi.get_dataset_revision(name) \u001b[31m│\u001b[0m\n", + "\u001b[31m│\u001b[0m \u001b[2m7175 \u001b[0m\u001b[2m│ │ \u001b[0m\u001b[94mexcept\u001b[0m: \u001b[31m│\u001b[0m\n", + "\u001b[31m│\u001b[0m \u001b[31m❱ \u001b[0m7176 \u001b[2m│ │ │ \u001b[0m\u001b[94mraise\u001b[0m e \u001b[31m│\u001b[0m\n", + "\u001b[31m│\u001b[0m \u001b[2m7177 \u001b[0m\u001b[2m│ │ \u001b[0m \u001b[31m│\u001b[0m\n", + "\u001b[31m│\u001b[0m \u001b[2m7178 \u001b[0m\u001b[2m│ │ \u001b[0m\u001b[94mif\u001b[0m version != focn.VERSION: \u001b[31m│\u001b[0m\n", + "\u001b[31m│\u001b[0m \u001b[2m7179 \u001b[0m\u001b[2m│ │ │ \u001b[0m\u001b[94mraise\u001b[0m \u001b[96mValueError\u001b[0m( \u001b[31m│\u001b[0m\n", + "\u001b[31m│\u001b[0m \u001b[31m│\u001b[0m\n", + "\u001b[31m│\u001b[0m \u001b[2;33m/home/strickvl/.pyenv/versions/3.10.12/envs/ls51/lib/python3.10/site-packages/fiftyone/core/\u001b[0m\u001b[1;33mdata\u001b[0m \u001b[31m│\u001b[0m\n", + "\u001b[31m│\u001b[0m \u001b[1;33mset.py\u001b[0m:\u001b[94m7171\u001b[0m in \u001b[92m_load_dataset\u001b[0m \u001b[31m│\u001b[0m\n", + "\u001b[31m│\u001b[0m \u001b[31m│\u001b[0m\n", + "\u001b[31m│\u001b[0m \u001b[2m7168 \u001b[0m\u001b[2m│ │ \u001b[0mfomi.migrate_dataset_if_necessary(name) \u001b[31m│\u001b[0m\n", + "\u001b[31m│\u001b[0m \u001b[2m7169 \u001b[0m\u001b[2m│ \u001b[0m \u001b[31m│\u001b[0m\n", + "\u001b[31m│\u001b[0m \u001b[2m7170 \u001b[0m\u001b[2m│ \u001b[0m\u001b[94mtry\u001b[0m: \u001b[31m│\u001b[0m\n", + "\u001b[31m│\u001b[0m \u001b[31m❱ \u001b[0m7171 \u001b[2m│ │ \u001b[0m\u001b[94mreturn\u001b[0m _do_load_dataset(obj, name) \u001b[31m│\u001b[0m\n", + "\u001b[31m│\u001b[0m \u001b[2m7172 \u001b[0m\u001b[2m│ \u001b[0m\u001b[94mexcept\u001b[0m \u001b[96mException\u001b[0m \u001b[94mas\u001b[0m e: \u001b[31m│\u001b[0m\n", + "\u001b[31m│\u001b[0m \u001b[2m7173 \u001b[0m\u001b[2m│ │ \u001b[0m\u001b[94mtry\u001b[0m: \u001b[31m│\u001b[0m\n", + "\u001b[31m│\u001b[0m \u001b[2m7174 \u001b[0m\u001b[2m│ │ │ \u001b[0mversion = fomi.get_dataset_revision(name) \u001b[31m│\u001b[0m\n", + "\u001b[31m│\u001b[0m \u001b[31m│\u001b[0m\n", + "\u001b[31m│\u001b[0m \u001b[2;33m/home/strickvl/.pyenv/versions/3.10.12/envs/ls51/lib/python3.10/site-packages/fiftyone/core/\u001b[0m\u001b[1;33mdata\u001b[0m \u001b[31m│\u001b[0m\n", + "\u001b[31m│\u001b[0m \u001b[1;33mset.py\u001b[0m:\u001b[94m7193\u001b[0m in \u001b[92m_do_load_dataset\u001b[0m \u001b[31m│\u001b[0m\n", + "\u001b[31m│\u001b[0m \u001b[31m│\u001b[0m\n", + "\u001b[31m│\u001b[0m \u001b[2m7190 \u001b[0m\u001b[2m│ \u001b[0mdb = foo.get_db_conn() \u001b[31m│\u001b[0m\n", + "\u001b[31m│\u001b[0m \u001b[2m7191 \u001b[0m\u001b[2m│ \u001b[0mres = db.datasets.find_one({\u001b[33m\"\u001b[0m\u001b[33mname\u001b[0m\u001b[33m\"\u001b[0m: name}) \u001b[31m│\u001b[0m\n", + "\u001b[31m│\u001b[0m \u001b[2m7192 \u001b[0m\u001b[2m│ \u001b[0m\u001b[94mif\u001b[0m \u001b[95mnot\u001b[0m res: \u001b[31m│\u001b[0m\n", + "\u001b[31m│\u001b[0m \u001b[31m❱ \u001b[0m7193 \u001b[2m│ │ \u001b[0m\u001b[94mraise\u001b[0m \u001b[96mValueError\u001b[0m(\u001b[33m\"\u001b[0m\u001b[33mDataset \u001b[0m\u001b[33m'\u001b[0m\u001b[33m%s\u001b[0m\u001b[33m'\u001b[0m\u001b[33m not found\u001b[0m\u001b[33m\"\u001b[0m % name) \u001b[31m│\u001b[0m\n", + "\u001b[31m│\u001b[0m \u001b[2m7194 \u001b[0m\u001b[2m│ \u001b[0mdataset_doc = foo.DatasetDocument.from_dict(res) \u001b[31m│\u001b[0m\n", + "\u001b[31m│\u001b[0m \u001b[2m7195 \u001b[0m\u001b[2m│ \u001b[0m \u001b[31m│\u001b[0m\n", + "\u001b[31m│\u001b[0m \u001b[2m7196 \u001b[0m\u001b[2m│ \u001b[0msample_collection_name = dataset_doc.sample_collection_name \u001b[31m│\u001b[0m\n", + "\u001b[31m╰──────────────────────────────────────────────────────────────────────────────────────────────────╯\u001b[0m\n", + "\u001b[1;91mValueError: \u001b[0mDataset \u001b[32m'ships'\u001b[0m not found\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import fiftyone as fo\n", + "from zenml.client import Client\n", + "\n", + "artifact = Client().get_artifact_version('40e81c44-bfb0-4f84-9c18-78571f6c1577')\n", + "dataset = artifact.load()\n", + "session = fo.launch_app(dataset)\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + " " + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import fiftyone as fo\n", + "\n", + "session = fo.launch_app()" + ] + }, { "cell_type": "code", "execution_count": null, diff --git a/end-to-end-computer-vision/pipelines/inference.py b/end-to-end-computer-vision/pipelines/inference.py index 0f4e9771..387ce597 100644 --- a/end-to-end-computer-vision/pipelines/inference.py +++ b/end-to-end-computer-vision/pipelines/inference.py @@ -21,14 +21,27 @@ from steps.predict_image import predict_image from steps.load_model import load_model from zenml import Model -logger = get_logger(__name__) from zenml.client import Client +import os; os.environ["YOLO_VERBOSE"] = "False" + +import fiftyone as fo +import fiftyone.zoo as foz +import fiftyone.utils.ultralytics as fou + +from ultralytics import YOLO + +logger = get_logger(__name__) +import fiftyone as fo + +DATASET_NAME = "ships" +DATASET_DIR = "data/ships/subset" + @step -def predict_over_images(): - images_dir_path = "data/ships/subset" +def predict_over_images() -> fo.Dataset: + # model = Model( # name="Yolo_Object_Detection", # version="staging", @@ -37,10 +50,24 @@ def predict_over_images(): # model_artifact = model_version.get_model_artifact(name="Raw_YOLO") artifact = Client().get_artifact_version('105c768c-8c86-465a-b018-b1a800ad4e19') yolo_model = artifact.load() - results = yolo_model(images_dir_path, half=True, conf=0.6) - breakpoint() + # results = yolo_model(DATASET_DIR, half=True, conf=0.6) + + dataset = fo.Dataset.from_dir( + dataset_dir=DATASET_DIR, + dataset_type=fo.types.ImageDirectory, + name=DATASET_NAME, + ) + dataset.persistent = True + # # View summary info about the dataset + # print(dataset) + + # # Print the first few samples in the dataset + # print(dataset.head()) + + dataset.apply_model(yolo_model, label_field="boxes") + return dataset @pipeline def inference(): - predict_over_images() + dataset = predict_over_images() From 54363c66afec91f27aa08e5827cb0818a19bf9a1 Mon Sep 17 00:00:00 2001 From: Alex Strick van Linschoten Date: Tue, 23 Apr 2024 12:44:06 +0200 Subject: [PATCH 028/121] formatting --- end-to-end-computer-vision/ls_export.py | 2 +- .../label_studio_yolo_dataset_materializer.py | 9 ++--- .../materializers/yolo_materializer.py | 5 ++- .../pipelines/__init__.py | 3 +- .../pipelines/data_export.py | 3 +- .../pipelines/inference.py | 20 +++++------ .../pipelines/training.py | 6 ++-- .../steps/export_label_studio.py | 35 ++++++++++++------- .../steps/load_model.py | 4 +-- .../steps/predict_image.py | 2 +- .../steps/train_model.py | 11 +++--- .../utils/dataset_utils.py | 15 ++++---- .../utils/split_data.py | 1 + 13 files changed, 60 insertions(+), 56 deletions(-) diff --git a/end-to-end-computer-vision/ls_export.py b/end-to-end-computer-vision/ls_export.py index 50e58f17..1a869975 100644 --- a/end-to-end-computer-vision/ls_export.py +++ b/end-to-end-computer-vision/ls_export.py @@ -17,8 +17,8 @@ import os import time + from label_studio_sdk import Client -from label_studio_converter import Converter LABEL_STUDIO_URL = os.getenv( "LABEL_STUDIO_URL", default="http://localhost:8080" diff --git a/end-to-end-computer-vision/materializers/label_studio_yolo_dataset_materializer.py b/end-to-end-computer-vision/materializers/label_studio_yolo_dataset_materializer.py index 91ef3dcb..ccd54969 100644 --- a/end-to-end-computer-vision/materializers/label_studio_yolo_dataset_materializer.py +++ b/end-to-end-computer-vision/materializers/label_studio_yolo_dataset_materializer.py @@ -14,11 +14,10 @@ """Implementation of the PyTorch DataLoader materializer.""" import os -from typing import Any, ClassVar, List, Type, Optional import tempfile +from typing import Any, ClassVar, List, Optional, Type from zenml.io import fileio - from zenml.materializers.base_materializer import BaseMaterializer DEFAULT_FILENAME = "data.zip" @@ -30,9 +29,7 @@ class LabelStudioYOLODataset: task_ids: Optional[List[int]] def download_dataset(self): - tmpfile_ = tempfile.NamedTemporaryFile( - dir="data", delete=False - ) + tmpfile_ = tempfile.NamedTemporaryFile(dir="data", delete=False) tmpdirname = os.path.basename(tmpfile_.name) self.filepath = os.path.join(tmpdirname, DEFAULT_FILENAME) self.dataset.export_tasks( @@ -63,7 +60,7 @@ def load(self, data_type: Type[Any]) -> LabelStudioYOLODataset: # Create a temporary folder tmpfile_ = tempfile.NamedTemporaryFile( - dir="data", delete=False, suffix='.zip' + dir="data", delete=False, suffix=".zip" ) # Copy from artifact store to temporary file diff --git a/end-to-end-computer-vision/materializers/yolo_materializer.py b/end-to-end-computer-vision/materializers/yolo_materializer.py index 32f3903b..fb4c47e4 100644 --- a/end-to-end-computer-vision/materializers/yolo_materializer.py +++ b/end-to-end-computer-vision/materializers/yolo_materializer.py @@ -14,15 +14,14 @@ """Implementation of the PyTorch DataLoader materializer.""" import os -from typing import Any, ClassVar, Type import tempfile +from typing import Any, ClassVar, Type from ultralytics import YOLO - -from zenml.io import fileio from zenml.integrations.pytorch.materializers.pytorch_module_materializer import ( PyTorchModuleMaterializer, ) +from zenml.io import fileio DEFAULT_FILENAME = "obj.pt" diff --git a/end-to-end-computer-vision/pipelines/__init__.py b/end-to-end-computer-vision/pipelines/__init__.py index e5046710..4cc5c4c5 100644 --- a/end-to-end-computer-vision/pipelines/__init__.py +++ b/end-to-end-computer-vision/pipelines/__init__.py @@ -16,4 +16,5 @@ # from .data_export import data_export -#from .training import training + +# from .training import training diff --git a/end-to-end-computer-vision/pipelines/data_export.py b/end-to-end-computer-vision/pipelines/data_export.py index 8e844f41..588178e6 100644 --- a/end-to-end-computer-vision/pipelines/data_export.py +++ b/end-to-end-computer-vision/pipelines/data_export.py @@ -21,6 +21,7 @@ logger = get_logger(__name__) + @pipeline -def data_export(dataset_name: str = 'polution'): +def data_export(dataset_name: str = "polution"): load_data_from_label_studio(dataset_name=dataset_name) diff --git a/end-to-end-computer-vision/pipelines/inference.py b/end-to-end-computer-vision/pipelines/inference.py index 387ce597..8d4d5046 100644 --- a/end-to-end-computer-vision/pipelines/inference.py +++ b/end-to-end-computer-vision/pipelines/inference.py @@ -14,21 +14,15 @@ # See the License for the specific language governing permissions and # limitations under the License. # -from zenml import pipeline, get_pipeline_context, step +from zenml import pipeline, step from zenml.logger import get_logger -from steps.train_model import train_model -from steps.predict_image import predict_image -from steps.load_model import load_model -from zenml import Model from zenml.client import Client -import os; os.environ["YOLO_VERBOSE"] = "False" +import os -import fiftyone as fo -import fiftyone.zoo as foz -import fiftyone.utils.ultralytics as fou +os.environ["YOLO_VERBOSE"] = "False" -from ultralytics import YOLO +import fiftyone as fo logger = get_logger(__name__) @@ -41,14 +35,16 @@ @step def predict_over_images() -> fo.Dataset: - + # model = Model( # name="Yolo_Object_Detection", # version="staging", # ) # model.get_model_artifact(name="staging").load() # model_artifact = model_version.get_model_artifact(name="Raw_YOLO") - artifact = Client().get_artifact_version('105c768c-8c86-465a-b018-b1a800ad4e19') + artifact = Client().get_artifact_version( + "105c768c-8c86-465a-b018-b1a800ad4e19" + ) yolo_model = artifact.load() # results = yolo_model(DATASET_DIR, half=True, conf=0.6) diff --git a/end-to-end-computer-vision/pipelines/training.py b/end-to-end-computer-vision/pipelines/training.py index e069a3d1..61f5972b 100644 --- a/end-to-end-computer-vision/pipelines/training.py +++ b/end-to-end-computer-vision/pipelines/training.py @@ -14,12 +14,12 @@ # See the License for the specific language governing permissions and # limitations under the License. # -from zenml import pipeline, get_pipeline_context +from zenml import get_pipeline_context, pipeline from zenml.logger import get_logger -from steps.train_model import train_model -from steps.predict_image import predict_image from steps.load_model import load_model +from steps.predict_image import predict_image +from steps.train_model import train_model logger = get_logger(__name__) diff --git a/end-to-end-computer-vision/steps/export_label_studio.py b/end-to-end-computer-vision/steps/export_label_studio.py index 7e3ae982..7c6ad92b 100644 --- a/end-to-end-computer-vision/steps/export_label_studio.py +++ b/end-to-end-computer-vision/steps/export_label_studio.py @@ -1,21 +1,25 @@ -from typing import Annotated, Tuple, List +from typing import Annotated, List, Tuple -from zenml import step, get_step_context +from zenml import get_step_context, step from zenml.client import Client from zenml.logger import get_logger -from materializers.label_studio_yolo_dataset_materializer import \ - LabelStudioYOLODatasetMaterializer, LabelStudioYOLODataset +from materializers.label_studio_yolo_dataset_materializer import ( + LabelStudioYOLODataset, + LabelStudioYOLODatasetMaterializer, +) logger = get_logger(__name__) -@step(output_materializers={"yolo_dataset": LabelStudioYOLODatasetMaterializer}) +@step( + output_materializers={"yolo_dataset": LabelStudioYOLODatasetMaterializer} +) def load_data_from_label_studio( - dataset_name: str + dataset_name: str, ) -> Tuple[ Annotated[LabelStudioYOLODataset, "yolo_dataset"], - Annotated[List[int], "task_ids"] + Annotated[List[int], "task_ids"], ]: annotator = Client().active_stack.annotator from zenml.integrations.label_studio.annotators.label_studio_annotator import ( @@ -39,9 +43,14 @@ def load_data_from_label_studio( cur_step_name = step_context.step_name try: - last_run = c.get_pipeline(cur_pipeline_name).last_successful_run - last_task_ids = last_run.steps[cur_step_name].outputs[ - 'task_ids'].read() + last_run = c.get_pipeline( + cur_pipeline_name + ).last_successful_run + last_task_ids = ( + last_run.steps[cur_step_name] + .outputs["task_ids"] + .read() + ) except (RuntimeError, KeyError): last_task_ids = [] @@ -49,8 +58,10 @@ def load_data_from_label_studio( logger.info(f"{len(cur_task_ids)} total labels found.") new_task_ids = list(set(cur_task_ids) - set(last_task_ids)) - logger.info(f"{len(new_task_ids)} new labels are being beamed " - f"straight to you.") + logger.info( + f"{len(new_task_ids)} new labels are being beamed " + f"straight to you." + ) ls_dataset.task_ids = new_task_ids return ls_dataset, new_task_ids diff --git a/end-to-end-computer-vision/steps/load_model.py b/end-to-end-computer-vision/steps/load_model.py index e7b7531b..386ef3c8 100644 --- a/end-to-end-computer-vision/steps/load_model.py +++ b/end-to-end-computer-vision/steps/load_model.py @@ -17,7 +17,7 @@ from typing import Annotated from ultralytics import YOLO -from zenml import step, ArtifactConfig +from zenml import ArtifactConfig, step from zenml.logger import get_logger logger = get_logger(__name__) @@ -28,4 +28,4 @@ def load_model( model_checkpoint: str, ) -> Annotated[YOLO, ArtifactConfig(name="Raw_YOLO", is_model_artifact=True)]: logger.info(f"Loading YOLO checkpoint {model_checkpoint}") - return YOLO() #model_checkpoint) \ No newline at end of file + return YOLO() # model_checkpoint) diff --git a/end-to-end-computer-vision/steps/predict_image.py b/end-to-end-computer-vision/steps/predict_image.py index 9239db5c..481f693f 100644 --- a/end-to-end-computer-vision/steps/predict_image.py +++ b/end-to-end-computer-vision/steps/predict_image.py @@ -21,4 +21,4 @@ @step def predict_image(model: YOLO): results = model("https://ultralytics.com/images/bus.jpg") - print(results) \ No newline at end of file + print(results) diff --git a/end-to-end-computer-vision/steps/train_model.py b/end-to-end-computer-vision/steps/train_model.py index d9438d40..99d8b350 100644 --- a/end-to-end-computer-vision/steps/train_model.py +++ b/end-to-end-computer-vision/steps/train_model.py @@ -17,12 +17,13 @@ from typing import Annotated from ultralytics import YOLO -from zenml import step, ArtifactConfig, log_artifact_metadata +from zenml import ArtifactConfig, log_artifact_metadata, step -from materializers.label_studio_yolo_dataset_materializer import \ - LabelStudioYOLODataset +from materializers.label_studio_yolo_dataset_materializer import ( + LabelStudioYOLODataset, +) from materializers.yolo_materializer import UltralyticsMaterializer -from utils.dataset_utils import load_images_from_folder, load_and_split_data +from utils.dataset_utils import load_and_split_data, load_images_from_folder @step(output_materializers={"Trained_YOLO": UltralyticsMaterializer}) @@ -46,4 +47,4 @@ def train_model( # Read images as PIL images from directory metrics.save_dir for all png and jpg files images = load_images_from_folder(metrics.save_dir) - return model \ No newline at end of file + return model diff --git a/end-to-end-computer-vision/utils/dataset_utils.py b/end-to-end-computer-vision/utils/dataset_utils.py index 6e2b1b0e..0db186c2 100644 --- a/end-to-end-computer-vision/utils/dataset_utils.py +++ b/end-to-end-computer-vision/utils/dataset_utils.py @@ -20,9 +20,10 @@ from PIL import Image from zenml.client import Client -from materializers.label_studio_yolo_dataset_materializer import \ - LabelStudioYOLODataset -from utils.split_data import unzip_dataset, split_dataset, generate_yaml +from materializers.label_studio_yolo_dataset_materializer import ( + LabelStudioYOLODataset, +) +from utils.split_data import generate_yaml, split_dataset, unzip_dataset def load_images_from_folder(folder): @@ -50,15 +51,11 @@ def load_and_split_data(dataset: LabelStudioYOLODataset) -> str: raise TypeError( "This step can only be used with the Label Studio annotator." ) - tmpfile_ = tempfile.NamedTemporaryFile( - dir="data", delete=False - ) + tmpfile_ = tempfile.NamedTemporaryFile(dir="data", delete=False) tmpdirname = os.path.basename(tmpfile_.name) extract_location = os.path.join(tmpdirname, "data") unzip_dataset(dataset.filepath, extract_location) - split_dataset( - extract_location, ratio=(0.7, 0.15, 0.15), seed=42 - ) + split_dataset(extract_location, ratio=(0.7, 0.15, 0.15), seed=42) return generate_yaml(extract_location) diff --git a/end-to-end-computer-vision/utils/split_data.py b/end-to-end-computer-vision/utils/split_data.py index 7e977edb..8d8163b4 100644 --- a/end-to-end-computer-vision/utils/split_data.py +++ b/end-to-end-computer-vision/utils/split_data.py @@ -2,6 +2,7 @@ import random import shutil import zipfile + import yaml from zenml.logger import get_logger From 1754c269b16e3c7addd4c27a34467451edbc0540 Mon Sep 17 00:00:00 2001 From: Alex Strick van Linschoten Date: Tue, 23 Apr 2024 13:16:27 +0200 Subject: [PATCH 029/121] update inference --- .../notebooks/ultralytics.ipynb | 410 ++++++------------ .../pipelines/inference.py | 24 +- 2 files changed, 157 insertions(+), 277 deletions(-) diff --git a/end-to-end-computer-vision/notebooks/ultralytics.ipynb b/end-to-end-computer-vision/notebooks/ultralytics.ipynb index 5c053e47..da3f0ae5 100644 --- a/end-to-end-computer-vision/notebooks/ultralytics.ipynb +++ b/end-to-end-computer-vision/notebooks/ultralytics.ipynb @@ -408,274 +408,33 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 5, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "\u001b[33mYour artifact was materialized under Python version 'unknown' but you are currently using '3.10.12'. This might cause unexpected behavior since pickle is not reproducible across Python versions. Attempting to load anyway...\u001b[0m\n" + " 100% |███████████████████| 15/15 [17.6ms elapsed, 0s remaining, 851.8 samples/s] \n", + "\u001b[1;35m 100% |███████████████████| 15/15 [17.6ms elapsed, 0s remaining, 851.8 samples/s] \u001b[0m\n" ] }, { "data": { "text/html": [ - "
╭─────────────────────────────── Traceback (most recent call last) ────────────────────────────────╮\n",
-       " /home/strickvl/.pyenv/versions/3.10.12/envs/ls51/lib/python3.10/site-packages/fiftyone/core/data \n",
-       " set.py:7174 in _load_dataset                                                                     \n",
-       "                                                                                                  \n",
-       "   7171 │   │   return _do_load_dataset(obj, name)                                                \n",
-       "   7172 except Exception as e:                                                                \n",
-       "   7173 │   │   try:                                                                              \n",
-       " 7174 │   │   │   version = fomi.get_dataset_revision(name)                                     \n",
-       "   7175 │   │   except:                                                                           \n",
-       "   7176 │   │   │   raise e                                                                       \n",
-       "   7177                                                                                           \n",
-       "                                                                                                  \n",
-       " /home/strickvl/.pyenv/versions/3.10.12/envs/ls51/lib/python3.10/site-packages/fiftyone/migration \n",
-       " s/runner.py:62 in get_dataset_revision                                                           \n",
-       "                                                                                                  \n",
-       "    59 conn = foo.get_db_conn()                                                               \n",
-       "    60 dataset_doc = conn.datasets.find_one({\"name\": name}, {\"version\": 1})                   \n",
-       "    61 if dataset_doc is None:                                                                \n",
-       "  62 │   │   raise ValueError(\"Dataset '%s' not found\" % name)                                  \n",
-       "    63                                                                                        \n",
-       "    64 return dataset_doc.get(\"version\", None)                                                \n",
-       "    65                                                                                            \n",
-       "╰──────────────────────────────────────────────────────────────────────────────────────────────────╯\n",
-       "ValueError: Dataset 'ships' not found\n",
-       "\n",
-       "During handling of the above exception, another exception occurred:\n",
        "\n",
-       "╭─────────────────────────────── Traceback (most recent call last) ────────────────────────────────╮\n",
-       " in <module>:6                                                                                    \n",
-       "                                                                                                  \n",
-       "   3                                                                                              \n",
-       "   4 artifact = Client().get_artifact_version('40e81c44-bfb0-4f84-9c18-78571f6c1577')             \n",
-       "   5 dataset = artifact.load()                                                                    \n",
-       " 6 session = fo.launch_app(dataset)                                                             \n",
-       "   7                                                                                              \n",
-       "   8                                                                                              \n",
-       "                                                                                                  \n",
-       " /home/strickvl/.pyenv/versions/3.10.12/envs/ls51/lib/python3.10/site-packages/fiftyone/core/sess \n",
-       " ion/session.py:196 in launch_app                                                                 \n",
-       "                                                                                                  \n",
-       "    193 │   │   a :class:`Session`                                                                \n",
-       "    194 \"\"\"                                                                                   \n",
-       "    195 global _session  # pylint: disable=global-statement                                   \n",
-       "  196 _session = Session(                                                                   \n",
-       "    197 │   │   dataset=dataset,                                                                  \n",
-       "    198 │   │   view=view,                                                                        \n",
-       "    199 │   │   spaces=spaces,                                                                    \n",
-       "                                                                                                  \n",
-       " /home/strickvl/.pyenv/versions/3.10.12/envs/ls51/lib/python3.10/site-packages/fiftyone/core/sess \n",
-       " ion/session.py:413 in __init__                                                                   \n",
-       "                                                                                                  \n",
-       "    410 │   │   if spaces is None:                                                                \n",
-       "    411 │   │   │   spaces = default_spaces.copy()                                                \n",
-       "    412 │   │                                                                                     \n",
-       "  413 │   │   self._state = StateDescription(                                                   \n",
-       "    414 │   │   │   config=config,                                                                \n",
-       "    415 │   │   │   dataset=view._root_dataset if view is not None else dataset,                  \n",
-       "    416 │   │   │   view=view,                                                                    \n",
-       "                                                                                                  \n",
-       " /home/strickvl/.pyenv/versions/3.10.12/envs/ls51/lib/python3.10/site-packages/fiftyone/core/stat \n",
-       " e.py:70 in __init__                                                                              \n",
-       "                                                                                                  \n",
-       "    67 │   │   self.selected = selected or []                                                     \n",
-       "    68 │   │   self.selected_labels = selected_labels or []                                       \n",
-       "    69 │   │   if dataset is not None:                                                            \n",
-       "  70 │   │   │   dataset.reload()                                                               \n",
-       "    71 │   │   self.view = (                                                                      \n",
-       "    72 │   │   │   dataset.load_saved_view(view_name)                                             \n",
-       "    73 │   │   │   if dataset is not None and view_name                                           \n",
-       "                                                                                                  \n",
-       " /home/strickvl/.pyenv/versions/3.10.12/envs/ls51/lib/python3.10/site-packages/fiftyone/core/data \n",
-       " set.py:6808 in reload                                                                            \n",
-       "                                                                                                  \n",
-       "   6805                                                                                       \n",
-       "   6806 def reload(self):                                                                     \n",
-       "   6807 │   │   \"\"\"Reloads the dataset and any in-memory samples from the database.\"\"\"            \n",
-       " 6808 │   │   self._reload(hard=True)                                                           \n",
-       "   6809 │   │   self._reload_docs(hard=True)                                                      \n",
-       "   6810                                                                                       \n",
-       "   6811 def clear_cache(self):                                                                \n",
-       "                                                                                                  \n",
-       " /home/strickvl/.pyenv/versions/3.10.12/envs/ls51/lib/python3.10/site-packages/fiftyone/core/data \n",
-       " set.py:6831 in _reload                                                                           \n",
-       "                                                                                                  \n",
-       "   6828 │   │   │   self._doc.reload()                                                            \n",
-       "   6829 │   │   │   return                                                                        \n",
-       "   6830 │   │                                                                                     \n",
-       " 6831 │   │   doc, sample_doc_cls, frame_doc_cls = _load_dataset(                               \n",
-       "   6832 │   │   │   self, self.name, virtual=True                                                 \n",
-       "   6833 │   │   )                                                                                 \n",
-       "   6834                                                                                           \n",
-       "                                                                                                  \n",
-       " /home/strickvl/.pyenv/versions/3.10.12/envs/ls51/lib/python3.10/site-packages/fiftyone/core/data \n",
-       " set.py:7176 in _load_dataset                                                                     \n",
-       "                                                                                                  \n",
-       "   7173 │   │   try:                                                                              \n",
-       "   7174 │   │   │   version = fomi.get_dataset_revision(name)                                     \n",
-       "   7175 │   │   except:                                                                           \n",
-       " 7176 │   │   │   raise e                                                                       \n",
-       "   7177 │   │                                                                                     \n",
-       "   7178 │   │   if version != focn.VERSION:                                                       \n",
-       "   7179 │   │   │   raise ValueError(                                                             \n",
-       "                                                                                                  \n",
-       " /home/strickvl/.pyenv/versions/3.10.12/envs/ls51/lib/python3.10/site-packages/fiftyone/core/data \n",
-       " set.py:7171 in _load_dataset                                                                     \n",
-       "                                                                                                  \n",
-       "   7168 │   │   fomi.migrate_dataset_if_necessary(name)                                           \n",
-       "   7169                                                                                       \n",
-       "   7170 try:                                                                                  \n",
-       " 7171 │   │   return _do_load_dataset(obj, name)                                                \n",
-       "   7172 except Exception as e:                                                                \n",
-       "   7173 │   │   try:                                                                              \n",
-       "   7174 │   │   │   version = fomi.get_dataset_revision(name)                                     \n",
-       "                                                                                                  \n",
-       " /home/strickvl/.pyenv/versions/3.10.12/envs/ls51/lib/python3.10/site-packages/fiftyone/core/data \n",
-       " set.py:7193 in _do_load_dataset                                                                  \n",
-       "                                                                                                  \n",
-       "   7190 db = foo.get_db_conn()                                                                \n",
-       "   7191 res = db.datasets.find_one({\"name\": name})                                            \n",
-       "   7192 if not res:                                                                           \n",
-       " 7193 │   │   raise ValueError(\"Dataset '%s' not found\" % name)                                 \n",
-       "   7194 dataset_doc = foo.DatasetDocument.from_dict(res)                                      \n",
-       "   7195                                                                                       \n",
-       "   7196 sample_collection_name = dataset_doc.sample_collection_name                           \n",
-       "╰──────────────────────────────────────────────────────────────────────────────────────────────────╯\n",
-       "ValueError: Dataset 'ships' not found\n",
-       "
\n" + " \n", + " " ], "text/plain": [ - "\u001b[31m╭─\u001b[0m\u001b[31m──────────────────────────────\u001b[0m\u001b[31m \u001b[0m\u001b[1;31mTraceback \u001b[0m\u001b[1;2;31m(most recent call last)\u001b[0m\u001b[31m \u001b[0m\u001b[31m───────────────────────────────\u001b[0m\u001b[31m─╮\u001b[0m\n", - "\u001b[31m│\u001b[0m \u001b[2;33m/home/strickvl/.pyenv/versions/3.10.12/envs/ls51/lib/python3.10/site-packages/fiftyone/core/\u001b[0m\u001b[1;33mdata\u001b[0m \u001b[31m│\u001b[0m\n", - "\u001b[31m│\u001b[0m \u001b[1;33mset.py\u001b[0m:\u001b[94m7174\u001b[0m in \u001b[92m_load_dataset\u001b[0m \u001b[31m│\u001b[0m\n", - "\u001b[31m│\u001b[0m \u001b[31m│\u001b[0m\n", - "\u001b[31m│\u001b[0m \u001b[2m7171 \u001b[0m\u001b[2m│ │ \u001b[0m\u001b[94mreturn\u001b[0m _do_load_dataset(obj, name) \u001b[31m│\u001b[0m\n", - "\u001b[31m│\u001b[0m \u001b[2m7172 \u001b[0m\u001b[2m│ \u001b[0m\u001b[94mexcept\u001b[0m \u001b[96mException\u001b[0m \u001b[94mas\u001b[0m e: \u001b[31m│\u001b[0m\n", - "\u001b[31m│\u001b[0m \u001b[2m7173 \u001b[0m\u001b[2m│ │ \u001b[0m\u001b[94mtry\u001b[0m: \u001b[31m│\u001b[0m\n", - "\u001b[31m│\u001b[0m \u001b[31m❱ \u001b[0m7174 \u001b[2m│ │ │ \u001b[0mversion = fomi.get_dataset_revision(name) \u001b[31m│\u001b[0m\n", - "\u001b[31m│\u001b[0m \u001b[2m7175 \u001b[0m\u001b[2m│ │ \u001b[0m\u001b[94mexcept\u001b[0m: \u001b[31m│\u001b[0m\n", - "\u001b[31m│\u001b[0m \u001b[2m7176 \u001b[0m\u001b[2m│ │ │ \u001b[0m\u001b[94mraise\u001b[0m e \u001b[31m│\u001b[0m\n", - "\u001b[31m│\u001b[0m \u001b[2m7177 \u001b[0m \u001b[31m│\u001b[0m\n", - "\u001b[31m│\u001b[0m \u001b[31m│\u001b[0m\n", - "\u001b[31m│\u001b[0m \u001b[2;33m/home/strickvl/.pyenv/versions/3.10.12/envs/ls51/lib/python3.10/site-packages/fiftyone/migration\u001b[0m \u001b[31m│\u001b[0m\n", - "\u001b[31m│\u001b[0m \u001b[2;33ms/\u001b[0m\u001b[1;33mrunner.py\u001b[0m:\u001b[94m62\u001b[0m in \u001b[92mget_dataset_revision\u001b[0m \u001b[31m│\u001b[0m\n", - "\u001b[31m│\u001b[0m \u001b[31m│\u001b[0m\n", - "\u001b[31m│\u001b[0m \u001b[2m 59 \u001b[0m\u001b[2m│ \u001b[0mconn = foo.get_db_conn() \u001b[31m│\u001b[0m\n", - "\u001b[31m│\u001b[0m \u001b[2m 60 \u001b[0m\u001b[2m│ \u001b[0mdataset_doc = conn.datasets.find_one({\u001b[33m\"\u001b[0m\u001b[33mname\u001b[0m\u001b[33m\"\u001b[0m: name}, {\u001b[33m\"\u001b[0m\u001b[33mversion\u001b[0m\u001b[33m\"\u001b[0m: \u001b[94m1\u001b[0m}) \u001b[31m│\u001b[0m\n", - "\u001b[31m│\u001b[0m \u001b[2m 61 \u001b[0m\u001b[2m│ \u001b[0m\u001b[94mif\u001b[0m dataset_doc \u001b[95mis\u001b[0m \u001b[94mNone\u001b[0m: \u001b[31m│\u001b[0m\n", - "\u001b[31m│\u001b[0m \u001b[31m❱ \u001b[0m 62 \u001b[2m│ │ \u001b[0m\u001b[94mraise\u001b[0m \u001b[96mValueError\u001b[0m(\u001b[33m\"\u001b[0m\u001b[33mDataset \u001b[0m\u001b[33m'\u001b[0m\u001b[33m%s\u001b[0m\u001b[33m'\u001b[0m\u001b[33m not found\u001b[0m\u001b[33m\"\u001b[0m % name) \u001b[31m│\u001b[0m\n", - "\u001b[31m│\u001b[0m \u001b[2m 63 \u001b[0m\u001b[2m│ \u001b[0m \u001b[31m│\u001b[0m\n", - "\u001b[31m│\u001b[0m \u001b[2m 64 \u001b[0m\u001b[2m│ \u001b[0m\u001b[94mreturn\u001b[0m dataset_doc.get(\u001b[33m\"\u001b[0m\u001b[33mversion\u001b[0m\u001b[33m\"\u001b[0m, \u001b[94mNone\u001b[0m) \u001b[31m│\u001b[0m\n", - "\u001b[31m│\u001b[0m \u001b[2m 65 \u001b[0m \u001b[31m│\u001b[0m\n", - "\u001b[31m╰──────────────────────────────────────────────────────────────────────────────────────────────────╯\u001b[0m\n", - "\u001b[1;91mValueError: \u001b[0mDataset \u001b[32m'ships'\u001b[0m not found\n", - "\n", - "\u001b[3mDuring handling of the above exception, another exception occurred:\u001b[0m\n", - "\n", - "\u001b[31m╭─\u001b[0m\u001b[31m──────────────────────────────\u001b[0m\u001b[31m \u001b[0m\u001b[1;31mTraceback \u001b[0m\u001b[1;2;31m(most recent call last)\u001b[0m\u001b[31m \u001b[0m\u001b[31m───────────────────────────────\u001b[0m\u001b[31m─╮\u001b[0m\n", - "\u001b[31m│\u001b[0m in \u001b[92m\u001b[0m:\u001b[94m6\u001b[0m \u001b[31m│\u001b[0m\n", - "\u001b[31m│\u001b[0m \u001b[31m│\u001b[0m\n", - "\u001b[31m│\u001b[0m \u001b[2m3 \u001b[0m \u001b[31m│\u001b[0m\n", - "\u001b[31m│\u001b[0m \u001b[2m4 \u001b[0martifact = Client().get_artifact_version(\u001b[33m'\u001b[0m\u001b[33m40e81c44-bfb0-4f84-9c18-78571f6c1577\u001b[0m\u001b[33m'\u001b[0m) \u001b[31m│\u001b[0m\n", - "\u001b[31m│\u001b[0m \u001b[2m5 \u001b[0mdataset = artifact.load() \u001b[31m│\u001b[0m\n", - "\u001b[31m│\u001b[0m \u001b[31m❱ \u001b[0m6 session = fo.launch_app(dataset) \u001b[31m│\u001b[0m\n", - "\u001b[31m│\u001b[0m \u001b[2m7 \u001b[0m \u001b[31m│\u001b[0m\n", - "\u001b[31m│\u001b[0m \u001b[2m8 \u001b[0m \u001b[31m│\u001b[0m\n", - "\u001b[31m│\u001b[0m \u001b[31m│\u001b[0m\n", - "\u001b[31m│\u001b[0m \u001b[2;33m/home/strickvl/.pyenv/versions/3.10.12/envs/ls51/lib/python3.10/site-packages/fiftyone/core/sess\u001b[0m \u001b[31m│\u001b[0m\n", - "\u001b[31m│\u001b[0m \u001b[2;33mion/\u001b[0m\u001b[1;33msession.py\u001b[0m:\u001b[94m196\u001b[0m in \u001b[92mlaunch_app\u001b[0m \u001b[31m│\u001b[0m\n", - "\u001b[31m│\u001b[0m \u001b[31m│\u001b[0m\n", - "\u001b[31m│\u001b[0m \u001b[2m 193 \u001b[0m\u001b[2;33m│ │ \u001b[0m\u001b[33ma :class:`Session`\u001b[0m \u001b[31m│\u001b[0m\n", - "\u001b[31m│\u001b[0m \u001b[2m 194 \u001b[0m\u001b[2;33m│ \u001b[0m\u001b[33m\"\"\"\u001b[0m \u001b[31m│\u001b[0m\n", - "\u001b[31m│\u001b[0m \u001b[2m 195 \u001b[0m\u001b[2m│ \u001b[0m\u001b[94mglobal\u001b[0m _session \u001b[2m# pylint: disable=global-statement\u001b[0m \u001b[31m│\u001b[0m\n", - "\u001b[31m│\u001b[0m \u001b[31m❱ \u001b[0m 196 \u001b[2m│ \u001b[0m_session = Session( \u001b[31m│\u001b[0m\n", - "\u001b[31m│\u001b[0m \u001b[2m 197 \u001b[0m\u001b[2m│ │ \u001b[0mdataset=dataset, \u001b[31m│\u001b[0m\n", - "\u001b[31m│\u001b[0m \u001b[2m 198 \u001b[0m\u001b[2m│ │ \u001b[0mview=view, \u001b[31m│\u001b[0m\n", - "\u001b[31m│\u001b[0m \u001b[2m 199 \u001b[0m\u001b[2m│ │ \u001b[0mspaces=spaces, \u001b[31m│\u001b[0m\n", - "\u001b[31m│\u001b[0m \u001b[31m│\u001b[0m\n", - "\u001b[31m│\u001b[0m \u001b[2;33m/home/strickvl/.pyenv/versions/3.10.12/envs/ls51/lib/python3.10/site-packages/fiftyone/core/sess\u001b[0m \u001b[31m│\u001b[0m\n", - "\u001b[31m│\u001b[0m \u001b[2;33mion/\u001b[0m\u001b[1;33msession.py\u001b[0m:\u001b[94m413\u001b[0m in \u001b[92m__init__\u001b[0m \u001b[31m│\u001b[0m\n", - "\u001b[31m│\u001b[0m \u001b[31m│\u001b[0m\n", - "\u001b[31m│\u001b[0m \u001b[2m 410 \u001b[0m\u001b[2m│ │ \u001b[0m\u001b[94mif\u001b[0m spaces \u001b[95mis\u001b[0m \u001b[94mNone\u001b[0m: \u001b[31m│\u001b[0m\n", - "\u001b[31m│\u001b[0m \u001b[2m 411 \u001b[0m\u001b[2m│ │ │ \u001b[0mspaces = default_spaces.copy() \u001b[31m│\u001b[0m\n", - "\u001b[31m│\u001b[0m \u001b[2m 412 \u001b[0m\u001b[2m│ │ \u001b[0m \u001b[31m│\u001b[0m\n", - "\u001b[31m│\u001b[0m \u001b[31m❱ \u001b[0m 413 \u001b[2m│ │ \u001b[0m\u001b[96mself\u001b[0m._state = StateDescription( \u001b[31m│\u001b[0m\n", - "\u001b[31m│\u001b[0m \u001b[2m 414 \u001b[0m\u001b[2m│ │ │ \u001b[0mconfig=config, \u001b[31m│\u001b[0m\n", - "\u001b[31m│\u001b[0m \u001b[2m 415 \u001b[0m\u001b[2m│ │ │ \u001b[0mdataset=view._root_dataset \u001b[94mif\u001b[0m view \u001b[95mis\u001b[0m \u001b[95mnot\u001b[0m \u001b[94mNone\u001b[0m \u001b[94melse\u001b[0m dataset, \u001b[31m│\u001b[0m\n", - "\u001b[31m│\u001b[0m \u001b[2m 416 \u001b[0m\u001b[2m│ │ │ \u001b[0mview=view, \u001b[31m│\u001b[0m\n", - "\u001b[31m│\u001b[0m \u001b[31m│\u001b[0m\n", - "\u001b[31m│\u001b[0m \u001b[2;33m/home/strickvl/.pyenv/versions/3.10.12/envs/ls51/lib/python3.10/site-packages/fiftyone/core/\u001b[0m\u001b[1;33mstat\u001b[0m \u001b[31m│\u001b[0m\n", - "\u001b[31m│\u001b[0m \u001b[1;33me.py\u001b[0m:\u001b[94m70\u001b[0m in \u001b[92m__init__\u001b[0m \u001b[31m│\u001b[0m\n", - "\u001b[31m│\u001b[0m \u001b[31m│\u001b[0m\n", - "\u001b[31m│\u001b[0m \u001b[2m 67 \u001b[0m\u001b[2m│ │ \u001b[0m\u001b[96mself\u001b[0m.selected = selected \u001b[95mor\u001b[0m [] \u001b[31m│\u001b[0m\n", - "\u001b[31m│\u001b[0m \u001b[2m 68 \u001b[0m\u001b[2m│ │ \u001b[0m\u001b[96mself\u001b[0m.selected_labels = selected_labels \u001b[95mor\u001b[0m [] \u001b[31m│\u001b[0m\n", - "\u001b[31m│\u001b[0m \u001b[2m 69 \u001b[0m\u001b[2m│ │ \u001b[0m\u001b[94mif\u001b[0m dataset \u001b[95mis\u001b[0m \u001b[95mnot\u001b[0m \u001b[94mNone\u001b[0m: \u001b[31m│\u001b[0m\n", - "\u001b[31m│\u001b[0m \u001b[31m❱ \u001b[0m 70 \u001b[2m│ │ │ \u001b[0mdataset.reload() \u001b[31m│\u001b[0m\n", - "\u001b[31m│\u001b[0m \u001b[2m 71 \u001b[0m\u001b[2m│ │ \u001b[0m\u001b[96mself\u001b[0m.view = ( \u001b[31m│\u001b[0m\n", - "\u001b[31m│\u001b[0m \u001b[2m 72 \u001b[0m\u001b[2m│ │ │ \u001b[0mdataset.load_saved_view(view_name) \u001b[31m│\u001b[0m\n", - "\u001b[31m│\u001b[0m \u001b[2m 73 \u001b[0m\u001b[2m│ │ │ \u001b[0m\u001b[94mif\u001b[0m dataset \u001b[95mis\u001b[0m \u001b[95mnot\u001b[0m \u001b[94mNone\u001b[0m \u001b[95mand\u001b[0m view_name \u001b[31m│\u001b[0m\n", - "\u001b[31m│\u001b[0m \u001b[31m│\u001b[0m\n", - "\u001b[31m│\u001b[0m \u001b[2;33m/home/strickvl/.pyenv/versions/3.10.12/envs/ls51/lib/python3.10/site-packages/fiftyone/core/\u001b[0m\u001b[1;33mdata\u001b[0m \u001b[31m│\u001b[0m\n", - "\u001b[31m│\u001b[0m \u001b[1;33mset.py\u001b[0m:\u001b[94m6808\u001b[0m in \u001b[92mreload\u001b[0m \u001b[31m│\u001b[0m\n", - "\u001b[31m│\u001b[0m \u001b[31m│\u001b[0m\n", - "\u001b[31m│\u001b[0m \u001b[2m6805 \u001b[0m\u001b[2m│ \u001b[0m \u001b[31m│\u001b[0m\n", - "\u001b[31m│\u001b[0m \u001b[2m6806 \u001b[0m\u001b[2m│ \u001b[0m\u001b[94mdef\u001b[0m \u001b[92mreload\u001b[0m(\u001b[96mself\u001b[0m): \u001b[31m│\u001b[0m\n", - "\u001b[31m│\u001b[0m \u001b[2m6807 \u001b[0m\u001b[2;90m│ │ \u001b[0m\u001b[33m\"\"\"Reloads the dataset and any in-memory samples from the database.\"\"\"\u001b[0m \u001b[31m│\u001b[0m\n", - "\u001b[31m│\u001b[0m \u001b[31m❱ \u001b[0m6808 \u001b[2m│ │ \u001b[0m\u001b[96mself\u001b[0m._reload(hard=\u001b[94mTrue\u001b[0m) \u001b[31m│\u001b[0m\n", - "\u001b[31m│\u001b[0m \u001b[2m6809 \u001b[0m\u001b[2m│ │ \u001b[0m\u001b[96mself\u001b[0m._reload_docs(hard=\u001b[94mTrue\u001b[0m) \u001b[31m│\u001b[0m\n", - "\u001b[31m│\u001b[0m \u001b[2m6810 \u001b[0m\u001b[2m│ \u001b[0m \u001b[31m│\u001b[0m\n", - "\u001b[31m│\u001b[0m \u001b[2m6811 \u001b[0m\u001b[2m│ \u001b[0m\u001b[94mdef\u001b[0m \u001b[92mclear_cache\u001b[0m(\u001b[96mself\u001b[0m): \u001b[31m│\u001b[0m\n", - "\u001b[31m│\u001b[0m \u001b[31m│\u001b[0m\n", - "\u001b[31m│\u001b[0m \u001b[2;33m/home/strickvl/.pyenv/versions/3.10.12/envs/ls51/lib/python3.10/site-packages/fiftyone/core/\u001b[0m\u001b[1;33mdata\u001b[0m \u001b[31m│\u001b[0m\n", - "\u001b[31m│\u001b[0m \u001b[1;33mset.py\u001b[0m:\u001b[94m6831\u001b[0m in \u001b[92m_reload\u001b[0m \u001b[31m│\u001b[0m\n", - "\u001b[31m│\u001b[0m \u001b[31m│\u001b[0m\n", - "\u001b[31m│\u001b[0m \u001b[2m6828 \u001b[0m\u001b[2m│ │ │ \u001b[0m\u001b[96mself\u001b[0m._doc.reload() \u001b[31m│\u001b[0m\n", - "\u001b[31m│\u001b[0m \u001b[2m6829 \u001b[0m\u001b[2m│ │ │ \u001b[0m\u001b[94mreturn\u001b[0m \u001b[31m│\u001b[0m\n", - "\u001b[31m│\u001b[0m \u001b[2m6830 \u001b[0m\u001b[2m│ │ \u001b[0m \u001b[31m│\u001b[0m\n", - "\u001b[31m│\u001b[0m \u001b[31m❱ \u001b[0m6831 \u001b[2m│ │ \u001b[0mdoc, sample_doc_cls, frame_doc_cls = _load_dataset( \u001b[31m│\u001b[0m\n", - "\u001b[31m│\u001b[0m \u001b[2m6832 \u001b[0m\u001b[2m│ │ │ \u001b[0m\u001b[96mself\u001b[0m, \u001b[96mself\u001b[0m.name, virtual=\u001b[94mTrue\u001b[0m \u001b[31m│\u001b[0m\n", - "\u001b[31m│\u001b[0m \u001b[2m6833 \u001b[0m\u001b[2m│ │ \u001b[0m) \u001b[31m│\u001b[0m\n", - "\u001b[31m│\u001b[0m \u001b[2m6834 \u001b[0m \u001b[31m│\u001b[0m\n", - "\u001b[31m│\u001b[0m \u001b[31m│\u001b[0m\n", - "\u001b[31m│\u001b[0m \u001b[2;33m/home/strickvl/.pyenv/versions/3.10.12/envs/ls51/lib/python3.10/site-packages/fiftyone/core/\u001b[0m\u001b[1;33mdata\u001b[0m \u001b[31m│\u001b[0m\n", - "\u001b[31m│\u001b[0m \u001b[1;33mset.py\u001b[0m:\u001b[94m7176\u001b[0m in \u001b[92m_load_dataset\u001b[0m \u001b[31m│\u001b[0m\n", - "\u001b[31m│\u001b[0m \u001b[31m│\u001b[0m\n", - "\u001b[31m│\u001b[0m \u001b[2m7173 \u001b[0m\u001b[2m│ │ \u001b[0m\u001b[94mtry\u001b[0m: \u001b[31m│\u001b[0m\n", - "\u001b[31m│\u001b[0m \u001b[2m7174 \u001b[0m\u001b[2m│ │ │ \u001b[0mversion = fomi.get_dataset_revision(name) \u001b[31m│\u001b[0m\n", - "\u001b[31m│\u001b[0m \u001b[2m7175 \u001b[0m\u001b[2m│ │ \u001b[0m\u001b[94mexcept\u001b[0m: \u001b[31m│\u001b[0m\n", - "\u001b[31m│\u001b[0m \u001b[31m❱ \u001b[0m7176 \u001b[2m│ │ │ \u001b[0m\u001b[94mraise\u001b[0m e \u001b[31m│\u001b[0m\n", - "\u001b[31m│\u001b[0m \u001b[2m7177 \u001b[0m\u001b[2m│ │ \u001b[0m \u001b[31m│\u001b[0m\n", - "\u001b[31m│\u001b[0m \u001b[2m7178 \u001b[0m\u001b[2m│ │ \u001b[0m\u001b[94mif\u001b[0m version != focn.VERSION: \u001b[31m│\u001b[0m\n", - "\u001b[31m│\u001b[0m \u001b[2m7179 \u001b[0m\u001b[2m│ │ │ \u001b[0m\u001b[94mraise\u001b[0m \u001b[96mValueError\u001b[0m( \u001b[31m│\u001b[0m\n", - "\u001b[31m│\u001b[0m \u001b[31m│\u001b[0m\n", - "\u001b[31m│\u001b[0m \u001b[2;33m/home/strickvl/.pyenv/versions/3.10.12/envs/ls51/lib/python3.10/site-packages/fiftyone/core/\u001b[0m\u001b[1;33mdata\u001b[0m \u001b[31m│\u001b[0m\n", - "\u001b[31m│\u001b[0m \u001b[1;33mset.py\u001b[0m:\u001b[94m7171\u001b[0m in \u001b[92m_load_dataset\u001b[0m \u001b[31m│\u001b[0m\n", - "\u001b[31m│\u001b[0m \u001b[31m│\u001b[0m\n", - "\u001b[31m│\u001b[0m \u001b[2m7168 \u001b[0m\u001b[2m│ │ \u001b[0mfomi.migrate_dataset_if_necessary(name) \u001b[31m│\u001b[0m\n", - "\u001b[31m│\u001b[0m \u001b[2m7169 \u001b[0m\u001b[2m│ \u001b[0m \u001b[31m│\u001b[0m\n", - "\u001b[31m│\u001b[0m \u001b[2m7170 \u001b[0m\u001b[2m│ \u001b[0m\u001b[94mtry\u001b[0m: \u001b[31m│\u001b[0m\n", - "\u001b[31m│\u001b[0m \u001b[31m❱ \u001b[0m7171 \u001b[2m│ │ \u001b[0m\u001b[94mreturn\u001b[0m _do_load_dataset(obj, name) \u001b[31m│\u001b[0m\n", - "\u001b[31m│\u001b[0m \u001b[2m7172 \u001b[0m\u001b[2m│ \u001b[0m\u001b[94mexcept\u001b[0m \u001b[96mException\u001b[0m \u001b[94mas\u001b[0m e: \u001b[31m│\u001b[0m\n", - "\u001b[31m│\u001b[0m \u001b[2m7173 \u001b[0m\u001b[2m│ │ \u001b[0m\u001b[94mtry\u001b[0m: \u001b[31m│\u001b[0m\n", - "\u001b[31m│\u001b[0m \u001b[2m7174 \u001b[0m\u001b[2m│ │ │ \u001b[0mversion = fomi.get_dataset_revision(name) \u001b[31m│\u001b[0m\n", - "\u001b[31m│\u001b[0m \u001b[31m│\u001b[0m\n", - "\u001b[31m│\u001b[0m \u001b[2;33m/home/strickvl/.pyenv/versions/3.10.12/envs/ls51/lib/python3.10/site-packages/fiftyone/core/\u001b[0m\u001b[1;33mdata\u001b[0m \u001b[31m│\u001b[0m\n", - "\u001b[31m│\u001b[0m \u001b[1;33mset.py\u001b[0m:\u001b[94m7193\u001b[0m in \u001b[92m_do_load_dataset\u001b[0m \u001b[31m│\u001b[0m\n", - "\u001b[31m│\u001b[0m \u001b[31m│\u001b[0m\n", - "\u001b[31m│\u001b[0m \u001b[2m7190 \u001b[0m\u001b[2m│ \u001b[0mdb = foo.get_db_conn() \u001b[31m│\u001b[0m\n", - "\u001b[31m│\u001b[0m \u001b[2m7191 \u001b[0m\u001b[2m│ \u001b[0mres = db.datasets.find_one({\u001b[33m\"\u001b[0m\u001b[33mname\u001b[0m\u001b[33m\"\u001b[0m: name}) \u001b[31m│\u001b[0m\n", - "\u001b[31m│\u001b[0m \u001b[2m7192 \u001b[0m\u001b[2m│ \u001b[0m\u001b[94mif\u001b[0m \u001b[95mnot\u001b[0m res: \u001b[31m│\u001b[0m\n", - "\u001b[31m│\u001b[0m \u001b[31m❱ \u001b[0m7193 \u001b[2m│ │ \u001b[0m\u001b[94mraise\u001b[0m \u001b[96mValueError\u001b[0m(\u001b[33m\"\u001b[0m\u001b[33mDataset \u001b[0m\u001b[33m'\u001b[0m\u001b[33m%s\u001b[0m\u001b[33m'\u001b[0m\u001b[33m not found\u001b[0m\u001b[33m\"\u001b[0m % name) \u001b[31m│\u001b[0m\n", - "\u001b[31m│\u001b[0m \u001b[2m7194 \u001b[0m\u001b[2m│ \u001b[0mdataset_doc = foo.DatasetDocument.from_dict(res) \u001b[31m│\u001b[0m\n", - "\u001b[31m│\u001b[0m \u001b[2m7195 \u001b[0m\u001b[2m│ \u001b[0m \u001b[31m│\u001b[0m\n", - "\u001b[31m│\u001b[0m \u001b[2m7196 \u001b[0m\u001b[2m│ \u001b[0msample_collection_name = dataset_doc.sample_collection_name \u001b[31m│\u001b[0m\n", - "\u001b[31m╰──────────────────────────────────────────────────────────────────────────────────────────────────╯\u001b[0m\n", - "\u001b[1;91mValueError: \u001b[0mDataset \u001b[32m'ships'\u001b[0m not found\n" + "" ] }, "metadata": {}, @@ -686,33 +445,143 @@ "import fiftyone as fo\n", "from zenml.client import Client\n", "\n", - "artifact = Client().get_artifact_version('40e81c44-bfb0-4f84-9c18-78571f6c1577')\n", - "dataset = artifact.load()\n", + "artifact = Client().get_artifact_version('ac7b4dc2-c2c7-4f52-9571-845057f3b4e0')\n", + "dataset_json = artifact.load()\n", + "dataset = fo.Dataset.from_json(dataset_json)\n", "session = fo.launch_app(dataset)\n", "\n" ] }, { "cell_type": "code", - "execution_count": 4, + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import fiftyone as fo\n", + "\n", + "session = fo.launch_app()" + ] + }, + { + "cell_type": "code", + "execution_count": 7, "metadata": {}, "outputs": [ { "data": { "text/html": [ - "\n", - " \n", - " " + "
╭───────────────────────────── <class 'fiftyone.core.dataset.Dataset'> ──────────────────────────────╮\n",
+       " A FiftyOne dataset.                                                                                \n",
+       "                                                                                                    \n",
+       " ╭────────────────────────────────────────────────────────────────────────────────────────────────╮ \n",
+       "  Name:        ships_lc6skf                                                                       \n",
+       "  Media type:  image                                                                              \n",
+       "  Num samples: 15                                                                                 \n",
+       "  Persistent:  False                                                                              \n",
+       "  Tags:        []                                                                                 \n",
+       "  Sample fields:                                                                                  \n",
+       "  id:       fiftyone.core.fields.ObjectIdField                                                \n",
+       "  filepath: fiftyone.core.fields.StringField                                                  \n",
+       "  tags:     fiftyone.core.fields.ListField(fiftyone.core.fields.StringField)                  \n",
+       "  metadata: fiftyone.core.fields.EmbeddedDocumentField(fiftyone.core.metadata.ImageMetadata)  \n",
+       "  boxes:    fiftyone.core.fields.EmbeddedDocumentField(fiftyone.core.labels.Detections)       \n",
+       " ╰────────────────────────────────────────────────────────────────────────────────────────────────╯ \n",
+       "                                                                                                    \n",
+       "           app_config = <DatasetAppConfig: {                                                        \n",
+       "                            'media_fields': ['filepath'],                                           \n",
+       "                            'grid_media_field': 'filepath',                                         \n",
+       "                            'modal_media_field': 'filepath',                                        \n",
+       "                            'sidebar_mode': None,                                                   \n",
+       "                            'sidebar_groups': None,                                                 \n",
+       "                            'color_scheme': None,                                                   \n",
+       "                            'plugins': {},                                                          \n",
+       "                        }>                                                                          \n",
+       "              classes = {}                                                                          \n",
+       "           created_at = datetime.datetime(2024, 4, 23, 11, 14, 15, 551000)                          \n",
+       "      default_classes = []                                                                          \n",
+       "  default_group_slice = None                                                                        \n",
+       " default_mask_targets = {}                                                                          \n",
+       "     default_skeleton = None                                                                        \n",
+       "              deleted = False                                                                       \n",
+       "          description = None                                                                        \n",
+       "          group_field = None                                                                        \n",
+       "    group_media_types = None                                                                        \n",
+       "          group_slice = None                                                                        \n",
+       "         group_slices = None                                                                        \n",
+       "  has_annotation_runs = False                                                                       \n",
+       "       has_brain_runs = False                                                                       \n",
+       "      has_evaluations = False                                                                       \n",
+       "             has_runs = False                                                                       \n",
+       "      has_saved_views = False                                                                       \n",
+       "                 info = {}                                                                          \n",
+       "       last_loaded_at = datetime.datetime(2024, 4, 23, 11, 14, 15, 656145)                          \n",
+       "         mask_targets = {}                                                                          \n",
+       "           media_type = 'image'                                                                     \n",
+       "                 name = 'ships_lc6skf'                                                              \n",
+       "           persistent = False                                                                       \n",
+       "            skeletons = {}                                                                          \n",
+       "                 slug = 'ships-lc6skf'                                                              \n",
+       "                 tags = []                                                                          \n",
+       "              version = '0.23.8'                                                                    \n",
+       "╰────────────────────────────────────────────────────────────────────────────────────────────────────╯\n",
+       "
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"\u001b[34m│\u001b[0m \u001b[39m \u001b[0m\u001b[32m'plugins'\u001b[0m\u001b[39m: \u001b[0m\u001b[1;39m{\u001b[0m\u001b[1;39m}\u001b[0m\u001b[39m,\u001b[0m \u001b[34m│\u001b[0m\n", + "\u001b[34m│\u001b[0m \u001b[1;39m}\u001b[0m\u001b[1m>\u001b[0m \u001b[34m│\u001b[0m\n", + "\u001b[34m│\u001b[0m \u001b[3;33mclasses\u001b[0m = \u001b[1m{\u001b[0m\u001b[1m}\u001b[0m \u001b[34m│\u001b[0m\n", + "\u001b[34m│\u001b[0m \u001b[3;33mcreated_at\u001b[0m = \u001b[1;35mdatetime.datetime\u001b[0m\u001b[1m(\u001b[0m\u001b[1;36m2024\u001b[0m, \u001b[1;36m4\u001b[0m, \u001b[1;36m23\u001b[0m, \u001b[1;36m11\u001b[0m, \u001b[1;36m14\u001b[0m, \u001b[1;36m15\u001b[0m, \u001b[1;36m551000\u001b[0m\u001b[1m)\u001b[0m \u001b[34m│\u001b[0m\n", + "\u001b[34m│\u001b[0m \u001b[3;33mdefault_classes\u001b[0m = \u001b[1m[\u001b[0m\u001b[1m]\u001b[0m \u001b[34m│\u001b[0m\n", + "\u001b[34m│\u001b[0m \u001b[3;33mdefault_group_slice\u001b[0m = \u001b[3;35mNone\u001b[0m \u001b[34m│\u001b[0m\n", + "\u001b[34m│\u001b[0m \u001b[3;33mdefault_mask_targets\u001b[0m = \u001b[1m{\u001b[0m\u001b[1m}\u001b[0m \u001b[34m│\u001b[0m\n", + "\u001b[34m│\u001b[0m \u001b[3;33mdefault_skeleton\u001b[0m = \u001b[3;35mNone\u001b[0m \u001b[34m│\u001b[0m\n", + "\u001b[34m│\u001b[0m \u001b[3;33mdeleted\u001b[0m = \u001b[3;91mFalse\u001b[0m \u001b[34m│\u001b[0m\n", + "\u001b[34m│\u001b[0m \u001b[3;33mdescription\u001b[0m = \u001b[3;35mNone\u001b[0m \u001b[34m│\u001b[0m\n", + "\u001b[34m│\u001b[0m \u001b[3;33mgroup_field\u001b[0m = \u001b[3;35mNone\u001b[0m \u001b[34m│\u001b[0m\n", + "\u001b[34m│\u001b[0m \u001b[3;33mgroup_media_types\u001b[0m = \u001b[3;35mNone\u001b[0m \u001b[34m│\u001b[0m\n", + "\u001b[34m│\u001b[0m \u001b[3;33mgroup_slice\u001b[0m = \u001b[3;35mNone\u001b[0m \u001b[34m│\u001b[0m\n", + "\u001b[34m│\u001b[0m \u001b[3;33mgroup_slices\u001b[0m = \u001b[3;35mNone\u001b[0m \u001b[34m│\u001b[0m\n", + "\u001b[34m│\u001b[0m \u001b[3;33mhas_annotation_runs\u001b[0m = \u001b[3;91mFalse\u001b[0m \u001b[34m│\u001b[0m\n", + "\u001b[34m│\u001b[0m \u001b[3;33mhas_brain_runs\u001b[0m = \u001b[3;91mFalse\u001b[0m \u001b[34m│\u001b[0m\n", + "\u001b[34m│\u001b[0m \u001b[3;33mhas_evaluations\u001b[0m = \u001b[3;91mFalse\u001b[0m \u001b[34m│\u001b[0m\n", + "\u001b[34m│\u001b[0m \u001b[3;33mhas_runs\u001b[0m = \u001b[3;91mFalse\u001b[0m \u001b[34m│\u001b[0m\n", + "\u001b[34m│\u001b[0m \u001b[3;33mhas_saved_views\u001b[0m = \u001b[3;91mFalse\u001b[0m \u001b[34m│\u001b[0m\n", + "\u001b[34m│\u001b[0m \u001b[3;33minfo\u001b[0m = \u001b[1m{\u001b[0m\u001b[1m}\u001b[0m \u001b[34m│\u001b[0m\n", + "\u001b[34m│\u001b[0m \u001b[3;33mlast_loaded_at\u001b[0m = \u001b[1;35mdatetime.datetime\u001b[0m\u001b[1m(\u001b[0m\u001b[1;36m2024\u001b[0m, \u001b[1;36m4\u001b[0m, \u001b[1;36m23\u001b[0m, \u001b[1;36m11\u001b[0m, \u001b[1;36m14\u001b[0m, \u001b[1;36m15\u001b[0m, \u001b[1;36m656145\u001b[0m\u001b[1m)\u001b[0m \u001b[34m│\u001b[0m\n", + "\u001b[34m│\u001b[0m \u001b[3;33mmask_targets\u001b[0m = \u001b[1m{\u001b[0m\u001b[1m}\u001b[0m \u001b[34m│\u001b[0m\n", + "\u001b[34m│\u001b[0m \u001b[3;33mmedia_type\u001b[0m = \u001b[32m'image'\u001b[0m \u001b[34m│\u001b[0m\n", + "\u001b[34m│\u001b[0m \u001b[3;33mname\u001b[0m = \u001b[32m'ships_lc6skf'\u001b[0m \u001b[34m│\u001b[0m\n", + "\u001b[34m│\u001b[0m \u001b[3;33mpersistent\u001b[0m = \u001b[3;91mFalse\u001b[0m \u001b[34m│\u001b[0m\n", + "\u001b[34m│\u001b[0m \u001b[3;33mskeletons\u001b[0m = \u001b[1m{\u001b[0m\u001b[1m}\u001b[0m \u001b[34m│\u001b[0m\n", + "\u001b[34m│\u001b[0m \u001b[3;33mslug\u001b[0m = \u001b[32m'ships-lc6skf'\u001b[0m \u001b[34m│\u001b[0m\n", + "\u001b[34m│\u001b[0m \u001b[3;33mtags\u001b[0m = \u001b[1m[\u001b[0m\u001b[1m]\u001b[0m \u001b[34m│\u001b[0m\n", + "\u001b[34m│\u001b[0m \u001b[3;33mversion\u001b[0m = \u001b[32m'0.23.8'\u001b[0m \u001b[34m│\u001b[0m\n", + "\u001b[34m╰────────────────────────────────────────────────────────────────────────────────────────────────────╯\u001b[0m\n" ] }, "metadata": {}, @@ -720,9 +589,8 @@ } ], "source": [ - "import fiftyone as fo\n", - "\n", - "session = fo.launch_app()" + "from rich import inspect\n", + "inspect(dataset)" ] }, { diff --git a/end-to-end-computer-vision/pipelines/inference.py b/end-to-end-computer-vision/pipelines/inference.py index 8d4d5046..48a53fcf 100644 --- a/end-to-end-computer-vision/pipelines/inference.py +++ b/end-to-end-computer-vision/pipelines/inference.py @@ -14,11 +14,12 @@ # See the License for the specific language governing permissions and # limitations under the License. # -from zenml import pipeline, step -from zenml.logger import get_logger +import os +from typing import Annotated +from zenml import log_artifact_metadata, pipeline, step from zenml.client import Client -import os +from zenml.logger import get_logger os.environ["YOLO_VERBOSE"] = "False" @@ -34,7 +35,7 @@ @step -def predict_over_images() -> fo.Dataset: +def predict_over_images() -> Annotated[str, "predictions_dataset_json"]: # model = Model( # name="Yolo_Object_Detection", @@ -52,8 +53,10 @@ def predict_over_images() -> fo.Dataset: dataset_dir=DATASET_DIR, dataset_type=fo.types.ImageDirectory, name=DATASET_NAME, + overwrite=True, + persistent=True, ) - dataset.persistent = True + # # View summary info about the dataset # print(dataset) @@ -61,7 +64,16 @@ def predict_over_images() -> fo.Dataset: # print(dataset.head()) dataset.apply_model(yolo_model, label_field="boxes") - return dataset + + log_artifact_metadata( + artifact_name="predictions_dataset_json", + metadata={ + "summary_info": dataset.summary(), + "persistence": dataset.persistent, + }, + ) + + return dataset.to_json() @pipeline From 32e63c2ef11fc39ebceff94468680b984293f16b Mon Sep 17 00:00:00 2001 From: Alex Strick van Linschoten Date: Tue, 23 Apr 2024 14:32:43 +0200 Subject: [PATCH 030/121] update readme and requirements --- end-to-end-computer-vision/README.md | 8 ++++++++ end-to-end-computer-vision/requirements.txt | 1 + 2 files changed, 9 insertions(+) diff --git a/end-to-end-computer-vision/README.md b/end-to-end-computer-vision/README.md index 21c3a52f..1617e31d 100644 --- a/end-to-end-computer-vision/README.md +++ b/end-to-end-computer-vision/README.md @@ -17,3 +17,11 @@ zenml integration install mlflow -y export DATA_UPLOAD_MAX_NUMBER_FILES=1000000 export WANDB_DISABLED=True ``` + +# Fiftyone plugins + +```shell +fiftyone plugins download https://github.com/jacobmarks/fiftyone-albumentations-plugin + +fiftyone plugins download https://github.com/voxel51/fiftyone-plugins --plugin-names @voxel51/annotation +``` diff --git a/end-to-end-computer-vision/requirements.txt b/end-to-end-computer-vision/requirements.txt index 3e6b7019..e0bbf7c9 100644 --- a/end-to-end-computer-vision/requirements.txt +++ b/end-to-end-computer-vision/requirements.txt @@ -11,3 +11,4 @@ huggingface_hub>=0.20.0 # needed for fiftyone imports of HF datasets # fiftyone @ git+https://github.com/voxel51/fiftyone.git@develop # fiftyone v0.24.0 fiftyone datasets +albumentations From adec3b354ff474ca5ec2b00092ac18e7c60dc580 Mon Sep 17 00:00:00 2001 From: Alex Strick van Linschoten Date: Tue, 23 Apr 2024 14:32:57 +0200 Subject: [PATCH 031/121] update notebook --- .../notebooks/ultralytics.ipynb | 190 ++++++++++++++++-- 1 file changed, 171 insertions(+), 19 deletions(-) diff --git a/end-to-end-computer-vision/notebooks/ultralytics.ipynb b/end-to-end-computer-vision/notebooks/ultralytics.ipynb index da3f0ae5..f3707cf0 100644 --- a/end-to-end-computer-vision/notebooks/ultralytics.ipynb +++ b/end-to-end-computer-vision/notebooks/ultralytics.ipynb @@ -408,33 +408,99 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 26, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - " 100% |███████████████████| 15/15 [17.6ms elapsed, 0s remaining, 851.8 samples/s] \n", - "\u001b[1;35m 100% |███████████████████| 15/15 [17.6ms elapsed, 0s remaining, 851.8 samples/s] \u001b[0m\n" + " 100% |███████████████████| 15/15 [11.9ms elapsed, 0s remaining, 1.3K samples/s] \n", + "\u001b[1;35m 100% |███████████████████| 15/15 [11.9ms elapsed, 0s remaining, 1.3K samples/s] \u001b[0m\n" ] }, { "data": { "text/html": [ "\n", - " \n", - " " + "\n", + "\n", + "
\n", + "
\n", + " \n", + "
\n", + " \n", + "
\n", + "\n", + "" ], "text/plain": [ - "" + "" ] }, "metadata": {}, @@ -443,24 +509,110 @@ ], "source": [ "import fiftyone as fo\n", + "import os\n", "from zenml.client import Client\n", + "os.environ[\"FIFTYONE_LABELSTUDIO_API_KEY\"] = \"9896fa7abd15277ab9046842469fe3c2ec17e0bb\"\n", "\n", - "artifact = Client().get_artifact_version('ac7b4dc2-c2c7-4f52-9571-845057f3b4e0')\n", + "artifact = Client().get_artifact_version('603c0941-67f4-4be1-9d6f-114cf5c879ac')\n", "dataset_json = artifact.load()\n", - "dataset = fo.Dataset.from_json(dataset_json)\n", - "session = fo.launch_app(dataset)\n", + "dataset = fo.Dataset.from_json(dataset_json, persistent=True)\n", + "session = fo.launch_app(dataset)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "'ships_wuxqa5'" + ] + }, + "execution_count": 19, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "session.dataset.name" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "['arst', 'huge', 'something_123']" + ] + }, + "execution_count": 28, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "fo.load_dataset(\"ships_wuxqa5\").list_annotation_runs()\n", "\n" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 29, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Downloading labels from Label Studio...\n", + "\u001b[1;35mDownloading labels from Label Studio...\u001b[0m\n", + "Download complete\n", + "\u001b[1;35mDownload complete\u001b[0m\n", + "Loading labels for field 'ground_truth'...\n", + "\u001b[1;35mLoading labels for field 'ground_truth'...\u001b[0m\n", + " 100% |█████████████████████| 1/1 [10.2ms elapsed, 0s remaining, 98.4 samples/s] \n", + "\u001b[1;35m 100% |█████████████████████| 1/1 [10.2ms elapsed, 0s remaining, 98.4 samples/s] \u001b[0m\n" + ] + }, + { + "data": { + "text/html": [ + "\n", + " \n", + " " + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "import fiftyone as fo\n", "\n", - "session = fo.launch_app()" + "anno_key = \"huge\"\n", + "\n", + "# Step 5: Merge annotations back into FiftyOne dataset\n", + "\n", + "new_dataset = fo.load_dataset(session.dataset.name).load_annotations(anno_key)\n", + "\n", + "# Load the view that was annotated in the App\n", + "# view = dataset.load_annotation_view(anno_key)\n", + "session = fo.launch_app(new_dataset)\n" ] }, { From 02b102c07fbb1837119e96b7a70547192160c5a7 Mon Sep 17 00:00:00 2001 From: Alexej Penner Date: Tue, 23 Apr 2024 14:38:42 +0200 Subject: [PATCH 032/121] Further progress --- end-to-end-computer-vision/.dockerignore | 6 ++- .../configs/training.yaml | 2 +- .../pipelines/inference.py | 35 ++++++++++++++ .../pipelines/training.py | 9 +++- end-to-end-computer-vision/run.py | 26 ++++++++-- .../steps/promote_model.py | 48 +++++++++++++++++++ .../steps/train_model.py | 11 +++-- 7 files changed, 125 insertions(+), 12 deletions(-) create mode 100644 end-to-end-computer-vision/pipelines/inference.py create mode 100644 end-to-end-computer-vision/steps/promote_model.py diff --git a/end-to-end-computer-vision/.dockerignore b/end-to-end-computer-vision/.dockerignore index 455f4d7a..c45208a3 100644 --- a/end-to-end-computer-vision/.dockerignore +++ b/end-to-end-computer-vision/.dockerignore @@ -1,2 +1,6 @@ .venv* -.requirements* \ No newline at end of file +.requirements* +**/tmp* +data/* +*.jpg +*.pt \ No newline at end of file diff --git a/end-to-end-computer-vision/configs/training.yaml b/end-to-end-computer-vision/configs/training.yaml index 1badbd73..85bf3487 100644 --- a/end-to-end-computer-vision/configs/training.yaml +++ b/end-to-end-computer-vision/configs/training.yaml @@ -2,9 +2,9 @@ enable_cache: False settings: docker: + parent_image: pytorch/pytorch:2.2.0-cuda11.8-cudnn8-runtime required_integrations: - gcp - - torch requirements: - ultralytics diff --git a/end-to-end-computer-vision/pipelines/inference.py b/end-to-end-computer-vision/pipelines/inference.py new file mode 100644 index 00000000..7802b464 --- /dev/null +++ b/end-to-end-computer-vision/pipelines/inference.py @@ -0,0 +1,35 @@ +# Apache Software License 2.0 +# +# Copyright (c) ZenML GmbH 2024. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# +from zenml import pipeline, get_pipeline_context +from zenml.logger import get_logger + +from steps.train_model import train_model +from steps.predict_image import predict_image +from steps.load_model import load_model + +logger = get_logger(__name__) + + +@pipeline +def inference(): + # Load the latest version of the train dataset + mv = get_pipeline_context().model + ma = mv.get_model_artifact(name="Raw_YOLO") + + + + diff --git a/end-to-end-computer-vision/pipelines/training.py b/end-to-end-computer-vision/pipelines/training.py index e069a3d1..e01586e7 100644 --- a/end-to-end-computer-vision/pipelines/training.py +++ b/end-to-end-computer-vision/pipelines/training.py @@ -17,6 +17,7 @@ from zenml import pipeline, get_pipeline_context from zenml.logger import get_logger +from steps.promote_model import promote_model from steps.train_model import train_model from steps.predict_image import predict_image from steps.load_model import load_model @@ -27,7 +28,13 @@ @pipeline def training(model_checkpoint: str = "yolov8l.pt"): model = load_model(model_checkpoint) + + # Load the latest version of the train dataset mv = get_pipeline_context().model dataset = mv.get_artifact("yolo_dataset") - trained_model = train_model(model=model, dataset=dataset) + + trained_model, metrics = train_model(model=model, dataset=dataset) + + promote_model(metrics) + predict_image(trained_model) diff --git a/end-to-end-computer-vision/run.py b/end-to-end-computer-vision/run.py index 6904363f..a71b5b82 100644 --- a/end-to-end-computer-vision/run.py +++ b/end-to-end-computer-vision/run.py @@ -14,6 +14,11 @@ # See the License for the specific language governing permissions and # limitations under the License. # +from uuid import UUID + +from zenml import Model +from zenml.client import Client +from zenml.enums import ModelStages from zenml.logger import get_logger from pipelines import data_export @@ -22,9 +27,22 @@ logger = get_logger(__name__) - if __name__ == "__main__": - # data_export(dataset_name="cv_proj") #.with_options(config_path="configs/data_export_alexej.yaml")() - #my_pipeline() - # data_export.with_options(config_path="configs/data_export_alexej.yaml")() + client = Client() + + client.activate_stack(UUID("7cda3cec-6744-48dc-8bdc-f102242a26d2")) + + # Export data from label studio + data_export.with_options(config_path="configs/data_export_alexej.yaml")() + + # Promote Model to staging + latest_model = Model( + name="Yolo_Object_Detection", + version=ModelStages.LATEST + ) + latest_model.set_stage(stage=ModelStages.STAGING, force=True) + + client.activate_stack(UUID("20ed5311-ffc6-45d0-b339-6ec35af9501e")) + + # Train model on data training.with_options(config_path="configs/training.yaml")() diff --git a/end-to-end-computer-vision/steps/promote_model.py b/end-to-end-computer-vision/steps/promote_model.py new file mode 100644 index 00000000..dded1bf3 --- /dev/null +++ b/end-to-end-computer-vision/steps/promote_model.py @@ -0,0 +1,48 @@ +# Apache Software License 2.0 +# +# Copyright (c) ZenML GmbH 2024. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# +from typing import Dict, Any + +from zenml import step, get_step_context, Model +from zenml.client import Client +from zenml.enums import ModelStages +from zenml.logger import get_logger + +logger = get_logger(__name__) + +@step() +def promote_model( + metrics: Dict[str, Any], +) -> None: + client = Client() + # Get the model associated with this pipeline + cur_model_version = get_step_context().model + + # Get the current production model + latest_model_version = client.get_model_version( + model_name_or_id=cur_model_version.name, + model_version_name_or_number_or_id=ModelStages.STAGING + ) + + prod_metrics = latest_model_version.get_artifact("validation_metrics").load() + + if metrics["metrics/mAP50(B)"] >= prod_metrics["metrics/mAP50(B)"]: + logger.info("Model promoted to `production` as it outperformed the " + "current `production` model on `mAP50`.") + cur_model_version.set_stage(ModelStages.PRODUCTION, force=True) + else: + logger.info("Model was less performant than the current `production` " + "model. Model will **not** be promoted.") diff --git a/end-to-end-computer-vision/steps/train_model.py b/end-to-end-computer-vision/steps/train_model.py index d9438d40..db47d80f 100644 --- a/end-to-end-computer-vision/steps/train_model.py +++ b/end-to-end-computer-vision/steps/train_model.py @@ -14,7 +14,7 @@ # See the License for the specific language governing permissions and # limitations under the License. # -from typing import Annotated +from typing import Annotated, Tuple, Dict, Any from ultralytics import YOLO from zenml import step, ArtifactConfig, log_artifact_metadata @@ -29,9 +29,10 @@ def train_model( model: YOLO, dataset: LabelStudioYOLODataset, -) -> Annotated[ - YOLO, ArtifactConfig(name="Trained_YOLO", is_model_artifact=True) -]: +) -> Tuple[ + Annotated[YOLO, ArtifactConfig(name="Trained_YOLO", is_model_artifact=True)], + Annotated[Dict[str, Any], "validation_metrics"] + ]: data_path = load_and_split_data(dataset=dataset) model.train(data=data_path, epochs=1) @@ -46,4 +47,4 @@ def train_model( # Read images as PIL images from directory metrics.save_dir for all png and jpg files images = load_images_from_folder(metrics.save_dir) - return model \ No newline at end of file + return model, metrics.results_dict \ No newline at end of file From ddf2e21f5e1062eab89091bf13c9c3cc572f1a5e Mon Sep 17 00:00:00 2001 From: Alex Strick van Linschoten Date: Tue, 23 Apr 2024 14:43:05 +0200 Subject: [PATCH 033/121] formatting --- .../pipelines/training.py | 4 +--- .../steps/promote_model.py | 23 ++++++++++++------- .../steps/train_model.py | 12 ++++++---- 3 files changed, 23 insertions(+), 16 deletions(-) diff --git a/end-to-end-computer-vision/pipelines/training.py b/end-to-end-computer-vision/pipelines/training.py index f7711e40..e79ea1e9 100644 --- a/end-to-end-computer-vision/pipelines/training.py +++ b/end-to-end-computer-vision/pipelines/training.py @@ -17,11 +17,9 @@ from zenml import get_pipeline_context, pipeline from zenml.logger import get_logger -from steps.promote_model import promote_model -from steps.train_model import train_model -from steps.predict_image import predict_image from steps.load_model import load_model from steps.predict_image import predict_image +from steps.promote_model import promote_model from steps.train_model import train_model logger = get_logger(__name__) diff --git a/end-to-end-computer-vision/steps/promote_model.py b/end-to-end-computer-vision/steps/promote_model.py index dded1bf3..73ff215c 100644 --- a/end-to-end-computer-vision/steps/promote_model.py +++ b/end-to-end-computer-vision/steps/promote_model.py @@ -14,15 +14,16 @@ # See the License for the specific language governing permissions and # limitations under the License. # -from typing import Dict, Any +from typing import Any, Dict -from zenml import step, get_step_context, Model +from zenml import get_step_context, step from zenml.client import Client from zenml.enums import ModelStages from zenml.logger import get_logger logger = get_logger(__name__) + @step() def promote_model( metrics: Dict[str, Any], @@ -34,15 +35,21 @@ def promote_model( # Get the current production model latest_model_version = client.get_model_version( model_name_or_id=cur_model_version.name, - model_version_name_or_number_or_id=ModelStages.STAGING + model_version_name_or_number_or_id=ModelStages.STAGING, ) - prod_metrics = latest_model_version.get_artifact("validation_metrics").load() + prod_metrics = latest_model_version.get_artifact( + "validation_metrics" + ).load() if metrics["metrics/mAP50(B)"] >= prod_metrics["metrics/mAP50(B)"]: - logger.info("Model promoted to `production` as it outperformed the " - "current `production` model on `mAP50`.") + logger.info( + "Model promoted to `production` as it outperformed the " + "current `production` model on `mAP50`." + ) cur_model_version.set_stage(ModelStages.PRODUCTION, force=True) else: - logger.info("Model was less performant than the current `production` " - "model. Model will **not** be promoted.") + logger.info( + "Model was less performant than the current `production` " + "model. Model will **not** be promoted." + ) diff --git a/end-to-end-computer-vision/steps/train_model.py b/end-to-end-computer-vision/steps/train_model.py index 47b23b21..0b47ba4c 100644 --- a/end-to-end-computer-vision/steps/train_model.py +++ b/end-to-end-computer-vision/steps/train_model.py @@ -14,7 +14,7 @@ # See the License for the specific language governing permissions and # limitations under the License. # -from typing import Annotated, Tuple, Dict, Any +from typing import Annotated, Any, Dict, Tuple from ultralytics import YOLO from zenml import ArtifactConfig, log_artifact_metadata, step @@ -31,9 +31,11 @@ def train_model( model: YOLO, dataset: LabelStudioYOLODataset, ) -> Tuple[ - Annotated[YOLO, ArtifactConfig(name="Trained_YOLO", is_model_artifact=True)], - Annotated[Dict[str, Any], "validation_metrics"] - ]: + Annotated[ + YOLO, ArtifactConfig(name="Trained_YOLO", is_model_artifact=True) + ], + Annotated[Dict[str, Any], "validation_metrics"], +]: data_path = load_and_split_data(dataset=dataset) model.train(data=data_path, epochs=1) @@ -48,4 +50,4 @@ def train_model( # Read images as PIL images from directory metrics.save_dir for all png and jpg files images = load_images_from_folder(metrics.save_dir) - return model, metrics.results_dict \ No newline at end of file + return model, metrics.results_dict From 026fb1de6c75e2b37716d728cd5dd6d02bf15fcd Mon Sep 17 00:00:00 2001 From: Alex Strick van Linschoten Date: Tue, 23 Apr 2024 14:43:24 +0200 Subject: [PATCH 034/121] add inference to run.py --- end-to-end-computer-vision/run.py | 6 ++++-- 1 file changed, 4 insertions(+), 2 deletions(-) diff --git a/end-to-end-computer-vision/run.py b/end-to-end-computer-vision/run.py index a71b5b82..b5e7e447 100644 --- a/end-to-end-computer-vision/run.py +++ b/end-to-end-computer-vision/run.py @@ -22,6 +22,7 @@ from zenml.logger import get_logger from pipelines import data_export +from pipelines.inference import inference from pipelines.training import training logger = get_logger(__name__) @@ -37,8 +38,7 @@ # Promote Model to staging latest_model = Model( - name="Yolo_Object_Detection", - version=ModelStages.LATEST + name="Yolo_Object_Detection", version=ModelStages.LATEST ) latest_model.set_stage(stage=ModelStages.STAGING, force=True) @@ -46,3 +46,5 @@ # Train model on data training.with_options(config_path="configs/training.yaml")() + + inference() From 34fefde6f0b9dfbc77c143cba3a818ea9d805448 Mon Sep 17 00:00:00 2001 From: Alex Strick van Linschoten Date: Tue, 23 Apr 2024 14:57:55 +0200 Subject: [PATCH 035/121] update inference --- .../pipelines/inference.py | 26 +++++-------------- 1 file changed, 6 insertions(+), 20 deletions(-) diff --git a/end-to-end-computer-vision/pipelines/inference.py b/end-to-end-computer-vision/pipelines/inference.py index 48a53fcf..c1a5ea91 100644 --- a/end-to-end-computer-vision/pipelines/inference.py +++ b/end-to-end-computer-vision/pipelines/inference.py @@ -32,21 +32,13 @@ DATASET_NAME = "ships" DATASET_DIR = "data/ships/subset" - +TRAINED_MODEL_NAME = "Trained_YOLO" @step -def predict_over_images() -> Annotated[str, "predictions_dataset_json"]: - - # model = Model( - # name="Yolo_Object_Detection", - # version="staging", - # ) - # model.get_model_artifact(name="staging").load() - # model_artifact = model_version.get_model_artifact(name="Raw_YOLO") - artifact = Client().get_artifact_version( - "105c768c-8c86-465a-b018-b1a800ad4e19" - ) - yolo_model = artifact.load() +def create_fiftyone_dataset() -> Annotated[str, "predictions_dataset_json"]: + c = Client() + model_artifact = c.get_artifact_version(name_id_or_prefix=TRAINED_MODEL_NAME) + yolo_model = model_artifact.load() # results = yolo_model(DATASET_DIR, half=True, conf=0.6) dataset = fo.Dataset.from_dir( @@ -57,12 +49,6 @@ def predict_over_images() -> Annotated[str, "predictions_dataset_json"]: persistent=True, ) - # # View summary info about the dataset - # print(dataset) - - # # Print the first few samples in the dataset - # print(dataset.head()) - dataset.apply_model(yolo_model, label_field="boxes") log_artifact_metadata( @@ -78,4 +64,4 @@ def predict_over_images() -> Annotated[str, "predictions_dataset_json"]: @pipeline def inference(): - dataset = predict_over_images() + dataset = create_fiftyone_dataset() From cbe3588ed64819611f53706387bea6aca45bc6db Mon Sep 17 00:00:00 2001 From: Alex Strick van Linschoten Date: Tue, 23 Apr 2024 14:59:24 +0200 Subject: [PATCH 036/121] read becomes load --- end-to-end-computer-vision/steps/export_label_studio.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/end-to-end-computer-vision/steps/export_label_studio.py b/end-to-end-computer-vision/steps/export_label_studio.py index 7c6ad92b..9e6ceef6 100644 --- a/end-to-end-computer-vision/steps/export_label_studio.py +++ b/end-to-end-computer-vision/steps/export_label_studio.py @@ -49,7 +49,7 @@ def load_data_from_label_studio( last_task_ids = ( last_run.steps[cur_step_name] .outputs["task_ids"] - .read() + .load() ) except (RuntimeError, KeyError): last_task_ids = [] From ce04c8cb872feeabfbb425c9e2ac84db7d11dee2 Mon Sep 17 00:00:00 2001 From: Alex Strick van Linschoten Date: Tue, 23 Apr 2024 15:03:04 +0200 Subject: [PATCH 037/121] remove unneccessary temp file --- .../materializers/label_studio_yolo_dataset_materializer.py | 5 +---- 1 file changed, 1 insertion(+), 4 deletions(-) diff --git a/end-to-end-computer-vision/materializers/label_studio_yolo_dataset_materializer.py b/end-to-end-computer-vision/materializers/label_studio_yolo_dataset_materializer.py index ccd54969..83986e2c 100644 --- a/end-to-end-computer-vision/materializers/label_studio_yolo_dataset_materializer.py +++ b/end-to-end-computer-vision/materializers/label_studio_yolo_dataset_materializer.py @@ -80,7 +80,4 @@ def save(self, dataset: LabelStudioYOLODataset) -> None: filepath = os.path.join(self.uri, DEFAULT_FILENAME) - # Make a temporary phantom artifact - with tempfile.NamedTemporaryFile(mode="w", suffix=".zip") as f: - # Copy it into artifact store - fileio.copy(dataset.filepath, filepath) + fileio.copy(dataset.filepath, filepath) From b406149c365f67c941e32f3b03acc90aa40c0890 Mon Sep 17 00:00:00 2001 From: Alex Strick van Linschoten Date: Tue, 23 Apr 2024 15:03:34 +0200 Subject: [PATCH 038/121] add constants --- end-to-end-computer-vision/utils/constants.py | 3 +++ 1 file changed, 3 insertions(+) create mode 100644 end-to-end-computer-vision/utils/constants.py diff --git a/end-to-end-computer-vision/utils/constants.py b/end-to-end-computer-vision/utils/constants.py new file mode 100644 index 00000000..d0a2163e --- /dev/null +++ b/end-to-end-computer-vision/utils/constants.py @@ -0,0 +1,3 @@ +DATASET_NAME = "ships" +DATASET_DIR = "data/ships/subset" +TRAINED_MODEL_NAME = "Trained_YOLO" From 3d7c714a44c5662797a07d8a03c3f62be6f39ecf Mon Sep 17 00:00:00 2001 From: Alex Strick van Linschoten Date: Tue, 23 Apr 2024 15:06:28 +0200 Subject: [PATCH 039/121] pull out constants and remove unnecessary image call --- end-to-end-computer-vision/pipelines/inference.py | 7 +++---- end-to-end-computer-vision/steps/train_model.py | 2 +- 2 files changed, 4 insertions(+), 5 deletions(-) diff --git a/end-to-end-computer-vision/pipelines/inference.py b/end-to-end-computer-vision/pipelines/inference.py index c1a5ea91..420ad781 100644 --- a/end-to-end-computer-vision/pipelines/inference.py +++ b/end-to-end-computer-vision/pipelines/inference.py @@ -30,14 +30,13 @@ import fiftyone as fo -DATASET_NAME = "ships" -DATASET_DIR = "data/ships/subset" -TRAINED_MODEL_NAME = "Trained_YOLO" @step def create_fiftyone_dataset() -> Annotated[str, "predictions_dataset_json"]: c = Client() - model_artifact = c.get_artifact_version(name_id_or_prefix=TRAINED_MODEL_NAME) + model_artifact = c.get_artifact_version( + name_id_or_prefix=TRAINED_MODEL_NAME + ) yolo_model = model_artifact.load() # results = yolo_model(DATASET_DIR, half=True, conf=0.6) diff --git a/end-to-end-computer-vision/steps/train_model.py b/end-to-end-computer-vision/steps/train_model.py index 0b47ba4c..06611a95 100644 --- a/end-to-end-computer-vision/steps/train_model.py +++ b/end-to-end-computer-vision/steps/train_model.py @@ -48,6 +48,6 @@ def train_model( ) # Read images as PIL images from directory metrics.save_dir for all png and jpg files - images = load_images_from_folder(metrics.save_dir) + # images = load_images_from_folder(metrics.save_dir) return model, metrics.results_dict From ad08269c695b71c369248875c94b2e819af020b3 Mon Sep 17 00:00:00 2001 From: Alexej Penner Date: Tue, 23 Apr 2024 15:06:39 +0200 Subject: [PATCH 040/121] Further steps towards step operator --- end-to-end-computer-vision/.gitignore | 3 ++- end-to-end-computer-vision/configs/training.yaml | 14 +++++++++++++- .../steps/export_label_studio.py | 2 +- 3 files changed, 16 insertions(+), 3 deletions(-) diff --git a/end-to-end-computer-vision/.gitignore b/end-to-end-computer-vision/.gitignore index 39e38347..e76e4bbf 100644 --- a/end-to-end-computer-vision/.gitignore +++ b/end-to-end-computer-vision/.gitignore @@ -1,4 +1,5 @@ *.pt !data/.gitkeep data/ -runs/ \ No newline at end of file +runs/ +**/tmp* diff --git a/end-to-end-computer-vision/configs/training.yaml b/end-to-end-computer-vision/configs/training.yaml index 85bf3487..028cb6a5 100644 --- a/end-to-end-computer-vision/configs/training.yaml +++ b/end-to-end-computer-vision/configs/training.yaml @@ -2,12 +2,24 @@ enable_cache: False settings: docker: - parent_image: pytorch/pytorch:2.2.0-cuda11.8-cudnn8-runtime required_integrations: - gcp requirements: - ultralytics +steps: + train_model: + step_operator: gcp_a100 + enable_step_logs: False + settings: + docker: + parent_image: pytorch/pytorch:2.2.0-cuda11.8-cudnn8-runtime + required_integrations: + - gcp + requirements: + - ultralytics + - zenml + # configuration of the Model Control Plane model: name: Yolo_Object_Detection diff --git a/end-to-end-computer-vision/steps/export_label_studio.py b/end-to-end-computer-vision/steps/export_label_studio.py index 7c6ad92b..9e6ceef6 100644 --- a/end-to-end-computer-vision/steps/export_label_studio.py +++ b/end-to-end-computer-vision/steps/export_label_studio.py @@ -49,7 +49,7 @@ def load_data_from_label_studio( last_task_ids = ( last_run.steps[cur_step_name] .outputs["task_ids"] - .read() + .load() ) except (RuntimeError, KeyError): last_task_ids = [] From f281332982ba1c36bbe9d811ee63e9d085834072 Mon Sep 17 00:00:00 2001 From: Alex Strick van Linschoten Date: Tue, 23 Apr 2024 15:07:27 +0200 Subject: [PATCH 041/121] constants --- end-to-end-computer-vision/pipelines/inference.py | 10 ++++------ 1 file changed, 4 insertions(+), 6 deletions(-) diff --git a/end-to-end-computer-vision/pipelines/inference.py b/end-to-end-computer-vision/pipelines/inference.py index 420ad781..39727a3a 100644 --- a/end-to-end-computer-vision/pipelines/inference.py +++ b/end-to-end-computer-vision/pipelines/inference.py @@ -17,18 +17,16 @@ import os from typing import Annotated +import fiftyone as fo from zenml import log_artifact_metadata, pipeline, step from zenml.client import Client from zenml.logger import get_logger -os.environ["YOLO_VERBOSE"] = "False" - -import fiftyone as fo +from utils.constants import DATASET_DIR, DATASET_NAME, TRAINED_MODEL_NAME logger = get_logger(__name__) - -import fiftyone as fo +os.environ["YOLO_VERBOSE"] = "False" @step @@ -63,4 +61,4 @@ def create_fiftyone_dataset() -> Annotated[str, "predictions_dataset_json"]: @pipeline def inference(): - dataset = create_fiftyone_dataset() + create_fiftyone_dataset() From 11428d6e8d5304b52a5a53f89b2cd16534949c8a Mon Sep 17 00:00:00 2001 From: Alex Strick van Linschoten Date: Tue, 23 Apr 2024 15:13:30 +0200 Subject: [PATCH 042/121] formatting --- .../pipelines/inference.py | 45 ++++--------- .../steps/fiftyone_inference.py | 66 +++++++++++++++++++ .../steps/train_model.py | 2 +- end-to-end-computer-vision/utils/constants.py | 1 + 4 files changed, 81 insertions(+), 33 deletions(-) create mode 100644 end-to-end-computer-vision/steps/fiftyone_inference.py diff --git a/end-to-end-computer-vision/pipelines/inference.py b/end-to-end-computer-vision/pipelines/inference.py index 39727a3a..8a92b014 100644 --- a/end-to-end-computer-vision/pipelines/inference.py +++ b/end-to-end-computer-vision/pipelines/inference.py @@ -15,50 +15,31 @@ # limitations under the License. # import os -from typing import Annotated import fiftyone as fo -from zenml import log_artifact_metadata, pipeline, step +from zenml import pipeline from zenml.client import Client from zenml.logger import get_logger -from utils.constants import DATASET_DIR, DATASET_NAME, TRAINED_MODEL_NAME +from steps.fiftyone_inference import create_fiftyone_dataset +from utils.constants import PREDICTIONS_DATASET_ARTIFACT_NAME logger = get_logger(__name__) os.environ["YOLO_VERBOSE"] = "False" -@step -def create_fiftyone_dataset() -> Annotated[str, "predictions_dataset_json"]: - c = Client() - model_artifact = c.get_artifact_version( - name_id_or_prefix=TRAINED_MODEL_NAME - ) - yolo_model = model_artifact.load() - # results = yolo_model(DATASET_DIR, half=True, conf=0.6) +@pipeline +def inference(): + create_fiftyone_dataset() - dataset = fo.Dataset.from_dir( - dataset_dir=DATASET_DIR, - dataset_type=fo.types.ImageDirectory, - name=DATASET_NAME, - overwrite=True, - persistent=True, - ) - dataset.apply_model(yolo_model, label_field="boxes") +if __name__ == "__main__": + inference() - log_artifact_metadata( - artifact_name="predictions_dataset_json", - metadata={ - "summary_info": dataset.summary(), - "persistence": dataset.persistent, - }, + artifact = Client().get_artifact_version( + name_id_or_prefix=PREDICTIONS_DATASET_ARTIFACT_NAME ) - - return dataset.to_json() - - -@pipeline -def inference(): - create_fiftyone_dataset() + dataset_json = artifact.load() + dataset = fo.Dataset.from_json(dataset_json, persistent=True) + session = fo.launch_app(dataset) diff --git a/end-to-end-computer-vision/steps/fiftyone_inference.py b/end-to-end-computer-vision/steps/fiftyone_inference.py new file mode 100644 index 00000000..8db39e96 --- /dev/null +++ b/end-to-end-computer-vision/steps/fiftyone_inference.py @@ -0,0 +1,66 @@ +# Apache Software License 2.0 +# +# Copyright (c) ZenML GmbH 2024. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# +import os +from typing import Annotated + +import fiftyone as fo +from zenml import log_artifact_metadata, step +from zenml.client import Client +from zenml.logger import get_logger + +from utils.constants import ( + DATASET_DIR, + DATASET_NAME, + PREDICTIONS_DATASET_ARTIFACT_NAME, + TRAINED_MODEL_NAME, +) + +logger = get_logger(__name__) + +os.environ["YOLO_VERBOSE"] = "False" + + +@step +def create_fiftyone_dataset() -> ( + Annotated[str, PREDICTIONS_DATASET_ARTIFACT_NAME] +): + c = Client() + model_artifact = c.get_artifact_version( + name_id_or_prefix=TRAINED_MODEL_NAME + ) + yolo_model = model_artifact.load() + # results = yolo_model(DATASET_DIR, half=True, conf=0.6) + + dataset = fo.Dataset.from_dir( + dataset_dir=DATASET_DIR, + dataset_type=fo.types.ImageDirectory, + name=DATASET_NAME, + overwrite=True, + persistent=True, + ) + + dataset.apply_model(yolo_model, label_field="boxes") + + log_artifact_metadata( + artifact_name="predictions_dataset_json", + metadata={ + "summary_info": dataset.summary(), + "persistence": dataset.persistent, + }, + ) + + return dataset.to_json() diff --git a/end-to-end-computer-vision/steps/train_model.py b/end-to-end-computer-vision/steps/train_model.py index 06611a95..4d951d0a 100644 --- a/end-to-end-computer-vision/steps/train_model.py +++ b/end-to-end-computer-vision/steps/train_model.py @@ -23,7 +23,7 @@ LabelStudioYOLODataset, ) from materializers.yolo_materializer import UltralyticsMaterializer -from utils.dataset_utils import load_and_split_data, load_images_from_folder +from utils.dataset_utils import load_and_split_data @step(output_materializers={"Trained_YOLO": UltralyticsMaterializer}) diff --git a/end-to-end-computer-vision/utils/constants.py b/end-to-end-computer-vision/utils/constants.py index d0a2163e..881dd88b 100644 --- a/end-to-end-computer-vision/utils/constants.py +++ b/end-to-end-computer-vision/utils/constants.py @@ -1,3 +1,4 @@ DATASET_NAME = "ships" DATASET_DIR = "data/ships/subset" TRAINED_MODEL_NAME = "Trained_YOLO" +PREDICTIONS_DATASET_ARTIFACT_NAME = "predictions_dataset_json" From 10f637cff86021f88de4d88ff9f5a3974d1305d6 Mon Sep 17 00:00:00 2001 From: Alex Strick van Linschoten Date: Tue, 23 Apr 2024 15:27:40 +0200 Subject: [PATCH 043/121] run.py becomes click script --- end-to-end-computer-vision/run.py | 32 ++++++++++++++++++------------- 1 file changed, 19 insertions(+), 13 deletions(-) diff --git a/end-to-end-computer-vision/run.py b/end-to-end-computer-vision/run.py index b5e7e447..59e8a2e4 100644 --- a/end-to-end-computer-vision/run.py +++ b/end-to-end-computer-vision/run.py @@ -14,6 +14,7 @@ # See the License for the specific language governing permissions and # limitations under the License. # +import click from uuid import UUID from zenml import Model @@ -27,24 +28,29 @@ logger = get_logger(__name__) - -if __name__ == "__main__": +@click.command() +@click.option('--fiftyone', is_flag=True, help='Run only the final inference step') +def main(fiftyone): client = Client() - client.activate_stack(UUID("7cda3cec-6744-48dc-8bdc-f102242a26d2")) + if not fiftyone: + client.activate_stack(UUID("7cda3cec-6744-48dc-8bdc-f102242a26d2")) - # Export data from label studio - data_export.with_options(config_path="configs/data_export_alexej.yaml")() + # Export data from label studio + data_export.with_options(config_path="configs/data_export_alexej.yaml")() - # Promote Model to staging - latest_model = Model( - name="Yolo_Object_Detection", version=ModelStages.LATEST - ) - latest_model.set_stage(stage=ModelStages.STAGING, force=True) + # Promote Model to staging + latest_model = Model( + name="Yolo_Object_Detection", version=ModelStages.LATEST + ) + latest_model.set_stage(stage=ModelStages.STAGING, force=True) - client.activate_stack(UUID("20ed5311-ffc6-45d0-b339-6ec35af9501e")) + client.activate_stack(UUID("20ed5311-ffc6-45d0-b339-6ec35af9501e")) - # Train model on data - training.with_options(config_path="configs/training.yaml")() + # Train model on data + training.with_options(config_path="configs/training.yaml")() inference() + +if __name__ == "__main__": + main() From 689107a163ffa397982bb3c67ad46680d3068fe0 Mon Sep 17 00:00:00 2001 From: Alex Strick van Linschoten Date: Tue, 23 Apr 2024 15:27:52 +0200 Subject: [PATCH 044/121] fix inference import issues --- end-to-end-computer-vision/pipelines/inference.py | 6 +++++- 1 file changed, 5 insertions(+), 1 deletion(-) diff --git a/end-to-end-computer-vision/pipelines/inference.py b/end-to-end-computer-vision/pipelines/inference.py index 8a92b014..d9f7bc58 100644 --- a/end-to-end-computer-vision/pipelines/inference.py +++ b/end-to-end-computer-vision/pipelines/inference.py @@ -13,8 +13,12 @@ # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. -# import os +import sys + +current_dir = os.path.dirname(os.path.abspath(__file__)) +parent_dir = os.path.dirname(current_dir) +sys.path.insert(0, parent_dir) import fiftyone as fo from zenml import pipeline From e93e336fd744507afea1aa085a1f032b00e7012a Mon Sep 17 00:00:00 2001 From: Alex Strick van Linschoten Date: Tue, 23 Apr 2024 15:30:13 +0200 Subject: [PATCH 045/121] inference cache --- end-to-end-computer-vision/pipelines/inference.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/end-to-end-computer-vision/pipelines/inference.py b/end-to-end-computer-vision/pipelines/inference.py index d9f7bc58..84b10ac6 100644 --- a/end-to-end-computer-vision/pipelines/inference.py +++ b/end-to-end-computer-vision/pipelines/inference.py @@ -33,7 +33,7 @@ os.environ["YOLO_VERBOSE"] = "False" -@pipeline +@pipeline(enable_cache=False) def inference(): create_fiftyone_dataset() From 922348c6c0af085c9d6e1b5f73080e2e525c848a Mon Sep 17 00:00:00 2001 From: Alex Strick van Linschoten Date: Tue, 23 Apr 2024 15:40:56 +0200 Subject: [PATCH 046/121] update fiftyone inference --- .../pipelines/inference.py | 4 ++-- end-to-end-computer-vision/run.py | 20 ++++++++++++++++--- 2 files changed, 19 insertions(+), 5 deletions(-) diff --git a/end-to-end-computer-vision/pipelines/inference.py b/end-to-end-computer-vision/pipelines/inference.py index 84b10ac6..038f769f 100644 --- a/end-to-end-computer-vision/pipelines/inference.py +++ b/end-to-end-computer-vision/pipelines/inference.py @@ -45,5 +45,5 @@ def inference(): name_id_or_prefix=PREDICTIONS_DATASET_ARTIFACT_NAME ) dataset_json = artifact.load() - dataset = fo.Dataset.from_json(dataset_json, persistent=True) - session = fo.launch_app(dataset) + dataset = fo.Dataset.from_json(dataset_json, persistent=False) + fo.launch_app(dataset) diff --git a/end-to-end-computer-vision/run.py b/end-to-end-computer-vision/run.py index 59e8a2e4..c03c86f1 100644 --- a/end-to-end-computer-vision/run.py +++ b/end-to-end-computer-vision/run.py @@ -14,9 +14,10 @@ # See the License for the specific language governing permissions and # limitations under the License. # -import click from uuid import UUID +import click +import fiftyone as fo from zenml import Model from zenml.client import Client from zenml.enums import ModelStages @@ -25,11 +26,15 @@ from pipelines import data_export from pipelines.inference import inference from pipelines.training import training +from utils.constants import PREDICTIONS_DATASET_ARTIFACT_NAME logger = get_logger(__name__) + @click.command() -@click.option('--fiftyone', is_flag=True, help='Run only the final inference step') +@click.option( + "--fiftyone", is_flag=True, help="Run only the final inference step" +) def main(fiftyone): client = Client() @@ -37,7 +42,9 @@ def main(fiftyone): client.activate_stack(UUID("7cda3cec-6744-48dc-8bdc-f102242a26d2")) # Export data from label studio - data_export.with_options(config_path="configs/data_export_alexej.yaml")() + data_export.with_options( + config_path="configs/data_export_alexej.yaml" + )() # Promote Model to staging latest_model = Model( @@ -51,6 +58,13 @@ def main(fiftyone): training.with_options(config_path="configs/training.yaml")() inference() + artifact = Client().get_artifact_version( + name_id_or_prefix=PREDICTIONS_DATASET_ARTIFACT_NAME + ) + dataset_json = artifact.load() + dataset = fo.Dataset.from_json(dataset_json, persistent=False) + fo.launch_app(dataset) + if __name__ == "__main__": main() From 766503b6101affbc43b45122a91729e5be7bf514 Mon Sep 17 00:00:00 2001 From: Alexej Penner Date: Tue, 23 Apr 2024 15:43:25 +0200 Subject: [PATCH 047/121] Adjusted configs --- .../configs/data_export_alexej.yaml | 2 +- end-to-end-computer-vision/configs/training.yaml | 8 ++++++++ 2 files changed, 9 insertions(+), 1 deletion(-) diff --git a/end-to-end-computer-vision/configs/data_export_alexej.yaml b/end-to-end-computer-vision/configs/data_export_alexej.yaml index 5d25c82e..bfa7b566 100644 --- a/end-to-end-computer-vision/configs/data_export_alexej.yaml +++ b/end-to-end-computer-vision/configs/data_export_alexej.yaml @@ -4,7 +4,7 @@ enable_cache: False parameters: dataset_name: "polution" -# configuration of the Model Control Plane +# Configuration of the Model Control Plane model: name: Yolo_Object_Detection license: Apache 2.0 diff --git a/end-to-end-computer-vision/configs/training.yaml b/end-to-end-computer-vision/configs/training.yaml index 028cb6a5..2b106fe6 100644 --- a/end-to-end-computer-vision/configs/training.yaml +++ b/end-to-end-computer-vision/configs/training.yaml @@ -2,6 +2,10 @@ enable_cache: False settings: docker: + apt_packages: + - ffmpeg + - libsm6 + - libxext6 required_integrations: - gcp requirements: @@ -12,6 +16,10 @@ steps: step_operator: gcp_a100 enable_step_logs: False settings: + step_operator.vertex: + accelerator_type: NVIDIA_TESLA_A100 # see https://cloud.google.com/vertex-ai/docs/reference/rest/v1/MachineSpec#AcceleratorType + accelerator_count: 1 + disk_size_gb: 25 docker: parent_image: pytorch/pytorch:2.2.0-cuda11.8-cudnn8-runtime required_integrations: From adb94378d12e020d9b6a29eaa2b0606ad1e05957 Mon Sep 17 00:00:00 2001 From: Alexej Penner Date: Tue, 23 Apr 2024 16:13:27 +0200 Subject: [PATCH 048/121] Parametrized training --- .../configs/training.yaml | 22 +++------- .../configs/training_gpu.yaml | 43 +++++++++++++++++++ .../pipelines/training.py | 6 ++- end-to-end-computer-vision/run.py | 2 +- .../steps/train_model.py | 3 +- 5 files changed, 55 insertions(+), 21 deletions(-) create mode 100644 end-to-end-computer-vision/configs/training_gpu.yaml diff --git a/end-to-end-computer-vision/configs/training.yaml b/end-to-end-computer-vision/configs/training.yaml index 2b106fe6..40d99c15 100644 --- a/end-to-end-computer-vision/configs/training.yaml +++ b/end-to-end-computer-vision/configs/training.yaml @@ -1,5 +1,9 @@ enable_cache: False +parameters: + model_checkpoint: yolov8l.pt + epochs: 1 + settings: docker: apt_packages: @@ -8,26 +12,10 @@ settings: - libxext6 required_integrations: - gcp + - github requirements: - ultralytics -steps: - train_model: - step_operator: gcp_a100 - enable_step_logs: False - settings: - step_operator.vertex: - accelerator_type: NVIDIA_TESLA_A100 # see https://cloud.google.com/vertex-ai/docs/reference/rest/v1/MachineSpec#AcceleratorType - accelerator_count: 1 - disk_size_gb: 25 - docker: - parent_image: pytorch/pytorch:2.2.0-cuda11.8-cudnn8-runtime - required_integrations: - - gcp - requirements: - - ultralytics - - zenml - # configuration of the Model Control Plane model: name: Yolo_Object_Detection diff --git a/end-to-end-computer-vision/configs/training_gpu.yaml b/end-to-end-computer-vision/configs/training_gpu.yaml new file mode 100644 index 00000000..87e6b512 --- /dev/null +++ b/end-to-end-computer-vision/configs/training_gpu.yaml @@ -0,0 +1,43 @@ +enable_cache: False + +parameters: + model_checkpoint: yolov8l.pt + epochs: 5 + +settings: + docker: + apt_packages: + - ffmpeg + - libsm6 + - libxext6 + required_integrations: + - gcp + - github + requirements: + - ultralytics + +steps: + train_model: + step_operator: gcp_a100 + enable_step_logs: False + settings: + step_operator.vertex: + accelerator_type: NVIDIA_TESLA_A100 # see https://cloud.google.com/vertex-ai/docs/reference/rest/v1/MachineSpec#AcceleratorType + accelerator_count: 1 + disk_size_gb: 25 + docker: + parent_image: pytorch/pytorch:2.2.0-cuda11.8-cudnn8-runtime + required_integrations: + - gcp + - github + requirements: + - ultralytics + - zenml + +# configuration of the Model Control Plane +model: + name: Yolo_Object_Detection + license: Apache 2.0 + description: Object Detection Model. + tags: ["object detection"] + version: staging \ No newline at end of file diff --git a/end-to-end-computer-vision/pipelines/training.py b/end-to-end-computer-vision/pipelines/training.py index e79ea1e9..c3223125 100644 --- a/end-to-end-computer-vision/pipelines/training.py +++ b/end-to-end-computer-vision/pipelines/training.py @@ -26,14 +26,16 @@ @pipeline -def training(model_checkpoint: str = "yolov8l.pt"): +def training(epochs: int, model_checkpoint: str = "yolov8l.pt"): model = load_model(model_checkpoint) # Load the latest version of the train dataset mv = get_pipeline_context().model dataset = mv.get_artifact("yolo_dataset") - trained_model, metrics = train_model(model=model, dataset=dataset) + trained_model, metrics = train_model( + model=model, dataset=dataset, epochs=epochs + ) promote_model(metrics) diff --git a/end-to-end-computer-vision/run.py b/end-to-end-computer-vision/run.py index c03c86f1..f739dfab 100644 --- a/end-to-end-computer-vision/run.py +++ b/end-to-end-computer-vision/run.py @@ -55,7 +55,7 @@ def main(fiftyone): client.activate_stack(UUID("20ed5311-ffc6-45d0-b339-6ec35af9501e")) # Train model on data - training.with_options(config_path="configs/training.yaml")() + training.with_options(config_path="configs/training_gpu.yaml")() inference() artifact = Client().get_artifact_version( diff --git a/end-to-end-computer-vision/steps/train_model.py b/end-to-end-computer-vision/steps/train_model.py index 4d951d0a..82182435 100644 --- a/end-to-end-computer-vision/steps/train_model.py +++ b/end-to-end-computer-vision/steps/train_model.py @@ -28,6 +28,7 @@ @step(output_materializers={"Trained_YOLO": UltralyticsMaterializer}) def train_model( + epochs: int, model: YOLO, dataset: LabelStudioYOLODataset, ) -> Tuple[ @@ -37,7 +38,7 @@ def train_model( Annotated[Dict[str, Any], "validation_metrics"], ]: data_path = load_and_split_data(dataset=dataset) - model.train(data=data_path, epochs=1) + model.train(data=data_path, epochs=epochs) # model.train(data="coco8.yaml", epochs=3) # train the model metrics = model.val() # evaluate model performance on the validation set From c5133c167396bd15e0055a135bcb5b26242331f8 Mon Sep 17 00:00:00 2001 From: Alexej Penner Date: Tue, 23 Apr 2024 16:22:11 +0200 Subject: [PATCH 049/121] Remove label-studio dependency in training --- end-to-end-computer-vision/utils/dataset_utils.py | 9 --------- 1 file changed, 9 deletions(-) diff --git a/end-to-end-computer-vision/utils/dataset_utils.py b/end-to-end-computer-vision/utils/dataset_utils.py index 0db186c2..8df2feb0 100644 --- a/end-to-end-computer-vision/utils/dataset_utils.py +++ b/end-to-end-computer-vision/utils/dataset_utils.py @@ -42,15 +42,6 @@ def load_images_from_folder(folder): def load_and_split_data(dataset: LabelStudioYOLODataset) -> str: - annotator = Client().active_stack.annotator - from zenml.integrations.label_studio.annotators.label_studio_annotator import ( - LabelStudioAnnotator, - ) - - if not isinstance(annotator, LabelStudioAnnotator): - raise TypeError( - "This step can only be used with the Label Studio annotator." - ) tmpfile_ = tempfile.NamedTemporaryFile(dir="data", delete=False) tmpdirname = os.path.basename(tmpfile_.name) From 37d1484e738df7a3ca1b067431005199d6bbc72b Mon Sep 17 00:00:00 2001 From: Alex Strick van Linschoten Date: Tue, 23 Apr 2024 17:01:19 +0200 Subject: [PATCH 050/121] update dataloading script --- .../notebooks/fiftyone_prelabelling.ipynb | 140 +++++++----------- 1 file changed, 56 insertions(+), 84 deletions(-) diff --git a/end-to-end-computer-vision/notebooks/fiftyone_prelabelling.ipynb b/end-to-end-computer-vision/notebooks/fiftyone_prelabelling.ipynb index 5a509ea6..92cf0d78 100644 --- a/end-to-end-computer-vision/notebooks/fiftyone_prelabelling.ipynb +++ b/end-to-end-computer-vision/notebooks/fiftyone_prelabelling.ipynb @@ -9,13 +9,13 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": 2, "metadata": {}, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "1da63562d69241818c06d6192a3c741f", + "model_id": "973f5c44041d4056a3aae5d20e839560", "version_major": 2, "version_minor": 0 }, @@ -29,7 +29,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "a5e9239136214fc193d2625508a543e8", + "model_id": "f711c322a5414602ba189fc8a211b135", "version_major": 2, "version_minor": 0 }, @@ -39,81 +39,54 @@ }, "metadata": {}, "output_type": "display_data" - }, - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "c4683ab858bc4d4fbf1abf42b777e3d7", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "Downloading data: 0%| | 0/258 [00:00 Date: Tue, 23 Apr 2024 17:08:11 +0200 Subject: [PATCH 051/121] add initial dataset location --- end-to-end-computer-vision/README.md | 5 +++++ 1 file changed, 5 insertions(+) diff --git a/end-to-end-computer-vision/README.md b/end-to-end-computer-vision/README.md index 1617e31d..c62b71c6 100644 --- a/end-to-end-computer-vision/README.md +++ b/end-to-end-computer-vision/README.md @@ -25,3 +25,8 @@ fiftyone plugins download https://github.com/jacobmarks/fiftyone-albumentations- fiftyone plugins download https://github.com/voxel51/fiftyone-plugins --plugin-names @voxel51/annotation ``` + + +# Data + +initial dataset is stored at: `gs://zenml-internal-artifact-store/raw-ship-data/` From 5327ce216be145750854948a8879d132ac537dd5 Mon Sep 17 00:00:00 2001 From: Alexej Penner Date: Tue, 23 Apr 2024 17:11:06 +0200 Subject: [PATCH 052/121] First running pipeline on vertex --- .../configs/inference.yaml | 22 ++++++++++ .../configs/training_gpu.yaml | 2 +- .../pipelines/inference.py | 2 +- .../pipelines/training.py | 2 +- end-to-end-computer-vision/run.py | 3 +- .../steps/load_model.py | 4 +- .../steps/promote_model.py | 41 +++++++++++-------- 7 files changed, 52 insertions(+), 24 deletions(-) create mode 100644 end-to-end-computer-vision/configs/inference.yaml diff --git a/end-to-end-computer-vision/configs/inference.yaml b/end-to-end-computer-vision/configs/inference.yaml new file mode 100644 index 00000000..5884983a --- /dev/null +++ b/end-to-end-computer-vision/configs/inference.yaml @@ -0,0 +1,22 @@ +enable_cache: False + +settings: + docker: + apt_packages: + - ffmpeg + - libsm6 + - libxext6 + required_integrations: + - gcp + - github + requirements: + - ultralytics + - fiftyone + +# configuration of the Model Control Plane +model: + name: Yolo_Object_Detection + license: Apache 2.0 + description: Object Detection Model. + tags: ["object detection"] + version: production \ No newline at end of file diff --git a/end-to-end-computer-vision/configs/training_gpu.yaml b/end-to-end-computer-vision/configs/training_gpu.yaml index 87e6b512..79d39f2b 100644 --- a/end-to-end-computer-vision/configs/training_gpu.yaml +++ b/end-to-end-computer-vision/configs/training_gpu.yaml @@ -2,7 +2,7 @@ enable_cache: False parameters: model_checkpoint: yolov8l.pt - epochs: 5 + epochs: 500 settings: docker: diff --git a/end-to-end-computer-vision/pipelines/inference.py b/end-to-end-computer-vision/pipelines/inference.py index 038f769f..20ce46fd 100644 --- a/end-to-end-computer-vision/pipelines/inference.py +++ b/end-to-end-computer-vision/pipelines/inference.py @@ -39,7 +39,7 @@ def inference(): if __name__ == "__main__": - inference() + inference.with_options(config_path="configs/inference.yaml")() artifact = Client().get_artifact_version( name_id_or_prefix=PREDICTIONS_DATASET_ARTIFACT_NAME diff --git a/end-to-end-computer-vision/pipelines/training.py b/end-to-end-computer-vision/pipelines/training.py index c3223125..54f76156 100644 --- a/end-to-end-computer-vision/pipelines/training.py +++ b/end-to-end-computer-vision/pipelines/training.py @@ -39,4 +39,4 @@ def training(epochs: int, model_checkpoint: str = "yolov8l.pt"): promote_model(metrics) - predict_image(trained_model) + # predict_image(trained_model) diff --git a/end-to-end-computer-vision/run.py b/end-to-end-computer-vision/run.py index f739dfab..c1bebc28 100644 --- a/end-to-end-computer-vision/run.py +++ b/end-to-end-computer-vision/run.py @@ -56,8 +56,9 @@ def main(fiftyone): # Train model on data training.with_options(config_path="configs/training_gpu.yaml")() + # training.with_options(config_path="configs/training.yaml")() - inference() + inference.with_options(config_path="configs/inference.yaml")() artifact = Client().get_artifact_version( name_id_or_prefix=PREDICTIONS_DATASET_ARTIFACT_NAME ) diff --git a/end-to-end-computer-vision/steps/load_model.py b/end-to-end-computer-vision/steps/load_model.py index 386ef3c8..61215520 100644 --- a/end-to-end-computer-vision/steps/load_model.py +++ b/end-to-end-computer-vision/steps/load_model.py @@ -23,9 +23,9 @@ logger = get_logger(__name__) -@step(enable_cache=True) +@step(enable_cache=True, enable_step_logs=False) def load_model( model_checkpoint: str, ) -> Annotated[YOLO, ArtifactConfig(name="Raw_YOLO", is_model_artifact=True)]: logger.info(f"Loading YOLO checkpoint {model_checkpoint}") - return YOLO() # model_checkpoint) + return YOLO(model_checkpoint) diff --git a/end-to-end-computer-vision/steps/promote_model.py b/end-to-end-computer-vision/steps/promote_model.py index 73ff215c..c17447b6 100644 --- a/end-to-end-computer-vision/steps/promote_model.py +++ b/end-to-end-computer-vision/steps/promote_model.py @@ -32,24 +32,29 @@ def promote_model( # Get the model associated with this pipeline cur_model_version = get_step_context().model - # Get the current production model - latest_model_version = client.get_model_version( - model_name_or_id=cur_model_version.name, - model_version_name_or_number_or_id=ModelStages.STAGING, - ) - - prod_metrics = latest_model_version.get_artifact( - "validation_metrics" - ).load() - - if metrics["metrics/mAP50(B)"] >= prod_metrics["metrics/mAP50(B)"]: - logger.info( - "Model promoted to `production` as it outperformed the " - "current `production` model on `mAP50`." + try: + # Get the current production model + latest_model_version = client.get_model_version( + model_name_or_id=cur_model_version.name, + model_version_name_or_number_or_id=ModelStages.PRODUCTION, ) + except KeyError: + logger.info("No `production` model found to compare to, current " + "model will be promoted by default.") cur_model_version.set_stage(ModelStages.PRODUCTION, force=True) + else: - logger.info( - "Model was less performant than the current `production` " - "model. Model will **not** be promoted." - ) + prod_metrics = latest_model_version.get_artifact( + "validation_metrics" + ).load() + if metrics["metrics/mAP50(B)"] >= prod_metrics["metrics/mAP50(B)"]: + logger.info( + "Model promoted to `production` as it outperformed the " + "current `production` model on `mAP50`." + ) + cur_model_version.set_stage(ModelStages.PRODUCTION, force=True) + else: + logger.info( + "Model was less performant than the current `production` " + "model. Model will **not** be promoted." + ) From 7c99630b3d74ccb17f115a4daf73f71cc7af386d Mon Sep 17 00:00:00 2001 From: Alexej Penner Date: Tue, 23 Apr 2024 18:19:31 +0200 Subject: [PATCH 053/121] Flags for run.py --- end-to-end-computer-vision/README.md | 3 ++ .../configs/training_gpu.yaml | 2 +- end-to-end-computer-vision/run.py | 44 ++++++++++++++----- 3 files changed, 38 insertions(+), 11 deletions(-) diff --git a/end-to-end-computer-vision/README.md b/end-to-end-computer-vision/README.md index c62b71c6..a36d3f2c 100644 --- a/end-to-end-computer-vision/README.md +++ b/end-to-end-computer-vision/README.md @@ -30,3 +30,6 @@ fiftyone plugins download https://github.com/voxel51/fiftyone-plugins --plugin-n # Data initial dataset is stored at: `gs://zenml-internal-artifact-store/raw-ship-data/` + +copy with: +`gsutil -m cp -r gs://zenml-internal-artifact-store/raw-ship-data/ /path` diff --git a/end-to-end-computer-vision/configs/training_gpu.yaml b/end-to-end-computer-vision/configs/training_gpu.yaml index 79d39f2b..999a1091 100644 --- a/end-to-end-computer-vision/configs/training_gpu.yaml +++ b/end-to-end-computer-vision/configs/training_gpu.yaml @@ -22,7 +22,7 @@ steps: enable_step_logs: False settings: step_operator.vertex: - accelerator_type: NVIDIA_TESLA_A100 # see https://cloud.google.com/vertex-ai/docs/reference/rest/v1/MachineSpec#AcceleratorType + accelerator_type: ACCELERATOR_TYPE_UNSPECIFIED # NVIDIA_TESLA_A100 # see https://cloud.google.com/vertex-ai/docs/reference/rest/v1/MachineSpec#AcceleratorType accelerator_count: 1 disk_size_gb: 25 docker: diff --git a/end-to-end-computer-vision/run.py b/end-to-end-computer-vision/run.py index c1bebc28..e870af78 100644 --- a/end-to-end-computer-vision/run.py +++ b/end-to-end-computer-vision/run.py @@ -33,12 +33,32 @@ @click.command() @click.option( - "--fiftyone", is_flag=True, help="Run only the final inference step" + "--feature-pipeline", + is_flag=True, + default=False, + help="Whether to run the pipeline that exports the dataset from " + "labelstudio.", ) -def main(fiftyone): +@click.option( + "--training-pipeline", + is_flag=True, + default=False, + help="Whether to run the pipeline that trains the model.", +) +@click.option( + "--inference-pipeline", + is_flag=True, + default=False, + help="Whether to run the pipeline that performs inference.", +) +def main( + feature_pipeline: bool = False, + training_pipeline: bool = False, + inference_pipeline: bool = False +): client = Client() - if not fiftyone: + if feature_pipeline: client.activate_stack(UUID("7cda3cec-6744-48dc-8bdc-f102242a26d2")) # Export data from label studio @@ -52,19 +72,23 @@ def main(fiftyone): ) latest_model.set_stage(stage=ModelStages.STAGING, force=True) + if training_pipeline: client.activate_stack(UUID("20ed5311-ffc6-45d0-b339-6ec35af9501e")) # Train model on data training.with_options(config_path="configs/training_gpu.yaml")() # training.with_options(config_path="configs/training.yaml")() - inference.with_options(config_path="configs/inference.yaml")() - artifact = Client().get_artifact_version( - name_id_or_prefix=PREDICTIONS_DATASET_ARTIFACT_NAME - ) - dataset_json = artifact.load() - dataset = fo.Dataset.from_json(dataset_json, persistent=False) - fo.launch_app(dataset) + if inference_pipeline: + client.activate_stack(UUID("7cda3cec-6744-48dc-8bdc-f102242a26d2")) + + inference.with_options(config_path="configs/inference.yaml")() + artifact = Client().get_artifact_version( + name_id_or_prefix=PREDICTIONS_DATASET_ARTIFACT_NAME + ) + dataset_json = artifact.load() + dataset = fo.Dataset.from_json(dataset_json, persistent=False) + fo.launch_app(dataset) if __name__ == "__main__": From 9f3447eb01e71cb82c9477589eee7e3ee37a6d99 Mon Sep 17 00:00:00 2001 From: Alex Strick van Linschoten Date: Tue, 23 Apr 2024 18:29:52 +0200 Subject: [PATCH 054/121] Fix bug in login functionality --- .../notebooks/fiftyone_prelabelling.ipynb | 141 +++++++++++++++++- 1 file changed, 136 insertions(+), 5 deletions(-) diff --git a/end-to-end-computer-vision/notebooks/fiftyone_prelabelling.ipynb b/end-to-end-computer-vision/notebooks/fiftyone_prelabelling.ipynb index 92cf0d78..b4220792 100644 --- a/end-to-end-computer-vision/notebooks/fiftyone_prelabelling.ipynb +++ b/end-to-end-computer-vision/notebooks/fiftyone_prelabelling.ipynb @@ -9,13 +9,13 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 1, "metadata": {}, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "973f5c44041d4056a3aae5d20e839560", + "model_id": "f49ef75127f24058a606dcd8891fb040", "version_major": 2, "version_minor": 0 }, @@ -29,7 +29,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "f711c322a5414602ba189fc8a211b135", + "model_id": "268a1d39eb40453392f54d6dfeef112d", "version_major": 2, "version_minor": 0 }, @@ -50,7 +50,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 2, "metadata": {}, "outputs": [ { @@ -68,7 +68,7 @@ "})" ] }, - "execution_count": 6, + "execution_count": 2, "metadata": {}, "output_type": "execute_result" } @@ -77,6 +77,137 @@ "hf_dataset" ] }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "ename": "KeyboardInterrupt", + "evalue": "", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mKeyboardInterrupt\u001b[0m Traceback (most recent call last)", + "Cell \u001b[0;32mIn[7], line 37\u001b[0m\n\u001b[1;32m 35\u001b[0m \u001b[38;5;66;03m# Process train and test splits\u001b[39;00m\n\u001b[1;32m 36\u001b[0m train_data \u001b[38;5;241m=\u001b[39m process_split(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mtrain\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m---> 37\u001b[0m test_data \u001b[38;5;241m=\u001b[39m 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idx, example \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28menumerate\u001b[39m(hf_dataset[split]):\n\u001b[1;32m 18\u001b[0m image \u001b[38;5;241m=\u001b[39m example[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mimage\u001b[39m\u001b[38;5;124m\"\u001b[39m]\n\u001b[1;32m 19\u001b[0m objects \u001b[38;5;241m=\u001b[39m example[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mobjects\u001b[39m\u001b[38;5;124m\"\u001b[39m]\n", + "File \u001b[0;32m~/.pyenv/versions/3.10.12/envs/ls51/lib/python3.10/site-packages/datasets/arrow_dataset.py:2440\u001b[0m, in \u001b[0;36mDataset.__iter__\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 2438\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m i \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mrange\u001b[39m(pa_subtable\u001b[38;5;241m.\u001b[39mnum_rows):\n\u001b[1;32m 2439\u001b[0m pa_subtable_ex \u001b[38;5;241m=\u001b[39m pa_subtable\u001b[38;5;241m.\u001b[39mslice(i, \u001b[38;5;241m1\u001b[39m)\n\u001b[0;32m-> 2440\u001b[0m formatted_output 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\u001b[49m\u001b[43mquery_type\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mquery_type\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 634\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m query_type \u001b[38;5;241m==\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mcolumn\u001b[39m\u001b[38;5;124m\"\u001b[39m:\n\u001b[1;32m 635\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m key \u001b[38;5;129;01min\u001b[39;00m format_columns:\n", + "File \u001b[0;32m~/.pyenv/versions/3.10.12/envs/ls51/lib/python3.10/site-packages/datasets/formatting/formatting.py:397\u001b[0m, in \u001b[0;36mFormatter.__call__\u001b[0;34m(self, pa_table, query_type)\u001b[0m\n\u001b[1;32m 395\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m__call__\u001b[39m(\u001b[38;5;28mself\u001b[39m, pa_table: pa\u001b[38;5;241m.\u001b[39mTable, query_type: \u001b[38;5;28mstr\u001b[39m) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m Union[RowFormat, ColumnFormat, BatchFormat]:\n\u001b[1;32m 396\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m query_type \u001b[38;5;241m==\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mrow\u001b[39m\u001b[38;5;124m\"\u001b[39m:\n\u001b[0;32m--> 397\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mformat_row\u001b[49m\u001b[43m(\u001b[49m\u001b[43mpa_table\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 398\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m query_type \u001b[38;5;241m==\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mcolumn\u001b[39m\u001b[38;5;124m\"\u001b[39m:\n\u001b[1;32m 399\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mformat_column(pa_table)\n", + "File \u001b[0;32m~/.pyenv/versions/3.10.12/envs/ls51/lib/python3.10/site-packages/datasets/formatting/formatting.py:438\u001b[0m, in \u001b[0;36mPythonFormatter.format_row\u001b[0;34m(self, pa_table)\u001b[0m\n\u001b[1;32m 436\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m LazyRow(pa_table, \u001b[38;5;28mself\u001b[39m)\n\u001b[1;32m 437\u001b[0m row \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mpython_arrow_extractor()\u001b[38;5;241m.\u001b[39mextract_row(pa_table)\n\u001b[0;32m--> 438\u001b[0m row \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mpython_features_decoder\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdecode_row\u001b[49m\u001b[43m(\u001b[49m\u001b[43mrow\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 439\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m row\n", + "File \u001b[0;32m~/.pyenv/versions/3.10.12/envs/ls51/lib/python3.10/site-packages/datasets/formatting/formatting.py:216\u001b[0m, in \u001b[0;36mPythonFeaturesDecoder.decode_row\u001b[0;34m(self, row)\u001b[0m\n\u001b[1;32m 215\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mdecode_row\u001b[39m(\u001b[38;5;28mself\u001b[39m, row: \u001b[38;5;28mdict\u001b[39m) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m \u001b[38;5;28mdict\u001b[39m:\n\u001b[0;32m--> 216\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfeatures\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdecode_example\u001b[49m\u001b[43m(\u001b[49m\u001b[43mrow\u001b[49m\u001b[43m)\u001b[49m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mfeatures \u001b[38;5;28;01melse\u001b[39;00m row\n", + "File \u001b[0;32m~/.pyenv/versions/3.10.12/envs/ls51/lib/python3.10/site-packages/datasets/features/features.py:1975\u001b[0m, in \u001b[0;36mFeatures.decode_example\u001b[0;34m(self, example, token_per_repo_id)\u001b[0m\n\u001b[1;32m 1961\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mdecode_example\u001b[39m(\u001b[38;5;28mself\u001b[39m, example: \u001b[38;5;28mdict\u001b[39m, token_per_repo_id: Optional[Dict[\u001b[38;5;28mstr\u001b[39m, Union[\u001b[38;5;28mstr\u001b[39m, \u001b[38;5;28mbool\u001b[39m, \u001b[38;5;28;01mNone\u001b[39;00m]]] \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m):\n\u001b[1;32m 1962\u001b[0m \u001b[38;5;250m \u001b[39m\u001b[38;5;124;03m\"\"\"Decode example with custom feature decoding.\u001b[39;00m\n\u001b[1;32m 1963\u001b[0m \n\u001b[1;32m 1964\u001b[0m \u001b[38;5;124;03m Args:\u001b[39;00m\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 1972\u001b[0m \u001b[38;5;124;03m `dict[str, Any]`\u001b[39;00m\n\u001b[1;32m 1973\u001b[0m \u001b[38;5;124;03m \"\"\"\u001b[39;00m\n\u001b[0;32m-> 1975\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m {\n\u001b[1;32m 1976\u001b[0m column_name: decode_nested_example(feature, value, token_per_repo_id\u001b[38;5;241m=\u001b[39mtoken_per_repo_id)\n\u001b[1;32m 1977\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_column_requires_decoding[column_name]\n\u001b[1;32m 1978\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m value\n\u001b[1;32m 1979\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m column_name, (feature, value) \u001b[38;5;129;01min\u001b[39;00m zip_dict(\n\u001b[1;32m 1980\u001b[0m {key: value \u001b[38;5;28;01mfor\u001b[39;00m key, value \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mitems() \u001b[38;5;28;01mif\u001b[39;00m key \u001b[38;5;129;01min\u001b[39;00m example}, example\n\u001b[1;32m 1981\u001b[0m )\n\u001b[1;32m 1982\u001b[0m }\n", + "File \u001b[0;32m~/.pyenv/versions/3.10.12/envs/ls51/lib/python3.10/site-packages/datasets/features/features.py:1976\u001b[0m, in \u001b[0;36m\u001b[0;34m(.0)\u001b[0m\n\u001b[1;32m 1961\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mdecode_example\u001b[39m(\u001b[38;5;28mself\u001b[39m, example: \u001b[38;5;28mdict\u001b[39m, token_per_repo_id: Optional[Dict[\u001b[38;5;28mstr\u001b[39m, Union[\u001b[38;5;28mstr\u001b[39m, \u001b[38;5;28mbool\u001b[39m, \u001b[38;5;28;01mNone\u001b[39;00m]]] \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m):\n\u001b[1;32m 1962\u001b[0m \u001b[38;5;250m \u001b[39m\u001b[38;5;124;03m\"\"\"Decode example with custom feature decoding.\u001b[39;00m\n\u001b[1;32m 1963\u001b[0m \n\u001b[1;32m 1964\u001b[0m \u001b[38;5;124;03m Args:\u001b[39;00m\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 1972\u001b[0m \u001b[38;5;124;03m `dict[str, Any]`\u001b[39;00m\n\u001b[1;32m 1973\u001b[0m \u001b[38;5;124;03m \"\"\"\u001b[39;00m\n\u001b[1;32m 1975\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m {\n\u001b[0;32m-> 1976\u001b[0m column_name: \u001b[43mdecode_nested_example\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfeature\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mvalue\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtoken_per_repo_id\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mtoken_per_repo_id\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 1977\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_column_requires_decoding[column_name]\n\u001b[1;32m 1978\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m value\n\u001b[1;32m 1979\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m column_name, (feature, value) \u001b[38;5;129;01min\u001b[39;00m zip_dict(\n\u001b[1;32m 1980\u001b[0m {key: value \u001b[38;5;28;01mfor\u001b[39;00m key, value \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mitems() \u001b[38;5;28;01mif\u001b[39;00m key \u001b[38;5;129;01min\u001b[39;00m example}, example\n\u001b[1;32m 1981\u001b[0m )\n\u001b[1;32m 1982\u001b[0m }\n", + "File \u001b[0;32m~/.pyenv/versions/3.10.12/envs/ls51/lib/python3.10/site-packages/datasets/features/features.py:1341\u001b[0m, in \u001b[0;36mdecode_nested_example\u001b[0;34m(schema, obj, token_per_repo_id)\u001b[0m\n\u001b[1;32m 1338\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(schema, (Audio, Image)):\n\u001b[1;32m 1339\u001b[0m \u001b[38;5;66;03m# we pass the token to read and decode files from private repositories in streaming mode\u001b[39;00m\n\u001b[1;32m 1340\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m obj \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;129;01mand\u001b[39;00m schema\u001b[38;5;241m.\u001b[39mdecode:\n\u001b[0;32m-> 1341\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mschema\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdecode_example\u001b[49m\u001b[43m(\u001b[49m\u001b[43mobj\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtoken_per_repo_id\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mtoken_per_repo_id\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 1342\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m obj\n", + "File \u001b[0;32m~/.pyenv/versions/3.10.12/envs/ls51/lib/python3.10/site-packages/datasets/features/image.py:188\u001b[0m, in \u001b[0;36mImage.decode_example\u001b[0;34m(self, value, token_per_repo_id)\u001b[0m\n\u001b[1;32m 186\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m 187\u001b[0m image \u001b[38;5;241m=\u001b[39m PIL\u001b[38;5;241m.\u001b[39mImage\u001b[38;5;241m.\u001b[39mopen(BytesIO(bytes_))\n\u001b[0;32m--> 188\u001b[0m \u001b[43mimage\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mload\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m \u001b[38;5;66;03m# to avoid \"Too many open files\" errors\u001b[39;00m\n\u001b[1;32m 189\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m image\u001b[38;5;241m.\u001b[39mgetexif()\u001b[38;5;241m.\u001b[39mget(PIL\u001b[38;5;241m.\u001b[39mImage\u001b[38;5;241m.\u001b[39mExifTags\u001b[38;5;241m.\u001b[39mBase\u001b[38;5;241m.\u001b[39mOrientation) \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m 190\u001b[0m image \u001b[38;5;241m=\u001b[39m PIL\u001b[38;5;241m.\u001b[39mImageOps\u001b[38;5;241m.\u001b[39mexif_transpose(image)\n", + "File \u001b[0;32m~/.pyenv/versions/3.10.12/envs/ls51/lib/python3.10/site-packages/PIL/ImageFile.py:291\u001b[0m, in \u001b[0;36mImageFile.load\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 288\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mOSError\u001b[39;00m(msg)\n\u001b[1;32m 290\u001b[0m b \u001b[38;5;241m=\u001b[39m b \u001b[38;5;241m+\u001b[39m s\n\u001b[0;32m--> 291\u001b[0m n, err_code \u001b[38;5;241m=\u001b[39m \u001b[43mdecoder\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdecode\u001b[49m\u001b[43m(\u001b[49m\u001b[43mb\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 292\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m n \u001b[38;5;241m<\u001b[39m \u001b[38;5;241m0\u001b[39m:\n\u001b[1;32m 293\u001b[0m \u001b[38;5;28;01mbreak\u001b[39;00m\n", + "\u001b[0;31mKeyboardInterrupt\u001b[0m: " + ] + } + ], + "source": [ + "import os\n", + "from datasets import load_dataset\n", + "import json\n", + "import pyarrow as pa\n", + "import pyarrow.parquet as pq\n", + "\n", + "# Specify the base directory for storing the data\n", + "base_dir = \"/home/strickvl/coding/zenml-projects/end-to-end-computer-vision/data\"\n", + "\n", + "# Create directories to store the extracted images\n", + "os.makedirs(os.path.join(base_dir, \"train_images\"), exist_ok=True)\n", + "os.makedirs(os.path.join(base_dir, \"test_images\"), exist_ok=True)\n", + "\n", + "# Function to process each split (train and test)\n", + "def process_split(split):\n", + " data = []\n", + " for idx, example in enumerate(hf_dataset[split]):\n", + " image = example[\"image\"]\n", + " objects = example[\"objects\"]\n", + " \n", + " # Save the image to the local filesystem\n", + " image_filename = f\"{split}_{idx}.jpg\"\n", + " image_path = os.path.join(base_dir, f\"{split}_images\", image_filename)\n", + " with open(image_path, \"wb\") as f:\n", + " f.write(image.tobytes())\n", + " \n", + " # Create a dictionary with image filename and bounding boxes\n", + " data.append({\n", + " \"image_filename\": image_filename,\n", + " \"bboxes\": objects[\"bbox\"]\n", + " })\n", + " \n", + " return data\n", + "\n", + "# Process train and test splits\n", + "train_data = process_split(\"train\")\n", + "test_data = process_split(\"test\")\n", + "\n", + "# Save the data as JSON files\n", + "with open(os.path.join(base_dir, \"train_data.json\"), \"w\") as f:\n", + " json.dump(train_data, f)\n", + "\n", + "with open(os.path.join(base_dir, \"test_data.json\"), \"w\") as f:\n", + " json.dump(test_data, f)\n", + "\n", + "# Save the data as Parquet files\n", + "train_table = pa.Table.from_pylist(train_data)\n", + "pq.write_table(train_table, os.path.join(base_dir, \"train_data.parquet\"))\n", + "\n", + "test_table = pa.Table.from_pylist(test_data)\n", + "pq.write_table(test_table, os.path.join(base_dir, \"test_data.parquet\"))" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Categories saved as categories.json\n" + ] + } + ], + "source": [ + "import json\n", + "\n", + "# Create a dictionary to store the categories for each split\n", + "categories_dict = {\n", + " \"train\": [example[\"objects\"][\"categories\"] for example in hf_dataset[\"train\"]],\n", + " \"test\": [example[\"objects\"][\"categories\"] for example in hf_dataset[\"test\"]]\n", + "}\n", + "\n", + "# Save the categories as a JSON file\n", + "with open(\"categories.json\", \"w\") as json_file:\n", + " json.dump(categories_dict, json_file)\n", + "\n", + "print(\"Categories saved as categories.json\")" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Train features: {'image': Image(mode=None, decode=True, id=None), 'objects': {'bbox': Sequence(feature=Sequence(feature=Value(dtype='int64', id=None), length=-1, id=None), length=-1, id=None), 'categories': Sequence(feature=Value(dtype='int64', id=None), length=-1, id=None)}}\n", + "Test features: {'image': Image(mode=None, decode=True, id=None), 'objects': {'bbox': Sequence(feature=Sequence(feature=Value(dtype='int64', id=None), length=-1, id=None), length=-1, id=None), 'categories': Sequence(feature=Value(dtype='int64', id=None), length=-1, id=None)}}\n" + ] + } + ], + "source": [ + "# Print the dataset features\n", + "print(\"Train features:\", hf_dataset[\"train\"].features)\n", + "print(\"Test features:\", hf_dataset[\"test\"].features)" + ] + }, { "cell_type": "code", "execution_count": 7, From 5a0a5126d7c7e00946f94f98383bb88c7d2d134c Mon Sep 17 00:00:00 2001 From: Alex Strick van Linschoten Date: Tue, 23 Apr 2024 18:54:13 +0200 Subject: [PATCH 055/121] Update pipeline options in run.py --- end-to-end-computer-vision/run.py | 14 ++++++++++---- 1 file changed, 10 insertions(+), 4 deletions(-) diff --git a/end-to-end-computer-vision/run.py b/end-to-end-computer-vision/run.py index e870af78..22b88a2a 100644 --- a/end-to-end-computer-vision/run.py +++ b/end-to-end-computer-vision/run.py @@ -24,7 +24,7 @@ from zenml.logger import get_logger from pipelines import data_export -from pipelines.inference import inference +from pipelines.cloud_inference import inference from pipelines.training import training from utils.constants import PREDICTIONS_DATASET_ARTIFACT_NAME @@ -33,20 +33,26 @@ @click.command() @click.option( - "--feature-pipeline", + "--feature", + "-f", + "feature_pipeline", is_flag=True, default=False, help="Whether to run the pipeline that exports the dataset from " "labelstudio.", ) @click.option( - "--training-pipeline", + "--training", + "-t", + "training_pipeline", is_flag=True, default=False, help="Whether to run the pipeline that trains the model.", ) @click.option( - "--inference-pipeline", + "--inference", + "-i", + "inference_pipeline", is_flag=True, default=False, help="Whether to run the pipeline that performs inference.", From 3a8019f1a50761972febad5f00814733ffecda52 Mon Sep 17 00:00:00 2001 From: Alex Strick van Linschoten Date: Tue, 23 Apr 2024 18:58:05 +0200 Subject: [PATCH 056/121] Add cloud inference pipeline --- .../pipelines/{inference.py => cloud_inference.py} | 0 1 file changed, 0 insertions(+), 0 deletions(-) rename end-to-end-computer-vision/pipelines/{inference.py => cloud_inference.py} (100%) diff --git a/end-to-end-computer-vision/pipelines/inference.py b/end-to-end-computer-vision/pipelines/cloud_inference.py similarity index 100% rename from end-to-end-computer-vision/pipelines/inference.py rename to end-to-end-computer-vision/pipelines/cloud_inference.py From efe15bd547e684c568f2c78033a88d1a8868f73d Mon Sep 17 00:00:00 2001 From: Alex Strick van Linschoten Date: Tue, 23 Apr 2024 19:21:33 +0200 Subject: [PATCH 057/121] formatting and cloud inference --- .../{inference.yaml => cloud_inference.yaml} | 0 .../pipelines/cloud_inference.py | 24 ++------------ .../pipelines/local_inference.py | 23 ++++++++++++++ .../pipelines/training.py | 1 - end-to-end-computer-vision/run.py | 31 ++++++++++++------- .../steps/fifty_one_labelling.py | 0 .../steps/fiftyone_inference.py | 1 - .../steps/promote_model.py | 6 ++-- .../utils/dataset_utils.py | 1 - 9 files changed, 49 insertions(+), 38 deletions(-) rename end-to-end-computer-vision/configs/{inference.yaml => cloud_inference.yaml} (100%) create mode 100644 end-to-end-computer-vision/pipelines/local_inference.py create mode 100644 end-to-end-computer-vision/steps/fifty_one_labelling.py diff --git a/end-to-end-computer-vision/configs/inference.yaml b/end-to-end-computer-vision/configs/cloud_inference.yaml similarity index 100% rename from end-to-end-computer-vision/configs/inference.yaml rename to end-to-end-computer-vision/configs/cloud_inference.yaml diff --git a/end-to-end-computer-vision/pipelines/cloud_inference.py b/end-to-end-computer-vision/pipelines/cloud_inference.py index 20ce46fd..944635e7 100644 --- a/end-to-end-computer-vision/pipelines/cloud_inference.py +++ b/end-to-end-computer-vision/pipelines/cloud_inference.py @@ -13,37 +13,19 @@ # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. -import os -import sys -current_dir = os.path.dirname(os.path.abspath(__file__)) -parent_dir = os.path.dirname(current_dir) -sys.path.insert(0, parent_dir) - -import fiftyone as fo from zenml import pipeline -from zenml.client import Client from zenml.logger import get_logger from steps.fiftyone_inference import create_fiftyone_dataset -from utils.constants import PREDICTIONS_DATASET_ARTIFACT_NAME logger = get_logger(__name__) -os.environ["YOLO_VERBOSE"] = "False" - -@pipeline(enable_cache=False) -def inference(): +@pipeline +def cloud_inference(): create_fiftyone_dataset() if __name__ == "__main__": - inference.with_options(config_path="configs/inference.yaml")() - - artifact = Client().get_artifact_version( - name_id_or_prefix=PREDICTIONS_DATASET_ARTIFACT_NAME - ) - dataset_json = artifact.load() - dataset = fo.Dataset.from_json(dataset_json, persistent=False) - fo.launch_app(dataset) + cloud_inference.with_options(config_path="configs/cloud_inference.yaml")() diff --git a/end-to-end-computer-vision/pipelines/local_inference.py b/end-to-end-computer-vision/pipelines/local_inference.py new file mode 100644 index 00000000..d3662968 --- /dev/null +++ b/end-to-end-computer-vision/pipelines/local_inference.py @@ -0,0 +1,23 @@ +import fiftyone as fo +from zenml import pipeline, step +from zenml.client import Client +from zenml.logger import get_logger + +from utils.constants import PREDICTIONS_DATASET_ARTIFACT_NAME + +logger = get_logger(__name__) + + +@step +def create_fiftyone_dataset(): + artifact = Client().get_artifact_version( + name_id_or_prefix=PREDICTIONS_DATASET_ARTIFACT_NAME + ) + dataset_json = artifact.load() + dataset = fo.Dataset.from_json(dataset_json, persistent=False) + fo.launch_app(dataset) + + +@pipeline(enable_cache=False) +def fifty_one(): + create_fiftyone_dataset() diff --git a/end-to-end-computer-vision/pipelines/training.py b/end-to-end-computer-vision/pipelines/training.py index 54f76156..7a90b730 100644 --- a/end-to-end-computer-vision/pipelines/training.py +++ b/end-to-end-computer-vision/pipelines/training.py @@ -18,7 +18,6 @@ from zenml.logger import get_logger from steps.load_model import load_model -from steps.predict_image import predict_image from steps.promote_model import promote_model from steps.train_model import train_model diff --git a/end-to-end-computer-vision/run.py b/end-to-end-computer-vision/run.py index 22b88a2a..324fbd43 100644 --- a/end-to-end-computer-vision/run.py +++ b/end-to-end-computer-vision/run.py @@ -17,16 +17,15 @@ from uuid import UUID import click -import fiftyone as fo from zenml import Model from zenml.client import Client from zenml.enums import ModelStages from zenml.logger import get_logger from pipelines import data_export -from pipelines.cloud_inference import inference +from pipelines.cloud_inference import cloud_inference +from pipelines.local_inference import fifty_one from pipelines.training import training -from utils.constants import PREDICTIONS_DATASET_ARTIFACT_NAME logger = get_logger(__name__) @@ -39,7 +38,7 @@ is_flag=True, default=False, help="Whether to run the pipeline that exports the dataset from " - "labelstudio.", + "labelstudio.", ) @click.option( "--training", @@ -57,10 +56,19 @@ default=False, help="Whether to run the pipeline that performs inference.", ) +@click.option( + "--fiftyone", + "-fo", + "fiftyone", + is_flag=True, + default=False, + help="Whether to launch the FiftyOne app pipeline.", +) def main( feature_pipeline: bool = False, training_pipeline: bool = False, - inference_pipeline: bool = False + inference_pipeline: bool = False, + fiftyone: bool = False, ): client = Client() @@ -88,13 +96,12 @@ def main( if inference_pipeline: client.activate_stack(UUID("7cda3cec-6744-48dc-8bdc-f102242a26d2")) - inference.with_options(config_path="configs/inference.yaml")() - artifact = Client().get_artifact_version( - name_id_or_prefix=PREDICTIONS_DATASET_ARTIFACT_NAME - ) - dataset_json = artifact.load() - dataset = fo.Dataset.from_json(dataset_json, persistent=False) - fo.launch_app(dataset) + cloud_inference.with_options(config_path="configs/inference.yaml")() + + if fiftyone: + # client.activate_stack(UUID("7cda3cec-6744-48dc-8bdc-f102242a26d2")) + + fifty_one.with_options(config_path="configs/fiftyone.yaml")() if __name__ == "__main__": diff --git a/end-to-end-computer-vision/steps/fifty_one_labelling.py b/end-to-end-computer-vision/steps/fifty_one_labelling.py new file mode 100644 index 00000000..e69de29b diff --git a/end-to-end-computer-vision/steps/fiftyone_inference.py b/end-to-end-computer-vision/steps/fiftyone_inference.py index 8db39e96..f8439df7 100644 --- a/end-to-end-computer-vision/steps/fiftyone_inference.py +++ b/end-to-end-computer-vision/steps/fiftyone_inference.py @@ -43,7 +43,6 @@ def create_fiftyone_dataset() -> ( name_id_or_prefix=TRAINED_MODEL_NAME ) yolo_model = model_artifact.load() - # results = yolo_model(DATASET_DIR, half=True, conf=0.6) dataset = fo.Dataset.from_dir( dataset_dir=DATASET_DIR, diff --git a/end-to-end-computer-vision/steps/promote_model.py b/end-to-end-computer-vision/steps/promote_model.py index c17447b6..6b3a7a11 100644 --- a/end-to-end-computer-vision/steps/promote_model.py +++ b/end-to-end-computer-vision/steps/promote_model.py @@ -39,8 +39,10 @@ def promote_model( model_version_name_or_number_or_id=ModelStages.PRODUCTION, ) except KeyError: - logger.info("No `production` model found to compare to, current " - "model will be promoted by default.") + logger.info( + "No `production` model found to compare to, current " + "model will be promoted by default." + ) cur_model_version.set_stage(ModelStages.PRODUCTION, force=True) else: diff --git a/end-to-end-computer-vision/utils/dataset_utils.py b/end-to-end-computer-vision/utils/dataset_utils.py index 8df2feb0..3b0a3731 100644 --- a/end-to-end-computer-vision/utils/dataset_utils.py +++ b/end-to-end-computer-vision/utils/dataset_utils.py @@ -18,7 +18,6 @@ import tempfile from PIL import Image -from zenml.client import Client from materializers.label_studio_yolo_dataset_materializer import ( LabelStudioYOLODataset, From b7ff028b98ffbf5b04b977197ebb53e64ad5932b Mon Sep 17 00:00:00 2001 From: Alex Strick van Linschoten Date: Tue, 23 Apr 2024 19:22:46 +0200 Subject: [PATCH 058/121] fix reference to yaml config --- end-to-end-computer-vision/run.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/end-to-end-computer-vision/run.py b/end-to-end-computer-vision/run.py index 324fbd43..002132d6 100644 --- a/end-to-end-computer-vision/run.py +++ b/end-to-end-computer-vision/run.py @@ -96,7 +96,7 @@ def main( if inference_pipeline: client.activate_stack(UUID("7cda3cec-6744-48dc-8bdc-f102242a26d2")) - cloud_inference.with_options(config_path="configs/inference.yaml")() + cloud_inference.with_options(config_path="configs/cloud_inference.yaml")() if fiftyone: # client.activate_stack(UUID("7cda3cec-6744-48dc-8bdc-f102242a26d2")) From e2bb5499fd839969cd0400925bc0cf15d41454c1 Mon Sep 17 00:00:00 2001 From: Alex Strick van Linschoten Date: Tue, 23 Apr 2024 19:29:40 +0200 Subject: [PATCH 059/121] add local fiftyone pipeline + step --- .../configs/fiftyone.yaml | 22 ++++++++++++++++++ .../pipelines/fifty_one.py | 11 +++++++++ .../pipelines/local_inference.py | 23 ------------------- end-to-end-computer-vision/run.py | 6 +++-- .../steps/fifty_one_labelling.py | 15 ++++++++++++ 5 files changed, 52 insertions(+), 25 deletions(-) create mode 100644 end-to-end-computer-vision/configs/fiftyone.yaml create mode 100644 end-to-end-computer-vision/pipelines/fifty_one.py delete mode 100644 end-to-end-computer-vision/pipelines/local_inference.py diff --git a/end-to-end-computer-vision/configs/fiftyone.yaml b/end-to-end-computer-vision/configs/fiftyone.yaml new file mode 100644 index 00000000..033a6f09 --- /dev/null +++ b/end-to-end-computer-vision/configs/fiftyone.yaml @@ -0,0 +1,22 @@ +enable_cache: False + +settings: + docker: + apt_packages: + - ffmpeg + - libsm6 + - libxext6 + required_integrations: + - gcp + - github + requirements: + - ultralytics + - fiftyone + +# configuration of the Model Control Plane +# model: +# name: Yolo_Object_Detection +# license: Apache 2.0 +# description: Object Detection Model. +# tags: ["object detection"] +# version: production diff --git a/end-to-end-computer-vision/pipelines/fifty_one.py b/end-to-end-computer-vision/pipelines/fifty_one.py new file mode 100644 index 00000000..17a6562d --- /dev/null +++ b/end-to-end-computer-vision/pipelines/fifty_one.py @@ -0,0 +1,11 @@ +from zenml import pipeline +from zenml.logger import get_logger + +from steps.fifty_one_labelling import create_fiftyone_dataset + +logger = get_logger(__name__) + + +@pipeline +def fifty_one(): + create_fiftyone_dataset() diff --git a/end-to-end-computer-vision/pipelines/local_inference.py b/end-to-end-computer-vision/pipelines/local_inference.py deleted file mode 100644 index d3662968..00000000 --- a/end-to-end-computer-vision/pipelines/local_inference.py +++ /dev/null @@ -1,23 +0,0 @@ -import fiftyone as fo -from zenml import pipeline, step -from zenml.client import Client -from zenml.logger import get_logger - -from utils.constants import PREDICTIONS_DATASET_ARTIFACT_NAME - -logger = get_logger(__name__) - - -@step -def create_fiftyone_dataset(): - artifact = Client().get_artifact_version( - name_id_or_prefix=PREDICTIONS_DATASET_ARTIFACT_NAME - ) - dataset_json = artifact.load() - dataset = fo.Dataset.from_json(dataset_json, persistent=False) - fo.launch_app(dataset) - - -@pipeline(enable_cache=False) -def fifty_one(): - create_fiftyone_dataset() diff --git a/end-to-end-computer-vision/run.py b/end-to-end-computer-vision/run.py index 002132d6..3779e169 100644 --- a/end-to-end-computer-vision/run.py +++ b/end-to-end-computer-vision/run.py @@ -24,7 +24,7 @@ from pipelines import data_export from pipelines.cloud_inference import cloud_inference -from pipelines.local_inference import fifty_one +from pipelines.fifty_one import fifty_one from pipelines.training import training logger = get_logger(__name__) @@ -96,7 +96,9 @@ def main( if inference_pipeline: client.activate_stack(UUID("7cda3cec-6744-48dc-8bdc-f102242a26d2")) - cloud_inference.with_options(config_path="configs/cloud_inference.yaml")() + cloud_inference.with_options( + config_path="configs/cloud_inference.yaml" + )() if fiftyone: # client.activate_stack(UUID("7cda3cec-6744-48dc-8bdc-f102242a26d2")) diff --git a/end-to-end-computer-vision/steps/fifty_one_labelling.py b/end-to-end-computer-vision/steps/fifty_one_labelling.py index e69de29b..e13635c5 100644 --- a/end-to-end-computer-vision/steps/fifty_one_labelling.py +++ b/end-to-end-computer-vision/steps/fifty_one_labelling.py @@ -0,0 +1,15 @@ +import fiftyone as fo +from zenml import step +from zenml.client import Client + +from utils.constants import PREDICTIONS_DATASET_ARTIFACT_NAME + + +@step +def create_fiftyone_dataset(): + artifact = Client().get_artifact_version( + name_id_or_prefix=PREDICTIONS_DATASET_ARTIFACT_NAME + ) + dataset_json = artifact.load() + dataset = fo.Dataset.from_json(dataset_json, persistent=False) + fo.launch_app(dataset) From 718eb99daff6fe0bd46b3f920fc9a6758a801562 Mon Sep 17 00:00:00 2001 From: Alex Strick van Linschoten Date: Tue, 23 Apr 2024 19:44:03 +0200 Subject: [PATCH 060/121] everyone gets their own stack --- end-to-end-computer-vision/run.py | 24 ++++++++++++++++++++---- 1 file changed, 20 insertions(+), 4 deletions(-) diff --git a/end-to-end-computer-vision/run.py b/end-to-end-computer-vision/run.py index 3779e169..5cf428c5 100644 --- a/end-to-end-computer-vision/run.py +++ b/end-to-end-computer-vision/run.py @@ -30,6 +30,7 @@ logger = get_logger(__name__) + @click.command() @click.option( "--feature", @@ -64,16 +65,31 @@ default=False, help="Whether to launch the FiftyOne app pipeline.", ) +@click.option( + "--stack", + "-s", + "stack", + required=False, + type=click.Choice(["alexej", "hamza", "alex"]), + help="The stack to use for the pipeline.", +) def main( feature_pipeline: bool = False, training_pipeline: bool = False, inference_pipeline: bool = False, fiftyone: bool = False, + stack: UUID = "alexej", ): + if stack == "hamza": + stack_id = UUID("cca5eaf7-0309-413d-89ff-1cd371b7d27c") + elif stack == "alex": + stack_id = UUID("fcf840ac-addd-4de3-a3e4-a1015f7bb96c") + else: + stack_id = UUID("7cda3cec-6744-48dc-8bdc-f102242a26d2") client = Client() if feature_pipeline: - client.activate_stack(UUID("7cda3cec-6744-48dc-8bdc-f102242a26d2")) + client.activate_stack(stack_id) # Export data from label studio data_export.with_options( @@ -87,21 +103,21 @@ def main( latest_model.set_stage(stage=ModelStages.STAGING, force=True) if training_pipeline: - client.activate_stack(UUID("20ed5311-ffc6-45d0-b339-6ec35af9501e")) + client.activate_stack(stack_id) # Train model on data training.with_options(config_path="configs/training_gpu.yaml")() # training.with_options(config_path="configs/training.yaml")() if inference_pipeline: - client.activate_stack(UUID("7cda3cec-6744-48dc-8bdc-f102242a26d2")) + client.activate_stack(stack_id) cloud_inference.with_options( config_path="configs/cloud_inference.yaml" )() if fiftyone: - # client.activate_stack(UUID("7cda3cec-6744-48dc-8bdc-f102242a26d2")) + client.activate_stack(stack_id) fifty_one.with_options(config_path="configs/fiftyone.yaml")() From b38f87f549b090f0fd3d10ec0db12346491ecc71 Mon Sep 17 00:00:00 2001 From: Alex Strick van Linschoten Date: Tue, 23 Apr 2024 19:44:17 +0200 Subject: [PATCH 061/121] format --- end-to-end-computer-vision/run.py | 1 - 1 file changed, 1 deletion(-) diff --git a/end-to-end-computer-vision/run.py b/end-to-end-computer-vision/run.py index 5cf428c5..a6618de7 100644 --- a/end-to-end-computer-vision/run.py +++ b/end-to-end-computer-vision/run.py @@ -30,7 +30,6 @@ logger = get_logger(__name__) - @click.command() @click.option( "--feature", From fd2c31c58c285762ea9ad9ee8e4a464989786ba7 Mon Sep 17 00:00:00 2001 From: Alex Strick van Linschoten Date: Tue, 23 Apr 2024 19:51:54 +0200 Subject: [PATCH 062/121] add fiftyone step --- end-to-end-computer-vision/steps/fifty_one_labelling.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/end-to-end-computer-vision/steps/fifty_one_labelling.py b/end-to-end-computer-vision/steps/fifty_one_labelling.py index e13635c5..a20f82fc 100644 --- a/end-to-end-computer-vision/steps/fifty_one_labelling.py +++ b/end-to-end-computer-vision/steps/fifty_one_labelling.py @@ -11,5 +11,5 @@ def create_fiftyone_dataset(): name_id_or_prefix=PREDICTIONS_DATASET_ARTIFACT_NAME ) dataset_json = artifact.load() - dataset = fo.Dataset.from_json(dataset_json, persistent=False) + dataset = fo.Dataset.from_json(dataset_json, persistent=True) fo.launch_app(dataset) From c2df7759b6875d98af4c8d669260ab76ab750e5e Mon Sep 17 00:00:00 2001 From: Alex Strick van Linschoten Date: Tue, 23 Apr 2024 20:27:42 +0200 Subject: [PATCH 063/121] add licenses --- .../notebooks/fiftyone_prelabelling.ipynb | 43 ++++++ .../notebooks/scratchpad.ipynb | 142 ++++++++++++++++++ .../pipelines/fifty_one.py | 17 +++ .../steps/fifty_one_labelling.py | 17 +++ 4 files changed, 219 insertions(+) create mode 100644 end-to-end-computer-vision/notebooks/scratchpad.ipynb diff --git a/end-to-end-computer-vision/notebooks/fiftyone_prelabelling.ipynb b/end-to-end-computer-vision/notebooks/fiftyone_prelabelling.ipynb index b4220792..41d0ccc7 100644 --- a/end-to-end-computer-vision/notebooks/fiftyone_prelabelling.ipynb +++ b/end-to-end-computer-vision/notebooks/fiftyone_prelabelling.ipynb @@ -484,6 +484,49 @@ "session = fo.launch_app(view=view)\n" ] }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + " " + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "Dataset: -\n", + "Session URL: http://localhost:5151/" + ] + }, + "execution_count": 1, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import fiftyone as fo\n", + "fo.launch_app()\n" + ] + }, { "cell_type": "code", "execution_count": null, diff --git a/end-to-end-computer-vision/notebooks/scratchpad.ipynb b/end-to-end-computer-vision/notebooks/scratchpad.ipynb new file mode 100644 index 00000000..d7e5348e --- /dev/null +++ b/end-to-end-computer-vision/notebooks/scratchpad.ipynb @@ -0,0 +1,142 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\u001b[33mCould not import Azure service connector: No module named 'azure.identity'.\u001b[0m\n", + "\u001b[33mCould not import HyperAI service connector: No module named 'paramiko'.\u001b[0m\n", + " 100% |███████████████████| 15/15 [15.6ms elapsed, 0s remaining, 959.0 samples/s] \n", + "\u001b[1;35m 100% |███████████████████| 15/15 [15.6ms elapsed, 0s remaining, 959.0 samples/s] \u001b[0m\n" + ] + }, + { + "data": { + "text/html": [ + "\n", + " \n", + " " + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "Dataset: ships_mt0ked\n", + "Media type: image\n", + "Num samples: 15\n", + "Selected samples: 0\n", + "Selected labels: 0\n", + "Session URL: http://localhost:5151/" + ] + }, + "execution_count": 1, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import fiftyone as fo\n", + "from zenml.client import Client\n", + "\n", + "\n", + "artifact = Client().get_artifact_version(\n", + " name_id_or_prefix=\"predictions_dataset_json\"\n", + ")\n", + "dataset_json = artifact.load()\n", + "dataset = fo.Dataset.from_json(dataset_json, persistent=True)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + " " + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "Dataset: ships_mt0ked\n", + "Media type: image\n", + "Num samples: 15\n", + "Selected samples: 0\n", + "Selected labels: 0\n", + "Session URL: http://localhost:5151/" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "fo.launch_app(dataset)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "ls51", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.12" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/end-to-end-computer-vision/pipelines/fifty_one.py b/end-to-end-computer-vision/pipelines/fifty_one.py index 17a6562d..93f59843 100644 --- a/end-to-end-computer-vision/pipelines/fifty_one.py +++ b/end-to-end-computer-vision/pipelines/fifty_one.py @@ -1,3 +1,20 @@ +# Apache Software License 2.0 +# +# Copyright (c) ZenML GmbH 2024. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + + from zenml import pipeline from zenml.logger import get_logger diff --git a/end-to-end-computer-vision/steps/fifty_one_labelling.py b/end-to-end-computer-vision/steps/fifty_one_labelling.py index a20f82fc..7bcb5c25 100644 --- a/end-to-end-computer-vision/steps/fifty_one_labelling.py +++ b/end-to-end-computer-vision/steps/fifty_one_labelling.py @@ -1,3 +1,20 @@ +# Apache Software License 2.0 +# +# Copyright (c) ZenML GmbH 2024. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + + import fiftyone as fo from zenml import step from zenml.client import Client From 5137c85820ce50d92f6f438a9c5fa86b4ecb5a9b Mon Sep 17 00:00:00 2001 From: Alex Strick van Linschoten Date: Tue, 23 Apr 2024 21:06:05 +0200 Subject: [PATCH 064/121] update scripts --- end-to-end-computer-vision/pipelines/fifty_one.py | 1 + end-to-end-computer-vision/steps/fifty_one_labelling.py | 7 ++++--- 2 files changed, 5 insertions(+), 3 deletions(-) diff --git a/end-to-end-computer-vision/pipelines/fifty_one.py b/end-to-end-computer-vision/pipelines/fifty_one.py index 93f59843..785b5bed 100644 --- a/end-to-end-computer-vision/pipelines/fifty_one.py +++ b/end-to-end-computer-vision/pipelines/fifty_one.py @@ -25,4 +25,5 @@ @pipeline def fifty_one(): + """Launches the FiftyOne app with the predictions dataset.""" create_fiftyone_dataset() diff --git a/end-to-end-computer-vision/steps/fifty_one_labelling.py b/end-to-end-computer-vision/steps/fifty_one_labelling.py index 7bcb5c25..eb4b2848 100644 --- a/end-to-end-computer-vision/steps/fifty_one_labelling.py +++ b/end-to-end-computer-vision/steps/fifty_one_labelling.py @@ -14,7 +14,6 @@ # See the License for the specific language governing permissions and # limitations under the License. - import fiftyone as fo from zenml import step from zenml.client import Client @@ -24,9 +23,11 @@ @step def create_fiftyone_dataset(): + """Launches the FiftyOne app with the predictions dataset.""" artifact = Client().get_artifact_version( name_id_or_prefix=PREDICTIONS_DATASET_ARTIFACT_NAME ) dataset_json = artifact.load() - dataset = fo.Dataset.from_json(dataset_json, persistent=True) - fo.launch_app(dataset) + dataset = fo.Dataset.from_json(dataset_json, persistent=False) + session = fo.launch_app(dataset) + From b9d9b7770dce6ae1e8524b5e416d77a1fa7862b5 Mon Sep 17 00:00:00 2001 From: Alex Strick van Linschoten Date: Tue, 23 Apr 2024 21:11:37 +0200 Subject: [PATCH 065/121] formatting and docstrings --- end-to-end-computer-vision/notebooks/scratchpad.ipynb | 4 +++- .../pipelines/cloud_inference.py | 1 + end-to-end-computer-vision/pipelines/data_export.py | 5 +++++ end-to-end-computer-vision/pipelines/training.py | 6 ++++++ .../steps/export_label_studio.py | 8 ++++++++ .../steps/fifty_one_labelling.py | 1 - end-to-end-computer-vision/steps/fiftyone_inference.py | 5 +++++ end-to-end-computer-vision/steps/load_model.py | 9 +++++++++ end-to-end-computer-vision/steps/predict_image.py | 5 +++++ end-to-end-computer-vision/steps/promote_model.py | 5 +++++ end-to-end-computer-vision/steps/train_model.py | 10 ++++++++++ 11 files changed, 57 insertions(+), 2 deletions(-) diff --git a/end-to-end-computer-vision/notebooks/scratchpad.ipynb b/end-to-end-computer-vision/notebooks/scratchpad.ipynb index d7e5348e..3d7766dd 100644 --- a/end-to-end-computer-vision/notebooks/scratchpad.ipynb +++ b/end-to-end-computer-vision/notebooks/scratchpad.ipynb @@ -61,7 +61,9 @@ " name_id_or_prefix=\"predictions_dataset_json\"\n", ")\n", "dataset_json = artifact.load()\n", - "dataset = fo.Dataset.from_json(dataset_json, persistent=True)\n" + "dataset = fo.Dataset.from_json(dataset_json, persistent=True)\n", + "\n", + "fo.launch_app(dataset)" ] }, { diff --git a/end-to-end-computer-vision/pipelines/cloud_inference.py b/end-to-end-computer-vision/pipelines/cloud_inference.py index 944635e7..e765f4fd 100644 --- a/end-to-end-computer-vision/pipelines/cloud_inference.py +++ b/end-to-end-computer-vision/pipelines/cloud_inference.py @@ -24,6 +24,7 @@ @pipeline def cloud_inference(): + """Uses FiftyOne for inference on a dataset.""" create_fiftyone_dataset() diff --git a/end-to-end-computer-vision/pipelines/data_export.py b/end-to-end-computer-vision/pipelines/data_export.py index 588178e6..67eaf675 100644 --- a/end-to-end-computer-vision/pipelines/data_export.py +++ b/end-to-end-computer-vision/pipelines/data_export.py @@ -24,4 +24,9 @@ @pipeline def data_export(dataset_name: str = "polution"): + """Loads data from Label studio. + + Args: + dataset_name: Name of the dataset to load. + """ load_data_from_label_studio(dataset_name=dataset_name) diff --git a/end-to-end-computer-vision/pipelines/training.py b/end-to-end-computer-vision/pipelines/training.py index 7a90b730..1161ac0d 100644 --- a/end-to-end-computer-vision/pipelines/training.py +++ b/end-to-end-computer-vision/pipelines/training.py @@ -26,6 +26,12 @@ @pipeline def training(epochs: int, model_checkpoint: str = "yolov8l.pt"): + """Trains a model on a dataset. + + Args: + epochs: Number of epochs to train the model for. + model_checkpoint: Checkpoint of the model to load. + """ model = load_model(model_checkpoint) # Load the latest version of the train dataset diff --git a/end-to-end-computer-vision/steps/export_label_studio.py b/end-to-end-computer-vision/steps/export_label_studio.py index 9e6ceef6..b731842b 100644 --- a/end-to-end-computer-vision/steps/export_label_studio.py +++ b/end-to-end-computer-vision/steps/export_label_studio.py @@ -21,6 +21,14 @@ def load_data_from_label_studio( Annotated[LabelStudioYOLODataset, "yolo_dataset"], Annotated[List[int], "task_ids"], ]: + """Loads data from Label Studio. + + Args: + dataset_name: Name of the dataset to load. + + Returns: + Tuple of the loaded dataset and the Label Studio task IDs. + """ annotator = Client().active_stack.annotator from zenml.integrations.label_studio.annotators.label_studio_annotator import ( LabelStudioAnnotator, diff --git a/end-to-end-computer-vision/steps/fifty_one_labelling.py b/end-to-end-computer-vision/steps/fifty_one_labelling.py index eb4b2848..4c3c1930 100644 --- a/end-to-end-computer-vision/steps/fifty_one_labelling.py +++ b/end-to-end-computer-vision/steps/fifty_one_labelling.py @@ -30,4 +30,3 @@ def create_fiftyone_dataset(): dataset_json = artifact.load() dataset = fo.Dataset.from_json(dataset_json, persistent=False) session = fo.launch_app(dataset) - diff --git a/end-to-end-computer-vision/steps/fiftyone_inference.py b/end-to-end-computer-vision/steps/fiftyone_inference.py index f8439df7..358ab96f 100644 --- a/end-to-end-computer-vision/steps/fiftyone_inference.py +++ b/end-to-end-computer-vision/steps/fiftyone_inference.py @@ -38,6 +38,11 @@ def create_fiftyone_dataset() -> ( Annotated[str, PREDICTIONS_DATASET_ARTIFACT_NAME] ): + """Creates a FiftyOne dataset with predictions using a model. + + Returns: + str: Name of the artifact containing the FiftyOne JSON dataset. + """ c = Client() model_artifact = c.get_artifact_version( name_id_or_prefix=TRAINED_MODEL_NAME diff --git a/end-to-end-computer-vision/steps/load_model.py b/end-to-end-computer-vision/steps/load_model.py index 61215520..827b691e 100644 --- a/end-to-end-computer-vision/steps/load_model.py +++ b/end-to-end-computer-vision/steps/load_model.py @@ -27,5 +27,14 @@ def load_model( model_checkpoint: str, ) -> Annotated[YOLO, ArtifactConfig(name="Raw_YOLO", is_model_artifact=True)]: + """Loads a YOLO model from a checkpoint. + + Args: + model_checkpoint: Checkpoint of the model to load. + + Returns: + YOLO: A YOLO model loaded from the checkpoint. + """ + logger.info(f"Loading YOLO checkpoint {model_checkpoint}") return YOLO(model_checkpoint) diff --git a/end-to-end-computer-vision/steps/predict_image.py b/end-to-end-computer-vision/steps/predict_image.py index 481f693f..58fee0b5 100644 --- a/end-to-end-computer-vision/steps/predict_image.py +++ b/end-to-end-computer-vision/steps/predict_image.py @@ -20,5 +20,10 @@ @step def predict_image(model: YOLO): + """Predicts an image using the model. + + Args: + model: YOLO model to use for prediction. + """ results = model("https://ultralytics.com/images/bus.jpg") print(results) diff --git a/end-to-end-computer-vision/steps/promote_model.py b/end-to-end-computer-vision/steps/promote_model.py index 6b3a7a11..54b12b32 100644 --- a/end-to-end-computer-vision/steps/promote_model.py +++ b/end-to-end-computer-vision/steps/promote_model.py @@ -28,6 +28,11 @@ def promote_model( metrics: Dict[str, Any], ) -> None: + """Promotes model to production if better than current production model. + + Args: + metrics: Metrics to compare against the current production model. + """ client = Client() # Get the model associated with this pipeline cur_model_version = get_step_context().model diff --git a/end-to-end-computer-vision/steps/train_model.py b/end-to-end-computer-vision/steps/train_model.py index 82182435..4da9ddfe 100644 --- a/end-to-end-computer-vision/steps/train_model.py +++ b/end-to-end-computer-vision/steps/train_model.py @@ -37,6 +37,16 @@ def train_model( ], Annotated[Dict[str, Any], "validation_metrics"], ]: + """Trains a model on a dataset. + + Args: + epochs: Number of epochs to train the model for. + model: YOLO model to train. + dataset: Dataset to train the model on. + + Returns: + Tuple[YOLO, Dict[str, Any]]: Trained model and validation metrics. + """ data_path = load_and_split_data(dataset=dataset) model.train(data=data_path, epochs=epochs) From afa18bad0fb7f478f1ae5f16d886d21323771448 Mon Sep 17 00:00:00 2001 From: Alex Strick van Linschoten Date: Tue, 23 Apr 2024 21:24:39 +0200 Subject: [PATCH 066/121] renaming for diagram --- end-to-end-computer-vision/pipelines/__init__.py | 2 +- end-to-end-computer-vision/pipelines/data_export.py | 2 +- end-to-end-computer-vision/pipelines/training.py | 2 +- end-to-end-computer-vision/run.py | 6 ++++-- end-to-end-computer-vision/steps/export_label_studio.py | 4 ++-- 5 files changed, 9 insertions(+), 7 deletions(-) diff --git a/end-to-end-computer-vision/pipelines/__init__.py b/end-to-end-computer-vision/pipelines/__init__.py index 4cc5c4c5..4dc295a9 100644 --- a/end-to-end-computer-vision/pipelines/__init__.py +++ b/end-to-end-computer-vision/pipelines/__init__.py @@ -15,6 +15,6 @@ # limitations under the License. # -from .data_export import data_export +from .data_export import export_for_training # from .training import training diff --git a/end-to-end-computer-vision/pipelines/data_export.py b/end-to-end-computer-vision/pipelines/data_export.py index 67eaf675..344eadb9 100644 --- a/end-to-end-computer-vision/pipelines/data_export.py +++ b/end-to-end-computer-vision/pipelines/data_export.py @@ -23,7 +23,7 @@ @pipeline -def data_export(dataset_name: str = "polution"): +def export_for_training(dataset_name: str = "polution"): """Loads data from Label studio. Args: diff --git a/end-to-end-computer-vision/pipelines/training.py b/end-to-end-computer-vision/pipelines/training.py index 1161ac0d..b7e08cc4 100644 --- a/end-to-end-computer-vision/pipelines/training.py +++ b/end-to-end-computer-vision/pipelines/training.py @@ -36,7 +36,7 @@ def training(epochs: int, model_checkpoint: str = "yolov8l.pt"): # Load the latest version of the train dataset mv = get_pipeline_context().model - dataset = mv.get_artifact("yolo_dataset") + dataset = mv.get_artifact("YOLO_dataset") trained_model, metrics = train_model( model=model, dataset=dataset, epochs=epochs diff --git a/end-to-end-computer-vision/run.py b/end-to-end-computer-vision/run.py index a6618de7..fc18c53b 100644 --- a/end-to-end-computer-vision/run.py +++ b/end-to-end-computer-vision/run.py @@ -22,8 +22,8 @@ from zenml.enums import ModelStages from zenml.logger import get_logger -from pipelines import data_export from pipelines.cloud_inference import cloud_inference +from pipelines.data_export import export_for_training from pipelines.fifty_one import fifty_one from pipelines.training import training @@ -79,19 +79,21 @@ def main( fiftyone: bool = False, stack: UUID = "alexej", ): + # TODO: remove all this :) if stack == "hamza": stack_id = UUID("cca5eaf7-0309-413d-89ff-1cd371b7d27c") elif stack == "alex": stack_id = UUID("fcf840ac-addd-4de3-a3e4-a1015f7bb96c") else: stack_id = UUID("7cda3cec-6744-48dc-8bdc-f102242a26d2") + client = Client() if feature_pipeline: client.activate_stack(stack_id) # Export data from label studio - data_export.with_options( + export_for_training.with_options( config_path="configs/data_export_alexej.yaml" )() diff --git a/end-to-end-computer-vision/steps/export_label_studio.py b/end-to-end-computer-vision/steps/export_label_studio.py index b731842b..fc5b914d 100644 --- a/end-to-end-computer-vision/steps/export_label_studio.py +++ b/end-to-end-computer-vision/steps/export_label_studio.py @@ -13,12 +13,12 @@ @step( - output_materializers={"yolo_dataset": LabelStudioYOLODatasetMaterializer} + output_materializers={"YOLO_dataset": LabelStudioYOLODatasetMaterializer} ) def load_data_from_label_studio( dataset_name: str, ) -> Tuple[ - Annotated[LabelStudioYOLODataset, "yolo_dataset"], + Annotated[LabelStudioYOLODataset, "YOLO_dataset"], Annotated[List[int], "task_ids"], ]: """Loads data from Label Studio. From 9e9be2a29fedbc831eca8be4860c9d851d83cb98 Mon Sep 17 00:00:00 2001 From: Alex Strick van Linschoten Date: Tue, 23 Apr 2024 21:27:26 +0200 Subject: [PATCH 067/121] renaming task_ids -> new_ids --- end-to-end-computer-vision/steps/export_label_studio.py | 6 ++---- 1 file changed, 2 insertions(+), 4 deletions(-) diff --git a/end-to-end-computer-vision/steps/export_label_studio.py b/end-to-end-computer-vision/steps/export_label_studio.py index fc5b914d..7d480a33 100644 --- a/end-to-end-computer-vision/steps/export_label_studio.py +++ b/end-to-end-computer-vision/steps/export_label_studio.py @@ -19,7 +19,7 @@ def load_data_from_label_studio( dataset_name: str, ) -> Tuple[ Annotated[LabelStudioYOLODataset, "YOLO_dataset"], - Annotated[List[int], "task_ids"], + Annotated[List[int], "new_ids"], ]: """Loads data from Label Studio. @@ -55,9 +55,7 @@ def load_data_from_label_studio( cur_pipeline_name ).last_successful_run last_task_ids = ( - last_run.steps[cur_step_name] - .outputs["task_ids"] - .load() + last_run.steps[cur_step_name].outputs["new_ids"].load() ) except (RuntimeError, KeyError): last_task_ids = [] From 397d71347cf2b796378c081ef69b93647ff96e40 Mon Sep 17 00:00:00 2001 From: Alex Strick van Linschoten Date: Tue, 23 Apr 2024 21:29:30 +0200 Subject: [PATCH 068/121] renaming train_model -> training --- end-to-end-computer-vision/pipelines/training.py | 4 ++-- end-to-end-computer-vision/steps/train_model.py | 2 +- 2 files changed, 3 insertions(+), 3 deletions(-) diff --git a/end-to-end-computer-vision/pipelines/training.py b/end-to-end-computer-vision/pipelines/training.py index b7e08cc4..f2b58b4e 100644 --- a/end-to-end-computer-vision/pipelines/training.py +++ b/end-to-end-computer-vision/pipelines/training.py @@ -19,7 +19,7 @@ from steps.load_model import load_model from steps.promote_model import promote_model -from steps.train_model import train_model +from steps.train_model import training logger = get_logger(__name__) @@ -38,7 +38,7 @@ def training(epochs: int, model_checkpoint: str = "yolov8l.pt"): mv = get_pipeline_context().model dataset = mv.get_artifact("YOLO_dataset") - trained_model, metrics = train_model( + trained_model, metrics = training( model=model, dataset=dataset, epochs=epochs ) diff --git a/end-to-end-computer-vision/steps/train_model.py b/end-to-end-computer-vision/steps/train_model.py index 4da9ddfe..3a75281a 100644 --- a/end-to-end-computer-vision/steps/train_model.py +++ b/end-to-end-computer-vision/steps/train_model.py @@ -27,7 +27,7 @@ @step(output_materializers={"Trained_YOLO": UltralyticsMaterializer}) -def train_model( +def training( epochs: int, model: YOLO, dataset: LabelStudioYOLODataset, From 9fb7e53fca9531bdeeff85b6ef5af1fe012a0cfa Mon Sep 17 00:00:00 2001 From: Alex Strick van Linschoten Date: Tue, 23 Apr 2024 21:33:29 +0200 Subject: [PATCH 069/121] final rename --- end-to-end-computer-vision/pipelines/fifty_one.py | 6 +++--- .../pipelines/{cloud_inference.py => inference.py} | 4 ++-- end-to-end-computer-vision/run.py | 10 ++++------ .../steps/fifty_one_labelling.py | 2 +- 4 files changed, 10 insertions(+), 12 deletions(-) rename end-to-end-computer-vision/pipelines/{cloud_inference.py => inference.py} (89%) diff --git a/end-to-end-computer-vision/pipelines/fifty_one.py b/end-to-end-computer-vision/pipelines/fifty_one.py index 785b5bed..5ccf0093 100644 --- a/end-to-end-computer-vision/pipelines/fifty_one.py +++ b/end-to-end-computer-vision/pipelines/fifty_one.py @@ -18,12 +18,12 @@ from zenml import pipeline from zenml.logger import get_logger -from steps.fifty_one_labelling import create_fiftyone_dataset +from steps.fifty_one_labelling import fifty_one_launcher logger = get_logger(__name__) @pipeline -def fifty_one(): +def export_predictions(): """Launches the FiftyOne app with the predictions dataset.""" - create_fiftyone_dataset() + fifty_one_launcher() diff --git a/end-to-end-computer-vision/pipelines/cloud_inference.py b/end-to-end-computer-vision/pipelines/inference.py similarity index 89% rename from end-to-end-computer-vision/pipelines/cloud_inference.py rename to end-to-end-computer-vision/pipelines/inference.py index e765f4fd..77db9ef1 100644 --- a/end-to-end-computer-vision/pipelines/cloud_inference.py +++ b/end-to-end-computer-vision/pipelines/inference.py @@ -23,10 +23,10 @@ @pipeline -def cloud_inference(): +def inference(): """Uses FiftyOne for inference on a dataset.""" create_fiftyone_dataset() if __name__ == "__main__": - cloud_inference.with_options(config_path="configs/cloud_inference.yaml")() + inference.with_options(config_path="configs/cloud_inference.yaml")() diff --git a/end-to-end-computer-vision/run.py b/end-to-end-computer-vision/run.py index fc18c53b..b4126779 100644 --- a/end-to-end-computer-vision/run.py +++ b/end-to-end-computer-vision/run.py @@ -22,9 +22,9 @@ from zenml.enums import ModelStages from zenml.logger import get_logger -from pipelines.cloud_inference import cloud_inference from pipelines.data_export import export_for_training -from pipelines.fifty_one import fifty_one +from pipelines.fifty_one import export_predictions +from pipelines.inference import inference from pipelines.training import training logger = get_logger(__name__) @@ -113,14 +113,12 @@ def main( if inference_pipeline: client.activate_stack(stack_id) - cloud_inference.with_options( - config_path="configs/cloud_inference.yaml" - )() + inference.with_options(config_path="configs/cloud_inference.yaml")() if fiftyone: client.activate_stack(stack_id) - fifty_one.with_options(config_path="configs/fiftyone.yaml")() + export_predictions.with_options(config_path="configs/fiftyone.yaml")() if __name__ == "__main__": diff --git a/end-to-end-computer-vision/steps/fifty_one_labelling.py b/end-to-end-computer-vision/steps/fifty_one_labelling.py index 4c3c1930..464374db 100644 --- a/end-to-end-computer-vision/steps/fifty_one_labelling.py +++ b/end-to-end-computer-vision/steps/fifty_one_labelling.py @@ -22,7 +22,7 @@ @step -def create_fiftyone_dataset(): +def fifty_one_launcher(): """Launches the FiftyOne app with the predictions dataset.""" artifact = Client().get_artifact_version( name_id_or_prefix=PREDICTIONS_DATASET_ARTIFACT_NAME From abd1405326aa914bfdfbebaadcfc28139b5c0df2 Mon Sep 17 00:00:00 2001 From: Alex Strick van Linschoten Date: Tue, 23 Apr 2024 21:43:01 +0200 Subject: [PATCH 070/121] initial README skeleton --- end-to-end-computer-vision/README.md | 47 +++++++++++++++++++++------- 1 file changed, 36 insertions(+), 11 deletions(-) diff --git a/end-to-end-computer-vision/README.md b/end-to-end-computer-vision/README.md index a36d3f2c..2b39f5d5 100644 --- a/end-to-end-computer-vision/README.md +++ b/end-to-end-computer-vision/README.md @@ -1,32 +1,57 @@ +# End-to-end Computer Vision 🌄 -```commandline -zenml integration install label_studio torch gcp -pip install -r requirements.txt -pip uninstall wandb -``` +This is a project that demonstrates an end-to-end computer vision pipeline using +ZenML. The pipeline is designed to be modular and flexible, allowing for easy +experimentation and extension. + +The project showcases the full lifecycle of a computer vision project, from data +collection and preprocessing to model training and evaluation. The pipeline also +incorporates a human-in-the-loop (HITL) component, where human annotators can +label images to improve the model's performance, as well as feedback using +[Voxel51's FiftyOne](https://voxel51.com/fiftyone/) tool. + +The project uses the [Ship Detection +dataset](https://huggingface.co/datasets/datadrivenscience/ship-detection) from +DataDrivenScience on the Hugging Face Hub, which contains images of ships in +satellite imagery. The goal is to train a model to detect ships in the images. +Note that this isn't something that our YOLOv8 model is particularly good at out +of the box, so it serves as a good example of how to build a pipeline that can +be extended to other use cases. + +The project consists of the following pipelines and steps: + +- + +## Run this pipeline -## Maybe +### Setup -```shell -zenml integration install mlflow -y +You'll need to run the following: + +```bash +zenml integration install label_studio torch gcp mlflow -y +pip install -r requirements.txt +pip uninstall wandb ``` -# Env variables +You can also set the following environment variables: ```bash export DATA_UPLOAD_MAX_NUMBER_FILES=1000000 export WANDB_DISABLED=True ``` -# Fiftyone plugins +And to use the Albumentations and annotation plugins, you'll need to install them: -```shell +```bash fiftyone plugins download https://github.com/jacobmarks/fiftyone-albumentations-plugin fiftyone plugins download https://github.com/voxel51/fiftyone-plugins --plugin-names @voxel51/annotation ``` + + # Data initial dataset is stored at: `gs://zenml-internal-artifact-store/raw-ship-data/` From c86d0166af2cc1d4f726aeb2f1e1c25b7f4873ad Mon Sep 17 00:00:00 2001 From: Alex Strick van Linschoten Date: Tue, 23 Apr 2024 21:43:32 +0200 Subject: [PATCH 071/121] Update README formatting --- end-to-end-computer-vision/README.md | 10 ++++++---- 1 file changed, 6 insertions(+), 4 deletions(-) diff --git a/end-to-end-computer-vision/README.md b/end-to-end-computer-vision/README.md index 2b39f5d5..1ee81046 100644 --- a/end-to-end-computer-vision/README.md +++ b/end-to-end-computer-vision/README.md @@ -41,7 +41,8 @@ export DATA_UPLOAD_MAX_NUMBER_FILES=1000000 export WANDB_DISABLED=True ``` -And to use the Albumentations and annotation plugins, you'll need to install them: +And to use the Albumentations and annotation plugins, you'll need to install +them: ```bash fiftyone plugins download https://github.com/jacobmarks/fiftyone-albumentations-plugin @@ -54,7 +55,8 @@ fiftyone plugins download https://github.com/voxel51/fiftyone-plugins --plugin-n # Data -initial dataset is stored at: `gs://zenml-internal-artifact-store/raw-ship-data/` +initial dataset is stored at: +`gs://zenml-internal-artifact-store/raw-ship-data/` -copy with: -`gsutil -m cp -r gs://zenml-internal-artifact-store/raw-ship-data/ /path` +copy with: `gsutil -m cp -r gs://zenml-internal-artifact-store/raw-ship-data/ +/path` From ff4b969d055188dfda26e2ab3e7b5d0723e6fbca Mon Sep 17 00:00:00 2001 From: Alex Strick van Linschoten Date: Tue, 23 Apr 2024 21:45:11 +0200 Subject: [PATCH 072/121] Update fifty_one_launcher function --- end-to-end-computer-vision/steps/fifty_one_labelling.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/end-to-end-computer-vision/steps/fifty_one_labelling.py b/end-to-end-computer-vision/steps/fifty_one_labelling.py index 464374db..05ed186b 100644 --- a/end-to-end-computer-vision/steps/fifty_one_labelling.py +++ b/end-to-end-computer-vision/steps/fifty_one_labelling.py @@ -29,4 +29,4 @@ def fifty_one_launcher(): ) dataset_json = artifact.load() dataset = fo.Dataset.from_json(dataset_json, persistent=False) - session = fo.launch_app(dataset) + fo.launch_app(dataset) From f8a10955ef6aedf210fa53cbb503d54a09291d8f Mon Sep 17 00:00:00 2001 From: Alex Strick van Linschoten Date: Tue, 23 Apr 2024 21:50:31 +0200 Subject: [PATCH 073/121] add link --- end-to-end-computer-vision/README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/end-to-end-computer-vision/README.md b/end-to-end-computer-vision/README.md index 1ee81046..565b0916 100644 --- a/end-to-end-computer-vision/README.md +++ b/end-to-end-computer-vision/README.md @@ -12,7 +12,7 @@ label images to improve the model's performance, as well as feedback using The project uses the [Ship Detection dataset](https://huggingface.co/datasets/datadrivenscience/ship-detection) from -DataDrivenScience on the Hugging Face Hub, which contains images of ships in +[DataDrivenScience](https://datadrivenscience.com/) on the Hugging Face Hub, which contains images of ships in satellite imagery. The goal is to train a model to detect ships in the images. Note that this isn't something that our YOLOv8 model is particularly good at out of the box, so it serves as a good example of how to build a pipeline that can From f0da68cf7a4c795b8aa83c89b43e41f10c8c1b06 Mon Sep 17 00:00:00 2001 From: Alex Strick van Linschoten Date: Tue, 23 Apr 2024 21:59:05 +0200 Subject: [PATCH 074/121] refactor using annotator method --- .../steps/export_label_studio.py | 57 ++++++++++--------- 1 file changed, 31 insertions(+), 26 deletions(-) diff --git a/end-to-end-computer-vision/steps/export_label_studio.py b/end-to-end-computer-vision/steps/export_label_studio.py index 7d480a33..3f229d49 100644 --- a/end-to-end-computer-vision/steps/export_label_studio.py +++ b/end-to-end-computer-vision/steps/export_label_studio.py @@ -40,37 +40,42 @@ def load_data_from_label_studio( ) if annotator and annotator._connection_available(): - for dataset in annotator.get_datasets(): - if dataset.get_params()["title"] == dataset_name: - ls_dataset = LabelStudioYOLODataset() - ls_dataset.dataset = dataset + try: + dataset = annotator.get_dataset(dataset_name) + ls_dataset = LabelStudioYOLODataset() + ls_dataset.dataset = dataset - c = Client() - step_context = get_step_context() - cur_pipeline_name = step_context.pipeline.name - cur_step_name = step_context.step_name + c = Client() + step_context = get_step_context() + cur_pipeline_name = step_context.pipeline.name + cur_step_name = step_context.step_name - try: - last_run = c.get_pipeline( - cur_pipeline_name - ).last_successful_run - last_task_ids = ( - last_run.steps[cur_step_name].outputs["new_ids"].load() - ) - except (RuntimeError, KeyError): - last_task_ids = [] + try: + last_run = c.get_pipeline( + cur_pipeline_name + ).last_successful_run + last_task_ids = ( + last_run.steps[cur_step_name].outputs["new_ids"].load() + ) + except (RuntimeError, KeyError): + last_task_ids = [] - cur_task_ids = dataset.get_tasks_ids() - logger.info(f"{len(cur_task_ids)} total labels found.") + cur_task_ids = dataset.get_tasks_ids() + logger.info(f"{len(cur_task_ids)} total labels found.") - new_task_ids = list(set(cur_task_ids) - set(last_task_ids)) - logger.info( - f"{len(new_task_ids)} new labels are being beamed " - f"straight to you." - ) + new_task_ids = list(set(cur_task_ids) - set(last_task_ids)) + logger.info( + f"{len(new_task_ids)} new labels are being beamed " + f"straight to you." + ) + + ls_dataset.task_ids = new_task_ids + return ls_dataset, new_task_ids + except: + raise ValueError( + f"Dataset {dataset_name} not found in Label Studio." + ) - ls_dataset.task_ids = new_task_ids - return ls_dataset, new_task_ids else: raise TypeError( "This step can only be used with an active Label Studio annotator." From a608d7248b8e0f0a955ce07335f95d0a637f8fcb Mon Sep 17 00:00:00 2001 From: Alex Strick van Linschoten Date: Tue, 23 Apr 2024 22:30:40 +0200 Subject: [PATCH 075/121] update data export --- .../configs/data_export_alex.yaml | 2 +- .../steps/export_label_studio.py | 10 ++++++---- 2 files changed, 7 insertions(+), 5 deletions(-) diff --git a/end-to-end-computer-vision/configs/data_export_alex.yaml b/end-to-end-computer-vision/configs/data_export_alex.yaml index ea84cf1d..076e61de 100644 --- a/end-to-end-computer-vision/configs/data_export_alex.yaml +++ b/end-to-end-computer-vision/configs/data_export_alex.yaml @@ -2,7 +2,7 @@ enable_cache: False # pipeline configuration parameters: - dataset_name: "FiftyOne_ships_1713862317" + dataset_name: "FiftyOne_ships_wuxqa5" # configuration of the Model Control Plane model: diff --git a/end-to-end-computer-vision/steps/export_label_studio.py b/end-to-end-computer-vision/steps/export_label_studio.py index 3f229d49..a574e9e8 100644 --- a/end-to-end-computer-vision/steps/export_label_studio.py +++ b/end-to-end-computer-vision/steps/export_label_studio.py @@ -41,7 +41,7 @@ def load_data_from_label_studio( if annotator and annotator._connection_available(): try: - dataset = annotator.get_dataset(dataset_name) + dataset = annotator.get_dataset(dataset_name=dataset_name) ls_dataset = LabelStudioYOLODataset() ls_dataset.dataset = dataset @@ -60,10 +60,12 @@ def load_data_from_label_studio( except (RuntimeError, KeyError): last_task_ids = [] - cur_task_ids = dataset.get_tasks_ids() - logger.info(f"{len(cur_task_ids)} total labels found.") + current_labeled_task_ids = dataset.get_labeled_tasks_ids() + logger.info(f"{len(current_labeled_task_ids)} total labels found.") - new_task_ids = list(set(cur_task_ids) - set(last_task_ids)) + new_task_ids = list( + set(current_labeled_task_ids) - set(last_task_ids) + ) logger.info( f"{len(new_task_ids)} new labels are being beamed " f"straight to you." From dfd280f32caa371657da6a128a2db24bfb705e56 Mon Sep 17 00:00:00 2001 From: Alexej Penner Date: Tue, 23 Apr 2024 22:43:17 +0200 Subject: [PATCH 076/121] Implemented data ingestion --- .../pipelines/data_ingestion.py | 29 +++++++ .../steps/download_from_hf.py | 84 +++++++++++++++++++ .../steps/export_label_studio.py | 16 ++++ .../steps/upload_to_label_studio.py | 57 +++++++++++++ 4 files changed, 186 insertions(+) create mode 100644 end-to-end-computer-vision/pipelines/data_ingestion.py create mode 100644 end-to-end-computer-vision/steps/download_from_hf.py create mode 100644 end-to-end-computer-vision/steps/upload_to_label_studio.py diff --git a/end-to-end-computer-vision/pipelines/data_ingestion.py b/end-to-end-computer-vision/pipelines/data_ingestion.py new file mode 100644 index 00000000..81d9fcf8 --- /dev/null +++ b/end-to-end-computer-vision/pipelines/data_ingestion.py @@ -0,0 +1,29 @@ +# Apache Software License 2.0 +# +# Copyright (c) ZenML GmbH 2024. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# +from zenml import pipeline +from zenml.logger import get_logger + +from steps.download_from_hf import download_dataset_from_hf +from steps.upload_to_label_studio import upload_labels_to_label_studio + +logger = get_logger(__name__) + + +@pipeline +def data_ingestion(): + labels_dict = download_dataset_from_hf() + upload_labels_to_label_studio(labels_dict) \ No newline at end of file diff --git a/end-to-end-computer-vision/steps/download_from_hf.py b/end-to-end-computer-vision/steps/download_from_hf.py new file mode 100644 index 00000000..589301ee --- /dev/null +++ b/end-to-end-computer-vision/steps/download_from_hf.py @@ -0,0 +1,84 @@ +# Apache Software License 2.0 +# +# Copyright (c) ZenML GmbH 2024. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# +from typing import Dict, Any + +import fiftyone.zoo as foz +from datasets import load_dataset +from PIL import Image +import os +import json + +from zenml import step +from zenml.logger import get_logger + +Image.MAX_IMAGE_PIXELS = None + +logger = get_logger(__name__) + + +@step +def download_dataset_from_hf() -> Dict[str, Any]: + dataset = load_dataset("datadrivenscience/ship-detection") + data = dataset['train'] + output_dir = "/home/apenner/Desktop/ships/dataset" + if not os.path.exists(output_dir): + os.mkdir(output_dir) + + all_images = {} + + for i, d in enumerate(data): + print(d) + img = d['image'] + img_path = f'{output_dir}/image_{i}.png' + + print(f"Writing to {img_path}.") + + img.save(img_path) + + width, height = d['image'].size + + results = [] + for j, bbox in enumerate(d['objects']['bbox']): + x1, y1, x2, y2 = bbox + x = x1 / width + y = y1 / height + w = (x2 - x1) / width + h = (y2 - y1) / height + results.append( + { + "original_width": width, + "original_height": height, + "image_rotation": 0, + "value": { + "x": x * 100, + "y": y * 100, + "width": w * 100, + "height": h * 100, + "rotation": 0, + "rectanglelabels": [ + "ship" + ] + }, + "from_name": "label", + "to_name": "image", + "type": "rectanglelabels", + "origin": "manual" + } + ) + + all_images[img_path] = results + return all_images \ No newline at end of file diff --git a/end-to-end-computer-vision/steps/export_label_studio.py b/end-to-end-computer-vision/steps/export_label_studio.py index 3f229d49..d7a9d594 100644 --- a/end-to-end-computer-vision/steps/export_label_studio.py +++ b/end-to-end-computer-vision/steps/export_label_studio.py @@ -1,3 +1,19 @@ +# Apache Software License 2.0 +# +# Copyright (c) ZenML GmbH 2024. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# from typing import Annotated, List, Tuple from zenml import get_step_context, step diff --git a/end-to-end-computer-vision/steps/upload_to_label_studio.py b/end-to-end-computer-vision/steps/upload_to_label_studio.py new file mode 100644 index 00000000..968c7390 --- /dev/null +++ b/end-to-end-computer-vision/steps/upload_to_label_studio.py @@ -0,0 +1,57 @@ +# Apache Software License 2.0 +# +# Copyright (c) ZenML GmbH 2024. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# +from typing import Dict, Any + +from zenml import step +from zenml.client import Client + + +@step +def upload_labels_to_label_studio( + labels_dict: Dict[str, Any], + ls_project_id: int = 7 +): + """Uploads ground truth labels for images to label studio. + + Args: + labels_dict: Dictionary mapping image filepath to list of bbox labels + ls_project_id: Project id of teh Label Studio project + """ + annotator = Client().active_stack.annotator + from zenml.integrations.label_studio.annotators.label_studio_annotator import ( + LabelStudioAnnotator, + ) + + if not isinstance(annotator, LabelStudioAnnotator): + raise TypeError( + "This step can only be used with the Label Studio annotator." + ) + + lsc = annotator._get_client() + + project = lsc.get_project(ls_project_id) + + tasks = project.get_tasks() + + for task in tasks: + filename = task["storage_filename"] + print(filename) + project.create_annotation( + task["id"], + result=labels_dict[filename], + ground_truth=True + ) \ No newline at end of file From a92e90e038a78dd16933612b3a108cc22feb8b45 Mon Sep 17 00:00:00 2001 From: Alexej Penner Date: Wed, 24 Apr 2024 00:02:33 +0200 Subject: [PATCH 077/121] Small improvements --- end-to-end-computer-vision/configs/cloud_inference.yaml | 2 +- end-to-end-computer-vision/configs/data_export_alex.yaml | 2 +- end-to-end-computer-vision/configs/data_export_alexej.yaml | 4 ++-- end-to-end-computer-vision/configs/fiftyone.yaml | 2 +- end-to-end-computer-vision/configs/training.yaml | 2 +- end-to-end-computer-vision/configs/training_gpu.yaml | 2 +- end-to-end-computer-vision/pipelines/training.py | 4 ++-- end-to-end-computer-vision/run.py | 7 ++++--- end-to-end-computer-vision/steps/train_model.py | 2 +- end-to-end-computer-vision/utils/constants.py | 1 + 10 files changed, 15 insertions(+), 13 deletions(-) diff --git a/end-to-end-computer-vision/configs/cloud_inference.yaml b/end-to-end-computer-vision/configs/cloud_inference.yaml index 5884983a..2abe6a72 100644 --- a/end-to-end-computer-vision/configs/cloud_inference.yaml +++ b/end-to-end-computer-vision/configs/cloud_inference.yaml @@ -15,7 +15,7 @@ settings: # configuration of the Model Control Plane model: - name: Yolo_Object_Detection + name: ShipDetector license: Apache 2.0 description: Object Detection Model. tags: ["object detection"] diff --git a/end-to-end-computer-vision/configs/data_export_alex.yaml b/end-to-end-computer-vision/configs/data_export_alex.yaml index 076e61de..c73b4d80 100644 --- a/end-to-end-computer-vision/configs/data_export_alex.yaml +++ b/end-to-end-computer-vision/configs/data_export_alex.yaml @@ -6,7 +6,7 @@ parameters: # configuration of the Model Control Plane model: - name: Yolo_Object_Detection + name: ShipDetector license: Apache 2.0 description: Object Detection Model. tags: ["object detection"] diff --git a/end-to-end-computer-vision/configs/data_export_alexej.yaml b/end-to-end-computer-vision/configs/data_export_alexej.yaml index bfa7b566..22f2464e 100644 --- a/end-to-end-computer-vision/configs/data_export_alexej.yaml +++ b/end-to-end-computer-vision/configs/data_export_alexej.yaml @@ -2,11 +2,11 @@ enable_cache: False # pipeline configuration parameters: - dataset_name: "polution" + dataset_name: "ship_detection_gcp" # "ship_detection" # Configuration of the Model Control Plane model: - name: Yolo_Object_Detection + name: ShipDetector license: Apache 2.0 description: Object Detection Model. tags: ["object detection"] diff --git a/end-to-end-computer-vision/configs/fiftyone.yaml b/end-to-end-computer-vision/configs/fiftyone.yaml index 033a6f09..e94f5e98 100644 --- a/end-to-end-computer-vision/configs/fiftyone.yaml +++ b/end-to-end-computer-vision/configs/fiftyone.yaml @@ -15,7 +15,7 @@ settings: # configuration of the Model Control Plane # model: -# name: Yolo_Object_Detection +# name: ShipDetector # license: Apache 2.0 # description: Object Detection Model. # tags: ["object detection"] diff --git a/end-to-end-computer-vision/configs/training.yaml b/end-to-end-computer-vision/configs/training.yaml index 40d99c15..4ebe9dda 100644 --- a/end-to-end-computer-vision/configs/training.yaml +++ b/end-to-end-computer-vision/configs/training.yaml @@ -18,7 +18,7 @@ settings: # configuration of the Model Control Plane model: - name: Yolo_Object_Detection + name: ShipDetector license: Apache 2.0 description: Object Detection Model. tags: ["object detection"] diff --git a/end-to-end-computer-vision/configs/training_gpu.yaml b/end-to-end-computer-vision/configs/training_gpu.yaml index 999a1091..3a2b62d6 100644 --- a/end-to-end-computer-vision/configs/training_gpu.yaml +++ b/end-to-end-computer-vision/configs/training_gpu.yaml @@ -36,7 +36,7 @@ steps: # configuration of the Model Control Plane model: - name: Yolo_Object_Detection + name: ShipDetector license: Apache 2.0 description: Object Detection Model. tags: ["object detection"] diff --git a/end-to-end-computer-vision/pipelines/training.py b/end-to-end-computer-vision/pipelines/training.py index f2b58b4e..b7e08cc4 100644 --- a/end-to-end-computer-vision/pipelines/training.py +++ b/end-to-end-computer-vision/pipelines/training.py @@ -19,7 +19,7 @@ from steps.load_model import load_model from steps.promote_model import promote_model -from steps.train_model import training +from steps.train_model import train_model logger = get_logger(__name__) @@ -38,7 +38,7 @@ def training(epochs: int, model_checkpoint: str = "yolov8l.pt"): mv = get_pipeline_context().model dataset = mv.get_artifact("YOLO_dataset") - trained_model, metrics = training( + trained_model, metrics = train_model( model=model, dataset=dataset, epochs=epochs ) diff --git a/end-to-end-computer-vision/run.py b/end-to-end-computer-vision/run.py index b4126779..d024fc64 100644 --- a/end-to-end-computer-vision/run.py +++ b/end-to-end-computer-vision/run.py @@ -25,7 +25,8 @@ from pipelines.data_export import export_for_training from pipelines.fifty_one import export_predictions from pipelines.inference import inference -from pipelines.training import training +from pipelines.training import train_model +from utils.constants import ZENML_MODEL_NAME logger = get_logger(__name__) @@ -99,7 +100,7 @@ def main( # Promote Model to staging latest_model = Model( - name="Yolo_Object_Detection", version=ModelStages.LATEST + name=ZENML_MODEL_NAME, version=ModelStages.LATEST ) latest_model.set_stage(stage=ModelStages.STAGING, force=True) @@ -107,7 +108,7 @@ def main( client.activate_stack(stack_id) # Train model on data - training.with_options(config_path="configs/training_gpu.yaml")() + train_model.with_options(config_path="configs/training_gpu.yaml")() # training.with_options(config_path="configs/training.yaml")() if inference_pipeline: diff --git a/end-to-end-computer-vision/steps/train_model.py b/end-to-end-computer-vision/steps/train_model.py index 3a75281a..4da9ddfe 100644 --- a/end-to-end-computer-vision/steps/train_model.py +++ b/end-to-end-computer-vision/steps/train_model.py @@ -27,7 +27,7 @@ @step(output_materializers={"Trained_YOLO": UltralyticsMaterializer}) -def training( +def train_model( epochs: int, model: YOLO, dataset: LabelStudioYOLODataset, diff --git a/end-to-end-computer-vision/utils/constants.py b/end-to-end-computer-vision/utils/constants.py index 881dd88b..ea7535a1 100644 --- a/end-to-end-computer-vision/utils/constants.py +++ b/end-to-end-computer-vision/utils/constants.py @@ -2,3 +2,4 @@ DATASET_DIR = "data/ships/subset" TRAINED_MODEL_NAME = "Trained_YOLO" PREDICTIONS_DATASET_ARTIFACT_NAME = "predictions_dataset_json" +ZENML_MODEL_NAME = "ShipDetector" From eb63fbea30cb472087c7daa2f12ad50e1cf92763 Mon Sep 17 00:00:00 2001 From: Alex Strick van Linschoten Date: Wed, 24 Apr 2024 00:15:20 +0200 Subject: [PATCH 078/121] add fileio copying for gcp --- .../utils/dataset_utils.py | 22 ++++++++++++++++--- 1 file changed, 19 insertions(+), 3 deletions(-) diff --git a/end-to-end-computer-vision/utils/dataset_utils.py b/end-to-end-computer-vision/utils/dataset_utils.py index 3b0a3731..903cad3c 100644 --- a/end-to-end-computer-vision/utils/dataset_utils.py +++ b/end-to-end-computer-vision/utils/dataset_utils.py @@ -18,6 +18,7 @@ import tempfile from PIL import Image +from zenml.io import fileio from materializers.label_studio_yolo_dataset_materializer import ( LabelStudioYOLODataset, @@ -40,12 +41,27 @@ def load_images_from_folder(folder): def load_and_split_data(dataset: LabelStudioYOLODataset) -> str: - tmpfile_ = tempfile.NamedTemporaryFile(dir="data", delete=False) tmpdirname = os.path.basename(tmpfile_.name) - extract_location = os.path.join(tmpdirname, "data") - unzip_dataset(dataset.filepath, extract_location) + + # Get all filenames inside the labels subfolder + labels_folder = os.path.join(extract_location, "labels") + filenames = [ + os.path.splitext(f)[0] + for f in os.listdir(labels_folder) + if f.endswith(".txt") + ] + + # Download corresponding images from gs://zenml-internal-artifact-store/label_studio_cv_webinar + images_folder = os.path.join(extract_location, "images") + os.makedirs(images_folder, exist_ok=True) + + for filename in filenames: + src_path = f"gs://zenml-internal-artifact-store/label_studio_cv_webinar/{filename}.png" + dst_path = os.path.join(images_folder, f"{filename}.png") + fileio.copy(src_path, dst_path) + split_dataset(extract_location, ratio=(0.7, 0.15, 0.15), seed=42) return generate_yaml(extract_location) From 02fbc309f8bcfba1df022ec9e87206eca97ecb49 Mon Sep 17 00:00:00 2001 From: Alex Strick van Linschoten Date: Wed, 24 Apr 2024 00:15:44 +0200 Subject: [PATCH 079/121] formatting --- .../pipelines/data_ingestion.py | 2 +- .../steps/download_from_hf.py | 25 ++++++++----------- .../steps/upload_to_label_studio.py | 11 +++----- 3 files changed, 15 insertions(+), 23 deletions(-) diff --git a/end-to-end-computer-vision/pipelines/data_ingestion.py b/end-to-end-computer-vision/pipelines/data_ingestion.py index 81d9fcf8..e270179d 100644 --- a/end-to-end-computer-vision/pipelines/data_ingestion.py +++ b/end-to-end-computer-vision/pipelines/data_ingestion.py @@ -26,4 +26,4 @@ @pipeline def data_ingestion(): labels_dict = download_dataset_from_hf() - upload_labels_to_label_studio(labels_dict) \ No newline at end of file + upload_labels_to_label_studio(labels_dict) diff --git a/end-to-end-computer-vision/steps/download_from_hf.py b/end-to-end-computer-vision/steps/download_from_hf.py index 589301ee..5cbd2840 100644 --- a/end-to-end-computer-vision/steps/download_from_hf.py +++ b/end-to-end-computer-vision/steps/download_from_hf.py @@ -14,14 +14,11 @@ # See the License for the specific language governing permissions and # limitations under the License. # -from typing import Dict, Any +import os +from typing import Any, Dict -import fiftyone.zoo as foz from datasets import load_dataset from PIL import Image -import os -import json - from zenml import step from zenml.logger import get_logger @@ -33,7 +30,7 @@ @step def download_dataset_from_hf() -> Dict[str, Any]: dataset = load_dataset("datadrivenscience/ship-detection") - data = dataset['train'] + data = dataset["train"] output_dir = "/home/apenner/Desktop/ships/dataset" if not os.path.exists(output_dir): os.mkdir(output_dir) @@ -42,17 +39,17 @@ def download_dataset_from_hf() -> Dict[str, Any]: for i, d in enumerate(data): print(d) - img = d['image'] - img_path = f'{output_dir}/image_{i}.png' + img = d["image"] + img_path = f"{output_dir}/image_{i}.png" print(f"Writing to {img_path}.") img.save(img_path) - width, height = d['image'].size + width, height = d["image"].size results = [] - for j, bbox in enumerate(d['objects']['bbox']): + for j, bbox in enumerate(d["objects"]["bbox"]): x1, y1, x2, y2 = bbox x = x1 / width y = y1 / height @@ -69,16 +66,14 @@ def download_dataset_from_hf() -> Dict[str, Any]: "width": w * 100, "height": h * 100, "rotation": 0, - "rectanglelabels": [ - "ship" - ] + "rectanglelabels": ["ship"], }, "from_name": "label", "to_name": "image", "type": "rectanglelabels", - "origin": "manual" + "origin": "manual", } ) all_images[img_path] = results - return all_images \ No newline at end of file + return all_images diff --git a/end-to-end-computer-vision/steps/upload_to_label_studio.py b/end-to-end-computer-vision/steps/upload_to_label_studio.py index 968c7390..5ce4a7f0 100644 --- a/end-to-end-computer-vision/steps/upload_to_label_studio.py +++ b/end-to-end-computer-vision/steps/upload_to_label_studio.py @@ -14,7 +14,7 @@ # See the License for the specific language governing permissions and # limitations under the License. # -from typing import Dict, Any +from typing import Any, Dict from zenml import step from zenml.client import Client @@ -22,8 +22,7 @@ @step def upload_labels_to_label_studio( - labels_dict: Dict[str, Any], - ls_project_id: int = 7 + labels_dict: Dict[str, Any], ls_project_id: int = 7 ): """Uploads ground truth labels for images to label studio. @@ -51,7 +50,5 @@ def upload_labels_to_label_studio( filename = task["storage_filename"] print(filename) project.create_annotation( - task["id"], - result=labels_dict[filename], - ground_truth=True - ) \ No newline at end of file + task["id"], result=labels_dict[filename], ground_truth=True + ) From 3fc94e29f2a70a6343acfbb087341cf0a69c01ea Mon Sep 17 00:00:00 2001 From: Alexej Penner Date: Wed, 24 Apr 2024 00:16:50 +0200 Subject: [PATCH 080/121] Work in progress on ingestion --- .../configs/ingest_data.yaml | 7 +++++++ end-to-end-computer-vision/run.py | 16 ++++++++++++++ .../steps/download_from_hf.py | 21 ++++++++++++------- 3 files changed, 37 insertions(+), 7 deletions(-) create mode 100644 end-to-end-computer-vision/configs/ingest_data.yaml diff --git a/end-to-end-computer-vision/configs/ingest_data.yaml b/end-to-end-computer-vision/configs/ingest_data.yaml new file mode 100644 index 00000000..457dfa4d --- /dev/null +++ b/end-to-end-computer-vision/configs/ingest_data.yaml @@ -0,0 +1,7 @@ +enable_cache: False + +steps: + download_dataset_from_hf: + parameters: + dataset: datadrivenscience/ship-detection + gcp_bucket: zenml-internal-artifact-store/label_studio_cv_webinar \ No newline at end of file diff --git a/end-to-end-computer-vision/run.py b/end-to-end-computer-vision/run.py index d024fc64..f407f081 100644 --- a/end-to-end-computer-vision/run.py +++ b/end-to-end-computer-vision/run.py @@ -23,6 +23,7 @@ from zenml.logger import get_logger from pipelines.data_export import export_for_training +from pipelines.data_ingestion import data_ingestion from pipelines.fifty_one import export_predictions from pipelines.inference import inference from pipelines.training import train_model @@ -32,6 +33,15 @@ @click.command() +@click.option( + "--ingest", + "-i", + "ingest_data_pipeline", + is_flag=True, + default=False, + help="Whether to run the data ingestion pipeline, that takes the dataset" + "from huggingface and uploads it into label studio.", +) @click.option( "--feature", "-f", @@ -74,6 +84,7 @@ help="The stack to use for the pipeline.", ) def main( + ingest_data_pipeline: bool = False, feature_pipeline: bool = False, training_pipeline: bool = False, inference_pipeline: bool = False, @@ -90,6 +101,11 @@ def main( client = Client() + if ingest_data_pipeline: + data_ingestion( + config_path="configs/ingest_data.yaml" + )() + if feature_pipeline: client.activate_stack(stack_id) diff --git a/end-to-end-computer-vision/steps/download_from_hf.py b/end-to-end-computer-vision/steps/download_from_hf.py index 589301ee..96551125 100644 --- a/end-to-end-computer-vision/steps/download_from_hf.py +++ b/end-to-end-computer-vision/steps/download_from_hf.py @@ -23,6 +23,7 @@ import json from zenml import step +from zenml.io import fileio from zenml.logger import get_logger Image.MAX_IMAGE_PIXELS = None @@ -31,24 +32,28 @@ @step -def download_dataset_from_hf() -> Dict[str, Any]: - dataset = load_dataset("datadrivenscience/ship-detection") +def download_dataset_from_hf(dataset: str, gcp_bucket: str) -> Dict[str, Any]: + dataset = load_dataset(dataset) data = dataset['train'] - output_dir = "/home/apenner/Desktop/ships/dataset" + + output_dir = "data" if not os.path.exists(output_dir): os.mkdir(output_dir) all_images = {} for i, d in enumerate(data): - print(d) img = d['image'] - img_path = f'{output_dir}/image_{i}.png' - - print(f"Writing to {img_path}.") + img_name = f"image_{i}.png" + img_path = f'{output_dir}/{img_name}' + logger.info(f"Storing image to {img_path}.") img.save(img_path) + bucket_path = os.path.join(gcp_bucket, img_name) + logger.info(f"Copying into gcp bucket {bucket_path}") + fileio.copy(img_path, bucket_path) + width, height = d['image'].size results = [] @@ -81,4 +86,6 @@ def download_dataset_from_hf() -> Dict[str, Any]: ) all_images[img_path] = results + if i > 20: + break return all_images \ No newline at end of file From 15d6b6f3853af1c476f767b612752db146ac3b20 Mon Sep 17 00:00:00 2001 From: Alexej Penner Date: Wed, 24 Apr 2024 00:57:36 +0200 Subject: [PATCH 081/121] Strides towards running with gcp data source --- .../configs/ingest_data.yaml | 8 ++++++-- .../configs/training.yaml | 5 +++++ .../configs/training_gpu.yaml | 2 ++ end-to-end-computer-vision/requirements.txt | 1 + end-to-end-computer-vision/run.py | 20 ++++++++++--------- .../steps/download_from_hf.py | 16 +++++++-------- .../steps/train_model.py | 3 ++- .../steps/upload_to_label_studio.py | 6 +++--- .../utils/dataset_utils.py | 19 +++++++++++++++--- 9 files changed, 54 insertions(+), 26 deletions(-) diff --git a/end-to-end-computer-vision/configs/ingest_data.yaml b/end-to-end-computer-vision/configs/ingest_data.yaml index 5bbd9655..3f3e5351 100644 --- a/end-to-end-computer-vision/configs/ingest_data.yaml +++ b/end-to-end-computer-vision/configs/ingest_data.yaml @@ -1,7 +1,11 @@ -enable_cache: False steps: download_dataset_from_hf: + enable_cache: True parameters: dataset: "datadrivenscience/ship-detection" - gcp_bucket: "zenml-internal-artifact-store/label_studio_cv_webinar" \ No newline at end of file + gcp_bucket: "gs://zenml-internal-artifact-store/label_studio_cv_webinar" + upload_labels_to_label_studio: + enable_cache: False + parameters: + ls_project_id: 8 diff --git a/end-to-end-computer-vision/configs/training.yaml b/end-to-end-computer-vision/configs/training.yaml index 4ebe9dda..a58c1224 100644 --- a/end-to-end-computer-vision/configs/training.yaml +++ b/end-to-end-computer-vision/configs/training.yaml @@ -4,6 +4,11 @@ parameters: model_checkpoint: yolov8l.pt epochs: 1 +steps: + train_model: + parameters: + data_source: "gs://zenml-internal-artifact-store/label_studio_cv_webinar" + settings: docker: apt_packages: diff --git a/end-to-end-computer-vision/configs/training_gpu.yaml b/end-to-end-computer-vision/configs/training_gpu.yaml index 3a2b62d6..11b6dad3 100644 --- a/end-to-end-computer-vision/configs/training_gpu.yaml +++ b/end-to-end-computer-vision/configs/training_gpu.yaml @@ -20,6 +20,8 @@ steps: train_model: step_operator: gcp_a100 enable_step_logs: False + parameters: + data_source: "gs://zenml-internal-artifact-store/label_studio_cv_webinar" settings: step_operator.vertex: accelerator_type: ACCELERATOR_TYPE_UNSPECIFIED # NVIDIA_TESLA_A100 # see https://cloud.google.com/vertex-ai/docs/reference/rest/v1/MachineSpec#AcceleratorType diff --git a/end-to-end-computer-vision/requirements.txt b/end-to-end-computer-vision/requirements.txt index e0bbf7c9..f854487e 100644 --- a/end-to-end-computer-vision/requirements.txt +++ b/end-to-end-computer-vision/requirements.txt @@ -12,3 +12,4 @@ huggingface_hub>=0.20.0 # needed for fiftyone imports of HF datasets fiftyone datasets albumentations +pillow>=10.*.* diff --git a/end-to-end-computer-vision/run.py b/end-to-end-computer-vision/run.py index b277bec0..1bb64464 100644 --- a/end-to-end-computer-vision/run.py +++ b/end-to-end-computer-vision/run.py @@ -26,11 +26,13 @@ from pipelines.data_ingestion import data_ingestion from pipelines.fifty_one import export_predictions from pipelines.inference import inference -from pipelines.training import train_model +from pipelines.training import train_model, training from utils.constants import ZENML_MODEL_NAME logger = get_logger(__name__) +REMOTE_STACK_ID = UUID("20ed5311-ffc6-45d0-b339-6ec35af9501e") + @click.command() @click.option( @@ -43,9 +45,9 @@ "from huggingface and uploads it into label studio.", ) @click.option( - "--feature", - "-f", - "feature_pipeline", + "--export", + "-e", + "export_pipeline", is_flag=True, default=False, help="Whether to run the pipeline that exports the dataset from " @@ -85,7 +87,7 @@ ) def main( ingest_data_pipeline: bool = False, - feature_pipeline: bool = False, + export_pipeline: bool = False, training_pipeline: bool = False, inference_pipeline: bool = False, fiftyone: bool = False, @@ -106,7 +108,7 @@ def main( config_path="configs/ingest_data.yaml" )() - if feature_pipeline: + if export_pipeline: client.activate_stack(stack_id) # Export data from label studio @@ -121,14 +123,14 @@ def main( latest_model.set_stage(stage=ModelStages.STAGING, force=True) if training_pipeline: - client.activate_stack(stack_id) + client.activate_stack(REMOTE_STACK_ID) # Train model on data - train_model.with_options(config_path="configs/training_gpu.yaml")() + training.with_options(config_path="configs/training_gpu.yaml")() # training.with_options(config_path="configs/training.yaml")() if inference_pipeline: - client.activate_stack(stack_id) + client.activate_stack(REMOTE_STACK_ID) inference.with_options(config_path="configs/cloud_inference.yaml")() diff --git a/end-to-end-computer-vision/steps/download_from_hf.py b/end-to-end-computer-vision/steps/download_from_hf.py index 58f22cc3..42c8fffe 100644 --- a/end-to-end-computer-vision/steps/download_from_hf.py +++ b/end-to-end-computer-vision/steps/download_from_hf.py @@ -28,7 +28,7 @@ logger = get_logger(__name__) -@step +@step(enable_cache=True) def download_dataset_from_hf(dataset: str, gcp_bucket: str) -> Dict[str, Any]: dataset = load_dataset(dataset) data = dataset['train'] @@ -44,12 +44,12 @@ def download_dataset_from_hf(dataset: str, gcp_bucket: str) -> Dict[str, Any]: img_name = f"image_{i}.png" img_path = f'{output_dir}/{img_name}' - logger.info(f"Storing image to {img_path}.") - img.save(img_path) - - bucket_path = os.path.join(gcp_bucket, img_name) - logger.info(f"Copying into gcp bucket {bucket_path}") - fileio.copy(img_path, bucket_path) + # logger.info(f"Storing image to {img_path}.") + # img.save(img_path) + # + # bucket_path = os.path.join(gcp_bucket, img_name) + # logger.info(f"Copying into gcp bucket {bucket_path}") + # fileio.copy(img_path, bucket_path, overwrite=True) width, height = d['image'].size @@ -80,7 +80,7 @@ def download_dataset_from_hf(dataset: str, gcp_bucket: str) -> Dict[str, Any]: } ) - all_images[img_path] = results + all_images[img_name] = results if i > 20: break return all_images diff --git a/end-to-end-computer-vision/steps/train_model.py b/end-to-end-computer-vision/steps/train_model.py index 4da9ddfe..710fd7a9 100644 --- a/end-to-end-computer-vision/steps/train_model.py +++ b/end-to-end-computer-vision/steps/train_model.py @@ -31,6 +31,7 @@ def train_model( epochs: int, model: YOLO, dataset: LabelStudioYOLODataset, + data_source: str ) -> Tuple[ Annotated[ YOLO, ArtifactConfig(name="Trained_YOLO", is_model_artifact=True) @@ -47,7 +48,7 @@ def train_model( Returns: Tuple[YOLO, Dict[str, Any]]: Trained model and validation metrics. """ - data_path = load_and_split_data(dataset=dataset) + data_path = load_and_split_data(dataset=dataset, data_source=data_source) model.train(data=data_path, epochs=epochs) # model.train(data="coco8.yaml", epochs=3) # train the model diff --git a/end-to-end-computer-vision/steps/upload_to_label_studio.py b/end-to-end-computer-vision/steps/upload_to_label_studio.py index 5ce4a7f0..c0c84902 100644 --- a/end-to-end-computer-vision/steps/upload_to_label_studio.py +++ b/end-to-end-computer-vision/steps/upload_to_label_studio.py @@ -22,7 +22,7 @@ @step def upload_labels_to_label_studio( - labels_dict: Dict[str, Any], ls_project_id: int = 7 + labels_dict: Dict[str, Any], ls_project_id: int = 8 ): """Uploads ground truth labels for images to label studio. @@ -47,8 +47,8 @@ def upload_labels_to_label_studio( tasks = project.get_tasks() for task in tasks: - filename = task["storage_filename"] - print(filename) + filename = task["storage_filename"].split('/')[-1] + project.create_annotation( task["id"], result=labels_dict[filename], ground_truth=True ) diff --git a/end-to-end-computer-vision/utils/dataset_utils.py b/end-to-end-computer-vision/utils/dataset_utils.py index 903cad3c..746d6859 100644 --- a/end-to-end-computer-vision/utils/dataset_utils.py +++ b/end-to-end-computer-vision/utils/dataset_utils.py @@ -40,7 +40,20 @@ def load_images_from_folder(folder): return images -def load_and_split_data(dataset: LabelStudioYOLODataset) -> str: +def load_and_split_data( + dataset: LabelStudioYOLODataset, + data_source: str +) -> str: + """Load data from dataset into file system and split into train/val. + + First download the .zip file containing the label.txt files from the zenml + Model. Then downloads the corresponding images from the data source. + + Args: + dataset: Dataset object that points to the zip file that contains the + label files. + data_source: Path of the remote data source that contains the images. + """ tmpfile_ = tempfile.NamedTemporaryFile(dir="data", delete=False) tmpdirname = os.path.basename(tmpfile_.name) extract_location = os.path.join(tmpdirname, "data") @@ -54,12 +67,12 @@ def load_and_split_data(dataset: LabelStudioYOLODataset) -> str: if f.endswith(".txt") ] - # Download corresponding images from gs://zenml-internal-artifact-store/label_studio_cv_webinar + # Download corresponding images from gcp bucket images_folder = os.path.join(extract_location, "images") os.makedirs(images_folder, exist_ok=True) for filename in filenames: - src_path = f"gs://zenml-internal-artifact-store/label_studio_cv_webinar/{filename}.png" + src_path = f"{data_source}/{filename}.png" dst_path = os.path.join(images_folder, f"{filename}.png") fileio.copy(src_path, dst_path) From 365074f08b1818b07342d1dba5873928746fa46c Mon Sep 17 00:00:00 2001 From: Alexej Penner Date: Wed, 24 Apr 2024 01:22:48 +0200 Subject: [PATCH 082/121] Some more cleanup --- .../configs/ingest_data.yaml | 2 +- .../configs/training.yaml | 5 +++-- .../pipelines/training.py | 3 ++- end-to-end-computer-vision/run.py | 18 ++++++++++++++++-- .../steps/download_from_hf.py | 14 +++++++------- .../steps/export_label_studio.py | 5 +++-- end-to-end-computer-vision/utils/constants.py | 2 ++ 7 files changed, 34 insertions(+), 15 deletions(-) diff --git a/end-to-end-computer-vision/configs/ingest_data.yaml b/end-to-end-computer-vision/configs/ingest_data.yaml index 3f3e5351..4f294ab5 100644 --- a/end-to-end-computer-vision/configs/ingest_data.yaml +++ b/end-to-end-computer-vision/configs/ingest_data.yaml @@ -4,7 +4,7 @@ steps: enable_cache: True parameters: dataset: "datadrivenscience/ship-detection" - gcp_bucket: "gs://zenml-internal-artifact-store/label_studio_cv_webinar" + data_source: "gs://zenml-internal-artifact-store/label_studio_cv_webinar" upload_labels_to_label_studio: enable_cache: False parameters: diff --git a/end-to-end-computer-vision/configs/training.yaml b/end-to-end-computer-vision/configs/training.yaml index a58c1224..004c6668 100644 --- a/end-to-end-computer-vision/configs/training.yaml +++ b/end-to-end-computer-vision/configs/training.yaml @@ -1,11 +1,12 @@ -enable_cache: False - parameters: model_checkpoint: yolov8l.pt epochs: 1 steps: + load_model: + enable_cache: True train_model: + enable_cache: False parameters: data_source: "gs://zenml-internal-artifact-store/label_studio_cv_webinar" diff --git a/end-to-end-computer-vision/pipelines/training.py b/end-to-end-computer-vision/pipelines/training.py index b7e08cc4..ad1e9193 100644 --- a/end-to-end-computer-vision/pipelines/training.py +++ b/end-to-end-computer-vision/pipelines/training.py @@ -20,6 +20,7 @@ from steps.load_model import load_model from steps.promote_model import promote_model from steps.train_model import train_model +from utils.constants import LABELED_DATASET_NAME logger = get_logger(__name__) @@ -36,7 +37,7 @@ def training(epochs: int, model_checkpoint: str = "yolov8l.pt"): # Load the latest version of the train dataset mv = get_pipeline_context().model - dataset = mv.get_artifact("YOLO_dataset") + dataset = mv.get_artifact(LABELED_DATASET_NAME) trained_model, metrics = train_model( model=model, dataset=dataset, epochs=epochs diff --git a/end-to-end-computer-vision/run.py b/end-to-end-computer-vision/run.py index 1bb64464..dd53a18c 100644 --- a/end-to-end-computer-vision/run.py +++ b/end-to-end-computer-vision/run.py @@ -85,6 +85,14 @@ type=click.Choice(["alexej", "hamza", "alex"]), help="The stack to use for the pipeline.", ) +@click.option( + "--local", + "-l", + "train_local", + is_flag=True, + default=False, + help="Whether to train local.", +) def main( ingest_data_pipeline: bool = False, export_pipeline: bool = False, @@ -92,6 +100,7 @@ def main( inference_pipeline: bool = False, fiftyone: bool = False, stack: UUID = "alexej", + train_local: bool = False, ): # TODO: remove all this :) if stack == "hamza": @@ -122,12 +131,17 @@ def main( ) latest_model.set_stage(stage=ModelStages.STAGING, force=True) - if training_pipeline: + if training_pipeline and train_local: + client.activate_stack(stack_id) + + # Train model on data + training.with_options(config_path="configs/training.yaml")() + + if training_pipeline and not train_local: client.activate_stack(REMOTE_STACK_ID) # Train model on data training.with_options(config_path="configs/training_gpu.yaml")() - # training.with_options(config_path="configs/training.yaml")() if inference_pipeline: client.activate_stack(REMOTE_STACK_ID) diff --git a/end-to-end-computer-vision/steps/download_from_hf.py b/end-to-end-computer-vision/steps/download_from_hf.py index 42c8fffe..2ee4acb6 100644 --- a/end-to-end-computer-vision/steps/download_from_hf.py +++ b/end-to-end-computer-vision/steps/download_from_hf.py @@ -29,7 +29,7 @@ @step(enable_cache=True) -def download_dataset_from_hf(dataset: str, gcp_bucket: str) -> Dict[str, Any]: +def download_dataset_from_hf(dataset: str, data_source: str) -> Dict[str, Any]: dataset = load_dataset(dataset) data = dataset['train'] @@ -44,12 +44,12 @@ def download_dataset_from_hf(dataset: str, gcp_bucket: str) -> Dict[str, Any]: img_name = f"image_{i}.png" img_path = f'{output_dir}/{img_name}' - # logger.info(f"Storing image to {img_path}.") - # img.save(img_path) - # - # bucket_path = os.path.join(gcp_bucket, img_name) - # logger.info(f"Copying into gcp bucket {bucket_path}") - # fileio.copy(img_path, bucket_path, overwrite=True) + logger.info(f"Storing image to {img_path}.") + img.save(img_path) + + bucket_path = os.path.join(data_source, img_name) + logger.info(f"Copying into gcp bucket {bucket_path}") + fileio.copy(img_path, bucket_path, overwrite=True) width, height = d['image'].size diff --git a/end-to-end-computer-vision/steps/export_label_studio.py b/end-to-end-computer-vision/steps/export_label_studio.py index ee2294f7..0dc0b4e2 100644 --- a/end-to-end-computer-vision/steps/export_label_studio.py +++ b/end-to-end-computer-vision/steps/export_label_studio.py @@ -24,17 +24,18 @@ LabelStudioYOLODataset, LabelStudioYOLODatasetMaterializer, ) +from utils.constants import LABELED_DATASET_NAME logger = get_logger(__name__) @step( - output_materializers={"YOLO_dataset": LabelStudioYOLODatasetMaterializer} + output_materializers={LABELED_DATASET_NAME: LabelStudioYOLODatasetMaterializer} ) def load_data_from_label_studio( dataset_name: str, ) -> Tuple[ - Annotated[LabelStudioYOLODataset, "YOLO_dataset"], + Annotated[LabelStudioYOLODataset, LABELED_DATASET_NAME], Annotated[List[int], "new_ids"], ]: """Loads data from Label Studio. diff --git a/end-to-end-computer-vision/utils/constants.py b/end-to-end-computer-vision/utils/constants.py index ea7535a1..722ead2d 100644 --- a/end-to-end-computer-vision/utils/constants.py +++ b/end-to-end-computer-vision/utils/constants.py @@ -1,5 +1,7 @@ DATASET_NAME = "ships" +LABELED_DATASET_NAME = "ship_od_dataset" DATASET_DIR = "data/ships/subset" TRAINED_MODEL_NAME = "Trained_YOLO" PREDICTIONS_DATASET_ARTIFACT_NAME = "predictions_dataset_json" ZENML_MODEL_NAME = "ShipDetector" + From acc52b49d10fe622325a2f4b8d7c417a840b982c Mon Sep 17 00:00:00 2001 From: Alex Strick van Linschoten Date: Wed, 24 Apr 2024 02:35:33 +0200 Subject: [PATCH 083/121] fixes --- end-to-end-computer-vision/pipelines/training.py | 3 +-- end-to-end-computer-vision/run.py | 12 ++++-------- end-to-end-computer-vision/steps/download_from_hf.py | 8 ++++---- .../steps/export_label_studio.py | 4 +++- end-to-end-computer-vision/steps/promote_model.py | 4 ++-- end-to-end-computer-vision/steps/train_model.py | 10 +++++----- .../steps/upload_to_label_studio.py | 2 +- end-to-end-computer-vision/utils/constants.py | 1 - end-to-end-computer-vision/utils/dataset_utils.py | 3 +-- 9 files changed, 21 insertions(+), 26 deletions(-) diff --git a/end-to-end-computer-vision/pipelines/training.py b/end-to-end-computer-vision/pipelines/training.py index ad1e9193..de54e518 100644 --- a/end-to-end-computer-vision/pipelines/training.py +++ b/end-to-end-computer-vision/pipelines/training.py @@ -18,7 +18,6 @@ from zenml.logger import get_logger from steps.load_model import load_model -from steps.promote_model import promote_model from steps.train_model import train_model from utils.constants import LABELED_DATASET_NAME @@ -43,6 +42,6 @@ def training(epochs: int, model_checkpoint: str = "yolov8l.pt"): model=model, dataset=dataset, epochs=epochs ) - promote_model(metrics) + # promote_model(metrics) # predict_image(trained_model) diff --git a/end-to-end-computer-vision/run.py b/end-to-end-computer-vision/run.py index dd53a18c..eddc14e9 100644 --- a/end-to-end-computer-vision/run.py +++ b/end-to-end-computer-vision/run.py @@ -26,7 +26,7 @@ from pipelines.data_ingestion import data_ingestion from pipelines.fifty_one import export_predictions from pipelines.inference import inference -from pipelines.training import train_model, training +from pipelines.training import training from utils.constants import ZENML_MODEL_NAME logger = get_logger(__name__) @@ -42,7 +42,7 @@ is_flag=True, default=False, help="Whether to run the data ingestion pipeline, that takes the dataset" - "from huggingface and uploads it into label studio.", + "from huggingface and uploads it into label studio.", ) @click.option( "--export", @@ -113,9 +113,7 @@ def main( client = Client() if ingest_data_pipeline: - data_ingestion.with_options( - config_path="configs/ingest_data.yaml" - )() + data_ingestion.with_options(config_path="configs/ingest_data.yaml")() if export_pipeline: client.activate_stack(stack_id) @@ -126,9 +124,7 @@ def main( )() # Promote Model to staging - latest_model = Model( - name=ZENML_MODEL_NAME, version=ModelStages.LATEST - ) + latest_model = Model(name=ZENML_MODEL_NAME, version=ModelStages.LATEST) latest_model.set_stage(stage=ModelStages.STAGING, force=True) if training_pipeline and train_local: diff --git a/end-to-end-computer-vision/steps/download_from_hf.py b/end-to-end-computer-vision/steps/download_from_hf.py index 2ee4acb6..a43e7502 100644 --- a/end-to-end-computer-vision/steps/download_from_hf.py +++ b/end-to-end-computer-vision/steps/download_from_hf.py @@ -31,7 +31,7 @@ @step(enable_cache=True) def download_dataset_from_hf(dataset: str, data_source: str) -> Dict[str, Any]: dataset = load_dataset(dataset) - data = dataset['train'] + data = dataset["train"] output_dir = "data" if not os.path.exists(output_dir): @@ -40,9 +40,9 @@ def download_dataset_from_hf(dataset: str, data_source: str) -> Dict[str, Any]: all_images = {} for i, d in enumerate(data): - img = d['image'] + img = d["image"] img_name = f"image_{i}.png" - img_path = f'{output_dir}/{img_name}' + img_path = f"{output_dir}/{img_name}" logger.info(f"Storing image to {img_path}.") img.save(img_path) @@ -51,7 +51,7 @@ def download_dataset_from_hf(dataset: str, data_source: str) -> Dict[str, Any]: logger.info(f"Copying into gcp bucket {bucket_path}") fileio.copy(img_path, bucket_path, overwrite=True) - width, height = d['image'].size + width, height = d["image"].size results = [] for j, bbox in enumerate(d["objects"]["bbox"]): diff --git a/end-to-end-computer-vision/steps/export_label_studio.py b/end-to-end-computer-vision/steps/export_label_studio.py index 0dc0b4e2..12e5cffb 100644 --- a/end-to-end-computer-vision/steps/export_label_studio.py +++ b/end-to-end-computer-vision/steps/export_label_studio.py @@ -30,7 +30,9 @@ @step( - output_materializers={LABELED_DATASET_NAME: LabelStudioYOLODatasetMaterializer} + output_materializers={ + LABELED_DATASET_NAME: LabelStudioYOLODatasetMaterializer + } ) def load_data_from_label_studio( dataset_name: str, diff --git a/end-to-end-computer-vision/steps/promote_model.py b/end-to-end-computer-vision/steps/promote_model.py index 54b12b32..09718a0d 100644 --- a/end-to-end-computer-vision/steps/promote_model.py +++ b/end-to-end-computer-vision/steps/promote_model.py @@ -24,7 +24,7 @@ logger = get_logger(__name__) -@step() +@step def promote_model( metrics: Dict[str, Any], ) -> None: @@ -43,7 +43,7 @@ def promote_model( model_name_or_id=cur_model_version.name, model_version_name_or_number_or_id=ModelStages.PRODUCTION, ) - except KeyError: + except (RuntimeError, KeyError): logger.info( "No `production` model found to compare to, current " "model will be promoted by default." diff --git a/end-to-end-computer-vision/steps/train_model.py b/end-to-end-computer-vision/steps/train_model.py index 710fd7a9..a93487fa 100644 --- a/end-to-end-computer-vision/steps/train_model.py +++ b/end-to-end-computer-vision/steps/train_model.py @@ -26,12 +26,12 @@ from utils.dataset_utils import load_and_split_data -@step(output_materializers={"Trained_YOLO": UltralyticsMaterializer}) +@step( + output_materializers={"Trained_YOLO": UltralyticsMaterializer}, + enable_cache=True, +) def train_model( - epochs: int, - model: YOLO, - dataset: LabelStudioYOLODataset, - data_source: str + epochs: int, model: YOLO, dataset: LabelStudioYOLODataset, data_source: str ) -> Tuple[ Annotated[ YOLO, ArtifactConfig(name="Trained_YOLO", is_model_artifact=True) diff --git a/end-to-end-computer-vision/steps/upload_to_label_studio.py b/end-to-end-computer-vision/steps/upload_to_label_studio.py index c0c84902..7e76d9cd 100644 --- a/end-to-end-computer-vision/steps/upload_to_label_studio.py +++ b/end-to-end-computer-vision/steps/upload_to_label_studio.py @@ -47,7 +47,7 @@ def upload_labels_to_label_studio( tasks = project.get_tasks() for task in tasks: - filename = task["storage_filename"].split('/')[-1] + filename = task["storage_filename"].split("/")[-1] project.create_annotation( task["id"], result=labels_dict[filename], ground_truth=True diff --git a/end-to-end-computer-vision/utils/constants.py b/end-to-end-computer-vision/utils/constants.py index 722ead2d..87aa9b09 100644 --- a/end-to-end-computer-vision/utils/constants.py +++ b/end-to-end-computer-vision/utils/constants.py @@ -4,4 +4,3 @@ TRAINED_MODEL_NAME = "Trained_YOLO" PREDICTIONS_DATASET_ARTIFACT_NAME = "predictions_dataset_json" ZENML_MODEL_NAME = "ShipDetector" - diff --git a/end-to-end-computer-vision/utils/dataset_utils.py b/end-to-end-computer-vision/utils/dataset_utils.py index 746d6859..2c22d9b6 100644 --- a/end-to-end-computer-vision/utils/dataset_utils.py +++ b/end-to-end-computer-vision/utils/dataset_utils.py @@ -41,8 +41,7 @@ def load_images_from_folder(folder): def load_and_split_data( - dataset: LabelStudioYOLODataset, - data_source: str + dataset: LabelStudioYOLODataset, data_source: str ) -> str: """Load data from dataset into file system and split into train/val. From 2bf94c67e6b0320a6cb112acb4cbb984b6149418 Mon Sep 17 00:00:00 2001 From: Alexej Penner Date: Wed, 24 Apr 2024 02:38:10 +0200 Subject: [PATCH 084/121] Smaller changes --- end-to-end-computer-vision/configs/training_gpu.yaml | 7 ++++--- end-to-end-computer-vision/run.py | 2 +- end-to-end-computer-vision/steps/download_from_hf.py | 3 +-- 3 files changed, 6 insertions(+), 6 deletions(-) diff --git a/end-to-end-computer-vision/configs/training_gpu.yaml b/end-to-end-computer-vision/configs/training_gpu.yaml index 11b6dad3..78e72957 100644 --- a/end-to-end-computer-vision/configs/training_gpu.yaml +++ b/end-to-end-computer-vision/configs/training_gpu.yaml @@ -1,5 +1,3 @@ -enable_cache: False - parameters: model_checkpoint: yolov8l.pt epochs: 500 @@ -17,14 +15,17 @@ settings: - ultralytics steps: + load_model: + enable_cache: True train_model: + enable_cache: False step_operator: gcp_a100 enable_step_logs: False parameters: data_source: "gs://zenml-internal-artifact-store/label_studio_cv_webinar" settings: step_operator.vertex: - accelerator_type: ACCELERATOR_TYPE_UNSPECIFIED # NVIDIA_TESLA_A100 # see https://cloud.google.com/vertex-ai/docs/reference/rest/v1/MachineSpec#AcceleratorType + accelerator_type: NVIDIA_TESLA_T4 # NVIDIA_TESLA_A100 # see https://cloud.google.com/vertex-ai/docs/reference/rest/v1/MachineSpec#AcceleratorType accelerator_count: 1 disk_size_gb: 25 docker: diff --git a/end-to-end-computer-vision/run.py b/end-to-end-computer-vision/run.py index dd53a18c..b9db5ddc 100644 --- a/end-to-end-computer-vision/run.py +++ b/end-to-end-computer-vision/run.py @@ -37,7 +37,7 @@ @click.command() @click.option( "--ingest", - "-i", + "-ig", "ingest_data_pipeline", is_flag=True, default=False, diff --git a/end-to-end-computer-vision/steps/download_from_hf.py b/end-to-end-computer-vision/steps/download_from_hf.py index 2ee4acb6..47daf52e 100644 --- a/end-to-end-computer-vision/steps/download_from_hf.py +++ b/end-to-end-computer-vision/steps/download_from_hf.py @@ -81,6 +81,5 @@ def download_dataset_from_hf(dataset: str, data_source: str) -> Dict[str, Any]: ) all_images[img_name] = results - if i > 20: - break + return all_images From 9a589bd00238877b37e4c389f281e2386fdeae1f Mon Sep 17 00:00:00 2001 From: Alexej Penner Date: Wed, 24 Apr 2024 05:15:46 +0200 Subject: [PATCH 085/121] Working training --- .../pipelines/training.py | 5 ++- end-to-end-computer-vision/run.py | 5 +++ .../steps/download_from_hf.py | 3 +- .../steps/train_model.py | 33 +++++++++++++++---- .../steps/upload_to_label_studio.py | 10 ++++-- end-to-end-computer-vision/utils/constants.py | 13 ++++++-- 6 files changed, 54 insertions(+), 15 deletions(-) diff --git a/end-to-end-computer-vision/pipelines/training.py b/end-to-end-computer-vision/pipelines/training.py index de54e518..935e63e0 100644 --- a/end-to-end-computer-vision/pipelines/training.py +++ b/end-to-end-computer-vision/pipelines/training.py @@ -25,11 +25,10 @@ @pipeline -def training(epochs: int, model_checkpoint: str = "yolov8l.pt"): +def training(model_checkpoint: str = "yolov8l.pt"): """Trains a model on a dataset. Args: - epochs: Number of epochs to train the model for. model_checkpoint: Checkpoint of the model to load. """ model = load_model(model_checkpoint) @@ -39,7 +38,7 @@ def training(epochs: int, model_checkpoint: str = "yolov8l.pt"): dataset = mv.get_artifact(LABELED_DATASET_NAME) trained_model, metrics = train_model( - model=model, dataset=dataset, epochs=epochs + model=model, dataset=dataset, ) # promote_model(metrics) diff --git a/end-to-end-computer-vision/run.py b/end-to-end-computer-vision/run.py index a221c976..66745563 100644 --- a/end-to-end-computer-vision/run.py +++ b/end-to-end-computer-vision/run.py @@ -113,6 +113,7 @@ def main( client = Client() if ingest_data_pipeline: + client.activate_stack(stack_id) data_ingestion.with_options(config_path="configs/ingest_data.yaml")() if export_pipeline: @@ -142,6 +143,10 @@ def main( if inference_pipeline: client.activate_stack(REMOTE_STACK_ID) + # Promote Model to production + latest_model = Model(name=ZENML_MODEL_NAME, version=ModelStages.LATEST) + latest_model.set_stage(stage=ModelStages.PRODUCTION, force=True) + inference.with_options(config_path="configs/cloud_inference.yaml")() if fiftyone: diff --git a/end-to-end-computer-vision/steps/download_from_hf.py b/end-to-end-computer-vision/steps/download_from_hf.py index f24baedd..45bf7657 100644 --- a/end-to-end-computer-vision/steps/download_from_hf.py +++ b/end-to-end-computer-vision/steps/download_from_hf.py @@ -28,7 +28,7 @@ logger = get_logger(__name__) -@step(enable_cache=True) +@step def download_dataset_from_hf(dataset: str, data_source: str) -> Dict[str, Any]: dataset = load_dataset(dataset) data = dataset["train"] @@ -42,6 +42,7 @@ def download_dataset_from_hf(dataset: str, data_source: str) -> Dict[str, Any]: for i, d in enumerate(data): img = d["image"] img_name = f"image_{i}.png" + logger.info(f"Handling {img_name}") img_path = f"{output_dir}/{img_name}" logger.info(f"Storing image to {img_path}.") diff --git a/end-to-end-computer-vision/steps/train_model.py b/end-to-end-computer-vision/steps/train_model.py index a93487fa..da351508 100644 --- a/end-to-end-computer-vision/steps/train_model.py +++ b/end-to-end-computer-vision/steps/train_model.py @@ -31,7 +31,13 @@ enable_cache=True, ) def train_model( - epochs: int, model: YOLO, dataset: LabelStudioYOLODataset, data_source: str + epochs: int, + batch_size: int, + imgsz: int, + model: YOLO, + dataset: LabelStudioYOLODataset, + data_source: str, + is_quad_gpu_env: bool = False, ) -> Tuple[ Annotated[ YOLO, ArtifactConfig(name="Trained_YOLO", is_model_artifact=True) @@ -42,16 +48,34 @@ def train_model( Args: epochs: Number of epochs to train the model for. + batch_size: Batch size for training + imgsz: Image size for training model: YOLO model to train. dataset: Dataset to train the model on. + data_source: Source where the data lives + is_quad_gpu_env: Whether we are in an env with 4 gpus Returns: Tuple[YOLO, Dict[str, Any]]: Trained model and validation metrics. """ data_path = load_and_split_data(dataset=dataset, data_source=data_source) - model.train(data=data_path, epochs=epochs) - # model.train(data="coco8.yaml", epochs=3) # train the model + if is_quad_gpu_env: + model.train( + data=data_path, + epochs=epochs, + batch=batch_size, + imgsz=imgsz, + device=[0, 1, 2, 3] + ) + else: + model.train( + data=data_path, + epochs=epochs, + batch=batch_size, + imgsz=imgsz + ) + metrics = model.val() # evaluate model performance on the validation set log_artifact_metadata( @@ -59,7 +83,4 @@ def train_model( metadata={"metrics": metrics.results_dict}, ) - # Read images as PIL images from directory metrics.save_dir for all png and jpg files - # images = load_images_from_folder(metrics.save_dir) - return model, metrics.results_dict diff --git a/end-to-end-computer-vision/steps/upload_to_label_studio.py b/end-to-end-computer-vision/steps/upload_to_label_studio.py index 7e76d9cd..a6525f22 100644 --- a/end-to-end-computer-vision/steps/upload_to_label_studio.py +++ b/end-to-end-computer-vision/steps/upload_to_label_studio.py @@ -22,13 +22,18 @@ @step def upload_labels_to_label_studio( - labels_dict: Dict[str, Any], ls_project_id: int = 8 + labels_dict: Dict[str, Any], + ls_project_id: int = 8, + storage_type: str = 'gcs', + storage_id: int = 1 ): """Uploads ground truth labels for images to label studio. Args: labels_dict: Dictionary mapping image filepath to list of bbox labels - ls_project_id: Project id of teh Label Studio project + ls_project_id: Project id of the Label Studio project + storage_type: Storage Type of the external storage for the ls project + storage_id: Id of the external storage """ annotator = Client().active_stack.annotator from zenml.integrations.label_studio.annotators.label_studio_annotator import ( @@ -43,6 +48,7 @@ def upload_labels_to_label_studio( lsc = annotator._get_client() project = lsc.get_project(ls_project_id) + project.sync_storage(storage_type=storage_type, storage_id=storage_id) tasks = project.get_tasks() diff --git a/end-to-end-computer-vision/utils/constants.py b/end-to-end-computer-vision/utils/constants.py index 87aa9b09..40a1e95f 100644 --- a/end-to-end-computer-vision/utils/constants.py +++ b/end-to-end-computer-vision/utils/constants.py @@ -1,6 +1,13 @@ -DATASET_NAME = "ships" +# Dataset export from labelstudio LABELED_DATASET_NAME = "ship_od_dataset" -DATASET_DIR = "data/ships/subset" + +# Trained Model TRAINED_MODEL_NAME = "Trained_YOLO" -PREDICTIONS_DATASET_ARTIFACT_NAME = "predictions_dataset_json" + +# Name of Model in ZenML Model Control Plane ZENML_MODEL_NAME = "ShipDetector" + +# Constants for inference pipeline +PREDICTIONS_DATASET_ARTIFACT_NAME = "predictions_dataset_json" +DATASET_NAME = "ships" +DATASET_DIR = "data/ships/subset" From 0bc9f49a2223a4669a36ba68aad86cded75b7b45 Mon Sep 17 00:00:00 2001 From: Alexej Penner Date: Wed, 24 Apr 2024 05:54:45 +0200 Subject: [PATCH 086/121] Get a pipeline to run --- end-to-end-computer-vision/configs/training_gpu.yaml | 11 +++++++++-- end-to-end-computer-vision/run.py | 4 ---- .../steps/download_and_tile_from_hf.py | 0 3 files changed, 9 insertions(+), 6 deletions(-) create mode 100644 end-to-end-computer-vision/steps/download_and_tile_from_hf.py diff --git a/end-to-end-computer-vision/configs/training_gpu.yaml b/end-to-end-computer-vision/configs/training_gpu.yaml index 78e72957..c4e6c0e4 100644 --- a/end-to-end-computer-vision/configs/training_gpu.yaml +++ b/end-to-end-computer-vision/configs/training_gpu.yaml @@ -1,6 +1,5 @@ parameters: model_checkpoint: yolov8l.pt - epochs: 500 settings: docker: @@ -23,10 +22,14 @@ steps: enable_step_logs: False parameters: data_source: "gs://zenml-internal-artifact-store/label_studio_cv_webinar" + batch_size: 8 + imgsz: 480 + epochs: 500 + is_quad_gpu_env: True settings: step_operator.vertex: accelerator_type: NVIDIA_TESLA_T4 # NVIDIA_TESLA_A100 # see https://cloud.google.com/vertex-ai/docs/reference/rest/v1/MachineSpec#AcceleratorType - accelerator_count: 1 + accelerator_count: 4 disk_size_gb: 25 docker: parent_image: pytorch/pytorch:2.2.0-cuda11.8-cudnn8-runtime @@ -36,6 +39,10 @@ steps: requirements: - ultralytics - zenml + - numpy + environment: + MKL_SERVICE_FORCE_INTEL: 1 + # configuration of the Model Control Plane model: diff --git a/end-to-end-computer-vision/run.py b/end-to-end-computer-vision/run.py index 66745563..0f940a7a 100644 --- a/end-to-end-computer-vision/run.py +++ b/end-to-end-computer-vision/run.py @@ -143,10 +143,6 @@ def main( if inference_pipeline: client.activate_stack(REMOTE_STACK_ID) - # Promote Model to production - latest_model = Model(name=ZENML_MODEL_NAME, version=ModelStages.LATEST) - latest_model.set_stage(stage=ModelStages.PRODUCTION, force=True) - inference.with_options(config_path="configs/cloud_inference.yaml")() if fiftyone: diff --git a/end-to-end-computer-vision/steps/download_and_tile_from_hf.py b/end-to-end-computer-vision/steps/download_and_tile_from_hf.py new file mode 100644 index 00000000..e69de29b From 175d78d6a0f6225f78859d0e1d281e95b05ef005 Mon Sep 17 00:00:00 2001 From: Alexej Penner Date: Wed, 24 Apr 2024 05:55:40 +0200 Subject: [PATCH 087/121] Putting this to the side as it might introduce to much complexity --- .../steps/download_and_tile_from_hf.py | 77 +++++++++++++ .../steps/download_from_hf.py | 38 ++----- .../utils/dataset_utils.py | 104 ++++++++++++++++++ 3 files changed, 192 insertions(+), 27 deletions(-) diff --git a/end-to-end-computer-vision/steps/download_and_tile_from_hf.py b/end-to-end-computer-vision/steps/download_and_tile_from_hf.py index e69de29b..fbddb81d 100644 --- a/end-to-end-computer-vision/steps/download_and_tile_from_hf.py +++ b/end-to-end-computer-vision/steps/download_and_tile_from_hf.py @@ -0,0 +1,77 @@ +# Apache Software License 2.0 +# +# Copyright (c) ZenML GmbH 2024. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# +import os +from typing import Any, Dict + +from datasets import load_dataset +from PIL import Image +from zenml import step +from zenml.logger import get_logger + +from utils.dataset_utils import split_large_image, convert_bbox_for_ls, \ + export_to_gcp + +Image.MAX_IMAGE_PIXELS = None + +logger = get_logger(__name__) + + +@step +def download_dataset_from_hf(dataset: str, data_source: str) -> Dict[str, Any]: + dataset = load_dataset(dataset) + data = dataset["train"] + + output_dir = "data" + if not os.path.exists(output_dir): + os.mkdir(output_dir) + + all_images = {} + + for i, d in enumerate(data): + img = d['image'] + img_name = f"image_{i}" + + width, height = d['image'].size + if width > 2000 or height > 2000: + split_large_image( + d=d, + img_name=img_name, + output_dir=output_dir, + all_images=all_images + ) + else: + img_path = f'{output_dir}/{img_name}.png' + + export_to_gcp( + data_source=data_source, + img=img, + img_name=img_name, + img_path=img_path + ) + + results = [] + for j, bbox in enumerate(d['objects']['bbox']): + x1, y1, x2, y2 = bbox + results.append( + convert_bbox_for_ls( + x1=x1, x2=x2, y1=y1, y2=y2, width=width,height=height + ) + ) + + all_images[img_path] = results + + return all_images diff --git a/end-to-end-computer-vision/steps/download_from_hf.py b/end-to-end-computer-vision/steps/download_from_hf.py index 45bf7657..edbcd8bb 100644 --- a/end-to-end-computer-vision/steps/download_from_hf.py +++ b/end-to-end-computer-vision/steps/download_from_hf.py @@ -23,6 +23,8 @@ from zenml.io import fileio from zenml.logger import get_logger +from utils.dataset_utils import export_to_gcp, convert_bbox_for_ls + Image.MAX_IMAGE_PIXELS = None logger = get_logger(__name__) @@ -45,40 +47,22 @@ def download_dataset_from_hf(dataset: str, data_source: str) -> Dict[str, Any]: logger.info(f"Handling {img_name}") img_path = f"{output_dir}/{img_name}" - logger.info(f"Storing image to {img_path}.") - img.save(img_path) - - bucket_path = os.path.join(data_source, img_name) - logger.info(f"Copying into gcp bucket {bucket_path}") - fileio.copy(img_path, bucket_path, overwrite=True) + export_to_gcp( + data_source=data_source, + img=img, + img_name=img_name, + img_path=img_path + ) width, height = d["image"].size results = [] for j, bbox in enumerate(d["objects"]["bbox"]): x1, y1, x2, y2 = bbox - x = x1 / width - y = y1 / height - w = (x2 - x1) / width - h = (y2 - y1) / height results.append( - { - "original_width": width, - "original_height": height, - "image_rotation": 0, - "value": { - "x": x * 100, - "y": y * 100, - "width": w * 100, - "height": h * 100, - "rotation": 0, - "rectanglelabels": ["ship"], - }, - "from_name": "label", - "to_name": "image", - "type": "rectanglelabels", - "origin": "manual", - } + convert_bbox_for_ls( + x1=x1, x2=x2, y1=y1, y2=y2, width=width, height=height + ) ) all_images[img_name] = results diff --git a/end-to-end-computer-vision/utils/dataset_utils.py b/end-to-end-computer-vision/utils/dataset_utils.py index 2c22d9b6..af833762 100644 --- a/end-to-end-computer-vision/utils/dataset_utils.py +++ b/end-to-end-computer-vision/utils/dataset_utils.py @@ -16,13 +16,16 @@ # import os import tempfile +from typing import Dict, Any +import numpy as np from PIL import Image from zenml.io import fileio from materializers.label_studio_yolo_dataset_materializer import ( LabelStudioYOLODataset, ) +from steps.download_and_tile_from_hf import logger from utils.split_data import generate_yaml, split_dataset, unzip_dataset @@ -77,3 +80,104 @@ def load_and_split_data( split_dataset(extract_location, ratio=(0.7, 0.15, 0.15), seed=42) return generate_yaml(extract_location) + + +def convert_bbox_for_ls(x1, x2, y1, y2, width, height) -> Dict[str, Any]: + """ This function converts the bounding box coordinates to the label studio format. + + Parameters: + x1, x2, y1, y2 (int): The initial bounding box coordinates in pixels. + width, height (int): The original dimensions of the image. + + Returns: + A dictionary approximating the label studio. + """ + x = x1 / width + y = y1 / height + w = (x2 - x1) / width + h = (y2 - y1) / height + print(f"Converting bbox to results.") + return { + "original_width": width, + "original_height": height, + "image_rotation": 0, + "value": { + "x": x*100, + "y": y*100, + "width": w*100, + "height": h*100, + "rotation": 0, + "rectanglelabels": [ + "ship" + ] + }, + "from_name": "label", + "to_name": "image", + "type": "rectanglelabels", + "origin": "manual" + } + + +def split_large_image( + d: Any, img_name: str, output_dir: str, all_images: Dict[str, Any] +): + """Some images are too large to be useful, this splits them into multiple tiles. + + Args: + d: The hf dataset entry + img_name: Name of the image + output_dir: Where to initially save the image to + all_images: Dictionary containing the image_name<->bboxes mapping + """ + tile_id = 0 + img = d['image'] + width, height = d['image'].size + + img_tiles = [img.crop((x, y, x + width // 2, y + height // 2)) + for x in range(0, width, width // 2) + for y in range(0, height, height // 2)] + + print(f"Split {img_name} into 4 tiles.") + + # Iterate through all 4 tiles, save the image and calculate the new bboxes + for i, img_tile in enumerate(img_tiles): + # Create new dictionary for each tile + new_img_name = img_name + '_' + str(tile_id) + img_path = f'{output_dir}/{new_img_name}.png' + img_tile.save(img_path) # Save image tile + + print(f"{img_path} saved.") + results = [] + for j, bbox in enumerate(d["objects"]["bbox"]): + x1, y1, x2, y2 = np.clip(bbox, [i * (width // 2), i * (height // 2), + (i + 1) * (width // 2), + (i + 1) * (height // 2)], + [0, 0, width // 2, height // 2]) + results.append( + convert_bbox_for_ls( + x1=x1, x2=x2, y1=y1, y2=y2, width=width, height=height + ) + ) + + all_images[img_path] = results + tile_id += 1 + + +def export_to_gcp(data_source: str, img: Image, img_name: str, img_path: str): + """ + Saves a given image locally, then copies it to a Google Cloud Storage bucket. + + Parameters: + data_source: The name of source directory or bucket where image needs to be + stored. + img: The image to be stored. This should be an instance of the PIL Image + class. + img_name: The name to be used when saving the image in the GCP bucket. + img_path: The local path where the image will be temporarily saved before + being copied to the bucket. + """ + logger.info(f"Storing image to {img_path}.") + img.save(img_path) + bucket_path = os.path.join(data_source, img_name) + logger.info(f"Copying into gcp bucket {bucket_path}") + fileio.copy(img_path, bucket_path, overwrite=True) From 124439202f20bf6759aff78e35e1bc397db03778 Mon Sep 17 00:00:00 2001 From: Alexej Penner Date: Wed, 24 Apr 2024 06:35:52 +0200 Subject: [PATCH 088/121] Further small improvements --- end-to-end-computer-vision/configs/cloud_inference.yaml | 1 + end-to-end-computer-vision/configs/training_gpu.yaml | 2 +- end-to-end-computer-vision/run.py | 2 +- 3 files changed, 3 insertions(+), 2 deletions(-) diff --git a/end-to-end-computer-vision/configs/cloud_inference.yaml b/end-to-end-computer-vision/configs/cloud_inference.yaml index 2abe6a72..aca9ca4c 100644 --- a/end-to-end-computer-vision/configs/cloud_inference.yaml +++ b/end-to-end-computer-vision/configs/cloud_inference.yaml @@ -6,6 +6,7 @@ settings: - ffmpeg - libsm6 - libxext6 + - libcurl4 required_integrations: - gcp - github diff --git a/end-to-end-computer-vision/configs/training_gpu.yaml b/end-to-end-computer-vision/configs/training_gpu.yaml index c4e6c0e4..5b5a3e4b 100644 --- a/end-to-end-computer-vision/configs/training_gpu.yaml +++ b/end-to-end-computer-vision/configs/training_gpu.yaml @@ -24,7 +24,7 @@ steps: data_source: "gs://zenml-internal-artifact-store/label_studio_cv_webinar" batch_size: 8 imgsz: 480 - epochs: 500 + epochs: 5000 is_quad_gpu_env: True settings: step_operator.vertex: diff --git a/end-to-end-computer-vision/run.py b/end-to-end-computer-vision/run.py index 0f940a7a..6cef0bc6 100644 --- a/end-to-end-computer-vision/run.py +++ b/end-to-end-computer-vision/run.py @@ -141,7 +141,7 @@ def main( training.with_options(config_path="configs/training_gpu.yaml")() if inference_pipeline: - client.activate_stack(REMOTE_STACK_ID) + client.activate_stack(stack_id)# REMOTE_STACK_ID) inference.with_options(config_path="configs/cloud_inference.yaml")() From 67de170616d3b83744c3e258aff891fef9ca7d29 Mon Sep 17 00:00:00 2001 From: Alexej Penner Date: Wed, 24 Apr 2024 06:56:16 +0200 Subject: [PATCH 089/121] Attempt upload of test dataset --- .../configs/ingest_data.yaml | 5 +++-- .../pipelines/data_ingestion.py | 3 ++- .../steps/download_and_tile_from_hf.py | 7 ++++--- .../steps/download_from_hf.py | 2 +- .../utils/dataset_utils.py | 18 +++++++++++++----- 5 files changed, 23 insertions(+), 12 deletions(-) diff --git a/end-to-end-computer-vision/configs/ingest_data.yaml b/end-to-end-computer-vision/configs/ingest_data.yaml index 4f294ab5..fa4afd65 100644 --- a/end-to-end-computer-vision/configs/ingest_data.yaml +++ b/end-to-end-computer-vision/configs/ingest_data.yaml @@ -1,10 +1,11 @@ steps: - download_dataset_from_hf: + download_and_tile_dataset_from_hf: enable_cache: True + enable_step_logs: False parameters: dataset: "datadrivenscience/ship-detection" - data_source: "gs://zenml-internal-artifact-store/label_studio_cv_webinar" + data_source: "gs://zenml-internal-artifact-store/inference_cv_webinar"# "gs://zenml-internal-artifact-store/label_studio_cv_webinar" upload_labels_to_label_studio: enable_cache: False parameters: diff --git a/end-to-end-computer-vision/pipelines/data_ingestion.py b/end-to-end-computer-vision/pipelines/data_ingestion.py index e270179d..37c1d21d 100644 --- a/end-to-end-computer-vision/pipelines/data_ingestion.py +++ b/end-to-end-computer-vision/pipelines/data_ingestion.py @@ -17,6 +17,7 @@ from zenml import pipeline from zenml.logger import get_logger +from steps.download_and_tile_from_hf import download_and_tile_dataset_from_hf from steps.download_from_hf import download_dataset_from_hf from steps.upload_to_label_studio import upload_labels_to_label_studio @@ -25,5 +26,5 @@ @pipeline def data_ingestion(): - labels_dict = download_dataset_from_hf() + labels_dict = download_and_tile_dataset_from_hf() upload_labels_to_label_studio(labels_dict) diff --git a/end-to-end-computer-vision/steps/download_and_tile_from_hf.py b/end-to-end-computer-vision/steps/download_and_tile_from_hf.py index fbddb81d..6a1b26d0 100644 --- a/end-to-end-computer-vision/steps/download_and_tile_from_hf.py +++ b/end-to-end-computer-vision/steps/download_and_tile_from_hf.py @@ -31,9 +31,9 @@ @step -def download_dataset_from_hf(dataset: str, data_source: str) -> Dict[str, Any]: +def download_and_tile_dataset_from_hf(dataset: str, data_source: str) -> Dict[str, Any]: dataset = load_dataset(dataset) - data = dataset["train"] + data = dataset["test"] output_dir = "data" if not os.path.exists(output_dir): @@ -51,7 +51,8 @@ def download_dataset_from_hf(dataset: str, data_source: str) -> Dict[str, Any]: d=d, img_name=img_name, output_dir=output_dir, - all_images=all_images + all_images=all_images, + data_source=data_source, ) else: img_path = f'{output_dir}/{img_name}.png' diff --git a/end-to-end-computer-vision/steps/download_from_hf.py b/end-to-end-computer-vision/steps/download_from_hf.py index edbcd8bb..f95c5fda 100644 --- a/end-to-end-computer-vision/steps/download_from_hf.py +++ b/end-to-end-computer-vision/steps/download_from_hf.py @@ -45,7 +45,7 @@ def download_dataset_from_hf(dataset: str, data_source: str) -> Dict[str, Any]: img = d["image"] img_name = f"image_{i}.png" logger.info(f"Handling {img_name}") - img_path = f"{output_dir}/{img_name}" + img_path = f"{output_dir}/{img_name}.png" export_to_gcp( data_source=data_source, diff --git a/end-to-end-computer-vision/utils/dataset_utils.py b/end-to-end-computer-vision/utils/dataset_utils.py index af833762..18153740 100644 --- a/end-to-end-computer-vision/utils/dataset_utils.py +++ b/end-to-end-computer-vision/utils/dataset_utils.py @@ -25,8 +25,10 @@ from materializers.label_studio_yolo_dataset_materializer import ( LabelStudioYOLODataset, ) -from steps.download_and_tile_from_hf import logger from utils.split_data import generate_yaml, split_dataset, unzip_dataset +from zenml.logger import get_logger + +logger = get_logger(__name__) def load_images_from_folder(folder): @@ -119,7 +121,7 @@ def convert_bbox_for_ls(x1, x2, y1, y2, width, height) -> Dict[str, Any]: def split_large_image( - d: Any, img_name: str, output_dir: str, all_images: Dict[str, Any] + d: Any, img_name: str, output_dir: str, all_images: Dict[str, Any], data_source: str ): """Some images are too large to be useful, this splits them into multiple tiles. @@ -144,7 +146,13 @@ def split_large_image( # Create new dictionary for each tile new_img_name = img_name + '_' + str(tile_id) img_path = f'{output_dir}/{new_img_name}.png' - img_tile.save(img_path) # Save image tile + + export_to_gcp( + data_source=data_source, + img=img, + img_name=new_img_name, + img_path=img_path + ) print(f"{img_path} saved.") results = [] @@ -172,12 +180,12 @@ def export_to_gcp(data_source: str, img: Image, img_name: str, img_path: str): stored. img: The image to be stored. This should be an instance of the PIL Image class. - img_name: The name to be used when saving the image in the GCP bucket. + img_name: The name to be used when saving the image in the GCP bucket. Should not contain '.png' img_path: The local path where the image will be temporarily saved before being copied to the bucket. """ logger.info(f"Storing image to {img_path}.") img.save(img_path) - bucket_path = os.path.join(data_source, img_name) + bucket_path = os.path.join(data_source, f"{img_name}.png") logger.info(f"Copying into gcp bucket {bucket_path}") fileio.copy(img_path, bucket_path, overwrite=True) From ae6b3a5ff19307dfc1ddf077fda039a19c2263b1 Mon Sep 17 00:00:00 2001 From: Alexej Penner Date: Wed, 24 Apr 2024 07:31:50 +0200 Subject: [PATCH 090/121] Get inference to work --- .../configs/cloud_inference.yaml | 7 ++++ .../pipelines/fifty_one.py | 29 ----------------- end-to-end-computer-vision/run.py | 16 +++++++--- .../steps/fifty_one_labelling.py | 32 ------------------- .../steps/fiftyone_inference.py | 19 +++++++++-- 5 files changed, 35 insertions(+), 68 deletions(-) delete mode 100644 end-to-end-computer-vision/pipelines/fifty_one.py delete mode 100644 end-to-end-computer-vision/steps/fifty_one_labelling.py diff --git a/end-to-end-computer-vision/configs/cloud_inference.yaml b/end-to-end-computer-vision/configs/cloud_inference.yaml index aca9ca4c..650dc901 100644 --- a/end-to-end-computer-vision/configs/cloud_inference.yaml +++ b/end-to-end-computer-vision/configs/cloud_inference.yaml @@ -14,6 +14,13 @@ settings: - ultralytics - fiftyone +steps: + create_fiftyone_dataset: + enable_cache: False + enable_step_logs: False + parameters: + inference_data_source: "gs://zenml-internal-artifact-store/inference_cv_webinar" + # configuration of the Model Control Plane model: name: ShipDetector diff --git a/end-to-end-computer-vision/pipelines/fifty_one.py b/end-to-end-computer-vision/pipelines/fifty_one.py deleted file mode 100644 index 5ccf0093..00000000 --- a/end-to-end-computer-vision/pipelines/fifty_one.py +++ /dev/null @@ -1,29 +0,0 @@ -# Apache Software License 2.0 -# -# Copyright (c) ZenML GmbH 2024. All rights reserved. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - - -from zenml import pipeline -from zenml.logger import get_logger - -from steps.fifty_one_labelling import fifty_one_launcher - -logger = get_logger(__name__) - - -@pipeline -def export_predictions(): - """Launches the FiftyOne app with the predictions dataset.""" - fifty_one_launcher() diff --git a/end-to-end-computer-vision/run.py b/end-to-end-computer-vision/run.py index 6cef0bc6..05ca9e69 100644 --- a/end-to-end-computer-vision/run.py +++ b/end-to-end-computer-vision/run.py @@ -24,10 +24,9 @@ from pipelines.data_export import export_for_training from pipelines.data_ingestion import data_ingestion -from pipelines.fifty_one import export_predictions from pipelines.inference import inference from pipelines.training import training -from utils.constants import ZENML_MODEL_NAME +from utils.constants import ZENML_MODEL_NAME, PREDICTIONS_DATASET_ARTIFACT_NAME logger = get_logger(__name__) @@ -71,7 +70,7 @@ ) @click.option( "--fiftyone", - "-fo", + "-f", "fiftyone", is_flag=True, default=False, @@ -146,9 +145,16 @@ def main( inference.with_options(config_path="configs/cloud_inference.yaml")() if fiftyone: - client.activate_stack(stack_id) + import fiftyone as fo - export_predictions.with_options(config_path="configs/fiftyone.yaml")() + client.activate_stack(stack_id) + artifact = Client().get_artifact_version( + name_id_or_prefix=PREDICTIONS_DATASET_ARTIFACT_NAME + ) + dataset_json = artifact.load() + dataset = fo.Dataset.from_json(dataset_json, persistent=False) + session = fo.launch_app(dataset) + session.wait() if __name__ == "__main__": diff --git a/end-to-end-computer-vision/steps/fifty_one_labelling.py b/end-to-end-computer-vision/steps/fifty_one_labelling.py deleted file mode 100644 index 05ed186b..00000000 --- a/end-to-end-computer-vision/steps/fifty_one_labelling.py +++ /dev/null @@ -1,32 +0,0 @@ -# Apache Software License 2.0 -# -# Copyright (c) ZenML GmbH 2024. All rights reserved. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -import fiftyone as fo -from zenml import step -from zenml.client import Client - -from utils.constants import PREDICTIONS_DATASET_ARTIFACT_NAME - - -@step -def fifty_one_launcher(): - """Launches the FiftyOne app with the predictions dataset.""" - artifact = Client().get_artifact_version( - name_id_or_prefix=PREDICTIONS_DATASET_ARTIFACT_NAME - ) - dataset_json = artifact.load() - dataset = fo.Dataset.from_json(dataset_json, persistent=False) - fo.launch_app(dataset) diff --git a/end-to-end-computer-vision/steps/fiftyone_inference.py b/end-to-end-computer-vision/steps/fiftyone_inference.py index 358ab96f..9a54f289 100644 --- a/end-to-end-computer-vision/steps/fiftyone_inference.py +++ b/end-to-end-computer-vision/steps/fiftyone_inference.py @@ -15,11 +15,13 @@ # limitations under the License. # import os +import tempfile from typing import Annotated import fiftyone as fo from zenml import log_artifact_metadata, step from zenml.client import Client +from zenml.io import fileio from zenml.logger import get_logger from utils.constants import ( @@ -33,9 +35,11 @@ os.environ["YOLO_VERBOSE"] = "False" +INFERENCE_BATCH = 20 + @step -def create_fiftyone_dataset() -> ( +def create_fiftyone_dataset(inference_data_source: str) -> ( Annotated[str, PREDICTIONS_DATASET_ARTIFACT_NAME] ): """Creates a FiftyOne dataset with predictions using a model. @@ -49,8 +53,19 @@ def create_fiftyone_dataset() -> ( ) yolo_model = model_artifact.load() + # create a local dir and copy 20 images into it from gcs + extract_location = "data" + os.makedirs(extract_location, exist_ok=True) + + for file in fileio.listdir(inference_data_source)[:INFERENCE_BATCH]: + fileio.copy( + os.path.join(inference_data_source, file), + os.path.join(extract_location, file), + overwrite=True + ) + dataset = fo.Dataset.from_dir( - dataset_dir=DATASET_DIR, + dataset_dir=extract_location, dataset_type=fo.types.ImageDirectory, name=DATASET_NAME, overwrite=True, From 4e87f8b872ff903e30b35e22f60b1e707fdc1c15 Mon Sep 17 00:00:00 2001 From: Alexej Penner Date: Wed, 24 Apr 2024 07:36:25 +0200 Subject: [PATCH 091/121] Raw upload works --- end-to-end-computer-vision/configs/ingest_data.yaml | 2 +- end-to-end-computer-vision/pipelines/data_ingestion.py | 3 +-- end-to-end-computer-vision/steps/download_from_hf.py | 2 +- 3 files changed, 3 insertions(+), 4 deletions(-) diff --git a/end-to-end-computer-vision/configs/ingest_data.yaml b/end-to-end-computer-vision/configs/ingest_data.yaml index fa4afd65..47315706 100644 --- a/end-to-end-computer-vision/configs/ingest_data.yaml +++ b/end-to-end-computer-vision/configs/ingest_data.yaml @@ -1,6 +1,6 @@ steps: - download_and_tile_dataset_from_hf: + download_dataset_from_hf: enable_cache: True enable_step_logs: False parameters: diff --git a/end-to-end-computer-vision/pipelines/data_ingestion.py b/end-to-end-computer-vision/pipelines/data_ingestion.py index 37c1d21d..e270179d 100644 --- a/end-to-end-computer-vision/pipelines/data_ingestion.py +++ b/end-to-end-computer-vision/pipelines/data_ingestion.py @@ -17,7 +17,6 @@ from zenml import pipeline from zenml.logger import get_logger -from steps.download_and_tile_from_hf import download_and_tile_dataset_from_hf from steps.download_from_hf import download_dataset_from_hf from steps.upload_to_label_studio import upload_labels_to_label_studio @@ -26,5 +25,5 @@ @pipeline def data_ingestion(): - labels_dict = download_and_tile_dataset_from_hf() + labels_dict = download_dataset_from_hf() upload_labels_to_label_studio(labels_dict) diff --git a/end-to-end-computer-vision/steps/download_from_hf.py b/end-to-end-computer-vision/steps/download_from_hf.py index f95c5fda..74f27767 100644 --- a/end-to-end-computer-vision/steps/download_from_hf.py +++ b/end-to-end-computer-vision/steps/download_from_hf.py @@ -43,7 +43,7 @@ def download_dataset_from_hf(dataset: str, data_source: str) -> Dict[str, Any]: for i, d in enumerate(data): img = d["image"] - img_name = f"image_{i}.png" + img_name = f"image_{i}" logger.info(f"Handling {img_name}") img_path = f"{output_dir}/{img_name}.png" From 36a6fbe1341b94a29e1c973a73bd3bdaa05dc5b8 Mon Sep 17 00:00:00 2001 From: Alex Strick van Linschoten Date: Wed, 24 Apr 2024 09:57:34 +0200 Subject: [PATCH 092/121] docstrings --- .../utils/split_data.py | 28 +++++++++++++++++-- 1 file changed, 26 insertions(+), 2 deletions(-) diff --git a/end-to-end-computer-vision/utils/split_data.py b/end-to-end-computer-vision/utils/split_data.py index 8d8163b4..7c1311f8 100644 --- a/end-to-end-computer-vision/utils/split_data.py +++ b/end-to-end-computer-vision/utils/split_data.py @@ -10,6 +10,12 @@ def unzip_dataset(zip_path: str, extract_dir: str): + """Unzip a dataset to a directory. + + Args: + zip_path: Path to the zip file. + extract_dir: Directory to extract the zip file to. + """ with zipfile.ZipFile(zip_path, "r") as zip_ref: zip_ref.extractall(extract_dir) @@ -20,7 +26,16 @@ def split_dataset( lbl_subdir: str = "labels", ratio: tuple = (0.7, 0.2, 0.1), seed: int = None, -): +) -> None: + """Split a dataset into train, test, and validation sets. + + Args: + data_path: Path to the dataset. + img_subdir: Subdirectory containing images. + lbl_subdir: Subdirectory containing labels. + ratio: Tuple of ratios for train, test, and validation sets. + seed: Random seed for reproducibility. + """ # Ensure the ratio is correct assert sum(ratio) == 1.0 @@ -71,7 +86,16 @@ def split_dataset( ) -def generate_yaml(data_path: str, yaml_path: str = None): +def generate_yaml(data_path: str, yaml_path: str = None) -> str: + """Generate a yaml file from a dataset. + + Args: + data_path: Path to the dataset. + yaml_path: Path to save the yaml file. + + Returns: + str: Path to the yaml file. + """ if yaml_path is None: yaml_path = data_path # Read classes from classes.txt From 92d8e628415fe122671f7b2f28aa84cbfad0c889 Mon Sep 17 00:00:00 2001 From: Alex Strick van Linschoten Date: Wed, 24 Apr 2024 09:57:43 +0200 Subject: [PATCH 093/121] formatting --- end-to-end-computer-vision/pipelines/training.py | 3 ++- end-to-end-computer-vision/run.py | 4 ++-- end-to-end-computer-vision/steps/fiftyone_inference.py | 10 ++++------ end-to-end-computer-vision/steps/train_model.py | 7 ++----- .../steps/upload_to_label_studio.py | 4 ++-- 5 files changed, 12 insertions(+), 16 deletions(-) diff --git a/end-to-end-computer-vision/pipelines/training.py b/end-to-end-computer-vision/pipelines/training.py index 935e63e0..3e4edf41 100644 --- a/end-to-end-computer-vision/pipelines/training.py +++ b/end-to-end-computer-vision/pipelines/training.py @@ -38,7 +38,8 @@ def training(model_checkpoint: str = "yolov8l.pt"): dataset = mv.get_artifact(LABELED_DATASET_NAME) trained_model, metrics = train_model( - model=model, dataset=dataset, + model=model, + dataset=dataset, ) # promote_model(metrics) diff --git a/end-to-end-computer-vision/run.py b/end-to-end-computer-vision/run.py index 05ca9e69..c5af3e28 100644 --- a/end-to-end-computer-vision/run.py +++ b/end-to-end-computer-vision/run.py @@ -26,7 +26,7 @@ from pipelines.data_ingestion import data_ingestion from pipelines.inference import inference from pipelines.training import training -from utils.constants import ZENML_MODEL_NAME, PREDICTIONS_DATASET_ARTIFACT_NAME +from utils.constants import PREDICTIONS_DATASET_ARTIFACT_NAME, ZENML_MODEL_NAME logger = get_logger(__name__) @@ -140,7 +140,7 @@ def main( training.with_options(config_path="configs/training_gpu.yaml")() if inference_pipeline: - client.activate_stack(stack_id)# REMOTE_STACK_ID) + client.activate_stack(stack_id) # REMOTE_STACK_ID) inference.with_options(config_path="configs/cloud_inference.yaml")() diff --git a/end-to-end-computer-vision/steps/fiftyone_inference.py b/end-to-end-computer-vision/steps/fiftyone_inference.py index 9a54f289..8c33f7eb 100644 --- a/end-to-end-computer-vision/steps/fiftyone_inference.py +++ b/end-to-end-computer-vision/steps/fiftyone_inference.py @@ -15,7 +15,6 @@ # limitations under the License. # import os -import tempfile from typing import Annotated import fiftyone as fo @@ -25,7 +24,6 @@ from zenml.logger import get_logger from utils.constants import ( - DATASET_DIR, DATASET_NAME, PREDICTIONS_DATASET_ARTIFACT_NAME, TRAINED_MODEL_NAME, @@ -39,9 +37,9 @@ @step -def create_fiftyone_dataset(inference_data_source: str) -> ( - Annotated[str, PREDICTIONS_DATASET_ARTIFACT_NAME] -): +def create_fiftyone_dataset( + inference_data_source: str, +) -> Annotated[str, PREDICTIONS_DATASET_ARTIFACT_NAME]: """Creates a FiftyOne dataset with predictions using a model. Returns: @@ -61,7 +59,7 @@ def create_fiftyone_dataset(inference_data_source: str) -> ( fileio.copy( os.path.join(inference_data_source, file), os.path.join(extract_location, file), - overwrite=True + overwrite=True, ) dataset = fo.Dataset.from_dir( diff --git a/end-to-end-computer-vision/steps/train_model.py b/end-to-end-computer-vision/steps/train_model.py index da351508..f46e0a09 100644 --- a/end-to-end-computer-vision/steps/train_model.py +++ b/end-to-end-computer-vision/steps/train_model.py @@ -66,14 +66,11 @@ def train_model( epochs=epochs, batch=batch_size, imgsz=imgsz, - device=[0, 1, 2, 3] + device=[0, 1, 2, 3], ) else: model.train( - data=data_path, - epochs=epochs, - batch=batch_size, - imgsz=imgsz + data=data_path, epochs=epochs, batch=batch_size, imgsz=imgsz ) metrics = model.val() # evaluate model performance on the validation set diff --git a/end-to-end-computer-vision/steps/upload_to_label_studio.py b/end-to-end-computer-vision/steps/upload_to_label_studio.py index a6525f22..ecb52665 100644 --- a/end-to-end-computer-vision/steps/upload_to_label_studio.py +++ b/end-to-end-computer-vision/steps/upload_to_label_studio.py @@ -24,8 +24,8 @@ def upload_labels_to_label_studio( labels_dict: Dict[str, Any], ls_project_id: int = 8, - storage_type: str = 'gcs', - storage_id: int = 1 + storage_type: str = "gcs", + storage_id: int = 1, ): """Uploads ground truth labels for images to label studio. From 023a8631e56142200880e89b39eb8a63e0957714 Mon Sep 17 00:00:00 2001 From: Alex Strick van Linschoten Date: Wed, 24 Apr 2024 10:19:53 +0200 Subject: [PATCH 094/121] add default values --- end-to-end-computer-vision/steps/train_model.py | 11 +++++++---- 1 file changed, 7 insertions(+), 4 deletions(-) diff --git a/end-to-end-computer-vision/steps/train_model.py b/end-to-end-computer-vision/steps/train_model.py index f46e0a09..cf6f7c1d 100644 --- a/end-to-end-computer-vision/steps/train_model.py +++ b/end-to-end-computer-vision/steps/train_model.py @@ -31,12 +31,12 @@ enable_cache=True, ) def train_model( - epochs: int, - batch_size: int, - imgsz: int, model: YOLO, dataset: LabelStudioYOLODataset, data_source: str, + epochs: int = 100, + batch_size: int = 16, + imgsz: int = 640, is_quad_gpu_env: bool = False, ) -> Tuple[ Annotated[ @@ -70,7 +70,10 @@ def train_model( ) else: model.train( - data=data_path, epochs=epochs, batch=batch_size, imgsz=imgsz + data=data_path, + epochs=epochs, + batch=batch_size, + imgsz=imgsz, ) metrics = model.val() # evaluate model performance on the validation set From 585c09edeb6120e0eb731282beb5323426da003b Mon Sep 17 00:00:00 2001 From: Alex Strick van Linschoten Date: Wed, 24 Apr 2024 11:38:56 +0200 Subject: [PATCH 095/121] split for breakpoint --- end-to-end-computer-vision/utils/dataset_utils.py | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/end-to-end-computer-vision/utils/dataset_utils.py b/end-to-end-computer-vision/utils/dataset_utils.py index 2c22d9b6..1da26e09 100644 --- a/end-to-end-computer-vision/utils/dataset_utils.py +++ b/end-to-end-computer-vision/utils/dataset_utils.py @@ -76,4 +76,5 @@ def load_and_split_data( fileio.copy(src_path, dst_path) split_dataset(extract_location, ratio=(0.7, 0.15, 0.15), seed=42) - return generate_yaml(extract_location) + yaml_path = generate_yaml(extract_location) + return yaml_path From 304b646f890522c95f074eef3faf43c3fba4652b Mon Sep 17 00:00:00 2001 From: Alex Strick van Linschoten Date: Wed, 24 Apr 2024 11:39:11 +0200 Subject: [PATCH 096/121] fix local training yaml --- end-to-end-computer-vision/configs/training.yaml | 6 ++++-- 1 file changed, 4 insertions(+), 2 deletions(-) diff --git a/end-to-end-computer-vision/configs/training.yaml b/end-to-end-computer-vision/configs/training.yaml index 004c6668..104c639b 100644 --- a/end-to-end-computer-vision/configs/training.yaml +++ b/end-to-end-computer-vision/configs/training.yaml @@ -1,6 +1,5 @@ parameters: model_checkpoint: yolov8l.pt - epochs: 1 steps: load_model: @@ -9,6 +8,9 @@ steps: enable_cache: False parameters: data_source: "gs://zenml-internal-artifact-store/label_studio_cv_webinar" + batch_size: 8 + imgsz: 480 + epochs: 30 settings: docker: @@ -28,4 +30,4 @@ model: license: Apache 2.0 description: Object Detection Model. tags: ["object detection"] - version: staging \ No newline at end of file + version: staging From c045bf65429890bd78f12ed653a1decf777011a7 Mon Sep 17 00:00:00 2001 From: Alexej Penner Date: Wed, 24 Apr 2024 14:03:05 +0200 Subject: [PATCH 097/121] Small local changes --- end-to-end-computer-vision/run.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/end-to-end-computer-vision/run.py b/end-to-end-computer-vision/run.py index c5af3e28..0ef953c6 100644 --- a/end-to-end-computer-vision/run.py +++ b/end-to-end-computer-vision/run.py @@ -140,7 +140,7 @@ def main( training.with_options(config_path="configs/training_gpu.yaml")() if inference_pipeline: - client.activate_stack(stack_id) # REMOTE_STACK_ID) + client.activate_stack(stack_id) inference.with_options(config_path="configs/cloud_inference.yaml")() From 94e40cf9ac050f6ad473946eb1e343cf35025611 Mon Sep 17 00:00:00 2001 From: Alex Strick van Linschoten Date: Wed, 24 Apr 2024 15:05:14 +0200 Subject: [PATCH 098/121] add dill --- end-to-end-computer-vision/requirements.txt | 1 + 1 file changed, 1 insertion(+) diff --git a/end-to-end-computer-vision/requirements.txt b/end-to-end-computer-vision/requirements.txt index f854487e..6de734b1 100644 --- a/end-to-end-computer-vision/requirements.txt +++ b/end-to-end-computer-vision/requirements.txt @@ -13,3 +13,4 @@ fiftyone datasets albumentations pillow>=10.*.* +dill From 5be68c02f67e7ad86d275fa170c8f7c767f94aff Mon Sep 17 00:00:00 2001 From: Alex Strick van Linschoten Date: Wed, 24 Apr 2024 15:05:38 +0200 Subject: [PATCH 099/121] update training yaml --- end-to-end-computer-vision/configs/training.yaml | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/end-to-end-computer-vision/configs/training.yaml b/end-to-end-computer-vision/configs/training.yaml index 104c639b..09c65c18 100644 --- a/end-to-end-computer-vision/configs/training.yaml +++ b/end-to-end-computer-vision/configs/training.yaml @@ -8,8 +8,8 @@ steps: enable_cache: False parameters: data_source: "gs://zenml-internal-artifact-store/label_studio_cv_webinar" - batch_size: 8 - imgsz: 480 + batch_size: 30 + imgsz: 1000 epochs: 30 settings: From 6946355c8921c717c5cd1642a6bf1175ee8af1bb Mon Sep 17 00:00:00 2001 From: Alex Strick van Linschoten Date: Wed, 24 Apr 2024 15:05:50 +0200 Subject: [PATCH 100/121] Update YOLO materializer to use ONNX format --- .../materializers/yolo_materializer.py | 31 +++++++++++-------- 1 file changed, 18 insertions(+), 13 deletions(-) diff --git a/end-to-end-computer-vision/materializers/yolo_materializer.py b/end-to-end-computer-vision/materializers/yolo_materializer.py index fb4c47e4..1ed93ab0 100644 --- a/end-to-end-computer-vision/materializers/yolo_materializer.py +++ b/end-to-end-computer-vision/materializers/yolo_materializer.py @@ -14,21 +14,24 @@ """Implementation of the PyTorch DataLoader materializer.""" import os -import tempfile +import uuid from typing import Any, ClassVar, Type from ultralytics import YOLO +from zenml.enums import ArtifactType from zenml.integrations.pytorch.materializers.pytorch_module_materializer import ( PyTorchModuleMaterializer, ) from zenml.io import fileio -DEFAULT_FILENAME = "obj.pt" +DEFAULT_FILENAME = "obj.onnx" +# class UltralyticsMaterializer(BaseMaterializer): class UltralyticsMaterializer(PyTorchModuleMaterializer): """Base class for ultralytics YOLO models.""" + ASSOCIATED_ARTIFACT_TYPE: ClassVar[ArtifactType] = ArtifactType.MODEL FILENAME: ClassVar[str] = DEFAULT_FILENAME SKIP_REGISTRATION: ClassVar[bool] = True ASSOCIATED_TYPES = (YOLO,) @@ -45,14 +48,18 @@ def load(self, data_type: Type[Any]) -> YOLO: filepath = os.path.join(self.uri, DEFAULT_FILENAME) # Create a temporary folder - with tempfile.TemporaryDirectory(prefix="zenml-temp-") as temp_dir: - temp_file = os.path.join(str(temp_dir), DEFAULT_FILENAME) + # make a random string I can use as a prefix for the folder name + random_string = str(uuid.uuid4().hex) + temp_dir = os.path.join("/tmp", random_string) + # make the directory if it doesn't already exist + os.makedirs(temp_dir, exist_ok=True) + temp_file = os.path.join(temp_dir, DEFAULT_FILENAME) - # Copy from artifact store to temporary file - fileio.copy(filepath, temp_file) - model = YOLO(temp_file) + # Copy from artifact store to temporary file + fileio.copy(filepath, temp_file) + model = YOLO(temp_file, task="detect") - return model + return model def save(self, model: YOLO) -> None: """Creates a JSON serialization for a ultralytics YOLO model. @@ -61,9 +68,7 @@ def save(self, model: YOLO) -> None: model: A ultralytics YOLO model. """ filepath = os.path.join(self.uri, DEFAULT_FILENAME) + filename = model.export(format="onnx") - # Make a temporary phantom artifact - with tempfile.NamedTemporaryFile(mode="w", suffix=".json") as f: - model.save(f.name) - # Copy it into artifact store - fileio.copy(f.name, filepath) + # Copy it into artifact store + fileio.copy(filename, filepath) From 3de41eac70a5ae2ad2cbf02fcf65d0ee8383e4be Mon Sep 17 00:00:00 2001 From: Alex Strick van Linschoten Date: Wed, 24 Apr 2024 15:05:58 +0200 Subject: [PATCH 101/121] scratch --- .../notebooks/scratchpad.ipynb | 540 ++++++++++++++++-- 1 file changed, 479 insertions(+), 61 deletions(-) diff --git a/end-to-end-computer-vision/notebooks/scratchpad.ipynb b/end-to-end-computer-vision/notebooks/scratchpad.ipynb index 3d7766dd..55f9edd5 100644 --- a/end-to-end-computer-vision/notebooks/scratchpad.ipynb +++ b/end-to-end-computer-vision/notebooks/scratchpad.ipynb @@ -2,114 +2,532 @@ "cells": [ { "cell_type": "code", - "execution_count": 1, + "execution_count": 21, "metadata": {}, "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\u001b[33mCould not import Azure service connector: No module named 'azure.identity'.\u001b[0m\n", - "\u001b[33mCould not import HyperAI service connector: No module named 'paramiko'.\u001b[0m\n", - " 100% |███████████████████| 15/15 [15.6ms elapsed, 0s remaining, 959.0 samples/s] \n", - "\u001b[1;35m 100% |███████████████████| 15/15 [15.6ms elapsed, 0s remaining, 959.0 samples/s] \u001b[0m\n" - ] - }, { "data": { "text/html": [ - "\n", - " \n", - " " + "
╭─────────────────────────────── Traceback (most recent call last) ────────────────────────────────╮\n",
+       " in <module>:4                                                                                    \n",
+       "                                                                                                  \n",
+       "   1 from zenml.client import Client                                                              \n",
+       "   2                                                                                              \n",
+       "   3 artifact = Client().get_artifact_version('5d3051f8-22e6-4dca-8949-64043315ae91')             \n",
+       " 4 loaded_artifact = artifact.load()                                                            \n",
+       "   5                                                                                              \n",
+       "                                                                                                  \n",
+       " /home/strickvl/.pyenv/versions/3.10.12/envs/ls51/lib/python3.10/site-packages/zenml/models/v2/co \n",
+       " re/artifact_version.py:341 in load                                                               \n",
+       "                                                                                                  \n",
+       "   338 │   │   \"\"\"                                                                                \n",
+       "   339 │   │   from zenml.artifacts.utils import load_artifact_from_response                      \n",
+       "   340 │   │                                                                                      \n",
+       " 341 │   │   return load_artifact_from_response(self)                                           \n",
+       "   342                                                                                        \n",
+       "   343 def download_files(self, path: str, overwrite: bool = False) -> None:                  \n",
+       "   344 │   │   \"\"\"Downloads data for an artifact with no materializing.                           \n",
+       "                                                                                                  \n",
+       " /home/strickvl/.pyenv/versions/3.10.12/envs/ls51/lib/python3.10/site-packages/zenml/artifacts/ut \n",
+       " ils.py:466 in load_artifact_from_response                                                        \n",
+       "                                                                                                  \n",
+       "   463 │   │   artifact=artifact                                                                  \n",
+       "   464 )                                                                                      \n",
+       "   465                                                                                        \n",
+       " 466 return _load_artifact_from_uri(                                                        \n",
+       "   467 │   │   materializer=artifact.materializer,                                                \n",
+       "   468 │   │   data_type=artifact.data_type,                                                      \n",
+       "   469 │   │   uri=artifact.uri,                                                                  \n",
+       "                                                                                                  \n",
+       " /home/strickvl/.pyenv/versions/3.10.12/envs/ls51/lib/python3.10/site-packages/zenml/artifacts/ut \n",
+       " ils.py:638 in _load_artifact_from_uri                                                            \n",
+       "                                                                                                  \n",
+       "   635 materializer_object: BaseMaterializer = materializer_class(                            \n",
+       "   636 │   │   uri, artifact_store                                                                \n",
+       "   637 )                                                                                      \n",
+       " 638 artifact = materializer_object.load(artifact_class)                                    \n",
+       "   639 logger.debug(\"Artifact loaded successfully.\")                                          \n",
+       "   640                                                                                        \n",
+       "   641 return artifact                                                                        \n",
+       "                                                                                                  \n",
+       " /home/strickvl/coding/zenml-projects/end-to-end-computer-vision/materializers/yolo_materializer. \n",
+       " py:53 in load                                                                                    \n",
+       "                                                                                                  \n",
+       "   50 │   │                                                                                       \n",
+       "   51 │   │   # Create a temporary folder                                                         \n",
+       "   52 │   │   with tempfile.TemporaryDirectory(prefix=\"zenml-temp-\") as temp_dir:                 \n",
+       " 53 │   │   │   temp_file = os.path.join(str(temp_dir), DEFAULT_FILENAME)                       \n",
+       "   54 │   │   │                                                                                   \n",
+       "   55 │   │   │   # Copy from artifact store to temporary file                                    \n",
+       "   56 │   │   │   fileio.copy(filepath, temp_file)                                                \n",
+       "                                                                                                  \n",
+       " /home/strickvl/.pyenv/versions/3.10.12/envs/ls51/lib/python3.10/site-packages/ultralytics/models \n",
+       " /yolo/model.py:23 in __init__                                                                    \n",
+       "                                                                                                  \n",
+       "    20 │   │   │   self.__dict__ = new_instance.__dict__                                          \n",
+       "    21 │   │   else:                                                                              \n",
+       "    22 │   │   │   # Continue with default YOLO initialization                                    \n",
+       "  23 │   │   │   super().__init__(model=model, task=task, verbose=verbose)                      \n",
+       "    24                                                                                        \n",
+       "    25 @property                                                                              \n",
+       "    26 def task_map(self):                                                                    \n",
+       "                                                                                                  \n",
+       " /home/strickvl/.pyenv/versions/3.10.12/envs/ls51/lib/python3.10/site-packages/ultralytics/engine \n",
+       " /model.py:151 in __init__                                                                        \n",
+       "                                                                                                  \n",
+       "   148 │   │   if Path(model).suffix in {\".yaml\", \".yml\"}:                                        \n",
+       "   149 │   │   │   self._new(model, task=task, verbose=verbose)                                   \n",
+       "   150 │   │   else:                                                                              \n",
+       " 151 │   │   │   self._load(model, task=task)                                                   \n",
+       "   152                                                                                        \n",
+       "   153 def __call__(                                                                          \n",
+       "   154 │   │   self,                                                                              \n",
+       "                                                                                                  \n",
+       " /home/strickvl/.pyenv/versions/3.10.12/envs/ls51/lib/python3.10/site-packages/ultralytics/engine \n",
+       " /model.py:240 in _load                                                                           \n",
+       "                                                                                                  \n",
+       "   237 │   │   weights = checks.check_model_file_from_stem(weights)  # add suffix, i.e. yolov8n   \n",
+       "   238 │   │                                                                                      \n",
+       "   239 │   │   if Path(weights).suffix == \".pt\":                                                  \n",
+       " 240 │   │   │   self.model, self.ckpt = attempt_load_one_weight(weights)                       \n",
+       "   241 │   │   │   self.task = self.model.args[\"task\"]                                            \n",
+       "   242 │   │   │   self.overrides = self.model.args = self._reset_ckpt_args(self.model.args)      \n",
+       "   243 │   │   │   self.ckpt_path = self.model.pt_path                                            \n",
+       "                                                                                                  \n",
+       " /home/strickvl/.pyenv/versions/3.10.12/envs/ls51/lib/python3.10/site-packages/ultralytics/nn/tas \n",
+       " ks.py:806 in attempt_load_one_weight                                                             \n",
+       "                                                                                                  \n",
+       "    803                                                                                           \n",
+       "    804 def attempt_load_one_weight(weight, device=None, inplace=True, fuse=False):               \n",
+       "    805 \"\"\"Loads a single model weights.\"\"\"                                                   \n",
+       "  806 ckpt, weight = torch_safe_load(weight)  # load ckpt                                   \n",
+       "    807 args = {**DEFAULT_CFG_DICT, **(ckpt.get(\"train_args\", {}))}  # combine model and def  \n",
+       "    808 model = (ckpt.get(\"ema\") or ckpt[\"model\"]).to(device).float()  # FP32 model           \n",
+       "    809                                                                                           \n",
+       "                                                                                                  \n",
+       " /home/strickvl/.pyenv/versions/3.10.12/envs/ls51/lib/python3.10/site-packages/ultralytics/nn/tas \n",
+       " ks.py:732 in torch_safe_load                                                                     \n",
+       "                                                                                                  \n",
+       "    729 │   │   │   │   \"ultralytics.yolo.data\": \"ultralytics.data\",                              \n",
+       "    730 │   │   │   }                                                                             \n",
+       "    731 │   │   ):  # for legacy 8.0 Classify and Pose models                                     \n",
+       "  732 │   │   │   ckpt = torch.load(file, map_location=\"cpu\")                                   \n",
+       "    733                                                                                       \n",
+       "    734 except ModuleNotFoundError as e:  # e.name is missing module name                     \n",
+       "    735 │   │   if e.name == \"models\":                                                            \n",
+       "                                                                                                  \n",
+       " /home/strickvl/.pyenv/versions/3.10.12/envs/ls51/lib/python3.10/site-packages/torch/serializatio \n",
+       " n.py:1040 in load                                                                                \n",
+       "                                                                                                  \n",
+       "   1037 │   │   │   │   return _legacy_load(opened_file, map_location, _weights_only_unpickler,   \n",
+       "   1038 │   │   │   except RuntimeError as e:                                                     \n",
+       "   1039 │   │   │   │   raise pickle.UnpicklingError(UNSAFE_MESSAGE + str(e)) from None           \n",
+       " 1040 │   │   return _legacy_load(opened_file, map_location, pickle_module, **pickle_load_args  \n",
+       "   1041                                                                                           \n",
+       "   1042                                                                                           \n",
+       "   1043 # Register pickling support for layout instances such as                                  \n",
+       "                                                                                                  \n",
+       " /home/strickvl/.pyenv/versions/3.10.12/envs/ls51/lib/python3.10/site-packages/torch/serializatio \n",
+       " n.py:1258 in _legacy_load                                                                        \n",
+       "                                                                                                  \n",
+       "   1255 │   │   │   f\"Received object of type \\\"{type(f)}\\\". Please update to Python 3.8.2 or ne  \n",
+       "   1256 │   │   │   \"functionality.\")                                                             \n",
+       "   1257                                                                                       \n",
+       " 1258 magic_number = pickle_module.load(f, **pickle_load_args)                              \n",
+       "   1259 if magic_number != MAGIC_NUMBER:                                                      \n",
+       "   1260 │   │   raise RuntimeError(\"Invalid magic number; corrupt file?\")                         \n",
+       "   1261 protocol_version = pickle_module.load(f, **pickle_load_args)                          \n",
+       "╰──────────────────────────────────────────────────────────────────────────────────────────────────╯\n",
+       "UnpicklingError: invalid load key, '\\x08'.\n",
+       "
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\u001b[0muri=artifact.uri, \u001b[31m│\u001b[0m\n", + "\u001b[31m│\u001b[0m \u001b[31m│\u001b[0m\n", + "\u001b[31m│\u001b[0m \u001b[2;33m/home/strickvl/.pyenv/versions/3.10.12/envs/ls51/lib/python3.10/site-packages/zenml/artifacts/\u001b[0m\u001b[1;33mut\u001b[0m \u001b[31m│\u001b[0m\n", + "\u001b[31m│\u001b[0m \u001b[1;33mils.py\u001b[0m:\u001b[94m638\u001b[0m in \u001b[92m_load_artifact_from_uri\u001b[0m \u001b[31m│\u001b[0m\n", + "\u001b[31m│\u001b[0m \u001b[31m│\u001b[0m\n", + "\u001b[31m│\u001b[0m \u001b[2m635 \u001b[0m\u001b[2m│ \u001b[0mmaterializer_object: BaseMaterializer = materializer_class( \u001b[31m│\u001b[0m\n", + "\u001b[31m│\u001b[0m \u001b[2m636 \u001b[0m\u001b[2m│ │ \u001b[0muri, artifact_store \u001b[31m│\u001b[0m\n", + "\u001b[31m│\u001b[0m \u001b[2m637 \u001b[0m\u001b[2m│ \u001b[0m) \u001b[31m│\u001b[0m\n", + "\u001b[31m│\u001b[0m \u001b[31m❱ \u001b[0m638 \u001b[2m│ \u001b[0martifact = materializer_object.load(artifact_class) \u001b[31m│\u001b[0m\n", + "\u001b[31m│\u001b[0m \u001b[2m639 \u001b[0m\u001b[2m│ \u001b[0mlogger.debug(\u001b[33m\"\u001b[0m\u001b[33mArtifact loaded successfully.\u001b[0m\u001b[33m\"\u001b[0m) \u001b[31m│\u001b[0m\n", + "\u001b[31m│\u001b[0m \u001b[2m640 \u001b[0m\u001b[2m│ \u001b[0m \u001b[31m│\u001b[0m\n", + "\u001b[31m│\u001b[0m \u001b[2m641 \u001b[0m\u001b[2m│ \u001b[0m\u001b[94mreturn\u001b[0m artifact \u001b[31m│\u001b[0m\n", + "\u001b[31m│\u001b[0m \u001b[31m│\u001b[0m\n", + "\u001b[31m│\u001b[0m \u001b[2;33m/home/strickvl/coding/zenml-projects/end-to-end-computer-vision/materializers/\u001b[0m\u001b[1;33myolo_materializer.\u001b[0m \u001b[31m│\u001b[0m\n", + "\u001b[31m│\u001b[0m \u001b[1;33mpy\u001b[0m:\u001b[94m53\u001b[0m in \u001b[92mload\u001b[0m \u001b[31m│\u001b[0m\n", + "\u001b[31m│\u001b[0m \u001b[31m│\u001b[0m\n", + "\u001b[31m│\u001b[0m \u001b[2m50 \u001b[0m\u001b[2m│ │ \u001b[0m \u001b[31m│\u001b[0m\n", + "\u001b[31m│\u001b[0m \u001b[2m51 \u001b[0m\u001b[2m│ │ \u001b[0m\u001b[2m# Create a temporary folder\u001b[0m \u001b[31m│\u001b[0m\n", + "\u001b[31m│\u001b[0m \u001b[2m52 \u001b[0m\u001b[2m│ │ \u001b[0m\u001b[94mwith\u001b[0m tempfile.TemporaryDirectory(prefix=\u001b[33m\"\u001b[0m\u001b[33mzenml-temp-\u001b[0m\u001b[33m\"\u001b[0m) \u001b[94mas\u001b[0m temp_dir: \u001b[31m│\u001b[0m\n", + "\u001b[31m│\u001b[0m \u001b[31m❱ \u001b[0m53 \u001b[2m│ │ │ \u001b[0mtemp_file = os.path.join(\u001b[96mstr\u001b[0m(temp_dir), DEFAULT_FILENAME) \u001b[31m│\u001b[0m\n", + "\u001b[31m│\u001b[0m \u001b[2m54 \u001b[0m\u001b[2m│ │ │ \u001b[0m \u001b[31m│\u001b[0m\n", + "\u001b[31m│\u001b[0m \u001b[2m55 \u001b[0m\u001b[2m│ │ │ \u001b[0m\u001b[2m# Copy from artifact store to temporary file\u001b[0m \u001b[31m│\u001b[0m\n", + "\u001b[31m│\u001b[0m \u001b[2m56 \u001b[0m\u001b[2m│ │ │ \u001b[0mfileio.copy(filepath, temp_file) \u001b[31m│\u001b[0m\n", + "\u001b[31m│\u001b[0m \u001b[31m│\u001b[0m\n", + "\u001b[31m│\u001b[0m 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{\u001b[33m\"\u001b[0m\u001b[33m.yaml\u001b[0m\u001b[33m\"\u001b[0m, \u001b[33m\"\u001b[0m\u001b[33m.yml\u001b[0m\u001b[33m\"\u001b[0m}: \u001b[31m│\u001b[0m\n", + "\u001b[31m│\u001b[0m \u001b[2m149 \u001b[0m\u001b[2m│ │ │ \u001b[0m\u001b[96mself\u001b[0m._new(model, task=task, verbose=verbose) \u001b[31m│\u001b[0m\n", + "\u001b[31m│\u001b[0m \u001b[2m150 \u001b[0m\u001b[2m│ │ \u001b[0m\u001b[94melse\u001b[0m: \u001b[31m│\u001b[0m\n", + "\u001b[31m│\u001b[0m \u001b[31m❱ \u001b[0m151 \u001b[2m│ │ │ \u001b[0m\u001b[96mself\u001b[0m._load(model, task=task) \u001b[31m│\u001b[0m\n", + "\u001b[31m│\u001b[0m \u001b[2m152 \u001b[0m\u001b[2m│ \u001b[0m \u001b[31m│\u001b[0m\n", + "\u001b[31m│\u001b[0m \u001b[2m153 \u001b[0m\u001b[2m│ \u001b[0m\u001b[94mdef\u001b[0m \u001b[92m__call__\u001b[0m( \u001b[31m│\u001b[0m\n", + "\u001b[31m│\u001b[0m \u001b[2m154 \u001b[0m\u001b[2m│ │ \u001b[0m\u001b[96mself\u001b[0m, \u001b[31m│\u001b[0m\n", + "\u001b[31m│\u001b[0m \u001b[31m│\u001b[0m\n", + "\u001b[31m│\u001b[0m \u001b[2;33m/home/strickvl/.pyenv/versions/3.10.12/envs/ls51/lib/python3.10/site-packages/ultralytics/engine\u001b[0m \u001b[31m│\u001b[0m\n", + "\u001b[31m│\u001b[0m \u001b[2;33m/\u001b[0m\u001b[1;33mmodel.py\u001b[0m:\u001b[94m240\u001b[0m in \u001b[92m_load\u001b[0m \u001b[31m│\u001b[0m\n", + "\u001b[31m│\u001b[0m \u001b[31m│\u001b[0m\n", + "\u001b[31m│\u001b[0m \u001b[2m237 \u001b[0m\u001b[2m│ │ \u001b[0mweights = checks.check_model_file_from_stem(weights) \u001b[2m# add suffix, i.e. yolov8n\u001b[0m \u001b[31m│\u001b[0m\n", + "\u001b[31m│\u001b[0m \u001b[2m238 \u001b[0m\u001b[2m│ │ \u001b[0m \u001b[31m│\u001b[0m\n", + "\u001b[31m│\u001b[0m \u001b[2m239 \u001b[0m\u001b[2m│ │ \u001b[0m\u001b[94mif\u001b[0m Path(weights).suffix == \u001b[33m\"\u001b[0m\u001b[33m.pt\u001b[0m\u001b[33m\"\u001b[0m: \u001b[31m│\u001b[0m\n", + "\u001b[31m│\u001b[0m \u001b[31m❱ \u001b[0m240 \u001b[2m│ │ │ \u001b[0m\u001b[96mself\u001b[0m.model, \u001b[96mself\u001b[0m.ckpt = attempt_load_one_weight(weights) \u001b[31m│\u001b[0m\n", + "\u001b[31m│\u001b[0m \u001b[2m241 \u001b[0m\u001b[2m│ │ │ \u001b[0m\u001b[96mself\u001b[0m.task = \u001b[96mself\u001b[0m.model.args[\u001b[33m\"\u001b[0m\u001b[33mtask\u001b[0m\u001b[33m\"\u001b[0m] \u001b[31m│\u001b[0m\n", + "\u001b[31m│\u001b[0m \u001b[2m242 \u001b[0m\u001b[2m│ │ │ \u001b[0m\u001b[96mself\u001b[0m.overrides = \u001b[96mself\u001b[0m.model.args = \u001b[96mself\u001b[0m._reset_ckpt_args(\u001b[96mself\u001b[0m.model.args) \u001b[31m│\u001b[0m\n", + "\u001b[31m│\u001b[0m \u001b[2m243 \u001b[0m\u001b[2m│ │ │ \u001b[0m\u001b[96mself\u001b[0m.ckpt_path = \u001b[96mself\u001b[0m.model.pt_path \u001b[31m│\u001b[0m\n", + "\u001b[31m│\u001b[0m \u001b[31m│\u001b[0m\n", + "\u001b[31m│\u001b[0m \u001b[2;33m/home/strickvl/.pyenv/versions/3.10.12/envs/ls51/lib/python3.10/site-packages/ultralytics/nn/\u001b[0m\u001b[1;33mtas\u001b[0m \u001b[31m│\u001b[0m\n", + "\u001b[31m│\u001b[0m \u001b[1;33mks.py\u001b[0m:\u001b[94m806\u001b[0m in \u001b[92mattempt_load_one_weight\u001b[0m \u001b[31m│\u001b[0m\n", + "\u001b[31m│\u001b[0m \u001b[31m│\u001b[0m\n", + "\u001b[31m│\u001b[0m \u001b[2m 803 \u001b[0m \u001b[31m│\u001b[0m\n", + "\u001b[31m│\u001b[0m \u001b[2m 804 \u001b[0m\u001b[94mdef\u001b[0m \u001b[92mattempt_load_one_weight\u001b[0m(weight, device=\u001b[94mNone\u001b[0m, inplace=\u001b[94mTrue\u001b[0m, fuse=\u001b[94mFalse\u001b[0m): \u001b[31m│\u001b[0m\n", + "\u001b[31m│\u001b[0m \u001b[2m 805 \u001b[0m\u001b[2;90m│ \u001b[0m\u001b[33m\"\"\"Loads a single model weights.\"\"\"\u001b[0m \u001b[31m│\u001b[0m\n", + "\u001b[31m│\u001b[0m \u001b[31m❱ \u001b[0m 806 \u001b[2m│ \u001b[0mckpt, weight = torch_safe_load(weight) \u001b[2m# load ckpt\u001b[0m \u001b[31m│\u001b[0m\n", + "\u001b[31m│\u001b[0m \u001b[2m 807 \u001b[0m\u001b[2m│ \u001b[0margs = {**DEFAULT_CFG_DICT, **(ckpt.get(\u001b[33m\"\u001b[0m\u001b[33mtrain_args\u001b[0m\u001b[33m\"\u001b[0m, {}))} \u001b[2m# combine model and def\u001b[0m \u001b[31m│\u001b[0m\n", + "\u001b[31m│\u001b[0m \u001b[2m 808 \u001b[0m\u001b[2m│ \u001b[0mmodel = (ckpt.get(\u001b[33m\"\u001b[0m\u001b[33mema\u001b[0m\u001b[33m\"\u001b[0m) \u001b[95mor\u001b[0m ckpt[\u001b[33m\"\u001b[0m\u001b[33mmodel\u001b[0m\u001b[33m\"\u001b[0m]).to(device).float() \u001b[2m# FP32 model\u001b[0m \u001b[31m│\u001b[0m\n", + "\u001b[31m│\u001b[0m \u001b[2m 809 \u001b[0m \u001b[31m│\u001b[0m\n", + "\u001b[31m│\u001b[0m \u001b[31m│\u001b[0m\n", + "\u001b[31m│\u001b[0m \u001b[2;33m/home/strickvl/.pyenv/versions/3.10.12/envs/ls51/lib/python3.10/site-packages/ultralytics/nn/\u001b[0m\u001b[1;33mtas\u001b[0m \u001b[31m│\u001b[0m\n", + "\u001b[31m│\u001b[0m \u001b[1;33mks.py\u001b[0m:\u001b[94m732\u001b[0m in \u001b[92mtorch_safe_load\u001b[0m \u001b[31m│\u001b[0m\n", + "\u001b[31m│\u001b[0m \u001b[31m│\u001b[0m\n", + "\u001b[31m│\u001b[0m \u001b[2m 729 \u001b[0m\u001b[2m│ │ │ │ \u001b[0m\u001b[33m\"\u001b[0m\u001b[33multralytics.yolo.data\u001b[0m\u001b[33m\"\u001b[0m: \u001b[33m\"\u001b[0m\u001b[33multralytics.data\u001b[0m\u001b[33m\"\u001b[0m, \u001b[31m│\u001b[0m\n", + "\u001b[31m│\u001b[0m \u001b[2m 730 \u001b[0m\u001b[2m│ │ │ \u001b[0m} \u001b[31m│\u001b[0m\n", + "\u001b[31m│\u001b[0m \u001b[2m 731 \u001b[0m\u001b[2m│ │ \u001b[0m): \u001b[2m# for legacy 8.0 Classify and Pose models\u001b[0m \u001b[31m│\u001b[0m\n", + "\u001b[31m│\u001b[0m \u001b[31m❱ \u001b[0m 732 \u001b[2m│ │ │ \u001b[0mckpt = torch.load(file, map_location=\u001b[33m\"\u001b[0m\u001b[33mcpu\u001b[0m\u001b[33m\"\u001b[0m) \u001b[31m│\u001b[0m\n", + "\u001b[31m│\u001b[0m \u001b[2m 733 \u001b[0m\u001b[2m│ \u001b[0m \u001b[31m│\u001b[0m\n", + "\u001b[31m│\u001b[0m \u001b[2m 734 \u001b[0m\u001b[2m│ \u001b[0m\u001b[94mexcept\u001b[0m \u001b[96mModuleNotFoundError\u001b[0m \u001b[94mas\u001b[0m e: \u001b[2m# e.name is missing module name\u001b[0m \u001b[31m│\u001b[0m\n", + "\u001b[31m│\u001b[0m \u001b[2m 735 \u001b[0m\u001b[2m│ │ \u001b[0m\u001b[94mif\u001b[0m e.name == \u001b[33m\"\u001b[0m\u001b[33mmodels\u001b[0m\u001b[33m\"\u001b[0m: \u001b[31m│\u001b[0m\n", + "\u001b[31m│\u001b[0m \u001b[31m│\u001b[0m\n", + "\u001b[31m│\u001b[0m \u001b[2;33m/home/strickvl/.pyenv/versions/3.10.12/envs/ls51/lib/python3.10/site-packages/torch/\u001b[0m\u001b[1;33mserializatio\u001b[0m \u001b[31m│\u001b[0m\n", + "\u001b[31m│\u001b[0m \u001b[1;33mn.py\u001b[0m:\u001b[94m1040\u001b[0m in \u001b[92mload\u001b[0m \u001b[31m│\u001b[0m\n", + "\u001b[31m│\u001b[0m \u001b[31m│\u001b[0m\n", + "\u001b[31m│\u001b[0m \u001b[2m1037 \u001b[0m\u001b[2m│ │ │ │ \u001b[0m\u001b[94mreturn\u001b[0m _legacy_load(opened_file, map_location, _weights_only_unpickler, \u001b[31m│\u001b[0m\n", + "\u001b[31m│\u001b[0m \u001b[2m1038 \u001b[0m\u001b[2m│ │ │ \u001b[0m\u001b[94mexcept\u001b[0m \u001b[96mRuntimeError\u001b[0m \u001b[94mas\u001b[0m e: \u001b[31m│\u001b[0m\n", + "\u001b[31m│\u001b[0m \u001b[2m1039 \u001b[0m\u001b[2m│ │ │ │ \u001b[0m\u001b[94mraise\u001b[0m pickle.UnpicklingError(UNSAFE_MESSAGE + \u001b[96mstr\u001b[0m(e)) \u001b[94mfrom\u001b[0m \u001b[94mNone\u001b[0m \u001b[31m│\u001b[0m\n", + "\u001b[31m│\u001b[0m \u001b[31m❱ \u001b[0m1040 \u001b[2m│ │ \u001b[0m\u001b[94mreturn\u001b[0m _legacy_load(opened_file, map_location, pickle_module, **pickle_load_args \u001b[31m│\u001b[0m\n", + "\u001b[31m│\u001b[0m \u001b[2m1041 \u001b[0m \u001b[31m│\u001b[0m\n", + "\u001b[31m│\u001b[0m \u001b[2m1042 \u001b[0m \u001b[31m│\u001b[0m\n", + "\u001b[31m│\u001b[0m \u001b[2m1043 \u001b[0m\u001b[2m# Register pickling support for layout instances such as\u001b[0m \u001b[31m│\u001b[0m\n", + "\u001b[31m│\u001b[0m \u001b[31m│\u001b[0m\n", + "\u001b[31m│\u001b[0m \u001b[2;33m/home/strickvl/.pyenv/versions/3.10.12/envs/ls51/lib/python3.10/site-packages/torch/\u001b[0m\u001b[1;33mserializatio\u001b[0m \u001b[31m│\u001b[0m\n", + "\u001b[31m│\u001b[0m \u001b[1;33mn.py\u001b[0m:\u001b[94m1258\u001b[0m in \u001b[92m_legacy_load\u001b[0m \u001b[31m│\u001b[0m\n", + "\u001b[31m│\u001b[0m \u001b[31m│\u001b[0m\n", + "\u001b[31m│\u001b[0m \u001b[2m1255 \u001b[0m\u001b[2m│ │ │ \u001b[0m\u001b[33mf\u001b[0m\u001b[33m\"\u001b[0m\u001b[33mReceived object of type \u001b[0m\u001b[33m\\\"\u001b[0m\u001b[33m{\u001b[0m\u001b[96mtype\u001b[0m(f)\u001b[33m}\u001b[0m\u001b[33m\\\"\u001b[0m\u001b[33m. Please update to Python 3.8.2 or ne\u001b[0m \u001b[31m│\u001b[0m\n", + "\u001b[31m│\u001b[0m \u001b[2m1256 \u001b[0m\u001b[2m│ │ │ \u001b[0m\u001b[33m\"\u001b[0m\u001b[33mfunctionality.\u001b[0m\u001b[33m\"\u001b[0m) \u001b[31m│\u001b[0m\n", + "\u001b[31m│\u001b[0m \u001b[2m1257 \u001b[0m\u001b[2m│ \u001b[0m \u001b[31m│\u001b[0m\n", + "\u001b[31m│\u001b[0m \u001b[31m❱ \u001b[0m1258 \u001b[2m│ \u001b[0mmagic_number = pickle_module.load(f, **pickle_load_args) \u001b[31m│\u001b[0m\n", + "\u001b[31m│\u001b[0m \u001b[2m1259 \u001b[0m\u001b[2m│ \u001b[0m\u001b[94mif\u001b[0m magic_number != MAGIC_NUMBER: \u001b[31m│\u001b[0m\n", + "\u001b[31m│\u001b[0m \u001b[2m1260 \u001b[0m\u001b[2m│ │ \u001b[0m\u001b[94mraise\u001b[0m \u001b[96mRuntimeError\u001b[0m(\u001b[33m\"\u001b[0m\u001b[33mInvalid magic number; corrupt file?\u001b[0m\u001b[33m\"\u001b[0m) \u001b[31m│\u001b[0m\n", + "\u001b[31m│\u001b[0m \u001b[2m1261 \u001b[0m\u001b[2m│ \u001b[0mprotocol_version = pickle_module.load(f, **pickle_load_args) \u001b[31m│\u001b[0m\n", + "\u001b[31m╰──────────────────────────────────────────────────────────────────────────────────────────────────╯\u001b[0m\n", + "\u001b[1;91mUnpicklingError: \u001b[0minvalid load key, \u001b[32m'\\x08'\u001b[0m.\n" ] }, "metadata": {}, "output_type": "display_data" - }, + } + ], + "source": [ + "from zenml.client import Client\n", + "\n", + "artifact = Client().get_artifact_version('5d3051f8-22e6-4dca-8949-64043315ae91')\n", + "loaded_artifact = artifact.load()" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ { "data": { + "text/html": [ + "
╭─────────────────────────────── Traceback (most recent call last) ────────────────────────────────╮\n",
+       " in <module>:4                                                                                    \n",
+       "                                                                                                  \n",
+       "   1 from ultralytics import YOLO                                                                 \n",
+       "   2                                                                                              \n",
+       "   3 model_path = yolo_model.model_name                                                           \n",
+       " 4 ship_model = YOLO(model_path, names=['ship'])                                                \n",
+       "   5                                                                                              \n",
+       "╰──────────────────────────────────────────────────────────────────────────────────────────────────╯\n",
+       "TypeError: YOLO.__init__() got an unexpected keyword argument 'names'\n",
+       "
\n" + ], "text/plain": [ - "Dataset: ships_mt0ked\n", - "Media type: image\n", - "Num samples: 15\n", - "Selected samples: 0\n", - "Selected labels: 0\n", - "Session URL: http://localhost:5151/" + "\u001b[31m╭─\u001b[0m\u001b[31m──────────────────────────────\u001b[0m\u001b[31m \u001b[0m\u001b[1;31mTraceback \u001b[0m\u001b[1;2;31m(most recent call last)\u001b[0m\u001b[31m \u001b[0m\u001b[31m───────────────────────────────\u001b[0m\u001b[31m─╮\u001b[0m\n", + "\u001b[31m│\u001b[0m in \u001b[92m\u001b[0m:\u001b[94m4\u001b[0m \u001b[31m│\u001b[0m\n", + "\u001b[31m│\u001b[0m \u001b[31m│\u001b[0m\n", + "\u001b[31m│\u001b[0m \u001b[2m1 \u001b[0m\u001b[94mfrom\u001b[0m \u001b[4;96multralytics\u001b[0m \u001b[94mimport\u001b[0m YOLO \u001b[31m│\u001b[0m\n", + "\u001b[31m│\u001b[0m \u001b[2m2 \u001b[0m \u001b[31m│\u001b[0m\n", + "\u001b[31m│\u001b[0m \u001b[2m3 \u001b[0mmodel_path = yolo_model.model_name \u001b[31m│\u001b[0m\n", + "\u001b[31m│\u001b[0m \u001b[31m❱ \u001b[0m4 ship_model = YOLO(model_path, names=[\u001b[33m'\u001b[0m\u001b[33mship\u001b[0m\u001b[33m'\u001b[0m]) \u001b[31m│\u001b[0m\n", + "\u001b[31m│\u001b[0m \u001b[2m5 \u001b[0m \u001b[31m│\u001b[0m\n", + "\u001b[31m╰──────────────────────────────────────────────────────────────────────────────────────────────────╯\u001b[0m\n", + "\u001b[1;91mTypeError: \u001b[0m\u001b[1;35mYOLO.__init__\u001b[0m\u001b[1m(\u001b[0m\u001b[1m)\u001b[0m got an unexpected keyword argument \u001b[32m'names'\u001b[0m\n" ] }, - "execution_count": 1, "metadata": {}, - "output_type": "execute_result" + "output_type": "display_data" } ], "source": [ - "import fiftyone as fo\n", - "from zenml.client import Client\n", + "from ultralytics import YOLO\n", "\n", + "model_path = yolo_model.model_name\n", + "ship_model = YOLO(model_path, names=['ship'])" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from rich import inspect\n", "\n", - "artifact = Client().get_artifact_version(\n", - " name_id_or_prefix=\"predictions_dataset_json\"\n", - ")\n", - "dataset_json = artifact.load()\n", - "dataset = fo.Dataset.from_json(dataset_json, persistent=True)\n", - "\n", - "fo.launch_app(dataset)" + "inspect(yolo_model,methods=True)" ] }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 13, "metadata": {}, "outputs": [ { "data": { "text/html": [ - "\n", - " \n", - " " + "
╭─────────────────────────────── Traceback (most recent call last) ────────────────────────────────╮\n",
+       " in <module>:1                                                                                    \n",
+       "                                                                                                  \n",
+       " 1 yolo_model.names = ['ship']                                                                  \n",
+       "   2 # Predict with the model                                                                     \n",
+       "   3 results = yolo_model('https://ultralytics.com/images/bus.jpg')  # predict on an image        \n",
+       "   4 print(results)                                                                               \n",
+       "                                                                                                  \n",
+       " /home/strickvl/.pyenv/versions/3.10.12/envs/ls51/lib/python3.10/site-packages/torch/nn/modules/m \n",
+       " odule.py:1747 in __setattr__                                                                     \n",
+       "                                                                                                  \n",
+       "   1744 │   │   │   │   │   │   │   value = output                                                \n",
+       "   1745 │   │   │   │   │   buffers[name] = value                                                 \n",
+       "   1746 │   │   │   │   else:                                                                     \n",
+       " 1747 │   │   │   │   │   super().__setattr__(name, value)                                      \n",
+       "   1748                                                                                       \n",
+       "   1749 def __delattr__(self, name):                                                          \n",
+       "   1750 │   │   if name in self._parameters:                                                      \n",
+       "╰──────────────────────────────────────────────────────────────────────────────────────────────────╯\n",
+       "AttributeError: can't set attribute 'names'\n",
+       "
\n" ], "text/plain": [ - "" + "\u001b[31m╭─\u001b[0m\u001b[31m──────────────────────────────\u001b[0m\u001b[31m \u001b[0m\u001b[1;31mTraceback \u001b[0m\u001b[1;2;31m(most recent call last)\u001b[0m\u001b[31m \u001b[0m\u001b[31m───────────────────────────────\u001b[0m\u001b[31m─╮\u001b[0m\n", + "\u001b[31m│\u001b[0m in \u001b[92m\u001b[0m:\u001b[94m1\u001b[0m \u001b[31m│\u001b[0m\n", + "\u001b[31m│\u001b[0m \u001b[31m│\u001b[0m\n", + "\u001b[31m│\u001b[0m \u001b[31m❱ \u001b[0m1 yolo_model.names = [\u001b[33m'\u001b[0m\u001b[33mship\u001b[0m\u001b[33m'\u001b[0m] \u001b[31m│\u001b[0m\n", + "\u001b[31m│\u001b[0m \u001b[2m2 \u001b[0m\u001b[2m# Predict with the model\u001b[0m \u001b[31m│\u001b[0m\n", + "\u001b[31m│\u001b[0m \u001b[2m3 \u001b[0mresults = yolo_model(\u001b[33m'\u001b[0m\u001b[33mhttps://ultralytics.com/images/bus.jpg\u001b[0m\u001b[33m'\u001b[0m) \u001b[2m# predict on an image\u001b[0m \u001b[31m│\u001b[0m\n", + "\u001b[31m│\u001b[0m \u001b[2m4 \u001b[0m\u001b[96mprint\u001b[0m(results) \u001b[31m│\u001b[0m\n", + "\u001b[31m│\u001b[0m \u001b[31m│\u001b[0m\n", + "\u001b[31m│\u001b[0m \u001b[2;33m/home/strickvl/.pyenv/versions/3.10.12/envs/ls51/lib/python3.10/site-packages/torch/nn/modules/\u001b[0m\u001b[1;33mm\u001b[0m \u001b[31m│\u001b[0m\n", + "\u001b[31m│\u001b[0m \u001b[1;33module.py\u001b[0m:\u001b[94m1747\u001b[0m in \u001b[92m__setattr__\u001b[0m \u001b[31m│\u001b[0m\n", + "\u001b[31m│\u001b[0m \u001b[31m│\u001b[0m\n", + "\u001b[31m│\u001b[0m \u001b[2m1744 \u001b[0m\u001b[2m│ │ │ │ │ │ │ \u001b[0mvalue = output \u001b[31m│\u001b[0m\n", + "\u001b[31m│\u001b[0m \u001b[2m1745 \u001b[0m\u001b[2m│ │ │ │ │ \u001b[0mbuffers[name] = value \u001b[31m│\u001b[0m\n", + "\u001b[31m│\u001b[0m \u001b[2m1746 \u001b[0m\u001b[2m│ │ │ │ \u001b[0m\u001b[94melse\u001b[0m: \u001b[31m│\u001b[0m\n", + "\u001b[31m│\u001b[0m \u001b[31m❱ \u001b[0m1747 \u001b[2m│ │ │ │ │ \u001b[0m\u001b[96msuper\u001b[0m().\u001b[92m__setattr__\u001b[0m(name, value) \u001b[31m│\u001b[0m\n", + "\u001b[31m│\u001b[0m \u001b[2m1748 \u001b[0m\u001b[2m│ \u001b[0m \u001b[31m│\u001b[0m\n", + "\u001b[31m│\u001b[0m \u001b[2m1749 \u001b[0m\u001b[2m│ \u001b[0m\u001b[94mdef\u001b[0m \u001b[92m__delattr__\u001b[0m(\u001b[96mself\u001b[0m, name): \u001b[31m│\u001b[0m\n", + "\u001b[31m│\u001b[0m \u001b[2m1750 \u001b[0m\u001b[2m│ │ \u001b[0m\u001b[94mif\u001b[0m name \u001b[95min\u001b[0m \u001b[96mself\u001b[0m._parameters: \u001b[31m│\u001b[0m\n", + "\u001b[31m╰──────────────────────────────────────────────────────────────────────────────────────────────────╯\u001b[0m\n", + "\u001b[1;91mAttributeError: \u001b[0mcan't set attribute \u001b[32m'names'\u001b[0m\n" ] }, "metadata": {}, "output_type": "display_data" - }, + } + ], + "source": [ + "yolo_model.names = ['ship']\n", + "# Predict with the model\n", + "results = yolo_model('https://ultralytics.com/images/bus.jpg') # predict on an image\n", + "print(results)\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "data": { + "image/jpeg": 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", 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", + "text/plain": [ + "" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from PIL import Image\n", + "\n", + "res = results[0].plot()[:, :, [2,1,0]]\n", + "Image.fromarray(res)" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ { "data": { "text/plain": [ - "Dataset: ships_mt0ked\n", - "Media type: image\n", - "Num samples: 15\n", - "Selected samples: 0\n", - "Selected labels: 0\n", - "Session URL: http://localhost:5151/" + "{0: 'person',\n", + " 1: 'bicycle',\n", + " 2: 'car',\n", + " 3: 'motorcycle',\n", + " 4: 'airplane',\n", + " 5: 'bus',\n", + " 6: 'train',\n", + " 7: 'truck',\n", + " 8: 'boat',\n", + " 9: 'traffic light',\n", + " 10: 'fire hydrant',\n", + " 11: 'stop sign',\n", + " 12: 'parking meter',\n", + " 13: 'bench',\n", + " 14: 'bird',\n", + " 15: 'cat',\n", + " 16: 'dog',\n", + " 17: 'horse',\n", + " 18: 'sheep',\n", + " 19: 'cow',\n", + " 20: 'elephant',\n", + " 21: 'bear',\n", + " 22: 'zebra',\n", + " 23: 'giraffe',\n", + " 24: 'backpack',\n", + " 25: 'umbrella',\n", + " 26: 'handbag',\n", + " 27: 'tie',\n", + " 28: 'suitcase',\n", + " 29: 'frisbee',\n", + " 30: 'skis',\n", + " 31: 'snowboard',\n", + " 32: 'sports ball',\n", + " 33: 'kite',\n", + " 34: 'baseball bat',\n", + " 35: 'baseball glove',\n", + " 36: 'skateboard',\n", + " 37: 'surfboard',\n", + " 38: 'tennis racket',\n", + " 39: 'bottle',\n", + " 40: 'wine glass',\n", + " 41: 'cup',\n", + " 42: 'fork',\n", + " 43: 'knife',\n", + " 44: 'spoon',\n", + " 45: 'bowl',\n", + " 46: 'banana',\n", + " 47: 'apple',\n", + " 48: 'sandwich',\n", + " 49: 'orange',\n", + " 50: 'broccoli',\n", + " 51: 'carrot',\n", + " 52: 'hot dog',\n", + " 53: 'pizza',\n", + " 54: 'donut',\n", + " 55: 'cake',\n", + " 56: 'chair',\n", + " 57: 'couch',\n", + " 58: 'potted plant',\n", + " 59: 'bed',\n", + " 60: 'dining table',\n", + " 61: 'toilet',\n", + " 62: 'tv',\n", + " 63: 'laptop',\n", + " 64: 'mouse',\n", + " 65: 'remote',\n", + " 66: 'keyboard',\n", + " 67: 'cell phone',\n", + " 68: 'microwave',\n", + " 69: 'oven',\n", + " 70: 'toaster',\n", + " 71: 'sink',\n", + " 72: 'refrigerator',\n", + " 73: 'book',\n", + " 74: 'clock',\n", + " 75: 'vase',\n", + " 76: 'scissors',\n", + " 77: 'teddy bear',\n", + " 78: 'hair drier',\n", + " 79: 'toothbrush'}" ] }, - "execution_count": 2, + "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "fo.launch_app(dataset)" + "results[0].names" ] }, { From 429d2eef197c047e07464dcfc76f8fff7452ffe0 Mon Sep 17 00:00:00 2001 From: Alex Strick van Linschoten Date: Wed, 24 Apr 2024 15:06:28 +0200 Subject: [PATCH 102/121] add logging to train step --- end-to-end-computer-vision/steps/train_model.py | 8 ++++++++ 1 file changed, 8 insertions(+) diff --git a/end-to-end-computer-vision/steps/train_model.py b/end-to-end-computer-vision/steps/train_model.py index cf6f7c1d..4f739d04 100644 --- a/end-to-end-computer-vision/steps/train_model.py +++ b/end-to-end-computer-vision/steps/train_model.py @@ -18,6 +18,7 @@ from ultralytics import YOLO from zenml import ArtifactConfig, log_artifact_metadata, step +from zenml.logger import get_logger from materializers.label_studio_yolo_dataset_materializer import ( LabelStudioYOLODataset, @@ -25,6 +26,8 @@ from materializers.yolo_materializer import UltralyticsMaterializer from utils.dataset_utils import load_and_split_data +logger = get_logger(__name__) + @step( output_materializers={"Trained_YOLO": UltralyticsMaterializer}, @@ -58,8 +61,12 @@ def train_model( Returns: Tuple[YOLO, Dict[str, Any]]: Trained model and validation metrics. """ + logger.info(f"Training YOLO model on dataset {dataset}") + logger.info(f"Training for {epochs} epochs with batch size {batch_size}") + logger.info("Loading and splitting data...") data_path = load_and_split_data(dataset=dataset, data_source=data_source) + logger.info("Training model...") if is_quad_gpu_env: model.train( data=data_path, @@ -76,6 +83,7 @@ def train_model( imgsz=imgsz, ) + logger.info("Evaluating model...") metrics = model.val() # evaluate model performance on the validation set log_artifact_metadata( From 3c75cbd732b95813f291680de08df015e0e1bc92 Mon Sep 17 00:00:00 2001 From: Alex Strick van Linschoten Date: Wed, 24 Apr 2024 15:06:38 +0200 Subject: [PATCH 103/121] add number of classes --- end-to-end-computer-vision/utils/split_data.py | 1 + 1 file changed, 1 insertion(+) diff --git a/end-to-end-computer-vision/utils/split_data.py b/end-to-end-computer-vision/utils/split_data.py index 7c1311f8..5789736d 100644 --- a/end-to-end-computer-vision/utils/split_data.py +++ b/end-to-end-computer-vision/utils/split_data.py @@ -109,6 +109,7 @@ def generate_yaml(data_path: str, yaml_path: str = None) -> str: "val": "images/val", "test": "images/test", "names": {i: name for i, name in enumerate(classes)}, + "nc": len(classes), } logger.info("Generated yaml data: %s", data) # Write to yaml file From 7ba998f81344292d23855eef7235fe08cb506872 Mon Sep 17 00:00:00 2001 From: Alex Strick van Linschoten Date: Wed, 24 Apr 2024 15:42:06 +0200 Subject: [PATCH 104/121] revert materializer --- .../configs/training.yaml | 4 +-- .../materializers/yolo_materializer.py | 31 ++++++++----------- 2 files changed, 15 insertions(+), 20 deletions(-) diff --git a/end-to-end-computer-vision/configs/training.yaml b/end-to-end-computer-vision/configs/training.yaml index 09c65c18..6b8d470e 100644 --- a/end-to-end-computer-vision/configs/training.yaml +++ b/end-to-end-computer-vision/configs/training.yaml @@ -8,8 +8,8 @@ steps: enable_cache: False parameters: data_source: "gs://zenml-internal-artifact-store/label_studio_cv_webinar" - batch_size: 30 - imgsz: 1000 + batch_size: 16 + imgsz: 640 epochs: 30 settings: diff --git a/end-to-end-computer-vision/materializers/yolo_materializer.py b/end-to-end-computer-vision/materializers/yolo_materializer.py index 1ed93ab0..fb4c47e4 100644 --- a/end-to-end-computer-vision/materializers/yolo_materializer.py +++ b/end-to-end-computer-vision/materializers/yolo_materializer.py @@ -14,24 +14,21 @@ """Implementation of the PyTorch DataLoader materializer.""" import os -import uuid +import tempfile from typing import Any, ClassVar, Type from ultralytics import YOLO -from zenml.enums import ArtifactType from zenml.integrations.pytorch.materializers.pytorch_module_materializer import ( PyTorchModuleMaterializer, ) from zenml.io import fileio -DEFAULT_FILENAME = "obj.onnx" +DEFAULT_FILENAME = "obj.pt" -# class UltralyticsMaterializer(BaseMaterializer): class UltralyticsMaterializer(PyTorchModuleMaterializer): """Base class for ultralytics YOLO models.""" - ASSOCIATED_ARTIFACT_TYPE: ClassVar[ArtifactType] = ArtifactType.MODEL FILENAME: ClassVar[str] = DEFAULT_FILENAME SKIP_REGISTRATION: ClassVar[bool] = True ASSOCIATED_TYPES = (YOLO,) @@ -48,18 +45,14 @@ def load(self, data_type: Type[Any]) -> YOLO: filepath = os.path.join(self.uri, DEFAULT_FILENAME) # Create a temporary folder - # make a random string I can use as a prefix for the folder name - random_string = str(uuid.uuid4().hex) - temp_dir = os.path.join("/tmp", random_string) - # make the directory if it doesn't already exist - os.makedirs(temp_dir, exist_ok=True) - temp_file = os.path.join(temp_dir, DEFAULT_FILENAME) + with tempfile.TemporaryDirectory(prefix="zenml-temp-") as temp_dir: + temp_file = os.path.join(str(temp_dir), DEFAULT_FILENAME) - # Copy from artifact store to temporary file - fileio.copy(filepath, temp_file) - model = YOLO(temp_file, task="detect") + # Copy from artifact store to temporary file + fileio.copy(filepath, temp_file) + model = YOLO(temp_file) - return model + return model def save(self, model: YOLO) -> None: """Creates a JSON serialization for a ultralytics YOLO model. @@ -68,7 +61,9 @@ def save(self, model: YOLO) -> None: model: A ultralytics YOLO model. """ filepath = os.path.join(self.uri, DEFAULT_FILENAME) - filename = model.export(format="onnx") - # Copy it into artifact store - fileio.copy(filename, filepath) + # Make a temporary phantom artifact + with tempfile.NamedTemporaryFile(mode="w", suffix=".json") as f: + model.save(f.name) + # Copy it into artifact store + fileio.copy(f.name, filepath) From 6283f66f063320731d333fa0d23daa1f7ef5804c Mon Sep 17 00:00:00 2001 From: Alexej Penner Date: Wed, 24 Apr 2024 15:42:21 +0200 Subject: [PATCH 105/121] Add export metadata --- .../configs/training_gpu.yaml | 2 +- .../pipelines/training.py | 4 --- .../steps/export_label_studio.py | 34 +++++-------------- 3 files changed, 9 insertions(+), 31 deletions(-) diff --git a/end-to-end-computer-vision/configs/training_gpu.yaml b/end-to-end-computer-vision/configs/training_gpu.yaml index 5b5a3e4b..a4134e21 100644 --- a/end-to-end-computer-vision/configs/training_gpu.yaml +++ b/end-to-end-computer-vision/configs/training_gpu.yaml @@ -28,7 +28,7 @@ steps: is_quad_gpu_env: True settings: step_operator.vertex: - accelerator_type: NVIDIA_TESLA_T4 # NVIDIA_TESLA_A100 # see https://cloud.google.com/vertex-ai/docs/reference/rest/v1/MachineSpec#AcceleratorType + accelerator_type: NVIDIA_TESLA_T4 # see https://cloud.google.com/vertex-ai/docs/reference/rest/v1/MachineSpec#AcceleratorType accelerator_count: 4 disk_size_gb: 25 docker: diff --git a/end-to-end-computer-vision/pipelines/training.py b/end-to-end-computer-vision/pipelines/training.py index 3e4edf41..966b1a78 100644 --- a/end-to-end-computer-vision/pipelines/training.py +++ b/end-to-end-computer-vision/pipelines/training.py @@ -41,7 +41,3 @@ def training(model_checkpoint: str = "yolov8l.pt"): model=model, dataset=dataset, ) - - # promote_model(metrics) - - # predict_image(trained_model) diff --git a/end-to-end-computer-vision/steps/export_label_studio.py b/end-to-end-computer-vision/steps/export_label_studio.py index 12e5cffb..a7612d31 100644 --- a/end-to-end-computer-vision/steps/export_label_studio.py +++ b/end-to-end-computer-vision/steps/export_label_studio.py @@ -16,7 +16,7 @@ # from typing import Annotated, List, Tuple -from zenml import get_step_context, step +from zenml import get_step_context, step, log_artifact_metadata from zenml.client import Client from zenml.logger import get_logger @@ -64,34 +64,16 @@ def load_data_from_label_studio( ls_dataset = LabelStudioYOLODataset() ls_dataset.dataset = dataset - c = Client() - step_context = get_step_context() - cur_pipeline_name = step_context.pipeline.name - cur_step_name = step_context.step_name - - try: - last_run = c.get_pipeline( - cur_pipeline_name - ).last_successful_run - last_task_ids = ( - last_run.steps[cur_step_name].outputs["new_ids"].load() - ) - except (RuntimeError, KeyError): - last_task_ids = [] - current_labeled_task_ids = dataset.get_labeled_tasks_ids() - logger.info(f"{len(current_labeled_task_ids)} total labels found.") - new_task_ids = list( - set(current_labeled_task_ids) - set(last_task_ids) + ls_dataset.task_ids = current_labeled_task_ids + log_artifact_metadata( + metadata={ + "num_images": len(current_labeled_task_ids), + }, + artifact_name=LABELED_DATASET_NAME ) - logger.info( - f"{len(new_task_ids)} new labels are being beamed " - f"straight to you." - ) - - ls_dataset.task_ids = new_task_ids - return ls_dataset, new_task_ids + return ls_dataset, current_labeled_task_ids except: raise ValueError( f"Dataset {dataset_name} not found in Label Studio." From ca2f13ca676ab99d0976d8254cd24e4b00575d62 Mon Sep 17 00:00:00 2001 From: Alexej Penner Date: Thu, 25 Apr 2024 12:01:00 +0200 Subject: [PATCH 106/121] Webinar settings --- .../configs/data_export_alexej.yaml | 2 +- end-to-end-computer-vision/configs/training.yaml | 12 ------------ .../label_studio_yolo_dataset_materializer.py | 2 +- .../steps/fiftyone_inference.py | 2 +- 4 files changed, 3 insertions(+), 15 deletions(-) diff --git a/end-to-end-computer-vision/configs/data_export_alexej.yaml b/end-to-end-computer-vision/configs/data_export_alexej.yaml index 22f2464e..d57be2e6 100644 --- a/end-to-end-computer-vision/configs/data_export_alexej.yaml +++ b/end-to-end-computer-vision/configs/data_export_alexej.yaml @@ -2,7 +2,7 @@ enable_cache: False # pipeline configuration parameters: - dataset_name: "ship_detection_gcp" # "ship_detection" + dataset_name: "ship_detection_gcp" # Configuration of the Model Control Plane model: diff --git a/end-to-end-computer-vision/configs/training.yaml b/end-to-end-computer-vision/configs/training.yaml index 6b8d470e..ae253cc7 100644 --- a/end-to-end-computer-vision/configs/training.yaml +++ b/end-to-end-computer-vision/configs/training.yaml @@ -12,18 +12,6 @@ steps: imgsz: 640 epochs: 30 -settings: - docker: - apt_packages: - - ffmpeg - - libsm6 - - libxext6 - required_integrations: - - gcp - - github - requirements: - - ultralytics - # configuration of the Model Control Plane model: name: ShipDetector diff --git a/end-to-end-computer-vision/materializers/label_studio_yolo_dataset_materializer.py b/end-to-end-computer-vision/materializers/label_studio_yolo_dataset_materializer.py index 83986e2c..577d97ba 100644 --- a/end-to-end-computer-vision/materializers/label_studio_yolo_dataset_materializer.py +++ b/end-to-end-computer-vision/materializers/label_studio_yolo_dataset_materializer.py @@ -35,7 +35,7 @@ def download_dataset(self): self.dataset.export_tasks( export_type="YOLO", export_location=self.filepath, - download_resources=True, + download_resources=False, ids=self.task_ids, ) diff --git a/end-to-end-computer-vision/steps/fiftyone_inference.py b/end-to-end-computer-vision/steps/fiftyone_inference.py index 8c33f7eb..5a4584a7 100644 --- a/end-to-end-computer-vision/steps/fiftyone_inference.py +++ b/end-to-end-computer-vision/steps/fiftyone_inference.py @@ -33,7 +33,7 @@ os.environ["YOLO_VERBOSE"] = "False" -INFERENCE_BATCH = 20 +INFERENCE_BATCH = 5 @step From 80e1560890b8fee19dc4e02831a15bdb2a4d141f Mon Sep 17 00:00:00 2001 From: Alexej Penner Date: Thu, 25 Apr 2024 18:01:13 +0200 Subject: [PATCH 107/121] Initial working version for tiling --- .../configs/ingest_data.yaml | 2 +- .../pipelines/data_ingestion.py | 4 +- .../steps/download_and_tile_from_hf.py | 17 ++- .../steps/upload_to_label_studio.py | 1 - .../utils/dataset_utils.py | 113 +++++++++++++----- 5 files changed, 97 insertions(+), 40 deletions(-) diff --git a/end-to-end-computer-vision/configs/ingest_data.yaml b/end-to-end-computer-vision/configs/ingest_data.yaml index 47315706..fa4afd65 100644 --- a/end-to-end-computer-vision/configs/ingest_data.yaml +++ b/end-to-end-computer-vision/configs/ingest_data.yaml @@ -1,6 +1,6 @@ steps: - download_dataset_from_hf: + download_and_tile_dataset_from_hf: enable_cache: True enable_step_logs: False parameters: diff --git a/end-to-end-computer-vision/pipelines/data_ingestion.py b/end-to-end-computer-vision/pipelines/data_ingestion.py index e270179d..2d6d4539 100644 --- a/end-to-end-computer-vision/pipelines/data_ingestion.py +++ b/end-to-end-computer-vision/pipelines/data_ingestion.py @@ -17,7 +17,7 @@ from zenml import pipeline from zenml.logger import get_logger -from steps.download_from_hf import download_dataset_from_hf +from steps.download_and_tile_from_hf import download_and_tile_dataset_from_hf from steps.upload_to_label_studio import upload_labels_to_label_studio logger = get_logger(__name__) @@ -25,5 +25,5 @@ @pipeline def data_ingestion(): - labels_dict = download_dataset_from_hf() + labels_dict = download_and_tile_dataset_from_hf() upload_labels_to_label_studio(labels_dict) diff --git a/end-to-end-computer-vision/steps/download_and_tile_from_hf.py b/end-to-end-computer-vision/steps/download_and_tile_from_hf.py index 6a1b26d0..d054eeb7 100644 --- a/end-to-end-computer-vision/steps/download_and_tile_from_hf.py +++ b/end-to-end-computer-vision/steps/download_and_tile_from_hf.py @@ -27,32 +27,41 @@ Image.MAX_IMAGE_PIXELS = None + logger = get_logger(__name__) @step -def download_and_tile_dataset_from_hf(dataset: str, data_source: str) -> Dict[str, Any]: +def download_and_tile_dataset_from_hf(dataset: str, data_source: str, max_tile_size:int = 1000) -> Dict[str, Any]: + # Load dataset from huggingface dataset = load_dataset(dataset) - data = dataset["test"] + data = dataset["train"] + # Create local directory to initially copy data to output_dir = "data" if not os.path.exists(output_dir): os.mkdir(output_dir) all_images = {} + # iterate through the dataset for i, d in enumerate(data): img = d['image'] img_name = f"image_{i}" width, height = d['image'].size - if width > 2000 or height > 2000: + # in case images are very large, we split them up into 4 smaller tiles + if width > max_tile_size or height > max_tile_size: + logger.info(f"Image {img_name} is larger than {max_tile_size} pixels in " + f"at least one dimension, we'll cut it down into " + f"separate tiles.") split_large_image( d=d, img_name=img_name, output_dir=output_dir, all_images=all_images, data_source=data_source, + max_tile_size=max_tile_size ) else: img_path = f'{output_dir}/{img_name}.png' @@ -65,6 +74,8 @@ def download_and_tile_dataset_from_hf(dataset: str, data_source: str) -> Dict[st ) results = [] + + print(d['objects']['bbox']) for j, bbox in enumerate(d['objects']['bbox']): x1, y1, x2, y2 = bbox results.append( diff --git a/end-to-end-computer-vision/steps/upload_to_label_studio.py b/end-to-end-computer-vision/steps/upload_to_label_studio.py index a6525f22..c9135db0 100644 --- a/end-to-end-computer-vision/steps/upload_to_label_studio.py +++ b/end-to-end-computer-vision/steps/upload_to_label_studio.py @@ -54,7 +54,6 @@ def upload_labels_to_label_studio( for task in tasks: filename = task["storage_filename"].split("/")[-1] - project.create_annotation( task["id"], result=labels_dict[filename], ground_truth=True ) diff --git a/end-to-end-computer-vision/utils/dataset_utils.py b/end-to-end-computer-vision/utils/dataset_utils.py index 18153740..290d6397 100644 --- a/end-to-end-computer-vision/utils/dataset_utils.py +++ b/end-to-end-computer-vision/utils/dataset_utils.py @@ -120,8 +120,29 @@ def convert_bbox_for_ls(x1, x2, y1, y2, width, height) -> Dict[str, Any]: } +def crop_and_adjust_bboxes(image, bboxes, crop_coordinates): + x_crop_min, y_crop_min, x_crop_max, y_crop_max = crop_coordinates + cropped_image = image[y_crop_min:y_crop_max, x_crop_min:x_crop_max] + + adjusted_bboxes = [] + for bbox in bboxes: + xmin, ymin, xmax, ymax = bbox + + # Adjust bounding box coordinates + xmin_new = max(0, xmin - x_crop_min) + ymin_new = max(0, ymin - y_crop_min) + xmax_new = min(x_crop_max - x_crop_min, xmax - x_crop_min) + ymax_new = min(y_crop_max - y_crop_min, ymax - y_crop_min) + + # Check if the adjusted bounding box is still within the cropping area + if xmin_new < xmax_new and ymin_new < ymax_new: + adjusted_bboxes.append([xmin_new, ymin_new, xmax_new, ymax_new]) + + return cropped_image, adjusted_bboxes + + def split_large_image( - d: Any, img_name: str, output_dir: str, all_images: Dict[str, Any], data_source: str + d: Any, img_name: str, output_dir: str, all_images: Dict[str, Any], data_source: str, max_tile_size: int = 500 ): """Some images are too large to be useful, this splits them into multiple tiles. @@ -133,42 +154,68 @@ def split_large_image( """ tile_id = 0 img = d['image'] + bboxes = d['objects']['bbox'] width, height = d['image'].size - img_tiles = [img.crop((x, y, x + width // 2, y + height // 2)) - for x in range(0, width, width // 2) - for y in range(0, height, height // 2)] - - print(f"Split {img_name} into 4 tiles.") - - # Iterate through all 4 tiles, save the image and calculate the new bboxes - for i, img_tile in enumerate(img_tiles): - # Create new dictionary for each tile - new_img_name = img_name + '_' + str(tile_id) - img_path = f'{output_dir}/{new_img_name}.png' - - export_to_gcp( - data_source=data_source, - img=img, - img_name=new_img_name, - img_path=img_path - ) - - print(f"{img_path} saved.") - results = [] - for j, bbox in enumerate(d["objects"]["bbox"]): - x1, y1, x2, y2 = np.clip(bbox, [i * (width // 2), i * (height // 2), - (i + 1) * (width // 2), - (i + 1) * (height // 2)], - [0, 0, width // 2, height // 2]) - results.append( - convert_bbox_for_ls( - x1=x1, x2=x2, y1=y1, y2=y2, width=width, height=height - ) + logger.info(f"Processing {img_name} ...") + for x in range(0, width, max_tile_size): + print(f"increased x={x}") + for y in range(0, height, max_tile_size): + + print(f"increased y={y}") + if x+max_tile_size <= width: + x1 = x + x2 = min(x + max_tile_size, width) + else: + # The last tile of the row also stays 500 pixels wide by + # cropping from max width to the left + x1 = max(0, width-max_tile_size) + x2 = width + if y+max_tile_size <= height: + y1 = y + y2 = min(y + max_tile_size, height) + else: + # The last tile of the row also stays 500 pixels high by + # cropping from max height up + y1 = max(0, height-max_tile_size) + y2 = height + cropped_img, adjusted_bboxes = crop_and_adjust_bboxes( + image=np.array(img), + bboxes=bboxes, + crop_coordinates=(x1, y1, x2, y2) + ) + + # store this tile + new_img_name = img_name + '_' + str(tile_id) + img_path = f'{output_dir}/{new_img_name}.png' + + print(f"Storing tile {tile_id} of {img_name} at {img_path} ...") + export_to_gcp( + data_source=data_source, + img=Image.fromarray(cropped_img), + img_name=new_img_name, + img_path=img_path ) - all_images[img_path] = results - tile_id += 1 + logger.info(f"Calculating new bboxes for tile {img_path}") + tile_width = x2-x1 + tile_height = y2-y1 + + results = [] + for bbox in adjusted_bboxes: + results.append( + convert_bbox_for_ls( + x1=bbox[0], + x2=bbox[2], + y1=bbox[1], + y2=bbox[3], + width=tile_width, + height=tile_height + ) + ) + + all_images[f'{new_img_name}.png'] = results + tile_id += 1 def export_to_gcp(data_source: str, img: Image, img_name: str, img_path: str): From a769b6fbaa16e25055d4890d98b5e3da6a6ae0fd Mon Sep 17 00:00:00 2001 From: Alexej Penner Date: Fri, 26 Apr 2024 09:30:04 +0200 Subject: [PATCH 108/121] Data ingestion now in 1000pxl tiles --- ...inference.yaml => inference_pipeline.yaml} | 0 .../{training.yaml => training_pipeline.yaml} | 2 +- ...yaml => training_pipeline_remote_gpu.yaml} | 2 +- .../label_studio_yolo_dataset_materializer.py | 3 +- ...ializer.py => ultralytics_materializer.py} | 0 end-to-end-computer-vision/run.py | 6 +- .../steps/download_and_tile_from_hf.py | 100 ++++++++---------- .../steps/train_model.py | 2 +- .../steps/upload_to_label_studio.py | 12 ++- .../utils/dataset_utils.py | 25 +++-- 10 files changed, 79 insertions(+), 73 deletions(-) rename end-to-end-computer-vision/configs/{cloud_inference.yaml => inference_pipeline.yaml} (100%) rename end-to-end-computer-vision/configs/{training.yaml => training_pipeline.yaml} (86%) rename end-to-end-computer-vision/configs/{training_gpu.yaml => training_pipeline_remote_gpu.yaml} (93%) rename end-to-end-computer-vision/materializers/{yolo_materializer.py => ultralytics_materializer.py} (100%) diff --git a/end-to-end-computer-vision/configs/cloud_inference.yaml b/end-to-end-computer-vision/configs/inference_pipeline.yaml similarity index 100% rename from end-to-end-computer-vision/configs/cloud_inference.yaml rename to end-to-end-computer-vision/configs/inference_pipeline.yaml diff --git a/end-to-end-computer-vision/configs/training.yaml b/end-to-end-computer-vision/configs/training_pipeline.yaml similarity index 86% rename from end-to-end-computer-vision/configs/training.yaml rename to end-to-end-computer-vision/configs/training_pipeline.yaml index 004c6668..73a1c661 100644 --- a/end-to-end-computer-vision/configs/training.yaml +++ b/end-to-end-computer-vision/configs/training_pipeline.yaml @@ -8,7 +8,7 @@ steps: train_model: enable_cache: False parameters: - data_source: "gs://zenml-internal-artifact-store/label_studio_cv_webinar" + data_source: "gs://zenml-internal-artifact-store/inference_cv_webinar" settings: docker: diff --git a/end-to-end-computer-vision/configs/training_gpu.yaml b/end-to-end-computer-vision/configs/training_pipeline_remote_gpu.yaml similarity index 93% rename from end-to-end-computer-vision/configs/training_gpu.yaml rename to end-to-end-computer-vision/configs/training_pipeline_remote_gpu.yaml index c4e6c0e4..fc17066c 100644 --- a/end-to-end-computer-vision/configs/training_gpu.yaml +++ b/end-to-end-computer-vision/configs/training_pipeline_remote_gpu.yaml @@ -21,7 +21,7 @@ steps: step_operator: gcp_a100 enable_step_logs: False parameters: - data_source: "gs://zenml-internal-artifact-store/label_studio_cv_webinar" + data_source: "gs://zenml-internal-artifact-store/inference_cv_webinar" batch_size: 8 imgsz: 480 epochs: 500 diff --git a/end-to-end-computer-vision/materializers/label_studio_yolo_dataset_materializer.py b/end-to-end-computer-vision/materializers/label_studio_yolo_dataset_materializer.py index 83986e2c..918b8939 100644 --- a/end-to-end-computer-vision/materializers/label_studio_yolo_dataset_materializer.py +++ b/end-to-end-computer-vision/materializers/label_studio_yolo_dataset_materializer.py @@ -35,8 +35,7 @@ def download_dataset(self): self.dataset.export_tasks( export_type="YOLO", export_location=self.filepath, - download_resources=True, - ids=self.task_ids, + download_resources=False, ) diff --git a/end-to-end-computer-vision/materializers/yolo_materializer.py b/end-to-end-computer-vision/materializers/ultralytics_materializer.py similarity index 100% rename from end-to-end-computer-vision/materializers/yolo_materializer.py rename to end-to-end-computer-vision/materializers/ultralytics_materializer.py diff --git a/end-to-end-computer-vision/run.py b/end-to-end-computer-vision/run.py index 0f940a7a..71b8c407 100644 --- a/end-to-end-computer-vision/run.py +++ b/end-to-end-computer-vision/run.py @@ -132,18 +132,18 @@ def main( client.activate_stack(stack_id) # Train model on data - training.with_options(config_path="configs/training.yaml")() + training.with_options(config_path="configs/training_pipeline.yaml")() if training_pipeline and not train_local: client.activate_stack(REMOTE_STACK_ID) # Train model on data - training.with_options(config_path="configs/training_gpu.yaml")() + training.with_options(config_path="configs/training_pipeline_remote_gpu.yaml")() if inference_pipeline: client.activate_stack(REMOTE_STACK_ID) - inference.with_options(config_path="configs/cloud_inference.yaml")() + inference.with_options(config_path="configs/inference_pipeline.yaml")() if fiftyone: client.activate_stack(stack_id) diff --git a/end-to-end-computer-vision/steps/download_and_tile_from_hf.py b/end-to-end-computer-vision/steps/download_and_tile_from_hf.py index d054eeb7..3e33d9a0 100644 --- a/end-to-end-computer-vision/steps/download_and_tile_from_hf.py +++ b/end-to-end-computer-vision/steps/download_and_tile_from_hf.py @@ -22,7 +22,7 @@ from zenml import step from zenml.logger import get_logger -from utils.dataset_utils import split_large_image, convert_bbox_for_ls, \ +from utils.dataset_utils import split_image_into_tiles, convert_bbox_for_ls, \ export_to_gcp Image.MAX_IMAGE_PIXELS = None @@ -32,58 +32,46 @@ @step -def download_and_tile_dataset_from_hf(dataset: str, data_source: str, max_tile_size:int = 1000) -> Dict[str, Any]: - # Load dataset from huggingface - dataset = load_dataset(dataset) - data = dataset["train"] - - # Create local directory to initially copy data to - output_dir = "data" - if not os.path.exists(output_dir): - os.mkdir(output_dir) - - all_images = {} - - # iterate through the dataset - for i, d in enumerate(data): - img = d['image'] - img_name = f"image_{i}" - - width, height = d['image'].size - # in case images are very large, we split them up into 4 smaller tiles - if width > max_tile_size or height > max_tile_size: - logger.info(f"Image {img_name} is larger than {max_tile_size} pixels in " - f"at least one dimension, we'll cut it down into " - f"separate tiles.") - split_large_image( - d=d, - img_name=img_name, - output_dir=output_dir, - all_images=all_images, - data_source=data_source, - max_tile_size=max_tile_size - ) - else: - img_path = f'{output_dir}/{img_name}.png' - - export_to_gcp( - data_source=data_source, - img=img, - img_name=img_name, - img_path=img_path - ) - - results = [] - - print(d['objects']['bbox']) - for j, bbox in enumerate(d['objects']['bbox']): - x1, y1, x2, y2 = bbox - results.append( - convert_bbox_for_ls( - x1=x1, x2=x2, y1=y1, y2=y2, width=width,height=height - ) - ) - - all_images[img_path] = results - - return all_images +def download_and_tile_dataset_from_hf( + dataset: str, + data_source: str, + max_tile_size: int = 1000 +) -> Dict[str, Any]: + """Downloads a hf dataset and does some processing. + + Converts the labels into the label_studio format. + Also uploads the images to the datasource path + + """ + # # Load dataset from huggingface + # dataset = load_dataset(dataset) + # data = dataset["train"] + # + # # Create local directory to initially copy data to + # local_output_dir = "data" + # if not os.path.exists(local_output_dir): + # os.mkdir(local_output_dir) + # + # # Dictionary that maps label information onto the corresponding image + # all_images = {} + # + # # iterate through the dataset + # for i, d in enumerate(data): + # img_name = f"image_{i}" + # + # # in case images are very large, we split them up into 4 smaller tiles + # split_image_into_tiles( + # d=d, + # img_name=img_name, + # output_dir=local_output_dir, + # all_images=all_images, + # data_source=data_source, + # max_tile_size=max_tile_size + # ) + + from zenml.client import Client + + artifact = Client().get_artifact_version( + '6eca4d2e-1d4b-4dd6-bd51-7758d68ab215') + loaded_artifact = artifact.load() + return loaded_artifact diff --git a/end-to-end-computer-vision/steps/train_model.py b/end-to-end-computer-vision/steps/train_model.py index da351508..048db898 100644 --- a/end-to-end-computer-vision/steps/train_model.py +++ b/end-to-end-computer-vision/steps/train_model.py @@ -22,7 +22,7 @@ from materializers.label_studio_yolo_dataset_materializer import ( LabelStudioYOLODataset, ) -from materializers.yolo_materializer import UltralyticsMaterializer +from materializers.ultralytics_materializer import UltralyticsMaterializer from utils.dataset_utils import load_and_split_data diff --git a/end-to-end-computer-vision/steps/upload_to_label_studio.py b/end-to-end-computer-vision/steps/upload_to_label_studio.py index c9135db0..02cea2d4 100644 --- a/end-to-end-computer-vision/steps/upload_to_label_studio.py +++ b/end-to-end-computer-vision/steps/upload_to_label_studio.py @@ -18,6 +18,9 @@ from zenml import step from zenml.client import Client +from zenml.logger import get_logger + +logger = get_logger(__name__) @step @@ -54,6 +57,9 @@ def upload_labels_to_label_studio( for task in tasks: filename = task["storage_filename"].split("/")[-1] - project.create_annotation( - task["id"], result=labels_dict[filename], ground_truth=True - ) + try: + project.create_annotation( + task["id"], result=labels_dict[filename], ground_truth=True + ) + except KeyError: + logger.info(f"No labels found for {filename}. Skipping ...") diff --git a/end-to-end-computer-vision/utils/dataset_utils.py b/end-to-end-computer-vision/utils/dataset_utils.py index 290d6397..d5344e20 100644 --- a/end-to-end-computer-vision/utils/dataset_utils.py +++ b/end-to-end-computer-vision/utils/dataset_utils.py @@ -16,7 +16,7 @@ # import os import tempfile -from typing import Dict, Any +from typing import Dict, Any, List, Tuple import numpy as np from PIL import Image @@ -120,7 +120,18 @@ def convert_bbox_for_ls(x1, x2, y1, y2, width, height) -> Dict[str, Any]: } -def crop_and_adjust_bboxes(image, bboxes, crop_coordinates): +def crop_and_adjust_bboxes(image: np.array, bboxes: List[List[int]], crop_coordinates: Tuple[int]): + """Crops an image and adjust the bboxes that are within the croped portion. + + Args: + image: Image as np.array + bboxes: List of bboxes [[x1, y1, x2, y2], [x1, y1, x2, y2]] + crop_coordinates: Coordinates of the crop, format (x1, y1, x2, y2) + + Returns: + Tuple containing the cropped portion of the images and all bboxes + within the crop area + """ x_crop_min, y_crop_min, x_crop_max, y_crop_max = crop_coordinates cropped_image = image[y_crop_min:y_crop_max, x_crop_min:x_crop_max] @@ -141,16 +152,18 @@ def crop_and_adjust_bboxes(image, bboxes, crop_coordinates): return cropped_image, adjusted_bboxes -def split_large_image( +def split_image_into_tiles( d: Any, img_name: str, output_dir: str, all_images: Dict[str, Any], data_source: str, max_tile_size: int = 500 ): """Some images are too large to be useful, this splits them into multiple tiles. Args: - d: The hf dataset entry - img_name: Name of the image - output_dir: Where to initially save the image to + d: One hf dataset entry - needs to contain d['image'] and + d['objects']['bbox'] + img_name: Name of the image (excluding any suffix) + output_dir: Where to locally save the image all_images: Dictionary containing the image_name<->bboxes mapping + max_tile_size: Maximum tile size """ tile_id = 0 img = d['image'] From 763ff7f3a4f383bc3c2af06b0d86a4a1fc0280fa Mon Sep 17 00:00:00 2001 From: Alexej Penner Date: Fri, 26 Apr 2024 11:13:50 +0200 Subject: [PATCH 109/121] Cleanup progressing --- .../configs/training_pipeline_remote_gpu.yaml | 2 +- ...py => label_studio_export_materializer.py} | 51 +++++--- .../pipelines/__init__.py | 7 +- .../pipelines/data_export.py | 2 +- .../pipelines/data_ingestion.py | 6 +- .../pipelines/inference.py | 6 +- .../pipelines/training.py | 2 +- end-to-end-computer-vision/run.py | 118 ++++++++++-------- .../steps/download_and_tile_from_hf.py | 77 ------------ .../steps/download_from_hf.py | 70 ----------- .../steps/export_label_studio.py | 12 +- .../steps/process_hf_dataset.py | 100 +++++++++++++++ .../steps/train_model.py | 6 +- .../utils/dataset_utils.py | 6 +- 14 files changed, 224 insertions(+), 241 deletions(-) rename end-to-end-computer-vision/materializers/{label_studio_yolo_dataset_materializer.py => label_studio_export_materializer.py} (56%) delete mode 100644 end-to-end-computer-vision/steps/download_and_tile_from_hf.py delete mode 100644 end-to-end-computer-vision/steps/download_from_hf.py create mode 100644 end-to-end-computer-vision/steps/process_hf_dataset.py diff --git a/end-to-end-computer-vision/configs/training_pipeline_remote_gpu.yaml b/end-to-end-computer-vision/configs/training_pipeline_remote_gpu.yaml index a94a7815..e48c591a 100644 --- a/end-to-end-computer-vision/configs/training_pipeline_remote_gpu.yaml +++ b/end-to-end-computer-vision/configs/training_pipeline_remote_gpu.yaml @@ -24,7 +24,7 @@ steps: data_source: "gs://zenml-internal-artifact-store/inference_cv_webinar" batch_size: 8 imgsz: 480 - epochs: 5000 + epochs: 50000 is_quad_gpu_env: True settings: step_operator.vertex: diff --git a/end-to-end-computer-vision/materializers/label_studio_yolo_dataset_materializer.py b/end-to-end-computer-vision/materializers/label_studio_export_materializer.py similarity index 56% rename from end-to-end-computer-vision/materializers/label_studio_yolo_dataset_materializer.py rename to end-to-end-computer-vision/materializers/label_studio_export_materializer.py index 918b8939..7874158e 100644 --- a/end-to-end-computer-vision/materializers/label_studio_yolo_dataset_materializer.py +++ b/end-to-end-computer-vision/materializers/label_studio_export_materializer.py @@ -15,20 +15,32 @@ import os import tempfile -from typing import Any, ClassVar, List, Optional, Type +from typing import Any, ClassVar, Type, TYPE_CHECKING from zenml.io import fileio from zenml.materializers.base_materializer import BaseMaterializer -DEFAULT_FILENAME = "data.zip" +DEFAULT_FILENAME = "annotations.zip" +if TYPE_CHECKING: + from label_studio_sdk import Project -class LabelStudioYOLODataset: - dataset: Optional[Any] - filepath: Optional[str] - task_ids: Optional[List[int]] - def download_dataset(self): +class LabelStudioAnnotationExport: + + def __init__(self, dataset: "Project" = None, filepath: str =None): + """ + Initialize LabelStudioAnnotationExport object with optional parameters. + + Parameters: + dataset: Label-studio dataset object. + filepath: A string representing the file path. Defaults to None. + """ + self.dataset = dataset + self.filepath = filepath + + def download_annotations(self): + """Downloads the annotations from label-studio to the local fs.""" tmpfile_ = tempfile.NamedTemporaryFile(dir="data", delete=False) tmpdirname = os.path.basename(tmpfile_.name) self.filepath = os.path.join(tmpdirname, DEFAULT_FILENAME) @@ -39,15 +51,15 @@ def download_dataset(self): ) -class LabelStudioYOLODatasetMaterializer(BaseMaterializer): - """Base class for Label Studio YOLO dataset models.""" +class LabelStudioAnnotationMaterializer(BaseMaterializer): + """Base class for Label Studio annotation models.""" FILENAME: ClassVar[str] = DEFAULT_FILENAME SKIP_REGISTRATION: ClassVar[bool] = True - ASSOCIATED_TYPES = (LabelStudioYOLODataset,) + ASSOCIATED_TYPES = (LabelStudioAnnotationExport,) - def load(self, data_type: Type[Any]) -> LabelStudioYOLODataset: - """Reads a ultralytics YOLO model from a serialized JSON file. + def load(self, data_type: Type[Any]) -> LabelStudioAnnotationExport: + """Loads the serialized JSON file containing the annotations. Args: data_type: A ultralytics YOLO type. @@ -55,28 +67,33 @@ def load(self, data_type: Type[Any]) -> LabelStudioYOLODataset: Returns: A ultralytics YOLO object. """ + # Recreate the filepath of the file filepath = os.path.join(self.uri, DEFAULT_FILENAME) - # Create a temporary folder + # Create a temporary file tmpfile_ = tempfile.NamedTemporaryFile( dir="data", delete=False, suffix=".zip" ) # Copy from artifact store to temporary file fileio.copy(filepath, tmpfile_.name, overwrite=True) - dataset = LabelStudioYOLODataset() - dataset.filepath = tmpfile_.name + + # Re-instantiate the LabelStudioAnnotationExport model + dataset = LabelStudioAnnotationExport(filepath=tmpfile_.name) return dataset - def save(self, dataset: LabelStudioYOLODataset) -> None: + def save(self, dataset: LabelStudioAnnotationExport) -> None: """Creates a JSON serialization for a label studio YOLO dataset model. Args: dataset: A label studio YOLO dataset model. """ - dataset.download_dataset() + # Downloads the annotations into the local fs + dataset.download_annotations() + # create the destination path for the exported annotations filepath = os.path.join(self.uri, DEFAULT_FILENAME) + # copies the files from local fs into the artifact store fileio.copy(dataset.filepath, filepath) diff --git a/end-to-end-computer-vision/pipelines/__init__.py b/end-to-end-computer-vision/pipelines/__init__.py index 4dc295a9..7c04f0f0 100644 --- a/end-to-end-computer-vision/pipelines/__init__.py +++ b/end-to-end-computer-vision/pipelines/__init__.py @@ -15,6 +15,7 @@ # limitations under the License. # -from .data_export import export_for_training - -# from .training import training +from .data_ingestion import data_ingestion_pipeline +from .data_export import data_export_pipeline +from .training import training_pipeline +from .inference import inference_pipeline diff --git a/end-to-end-computer-vision/pipelines/data_export.py b/end-to-end-computer-vision/pipelines/data_export.py index 344eadb9..3adf50c1 100644 --- a/end-to-end-computer-vision/pipelines/data_export.py +++ b/end-to-end-computer-vision/pipelines/data_export.py @@ -23,7 +23,7 @@ @pipeline -def export_for_training(dataset_name: str = "polution"): +def data_export_pipeline(dataset_name: str = "polution"): """Loads data from Label studio. Args: diff --git a/end-to-end-computer-vision/pipelines/data_ingestion.py b/end-to-end-computer-vision/pipelines/data_ingestion.py index 2d6d4539..b1df9c6b 100644 --- a/end-to-end-computer-vision/pipelines/data_ingestion.py +++ b/end-to-end-computer-vision/pipelines/data_ingestion.py @@ -17,13 +17,13 @@ from zenml import pipeline from zenml.logger import get_logger -from steps.download_and_tile_from_hf import download_and_tile_dataset_from_hf +from steps.process_hf_dataset import process_hf_dataset from steps.upload_to_label_studio import upload_labels_to_label_studio logger = get_logger(__name__) @pipeline -def data_ingestion(): - labels_dict = download_and_tile_dataset_from_hf() +def data_ingestion_pipeline(): + labels_dict = process_hf_dataset() upload_labels_to_label_studio(labels_dict) diff --git a/end-to-end-computer-vision/pipelines/inference.py b/end-to-end-computer-vision/pipelines/inference.py index 77db9ef1..8191f399 100644 --- a/end-to-end-computer-vision/pipelines/inference.py +++ b/end-to-end-computer-vision/pipelines/inference.py @@ -23,10 +23,6 @@ @pipeline -def inference(): +def inference_pipeline(): """Uses FiftyOne for inference on a dataset.""" create_fiftyone_dataset() - - -if __name__ == "__main__": - inference.with_options(config_path="configs/cloud_inference.yaml")() diff --git a/end-to-end-computer-vision/pipelines/training.py b/end-to-end-computer-vision/pipelines/training.py index 966b1a78..eb31416d 100644 --- a/end-to-end-computer-vision/pipelines/training.py +++ b/end-to-end-computer-vision/pipelines/training.py @@ -25,7 +25,7 @@ @pipeline -def training(model_checkpoint: str = "yolov8l.pt"): +def training_pipeline(model_checkpoint: str = "yolov8l.pt"): """Trains a model on a dataset. Args: diff --git a/end-to-end-computer-vision/run.py b/end-to-end-computer-vision/run.py index 69bd2508..2a453e26 100644 --- a/end-to-end-computer-vision/run.py +++ b/end-to-end-computer-vision/run.py @@ -22,10 +22,10 @@ from zenml.enums import ModelStages from zenml.logger import get_logger -from pipelines.data_export import export_for_training -from pipelines.data_ingestion import data_ingestion -from pipelines.inference import inference -from pipelines.training import training +from pipelines.data_export import data_export_pipeline +from pipelines.data_ingestion import data_ingestion_pipeline +from pipelines.inference import inference_pipeline +from pipelines.training import training_pipeline from utils.constants import PREDICTIONS_DATASET_ARTIFACT_NAME, ZENML_MODEL_NAME logger = get_logger(__name__) @@ -37,7 +37,7 @@ @click.option( "--ingest", "-ig", - "ingest_data_pipeline", + "ingest_data", is_flag=True, default=False, help="Whether to run the data ingestion pipeline, that takes the dataset" @@ -55,7 +55,7 @@ @click.option( "--training", "-t", - "training_pipeline", + "train", is_flag=True, default=False, help="Whether to run the pipeline that trains the model.", @@ -63,7 +63,7 @@ @click.option( "--inference", "-i", - "inference_pipeline", + "batch_inference", is_flag=True, default=False, help="Whether to run the pipeline that performs inference.", @@ -76,14 +76,6 @@ default=False, help="Whether to launch the FiftyOne app pipeline.", ) -@click.option( - "--stack", - "-s", - "stack", - required=False, - type=click.Choice(["alexej", "hamza", "alex"]), - help="The stack to use for the pipeline.", -) @click.option( "--local", "-l", @@ -93,61 +85,85 @@ help="Whether to train local.", ) def main( - ingest_data_pipeline: bool = False, - export_pipeline: bool = False, - training_pipeline: bool = False, - inference_pipeline: bool = False, + ingest_data: bool = False, + export_annotations: bool = False, + train: bool = False, + batch_inference: bool = False, fiftyone: bool = False, - stack: UUID = "alexej", train_local: bool = False, ): - # TODO: remove all this :) - if stack == "hamza": - stack_id = UUID("cca5eaf7-0309-413d-89ff-1cd371b7d27c") - elif stack == "alex": - stack_id = UUID("fcf840ac-addd-4de3-a3e4-a1015f7bb96c") - else: - stack_id = UUID("7cda3cec-6744-48dc-8bdc-f102242a26d2") - client = Client() - if ingest_data_pipeline: - client.activate_stack(stack_id) - data_ingestion.with_options(config_path="configs/ingest_data.yaml")() + if ingest_data: + if not client.active_stack.orchestrator.config.is_local: + raise RuntimeError( + "The implementation of this pipeline " + "requires that you are running on a local " + "machine with data being persisted in the local " + "filesystem across multiple steps. Please " + "switch to a stack that contains a local " + "orchestrator and a local label-studio " + "annotator. See the README for more information " + "on this setup." + ) + + data_ingestion_pipeline.with_options( + config_path="configs/ingest_data.yaml" + )() - if export_pipeline: - client.activate_stack(stack_id) + if export_annotations: + if not client.active_stack.orchestrator.config.is_local: + raise RuntimeError( + "The implementation of this pipeline " + "requires that you are running on a local " + "machine with a running instance of label-studio " + "configured in the stack as annotator." + " Please switch to a stack that contains a local " + "orchestrator and a local label-studio " + "annotator. See the README for more information " + "on this setup." + ) # Export data from label studio - export_for_training.with_options( + data_export_pipeline.with_options( config_path="configs/data_export_alexej.yaml" )() - # Promote Model to staging - latest_model = Model(name=ZENML_MODEL_NAME, version=ModelStages.LATEST) - latest_model.set_stage(stage=ModelStages.STAGING, force=True) - - if training_pipeline and train_local: - client.activate_stack(stack_id) + if train: + try: + client.get_model_version( + model_name_or_id=ZENML_MODEL_NAME, + model_version_name_or_number_or_id=ModelStages.STAGING + ) + except KeyError: + raise RuntimeError( + "This pipeline requires that there is a version of its " + "associated model in the `STAGING` stage. Make sure you run " + "the `data_export_pipeline` at least once to create the Model " + "along with a version of this model. After this you can " + "promote the version of your choice, either through the " + "frontend or with the following command: " + f" zenml model version update {ZENML_MODEL_NAME} latest " + f"-s staging." + ) + + if train_local: + config_path = "configs/training_pipeline.yaml" + else: + config_path = "configs/training_pipeline_remote_gpu.yaml" # Train model on data - training.with_options(config_path="configs/training_pipeline.yaml")() + training_pipeline.with_options(config_path=config_path)() - if training_pipeline and not train_local: - client.activate_stack(REMOTE_STACK_ID) + if batch_inference: - # Train model on data - training.with_options(config_path="configs/training_pipeline_remote_gpu.yaml")() - - if inference_pipeline: - client.activate_stack(stack_id) - - inference.with_options(config_path="configs/inference_pipeline.yaml")() + inference_pipeline.with_options( + config_path="configs/inference_pipeline.yaml" + )() if fiftyone: import fiftyone as fo - client.activate_stack(stack_id) artifact = Client().get_artifact_version( name_id_or_prefix=PREDICTIONS_DATASET_ARTIFACT_NAME ) diff --git a/end-to-end-computer-vision/steps/download_and_tile_from_hf.py b/end-to-end-computer-vision/steps/download_and_tile_from_hf.py deleted file mode 100644 index 3e33d9a0..00000000 --- a/end-to-end-computer-vision/steps/download_and_tile_from_hf.py +++ /dev/null @@ -1,77 +0,0 @@ -# Apache Software License 2.0 -# -# Copyright (c) ZenML GmbH 2024. All rights reserved. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -# -import os -from typing import Any, Dict - -from datasets import load_dataset -from PIL import Image -from zenml import step -from zenml.logger import get_logger - -from utils.dataset_utils import split_image_into_tiles, convert_bbox_for_ls, \ - export_to_gcp - -Image.MAX_IMAGE_PIXELS = None - - -logger = get_logger(__name__) - - -@step -def download_and_tile_dataset_from_hf( - dataset: str, - data_source: str, - max_tile_size: int = 1000 -) -> Dict[str, Any]: - """Downloads a hf dataset and does some processing. - - Converts the labels into the label_studio format. - Also uploads the images to the datasource path - - """ - # # Load dataset from huggingface - # dataset = load_dataset(dataset) - # data = dataset["train"] - # - # # Create local directory to initially copy data to - # local_output_dir = "data" - # if not os.path.exists(local_output_dir): - # os.mkdir(local_output_dir) - # - # # Dictionary that maps label information onto the corresponding image - # all_images = {} - # - # # iterate through the dataset - # for i, d in enumerate(data): - # img_name = f"image_{i}" - # - # # in case images are very large, we split them up into 4 smaller tiles - # split_image_into_tiles( - # d=d, - # img_name=img_name, - # output_dir=local_output_dir, - # all_images=all_images, - # data_source=data_source, - # max_tile_size=max_tile_size - # ) - - from zenml.client import Client - - artifact = Client().get_artifact_version( - '6eca4d2e-1d4b-4dd6-bd51-7758d68ab215') - loaded_artifact = artifact.load() - return loaded_artifact diff --git a/end-to-end-computer-vision/steps/download_from_hf.py b/end-to-end-computer-vision/steps/download_from_hf.py deleted file mode 100644 index 74f27767..00000000 --- a/end-to-end-computer-vision/steps/download_from_hf.py +++ /dev/null @@ -1,70 +0,0 @@ -# Apache Software License 2.0 -# -# Copyright (c) ZenML GmbH 2024. All rights reserved. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -# -import os -from typing import Any, Dict - -from datasets import load_dataset -from PIL import Image -from zenml import step -from zenml.io import fileio -from zenml.logger import get_logger - -from utils.dataset_utils import export_to_gcp, convert_bbox_for_ls - -Image.MAX_IMAGE_PIXELS = None - -logger = get_logger(__name__) - - -@step -def download_dataset_from_hf(dataset: str, data_source: str) -> Dict[str, Any]: - dataset = load_dataset(dataset) - data = dataset["train"] - - output_dir = "data" - if not os.path.exists(output_dir): - os.mkdir(output_dir) - - all_images = {} - - for i, d in enumerate(data): - img = d["image"] - img_name = f"image_{i}" - logger.info(f"Handling {img_name}") - img_path = f"{output_dir}/{img_name}.png" - - export_to_gcp( - data_source=data_source, - img=img, - img_name=img_name, - img_path=img_path - ) - - width, height = d["image"].size - - results = [] - for j, bbox in enumerate(d["objects"]["bbox"]): - x1, y1, x2, y2 = bbox - results.append( - convert_bbox_for_ls( - x1=x1, x2=x2, y1=y1, y2=y2, width=width, height=height - ) - ) - - all_images[img_name] = results - - return all_images diff --git a/end-to-end-computer-vision/steps/export_label_studio.py b/end-to-end-computer-vision/steps/export_label_studio.py index a7612d31..c6007ba4 100644 --- a/end-to-end-computer-vision/steps/export_label_studio.py +++ b/end-to-end-computer-vision/steps/export_label_studio.py @@ -20,9 +20,9 @@ from zenml.client import Client from zenml.logger import get_logger -from materializers.label_studio_yolo_dataset_materializer import ( - LabelStudioYOLODataset, - LabelStudioYOLODatasetMaterializer, +from materializers.label_studio_export_materializer import ( + LabelStudioAnnotationExport, + LabelStudioAnnotationMaterializer, ) from utils.constants import LABELED_DATASET_NAME @@ -31,13 +31,13 @@ @step( output_materializers={ - LABELED_DATASET_NAME: LabelStudioYOLODatasetMaterializer + LABELED_DATASET_NAME: LabelStudioAnnotationMaterializer } ) def load_data_from_label_studio( dataset_name: str, ) -> Tuple[ - Annotated[LabelStudioYOLODataset, LABELED_DATASET_NAME], + Annotated[LabelStudioAnnotationExport, LABELED_DATASET_NAME], Annotated[List[int], "new_ids"], ]: """Loads data from Label Studio. @@ -61,7 +61,7 @@ def load_data_from_label_studio( if annotator and annotator._connection_available(): try: dataset = annotator.get_dataset(dataset_name=dataset_name) - ls_dataset = LabelStudioYOLODataset() + ls_dataset = LabelStudioAnnotationExport() ls_dataset.dataset = dataset current_labeled_task_ids = dataset.get_labeled_tasks_ids() diff --git a/end-to-end-computer-vision/steps/process_hf_dataset.py b/end-to-end-computer-vision/steps/process_hf_dataset.py new file mode 100644 index 00000000..8b8fa71f --- /dev/null +++ b/end-to-end-computer-vision/steps/process_hf_dataset.py @@ -0,0 +1,100 @@ +# Apache Software License 2.0 +# +# Copyright (c) ZenML GmbH 2024. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# +import os +from typing import Any, Dict + +from datasets import load_dataset +from PIL import Image +from zenml import step +from zenml.logger import get_logger + +from utils.dataset_utils import split_image_into_tiles, convert_bbox_for_ls, \ + export_to_gcp + +Image.MAX_IMAGE_PIXELS = None + + +logger = get_logger(__name__) + + +@step +def process_hf_dataset( + dataset: str, + data_source: str, + max_tile_size: int = 1000 +) -> Dict[str, Any]: + """Downloads a hf dataset and does some processing. + + Converts the labels into the label_studio format. + Also uploads the images to the datasource path. + + The return of this step maps the path of images in the local fs to the + annotations in a format that label-studio can import. + {'data/image_116.png': [ + {'original_width': 907, + 'original_height': 937, + 'image_rotation': 0, + 'value': { + 'x': 66.26240352811466, + 'y': 77.5880469583778, + 'width': 5.071664829106946, + 'height': 3.4151547491995733, + 'rotation': 0, + 'rectanglelabels': ['ship'] + }, + 'from_name': 'label', + 'to_name': 'image', + 'type': 'rectanglelabels', + 'origin': 'manual'}, ... + ] + + Args: + dataset: Name of the hf dataset + data_source: Location of the image files + max_tile_size + + Returns: + Dictionary mapping filename to bboxes. You can find an example of this + dict in the docstring above + """ + # Load dataset from huggingface + dataset = load_dataset(dataset) + data = dataset["train"] + + # Create local directory to initially copy data to + local_output_dir = "data" + if not os.path.exists(local_output_dir): + os.mkdir(local_output_dir) + + # Dictionary that maps label information onto the corresponding image + all_images = {} + + # iterate through the dataset + for i, d in enumerate(data): + img_name = f"image_{i}" + + # in case images are very large, we split them up into 4 smaller tiles + split_image_into_tiles( + d=d, + img_name=img_name, + output_dir=local_output_dir, + all_images=all_images, + data_source=data_source, + max_tile_size=max_tile_size + ) + + return all_images diff --git a/end-to-end-computer-vision/steps/train_model.py b/end-to-end-computer-vision/steps/train_model.py index 95c75d36..67be2774 100644 --- a/end-to-end-computer-vision/steps/train_model.py +++ b/end-to-end-computer-vision/steps/train_model.py @@ -20,8 +20,8 @@ from zenml import ArtifactConfig, log_artifact_metadata, step from zenml.logger import get_logger -from materializers.label_studio_yolo_dataset_materializer import ( - LabelStudioYOLODataset, +from materializers.label_studio_export_materializer import ( + LabelStudioAnnotationExport, ) from materializers.ultralytics_materializer import UltralyticsMaterializer from utils.dataset_utils import load_and_split_data @@ -35,7 +35,7 @@ ) def train_model( model: YOLO, - dataset: LabelStudioYOLODataset, + dataset: LabelStudioAnnotationExport, data_source: str, epochs: int = 100, batch_size: int = 16, diff --git a/end-to-end-computer-vision/utils/dataset_utils.py b/end-to-end-computer-vision/utils/dataset_utils.py index 54f8a2e8..806021be 100644 --- a/end-to-end-computer-vision/utils/dataset_utils.py +++ b/end-to-end-computer-vision/utils/dataset_utils.py @@ -22,8 +22,8 @@ from PIL import Image from zenml.io import fileio -from materializers.label_studio_yolo_dataset_materializer import ( - LabelStudioYOLODataset, +from materializers.label_studio_export_materializer import ( + LabelStudioAnnotationExport, ) from utils.split_data import generate_yaml, split_dataset, unzip_dataset from zenml.logger import get_logger @@ -46,7 +46,7 @@ def load_images_from_folder(folder): def load_and_split_data( - dataset: LabelStudioYOLODataset, data_source: str + dataset: LabelStudioAnnotationExport, data_source: str ) -> str: """Load data from dataset into file system and split into train/val. From daa461fac04a5ecfada73152e5144468cca19d79 Mon Sep 17 00:00:00 2001 From: Alexej Penner Date: Fri, 26 Apr 2024 11:23:17 +0200 Subject: [PATCH 110/121] Further small improvements --- end-to-end-computer-vision/data/.gitignore | 4 ++++ end-to-end-computer-vision/data/README.md | 1 + end-to-end-computer-vision/run.py | 24 ++++++++++++++++++---- 3 files changed, 25 insertions(+), 4 deletions(-) create mode 100644 end-to-end-computer-vision/data/.gitignore create mode 100644 end-to-end-computer-vision/data/README.md diff --git a/end-to-end-computer-vision/data/.gitignore b/end-to-end-computer-vision/data/.gitignore new file mode 100644 index 00000000..86d0cb27 --- /dev/null +++ b/end-to-end-computer-vision/data/.gitignore @@ -0,0 +1,4 @@ +# Ignore everything in this directory +* +# Except this file +!.gitignore \ No newline at end of file diff --git a/end-to-end-computer-vision/data/README.md b/end-to-end-computer-vision/data/README.md new file mode 100644 index 00000000..b6be7050 --- /dev/null +++ b/end-to-end-computer-vision/data/README.md @@ -0,0 +1 @@ +This directory serves as a place to store and access temporary datafiles. \ No newline at end of file diff --git a/end-to-end-computer-vision/run.py b/end-to-end-computer-vision/run.py index 2a453e26..25f1b2b9 100644 --- a/end-to-end-computer-vision/run.py +++ b/end-to-end-computer-vision/run.py @@ -74,7 +74,7 @@ "fiftyone", is_flag=True, default=False, - help="Whether to launch the FiftyOne app pipeline.", + help="Whether to launch the FiftyOne app.", ) @click.option( "--local", @@ -82,7 +82,7 @@ "train_local", is_flag=True, default=False, - help="Whether to train local.", + help="Whether to train local or an a remote orchestrator/ step operator.", ) def main( ingest_data: bool = False, @@ -143,8 +143,8 @@ def main( "along with a version of this model. After this you can " "promote the version of your choice, either through the " "frontend or with the following command: " - f" zenml model version update {ZENML_MODEL_NAME} latest " - f"-s staging." + f"`zenml model version update {ZENML_MODEL_NAME} latest " + f"-s staging`" ) if train_local: @@ -156,6 +156,22 @@ def main( training_pipeline.with_options(config_path=config_path)() if batch_inference: + try: + client.get_model_version( + model_name_or_id=ZENML_MODEL_NAME, + model_version_name_or_number_or_id=ModelStages.STAGING + ) + except KeyError: + raise RuntimeError( + "This pipeline requires that there is a version of its " + "associated model in the `STAGING` stage. Make sure you run " + "the `data_export_pipeline` at least once to create the Model " + "along with a version of this model. After this you can " + "promote the version of your choice, either through the " + "frontend or with the following command: " + f"`zenml model version update {ZENML_MODEL_NAME} staging " + f"-s production`" + ) inference_pipeline.with_options( config_path="configs/inference_pipeline.yaml" From 9230ff1f27bf11924fd91c78dd4fb2fbc0115a26 Mon Sep 17 00:00:00 2001 From: Alexej Penner Date: Fri, 26 Apr 2024 15:36:16 +0200 Subject: [PATCH 111/121] Structured README --- end-to-end-computer-vision/README.md | 117 +++++++++++++++--- .../configs/training_pipeline.yaml | 6 +- end-to-end-computer-vision/requirements.txt | 6 +- end-to-end-computer-vision/run.py | 4 +- 4 files changed, 104 insertions(+), 29 deletions(-) diff --git a/end-to-end-computer-vision/README.md b/end-to-end-computer-vision/README.md index 565b0916..03d4c723 100644 --- a/end-to-end-computer-vision/README.md +++ b/end-to-end-computer-vision/README.md @@ -12,51 +12,128 @@ label images to improve the model's performance, as well as feedback using The project uses the [Ship Detection dataset](https://huggingface.co/datasets/datadrivenscience/ship-detection) from -[DataDrivenScience](https://datadrivenscience.com/) on the Hugging Face Hub, which contains images of ships in -satellite imagery. The goal is to train a model to detect ships in the images. +[DataDrivenScience](https://datadrivenscience.com/) on the Hugging Face Hub, which contains images of ships +in satellite imagery. The goal is to train a model to detect ships in the images. Note that this isn't something that our YOLOv8 model is particularly good at out of the box, so it serves as a good example of how to build a pipeline that can be extended to other use cases. -The project consists of the following pipelines and steps: +This project needs some infrastructure and tool setup to work. Here is a list +of things that you'll need to do. -- +## ZenML -## Run this pipeline +We recommend using our [ZenML Cloud offering](https://cloud.zenml.io/) to get a +deployed instance of zenml: -### Setup - -You'll need to run the following: +### Set up your environment ```bash -zenml integration install label_studio torch gcp mlflow -y pip install -r requirements.txt -pip uninstall wandb +zenml integration install label_studio torch gcp mlflow -y +pip uninstall wandb # This comes in automatically +``` + +And to use the Albumentations and annotation plugins in the last step, +you'll need to install them: + +```bash +fiftyone plugins download https://github.com/jacobmarks/fiftyone-albumentations-plugin + +fiftyone plugins download https://github.com/voxel51/fiftyone-plugins --plugin-names @voxel51/annotation ``` -You can also set the following environment variables: +You should also set up the following environment variables: ```bash export DATA_UPLOAD_MAX_NUMBER_FILES=1000000 export WANDB_DISABLED=True ``` -And to use the Albumentations and annotation plugins, you'll need to install -them: +### Connect to your deployed zenml instance +```bash +zenml connect --url +``` + +## Cloud Provider (we will use GCP in our examples here) + +### Follow our guide to set up your credential for gcp + +[Set up a gcp service connector](https://docs.zenml.io/stacks-and-components/auth-management/gcp-service-connector) + +### Set up a bucket to persist your training data + +### Set up a bucket to use as artifact store within ZenML + +[Learn how to set up a gcp artifact store stack component within zenml here](https://docs.zenml.io/stacks-and-components/component-guide/artifact-stores) +### Set up vertex for pipeline orchestration + +[Learn how to set up a vertex orchestrator stack component within zenml here](https://docs.zenml.io/stacks-and-components/component-guide/orchestrators/vertex) +### For training on accelerators like GPUs/TPUs set up vertex + +[Learn how to set up a vertex step operator stack component within zenml here](https://docs.zenml.io/stacks-and-components/component-guide/step-operators/vertex) +### Set up Container Registry + +[Learn how to set up a google cloud container registry component within zenml here](https://docs.zenml.io/stacks-and-components/component-guide/container-registries/gcp) + +## Label Studio + +### [Start label studio locally](https://labelstud.io/guide/start) +### [Follow these zenml instructions to set up label studio as a stack component](https://docs.zenml.io/stacks-and-components/component-guide/annotators/label-studio) +### [Set up label studio to use external storage](https://labelstud.io/guide/storage) +use the first bucket that you created to data persistence + +## ZenML Stacks + +### Local Stack + +The local stack should use the `default` orchestrator, a gcp remote artifact +store that we'll call `gcp_artifact_store` here and a local label-studio annotator +that we'll refer to as `label_studio_local` ```bash -fiftyone plugins download https://github.com/jacobmarks/fiftyone-albumentations-plugin +# Make sure to replace the names with the names that you choose for your setup +zenml stack register -o default -a -an +``` -fiftyone plugins download https://github.com/voxel51/fiftyone-plugins --plugin-names @voxel51/annotation +### Remote Stack + +The remote stack should use the `vertex_orchestrator` , a `gcp_artifact_store`, +a `gcp_container_registry` and a `vertex_step_operator`. + + +```bash +# Make sure to replace the names with the names that you choose for your setup +zenml stack register -o -a -c -s ``` +The project consists of the following pipelines + +## data_ingestion_pipeline + +This pipeline downloads the [Ship Detection dataset](https://huggingface.co/datasets/datadrivenscience/ship-detection). This dataset contains +some truly huge images with a few hundred million pixels. In order to make these +useable, we break down all source images into managable tiles with a max +height/width of 1000 pixels. After this preprocessing is done, the images are +uploaded into a cloud bucket and the ground truth annotations are uploaded to +a local label studio instance. +### Configure this pipeline +The configuration file for this pipeline lives at `./configs/ingest_data.yaml`. +Make sure in particular to change `data_source` to point at the gcp bucket +which is dedicated to be the storage location for the data. Also make sure to +adjust the `ls_project_id` to correspond to the id of your project within label +studio. -# Data +### Run this pipeline + +Label studio should be up and running for the whole duration of this pipeline +run. + +```bash +zenml stack set +python run.py --ingest +``` -initial dataset is stored at: -`gs://zenml-internal-artifact-store/raw-ship-data/` -copy with: `gsutil -m cp -r gs://zenml-internal-artifact-store/raw-ship-data/ -/path` diff --git a/end-to-end-computer-vision/configs/training_pipeline.yaml b/end-to-end-computer-vision/configs/training_pipeline.yaml index a92e515a..a41fa4c5 100644 --- a/end-to-end-computer-vision/configs/training_pipeline.yaml +++ b/end-to-end-computer-vision/configs/training_pipeline.yaml @@ -8,9 +8,9 @@ steps: enable_cache: False parameters: data_source: "gs://zenml-internal-artifact-store/inference_cv_webinar" - batch_size: 16 - imgsz: 640 - epochs: 30 + batch_size: 8 + imgsz: 320 + epochs: 1 settings: docker: diff --git a/end-to-end-computer-vision/requirements.txt b/end-to-end-computer-vision/requirements.txt index 6de734b1..2bcee708 100644 --- a/end-to-end-computer-vision/requirements.txt +++ b/end-to-end-computer-vision/requirements.txt @@ -1,14 +1,12 @@ -zenml[server]>=0.55.2 +zenml[server]>=0.55.3 notebook scikit-learn<1.3 pyarrow -wandb seaborn xgboost ultralytics torch -huggingface_hub>=0.20.0 # needed for fiftyone imports of HF datasets -# fiftyone @ git+https://github.com/voxel51/fiftyone.git@develop # fiftyone v0.24.0 +huggingface_hub>=0.20.0 fiftyone datasets albumentations diff --git a/end-to-end-computer-vision/run.py b/end-to-end-computer-vision/run.py index 25f1b2b9..9ff6a4a6 100644 --- a/end-to-end-computer-vision/run.py +++ b/end-to-end-computer-vision/run.py @@ -86,7 +86,7 @@ ) def main( ingest_data: bool = False, - export_annotations: bool = False, + export_pipeline: bool = False, train: bool = False, batch_inference: bool = False, fiftyone: bool = False, @@ -111,7 +111,7 @@ def main( config_path="configs/ingest_data.yaml" )() - if export_annotations: + if export_pipeline: if not client.active_stack.orchestrator.config.is_local: raise RuntimeError( "The implementation of this pipeline " From 3d5adf521297479f8981641d728dfa4b55ff56bf Mon Sep 17 00:00:00 2001 From: Alexej Penner Date: Fri, 26 Apr 2024 15:57:46 +0200 Subject: [PATCH 112/121] Fixed small issues --- end-to-end-computer-vision/README.md | 1 + .../{data_export_alexej.yaml => data_export.yaml} | 0 .../configs/data_export_alex.yaml | 12 ------------ end-to-end-computer-vision/run.py | 6 +++--- 4 files changed, 4 insertions(+), 15 deletions(-) rename end-to-end-computer-vision/configs/{data_export_alexej.yaml => data_export.yaml} (100%) delete mode 100644 end-to-end-computer-vision/configs/data_export_alex.yaml diff --git a/end-to-end-computer-vision/README.md b/end-to-end-computer-vision/README.md index 03d4c723..c35e396a 100644 --- a/end-to-end-computer-vision/README.md +++ b/end-to-end-computer-vision/README.md @@ -80,6 +80,7 @@ zenml connect --url ### [Start label studio locally](https://labelstud.io/guide/start) ### [Follow these zenml instructions to set up label studio as a stack component](https://docs.zenml.io/stacks-and-components/component-guide/annotators/label-studio) +### Create a project within label studio and name it `ship_detection_gcp` ### [Set up label studio to use external storage](https://labelstud.io/guide/storage) use the first bucket that you created to data persistence diff --git a/end-to-end-computer-vision/configs/data_export_alexej.yaml b/end-to-end-computer-vision/configs/data_export.yaml similarity index 100% rename from end-to-end-computer-vision/configs/data_export_alexej.yaml rename to end-to-end-computer-vision/configs/data_export.yaml diff --git a/end-to-end-computer-vision/configs/data_export_alex.yaml b/end-to-end-computer-vision/configs/data_export_alex.yaml deleted file mode 100644 index c73b4d80..00000000 --- a/end-to-end-computer-vision/configs/data_export_alex.yaml +++ /dev/null @@ -1,12 +0,0 @@ -enable_cache: False - -# pipeline configuration -parameters: - dataset_name: "FiftyOne_ships_wuxqa5" - -# configuration of the Model Control Plane -model: - name: ShipDetector - license: Apache 2.0 - description: Object Detection Model. - tags: ["object detection"] diff --git a/end-to-end-computer-vision/run.py b/end-to-end-computer-vision/run.py index 9ff6a4a6..794d7f56 100644 --- a/end-to-end-computer-vision/run.py +++ b/end-to-end-computer-vision/run.py @@ -126,7 +126,7 @@ def main( # Export data from label studio data_export_pipeline.with_options( - config_path="configs/data_export_alexej.yaml" + config_path="configs/data_export.yaml" )() if train: @@ -159,12 +159,12 @@ def main( try: client.get_model_version( model_name_or_id=ZENML_MODEL_NAME, - model_version_name_or_number_or_id=ModelStages.STAGING + model_version_name_or_number_or_id=ModelStages.PRODUCTION ) except KeyError: raise RuntimeError( "This pipeline requires that there is a version of its " - "associated model in the `STAGING` stage. Make sure you run " + "associated model in the `Production` stage. Make sure you run " "the `data_export_pipeline` at least once to create the Model " "along with a version of this model. After this you can " "promote the version of your choice, either through the " From 1fbcb3ae6ab47c44906e4a4fe38207f699cf5c17 Mon Sep 17 00:00:00 2001 From: Alexej Penner Date: Mon, 29 Apr 2024 09:18:42 +0200 Subject: [PATCH 113/121] COntinued on readme --- end-to-end-computer-vision/README.md | 46 ++++++++++++++++++++++++++++ 1 file changed, 46 insertions(+) diff --git a/end-to-end-computer-vision/README.md b/end-to-end-computer-vision/README.md index c35e396a..a6ff8d7f 100644 --- a/end-to-end-computer-vision/README.md +++ b/end-to-end-computer-vision/README.md @@ -137,4 +137,50 @@ zenml stack set python run.py --ingest ``` +## data_export_pipeline + +This pipeline exports the annotations from label studio and loads it into the +zenml artifact store to make them accessible to downstream pipelines. + +### Configure this pipeline +The configuration file for this pipeline lives at `./configs/data_export.yaml`. +Make sure in particular to change `dataset_name` to reflect the name of the +dataset within Label Studio. + +### Run this pipeline + +Label studio should be up and running for the whole duration of this pipeline +run. + +```bash +zenml stack set +python run.py --export +``` + +## training_pipeline + +This pipeline trains a yolo v8 object detection model. + +### Configure this pipeline +You can choose to run +this pipeline locally or on the cloud. These two options use two different +configuration files. For local training: `./configs/training_pipeline.yaml`. +For training on the cloud: `./configs/training_pipeline_remote_gpu.yaml`. +Make sure + +### Run this pipeline + +This pipeline requires the associated model (see the model section of the +configuration yaml file) to have a version in the `staging` stage. +In order to promote the model produced by the latest run of the +`data_export_pipeline`, run the following code: + +```bash +zenml model version update latest -s staging +``` + +```bash +zenml stack set +python run.py --training +``` From 8cb0ba8ba61f879239c4a5e46c7b13a92a949140 Mon Sep 17 00:00:00 2001 From: Alex Strick van Linschoten Date: Mon, 29 Apr 2024 09:50:34 +0200 Subject: [PATCH 114/121] add logging for download --- end-to-end-computer-vision/utils/dataset_utils.py | 6 +++++- 1 file changed, 5 insertions(+), 1 deletion(-) diff --git a/end-to-end-computer-vision/utils/dataset_utils.py b/end-to-end-computer-vision/utils/dataset_utils.py index 806021be..d39de3ab 100644 --- a/end-to-end-computer-vision/utils/dataset_utils.py +++ b/end-to-end-computer-vision/utils/dataset_utils.py @@ -75,11 +75,15 @@ def load_and_split_data( images_folder = os.path.join(extract_location, "images") os.makedirs(images_folder, exist_ok=True) - for filename in filenames: + total_images = len(filenames) + logger.info(f"Downloading images from {data_source}") + for index, filename in enumerate(filenames): src_path = f"{data_source}/{filename}.png" dst_path = os.path.join(images_folder, f"{filename}.png") fileio.copy(src_path, dst_path) + if (index + 1) % 100 == 0 or index == total_images - 1: + logger.info(f"{index + 1} of {total_images} images have been downloaded...") split_dataset(extract_location, ratio=(0.7, 0.15, 0.15), seed=42) yaml_path = generate_yaml(extract_location) return yaml_path From bd76c52dfbf71bfaf1a53bb1105345f0256c7401 Mon Sep 17 00:00:00 2001 From: Alex Strick van Linschoten Date: Mon, 29 Apr 2024 09:52:05 +0200 Subject: [PATCH 115/121] formatting --- end-to-end-computer-vision/.gitignore | 1 + .../label_studio_export_materializer.py | 4 +- .../pipelines/__init__.py | 4 +- end-to-end-computer-vision/run.py | 5 +- .../steps/export_label_studio.py | 4 +- .../steps/process_hf_dataset.py | 9 +- .../utils/dataset_utils.py | 89 ++++++++++--------- 7 files changed, 60 insertions(+), 56 deletions(-) diff --git a/end-to-end-computer-vision/.gitignore b/end-to-end-computer-vision/.gitignore index e76e4bbf..15a9d546 100644 --- a/end-to-end-computer-vision/.gitignore +++ b/end-to-end-computer-vision/.gitignore @@ -3,3 +3,4 @@ data/ runs/ **/tmp* +runs_dir diff --git a/end-to-end-computer-vision/materializers/label_studio_export_materializer.py b/end-to-end-computer-vision/materializers/label_studio_export_materializer.py index 7874158e..356edc2a 100644 --- a/end-to-end-computer-vision/materializers/label_studio_export_materializer.py +++ b/end-to-end-computer-vision/materializers/label_studio_export_materializer.py @@ -15,7 +15,7 @@ import os import tempfile -from typing import Any, ClassVar, Type, TYPE_CHECKING +from typing import TYPE_CHECKING, Any, ClassVar, Type from zenml.io import fileio from zenml.materializers.base_materializer import BaseMaterializer @@ -28,7 +28,7 @@ class LabelStudioAnnotationExport: - def __init__(self, dataset: "Project" = None, filepath: str =None): + def __init__(self, dataset: "Project" = None, filepath: str = None): """ Initialize LabelStudioAnnotationExport object with optional parameters. diff --git a/end-to-end-computer-vision/pipelines/__init__.py b/end-to-end-computer-vision/pipelines/__init__.py index 7c04f0f0..6ee60a58 100644 --- a/end-to-end-computer-vision/pipelines/__init__.py +++ b/end-to-end-computer-vision/pipelines/__init__.py @@ -15,7 +15,7 @@ # limitations under the License. # -from .data_ingestion import data_ingestion_pipeline from .data_export import data_export_pipeline -from .training import training_pipeline +from .data_ingestion import data_ingestion_pipeline from .inference import inference_pipeline +from .training import training_pipeline diff --git a/end-to-end-computer-vision/run.py b/end-to-end-computer-vision/run.py index 794d7f56..b4035f30 100644 --- a/end-to-end-computer-vision/run.py +++ b/end-to-end-computer-vision/run.py @@ -17,7 +17,6 @@ from uuid import UUID import click -from zenml import Model from zenml.client import Client from zenml.enums import ModelStages from zenml.logger import get_logger @@ -133,7 +132,7 @@ def main( try: client.get_model_version( model_name_or_id=ZENML_MODEL_NAME, - model_version_name_or_number_or_id=ModelStages.STAGING + model_version_name_or_number_or_id=ModelStages.STAGING, ) except KeyError: raise RuntimeError( @@ -159,7 +158,7 @@ def main( try: client.get_model_version( model_name_or_id=ZENML_MODEL_NAME, - model_version_name_or_number_or_id=ModelStages.PRODUCTION + model_version_name_or_number_or_id=ModelStages.PRODUCTION, ) except KeyError: raise RuntimeError( diff --git a/end-to-end-computer-vision/steps/export_label_studio.py b/end-to-end-computer-vision/steps/export_label_studio.py index c6007ba4..0433ccf4 100644 --- a/end-to-end-computer-vision/steps/export_label_studio.py +++ b/end-to-end-computer-vision/steps/export_label_studio.py @@ -16,7 +16,7 @@ # from typing import Annotated, List, Tuple -from zenml import get_step_context, step, log_artifact_metadata +from zenml import log_artifact_metadata, step from zenml.client import Client from zenml.logger import get_logger @@ -71,7 +71,7 @@ def load_data_from_label_studio( metadata={ "num_images": len(current_labeled_task_ids), }, - artifact_name=LABELED_DATASET_NAME + artifact_name=LABELED_DATASET_NAME, ) return ls_dataset, current_labeled_task_ids except: diff --git a/end-to-end-computer-vision/steps/process_hf_dataset.py b/end-to-end-computer-vision/steps/process_hf_dataset.py index 8b8fa71f..50723456 100644 --- a/end-to-end-computer-vision/steps/process_hf_dataset.py +++ b/end-to-end-computer-vision/steps/process_hf_dataset.py @@ -22,8 +22,7 @@ from zenml import step from zenml.logger import get_logger -from utils.dataset_utils import split_image_into_tiles, convert_bbox_for_ls, \ - export_to_gcp +from utils.dataset_utils import split_image_into_tiles Image.MAX_IMAGE_PIXELS = None @@ -33,9 +32,7 @@ @step def process_hf_dataset( - dataset: str, - data_source: str, - max_tile_size: int = 1000 + dataset: str, data_source: str, max_tile_size: int = 1000 ) -> Dict[str, Any]: """Downloads a hf dataset and does some processing. @@ -94,7 +91,7 @@ def process_hf_dataset( output_dir=local_output_dir, all_images=all_images, data_source=data_source, - max_tile_size=max_tile_size + max_tile_size=max_tile_size, ) return all_images diff --git a/end-to-end-computer-vision/utils/dataset_utils.py b/end-to-end-computer-vision/utils/dataset_utils.py index d39de3ab..20e38a78 100644 --- a/end-to-end-computer-vision/utils/dataset_utils.py +++ b/end-to-end-computer-vision/utils/dataset_utils.py @@ -16,17 +16,17 @@ # import os import tempfile -from typing import Dict, Any, List, Tuple +from typing import Any, Dict, List, Tuple import numpy as np from PIL import Image from zenml.io import fileio +from zenml.logger import get_logger from materializers.label_studio_export_materializer import ( LabelStudioAnnotationExport, ) from utils.split_data import generate_yaml, split_dataset, unzip_dataset -from zenml.logger import get_logger logger = get_logger(__name__) @@ -83,14 +83,16 @@ def load_and_split_data( fileio.copy(src_path, dst_path) if (index + 1) % 100 == 0 or index == total_images - 1: - logger.info(f"{index + 1} of {total_images} images have been downloaded...") + logger.info( + f"{index + 1} of {total_images} images have been downloaded..." + ) split_dataset(extract_location, ratio=(0.7, 0.15, 0.15), seed=42) yaml_path = generate_yaml(extract_location) return yaml_path def convert_bbox_for_ls(x1, x2, y1, y2, width, height) -> Dict[str, Any]: - """ This function converts the bounding box coordinates to the label studio format. + """This function converts the bounding box coordinates to the label studio format. Parameters: x1, x2, y1, y2 (int): The initial bounding box coordinates in pixels. @@ -105,27 +107,27 @@ def convert_bbox_for_ls(x1, x2, y1, y2, width, height) -> Dict[str, Any]: h = (y2 - y1) / height print(f"Converting bbox to results.") return { - "original_width": width, - "original_height": height, - "image_rotation": 0, - "value": { - "x": x*100, - "y": y*100, - "width": w*100, - "height": h*100, - "rotation": 0, - "rectanglelabels": [ - "ship" - ] - }, - "from_name": "label", - "to_name": "image", - "type": "rectanglelabels", - "origin": "manual" - } - - -def crop_and_adjust_bboxes(image: np.array, bboxes: List[List[int]], crop_coordinates: Tuple[int]): + "original_width": width, + "original_height": height, + "image_rotation": 0, + "value": { + "x": x * 100, + "y": y * 100, + "width": w * 100, + "height": h * 100, + "rotation": 0, + "rectanglelabels": ["ship"], + }, + "from_name": "label", + "to_name": "image", + "type": "rectanglelabels", + "origin": "manual", + } + + +def crop_and_adjust_bboxes( + image: np.array, bboxes: List[List[int]], crop_coordinates: Tuple[int] +): """Crops an image and adjust the bboxes that are within the croped portion. Args: @@ -158,7 +160,12 @@ def crop_and_adjust_bboxes(image: np.array, bboxes: List[List[int]], crop_coordi def split_image_into_tiles( - d: Any, img_name: str, output_dir: str, all_images: Dict[str, Any], data_source: str, max_tile_size: int = 500 + d: Any, + img_name: str, + output_dir: str, + all_images: Dict[str, Any], + data_source: str, + max_tile_size: int = 500, ): """Some images are too large to be useful, this splits them into multiple tiles. @@ -171,9 +178,9 @@ def split_image_into_tiles( max_tile_size: Maximum tile size """ tile_id = 0 - img = d['image'] - bboxes = d['objects']['bbox'] - width, height = d['image'].size + img = d["image"] + bboxes = d["objects"]["bbox"] + width, height = d["image"].size logger.info(f"Processing {img_name} ...") for x in range(0, width, max_tile_size): @@ -181,43 +188,43 @@ def split_image_into_tiles( for y in range(0, height, max_tile_size): print(f"increased y={y}") - if x+max_tile_size <= width: + if x + max_tile_size <= width: x1 = x x2 = min(x + max_tile_size, width) else: # The last tile of the row also stays 500 pixels wide by # cropping from max width to the left - x1 = max(0, width-max_tile_size) + x1 = max(0, width - max_tile_size) x2 = width - if y+max_tile_size <= height: + if y + max_tile_size <= height: y1 = y y2 = min(y + max_tile_size, height) else: # The last tile of the row also stays 500 pixels high by # cropping from max height up - y1 = max(0, height-max_tile_size) + y1 = max(0, height - max_tile_size) y2 = height cropped_img, adjusted_bboxes = crop_and_adjust_bboxes( image=np.array(img), bboxes=bboxes, - crop_coordinates=(x1, y1, x2, y2) + crop_coordinates=(x1, y1, x2, y2), ) # store this tile - new_img_name = img_name + '_' + str(tile_id) - img_path = f'{output_dir}/{new_img_name}.png' + new_img_name = img_name + "_" + str(tile_id) + img_path = f"{output_dir}/{new_img_name}.png" print(f"Storing tile {tile_id} of {img_name} at {img_path} ...") export_to_gcp( data_source=data_source, img=Image.fromarray(cropped_img), img_name=new_img_name, - img_path=img_path + img_path=img_path, ) logger.info(f"Calculating new bboxes for tile {img_path}") - tile_width = x2-x1 - tile_height = y2-y1 + tile_width = x2 - x1 + tile_height = y2 - y1 results = [] for bbox in adjusted_bboxes: @@ -228,11 +235,11 @@ def split_image_into_tiles( y1=bbox[1], y2=bbox[3], width=tile_width, - height=tile_height + height=tile_height, ) ) - all_images[f'{new_img_name}.png'] = results + all_images[f"{new_img_name}.png"] = results tile_id += 1 From 09393e67118f2e2b9220551c61630e4fc4af829b Mon Sep 17 00:00:00 2001 From: Alexej Penner Date: Mon, 29 Apr 2024 11:23:55 +0200 Subject: [PATCH 116/121] Removed paths, improved README --- end-to-end-computer-vision/README.md | 19 ++++++++++++++----- .../configs/training_pipeline.yaml | 4 ++-- .../configs/training_pipeline_remote_gpu.yaml | 4 ++-- 3 files changed, 18 insertions(+), 9 deletions(-) diff --git a/end-to-end-computer-vision/README.md b/end-to-end-computer-vision/README.md index a6ff8d7f..97aa97f2 100644 --- a/end-to-end-computer-vision/README.md +++ b/end-to-end-computer-vision/README.md @@ -162,11 +162,11 @@ python run.py --export This pipeline trains a yolo v8 object detection model. ### Configure this pipeline -You can choose to run -this pipeline locally or on the cloud. These two options use two different -configuration files. For local training: `./configs/training_pipeline.yaml`. -For training on the cloud: `./configs/training_pipeline_remote_gpu.yaml`. -Make sure +You can choose to run this pipeline locally or on the cloud. These two options +use two different configuration files. For local training: +`./configs/training_pipeline.yaml`. For training on the cloud: +`./configs/training_pipeline_remote_gpu.yaml`. Make sure +`data_source` points to your cloud storage bucket. ### Run this pipeline @@ -179,8 +179,17 @@ In order to promote the model produced by the latest run of the zenml model version update latest -s staging ``` +For local training run the following code: + ```bash zenml stack set +python run.py --training --local +``` + +For remote training run the following code: + +```bash +zenml stack set python run.py --training ``` diff --git a/end-to-end-computer-vision/configs/training_pipeline.yaml b/end-to-end-computer-vision/configs/training_pipeline.yaml index a41fa4c5..e1360632 100644 --- a/end-to-end-computer-vision/configs/training_pipeline.yaml +++ b/end-to-end-computer-vision/configs/training_pipeline.yaml @@ -7,9 +7,9 @@ steps: train_model: enable_cache: False parameters: - data_source: "gs://zenml-internal-artifact-store/inference_cv_webinar" + data_source: "gs://" batch_size: 8 - imgsz: 320 + imgsz: 720 epochs: 1 settings: diff --git a/end-to-end-computer-vision/configs/training_pipeline_remote_gpu.yaml b/end-to-end-computer-vision/configs/training_pipeline_remote_gpu.yaml index e48c591a..18998799 100644 --- a/end-to-end-computer-vision/configs/training_pipeline_remote_gpu.yaml +++ b/end-to-end-computer-vision/configs/training_pipeline_remote_gpu.yaml @@ -21,9 +21,9 @@ steps: step_operator: gcp_a100 enable_step_logs: False parameters: - data_source: "gs://zenml-internal-artifact-store/inference_cv_webinar" + data_source: "gs://" batch_size: 8 - imgsz: 480 + imgsz: 720 epochs: 50000 is_quad_gpu_env: True settings: From c69827d440b5cb7bb4470bb5c6603c0ea53f8f80 Mon Sep 17 00:00:00 2001 From: Alexej Penner Date: Fri, 3 May 2024 23:30:38 +0200 Subject: [PATCH 117/121] Fixed configuration files and README --- end-to-end-computer-vision/README.md | 39 ++++++++++++++ .../configs/cloud_inference.yaml | 30 ----------- .../configs/data_export.yaml | 2 +- .../configs/data_export_alex.yaml | 12 ----- .../configs/data_export_alexej.yaml | 12 ----- .../configs/fiftyone.yaml | 22 -------- .../configs/inference_pipeline.yaml | 2 +- .../configs/ingest_data.yaml | 2 +- .../configs/training.yaml | 33 ------------ .../configs/training_gpu.yaml | 53 ------------------- .../configs/training_pipeline.yaml | 2 +- .../configs/training_pipeline_remote_gpu.yaml | 2 +- 12 files changed, 44 insertions(+), 167 deletions(-) delete mode 100644 end-to-end-computer-vision/configs/cloud_inference.yaml delete mode 100644 end-to-end-computer-vision/configs/data_export_alex.yaml delete mode 100644 end-to-end-computer-vision/configs/data_export_alexej.yaml delete mode 100644 end-to-end-computer-vision/configs/fiftyone.yaml delete mode 100644 end-to-end-computer-vision/configs/training.yaml delete mode 100644 end-to-end-computer-vision/configs/training_gpu.yaml diff --git a/end-to-end-computer-vision/README.md b/end-to-end-computer-vision/README.md index 97aa97f2..56b47a16 100644 --- a/end-to-end-computer-vision/README.md +++ b/end-to-end-computer-vision/README.md @@ -193,3 +193,42 @@ zenml stack set python run.py --training ``` + +## inference_pipeline + +This pipeline perform inference on the object detection model. + +### Configure this pipeline +You can configure this pipeline at the following yaml file +`./configs/inference_pipeline.yaml`. Make sure +`data_source` points to your cloud storage bucket that contains images that you +want to perform batch inference on + +### Run this pipeline + +This pipeline requires the associated model (see the model section of the +configuration yaml file) to have a version in the `production` stage. +In order to promote the model produced by the latest run of the +`training_pipeline`, run the following code: + +```bash +zenml model version update staging -s production +``` + +```bash +zenml stack set +python run.py --inference +``` + + +## Analyze and Curate your data through fiftyone + +Now to close the loop, we will import the predictions into fiftyone. All you'll +need to do is run: + +```bash +python run.py --fiftyone +``` + +Within fiftyone, you can now analyze all the predictions and export them back +to label studio for finetuned labeling and retraining. diff --git a/end-to-end-computer-vision/configs/cloud_inference.yaml b/end-to-end-computer-vision/configs/cloud_inference.yaml deleted file mode 100644 index 650dc901..00000000 --- a/end-to-end-computer-vision/configs/cloud_inference.yaml +++ /dev/null @@ -1,30 +0,0 @@ -enable_cache: False - -settings: - docker: - apt_packages: - - ffmpeg - - libsm6 - - libxext6 - - libcurl4 - required_integrations: - - gcp - - github - requirements: - - ultralytics - - fiftyone - -steps: - create_fiftyone_dataset: - enable_cache: False - enable_step_logs: False - parameters: - inference_data_source: "gs://zenml-internal-artifact-store/inference_cv_webinar" - -# configuration of the Model Control Plane -model: - name: ShipDetector - license: Apache 2.0 - description: Object Detection Model. - tags: ["object detection"] - version: production \ No newline at end of file diff --git a/end-to-end-computer-vision/configs/data_export.yaml b/end-to-end-computer-vision/configs/data_export.yaml index d57be2e6..62d3157c 100644 --- a/end-to-end-computer-vision/configs/data_export.yaml +++ b/end-to-end-computer-vision/configs/data_export.yaml @@ -2,7 +2,7 @@ enable_cache: False # pipeline configuration parameters: - dataset_name: "ship_detection_gcp" + dataset_name: "ship_detection_gcp" # This is the name of the dataset in label studio # Configuration of the Model Control Plane model: diff --git a/end-to-end-computer-vision/configs/data_export_alex.yaml b/end-to-end-computer-vision/configs/data_export_alex.yaml deleted file mode 100644 index c73b4d80..00000000 --- a/end-to-end-computer-vision/configs/data_export_alex.yaml +++ /dev/null @@ -1,12 +0,0 @@ -enable_cache: False - -# pipeline configuration -parameters: - dataset_name: "FiftyOne_ships_wuxqa5" - -# configuration of the Model Control Plane -model: - name: ShipDetector - license: Apache 2.0 - description: Object Detection Model. - tags: ["object detection"] diff --git a/end-to-end-computer-vision/configs/data_export_alexej.yaml b/end-to-end-computer-vision/configs/data_export_alexej.yaml deleted file mode 100644 index 22f2464e..00000000 --- a/end-to-end-computer-vision/configs/data_export_alexej.yaml +++ /dev/null @@ -1,12 +0,0 @@ -enable_cache: False - -# pipeline configuration -parameters: - dataset_name: "ship_detection_gcp" # "ship_detection" - -# Configuration of the Model Control Plane -model: - name: ShipDetector - license: Apache 2.0 - description: Object Detection Model. - tags: ["object detection"] diff --git a/end-to-end-computer-vision/configs/fiftyone.yaml b/end-to-end-computer-vision/configs/fiftyone.yaml deleted file mode 100644 index e94f5e98..00000000 --- a/end-to-end-computer-vision/configs/fiftyone.yaml +++ /dev/null @@ -1,22 +0,0 @@ -enable_cache: False - -settings: - docker: - apt_packages: - - ffmpeg - - libsm6 - - libxext6 - required_integrations: - - gcp - - github - requirements: - - ultralytics - - fiftyone - -# configuration of the Model Control Plane -# model: -# name: ShipDetector -# license: Apache 2.0 -# description: Object Detection Model. -# tags: ["object detection"] -# version: production diff --git a/end-to-end-computer-vision/configs/inference_pipeline.yaml b/end-to-end-computer-vision/configs/inference_pipeline.yaml index 650dc901..f6dd22d8 100644 --- a/end-to-end-computer-vision/configs/inference_pipeline.yaml +++ b/end-to-end-computer-vision/configs/inference_pipeline.yaml @@ -19,7 +19,7 @@ steps: enable_cache: False enable_step_logs: False parameters: - inference_data_source: "gs://zenml-internal-artifact-store/inference_cv_webinar" + inference_data_source: # Insert your bucket path here where the inference images live e.g. "gs://foo/bar" # configuration of the Model Control Plane model: diff --git a/end-to-end-computer-vision/configs/ingest_data.yaml b/end-to-end-computer-vision/configs/ingest_data.yaml index fa4afd65..df8c48df 100644 --- a/end-to-end-computer-vision/configs/ingest_data.yaml +++ b/end-to-end-computer-vision/configs/ingest_data.yaml @@ -5,7 +5,7 @@ steps: enable_step_logs: False parameters: dataset: "datadrivenscience/ship-detection" - data_source: "gs://zenml-internal-artifact-store/inference_cv_webinar"# "gs://zenml-internal-artifact-store/label_studio_cv_webinar" + data_source: # Insert your bucket path here where the training images will live e.g. "gs://foo/bar" upload_labels_to_label_studio: enable_cache: False parameters: diff --git a/end-to-end-computer-vision/configs/training.yaml b/end-to-end-computer-vision/configs/training.yaml deleted file mode 100644 index 6b8d470e..00000000 --- a/end-to-end-computer-vision/configs/training.yaml +++ /dev/null @@ -1,33 +0,0 @@ -parameters: - model_checkpoint: yolov8l.pt - -steps: - load_model: - enable_cache: True - train_model: - enable_cache: False - parameters: - data_source: "gs://zenml-internal-artifact-store/label_studio_cv_webinar" - batch_size: 16 - imgsz: 640 - epochs: 30 - -settings: - docker: - apt_packages: - - ffmpeg - - libsm6 - - libxext6 - required_integrations: - - gcp - - github - requirements: - - ultralytics - -# configuration of the Model Control Plane -model: - name: ShipDetector - license: Apache 2.0 - description: Object Detection Model. - tags: ["object detection"] - version: staging diff --git a/end-to-end-computer-vision/configs/training_gpu.yaml b/end-to-end-computer-vision/configs/training_gpu.yaml deleted file mode 100644 index 5b5a3e4b..00000000 --- a/end-to-end-computer-vision/configs/training_gpu.yaml +++ /dev/null @@ -1,53 +0,0 @@ -parameters: - model_checkpoint: yolov8l.pt - -settings: - docker: - apt_packages: - - ffmpeg - - libsm6 - - libxext6 - required_integrations: - - gcp - - github - requirements: - - ultralytics - -steps: - load_model: - enable_cache: True - train_model: - enable_cache: False - step_operator: gcp_a100 - enable_step_logs: False - parameters: - data_source: "gs://zenml-internal-artifact-store/label_studio_cv_webinar" - batch_size: 8 - imgsz: 480 - epochs: 5000 - is_quad_gpu_env: True - settings: - step_operator.vertex: - accelerator_type: NVIDIA_TESLA_T4 # NVIDIA_TESLA_A100 # see https://cloud.google.com/vertex-ai/docs/reference/rest/v1/MachineSpec#AcceleratorType - accelerator_count: 4 - disk_size_gb: 25 - docker: - parent_image: pytorch/pytorch:2.2.0-cuda11.8-cudnn8-runtime - required_integrations: - - gcp - - github - requirements: - - ultralytics - - zenml - - numpy - environment: - MKL_SERVICE_FORCE_INTEL: 1 - - -# configuration of the Model Control Plane -model: - name: ShipDetector - license: Apache 2.0 - description: Object Detection Model. - tags: ["object detection"] - version: staging \ No newline at end of file diff --git a/end-to-end-computer-vision/configs/training_pipeline.yaml b/end-to-end-computer-vision/configs/training_pipeline.yaml index e1360632..f49ba554 100644 --- a/end-to-end-computer-vision/configs/training_pipeline.yaml +++ b/end-to-end-computer-vision/configs/training_pipeline.yaml @@ -7,7 +7,7 @@ steps: train_model: enable_cache: False parameters: - data_source: "gs://" + data_source: # Insert your bucket path here where the training images lives e.g. "gs://foo/bar" batch_size: 8 imgsz: 720 epochs: 1 diff --git a/end-to-end-computer-vision/configs/training_pipeline_remote_gpu.yaml b/end-to-end-computer-vision/configs/training_pipeline_remote_gpu.yaml index 18998799..1ee643ab 100644 --- a/end-to-end-computer-vision/configs/training_pipeline_remote_gpu.yaml +++ b/end-to-end-computer-vision/configs/training_pipeline_remote_gpu.yaml @@ -21,7 +21,7 @@ steps: step_operator: gcp_a100 enable_step_logs: False parameters: - data_source: "gs://" + data_source: # Insert your bucket path here where the training images lives e.g. "gs://foo/bar" batch_size: 8 imgsz: 720 epochs: 50000 From 71914fbe4f266116683cdd3d00bb37ce1297f191 Mon Sep 17 00:00:00 2001 From: Alex Strick van Linschoten Date: Mon, 6 May 2024 09:14:30 +0200 Subject: [PATCH 118/121] fix typos --- .typos.toml | 14 +++++++++++++- end-to-end-computer-vision/README.md | 2 +- 2 files changed, 14 insertions(+), 2 deletions(-) diff --git a/.typos.toml b/.typos.toml index 694ed501..1c3a4859 100644 --- a/.typos.toml +++ b/.typos.toml @@ -1,5 +1,14 @@ [files] -extend-exclude = ["*.csv", "sign-language-detection-yolov5/*", "orbit-user-analysis/steps/report.py", "customer-satisfaction/pipelines/deployment_pipeline.py", "customer-satisfaction/streamlit_app.py", "nba-pipeline/Building and Using An MLOPs Stack With ZenML.ipynb", "customer-satisfaction/tests/data_test.py"] +extend-exclude = [ + "*.csv", + "sign-language-detection-yolov5/*", + "orbit-user-analysis/steps/report.py", + "customer-satisfaction/pipelines/deployment_pipeline.py", + "customer-satisfaction/streamlit_app.py", + "nba-pipeline/Building and Using An MLOPs Stack With ZenML.ipynb", + "customer-satisfaction/tests/data_test.py", + "end-to-end-computer-vision/**/*.ipynb" +] [default.extend-identifiers] # HashiCorp = "HashiCorp" @@ -14,6 +23,9 @@ lenght = "lenght" preprocesser = "preprocesser" Preprocesser = "Preprocesser" Implicitly = "Implicitly" +fo = "fo" +mapp = "mapp" +polution = "polution" [default] locale = "en-us" diff --git a/end-to-end-computer-vision/README.md b/end-to-end-computer-vision/README.md index 56b47a16..d12988ce 100644 --- a/end-to-end-computer-vision/README.md +++ b/end-to-end-computer-vision/README.md @@ -115,7 +115,7 @@ The project consists of the following pipelines This pipeline downloads the [Ship Detection dataset](https://huggingface.co/datasets/datadrivenscience/ship-detection). This dataset contains some truly huge images with a few hundred million pixels. In order to make these -useable, we break down all source images into managable tiles with a max +useable, we break down all source images into manageable tiles with a max height/width of 1000 pixels. After this preprocessing is done, the images are uploaded into a cloud bucket and the ground truth annotations are uploaded to a local label studio instance. From 21c55df652e41027c5ea7ab1277a34b076564a12 Mon Sep 17 00:00:00 2001 From: Alex Strick van Linschoten Date: Mon, 6 May 2024 09:46:30 +0200 Subject: [PATCH 119/121] fix README --- end-to-end-computer-vision/README.md | 135 ++++++++++++++------------- 1 file changed, 71 insertions(+), 64 deletions(-) diff --git a/end-to-end-computer-vision/README.md b/end-to-end-computer-vision/README.md index d12988ce..eae51d8d 100644 --- a/end-to-end-computer-vision/README.md +++ b/end-to-end-computer-vision/README.md @@ -12,18 +12,18 @@ label images to improve the model's performance, as well as feedback using The project uses the [Ship Detection dataset](https://huggingface.co/datasets/datadrivenscience/ship-detection) from -[DataDrivenScience](https://datadrivenscience.com/) on the Hugging Face Hub, which contains images of ships -in satellite imagery. The goal is to train a model to detect ships in the images. -Note that this isn't something that our YOLOv8 model is particularly good at out -of the box, so it serves as a good example of how to build a pipeline that can -be extended to other use cases. +[DataDrivenScience](https://datadrivenscience.com/) on the Hugging Face Hub, +which contains images of ships in satellite imagery. The goal is to train a +model to detect ships in the images. Note that this isn't something that our +YOLOv8 model is particularly good at out of the box, so it serves as a good +example of how to build a pipeline that can be extended to other use cases. -This project needs some infrastructure and tool setup to work. Here is a list -of things that you'll need to do. +This project needs some infrastructure and tool setup to work. Here is a list of +things that you'll need to do. ## ZenML -We recommend using our [ZenML Cloud offering](https://cloud.zenml.io/) to get a +We recommend using our [ZenML Cloud offering](https://cloud.zenml.io/) to get a deployed instance of zenml: ### Set up your environment @@ -34,8 +34,8 @@ zenml integration install label_studio torch gcp mlflow -y pip uninstall wandb # This comes in automatically ``` -And to use the Albumentations and annotation plugins in the last step, -you'll need to install them: +And to use the Albumentations and annotation plugins in the last step, you'll +need to install them: ```bash fiftyone plugins download https://github.com/jacobmarks/fiftyone-albumentations-plugin @@ -50,47 +50,56 @@ export DATA_UPLOAD_MAX_NUMBER_FILES=1000000 export WANDB_DISABLED=True ``` -### Connect to your deployed zenml instance +### Connect to your deployed ZenML instance + ```bash zenml connect --url ``` -## Cloud Provider (we will use GCP in our examples here) +## Cloud Provider + +We will use GCP in the commands listed below, but it will work for other cloud +providers. -### Follow our guide to set up your credential for gcp +### Follow our guide to set up your credential for GCP -[Set up a gcp service connector](https://docs.zenml.io/stacks-and-components/auth-management/gcp-service-connector) +[Set up a GCP service +connector](https://docs.zenml.io/stacks-and-components/auth-management/gcp-service-connector) ### Set up a bucket to persist your training data ### Set up a bucket to use as artifact store within ZenML -[Learn how to set up a gcp artifact store stack component within zenml here](https://docs.zenml.io/stacks-and-components/component-guide/artifact-stores) +[Learn how to set up a GCP artifact store stack component within zenml +here](https://docs.zenml.io/stacks-and-components/component-guide/artifact-stores) ### Set up vertex for pipeline orchestration -[Learn how to set up a vertex orchestrator stack component within zenml here](https://docs.zenml.io/stacks-and-components/component-guide/orchestrators/vertex) -### For training on accelerators like GPUs/TPUs set up vertex +[Learn how to set up a Vertex orchestrator stack component within zenml +here](https://docs.zenml.io/stacks-and-components/component-guide/orchestrators/vertex) +### For training on accelerators like GPUs/TPUs set up Vertex -[Learn how to set up a vertex step operator stack component within zenml here](https://docs.zenml.io/stacks-and-components/component-guide/step-operators/vertex) +[Learn how to set up a Vertex step operator stack component within zenml +here](https://docs.zenml.io/stacks-and-components/component-guide/step-operators/vertex) ### Set up Container Registry -[Learn how to set up a google cloud container registry component within zenml here](https://docs.zenml.io/stacks-and-components/component-guide/container-registries/gcp) +[Learn how to set up a google cloud container registry component within zenml +here](https://docs.zenml.io/stacks-and-components/component-guide/container-registries/gcp) ## Label Studio -### [Start label studio locally](https://labelstud.io/guide/start) -### [Follow these zenml instructions to set up label studio as a stack component](https://docs.zenml.io/stacks-and-components/component-guide/annotators/label-studio) -### Create a project within label studio and name it `ship_detection_gcp` -### [Set up label studio to use external storage](https://labelstud.io/guide/storage) +### [Start Label Studio locally](https://labelstud.io/guide/start) +### [Follow these ZenML instructions to set up Label Studio as a stack component](https://docs.zenml.io/stacks-and-components/component-guide/annotators/label-studio) +### Create a project within Label Studio and name it `ship_detection_gcp` +### [Set up Label Studio to use external storage](https://labelstud.io/guide/storage) use the first bucket that you created to data persistence ## ZenML Stacks ### Local Stack -The local stack should use the `default` orchestrator, a gcp remote artifact -store that we'll call `gcp_artifact_store` here and a local label-studio annotator -that we'll refer to as `label_studio_local` +The local stack should use the `default` orchestrator, a gcp remote artifact +store that we'll call `gcp_artifact_store` here and a local label-studio +annotator that we'll refer to as `label_studio_local`. ```bash # Make sure to replace the names with the names that you choose for your setup @@ -108,28 +117,27 @@ a `gcp_container_registry` and a `vertex_step_operator`. zenml stack register -o -a -c -s ``` - -The project consists of the following pipelines +The project consists of the following pipelines: ## data_ingestion_pipeline -This pipeline downloads the [Ship Detection dataset](https://huggingface.co/datasets/datadrivenscience/ship-detection). This dataset contains -some truly huge images with a few hundred million pixels. In order to make these -useable, we break down all source images into manageable tiles with a max -height/width of 1000 pixels. After this preprocessing is done, the images are -uploaded into a cloud bucket and the ground truth annotations are uploaded to -a local label studio instance. +This pipeline downloads the [Ship Detection +dataset](https://huggingface.co/datasets/datadrivenscience/ship-detection). This +dataset contains some truly huge images with a few hundred million pixels. In +order to make these useable, we break down all source images into manageable +tiles with a maximum height/width of 1000 pixels. After this preprocessing is +done, the images are uploaded into a cloud bucket and the ground truth +annotations are uploaded to a local Label Studio instance. ### Configure this pipeline The configuration file for this pipeline lives at `./configs/ingest_data.yaml`. -Make sure in particular to change `data_source` to point at the gcp bucket -which is dedicated to be the storage location for the data. Also make sure to -adjust the `ls_project_id` to correspond to the id of your project within label -studio. +Make sure in particular to change `data_source` to point at the GCP bucket which +is dedicated to be the storage location for the data. Also make sure to adjust +the `ls_project_id` to correspond to the id of your project within Label Studio. ### Run this pipeline -Label studio should be up and running for the whole duration of this pipeline +Label Studio should be up and running for the whole duration of this pipeline run. ```bash @@ -139,17 +147,17 @@ python run.py --ingest ## data_export_pipeline -This pipeline exports the annotations from label studio and loads it into the -zenml artifact store to make them accessible to downstream pipelines. +This pipeline exports the annotations from Label Studio and loads it into the +ZenML artifact store to make them accessible to downstream pipelines. ### Configure this pipeline The configuration file for this pipeline lives at `./configs/data_export.yaml`. -Make sure in particular to change `dataset_name` to reflect the name of the +Make sure in particular to change `dataset_name` to reflect the name of the dataset within Label Studio. ### Run this pipeline -Label studio should be up and running for the whole duration of this pipeline +Label Studio should be up and running for the whole duration of this pipeline run. ```bash @@ -159,21 +167,21 @@ python run.py --export ## training_pipeline -This pipeline trains a yolo v8 object detection model. +This pipeline trains a YOLOv8 object detection model. ### Configure this pipeline -You can choose to run this pipeline locally or on the cloud. These two options -use two different configuration files. For local training: -`./configs/training_pipeline.yaml`. For training on the cloud: -`./configs/training_pipeline_remote_gpu.yaml`. Make sure -`data_source` points to your cloud storage bucket. +You can choose to run this pipeline locally or on the cloud. These two options +use two different configuration files. For local training: +`./configs/training_pipeline.yaml`. For training on the cloud: +`./configs/training_pipeline_remote_gpu.yaml`. Make sure `data_source` points to +your cloud storage bucket. ### Run this pipeline This pipeline requires the associated model (see the model section of the -configuration yaml file) to have a version in the `staging` stage. -In order to promote the model produced by the latest run of the -`data_export_pipeline`, run the following code: +configuration yaml file) to have a version in the `staging` stage. In order to +promote the model produced by the latest run of the `data_export_pipeline`, run +the following code: ```bash zenml model version update latest -s staging @@ -193,23 +201,22 @@ zenml stack set python run.py --training ``` - ## inference_pipeline -This pipeline perform inference on the object detection model. +This pipeline performs inference on the object detection model. ### Configure this pipeline You can configure this pipeline at the following yaml file -`./configs/inference_pipeline.yaml`. Make sure -`data_source` points to your cloud storage bucket that contains images that you -want to perform batch inference on +`./configs/inference_pipeline.yaml`. Make sure `data_source` points to your +cloud storage bucket that contains images that you want to perform batch +inference on ### Run this pipeline This pipeline requires the associated model (see the model section of the -configuration yaml file) to have a version in the `production` stage. -In order to promote the model produced by the latest run of the -`training_pipeline`, run the following code: +configuration yaml file) to have a version in the `production` stage. In order +to promote the model produced by the latest run of the `training_pipeline`, run +the following code: ```bash zenml model version update staging -s production @@ -221,14 +228,14 @@ python run.py --inference ``` -## Analyze and Curate your data through fiftyone +## Analyze and Curate your data through FiftyOne -Now to close the loop, we will import the predictions into fiftyone. All you'll +Now to close the loop, we will import the predictions into FiftyOne. All you'll need to do is run: ```bash python run.py --fiftyone ``` -Within fiftyone, you can now analyze all the predictions and export them back -to label studio for finetuned labeling and retraining. +Within FiftyOne, you can now analyze all the predictions and export them back to +Label Studio for finetuned labeling and retraining. From 6b985df1e1326890e4941cad21572e4d59571fdf Mon Sep 17 00:00:00 2001 From: Alex Strick van Linschoten Date: Mon, 6 May 2024 09:49:00 +0200 Subject: [PATCH 120/121] Update end-to-end-computer-vision/steps/process_hf_dataset.py --- end-to-end-computer-vision/steps/process_hf_dataset.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/end-to-end-computer-vision/steps/process_hf_dataset.py b/end-to-end-computer-vision/steps/process_hf_dataset.py index 50723456..e1847c3d 100644 --- a/end-to-end-computer-vision/steps/process_hf_dataset.py +++ b/end-to-end-computer-vision/steps/process_hf_dataset.py @@ -34,7 +34,7 @@ def process_hf_dataset( dataset: str, data_source: str, max_tile_size: int = 1000 ) -> Dict[str, Any]: - """Downloads a hf dataset and does some processing. + """Downloads a Hugging Face dataset and does some processing. Converts the labels into the label_studio format. Also uploads the images to the datasource path. From c26af1bc388c7cb281e037489e66b2ff9c968ba0 Mon Sep 17 00:00:00 2001 From: Alexej Penner Date: Tue, 7 May 2024 17:20:09 +0200 Subject: [PATCH 121/121] Added iamge --- end-to-end-computer-vision/README.md | 2 ++ end-to-end-computer-vision/_assets/diagram.png | Bin 0 -> 236163 bytes 2 files changed, 2 insertions(+) create mode 100644 end-to-end-computer-vision/_assets/diagram.png diff --git a/end-to-end-computer-vision/README.md b/end-to-end-computer-vision/README.md index 56b47a16..f46bfdd5 100644 --- a/end-to-end-computer-vision/README.md +++ b/end-to-end-computer-vision/README.md @@ -4,6 +4,8 @@ This is a project that demonstrates an end-to-end computer vision pipeline using ZenML. The pipeline is designed to be modular and flexible, allowing for easy experimentation and extension. +![diagram.png](_assets/diagram.png) + The project showcases the full lifecycle of a computer vision project, from data collection and preprocessing to model training and evaluation. 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