Bump dependabot security alerts (opencv, torch, lightning, jupyterlab) (

#61)

* Bump opencv-python from 4.7.0.72 to 4.8.1.78

Bumps [opencv-python](https://github.com/opencv/opencv-python) from 4.7.0.72 to 4.8.1.78.
- [Release notes](https://github.com/opencv/opencv-python/releases)
- [Commits](https://github.com/opencv/opencv-python/commits)

---
updated-dependencies:
- dependency-name: opencv-python
  dependency-type: direct:production
...

Signed-off-by: dependabot[bot] <[email protected]>

* Patched all package versions flagged by dependabot security alerts

* Fixed malpolon.models.standard_prediction_system.predict_point not casting input to gpu correctly for certain data types.
Updated test_examples to correctly run all train, transfer_learning and inference runs

* Updated python package requirements to make the whole project compatible with dependabot security alerts

---------

Signed-off-by: dependabot[bot] <[email protected]>
Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>
Co-authored-by: aerodynamic-sauce-pan <[email protected]>

examples/custom_train/sentinel-2a-rgbnir/cnn_on_rgbnir_torchgeo.py

@@ -83,7 +83,7 @@ def main(cfg: DictConfig) -> None:
         test_data_point = test_data_point.resize_(1, *test_data_point.shape)
 
         prediction = model_loaded.predict_point(cfg.run.checkpoint_path,
-                                                test_data_point])
+                                                test_data_point)
         preds, probas = datamodule.predict_logits_to_class(prediction,
                                                            datamodule.get_test_dataset().unique_labels)
         datamodule.export_predict_csv(preds, probas,

malpolon/models/standard_prediction_systems.py

@@ -339,7 +339,7 @@ def predict_point(
         self.load_state_dict(ckpt['state_dict'])
         self.model.eval()
         with torch.no_grad():
-            if '__iter__' in dir(data):
+            if isinstance(data, (tuple, list, set, dict)):
                 for i, d in enumerate(data):
                     data[i] = d.to(device) if isinstance(d, torch.Tensor) else d
                 prediction = self.model(*data)

malpolon/tests/test_examples.py

@@ -258,7 +258,7 @@
     ],
 }
 
-# @pytest.mark.skip(reason="Slow or no guarantee of having the data available.")
+@pytest.mark.skip(reason="Slow or no guarantee of having the data available.")
 def test_train_inference_examples():
     ckpt_path = ''
     for expe_name, v in EXAMPLE_PATHS.items():
@@ -304,7 +304,7 @@ def test_train_inference_examples():
     print(f'\n{INFO}[INFO] Done. {RESET}')
 
 
-@pytest.mark.skip(reason="Slow or no guarantee of having the data available.")
+# @pytest.mark.skip(reason="Slow or no guarantee of having the data available.")
 def test_GLC22_examples():
     ckpt_path = ''
     for expe_name, v in GLC22_EXAMPLE_PATHS.items():
@@ -353,7 +353,7 @@ def test_GLC22_examples():
     print(f'\n{INFO}[INFO] Done. {RESET}')
 
 
-# @pytest.mark.skip(reason="Slow or no guarantee of having the data available.")
+@pytest.mark.skip(reason="Slow or no guarantee of having the data available.")
 def test_GLC23_examples():
     ckpt_path = ''
     for expe_name, v in GLC23_EXAMPLE_PATHS.items():
@@ -402,7 +402,7 @@ def test_GLC23_examples():
     print(f'\n{INFO}[INFO] Done. {RESET}')
 
 
-@pytest.mark.skip(reason="Impossible for pytest to run because user input is needed to validate data download.")
+# @pytest.mark.skip(reason="Impossible for pytest to run because user input is needed to validate data download.")
 def test_GLC24_pre_extracted_examples():
     ckpt_path = ''
     for expe_name, v in GLC24_PRE_EXTRACTED_EXAMPLE_PATHS.items():

requirements_python3.10.txt

@@ -9,7 +9,7 @@ ipykernel==6.18.2
 ipython==8.17.2
 jupyter==1.0.0
 jupyter-server==2.11.2
-jupyterlab==3.6.7
+jupyterlab==3.6.8
 kaggle==1.5.16
 lightning==2.0.9.post0
 Markdown==3.4.1
@@ -19,7 +19,7 @@ numpy==1.26.4
 odc-geo==0.3.2
 odc-stac==0.3.3
 omegaconf==2.3.0
-opencv-python==4.7.0.72
+opencv-python==4.8.1.78
 pandas==2.2.1
 Pillow==10.3.0
 planetary-computer==1.0.0
@@ -30,7 +30,7 @@ pyproj==3.6.1
 pystac==1.6.1
 pystac-client==0.5.1
 pytest==7.2.2
-pytorch-lightning==2.1.0
+pytorch-lightning==2.3.3
 PyYAML==6.0.1
 rasterio==1.3.8.post1
 scikit-learn==1.5.0
@@ -42,10 +42,10 @@ tensorboard==2.14.1
 protobuf==4.25
 tifffile==2022.10.10
 timm==0.9.2
-torch==2.1.0
+torch==2.2.0
 torchgeo==0.5.0
 torchmetrics==1.2.0
-torchvision==0.16.0
+torchvision==0.17.0
 tqdm==4.66.3
 verde==1.8.0
 yarl==1.9.2