1_Test_LR_shedulers.ipynb

{
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      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 448
        },
        "id": "P3bFvrXa-PgC",
        "outputId": "ba4b3cf3-79a9-48ea-839c-6aba58b896b5"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "check environment \n",
            "I'm running on Colab\n",
            "Cuda available True\n",
            "installing required packages ...\n",
            "Manage dataset\n",
            "Mounted at /content/drive\n"
          ]
        },
        {
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              "            const handshake = new Postmate({\n",
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              "                url: 'https://wandb.ai/authorize'\n",
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              "            const timeout = setTimeout(() => reject(\"Couldn't auto authenticate\"), 5000)\n",
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          "text": [
            "\u001b[34m\u001b[1mwandb\u001b[0m: Logging into wandb.ai. (Learn how to deploy a W&B server locally: https://wandb.me/wandb-server)\n",
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            "wandb: Paste an API key from your profile and hit enter, or press ctrl+c to quit:"
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          "text": [
            " ··········\n"
          ]
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        {
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          "text": [
            "\u001b[34m\u001b[1mwandb\u001b[0m: Appending key for api.wandb.ai to your netrc file: /root/.netrc\n"
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        {
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              "Tracking run with wandb version 0.15.7"
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              "Run data is saved locally in <code>/content/wandb/run-20230731_153253-ack48lwj</code>"
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            "text/html": [
              "Syncing run <strong><a href='https://wandb.ai/team-invonto/Card_detection/runs/ack48lwj' target=\"_blank\">zany-waterfall-326</a></strong> to <a href='https://wandb.ai/team-invonto/Card_detection' target=\"_blank\">Weights & Biases</a> (<a href='https://wandb.me/run' target=\"_blank\">docs</a>)<br/>"
            ]
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              "<IPython.core.display.HTML object>"
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            "text/html": [
              " View project at <a href='https://wandb.ai/team-invonto/Card_detection' target=\"_blank\">https://wandb.ai/team-invonto/Card_detection</a>"
            ]
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              " View run at <a href='https://wandb.ai/team-invonto/Card_detection/runs/ack48lwj' target=\"_blank\">https://wandb.ai/team-invonto/Card_detection/runs/ack48lwj</a>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "dowload data from the wandb to temp\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\u001b[34m\u001b[1mwandb\u001b[0m: Downloading large artifact Dataset_July2023_train213_val31:v0, 73.86MB. 2 files... \n",
            "\u001b[34m\u001b[1mwandb\u001b[0m:   2 of 2 files downloaded.  \n",
            "Done. 0:0:6.5\n"
          ]
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              "Waiting for W&B process to finish... <strong style=\"color:green\">(success).</strong>"
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              " View run <strong style=\"color:#cdcd00\">zany-waterfall-326</strong> at: <a href='https://wandb.ai/team-invonto/Card_detection/runs/ack48lwj' target=\"_blank\">https://wandb.ai/team-invonto/Card_detection/runs/ack48lwj</a><br/>Synced 5 W&B file(s), 0 media file(s), 0 artifact file(s) and 0 other file(s)"
            ]
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              "Find logs at: <code>./wandb/run-20230731_153253-ack48lwj/logs</code>"
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      "source": [
        "import os\n",
        "import subprocess\n",
        "import sys\n",
        "import torch\n",
        "import matplotlib.pyplot as plt\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "import os\n",
        "import cv2\n",
        "import zipfile\n",
        "from torchvision import models\n",
        "import torchvision.transforms as transforms\n",
        "import torchvision\n",
        "from torch.utils.data import Dataset, DataLoader\n",
        "import json\n",
        "\n",
        "\n",
        "##############################################################################\n",
        "print(\"check environment \")\n",
        "##############################################################################\n",
        "\n",
        "if 'COLAB_GPU' in os.environ:\n",
        "   print(\"I'm running on Colab\")\n",
        "\n",
        "   def install(package):\n",
        "       subprocess.check_call([sys.executable, \"-m\", \"pip\", \"install\", package])\n",
        "\n",
        "   print(\"Cuda available\",torch.cuda.is_available())\n",
        "   print(\"installing required packages ...\")\n",
        "   install(\"segmentation-models-pytorch\")\n",
        "   install(\"lightning\")\n",
        "   install(\"wandb\")\n",
        "   install(\"onnx\")\n",
        "   install(\"albumentations\")\n",
        "\n",
        "\n",
        "import segmentation_models_pytorch as smp\n",
        "import albumentations as A\n",
        "import albumentations.augmentations.functional as F\n",
        "from albumentations.pytorch import ToTensorV2\n",
        "\n",
        "import warnings\n",
        "warnings.filterwarnings('ignore')\n",
        "\n",
        "import pytorch_lightning as pl\n",
        "from lightning.pytorch.callbacks import LearningRateMonitor\n",
        "from lightning.pytorch.utilities.model_summary import ModelSummary\n",
        "from pytorch_lightning.loggers import WandbLogger\n",
        "\n",
        "import wandb\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "##############################################################################\n",
        "print(\"Manage dataset\")\n",
        "##############################################################################\n",
        "\n",
        "if 'COLAB_GPU' in os.environ:\n",
        "\n",
        "   from google.colab import drive\n",
        "   drive.mount('/content/drive')\n",
        "\n",
        "\n",
        "   def load_data_to_tmp(folder_name):\n",
        "    zip_ref = zipfile.ZipFile('/tmp/'+folder_name, 'r') #Opens the zip file in read mode\n",
        "    zip_ref.extractall('/tmp') #Extracts the files into the /tmp folder\n",
        "    zip_ref.close()\n",
        "\n",
        "   run=wandb.init(project=\"Card_detection\")\n",
        "   print(\"dowload data from the wandb to temp\")\n",
        "   artifact = run.use_artifact('team-invonto/Card_detection/Dataset_July2023_train213_val31:v0', type='compressed_480x320')\n",
        "   artifact.download('/tmp/')\n",
        "   load_data_to_tmp('Train.zip')\n",
        "   load_data_to_tmp('Validation.zip')\n",
        "   data_path='/tmp/'\n",
        "   wandb.finish()\n",
        "\n",
        "\n",
        "else:\n",
        "   data_path='/home/tatyana/Work_Invonto/Dataset/Card_Dataset/Dataset_ready_July2023'\n",
        "   print('running local')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "metadata": {
        "id": "Vy21psk59sLq"
      },
      "outputs": [],
      "source": [
        "############################################################################################################33\n",
        "#### Card Dataset\n",
        "\n",
        "class CardDataset(Dataset):\n",
        "    \"\"\"NailDataset dataset.\n",
        "    img generator that take file names and selected poligons from csv_file\n",
        "    \"\"\"\n",
        "\n",
        "    def __init__(self, csv_file, root_dir, transform=None, augmentation=None):\n",
        "        \"\"\"\n",
        "        Args:\n",
        "            csv_file (string): Path to the csv file with annotations.\n",
        "            root_dir (string): Directory with all the images.\n",
        "            transform (callable, optional): Optional transform to be applied\n",
        "                on a sample.\n",
        "\n",
        "            nail_dataset[img_number_in_csv][1] -image\n",
        "            nail_dataset[img_number_in_csv][0] -corresponding mask image\n",
        "        \"\"\"\n",
        "        self.nail_frame = pd.read_csv(csv_file)\n",
        "        self.root_dir = root_dir\n",
        "        self.transform = transform\n",
        "        self.augmentation = augmentation\n",
        "\n",
        "    def __len__(self):\n",
        "        return self.nail_frame.shape[0]\n",
        "\n",
        "    def __getitem__(self, idx):\n",
        "        file_id=self.nail_frame.iloc[idx][\"filename\"]\n",
        "        #\"read image\"\n",
        "        img_name = os.path.join(self.root_dir,self.nail_frame.iloc[idx][\"filename\"])\n",
        "        img_name=img_name.split('.')[0]+'.png'\n",
        "        image = cv2.imread(img_name)\n",
        "        image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)\n",
        "\n",
        "        # read mask\n",
        "        mask_name=os.path.join(self.root_dir,self.nail_frame.iloc[idx][\"mask\"])\n",
        "        mask = cv2.imread(mask_name,cv2.IMREAD_UNCHANGED)\n",
        "\n",
        "        # required for albumentation\n",
        "        mask = mask.astype(np.float32)\n",
        "        mask[mask == 0] = 0.0\n",
        "        mask[mask == 255] = 1.0\n",
        "\n",
        "        if self.transform:\n",
        "            sample = self.transform(image=image, mask=mask)\n",
        "            image = sample['image']\n",
        "            mask=sample['mask']\n",
        "            mask=mask.unsqueeze(0)\n",
        "\n",
        "        return image,mask\n",
        "\n",
        "train_transform=A.Compose([\n",
        "        A.ShiftScaleRotate(shift_limit=0.2, scale_limit=0.2, rotate_limit=30, p=0.5,border_mode=cv2.BORDER_CONSTANT),\n",
        "        A.RGBShift(r_shift_limit=25, g_shift_limit=25, b_shift_limit=25, p=0.5),\n",
        "        A.RandomBrightnessContrast(brightness_limit=0.3, contrast_limit=0.3, p=0.5),\n",
        "        A.Normalize(mean=(0.485, 0.456, 0.406), std=(0.229, 0.224, 0.225)),\n",
        "        ToTensorV2()])\n",
        "\n",
        "\n",
        "validation_transform =A.Compose([\n",
        "        A.Normalize(mean=(0.485, 0.456, 0.406), std=(0.229, 0.224, 0.225)),\n",
        "        ToTensorV2()])\n",
        "\n",
        "\n",
        "############################################################################################################33\n",
        "#### Card Model\n",
        "\n",
        "class CardModel(pl.LightningModule):\n",
        "\n",
        "    def __init__(self,arch,encoder_name,encoder_weights,learning_rate,name_optimizer,frozen_encoder=True):\n",
        "        super().__init__()\n",
        "        self.lr=learning_rate\n",
        "        self.optimizer_name=name_optimizer\n",
        "        self.save_hyperparameters()\n",
        "        self.training_step_outputs = []\n",
        "        self.validation_step_outputs = []\n",
        "\n",
        "        self.model = smp.create_model(\n",
        "            arch,\n",
        "            encoder_name=encoder_name,\n",
        "            encoder_weights=encoder_weights,\n",
        "            classes=1,\n",
        "            #activation='sigmoid'\n",
        "            )\n",
        "\n",
        "        # for image segmentation dice loss could be the best first choice\n",
        "        self.loss_fn = smp.losses.DiceLoss(smp.losses.BINARY_MODE,from_logits=True)\n",
        "\n",
        "        if frozen_encoder==True:\n",
        "            for child in self.model.encoder.children():\n",
        "                for param in child.parameters():\n",
        "                    param.requires_grad = False\n",
        "\n",
        "\n",
        "    def forward(self, image):\n",
        "        mask = self.model(image)\n",
        "        return mask\n",
        "\n",
        "    def training_step(self, batch, batch_idx):\n",
        "        # training_step defines the train loop.\n",
        "        # it is independent of forward\n",
        "\n",
        "        image=batch[0]\n",
        "        # assert image (batch_size,num_channels,height,width)\n",
        "        assert image.ndim == 4\n",
        "        # assert the image dim is suitable for Unet\n",
        "        h, w = image.shape[2:]\n",
        "        assert h % 32 == 0 and w % 32 == 0\n",
        "\n",
        "        mask=batch[1]\n",
        "        assert mask.ndim == 4\n",
        "        assert mask.max()<=1 and  mask.min()>=0 # check that the mask between [0,1] not in range [0,255]\n",
        "\n",
        "        # calculate loss\n",
        "        output_mask = self.forward(image)\n",
        "        loss=self.loss_fn(output_mask,mask)\n",
        "\n",
        "        pred_mask=(output_mask.sigmoid() >= 0.5).float()\n",
        "\n",
        "\n",
        "\n",
        "        # calculate logs\n",
        "        tp, fp, fn, tn = smp.metrics.get_stats(pred_mask.long(), mask.long(), mode=\"binary\")\n",
        "\n",
        "        train_batch_dictionary={\"tp\":tp,\n",
        "                          \"fp\":fp,\n",
        "                          \"fn\":fn,\n",
        "                          \"tn\":tn}\n",
        "\n",
        "        self.training_step_outputs.append(train_batch_dictionary)\n",
        "        # logs metrics for each training_step,\n",
        "        # and the average across the epoch, to the progress bar and logger\n",
        "        self.log(\"train_loss\", loss, on_epoch=True, prog_bar=True, logger=True)\n",
        "\n",
        "        return loss\n",
        "\n",
        "    def on_train_epoch_end(self):\n",
        "        # aggregate step metics\n",
        "        outputs=self.training_step_outputs\n",
        "        tp = torch.cat([x[\"tp\"] for x in outputs])\n",
        "        fp = torch.cat([x[\"fp\"] for x in outputs])\n",
        "        fn = torch.cat([x[\"fn\"] for x in outputs])\n",
        "        tn = torch.cat([x[\"tn\"] for x in outputs])\n",
        "\n",
        "\n",
        "        per_image_iou = smp.metrics.iou_score(tp, fp, fn, tn, reduction=\"micro-imagewise\") # mean (per image)\n",
        "        dataset_iou = smp.metrics.iou_score(tp, fp, fn, tn, reduction=\"micro\") # sum tp,fp,fn,tn over all dataset\n",
        "\n",
        "        metrics = {\n",
        "            \"train_per_image_iou\": per_image_iou,\n",
        "        #    \"train_dataset_iou\": dataset_iou,\n",
        "        }\n",
        "\n",
        "        self.log_dict(metrics, prog_bar=True)\n",
        "        self.training_step_outputs.clear()\n",
        "\n",
        "\n",
        "\n",
        "    def validation_step(self, batch, batch_idx):\n",
        "        # training_step defines the train loop.\n",
        "        # it is independent of forward\n",
        "\n",
        "        image=batch[0]\n",
        "        # assert image (batch_size,num_channels,height,width)\n",
        "        assert image.ndim == 4\n",
        "        # assert the image dim is suitable for Unet\n",
        "        h, w = image.shape[2:]\n",
        "        assert h % 32 == 0 and w % 32 == 0\n",
        "\n",
        "        mask=batch[1]\n",
        "        assert mask.ndim == 4\n",
        "        assert mask.max()<=1 and  mask.min()>=0 # check that the mask between [0,1] not in range [0,255]\n",
        "\n",
        "        # calculate loss\n",
        "        output_mask = self.forward(image)\n",
        "        loss=self.loss_fn(output_mask,mask)\n",
        "\n",
        "        pred_mask=(output_mask.sigmoid() >= 0.5).float()\n",
        "\n",
        "\n",
        "        # calculate logs\n",
        "        tp, fp, fn, tn = smp.metrics.get_stats(pred_mask.long(), mask.long(), mode=\"binary\")\n",
        "\n",
        "        validation_batch_dictionary={\"loss\":loss,\n",
        "                          \"tp\":tp,\n",
        "                          \"fp\":fp,\n",
        "                          \"fn\":fn,\n",
        "                          \"tn\":tn}\n",
        "\n",
        "        self.validation_step_outputs.append(validation_batch_dictionary)\n",
        "        # logs metrics for each training_step,\n",
        "        # and the average across the epoch, to the progress bar and logger\n",
        "        self.log(\"valid_loss\", loss, on_epoch=True, prog_bar=True, logger=True)\n",
        "\n",
        "        return loss\n",
        "\n",
        "\n",
        "    def on_validation_epoch_end(self):\n",
        "        # aggregate step metics\n",
        "        outputs= self.validation_step_outputs\n",
        "        tp = torch.cat([x[\"tp\"] for x in outputs])\n",
        "        fp = torch.cat([x[\"fp\"] for x in outputs])\n",
        "        fn = torch.cat([x[\"fn\"] for x in outputs])\n",
        "        tn = torch.cat([x[\"tn\"] for x in outputs])\n",
        "\n",
        "\n",
        "        per_image_iou = smp.metrics.iou_score(tp, fp, fn, tn, reduction=\"micro-imagewise\") # mean (per image)\n",
        "        #dataset_iou = smp.metrics.iou_score(tp, fp, fn, tn, reduction=\"micro\") # sum tp,fp,fn,tn over all dataset\n",
        "\n",
        "        metrics = {\n",
        "            \"validation_per_image_iou\": per_image_iou,\n",
        "            #\"validation_dataset_iou\": dataset_iou,\n",
        "        }\n",
        "\n",
        "        self.log_dict(metrics, prog_bar=True)\n",
        "        self.validation_step_outputs.clear()\n",
        "\n",
        "    def configure_optimizers(self):\n",
        "        opt=torch.optim.Adam(self.parameters(), lr=self.lr)\n",
        "        lr_dict = {\"scheduler\": torch.optim.lr_scheduler.ExponentialLR(opt, 0.99),\n",
        "                    \"interval\": 'epoch',\n",
        "                    \"frequency\": 1}\n",
        "        return {\"optimizer\": opt,\"lr_scheduler\": lr_dict}\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 33,
      "metadata": {
        "id": "wW_DV87i9sLt"
      },
      "outputs": [],
      "source": [
        "def train_run(config=None):\n",
        "\n",
        "    # Get hyperparameters from the run configs\n",
        "\n",
        "    with wandb.init(config=config) as run:\n",
        "        # Overwrite the random run names chosen by wandb\n",
        "        run_config = wandb.config\n",
        "        name_str =f\"Sweep_{run_config.encoder}_lr={run_config.learning_rate:.3f}\" # Code for determining run names here\n",
        "        run.name = name_str\n",
        "\n",
        "    print(run_config.batch_size)\n",
        "    #data_path='/home/tatyana/Work_Invonto/Dataset/Card_Dataset/Dataset_ready_July2023'\n",
        "    data_path='/tmp/'\n",
        "\n",
        "    ### Load Data\n",
        "    n_cpu = os.cpu_count()\n",
        "\n",
        "    #train\n",
        "    x_train_path=os.path.join(data_path,'Train')\n",
        "    #y_train_dataloader=os.path.join(x_train_path,'Train_DataLoader.csv')\n",
        "    y_train_dataloader=os.path.join(x_train_path,'Train_DataLoader_sample96.csv')\n",
        "    train_dataset = CardDataset(y_train_dataloader, x_train_path,transform=train_transform, augmentation=None)\n",
        "    train_loader = DataLoader(train_dataset, batch_size=run_config.batch_size, shuffle=True,drop_last=True,num_workers=n_cpu)\n",
        "    #print('Number of training samples:', len(train_dataset))\n",
        "\n",
        "    # validation\n",
        "    x_valid_path = os.path.join(data_path, 'Validation')\n",
        "    y_valid_dataloader = os.path.join(x_valid_path, 'Validation_DataLoader.csv')\n",
        "    valid_dataset = CardDataset(y_valid_dataloader,x_valid_path,transform=validation_transform, augmentation=None)\n",
        "    valid_loader = DataLoader(valid_dataset, batch_size=run_config.batch_size, shuffle=False,num_workers=n_cpu)\n",
        "    #print('Number of validation samples:', len(valid_dataset))\n",
        "\n",
        "\n",
        "    ### Define Model\n",
        "    card_model = CardModel(\"Unet\",\n",
        "                               run_config.encoder,\n",
        "                               \"imagenet\",\n",
        "                               run_config.learning_rate,\n",
        "                               run_config.optimizer,\n",
        "                               frozen_encoder=run_config.Frozen_Encoder_Flag)\n",
        "\n",
        "    wandb_logger = WandbLogger(project=\"Card_detection\")\n",
        "    lr_monitor=pl.callbacks.LearningRateMonitor(logging_interval=\"step\")\n",
        "    trainer = pl.Trainer(callbacks=[lr_monitor],\n",
        "                         max_epochs=run_config.epochs,\n",
        "                        logger=wandb_logger,\n",
        "                        log_every_n_steps=1,\n",
        "                        default_root_dir=\"/content/drive/MyDrive/Dataset/\")\n",
        "    trainer.fit(\n",
        "            model=card_model,\n",
        "            train_dataloaders=train_loader,\n",
        "            val_dataloaders=valid_loader)"
      ]
    },
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              "Syncing run <strong><a href='https://wandb.ai/team-invonto/Card_detection/runs/skaf1v1g' target=\"_blank\">exalted-night-339</a></strong> to <a href='https://wandb.ai/team-invonto/Card_detection' target=\"_blank\">Weights & Biases</a> (<a href='https://wandb.me/run' target=\"_blank\">docs</a>)<br/>"
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              " View project at <a href='https://wandb.ai/team-invonto/Card_detection' target=\"_blank\">https://wandb.ai/team-invonto/Card_detection</a>"
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              " View run at <a href='https://wandb.ai/team-invonto/Card_detection/runs/skaf1v1g' target=\"_blank\">https://wandb.ai/team-invonto/Card_detection/runs/skaf1v1g</a>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "INFO: GPU available: True (cuda), used: True\n",
            "INFO:lightning.pytorch.utilities.rank_zero:GPU available: True (cuda), used: True\n",
            "INFO: TPU available: False, using: 0 TPU cores\n",
            "INFO:lightning.pytorch.utilities.rank_zero:TPU available: False, using: 0 TPU cores\n",
            "INFO: IPU available: False, using: 0 IPUs\n",
            "INFO:lightning.pytorch.utilities.rank_zero:IPU available: False, using: 0 IPUs\n",
            "INFO: HPU available: False, using: 0 HPUs\n",
            "INFO:lightning.pytorch.utilities.rank_zero:HPU available: False, using: 0 HPUs\n",
            "INFO:pytorch_lightning.accelerators.cuda:LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0]\n",
            "INFO:pytorch_lightning.callbacks.model_summary:\n",
            "  | Name    | Type     | Params\n",
            "-------------------------------------\n",
            "0 | model   | Unet     | 31.2 M\n",
            "1 | loss_fn | DiceLoss | 0     \n",
            "-------------------------------------\n",
            "2.9 M     Trainable params\n",
            "28.3 M    Non-trainable params\n",
            "31.2 M    Total params\n",
            "124.864   Total estimated model params size (MB)\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Sanity Checking: 0it [00:00, ?it/s]"
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              "Training: 0it [00:00, ?it/s]"
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          "text": [
            "INFO: `Trainer.fit` stopped: `max_epochs=5` reached.\n",
            "INFO:lightning.pytorch.utilities.rank_zero:`Trainer.fit` stopped: `max_epochs=5` reached.\n"
          ]
        }
      ],
      "source": [
        "test_config = {\n",
        "        # const values\n",
        "        'encoder':'efficientnet-b5',\n",
        "        'epochs': 5,\n",
        "        'batch_size':16,\n",
        "        'optimizer':\"Adam\",\n",
        "        \"Frozen_Encoder_Flag\":True,\n",
        "        \"learning_rate\":0.02\n",
        "    }\n",
        "\n",
        "train_run(test_config)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "qYr94Fth9sLu"
      },
      "outputs": [],
      "source": [
        "# sweep_config = {\n",
        "#     \"method\": \"bayes\",\n",
        "#     \"name\":\"EfficientNet_b5_learning_rate\",\n",
        "#     \"metric\":{\"name\":\"validation_per_image_iou\",\"goal\":\"maximize\"},\n",
        "#     \"parameters\":{\n",
        "#         # const values\n",
        "#         'encoder':{'value':'efficientnet-b5'},\n",
        "#         'epochs': {'value':20},\n",
        "#         'batch_size':{'value':16},\n",
        "#         'optimizer':{'value':\"Adam\"},\n",
        "#         \"Frozen_Encoder_Flag\":{'value':True},\n",
        "\n",
        "#         # sweep values\n",
        "#         \"learning_rate\": {\"distribution\": \"uniform\", \"min\": 1e-5, \"max\": 0.1}\n",
        "#         }\n",
        "#     }\n",
        "\n",
        "# sweep_id = wandb.sweep(sweep_config, project=\"Card_detection\")\n",
        "# wandb.agent(sweep_id, train_run, count=50)\n",
        "# #wandb.finish()"
      ]
    }
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