GoogleNet_PyTorch_Lightning.ipynb

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            "\u001b[?25h  Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m62.7/62.7 kB\u001b[0m \u001b[31m7.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h  Building wheel for pathtools (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m519.6/519.6 kB\u001b[0m \u001b[31m7.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
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            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m134.8/134.8 kB\u001b[0m \u001b[31m10.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
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            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m7.6/7.6 MB\u001b[0m \u001b[31m34.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m7.8/7.8 MB\u001b[0m \u001b[31m85.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m1.3/1.3 MB\u001b[0m \u001b[31m86.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h"
          ]
        }
      ],
      "source": [
        "!pip install pytorch-lightning -q\n",
        "!pip install wandb -q\n",
        "!pip install datasets -q\n",
        "!pip install transformers -q"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## PyTorch Lightning: Streamlined Deep Learning\n",
        "\n",
        "PyTorch Lightning simplifies deep learning in PyTorch. It abstracts lower-level details, organizes code, and accelerates experimentation. Key features:\n",
        "\n",
        "**Structured Code**: Use LightningModule for organized model code.\n",
        "**Data Handling**: Simplified data loading with data modules.\n",
        "**Training Loop**: Automate the training loop with the Trainer class.\n",
        "**Monitoring**: Seamlessly integrate with monitoring tools like TensorBoard and WandB.\n",
        "**Reproducibility**: Encourages reproducible research practices.\n",
        "For detailed information, refer to the PyTorch Lightning [documentation](https://pytorch-lightning.readthedocs.io/en/latest/).\n",
        "\n",
        "You're gonna need these imports.\n"
      ],
      "metadata": {
        "id": "aHxky_Xd-2jr"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import random\n",
        "import numpy as np\n",
        "import argparse\n",
        "from typing import List, Dict, Union, Optional\n",
        "\n",
        "import wandb\n",
        "import transformers\n",
        "import pytorch_lightning as pl\n",
        "from pytorch_lightning.callbacks import ModelCheckpoint, EarlyStopping, LearningRateMonitor\n",
        "from pytorch_lightning.loggers import WandbLogger\n",
        "\n",
        "import torch\n",
        "from torch import nn\n",
        "from torch.utils.data import random_split, DataLoader, Dataset\n",
        "\n",
        "from torchvision import transforms\n",
        "from torchmetrics.classification import AUROC, F1Score, Accuracy\n",
        "from torchvision.models import list_models, get_model, get_model_weights, get_weight\n",
        "from datasets import load_dataset, list_datasets"
      ],
      "metadata": {
        "id": "iZeBsTkx2Rkt"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Now you'll need to login to you wandb account."
      ],
      "metadata": {
        "id": "0OxG2NL4-9Tj"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "wandb.login()"
      ],
      "metadata": {
        "id": "8iyUnq_9_B4m",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 125
        },
        "outputId": "a9002d9a-8721-49ac-fd1f-ef1814457173"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
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            "text/plain": [
              "<IPython.core.display.Javascript object>"
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            "application/javascript": [
              "\n",
              "        window._wandbApiKey = new Promise((resolve, reject) => {\n",
              "            function loadScript(url) {\n",
              "            return new Promise(function(resolve, reject) {\n",
              "                let newScript = document.createElement(\"script\");\n",
              "                newScript.onerror = reject;\n",
              "                newScript.onload = resolve;\n",
              "                document.body.appendChild(newScript);\n",
              "                newScript.src = url;\n",
              "            });\n",
              "            }\n",
              "            loadScript(\"https://cdn.jsdelivr.net/npm/postmate/build/postmate.min.js\").then(() => {\n",
              "            const iframe = document.createElement('iframe')\n",
              "            iframe.style.cssText = \"width:0;height:0;border:none\"\n",
              "            document.body.appendChild(iframe)\n",
              "            const handshake = new Postmate({\n",
              "                container: iframe,\n",
              "                url: 'https://wandb.ai/authorize'\n",
              "            });\n",
              "            const timeout = setTimeout(() => reject(\"Couldn't auto authenticate\"), 5000)\n",
              "            handshake.then(function(child) {\n",
              "                child.on('authorize', data => {\n",
              "                    clearTimeout(timeout)\n",
              "                    resolve(data)\n",
              "                });\n",
              "            });\n",
              "            })\n",
              "        });\n",
              "    "
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "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",
            "\u001b[34m\u001b[1mwandb\u001b[0m: You can find your API key in your browser here: https://wandb.ai/authorize\n",
            "wandb: Paste an API key from your profile and hit enter, or press ctrl+c to quit:"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            " ··········\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\u001b[34m\u001b[1mwandb\u001b[0m: Appending key for api.wandb.ai to your netrc file: /root/.netrc\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "True"
            ]
          },
          "metadata": {},
          "execution_count": 5
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      ]
    },
    {
      "cell_type": "code",
      "source": [
        "class DataModule(pl.LightningDataModule):\n",
        "    \"\"\"\n",
        "    A DataModule implements 5 key methods:\n",
        "        - prepare_data (things to do on 1 GPU/TPU, not on every GPU/TPU in distributed mode)\n",
        "        - setup (things to do on every accelerator in distributed mode)\n",
        "        - train_dataloader (the training dataloader)\n",
        "        - val_dataloader (the validation dataloader(s))\n",
        "        - test_dataloader (the test dataloader(s))\n",
        "    This allows you to share a full dataset without explaining how to download,\n",
        "    split, transform and process the data.\n",
        "    \"\"\"\n",
        "\n",
        "    def __init__(\n",
        "        self,\n",
        "        dataset_name: str=\"cifar100\",\n",
        "        batch_size: int=32,\n",
        "        data_dir: str=\"~/cache\",\n",
        "        num_workers: int=4,\n",
        "        transform_custom: bool=True,\n",
        "        **kwargs\n",
        "    ):\n",
        "        super().__init__()\n",
        "\n",
        "        self.dataset_name = dataset_name\n",
        "        self.data_dir = data_dir\n",
        "        self.batch_size = batch_size\n",
        "        self.num_workers = num_workers\n",
        "        self.transform_custom = transform_custom\n",
        "\n",
        "        self.data_train: Optional[Dataset] = None\n",
        "        self.data_val: Optional[Dataset] = None\n",
        "        self.data_test: Optional[Dataset] = None\n",
        "\n",
        "    def prepare_data(self):\n",
        "        \"\"\"Download data if needed. This method is called only from a single GPU.\"\"\"\n",
        "        datasets_list = list_datasets()\n",
        "        assert self.dataset_name in datasets_list, \"the dataset was not found in HF hub\"\n",
        "        self.train_dataset = load_dataset(self.dataset_name, cache_dir=self.data_dir, split=\"train\")\n",
        "        self.data_test = load_dataset(self.dataset_name, cache_dir=self.data_dir, split=\"test\")\n",
        "\n",
        "    @staticmethod\n",
        "    def transforms_train(examples):\n",
        "        transform_train = transforms.Compose([\n",
        "              transforms.ToTensor(),\n",
        "              transforms.Normalize(mean=[0.5073, 0.4868, 0.4410], std=[0.2623, 0.2515, 0.2716]),\n",
        "              transforms.RandomHorizontalFlip(),\n",
        "              transforms.RandomRotation(degrees=5),\n",
        "\n",
        "        ])\n",
        "        examples[\"pixel_values\"] = [transform_train(img.convert(\"RGB\")) for img in examples['img']]\n",
        "        return examples\n",
        "\n",
        "    @staticmethod\n",
        "    def transforms_test(examples):\n",
        "        transform_test = transforms.Compose([\n",
        "              transforms.ToTensor(),\n",
        "              transforms.Normalize(mean=[0.5073, 0.4868, 0.4410], std=[0.2623, 0.2515, 0.2716]),\n",
        "        ])\n",
        "\n",
        "        examples[\"pixel_values\"] = [transform_test(img.convert(\"RGB\")) for img in examples['img']]\n",
        "        return examples\n",
        "\n",
        "    def setup(self, stage: Optional[str]=None):\n",
        "        train_valid = self.train_dataset.train_test_split(test_size=0.2)\n",
        "        self.data_train = train_valid['train']\n",
        "        self.data_val = train_valid['test']\n",
        "        self.data_train = self.data_train.map(lambda example: {\"labels\": example[\"fine_label\"]}, remove_columns=[\"fine_label\", \"coarse_label\"])\n",
        "        self.data_val = self.data_val.map(lambda example: {\"labels\": example[\"fine_label\"]}, remove_columns=[\"fine_label\", \"coarse_label\"])\n",
        "        self.data_test = self.data_test.map(lambda example: {\"labels\": example[\"fine_label\"]}, remove_columns=[\"fine_label\", \"coarse_label\"])\n",
        "\n",
        "        if self.transform_custom:\n",
        "            self.data_train = self.data_train.map(self.transforms_train, batched=True, remove_columns=[\"img\"])\n",
        "            self.data_val = self.data_val.map(self.transforms_test, batched=True, remove_columns=[\"img\"])\n",
        "            self.data_test = self.data_test.map(self.transforms_test, batched=True, remove_columns=[\"img\"])\n",
        "            self.data_train.set_format(\"pt\", columns=[\"pixel_values\"], output_all_columns=True)\n",
        "            self.data_val.set_format(\"pt\", columns=[\"pixel_values\"], output_all_columns=True)\n",
        "            self.data_test.set_format(\"pt\", columns=[\"pixel_values\"], output_all_columns=True)\n",
        "\n",
        "    def train_dataloader(self):\n",
        "        return DataLoader(\n",
        "            dataset=self.data_train,\n",
        "            batch_size=self.batch_size,\n",
        "            num_workers=self.num_workers,\n",
        "            shuffle=True,\n",
        "        )\n",
        "\n",
        "    def val_dataloader(self):\n",
        "        return DataLoader(\n",
        "            dataset=self.data_val,\n",
        "            batch_size=self.batch_size,\n",
        "            num_workers=self.num_workers,\n",
        "            shuffle=False,\n",
        "        )\n",
        "\n",
        "    def test_dataloader(self):\n",
        "        return DataLoader(\n",
        "            dataset=self.data_test,\n",
        "            batch_size=self.batch_size,\n",
        "            num_workers=self.num_workers,\n",
        "            shuffle=False,\n",
        "        )"
      ],
      "metadata": {
        "id": "f65omx9A2Tbd"
      },
      "execution_count": 40,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Define the LightningModule for image classification\n",
        "class ImageModel(pl.LightningModule):\n",
        "    def __init__(self, *args, **kwargs):\n",
        "        super().__init__()\n",
        "        self.save_hyperparameters()\n",
        "\n",
        "        # Load model weights if not specified\n",
        "        if not self.hparams.weight_name:\n",
        "            weights = torch.hub.load(\"pytorch/vision\", \"get_model_weights\", name=self.hparams.model_name)\n",
        "            self.hparams.weight_name = [weight for weight in weights][0]\n",
        "            print(f\"set weights to {self.hparams.weight_name}\")\n",
        "\n",
        "        # Load the model with specified weights and replace the final classification layer\n",
        "        self.weights = get_weight(str(self.hparams.weight_name))\n",
        "        self.model = torch.hub.load(\"pytorch/vision\", self.hparams.model_name, weights=self.hparams.weight_name)\n",
        "        self.model.fc = nn.Linear(self.model.fc.in_features, self.hparams.num_classes)\n",
        "        self.preprocess = self.weights.transforms()\n",
        "        self.test_outputs = []\n",
        "\n",
        "        # Initialize metrics based on the number of classes\n",
        "        if self.hparams.num_classes > 2:\n",
        "            self.aucroc = AUROC(task=\"multiclass\", num_classes=self.hparams.num_classes, average=\"weighted\")\n",
        "            self.accuracy = Accuracy(task=\"multiclass\", num_classes=self.hparams.num_classes, average='weighted')\n",
        "            self.f1 = F1Score(task=\"multiclass\", num_classes=self.hparams.num_classes, average='weighted')\n",
        "        elif self.hparams.num_classes == 2:\n",
        "            self.aucroc = AUROC(task=\"binary\")\n",
        "            self.accuracy = Accuracy(task=\"binary\")\n",
        "            self.f1 = F1Score(task=\"binary\")\n",
        "        else:\n",
        "            raise ValueError(f\"num_classes should be 2 or more, regression not supported. Got value {self.hparams.num_classes}\")\n",
        "\n",
        "    def forward(self, x):\n",
        "        # Preprocess input and pass through the model\n",
        "        x = self.preprocess(x)\n",
        "        logits = self.model(x)\n",
        "        return logits\n",
        "\n",
        "    def training_step(self, batch, batch_idx=None):\n",
        "        # Forward pass during training\n",
        "        images, labels = batch['pixel_values'], batch['labels']\n",
        "        logits = self(images)\n",
        "\n",
        "        # Calculate loss and metrics\n",
        "        loss = nn.CrossEntropyLoss()(logits, labels)\n",
        "        preds = torch.argmax(logits, dim=1)\n",
        "        acc = self.accuracy(preds, labels)\n",
        "        aucroc = self.aucroc(logits, labels)\n",
        "        f1 = self.f1(preds, labels)\n",
        "        values = {'train_loss': loss, 'train_acc': acc, 'train_aucroc': aucroc, 'train_f1': f1}\n",
        "\n",
        "        # Log metrics\n",
        "        self.log_dict(values, on_step=True, on_epoch=True, logger=True)\n",
        "        return loss\n",
        "\n",
        "    def validation_step(self, batch, batch_idx=None):\n",
        "        # Forward pass during validation\n",
        "        images, labels = batch['pixel_values'], batch['labels']\n",
        "        logits = self(images)\n",
        "        loss = nn.CrossEntropyLoss()(logits, labels)\n",
        "        preds = torch.argmax(logits, dim=1)\n",
        "\n",
        "        # Calculate metrics\n",
        "        acc = self.accuracy(preds, labels)\n",
        "        aucroc = self.aucroc(logits, labels)\n",
        "        f1 = self.f1(preds, labels)\n",
        "        values = {'val_loss': loss, 'val_acc': acc, 'val_aucroc': aucroc, 'val_f1': f1}\n",
        "\n",
        "        # Log metrics\n",
        "        self.log_dict(values, prog_bar=True)\n",
        "        return loss\n",
        "\n",
        "    def test_step(self, batch, batch_idx=None):\n",
        "        # Forward pass during testing\n",
        "        images, labels = batch['pixel_values'], batch['labels']\n",
        "        logits = self(images)\n",
        "        preds = torch.argmax(logits, dim=1)\n",
        "\n",
        "        # Calculate metrics\n",
        "        acc = self.accuracy(preds, labels)\n",
        "        aucroc = self.aucroc(logits, labels)\n",
        "        f1 = self.f1(preds, labels)\n",
        "        values = {'test_acc': acc, 'test_aucroc': aucroc, 'test_f1': f1}\n",
        "\n",
        "        # Log metrics\n",
        "        self.log_dict(values, prog_bar=True)\n",
        "\n",
        "        # Store test outputs for visualization\n",
        "        self.test_outputs.append((batch, preds, labels))\n",
        "\n",
        "    def test_epoch_end(self):\n",
        "        # Visualize some test examples and predictions\n",
        "        imgs, preds, labels = random.sample(self.test_outputs, 5)\n",
        "        self.log({\n",
        "            \"examples\":[wandb.Image(img, caption=f\"Pred:{pred}, Label:{lbl}\") for img, pred, lbl in zip(imgs, preds, labels)]\n",
        "        })\n",
        "        self.test_outputs.clear()\n",
        "\n",
        "    def configure_optimizers(self):\n",
        "        # Configure optimizer and learning rate scheduler\n",
        "        no_decay = [\"bias\", \"LayerNorm.weight\"]\n",
        "        optimizer_grouped_parameters = [\n",
        "            {\n",
        "                \"params\": [p for n, p in self.model.named_parameters() if not any(nd in n for nd in no_decay)],\n",
        "                \"weight_decay\": self.hparams.weight_decay,\n",
        "                \"betas\": (0.9, 0.999),\n",
        "                \"eps\": self.hparams.adam_eps,\n",
        "            },\n",
        "            {\n",
        "                \"params\": [p for n, p in self.model.named_parameters() if any(nd in n for nd in no_decay)],\n",
        "                \"weight_decay\": 0.0,\n",
        "                \"betas\": (0.9, 0.999),\n",
        "                \"eps\": self.hparams.adam_eps,\n",
        "            },\n",
        "        ]\n",
        "\n",
        "        optimizer = torch.optim.AdamW(\n",
        "            optimizer_grouped_parameters,\n",
        "            lr=self.hparams.lr,\n",
        "            weight_decay=self.hparams.weight_decay,\n",
        "        )\n",
        "\n",
        "        scheduler = transformers.get_linear_schedule_with_warmup(\n",
        "            optimizer,\n",
        "            num_warmup_steps=self.hparams.warmup_updates,\n",
        "            num_training_steps=self.hparams.total_num_updates,\n",
        "        )\n",
        "\n",
        "        lr_dict = {\n",
        "            \"scheduler\": scheduler,\n",
        "            \"interval\": \"step\",\n",
        "            \"frequency\": 1,\n",
        "            \"name\": f\"LearningRateLinearScheduler\",\n",
        "        }\n",
        "\n",
        "        return [optimizer], [lr_dict]"
      ],
      "metadata": {
        "id": "Svg_ACPc4MfL"
      },
      "execution_count": 70,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def get_args() -> argparse.Namespace:\n",
        "    \"\"\"\n",
        "    Parse command line arguments and return them as an `argparse.Namespace` object.\n",
        "\n",
        "    Returns:\n",
        "        argparse.Namespace: Parsed command line arguments.\n",
        "    \"\"\"\n",
        "    parser = argparse.ArgumentParser()\n",
        "    parser.add_argument('--num_classes', type=int, default=100, help='Number of classes')\n",
        "    parser.add_argument('--savedir', type=str, default='./checkpoints', help='Save checkpoint directory')\n",
        "    parser.add_argument('--model_name', type=str, default='googlenet', help='Model name from torchvision')\n",
        "    parser.add_argument('--weight_name', type=str, default=None, help='Model weights name from torchvision')\n",
        "    parser.add_argument('--num_sanity_val_steps', type=int, default=2, help='Number of sanity validation steps')\n",
        "    parser.add_argument('--max_steps', type=int, default=1000, help='Maximum training steps')\n",
        "    parser.add_argument('--batch-size', type=int, default=32, help='Batch size')\n",
        "    parser.add_argument('--accelerator', type=str, default=\"gpu\", help='Accelerator (e.g., gpu or tpu)')\n",
        "    parser.add_argument('--lr', type=float, default=1e-05, help='Learning rate')\n",
        "    parser.add_argument('--weight_decay', type=float, default=0.01, help='Weight decay')\n",
        "    parser.add_argument('--adam_eps', type=float, default=1.0e-08, help='Adam optimizer epsilon')\n",
        "    parser.add_argument('--warmup_updates', type=int, default=100, help='Number of warmup updates')\n",
        "    parser.add_argument('--total_num_updates', type=int, default=1000, help='Total number of updates')\n",
        "    parser.add_argument('--accumulate_grad_batches', type=int, default=8, help='Number of gradient accumulation batches')\n",
        "    group = parser.add_mutually_exclusive_group()\n",
        "    group.add_argument('--gpu_ids', type=int, default=None, nargs='+', help='List of GPU IDs')\n",
        "    group.add_argument('--gpus', type=int, default=1, help='Number of GPUs to use')\n",
        "    args, unknown = parser.parse_known_args()\n",
        "    return args\n"
      ],
      "metadata": {
        "id": "Z9QJRw0LW76P"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def get_gpu_settings(gpu_ids: List[int] | None, gpus: int | None) -> Tuple[str, Union[int, List[int], None], Union[str, None]]:\n",
        "    \"\"\"\n",
        "    Determine GPU settings based on provided GPU IDs and the number of GPUs to use.\n",
        "\n",
        "    :param gpu_ids: List of GPU IDs to use, or None if not specified.\n",
        "    :param gpus: Number of GPUs to use, or None if not specified.\n",
        "    :return: A tuple containing:\n",
        "        - A string indicating the device type ('gpu' or 'cpu').\n",
        "        - An integer or list of integers representing the selected GPU(s) or None for CPU.\n",
        "        - A string indicating the strategy ('gpu', 'ddp', or None).\n",
        "    \"\"\"\n",
        "    if not torch.cuda.is_available():\n",
        "        return \"cpu\", -1, \"cpu\"\n",
        "\n",
        "    if gpu_ids is not None:\n",
        "        gpus = gpu_ids\n",
        "        strategy = \"ddp\" if len(gpu_ids) > 1 else \"gpu\"\n",
        "    elif gpus is not None:\n",
        "        gpus = gpus\n",
        "        strategy = \"ddp\" if gpus > 1 else \"gpu\"\n",
        "    else:\n",
        "        gpus = 1\n",
        "        strategy = \"gpu\"\n",
        "\n",
        "    return \"gpu\", gpus, strategy"
      ],
      "metadata": {
        "id": "iQBqHX-Jd0nN"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Parse command line arguments\n",
        "args = get_args()\n",
        "\n",
        "# Determine GPU settings\n",
        "device_type, gpus, strategy = get_gpu_settings(gpu_ids=args.gpu_ids, gpus=args.gpus)\n",
        "\n",
        "# Ensure total_num_updates matches max_steps\n",
        "if args.total_num_updates != args.max_steps:\n",
        "    args.total_num_updates = args.max_steps\n",
        "\n",
        "# Initialize DataModule\n",
        "data_module = DataModule(batch_size=args.batch_size, transform_custom=True)\n",
        "\n",
        "# Initialize ImageModel with arguments from parsed command line arguments\n",
        "model = ImageModel(**vars(args))\n",
        "\n",
        "# Initialize WandbLogger\n",
        "wandb_logger = WandbLogger(project='wandb-lightning', job_type='train')\n",
        "\n",
        "# Initialize callbacks for Lightning Trainer\n",
        "early_stop_callback = EarlyStopping(monitor=\"val_f1\", mode='max', patience=5, verbose=True)\n",
        "lr_monitor = LearningRateMonitor(logging_interval='step')\n",
        "checkpoint_callback = ModelCheckpoint(\n",
        "    monitor=\"val_f1\", mode=\"max\", save_top_k=2, save_last=True,\n",
        "    dirpath=\"./checkpoints\", filename=\"model-epoch_{epoch:03d}-val_loss_{val_loss:.2f}-val_f1_{val_f1:.2f}\",\n",
        "    auto_insert_metric_name=False, save_on_train_epoch_end=False\n",
        ")\n"
      ],
      "metadata": {
        "id": "Ylu4mwV65BC0",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "648aabaa-b5d7-45d3-c9a9-6577cc40c91a"
      },
      "execution_count": 71,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Using cache found in /root/.cache/torch/hub/pytorch_vision_main\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "set weights to GoogLeNet_Weights.IMAGENET1K_V1\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Using cache found in /root/.cache/torch/hub/pytorch_vision_main\n",
            "/usr/local/lib/python3.10/dist-packages/pytorch_lightning/loggers/wandb.py:398: UserWarning: There is a wandb run already in progress and newly created instances of `WandbLogger` will reuse this run. If this is not desired, call `wandb.finish()` before instantiating `WandbLogger`.\n",
            "  rank_zero_warn(\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Initialize Lightning Trainer\n",
        "trainer = pl.Trainer(\n",
        "    max_steps=args.max_steps,\n",
        "    default_root_dir=args.savedir,\n",
        "    devices=gpus,\n",
        "    accelerator=strategy,\n",
        "    num_sanity_val_steps=args.num_sanity_val_steps,\n",
        "    accumulate_grad_batches=args.accumulate_grad_batches,\n",
        "    deterministic=True,\n",
        "    logger=wandb_logger,\n",
        "    callbacks=[early_stop_callback, checkpoint_callback, lr_monitor],\n",
        ")\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "E1FmfCa5k3rT",
        "outputId": "cc672cb9-1353-4d88-edcd-9cb806026a70"
      },
      "execution_count": 42,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "INFO:pytorch_lightning.utilities.rank_zero:GPU available: True (cuda), used: True\n",
            "INFO:pytorch_lightning.utilities.rank_zero:TPU available: False, using: 0 TPU cores\n",
            "INFO:pytorch_lightning.utilities.rank_zero:IPU available: False, using: 0 IPUs\n",
            "INFO:pytorch_lightning.utilities.rank_zero:HPU available: False, using: 0 HPUs\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "trainer.fit(model, data_module)\n",
        "trainer.test(dataloaders=data_module.test_dataloader())\n",
        "wandb.finish()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 727,
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        "outputId": "84eded1b-5ba6-4777-eb68-5b9cd78064b1"
      },
      "execution_count": null,
      "outputs": [
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "b24656a9442344d79843f940fc62eefc",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Map:   0%|          | 0/40000 [00:00<?, ? examples/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "0da90721e7a24a8fa650a4b0a4ad025c",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Map:   0%|          | 0/10000 [00:00<?, ? examples/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "9dea627a6cc04222b56b7205fc7dc369",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Map:   0%|          | 0/40000 [00:00<?, ? examples/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "b0898a7a178144259a0b0f210d5ee724",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Map:   0%|          | 0/10000 [00:00<?, ? examples/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "metadata": {
            "tags": null
          },
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "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      | GoogLeNet           | 5.7 M \n",
            "1 | preprocess | ImageClassification | 0     \n",
            "2 | aucroc     | MulticlassAUROC     | 0     \n",
            "3 | accuracy   | MulticlassAccuracy  | 0     \n",
            "4 | f1         | MulticlassF1Score   | 0     \n",
            "---------------------------------------------------\n",
            "5.7 M     Trainable params\n",
            "0         Non-trainable params\n",
            "5.7 M     Total params\n",
            "22.810    Total estimated model params size (MB)\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "f1b2d4ab0ef04b1c90b0c3c6e880ce88",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Sanity Checking: 0it [00:00, ?it/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "metadata": {
            "tags": null
          },
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/torch/utils/data/dataloader.py:560: UserWarning: This DataLoader will create 4 worker processes in total. Our suggested max number of worker in current system is 2, which is smaller than what this DataLoader is going to create. Please be aware that excessive worker creation might get DataLoader running slow or even freeze, lower the worker number to avoid potential slowness/freeze if necessary.\n",
            "  warnings.warn(_create_warning_msg(\n",
            "/usr/local/lib/python3.10/dist-packages/torchvision/transforms/functional.py:1603: UserWarning: The default value of the antialias parameter of all the resizing transforms (Resize(), RandomResizedCrop(), etc.) will change from None to True in v0.17, in order to be consistent across the PIL and Tensor backends. To suppress this warning, directly pass antialias=True (recommended, future default), antialias=None (current default, which means False for Tensors and True for PIL), or antialias=False (only works on Tensors - PIL will still use antialiasing). This also applies if you are using the inference transforms from the models weights: update the call to weights.transforms(antialias=True).\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/torchmetrics/utilities/prints.py:43: TorchMetricsUserWarning: You are trying to use a metric in deterministic mode on GPU that uses `torch.cumsum`, which is currently not supported. The tensor will be copied to the CPU memory to compute it and then copied back to GPU. Expect some slowdowns.\n",
            "  warnings.warn(*args, **kwargs)  # noqa: B028\n",
            "/usr/local/lib/python3.10/dist-packages/torchmetrics/utilities/prints.py:43: UserWarning: No positive samples in targets, true positive value should be meaningless. Returning zero tensor in true positive score\n",
            "  warnings.warn(*args, **kwargs)  # noqa: B028\n"
          ]
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              "Training: 0it [00:00, ?it/s]"
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              "Validation: 0it [00:00, ?it/s]"
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        },
        {
          "metadata": {
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          "name": "stderr",
          "output_type": "stream",
          "text": [
            "INFO:pytorch_lightning.callbacks.early_stopping:Metric val_f1 improved. New best score: 0.045\n"
          ]
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              "Validation: 0it [00:00, ?it/s]"
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        },
        {
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            "tags": null
          },
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "INFO:pytorch_lightning.callbacks.early_stopping:Metric val_f1 improved by 0.121 >= min_delta = 0.0. New best score: 0.166\n"
          ]
        }
      ]
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      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "y780QzAgt47Z"
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      "execution_count": 60,
      "outputs": []
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      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "XTS-eEqjVc5e"
      },
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      "outputs": []
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}