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  "cells": [
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "MV54Pk6lbbr0",
        "outputId": "c566197e-cb8f-4d53-ddcc-60ff7845a309"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Cloning into 'backbones'...\n",
            "remote: Enumerating objects: 61, done.\u001b[K\n",
            "remote: Counting objects: 100% (61/61), done.\u001b[K\n",
            "remote: Compressing objects: 100% (41/41), done.\u001b[K\n",
            "remote: Total 61 (delta 18), reused 46 (delta 9), pack-reused 0\u001b[K\n",
            "Unpacking objects: 100% (61/61), 11.80 KiB | 805.00 KiB/s, done.\n"
          ]
        }
      ],
      "source": [
        "!git clone https://github.com/namnh194/backbones.git"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "cd /content/backbones"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "G9J-QjAtbvo0",
        "outputId": "271b9d38-af51-401a-c678-4198e44c0948"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "/content/backbones\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Alexnet"
      ],
      "metadata": {
        "id": "RhaiuhwJbob7"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from backbones.dataset.cifar10 import get_train_valid_loader, get_test_loader\n",
        "import torch\n",
        "from tqdm import tqdm_notebook\n",
        "from model.alexnet import *\n",
        "\n",
        "# hyperparam\n",
        "num_classes = 10\n",
        "num_epochs = 20\n",
        "batch_size = 128\n",
        "learning_rate = 0.005\n",
        "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
        "train_loader, valid_loader = get_train_valid_loader(data_dir='./data', batch_size=batch_size,\n",
        "                                                    augment=False, random_seed=1)\n",
        "test_loader = get_test_loader(data_dir='./data',\n",
        "                              batch_size=batch_size)\n",
        "model = AlexNet(num_classes).to(device)\n",
        "\n",
        "\n",
        "# Loss and optimizer\n",
        "criterion = nn.CrossEntropyLoss()\n",
        "# optimizer = torch.optim.Adam(model.parameters(), lr=learning_rate, weight_decay=0.005)\n",
        "optimizer = torch.optim.SGD(model.parameters(), lr=learning_rate, momentum=0.9, weight_decay=1e-4)\n",
        "lr_scheduler = torch.optim.lr_scheduler.MultiStepLR(optimizer, milestones=[100, 150], gamma=0.1)\n",
        "\n",
        "\n",
        "# Train the model\n",
        "total_step = len(train_loader)\n",
        "for epoch in range(num_epochs):\n",
        "    for i, (images, labels) in enumerate(tqdm_notebook(train_loader)):\n",
        "        # Move tensors to the configured device\n",
        "        images = images.to(device)\n",
        "        labels = labels.to(device)\n",
        "\n",
        "        # Forward pass\n",
        "        outputs = model(images)\n",
        "        loss = criterion(outputs, labels)\n",
        "\n",
        "        # Backward and optimize\n",
        "        optimizer.zero_grad()\n",
        "        loss.backward()\n",
        "        optimizer.step()\n",
        "        lr_scheduler.step()\n",
        "\n",
        "    print('Epoch [{}/{}], Step [{}/{}], Loss: {:.4f}'\n",
        "          .format(epoch + 1, num_epochs, i + 1, total_step, loss.item()))\n",
        "\n",
        "    # Validation\n",
        "    with torch.no_grad():\n",
        "        correct = 0\n",
        "        total = 0\n",
        "        for images, labels in valid_loader:\n",
        "            images = images.to(device)\n",
        "            labels = labels.to(device)\n",
        "            outputs = model(images)\n",
        "            _, predicted = torch.max(outputs.data, 1)\n",
        "            total += labels.size(0)\n",
        "            correct += (predicted == labels).sum().item()\n",
        "            del images, labels, outputs\n",
        "        print('Accuracy of the network on the {} validation images: {} %'.format(5000, 100 * correct / total))"
      ],
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      "execution_count": null,
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        {
          "metadata": {
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          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Files already downloaded and verified\n",
            "Files already downloaded and verified\n",
            "Files already downloaded and verified\n"
          ]
        },
        {
          "metadata": {
            "tags": null
          },
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "<ipython-input-5-a69e79e4dd86>:29: TqdmDeprecationWarning: This function will be removed in tqdm==5.0.0\n",
            "Please use `tqdm.notebook.tqdm` instead of `tqdm.tqdm_notebook`\n",
            "  for i, (images, labels) in enumerate(tqdm_notebook(train_loader)):\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "9bd1f545871f4798a88f1f12109cdcd0",
              "version_major": 2,
              "version_minor": 0
            },
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            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "metadata": {
            "tags": null
          },
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Epoch [1/20], Step [352/352], Loss: 1.4502\n",
            "Accuracy of the network on the 5000 validation images: 49.82 %\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "901545424ac84ed78ea4902d5241c1a2",
              "version_major": 2,
              "version_minor": 0
            },
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            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "metadata": {
            "tags": null
          },
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Epoch [2/20], Step [352/352], Loss: 1.0886\n",
            "Accuracy of the network on the 5000 validation images: 52.06 %\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "45d1109aee9e4bec8c935351a5ee9194",
              "version_major": 2,
              "version_minor": 0
            },
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            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "metadata": {
            "tags": null
          },
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Epoch [3/20], Step [352/352], Loss: 1.3477\n",
            "Accuracy of the network on the 5000 validation images: 52.6 %\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "d4a2daa46d064c339cffaf4023d5f426",
              "version_major": 2,
              "version_minor": 0
            },
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            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "metadata": {
            "tags": null
          },
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Epoch [4/20], Step [352/352], Loss: 1.1201\n",
            "Accuracy of the network on the 5000 validation images: 53.58 %\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "dd60cc92cc0d4d38ae9ae0b35eeb1abc",
              "version_major": 2,
              "version_minor": 0
            },
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            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "metadata": {
            "tags": null
          },
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Epoch [5/20], Step [352/352], Loss: 1.4390\n",
            "Accuracy of the network on the 5000 validation images: 53.38 %\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "0fbea6e1019642d381758c23ab48b6e6",
              "version_major": 2,
              "version_minor": 0
            },
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            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "metadata": {
            "tags": null
          },
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Epoch [6/20], Step [352/352], Loss: 1.4709\n",
            "Accuracy of the network on the 5000 validation images: 54.7 %\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "4f4129b1b5b24e208858dca14b9bd645",
              "version_major": 2,
              "version_minor": 0
            },
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            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "metadata": {
            "tags": null
          },
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Epoch [7/20], Step [352/352], Loss: 1.2811\n",
            "Accuracy of the network on the 5000 validation images: 56.26 %\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "c8f4f5273fcc4b098c5734f0e4d7311c",
              "version_major": 2,
              "version_minor": 0
            },
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            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "metadata": {
            "tags": null
          },
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Epoch [8/20], Step [352/352], Loss: 1.2669\n",
            "Accuracy of the network on the 5000 validation images: 55.62 %\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "4f716b5c33124cacb3f719342826ae17",
              "version_major": 2,
              "version_minor": 0
            },
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            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "metadata": {
            "tags": null
          },
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Epoch [9/20], Step [352/352], Loss: 1.0984\n",
            "Accuracy of the network on the 5000 validation images: 57.76 %\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "0c3b66cc51054d79a7e7a68c99da266c",
              "version_major": 2,
              "version_minor": 0
            },
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            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "metadata": {
            "tags": null
          },
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Epoch [10/20], Step [352/352], Loss: 0.9889\n",
            "Accuracy of the network on the 5000 validation images: 58.38 %\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "eadc04398ab6499d8dc3aca165dfa799",
              "version_major": 2,
              "version_minor": 0
            },
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            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "metadata": {
            "tags": null
          },
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Epoch [11/20], Step [352/352], Loss: 1.0913\n",
            "Accuracy of the network on the 5000 validation images: 58.88 %\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "341cd0599ecc42009a565cbad8cac75c",
              "version_major": 2,
              "version_minor": 0
            },
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            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "metadata": {
            "tags": null
          },
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Epoch [12/20], Step [352/352], Loss: 0.9108\n",
            "Accuracy of the network on the 5000 validation images: 60.18 %\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "08aca9f2870040f8925e2b13806a80c5",
              "version_major": 2,
              "version_minor": 0
            },
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            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "metadata": {
            "tags": null
          },
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Epoch [13/20], Step [352/352], Loss: 1.0868\n",
            "Accuracy of the network on the 5000 validation images: 60.34 %\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "ffb2b9978fc04d2caa4883a91e89b6b7",
              "version_major": 2,
              "version_minor": 0
            },
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            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "metadata": {
            "tags": null
          },
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Epoch [14/20], Step [352/352], Loss: 1.0131\n",
            "Accuracy of the network on the 5000 validation images: 61.02 %\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "036e078de651431ebea53414be227fb1",
              "version_major": 2,
              "version_minor": 0
            },
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            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "metadata": {
            "tags": null
          },
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Epoch [15/20], Step [352/352], Loss: 1.0121\n",
            "Accuracy of the network on the 5000 validation images: 62.12 %\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "fe51f77887d74a93a586d2361b4b2e09",
              "version_major": 2,
              "version_minor": 0
            },
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            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "metadata": {
            "tags": null
          },
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Epoch [16/20], Step [352/352], Loss: 1.0427\n",
            "Accuracy of the network on the 5000 validation images: 62.84 %\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "4a00b66d219647748cbc83873159b379",
              "version_major": 2,
              "version_minor": 0
            },
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            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "metadata": {
            "tags": null
          },
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Epoch [17/20], Step [352/352], Loss: 0.7708\n",
            "Accuracy of the network on the 5000 validation images: 63.32 %\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "562515d67b784c6e9b1fec5b459b4c0d",
              "version_major": 2,
              "version_minor": 0
            },
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            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "metadata": {
            "tags": null
          },
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Epoch [18/20], Step [352/352], Loss: 1.0038\n",
            "Accuracy of the network on the 5000 validation images: 64.3 %\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "48b76ad251d5414fbbf47fd0310a675a",
              "version_major": 2,
              "version_minor": 0
            },
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            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "metadata": {
            "tags": null
          },
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Epoch [19/20], Step [352/352], Loss: 0.8796\n",
            "Accuracy of the network on the 5000 validation images: 64.92 %\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "8ace4a8932a94c5286007c6eba4a0149",
              "version_major": 2,
              "version_minor": 0
            },
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            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch [20/20], Step [352/352], Loss: 0.7605\n",
            "Accuracy of the network on the 5000 validation images: 65.68 %\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "with torch.no_grad():\n",
        "    correct = 0\n",
        "    total = 0\n",
        "    for images, labels in test_loader:\n",
        "        images = images.to(device)\n",
        "        labels = labels.to(device)\n",
        "        outputs = model(images)\n",
        "        _, predicted = torch.max(outputs.data, 1)\n",
        "        total += labels.size(0)\n",
        "        correct += (predicted == labels).sum().item()\n",
        "        del images, labels, outputs\n",
        "    print('Accuracy of the network on the {} test images: {} %'.format(5000, 100 * correct / total))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "o8450jGEb72v",
        "outputId": "ee49317f-cccc-43c7-877f-dbaf3ebf41f3"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy of the network on the 5000 test images: 65.09 %\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# VGG16"
      ],
      "metadata": {
        "id": "iZBi-hOQ9UOB"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from backbones.dataset.cifar10 import get_train_valid_loader, get_test_loader\n",
        "import torch\n",
        "from backbones.model.vgg16 import *\n",
        "from tqdm import tqdm_notebook\n",
        "\n",
        "# hyperparam\n",
        "num_classes = 10\n",
        "num_epochs = 20\n",
        "batch_size = 64\n",
        "learning_rate = 0.1\n",
        "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
        "train_loader, valid_loader = get_train_valid_loader(data_dir='./data', batch_size=batch_size, resize=(224,224),\n",
        "                                                    augment=False, random_seed=1)\n",
        "test_loader = get_test_loader(data_dir='./data', resize=(224,224),\n",
        "                              batch_size=batch_size)\n",
        "model = VGG16(num_classes).to(device)\n",
        "\n",
        "\n",
        "# Loss and optimizer\n",
        "criterion = nn.CrossEntropyLoss()\n",
        "optimizer = torch.optim.Adam(model.parameters(), lr=learning_rate, weight_decay=0.005)\n",
        "# optimizer = torch.optim.SGD(model.parameters(), lr=learning_rate, momentum=0.9, weight_decay=1e-4)\n",
        "lr_scheduler = torch.optim.lr_scheduler.MultiStepLR(optimizer, milestones=[100, 150], gamma=0.1)\n",
        "\n",
        "\n",
        "# Train the model\n",
        "total_step = len(train_loader)\n",
        "for epoch in range(num_epochs):\n",
        "    for i, (images, labels) in enumerate(tqdm_notebook(train_loader)):\n",
        "        # Move tensors to the configured device\n",
        "        images = images.to(device)\n",
        "        labels = labels.to(device)\n",
        "\n",
        "        # Forward pass\n",
        "        outputs = model(images)\n",
        "        loss = criterion(outputs, labels)\n",
        "\n",
        "        # Backward and optimize\n",
        "        optimizer.zero_grad()\n",
        "        loss.backward()\n",
        "        optimizer.step()\n",
        "        lr_scheduler.step()\n",
        "\n",
        "    print('Epoch [{}/{}], Step [{}/{}], Loss: {:.4f}'\n",
        "          .format(epoch + 1, num_epochs, i + 1, total_step, loss.item()))\n",
        "\n",
        "    # Validation\n",
        "    with torch.no_grad():\n",
        "        correct = 0\n",
        "        total = 0\n",
        "        for images, labels in valid_loader:\n",
        "            images = images.to(device)\n",
        "            labels = labels.to(device)\n",
        "            outputs = model(images)\n",
        "            _, predicted = torch.max(outputs.data, 1)\n",
        "            total += labels.size(0)\n",
        "            correct += (predicted == labels).sum().item()\n",
        "            del images, labels, outputs\n",
        "        print('Accuracy of the network on the {} validation images: {} %'.format(5000, 100 * correct / total))"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2023-07-13T09:06:55.325774Z",
          "iopub.execute_input": "2023-07-13T09:06:55.326418Z"
        },
        "trusted": true,
        "id": "9mlTmAKp3XcD",
        "outputId": "e3e9da42-3549-475f-ce9e-fc7d2b4a8532",
        "colab": {
          "referenced_widgets": [
            "19636d3ad1c74cb29c1548d19bea2f1d",
            "ddbf1c3ddf7342d9b1a8099d1c4df7f1",
            "49b8559f00174e789b43c6acaf894a3a",
            "614952e156104a5d9526d69aea8ba6ed",
            "4d5a4ed647044b26bc458d213b46b140",
            "2b7222a85100405f8e264f24e6f43f61",
            "a4489a6510ad42d4b2a8c7d45d598c75",
            "c275538a1c9e4362b8c2ba301e9421f9",
            "c01cb062614e458ea8d192cfe0a86ca3",
            "ec48088dadf34f7f9f4d1420518b0533",
            "d416e422dd0f4da2b9c5aa01542d88b2",
            "1a882c28f195436b85e17a9fc995ebcc",
            "61aa5c36483d4d1e8762b312ff16b514",
            "92ff81b1aeb14bf78b4ec34a3931bc5d",
            "bcbf04b265584c81ac2e14b4e5f9b2ba"
          ]
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "name": "stdout",
          "text": "Downloading https://www.cs.toronto.edu/~kriz/cifar-10-python.tar.gz to ./data/cifar-10-python.tar.gz\n",
          "output_type": "stream"
        },
        {
          "name": "stderr",
          "text": "100%|██████████| 170498071/170498071 [00:03<00:00, 48330878.64it/s]\n",
          "output_type": "stream"
        },
        {
          "name": "stdout",
          "text": "Extracting ./data/cifar-10-python.tar.gz to ./data\nFiles already downloaded and verified\nFiles already downloaded and verified\n",
          "output_type": "stream"
        },
        {
          "name": "stderr",
          "text": "/tmp/ipykernel_28/4171700271.py:29: TqdmDeprecationWarning: This function will be removed in tqdm==5.0.0\nPlease use `tqdm.notebook.tqdm` instead of `tqdm.tqdm_notebook`\n  for i, (images, labels) in enumerate(tqdm_notebook(train_loader)):\n",
          "output_type": "stream"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "  0%|          | 0/704 [00:00<?, ?it/s]",
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "19636d3ad1c74cb29c1548d19bea2f1d"
            }
          },
          "metadata": {}
        },
        {
          "name": "stdout",
          "text": "Epoch [1/20], Step [704/704], Loss: 2.2898\nAccuracy of the network on the 5000 validation images: 9.36 %\n",
          "output_type": "stream"
        },
        {
          "output_type": "display_data",
          "data": {
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            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "ddbf1c3ddf7342d9b1a8099d1c4df7f1"
            }
          },
          "metadata": {}
        },
        {
          "name": "stdout",
          "text": "Epoch [2/20], Step [704/704], Loss: 2.5977\nAccuracy of the network on the 5000 validation images: 9.24 %\n",
          "output_type": "stream"
        },
        {
          "output_type": "display_data",
          "data": {
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            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "49b8559f00174e789b43c6acaf894a3a"
            }
          },
          "metadata": {}
        },
        {
          "name": "stdout",
          "text": "Epoch [3/20], Step [704/704], Loss: 3.5913\nAccuracy of the network on the 5000 validation images: 9.48 %\n",
          "output_type": "stream"
        },
        {
          "output_type": "display_data",
          "data": {
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            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "614952e156104a5d9526d69aea8ba6ed"
            }
          },
          "metadata": {}
        },
        {
          "name": "stdout",
          "text": "Epoch [4/20], Step [704/704], Loss: 2.2992\nAccuracy of the network on the 5000 validation images: 9.5 %\n",
          "output_type": "stream"
        },
        {
          "output_type": "display_data",
          "data": {
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            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "4d5a4ed647044b26bc458d213b46b140"
            }
          },
          "metadata": {}
        },
        {
          "name": "stdout",
          "text": "Epoch [5/20], Step [704/704], Loss: 2.3139\nAccuracy of the network on the 5000 validation images: 9.64 %\n",
          "output_type": "stream"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "  0%|          | 0/704 [00:00<?, ?it/s]",
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "2b7222a85100405f8e264f24e6f43f61"
            }
          },
          "metadata": {}
        },
        {
          "name": "stdout",
          "text": "Epoch [6/20], Step [704/704], Loss: 2.3132\nAccuracy of the network on the 5000 validation images: 9.74 %\n",
          "output_type": "stream"
        },
        {
          "output_type": "display_data",
          "data": {
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            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "a4489a6510ad42d4b2a8c7d45d598c75"
            }
          },
          "metadata": {}
        },
        {
          "name": "stdout",
          "text": "Epoch [7/20], Step [704/704], Loss: 2.3492\nAccuracy of the network on the 5000 validation images: 9.32 %\n",
          "output_type": "stream"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "  0%|          | 0/704 [00:00<?, ?it/s]",
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "c275538a1c9e4362b8c2ba301e9421f9"
            }
          },
          "metadata": {}
        },
        {
          "name": "stdout",
          "text": "Epoch [8/20], Step [704/704], Loss: 2.4327\nAccuracy of the network on the 5000 validation images: 10.26 %\n",
          "output_type": "stream"
        },
        {
          "output_type": "display_data",
          "data": {
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            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "c01cb062614e458ea8d192cfe0a86ca3"
            }
          },
          "metadata": {}
        },
        {
          "name": "stdout",
          "text": "Epoch [9/20], Step [704/704], Loss: 2.3229\nAccuracy of the network on the 5000 validation images: 10.18 %\n",
          "output_type": "stream"
        },
        {
          "output_type": "display_data",
          "data": {
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            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "ec48088dadf34f7f9f4d1420518b0533"
            }
          },
          "metadata": {}
        },
        {
          "name": "stdout",
          "text": "Epoch [10/20], Step [704/704], Loss: 2.3000\nAccuracy of the network on the 5000 validation images: 10.3 %\n",
          "output_type": "stream"
        },
        {
          "output_type": "display_data",
          "data": {
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            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "d416e422dd0f4da2b9c5aa01542d88b2"
            }
          },
          "metadata": {}
        },
        {
          "name": "stdout",
          "text": "Epoch [11/20], Step [704/704], Loss: 2.2600\nAccuracy of the network on the 5000 validation images: 9.78 %\n",
          "output_type": "stream"
        },
        {
          "output_type": "display_data",
          "data": {
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            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "1a882c28f195436b85e17a9fc995ebcc"
            }
          },
          "metadata": {}
        },
        {
          "name": "stdout",
          "text": "Epoch [12/20], Step [704/704], Loss: 2.3569\nAccuracy of the network on the 5000 validation images: 9.98 %\n",
          "output_type": "stream"
        },
        {
          "output_type": "display_data",
          "data": {
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            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "61aa5c36483d4d1e8762b312ff16b514"
            }
          },
          "metadata": {}
        },
        {
          "name": "stdout",
          "text": "Epoch [13/20], Step [704/704], Loss: 2.3270\nAccuracy of the network on the 5000 validation images: 9.84 %\n",
          "output_type": "stream"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "  0%|          | 0/704 [00:00<?, ?it/s]",
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "92ff81b1aeb14bf78b4ec34a3931bc5d"
            }
          },
          "metadata": {}
        },
        {
          "name": "stdout",
          "text": "Epoch [14/20], Step [704/704], Loss: 2.3872\nAccuracy of the network on the 5000 validation images: 10.52 %\n",
          "output_type": "stream"
        },
        {
          "output_type": "display_data",
          "data": {
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            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "bcbf04b265584c81ac2e14b4e5f9b2ba"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# ResNet34"
      ],
      "metadata": {
        "id": "jfv0B_9vGZft"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from backbones.dataset.cifar10 import get_train_valid_loader, get_test_loader\n",
        "import torch\n",
        "from backbones.model.resnet34 import *\n",
        "from tqdm import tqdm_notebook\n",
        "\n",
        "# hyperparam\n",
        "num_classes = 10\n",
        "num_epochs = 20\n",
        "batch_size = 128\n",
        "learning_rate = 0.1\n",
        "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
        "train_loader, valid_loader = get_train_valid_loader(data_dir='./data', batch_size=batch_size, resize=(227,227),\n",
        "                                                    augment=False, random_seed=1)\n",
        "test_loader = get_test_loader(data_dir='./data', resize=(227,227),\n",
        "                              batch_size=batch_size)\n",
        "model = ResNet34(ResidualBlock, [3,4,6,3], num_classes=num_classes).to(device)\n",
        "\n",
        "\n",
        "# Loss and optimizer\n",
        "criterion = nn.CrossEntropyLoss()\n",
        "# optimizer = torch.optim.Adam(model.parameters(), lr=learning_rate, weight_decay=0.01)\n",
        "optimizer = torch.optim.SGD(model.parameters(), lr=learning_rate, momentum=0.9, weight_decay=1e-4)\n",
        "lr_scheduler = torch.optim.lr_scheduler.MultiStepLR(optimizer, milestones=[100, 150], gamma=0.1)\n",
        "\n",
        "\n",
        "# Train the model\n",
        "total_step = len(train_loader)\n",
        "for epoch in range(num_epochs):\n",
        "    for i, (images, labels) in enumerate(tqdm_notebook(train_loader)):\n",
        "        # Move tensors to the configured device\n",
        "        images = images.to(device)\n",
        "        labels = labels.to(device)\n",
        "\n",
        "        # Forward pass\n",
        "        outputs = model(images)\n",
        "        loss = criterion(outputs, labels)\n",
        "\n",
        "        # Backward and optimize\n",
        "        optimizer.zero_grad()\n",
        "        loss.backward()\n",
        "        optimizer.step()\n",
        "        lr_scheduler.step()\n",
        "\n",
        "    print('Epoch [{}/{}], Step [{}/{}], Loss: {:.4f}'\n",
        "          .format(epoch + 1, num_epochs, i + 1, total_step, loss.item()))\n",
        "\n",
        "    # Validation\n",
        "    with torch.no_grad():\n",
        "        correct = 0\n",
        "        total = 0\n",
        "        for i, (images, labels) in enumerate(tqdm_notebook(valid_loader)):\n",
        "            images = images.to(device)\n",
        "            labels = labels.to(device)\n",
        "            outputs = model(images)\n",
        "            _, predicted = torch.max(outputs.data, 1)\n",
        "            total += labels.size(0)\n",
        "            correct += (predicted == labels).sum().item()\n",
        "            del images, labels, outputs\n",
        "        print('Accuracy of the network on the {} validation images: {} %'.format(5000, 100 * correct / total))"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2023-07-13T08:05:30.439486Z",
          "iopub.execute_input": "2023-07-13T08:05:30.439847Z",
          "iopub.status.idle": "2023-07-13T08:48:49.543923Z",
          "shell.execute_reply.started": "2023-07-13T08:05:30.439820Z",
          "shell.execute_reply": "2023-07-13T08:48:49.542819Z"
        },
        "trusted": true,
        "id": "m3O_Z7cx3XcA",
        "outputId": "ba92064c-4987-436a-f099-7b34851f388e",
        "colab": {
          "referenced_widgets": [
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            "85c9f9b88add4a8ca92ce52fab344248"
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        }
      },
      "execution_count": null,
      "outputs": [
        {
          "name": "stdout",
          "text": "Files already downloaded and verified\nFiles already downloaded and verified\nFiles already downloaded and verified\n",
          "output_type": "stream"
        },
        {
          "name": "stderr",
          "text": "/tmp/ipykernel_28/3664063992.py:29: TqdmDeprecationWarning: This function will be removed in tqdm==5.0.0\nPlease use `tqdm.notebook.tqdm` instead of `tqdm.tqdm_notebook`\n  for i, (images, labels) in enumerate(tqdm_notebook(train_loader)):\n",
          "output_type": "stream"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "  0%|          | 0/352 [00:00<?, ?it/s]",
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "43f8f4034352445eadc5d145917ac938"
            }
          },
          "metadata": {}
        },
        {
          "name": "stdout",
          "text": "Epoch [1/20], Step [352/352], Loss: 1.9307\n",
          "output_type": "stream"
        },
        {
          "name": "stderr",
          "text": "/tmp/ipykernel_28/3664063992.py:51: TqdmDeprecationWarning: This function will be removed in tqdm==5.0.0\nPlease use `tqdm.notebook.tqdm` instead of `tqdm.tqdm_notebook`\n  for i, (images, labels) in enumerate(tqdm_notebook(valid_loader)):\n",
          "output_type": "stream"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "  0%|          | 0/40 [00:00<?, ?it/s]",
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "549d9c567d3b40e8a9a04b488f3345c5"
            }
          },
          "metadata": {}
        },
        {
          "name": "stdout",
          "text": "Accuracy of the network on the 5000 validation images: 24.74 %\n",
          "output_type": "stream"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "  0%|          | 0/352 [00:00<?, ?it/s]",
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "dcaa69c0d2b14a329db0b2723960499a"
            }
          },
          "metadata": {}
        },
        {
          "name": "stdout",
          "text": "Epoch [2/20], Step [352/352], Loss: 1.9259\n",
          "output_type": "stream"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "  0%|          | 0/40 [00:00<?, ?it/s]",
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "cda1afc50fdc44749259525bbb1acc8c"
            }
          },
          "metadata": {}
        },
        {
          "name": "stdout",
          "text": "Accuracy of the network on the 5000 validation images: 26.9 %\n",
          "output_type": "stream"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "  0%|          | 0/352 [00:00<?, ?it/s]",
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "93edf86c8d444a56b341d284de477ca1"
            }
          },
          "metadata": {}
        },
        {
          "name": "stdout",
          "text": "Epoch [3/20], Step [352/352], Loss: 1.8299\n",
          "output_type": "stream"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "  0%|          | 0/40 [00:00<?, ?it/s]",
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "05a0701dcce64b0b959c6c7d6da6fce8"
            }
          },
          "metadata": {}
        },
        {
          "name": "stdout",
          "text": "Accuracy of the network on the 5000 validation images: 28.36 %\n",
          "output_type": "stream"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "  0%|          | 0/352 [00:00<?, ?it/s]",
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "9312a6ea3c6f47de8820a9a2aafcc1e1"
            }
          },
          "metadata": {}
        },
        {
          "name": "stdout",
          "text": "Epoch [4/20], Step [352/352], Loss: 1.8703\n",
          "output_type": "stream"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "  0%|          | 0/40 [00:00<?, ?it/s]",
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "c39456b34e5d44e989c70ac06095ad7d"
            }
          },
          "metadata": {}
        },
        {
          "name": "stdout",
          "text": "Accuracy of the network on the 5000 validation images: 29.1 %\n",
          "output_type": "stream"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "  0%|          | 0/352 [00:00<?, ?it/s]",
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "ce50ea5612f24cc488eba0bb57c7f19c"
            }
          },
          "metadata": {}
        },
        {
          "name": "stdout",
          "text": "Epoch [5/20], Step [352/352], Loss: 1.8373\n",
          "output_type": "stream"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "  0%|          | 0/40 [00:00<?, ?it/s]",
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "e49aa6a62bb84d79915dc60f753a0e7a"
            }
          },
          "metadata": {}
        },
        {
          "name": "stdout",
          "text": "Accuracy of the network on the 5000 validation images: 30.28 %\n",
          "output_type": "stream"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "  0%|          | 0/352 [00:00<?, ?it/s]",
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "3eb3938bd00c416193d8145473d5821e"
            }
          },
          "metadata": {}
        },
        {
          "name": "stdout",
          "text": "Epoch [6/20], Step [352/352], Loss: 1.8589\n",
          "output_type": "stream"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "  0%|          | 0/40 [00:00<?, ?it/s]",
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "319c4fce520b442e96a0d0ad02ab5b9c"
            }
          },
          "metadata": {}
        },
        {
          "name": "stdout",
          "text": "Accuracy of the network on the 5000 validation images: 32.38 %\n",
          "output_type": "stream"
        },
        {
          "output_type": "display_data",
          "data": {
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            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "76fdccd4c49d4ae7be15c1d96b0323ce"
            }
          },
          "metadata": {}
        },
        {
          "name": "stdout",
          "text": "Epoch [7/20], Step [352/352], Loss: 1.7079\n",
          "output_type": "stream"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "  0%|          | 0/40 [00:00<?, ?it/s]",
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "b8081cf3808e48dda603b43a0b9f66e7"
            }
          },
          "metadata": {}
        },
        {
          "name": "stdout",
          "text": "Accuracy of the network on the 5000 validation images: 32.18 %\n",
          "output_type": "stream"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "  0%|          | 0/352 [00:00<?, ?it/s]",
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "0e4cc3d367864af2bbc00d1aca75d4fc"
            }
          },
          "metadata": {}
        },
        {
          "name": "stdout",
          "text": "Epoch [8/20], Step [352/352], Loss: 1.7989\n",
          "output_type": "stream"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "  0%|          | 0/40 [00:00<?, ?it/s]",
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "6e7f0c3cfeda44ed94ab847f6bd68fc3"
            }
          },
          "metadata": {}
        },
        {
          "name": "stdout",
          "text": "Accuracy of the network on the 5000 validation images: 35.14 %\n",
          "output_type": "stream"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "  0%|          | 0/352 [00:00<?, ?it/s]",
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "a5484a37ceef44a6b51ae97bed10a6ad"
            }
          },
          "metadata": {}
        },
        {
          "name": "stdout",
          "text": "Epoch [9/20], Step [352/352], Loss: 1.9999\n",
          "output_type": "stream"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "  0%|          | 0/40 [00:00<?, ?it/s]",
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "d7db54e2785b4910826c169953046beb"
            }
          },
          "metadata": {}
        },
        {
          "name": "stdout",
          "text": "Accuracy of the network on the 5000 validation images: 35.86 %\n",
          "output_type": "stream"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "  0%|          | 0/352 [00:00<?, ?it/s]",
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "8f24c3b17d53452e865eec9901ddf76b"
            }
          },
          "metadata": {}
        },
        {
          "name": "stdout",
          "text": "Epoch [10/20], Step [352/352], Loss: 1.7327\n",
          "output_type": "stream"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "  0%|          | 0/40 [00:00<?, ?it/s]",
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "689697cf241146158893f9888d749f1f"
            }
          },
          "metadata": {}
        },
        {
          "name": "stdout",
          "text": "Accuracy of the network on the 5000 validation images: 37.02 %\n",
          "output_type": "stream"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "  0%|          | 0/352 [00:00<?, ?it/s]",
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "0130bcea23a3429cb8f8064e64137bf7"
            }
          },
          "metadata": {}
        },
        {
          "name": "stdout",
          "text": "Epoch [11/20], Step [352/352], Loss: 1.5495\n",
          "output_type": "stream"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "  0%|          | 0/40 [00:00<?, ?it/s]",
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "aed8a2cf111b4fb98c67510e4461d5ad"
            }
          },
          "metadata": {}
        },
        {
          "name": "stdout",
          "text": "Accuracy of the network on the 5000 validation images: 38.04 %\n",
          "output_type": "stream"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "  0%|          | 0/352 [00:00<?, ?it/s]",
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "4b205872574949238919d8a911bf6de5"
            }
          },
          "metadata": {}
        },
        {
          "name": "stdout",
          "text": "Epoch [12/20], Step [352/352], Loss: 1.6942\n",
          "output_type": "stream"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "  0%|          | 0/40 [00:00<?, ?it/s]",
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "24d503afaa1b4a6fa4facbc0c28b4c5e"
            }
          },
          "metadata": {}
        },
        {
          "name": "stdout",
          "text": "Accuracy of the network on the 5000 validation images: 39.84 %\n",
          "output_type": "stream"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "  0%|          | 0/352 [00:00<?, ?it/s]",
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "5d1b0e0d095a4be5935d2f6c3e994b82"
            }
          },
          "metadata": {}
        },
        {
          "name": "stdout",
          "text": "Epoch [13/20], Step [352/352], Loss: 1.4498\n",
          "output_type": "stream"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "  0%|          | 0/40 [00:00<?, ?it/s]",
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "3c09d7df6c58415eae60c4550d4d5ae4"
            }
          },
          "metadata": {}
        },
        {
          "name": "stdout",
          "text": "Accuracy of the network on the 5000 validation images: 41.44 %\n",
          "output_type": "stream"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "  0%|          | 0/352 [00:00<?, ?it/s]",
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "1c2d61b7b30c4c4da8ee674fb8af003d"
            }
          },
          "metadata": {}
        },
        {
          "name": "stdout",
          "text": "Epoch [14/20], Step [352/352], Loss: 1.3904\n",
          "output_type": "stream"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "  0%|          | 0/40 [00:00<?, ?it/s]",
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "ca5dac5159164bd3a8c8bc9b912f00fb"
            }
          },
          "metadata": {}
        },
        {
          "name": "stdout",
          "text": "Accuracy of the network on the 5000 validation images: 41.68 %\n",
          "output_type": "stream"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "  0%|          | 0/352 [00:00<?, ?it/s]",
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "1e08bdf6ab1d4a3e89dede677bf1ac42"
            }
          },
          "metadata": {}
        },
        {
          "name": "stdout",
          "text": "Epoch [15/20], Step [352/352], Loss: 1.6248\n",
          "output_type": "stream"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "  0%|          | 0/40 [00:00<?, ?it/s]",
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "48b6ded3b24f42c0942490f82a3b423e"
            }
          },
          "metadata": {}
        },
        {
          "name": "stdout",
          "text": "Accuracy of the network on the 5000 validation images: 43.24 %\n",
          "output_type": "stream"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "  0%|          | 0/352 [00:00<?, ?it/s]",
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "e05eab0aed7b4ad2826fc1097e36590c"
            }
          },
          "metadata": {}
        },
        {
          "name": "stdout",
          "text": "Epoch [16/20], Step [352/352], Loss: 1.5435\n",
          "output_type": "stream"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "  0%|          | 0/40 [00:00<?, ?it/s]",
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "1fbfd9b4942043a98ac3e1ab8462972b"
            }
          },
          "metadata": {}
        },
        {
          "name": "stdout",
          "text": "Accuracy of the network on the 5000 validation images: 44.06 %\n",
          "output_type": "stream"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "  0%|          | 0/352 [00:00<?, ?it/s]",
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "b40be8b1cc40485eb6340b000f4d0354"
            }
          },
          "metadata": {}
        },
        {
          "name": "stdout",
          "text": "Epoch [17/20], Step [352/352], Loss: 1.5179\n",
          "output_type": "stream"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "  0%|          | 0/40 [00:00<?, ?it/s]",
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "2f60335a39cf4775abb47c6912444690"
            }
          },
          "metadata": {}
        },
        {
          "name": "stdout",
          "text": "Accuracy of the network on the 5000 validation images: 43.38 %\n",
          "output_type": "stream"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "  0%|          | 0/352 [00:00<?, ?it/s]",
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "3b122312d825487aaeccd14b47b7ac90"
            }
          },
          "metadata": {}
        },
        {
          "name": "stdout",
          "text": "Epoch [18/20], Step [352/352], Loss: 1.4446\n",
          "output_type": "stream"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "  0%|          | 0/40 [00:00<?, ?it/s]",
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "0ada34a2e4f14932a99aa604f26bfcdb"
            }
          },
          "metadata": {}
        },
        {
          "name": "stdout",
          "text": "Accuracy of the network on the 5000 validation images: 44.76 %\n",
          "output_type": "stream"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "  0%|          | 0/352 [00:00<?, ?it/s]",
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "60b2a945719c4488a546c669ec4cb7ab"
            }
          },
          "metadata": {}
        },
        {
          "name": "stdout",
          "text": "Epoch [19/20], Step [352/352], Loss: 1.6356\n",
          "output_type": "stream"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "  0%|          | 0/40 [00:00<?, ?it/s]",
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "d934e5b883974c718afe1f62c34c0fa1"
            }
          },
          "metadata": {}
        },
        {
          "name": "stdout",
          "text": "Accuracy of the network on the 5000 validation images: 45.72 %\n",
          "output_type": "stream"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "  0%|          | 0/352 [00:00<?, ?it/s]",
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "9685b0a16be34348a2f4e153cc7a1f9e"
            }
          },
          "metadata": {}
        },
        {
          "name": "stdout",
          "text": "Epoch [20/20], Step [352/352], Loss: 1.2291\n",
          "output_type": "stream"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "  0%|          | 0/40 [00:00<?, ?it/s]",
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "85c9f9b88add4a8ca92ce52fab344248"
            }
          },
          "metadata": {}
        },
        {
          "name": "stdout",
          "text": "Accuracy of the network on the 5000 validation images: 45.68 %\n",
          "output_type": "stream"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Inceptionv1"
      ],
      "metadata": {
        "id": "_Qtu6DhYahrH"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from backbones.dataset.cifar10 import get_train_valid_loader, get_test_loader\n",
        "import torch\n",
        "from backbones.model.inceptionv1 import *\n",
        "from tqdm import tqdm_notebook\n",
        "\n",
        "# hyperparam\n",
        "num_classes = 10\n",
        "num_epochs = 20\n",
        "batch_size = 128\n",
        "learning_rate = 0.1\n",
        "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
        "train_loader, valid_loader = get_train_valid_loader(data_dir='./data', batch_size=batch_size, resize=(224,224),\n",
        "                                                    augment=False, random_seed=1)\n",
        "test_loader = get_test_loader(data_dir='./data', resize=(224,224),\n",
        "                              batch_size=batch_size)\n",
        "model = InceptionV1(input_channel=3, num_classes=10).to(device)\n",
        "\n",
        "\n",
        "# Loss and optimizer\n",
        "criterion = nn.CrossEntropyLoss()\n",
        "#optimizer = torch.optim.Adam(model.parameters(), lr=learning_rate, weight_decay=0.01)\n",
        "optimizer = torch.optim.SGD(model.parameters(), lr=learning_rate, momentum=0.9, weight_decay=1e-4)\n",
        "lr_scheduler = torch.optim.lr_scheduler.MultiStepLR(optimizer, milestones=[100, 150], gamma=0.1)\n",
        "\n",
        "\n",
        "# Train the model\n",
        "total_step = len(train_loader)\n",
        "for epoch in range(num_epochs):\n",
        "    for i, (images, labels) in enumerate(tqdm_notebook(train_loader)):\n",
        "        # Move tensors to the configured device\n",
        "        images = images.to(device)\n",
        "        labels = labels.to(device)\n",
        "\n",
        "        # Forward pass\n",
        "        outputs = model(images)\n",
        "        loss = criterion(outputs, labels)\n",
        "\n",
        "        # Backward and optimize\n",
        "        optimizer.zero_grad()\n",
        "        loss.backward()\n",
        "        optimizer.step()\n",
        "        lr_scheduler.step()\n",
        "\n",
        "    print('Epoch [{}/{}], Step [{}/{}], Loss: {:.4f}'\n",
        "          .format(epoch + 1, num_epochs, i + 1, total_step, loss.item()))\n",
        "\n",
        "    # Validation\n",
        "    with torch.no_grad():\n",
        "        correct = 0\n",
        "        total = 0\n",
        "        for i, (images, labels) in enumerate(tqdm_notebook(valid_loader)):\n",
        "            images = images.to(device)\n",
        "            labels = labels.to(device)\n",
        "            outputs = model(images)\n",
        "            _, predicted = torch.max(outputs.data, 1)\n",
        "            total += labels.size(0)\n",
        "            correct += (predicted == labels).sum().item()\n",
        "            del images, labels, outputs\n",
        "        print('Accuracy of the network on the {} validation images: {} %'.format(5000, 100 * correct / total))"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2023-07-13T07:22:35.426791Z",
          "iopub.execute_input": "2023-07-13T07:22:35.427721Z",
          "iopub.status.idle": "2023-07-13T08:05:30.360746Z",
          "shell.execute_reply.started": "2023-07-13T07:22:35.427666Z",
          "shell.execute_reply": "2023-07-13T08:05:30.359737Z"
        },
        "trusted": true,
        "id": "IesxQ8Ok3Xb_"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "_Ge6g9aa9l7Z"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# DenseNet121"
      ],
      "metadata": {
        "id": "hsLET5059mxG"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Adam optimizer (not AdamW) is suck idk why"
      ],
      "metadata": {
        "id": "-9A4Bkx_Gp75"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from backbones.dataset.cifar10 import get_train_valid_loader, get_test_loader\n",
        "import torch\n",
        "from backbones.model.densenet import *\n",
        "from tqdm import tqdm_notebook\n",
        "\n",
        "# hyperparam\n",
        "num_classes = 10\n",
        "num_epochs = 20\n",
        "batch_size = 128\n",
        "learning_rate = 0.005\n",
        "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
        "train_loader, valid_loader = get_train_valid_loader(data_dir='./data', batch_size=batch_size, resize=(224,224),\n",
        "                                                    augment=False, random_seed=1)\n",
        "test_loader = get_test_loader(data_dir='./data', resize=(224,224),\n",
        "                              batch_size=batch_size)\n",
        "model = DenseNet(num_classes=10, block_config=[6,12,24,16], growth_rate=32).to(device)\n",
        "\n",
        "\n",
        "# Loss and optimizer\n",
        "criterion = nn.CrossEntropyLoss()\n",
        "# optimizer = torch.optim.Adam(model.parameters(), lr=learning_rate, weight_decay=0.01)\n",
        "optimizer = torch.optim.SGD(model.parameters(), lr=learning_rate, momentum=0.9, weight_decay=1e-4)\n",
        "lr_scheduler = torch.optim.lr_scheduler.MultiStepLR(optimizer, milestones=[100, 150], gamma=0.1)\n",
        "\n",
        "\n",
        "# Train the model\n",
        "total_step = len(train_loader)\n",
        "for epoch in range(num_epochs):\n",
        "    for i, (images, labels) in enumerate(tqdm_notebook(train_loader)):\n",
        "        # Move tensors to the configured device\n",
        "        images = images.to(device)\n",
        "        labels = labels.to(device)\n",
        "\n",
        "        # Forward pass\n",
        "        outputs = model(images).to(device)\n",
        "        loss = criterion(outputs, labels)\n",
        "\n",
        "        # Backward and optimize\n",
        "        optimizer.zero_grad()\n",
        "        loss.backward()\n",
        "        optimizer.step()\n",
        "\n",
        "    print('Epoch [{}/{}], Step [{}/{}], Loss: {:.4f}'\n",
        "          .format(epoch + 1, num_epochs, i + 1, total_step, loss.item()))\n",
        "\n",
        "    # Validation\n",
        "    with torch.no_grad():\n",
        "        correct = 0\n",
        "        total = 0\n",
        "        for i, (images, labels) in enumerate(tqdm_notebook(valid_loader)):\n",
        "            images = images.to(device)\n",
        "            labels = labels.to(device)\n",
        "            outputs = model(images).to(device)\n",
        "            _, predicted = torch.max(outputs.data, 1)\n",
        "            total += labels.size(0)\n",
        "            correct += (predicted == labels).sum().item()\n",
        "            del images, labels, outputs\n",
        "        print('Accuracy of the network on the {} validation images: {} %'.format(5000, 100 * correct / total))"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2023-07-17T07:13:09.839990Z",
          "iopub.execute_input": "2023-07-17T07:13:09.840352Z",
          "iopub.status.idle": "2023-07-17T07:35:01.053870Z",
          "shell.execute_reply.started": "2023-07-17T07:13:09.840323Z",
          "shell.execute_reply": "2023-07-17T07:35:01.051558Z"
        },
        "trusted": true,
        "id": "HolAjfmp9iGo",
        "outputId": "b629d852-6704-496d-8487-e294cafe21d9",
        "colab": {
          "referenced_widgets": [
            "7054deeedb0f4f2ab6c6d36ff60423a6",
            "156dc8e1c69a458cb4a13c21abfa54ae",
            "fa5ee457d0634144a8b446770330a254",
            "ffc0974f30714034b6ad893c93d200db",
            "c2bca1b14b8b4eaeab3fb4f38d1241c8",
            "c1664cac692c48bfb6960046074e3d17",
            "7178992fda0e4ff48a75ae59d70cce66",
            "7edede8e720c496cb339a204c42e3340",
            "8bb101805e7c498a8067f409e15a0900",
            "e9d3eec20eb54313b1120feb830227ee",
            "efb82185383b4870bee224109fd77baa",
            "96ded37339b04e22a38102af77580bde",
            "ff94fdc742f346d9a4de70083fd58ca9",
            "7cdceb2ad9b542949403bb0a30491ef8",
            "58cdd01f251844008dad924a6f7a9eed",
            "dca41d111a944b4199c96eeed238eb88",
            "d5af2a84c99847e3b0e98c6e96eb4bc4",
            "ac29b28d87bc4c4db2579c2f7853fbbe"
          ]
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "name": "stdout",
          "text": "Files already downloaded and verified\nFiles already downloaded and verified\nFiles already downloaded and verified\n",
          "output_type": "stream"
        },
        {
          "name": "stderr",
          "text": "/tmp/ipykernel_29/747884710.py:29: TqdmDeprecationWarning: This function will be removed in tqdm==5.0.0\nPlease use `tqdm.notebook.tqdm` instead of `tqdm.tqdm_notebook`\n  for i, (images, labels) in enumerate(tqdm_notebook(train_loader)):\n",
          "output_type": "stream"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "  0%|          | 0/352 [00:00<?, ?it/s]",
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "7054deeedb0f4f2ab6c6d36ff60423a6"
            }
          },
          "metadata": {}
        },
        {
          "name": "stdout",
          "text": "Epoch [1/20], Step [352/352], Loss: 1.2048\n",
          "output_type": "stream"
        },
        {
          "name": "stderr",
          "text": "/tmp/ipykernel_29/747884710.py:50: TqdmDeprecationWarning: This function will be removed in tqdm==5.0.0\nPlease use `tqdm.notebook.tqdm` instead of `tqdm.tqdm_notebook`\n  for i, (images, labels) in enumerate(tqdm_notebook(valid_loader)):\n",
          "output_type": "stream"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "  0%|          | 0/40 [00:00<?, ?it/s]",
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "156dc8e1c69a458cb4a13c21abfa54ae"
            }
          },
          "metadata": {}
        },
        {
          "name": "stdout",
          "text": "Accuracy of the network on the 5000 validation images: 55.5 %\n",
          "output_type": "stream"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "  0%|          | 0/352 [00:00<?, ?it/s]",
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "fa5ee457d0634144a8b446770330a254"
            }
          },
          "metadata": {}
        },
        {
          "name": "stdout",
          "text": "Epoch [2/20], Step [352/352], Loss: 0.9973\n",
          "output_type": "stream"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "  0%|          | 0/40 [00:00<?, ?it/s]",
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "ffc0974f30714034b6ad893c93d200db"
            }
          },
          "metadata": {}
        },
        {
          "name": "stdout",
          "text": "Accuracy of the network on the 5000 validation images: 67.12 %\n",
          "output_type": "stream"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "  0%|          | 0/352 [00:00<?, ?it/s]",
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "c2bca1b14b8b4eaeab3fb4f38d1241c8"
            }
          },
          "metadata": {}
        },
        {
          "name": "stdout",
          "text": "Epoch [3/20], Step [352/352], Loss: 0.7697\n",
          "output_type": "stream"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "  0%|          | 0/40 [00:00<?, ?it/s]",
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "c1664cac692c48bfb6960046074e3d17"
            }
          },
          "metadata": {}
        },
        {
          "name": "stdout",
          "text": "Accuracy of the network on the 5000 validation images: 72.28 %\n",
          "output_type": "stream"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "  0%|          | 0/352 [00:00<?, ?it/s]",
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "7178992fda0e4ff48a75ae59d70cce66"
            }
          },
          "metadata": {}
        },
        {
          "name": "stdout",
          "text": "Epoch [4/20], Step [352/352], Loss: 0.4548\n",
          "output_type": "stream"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "  0%|          | 0/40 [00:00<?, ?it/s]",
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "7edede8e720c496cb339a204c42e3340"
            }
          },
          "metadata": {}
        },
        {
          "name": "stdout",
          "text": "Accuracy of the network on the 5000 validation images: 78.56 %\n",
          "output_type": "stream"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "  0%|          | 0/352 [00:00<?, ?it/s]",
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "8bb101805e7c498a8067f409e15a0900"
            }
          },
          "metadata": {}
        },
        {
          "name": "stdout",
          "text": "Epoch [5/20], Step [352/352], Loss: 0.5397\n",
          "output_type": "stream"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "  0%|          | 0/40 [00:00<?, ?it/s]",
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "e9d3eec20eb54313b1120feb830227ee"
            }
          },
          "metadata": {}
        },
        {
          "name": "stdout",
          "text": "Accuracy of the network on the 5000 validation images: 79.86 %\n",
          "output_type": "stream"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "  0%|          | 0/352 [00:00<?, ?it/s]",
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "efb82185383b4870bee224109fd77baa"
            }
          },
          "metadata": {}
        },
        {
          "name": "stdout",
          "text": "Epoch [6/20], Step [352/352], Loss: 0.5852\n",
          "output_type": "stream"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "  0%|          | 0/40 [00:00<?, ?it/s]",
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "96ded37339b04e22a38102af77580bde"
            }
          },
          "metadata": {}
        },
        {
          "name": "stdout",
          "text": "Accuracy of the network on the 5000 validation images: 82.18 %\n",
          "output_type": "stream"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "  0%|          | 0/352 [00:00<?, ?it/s]",
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "ff94fdc742f346d9a4de70083fd58ca9"
            }
          },
          "metadata": {}
        },
        {
          "name": "stdout",
          "text": "Epoch [7/20], Step [352/352], Loss: 0.4795\n",
          "output_type": "stream"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "  0%|          | 0/40 [00:00<?, ?it/s]",
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "7cdceb2ad9b542949403bb0a30491ef8"
            }
          },
          "metadata": {}
        },
        {
          "name": "stdout",
          "text": "Accuracy of the network on the 5000 validation images: 82.38 %\n",
          "output_type": "stream"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "  0%|          | 0/352 [00:00<?, ?it/s]",
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "58cdd01f251844008dad924a6f7a9eed"
            }
          },
          "metadata": {}
        },
        {
          "name": "stdout",
          "text": "Epoch [8/20], Step [352/352], Loss: 0.2815\n",
          "output_type": "stream"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "  0%|          | 0/40 [00:00<?, ?it/s]",
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "dca41d111a944b4199c96eeed238eb88"
            }
          },
          "metadata": {}
        },
        {
          "name": "stdout",
          "text": "Accuracy of the network on the 5000 validation images: 83.64 %\n",
          "output_type": "stream"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "  0%|          | 0/352 [00:00<?, ?it/s]",
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "d5af2a84c99847e3b0e98c6e96eb4bc4"
            }
          },
          "metadata": {}
        },
        {
          "name": "stdout",
          "text": "Epoch [9/20], Step [352/352], Loss: 0.4508\n",
          "output_type": "stream"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "  0%|          | 0/40 [00:00<?, ?it/s]",
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "ac29b28d87bc4c4db2579c2f7853fbbe"
            }
          },
          "metadata": {}
        },
        {
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
            "Cell \u001b[0;32mIn[7], line 50\u001b[0m\n\u001b[1;32m     48\u001b[0m correct \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m0\u001b[39m\n\u001b[1;32m     49\u001b[0m total \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m0\u001b[39m\n\u001b[0;32m---> 50\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m i, (images, labels) \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28menumerate\u001b[39m(tqdm_notebook(valid_loader)):\n\u001b[1;32m     51\u001b[0m     images \u001b[38;5;241m=\u001b[39m images\u001b[38;5;241m.\u001b[39mto(device)\n\u001b[1;32m     52\u001b[0m     labels \u001b[38;5;241m=\u001b[39m labels\u001b[38;5;241m.\u001b[39mto(device)\n",
            "File \u001b[0;32m/opt/conda/lib/python3.10/site-packages/tqdm/notebook.py:254\u001b[0m, in \u001b[0;36mtqdm_notebook.__iter__\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    252\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m    253\u001b[0m     it \u001b[38;5;241m=\u001b[39m \u001b[38;5;28msuper\u001b[39m(tqdm_notebook, \u001b[38;5;28mself\u001b[39m)\u001b[38;5;241m.\u001b[39m\u001b[38;5;21m__iter__\u001b[39m()\n\u001b[0;32m--> 254\u001b[0m     \u001b[38;5;28;01mfor\u001b[39;00m obj \u001b[38;5;129;01min\u001b[39;00m it:\n\u001b[1;32m    255\u001b[0m         \u001b[38;5;66;03m# return super(tqdm...) will not catch exception\u001b[39;00m\n\u001b[1;32m    256\u001b[0m         \u001b[38;5;28;01myield\u001b[39;00m obj\n\u001b[1;32m    257\u001b[0m \u001b[38;5;66;03m# NB: except ... [ as ...] breaks IPython async KeyboardInterrupt\u001b[39;00m\n",
            "File \u001b[0;32m/opt/conda/lib/python3.10/site-packages/tqdm/std.py:1178\u001b[0m, in \u001b[0;36mtqdm.__iter__\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m   1175\u001b[0m time \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_time\n\u001b[1;32m   1177\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m-> 1178\u001b[0m     \u001b[38;5;28;01mfor\u001b[39;00m obj \u001b[38;5;129;01min\u001b[39;00m iterable:\n\u001b[1;32m   1179\u001b[0m         \u001b[38;5;28;01myield\u001b[39;00m obj\n\u001b[1;32m   1180\u001b[0m         \u001b[38;5;66;03m# Update and possibly print the progressbar.\u001b[39;00m\n\u001b[1;32m   1181\u001b[0m         \u001b[38;5;66;03m# Note: does not call self.update(1) for speed optimisation.\u001b[39;00m\n",
            "File \u001b[0;32m/opt/conda/lib/python3.10/site-packages/torch/utils/data/dataloader.py:634\u001b[0m, in \u001b[0;36m_BaseDataLoaderIter.__next__\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    631\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_sampler_iter \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m    632\u001b[0m     \u001b[38;5;66;03m# TODO(https://github.com/pytorch/pytorch/issues/76750)\u001b[39;00m\n\u001b[1;32m    633\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_reset()  \u001b[38;5;66;03m# type: ignore[call-arg]\u001b[39;00m\n\u001b[0;32m--> 634\u001b[0m data \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_next_data\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    635\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_num_yielded \u001b[38;5;241m+\u001b[39m\u001b[38;5;241m=\u001b[39m \u001b[38;5;241m1\u001b[39m\n\u001b[1;32m    636\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_dataset_kind \u001b[38;5;241m==\u001b[39m _DatasetKind\u001b[38;5;241m.\u001b[39mIterable \u001b[38;5;129;01mand\u001b[39;00m \\\n\u001b[1;32m    637\u001b[0m         \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_IterableDataset_len_called \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 \\\n\u001b[1;32m    638\u001b[0m         \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_num_yielded \u001b[38;5;241m>\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_IterableDataset_len_called:\n",
            "File \u001b[0;32m/opt/conda/lib/python3.10/site-packages/torch/utils/data/dataloader.py:678\u001b[0m, in \u001b[0;36m_SingleProcessDataLoaderIter._next_data\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    676\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m_next_data\u001b[39m(\u001b[38;5;28mself\u001b[39m):\n\u001b[1;32m    677\u001b[0m     index \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_next_index()  \u001b[38;5;66;03m# may raise StopIteration\u001b[39;00m\n\u001b[0;32m--> 678\u001b[0m     data \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_dataset_fetcher\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfetch\u001b[49m\u001b[43m(\u001b[49m\u001b[43mindex\u001b[49m\u001b[43m)\u001b[49m  \u001b[38;5;66;03m# may raise StopIteration\u001b[39;00m\n\u001b[1;32m    679\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_pin_memory:\n\u001b[1;32m    680\u001b[0m         data \u001b[38;5;241m=\u001b[39m _utils\u001b[38;5;241m.\u001b[39mpin_memory\u001b[38;5;241m.\u001b[39mpin_memory(data, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_pin_memory_device)\n",
            "File \u001b[0;32m/opt/conda/lib/python3.10/site-packages/torch/utils/data/_utils/fetch.py:54\u001b[0m, in \u001b[0;36m_MapDatasetFetcher.fetch\u001b[0;34m(self, possibly_batched_index)\u001b[0m\n\u001b[1;32m     52\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m     53\u001b[0m     data \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mdataset[possibly_batched_index]\n\u001b[0;32m---> 54\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[43mcollate_fn\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdata\u001b[49m\u001b[43m)\u001b[49m\n",
            "File \u001b[0;32m/opt/conda/lib/python3.10/site-packages/torch/utils/data/_utils/collate.py:264\u001b[0m, in \u001b[0;36mdefault_collate\u001b[0;34m(batch)\u001b[0m\n\u001b[1;32m    203\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mdefault_collate\u001b[39m(batch):\n\u001b[1;32m    204\u001b[0m \u001b[38;5;250m    \u001b[39m\u001b[38;5;124mr\u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m    205\u001b[0m \u001b[38;5;124;03m        Function that takes in a batch of data and puts the elements within the batch\u001b[39;00m\n\u001b[1;32m    206\u001b[0m \u001b[38;5;124;03m        into a tensor with an additional outer dimension - batch size. The exact output type can be\u001b[39;00m\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m    262\u001b[0m \u001b[38;5;124;03m            >>> default_collate(batch)  # Handle `CustomType` automatically\u001b[39;00m\n\u001b[1;32m    263\u001b[0m \u001b[38;5;124;03m    \"\"\"\u001b[39;00m\n\u001b[0;32m--> 264\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mcollate\u001b[49m\u001b[43m(\u001b[49m\u001b[43mbatch\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcollate_fn_map\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdefault_collate_fn_map\u001b[49m\u001b[43m)\u001b[49m\n",
            "File \u001b[0;32m/opt/conda/lib/python3.10/site-packages/torch/utils/data/_utils/collate.py:142\u001b[0m, in \u001b[0;36mcollate\u001b[0;34m(batch, collate_fn_map)\u001b[0m\n\u001b[1;32m    139\u001b[0m transposed \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mlist\u001b[39m(\u001b[38;5;28mzip\u001b[39m(\u001b[38;5;241m*\u001b[39mbatch))  \u001b[38;5;66;03m# It may be accessed twice, so we use a list.\u001b[39;00m\n\u001b[1;32m    141\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(elem, \u001b[38;5;28mtuple\u001b[39m):\n\u001b[0;32m--> 142\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m [collate(samples, collate_fn_map\u001b[38;5;241m=\u001b[39mcollate_fn_map) \u001b[38;5;28;01mfor\u001b[39;00m samples \u001b[38;5;129;01min\u001b[39;00m transposed]  \u001b[38;5;66;03m# Backwards compatibility.\u001b[39;00m\n\u001b[1;32m    143\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m    144\u001b[0m     \u001b[38;5;28;01mtry\u001b[39;00m:\n",
            "File \u001b[0;32m/opt/conda/lib/python3.10/site-packages/torch/utils/data/_utils/collate.py:142\u001b[0m, in \u001b[0;36m<listcomp>\u001b[0;34m(.0)\u001b[0m\n\u001b[1;32m    139\u001b[0m transposed \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mlist\u001b[39m(\u001b[38;5;28mzip\u001b[39m(\u001b[38;5;241m*\u001b[39mbatch))  \u001b[38;5;66;03m# It may be accessed twice, so we use a list.\u001b[39;00m\n\u001b[1;32m    141\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(elem, \u001b[38;5;28mtuple\u001b[39m):\n\u001b[0;32m--> 142\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m [\u001b[43mcollate\u001b[49m\u001b[43m(\u001b[49m\u001b[43msamples\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcollate_fn_map\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcollate_fn_map\u001b[49m\u001b[43m)\u001b[49m \u001b[38;5;28;01mfor\u001b[39;00m samples \u001b[38;5;129;01min\u001b[39;00m transposed]  \u001b[38;5;66;03m# Backwards compatibility.\u001b[39;00m\n\u001b[1;32m    143\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m    144\u001b[0m     \u001b[38;5;28;01mtry\u001b[39;00m:\n",
            "File \u001b[0;32m/opt/conda/lib/python3.10/site-packages/torch/utils/data/_utils/collate.py:119\u001b[0m, in \u001b[0;36mcollate\u001b[0;34m(batch, collate_fn_map)\u001b[0m\n\u001b[1;32m    117\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m collate_fn_map \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    118\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m elem_type \u001b[38;5;129;01min\u001b[39;00m collate_fn_map:\n\u001b[0;32m--> 119\u001b[0m         \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mcollate_fn_map\u001b[49m\u001b[43m[\u001b[49m\u001b[43melem_type\u001b[49m\u001b[43m]\u001b[49m\u001b[43m(\u001b[49m\u001b[43mbatch\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcollate_fn_map\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcollate_fn_map\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    121\u001b[0m     \u001b[38;5;28;01mfor\u001b[39;00m collate_type \u001b[38;5;129;01min\u001b[39;00m collate_fn_map:\n\u001b[1;32m    122\u001b[0m         \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(elem, collate_type):\n",
            "File \u001b[0;32m/opt/conda/lib/python3.10/site-packages/torch/utils/data/_utils/collate.py:162\u001b[0m, in \u001b[0;36mcollate_tensor_fn\u001b[0;34m(batch, collate_fn_map)\u001b[0m\n\u001b[1;32m    160\u001b[0m     storage \u001b[38;5;241m=\u001b[39m elem\u001b[38;5;241m.\u001b[39m_typed_storage()\u001b[38;5;241m.\u001b[39m_new_shared(numel, device\u001b[38;5;241m=\u001b[39melem\u001b[38;5;241m.\u001b[39mdevice)\n\u001b[1;32m    161\u001b[0m     out \u001b[38;5;241m=\u001b[39m elem\u001b[38;5;241m.\u001b[39mnew(storage)\u001b[38;5;241m.\u001b[39mresize_(\u001b[38;5;28mlen\u001b[39m(batch), \u001b[38;5;241m*\u001b[39m\u001b[38;5;28mlist\u001b[39m(elem\u001b[38;5;241m.\u001b[39msize()))\n\u001b[0;32m--> 162\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mtorch\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mstack\u001b[49m\u001b[43m(\u001b[49m\u001b[43mbatch\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m0\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mout\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mout\u001b[49m\u001b[43m)\u001b[49m\n",
            "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
          ],
          "ename": "KeyboardInterrupt",
          "evalue": "",
          "output_type": "error"
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from backbones.dataset.cifar10 import get_train_valid_loader, get_test_loader\n",
        "import torch\n",
        "from backbones.model.densenet import *\n",
        "from tqdm import tqdm_notebook\n",
        "\n",
        "# hyperparam\n",
        "num_classes = 10\n",
        "num_epochs = 20\n",
        "batch_size = 128\n",
        "learning_rate = 0.005\n",
        "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
        "train_loader, valid_loader = get_train_valid_loader(data_dir='./data', batch_size=batch_size, resize=(224,224),\n",
        "                                                    augment=False, random_seed=1)\n",
        "test_loader = get_test_loader(data_dir='./data', resize=(224,224),\n",
        "                              batch_size=batch_size)\n",
        "model = DenseNet(num_classes=10, block_config=[6,12,24,16], growth_rate=32).to(device)\n",
        "\n",
        "\n",
        "# Loss and optimizer\n",
        "criterion = nn.CrossEntropyLoss()\n",
        "optimizer = torch.optim.Adam(model.parameters(), lr=learning_rate, weight_decay=0.01)\n",
        "# optimizer = torch.optim.SGD(model.parameters(), lr=learning_rate, momentum=0.9, weight_decay=1e-4)\n",
        "lr_scheduler = torch.optim.lr_scheduler.MultiStepLR(optimizer, milestones=[100, 150], gamma=0.1)\n",
        "\n",
        "\n",
        "# Train the model\n",
        "total_step = len(train_loader)\n",
        "for epoch in range(num_epochs):\n",
        "    for i, (images, labels) in enumerate(tqdm_notebook(train_loader)):\n",
        "        # Move tensors to the configured device\n",
        "        images = images.to(device)\n",
        "        labels = labels.to(device)\n",
        "\n",
        "        # Forward pass\n",
        "        outputs = model(images).to(device)\n",
        "        loss = criterion(outputs, labels)\n",
        "\n",
        "        # Backward and optimize\n",
        "        optimizer.zero_grad()\n",
        "        loss.backward()\n",
        "        optimizer.step()\n",
        "\n",
        "    print('Epoch [{}/{}], Step [{}/{}], Loss: {:.4f}'\n",
        "          .format(epoch + 1, num_epochs, i + 1, total_step, loss.item()))\n",
        "\n",
        "    # Validation\n",
        "    with torch.no_grad():\n",
        "        correct = 0\n",
        "        total = 0\n",
        "        for i, (images, labels) in enumerate(tqdm_notebook(valid_loader)):\n",
        "            images = images.to(device)\n",
        "            labels = labels.to(device)\n",
        "            outputs = model(images).to(device)\n",
        "            _, predicted = torch.max(outputs.data, 1)\n",
        "            total += labels.size(0)\n",
        "            correct += (predicted == labels).sum().item()\n",
        "            del images, labels, outputs\n",
        "        print('Accuracy of the network on the {} validation images: {} %'.format(5000, 100 * correct / total))"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2023-07-17T07:35:04.962614Z",
          "iopub.execute_input": "2023-07-17T07:35:04.962976Z",
          "iopub.status.idle": "2023-07-17T08:24:50.839525Z",
          "shell.execute_reply.started": "2023-07-17T07:35:04.962949Z",
          "shell.execute_reply": "2023-07-17T08:24:50.838476Z"
        },
        "trusted": true,
        "id": "n6srAGYC9iGq",
        "outputId": "666a833e-1aa6-4d7a-9d41-9327d15b9017",
        "colab": {
          "referenced_widgets": [
            "22d0ba61cad74373a0282658f5eb2ebe",
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            "1e2af441c38a4d409e5eff6201aabc99"
          ]
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "name": "stdout",
          "text": "Files already downloaded and verified\nFiles already downloaded and verified\nFiles already downloaded and verified\n",
          "output_type": "stream"
        },
        {
          "name": "stderr",
          "text": "/tmp/ipykernel_29/2050497277.py:29: TqdmDeprecationWarning: This function will be removed in tqdm==5.0.0\nPlease use `tqdm.notebook.tqdm` instead of `tqdm.tqdm_notebook`\n  for i, (images, labels) in enumerate(tqdm_notebook(train_loader)):\n",
          "output_type": "stream"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "  0%|          | 0/352 [00:00<?, ?it/s]",
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "22d0ba61cad74373a0282658f5eb2ebe"
            }
          },
          "metadata": {}
        },
        {
          "name": "stdout",
          "text": "Epoch [1/20], Step [352/352], Loss: 1.8493\n",
          "output_type": "stream"
        },
        {
          "name": "stderr",
          "text": "/tmp/ipykernel_29/2050497277.py:50: TqdmDeprecationWarning: This function will be removed in tqdm==5.0.0\nPlease use `tqdm.notebook.tqdm` instead of `tqdm.tqdm_notebook`\n  for i, (images, labels) in enumerate(tqdm_notebook(valid_loader)):\n",
          "output_type": "stream"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "  0%|          | 0/40 [00:00<?, ?it/s]",
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "aeccadd4ab1d48fe9bc6c350756b1049"
            }
          },
          "metadata": {}
        },
        {
          "name": "stdout",
          "text": "Accuracy of the network on the 5000 validation images: 29.3 %\n",
          "output_type": "stream"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "  0%|          | 0/352 [00:00<?, ?it/s]",
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "ac1241a682434c8792eb23fdb8e6b6e5"
            }
          },
          "metadata": {}
        },
        {
          "name": "stdout",
          "text": "Epoch [2/20], Step [352/352], Loss: 1.8627\n",
          "output_type": "stream"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "  0%|          | 0/40 [00:00<?, ?it/s]",
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "d6c0f60c5ccb48f2aec80e681449bf7e"
            }
          },
          "metadata": {}
        },
        {
          "name": "stdout",
          "text": "Accuracy of the network on the 5000 validation images: 26.64 %\n",
          "output_type": "stream"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "  0%|          | 0/352 [00:00<?, ?it/s]",
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "a819ab82308c4464855660d51e4b95ee"
            }
          },
          "metadata": {}
        },
        {
          "name": "stdout",
          "text": "Epoch [3/20], Step [352/352], Loss: 1.8033\n",
          "output_type": "stream"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "  0%|          | 0/40 [00:00<?, ?it/s]",
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "19edc38b66604e69a35e91823f8c4da6"
            }
          },
          "metadata": {}
        },
        {
          "name": "stdout",
          "text": "Accuracy of the network on the 5000 validation images: 32.26 %\n",
          "output_type": "stream"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "  0%|          | 0/352 [00:00<?, ?it/s]",
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "9d9ecfb8b14f4f1db6e4e47350ed9c7b"
            }
          },
          "metadata": {}
        },
        {
          "name": "stdout",
          "text": "Epoch [4/20], Step [352/352], Loss: 1.9314\n",
          "output_type": "stream"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "  0%|          | 0/40 [00:00<?, ?it/s]",
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "af25f0ac8759403b8901068129149496"
            }
          },
          "metadata": {}
        },
        {
          "name": "stdout",
          "text": "Accuracy of the network on the 5000 validation images: 32.12 %\n",
          "output_type": "stream"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "  0%|          | 0/352 [00:00<?, ?it/s]",
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "b33570d0149245c5adeec6920d9df0ed"
            }
          },
          "metadata": {}
        },
        {
          "name": "stdout",
          "text": "Epoch [5/20], Step [352/352], Loss: 1.7453\n",
          "output_type": "stream"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "  0%|          | 0/40 [00:00<?, ?it/s]",
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "a3e198195c6f4be2af6b873dfe47c0f5"
            }
          },
          "metadata": {}
        },
        {
          "name": "stdout",
          "text": "Accuracy of the network on the 5000 validation images: 39.64 %\n",
          "output_type": "stream"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "  0%|          | 0/352 [00:00<?, ?it/s]",
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "470cc4c2cc54439cbd32791faf00ee84"
            }
          },
          "metadata": {}
        },
        {
          "name": "stdout",
          "text": "Epoch [6/20], Step [352/352], Loss: 1.4584\n",
          "output_type": "stream"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "  0%|          | 0/40 [00:00<?, ?it/s]",
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "f3ed2d4ac3014234a946c88fa84bc068"
            }
          },
          "metadata": {}
        },
        {
          "name": "stdout",
          "text": "Accuracy of the network on the 5000 validation images: 44.32 %\n",
          "output_type": "stream"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "  0%|          | 0/352 [00:00<?, ?it/s]",
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "f08c160a17c5420a8548add8884960ba"
            }
          },
          "metadata": {}
        },
        {
          "name": "stdout",
          "text": "Epoch [7/20], Step [352/352], Loss: 1.2829\n",
          "output_type": "stream"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "  0%|          | 0/40 [00:00<?, ?it/s]",
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "b4b92e9aec2847ed8cc750add345744d"
            }
          },
          "metadata": {}
        },
        {
          "name": "stdout",
          "text": "Accuracy of the network on the 5000 validation images: 44.2 %\n",
          "output_type": "stream"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "  0%|          | 0/352 [00:00<?, ?it/s]",
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "a314e989b1e64ef29c21d76536c220c7"
            }
          },
          "metadata": {}
        },
        {
          "name": "stdout",
          "text": "Epoch [8/20], Step [352/352], Loss: 1.4919\n",
          "output_type": "stream"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "  0%|          | 0/40 [00:00<?, ?it/s]",
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "41f6f21615b54ffd8dd27180cd69b6d8"
            }
          },
          "metadata": {}
        },
        {
          "name": "stdout",
          "text": "Accuracy of the network on the 5000 validation images: 47.18 %\n",
          "output_type": "stream"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "  0%|          | 0/352 [00:00<?, ?it/s]",
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "593e6d368b974a98a9d784926879b358"
            }
          },
          "metadata": {}
        },
        {
          "name": "stdout",
          "text": "Epoch [9/20], Step [352/352], Loss: 1.4827\n",
          "output_type": "stream"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "  0%|          | 0/40 [00:00<?, ?it/s]",
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "3d7f8e89901c49ac82b0a3a3111fe5a1"
            }
          },
          "metadata": {}
        },
        {
          "name": "stdout",
          "text": "Accuracy of the network on the 5000 validation images: 48.3 %\n",
          "output_type": "stream"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "  0%|          | 0/352 [00:00<?, ?it/s]",
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "785a173c90a34c79bc085601c18bcdc5"
            }
          },
          "metadata": {}
        },
        {
          "name": "stdout",
          "text": "Epoch [10/20], Step [352/352], Loss: 1.4342\n",
          "output_type": "stream"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "  0%|          | 0/40 [00:00<?, ?it/s]",
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "65e2316b58084452a37f06ca62dd64a6"
            }
          },
          "metadata": {}
        },
        {
          "name": "stdout",
          "text": "Accuracy of the network on the 5000 validation images: 50.34 %\n",
          "output_type": "stream"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "  0%|          | 0/352 [00:00<?, ?it/s]",
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "11bf972a54124c85811575d8d52aecb8"
            }
          },
          "metadata": {}
        },
        {
          "name": "stdout",
          "text": "Epoch [11/20], Step [352/352], Loss: 1.3801\n",
          "output_type": "stream"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "  0%|          | 0/40 [00:00<?, ?it/s]",
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "5b53167a6a6743aa8c83e72038ddd40e"
            }
          },
          "metadata": {}
        },
        {
          "name": "stdout",
          "text": "Accuracy of the network on the 5000 validation images: 49.82 %\n",
          "output_type": "stream"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "  0%|          | 0/352 [00:00<?, ?it/s]",
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "8e1a78ca9b8a449c8afe4381dd98c9e2"
            }
          },
          "metadata": {}
        },
        {
          "name": "stdout",
          "text": "Epoch [12/20], Step [352/352], Loss: 1.3949\n",
          "output_type": "stream"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "  0%|          | 0/40 [00:00<?, ?it/s]",
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "824cfbf62c564936abe3a040414c32f3"
            }
          },
          "metadata": {}
        },
        {
          "name": "stdout",
          "text": "Accuracy of the network on the 5000 validation images: 49.66 %\n",
          "output_type": "stream"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "  0%|          | 0/352 [00:00<?, ?it/s]",
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "e8a3068788b9491686363dc6ea611eba"
            }
          },
          "metadata": {}
        },
        {
          "name": "stdout",
          "text": "Epoch [13/20], Step [352/352], Loss: 1.5267\n",
          "output_type": "stream"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "  0%|          | 0/40 [00:00<?, ?it/s]",
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "24aeaf4a14834db283e30902ec8fdf52"
            }
          },
          "metadata": {}
        },
        {
          "name": "stdout",
          "text": "Accuracy of the network on the 5000 validation images: 51.38 %\n",
          "output_type": "stream"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "  0%|          | 0/352 [00:00<?, ?it/s]",
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "d8aecb0df2ab4ab3b0d049b015da3f9b"
            }
          },
          "metadata": {}
        },
        {
          "name": "stdout",
          "text": "Epoch [14/20], Step [352/352], Loss: 1.3664\n",
          "output_type": "stream"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "  0%|          | 0/40 [00:00<?, ?it/s]",
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "d629a4aef8fd49ff876e1869023dfa1d"
            }
          },
          "metadata": {}
        },
        {
          "name": "stdout",
          "text": "Accuracy of the network on the 5000 validation images: 51.56 %\n",
          "output_type": "stream"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "  0%|          | 0/352 [00:00<?, ?it/s]",
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "cba4dd3a3f8a49cbb3dd77b2ce63c246"
            }
          },
          "metadata": {}
        },
        {
          "name": "stdout",
          "text": "Epoch [15/20], Step [352/352], Loss: 1.2277\n",
          "output_type": "stream"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "  0%|          | 0/40 [00:00<?, ?it/s]",
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "2ef1f0634e994cba8a27e8d07a15c1b5"
            }
          },
          "metadata": {}
        },
        {
          "name": "stdout",
          "text": "Accuracy of the network on the 5000 validation images: 51.38 %\n",
          "output_type": "stream"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "  0%|          | 0/352 [00:00<?, ?it/s]",
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "5202e794ecef4f1abf0d419f72b6544d"
            }
          },
          "metadata": {}
        },
        {
          "name": "stdout",
          "text": "Epoch [16/20], Step [352/352], Loss: 1.1459\n",
          "output_type": "stream"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "  0%|          | 0/40 [00:00<?, ?it/s]",
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "d4ab3f350686475da4c5003e61423161"
            }
          },
          "metadata": {}
        },
        {
          "name": "stdout",
          "text": "Accuracy of the network on the 5000 validation images: 52.66 %\n",
          "output_type": "stream"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "  0%|          | 0/352 [00:00<?, ?it/s]",
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "41764b10dfee4dc8b6c61f4a1d110559"
            }
          },
          "metadata": {}
        },
        {
          "name": "stdout",
          "text": "Epoch [17/20], Step [352/352], Loss: 1.3006\n",
          "output_type": "stream"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "  0%|          | 0/40 [00:00<?, ?it/s]",
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "4cd53a1ff46c417a80c79a5cab86aabf"
            }
          },
          "metadata": {}
        },
        {
          "name": "stdout",
          "text": "Accuracy of the network on the 5000 validation images: 53.72 %\n",
          "output_type": "stream"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "  0%|          | 0/352 [00:00<?, ?it/s]",
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "e5c5d9bca3fc4481a16eb01d77864dd5"
            }
          },
          "metadata": {}
        },
        {
          "name": "stdout",
          "text": "Epoch [18/20], Step [352/352], Loss: 1.2286\n",
          "output_type": "stream"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "  0%|          | 0/40 [00:00<?, ?it/s]",
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "b2eb690425a64f098f6a753122a518d8"
            }
          },
          "metadata": {}
        },
        {
          "name": "stdout",
          "text": "Accuracy of the network on the 5000 validation images: 51.68 %\n",
          "output_type": "stream"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "  0%|          | 0/352 [00:00<?, ?it/s]",
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "4a412bdbbc6240039f08d20ef859535f"
            }
          },
          "metadata": {}
        },
        {
          "name": "stdout",
          "text": "Epoch [19/20], Step [352/352], Loss: 1.1880\n",
          "output_type": "stream"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "  0%|          | 0/40 [00:00<?, ?it/s]",
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "bb740780313d4d458eb140912f594944"
            }
          },
          "metadata": {}
        },
        {
          "name": "stdout",
          "text": "Accuracy of the network on the 5000 validation images: 53.48 %\n",
          "output_type": "stream"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "  0%|          | 0/352 [00:00<?, ?it/s]",
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "c350b670f1e140d2aa141dadfaeb690b"
            }
          },
          "metadata": {}
        },
        {
          "name": "stdout",
          "text": "Epoch [20/20], Step [352/352], Loss: 1.2792\n",
          "output_type": "stream"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "  0%|          | 0/40 [00:00<?, ?it/s]",
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "1e2af441c38a4d409e5eff6201aabc99"
            }
          },
          "metadata": {}
        },
        {
          "name": "stdout",
          "text": "Accuracy of the network on the 5000 validation images: 53.3 %\n",
          "output_type": "stream"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# ResNext50"
      ],
      "metadata": {
        "id": "jIl3xsG79xye"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from backbones.dataset.cifar10 import get_train_valid_loader, get_test_loader\n",
        "import torch\n",
        "from backbones.model.resnext50 import *\n",
        "from tqdm import tqdm_notebook\n",
        "\n",
        "# hyperparam\n",
        "num_classes = 10\n",
        "num_epochs = 20\n",
        "batch_size = 128\n",
        "learning_rate = 0.1\n",
        "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
        "train_loader, valid_loader = get_train_valid_loader(data_dir='./data', batch_size=batch_size, resize=(224,224),\n",
        "                                                    augment=False, random_seed=1)\n",
        "test_loader = get_test_loader(data_dir='./data', resize=(224,224),\n",
        "                              batch_size=batch_size)\n",
        "#model = ResNext50(num_classes=10, block=ResidualBlock, config=[3,4,6,3]).to(device)\n",
        "model = Resnext50(10, Resnext_block, [3,4,6,3]).cuda()\n",
        "\n",
        "\n",
        "# Loss and optimizer\n",
        "criterion = nn.CrossEntropyLoss()\n",
        "# optimizer = torch.optim.Adam(model.parameters(), lr=learning_rate, weight_decay=0.01)\n",
        "optimizer = torch.optim.SGD(model.parameters(), lr=learning_rate, momentum=0.9, weight_decay=1e-4)\n",
        "lr_scheduler = torch.optim.lr_scheduler.MultiStepLR(optimizer, milestones=[10, 15], gamma=0.1)\n",
        "\n",
        "\n",
        "# Train the model\n",
        "total_step = len(train_loader)\n",
        "for epoch in range(num_epochs):\n",
        "    for i, (images, labels) in enumerate(tqdm_notebook(train_loader)):\n",
        "        # Move tensors to the configured device\n",
        "        images = images.to(device)\n",
        "        labels = labels.to(device)\n",
        "\n",
        "        # Forward pass\n",
        "        outputs = model(images).to(device)\n",
        "        loss = criterion(outputs, labels)\n",
        "\n",
        "        # Backward and optimize\n",
        "        optimizer.zero_grad()\n",
        "        loss.backward()\n",
        "        optimizer.step()\n",
        "\n",
        "    print('Epoch [{}/{}], Step [{}/{}], Loss: {:.4f}'\n",
        "          .format(epoch + 1, num_epochs, i + 1, total_step, loss.item()))\n",
        "\n",
        "    # Validation\n",
        "    with torch.no_grad():\n",
        "        correct = 0\n",
        "        total = 0\n",
        "        for i, (images, labels) in enumerate(tqdm_notebook(valid_loader)):\n",
        "            images = images.to(device)\n",
        "            labels = labels.to(device)\n",
        "            outputs = model(images).to(device)\n",
        "            _, predicted = torch.max(outputs.data, 1)\n",
        "            total += labels.size(0)\n",
        "            correct += (predicted == labels).sum().item()\n",
        "            del images, labels, outputs\n",
        "        print('Accuracy of the network on the {} validation images: {} %'.format(5000, 100 * correct / total))"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2023-07-19T08:37:51.146677Z",
          "iopub.execute_input": "2023-07-19T08:37:51.147064Z",
          "iopub.status.idle": "2023-07-19T10:36:29.311974Z",
          "shell.execute_reply.started": "2023-07-19T08:37:51.147026Z",
          "shell.execute_reply": "2023-07-19T10:36:29.310965Z"
        },
        "trusted": true,
        "id": "ctE095MIvHD9",
        "outputId": "00ca8e4d-c220-47d3-ed43-ca4fca780a87",
        "colab": {
          "referenced_widgets": [
            "f9e61c55959a4244bbae78900901133b",
            "389fc185060646d8b96d03158be545db",
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        }
      },
      "execution_count": null,
      "outputs": [
        {
          "name": "stdout",
          "text": "Downloading https://www.cs.toronto.edu/~kriz/cifar-10-python.tar.gz to ./data/cifar-10-python.tar.gz\n",
          "output_type": "stream"
        },
        {
          "name": "stderr",
          "text": "100%|██████████| 170498071/170498071 [00:04<00:00, 41316522.02it/s]\n",
          "output_type": "stream"
        },
        {
          "name": "stdout",
          "text": "Extracting ./data/cifar-10-python.tar.gz to ./data\nFiles already downloaded and verified\nFiles already downloaded and verified\n",
          "output_type": "stream"
        },
        {
          "name": "stderr",
          "text": "/tmp/ipykernel_28/1643724782.py:30: TqdmDeprecationWarning: This function will be removed in tqdm==5.0.0\nPlease use `tqdm.notebook.tqdm` instead of `tqdm.tqdm_notebook`\n  for i, (images, labels) in enumerate(tqdm_notebook(train_loader)):\n",
          "output_type": "stream"
        },
        {
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            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "f9e61c55959a4244bbae78900901133b"
            }
          },
          "metadata": {}
        },
        {
          "name": "stdout",
          "text": "Epoch [1/20], Step [352/352], Loss: 2.0024\n",
          "output_type": "stream"
        },
        {
          "name": "stderr",
          "text": "/tmp/ipykernel_28/1643724782.py:51: TqdmDeprecationWarning: This function will be removed in tqdm==5.0.0\nPlease use `tqdm.notebook.tqdm` instead of `tqdm.tqdm_notebook`\n  for i, (images, labels) in enumerate(tqdm_notebook(valid_loader)):\n",
          "output_type": "stream"
        },
        {
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              "version_major": 2,
              "version_minor": 0,
              "model_id": "389fc185060646d8b96d03158be545db"
            }
          },
          "metadata": {}
        },
        {
          "name": "stdout",
          "text": "Accuracy of the network on the 5000 validation images: 21.72 %\n",
          "output_type": "stream"
        },
        {
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              "version_major": 2,
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              "model_id": "d19f4cdfde3b4e989a3db5d8a905aaf1"
            }
          },
          "metadata": {}
        },
        {
          "name": "stdout",
          "text": "Epoch [2/20], Step [352/352], Loss: 1.6000\n",
          "output_type": "stream"
        },
        {
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              "version_major": 2,
              "version_minor": 0,
              "model_id": "497b798bc20e4562adb145312dc59ac8"
            }
          },
          "metadata": {}
        },
        {
          "name": "stdout",
          "text": "Accuracy of the network on the 5000 validation images: 31.16 %\n",
          "output_type": "stream"
        },
        {
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              "version_major": 2,
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              "model_id": "e18944a30edb46e0998bfe81ddd62c59"
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          },
          "metadata": {}
        },
        {
          "name": "stdout",
          "text": "Epoch [3/20], Step [352/352], Loss: 1.4427\n",
          "output_type": "stream"
        },
        {
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              "version_major": 2,
              "version_minor": 0,
              "model_id": "e263c0d3463442edbd4e585fdcac2428"
            }
          },
          "metadata": {}
        },
        {
          "name": "stdout",
          "text": "Accuracy of the network on the 5000 validation images: 40.46 %\n",
          "output_type": "stream"
        },
        {
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              "model_id": "9788a0ab5a57469d9e8dc2381c73a267"
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          },
          "metadata": {}
        },
        {
          "name": "stdout",
          "text": "Epoch [4/20], Step [352/352], Loss: 1.2689\n",
          "output_type": "stream"
        },
        {
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              "version_major": 2,
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              "model_id": "75c4027ea6264a44bf8f4ec1b0a74121"
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          },
          "metadata": {}
        },
        {
          "name": "stdout",
          "text": "Accuracy of the network on the 5000 validation images: 46.64 %\n",
          "output_type": "stream"
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              "model_id": "81a6a32fd6bc40538e19cf6f57bf7566"
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          },
          "metadata": {}
        },
        {
          "name": "stdout",
          "text": "Epoch [5/20], Step [352/352], Loss: 1.3229\n",
          "output_type": "stream"
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              "version_major": 2,
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              "model_id": "372dce8917f849cdb465b29eddeb8d45"
            }
          },
          "metadata": {}
        },
        {
          "name": "stdout",
          "text": "Accuracy of the network on the 5000 validation images: 52.36 %\n",
          "output_type": "stream"
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        {
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            }
          },
          "metadata": {}
        },
        {
          "name": "stdout",
          "text": "Epoch [6/20], Step [352/352], Loss: 1.0735\n",
          "output_type": "stream"
        },
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              "version_major": 2,
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              "model_id": "80fd4bf4464842dcbf79b2cb069413ee"
            }
          },
          "metadata": {}
        },
        {
          "name": "stdout",
          "text": "Accuracy of the network on the 5000 validation images: 59.6 %\n",
          "output_type": "stream"
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        {
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              "model_id": "5034d7c9f6bf452382a1221d870301dc"
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          },
          "metadata": {}
        },
        {
          "name": "stdout",
          "text": "Epoch [7/20], Step [352/352], Loss: 0.9137\n",
          "output_type": "stream"
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        {
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              "model_id": "13729a8402d84d088a2361a74da80b31"
            }
          },
          "metadata": {}
        },
        {
          "name": "stdout",
          "text": "Accuracy of the network on the 5000 validation images: 63.8 %\n",
          "output_type": "stream"
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          },
          "metadata": {}
        },
        {
          "name": "stdout",
          "text": "Epoch [8/20], Step [352/352], Loss: 1.3136\n",
          "output_type": "stream"
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              "model_id": "a28b705c21464706aa84002c7806039b"
            }
          },
          "metadata": {}
        },
        {
          "name": "stdout",
          "text": "Accuracy of the network on the 5000 validation images: 67.52 %\n",
          "output_type": "stream"
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          },
          "metadata": {}
        },
        {
          "name": "stdout",
          "text": "Epoch [9/20], Step [352/352], Loss: 0.9072\n",
          "output_type": "stream"
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              "model_id": "615d1b10227341f2a11d9717f50d8d1e"
            }
          },
          "metadata": {}
        },
        {
          "name": "stdout",
          "text": "Accuracy of the network on the 5000 validation images: 69.48 %\n",
          "output_type": "stream"
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          },
          "metadata": {}
        },
        {
          "name": "stdout",
          "text": "Epoch [10/20], Step [352/352], Loss: 0.9475\n",
          "output_type": "stream"
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              "model_id": "9f41f6bc22154cbdb5380996b0c361b6"
            }
          },
          "metadata": {}
        },
        {
          "name": "stdout",
          "text": "Accuracy of the network on the 5000 validation images: 72.42 %\n",
          "output_type": "stream"
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          },
          "metadata": {}
        },
        {
          "name": "stdout",
          "text": "Epoch [11/20], Step [352/352], Loss: 0.7800\n",
          "output_type": "stream"
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            }
          },
          "metadata": {}
        },
        {
          "name": "stdout",
          "text": "Accuracy of the network on the 5000 validation images: 75.4 %\n",
          "output_type": "stream"
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          },
          "metadata": {}
        },
        {
          "name": "stdout",
          "text": "Epoch [12/20], Step [352/352], Loss: 0.7027\n",
          "output_type": "stream"
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              "model_id": "511363d490b64773a9b334c270bee493"
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        },
        {
          "name": "stdout",
          "text": "Accuracy of the network on the 5000 validation images: 76.56 %\n",
          "output_type": "stream"
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        },
        {
          "name": "stdout",
          "text": "Epoch [13/20], Step [352/352], Loss: 0.5561\n",
          "output_type": "stream"
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          },
          "metadata": {}
        },
        {
          "name": "stdout",
          "text": "Accuracy of the network on the 5000 validation images: 77.88 %\n",
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          },
          "metadata": {}
        },
        {
          "name": "stdout",
          "text": "Epoch [14/20], Step [352/352], Loss: 0.6148\n",
          "output_type": "stream"
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              "model_id": "032750ec418343daa452faf6b1ffab3f"
            }
          },
          "metadata": {}
        },
        {
          "name": "stdout",
          "text": "Accuracy of the network on the 5000 validation images: 79.66 %\n",
          "output_type": "stream"
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          },
          "metadata": {}
        },
        {
          "name": "stdout",
          "text": "Epoch [15/20], Step [352/352], Loss: 0.3294\n",
          "output_type": "stream"
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            }
          },
          "metadata": {}
        },
        {
          "name": "stdout",
          "text": "Accuracy of the network on the 5000 validation images: 79.96 %\n",
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          },
          "metadata": {}
        },
        {
          "name": "stdout",
          "text": "Epoch [16/20], Step [352/352], Loss: 0.4337\n",
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              "model_id": "80f089b937bf438e91827012097d32f6"
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          },
          "metadata": {}
        },
        {
          "name": "stdout",
          "text": "Accuracy of the network on the 5000 validation images: 80.1 %\n",
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        },
        {
          "name": "stdout",
          "text": "Epoch [17/20], Step [352/352], Loss: 0.4676\n",
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          },
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        },
        {
          "name": "stdout",
          "text": "Accuracy of the network on the 5000 validation images: 81.22 %\n",
          "output_type": "stream"
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        {
          "output_type": "display_data",
          "data": {
            "text/plain": "  0%|          | 0/352 [00:00<?, ?it/s]",
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "8713c3e059a04ad4bc3b37ab7387dfcb"
            }
          },
          "metadata": {}
        },
        {
          "name": "stdout",
          "text": "Epoch [18/20], Step [352/352], Loss: 0.3132\n",
          "output_type": "stream"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "  0%|          | 0/40 [00:00<?, ?it/s]",
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "31594991aca548408b6344c75f2f7c50"
            }
          },
          "metadata": {}
        },
        {
          "name": "stdout",
          "text": "Accuracy of the network on the 5000 validation images: 81.24 %\n",
          "output_type": "stream"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "  0%|          | 0/352 [00:00<?, ?it/s]",
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "c82c88bc11c04329b5063c4bf49b7e9e"
            }
          },
          "metadata": {}
        },
        {
          "name": "stdout",
          "text": "Epoch [19/20], Step [352/352], Loss: 0.4159\n",
          "output_type": "stream"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "  0%|          | 0/40 [00:00<?, ?it/s]",
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "495827dd221540d7a959147073fd4fd2"
            }
          },
          "metadata": {}
        },
        {
          "name": "stdout",
          "text": "Accuracy of the network on the 5000 validation images: 82.1 %\n",
          "output_type": "stream"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "  0%|          | 0/352 [00:00<?, ?it/s]",
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "7c77dba217c944b98accb1201b577b73"
            }
          },
          "metadata": {}
        },
        {
          "name": "stdout",
          "text": "Epoch [20/20], Step [352/352], Loss: 0.1898\n",
          "output_type": "stream"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "  0%|          | 0/40 [00:00<?, ?it/s]",
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "a70426d0e3db4b74a13582546ecbfbc7"
            }
          },
          "metadata": {}
        },
        {
          "name": "stdout",
          "text": "Accuracy of the network on the 5000 validation images: 82.82 %\n",
          "output_type": "stream"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Darknet19"
      ],
      "metadata": {
        "id": "OE8DVBvtvLzy"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from backbones.dataset.cifar10 import get_train_valid_loader, get_test_loader\n",
        "import torch\n",
        "from backbones.model.darknet19 import *\n",
        "from tqdm import tqdm_notebook\n",
        "\n",
        "# hyperparam\n",
        "num_classes = 10\n",
        "num_epochs = 20\n",
        "batch_size = 128\n",
        "learning_rate = 0.1\n",
        "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
        "train_loader, valid_loader = get_train_valid_loader(data_dir='./data', batch_size=batch_size, resize=(224,224),\n",
        "                                                    augment=False, random_seed=1)\n",
        "test_loader = get_test_loader(data_dir='./data', resize=(224,224),\n",
        "                              batch_size=batch_size)\n",
        "#model = ResNext50(num_classes=10, block=ResidualBlock, config=[3,4,6,3]).to(device)\n",
        "model = Darknet19().cuda()\n",
        "\n",
        "\n",
        "# Loss and optimizer\n",
        "criterion = nn.CrossEntropyLoss()\n",
        "# optimizer = torch.optim.Adam(model.parameters(), lr=learning_rate, weight_decay=0.01)\n",
        "optimizer = torch.optim.SGD(model.parameters(), lr=learning_rate, momentum=0.9, weight_decay=1e-4)\n",
        "lr_scheduler = torch.optim.lr_scheduler.MultiStepLR(optimizer, milestones=[100, 150], gamma=0.1)\n",
        "\n",
        "\n",
        "# Train the model\n",
        "total_step = len(train_loader)\n",
        "for epoch in range(num_epochs):\n",
        "    for i, (images, labels) in enumerate(tqdm_notebook(train_loader)):\n",
        "        # Move tensors to the configured device\n",
        "        images = images.to(device)\n",
        "        labels = labels.to(device)\n",
        "\n",
        "        # Forward pass\n",
        "        outputs = model(images).to(device)\n",
        "        loss = criterion(outputs, labels)\n",
        "\n",
        "        # Backward and optimize\n",
        "        optimizer.zero_grad()\n",
        "        loss.backward()\n",
        "        optimizer.step()\n",
        "\n",
        "    print('Epoch [{}/{}], Step [{}/{}], Loss: {:.4f}'\n",
        "          .format(epoch + 1, num_epochs, i + 1, total_step, loss.item()))\n",
        "\n",
        "    # Validation\n",
        "    with torch.no_grad():\n",
        "        correct = 0\n",
        "        total = 0\n",
        "        for i, (images, labels) in enumerate(tqdm_notebook(valid_loader)):\n",
        "            images = images.to(device)\n",
        "            labels = labels.to(device)\n",
        "            outputs = model(images).to(device)\n",
        "            _, predicted = torch.max(outputs.data, 1)\n",
        "            total += labels.size(0)\n",
        "            correct += (predicted == labels).sum().item()\n",
        "            del images, labels, outputs\n",
        "        print('Accuracy of the network on the {} validation images: {} %'.format(5000, 100 * correct / total))"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2023-07-19T10:52:40.434843Z",
          "iopub.execute_input": "2023-07-19T10:52:40.435332Z"
        },
        "trusted": true,
        "id": "D4_1b45v0MTT",
        "outputId": "8c7403a3-aa71-4e77-cfdc-dd60999bc4b0",
        "colab": {
          "referenced_widgets": [
            "01e2f60c6ae94f7c9bac8dab248b0ecc",
            "8bfeb739a3fa45439850ab23910ef50d",
            "5fd59770f62b4a4aa205e3c317126357",
            "a2bfc314ee544c5880c25726e921b75a",
            "db5789a0ec094f9b81da41b984336721",
            "8ab552bab92c4138a6e7ae8c5e2e1774",
            "d5e049b16894498d8ff0725c9a80fd80",
            "b6d5de41ad7d4e72a026a7802df72add",
            "6da72a7b24ca48a5a18380322e61afda"
          ]
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "name": "stdout",
          "text": "Downloading https://www.cs.toronto.edu/~kriz/cifar-10-python.tar.gz to ./data/cifar-10-python.tar.gz\n",
          "output_type": "stream"
        },
        {
          "name": "stderr",
          "text": "100%|██████████| 170498071/170498071 [00:05<00:00, 28473355.64it/s]\n",
          "output_type": "stream"
        },
        {
          "name": "stdout",
          "text": "Extracting ./data/cifar-10-python.tar.gz to ./data\nFiles already downloaded and verified\nFiles already downloaded and verified\n",
          "output_type": "stream"
        },
        {
          "name": "stderr",
          "text": "/tmp/ipykernel_28/1531229108.py:30: TqdmDeprecationWarning: This function will be removed in tqdm==5.0.0\nPlease use `tqdm.notebook.tqdm` instead of `tqdm.tqdm_notebook`\n  for i, (images, labels) in enumerate(tqdm_notebook(train_loader)):\n",
          "output_type": "stream"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "  0%|          | 0/352 [00:00<?, ?it/s]",
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "01e2f60c6ae94f7c9bac8dab248b0ecc"
            }
          },
          "metadata": {}
        },
        {
          "name": "stdout",
          "text": "Epoch [1/20], Step [352/352], Loss: 1.8831\n",
          "output_type": "stream"
        },
        {
          "name": "stderr",
          "text": "/tmp/ipykernel_28/1531229108.py:51: TqdmDeprecationWarning: This function will be removed in tqdm==5.0.0\nPlease use `tqdm.notebook.tqdm` instead of `tqdm.tqdm_notebook`\n  for i, (images, labels) in enumerate(tqdm_notebook(valid_loader)):\n",
          "output_type": "stream"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "  0%|          | 0/40 [00:00<?, ?it/s]",
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "8bfeb739a3fa45439850ab23910ef50d"
            }
          },
          "metadata": {}
        },
        {
          "name": "stdout",
          "text": "Accuracy of the network on the 5000 validation images: 34.34 %\n",
          "output_type": "stream"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "  0%|          | 0/352 [00:00<?, ?it/s]",
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "5fd59770f62b4a4aa205e3c317126357"
            }
          },
          "metadata": {}
        },
        {
          "name": "stdout",
          "text": "Epoch [2/20], Step [352/352], Loss: 1.5135\n",
          "output_type": "stream"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "  0%|          | 0/40 [00:00<?, ?it/s]",
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "a2bfc314ee544c5880c25726e921b75a"
            }
          },
          "metadata": {}
        },
        {
          "name": "stdout",
          "text": "Accuracy of the network on the 5000 validation images: 41.38 %\n",
          "output_type": "stream"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "  0%|          | 0/352 [00:00<?, ?it/s]",
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "db5789a0ec094f9b81da41b984336721"
            }
          },
          "metadata": {}
        },
        {
          "name": "stdout",
          "text": "Epoch [3/20], Step [352/352], Loss: 1.3769\n",
          "output_type": "stream"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "  0%|          | 0/40 [00:00<?, ?it/s]",
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "8ab552bab92c4138a6e7ae8c5e2e1774"
            }
          },
          "metadata": {}
        },
        {
          "name": "stdout",
          "text": "Accuracy of the network on the 5000 validation images: 48.96 %\n",
          "output_type": "stream"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "  0%|          | 0/352 [00:00<?, ?it/s]",
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "d5e049b16894498d8ff0725c9a80fd80"
            }
          },
          "metadata": {}
        },
        {
          "name": "stdout",
          "text": "Epoch [4/20], Step [352/352], Loss: 1.1971\n",
          "output_type": "stream"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "  0%|          | 0/40 [00:00<?, ?it/s]",
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "b6d5de41ad7d4e72a026a7802df72add"
            }
          },
          "metadata": {}
        },
        {
          "name": "stdout",
          "text": "Accuracy of the network on the 5000 validation images: 55.32 %\n",
          "output_type": "stream"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "  0%|          | 0/352 [00:00<?, ?it/s]",
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "6da72a7b24ca48a5a18380322e61afda"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# ShuffleNet v1"
      ],
      "metadata": {
        "id": "IonEVg6q0Iiq"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## this backbones the Adam better much"
      ],
      "metadata": {
        "id": "NHezOzWf5njA"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from backbones.dataset.cifar10 import get_train_valid_loader, get_test_loader\n",
        "import torch\n",
        "from backbones.model.shufflenetv1 import *\n",
        "from tqdm import tqdm_notebook\n",
        "\n",
        "# hyperparam\n",
        "num_classes = 10\n",
        "num_epochs = 50\n",
        "batch_size = 128\n",
        "learning_rate = 0.05\n",
        "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
        "train_loader, valid_loader = get_train_valid_loader(data_dir='./data', batch_size=batch_size, resize=(224,224),\n",
        "                                                    augment=False, random_seed=1)\n",
        "test_loader = get_test_loader(data_dir='./data', resize=(224,224),\n",
        "                              batch_size=batch_size)\n",
        "#model = ResNext50(num_classes=10, block=ResidualBlock, config=[3,4,6,3]).to(device)\n",
        "model = ShuffleNet(10, Shuffle_unit, group=3, output_group=[240, 480, 960], block_config=[4, 8, 4]).cuda()\n",
        "\n",
        "\n",
        "# Loss and optimizer\n",
        "criterion = nn.CrossEntropyLoss()\n",
        "optimizer = torch.optim.Adam(model.parameters(), lr=learning_rate, weight_decay=0.01)\n",
        "# optimizer = torch.optim.SGD(model.parameters(), lr=learning_rate, momentum=0.9, weight_decay=1e-4)\n",
        "lr_scheduler = torch.optim.lr_scheduler.MultiStepLR(optimizer, milestones=[5, 10], gamma=0.1)\n",
        "\n",
        "\n",
        "# Train the model\n",
        "total_step = len(train_loader)\n",
        "for epoch in range(num_epochs):\n",
        "    for i, (images, labels) in enumerate(tqdm_notebook(train_loader)):\n",
        "        # Move tensors to the configured device\n",
        "        images = images.to(device)\n",
        "        labels = labels.to(device)\n",
        "\n",
        "        # Forward pass\n",
        "        outputs = model(images).to(device)\n",
        "        loss = criterion(outputs, labels)\n",
        "\n",
        "        # Backward and optimize\n",
        "        optimizer.zero_grad()\n",
        "        loss.backward()\n",
        "        optimizer.step()\n",
        "\n",
        "    print('Epoch [{}/{}], Step [{}/{}], Loss: {:.4f}'\n",
        "          .format(epoch + 1, num_epochs, i + 1, total_step, loss.item()))\n",
        "\n",
        "    # Validation\n",
        "    with torch.no_grad():\n",
        "        correct = 0\n",
        "        total = 0\n",
        "        for i, (images, labels) in enumerate(tqdm_notebook(valid_loader)):\n",
        "            images = images.to(device)\n",
        "            labels = labels.to(device)\n",
        "            outputs = model(images).to(device)\n",
        "            _, predicted = torch.max(outputs.data, 1)\n",
        "            total += labels.size(0)\n",
        "            correct += (predicted == labels).sum().item()\n",
        "            del images, labels, outputs\n",
        "        print('Accuracy of the network on the {} validation images: {} %'.format(5000, 100 * correct / total))"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2023-07-20T10:56:28.992922Z",
          "iopub.execute_input": "2023-07-20T10:56:28.993883Z"
        },
        "trusted": true,
        "id": "wyJmJxD9HUmU",
        "outputId": "8dedb94c-f619-4490-99aa-62988c8b32ef",
        "colab": {
          "referenced_widgets": [
            "f25ae9520edd4e0f8a0418202c7b6503",
            "6001c8a4fb2249d381b5c3db1414d33c",
            "5f86b1b29a3b4d188eee7a183cfd28da",
            "634b5882131d40fdb03f79ae300452ae",
            "d20056cf64b34660a8a9d91e436184d3",
            "81354207d9e34b609b1c5b3f1fb5235b",
            "0210561218594a90869137f5459a9ee5",
            "fcdaf5b55e3e4a379ce4adc749684722",
            "65291f13d4474e7e8869517767a792e0",
            "1f9dffb0f34b4d2b8130e5de7b403e14",
            "087ca5af744f4ceaacf4cfa0dadd96a3"
          ]
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "name": "stdout",
          "text": "Downloading https://www.cs.toronto.edu/~kriz/cifar-10-python.tar.gz to ./data/cifar-10-python.tar.gz\n",
          "output_type": "stream"
        },
        {
          "name": "stderr",
          "text": "100%|██████████| 170498071/170498071 [00:06<00:00, 28132417.71it/s]\n",
          "output_type": "stream"
        },
        {
          "name": "stdout",
          "text": "Extracting ./data/cifar-10-python.tar.gz to ./data\nFiles already downloaded and verified\nFiles already downloaded and verified\n",
          "output_type": "stream"
        },
        {
          "name": "stderr",
          "text": "/tmp/ipykernel_28/2068592809.py:30: TqdmDeprecationWarning: This function will be removed in tqdm==5.0.0\nPlease use `tqdm.notebook.tqdm` instead of `tqdm.tqdm_notebook`\n  for i, (images, labels) in enumerate(tqdm_notebook(train_loader)):\n",
          "output_type": "stream"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "  0%|          | 0/352 [00:00<?, ?it/s]",
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "f25ae9520edd4e0f8a0418202c7b6503"
            }
          },
          "metadata": {}
        },
        {
          "name": "stdout",
          "text": "Epoch [1/50], Step [352/352], Loss: 2.4119\n",
          "output_type": "stream"
        },
        {
          "name": "stderr",
          "text": "/tmp/ipykernel_28/2068592809.py:51: TqdmDeprecationWarning: This function will be removed in tqdm==5.0.0\nPlease use `tqdm.notebook.tqdm` instead of `tqdm.tqdm_notebook`\n  for i, (images, labels) in enumerate(tqdm_notebook(valid_loader)):\n",
          "output_type": "stream"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "  0%|          | 0/40 [00:00<?, ?it/s]",
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "6001c8a4fb2249d381b5c3db1414d33c"
            }
          },
          "metadata": {}
        },
        {
          "name": "stdout",
          "text": "Accuracy of the network on the 5000 validation images: 11.76 %\n",
          "output_type": "stream"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "  0%|          | 0/352 [00:00<?, ?it/s]",
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "5f86b1b29a3b4d188eee7a183cfd28da"
            }
          },
          "metadata": {}
        },
        {
          "name": "stdout",
          "text": "Epoch [2/50], Step [352/352], Loss: 1.9821\n",
          "output_type": "stream"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "  0%|          | 0/40 [00:00<?, ?it/s]",
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "634b5882131d40fdb03f79ae300452ae"
            }
          },
          "metadata": {}
        },
        {
          "name": "stdout",
          "text": "Accuracy of the network on the 5000 validation images: 22.8 %\n",
          "output_type": "stream"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "  0%|          | 0/352 [00:00<?, ?it/s]",
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "d20056cf64b34660a8a9d91e436184d3"
            }
          },
          "metadata": {}
        },
        {
          "name": "stdout",
          "text": "Epoch [3/50], Step [352/352], Loss: 1.9291\n",
          "output_type": "stream"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "  0%|          | 0/40 [00:00<?, ?it/s]",
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "81354207d9e34b609b1c5b3f1fb5235b"
            }
          },
          "metadata": {}
        },
        {
          "name": "stdout",
          "text": "Accuracy of the network on the 5000 validation images: 24.4 %\n",
          "output_type": "stream"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "  0%|          | 0/352 [00:00<?, ?it/s]",
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "0210561218594a90869137f5459a9ee5"
            }
          },
          "metadata": {}
        },
        {
          "name": "stdout",
          "text": "Epoch [4/50], Step [352/352], Loss: 1.8563\n",
          "output_type": "stream"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "  0%|          | 0/40 [00:00<?, ?it/s]",
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "fcdaf5b55e3e4a379ce4adc749684722"
            }
          },
          "metadata": {}
        },
        {
          "name": "stdout",
          "text": "Accuracy of the network on the 5000 validation images: 24.14 %\n",
          "output_type": "stream"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "  0%|          | 0/352 [00:00<?, ?it/s]",
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "65291f13d4474e7e8869517767a792e0"
            }
          },
          "metadata": {}
        },
        {
          "name": "stdout",
          "text": "Epoch [5/50], Step [352/352], Loss: 1.7669\n",
          "output_type": "stream"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "  0%|          | 0/40 [00:00<?, ?it/s]",
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "1f9dffb0f34b4d2b8130e5de7b403e14"
            }
          },
          "metadata": {}
        },
        {
          "name": "stdout",
          "text": "Accuracy of the network on the 5000 validation images: 27.62 %\n",
          "output_type": "stream"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "  0%|          | 0/352 [00:00<?, ?it/s]",
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "087ca5af744f4ceaacf4cfa0dadd96a3"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# DetNet"
      ],
      "metadata": {
        "id": "l3jYdhLkHYju"
      }
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "6RHG00SrHdMn"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Squeezenet"
      ],
      "metadata": {
        "id": "eSriloXowwRL"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from backbones.dataset.cifar10 import get_train_valid_loader, get_test_loader\n",
        "import torch\n",
        "import torch.nn as nn\n",
        "from backbones.model.squeezenet import *\n",
        "from tqdm import tqdm_notebook\n",
        "\n",
        "# hyperparam\n",
        "num_classes = 10\n",
        "num_epochs = 20\n",
        "batch_size = 128\n",
        "learning_rate = 0.04\n",
        "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
        "train_loader, valid_loader = get_train_valid_loader(data_dir='./data', batch_size=batch_size, resize=(224,224),\n",
        "                                                    augment=False, random_seed=1)\n",
        "test_loader = get_test_loader(data_dir='./data', resize=(224,224),\n",
        "                              batch_size=batch_size)\n",
        "model = SqueezeNet(fire_module, num_classes=10).cuda()\n",
        "\n",
        "\n",
        "# Loss and optimizer\n",
        "criterion = nn.CrossEntropyLoss()\n",
        "# optimizer = torch.optim.Adam(model.parameters(), lr=learning_rate, weight_decay=0.01)\n",
        "optimizer = torch.optim.SGD(model.parameters(), lr=learning_rate, momentum=0.9, weight_decay=1e-4)\n",
        "lr_scheduler = torch.optim.lr_scheduler.MultiStepLR(optimizer, milestones=[10, 15], gamma=0.1)\n",
        "\n",
        "\n",
        "# Train the model\n",
        "total_step = len(train_loader)\n",
        "for epoch in range(num_epochs):\n",
        "    for i, (images, labels) in enumerate(tqdm_notebook(train_loader)):\n",
        "        # Move tensors to the configured device\n",
        "        images = images.to(device)\n",
        "        labels = labels.to(device)\n",
        "\n",
        "        # Forward pass\n",
        "        outputs = model(images).to(device)\n",
        "        loss = criterion(outputs, labels)\n",
        "\n",
        "        # Backward and optimize\n",
        "        optimizer.zero_grad()\n",
        "        loss.backward()\n",
        "        optimizer.step()\n",
        "\n",
        "    print('Epoch [{}/{}], Step [{}/{}], Loss: {:.4f}'\n",
        "          .format(epoch + 1, num_epochs, i + 1, total_step, loss.item()))\n",
        "\n",
        "    # Validation\n",
        "    with torch.no_grad():\n",
        "        correct = 0\n",
        "        total = 0\n",
        "        for i, (images, labels) in enumerate(tqdm_notebook(valid_loader)):\n",
        "            images = images.to(device)\n",
        "            labels = labels.to(device)\n",
        "            outputs = model(images).to(device)\n",
        "            _, predicted = torch.max(outputs.data, 1)\n",
        "            total += labels.size(0)\n",
        "            correct += (predicted == labels).sum().item()\n",
        "            del images, labels, outputs\n",
        "        print('Accuracy of the network on the {} validation images: {} %'.format(5000, 100 * correct / total))"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2023-07-27T09:04:26.827341Z",
          "iopub.execute_input": "2023-07-27T09:04:26.827946Z",
          "iopub.status.idle": "2023-07-27T09:50:49.139095Z",
          "shell.execute_reply.started": "2023-07-27T09:04:26.827904Z",
          "shell.execute_reply": "2023-07-27T09:50:49.138018Z"
        },
        "trusted": true,
        "id": "17DYl9Xiwwzc",
        "outputId": "c3681535-20cb-4405-9f72-96261f42a391",
        "colab": {
          "referenced_widgets": [
            "36196e69f609455f9bf46a2322502074",
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            "91a09176d5424067948a6404e3c182d5"
          ]
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "name": "stdout",
          "text": "Files already downloaded and verified\nFiles already downloaded and verified\nFiles already downloaded and verified\n",
          "output_type": "stream"
        },
        {
          "name": "stderr",
          "text": "/tmp/ipykernel_28/3351113438.py:30: TqdmDeprecationWarning: This function will be removed in tqdm==5.0.0\nPlease use `tqdm.notebook.tqdm` instead of `tqdm.tqdm_notebook`\n  for i, (images, labels) in enumerate(tqdm_notebook(train_loader)):\n",
          "output_type": "stream"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "  0%|          | 0/352 [00:00<?, ?it/s]",
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "36196e69f609455f9bf46a2322502074"
            }
          },
          "metadata": {}
        },
        {
          "name": "stdout",
          "text": "Epoch [1/20], Step [352/352], Loss: 1.1844\n",
          "output_type": "stream"
        },
        {
          "name": "stderr",
          "text": "/tmp/ipykernel_28/3351113438.py:51: TqdmDeprecationWarning: This function will be removed in tqdm==5.0.0\nPlease use `tqdm.notebook.tqdm` instead of `tqdm.tqdm_notebook`\n  for i, (images, labels) in enumerate(tqdm_notebook(valid_loader)):\n",
          "output_type": "stream"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "  0%|          | 0/40 [00:00<?, ?it/s]",
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "dbe1a1e26c8d4947bef19e75014b6b27"
            }
          },
          "metadata": {}
        },
        {
          "name": "stdout",
          "text": "Accuracy of the network on the 5000 validation images: 56.48 %\n",
          "output_type": "stream"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "  0%|          | 0/352 [00:00<?, ?it/s]",
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "dee9a4f0ae654f5c8fe935610d8ff79e"
            }
          },
          "metadata": {}
        },
        {
          "name": "stdout",
          "text": "Epoch [2/20], Step [352/352], Loss: 1.1652\n",
          "output_type": "stream"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "  0%|          | 0/40 [00:00<?, ?it/s]",
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "dcd10aff66b544dcbbc04031770e0c88"
            }
          },
          "metadata": {}
        },
        {
          "name": "stdout",
          "text": "Accuracy of the network on the 5000 validation images: 61.74 %\n",
          "output_type": "stream"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "  0%|          | 0/352 [00:00<?, ?it/s]",
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "1206cfd6637c4914ad1a82336064119a"
            }
          },
          "metadata": {}
        },
        {
          "name": "stdout",
          "text": "Epoch [3/20], Step [352/352], Loss: 0.7456\n",
          "output_type": "stream"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "  0%|          | 0/40 [00:00<?, ?it/s]",
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "11cdfe74b8b04db2a9cf0dbda7b5c52d"
            }
          },
          "metadata": {}
        },
        {
          "name": "stdout",
          "text": "Accuracy of the network on the 5000 validation images: 68.58 %\n",
          "output_type": "stream"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "  0%|          | 0/352 [00:00<?, ?it/s]",
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "bc037b965c5c4e30bacee89f00a2368c"
            }
          },
          "metadata": {}
        },
        {
          "name": "stdout",
          "text": "Epoch [4/20], Step [352/352], Loss: 0.9494\n",
          "output_type": "stream"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "  0%|          | 0/40 [00:00<?, ?it/s]",
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "3cd20be6781a4485a267caa152738c70"
            }
          },
          "metadata": {}
        },
        {
          "name": "stdout",
          "text": "Accuracy of the network on the 5000 validation images: 71.04 %\n",
          "output_type": "stream"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "  0%|          | 0/352 [00:00<?, ?it/s]",
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "fb445d55dadf4e92b590249af92fb3a4"
            }
          },
          "metadata": {}
        },
        {
          "name": "stdout",
          "text": "Epoch [5/20], Step [352/352], Loss: 0.7568\n",
          "output_type": "stream"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "  0%|          | 0/40 [00:00<?, ?it/s]",
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "b51a19c189c84c7188dfee04e6b2ae18"
            }
          },
          "metadata": {}
        },
        {
          "name": "stdout",
          "text": "Accuracy of the network on the 5000 validation images: 74.32 %\n",
          "output_type": "stream"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "  0%|          | 0/352 [00:00<?, ?it/s]",
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "33f2f44680ff4d54b27277e417b05061"
            }
          },
          "metadata": {}
        },
        {
          "name": "stdout",
          "text": "Epoch [6/20], Step [352/352], Loss: 0.6720\n",
          "output_type": "stream"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "  0%|          | 0/40 [00:00<?, ?it/s]",
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "a051d58ec3f3496c9de8d0814bc72b71"
            }
          },
          "metadata": {}
        },
        {
          "name": "stdout",
          "text": "Accuracy of the network on the 5000 validation images: 76.16 %\n",
          "output_type": "stream"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "  0%|          | 0/352 [00:00<?, ?it/s]",
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "94bb1457e0bc4d629635f2efbc821c01"
            }
          },
          "metadata": {}
        },
        {
          "name": "stdout",
          "text": "Epoch [7/20], Step [352/352], Loss: 0.6301\n",
          "output_type": "stream"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "  0%|          | 0/40 [00:00<?, ?it/s]",
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "1c4b3873245f48989fb135513810029d"
            }
          },
          "metadata": {}
        },
        {
          "name": "stdout",
          "text": "Accuracy of the network on the 5000 validation images: 77.44 %\n",
          "output_type": "stream"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "  0%|          | 0/352 [00:00<?, ?it/s]",
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "94d63ace1bbb42489a83421b3fb7a7a1"
            }
          },
          "metadata": {}
        },
        {
          "name": "stdout",
          "text": "Epoch [8/20], Step [352/352], Loss: 0.7378\n",
          "output_type": "stream"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "  0%|          | 0/40 [00:00<?, ?it/s]",
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "9b3ae47566ca4e1a9f2e50bed572039a"
            }
          },
          "metadata": {}
        },
        {
          "name": "stdout",
          "text": "Accuracy of the network on the 5000 validation images: 79.08 %\n",
          "output_type": "stream"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "  0%|          | 0/352 [00:00<?, ?it/s]",
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "38bb3c6c26ba41968516ed0d77429a27"
            }
          },
          "metadata": {}
        },
        {
          "name": "stdout",
          "text": "Epoch [9/20], Step [352/352], Loss: 0.4960\n",
          "output_type": "stream"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "  0%|          | 0/40 [00:00<?, ?it/s]",
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "df8cc733dd2f401eb2efdcf3624d51f6"
            }
          },
          "metadata": {}
        },
        {
          "name": "stdout",
          "text": "Accuracy of the network on the 5000 validation images: 80.92 %\n",
          "output_type": "stream"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "  0%|          | 0/352 [00:00<?, ?it/s]",
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "e395c640dbee4c8eb968e57f8ef7e14f"
            }
          },
          "metadata": {}
        },
        {
          "name": "stdout",
          "text": "Epoch [10/20], Step [352/352], Loss: 0.5312\n",
          "output_type": "stream"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "  0%|          | 0/40 [00:00<?, ?it/s]",
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "92ddf19fcad945a09ccef0d653b439f0"
            }
          },
          "metadata": {}
        },
        {
          "name": "stdout",
          "text": "Accuracy of the network on the 5000 validation images: 81.3 %\n",
          "output_type": "stream"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "  0%|          | 0/352 [00:00<?, ?it/s]",
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "0c2aae73d73c44d7a3b5da15533380cf"
            }
          },
          "metadata": {}
        },
        {
          "name": "stdout",
          "text": "Epoch [11/20], Step [352/352], Loss: 0.5864\n",
          "output_type": "stream"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "  0%|          | 0/40 [00:00<?, ?it/s]",
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "a3e437d9ea7d481f9a37601645b52fa4"
            }
          },
          "metadata": {}
        },
        {
          "name": "stdout",
          "text": "Accuracy of the network on the 5000 validation images: 78.66 %\n",
          "output_type": "stream"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "  0%|          | 0/352 [00:00<?, ?it/s]",
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "e2200c9c211143e5ad89106adc6e09c6"
            }
          },
          "metadata": {}
        },
        {
          "name": "stdout",
          "text": "Epoch [12/20], Step [352/352], Loss: 0.2413\n",
          "output_type": "stream"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "  0%|          | 0/40 [00:00<?, ?it/s]",
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "76543b47e18d43c6a9f7fcba62b9d138"
            }
          },
          "metadata": {}
        },
        {
          "name": "stdout",
          "text": "Accuracy of the network on the 5000 validation images: 80.48 %\n",
          "output_type": "stream"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "  0%|          | 0/352 [00:00<?, ?it/s]",
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "f54ea524134d427cb2a4d401388a57e5"
            }
          },
          "metadata": {}
        },
        {
          "name": "stdout",
          "text": "Epoch [13/20], Step [352/352], Loss: 0.2959\n",
          "output_type": "stream"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "  0%|          | 0/40 [00:00<?, ?it/s]",
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "24fd4bb47f804d10ba83f0d503ba156f"
            }
          },
          "metadata": {}
        },
        {
          "name": "stdout",
          "text": "Accuracy of the network on the 5000 validation images: 81.88 %\n",
          "output_type": "stream"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "  0%|          | 0/352 [00:00<?, ?it/s]",
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "0ae1ee9fd7344a888af4e20a067b9a03"
            }
          },
          "metadata": {}
        },
        {
          "name": "stdout",
          "text": "Epoch [14/20], Step [352/352], Loss: 0.3511\n",
          "output_type": "stream"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "  0%|          | 0/40 [00:00<?, ?it/s]",
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "6a929f6733274aa5a9f89143c7326388"
            }
          },
          "metadata": {}
        },
        {
          "name": "stdout",
          "text": "Accuracy of the network on the 5000 validation images: 82.04 %\n",
          "output_type": "stream"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "  0%|          | 0/352 [00:00<?, ?it/s]",
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "2c0268283a804a26b74f72364328ed60"
            }
          },
          "metadata": {}
        },
        {
          "name": "stdout",
          "text": "Epoch [15/20], Step [352/352], Loss: 0.2567\n",
          "output_type": "stream"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "  0%|          | 0/40 [00:00<?, ?it/s]",
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "f0dafeb93e484b0e87e78c9112a9ed0f"
            }
          },
          "metadata": {}
        },
        {
          "name": "stdout",
          "text": "Accuracy of the network on the 5000 validation images: 81.54 %\n",
          "output_type": "stream"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "  0%|          | 0/352 [00:00<?, ?it/s]",
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "04bf28f871ee46abb026c1c9e2375809"
            }
          },
          "metadata": {}
        },
        {
          "name": "stdout",
          "text": "Epoch [16/20], Step [352/352], Loss: 0.3382\n",
          "output_type": "stream"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "  0%|          | 0/40 [00:00<?, ?it/s]",
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "88f7fba4db1140918923007a1f84407d"
            }
          },
          "metadata": {}
        },
        {
          "name": "stdout",
          "text": "Accuracy of the network on the 5000 validation images: 82.4 %\n",
          "output_type": "stream"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "  0%|          | 0/352 [00:00<?, ?it/s]",
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "d67ffc65b2c54c14a3a9777bfad97318"
            }
          },
          "metadata": {}
        },
        {
          "name": "stdout",
          "text": "Epoch [17/20], Step [352/352], Loss: 0.2963\n",
          "output_type": "stream"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "  0%|          | 0/40 [00:00<?, ?it/s]",
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "0386fbb381a64a71869aad1f4d9e9c4d"
            }
          },
          "metadata": {}
        },
        {
          "name": "stdout",
          "text": "Accuracy of the network on the 5000 validation images: 81.6 %\n",
          "output_type": "stream"
        },
        {
          "output_type": "display_data",
          "data": {
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            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "68eda7c00d234f1982d5afb2401e94a0"
            }
          },
          "metadata": {}
        },
        {
          "name": "stdout",
          "text": "Epoch [18/20], Step [352/352], Loss: 0.1339\n",
          "output_type": "stream"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "  0%|          | 0/40 [00:00<?, ?it/s]",
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "e6507e3816384e5aaae8c8b6a36dd236"
            }
          },
          "metadata": {}
        },
        {
          "name": "stdout",
          "text": "Accuracy of the network on the 5000 validation images: 82.04 %\n",
          "output_type": "stream"
        },
        {
          "output_type": "display_data",
          "data": {
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            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "2fe451937c8144df84c9811c8c66516c"
            }
          },
          "metadata": {}
        },
        {
          "name": "stdout",
          "text": "Epoch [19/20], Step [352/352], Loss: 0.2398\n",
          "output_type": "stream"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "  0%|          | 0/40 [00:00<?, ?it/s]",
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "064354c7d66a40bd994cc1bb3108bd68"
            }
          },
          "metadata": {}
        },
        {
          "name": "stdout",
          "text": "Accuracy of the network on the 5000 validation images: 83.38 %\n",
          "output_type": "stream"
        },
        {
          "output_type": "display_data",
          "data": {
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            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "b3afe008d31742cd8de5940aff0a58f9"
            }
          },
          "metadata": {}
        },
        {
          "name": "stdout",
          "text": "Epoch [20/20], Step [352/352], Loss: 0.2443\n",
          "output_type": "stream"
        },
        {
          "output_type": "display_data",
          "data": {
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            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "91a09176d5424067948a6404e3c182d5"
            }
          },
          "metadata": {}
        },
        {
          "name": "stdout",
          "text": "Accuracy of the network on the 5000 validation images: 81.94 %\n",
          "output_type": "stream"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Mobilenet v1\n",
        "(introduce depthwise separable convolution = depthwise conv + pointwise conv)"
      ],
      "metadata": {
        "id": "69tu0FRWwx0Q"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from backbones.dataset.cifar10 import get_train_valid_loader, get_test_loader\n",
        "import torch\n",
        "import torch.nn as nn\n",
        "from tqdm import tqdm_notebook\n",
        "\n",
        "# hyperparam\n",
        "num_classes = 10\n",
        "num_epochs = 20\n",
        "batch_size = 256\n",
        "learning_rate = 0.1\n",
        "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
        "train_loader, valid_loader = get_train_valid_loader(data_dir='./data', batch_size=batch_size, resize=(224, 224),\n",
        "                                                    augment=False, random_seed=1)\n",
        "test_loader = get_test_loader(data_dir='./data', resize=(224, 224),\n",
        "                              batch_size=batch_size)\n",
        "model = mobilenetv1().cuda()\n",
        "\n",
        "\n",
        "# Loss and optimizer\n",
        "criterion = nn.CrossEntropyLoss()\n",
        "# optimizer = torch.optim.Adam(model.parameters(), lr=learning_rate, weight_decay=0.01)\n",
        "optimizer = torch.optim.SGD(model.parameters(), lr=learning_rate, momentum=0.9, weight_decay=1e-4)\n",
        "lr_scheduler = torch.optim.lr_scheduler.MultiStepLR(optimizer, milestones=[10, 15], gamma=0.1)\n",
        "\n",
        "\n",
        "# Train the model\n",
        "total_step = len(train_loader)\n",
        "for epoch in range(num_epochs):\n",
        "    for i, (images, labels) in enumerate(tqdm_notebook(train_loader)):\n",
        "        # Move tensors to the configured device\n",
        "        images = images.to(device)\n",
        "        labels = labels.to(device)\n",
        "\n",
        "        # Forward pass\n",
        "        outputs = model(images).to(device)\n",
        "        loss = criterion(outputs, labels)\n",
        "\n",
        "        # Backward and optimize\n",
        "        optimizer.zero_grad()\n",
        "        loss.backward()\n",
        "        optimizer.step()\n",
        "\n",
        "    print('Epoch [{}/{}], Step [{}/{}], Loss: {:.4f}'\n",
        "          .format(epoch + 1, num_epochs, i + 1, total_step, loss.item()))\n",
        "\n",
        "    # Validation\n",
        "    with torch.no_grad():\n",
        "        correct = 0\n",
        "        total = 0\n",
        "        for i, (images, labels) in enumerate(tqdm_notebook(valid_loader)):\n",
        "            images = images.to(device)\n",
        "            labels = labels.to(device)\n",
        "            outputs = model(images).to(device)\n",
        "            _, predicted = torch.max(outputs.data, 1)\n",
        "            total += labels.size(0)\n",
        "            correct += (predicted == labels).sum().item()\n",
        "            del images, labels, outputs\n",
        "        print('Accuracy of the network on the {} validation images: {} %'.format(5000, 100 * correct / total))"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2023-08-07T08:23:46.308344Z",
          "iopub.execute_input": "2023-08-07T08:23:46.309086Z",
          "iopub.status.idle": "2023-08-07T08:58:43.942702Z",
          "shell.execute_reply.started": "2023-08-07T08:23:46.309050Z",
          "shell.execute_reply": "2023-08-07T08:58:43.941533Z"
        },
        "trusted": true,
        "id": "zpBm07rzVsdp",
        "outputId": "9d88f6f7-2ea2-4fdf-a4b4-8d12efbcefe5",
        "colab": {
          "referenced_widgets": [
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            "25d79fac6c184d98980fa1ed195f7247"
          ]
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "name": "stdout",
          "text": "Files already downloaded and verified\nFiles already downloaded and verified\nFiles already downloaded and verified\n",
          "output_type": "stream"
        },
        {
          "name": "stderr",
          "text": "/tmp/ipykernel_28/529819025.py:29: TqdmDeprecationWarning: This function will be removed in tqdm==5.0.0\nPlease use `tqdm.notebook.tqdm` instead of `tqdm.tqdm_notebook`\n  for i, (images, labels) in enumerate(tqdm_notebook(train_loader)):\n",
          "output_type": "stream"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "  0%|          | 0/176 [00:00<?, ?it/s]",
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "78d7044f72a240059ece102accb1d47a"
            }
          },
          "metadata": {}
        },
        {
          "name": "stdout",
          "text": "Epoch [1/20], Step [176/176], Loss: 1.2766\n",
          "output_type": "stream"
        },
        {
          "name": "stderr",
          "text": "/tmp/ipykernel_28/529819025.py:50: TqdmDeprecationWarning: This function will be removed in tqdm==5.0.0\nPlease use `tqdm.notebook.tqdm` instead of `tqdm.tqdm_notebook`\n  for i, (images, labels) in enumerate(tqdm_notebook(valid_loader)):\n",
          "output_type": "stream"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "  0%|          | 0/20 [00:00<?, ?it/s]",
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "f3dadde47d5f47128333782e6c24fb37"
            }
          },
          "metadata": {}
        },
        {
          "name": "stdout",
          "text": "Accuracy of the network on the 5000 validation images: 56.18 %\n",
          "output_type": "stream"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "  0%|          | 0/176 [00:00<?, ?it/s]",
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "0835e5a9a72a49e49a6ea172484b589e"
            }
          },
          "metadata": {}
        },
        {
          "name": "stdout",
          "text": "Epoch [2/20], Step [176/176], Loss: 0.9093\n",
          "output_type": "stream"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "  0%|          | 0/20 [00:00<?, ?it/s]",
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "e3dd51aabdf3412793cb1498bd79d429"
            }
          },
          "metadata": {}
        },
        {
          "name": "stdout",
          "text": "Accuracy of the network on the 5000 validation images: 67.24 %\n",
          "output_type": "stream"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "  0%|          | 0/176 [00:00<?, ?it/s]",
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "3788cb030fb349e9a6912f834cfb61b1"
            }
          },
          "metadata": {}
        },
        {
          "name": "stdout",
          "text": "Epoch [3/20], Step [176/176], Loss: 0.7994\n",
          "output_type": "stream"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "  0%|          | 0/20 [00:00<?, ?it/s]",
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "0c63a68f942f49289dd0b69bc33a2706"
            }
          },
          "metadata": {}
        },
        {
          "name": "stdout",
          "text": "Accuracy of the network on the 5000 validation images: 75.04 %\n",
          "output_type": "stream"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "  0%|          | 0/176 [00:00<?, ?it/s]",
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "3affc6cd3bae4df2be291d87c722dba4"
            }
          },
          "metadata": {}
        },
        {
          "name": "stdout",
          "text": "Epoch [4/20], Step [176/176], Loss: 0.5424\n",
          "output_type": "stream"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "  0%|          | 0/20 [00:00<?, ?it/s]",
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "80a784a2fbb546308608f4175000c23a"
            }
          },
          "metadata": {}
        },
        {
          "name": "stdout",
          "text": "Accuracy of the network on the 5000 validation images: 76.76 %\n",
          "output_type": "stream"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "  0%|          | 0/176 [00:00<?, ?it/s]",
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "b3c42f056ef54343b2af3b7aa31f09f0"
            }
          },
          "metadata": {}
        },
        {
          "name": "stdout",
          "text": "Epoch [5/20], Step [176/176], Loss: 0.4800\n",
          "output_type": "stream"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "  0%|          | 0/20 [00:00<?, ?it/s]",
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "13c08e5cf6ce4cd3afa9daf1c708af5d"
            }
          },
          "metadata": {}
        },
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