finetune-squeezenet.ipynb

{
  "cells": [
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "id": "ly7jYi2e-l2n"
      },
      "outputs": [],
      "source": [
        "import os\n",
        "import cv2\n",
        "import numpy as np\n",
        "from torch.utils.data import Dataset, DataLoader\n",
        "import torchvision.transforms as transforms\n",
        "import torch\n",
        "import tqdm\n",
        "import torch.nn as nn"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 71,
      "metadata": {
        "id": "Z2VVPnty-l2o"
      },
      "outputs": [],
      "source": [
        "transform = transforms.Compose([\n",
        "    transforms.ToPILImage(),\n",
        "    transforms.Resize((256, 256)),\n",
        "    transforms.ToTensor(),\n",
        "    transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),\n",
        "])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 72,
      "metadata": {
        "id": "pYca-Cyy-l2o"
      },
      "outputs": [],
      "source": [
        "# check device\n",
        "device = 'cuda' if torch.cuda.is_available() else 'cpu'"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 73,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jRAFAJCV-l2o",
        "outputId": "ba240792-e740-4e61-ffd8-b4a26cd46444"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "torch.Size([999, 3, 256, 256]) torch.Size([999])\n"
          ]
        }
      ],
      "source": [
        "classes = os.listdir('Data/images_original')\n",
        "classes.sort()\n",
        "class_to_idx = {cls_name: i for i, cls_name in enumerate(classes)}\n",
        "idx_to_class = {i: cls_name for i, cls_name in enumerate(classes)}\n",
        "\n",
        "images = []\n",
        "labels = []\n",
        "for i, cls_name in enumerate(classes):\n",
        "    path = os.path.join('Data/images_original', cls_name)\n",
        "    for img_file in os.listdir(path):\n",
        "        img_path = os.path.join(path, img_file)\n",
        "        img = cv2.imread(img_path)\n",
        "        img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)\n",
        "        img = transform(img)\n",
        "        images.append(img)\n",
        "        labels.append(i)\n",
        "images = torch.stack(images).to(device)\n",
        "labels = torch.tensor(labels).to(device)\n",
        "print(images.shape, labels.shape)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "torch.manual_seed(42)\n",
        "if device == 'cuda':\n",
        "    torch.cuda.manual_seed_all(42)"
      ],
      "metadata": {
        "id": "f7YQsj_qEH1e"
      },
      "execution_count": 74,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": 75,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "89U9163y-l2p",
        "outputId": "c1980a23-c4bd-4e28-e98f-d7b22715fb5f"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "torch.Size([799, 3, 256, 256]) torch.Size([200, 3, 256, 256]) torch.Size([799]) torch.Size([200])\n"
          ]
        }
      ],
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "\n",
        "train_images, test_images, train_labels, test_labels = train_test_split(images, labels, shuffle=True, test_size=0.2, random_state=42)\n",
        "print(train_images.shape, test_images.shape, train_labels.shape, test_labels.shape)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 76,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 507
        },
        "id": "5jyvFSli-l2p",
        "outputId": "c509b842-bed9-4833-8f8f-a41e94206ee2"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:matplotlib.image:Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, 'jazz')"
            ]
          },
          "metadata": {},
          "execution_count": 76
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "# print a image from the dataset\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "plt.imshow(train_images[110].cpu().numpy().transpose(1, 2, 0))\n",
        "plt.title(idx_to_class[train_labels[110].item()])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 77,
      "metadata": {
        "id": "yi6aLK5M-l2p"
      },
      "outputs": [],
      "source": [
        "\n",
        "class MusicDataset(Dataset):\n",
        "    def __init__(self, images, labels):\n",
        "        self.images = images\n",
        "        self.labels = labels\n",
        "\n",
        "    def __len__(self):\n",
        "        return len(self.images)\n",
        "\n",
        "    def __getitem__(self, idx):\n",
        "        return self.images[idx], self.labels[idx]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 78,
      "metadata": {
        "id": "TEwExQEc-l2p"
      },
      "outputs": [],
      "source": [
        "dataset_train = MusicDataset(train_images, train_labels)\n",
        "DataLoader_train = DataLoader(dataset_train, batch_size=50, shuffle=True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 79,
      "metadata": {
        "id": "sr4SU1JM-l2p"
      },
      "outputs": [],
      "source": [
        "import torch.nn as nn\n",
        "\n",
        "class Identity(nn.Module):\n",
        "    def __init__(self):\n",
        "        super(Identity, self).__init__()\n",
        "\n",
        "    def forward(self, x):\n",
        "        return x"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from torchvision.models import squeezenet1_1\n",
        "model = squeezenet1_1(pretrained=True)\n",
        "model"
      ],
      "metadata": {
        "id": "nbif9zjDN56J",
        "outputId": "2d3eddf7-c199-4162-9f42-681a5c39cf6e",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 87,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "SqueezeNet(\n",
              "  (features): Sequential(\n",
              "    (0): Conv2d(3, 64, kernel_size=(3, 3), stride=(2, 2))\n",
              "    (1): ReLU(inplace=True)\n",
              "    (2): MaxPool2d(kernel_size=3, stride=2, padding=0, dilation=1, ceil_mode=True)\n",
              "    (3): Fire(\n",
              "      (squeeze): Conv2d(64, 16, kernel_size=(1, 1), stride=(1, 1))\n",
              "      (squeeze_activation): ReLU(inplace=True)\n",
              "      (expand1x1): Conv2d(16, 64, kernel_size=(1, 1), stride=(1, 1))\n",
              "      (expand1x1_activation): ReLU(inplace=True)\n",
              "      (expand3x3): Conv2d(16, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
              "      (expand3x3_activation): ReLU(inplace=True)\n",
              "    )\n",
              "    (4): Fire(\n",
              "      (squeeze): Conv2d(128, 16, kernel_size=(1, 1), stride=(1, 1))\n",
              "      (squeeze_activation): ReLU(inplace=True)\n",
              "      (expand1x1): Conv2d(16, 64, kernel_size=(1, 1), stride=(1, 1))\n",
              "      (expand1x1_activation): ReLU(inplace=True)\n",
              "      (expand3x3): Conv2d(16, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
              "      (expand3x3_activation): ReLU(inplace=True)\n",
              "    )\n",
              "    (5): MaxPool2d(kernel_size=3, stride=2, padding=0, dilation=1, ceil_mode=True)\n",
              "    (6): Fire(\n",
              "      (squeeze): Conv2d(128, 32, kernel_size=(1, 1), stride=(1, 1))\n",
              "      (squeeze_activation): ReLU(inplace=True)\n",
              "      (expand1x1): Conv2d(32, 128, kernel_size=(1, 1), stride=(1, 1))\n",
              "      (expand1x1_activation): ReLU(inplace=True)\n",
              "      (expand3x3): Conv2d(32, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
              "      (expand3x3_activation): ReLU(inplace=True)\n",
              "    )\n",
              "    (7): Fire(\n",
              "      (squeeze): Conv2d(256, 32, kernel_size=(1, 1), stride=(1, 1))\n",
              "      (squeeze_activation): ReLU(inplace=True)\n",
              "      (expand1x1): Conv2d(32, 128, kernel_size=(1, 1), stride=(1, 1))\n",
              "      (expand1x1_activation): ReLU(inplace=True)\n",
              "      (expand3x3): Conv2d(32, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
              "      (expand3x3_activation): ReLU(inplace=True)\n",
              "    )\n",
              "    (8): MaxPool2d(kernel_size=3, stride=2, padding=0, dilation=1, ceil_mode=True)\n",
              "    (9): Fire(\n",
              "      (squeeze): Conv2d(256, 48, kernel_size=(1, 1), stride=(1, 1))\n",
              "      (squeeze_activation): ReLU(inplace=True)\n",
              "      (expand1x1): Conv2d(48, 192, kernel_size=(1, 1), stride=(1, 1))\n",
              "      (expand1x1_activation): ReLU(inplace=True)\n",
              "      (expand3x3): Conv2d(48, 192, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
              "      (expand3x3_activation): ReLU(inplace=True)\n",
              "    )\n",
              "    (10): Fire(\n",
              "      (squeeze): Conv2d(384, 48, kernel_size=(1, 1), stride=(1, 1))\n",
              "      (squeeze_activation): ReLU(inplace=True)\n",
              "      (expand1x1): Conv2d(48, 192, kernel_size=(1, 1), stride=(1, 1))\n",
              "      (expand1x1_activation): ReLU(inplace=True)\n",
              "      (expand3x3): Conv2d(48, 192, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
              "      (expand3x3_activation): ReLU(inplace=True)\n",
              "    )\n",
              "    (11): Fire(\n",
              "      (squeeze): Conv2d(384, 64, kernel_size=(1, 1), stride=(1, 1))\n",
              "      (squeeze_activation): ReLU(inplace=True)\n",
              "      (expand1x1): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1))\n",
              "      (expand1x1_activation): ReLU(inplace=True)\n",
              "      (expand3x3): Conv2d(64, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
              "      (expand3x3_activation): ReLU(inplace=True)\n",
              "    )\n",
              "    (12): Fire(\n",
              "      (squeeze): Conv2d(512, 64, kernel_size=(1, 1), stride=(1, 1))\n",
              "      (squeeze_activation): ReLU(inplace=True)\n",
              "      (expand1x1): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1))\n",
              "      (expand1x1_activation): ReLU(inplace=True)\n",
              "      (expand3x3): Conv2d(64, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
              "      (expand3x3_activation): ReLU(inplace=True)\n",
              "    )\n",
              "  )\n",
              "  (classifier): Sequential(\n",
              "    (0): Dropout(p=0.5, inplace=False)\n",
              "    (1): Conv2d(512, 1000, kernel_size=(1, 1), stride=(1, 1))\n",
              "    (2): ReLU(inplace=True)\n",
              "    (3): AdaptiveAvgPool2d(output_size=(1, 1))\n",
              "  )\n",
              ")"
            ]
          },
          "metadata": {},
          "execution_count": 87
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 92,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fp_Kq3My-l2p",
        "outputId": "bdab8fa2-58e3-4b51-fa65-4619e4baf5cb"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "SqueezeNet(\n",
              "  (features): Sequential(\n",
              "    (0): Conv2d(3, 64, kernel_size=(3, 3), stride=(2, 2))\n",
              "    (1): ReLU(inplace=True)\n",
              "    (2): MaxPool2d(kernel_size=3, stride=2, padding=0, dilation=1, ceil_mode=True)\n",
              "    (3): Fire(\n",
              "      (squeeze): Conv2d(64, 16, kernel_size=(1, 1), stride=(1, 1))\n",
              "      (squeeze_activation): ReLU(inplace=True)\n",
              "      (expand1x1): Conv2d(16, 64, kernel_size=(1, 1), stride=(1, 1))\n",
              "      (expand1x1_activation): ReLU(inplace=True)\n",
              "      (expand3x3): Conv2d(16, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
              "      (expand3x3_activation): ReLU(inplace=True)\n",
              "    )\n",
              "    (4): Fire(\n",
              "      (squeeze): Conv2d(128, 16, kernel_size=(1, 1), stride=(1, 1))\n",
              "      (squeeze_activation): ReLU(inplace=True)\n",
              "      (expand1x1): Conv2d(16, 64, kernel_size=(1, 1), stride=(1, 1))\n",
              "      (expand1x1_activation): ReLU(inplace=True)\n",
              "      (expand3x3): Conv2d(16, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
              "      (expand3x3_activation): ReLU(inplace=True)\n",
              "    )\n",
              "    (5): MaxPool2d(kernel_size=3, stride=2, padding=0, dilation=1, ceil_mode=True)\n",
              "    (6): Fire(\n",
              "      (squeeze): Conv2d(128, 32, kernel_size=(1, 1), stride=(1, 1))\n",
              "      (squeeze_activation): ReLU(inplace=True)\n",
              "      (expand1x1): Conv2d(32, 128, kernel_size=(1, 1), stride=(1, 1))\n",
              "      (expand1x1_activation): ReLU(inplace=True)\n",
              "      (expand3x3): Conv2d(32, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
              "      (expand3x3_activation): ReLU(inplace=True)\n",
              "    )\n",
              "    (7): Fire(\n",
              "      (squeeze): Conv2d(256, 32, kernel_size=(1, 1), stride=(1, 1))\n",
              "      (squeeze_activation): ReLU(inplace=True)\n",
              "      (expand1x1): Conv2d(32, 128, kernel_size=(1, 1), stride=(1, 1))\n",
              "      (expand1x1_activation): ReLU(inplace=True)\n",
              "      (expand3x3): Conv2d(32, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
              "      (expand3x3_activation): ReLU(inplace=True)\n",
              "    )\n",
              "    (8): MaxPool2d(kernel_size=3, stride=2, padding=0, dilation=1, ceil_mode=True)\n",
              "    (9): Fire(\n",
              "      (squeeze): Conv2d(256, 48, kernel_size=(1, 1), stride=(1, 1))\n",
              "      (squeeze_activation): ReLU(inplace=True)\n",
              "      (expand1x1): Conv2d(48, 192, kernel_size=(1, 1), stride=(1, 1))\n",
              "      (expand1x1_activation): ReLU(inplace=True)\n",
              "      (expand3x3): Conv2d(48, 192, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
              "      (expand3x3_activation): ReLU(inplace=True)\n",
              "    )\n",
              "    (10): Fire(\n",
              "      (squeeze): Conv2d(384, 48, kernel_size=(1, 1), stride=(1, 1))\n",
              "      (squeeze_activation): ReLU(inplace=True)\n",
              "      (expand1x1): Conv2d(48, 192, kernel_size=(1, 1), stride=(1, 1))\n",
              "      (expand1x1_activation): ReLU(inplace=True)\n",
              "      (expand3x3): Conv2d(48, 192, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
              "      (expand3x3_activation): ReLU(inplace=True)\n",
              "    )\n",
              "    (11): Fire(\n",
              "      (squeeze): Conv2d(384, 64, kernel_size=(1, 1), stride=(1, 1))\n",
              "      (squeeze_activation): ReLU(inplace=True)\n",
              "      (expand1x1): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1))\n",
              "      (expand1x1_activation): ReLU(inplace=True)\n",
              "      (expand3x3): Conv2d(64, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
              "      (expand3x3_activation): ReLU(inplace=True)\n",
              "    )\n",
              "    (12): Fire(\n",
              "      (squeeze): Conv2d(512, 64, kernel_size=(1, 1), stride=(1, 1))\n",
              "      (squeeze_activation): ReLU(inplace=True)\n",
              "      (expand1x1): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1))\n",
              "      (expand1x1_activation): ReLU(inplace=True)\n",
              "      (expand3x3): Conv2d(64, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
              "      (expand3x3_activation): ReLU(inplace=True)\n",
              "    )\n",
              "  )\n",
              "  (classifier): Sequential(\n",
              "    (0): Dropout(p=0.5, inplace=False)\n",
              "    (1): Conv2d(512, 10, kernel_size=(1, 1), stride=(1, 1))\n",
              "    (2): ReLU(inplace=True)\n",
              "    (3): AdaptiveAvgPool2d(output_size=(1, 1))\n",
              "  )\n",
              ")"
            ]
          },
          "metadata": {},
          "execution_count": 92
        }
      ],
      "source": [
        "for param in model.parameters():\n",
        "    param.requires_grad = False\n",
        "\n",
        "model.classifier[1] = nn.Conv2d(512, 10, kernel_size=1)\n",
        "\n",
        "model = model.to(device)\n",
        "model"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 93,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Ffrtx1T8-l2p",
        "outputId": "65b949ce-1675-448f-f265-b902d24eea08"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 16/16 [00:00<00:00, 52.99it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 0, loss_train: 0.12416445463895798, loss_val: 2.0200884342193604, accuracy_val: 0.275\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 16/16 [00:00<00:00, 53.11it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1, loss_train: 0.10643300414085388, loss_val: 1.7283004522323608, accuracy_val: 0.455\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 16/16 [00:00<00:00, 52.20it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 2, loss_train: 0.09018762409687042, loss_val: 1.5840150117874146, accuracy_val: 0.475\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 16/16 [00:00<00:00, 52.18it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 3, loss_train: 0.0931943953037262, loss_val: 1.4433772563934326, accuracy_val: 0.5\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 16/16 [00:00<00:00, 52.38it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 4, loss_train: 0.08004364371299744, loss_val: 1.3857927322387695, accuracy_val: 0.565\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 16/16 [00:00<00:00, 52.35it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 5, loss_train: 0.08310551196336746, loss_val: 1.3514965772628784, accuracy_val: 0.53\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 16/16 [00:00<00:00, 52.93it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 6, loss_train: 0.08129201829433441, loss_val: 1.2697299718856812, accuracy_val: 0.59\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 16/16 [00:00<00:00, 52.26it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 7, loss_train: 0.0742608979344368, loss_val: 1.2232805490493774, accuracy_val: 0.6\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 16/16 [00:00<00:00, 51.57it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 8, loss_train: 0.06903985142707825, loss_val: 1.2494025230407715, accuracy_val: 0.55\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 16/16 [00:00<00:00, 52.01it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 9, loss_train: 0.07436389476060867, loss_val: 1.170832872390747, accuracy_val: 0.6\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 16/16 [00:00<00:00, 52.65it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 10, loss_train: 0.06815861165523529, loss_val: 1.1899926662445068, accuracy_val: 0.555\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 16/16 [00:00<00:00, 52.52it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 11, loss_train: 0.07175420969724655, loss_val: 1.174034833908081, accuracy_val: 0.58\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 16/16 [00:00<00:00, 52.15it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 12, loss_train: 0.054589416831731796, loss_val: 1.119057059288025, accuracy_val: 0.605\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 16/16 [00:00<00:00, 52.07it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 13, loss_train: 0.07673822343349457, loss_val: 1.114518642425537, accuracy_val: 0.6\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 16/16 [00:00<00:00, 51.83it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 14, loss_train: 0.056054119020700455, loss_val: 1.1885881423950195, accuracy_val: 0.605\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 16/16 [00:00<00:00, 51.44it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 15, loss_train: 0.0737953633069992, loss_val: 1.1107020378112793, accuracy_val: 0.595\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 16/16 [00:00<00:00, 51.86it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 16, loss_train: 0.058297064155340195, loss_val: 1.1193195581436157, accuracy_val: 0.6\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 16/16 [00:00<00:00, 51.92it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 17, loss_train: 0.05438948795199394, loss_val: 1.1124988794326782, accuracy_val: 0.62\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 16/16 [00:00<00:00, 51.42it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 18, loss_train: 0.055305659770965576, loss_val: 1.0916880369186401, accuracy_val: 0.63\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 16/16 [00:00<00:00, 51.79it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 19, loss_train: 0.06583883613348007, loss_val: 1.0677317380905151, accuracy_val: 0.595\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 16/16 [00:00<00:00, 51.71it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 20, loss_train: 0.048212986439466476, loss_val: 1.069949746131897, accuracy_val: 0.615\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 16/16 [00:00<00:00, 50.79it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 21, loss_train: 0.0622420571744442, loss_val: 1.0780938863754272, accuracy_val: 0.625\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 16/16 [00:00<00:00, 51.54it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 22, loss_train: 0.06695714592933655, loss_val: 1.0920382738113403, accuracy_val: 0.61\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 16/16 [00:00<00:00, 51.23it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 23, loss_train: 0.0640178769826889, loss_val: 1.051827073097229, accuracy_val: 0.63\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 16/16 [00:00<00:00, 52.07it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 24, loss_train: 0.05444328486919403, loss_val: 1.0290162563323975, accuracy_val: 0.62\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 16/16 [00:00<00:00, 52.16it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 25, loss_train: 0.06307078152894974, loss_val: 1.038282036781311, accuracy_val: 0.65\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 16/16 [00:00<00:00, 50.74it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 26, loss_train: 0.05282217636704445, loss_val: 1.0514127016067505, accuracy_val: 0.62\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 16/16 [00:00<00:00, 51.02it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 27, loss_train: 0.05517624318599701, loss_val: 1.0355225801467896, accuracy_val: 0.615\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 16/16 [00:00<00:00, 51.19it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 28, loss_train: 0.05062290281057358, loss_val: 1.033117651939392, accuracy_val: 0.615\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 16/16 [00:00<00:00, 51.43it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 29, loss_train: 0.04033927991986275, loss_val: 1.0498225688934326, accuracy_val: 0.62\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
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          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 30, loss_train: 0.060398899018764496, loss_val: 1.0733380317687988, accuracy_val: 0.62\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 16/16 [00:00<00:00, 51.32it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 31, loss_train: 0.049534816294908524, loss_val: 1.0568912029266357, accuracy_val: 0.625\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 16/16 [00:00<00:00, 51.40it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 32, loss_train: 0.06152595952153206, loss_val: 1.0080407857894897, accuracy_val: 0.62\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 16/16 [00:00<00:00, 52.40it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 33, loss_train: 0.056362517178058624, loss_val: 0.999158501625061, accuracy_val: 0.645\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
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          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 34, loss_train: 0.04105193167924881, loss_val: 1.0200893878936768, accuracy_val: 0.615\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
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          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 35, loss_train: 0.06141703948378563, loss_val: 1.0266830921173096, accuracy_val: 0.665\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
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          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 36, loss_train: 0.05313534662127495, loss_val: 1.0113823413848877, accuracy_val: 0.635\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
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          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 37, loss_train: 0.049677614122629166, loss_val: 1.006187915802002, accuracy_val: 0.645\n"
          ]
        },
        {
          "output_type": "stream",
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          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 38, loss_train: 0.051464591175317764, loss_val: 1.012698769569397, accuracy_val: 0.645\n"
          ]
        },
        {
          "output_type": "stream",
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          ]
        },
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          "name": "stderr",
          "text": [
            "100%|██████████| 16/16 [00:00<00:00, 51.93it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 91, loss_train: 0.034178733825683594, loss_val: 0.9588351249694824, accuracy_val: 0.63\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 16/16 [00:00<00:00, 51.99it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 92, loss_train: 0.03457396477460861, loss_val: 0.9595862030982971, accuracy_val: 0.655\n",
            "Early stopping at epoch 92\n"
          ]
        }
      ],
      "source": [
        "model = model.to(device)\n",
        "\n",
        "# 定义损失函数和优化器\n",
        "criterion = nn.CrossEntropyLoss()\n",
        "optimizer = torch.optim.Adam(model.parameters())\n",
        "\n",
        "# 训练模型\n",
        "num_epochs = 300\n",
        "patience = 15  # Number of epochs to wait for improvement before stopping\n",
        "best_val_loss = float('inf')  # Initialize best validation loss\n",
        "patience_counter = 0  # Counter to keep track of the number of epochs without improvement\n",
        "log = {}\n",
        "\n",
        "\n",
        "for epoch in range(num_epochs):\n",
        "    model.train()\n",
        "    run_loss = 0.0\n",
        "    for images, labels in tqdm.tqdm(DataLoader_train):\n",
        "        optimizer.zero_grad()\n",
        "        outputs = model(images)\n",
        "        loss = criterion(outputs, labels)\n",
        "        loss.backward()\n",
        "        optimizer.step()\n",
        "        run_loss += loss.item()\n",
        "\n",
        "    model.eval()\n",
        "    with torch.no_grad():\n",
        "        test_outputs = model(test_images)\n",
        "        test_loss = criterion(test_outputs, test_labels)\n",
        "        pred = torch.argmax(test_outputs, dim=1)\n",
        "        acc = (pred == test_labels).sum().item() / len(test_labels)\n",
        "\n",
        "        # save the loss and accuracy\n",
        "        log[epoch] = {'loss_train': loss.item(), 'loss_val': test_loss.item(), 'accuracy_val': acc}\n",
        "        print(f'Epoch {epoch}, loss_train: {loss.item() / len(DataLoader_train)}, loss_val: {test_loss.item()}, accuracy_val: {acc}')\n",
        "\n",
        "    if test_loss < best_val_loss:\n",
        "        best_val_loss = test_loss\n",
        "        patience_counter = 0\n",
        "        torch.save(model.state_dict(), 'find-tuned-squeezenet.pth')\n",
        "    else:\n",
        "        patience_counter += 1\n",
        "        if patience_counter >= patience:\n",
        "            print(f'Early stopping at epoch {epoch}')\n",
        "            break"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 94,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 430
        },
        "id": "Ws8_5U3h-l2q",
        "outputId": "504d9a84-8066-46c4-f6cc-ee3b3479259b"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "# draw the loss and accuracy\n",
        "import matplotlib.pyplot as plt\n",
        "plt.figure()\n",
        "plt.plot([log[k]['loss_train'] for k in log], label='train loss')\n",
        "plt.plot([log[k]['loss_val'] for k in log], label='val loss')\n",
        "plt.legend()\n",
        "plt.show()\n"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# load the model\n",
        "model.load_state_dict(torch.load('find-tuned-squeezenet.pth'))\n",
        "\n",
        "# test the model\n",
        "model.eval()\n",
        "from sklearn.metrics import classification_report\n",
        "\n",
        "print(classification_report(test_labels.detach().cpu().numpy(), torch.argmax(model(test_images), dim=1).detach().cpu().numpy()))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "G9F2qUZzB4Gx",
        "outputId": "85faabf5-2576-41f5-afa2-38671cfb9157"
      },
      "execution_count": 95,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.61      0.67      0.64        21\n",
            "           1       0.80      1.00      0.89        12\n",
            "           2       0.60      0.50      0.55        24\n",
            "           3       0.65      0.68      0.67        22\n",
            "           4       0.71      0.80      0.75        15\n",
            "           5       0.81      0.78      0.79        27\n",
            "           6       0.94      0.94      0.94        18\n",
            "           7       0.53      0.47      0.50        19\n",
            "           8       0.59      0.59      0.59        22\n",
            "           9       0.42      0.40      0.41        20\n",
            "\n",
            "    accuracy                           0.67       200\n",
            "   macro avg       0.67      0.68      0.67       200\n",
            "weighted avg       0.66      0.67      0.66       200\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "1qcY4anlMxzF"
      },
      "execution_count": null,
      "outputs": []
    }
  ],
  "metadata": {
    "kernelspec": {
      "display_name": "Python 3",
      "language": "python",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.8.18"
    },
    "colab": {
      "provenance": []
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}