EfficientDet_Train.ipynb

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            "_model_module_version": "1.2.0",
            "_view_count": null,
            "flex_flow": null,
            "width": null,
            "min_width": null,
            "border": null,
            "align_items": null,
            "bottom": null,
            "_model_module": "@jupyter-widgets/base",
            "top": null,
            "grid_column": null,
            "overflow_y": null,
            "overflow_x": null,
            "grid_auto_flow": null,
            "grid_area": null,
            "grid_template_columns": null,
            "flex": null,
            "_model_name": "LayoutModel",
            "justify_items": null,
            "grid_row": null,
            "max_height": null,
            "align_content": null,
            "visibility": null,
            "align_self": null,
            "height": null,
            "min_height": null,
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            "grid_auto_rows": null,
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            "_view_module_version": "1.2.0",
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          }
        }
      }
    }
  },
  "cells": [
    {
      "cell_type": "code",
      "metadata": {
        "id": "k1aNGfql5Smf",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "ec586ee3-0adc-4eee-c7d9-d0ea5a221d47"
      },
      "source": [
        "!pip install manga109api\n",
        "!pip install efficientnet_pytorch"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Requirement already satisfied: manga109api in /usr/local/lib/python3.7/dist-packages (0.3.1)\n",
            "Requirement already satisfied: efficientnet_pytorch in /usr/local/lib/python3.7/dist-packages (0.7.1)\n",
            "Requirement already satisfied: torch in /usr/local/lib/python3.7/dist-packages (from efficientnet_pytorch) (1.9.0+cu102)\n",
            "Requirement already satisfied: typing-extensions in /usr/local/lib/python3.7/dist-packages (from torch->efficientnet_pytorch) (3.7.4.3)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ccHD3ElYNbLW",
        "outputId": "adf9d63c-a264-482d-fa0b-a02e746c5d87"
      },
      "source": [
        "!unzip src.zip"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Archive:  src.zip\n",
            "replace src/config.py? [y]es, [n]o, [A]ll, [N]one, [r]ename: n\n",
            "replace src/dataset.py? [y]es, [n]o, [A]ll, [N]one, [r]ename: n\n",
            "replace src/loss.py? [y]es, [n]o, [A]ll, [N]one, [r]ename: n\n",
            "replace src/model.py? [y]es, [n]o, [A]ll, [N]one, [r]ename: n\n",
            "replace src/utils.py? [y]es, [n]o, [A]ll, [N]one, [r]ename: N\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lwZ-7ZTYMVR3",
        "outputId": "919370af-ed03-4b26-b892-00916ac987c0"
      },
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4s-B7snq461g"
      },
      "source": [
        "import os\n",
        "import numpy as np\n",
        "import torch\n",
        "import glob\n",
        "from PIL import Image\n",
        "import cv2\n",
        "import manga109api\n",
        "import torchvision\n",
        "from torchvision import transforms\n",
        "from src.model import EfficientDet\n",
        "from torch.utils.data import DataLoader\n",
        "import matplotlib.pyplot as plt\n",
        "from tqdm.notebook import tqdm"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QJewYQM04612"
      },
      "source": [
        "manga109_root_dir = \"/content/drive/MyDrive/Manga109/\"\n",
        "root_dir = \"/content/drive/MyDrive/Manga109/save/\"\n",
        "p = manga109api.Parser(root_dir=manga109_root_dir)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PNYFQ1tKxl5l"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "a05mQzkr4615"
      },
      "source": [
        "class Manga109Dataset(torch.utils.data.Dataset):\n",
        "    def __init__(self, root, manga109_root_dir, transforms):\n",
        "        self.root = root\n",
        "        self.transforms = transforms\n",
        "        self.parser = manga109api.Parser(root_dir=manga109_root_dir)\n",
        "        self.imgs = list(sorted(os.listdir(os.path.join(root, \"images\"))))\n",
        "        \n",
        "    def __getitem__(self, idx):\n",
        "        img_path = os.path.join(self.root, \"images\", self.imgs[idx])\n",
        "        img = cv2.imread(img_path)\n",
        "        img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)\n",
        "        img = img.astype(np.float32) / 255.\n",
        "        width, height, _ = img.shape\n",
        "        h_scale, w_scale = width/512, height/512\n",
        "        splitted = self.imgs[idx].split(\"_\")\n",
        "        book = \"_\".join(splitted[:-1])\n",
        "        page_index = splitted[-1]\n",
        "        page_index, extension = page_index.split(\".\")\n",
        "        page_index = int(page_index)\n",
        "        annotation = p.get_annotation(book=book)\n",
        "        boxes = []\n",
        "        rois = annotation[\"page\"][page_index][\"text\"]\n",
        "        for roi in rois:\n",
        "            xmin = roi[\"@xmin\"] \n",
        "            xmax = roi[\"@xmax\"] \n",
        "            ymin = roi[\"@ymin\"] \n",
        "            ymax = roi[\"@ymax\"] \n",
        "            boxes.append([xmin/w_scale, ymin/h_scale, xmax/w_scale, ymax/h_scale, 1])\n",
        "            #boxes.append([xmin, ymin, xmax, ymax, 1])\n",
        "        \n",
        "        for i in range(55 - len(boxes)):\n",
        "            boxes.append([-1, -1, -1, -1, -1])\n",
        "        \n",
        "        target = np.array(boxes).astype(np.float32)\n",
        "        #img = Image.open(p.img_path(book=book, index=page_index))\n",
        "        img = torch.from_numpy(img)\n",
        "        img = img.permute(2, 0, 1)\n",
        "        #sample = {'img': img, 'annot': target}\n",
        "        if self.transforms:\n",
        "            sample = self.transforms(img)\n",
        "            \n",
        "        return sample, target, h_scale, w_scale\n",
        "\n",
        "    def __len__(self):\n",
        "        return len(self.imgs)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MgaQ5cmM461_"
      },
      "source": [
        "transform= transforms.Compose([transforms.Resize((512,512)), transforms.Normalize(0,1)])\n",
        "\n",
        "dataset = Manga109Dataset(root_dir, manga109_root_dir, transform)\n",
        "train_dataloader = DataLoader(dataset, batch_size=10, shuffle=True)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2pgKbtST462E"
      },
      "source": [
        "PATH = \"/content/drive/MyDrive/Manga109/mangadet.pth\""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dk3KQpuJ462I",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "d0a31241-86b4-466e-ef85-bb6e9e06ed32"
      },
      "source": [
        "model = EfficientDet(num_classes = 2)\n",
        "if torch.cuda.is_available():\n",
        "  model = model.cuda()\n",
        "optimizer = torch.optim.Adam(model.parameters(), lr = 1e-3)\n",
        "model.load_state_dict(torch.load(PATH))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Loaded pretrained weights for efficientnet-b0\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<All keys matched successfully>"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 135,
          "referenced_widgets": [
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        "id": "ls3zfASP462R",
        "outputId": "2df20915-567f-431d-91fd-892d03a21636"
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      "source": [
        "loss_hist = []\n",
        "epochs = 2\n",
        "for epoch in range(epochs):\n",
        "  batch_loss = 0\n",
        "  for c, sample in enumerate(tqdm(train_dataloader)):\n",
        "    optimizer.zero_grad()\n",
        "    img = sample[0].double()\n",
        "    annot = sample[1]\n",
        "    if torch.cuda.is_available():\n",
        "        img = img.cuda()\n",
        "        annot = annot.cuda()\n",
        "    losses = model([img,annot])\n",
        "    loss = losses[0] + losses[1]\n",
        "    batch_loss += loss.item()\n",
        "    loss.backward()\n",
        "    optimizer.step()\n",
        "    if c%100 == 0:\n",
        "      torch.save(model.state_dict(), PATH)\n",
        "      loss_hist.append(batch_loss)\n",
        "      batch_loss = 0"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "63b63e74aa314a1abb37117be200d2fc",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "  0%|          | 0/999 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/torch/nn/functional.py:718: UserWarning: Named tensors and all their associated APIs are an experimental feature and subject to change. Please do not use them for anything important until they are released as stable. (Triggered internally at  /pytorch/c10/core/TensorImpl.h:1156.)\n",
            "  return torch.max_pool2d(input, kernel_size, stride, padding, dilation, ceil_mode)\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "8454250161b34587aeff230cd8e3b5a9",
              "version_minor": 0,
              "version_major": 2
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            "text/plain": [
              "  0%|          | 0/999 [00:00<?, ?it/s]"
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      "cell_type": "code",
      "metadata": {
        "id": "WpCEuN3h462W",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "outputId": "51a1c3e5-4b93-41a9-f70d-034b4de2a5d6"
      },
      "source": [
        "plt.plot(loss_hist)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x7f800d8f53d0>]"
            ]
          },
          "metadata": {},
          "execution_count": 8
        },
        {
          "output_type": "display_data",
          "data": {
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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VMZx-mfm462a"
      },
      "source": [
        "torch.save(model.state_dict(), PATH)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-NKf93O-462e"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YwuF2TWT462h"
      },
      "source": [
        "sample = next(iter(train_dataloader))\n",
        "img = sample[0].permute(0, 2, 3, 1).cpu().numpy()[0]\n",
        "print(img.shape)\n",
        "img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)\n",
        "for co in sample[1][0]:\n",
        "    color = (255, 0, 0)\n",
        "    thickness = 2\n",
        "    start_point = (int(co[0]), int(co[1]))\n",
        "    end_point = (int(co[2]), int(co[3]))\n",
        "    img = cv2.rectangle(img, start_point, end_point, color, thickness)\n",
        "plt.figure(figsize=(8,8)) \n",
        "plt.imshow(img)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dhGKJOqt462l"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fZVcEcfa462m"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "B4GODfIk462n"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}