TblSrcUnet.ipynb

{
  "cells": [
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "SsvrqodxNu4r",
        "outputId": "ea8aab2f-d612-4905-dbd3-9a8c45d314e6"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n",
            "\u001b[0m\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n",
            "\u001b[0m"
          ]
        }
      ],
      "source": [
        "!pip install -q transformers datasets torch torchvision albumentations matplotlib load_dotenv\n",
        "!pip install -q -U \"huggingface_hub[cli]\"\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Token is valid (permission: write).\n",
            "\u001b[1m\u001b[31mCannot authenticate through git-credential as no helper is defined on your machine.\n",
            "You might have to re-authenticate when pushing to the Hugging Face Hub.\n",
            "Run the following command in your terminal in case you want to set the 'store' credential helper as default.\n",
            "\n",
            "git config --global credential.helper store\n",
            "\n",
            "Read https://git-scm.com/book/en/v2/Git-Tools-Credential-Storage for more details.\u001b[0m\n",
            "Token has not been saved to git credential helper.\n",
            "Your token has been saved to /root/.cache/huggingface/token\n",
            "Login successful\n"
          ]
        }
      ],
      "source": [
        "from dotenv import load_dotenv\n",
        "import os\n",
        "# Load environment variables from a file named '.env' in the same directory\n",
        "load_dotenv()\n",
        "\n",
        "HUGGINGFACE_TOKEN = os.environ['HUGGINGFACE_TOKEN']\n",
        "!huggingface-cli login --token $HUGGINGFACE_TOKEN --add-to-git-credential\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Consider using `hf_transfer` for faster downloads. This solution comes with some limitations. See https://huggingface.co/docs/huggingface_hub/hf_transfer for more details.\n",
            "Fetching 3 files: 100%|███████████████████████████| 3/3 [00:00<00:00, 11.71it/s]\n",
            "/root/tstSegFormer/tbl-unet\n"
          ]
        }
      ],
      "source": [
        "# !huggingface-cli download doc2txt/tbl-unet --local-dir tbl-unet"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "aVKEGt3rOiYb"
      },
      "source": [
        "# init"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 28,
      "metadata": {
        "id": "cTClQ-EqNmjj"
      },
      "outputs": [],
      "source": [
        "import torch\n",
        "import albumentations as A\n",
        "from albumentations.pytorch import ToTensorV2\n",
        "import os\n",
        "import torch.nn as nn\n",
        "import torchvision\n",
        "import os\n",
        "from PIL import Image\n",
        "import numpy as np\n",
        "from torch.utils.data import Dataset\n",
        "from torch.utils.data import DataLoader\n",
        "from matplotlib import pyplot as plt\n",
        "import torchvision.transforms.functional as TF\n",
        "import torch.optim as optim\n",
        "from tqdm import tqdm\n",
        "import time\n",
        "\n",
        "BASE_PATH = \"../pdfBLines/tblDS/\"\n",
        "# BASE_PATH = \"/home/ubuntu/work/carvana/dataset/\"\n",
        "CHECKPOINT_PATH = \"./tbl-unet/tblSrc.pth.tar\"\n",
        "\n",
        "\n",
        "# Hyperparameters etc.\n",
        "LEARNING_RATE = 1e-5\n",
        "DEVICE = \"cuda\" if torch.cuda.is_available() else \"cpu\"\n",
        "# print(\"DEVICE\", DEVICE)\n",
        "BATCH_SIZE = 8\n",
        "NUM_EPOCHS = 100\n",
        "NUM_WORKERS = 2\n",
        "IMAGE_HEIGHT = 512 \n",
        "IMAGE_WIDTH = 512  \n",
        "PIN_MEMORY = True\n",
        "LOAD_MODEL = False\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zBaoEt-DOYTs"
      },
      "source": [
        "# Dataset"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 29,
      "metadata": {
        "id": "d4IhSuFiOLQ3"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "1114"
            ]
          },
          "execution_count": 29,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "\n",
        "class TblDataset(Dataset):\n",
        "    def __init__(self, root_dir, transform=None):     \n",
        "        self.root_dir = root_dir\n",
        "        self.transform = transform\n",
        "        \n",
        "        self.images = os.listdir(self.root_dir)    \n",
        "        self.images = list(filter(lambda x: \"_src.jpg\" in x, self.images))\n",
        "        \n",
        "    def __len__(self):\n",
        "        return len(self.images)\n",
        "    \n",
        "    def __getitem__(self, index):\n",
        "        img_path = os.path.join(self.root_dir, self.images[index])\n",
        "        mask_path = os.path.join(self.root_dir, self.images[index].replace(\"_src.jpg\", \"_mask.png\"))\n",
        "        image = Image.open(img_path).convert(\"L\")\n",
        "        mask = Image.open(mask_path).convert(\"L\")\n",
        "        image = image.resize(mask.size)\n",
        "        # mask = mask.resize(image.size)\n",
        "        image = np.array(image, dtype=np.float32)\n",
        "        image = image / 255.0\n",
        "        mask = np.array(mask, dtype=np.float32)\n",
        "        mask[mask != 255] = 0\n",
        "        mask[mask == 255] = 1.0\n",
        "        \n",
        "        image1,mask1 = image,mask\n",
        "        if self.transform is not None:\n",
        "            augmentations = self.transform(image=image,mask=mask)\n",
        "            image1 = augmentations['image']\n",
        "            # assert image1.shape == image.shape, f\"{ image1.shape},  {image.shape}\"\n",
        "            mask1 = augmentations['mask']\n",
        "        \n",
        "            \n",
        "        return image1,mask1\n",
        "    \n",
        "root_dir=os.path.join(BASE_PATH,\"train\")\n",
        "images = os.listdir(root_dir)   \n",
        "images1 = list(filter(lambda x: \"_src.jpg\" in x, images))\n",
        "len(images1)        "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 30,
      "metadata": {
        "id": "ozliBet2Nmjn"
      },
      "outputs": [],
      "source": [
        "train_transform = A.Compose(\n",
        "    [\n",
        "        A. Resize(height=IMAGE_HEIGHT, width=IMAGE_WIDTH),\n",
        "        A. Rotate(limit=2.5, p=0.5),\n",
        "        A.HorizontalFlip(p=0.5),\n",
        "        ToTensorV2(),\n",
        "    ],\n",
        ")\n",
        "val_transform = A.Compose(\n",
        "    [\n",
        "        A.Resize(height=IMAGE_HEIGHT, width=IMAGE_WIDTH),\n",
        "        ToTensorV2(),\n",
        "    ],\n",
        ")\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 33,
      "metadata": {
        "id": "J43XxUNZOXBq"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "../pdfBLines/tblDS/train\n",
            "1114\n",
            "images.shape torch.Size([8, 1, 512, 512])\n",
            "masks.shape torch.Size([8, 512, 512])\n",
            "data.dtype torch.float32\n",
            "targets.dtype torch.float32\n"
          ]
        }
      ],
      "source": [
        "UnetDataset = TblDataset\n",
        "train_ds = UnetDataset(\n",
        "    root_dir=os.path.join(BASE_PATH,\"train\"),\n",
        "    transform=train_transform,\n",
        ")\n",
        "print(os.path.join(BASE_PATH,\"train\"))\n",
        "train_loaders = DataLoader(\n",
        "    train_ds,\n",
        "    batch_size=BATCH_SIZE,\n",
        "    # num_workers=NUM_WORKERS,\n",
        "    pin_memory=PIN_MEMORY,\n",
        "    shuffle=True,\n",
        ")\n",
        "print(len(train_ds))\n",
        "# val_ds = UnetDataset(\n",
        "#     root_dir=os.path.join(BASE_PATH,\"valid\"),\n",
        "\n",
        "#     transform=val_transform,\n",
        "# )\n",
        "# val_loaders = DataLoader(\n",
        "#     val_ds,\n",
        "#     batch_size=BATCH_SIZE,\n",
        "#     num_workers=NUM_WORKERS,\n",
        "#     pin_memory=PIN_MEMORY,\n",
        "#     shuffle=False,\n",
        "# )\n",
        "for batch in train_loaders:\n",
        "    images, masks = batch\n",
        "    break\n",
        "print(\"images.shape\", images.shape)\n",
        "print(\"masks.shape\", masks.shape)\n",
        "data, targets = next(iter(train_loaders))\n",
        "print (\"data.dtype\", data.dtype)\n",
        "print (\"targets.dtype\", targets.dtype)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 34,
      "metadata": {},
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 1500x1500 with 16 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "color_map = {\n",
        "    0:(0,0,0),\n",
        "    1:(255,0,0),\n",
        "}\n",
        "\n",
        "def prediction_to_vis(prediction):\n",
        "    vis_shape = prediction.shape + (3,)\n",
        "    vis = np.zeros(vis_shape)\n",
        "    for i,c in color_map.items():\n",
        "        vis[prediction == i] = color_map[i]\n",
        "    return Image.fromarray(vis.astype(np.uint8))\n",
        "\n",
        "for batch in train_loaders:\n",
        "    images, masks = batch\n",
        "    break\n",
        "\n",
        "n_plots = BATCH_SIZE\n",
        "f, axarr = plt.subplots(n_plots,2)\n",
        "f.set_figheight(15)\n",
        "f.set_figwidth(15)\n",
        "\n",
        "for i in range(n_plots):\n",
        "    image =images[i,:,:].permute(1, 2, 0)\n",
        "    axarr[i,0].imshow(image)\n",
        "    axarr[i,1].imshow(prediction_to_vis(masks[i,:,:]))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ulrYwlGnPSLY"
      },
      "source": [
        "# Unet model"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 18,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LnSiB7XTPUTl",
        "outputId": "f1f02b0a-adc5-40ec-cc90-541639b8cbec"
      },
      "outputs": [],
      "source": [
        "\n",
        "class DoubleConv(nn.Module):\n",
        "    def __init__(self, in_channels, out_channels) -> None:\n",
        "        super(DoubleConv, self).__init__()\n",
        "        self.conv = nn.Sequential(\n",
        "            # kernel, stride, padding - same convolution\n",
        "            nn.Conv2d(in_channels, out_channels, 3, 1, 1, bias=False),\n",
        "            nn.BatchNorm2d(out_channels),\n",
        "            nn.ReLU(inplace=True),\n",
        "            # kernel, stride, padding - same convolution\n",
        "            nn.Conv2d(out_channels, out_channels, 3, 1, 1, bias=False),\n",
        "            nn.BatchNorm2d(out_channels),\n",
        "            nn.ReLU(inplace=True),\n",
        "        )\n",
        "\n",
        "    def forward(self, x):\n",
        "        return self.conv(x)\n",
        "\n",
        "\n",
        "class UNET(nn.Module):\n",
        "    def __init__(self,\n",
        "                 in_channels=3, out_channels=1,\n",
        "                 features=[64, 128, 256, 512]\n",
        "                 ) -> None:\n",
        "        super(UNET, self).__init__()\n",
        "        self.downs = nn.ModuleList()\n",
        "        self.ups = nn.ModuleList()\n",
        "\n",
        "        self.pool = nn.MaxPool2d(kernel_size=2, stride=2)\n",
        "\n",
        "        # Down part\n",
        "        for feature in features:\n",
        "            self.downs.append(DoubleConv(in_channels, feature))\n",
        "            in_channels = feature\n",
        "\n",
        "        # Up part\n",
        "        for feature in reversed(features):\n",
        "            self.ups.append(\n",
        "                nn.ConvTranspose2d(feature*2, feature, kernel_size=2, stride=2)\n",
        "            )\n",
        "            self.ups.append(DoubleConv(feature*2, feature))\n",
        "\n",
        "        self.bottleneck = DoubleConv(features[-1], features[-1]*2)\n",
        "\n",
        "        self.final_conv = nn.Conv2d(features[0], out_channels, kernel_size=1)\n",
        "\n",
        "    def forward(self, x):\n",
        "        # print(\"Forward\",\"x.dtype\",x.dtype)\n",
        "        skip_connections = []\n",
        "        for down in self.downs:\n",
        "            x = down(x)\n",
        "            skip_connections.append(x)\n",
        "            x = self.pool(x)\n",
        "\n",
        "        x = self.bottleneck(x)\n",
        "\n",
        "        skip_connections = skip_connections[::-1]\n",
        "\n",
        "        for idx in range(0, len(self.ups), 2):\n",
        "            x = self.ups[idx](x)\n",
        "            skip_connection = skip_connections[idx//2]\n",
        "\n",
        "            if x.shape != skip_connection.shape:\n",
        "                x = TF.resize(x, size=skip_connection.shape[2:])\n",
        "\n",
        "            concat_skip = torch.cat((skip_connection, x), dim=1)\n",
        "            x = self.ups[idx+1](concat_skip)\n",
        "\n",
        "        return self.final_conv(x)\n",
        "\n",
        "\n",
        "def test():\n",
        "    x = torch.randn((3, 1, 161, 161))\n",
        "    model = UNET(in_channels=1, out_channels=1)\n",
        "    pred = model(x)\n",
        "    print(pred.shape)\n",
        "    print(x.shape)\n",
        "    assert pred.shape == x.shape\n",
        "\n",
        "\n",
        "# test()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "# Utils"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 19,
      "metadata": {},
      "outputs": [],
      "source": [
        "def load_checkpoint(checkpoint, model):\n",
        "    print(\"=> Loading checkpoint\")\n",
        "    model.load_state_dict(checkpoint[\"state_dict\"])\n",
        "\n",
        "def save_checkpoint(state, filename=\"my_checkpoint.pth.tar\"):\n",
        "    print(\"=> Saving checkpoint\",filename)\n",
        "    torch.save(state, filename)\n",
        "\n",
        "def check_accuracy(loader, model, device=\"cuda\"):\n",
        "    print(\"Checking accuracy\")\n",
        "    num_correct = 0\n",
        "    num_pixels = 0\n",
        "    dice_score = 0\n",
        "    model.eval()\n",
        "\n",
        "    with torch.no_grad():\n",
        "        for idx, (x, y) in enumerate(loader):\n",
        "            x = x.to(device)\n",
        "            y = y.to(device).unsqueeze(1)\n",
        "            preds = torch.sigmoid(model(x))\n",
        "            preds = (preds > 0.5).float()\n",
        "            \n",
        "            acc = (preds == y).sum()\n",
        "            numElem = torch.numel(preds)\n",
        "            # print(idx, acc/numElem)\n",
        "            \n",
        "            num_correct += acc\n",
        "            num_pixels += numElem\n",
        "            dice_score += (2*(preds*y).sum())/(\n",
        "                (preds+y).sum() + 1e-8\n",
        "            )\n",
        "\n",
        "        print(\n",
        "            f\"Got {num_correct}/{num_pixels} with acc {num_correct/num_pixels*100:.2f}\"\n",
        "        )\n",
        "\n",
        "        dice = dice_score/len(loader)\n",
        "        print(f\"Dice score: {dice}\")\n",
        "\n",
        "        model.train()\n",
        "    return dice\n",
        "\n",
        "\n",
        "def save_predictions_as_imgs(\n",
        "    loader, model, folder=\"saved_images/\", device=\"cuda\"\n",
        "):\n",
        "    print(\"Saving predictions\")\n",
        "    start = time.time()\n",
        "    model.eval()\n",
        "    for idx, (x, y) in enumerate(loader):\n",
        "        x = x.to(device=device)\n",
        "        with torch.no_grad():\n",
        "            preds = torch.sigmoid(model(x))\n",
        "            preds = (preds > 0.5).float()\n",
        "        torchvision.utils.save_image(\n",
        "            preds, os.path.join(folder,f\"pred_{idx}.png\")\n",
        "        )\n",
        "        torchvision.utils.save_image(y.unsqueeze(1), \n",
        "                                    os.path.join(folder,f\"correct_{idx}.png\")\n",
        "                                     )\n",
        "    print(f\"Saved predictions in {time.time()-start:.2f} seconds\")\n",
        "    model.train()\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "MAeqwQFJOb2Y"
      },
      "source": [
        "# Train"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 20,
      "metadata": {
        "id": "1YtcwzN1Nmjn"
      },
      "outputs": [],
      "source": [
        "model = UNET(in_channels=1, out_channels=1).to(DEVICE)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 21,
      "metadata": {},
      "outputs": [],
      "source": [
        "# load_checkpoint(torch.load(CHECKPOINT_PATH), model)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 22,
      "metadata": {},
      "outputs": [],
      "source": [
        "def train_fn(loader, model, optimizer, loss_fn, scaler):\n",
        "    loop = tqdm(loader)\n",
        "    for batch_idx, (data, targets) in enumerate(loop):\n",
        "    # for i in range(10):\n",
        "    #     data, targets = next(iter(loader))\n",
        "        \n",
        "        data = data.to(device=DEVICE)\n",
        "        targets = targets.float().unsqueeze(1).to(device=DEVICE)\n",
        "\n",
        "        # forward\n",
        "        with torch.cuda.amp.autocast():\n",
        "            predictions = model(data)\n",
        "            loss = loss_fn(predictions, targets)\n",
        "\n",
        "        # backward\n",
        "        optimizer.zero_grad()\n",
        "        scaler.scale(loss).backward()\n",
        "        scaler.step(optimizer)\n",
        "        scaler.update()\n",
        "        # print(\"loss\", loss.item())\n",
        "\n",
        "        # update tqdm\n",
        "        loop.set_postfix(loss=loss.item())"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 23,
      "metadata": {},
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "/Users/eliaweiss/opt/anaconda3/envs/LayoutLMv3/lib/python3.10/site-packages/torch/cuda/amp/grad_scaler.py:125: UserWarning: torch.cuda.amp.GradScaler is enabled, but CUDA is not available.  Disabling.\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "epoch 0\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "  0%|          | 0/140 [00:00<?, ?it/s]"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "/Users/eliaweiss/opt/anaconda3/envs/LayoutLMv3/lib/python3.10/site-packages/torch/amp/autocast_mode.py:250: UserWarning: User provided device_type of 'cuda', but CUDA is not available. Disabling\n",
            "  warnings.warn(\n",
            "  6%|▌         | 8/140 [12:52<3:32:26, 96.56s/it, loss=0.692]\n"
          ]
        },
        {
          "ename": "KeyboardInterrupt",
          "evalue": "",
          "output_type": "error",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
            "Cell \u001b[0;32mIn[23], line 10\u001b[0m\n\u001b[1;32m      8\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m epoch \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mrange\u001b[39m(NUM_EPOCHS):\n\u001b[1;32m      9\u001b[0m     \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mepoch\u001b[39m\u001b[38;5;124m\"\u001b[39m, epoch)\n\u001b[0;32m---> 10\u001b[0m     \u001b[43mtrain_fn\u001b[49m\u001b[43m(\u001b[49m\u001b[43mtrain_loaders\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmodel\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43moptimizer\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mloss_fn\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mscaler\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m     16\u001b[0m     \u001b[38;5;66;03m# print some example to folder\u001b[39;00m\n\u001b[1;32m     17\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m epoch \u001b[38;5;241m%\u001b[39m \u001b[38;5;241m10\u001b[39m \u001b[38;5;241m==\u001b[39m \u001b[38;5;241m0\u001b[39m:\n\u001b[1;32m     18\u001b[0m         \u001b[38;5;66;03m# save model\u001b[39;00m\n",
            "Cell \u001b[0;32mIn[22], line 12\u001b[0m, in \u001b[0;36mtrain_fn\u001b[0;34m(loader, model, optimizer, loss_fn, scaler)\u001b[0m\n\u001b[1;32m     10\u001b[0m \u001b[38;5;66;03m# forward\u001b[39;00m\n\u001b[1;32m     11\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m torch\u001b[38;5;241m.\u001b[39mcuda\u001b[38;5;241m.\u001b[39mamp\u001b[38;5;241m.\u001b[39mautocast():\n\u001b[0;32m---> 12\u001b[0m     predictions \u001b[38;5;241m=\u001b[39m \u001b[43mmodel\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdata\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m     13\u001b[0m     loss \u001b[38;5;241m=\u001b[39m loss_fn(predictions, targets)\n\u001b[1;32m     15\u001b[0m \u001b[38;5;66;03m# backward\u001b[39;00m\n",
            "File \u001b[0;32m~/opt/anaconda3/envs/LayoutLMv3/lib/python3.10/site-packages/torch/nn/modules/module.py:1518\u001b[0m, in \u001b[0;36mModule._wrapped_call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1516\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_compiled_call_impl(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)  \u001b[38;5;66;03m# type: ignore[misc]\u001b[39;00m\n\u001b[1;32m   1517\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m-> 1518\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[43m_call_impl\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
            "File \u001b[0;32m~/opt/anaconda3/envs/LayoutLMv3/lib/python3.10/site-packages/torch/nn/modules/module.py:1527\u001b[0m, in \u001b[0;36mModule._call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1522\u001b[0m \u001b[38;5;66;03m# If we don't have any hooks, we want to skip the rest of the logic in\u001b[39;00m\n\u001b[1;32m   1523\u001b[0m \u001b[38;5;66;03m# this function, and just call forward.\u001b[39;00m\n\u001b[1;32m   1524\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m (\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_pre_hooks\n\u001b[1;32m   1525\u001b[0m         \u001b[38;5;129;01mor\u001b[39;00m _global_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_backward_hooks\n\u001b[1;32m   1526\u001b[0m         \u001b[38;5;129;01mor\u001b[39;00m _global_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_forward_pre_hooks):\n\u001b[0;32m-> 1527\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mforward_call\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   1529\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m   1530\u001b[0m     result \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n",
            "Cell \u001b[0;32mIn[18], line 66\u001b[0m, in \u001b[0;36mUNET.forward\u001b[0;34m(self, x)\u001b[0m\n\u001b[1;32m     63\u001b[0m         x \u001b[38;5;241m=\u001b[39m TF\u001b[38;5;241m.\u001b[39mresize(x, size\u001b[38;5;241m=\u001b[39mskip_connection\u001b[38;5;241m.\u001b[39mshape[\u001b[38;5;241m2\u001b[39m:])\n\u001b[1;32m     65\u001b[0m     concat_skip \u001b[38;5;241m=\u001b[39m torch\u001b[38;5;241m.\u001b[39mcat((skip_connection, x), dim\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m1\u001b[39m)\n\u001b[0;32m---> 66\u001b[0m     x \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mups\u001b[49m\u001b[43m[\u001b[49m\u001b[43midx\u001b[49m\u001b[38;5;241;43m+\u001b[39;49m\u001b[38;5;241;43m1\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m(\u001b[49m\u001b[43mconcat_skip\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m     68\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mfinal_conv(x)\n",
            "File \u001b[0;32m~/opt/anaconda3/envs/LayoutLMv3/lib/python3.10/site-packages/torch/nn/modules/module.py:1518\u001b[0m, in \u001b[0;36mModule._wrapped_call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1516\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_compiled_call_impl(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)  \u001b[38;5;66;03m# type: ignore[misc]\u001b[39;00m\n\u001b[1;32m   1517\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m-> 1518\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[43m_call_impl\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
            "File \u001b[0;32m~/opt/anaconda3/envs/LayoutLMv3/lib/python3.10/site-packages/torch/nn/modules/module.py:1527\u001b[0m, in \u001b[0;36mModule._call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1522\u001b[0m \u001b[38;5;66;03m# If we don't have any hooks, we want to skip the rest of the logic in\u001b[39;00m\n\u001b[1;32m   1523\u001b[0m \u001b[38;5;66;03m# this function, and just call forward.\u001b[39;00m\n\u001b[1;32m   1524\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m (\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_pre_hooks\n\u001b[1;32m   1525\u001b[0m         \u001b[38;5;129;01mor\u001b[39;00m _global_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_backward_hooks\n\u001b[1;32m   1526\u001b[0m         \u001b[38;5;129;01mor\u001b[39;00m _global_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_forward_pre_hooks):\n\u001b[0;32m-> 1527\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mforward_call\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   1529\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m   1530\u001b[0m     result \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n",
            "Cell \u001b[0;32mIn[18], line 16\u001b[0m, in \u001b[0;36mDoubleConv.forward\u001b[0;34m(self, x)\u001b[0m\n\u001b[1;32m     15\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mforward\u001b[39m(\u001b[38;5;28mself\u001b[39m, x):\n\u001b[0;32m---> 16\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[43mconv\u001b[49m\u001b[43m(\u001b[49m\u001b[43mx\u001b[49m\u001b[43m)\u001b[49m\n",
            "File \u001b[0;32m~/opt/anaconda3/envs/LayoutLMv3/lib/python3.10/site-packages/torch/nn/modules/module.py:1518\u001b[0m, in \u001b[0;36mModule._wrapped_call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1516\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_compiled_call_impl(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)  \u001b[38;5;66;03m# type: ignore[misc]\u001b[39;00m\n\u001b[1;32m   1517\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m-> 1518\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[43m_call_impl\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
            "File \u001b[0;32m~/opt/anaconda3/envs/LayoutLMv3/lib/python3.10/site-packages/torch/nn/modules/module.py:1527\u001b[0m, in \u001b[0;36mModule._call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1522\u001b[0m \u001b[38;5;66;03m# If we don't have any hooks, we want to skip the rest of the logic in\u001b[39;00m\n\u001b[1;32m   1523\u001b[0m \u001b[38;5;66;03m# this function, and just call forward.\u001b[39;00m\n\u001b[1;32m   1524\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m (\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_pre_hooks\n\u001b[1;32m   1525\u001b[0m         \u001b[38;5;129;01mor\u001b[39;00m _global_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_backward_hooks\n\u001b[1;32m   1526\u001b[0m         \u001b[38;5;129;01mor\u001b[39;00m _global_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_forward_pre_hooks):\n\u001b[0;32m-> 1527\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mforward_call\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   1529\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m   1530\u001b[0m     result \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n",
            "File \u001b[0;32m~/opt/anaconda3/envs/LayoutLMv3/lib/python3.10/site-packages/torch/nn/modules/container.py:215\u001b[0m, in \u001b[0;36mSequential.forward\u001b[0;34m(self, input)\u001b[0m\n\u001b[1;32m    213\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mforward\u001b[39m(\u001b[38;5;28mself\u001b[39m, \u001b[38;5;28minput\u001b[39m):\n\u001b[1;32m    214\u001b[0m     \u001b[38;5;28;01mfor\u001b[39;00m module \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m:\n\u001b[0;32m--> 215\u001b[0m         \u001b[38;5;28minput\u001b[39m \u001b[38;5;241m=\u001b[39m \u001b[43mmodule\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43minput\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[1;32m    216\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28minput\u001b[39m\n",
            "File \u001b[0;32m~/opt/anaconda3/envs/LayoutLMv3/lib/python3.10/site-packages/torch/nn/modules/module.py:1518\u001b[0m, in \u001b[0;36mModule._wrapped_call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1516\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_compiled_call_impl(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)  \u001b[38;5;66;03m# type: ignore[misc]\u001b[39;00m\n\u001b[1;32m   1517\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m-> 1518\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[43m_call_impl\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
            "File \u001b[0;32m~/opt/anaconda3/envs/LayoutLMv3/lib/python3.10/site-packages/torch/nn/modules/module.py:1527\u001b[0m, in \u001b[0;36mModule._call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1522\u001b[0m \u001b[38;5;66;03m# If we don't have any hooks, we want to skip the rest of the logic in\u001b[39;00m\n\u001b[1;32m   1523\u001b[0m \u001b[38;5;66;03m# this function, and just call forward.\u001b[39;00m\n\u001b[1;32m   1524\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m (\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_pre_hooks\n\u001b[1;32m   1525\u001b[0m         \u001b[38;5;129;01mor\u001b[39;00m _global_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_backward_hooks\n\u001b[1;32m   1526\u001b[0m         \u001b[38;5;129;01mor\u001b[39;00m _global_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_forward_pre_hooks):\n\u001b[0;32m-> 1527\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mforward_call\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   1529\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m   1530\u001b[0m     result \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n",
            "File \u001b[0;32m~/opt/anaconda3/envs/LayoutLMv3/lib/python3.10/site-packages/torch/nn/modules/conv.py:460\u001b[0m, in \u001b[0;36mConv2d.forward\u001b[0;34m(self, input)\u001b[0m\n\u001b[1;32m    459\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mforward\u001b[39m(\u001b[38;5;28mself\u001b[39m, \u001b[38;5;28minput\u001b[39m: Tensor) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m Tensor:\n\u001b[0;32m--> 460\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[43m_conv_forward\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43minput\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mweight\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mbias\u001b[49m\u001b[43m)\u001b[49m\n",
            "File \u001b[0;32m~/opt/anaconda3/envs/LayoutLMv3/lib/python3.10/site-packages/torch/nn/modules/conv.py:456\u001b[0m, in \u001b[0;36mConv2d._conv_forward\u001b[0;34m(self, input, weight, bias)\u001b[0m\n\u001b[1;32m    452\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mpadding_mode \u001b[38;5;241m!=\u001b[39m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mzeros\u001b[39m\u001b[38;5;124m'\u001b[39m:\n\u001b[1;32m    453\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m F\u001b[38;5;241m.\u001b[39mconv2d(F\u001b[38;5;241m.\u001b[39mpad(\u001b[38;5;28minput\u001b[39m, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_reversed_padding_repeated_twice, mode\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mpadding_mode),\n\u001b[1;32m    454\u001b[0m                     weight, bias, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mstride,\n\u001b[1;32m    455\u001b[0m                     _pair(\u001b[38;5;241m0\u001b[39m), \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mdilation, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mgroups)\n\u001b[0;32m--> 456\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mF\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mconv2d\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43minput\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mweight\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mbias\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mstride\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    457\u001b[0m \u001b[43m                \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mpadding\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdilation\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mgroups\u001b[49m\u001b[43m)\u001b[49m\n",
            "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
          ]
        }
      ],
      "source": [
        "# pos_weight = torch.ones([224]).to(DEVICE)\n",
        "# loss_fn = nn.BCEWithLogitsLoss(pos_weight=pos_weight)\n",
        "loss_fn = nn.BCEWithLogitsLoss()\n",
        "# loss_fn = DiceLoss()\n",
        "optimizer = optim.Adam(model.parameters(), lr=LEARNING_RATE)\n",
        "\n",
        "scaler = torch.cuda.amp.grad_scaler.GradScaler()\n",
        "for epoch in range(NUM_EPOCHS):\n",
        "    print(\"epoch\", epoch)\n",
        "    train_fn(train_loaders, model, optimizer, loss_fn, scaler)\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "    # print some example to folder\n",
        "    if epoch % 10 == 0:\n",
        "        # save model\n",
        "        check_point = {\n",
        "            \"state_dict\": model.state_dict(),\n",
        "            \"optimizer\": optimizer.state_dict(),\n",
        "        }\n",
        "        # dice_score =check_accuracy(val_loaders, model, device=DEVICE)        \n",
        "        # save_checkpoint(check_point, os.path.join(CHECKPOINT_PATH,f\"blines_dice{dice_score}.pth.tar\"))        \n",
        "        # save_predictions_as_imgs(\n",
        "        #     val_loaders, model, folder=\"save_images/\", device=DEVICE\n",
        "        # )\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "=> Saving checkpoint ./tbl-unet/tbl_dice1.00.pth.tar\n"
          ]
        }
      ],
      "source": [
        "# dice_score =check_accuracy(val_loaders, model, device=DEVICE)        \n",
        "save_checkpoint(check_point, CHECKPOINT_PATH)        \n",
        "# save_checkpoint(check_point, os.path.join(CHECKPOINT_PATH,f\"blines_dice{dice_score:.2F}.pth.tar\"))        \n",
        "COMMIT_MSG = f\"from scrach IMAGE_HEIGHT {IMAGE_HEIGHT} BATCH_SIZE {BATCH_SIZE} epoch {epoch} loss 0.002 Transform rotate 2.5 only line tbls\"\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "IMAGE_HEIGHT 512 BATCH_SIZE 8 epoch 156 loss 0.002 Transform rotate 2.5 only line tbls\n"
          ]
        }
      ],
      "source": [
        "!echo \"$COMMIT_MSG\""
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Consider using `hf_transfer` for faster uploads. This solution comes with some limitations. See https://huggingface.co/docs/huggingface_hub/hf_transfer for more details.\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "/opt/conda/lib/python3.10/site-packages/huggingface_hub/hf_api.py:3757: UserWarning: Warnings while validating metadata in README.md:\n",
            "- empty or missing yaml metadata in repo card\n",
            "  warnings.warn(f\"Warnings while validating metadata in README.md:\\n{message}\")\n",
            "tbl_dice1.00.pth.tar: 100%|██████████████████| 373M/373M [02:10<00:00, 2.85MB/s]\n",
            "https://huggingface.co/doc2txt/tbl-unet/tree/main/.\n"
          ]
        }
      ],
      "source": [
        "!huggingface-cli upload doc2txt/tblSrc-unet ./tbl-unet . --commit-message=\"$COMMIT_MSG\""
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 23,
      "metadata": {},
      "outputs": [],
      "source": [
        "# model = model.half()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NqDw7yp_SOZN"
      },
      "source": [
        " # view results"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "metadata": {},
      "outputs": [],
      "source": [
        "color_map = {\n",
        "    0:(0,0,0),\n",
        "    1:(255,0,0),\n",
        "}\n",
        "def prediction_to_vis(prediction):\n",
        "    vis_shape = prediction.shape + (3,)\n",
        "    vis = np.zeros(vis_shape)\n",
        "    for i,c in color_map.items():\n",
        "        vis[prediction == i] = color_map[i]\n",
        "    return Image.fromarray(vis.astype(np.uint8).squeeze())"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ALbqPVetQJhR",
        "outputId": "5ac5b393-f9ff-4ad3-b96c-37fd2b96b2d0"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "0 torch.Size([8, 1, 512, 512])\n"
          ]
        },
        {
          "ename": "OutOfMemoryError",
          "evalue": "CUDA out of memory. Tried to allocate 256.00 MiB. GPU 0 has a total capacity of 10.75 GiB of which 159.62 MiB is free. Process 2737557 has 10.59 GiB memory in use. Of the allocated memory 10.03 GiB is allocated by PyTorch, and 385.04 MiB is reserved by PyTorch but unallocated. If reserved but unallocated memory is large try setting PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True to avoid fragmentation.  See documentation for Memory Management  (https://pytorch.org/docs/stable/notes/cuda.html#environment-variables)",
          "output_type": "error",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mOutOfMemoryError\u001b[0m                          Traceback (most recent call last)",
            "Cell \u001b[0;32mIn[14], line 6\u001b[0m\n\u001b[1;32m      4\u001b[0m pixel_values \u001b[38;5;241m=\u001b[39m pixel_values\u001b[38;5;241m.\u001b[39mhalf()\n\u001b[1;32m      5\u001b[0m \u001b[38;5;28mprint\u001b[39m(idx, pixel_values\u001b[38;5;241m.\u001b[39mshape)\n\u001b[0;32m----> 6\u001b[0m preds \u001b[38;5;241m=\u001b[39m torch\u001b[38;5;241m.\u001b[39msigmoid(\u001b[43mmodel\u001b[49m\u001b[43m(\u001b[49m\u001b[43mpixel_values\u001b[49m\u001b[43m)\u001b[49m)\n\u001b[1;32m      7\u001b[0m predicted_mask \u001b[38;5;241m=\u001b[39m (preds \u001b[38;5;241m>\u001b[39m \u001b[38;5;241m0.5\u001b[39m)\u001b[38;5;241m.\u001b[39mfloat()\n\u001b[1;32m      8\u001b[0m masks \u001b[38;5;241m=\u001b[39m masks\u001b[38;5;241m.\u001b[39mcpu()\u001b[38;5;241m.\u001b[39mnumpy()\n",
            "File \u001b[0;32m/opt/conda/lib/python3.10/site-packages/torch/nn/modules/module.py:1511\u001b[0m, in \u001b[0;36mModule._wrapped_call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1509\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_compiled_call_impl(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)  \u001b[38;5;66;03m# type: ignore[misc]\u001b[39;00m\n\u001b[1;32m   1510\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m-> 1511\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[43m_call_impl\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
            "File \u001b[0;32m/opt/conda/lib/python3.10/site-packages/torch/nn/modules/module.py:1520\u001b[0m, in \u001b[0;36mModule._call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1515\u001b[0m \u001b[38;5;66;03m# If we don't have any hooks, we want to skip the rest of the logic in\u001b[39;00m\n\u001b[1;32m   1516\u001b[0m \u001b[38;5;66;03m# this function, and just call forward.\u001b[39;00m\n\u001b[1;32m   1517\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m (\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_pre_hooks\n\u001b[1;32m   1518\u001b[0m         \u001b[38;5;129;01mor\u001b[39;00m _global_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_backward_hooks\n\u001b[1;32m   1519\u001b[0m         \u001b[38;5;129;01mor\u001b[39;00m _global_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_forward_pre_hooks):\n\u001b[0;32m-> 1520\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mforward_call\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   1522\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m   1523\u001b[0m     result \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n",
            "Cell \u001b[0;32mIn[7], line 50\u001b[0m, in \u001b[0;36mUNET.forward\u001b[0;34m(self, x)\u001b[0m\n\u001b[1;32m     48\u001b[0m skip_connections \u001b[38;5;241m=\u001b[39m []\n\u001b[1;32m     49\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m down \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mdowns:\n\u001b[0;32m---> 50\u001b[0m     x \u001b[38;5;241m=\u001b[39m \u001b[43mdown\u001b[49m\u001b[43m(\u001b[49m\u001b[43mx\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m     51\u001b[0m     skip_connections\u001b[38;5;241m.\u001b[39mappend(x)\n\u001b[1;32m     52\u001b[0m     x \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mpool(x)\n",
            "File \u001b[0;32m/opt/conda/lib/python3.10/site-packages/torch/nn/modules/module.py:1511\u001b[0m, in \u001b[0;36mModule._wrapped_call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1509\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_compiled_call_impl(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)  \u001b[38;5;66;03m# type: ignore[misc]\u001b[39;00m\n\u001b[1;32m   1510\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m-> 1511\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[43m_call_impl\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
            "File \u001b[0;32m/opt/conda/lib/python3.10/site-packages/torch/nn/modules/module.py:1520\u001b[0m, in \u001b[0;36mModule._call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1515\u001b[0m \u001b[38;5;66;03m# If we don't have any hooks, we want to skip the rest of the logic in\u001b[39;00m\n\u001b[1;32m   1516\u001b[0m \u001b[38;5;66;03m# this function, and just call forward.\u001b[39;00m\n\u001b[1;32m   1517\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m (\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_pre_hooks\n\u001b[1;32m   1518\u001b[0m         \u001b[38;5;129;01mor\u001b[39;00m _global_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_backward_hooks\n\u001b[1;32m   1519\u001b[0m         \u001b[38;5;129;01mor\u001b[39;00m _global_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_forward_pre_hooks):\n\u001b[0;32m-> 1520\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mforward_call\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   1522\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m   1523\u001b[0m     result \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n",
            "Cell \u001b[0;32mIn[7], line 16\u001b[0m, in \u001b[0;36mDoubleConv.forward\u001b[0;34m(self, x)\u001b[0m\n\u001b[1;32m     15\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mforward\u001b[39m(\u001b[38;5;28mself\u001b[39m, x):\n\u001b[0;32m---> 16\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[43mconv\u001b[49m\u001b[43m(\u001b[49m\u001b[43mx\u001b[49m\u001b[43m)\u001b[49m\n",
            "File \u001b[0;32m/opt/conda/lib/python3.10/site-packages/torch/nn/modules/module.py:1511\u001b[0m, in \u001b[0;36mModule._wrapped_call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1509\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_compiled_call_impl(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)  \u001b[38;5;66;03m# type: ignore[misc]\u001b[39;00m\n\u001b[1;32m   1510\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m-> 1511\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[43m_call_impl\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
            "File \u001b[0;32m/opt/conda/lib/python3.10/site-packages/torch/nn/modules/module.py:1520\u001b[0m, in \u001b[0;36mModule._call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1515\u001b[0m \u001b[38;5;66;03m# If we don't have any hooks, we want to skip the rest of the logic in\u001b[39;00m\n\u001b[1;32m   1516\u001b[0m \u001b[38;5;66;03m# this function, and just call forward.\u001b[39;00m\n\u001b[1;32m   1517\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m (\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_pre_hooks\n\u001b[1;32m   1518\u001b[0m         \u001b[38;5;129;01mor\u001b[39;00m _global_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_backward_hooks\n\u001b[1;32m   1519\u001b[0m         \u001b[38;5;129;01mor\u001b[39;00m _global_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_forward_pre_hooks):\n\u001b[0;32m-> 1520\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mforward_call\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   1522\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m   1523\u001b[0m     result \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n",
            "File \u001b[0;32m/opt/conda/lib/python3.10/site-packages/torch/nn/modules/container.py:217\u001b[0m, in \u001b[0;36mSequential.forward\u001b[0;34m(self, input)\u001b[0m\n\u001b[1;32m    215\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mforward\u001b[39m(\u001b[38;5;28mself\u001b[39m, \u001b[38;5;28minput\u001b[39m):\n\u001b[1;32m    216\u001b[0m     \u001b[38;5;28;01mfor\u001b[39;00m module \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m:\n\u001b[0;32m--> 217\u001b[0m         \u001b[38;5;28minput\u001b[39m \u001b[38;5;241m=\u001b[39m \u001b[43mmodule\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43minput\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[1;32m    218\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28minput\u001b[39m\n",
            "File \u001b[0;32m/opt/conda/lib/python3.10/site-packages/torch/nn/modules/module.py:1511\u001b[0m, in \u001b[0;36mModule._wrapped_call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1509\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_compiled_call_impl(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)  \u001b[38;5;66;03m# type: ignore[misc]\u001b[39;00m\n\u001b[1;32m   1510\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m-> 1511\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[43m_call_impl\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
            "File \u001b[0;32m/opt/conda/lib/python3.10/site-packages/torch/nn/modules/module.py:1520\u001b[0m, in \u001b[0;36mModule._call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1515\u001b[0m \u001b[38;5;66;03m# If we don't have any hooks, we want to skip the rest of the logic in\u001b[39;00m\n\u001b[1;32m   1516\u001b[0m \u001b[38;5;66;03m# this function, and just call forward.\u001b[39;00m\n\u001b[1;32m   1517\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m (\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_pre_hooks\n\u001b[1;32m   1518\u001b[0m         \u001b[38;5;129;01mor\u001b[39;00m _global_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_backward_hooks\n\u001b[1;32m   1519\u001b[0m         \u001b[38;5;129;01mor\u001b[39;00m _global_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_forward_pre_hooks):\n\u001b[0;32m-> 1520\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mforward_call\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   1522\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m   1523\u001b[0m     result \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n",
            "File \u001b[0;32m/opt/conda/lib/python3.10/site-packages/torch/nn/modules/batchnorm.py:175\u001b[0m, in \u001b[0;36m_BatchNorm.forward\u001b[0;34m(self, input)\u001b[0m\n\u001b[1;32m    168\u001b[0m     bn_training \u001b[38;5;241m=\u001b[39m (\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mrunning_mean \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m) \u001b[38;5;129;01mand\u001b[39;00m (\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mrunning_var \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m)\n\u001b[1;32m    170\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    171\u001b[0m \u001b[38;5;124;03mBuffers are only updated if they are to be tracked and we are in training mode. Thus they only need to be\u001b[39;00m\n\u001b[1;32m    172\u001b[0m \u001b[38;5;124;03mpassed when the update should occur (i.e. in training mode when they are tracked), or when buffer stats are\u001b[39;00m\n\u001b[1;32m    173\u001b[0m \u001b[38;5;124;03mused for normalization (i.e. in eval mode when buffers are not None).\u001b[39;00m\n\u001b[1;32m    174\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[0;32m--> 175\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mF\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mbatch_norm\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m    176\u001b[0m \u001b[43m    \u001b[49m\u001b[38;5;28;43minput\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m    177\u001b[0m \u001b[43m    \u001b[49m\u001b[38;5;66;43;03m# If buffers are not to be tracked, ensure that they won't be updated\u001b[39;49;00m\n\u001b[1;32m    178\u001b[0m \u001b[43m    \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mrunning_mean\u001b[49m\n\u001b[1;32m    179\u001b[0m \u001b[43m    \u001b[49m\u001b[38;5;28;43;01mif\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[38;5;129;43;01mnot\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mtraining\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;129;43;01mor\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mtrack_running_stats\u001b[49m\n\u001b[1;32m    180\u001b[0m \u001b[43m    \u001b[49m\u001b[38;5;28;43;01melse\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mNone\u001b[39;49;00m\u001b[43m,\u001b[49m\n\u001b[1;32m    181\u001b[0m \u001b[43m    \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mrunning_var\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mif\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[38;5;129;43;01mnot\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mtraining\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;129;43;01mor\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mtrack_running_stats\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01melse\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mNone\u001b[39;49;00m\u001b[43m,\u001b[49m\n\u001b[1;32m    182\u001b[0m \u001b[43m    \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mweight\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    183\u001b[0m \u001b[43m    \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mbias\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    184\u001b[0m \u001b[43m    \u001b[49m\u001b[43mbn_training\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    185\u001b[0m \u001b[43m    \u001b[49m\u001b[43mexponential_average_factor\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    186\u001b[0m \u001b[43m    \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43meps\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    187\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n",
            "File \u001b[0;32m/opt/conda/lib/python3.10/site-packages/torch/nn/functional.py:2482\u001b[0m, in \u001b[0;36mbatch_norm\u001b[0;34m(input, running_mean, running_var, weight, bias, training, momentum, eps)\u001b[0m\n\u001b[1;32m   2479\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m training:\n\u001b[1;32m   2480\u001b[0m     _verify_batch_size(\u001b[38;5;28minput\u001b[39m\u001b[38;5;241m.\u001b[39msize())\n\u001b[0;32m-> 2482\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mtorch\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mbatch_norm\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m   2483\u001b[0m \u001b[43m    \u001b[49m\u001b[38;5;28;43minput\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mweight\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mbias\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mrunning_mean\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mrunning_var\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtraining\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmomentum\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43meps\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtorch\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mbackends\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcudnn\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43menabled\u001b[49m\n\u001b[1;32m   2484\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n",
            "\u001b[0;31mOutOfMemoryError\u001b[0m: CUDA out of memory. Tried to allocate 256.00 MiB. GPU 0 has a total capacity of 10.75 GiB of which 159.62 MiB is free. Process 2737557 has 10.59 GiB memory in use. Of the allocated memory 10.03 GiB is allocated by PyTorch, and 385.04 MiB is reserved by PyTorch but unallocated. If reserved but unallocated memory is large try setting PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True to avoid fragmentation.  See documentation for Memory Management  (https://pytorch.org/docs/stable/notes/cuda.html#environment-variables)"
          ]
        }
      ],
      "source": [
        "\n",
        "for idx, (pixel_values,masks) in enumerate(train_loaders):\n",
        "# for idx, (pixel_values,masks) in enumerate(val_loaders):\n",
        "    pixel_values = pixel_values.to(DEVICE)\n",
        "    pixel_values = pixel_values.half()\n",
        "    print(idx, pixel_values.shape)\n",
        "    preds = torch.sigmoid(model(pixel_values))\n",
        "    predicted_mask = (preds > 0.5).float()\n",
        "    masks = masks.cpu().numpy()\n",
        "    break # only one batch\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 58,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1t7MJWLiSoFC",
        "outputId": "8cac0a06-c1b2-4d54-f838-b79f81ca377b"
      },
      "outputs": [
        {
          "ename": "NameError",
          "evalue": "name 'predicted_mask' is not defined",
          "output_type": "error",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
            "Cell \u001b[0;32mIn[58], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mpredicted_mask.shape\u001b[39m\u001b[38;5;124m\"\u001b[39m,\u001b[43mpredicted_mask\u001b[49m\u001b[38;5;241m.\u001b[39mshape)\n\u001b[1;32m      2\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mmasks.shape\u001b[39m\u001b[38;5;124m\"\u001b[39m,masks\u001b[38;5;241m.\u001b[39mshape)\n",
            "\u001b[0;31mNameError\u001b[0m: name 'predicted_mask' is not defined"
          ]
        }
      ],
      "source": [
        "print(\"predicted_mask.shape\",predicted_mask.shape)\n",
        "print(\"masks.shape\",masks.shape)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "Sth5I5x9R-xu",
        "outputId": "f91830f7-a8b5-41c7-f8ec-57b23c793cab"
      },
      "outputs": [],
      "source": [
        "\n",
        "\n",
        "\n",
        "n_plots = 8\n",
        "from matplotlib import pyplot as plt\n",
        "f, axarr = plt.subplots(n_plots,2)\n",
        "f.set_figheight(15)\n",
        "f.set_figwidth(15)\n",
        "for i in range(n_plots):\n",
        "    axarr[i,0].imshow(prediction_to_vis(predicted_mask[i,:,:].cpu()))\n",
        "    axarr[i,1].imshow(prediction_to_vis(masks[i,:,:]))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 26,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 529
        },
        "id": "HxpXpZQ5Nmjq",
        "outputId": "85a9ef9d-b393-40a6-9b6c-cea17ffc58dd"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<PIL.Image.Image image mode=RGBA size=1024x1440>"
            ]
          },
          "execution_count": 26,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "#Predict on a test image and overlay the mask on the original image\n",
        "ds = train_ds\n",
        "test_idx = 0\n",
        "input_image_file = os.path.join(ds.root_dir,ds.images[test_idx])\n",
        "input_image = Image.open(input_image_file)\n",
        "test_batch = ds[test_idx]\n",
        "images, masks = test_batch\n",
        "images = torch.unsqueeze(images, 0)\n",
        "masks = torch.unsqueeze(masks, 0)\n",
        "images= images.to(DEVICE)\n",
        "# outputs = model(images.half())\n",
        "outputs = model(images)\n",
        "preds = torch.sigmoid(outputs)\n",
        "predicted_mask = (preds > 0.5).float()\n",
        "\n",
        "\n",
        "mask = prediction_to_vis(predicted_mask[test_idx,:,:].cpu())\n",
        "mask = mask.resize(input_image.size)\n",
        "mask = mask.convert(\"RGBA\")\n",
        "input_image = input_image.convert(\"RGBA\")\n",
        "overlay_img = Image.blend(input_image, mask, 0.5)\n",
        "overlay_img"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "QXfIs7RvVOam"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": []
    }
  ],
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