Image_Inpainting.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "# **Image Inpainting for Irregular Holes Using Partial Convolutions**\n"
      ],
      "metadata": {
        "id": "M2zg70vdAoiQ"
      }
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "JbY1ZFG7TTmx"
      },
      "source": [
        "# **Importing Libraries**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "iMqxpt4BStRA"
      },
      "outputs": [],
      "source": [
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import pandas as pd\n",
        "import torch\n",
        "import torchvision\n",
        "import torchvision.transforms as transforms\n",
        "import torch.nn as nn\n",
        "import torch.nn.functional as F\n",
        "import torch.optim as optim\n",
        "from torchvision import models\n",
        "import copy\n",
        "import cv2\n",
        "from PIL import Image\n",
        "import os\n",
        "import seaborn as sns"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Zej32gl0Ta6Q"
      },
      "source": [
        "# **Data Visualization**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-SLff5ULTNBN",
        "outputId": "281a912a-14c1-4a5c-c94d-d2cf52e43b24"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "cpu\n"
          ]
        }
      ],
      "source": [
        "device = torch.device(\"cuda:0\" if torch.cuda.is_available() else \"cpu\")\n",
        "print(device)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "vb1VTMOTTozH"
      },
      "outputs": [],
      "source": [
        "transform_train = transforms.Compose([transforms.ToTensor(),\n",
        "                                      transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5)),\n",
        "])\n",
        "\n",
        "transform_test = transforms.Compose([transforms.ToTensor(),\n",
        "                                      transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5)),\n",
        "])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cFQCvSmMTsLr",
        "outputId": "e6d1748a-8728-44e0-ffc6-046b7172d8d6"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Downloading https://www.cs.toronto.edu/~kriz/cifar-10-python.tar.gz to ./data/cifar-10-python.tar.gz\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 170498071/170498071 [00:03<00:00, 47804256.05it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Extracting ./data/cifar-10-python.tar.gz to ./data\n",
            "Files already downloaded and verified\n"
          ]
        }
      ],
      "source": [
        "trainset = torchvision.datasets.CIFAR10(root = './data', train = True, download = True, transform = transform_train)\n",
        "testset = torchvision.datasets.CIFAR10(root = './data', train = False, download = True, transform = transform_test)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "U5dJ9gkrUfJO",
        "outputId": "8196b036-0078-4030-a51f-4a5945352c58"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Dataset CIFAR10\n",
            "    Number of datapoints: 50000\n",
            "    Root location: ./data\n",
            "    Split: Train\n",
            "    StandardTransform\n",
            "Transform: Compose(\n",
            "               ToTensor()\n",
            "               Normalize(mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5))\n",
            "           )\n"
          ]
        }
      ],
      "source": [
        "print(trainset)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "TI_sA32EUmOe",
        "outputId": "9df968f6-c438-442d-8e64-b9a79551ed47"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Dataset CIFAR10\n",
            "    Number of datapoints: 10000\n",
            "    Root location: ./data\n",
            "    Split: Test\n",
            "    StandardTransform\n",
            "Transform: Compose(\n",
            "               ToTensor()\n",
            "               Normalize(mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5))\n",
            "           )\n"
          ]
        }
      ],
      "source": [
        "print(testset)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "jqCTy7cCUpFy"
      },
      "outputs": [],
      "source": [
        "trainloader = torch.utils.data.DataLoader(trainset, batch_size=4, shuffle=True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Wwho8AkZUrZg",
        "outputId": "615b8823-9e67-428d-c46d-6bdfb4248900"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "12500\n"
          ]
        }
      ],
      "source": [
        "print(len(trainloader))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "WBSE-dSkUvH0",
        "outputId": "f6b7c48f-ee20-4f2f-e7e3-327abad8174c"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "torch.Size([4, 3, 32, 32])\n",
            "torch.Size([3, 32, 32])\n",
            "6\n"
          ]
        }
      ],
      "source": [
        "dataiter = iter(trainloader)\n",
        "images, labels = next(dataiter)\n",
        "\n",
        "print(images.shape)\n",
        "\n",
        "print(images[1].shape)\n",
        "print(labels[1].item())"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "U4j7HNQ5VGlN",
        "outputId": "f5de19b4-73b8-4b57-e53e-3ea5f1708486"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'torch.Tensor'>\n"
          ]
        }
      ],
      "source": [
        "img = images[1]\n",
        "print(type(img))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "RwWRyS8AVJGG",
        "outputId": "611f7637-dafb-4216-ce6c-913e9c31191f"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(3, 32, 32)\n",
            "(32, 32, 3)\n"
          ]
        }
      ],
      "source": [
        "npimg = img.numpy()\n",
        "print(npimg.shape)\n",
        "npimg = np.transpose(npimg, (1,2,0))\n",
        "print(npimg.shape)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "kps_cifUVNAx"
      },
      "outputs": [],
      "source": [
        "batch_size = 4"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "lw_wQpk3VPmD"
      },
      "outputs": [],
      "source": [
        "def imshow(img, title):\n",
        "  npimg = img.numpy() / 2 + 0.5\n",
        "  plt.figure(figsize = (batch_size, 1))\n",
        "  plt.axis('off')\n",
        "  plt.imshow(np.transpose(npimg, (1,2,0)))\n",
        "  plt.title(title)\n",
        "  plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 159
        },
        "id": "huwpih0oVR4A",
        "outputId": "0e86287d-7b06-4815-9aa8-0149acf09cfb"
      },
      "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": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 100x100 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "plt.figure(figsize = (1,1))\n",
        "plt.imshow(npimg)\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "n6K1mKkyVWQE"
      },
      "outputs": [],
      "source": [
        "def show_batch_images(dataloader):\n",
        "  images, labels = next(iter(dataloader))\n",
        "  img = torchvision.utils.make_grid(images)\n",
        "  imshow(img, title = [str(x.item()) for x in labels])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 493
        },
        "id": "-eITv3z9Vawp",
        "outputId": "d6f4922a-6342-44e8-d096-96c0b918027e"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 400x100 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 400x100 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 400x100 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 400x100 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "for i in range(4):\n",
        "  show_batch_images(trainloader)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9sJ2NPhGVnH2"
      },
      "source": [
        "# **Data Pre Processing**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "0XxTnmWRVtly"
      },
      "outputs": [],
      "source": [
        "image_dims = (32, 32)\n",
        "batch_size = 4"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "NJ6a_yOvVvjC"
      },
      "outputs": [],
      "source": [
        "def imshow_masked(img):\n",
        "  npimg = img.numpy()\n",
        "  plt.figure(figsize = (3, 3))\n",
        "  plt.axis('off')\n",
        "  plt.imshow(np.transpose(npimg, (1,2,0)))\n",
        "  plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "kqyyupunVw6b"
      },
      "outputs": [],
      "source": [
        "class Dataset(torchvision.datasets.CIFAR10):\n",
        "  def __init__(self, path, image_dims = (32, 32), n_channels = 3, train = True):\n",
        "    super().__init__(path, train, download = True)\n",
        "    self.n_classes = 10\n",
        "    self.image_dims = image_dims\n",
        "    self.n_channels = n_channels\n",
        "    self.resize = transforms.Resize(image_dims)\n",
        "    self.to_tensor = transforms.ToTensor()\n",
        "\n",
        "  def __getitem__(self, index):\n",
        "    image, label = super().__getitem__(index)\n",
        "\n",
        "    Y_output = self.resize(image)\n",
        "    X_input, X_mask = self.Create_Mask(Y_output)\n",
        "    return self.to_tensor(X_input), self.to_tensor(X_mask), self.to_tensor(Y_output)\n",
        "\n",
        "  def Create_Mask(self, image):\n",
        "    mask = np.full((self.image_dims[0], self.image_dims[1], self.n_channels), 255, dtype = np.uint8)\n",
        "\n",
        "    for _ in range(np.random.randint(1, 10)):\n",
        "      x1, x2 = np.random.randint(1, self.image_dims[0]), np.random.randint(1, self.image_dims[0])\n",
        "      y1, y2 = np.random.randint(1, self.image_dims[1]), np.random.randint(1, self.image_dims[1])\n",
        "      thickness = np.random.randint(1, 3)\n",
        "\n",
        "      cv2.line(mask, (x1, y1), (x2, y2), (1, 1, 1), thickness)\n",
        "\n",
        "    image = np.asarray(image)\n",
        "    masked_image = cv2.bitwise_and(image, mask)\n",
        "    return masked_image, mask"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LnJq8bZFV3T1",
        "outputId": "b1d50a24-3e04-4b5c-9c5b-14b9009d83e1"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Files already downloaded and verified\n",
            "Files already downloaded and verified\n"
          ]
        }
      ],
      "source": [
        "Trainset = Dataset('./data', image_dims)\n",
        "Testset = Dataset('./data', image_dims, train=False)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "9F6oh13WV6da"
      },
      "outputs": [],
      "source": [
        "Trainloader = torch.utils.data.DataLoader(Trainset, batch_size = batch_size, shuffle = True)\n",
        "Testloader = torch.utils.data.DataLoader(Testset, batch_size = batch_size, shuffle = False)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "5jPiMcCxV8X2"
      },
      "outputs": [],
      "source": [
        "Train_dataiter = iter(Trainloader)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "yaSkFFDLV-Hc"
      },
      "outputs": [],
      "source": [
        "Test_dataiter = iter(Testloader)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "s2paSAy0WAHk",
        "outputId": "1f65b465-8e63-4917-ad97-002de5051746"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "torch.Size([4, 3, 32, 32]) torch.Size([4, 3, 32, 32]) torch.Size([4, 3, 32, 32])\n"
          ]
        }
      ],
      "source": [
        "X_in, X_mask, Y_out = next(Train_dataiter)\n",
        "print(X_in.shape, X_mask.shape, Y_out.shape)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 309
        },
        "id": "FDUBtmMDWCAq",
        "outputId": "8873b952-23d0-4b0a-af94-2e085c31efa4"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Masked Image\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 300x300 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Masking Pattern\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 300x300 with 1 Axes>"
            ],
            "image/png": 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F8Lt37+L69esAmqYgy5Yta1W+1PWemZmZWLVqFdLT0zWK67///W+L97S5XC42bNhATwc8PT210qOXlZVh9+7d9HrAu4wYMQJLlixRSGfIkCE4cOAAXr9+jfDwcKxdu1ZnbqIePXqEgwcPYtasWZg8ebJO0qivr8e2bdtga2tL+1lgs9no37+/2vKV/7Z8+XJMmTIFQJPpuHynycjICOvXr8eIESM6ZldD07ceWtHD19fXE5FIREQiEUlLSyOGhobNnkQyNTUl8fHxKuOpqKgg7u7uzT5PURT54osviEgk0rhX/sMf/kBMTEyIiYkJmT9/PpHJZG3yWquqh+dyucTExETpra3Nq1+/fiQ3N5cuZ5FIRKqqqpR6Dzkf6uFlMhmpqakh2dnZxMzMTGWas2bNIpWVlQppyq9Vq1YRQ0NDkpqaSofV19drXI6akJCQQNhsNomKilL5e0t7eKlUSqqqqmh5ly5dSo8i/f392+y9+PTp03RbGzBgAHnx4oXK+97v4Y2MjMi4ceOaPTXZ6Xr4o0eP4vDhwwCa5t/Nea0NCAjAjh07mu0RjI2NcebMGSQnJ2P58uUqDUe+//57XLp0CceOHcPIkSPVykVRFL766iusXbsWAGBiYtKSbGnM1KlTsXv3bkRGRuL8+fM6SaO0tBTTpk1TWPAzMzPDqVOnWtXDEkLw+eefIzk5udn5eGJiYrPuuUpKSlBfX4+QkBB6ZyQsLAyrVq1qsSztRUVFBYKCgmgPRcXFxTAyMsLJkyfh6uqq1pmLJkyZMoU+RMZms9G3b98PPsPlcvHdd99BIBBodYdJJwovEomQk5ODrKwsPHz4UOU9rq6uMDc3B9BkOefi4tJsfGw2Gx999BGEQiG8vLzw7NkzpZX9N2/eQCgU0gXr7u6utqLawyspn8/HkCFDMGrUKJSVlSE7O1vJDrutSCQS2jOwHFNTU6SmpsLFxQWurq4tHjqamprC3NwcL168UPl7VVVVs/Uq511HEllZWbh27ZrG6Ts7O6u1MDMzM4OXl1eb6rC0tBSPHz8G0HQ2wNDQEJaWlgAAS0tL6Ovro6GhASKRqNVpyOHz+WqtJKVSKbKzs5GTk0Mb31AUBQcHB+1/JETTYQlaMKRPTU0lhoaGhM1mN+v44sKFC0QsFhOxWEwkEolGMkilUiIWi0l4eHizQ1wOh0OGDx9OqqqqNM0ajbaH9PJFO4lEQoqKikj//v11NrRXNZ0ICAhQGtprMqRvbGwkjx49Iubm5lqRhc1mEy6Xq/F1+vRptWUtbwfNtRtNhvQ//PADnZ6NjQ3Jy8uj26NYLCZv374lrq6uxNfXV+cfJKmqqiJubm6Ew+HQZaavr0/S09PVPtehQ/qLFy/SvWt+fj4aGhpUDr1Hjx4Nf39/DB48uMUnrFgsFlgsFvz9/WFkZITvvvtOwVEk0PS2fP36Nb7++muMHz9eabFOV4waNQpbt25FXFwcnjx5AqDp9NvWrVvxySefqD3ea29vj8WLFyMpKYnuCa2trbF06VK1e/MnT57Eo0ePlMJ5PB6WLVsGgUDQ4t6doihwOBz07t0bkZGRKqdh9+/fx5kzZzSOU51TU1WcPn0a9+7dUwo3MDDAsmXLYGlp2WKLPkKIQt1QFIXNmzcDaFpIs7CwUGiPHA4HUqm0Xc4JEEIgkUja50yCpm8hfKCH//Of/6y2t9HT0yN6enpk7dq1miaplpqaGiIQCNQuiK1cuZI0NDQ0u4D1Pm391JRMJiMzZsxQkiM2NpaUlpY228OPHz+e1NTUkI0bN9LlNGrUKLUurmQyGZkzZw59/7uXtbU1ycvLU/mcNrblzp49qzLd9y9tL1by+Xxy//59IhaL1X62TFUPL5VKyaxZs2jZQkND1eaxsrKSODs7t8snx0QiEXFxcVHIa6fv4ZuDw+HgwIEDGDZsGABotGChCfr6+oiOjkZGRgZWrlypdKIOAH788UdkZ2cjKioKY8eO1Uq6uiAnJweTJ0/GtGnTaJNhIyOjDy7WbN++HeHh4UrhHA5Ha+WsCm9vbyXTZlXcu3cPq1atUlk3raGmpgYhISHw8vLC3/72txYtplEUhZ07d9Jmzdo+hdZV0JrC9+3bFy4uLnj27Bm9MGVlZQUbGxt4enqqtIJrC2w2G8OHDwdFURg2bBgKCwuVvuxSXFyMkpISZGdnw9LSEoMGDepUvsVZLBYGDRoEfX19VFdXw9raWuMXE0VRao/a6pJevXpp9OkuIyMjDB06FEVFRSgtLYWDg4NG5/lfvXqlcrGMEILq6mqVZsofgqIoDBkypMXPdTe01vpXr16N0NBQ+Pr60qazS5YswYYNG3TqcNLV1RXJycmIiopSeSiE/OYD38HBASkpKfRKbGfA0NAQcXFx9IEKbR2Q6CwMHToUKSkp2LVrF6KionDkyBGMHj36g88tXrwY8fHxSuHGxsY4deoUBg8e3CNO5ekCrSm8/PRPcHAwioqKADT5pdP111/YbDaMjY0xduxYrFixAvHx8Xj9+rXCPfX19SgpKUF0dDR9VLU9SUhIQH5+vsKZgQkTJkAgEKBfv37t8oWcjkBeN15eXqiqqsKtW7dQWlqKoKCgZhdsyW8eilWdczAwMICNjU2n8FjcVdHq+FZfX5/+plt74+fnB19fX9y9e5deuSfvOIAoLy/Hxo0bsXDhQvj6+tLh1G8nybSFPL530373o5pygoKCsHLlSq2l25nx8/PDpEmT4Ovri8rKSsycObNZhacoCp9++ik+/fTTdpaydcjrmbyzf67N9qRtOs+EVguw2Wzs2rWLPpF0/vx5JccaSUlJtDttPp+Pv//971r9lPSXX36J2bNnIzw8HJWVlVqLl6FzIpPJsHnzZvqkpL+/f6d+kXcrhWexWAomn2VlZbh48SJKS0vp/eTCwkLaSs/S0hK5ubmQyWRtXrWtqqpCeXk5evfuDScnpzabY3Y3qN/O43O5XOTn58PKyor2FddVqaysxNu3b5GcnEzboJibmyMgIAC9e/fulI5AuvXKx7x585CRkQEvLy+Vv5eXlyMgIAARERFtTuvs2bMQCAQQCAQqvdb2dDgcDo4ePYodO3Zg6tSp2Lt3b0eL1GaOHz+O0aNHIysriw77z3/+07O91nYkPB4PBgYG+Pjjj2FoaIhLly4pOMwkhKC8vBwPHjxAXFwccnNzNY5bJpMhMTGRdnIg93IDNO0XX7p0SaXdvI2NDby9veHk5NTG3HUtKIqCmZkZ7OzsMHnyZFAUhbi4OPj4+HSJj4xkZ2fTu08WFhaYMmUKamtrlRxZ1NfXo76+Xq1j1g/h5uYGd3d3newodWuFB5oaWkREBIKCgvC73/2Ont+/y61btxASEqL0SSF1yGQyfP311/SbPCwsjF6cy8/Ph0AgUDi/L2fEiBE4ceJEp17Y0SV2dnY4evQoDhw4gJCQEJw/f75LKPypU6ewe/duAE3ekiZOnKiztObMmYNNmzbppI10e4UHmpS+V69eOHToEBITExEdHd3mOFksFiIjI+nV5MGDB9MVZGlpiQMHDiApKanNrqa7I13xZRcUFEQb7lRVVSEsLEzp24AAMHHiRISGhsLDw0OjeGNjY3Hx4kV6K1uOrsqoRyg80GS0MW/ePABN86zq6mp62MXlcmFiYoLq6mqNj6+yWCxMnTpV5W9GRkaYO3cuGhsbGYVvBgMDA1haWqK+vh5CoRB8Pr9TG9N4eHhgxIgRqKysxK1btxAREaGyrTg5OSEkJETjeDMzMzVyda0tOm8J64hp06YhMzNTQVm9vb2RkZGBTz75pAMl61nMmzcPaWlpOHXqFGbNmqWVc+e6RiwWY/HixVi0aFGb5ugdSY/p4eWYmJjQlnnyShs9ejQcHBy05vmmrq4OaWlpCkM+LpeLMWPGaDzU6+7w+XyYmJjA0dERBgYGnW4bk8PhwMvLC+bm5qAoCk+ePMHjx4/x8OFDpeG3QCCgtxjVOXLpDPQ4hQea5kfr1q2jT07Jw7RFWVkZgoODFQ7z8Pl8xMTEfNAJYk+C+s3VmPzvzgSPx6OnYxRFISYmBrt371Za2GWz2fjmm2/g4+PTAVK2nB6p8AB0Pl+UyWRKjUPuwIOhic5shvq+bISQZndxKIrqMvXaNaTsYlAUBR6PR59+09PTA4/H67SNm+HDcLlcpTrkcDjg8Xhtmo68Gy+LxQKPx9PpEW5G4XVAnz59cOHCBfzlL38BAGzZsgW//PIL+vTp08GSMbSWsLAwXL16VcEHQUhICK5fv96mdZk1a9bgypUrcHBwwPDhw3Ht2jWdHhzqsUN6XaKnpwdXV1cUFBTA09MTHh4eKj9vzdB16Nu3LywtLTFq1CiYmpoCAEaOHAk3N7c2xduvXz+Ym5tDIBCAz+fDzc1Npz08o/A6ZPLkyfDx8elUXnYYWg+Xy8WRI0fouby2dhZ4PB6OHz8OiqJ0vlvBtEQdwmazO912k67Iy8vDyZMnFRa2+vXrh5CQkG5TBhRF6cQrka7iVQWj8Axa4dmzZ/jqq68U3FF7enpi7ty5GjVmNpvNLGq2A4zCM2iFkSNH4n//+59CD19QUIDAwMAP+lu3trbGkSNH6Lkxg+5gFJ5BK1hYWMDb21shLDs7G6WlpWhsbFT7LCEET548UfDtx+FwYG9v3+KPlTCoh1F4Bp0xdOhQXL9+/YPHjvPz8xEQEICKigo6zMrKClevXu0SR2e7Eq1S+KtXr3bZwwPqePcDiUVFRYiOju4yFlSaInfpBTRZA/7000+4c+dOh8r05s0bvHnzBjU1NXQYIQSxsbEwMzNrcXxSqVThE2T379/vlqcWW/KBTjkU0dDrA7OgwsDQudFElbtX98XAwKAWRuEZGHoQGs/hW+LvjYGBoXPC9PAMDD0IRuEZGHoQjMIzMPQgGIVnYOhBMArPwNCDYBSegaEHwSg8A0MPglF4BoYeBKPwDAw9iP8D3myK9c+5nNUAAAAASUVORK5CYII=\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Original Image\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 300x300 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "print('Masked Image')\n",
        "imshow_masked(torchvision.utils.make_grid(X_in))\n",
        "print('Masking Pattern')\n",
        "imshow_masked(torchvision.utils.make_grid(X_mask))\n",
        "print('Original Image')\n",
        "imshow_masked(torchvision.utils.make_grid(Y_out))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "90e-v1TaWFNG"
      },
      "source": [
        "# **Training**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "5iw4b5yiWDyt"
      },
      "outputs": [],
      "source": [
        "class PartialConv(nn.Module):\n",
        "    def __init__(self, in_filters, out_filters, kernel_size, strides=1, padding=0, dilation=1, groups=1, bias=True):\n",
        "        super(PartialConv, self).__init__()\n",
        "\n",
        "        padding = (kernel_size[0] // 2, kernel_size[1] // 2)\n",
        "        self.input_conv = nn.Conv2d(in_filters, out_filters, kernel_size, strides, padding, bias=bias)\n",
        "        self.mask_conv = nn.Conv2d(in_filters, out_filters, kernel_size, strides, padding, bias=False)\n",
        "\n",
        "        self.window_size = kernel_size[0] * kernel_size[1]\n",
        "\n",
        "        nn.init.constant_(self.mask_conv.weight, 1.0)\n",
        "\n",
        "        for param in self.mask_conv.parameters():\n",
        "            param.requires_grad = False\n",
        "\n",
        "    def forward(self, input, mask):\n",
        "        output = self.input_conv(input * mask)\n",
        "        if self.input_conv.bias is not None:\n",
        "            output_bias = self.input_conv.bias.view(1, -1, 1, 1).expand_as(output)\n",
        "        else:\n",
        "            output_bias = torch.zeros_like(output)\n",
        "\n",
        "        with torch.no_grad():\n",
        "            output_mask = self.mask_conv(mask)\n",
        "\n",
        "        mask_ratio = self.window_size / (output_mask + 1e-8)\n",
        "\n",
        "        output_mask = torch.clamp(output_mask, 0, 1)\n",
        "\n",
        "        mask_ratio = mask_ratio * output_mask\n",
        "\n",
        "        output = output * mask_ratio\n",
        "\n",
        "        output = output + output_bias\n",
        "\n",
        "        return output, output_mask"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "eIU8Tum-WSv-"
      },
      "outputs": [],
      "source": [
        "class EncoderLayer(nn.Module):\n",
        "    def __init__(self, in_filters, out_filters):\n",
        "        super(EncoderLayer, self).__init__()\n",
        "        self.p_conv_1 = PartialConv(in_filters, out_filters, (3, 3), strides=1)\n",
        "        self.act_1 = nn.ReLU()\n",
        "        self.p_conv_2 = PartialConv(out_filters, out_filters, (3, 3), strides=2)\n",
        "        self.act_2 = nn.ReLU()\n",
        "\n",
        "    def forward(self, inputs, masks):\n",
        "        conv_out_1, mask_out_1 = self.p_conv_1(inputs, masks)\n",
        "        conv_out_1 = self.act_1(conv_out_1)\n",
        "\n",
        "        conv_out_2, mask_out_2 = self.p_conv_2(conv_out_1, mask_out_1)\n",
        "        conv_out_2 = self.act_2(conv_out_2)\n",
        "\n",
        "        return conv_out_1, mask_out_1, conv_out_2, mask_out_2"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "toLXtTBxWWsk"
      },
      "outputs": [],
      "source": [
        "class DecoderLayer(nn.Module):\n",
        "    def __init__(self, in_filters_1, in_filters_2):\n",
        "        super(DecoderLayer, self).__init__()\n",
        "\n",
        "        self.up_image = nn.Upsample(scale_factor=(2, 2))\n",
        "        self.up_mask = nn.Upsample(scale_factor=(2, 2))\n",
        "\n",
        "        self.p_conv_1 = PartialConv(in_filters_1 * 2, in_filters_1, (3, 3))\n",
        "        self.act_1 = nn.LeakyReLU(negative_slope=0.2)\n",
        "        self.p_conv_2 = PartialConv(in_filters_1, in_filters_2, (3, 3))\n",
        "        self.act_2 = nn.LeakyReLU(negative_slope=0.2)\n",
        "\n",
        "    def forward(self, in_image, in_mask, share_image, share_mask):\n",
        "        up_out = self.up_image(in_image)\n",
        "        mask_out = self.up_mask(in_mask)\n",
        "\n",
        "        concat_image = torch.cat([share_image, up_out], axis=1)\n",
        "        concat_mask = torch.cat([share_mask, mask_out], axis=1)\n",
        "\n",
        "        conv_out_1, mask_out_1 = self.p_conv_1(concat_image, concat_mask)\n",
        "        conv_out_1 = self.act_1(conv_out_1)\n",
        "\n",
        "        conv_out_2, mask_out_2 = self.p_conv_2(conv_out_1, mask_out_1)\n",
        "        conv_out_2 = self.act_2(conv_out_2)\n",
        "\n",
        "        return conv_out_1, mask_out_1, conv_out_2, mask_out_2"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "peSJLjqhWax4"
      },
      "outputs": [],
      "source": [
        "class InpaintingModel(nn.Module):\n",
        "    def __init__(self, input_dims=(32, 32), num_channels=3):\n",
        "        super(InpaintingModel, self).__init__()\n",
        "\n",
        "        self.encoder_1 = EncoderLayer(num_channels, 32)\n",
        "        self.encoder_2 = EncoderLayer(32, 64)\n",
        "        self.encoder_3 = EncoderLayer(64, 128)\n",
        "        self.encoder_4 = EncoderLayer(128, 256)\n",
        "\n",
        "        self.decoder_1 = DecoderLayer(256, 128)\n",
        "        self.decoder_2 = DecoderLayer(128, 64)\n",
        "        self.decoder_3 = DecoderLayer(64, 32)\n",
        "        self.decoder_4 = DecoderLayer(32, 3)\n",
        "\n",
        "        self.output_layer = nn.Sequential(\n",
        "            nn.Conv2d(3, 3, (3, 3), padding=1),\n",
        "            nn.Sigmoid()\n",
        "        )\n",
        "\n",
        "    def forward(self, inputs, masks):\n",
        "        conv_out_1, mask_out_1, conv_out_2, mask_out_2 = self.encoder_1(inputs, masks)\n",
        "        conv_out_3, mask_out_3, conv_out_4, mask_out_4 = self.encoder_2(conv_out_2, mask_out_2)\n",
        "        conv_out_5, mask_out_5, conv_out_6, mask_out_6 = self.encoder_3(conv_out_4, mask_out_4)\n",
        "        conv_out_7, mask_out_7, conv_out_8, mask_out_8 = self.encoder_4(conv_out_6, mask_out_6)\n",
        "\n",
        "        conv_out_9, mask_out_9, conv_out_10, mask_out_10 = self.decoder_1(conv_out_8, mask_out_8, conv_out_7, mask_out_7)\n",
        "        conv_out_11, mask_out_11, conv_out_12, mask_out_12 = self.decoder_2(conv_out_6, mask_out_6, conv_out_5, mask_out_5)\n",
        "        conv_out_13, mask_out_13, conv_out_14, mask_out_14 = self.decoder_3(conv_out_4, mask_out_4, conv_out_3, mask_out_3)\n",
        "        conv_out_15, mask_out_15, conv_out_16, mask_out_16 = self.decoder_4(conv_out_2, mask_out_2, conv_out_1, mask_out_1)\n",
        "\n",
        "        outputs = self.output_layer(conv_out_16)\n",
        "        return outputs"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "vuu2J_G3Wc5S"
      },
      "outputs": [],
      "source": [
        "def jaccard_coeff(dataloader):\n",
        "  for (X_input, X_mask, Y_output) in dataloader:\n",
        "    X_input, X_mask, Y_output = X_input.to(device), X_mask.to(device), Y_output.to(device)\n",
        "    outputs = model(X_input, X_mask)\n",
        "    eps = 1e-15\n",
        "    outputs = outputs.view(-1)\n",
        "    Y_output = Y_output.view(-1)\n",
        "    intersection = (outputs * Y_output).sum()\n",
        "    union = (outputs.sum() + Y_output.sum()) - intersection\n",
        "  return (intersection / (union + eps))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "gEBQahS5WfBF"
      },
      "outputs": [],
      "source": [
        "model = InpaintingModel()\n",
        "model = model.to(device)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "W-mcIi4XWjg0"
      },
      "outputs": [],
      "source": [
        "loss_fn = nn.MSELoss()\n",
        "opt = optim.Adam(model.parameters())"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 511
        },
        "id": "8WGu5hBMWm_8",
        "outputId": "e66639da-c679-4270-c3b3-a427e132a8d9"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Min loss 0.00\n",
            "Iteration: 0/12500, Loss: 0.00\n",
            "Min loss 0.00\n",
            "Min loss 0.00\n",
            "Iteration: 100/12500, Loss: 0.00\n"
          ]
        },
        {
          "output_type": "error",
          "ename": "KeyboardInterrupt",
          "evalue": "",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
            "\u001b[0;32m<timed exec>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/torch/nn/modules/module.py\u001b[0m in \u001b[0;36m_wrapped_call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1516\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_compiled_call_impl\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m  \u001b[0;31m# type: ignore[misc]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1517\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1518\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_call_impl\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1519\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1520\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_call_impl\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/torch/nn/modules/module.py\u001b[0m in \u001b[0;36m_call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1525\u001b[0m                 \u001b[0;32mor\u001b[0m \u001b[0m_global_backward_pre_hooks\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0m_global_backward_hooks\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1526\u001b[0m                 or _global_forward_hooks or _global_forward_pre_hooks):\n\u001b[0;32m-> 1527\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mforward_call\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1528\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1529\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m<ipython-input-29-629a1cb2ebf0>\u001b[0m in \u001b[0;36mforward\u001b[0;34m(self, inputs, masks)\u001b[0m\n\u001b[1;32m     19\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     20\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mforward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minputs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmasks\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 21\u001b[0;31m         \u001b[0mconv_out_1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmask_out_1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mconv_out_2\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmask_out_2\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mencoder_1\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minputs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmasks\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     22\u001b[0m         \u001b[0mconv_out_3\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmask_out_3\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mconv_out_4\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmask_out_4\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mencoder_2\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mconv_out_2\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmask_out_2\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     23\u001b[0m         \u001b[0mconv_out_5\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmask_out_5\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mconv_out_6\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmask_out_6\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mencoder_3\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mconv_out_4\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmask_out_4\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/torch/nn/modules/module.py\u001b[0m in \u001b[0;36m_wrapped_call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1516\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_compiled_call_impl\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m  \u001b[0;31m# type: ignore[misc]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1517\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1518\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_call_impl\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1519\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1520\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_call_impl\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/torch/nn/modules/module.py\u001b[0m in \u001b[0;36m_call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1525\u001b[0m                 \u001b[0;32mor\u001b[0m \u001b[0m_global_backward_pre_hooks\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0m_global_backward_hooks\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1526\u001b[0m                 or _global_forward_hooks or _global_forward_pre_hooks):\n\u001b[0;32m-> 1527\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mforward_call\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1528\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1529\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m<ipython-input-27-bcd23bf7edee>\u001b[0m in \u001b[0;36mforward\u001b[0;34m(self, inputs, masks)\u001b[0m\n\u001b[1;32m     11\u001b[0m         \u001b[0mconv_out_1\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mact_1\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mconv_out_1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     12\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 13\u001b[0;31m         \u001b[0mconv_out_2\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmask_out_2\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mp_conv_2\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mconv_out_1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmask_out_1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     14\u001b[0m         \u001b[0mconv_out_2\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mact_2\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mconv_out_2\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     15\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/torch/nn/modules/module.py\u001b[0m in \u001b[0;36m_wrapped_call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1516\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_compiled_call_impl\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m  \u001b[0;31m# type: ignore[misc]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1517\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1518\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_call_impl\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1519\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1520\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_call_impl\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/torch/nn/modules/module.py\u001b[0m in \u001b[0;36m_call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1525\u001b[0m                 \u001b[0;32mor\u001b[0m \u001b[0m_global_backward_pre_hooks\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0m_global_backward_hooks\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1526\u001b[0m                 or _global_forward_hooks or _global_forward_pre_hooks):\n\u001b[0;32m-> 1527\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mforward_call\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1528\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1529\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m<ipython-input-26-902c08ebd657>\u001b[0m in \u001b[0;36mforward\u001b[0;34m(self, input, mask)\u001b[0m\n\u001b[1;32m     15\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     16\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mforward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmask\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 17\u001b[0;31m         \u001b[0moutput\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0minput_conv\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minput\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0mmask\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     18\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0minput_conv\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbias\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     19\u001b[0m             \u001b[0moutput_bias\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0minput_conv\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbias\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mview\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m-\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mexpand_as\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0moutput\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/torch/nn/modules/module.py\u001b[0m in \u001b[0;36m_wrapped_call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1516\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_compiled_call_impl\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m  \u001b[0;31m# type: ignore[misc]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1517\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1518\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_call_impl\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1519\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1520\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_call_impl\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/torch/nn/modules/module.py\u001b[0m in \u001b[0;36m_call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1525\u001b[0m                 \u001b[0;32mor\u001b[0m \u001b[0m_global_backward_pre_hooks\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0m_global_backward_hooks\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1526\u001b[0m                 or _global_forward_hooks or _global_forward_pre_hooks):\n\u001b[0;32m-> 1527\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mforward_call\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1528\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1529\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/torch/nn/modules/conv.py\u001b[0m in \u001b[0;36mforward\u001b[0;34m(self, input)\u001b[0m\n\u001b[1;32m    458\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    459\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mforward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minput\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mTensor\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m->\u001b[0m \u001b[0mTensor\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 460\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_conv_forward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mweight\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbias\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    461\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    462\u001b[0m \u001b[0;32mclass\u001b[0m \u001b[0mConv3d\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0m_ConvNd\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/torch/nn/modules/conv.py\u001b[0m in \u001b[0;36m_conv_forward\u001b[0;34m(self, input, weight, bias)\u001b[0m\n\u001b[1;32m    454\u001b[0m                             \u001b[0mweight\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbias\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstride\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    455\u001b[0m                             _pair(0), self.dilation, self.groups)\n\u001b[0;32m--> 456\u001b[0;31m         return F.conv2d(input, weight, bias, self.stride,\n\u001b[0m\u001b[1;32m    457\u001b[0m                         self.padding, self.dilation, self.groups)\n\u001b[1;32m    458\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
          ]
        }
      ],
      "source": [
        "%%time\n",
        "loss_epoch_arr = []\n",
        "max_epochs = 15\n",
        "\n",
        "min_loss = 1000\n",
        "\n",
        "n_iters = np.ceil(50000/batch_size)\n",
        "for epoch in range(max_epochs):\n",
        "    model.train()\n",
        "    for i, (X_input, X_mask, Y_output) in enumerate(Trainloader, 0):\n",
        "\n",
        "        X_input, X_mask, Y_output = X_input.to(device), X_mask.to(device), Y_output.to(device)\n",
        "\n",
        "        opt.zero_grad()\n",
        "\n",
        "        outputs = model(X_input, X_mask)\n",
        "        loss = loss_fn(Y_output, outputs)\n",
        "        loss.backward()\n",
        "\n",
        "        opt.step()\n",
        "\n",
        "        if min_loss > loss.item():\n",
        "            min_loss = loss.item()\n",
        "            best_model = copy.deepcopy(model.state_dict())\n",
        "            print('Min loss %0.2f' % min_loss)\n",
        "\n",
        "        if i % 100 == 0:\n",
        "            print('Iteration: %d/%d, Loss: %0.002f' % (i, n_iters, loss.item()))\n",
        "\n",
        "        del X_input, X_mask, Y_output\n",
        "        torch.cuda.empty_cache()\n",
        "\n",
        "    loss_epoch_arr.append(loss.item())\n",
        "\n",
        "plt.plot(loss_epoch_arr)\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "ov4OxrZ7WwBS",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "9d340698-5bdd-4651-b781-99e509b98595"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<All keys matched successfully>"
            ]
          },
          "metadata": {},
          "execution_count": 34
        }
      ],
      "source": [
        "model.load_state_dict(best_model)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "yHe7wOjNWzC_",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "38cae6e4-defd-4898-e7e9-eb4172aa99f9"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "InpaintingModel(\n",
            "  (encoder_1): EncoderLayer(\n",
            "    (p_conv_1): PartialConv(\n",
            "      (input_conv): Conv2d(3, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "      (mask_conv): Conv2d(3, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
            "    )\n",
            "    (act_1): ReLU()\n",
            "    (p_conv_2): PartialConv(\n",
            "      (input_conv): Conv2d(32, 32, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1))\n",
            "      (mask_conv): Conv2d(32, 32, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n",
            "    )\n",
            "    (act_2): ReLU()\n",
            "  )\n",
            "  (encoder_2): EncoderLayer(\n",
            "    (p_conv_1): PartialConv(\n",
            "      (input_conv): Conv2d(32, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "      (mask_conv): Conv2d(32, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
            "    )\n",
            "    (act_1): ReLU()\n",
            "    (p_conv_2): PartialConv(\n",
            "      (input_conv): Conv2d(64, 64, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1))\n",
            "      (mask_conv): Conv2d(64, 64, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n",
            "    )\n",
            "    (act_2): ReLU()\n",
            "  )\n",
            "  (encoder_3): EncoderLayer(\n",
            "    (p_conv_1): PartialConv(\n",
            "      (input_conv): Conv2d(64, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "      (mask_conv): Conv2d(64, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
            "    )\n",
            "    (act_1): ReLU()\n",
            "    (p_conv_2): PartialConv(\n",
            "      (input_conv): Conv2d(128, 128, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1))\n",
            "      (mask_conv): Conv2d(128, 128, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n",
            "    )\n",
            "    (act_2): ReLU()\n",
            "  )\n",
            "  (encoder_4): EncoderLayer(\n",
            "    (p_conv_1): PartialConv(\n",
            "      (input_conv): Conv2d(128, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "      (mask_conv): Conv2d(128, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
            "    )\n",
            "    (act_1): ReLU()\n",
            "    (p_conv_2): PartialConv(\n",
            "      (input_conv): Conv2d(256, 256, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1))\n",
            "      (mask_conv): Conv2d(256, 256, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n",
            "    )\n",
            "    (act_2): ReLU()\n",
            "  )\n",
            "  (decoder_1): DecoderLayer(\n",
            "    (up_image): Upsample(scale_factor=(2.0, 2.0), mode='nearest')\n",
            "    (up_mask): Upsample(scale_factor=(2.0, 2.0), mode='nearest')\n",
            "    (p_conv_1): PartialConv(\n",
            "      (input_conv): Conv2d(512, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "      (mask_conv): Conv2d(512, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
            "    )\n",
            "    (act_1): LeakyReLU(negative_slope=0.2)\n",
            "    (p_conv_2): PartialConv(\n",
            "      (input_conv): Conv2d(256, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "      (mask_conv): Conv2d(256, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
            "    )\n",
            "    (act_2): LeakyReLU(negative_slope=0.2)\n",
            "  )\n",
            "  (decoder_2): DecoderLayer(\n",
            "    (up_image): Upsample(scale_factor=(2.0, 2.0), mode='nearest')\n",
            "    (up_mask): Upsample(scale_factor=(2.0, 2.0), mode='nearest')\n",
            "    (p_conv_1): PartialConv(\n",
            "      (input_conv): Conv2d(256, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "      (mask_conv): Conv2d(256, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
            "    )\n",
            "    (act_1): LeakyReLU(negative_slope=0.2)\n",
            "    (p_conv_2): PartialConv(\n",
            "      (input_conv): Conv2d(128, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "      (mask_conv): Conv2d(128, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
            "    )\n",
            "    (act_2): LeakyReLU(negative_slope=0.2)\n",
            "  )\n",
            "  (decoder_3): DecoderLayer(\n",
            "    (up_image): Upsample(scale_factor=(2.0, 2.0), mode='nearest')\n",
            "    (up_mask): Upsample(scale_factor=(2.0, 2.0), mode='nearest')\n",
            "    (p_conv_1): PartialConv(\n",
            "      (input_conv): Conv2d(128, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "      (mask_conv): Conv2d(128, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
            "    )\n",
            "    (act_1): LeakyReLU(negative_slope=0.2)\n",
            "    (p_conv_2): PartialConv(\n",
            "      (input_conv): Conv2d(64, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "      (mask_conv): Conv2d(64, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
            "    )\n",
            "    (act_2): LeakyReLU(negative_slope=0.2)\n",
            "  )\n",
            "  (decoder_4): DecoderLayer(\n",
            "    (up_image): Upsample(scale_factor=(2.0, 2.0), mode='nearest')\n",
            "    (up_mask): Upsample(scale_factor=(2.0, 2.0), mode='nearest')\n",
            "    (p_conv_1): PartialConv(\n",
            "      (input_conv): Conv2d(64, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "      (mask_conv): Conv2d(64, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
            "    )\n",
            "    (act_1): LeakyReLU(negative_slope=0.2)\n",
            "    (p_conv_2): PartialConv(\n",
            "      (input_conv): Conv2d(32, 3, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "      (mask_conv): Conv2d(32, 3, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
            "    )\n",
            "    (act_2): LeakyReLU(negative_slope=0.2)\n",
            "  )\n",
            "  (output_layer): Sequential(\n",
            "    (0): Conv2d(3, 3, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "    (1): Sigmoid()\n",
            "  )\n",
            ")\n"
          ]
        }
      ],
      "source": [
        "print(model)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "sA07kKfoW14f"
      },
      "outputs": [],
      "source": [
        "weight_layer0 = list(model.parameters())[0].cpu()\n",
        "weight_layer0 = weight_layer0.data.numpy()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "CYE7ZBgjW3oN",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "7c8bafe4-41cc-45a6-ea0b-003c541b2090"
      },
      "outputs": [
        {
          "metadata": {
            "tags": null
          },
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "(32, 3, 3, 3)\n"
          ]
        }
      ],
      "source": [
        "print(weight_layer0.shape)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "b89V0Ob7W7hJ",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 638
        },
        "outputId": "b218f1d3-ece3-4def-9ee8-b13e2f426ed8"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-38-dfd95a8f0242>:1: UserWarning: \n",
            "\n",
            "`distplot` is a deprecated function and will be removed in seaborn v0.14.0.\n",
            "\n",
            "Please adapt your code to use either `displot` (a figure-level function with\n",
            "similar flexibility) or `histplot` (an axes-level function for histograms).\n",
            "\n",
            "For a guide to updating your code to use the new functions, please see\n",
            "https://gist.github.com/mwaskom/de44147ed2974457ad6372750bbe5751\n",
            "\n",
            "  sns.distplot(weight_layer0.ravel())\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[]"
            ]
          },
          "metadata": {},
          "execution_count": 38
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "sns.distplot(weight_layer0.ravel())\n",
        "plt.plot()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "6voUj20MW-mr",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 807
        },
        "outputId": "6287e9cb-433c-4f4f-a4d6-7ab23dd6139e"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x800 with 16 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "sample_inputs, sample_masks, sample_truths = next(iter(Train_dataiter))\n",
        "\n",
        "model.eval()\n",
        "with torch.no_grad():\n",
        "    sample_preds = model(sample_inputs, sample_masks)\n",
        "\n",
        "sample_inputs, sample_masks, sample_truths, sample_preds = (\n",
        "    sample_inputs.cpu().numpy(),\n",
        "    sample_masks.cpu().numpy(),\n",
        "    sample_truths.cpu().numpy(),\n",
        "    sample_preds.cpu().numpy(),\n",
        ")\n",
        "\n",
        "fig, axs = plt.subplots(nrows=4, ncols=4, figsize=(8, 8))\n",
        "\n",
        "for i in range(4):\n",
        "    axs[i][0].imshow(np.transpose(sample_truths[i], (1, 2, 0)))\n",
        "    axs[i][0].set_title('Ground Truth')\n",
        "\n",
        "    axs[i][1].imshow(np.transpose(sample_masks[i], (1, 2, 0)))\n",
        "    axs[i][1].set_title('Input Mask')\n",
        "\n",
        "    axs[i][2].imshow(np.transpose(sample_inputs[i], (1, 2, 0)))\n",
        "    axs[i][2].set_title('Input Image')\n",
        "\n",
        "    axs[i][3].imshow(np.transpose(sample_preds[i], (1, 2, 0)))\n",
        "    axs[i][3].set_title('Predicted Mask')\n",
        "\n",
        "plt.tight_layout()\n",
        "plt.show()\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "UfEG_o43W_PJ",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 807
        },
        "outputId": "ce4ec926-649e-46fd-ac40-5c7a2c899879"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x800 with 16 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "sample_inputs, sample_masks, sample_truths = next(iter(Test_dataiter))\n",
        "\n",
        "model.eval()\n",
        "with torch.no_grad():\n",
        "    sample_preds = model(sample_inputs, sample_masks)\n",
        "\n",
        "sample_inputs, sample_masks, sample_truths, sample_preds = (\n",
        "    sample_inputs.cpu().numpy(),\n",
        "    sample_masks.cpu().numpy(),\n",
        "    sample_truths.cpu().numpy(),\n",
        "    sample_preds.cpu().numpy(),\n",
        ")\n",
        "\n",
        "fig, axs = plt.subplots(nrows=4, ncols=4, figsize=(8, 8))\n",
        "\n",
        "for i in range(4):\n",
        "    axs[i][0].imshow(np.transpose(sample_truths[i], (1, 2, 0)))\n",
        "    axs[i][0].set_title('Ground Truth')\n",
        "\n",
        "    axs[i][1].imshow(np.transpose(sample_masks[i], (1, 2, 0)))\n",
        "    axs[i][1].set_title('Input Mask')\n",
        "\n",
        "    axs[i][2].imshow(np.transpose(sample_inputs[i], (1, 2, 0)))\n",
        "    axs[i][2].set_title('Input Image')\n",
        "\n",
        "    axs[i][3].imshow(np.transpose(sample_preds[i], (1, 2, 0)))\n",
        "    axs[i][3].set_title('Predicted Mask')\n",
        "\n",
        "plt.tight_layout()\n",
        "plt.show()\n"
      ]
    }
  ],
  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}