Egyptian_model_with_ResNet.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Egyptian_model_with_ResNet.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "authorship_tag": "ABX9TyMzK9IliUi/PTdK0FE1/BTi",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/dqj5182/egyptian_hieroglyph_recognition_convnet/blob/main/Egyptian_model_with_ResNet.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8_wPy3PTj15A"
      },
      "source": [
        "import os\n",
        "import numpy as np\n",
        "import torch\n",
        "\n",
        "import torchvision\n",
        "from torchvision import datasets, models, transforms\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "%matplotlib inline"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "AoGYGRZBlS9a",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "99b44aeb-01d2-40db-b645-bc4eab1cb382"
      },
      "source": [
        "# Connecting and Mounting to the Google Drive\n",
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HfF8iFPVj7dO",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "a5a5e0ec-ccb1-4bc2-98b7-57dbfa70a648"
      },
      "source": [
        "# check if CUDA is available\n",
        "train_on_gpu = torch.cuda.is_available()\n",
        "\n",
        "if not train_on_gpu:\n",
        "    print('CUDA is not available.  Training on CPU ...')\n",
        "else:\n",
        "    print('CUDA is available!  Training on GPU ...')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "CUDA is available!  Training on GPU ...\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-n0zxPziNP_G"
      },
      "source": [
        "#!pip install split-folders"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HVPiflB_j9S_"
      },
      "source": [
        "# define training and test data directories\n",
        "# data_dir = '/content/drive/MyDrive/FinalEdgeDetectedDataset/'\n",
        "data_dir = '/content/drive/MyDrive/EgyptianHieroglyphDataset/'\n",
        "train_dir = os.path.join(data_dir, 'train/')\n",
        "test_dir = os.path.join(data_dir, 'test/')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "34gVTZ7YBs1D",
        "outputId": "67f9dddf-0b80-4867-98b2-59b9ccd82f1d"
      },
      "source": [
        "hieroglyph_for_train = []\n",
        "\n",
        "for name in os.listdir('/content/drive/MyDrive/EgyptianHieroglyphDataset/'):\n",
        "  path, dirs, files = next(os.walk(\"/content/drive/MyDrive/EgyptianHieroglyphDataset/\"+name))\n",
        "  file_count = len(files)\n",
        "  if file_count > 100:\n",
        "    print(name, file_count)\n",
        "    hieroglyph_for_train.append(name)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "E34 122\n",
            "D21 183\n",
            "X1 232\n",
            "V31 133\n",
            "S29 265\n",
            "O50 106\n",
            "I9 146\n",
            "G17 195\n",
            "M17 364\n",
            "N35 448\n",
            "G43 197\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HJLttkcJNRpm"
      },
      "source": [
        "#import splitfolders\n",
        "\n",
        "#splitfolders.ratio(data_dir, output=data_dir, seed=1337, ratio=(.8, 0,0.2)) "
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YJUNV9TeWwFe"
      },
      "source": [
        "# print(hieroglyph_for_train)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 69
        },
        "id": "1z1ebVd0WOVE",
        "outputId": "0fdfe4fd-0af1-44a2-dfc0-515918421dc2"
      },
      "source": [
        "import os, os.path\n",
        "import shutil, sys\n",
        "'''\n",
        "for name in os.listdir('/content/drive/MyDrive/EgyptianHieroglyphDataset/train/'):\n",
        "  if name in hieroglyph_for_train:\n",
        "    pass\n",
        "  else:\n",
        "    shutil.rmtree('/content/drive/MyDrive/EgyptianHieroglyphDataset/train/'+name)\n",
        "\n",
        "for name in os.listdir('/content/drive/MyDrive/EgyptianHieroglyphDataset/test/'):\n",
        "  if name in hieroglyph_for_train:\n",
        "    pass\n",
        "  else:\n",
        "    shutil.rmtree('/content/drive/MyDrive/EgyptianHieroglyphDataset/test/'+name)\n",
        "'''"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            },
            "text/plain": [
              "\"\\nfor name in os.listdir('/content/drive/MyDrive/EgyptianHieroglyphDataset/train/'):\\n  if name in hieroglyph_for_train:\\n    pass\\n  else:\\n    shutil.rmtree('/content/drive/MyDrive/EgyptianHieroglyphDataset/train/'+name)\\n\\nfor name in os.listdir('/content/drive/MyDrive/EgyptianHieroglyphDataset/test/'):\\n  if name in hieroglyph_for_train:\\n    pass\\n  else:\\n    shutil.rmtree('/content/drive/MyDrive/EgyptianHieroglyphDataset/test/'+name)\\n\""
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LDW4GlhM9Obc"
      },
      "source": [
        "# classes are folders in each directory with these names\n",
        "classes = []\n",
        "\n",
        "for filename in os.listdir(train_dir):\n",
        "    classes.append(filename)\n",
        "\n",
        "classes.sort()\n",
        "# CLASSES: "
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "BvJq2LHO9aDt",
        "outputId": "57b5d47a-3a1a-4dbe-baf9-dba3069b8ce8"
      },
      "source": [
        "print(classes)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "['D21', 'E34', 'G17', 'G43', 'I9', 'M17', 'N35', 'O50', 'S29', 'V31', 'X1']\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SHqGkfy_b9zY"
      },
      "source": [
        "# Generate more training data from data augmentation\n",
        "data_transform_main = transforms.Compose([\n",
        "                            transforms.ToTensor(),\n",
        "                            #transforms.RandomApply([transforms.RandomHorizontalFlip()]),\n",
        "                            #transforms.RandomApply([transforms.ColorJitter(brightness=0.1, contrast=0.2, saturation=0, hue=0)]),\n",
        "                            #transforms.RandomRotation(degrees=(-30, 30)),\n",
        "                            #transforms.RandomHorizontalFlip(p = 0.5),\n",
        "                            #transforms.RandomAffine(degrees = 0, translate=(.1, .1)),\n",
        "                            #transforms.RandomErasing(p=0.1, scale=(0.05, 0.05), ratio=(0.3, 3.3), value=0, inplace=False),\n",
        "                            #transforms.RandomApply([transforms.ColorJitter()]),\n",
        "                            transforms.Normalize((0.5,), (0.5,))\n",
        "                        ])\n",
        "\n",
        "# Generate more training data from data augmentation\n",
        "data_transform_more = transforms.Compose([\n",
        "                            transforms.ToTensor(),\n",
        "                            transforms.RandomApply([transforms.RandomHorizontalFlip()]),\n",
        "                            transforms.RandomApply([transforms.ColorJitter(brightness=0.1, contrast=0.2, saturation=0, hue=0)]),\n",
        "                            transforms.RandomRotation(degrees=(-30, 30)),\n",
        "                            transforms.RandomHorizontalFlip(p = 0.5),\n",
        "                            transforms.RandomAffine(degrees = 0, translate=(.1, .1)),\n",
        "                            transforms.RandomErasing(p=0.1, scale=(0.05, 0.05), ratio=(0.3, 3.3), value=0, inplace=False),\n",
        "                            #transforms.RandomApply([transforms.ColorJitter()]),\n",
        "                            transforms.Normalize((0.5,), (0.5,))\n",
        "                        ])"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QyfJiIsbj_SA",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "88d9e592-9ca7-4cf4-91d4-065fc178ef27"
      },
      "source": [
        "# load and transform data using ImageFolder\n",
        "\n",
        "# VGG-16 Takes 224x224 images as input, so we resize all of them\n",
        "data_transform_train = transforms.Compose([transforms.ToTensor(),\n",
        "                                           transforms.RandomHorizontalFlip(p = 0.5),\n",
        "                                           #transforms.CenterCrop((10, 10)),\n",
        "                                           transforms.ColorJitter(brightness=0.1, contrast=0.2, saturation=0, hue=0),\n",
        "                                           #transforms.GaussianBlur(kernel_size=501),\n",
        "                                           transforms.RandomRotation(degrees=(-30, 30)),\n",
        "                                           transforms.RandomAffine(degrees = 0, translate=(.1, .1)),\n",
        "                                           #transforms.RandomErasing(p=0.5, scale=(0.05, 0.05), ratio=(0.3, 3.3), value=0, inplace=False),\n",
        "                                           transforms.Normalize((0.5,), (0.5,))])\n",
        "\n",
        "data_transform_test = transforms.Compose([transforms.ToTensor(),\n",
        "                                          transforms.RandomRotation(degrees=(-5, 5)),\n",
        "                                          transforms.RandomAffine(degrees = 0, translate=(.1, .1)),\n",
        "                                          transforms.RandomApply([transforms.ColorJitter(brightness=(1, 1.2), contrast=(1, 1.5), saturation=(1, 1.5), hue=(0, 0.5))]),\n",
        "                                          transforms.Normalize((0.5,), (0.5,))])\n",
        "\n",
        "train_data = datasets.ImageFolder(train_dir, transform=data_transform_test)\n",
        "test_data = datasets.ImageFolder(test_dir, transform=data_transform_test)\n",
        "\n",
        "more_data = datasets.ImageFolder(train_dir, transform=data_transform_more)\n",
        "\n",
        "# print out some data stats\n",
        "print('Num training images: ', len(train_data))\n",
        "print('Num test images: ', len(test_data))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Num training images:  1908\n",
            "Num test images:  483\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 52
        },
        "id": "HeZU4ynH8IE3",
        "outputId": "dc18845b-b904-4686-a548-c1730cfbb2ed"
      },
      "source": [
        "from torchvision.utils import save_image\n",
        "from google.colab.patches import cv2_imshow\n",
        "\n",
        "'''\n",
        "img_num = 0\n",
        "for _ in range(10):\n",
        "  for img, label in more_data:\n",
        "    download_directory = train_dir+ '/' + classes[label] + '/' + '100000' + img_num + classes[label] + '.png'\n",
        "    save_image(img, download_directory)\n",
        "    img_num += 1\n",
        "'''\n",
        "\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            },
            "text/plain": [
              "\"\\nimg_num = 0\\nfor _ in range(10):\\n  for img, label in more_data:\\n    download_directory = train_dir+ '/' + classes[label] + '/' + '100000' + img_num + classes[label] + '.png'\\n    save_image(img, download_directory)\\n    img_num += 1\\n\""
            ]
          },
          "metadata": {},
          "execution_count": 37
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8a6qPwTakBXG"
      },
      "source": [
        "# define dataloader parameters\n",
        "batch_size = 20\n",
        "num_workers=0\n",
        "\n",
        "# prepare data loaders\n",
        "train_loader = torch.utils.data.DataLoader(train_data, batch_size=batch_size, \n",
        "                                           num_workers=num_workers, shuffle=True)\n",
        "test_loader = torch.utils.data.DataLoader(test_data, batch_size=batch_size, \n",
        "                                          num_workers=num_workers, shuffle=True)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hh9byoNokDVA",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 604
        },
        "outputId": "170ddd31-2250-46a9-fb44-0700455ea8ce"
      },
      "source": [
        "# Visualize some sample data\n",
        "\n",
        "# obtain one batch of training images\n",
        "dataiter = iter(train_loader)\n",
        "images, labels = dataiter.next()\n",
        "images = images.numpy() # convert images to numpy for display\n",
        "\n",
        "# plot the images in the batch, along with the corresponding labels\n",
        "fig = plt.figure(figsize=(25, 4))\n",
        "for idx in np.arange(20):\n",
        "    ax = fig.add_subplot(2, 20/2, idx+1, xticks=[], yticks=[])\n",
        "    plt.imshow(np.transpose(images[idx], (1, 2, 0)))\n",
        "    ax.set_title(classes[labels[idx]])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n",
            "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n",
            "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n",
            "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n",
            "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n",
            "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n",
            "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n",
            "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n",
            "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n",
            "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n",
            "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n",
            "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n",
            "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n",
            "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n",
            "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n",
            "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n",
            "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n",
            "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n",
            "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n",
            "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": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1800x288 with 20 Axes>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "E1JUwFIgkFOg",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "44e1b2a2-a3d9-4a4b-8b31-7d3587c33844"
      },
      "source": [
        "# Load the pretrained model from pytorch\n",
        "resnet152 = models.resnet152(pretrained=True)\n",
        "\n",
        "# print out the model structure\n",
        "print(resnet152)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "ResNet(\n",
            "  (conv1): Conv2d(3, 64, kernel_size=(7, 7), stride=(2, 2), padding=(3, 3), bias=False)\n",
            "  (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "  (relu): ReLU(inplace=True)\n",
            "  (maxpool): MaxPool2d(kernel_size=3, stride=2, padding=1, dilation=1, ceil_mode=False)\n",
            "  (layer1): Sequential(\n",
            "    (0): Bottleneck(\n",
            "      (conv1): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
            "      (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "      (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
            "      (bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "      (conv3): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
            "      (bn3): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "      (relu): ReLU(inplace=True)\n",
            "      (downsample): Sequential(\n",
            "        (0): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
            "        (1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "      )\n",
            "    )\n",
            "    (1): Bottleneck(\n",
            "      (conv1): Conv2d(256, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
            "      (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "      (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
            "      (bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "      (conv3): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
            "      (bn3): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "      (relu): ReLU(inplace=True)\n",
            "    )\n",
            "    (2): Bottleneck(\n",
            "      (conv1): Conv2d(256, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
            "      (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "      (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
            "      (bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "      (conv3): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
            "      (bn3): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "      (relu): ReLU(inplace=True)\n",
            "    )\n",
            "  )\n",
            "  (layer2): Sequential(\n",
            "    (0): Bottleneck(\n",
            "      (conv1): Conv2d(256, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
            "      (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "      (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n",
            "      (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "      (conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
            "      (bn3): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "      (relu): ReLU(inplace=True)\n",
            "      (downsample): Sequential(\n",
            "        (0): Conv2d(256, 512, kernel_size=(1, 1), stride=(2, 2), bias=False)\n",
            "        (1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "      )\n",
            "    )\n",
            "    (1): Bottleneck(\n",
            "      (conv1): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
            "      (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "      (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
            "      (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "      (conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
            "      (bn3): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "      (relu): ReLU(inplace=True)\n",
            "    )\n",
            "    (2): Bottleneck(\n",
            "      (conv1): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
            "      (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "      (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
            "      (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "      (conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
            "      (bn3): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "      (relu): ReLU(inplace=True)\n",
            "    )\n",
            "    (3): Bottleneck(\n",
            "      (conv1): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
            "      (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "      (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
            "      (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "      (conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
            "      (bn3): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "      (relu): ReLU(inplace=True)\n",
            "    )\n",
            "    (4): Bottleneck(\n",
            "      (conv1): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
            "      (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "      (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
            "      (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "      (conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
            "      (bn3): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "      (relu): ReLU(inplace=True)\n",
            "    )\n",
            "    (5): Bottleneck(\n",
            "      (conv1): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
            "      (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "      (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
            "      (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "      (conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
            "      (bn3): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "      (relu): ReLU(inplace=True)\n",
            "    )\n",
            "    (6): Bottleneck(\n",
            "      (conv1): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
            "      (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "      (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
            "      (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "      (conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
            "      (bn3): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "      (relu): ReLU(inplace=True)\n",
            "    )\n",
            "    (7): Bottleneck(\n",
            "      (conv1): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
            "      (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "      (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
            "      (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "      (conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
            "      (bn3): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "      (relu): ReLU(inplace=True)\n",
            "    )\n",
            "  )\n",
            "  (layer3): Sequential(\n",
            "    (0): Bottleneck(\n",
            "      (conv1): Conv2d(512, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
            "      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n",
            "      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
            "      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "      (relu): ReLU(inplace=True)\n",
            "      (downsample): Sequential(\n",
            "        (0): Conv2d(512, 1024, kernel_size=(1, 1), stride=(2, 2), bias=False)\n",
            "        (1): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "      )\n",
            "    )\n",
            "    (1): Bottleneck(\n",
            "      (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
            "      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
            "      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
            "      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "      (relu): ReLU(inplace=True)\n",
            "    )\n",
            "    (2): Bottleneck(\n",
            "      (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
            "      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
            "      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
            "      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "      (relu): ReLU(inplace=True)\n",
            "    )\n",
            "    (3): Bottleneck(\n",
            "      (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
            "      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
            "      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
            "      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "      (relu): ReLU(inplace=True)\n",
            "    )\n",
            "    (4): Bottleneck(\n",
            "      (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
            "      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
            "      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
            "      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "      (relu): ReLU(inplace=True)\n",
            "    )\n",
            "    (5): Bottleneck(\n",
            "      (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
            "      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
            "      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
            "      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "      (relu): ReLU(inplace=True)\n",
            "    )\n",
            "    (6): Bottleneck(\n",
            "      (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
            "      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
            "      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
            "      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "      (relu): ReLU(inplace=True)\n",
            "    )\n",
            "    (7): Bottleneck(\n",
            "      (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
            "      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
            "      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
            "      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "      (relu): ReLU(inplace=True)\n",
            "    )\n",
            "    (8): Bottleneck(\n",
            "      (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
            "      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
            "      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
            "      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "      (relu): ReLU(inplace=True)\n",
            "    )\n",
            "    (9): Bottleneck(\n",
            "      (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
            "      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
            "      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
            "      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "      (relu): ReLU(inplace=True)\n",
            "    )\n",
            "    (10): Bottleneck(\n",
            "      (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
            "      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
            "      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
            "      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "      (relu): ReLU(inplace=True)\n",
            "    )\n",
            "    (11): Bottleneck(\n",
            "      (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
            "      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
            "      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
            "      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "      (relu): ReLU(inplace=True)\n",
            "    )\n",
            "    (12): Bottleneck(\n",
            "      (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
            "      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
            "      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
            "      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "      (relu): ReLU(inplace=True)\n",
            "    )\n",
            "    (13): Bottleneck(\n",
            "      (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
            "      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
            "      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
            "      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "      (relu): ReLU(inplace=True)\n",
            "    )\n",
            "    (14): Bottleneck(\n",
            "      (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
            "      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
            "      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
            "      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "      (relu): ReLU(inplace=True)\n",
            "    )\n",
            "    (15): Bottleneck(\n",
            "      (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
            "      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
            "      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
            "      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "      (relu): ReLU(inplace=True)\n",
            "    )\n",
            "    (16): Bottleneck(\n",
            "      (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
            "      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
            "      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
            "      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "      (relu): ReLU(inplace=True)\n",
            "    )\n",
            "    (17): Bottleneck(\n",
            "      (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
            "      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
            "      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
            "      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "      (relu): ReLU(inplace=True)\n",
            "    )\n",
            "    (18): Bottleneck(\n",
            "      (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
            "      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
            "      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
            "      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "      (relu): ReLU(inplace=True)\n",
            "    )\n",
            "    (19): Bottleneck(\n",
            "      (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
            "      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
            "      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
            "      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "      (relu): ReLU(inplace=True)\n",
            "    )\n",
            "    (20): Bottleneck(\n",
            "      (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
            "      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
            "      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
            "      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "      (relu): ReLU(inplace=True)\n",
            "    )\n",
            "    (21): Bottleneck(\n",
            "      (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
            "      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
            "      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
            "      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "      (relu): ReLU(inplace=True)\n",
            "    )\n",
            "    (22): Bottleneck(\n",
            "      (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
            "      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
            "      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
            "      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "      (relu): ReLU(inplace=True)\n",
            "    )\n",
            "    (23): Bottleneck(\n",
            "      (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
            "      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
            "      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
            "      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "      (relu): ReLU(inplace=True)\n",
            "    )\n",
            "    (24): Bottleneck(\n",
            "      (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
            "      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
            "      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
            "      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "      (relu): ReLU(inplace=True)\n",
            "    )\n",
            "    (25): Bottleneck(\n",
            "      (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
            "      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
            "      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
            "      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "      (relu): ReLU(inplace=True)\n",
            "    )\n",
            "    (26): Bottleneck(\n",
            "      (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
            "      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
            "      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
            "      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "      (relu): ReLU(inplace=True)\n",
            "    )\n",
            "    (27): Bottleneck(\n",
            "      (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
            "      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
            "      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
            "      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "      (relu): ReLU(inplace=True)\n",
            "    )\n",
            "    (28): Bottleneck(\n",
            "      (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
            "      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
            "      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
            "      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "      (relu): ReLU(inplace=True)\n",
            "    )\n",
            "    (29): Bottleneck(\n",
            "      (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
            "      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
            "      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
            "      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "      (relu): ReLU(inplace=True)\n",
            "    )\n",
            "    (30): Bottleneck(\n",
            "      (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
            "      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
            "      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
            "      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "      (relu): ReLU(inplace=True)\n",
            "    )\n",
            "    (31): Bottleneck(\n",
            "      (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
            "      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
            "      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
            "      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "      (relu): ReLU(inplace=True)\n",
            "    )\n",
            "    (32): Bottleneck(\n",
            "      (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
            "      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
            "      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
            "      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "      (relu): ReLU(inplace=True)\n",
            "    )\n",
            "    (33): Bottleneck(\n",
            "      (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
            "      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
            "      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
            "      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "      (relu): ReLU(inplace=True)\n",
            "    )\n",
            "    (34): Bottleneck(\n",
            "      (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
            "      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
            "      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
            "      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "      (relu): ReLU(inplace=True)\n",
            "    )\n",
            "    (35): Bottleneck(\n",
            "      (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
            "      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
            "      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
            "      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "      (relu): ReLU(inplace=True)\n",
            "    )\n",
            "  )\n",
            "  (layer4): Sequential(\n",
            "    (0): Bottleneck(\n",
            "      (conv1): Conv2d(1024, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
            "      (bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "      (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n",
            "      (bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "      (conv3): Conv2d(512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
            "      (bn3): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "      (relu): ReLU(inplace=True)\n",
            "      (downsample): Sequential(\n",
            "        (0): Conv2d(1024, 2048, kernel_size=(1, 1), stride=(2, 2), bias=False)\n",
            "        (1): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "      )\n",
            "    )\n",
            "    (1): Bottleneck(\n",
            "      (conv1): Conv2d(2048, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
            "      (bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "      (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
            "      (bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "      (conv3): Conv2d(512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
            "      (bn3): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "      (relu): ReLU(inplace=True)\n",
            "    )\n",
            "    (2): Bottleneck(\n",
            "      (conv1): Conv2d(2048, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
            "      (bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "      (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
            "      (bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "      (conv3): Conv2d(512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
            "      (bn3): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "      (relu): ReLU(inplace=True)\n",
            "    )\n",
            "  )\n",
            "  (avgpool): AdaptiveAvgPool2d(output_size=(1, 1))\n",
            "  (fc): Linear(in_features=2048, out_features=1000, bias=True)\n",
            ")\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lQGpJexdkISQ",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "81dfcec8-9259-44e8-8695-2f2e82f2f94c"
      },
      "source": [
        "print(resnet152.fc.in_features) \n",
        "print(resnet152.fc.out_features) "
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "2048\n",
            "1000\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "P-00EnwhkLED"
      },
      "source": [
        "# Freeze training for all \"features\" layers\n",
        "for param in resnet152.parameters():\n",
        "    param.requires_grad = False"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bSCv4gqkkM3z"
      },
      "source": [
        "import torch.nn as nn\n",
        "\n",
        "n_inputs = resnet152.fc.in_features\n",
        "\n",
        "# add last linear layer (n_inputs -> 5 flower classes)\n",
        "# new layers automatically have requires_grad = True\n",
        "last_layer = nn.Sequential(\n",
        "                nn.Linear(n_inputs, 1024),\n",
        "                nn.ReLU(),\n",
        "                nn.Linear(1024, 256),\n",
        "                nn.ReLU(),\n",
        "                nn.Linear(256, len(classes)))\n",
        "\n",
        "resnet152.fc = last_layer\n",
        "\n",
        "# if GPU is available, move the model to GPU\n",
        "if train_on_gpu:\n",
        "    resnet152.cuda()\n",
        "\n",
        "# check to see that your last layer produces the expected number of outputs\n",
        "# print(resnet152.fc.out_features)\n",
        "#print(resnet152)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BR9N2l2x9xRl",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "25c8bc09-dade-49d1-89f9-f1b0f1901c21"
      },
      "source": [
        "# Initialize weights\n",
        "def init_weights(m):\n",
        "    if isinstance(m, nn.Linear):\n",
        "        torch.nn.init.xavier_uniform(m.weight)\n",
        "        m.bias.data.fill_(0.01)\n",
        "\n",
        "resnet152.fc.apply(init_weights)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:4: UserWarning: nn.init.xavier_uniform is now deprecated in favor of nn.init.xavier_uniform_.\n",
            "  after removing the cwd from sys.path.\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Sequential(\n",
              "  (0): Linear(in_features=2048, out_features=1024, bias=True)\n",
              "  (1): ReLU()\n",
              "  (2): Linear(in_features=1024, out_features=256, bias=True)\n",
              "  (3): ReLU()\n",
              "  (4): Linear(in_features=256, out_features=11, bias=True)\n",
              ")"
            ]
          },
          "metadata": {},
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "n_HwrUmnkPKz"
      },
      "source": [
        "import torch.optim as optim\n",
        "\n",
        "# specify loss function (categorical cross-entropy)\n",
        "criterion = nn.CrossEntropyLoss()\n",
        "\n",
        "# specify optimizer (Adam) and learning rate = 0.001\n",
        "# Adam is better than SGD\n",
        "optimizer = optim.SGD(resnet152.fc.parameters(), lr=0.001)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "iMK4r5f0kRRE",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "22003114-42ef-43f2-9796-f01d689129ff"
      },
      "source": [
        "# number of epochs to train the model\n",
        "n_epochs = 80\n",
        "\n",
        "for epoch in range(1, n_epochs+1):\n",
        "\n",
        "    # keep track of training and validation loss\n",
        "    train_loss = 0.0\n",
        "    \n",
        "    ###################\n",
        "    # train the model #\n",
        "    ###################\n",
        "    # model by default is set to train\n",
        "    for batch_i, (data, target) in enumerate(train_loader):\n",
        "        # move tensors to GPU if CUDA is available\n",
        "        if train_on_gpu:\n",
        "            data, target = data.cuda(), target.cuda()\n",
        "        # clear the gradients of all optimized variables\n",
        "        optimizer.zero_grad()\n",
        "        # forward pass: compute predicted outputs by passing inputs to the model\n",
        "        output = resnet152(data)\n",
        "        # calculate the batch loss\n",
        "        loss = criterion(output, target)\n",
        "        # backward pass: compute gradient of the loss with respect to model parameters\n",
        "        loss.backward()\n",
        "        # perform a single optimization step (parameter update)\n",
        "        optimizer.step()\n",
        "        # update training loss \n",
        "        train_loss += loss.item()\n",
        "        \n",
        "        if batch_i % 20 == 19:    # print training loss every specified number of mini-batches\n",
        "            print('Epoch %d, Batch %d loss: %.16f' %\n",
        "                  (epoch, batch_i + 1, train_loss / 20))\n",
        "            train_loss = 0.0"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/torch/nn/functional.py:718: UserWarning: Named tensors and all their associated APIs are an experimental feature and subject to change. Please do not use them for anything important until they are released as stable. (Triggered internally at  /pytorch/c10/core/TensorImpl.h:1156.)\n",
            "  return torch.max_pool2d(input, kernel_size, stride, padding, dilation, ceil_mode)\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1, Batch 20 loss: 2.4305363297462463\n",
            "Epoch 1, Batch 40 loss: 2.3455267786979674\n",
            "Epoch 1, Batch 60 loss: 2.2354951024055483\n",
            "Epoch 1, Batch 80 loss: 2.1569874882698059\n",
            "Epoch 2, Batch 20 loss: 1.9895688951015473\n",
            "Epoch 2, Batch 40 loss: 1.9212724924087525\n",
            "Epoch 2, Batch 60 loss: 1.8119388043880462\n",
            "Epoch 2, Batch 80 loss: 1.8171261847019196\n",
            "Epoch 3, Batch 20 loss: 1.6855978727340699\n",
            "Epoch 3, Batch 40 loss: 1.5585260391235352\n",
            "Epoch 3, Batch 60 loss: 1.5119893610477448\n",
            "Epoch 3, Batch 80 loss: 1.5178697586059571\n",
            "Epoch 4, Batch 20 loss: 1.4591014623641967\n",
            "Epoch 4, Batch 40 loss: 1.2951612114906310\n",
            "Epoch 4, Batch 60 loss: 1.2604150831699372\n",
            "Epoch 4, Batch 80 loss: 1.2538837045431137\n",
            "Epoch 5, Batch 20 loss: 1.1526994824409484\n",
            "Epoch 5, Batch 40 loss: 1.2429156184196473\n",
            "Epoch 5, Batch 60 loss: 1.0991425365209579\n",
            "Epoch 5, Batch 80 loss: 1.1001380205154419\n",
            "Epoch 6, Batch 20 loss: 1.0101185292005539\n",
            "Epoch 6, Batch 40 loss: 0.9468287587165832\n",
            "Epoch 6, Batch 60 loss: 0.9533349841833114\n",
            "Epoch 6, Batch 80 loss: 0.9045109212398529\n",
            "Epoch 7, Batch 20 loss: 0.8540160059928894\n",
            "Epoch 7, Batch 40 loss: 0.8763370811939240\n",
            "Epoch 7, Batch 60 loss: 0.8161436051130295\n",
            "Epoch 7, Batch 80 loss: 0.8299627482891083\n",
            "Epoch 8, Batch 20 loss: 0.7819510787725449\n",
            "Epoch 8, Batch 40 loss: 0.7677240043878555\n",
            "Epoch 8, Batch 60 loss: 0.7664837181568146\n",
            "Epoch 8, Batch 80 loss: 0.8078772097826004\n",
            "Epoch 9, Batch 20 loss: 0.6866414368152618\n",
            "Epoch 9, Batch 40 loss: 0.7813974946737290\n",
            "Epoch 9, Batch 60 loss: 0.6571548402309417\n",
            "Epoch 9, Batch 80 loss: 0.6147323593497276\n",
            "Epoch 10, Batch 20 loss: 0.6252274423837662\n",
            "Epoch 10, Batch 40 loss: 0.6627659574151039\n",
            "Epoch 10, Batch 60 loss: 0.6023144721984863\n",
            "Epoch 10, Batch 80 loss: 0.6393804699182510\n",
            "Epoch 11, Batch 20 loss: 0.5689664527773857\n",
            "Epoch 11, Batch 40 loss: 0.6113986641168594\n",
            "Epoch 11, Batch 60 loss: 0.5635847151279449\n",
            "Epoch 11, Batch 80 loss: 0.5676831915974617\n",
            "Epoch 12, Batch 20 loss: 0.5475913852453231\n",
            "Epoch 12, Batch 40 loss: 0.5173442527651787\n",
            "Epoch 12, Batch 60 loss: 0.5747990638017655\n",
            "Epoch 12, Batch 80 loss: 0.5049161791801453\n",
            "Epoch 13, Batch 20 loss: 0.5375873863697052\n",
            "Epoch 13, Batch 40 loss: 0.4945814684033394\n",
            "Epoch 13, Batch 60 loss: 0.4919866606593132\n",
            "Epoch 13, Batch 80 loss: 0.5451627738773823\n",
            "Epoch 14, Batch 20 loss: 0.4767457932233811\n",
            "Epoch 14, Batch 40 loss: 0.4693789228796959\n",
            "Epoch 14, Batch 60 loss: 0.5143773168325424\n",
            "Epoch 14, Batch 80 loss: 0.4883735299110413\n",
            "Epoch 15, Batch 20 loss: 0.4805655077099800\n",
            "Epoch 15, Batch 40 loss: 0.4399418652057648\n",
            "Epoch 15, Batch 60 loss: 0.4849858522415161\n",
            "Epoch 15, Batch 80 loss: 0.4408998519182205\n",
            "Epoch 16, Batch 20 loss: 0.4509177416563034\n",
            "Epoch 16, Batch 40 loss: 0.4622396543622017\n",
            "Epoch 16, Batch 60 loss: 0.4425972901284695\n",
            "Epoch 16, Batch 80 loss: 0.4240083843469620\n",
            "Epoch 17, Batch 20 loss: 0.4557189814746380\n",
            "Epoch 17, Batch 40 loss: 0.4291421219706535\n",
            "Epoch 17, Batch 60 loss: 0.4634676307439804\n",
            "Epoch 17, Batch 80 loss: 0.4064415931701660\n",
            "Epoch 18, Batch 20 loss: 0.4196372449398041\n",
            "Epoch 18, Batch 40 loss: 0.4083020031452179\n",
            "Epoch 18, Batch 60 loss: 0.3930700249969959\n",
            "Epoch 18, Batch 80 loss: 0.4072547674179077\n",
            "Epoch 19, Batch 20 loss: 0.4015008978545666\n",
            "Epoch 19, Batch 40 loss: 0.4009467765688896\n",
            "Epoch 19, Batch 60 loss: 0.3557385206222534\n",
            "Epoch 19, Batch 80 loss: 0.3743292391300201\n",
            "Epoch 20, Batch 20 loss: 0.3641953319311142\n",
            "Epoch 20, Batch 40 loss: 0.3046956643462181\n",
            "Epoch 20, Batch 60 loss: 0.4145664364099503\n",
            "Epoch 20, Batch 80 loss: 0.3246091701090336\n",
            "Epoch 21, Batch 20 loss: 0.3824844367802143\n",
            "Epoch 21, Batch 40 loss: 0.3346982143819332\n",
            "Epoch 21, Batch 60 loss: 0.3465232983231544\n",
            "Epoch 21, Batch 80 loss: 0.3453983858227730\n",
            "Epoch 22, Batch 20 loss: 0.3625137165188789\n",
            "Epoch 22, Batch 40 loss: 0.3157581128180027\n",
            "Epoch 22, Batch 60 loss: 0.3461822569370270\n",
            "Epoch 22, Batch 80 loss: 0.3524002879858017\n",
            "Epoch 23, Batch 20 loss: 0.3231190674006939\n",
            "Epoch 23, Batch 40 loss: 0.3548976309597492\n",
            "Epoch 23, Batch 60 loss: 0.3568963006138802\n",
            "Epoch 23, Batch 80 loss: 0.3103739626705647\n",
            "Epoch 24, Batch 20 loss: 0.3418666504323483\n",
            "Epoch 24, Batch 40 loss: 0.3329802416265011\n",
            "Epoch 24, Batch 60 loss: 0.3161982089281082\n",
            "Epoch 24, Batch 80 loss: 0.3316157214343548\n",
            "Epoch 25, Batch 20 loss: 0.3253597199916839\n",
            "Epoch 25, Batch 40 loss: 0.2911170646548271\n",
            "Epoch 25, Batch 60 loss: 0.2802154138684273\n",
            "Epoch 25, Batch 80 loss: 0.3239815175533295\n",
            "Epoch 26, Batch 20 loss: 0.2729312617331743\n",
            "Epoch 26, Batch 40 loss: 0.3242640122771263\n",
            "Epoch 26, Batch 60 loss: 0.3325759992003441\n",
            "Epoch 26, Batch 80 loss: 0.3167105313390494\n",
            "Epoch 27, Batch 20 loss: 0.3451534301042557\n",
            "Epoch 27, Batch 40 loss: 0.3165863767266274\n",
            "Epoch 27, Batch 60 loss: 0.2977093145251274\n",
            "Epoch 27, Batch 80 loss: 0.3230456657707691\n",
            "Epoch 28, Batch 20 loss: 0.2952420279383660\n",
            "Epoch 28, Batch 40 loss: 0.2773568943142891\n",
            "Epoch 28, Batch 60 loss: 0.2871602326631546\n",
            "Epoch 28, Batch 80 loss: 0.3227020218968392\n",
            "Epoch 29, Batch 20 loss: 0.2854890309274197\n",
            "Epoch 29, Batch 40 loss: 0.2744206540286541\n",
            "Epoch 29, Batch 60 loss: 0.3158603675663471\n",
            "Epoch 29, Batch 80 loss: 0.3416167460381985\n",
            "Epoch 30, Batch 20 loss: 0.3021197654306889\n",
            "Epoch 30, Batch 40 loss: 0.3215037129819393\n",
            "Epoch 30, Batch 60 loss: 0.2841058351099491\n",
            "Epoch 30, Batch 80 loss: 0.2997080501168966\n",
            "Epoch 31, Batch 20 loss: 0.2417205128818750\n",
            "Epoch 31, Batch 40 loss: 0.2713681418448687\n",
            "Epoch 31, Batch 60 loss: 0.2712053783237934\n",
            "Epoch 31, Batch 80 loss: 0.2768015816807747\n",
            "Epoch 32, Batch 20 loss: 0.3017915286123752\n",
            "Epoch 32, Batch 40 loss: 0.2607952468097210\n",
            "Epoch 32, Batch 60 loss: 0.3418838515877723\n",
            "Epoch 32, Batch 80 loss: 0.2125559184700251\n",
            "Epoch 33, Batch 20 loss: 0.2806991826742887\n",
            "Epoch 33, Batch 40 loss: 0.2683815367519856\n",
            "Epoch 33, Batch 60 loss: 0.2556200828403234\n",
            "Epoch 33, Batch 80 loss: 0.1993982426822186\n",
            "Epoch 34, Batch 20 loss: 0.2654631018638611\n",
            "Epoch 34, Batch 40 loss: 0.2534983795136213\n",
            "Epoch 34, Batch 60 loss: 0.2380519673228264\n",
            "Epoch 34, Batch 80 loss: 0.2518879946321249\n",
            "Epoch 35, Batch 20 loss: 0.2748553603887558\n",
            "Epoch 35, Batch 40 loss: 0.3146019961684942\n",
            "Epoch 35, Batch 60 loss: 0.2563026294112206\n",
            "Epoch 35, Batch 80 loss: 0.2795726682990790\n",
            "Epoch 36, Batch 20 loss: 0.2764560084789991\n",
            "Epoch 36, Batch 40 loss: 0.2317697511985898\n",
            "Epoch 36, Batch 60 loss: 0.2471144165843725\n",
            "Epoch 36, Batch 80 loss: 0.2352018591016531\n",
            "Epoch 37, Batch 20 loss: 0.2406485199928284\n",
            "Epoch 37, Batch 40 loss: 0.2500678803771734\n",
            "Epoch 37, Batch 60 loss: 0.2576462015509605\n",
            "Epoch 37, Batch 80 loss: 0.2967832155525684\n",
            "Epoch 38, Batch 20 loss: 0.2678757235407829\n",
            "Epoch 38, Batch 40 loss: 0.2181642785668373\n",
            "Epoch 38, Batch 60 loss: 0.2602559797465801\n",
            "Epoch 38, Batch 80 loss: 0.2471770223230124\n",
            "Epoch 39, Batch 20 loss: 0.2489694796502590\n",
            "Epoch 39, Batch 40 loss: 0.2319923616945744\n",
            "Epoch 39, Batch 60 loss: 0.2452971614897251\n",
            "Epoch 39, Batch 80 loss: 0.2354965522885323\n",
            "Epoch 40, Batch 20 loss: 0.2333155926316977\n",
            "Epoch 40, Batch 40 loss: 0.2185493044555187\n",
            "Epoch 40, Batch 60 loss: 0.2720241118222475\n",
            "Epoch 40, Batch 80 loss: 0.2341146521270275\n",
            "Epoch 41, Batch 20 loss: 0.2174005620181561\n",
            "Epoch 41, Batch 40 loss: 0.2295612988993526\n",
            "Epoch 41, Batch 60 loss: 0.1974763549864292\n",
            "Epoch 41, Batch 80 loss: 0.2062261126935482\n",
            "Epoch 42, Batch 20 loss: 0.2888405606150627\n",
            "Epoch 42, Batch 40 loss: 0.1993015531450510\n",
            "Epoch 42, Batch 60 loss: 0.2180170003324747\n",
            "Epoch 42, Batch 80 loss: 0.1993181072175503\n",
            "Epoch 43, Batch 20 loss: 0.2169026073068380\n",
            "Epoch 43, Batch 40 loss: 0.2238857857882977\n",
            "Epoch 43, Batch 60 loss: 0.2163349859416485\n",
            "Epoch 43, Batch 80 loss: 0.1968668784946203\n",
            "Epoch 44, Batch 20 loss: 0.2286163318902254\n",
            "Epoch 44, Batch 40 loss: 0.2053101141005755\n",
            "Epoch 44, Batch 60 loss: 0.2139745250344277\n",
            "Epoch 44, Batch 80 loss: 0.2149954654276371\n",
            "Epoch 45, Batch 20 loss: 0.1645495034754276\n",
            "Epoch 45, Batch 40 loss: 0.2333878597244620\n",
            "Epoch 45, Batch 60 loss: 0.2161024687811732\n",
            "Epoch 45, Batch 80 loss: 0.2274052537977695\n",
            "Epoch 46, Batch 20 loss: 0.2145312895998359\n",
            "Epoch 46, Batch 40 loss: 0.1991964731365442\n",
            "Epoch 46, Batch 60 loss: 0.2270987264811993\n",
            "Epoch 46, Batch 80 loss: 0.2000548508018255\n",
            "Epoch 47, Batch 20 loss: 0.2040917515754700\n",
            "Epoch 47, Batch 40 loss: 0.2086287137120962\n",
            "Epoch 47, Batch 60 loss: 0.1932829957455397\n",
            "Epoch 47, Batch 80 loss: 0.2039289791136980\n",
            "Epoch 48, Batch 20 loss: 0.1890340378507972\n",
            "Epoch 48, Batch 40 loss: 0.2473689951002598\n",
            "Epoch 48, Batch 60 loss: 0.2226085629314184\n",
            "Epoch 48, Batch 80 loss: 0.2233647219836712\n",
            "Epoch 49, Batch 20 loss: 0.1870260072872043\n",
            "Epoch 49, Batch 40 loss: 0.2629931595176458\n",
            "Epoch 49, Batch 60 loss: 0.2177107887342572\n",
            "Epoch 49, Batch 80 loss: 0.2016007127240300\n",
            "Epoch 50, Batch 20 loss: 0.2286106355488300\n",
            "Epoch 50, Batch 40 loss: 0.2240893032401800\n",
            "Epoch 50, Batch 60 loss: 0.2566613610833883\n",
            "Epoch 50, Batch 80 loss: 0.1898746261373162\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vGxdhaPbkTmC",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "9c41d2a0-fc72-465b-92b0-5267cc98cc64"
      },
      "source": [
        "# track test loss \n",
        "# over 5 flower classes\n",
        "test_loss = 0.0\n",
        "class_correct = list(0. for i in range(len(classes)))\n",
        "class_total = list(0. for i in range(len(classes)))\n",
        "\n",
        "resnet152.eval() # eval mode\n",
        "\n",
        "# iterate over test data\n",
        "for data, target in test_loader:\n",
        "    # move tensors to GPU if CUDA is available\n",
        "    if train_on_gpu:\n",
        "        data, target = data.cuda(), target.cuda()\n",
        "    # forward pass: compute predicted outputs by passing inputs to the model\n",
        "    output = resnet152(data)\n",
        "    # calculate the batch loss\n",
        "    loss = criterion(output, target)\n",
        "    # update  test loss \n",
        "    test_loss += loss.item()*data.size(0)\n",
        "    # convert output probabilities to predicted class\n",
        "    _, pred = torch.max(output, 1)    \n",
        "    # compare predictions to true label\n",
        "    correct_tensor = pred.eq(target.data.view_as(pred))\n",
        "    correct = np.squeeze(correct_tensor.numpy()) if not train_on_gpu else np.squeeze(correct_tensor.cpu().numpy())\n",
        "    # calculate test accuracy for each object class\n",
        "    for i in range(len(target.data)):\n",
        "        label = target.data[i]\n",
        "        class_correct[label] += correct[i].item()\n",
        "        class_total[label] += 1\n",
        "\n",
        "# calculate avg test loss\n",
        "test_loss = test_loss/len(test_loader.dataset)\n",
        "print('Test Loss: {:.6f}\\n'.format(test_loss))\n",
        "\n",
        "for i in range(len(classes)):\n",
        "    if class_total[i] > 0:\n",
        "        print('Test Accuracy of %5s: %2d%% (%2d/%2d)' % (\n",
        "            classes[i], 100 * class_correct[i] / class_total[i],\n",
        "            np.sum(class_correct[i]), np.sum(class_total[i])))\n",
        "    else:\n",
        "        print('Test Accuracy of %5s: N/A (no training examples)' % (classes[i]))\n",
        "\n",
        "print('\\nTest Accuracy (Overall): %2d%% (%2d/%2d)' % (\n",
        "    100. * np.sum(class_correct) / np.sum(class_total),\n",
        "    np.sum(class_correct), np.sum(class_total)))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Test Loss: 1.910048\n",
            "\n",
            "Test Accuracy of   D21: 91% (34/37)\n",
            "Test Accuracy of   E34: 96% (24/25)\n",
            "Test Accuracy of   G17: 82% (32/39)\n",
            "Test Accuracy of   G43: 92% (37/40)\n",
            "Test Accuracy of    I9: 93% (28/30)\n",
            "Test Accuracy of   M17: 93% (68/73)\n",
            "Test Accuracy of   N35: 95% (86/90)\n",
            "Test Accuracy of   O50: 100% (22/22)\n",
            "Test Accuracy of   S29: 98% (52/53)\n",
            "Test Accuracy of   V31: 96% (26/27)\n",
            "Test Accuracy of    X1: 95% (45/47)\n",
            "\n",
            "Test Accuracy (Overall): 93% (454/483)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UPP_1RcykY0a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 600
        },
        "outputId": "8da18b63-7d96-42fd-95c0-d6ec520dd9e3"
      },
      "source": [
        "# obtain one batch of test images\n",
        "dataiter = iter(test_loader)\n",
        "images, labels = dataiter.next()\n",
        "images.numpy()\n",
        "\n",
        "# move model inputs to cuda, if GPU available\n",
        "if train_on_gpu:\n",
        "    images = images.cuda()\n",
        "\n",
        "# get sample outputs\n",
        "output = resnet152(images)\n",
        "# convert output probabilities to predicted class\n",
        "_, preds_tensor = torch.max(output, 1)\n",
        "preds = np.squeeze(preds_tensor.numpy()) if not train_on_gpu else np.squeeze(preds_tensor.cpu().numpy())\n",
        "\n",
        "# plot the images in the batch, along with predicted and true labels\n",
        "fig = plt.figure(figsize=(25, 4))\n",
        "for idx in np.arange(20):\n",
        "    ax = fig.add_subplot(2, 20/2, idx+1, xticks=[], yticks=[])\n",
        "    plt.imshow(np.transpose(images.cpu()[idx], (1, 2, 0)))\n",
        "    ax.set_title(\"{} ({})\".format(classes[preds[idx]], classes[labels[idx]]),\n",
        "                 color=(\"green\" if preds[idx]==labels[idx].item() else \"red\"))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n",
            "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n",
            "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n",
            "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n",
            "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n",
            "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n",
            "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n",
            "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n",
            "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n",
            "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n",
            "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n",
            "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n",
            "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n",
            "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n",
            "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n",
            "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n",
            "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n",
            "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n",
            "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n",
            "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": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1800x288 with 20 Axes>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "QwQGm2DkKHo6"
      },
      "source": [
        "Flower dataset\n",
        "1.   Wide ResNet (epoch = 5, batch = 140)\n",
        "> Loss: 0.86 | Test Accuracy: 79% (429/540)\n",
        "2.   ResNet\n",
        "> Loss: 0.67 | Test Accuracy: 83% (450/540)\n",
        "3.   VGG\n",
        "4.   SqueezeNet\n",
        "\n",
        "\n"
      ]
    }
  ]
}