PatchNet.ipynb

{
  "cells": [
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {
        "id": "ACJRSdkJjhKB",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "6e697585-ac49-4340-d9f9-1d7b4fbb3328"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n"
          ]
        }
      ],
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "metadata": {
        "id": "LYttvZIriFLg"
      },
      "outputs": [],
      "source": [
        "import torch\n",
        "import torch.nn as nn\n",
        "import torch.optim as optim\n",
        "from torch.optim import lr_scheduler\n",
        "import torch.backends.cudnn as cudnn\n",
        "import numpy as np\n",
        "import torchvision\n",
        "from torchvision import datasets, models, transforms\n",
        "import matplotlib.pyplot as plt\n",
        "import time\n",
        "import os\n",
        "import copy"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "metadata": {
        "id": "m2_bVrNpi1_-",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "39fe25d7-9f65-462b-db00-697934b944ee"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "device(type='cuda', index=0)"
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ],
      "source": [
        "device = torch.device(\"cuda:0\" if torch.cuda.is_available() else \"cpu\")\n",
        "device"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "metadata": {
        "id": "O0WAu0Qei3o9"
      },
      "outputs": [],
      "source": [
        "import torch\n",
        "import torch.nn as nn\n",
        "import torch.nn.functional as F\n",
        "\n",
        "live_lbls = [0,1,6]\n",
        "class AsymAdditiveMarginSoftmax(nn.Module):\n",
        "    def __init__(self, in_features, out_features, s=30, ml=0.4,ms=0.1):\n",
        "        super(AsymAdditiveMarginSoftmax, self).__init__()\n",
        "        self.s = s\n",
        "        self.ml = ml\n",
        "        self.ms = ms\n",
        "        self.in_features = in_features\n",
        "        self.out_features = out_features\n",
        "        self.fc = nn.Linear(in_features, out_features, bias=False)\n",
        "    def forward(self, x, labels):\n",
        "        assert len(x) == len(labels)\n",
        "        assert torch.min(labels) >= 0\n",
        "        assert torch.max(labels) < self.out_features\n",
        "        m = torch.tensor([[self.ml if lbl in live_lbls else self.ms for lbl in labels]]).to(device)\n",
        "        self.fc.weight.data = F.normalize(self.fc.weight.data, p=2, dim=1)\n",
        "        x = F.normalize(x, p=2, dim=1)\n",
        "        wf = self.fc(x)\n",
        "        out = F.softmax(self.s*wf)\n",
        "        numerator = self.s * (torch.diagonal(wf.transpose(0, 1)[labels]) - m)\n",
        "        excl = torch.cat([torch.cat((wf[i, :y], wf[i, y+1:])).unsqueeze(0) for i, y in enumerate(labels)], dim=0)\n",
        "        denominator = torch.exp(numerator) + torch.sum(torch.exp(self.s * excl), dim=1)\n",
        "        L = numerator - torch.log(denominator)\n",
        "        return out, -torch.mean(L)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from torchvision.models.resnet import resnet18\n",
        "\n",
        "class SiameseNetwork(nn.Module):\n",
        "    def __init__(self):\n",
        "        super(SiameseNetwork, self).__init__()\n",
        "        self.encoder = resnet18(pretrained=True)\n",
        "        self.encoder.requires_grad = False\n",
        "        self.encoder.fc = nn.Sequential()\n",
        "        self.ams = AsymAdditiveMarginSoftmax(512,9)\n",
        "    def forward_once(self,input,labels):\n",
        "        f = self.encoder(input)\n",
        "        out, loss = self.ams(f,labels)\n",
        "        return out, f, loss\n",
        "    \n",
        "    def forward(self, input1, input2,labels):\n",
        "        out1, f1, loss1 = self.forward_once(input1,labels)\n",
        "        out2, f2, loss2 = self.forward_once(input2,labels)\n",
        "        f1_norm = F.normalize(f1,p=2,dim=1)\n",
        "        f2_norm = F.normalize(f2,p=2,dim=1)\n",
        "        loss =  loss1 + loss2 + torch.mean(torch.norm(f1_norm-f2_norm))\n",
        "        return out1, out2, loss"
      ],
      "metadata": {
        "id": "d0qVV94dc6XS"
      },
      "execution_count": 16,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": 17,
      "metadata": {
        "id": "6c1k0XKljEVJ"
      },
      "outputs": [],
      "source": [
        "from torch.utils.data import Dataset\n",
        "from PIL import Image\n",
        "class SiameseNetworkDataset(Dataset):\n",
        "    def __init__(self,imageFolderDataset,transform):\n",
        "        self.imageFolderDataset = imageFolderDataset    \n",
        "        self.transform = transform\n",
        "    def __getitem__(self,index):   \n",
        "        data = self.imageFolderDataset.imgs[index]\n",
        "        img = Image.open(data[0])\n",
        "        img0 = self.transform[0](img)\n",
        "        img1 = self.transform[1](img)\n",
        "        return img0, img1, data[1]\n",
        "    def __len__(self):\n",
        "        return len(self.imageFolderDataset.imgs)\n",
        "class TestDataset(Dataset):\n",
        "    def __init__(self,imageFolderDataset,transform):\n",
        "        self.imageFolderDataset = imageFolderDataset    \n",
        "        self.transform = transform\n",
        "    def __getitem__(self,index):   \n",
        "        data = self.imageFolderDataset.imgs[index]\n",
        "        img = Image.open(data[0])\n",
        "        img = self.transform(img)\n",
        "        return img, data[1]\n",
        "    def __len__(self):\n",
        "        return len(self.imageFolderDataset.imgs)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 18,
      "metadata": {
        "id": "TkEIvR5ijH4S"
      },
      "outputs": [],
      "source": [
        "from torchvision.transforms.transforms import CenterCrop\n",
        "import time\n",
        "import copy\n",
        "from torchvision import datasets, transforms\n",
        "import os\n",
        "import torch\n",
        "\n",
        "data_transforms = {\n",
        "    'train': [\n",
        "        transforms.Compose([\n",
        "          transforms.RandomRotation(180),\n",
        "          transforms.RandomHorizontalFlip(p=0.5),\n",
        "          transforms.RandomCrop(100),\n",
        "          transforms.ToTensor(),\n",
        "        ]),\n",
        "        transforms.Compose([\n",
        "          transforms.RandomRotation(180),\n",
        "          transforms.RandomHorizontalFlip(p=0.5),\n",
        "          transforms.RandomCrop(100),\n",
        "          transforms.ToTensor(),\n",
        "        ]),\n",
        "    ],\n",
        "    'test': transforms.Compose([\n",
        "          transforms.RandomRotation(180),\n",
        "          transforms.RandomHorizontalFlip(p=0.5),\n",
        "          transforms.RandomCrop(100),\n",
        "          transforms.ToTensor(),\n",
        "        ]),\n",
        "}\n",
        "data_dir = '/content/drive/MyDrive/casia'\n",
        "image_datasets = {\n",
        "    'train': datasets.ImageFolder(os.path.join(data_dir, 'train')),\n",
        "     'test': datasets.ImageFolder(os.path.join(data_dir, 'test'))\n",
        "}\n",
        "siameseDataset = SiameseNetworkDataset(image_datasets['train'],data_transforms['train'])\n",
        "testDataset = TestDataset(image_datasets['test'], data_transforms['test'])\n",
        "dataloaders = {\n",
        "    'train': torch.utils.data.DataLoader(siameseDataset,shuffle=True,num_workers=2),\n",
        "    'test': torch.utils.data.DataLoader(testDataset,shuffle=True,num_workers=2)\n",
        "}\n",
        "dataset_sizes = {x: len(image_datasets[x]) for x in ['train','test']}\n",
        "class_names = image_datasets['train'].classes"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 19,
      "metadata": {
        "id": "MU3b90pVwn4Z"
      },
      "outputs": [],
      "source": [
        "def imshow(img, text=None):\n",
        "    npimg = img.numpy()\n",
        "    plt.axis(\"off\")\n",
        "    if text:\n",
        "        plt.text(75, 8, text, style='italic',fontweight='bold',\n",
        "            bbox={'facecolor':'white', 'alpha':0.8, 'pad':10})\n",
        "        \n",
        "    plt.imshow(np.transpose(npimg, (1, 2, 0)))\n",
        "    plt.show()    \n",
        "\n",
        "def show_plot(iteration,loss):\n",
        "    plt.plot(iteration,loss)\n",
        "    plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 20,
      "metadata": {
        "id": "-TkCM5Z-wTac",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 132
        },
        "outputId": "48b96bdf-88ab-4509-d501-e4739f3251f6"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[5 6 3 5 7 4 8 3]\n"
          ]
        }
      ],
      "source": [
        "vis_dataloader = torch.utils.data.DataLoader(siameseDataset,\n",
        "                        shuffle=True,\n",
        "                        num_workers=2,\n",
        "                        batch_size=8)\n",
        "example_batch = next(iter(vis_dataloader))\n",
        "concatenated = torch.cat((example_batch[0], example_batch[1]),0)\n",
        "imshow(torchvision.utils.make_grid(concatenated))\n",
        "print(example_batch[2].numpy().reshape(-1))"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "vis_dataloader = torch.utils.data.DataLoader(testDataset,\n",
        "                        shuffle=True,\n",
        "                        num_workers=2,\n",
        "                        batch_size=8)\n",
        "example_batch = next(iter(vis_dataloader))\n",
        "concatenated = example_batch[0]\n",
        "imshow(torchvision.utils.make_grid(concatenated))\n",
        "print(example_batch[1].numpy().reshape(-1))"
      ],
      "metadata": {
        "id": "4ssplXYqDIaN",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 90
        },
        "outputId": "c6c8801d-828f-4479-f90e-68656566c69e"
      },
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[2 7 5 2 6 6 2 8]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "loss_history_train = []\n",
        "loss_history_test = []\n",
        "acc_history_train = []\n",
        "acc_history_test = []"
      ],
      "metadata": {
        "id": "i86Fz9nGfzXM"
      },
      "execution_count": 25,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "ZIVyvlOcjU9w",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "8e0176e3-7d79-4a4c-e848-7fd712dba74d"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/200\n",
            "----------------------------------------\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:23: UserWarning: Implicit dimension choice for softmax has been deprecated. Change the call to include dim=X as an argument.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "train Loss: 19.3015 Acc: 0.1222\n",
            "test Loss: 10.2233 Acc: 0.1111\n",
            "\n",
            "Epoch 2/200\n",
            "----------------------------------------\n",
            "train Loss: 18.7525 Acc: 0.1389\n",
            "test Loss: 8.8522 Acc: 0.2062\n",
            "\n",
            "Epoch 3/200\n",
            "----------------------------------------\n",
            "train Loss: 18.3327 Acc: 0.1667\n",
            "test Loss: 8.7024 Acc: 0.2173\n",
            "\n",
            "Epoch 4/200\n",
            "----------------------------------------\n",
            "train Loss: 18.0328 Acc: 0.1963\n",
            "test Loss: 9.4142 Acc: 0.1975\n",
            "\n",
            "Epoch 5/200\n",
            "----------------------------------------\n",
            "train Loss: 17.8940 Acc: 0.2074\n",
            "test Loss: 8.4071 Acc: 0.2481\n",
            "\n",
            "Epoch 6/200\n",
            "----------------------------------------\n",
            "train Loss: 17.6007 Acc: 0.2537\n",
            "test Loss: 8.8876 Acc: 0.2617\n",
            "\n",
            "Epoch 7/200\n",
            "----------------------------------------\n",
            "train Loss: 17.4767 Acc: 0.2722\n",
            "test Loss: 8.4048 Acc: 0.3025\n",
            "\n",
            "Epoch 8/200\n",
            "----------------------------------------\n",
            "train Loss: 17.2967 Acc: 0.3093\n",
            "test Loss: 9.3758 Acc: 0.1926\n",
            "\n",
            "Epoch 9/200\n",
            "----------------------------------------\n",
            "train Loss: 16.8644 Acc: 0.3259\n",
            "test Loss: 7.8641 Acc: 0.3741\n",
            "\n",
            "Epoch 10/200\n",
            "----------------------------------------\n",
            "train Loss: 16.7102 Acc: 0.3333\n",
            "test Loss: 8.1593 Acc: 0.2790\n",
            "\n",
            "Epoch 11/200\n",
            "----------------------------------------\n",
            "train Loss: 16.8246 Acc: 0.3296\n",
            "test Loss: 7.1376 Acc: 0.3914\n",
            "\n",
            "Epoch 12/200\n",
            "----------------------------------------\n",
            "train Loss: 16.3662 Acc: 0.3259\n",
            "test Loss: 9.0840 Acc: 0.3185\n",
            "\n",
            "Epoch 13/200\n",
            "----------------------------------------\n",
            "train Loss: 16.3256 Acc: 0.3426\n",
            "test Loss: 6.8387 Acc: 0.4086\n",
            "\n",
            "Epoch 14/200\n",
            "----------------------------------------\n",
            "train Loss: 16.0934 Acc: 0.3463\n",
            "test Loss: 10.4708 Acc: 0.3074\n",
            "\n",
            "Epoch 15/200\n",
            "----------------------------------------\n",
            "train Loss: 15.9183 Acc: 0.3704\n",
            "test Loss: 7.8157 Acc: 0.3235\n",
            "\n",
            "Epoch 16/200\n",
            "----------------------------------------\n",
            "train Loss: 15.8270 Acc: 0.3537\n",
            "test Loss: 8.7603 Acc: 0.3667\n",
            "\n",
            "Epoch 17/200\n",
            "----------------------------------------\n",
            "train Loss: 15.3888 Acc: 0.3963\n",
            "test Loss: 7.5943 Acc: 0.3247\n",
            "\n",
            "Epoch 18/200\n",
            "----------------------------------------\n",
            "train Loss: 15.7005 Acc: 0.3759\n",
            "test Loss: 9.1690 Acc: 0.2914\n",
            "\n",
            "Epoch 19/200\n",
            "----------------------------------------\n",
            "train Loss: 14.9831 Acc: 0.3944\n",
            "test Loss: 8.5705 Acc: 0.2765\n",
            "\n",
            "Epoch 20/200\n",
            "----------------------------------------\n",
            "train Loss: 15.4143 Acc: 0.3741\n",
            "test Loss: 9.4846 Acc: 0.2420\n",
            "\n",
            "Epoch 21/200\n",
            "----------------------------------------\n",
            "train Loss: 15.0825 Acc: 0.4019\n",
            "test Loss: 7.0646 Acc: 0.3815\n",
            "\n",
            "Epoch 22/200\n",
            "----------------------------------------\n",
            "train Loss: 14.9821 Acc: 0.3963\n",
            "test Loss: 9.9047 Acc: 0.2691\n",
            "\n",
            "Epoch 23/200\n",
            "----------------------------------------\n",
            "train Loss: 14.7864 Acc: 0.4019\n",
            "test Loss: 6.7842 Acc: 0.4160\n",
            "\n",
            "Epoch 24/200\n",
            "----------------------------------------\n",
            "train Loss: 14.3067 Acc: 0.4167\n",
            "test Loss: 6.0597 Acc: 0.4617\n",
            "\n",
            "Epoch 25/200\n",
            "----------------------------------------\n",
            "train Loss: 14.3892 Acc: 0.4315\n",
            "test Loss: 7.1144 Acc: 0.4333\n",
            "\n",
            "Epoch 26/200\n",
            "----------------------------------------\n",
            "train Loss: 14.0775 Acc: 0.4630\n",
            "test Loss: 6.3136 Acc: 0.4506\n",
            "\n",
            "Epoch 27/200\n",
            "----------------------------------------\n",
            "train Loss: 14.0711 Acc: 0.4593\n",
            "test Loss: 8.0260 Acc: 0.3741\n",
            "\n",
            "Epoch 28/200\n",
            "----------------------------------------\n",
            "train Loss: 13.9384 Acc: 0.4389\n",
            "test Loss: 6.5405 Acc: 0.4877\n",
            "\n",
            "Epoch 29/200\n",
            "----------------------------------------\n",
            "train Loss: 13.3541 Acc: 0.4704\n",
            "test Loss: 6.2987 Acc: 0.5160\n",
            "\n",
            "Epoch 30/200\n",
            "----------------------------------------\n",
            "train Loss: 13.0349 Acc: 0.4833\n",
            "test Loss: 8.7648 Acc: 0.4111\n",
            "\n",
            "Epoch 31/200\n",
            "----------------------------------------\n",
            "train Loss: 13.8882 Acc: 0.4352\n",
            "test Loss: 6.3633 Acc: 0.5074\n",
            "\n",
            "Epoch 32/200\n",
            "----------------------------------------\n",
            "train Loss: 12.4102 Acc: 0.4907\n",
            "test Loss: 7.7330 Acc: 0.3605\n",
            "\n",
            "Epoch 33/200\n",
            "----------------------------------------\n",
            "train Loss: 12.8817 Acc: 0.4907\n",
            "test Loss: 8.2598 Acc: 0.3864\n",
            "\n",
            "Epoch 34/200\n",
            "----------------------------------------\n",
            "train Loss: 13.0066 Acc: 0.4519\n",
            "test Loss: 5.8463 Acc: 0.5198\n",
            "\n",
            "Epoch 35/200\n",
            "----------------------------------------\n",
            "train Loss: 12.2772 Acc: 0.5222\n",
            "test Loss: 7.0981 Acc: 0.5025\n",
            "\n",
            "Epoch 36/200\n",
            "----------------------------------------\n",
            "train Loss: 12.6098 Acc: 0.4704\n",
            "test Loss: 7.3055 Acc: 0.4259\n",
            "\n",
            "Epoch 37/200\n",
            "----------------------------------------\n",
            "train Loss: 11.9009 Acc: 0.5093\n",
            "test Loss: 5.1451 Acc: 0.5407\n",
            "\n",
            "Epoch 38/200\n",
            "----------------------------------------\n",
            "train Loss: 11.7892 Acc: 0.4963\n",
            "test Loss: 5.9496 Acc: 0.5321\n",
            "\n",
            "Epoch 39/200\n",
            "----------------------------------------\n",
            "train Loss: 12.7857 Acc: 0.4815\n",
            "test Loss: 5.9061 Acc: 0.4568\n",
            "\n",
            "Epoch 40/200\n",
            "----------------------------------------\n",
            "train Loss: 11.2537 Acc: 0.5407\n",
            "test Loss: 5.2977 Acc: 0.5617\n",
            "\n",
            "Epoch 41/200\n",
            "----------------------------------------\n"
          ]
        }
      ],
      "source": [
        "def train_model(model, optimizer, scheduler, num_epochs):\n",
        "    since = time.time()\n",
        "    best_model_wts = copy.deepcopy(model.state_dict())\n",
        "    best_acc = 0.0\n",
        "\n",
        "\n",
        "    for epoch in range(num_epochs):\n",
        "        print(f'Epoch {epoch+1}/{num_epochs}')\n",
        "        print('-' * 40)\n",
        "        for phase in ['train' , 'test']:\n",
        "            if phase == 'train':\n",
        "                model.train()\n",
        "            running_loss = 0.0\n",
        "            running_corrects = 0\n",
        "            for dataloader in dataloaders[phase]:\n",
        "                optimizer.zero_grad()\n",
        "                if phase == 'train':\n",
        "                  inputs1, inputs2, labels = dataloader\n",
        "                  inputs1 = inputs1.to(device)\n",
        "                  inputs2 = inputs2.to(device)\n",
        "                  labels = labels.to(device)\n",
        "                  out1, out2, loss = model(inputs1, inputs2, labels)\n",
        "                  loss.backward()\n",
        "                  optimizer.step()\n",
        "                  _, preds = torch.max(out1,1)\n",
        "                  running_corrects += torch.sum(preds == labels.data)\n",
        "                  running_loss += loss.item() * inputs1.size(0)\n",
        "                else:\n",
        "                  inputs, labels = dataloader\n",
        "                  inputs = inputs.to(device)\n",
        "                  labels = labels.to(device)\n",
        "                  with torch.no_grad():\n",
        "                    out, f, loss = net.forward_once(inputs, labels)\n",
        "                  _, preds = torch.max(out,1)\n",
        "                  running_corrects += torch.sum(preds == labels.data)\n",
        "                  running_loss += loss.item() * inputs.size(0)\n",
        "            epoch_loss = running_loss / dataset_sizes[phase]\n",
        "            epoch_acc = running_corrects / dataset_sizes[phase]\n",
        "            if phase == 'train':\n",
        "              scheduler.step()\n",
        "              loss_history_train.append(epoch_loss)\n",
        "              acc_history_train.append(epoch_acc.cpu().detach().numpy())\n",
        "            else:\n",
        "              loss_history_test.append(epoch_loss)\n",
        "              acc_history_test.append(epoch_acc.cpu().detach().numpy())\n",
        "            print(f'{phase} Loss: {epoch_loss:.4f} Acc: {epoch_acc:.4f}')\n",
        "\n",
        "            if phase == 'test' and epoch_acc > best_acc:\n",
        "                best_acc = epoch_acc\n",
        "                best_model_wts = copy.deepcopy(model.state_dict())\n",
        "\n",
        "        print()\n",
        "\n",
        "    time_elapsed = time.time() - since\n",
        "    print(f'Training complete in {time_elapsed // 60:.0f}m {time_elapsed % 60:.0f}s')\n",
        "    print(f'Best val Acc: {best_acc:4f}')\n",
        "    model.load_state_dict(best_model_wts)\n",
        "    return model\n",
        "net = SiameseNetwork().to(device)\n",
        "optimizer = optim.SGD(net.parameters(), lr = 0.002 )\n",
        "exp_lr_scheduler = lr_scheduler.StepLR(optimizer, step_size=90, gamma=0.5)\n",
        "net = train_model(net,optimizer,exp_lr_scheduler,200)\n"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.plot(loss_history_train,c='r')\n",
        "plt.plot(loss_history_test,c='b')\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "1flLH1vgbqB1"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "plt.plot(acc_history_train,c='r')\n",
        "plt.plot(acc_history_test,c='b')\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "gxJqhKHgiLt6"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "yjo-swx6Nrxc"
      },
      "outputs": [],
      "source": [
        "class PatchNet(nn.Module):\n",
        "    def __init__(self):\n",
        "        super(PatchNet,self).__init__()\n",
        "        self.encoder = resnet18()\n",
        "        self.encoder.fc = nn.Sequential()\n",
        "        self.fc = nn.Linear(512,9, bias=False)\n",
        "    def forward(self,x):\n",
        "        x = self.encoder(x)\n",
        "        self.fc.weight.data = F.normalize(self.fc.weight.data, p=2, dim=1) \n",
        "\n",
        "        x = F.normalize(x, p=2, dim=1)\n",
        "\n",
        "        wf = self.fc(x)\n",
        "        x = F.softmax(30 * wf)\n",
        "        return x\n",
        "\n",
        "model = PatchNet().cuda()\n",
        "model.encoder = net.encoder\n",
        "model.fc = net.ams.fc\n",
        "torch.save(model,'/content/drive/MyDrive/casia/patchnet.pt')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "azDNvnZcaao7"
      },
      "outputs": [],
      "source": [
        "running_corrects = 0\n",
        "for i, (img1, label) in enumerate(dataloaders['test']):\n",
        "        img1, label = img1.cuda(), label.cuda()\n",
        "        with torch.no_grad():\n",
        "            output = model(img1)\n",
        "            _, preds = torch.max(output,1)\n",
        "        running_corrects += torch.sum(preds == label.data)\n",
        "acc = running_corrects/dataset_sizes['test']\n",
        "print(f'Acc: {acc:.4f}')\n",
        "  "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "9aSeWuglbEDu"
      },
      "outputs": [],
      "source": [
        ""
      ]
    }
  ],
  "metadata": {
    "accelerator": "GPU",
    "colab": {
      "collapsed_sections": [],
      "name": "PatchNet.ipynb",
      "provenance": []
    },
    "gpuClass": "standard",
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}