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  },
  "cells": [
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      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
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      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/ZackPashkin/swin-transformer-pytorch-starter/blob/main/swin_transformer_pytorch_starter_wb.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
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        "outputId": "f3fea07a-4e20-4f71-99e3-d7f81c070ea5"
      },
      "source": [
        "!nvidia-smi"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Tue Jul 20 13:19:54 2021       \n",
            "+-----------------------------------------------------------------------------+\n",
            "| NVIDIA-SMI 470.42.01    Driver Version: 460.32.03    CUDA Version: 11.2     |\n",
            "|-------------------------------+----------------------+----------------------+\n",
            "| GPU  Name        Persistence-M| Bus-Id        Disp.A | Volatile Uncorr. ECC |\n",
            "| Fan  Temp  Perf  Pwr:Usage/Cap|         Memory-Usage | GPU-Util  Compute M. |\n",
            "|                               |                      |               MIG M. |\n",
            "|===============================+======================+======================|\n",
            "|   0  Tesla V100-SXM2...  Off  | 00000000:00:04.0 Off |                    0 |\n",
            "| N/A   38C    P0    39W / 300W |   1541MiB / 16160MiB |      0%      Default |\n",
            "|                               |                      |                  N/A |\n",
            "+-------------------------------+----------------------+----------------------+\n",
            "                                                                               \n",
            "+-----------------------------------------------------------------------------+\n",
            "| Processes:                                                                  |\n",
            "|  GPU   GI   CI        PID   Type   Process name                  GPU Memory |\n",
            "|        ID   ID                                                   Usage      |\n",
            "|=============================================================================|\n",
            "|  No running processes found                                                 |\n",
            "+-----------------------------------------------------------------------------+\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FTaik_C2ym7S"
      },
      "source": [
        "# uncomment for the first run\n",
        "!git clone https://github.com/microsoft/Swin-Transformer.git\n",
        "!pip install timm\n",
        "!wget https://github.com/SwinTransformer/storage/releases/download/v1.0.0/swin_tiny_patch4_window7_224.pth ./"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "pGEZGPcL7b_q",
        "outputId": "dfbae1b5-df5b-436f-8d44-32f7b40b993c"
      },
      "source": [
        "!pip install wandb -qqq\n",
        "import wandb\n",
        "# Log in to your W&B account\n",
        "wandb.login()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "True"
            ]
          },
          "metadata": {
            "tags": []
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          "execution_count": 15
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      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Bb0WI13yAJyI"
      },
      "source": [
        "%matplotlib inline\n",
        "import torch\n",
        "import torchvision\n",
        "import torchvision.transforms as transforms\n",
        "import torch.nn as nn\n",
        "\n",
        "import matplotlib.pyplot as plt\n",
        "import numpy as np"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vVHzEHkrEBzP"
      },
      "source": [
        "torch.manual_seed(42)\n",
        "device = torch.device(\"cuda:0\" if torch.cuda.is_available() else \"cpu\")\n",
        "batch_size = 128\n",
        "epochs = 30"
      ],
      "execution_count": null,
      "outputs": []
    },
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      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 117,
          "referenced_widgets": [
            "3cc3370959954d4096a0768ca637235a",
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        "outputId": "9fa63625-2b50-4727-e6d4-ecf24fbbea57"
      },
      "source": [
        "transform = transforms.Compose(\n",
        "    [transforms.Resize((224,224)),\n",
        "     transforms.ToTensor(),\n",
        "     transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))])\n",
        "\n",
        "\n",
        "trainset = torchvision.datasets.CIFAR10(root='./data', train=True,\n",
        "                                        download=True, transform=transform)\n",
        "\n",
        "trainloader = torch.utils.data.DataLoader(trainset, batch_size=batch_size,\n",
        "                                          shuffle=True, num_workers=2)\n",
        "\n",
        "\n",
        "valset = torchvision.datasets.CIFAR10(root='./data', train=False,\n",
        "                                       download=True, transform=transform)\n",
        "\n",
        "valloader = torch.utils.data.DataLoader(valset, batch_size=batch_size,\n",
        "                                         shuffle=False, num_workers=2)\n",
        "\n",
        "classes = ('plane', 'car', 'bird', 'cat',\n",
        "           'deer', 'dog', 'frog', 'horse', 'ship', 'truck')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Downloading https://www.cs.toronto.edu/~kriz/cifar-10-python.tar.gz to ./data/cifar-10-python.tar.gz\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "3cc3370959954d4096a0768ca637235a",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "HBox(children=(FloatProgress(value=0.0, max=170498071.0), HTML(value='')))"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "Extracting ./data/cifar-10-python.tar.gz to ./data\n",
            "Files already downloaded and verified\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 306
        },
        "id": "1B20jtiOAJyK",
        "outputId": "11d6b650-d9b8-4ad3-c85b-e4ba2a8ecc8c"
      },
      "source": [
        "def imshow(img):\n",
        "    img = img / 2 + 0.5     # unnormalize\n",
        "    npimg = img.numpy()\n",
        "    plt.imshow(np.transpose(npimg, (1, 2, 0)))\n",
        "    plt.show()\n",
        "\n",
        "\n",
        "# get some random training images\n",
        "dataiter = iter(trainloader)\n",
        "images, labels = dataiter.next()\n",
        "\n",
        "# show images\n",
        "imshow(torchvision.utils.make_grid(images))\n",
        "# print labels\n",
        "print(' '.join('%5s' % classes[labels[j]] for j in range(batch_size)))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            " frog plane  deer   car  bird horse truck  deer horse  ship  deer   dog  frog plane  deer  bird plane   car  frog   car  deer   cat  bird   cat  bird  deer plane horse   dog   car  ship  frog   cat   cat horse plane  deer   car horse   car   cat   car   car   dog  frog   cat horse  ship  bird   cat  deer  ship  bird   car   dog  frog truck truck  frog truck  ship   cat  deer horse  ship horse plane  bird   dog  bird   dog  ship  frog horse  deer plane   dog  bird plane   dog   car   dog   dog   dog   car  ship  frog   dog horse  deer truck   cat  bird  frog  frog horse plane   car  bird   cat  deer  frog   dog truck   cat   dog  ship  ship  bird  frog   dog plane plane  bird  bird plane  deer  deer   dog  bird  ship horse  deer horse  bird  bird plane   cat\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ZOqJdc5_B_en",
        "outputId": "ac1008f6-6fd2-4305-9a95-57af404a5258"
      },
      "source": [
        "%cd \"/content/Swin-Transformer\"\n",
        "from models.swin_transformer import SwinTransformer\n",
        "net = SwinTransformer(img_size=224,\n",
        "                                patch_size=4,\n",
        "                                in_chans=3,\n",
        "                                num_classes=1000,\n",
        "                                embed_dim=96,\n",
        "                                depths=[2, 2, 6, 2],\n",
        "                                num_heads=[ 3, 6, 12, 24 ],\n",
        "                                window_size=7,\n",
        "                                mlp_ratio=4.,\n",
        "                                qkv_bias=True,\n",
        "                                qk_scale=True,\n",
        "                                drop_rate=0.0,\n",
        "                                drop_path_rate=0.2,\n",
        "                                ape=False,\n",
        "                                patch_norm=True,\n",
        "                                use_checkpoint=True)\n",
        "\n",
        "net.load_state_dict(torch.load('/content/swin_tiny_patch4_window7_224.pth')['model'],strict=True)\n",
        "net.head = nn.Linear(net.head.in_features, 10)\n",
        "\n",
        "dummy_x = torch.randn(1, 3, 224, 224)\n",
        "logits = net(dummy_x) \n",
        "net = net.to(device)\n",
        "print(logits.shape)\n",
        "print(net.head.bias.shape)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/content/Swin-Transformer\n",
            "torch.Size([1, 10])\n",
            "torch.Size([10])\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "a8lwq9-yH9bv",
        "outputId": "c8185af3-4afc-4388-e78a-0f3308905087"
      },
      "source": [
        "# model description\n",
        "import pandas as pd\n",
        "import torch\n",
        "import torch.nn as nn\n",
        "from torch.autograd import Variable\n",
        "\n",
        "from collections import OrderedDict\n",
        "import numpy as np\n",
        "\n",
        "model = net\n",
        "input_size = (3,224,224)\n",
        "batch_size= 128\n",
        "device=\"cuda\"\n",
        "\n",
        "def register_hook(module):\n",
        "\n",
        "    def hook(module, input, output):\n",
        "        class_name = str(module.__class__).split(\".\")[-1].split(\"'\")[0]\n",
        "        module_idx = len(summary)\n",
        "\n",
        "        m_key = \"%s-%i\" % (class_name, module_idx + 1)\n",
        "        summary[m_key] = OrderedDict()\n",
        "        summary[m_key][\"input_shape\"] = list(input[0].size())\n",
        "        summary[m_key][\"input_shape\"][0] = batch_size\n",
        "        if isinstance(output, (list, tuple)):\n",
        "            summary[m_key][\"output_shape\"] = [\n",
        "                [-1] + list(o.size())[1:] for o in output\n",
        "            ]\n",
        "        else:\n",
        "            summary[m_key][\"output_shape\"] = list(output.size())\n",
        "            summary[m_key][\"output_shape\"][0] = batch_size\n",
        "\n",
        "        params = 0\n",
        "        if hasattr(module, \"weight\") and hasattr(module.weight, \"size\"):\n",
        "            params += torch.prod(torch.LongTensor(list(module.weight.size())))\n",
        "            summary[m_key][\"trainable\"] = module.weight.requires_grad\n",
        "        if hasattr(module, \"bias\") and hasattr(module.bias, \"size\"):\n",
        "            params += torch.prod(torch.LongTensor(list(module.bias.size())))\n",
        "        summary[m_key][\"nb_params\"] = params\n",
        "\n",
        "    if (\n",
        "        not isinstance(module, nn.Sequential)\n",
        "        and not isinstance(module, nn.ModuleList)\n",
        "        and not (module == model)\n",
        "    ):\n",
        "        hooks.append(module.register_forward_hook(hook))\n",
        "\n",
        "device = device.lower()\n",
        "assert device in [\n",
        "    \"cuda\",\n",
        "    \"cpu\",\n",
        "], \"Input device is not valid, please specify 'cuda' or 'cpu'\"\n",
        "\n",
        "if device == \"cuda\" and torch.cuda.is_available():\n",
        "    dtype = torch.cuda.FloatTensor\n",
        "else:\n",
        "    dtype = torch.FloatTensor\n",
        "\n",
        "# multiple inputs to the network\n",
        "if isinstance(input_size, tuple):\n",
        "    input_size = [input_size]\n",
        "\n",
        "# batch_size of 2 for batchnorm\n",
        "x = [torch.rand(2, *in_size).type(dtype) for in_size in input_size]\n",
        "# print(type(x[0]))\n",
        "\n",
        "# create properties\n",
        "summary = OrderedDict()\n",
        "hooks = []\n",
        "\n",
        "# register hook\n",
        "model.apply(register_hook)\n",
        "\n",
        "# make a forward pass\n",
        "# print(x.shape)\n",
        "model(*x)\n",
        "\n",
        "# remove these hooks\n",
        "for h in hooks:\n",
        "    h.remove()\n",
        "\n",
        "print(\"----------------------------------------------------------------\")\n",
        "line_new = \"{:>20}  {:>25} {:>15}\".format(\"Layer (type)\", \"Output Shape\", \"Param #\")\n",
        "print(line_new)\n",
        "print(\"================================================================\")\n",
        "total_params = 0\n",
        "total_output = 0\n",
        "trainable_params = 0\n",
        "for layer in summary:\n",
        "    # input_shape, output_shape, trainable, nb_params\n",
        "    line_new = \"{:>20}  {:>25} {:>15}\".format(\n",
        "        layer,\n",
        "        str(summary[layer][\"output_shape\"]),\n",
        "        \"{0:,}\".format(summary[layer][\"nb_params\"]),\n",
        "    )\n",
        "    total_params += summary[layer][\"nb_params\"]\n",
        "    total_output += np.prod(summary[layer][\"output_shape\"])\n",
        "    if \"trainable\" in summary[layer]:\n",
        "        if summary[layer][\"trainable\"] == True:\n",
        "            trainable_params += summary[layer][\"nb_params\"]\n",
        "    print(line_new)\n",
        "\n",
        "# assume 4 bytes/number (float on cuda).\n",
        "total_input_size = abs(np.prod(input_size) * batch_size * 4. / (1024 ** 2.))\n",
        "total_output_size = abs(2. * total_output * 4. / (1024 ** 2.))  # x2 for gradients\n",
        "total_params_size = abs(total_params.numpy() * 4. / (1024 ** 2.))\n",
        "total_size = total_params_size + total_output_size + total_input_size\n",
        "\n",
        "\n",
        "summary_df = pd.DataFrame({\"Total params\": int(total_params),\n",
        "                   \"Trainable params\": int(trainable_params),\n",
        "                   \"Non-trainable params\": int(total_params - trainable_params),\n",
        "                   \"Input size (MB)\": total_input_size,\n",
        "                   \"Forward/backward pass size (MB)\":total_output_size,\n",
        "                   \"Params size (MB)\":total_params_size,\n",
        "                   \"Estimated Total Size (MB)\": total_size},index=[0]).reset_index(drop=True).T\n",
        "\n",
        "summary_df"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "----------------------------------------------------------------\n",
            "        Layer (type)               Output Shape         Param #\n",
            "================================================================\n",
            "            Conv2d-1          [128, 96, 56, 56]           4,704\n",
            "         LayerNorm-2            [128, 3136, 96]             192\n",
            "        PatchEmbed-3            [128, 3136, 96]               0\n",
            "           Dropout-4            [128, 3136, 96]               0\n",
            "         LayerNorm-5            [128, 3136, 96]             192\n",
            "            Linear-6             [128, 49, 288]          27,936\n",
            "           Softmax-7           [128, 3, 49, 49]               0\n",
            "           Dropout-8           [128, 3, 49, 49]               0\n",
            "            Linear-9              [128, 49, 96]           9,312\n",
            "          Dropout-10              [128, 49, 96]               0\n",
            "  WindowAttention-11              [128, 49, 96]               0\n",
            "         Identity-12            [128, 3136, 96]               0\n",
            "        LayerNorm-13            [128, 3136, 96]             192\n",
            "           Linear-14           [128, 3136, 384]          37,248\n",
            "             GELU-15           [128, 3136, 384]               0\n",
            "          Dropout-16           [128, 3136, 384]               0\n",
            "           Linear-17            [128, 3136, 96]          36,960\n",
            "          Dropout-18            [128, 3136, 96]               0\n",
            "              Mlp-19            [128, 3136, 96]               0\n",
            "         Identity-20            [128, 3136, 96]               0\n",
            "SwinTransformerBlock-21            [128, 3136, 96]               0\n",
            "        LayerNorm-22            [128, 3136, 96]             192\n",
            "           Linear-23             [128, 49, 288]          27,936\n",
            "          Softmax-24           [128, 3, 49, 49]               0\n",
            "          Dropout-25           [128, 3, 49, 49]               0\n",
            "           Linear-26              [128, 49, 96]           9,312\n",
            "          Dropout-27              [128, 49, 96]               0\n",
            "  WindowAttention-28              [128, 49, 96]               0\n",
            "         DropPath-29            [128, 3136, 96]               0\n",
            "        LayerNorm-30            [128, 3136, 96]             192\n",
            "           Linear-31           [128, 3136, 384]          37,248\n",
            "             GELU-32           [128, 3136, 384]               0\n",
            "          Dropout-33           [128, 3136, 384]               0\n",
            "           Linear-34            [128, 3136, 96]          36,960\n",
            "          Dropout-35            [128, 3136, 96]               0\n",
            "              Mlp-36            [128, 3136, 96]               0\n",
            "         DropPath-37            [128, 3136, 96]               0\n",
            "SwinTransformerBlock-38            [128, 3136, 96]               0\n",
            "        LayerNorm-39            [128, 784, 384]             768\n",
            "           Linear-40            [128, 784, 192]          73,728\n",
            "     PatchMerging-41            [128, 784, 192]               0\n",
            "       BasicLayer-42            [128, 784, 192]               0\n",
            "        LayerNorm-43            [128, 784, 192]             384\n",
            "           Linear-44             [128, 49, 576]         111,168\n",
            "          Softmax-45           [128, 6, 49, 49]               0\n",
            "          Dropout-46           [128, 6, 49, 49]               0\n",
            "           Linear-47             [128, 49, 192]          37,056\n",
            "          Dropout-48             [128, 49, 192]               0\n",
            "  WindowAttention-49             [128, 49, 192]               0\n",
            "         DropPath-50            [128, 784, 192]               0\n",
            "        LayerNorm-51            [128, 784, 192]             384\n",
            "           Linear-52            [128, 784, 768]         148,224\n",
            "             GELU-53            [128, 784, 768]               0\n",
            "          Dropout-54            [128, 784, 768]               0\n",
            "           Linear-55            [128, 784, 192]         147,648\n",
            "          Dropout-56            [128, 784, 192]               0\n",
            "              Mlp-57            [128, 784, 192]               0\n",
            "         DropPath-58            [128, 784, 192]               0\n",
            "SwinTransformerBlock-59            [128, 784, 192]               0\n",
            "        LayerNorm-60            [128, 784, 192]             384\n",
            "           Linear-61             [128, 49, 576]         111,168\n",
            "          Softmax-62           [128, 6, 49, 49]               0\n",
            "          Dropout-63           [128, 6, 49, 49]               0\n",
            "           Linear-64             [128, 49, 192]          37,056\n",
            "          Dropout-65             [128, 49, 192]               0\n",
            "  WindowAttention-66             [128, 49, 192]               0\n",
            "         DropPath-67            [128, 784, 192]               0\n",
            "        LayerNorm-68            [128, 784, 192]             384\n",
            "           Linear-69            [128, 784, 768]         148,224\n",
            "             GELU-70            [128, 784, 768]               0\n",
            "          Dropout-71            [128, 784, 768]               0\n",
            "           Linear-72            [128, 784, 192]         147,648\n",
            "          Dropout-73            [128, 784, 192]               0\n",
            "              Mlp-74            [128, 784, 192]               0\n",
            "         DropPath-75            [128, 784, 192]               0\n",
            "SwinTransformerBlock-76            [128, 784, 192]               0\n",
            "        LayerNorm-77            [128, 196, 768]           1,536\n",
            "           Linear-78            [128, 196, 384]         294,912\n",
            "     PatchMerging-79            [128, 196, 384]               0\n",
            "       BasicLayer-80            [128, 196, 384]               0\n",
            "        LayerNorm-81            [128, 196, 384]             768\n",
            "           Linear-82            [128, 49, 1152]         443,520\n",
            "          Softmax-83          [128, 12, 49, 49]               0\n",
            "          Dropout-84          [128, 12, 49, 49]               0\n",
            "           Linear-85             [128, 49, 384]         147,840\n",
            "          Dropout-86             [128, 49, 384]               0\n",
            "  WindowAttention-87             [128, 49, 384]               0\n",
            "         DropPath-88            [128, 196, 384]               0\n",
            "        LayerNorm-89            [128, 196, 384]             768\n",
            "           Linear-90           [128, 196, 1536]         591,360\n",
            "             GELU-91           [128, 196, 1536]               0\n",
            "          Dropout-92           [128, 196, 1536]               0\n",
            "           Linear-93            [128, 196, 384]         590,208\n",
            "          Dropout-94            [128, 196, 384]               0\n",
            "              Mlp-95            [128, 196, 384]               0\n",
            "         DropPath-96            [128, 196, 384]               0\n",
            "SwinTransformerBlock-97            [128, 196, 384]               0\n",
            "        LayerNorm-98            [128, 196, 384]             768\n",
            "           Linear-99            [128, 49, 1152]         443,520\n",
            "         Softmax-100          [128, 12, 49, 49]               0\n",
            "         Dropout-101          [128, 12, 49, 49]               0\n",
            "          Linear-102             [128, 49, 384]         147,840\n",
            "         Dropout-103             [128, 49, 384]               0\n",
            " WindowAttention-104             [128, 49, 384]               0\n",
            "        DropPath-105            [128, 196, 384]               0\n",
            "       LayerNorm-106            [128, 196, 384]             768\n",
            "          Linear-107           [128, 196, 1536]         591,360\n",
            "            GELU-108           [128, 196, 1536]               0\n",
            "         Dropout-109           [128, 196, 1536]               0\n",
            "          Linear-110            [128, 196, 384]         590,208\n",
            "         Dropout-111            [128, 196, 384]               0\n",
            "             Mlp-112            [128, 196, 384]               0\n",
            "        DropPath-113            [128, 196, 384]               0\n",
            "SwinTransformerBlock-114            [128, 196, 384]               0\n",
            "       LayerNorm-115            [128, 196, 384]             768\n",
            "          Linear-116            [128, 49, 1152]         443,520\n",
            "         Softmax-117          [128, 12, 49, 49]               0\n",
            "         Dropout-118          [128, 12, 49, 49]               0\n",
            "          Linear-119             [128, 49, 384]         147,840\n",
            "         Dropout-120             [128, 49, 384]               0\n",
            " WindowAttention-121             [128, 49, 384]               0\n",
            "        DropPath-122            [128, 196, 384]               0\n",
            "       LayerNorm-123            [128, 196, 384]             768\n",
            "          Linear-124           [128, 196, 1536]         591,360\n",
            "            GELU-125           [128, 196, 1536]               0\n",
            "         Dropout-126           [128, 196, 1536]               0\n",
            "          Linear-127            [128, 196, 384]         590,208\n",
            "         Dropout-128            [128, 196, 384]               0\n",
            "             Mlp-129            [128, 196, 384]               0\n",
            "        DropPath-130            [128, 196, 384]               0\n",
            "SwinTransformerBlock-131            [128, 196, 384]               0\n",
            "       LayerNorm-132            [128, 196, 384]             768\n",
            "          Linear-133            [128, 49, 1152]         443,520\n",
            "         Softmax-134          [128, 12, 49, 49]               0\n",
            "         Dropout-135          [128, 12, 49, 49]               0\n",
            "          Linear-136             [128, 49, 384]         147,840\n",
            "         Dropout-137             [128, 49, 384]               0\n",
            " WindowAttention-138             [128, 49, 384]               0\n",
            "        DropPath-139            [128, 196, 384]               0\n",
            "       LayerNorm-140            [128, 196, 384]             768\n",
            "          Linear-141           [128, 196, 1536]         591,360\n",
            "            GELU-142           [128, 196, 1536]               0\n",
            "         Dropout-143           [128, 196, 1536]               0\n",
            "          Linear-144            [128, 196, 384]         590,208\n",
            "         Dropout-145            [128, 196, 384]               0\n",
            "             Mlp-146            [128, 196, 384]               0\n",
            "        DropPath-147            [128, 196, 384]               0\n",
            "SwinTransformerBlock-148            [128, 196, 384]               0\n",
            "       LayerNorm-149            [128, 196, 384]             768\n",
            "          Linear-150            [128, 49, 1152]         443,520\n",
            "         Softmax-151          [128, 12, 49, 49]               0\n",
            "         Dropout-152          [128, 12, 49, 49]               0\n",
            "          Linear-153             [128, 49, 384]         147,840\n",
            "         Dropout-154             [128, 49, 384]               0\n",
            " WindowAttention-155             [128, 49, 384]               0\n",
            "        DropPath-156            [128, 196, 384]               0\n",
            "       LayerNorm-157            [128, 196, 384]             768\n",
            "          Linear-158           [128, 196, 1536]         591,360\n",
            "            GELU-159           [128, 196, 1536]               0\n",
            "         Dropout-160           [128, 196, 1536]               0\n",
            "          Linear-161            [128, 196, 384]         590,208\n",
            "         Dropout-162            [128, 196, 384]               0\n",
            "             Mlp-163            [128, 196, 384]               0\n",
            "        DropPath-164            [128, 196, 384]               0\n",
            "SwinTransformerBlock-165            [128, 196, 384]               0\n",
            "       LayerNorm-166            [128, 196, 384]             768\n",
            "          Linear-167            [128, 49, 1152]         443,520\n",
            "         Softmax-168          [128, 12, 49, 49]               0\n",
            "         Dropout-169          [128, 12, 49, 49]               0\n",
            "          Linear-170             [128, 49, 384]         147,840\n",
            "         Dropout-171             [128, 49, 384]               0\n",
            " WindowAttention-172             [128, 49, 384]               0\n",
            "        DropPath-173            [128, 196, 384]               0\n",
            "       LayerNorm-174            [128, 196, 384]             768\n",
            "          Linear-175           [128, 196, 1536]         591,360\n",
            "            GELU-176           [128, 196, 1536]               0\n",
            "         Dropout-177           [128, 196, 1536]               0\n",
            "          Linear-178            [128, 196, 384]         590,208\n",
            "         Dropout-179            [128, 196, 384]               0\n",
            "             Mlp-180            [128, 196, 384]               0\n",
            "        DropPath-181            [128, 196, 384]               0\n",
            "SwinTransformerBlock-182            [128, 196, 384]               0\n",
            "       LayerNorm-183            [128, 49, 1536]           3,072\n",
            "          Linear-184             [128, 49, 768]       1,179,648\n",
            "    PatchMerging-185             [128, 49, 768]               0\n",
            "      BasicLayer-186             [128, 49, 768]               0\n",
            "       LayerNorm-187             [128, 49, 768]           1,536\n",
            "          Linear-188            [128, 49, 2304]       1,771,776\n",
            "         Softmax-189          [128, 24, 49, 49]               0\n",
            "         Dropout-190          [128, 24, 49, 49]               0\n",
            "          Linear-191             [128, 49, 768]         590,592\n",
            "         Dropout-192             [128, 49, 768]               0\n",
            " WindowAttention-193             [128, 49, 768]               0\n",
            "        DropPath-194             [128, 49, 768]               0\n",
            "       LayerNorm-195             [128, 49, 768]           1,536\n",
            "          Linear-196            [128, 49, 3072]       2,362,368\n",
            "            GELU-197            [128, 49, 3072]               0\n",
            "         Dropout-198            [128, 49, 3072]               0\n",
            "          Linear-199             [128, 49, 768]       2,360,064\n",
            "         Dropout-200             [128, 49, 768]               0\n",
            "             Mlp-201             [128, 49, 768]               0\n",
            "        DropPath-202             [128, 49, 768]               0\n",
            "SwinTransformerBlock-203             [128, 49, 768]               0\n",
            "       LayerNorm-204             [128, 49, 768]           1,536\n",
            "          Linear-205            [128, 49, 2304]       1,771,776\n",
            "         Softmax-206          [128, 24, 49, 49]               0\n",
            "         Dropout-207          [128, 24, 49, 49]               0\n",
            "          Linear-208             [128, 49, 768]         590,592\n",
            "         Dropout-209             [128, 49, 768]               0\n",
            " WindowAttention-210             [128, 49, 768]               0\n",
            "        DropPath-211             [128, 49, 768]               0\n",
            "       LayerNorm-212             [128, 49, 768]           1,536\n",
            "          Linear-213            [128, 49, 3072]       2,362,368\n",
            "            GELU-214            [128, 49, 3072]               0\n",
            "         Dropout-215            [128, 49, 3072]               0\n",
            "          Linear-216             [128, 49, 768]       2,360,064\n",
            "         Dropout-217             [128, 49, 768]               0\n",
            "             Mlp-218             [128, 49, 768]               0\n",
            "        DropPath-219             [128, 49, 768]               0\n",
            "SwinTransformerBlock-220             [128, 49, 768]               0\n",
            "      BasicLayer-221             [128, 49, 768]               0\n",
            "       LayerNorm-222             [128, 49, 768]           1,536\n",
            "AdaptiveAvgPool1d-223              [128, 768, 1]               0\n",
            "          Linear-224                  [128, 10]           7,690\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
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              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Total params</th>\n",
              "      <td>2.750372e+07</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Trainable params</th>\n",
              "      <td>2.750372e+07</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Non-trainable params</th>\n",
              "      <td>0.000000e+00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Input size (MB)</th>\n",
              "      <td>7.350000e+01</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Forward/backward pass size (MB)</th>\n",
              "      <td>3.238153e+04</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Params size (MB)</th>\n",
              "      <td>1.049184e+02</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Estimated Total Size (MB)</th>\n",
              "      <td>3.255995e+04</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                                            0\n",
              "Total params                     2.750372e+07\n",
              "Trainable params                 2.750372e+07\n",
              "Non-trainable params             0.000000e+00\n",
              "Input size (MB)                  7.350000e+01\n",
              "Forward/backward pass size (MB)  3.238153e+04\n",
              "Params size (MB)                 1.049184e+02\n",
              "Estimated Total Size (MB)        3.255995e+04"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WyhPsy4WAJyM"
      },
      "source": [
        "import torch.optim as optim\n",
        "lr = 5e-4\n",
        "criterion = nn.CrossEntropyLoss()\n",
        "optimizer = optim.SGD(net.parameters(), lr=lr, momentum=0.9)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 119
        },
        "id": "-NpCkoiYvd8J",
        "outputId": "5606d03f-5dad-44b8-d659-dec7c35e4278"
      },
      "source": [
        "# Start a run, tracking hyperparameters\n",
        "wandb.init(\n",
        "  project=\"resnet50 vs swin transformer tiny\",\n",
        "  config={\n",
        "      \"summary\": summary_df,\n",
        "      \"optimizer\": optimizer,\n",
        "      \"loss\": criterion,\n",
        "      \"learning rate\": lr,\n",
        "      \"metric\": \"accuracy\",\n",
        "      \"epoch\": epochs,\n",
        "      \"batch_size\": batch_size,\n",
        "  })\n",
        "config = wandb.config"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "                Tracking run with wandb version 0.11.0<br/>\n",
              "                Syncing run <strong style=\"color:#cdcd00\">earnest-thunder-2</strong> to <a href=\"https://wandb.ai\" target=\"_blank\">Weights & Biases</a> <a href=\"https://docs.wandb.com/integrations/jupyter.html\" target=\"_blank\">(Documentation)</a>.<br/>\n",
              "                Project page: <a href=\"https://wandb.ai/zack_/resnet50%20vs%20swin%20transformer%20tiny\" target=\"_blank\">https://wandb.ai/zack_/resnet50%20vs%20swin%20transformer%20tiny</a><br/>\n",
              "                Run page: <a href=\"https://wandb.ai/zack_/resnet50%20vs%20swin%20transformer%20tiny/runs/3j4c1vs3\" target=\"_blank\">https://wandb.ai/zack_/resnet50%20vs%20swin%20transformer%20tiny/runs/3j4c1vs3</a><br/>\n",
              "                Run data is saved locally in <code>/content/Swin-Transformer/wandb/run-20210720_132012-3j4c1vs3</code><br/><br/>\n",
              "            "
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
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          "metadata": {
            "tags": []
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      "cell_type": "code",
      "metadata": {
        "colab": {
          "background_save": true,
          "base_uri": "https://localhost:8080/",
          "height": 1000,
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        },
        "id": "OyPySHxtAJyM",
        "outputId": "8036a6ee-e818-4f98-c16c-aad68a0750ca"
      },
      "source": [
        "from tqdm.notebook import tqdm\n",
        "from time import time\n",
        "\n",
        "for epoch in tqdm(range(epochs), \"Epoch: \"): \n",
        "    net.train()\n",
        "    optimizer.zero_grad()\n",
        "\n",
        "    start = time()\n",
        "\n",
        "\n",
        "    running_loss = 0.0\n",
        "    tqdm_train = tqdm(enumerate(trainloader, 0), \"Train step: \", total=len(trainloader))\n",
        "    for i, data in tqdm_train:\n",
        "\n",
        "        inputs, labels = data[0].to(device), data[1].to(device)\n",
        "\n",
        "        # zero the parameter gradients\n",
        "        optimizer.zero_grad()\n",
        "\n",
        "        # forward + backward + optimize\n",
        "        outputs = net(inputs)\n",
        "        loss = criterion(outputs, labels)\n",
        "        loss.backward()\n",
        "        optimizer.step()\n",
        "\n",
        "        # print statistics\n",
        "        running_loss += loss.item()\n",
        "        tqdm_train.set_postfix(loss=loss.item())\n",
        "        \n",
        "\n",
        "    \n",
        "    epoch_time = time() - start\n",
        "    print(f\"Epoch time: {epoch_time/60:.3f} min\" )\n",
        "    print(f\"Epoch {epoch + 1} Train loss: {running_loss / len(trainloader):.3f}\")\n",
        "    len_trainloader = len(trainloader)\n",
        "    wandb.log({\"Epoch\":epoch + 1,\n",
        "               \"Epoch time\": epoch_time/60,\n",
        "               \"Train loss\":running_loss/len_trainloader,\n",
        "               })\n",
        "    \n",
        "    running_loss = 0.0\n",
        "    correct_pred = {classname: 0 for classname in classes}\n",
        "    total_pred = {classname: 0 for classname in classes}\n",
        "\n",
        "    with torch.no_grad():\n",
        "        net.eval()\n",
        "        tqdm_val = tqdm(enumerate(valloader, 0), \"Val step: \", total=len(valloader))\n",
        "        for i, data in tqdm_val:\n",
        "            inputs, labels = data[0].to(device), data[1].to(device)  \n",
        "            outputs = net(inputs)\n",
        "            loss = criterion(outputs, labels)\n",
        "            # print statistics\n",
        "            running_loss += loss.item()\n",
        "            tqdm_val.set_postfix(loss=loss.item())\n",
        "            _, predictions = torch.max(outputs, 1)\n",
        "            # collect the correct predictions for each class\n",
        "            for label, prediction in list(zip(labels, predictions)):\n",
        "                if label == prediction:\n",
        "                    correct_pred[classes[label]] += 1\n",
        "                total_pred[classes[label]] += 1 \n",
        "               \n",
        "    # print accuracy for each class\n",
        "    accuracy_sum = []\n",
        "    classnames = []\n",
        "    for classname, correct_count in correct_pred.items():\n",
        "        accuracy = 100 * float(correct_count) / total_pred[classname]\n",
        "        accuracy_sum.append(accuracy)\n",
        "        classnames.append(classname)\n",
        "        print(f\"Accuracy for class {classname}: {accuracy:.1f}%\")  \n",
        "        \n",
        "    classnames_accuracy = list(zip(classnames,accuracy_sum))\n",
        "    print(f\"Epoch {epoch + 1} Val loss: {running_loss / len(valloader):.3f}\")\n",
        "    print(f\"Total accuracy: {sum(accuracy_sum)/len(accuracy_sum)}\")\n",
        "    len_valloader = len(valloader)\n",
        "    wandb.log({\"Epoch\":epoch + 1, \n",
        "               \"Val loss\":running_loss / len_valloader,\n",
        "               \"Accuracy by class\": classnames_accuracy,\n",
        "               \"Total accuracy\": sum(accuracy_sum)/len(accuracy_sum),\n",
        "               })\n",
        "\n",
        "# Mark the run as finished\n",
        "wandb.finish()\n",
        "print('Training comleted')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "6383f7880e49406d81f91ab973810305",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "HBox(children=(FloatProgress(value=0.0, description='Epoch: ', max=30.0, style=ProgressStyle(description_width…"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "f624a2a0a2d243b999f332895c0281dc",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "HBox(children=(FloatProgress(value=0.0, description='Train step: ', max=391.0, style=ProgressStyle(description…"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "Epoch time: 5.251 min\n",
            "Epoch 1 Train loss: 2.085\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "bdeb28005a2f49f4bf1a28a70d371f20",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "HBox(children=(FloatProgress(value=0.0, description='Val step: ', max=79.0, style=ProgressStyle(description_wi…"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/torch/utils/checkpoint.py:25: UserWarning: None of the inputs have requires_grad=True. Gradients will be None\n",
            "  warnings.warn(\"None of the inputs have requires_grad=True. Gradients will be None\")\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "Accuracy for class plane: 46.6%\n",
            "Accuracy for class car: 38.5%\n",
            "Accuracy for class bird: 17.2%\n",
            "Accuracy for class cat: 11.6%\n",
            "Accuracy for class deer: 21.9%\n",
            "Accuracy for class dog: 55.4%\n",
            "Accuracy for class frog: 32.9%\n",
            "Accuracy for class horse: 38.0%\n",
            "Accuracy for class ship: 33.8%\n",
            "Accuracy for class truck: 42.7%\n",
            "Epoch 1 Val loss: 1.758\n",
            "Total accuracy: 33.86\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "b72e086fd94141a58d9f0589001d58bd",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "HBox(children=(FloatProgress(value=0.0, description='Train step: ', max=391.0, style=ProgressStyle(description…"
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          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "Epoch time: 5.275 min\n",
            "Epoch 2 Train loss: 1.691\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "0140b6d2c5e8454f92e474cae1bad669",
              "version_major": 2,
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              "HBox(children=(FloatProgress(value=0.0, description='Val step: ', max=79.0, style=ProgressStyle(description_wi…"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "Accuracy for class plane: 52.3%\n",
            "Accuracy for class car: 62.1%\n",
            "Accuracy for class bird: 29.6%\n",
            "Accuracy for class cat: 28.5%\n",
            "Accuracy for class deer: 42.4%\n",
            "Accuracy for class dog: 51.4%\n",
            "Accuracy for class frog: 64.4%\n",
            "Accuracy for class horse: 50.3%\n",
            "Accuracy for class ship: 67.4%\n",
            "Accuracy for class truck: 34.0%\n",
            "Epoch 2 Val loss: 1.426\n",
            "Total accuracy: 48.24000000000001\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "d286a0e06b734c4d91c1a9875548ac96",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "HBox(children=(FloatProgress(value=0.0, description='Train step: ', max=391.0, style=ProgressStyle(description…"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "Epoch time: 5.299 min\n",
            "Epoch 3 Train loss: 1.306\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "7eb0c6574a8240499e9c720101e0883e",
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              "HBox(children=(FloatProgress(value=0.0, description='Val step: ', max=79.0, style=ProgressStyle(description_wi…"
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          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "Accuracy for class plane: 66.3%\n",
            "Accuracy for class car: 81.5%\n",
            "Accuracy for class bird: 59.8%\n",
            "Accuracy for class cat: 60.3%\n",
            "Accuracy for class deer: 51.1%\n",
            "Accuracy for class dog: 54.6%\n",
            "Accuracy for class frog: 62.3%\n",
            "Accuracy for class horse: 70.1%\n",
            "Accuracy for class ship: 78.1%\n",
            "Accuracy for class truck: 72.6%\n",
            "Epoch 3 Val loss: 0.973\n",
            "Total accuracy: 65.67000000000002\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "793ad592dc4743359999ddf8f3a56fbf",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "HBox(children=(FloatProgress(value=0.0, description='Train step: ', max=391.0, style=ProgressStyle(description…"
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          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "Epoch time: 5.288 min\n",
            "Epoch 4 Train loss: 0.897\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
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              "HBox(children=(FloatProgress(value=0.0, description='Val step: ', max=79.0, style=ProgressStyle(description_wi…"
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          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "Accuracy for class plane: 86.5%\n",
            "Accuracy for class car: 89.3%\n",
            "Accuracy for class bird: 66.9%\n",
            "Accuracy for class cat: 61.3%\n",
            "Accuracy for class deer: 83.1%\n",
            "Accuracy for class dog: 74.9%\n",
            "Accuracy for class frog: 84.3%\n",
            "Accuracy for class horse: 80.4%\n",
            "Accuracy for class ship: 82.8%\n",
            "Accuracy for class truck: 90.7%\n",
            "Epoch 4 Val loss: 0.590\n",
            "Total accuracy: 80.02\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
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          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "Epoch time: 5.284 min\n",
            "Epoch 5 Train loss: 0.581\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
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              "HBox(children=(FloatProgress(value=0.0, description='Val step: ', max=79.0, style=ProgressStyle(description_wi…"
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          },
          "metadata": {
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          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "Accuracy for class plane: 86.8%\n",
            "Accuracy for class car: 96.1%\n",
            "Accuracy for class bird: 80.6%\n",
            "Accuracy for class cat: 80.4%\n",
            "Accuracy for class deer: 89.4%\n",
            "Accuracy for class dog: 87.3%\n",
            "Accuracy for class frog: 89.6%\n",
            "Accuracy for class horse: 90.8%\n",
            "Accuracy for class ship: 92.8%\n",
            "Accuracy for class truck: 91.2%\n",
            "Epoch 5 Val loss: 0.351\n",
            "Total accuracy: 88.49999999999999\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
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              "HBox(children=(FloatProgress(value=0.0, description='Train step: ', max=391.0, style=ProgressStyle(description…"
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          },
          "metadata": {
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          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "Epoch time: 5.283 min\n",
            "Epoch 6 Train loss: 0.390\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
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              "HBox(children=(FloatProgress(value=0.0, description='Val step: ', max=79.0, style=ProgressStyle(description_wi…"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "Accuracy for class plane: 90.9%\n",
            "Accuracy for class car: 95.8%\n",
            "Accuracy for class bird: 91.3%\n",
            "Accuracy for class cat: 81.4%\n",
            "Accuracy for class deer: 90.0%\n",
            "Accuracy for class dog: 90.3%\n",
            "Accuracy for class frog: 90.6%\n",
            "Accuracy for class horse: 93.9%\n",
            "Accuracy for class ship: 94.3%\n",
            "Accuracy for class truck: 95.3%\n",
            "Epoch 6 Val loss: 0.249\n",
            "Total accuracy: 91.37999999999998\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "9d2d7e3cfc3d419a919e0e3f043b5762",
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              "version_minor": 0
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              "HBox(children=(FloatProgress(value=0.0, description='Train step: ', max=391.0, style=ProgressStyle(description…"
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          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "Epoch time: 5.281 min\n",
            "Epoch 7 Train loss: 0.309\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
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          },
          "metadata": {
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          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "Accuracy for class plane: 95.3%\n",
            "Accuracy for class car: 95.1%\n",
            "Accuracy for class bird: 91.3%\n",
            "Accuracy for class cat: 80.3%\n",
            "Accuracy for class deer: 90.3%\n",
            "Accuracy for class dog: 93.1%\n",
            "Accuracy for class frog: 95.7%\n",
            "Accuracy for class horse: 94.7%\n",
            "Accuracy for class ship: 95.1%\n",
            "Accuracy for class truck: 96.0%\n",
            "Epoch 7 Val loss: 0.211\n",
            "Total accuracy: 92.69000000000001\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "d2faa5d1ba6b4ed3af732ae284f803f6",
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              "version_minor": 0
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              "HBox(children=(FloatProgress(value=0.0, description='Train step: ', max=391.0, style=ProgressStyle(description…"
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          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "Epoch time: 5.277 min\n",
            "Epoch 8 Train loss: 0.253\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
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          },
          "metadata": {
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          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "Accuracy for class plane: 93.5%\n",
            "Accuracy for class car: 98.4%\n",
            "Accuracy for class bird: 93.3%\n",
            "Accuracy for class cat: 86.7%\n",
            "Accuracy for class deer: 92.7%\n",
            "Accuracy for class dog: 90.9%\n",
            "Accuracy for class frog: 97.2%\n",
            "Accuracy for class horse: 96.3%\n",
            "Accuracy for class ship: 96.8%\n",
            "Accuracy for class truck: 95.1%\n",
            "Epoch 8 Val loss: 0.181\n",
            "Total accuracy: 94.09\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
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          },
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        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "Epoch time: 5.283 min\n",
            "Epoch 9 Train loss: 0.223\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
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              "HBox(children=(FloatProgress(value=0.0, description='Val step: ', max=79.0, style=ProgressStyle(description_wi…"
            ]
          },
          "metadata": {
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          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "Accuracy for class plane: 95.1%\n",
            "Accuracy for class car: 97.6%\n",
            "Accuracy for class bird: 92.0%\n",
            "Accuracy for class cat: 85.9%\n",
            "Accuracy for class deer: 93.8%\n",
            "Accuracy for class dog: 92.7%\n",
            "Accuracy for class frog: 97.5%\n",
            "Accuracy for class horse: 94.5%\n",
            "Accuracy for class ship: 97.5%\n",
            "Accuracy for class truck: 94.9%\n",
            "Epoch 9 Val loss: 0.172\n",
            "Total accuracy: 94.15\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "c8fdc452869b4be5825829114beb9eb3",
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              "HBox(children=(FloatProgress(value=0.0, description='Train step: ', max=391.0, style=ProgressStyle(description…"
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          },
          "metadata": {
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          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "Epoch time: 5.275 min\n",
            "Epoch 10 Train loss: 0.198\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "0987db6a414d4b5f9c32f93f28d80fe7",
              "version_major": 2,
              "version_minor": 0
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              "HBox(children=(FloatProgress(value=0.0, description='Val step: ', max=79.0, style=ProgressStyle(description_wi…"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "Accuracy for class plane: 96.4%\n",
            "Accuracy for class car: 98.6%\n",
            "Accuracy for class bird: 95.8%\n",
            "Accuracy for class cat: 88.4%\n",
            "Accuracy for class deer: 94.7%\n",
            "Accuracy for class dog: 91.8%\n",
            "Accuracy for class frog: 95.9%\n",
            "Accuracy for class horse: 95.1%\n",
            "Accuracy for class ship: 95.2%\n",
            "Accuracy for class truck: 95.6%\n",
            "Epoch 10 Val loss: 0.159\n",
            "Total accuracy: 94.75000000000001\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "ad4e4d4579a2423298305272452446ea",
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              "version_minor": 0
            },
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              "HBox(children=(FloatProgress(value=0.0, description='Train step: ', max=391.0, style=ProgressStyle(description…"
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          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "Epoch time: 5.281 min\n",
            "Epoch 11 Train loss: 0.180\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "f5f945c8e0c74e16bb2f0ccdc37ebc6d",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "HBox(children=(FloatProgress(value=0.0, description='Val step: ', max=79.0, style=ProgressStyle(description_wi…"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "Accuracy for class plane: 97.2%\n",
            "Accuracy for class car: 98.0%\n",
            "Accuracy for class bird: 91.6%\n",
            "Accuracy for class cat: 89.5%\n",
            "Accuracy for class deer: 95.1%\n",
            "Accuracy for class dog: 93.1%\n",
            "Accuracy for class frog: 96.7%\n",
            "Accuracy for class horse: 95.3%\n",
            "Accuracy for class ship: 95.2%\n",
            "Accuracy for class truck: 96.9%\n",
            "Epoch 11 Val loss: 0.155\n",
            "Total accuracy: 94.86\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "f1c6e99e4bc7449e97ebf1187a02baa2",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "HBox(children=(FloatProgress(value=0.0, description='Train step: ', max=391.0, style=ProgressStyle(description…"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "Epoch time: 5.276 min\n",
            "Epoch 12 Train loss: 0.160\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "9616372c9c3243f59abfb56390281429",
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              "HBox(children=(FloatProgress(value=0.0, description='Val step: ', max=79.0, style=ProgressStyle(description_wi…"
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          },
          "metadata": {
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          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "Accuracy for class plane: 95.5%\n",
            "Accuracy for class car: 98.2%\n",
            "Accuracy for class bird: 94.1%\n",
            "Accuracy for class cat: 88.9%\n",
            "Accuracy for class deer: 95.8%\n",
            "Accuracy for class dog: 93.9%\n",
            "Accuracy for class frog: 98.5%\n",
            "Accuracy for class horse: 93.7%\n",
            "Accuracy for class ship: 97.9%\n",
            "Accuracy for class truck: 96.0%\n",
            "Epoch 12 Val loss: 0.145\n",
            "Total accuracy: 95.25\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "ae73a2fc0c0c4e3fa343c7f05f31f843",
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              "version_minor": 0
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              "HBox(children=(FloatProgress(value=0.0, description='Train step: ', max=391.0, style=ProgressStyle(description…"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "Epoch time: 5.273 min\n",
            "Epoch 13 Train loss: 0.147\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "1c1e171b71be4aa89cd155ed9021917e",
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              "HBox(children=(FloatProgress(value=0.0, description='Val step: ', max=79.0, style=ProgressStyle(description_wi…"
            ]
          },
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          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "Accuracy for class plane: 95.0%\n",
            "Accuracy for class car: 98.2%\n",
            "Accuracy for class bird: 94.9%\n",
            "Accuracy for class cat: 89.1%\n",
            "Accuracy for class deer: 93.3%\n",
            "Accuracy for class dog: 93.5%\n",
            "Accuracy for class frog: 98.2%\n",
            "Accuracy for class horse: 96.3%\n",
            "Accuracy for class ship: 97.3%\n",
            "Accuracy for class truck: 96.6%\n",
            "Epoch 13 Val loss: 0.136\n",
            "Total accuracy: 95.24\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "b21a5d35126f4e209ddcf091e186f0bd",
              "version_major": 2,
              "version_minor": 0
            },
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              "HBox(children=(FloatProgress(value=0.0, description='Train step: ', max=391.0, style=ProgressStyle(description…"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "Epoch time: 5.278 min\n",
            "Epoch 14 Train loss: 0.137\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
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              "HBox(children=(FloatProgress(value=0.0, description='Val step: ', max=79.0, style=ProgressStyle(description_wi…"
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          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "Accuracy for class plane: 97.1%\n",
            "Accuracy for class car: 98.6%\n",
            "Accuracy for class bird: 94.5%\n",
            "Accuracy for class cat: 87.9%\n",
            "Accuracy for class deer: 95.3%\n",
            "Accuracy for class dog: 94.7%\n",
            "Accuracy for class frog: 97.1%\n",
            "Accuracy for class horse: 96.2%\n",
            "Accuracy for class ship: 96.2%\n",
            "Accuracy for class truck: 96.9%\n",
            "Epoch 14 Val loss: 0.136\n",
            "Total accuracy: 95.45000000000002\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "eff4a43ada064d5faf3787cb99102810",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "HBox(children=(FloatProgress(value=0.0, description='Train step: ', max=391.0, style=ProgressStyle(description…"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "Epoch time: 5.281 min\n",
            "Epoch 15 Train loss: 0.122\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "78801a7ee9044ad3aacd8533bc6d55f6",
              "version_major": 2,
              "version_minor": 0
            },
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              "HBox(children=(FloatProgress(value=0.0, description='Val step: ', max=79.0, style=ProgressStyle(description_wi…"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "Accuracy for class plane: 97.9%\n",
            "Accuracy for class car: 98.7%\n",
            "Accuracy for class bird: 93.4%\n",
            "Accuracy for class cat: 90.4%\n",
            "Accuracy for class deer: 95.3%\n",
            "Accuracy for class dog: 93.2%\n",
            "Accuracy for class frog: 98.4%\n",
            "Accuracy for class horse: 96.6%\n",
            "Accuracy for class ship: 96.2%\n",
            "Accuracy for class truck: 95.9%\n",
            "Epoch 15 Val loss: 0.141\n",
            "Total accuracy: 95.6\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "1291defabd7045c1958ccd112db1c51c",
              "version_major": 2,
              "version_minor": 0
            },
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              "HBox(children=(FloatProgress(value=0.0, description='Train step: ', max=391.0, style=ProgressStyle(description…"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Fq-4dOMkE0xN"
      },
      "source": [
        "# gpu load  7107MiB"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ncSixb9iAJyN"
      },
      "source": [
        "PATH = './model.pt'\n",
        "torch.save(net.state_dict(), PATH)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KZUN9MxdAJyO"
      },
      "source": [
        "dataiter = iter(valloader)\n",
        "images, labels = dataiter.next()\n",
        "\n",
        "# print images\n",
        "imshow(torchvision.utils.make_grid(images))\n",
        "print('GroundTruth: ', ' '.join('%5s' % classes[labels[j]] for j in range(4)))"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "u7IT8DjqAJyO"
      },
      "source": [
        "net.load_state_dict(torch.load(PATH))"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5IJCaF7ZAJyO"
      },
      "source": [
        "outputs = net(images)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8ehbINgTAJyP"
      },
      "source": [
        "_, predicted = torch.max(outputs, 1)\n",
        "\n",
        "print('Predicted: ', ' '.join('%5s' % classes[predicted[j]]\n",
        "                              for j in range(4)))"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "528G3Uij-oHd"
      },
      "source": [
        "# Note: \n",
        "## model specs\n",
        "\n",
        "Total params: 27,486,250\n",
        "\n",
        "Trainable params: 27,486,250\n",
        "\n",
        "Non-trainable params: 0\n",
        "----------------------------------------------------------------\n",
        "Input size (MB): 0.57\n",
        "\n",
        "Forward/backward pass size (MB): 269.03\n",
        "\n",
        "Params size (MB): 104.85\n",
        "\n",
        "Estimated Total Size (MB): 374.45\n",
        "\n",
        "\n",
        "See available weights here https://github.com/microsoft/Swin-Transformer\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "oHxpQG2s_NL1"
      },
      "source": [
        "# Reference\n",
        "\n",
        "\n",
        "https://github.com/sksq96/pytorch-summary#multiple-inputs\n",
        "\n",
        "https://github.com/berniwal/swin-transformer-pytorch\n",
        "\n",
        "https://arxiv.org/pdf/2103.14030.pdf"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DroAy-8R-noL"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}