advance/MLP Mixer.ipynb

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  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/himanshu-dutta/MLPMixer-pytorch/blob/master/MLP_Mixer_CIFAR_10.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/"
        },
        "id": "XoNVRTnQpznV",
        "outputId": "3689d34b-50b8-4a27-ce0f-a9ec00732d7b"
      },
      "source": [
        "!nvidia-smi"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Fri May 28 12:27:23 2021       \n",
            "+-----------------------------------------------------------------------------+\n",
            "| NVIDIA-SMI 465.19.01    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 P100-PCIE...  Off  | 00000000:00:04.0 Off |                    0 |\n",
            "| N/A   38C    P0    28W / 250W |      0MiB / 16280MiB |      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": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "WXmbV4v8pqfo",
        "outputId": "eed9dd44-8d02-4fb3-abf8-05c45fa99e49"
      },
      "source": [
        "!pip install -qq pytorch-lightning\n",
        "!pip install -qq torchinfo"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "\u001b[K     |████████████████████████████████| 808kB 12.9MB/s \n",
            "\u001b[K     |████████████████████████████████| 112kB 36.0MB/s \n",
            "\u001b[K     |████████████████████████████████| 645kB 26.5MB/s \n",
            "\u001b[K     |████████████████████████████████| 10.6MB 39.4MB/s \n",
            "\u001b[K     |████████████████████████████████| 276kB 32.8MB/s \n",
            "\u001b[K     |████████████████████████████████| 829kB 30.4MB/s \n",
            "\u001b[K     |████████████████████████████████| 1.3MB 27.2MB/s \n",
            "\u001b[K     |████████████████████████████████| 296kB 37.9MB/s \n",
            "\u001b[K     |████████████████████████████████| 143kB 40.0MB/s \n",
            "\u001b[?25h  Building wheel for future (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "\u001b[31mERROR: tensorflow 2.5.0 has requirement tensorboard~=2.5, but you'll have tensorboard 2.4.1 which is incompatible.\u001b[0m\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TH0PapF9oQlZ"
      },
      "source": [
        "import os\n",
        "import torch\n",
        "import torchvision\n",
        "import torch.nn as nn\n",
        "import pytorch_lightning as pl\n",
        "\n",
        "from tqdm.notebook import tqdm\n",
        "from torchinfo import summary\n",
        "\n",
        "import matplotlib.pyplot as plt\n",
        "from sklearn.decomposition import PCA\n",
        "from typing import Iterable, Callable, Dict"
      ],
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5mk5wp9q3ZhJ"
      },
      "source": [
        "BS = 64\n",
        "\n",
        "IMG_SZ = 32\n",
        "IMG_CHANNELS = 3\n",
        "NUM_CLASSES = 10\n",
        "\n",
        "NUM_PATCHES = 64\n",
        "NUM_CHANNELS = 256\n",
        "\n",
        "DEPTH = 20\n",
        "EPOCHS = 10\n",
        "EXPANSION = 8\n",
        "DROPOUT = 0.1"
      ],
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_FqWJRwwohez"
      },
      "source": [
        "# Data"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 117,
          "referenced_widgets": [
            "2ffee2cd78f14cfaa10c5675fe9bc675",
            "d6f880c6684c452b971a027e4a155cd0",
            "b60edfae50144ddab0700c32e4d3b1aa",
            "e754bda4d7c1481ab8b54221586f49cc",
            "6d59037ba0234654bc537d4d13bd2ae9",
            "b1191bfbaf4d4f418ebb4eb84b433c3c",
            "ec7b615c55f2495dbd14384e0f2e41db",
            "72701797b27c41c9bdd67d20b93774f2"
          ]
        },
        "id": "T1KeTTDdoiYx",
        "outputId": "a4afb2fc-e5dc-4bda-a6df-9713653ebe1b"
      },
      "source": [
        "train_dl = torch.utils.data.DataLoader(\n",
        "    torchvision.datasets.CIFAR10(\n",
        "        \"./data/\",\n",
        "        train=True,\n",
        "        download=True,\n",
        "        transform=torchvision.transforms.Compose(\n",
        "            [   \n",
        "                torchvision.transforms.RandomHorizontalFlip(),\n",
        "                torchvision.transforms.ToTensor(),\n",
        "                torchvision.transforms.Normalize((0.1307,), (0.3081,)),\n",
        "            ]\n",
        "        ),\n",
        "    ),\n",
        "    batch_size=BS,\n",
        "    shuffle=True,\n",
        "    num_workers=os.cpu_count(),\n",
        ")\n",
        "\n",
        "val_dl = torch.utils.data.DataLoader(\n",
        "    torchvision.datasets.CIFAR10(\n",
        "        \"./data/\",\n",
        "        train=False,\n",
        "        download=True,\n",
        "        transform=torchvision.transforms.Compose(\n",
        "            [\n",
        "                torchvision.transforms.ToTensor(),\n",
        "                torchvision.transforms.Normalize((0.1307,), (0.3081,)),\n",
        "            ]\n",
        "        ),\n",
        "    ),\n",
        "    batch_size=BS,\n",
        "    num_workers=os.cpu_count(),\n",
        ")\n"
      ],
      "execution_count": 5,
      "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": "2ffee2cd78f14cfaa10c5675fe9bc675",
              "version_minor": 0,
              "version_major": 2
            },
            "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": "markdown",
      "metadata": {
        "id": "6ZYLBSb3oV5M"
      },
      "source": [
        "# MLP Mixer"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "f7xBI5o3oUvI"
      },
      "source": [
        "class MLP(nn.Module):\n",
        "    def __init__(self, dim, expansion, dropout):\n",
        "        super(MLP, self).__init__()\n",
        "        self.lin1 = nn.Linear(dim, dim * expansion)\n",
        "        self.act = nn.GELU()\n",
        "        self.lin2 = nn.Linear(dim * expansion, dim)\n",
        "        self.drop = nn.Dropout(dropout)\n",
        "\n",
        "    def forward(self, x):\n",
        "        x = self.lin1(x)\n",
        "        x = self.act(x)\n",
        "        x = self.drop(x)\n",
        "        \n",
        "        x = self.lin2(x)\n",
        "        x = self.drop(x)\n",
        "\n",
        "        return x"
      ],
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5ASe9y6moYPh"
      },
      "source": [
        "class MixerLayer(nn.Module):\n",
        "    def __init__(self, num_patches, num_channels, expansion, dropout):\n",
        "        super(MixerLayer, self).__init__()\n",
        "        self.norm1 = nn.LayerNorm(num_channels)\n",
        "        self.by_patch = MLP(num_patches, expansion, dropout)\n",
        "        self.by_channel = MLP(num_channels, expansion, dropout)\n",
        "        self.norm2 = nn.LayerNorm(num_channels)\n",
        "\n",
        "    def forward(self, x):\n",
        "        # x.shape -> B, P, C\n",
        "        identity = x\n",
        "        x = self.norm1(x)\n",
        "\n",
        "        # x.shape -> B, C, P\n",
        "        x = self.by_patch(torch.transpose(x, 1, 2))\n",
        "\n",
        "        # x.shape -> B, P, C\n",
        "        x = torch.transpose(x, 1, 2) + identity\n",
        "\n",
        "        # x.shape -> B, P, C\n",
        "        identity = x\n",
        "        x = self.norm2(x)\n",
        "        x = self.by_channel(x) + identity\n",
        "\n",
        "        return x"
      ],
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RZzplR-DoZlc"
      },
      "source": [
        "class MLPMixer(pl.LightningModule):\n",
        "    def __init__(\n",
        "        self,\n",
        "        img_sz,\n",
        "        img_channels,\n",
        "        num_classes,\n",
        "        depth,\n",
        "        num_patches,\n",
        "        num_channels,\n",
        "        expansion,\n",
        "        dropout,\n",
        "        lr=2e-4\n",
        "    ):\n",
        "        super(MLPMixer, self).__init__()\n",
        "        self.lr = lr\n",
        "        self.img_sz = img_sz\n",
        "        self.img_channels = img_channels\n",
        "\n",
        "        self.num_patches = num_patches\n",
        "        self.num_channels = num_channels\n",
        "\n",
        "        self.patch_sz = int(((self.img_sz ** 2) // self.num_patches) ** (1 / 2))\n",
        "\n",
        "        inp_channels = ((img_sz ** 2) // num_patches) * img_channels\n",
        "\n",
        "        self.per_patch = nn.Linear(inp_channels, num_channels)\n",
        "\n",
        "        self.mixer_layers = nn.ModuleList(\n",
        "            [MixerLayer(num_patches, num_channels, expansion, dropout) for _ in range(depth)]\n",
        "        )\n",
        "\n",
        "        self.identity = nn.Identity()\n",
        "        self.classifier = nn.Linear(num_channels, num_classes)\n",
        "\n",
        "        self.loss = nn.CrossEntropyLoss()\n",
        "\n",
        "    def forward(self, x):\n",
        "        if len(x.shape) == 3:\n",
        "            x = x.unsqueeze(0)\n",
        "\n",
        "        bs = x.shape[0]\n",
        "\n",
        "        # creating patches of the images\n",
        "        x = (\n",
        "            x.data.unfold(1, self.img_channels, self.img_channels)\n",
        "            .unfold(2, self.patch_sz, self.patch_sz)\n",
        "            .unfold(3, self.patch_sz, self.patch_sz)\n",
        "        )\n",
        "\n",
        "        x = x.reshape(bs, -1, self.img_channels * self.patch_sz * self.patch_sz)\n",
        "\n",
        "        # per patch fc\n",
        "        x = self.per_patch(x)\n",
        "\n",
        "        # mixer layers\n",
        "        for layer in self.mixer_layers:\n",
        "            x = layer(x)\n",
        "\n",
        "        # classifier\n",
        "        x = x.mean(1)\n",
        "        x = self.identity(x) # for feature extraction\n",
        "        return self.classifier(x)\n",
        "\n",
        "    def training_step(self, xb, batch_idx):\n",
        "        inp, labels = xb\n",
        "        out = self(inp)\n",
        "\n",
        "        return self.loss(out, labels)\n",
        "\n",
        "    def validation_step(self, xb, batch_idx):\n",
        "        inp, labels = xb\n",
        "        out = self(inp)\n",
        "\n",
        "        labels_hat = torch.argmax(out, dim=1)\n",
        "        val_acc = torch.sum(labels == labels_hat).item() / (len(labels) * 1.0)\n",
        "\n",
        "        self.log(\n",
        "            \"val_loss\",\n",
        "            self.loss(out, labels),\n",
        "            prog_bar=True,\n",
        "            on_step=False,\n",
        "            on_epoch=True,\n",
        "        )\n",
        "        self.log(\"val_acc\", val_acc, prog_bar=True, on_step=False, on_epoch=True)\n",
        "\n",
        "        return val_acc\n",
        "\n",
        "    def configure_optimizers(self):\n",
        "        return torch.optim.Adam(self.parameters(), lr=self.lr)"
      ],
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "e10I88ttcvfW"
      },
      "source": [
        "# Evaluation"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zSXX8tGwZG43"
      },
      "source": [
        "class FeatureExtractor(nn.Module):\n",
        "    def __init__(self, model: nn.Module, layers: Iterable[str]):\n",
        "        super().__init__()\n",
        "        self.model = model\n",
        "        self.layers = layers\n",
        "        self._features = {layer: torch.empty(0) for layer in layers}\n",
        "\n",
        "        for layer_id in layers:\n",
        "            layer = dict([*self.model.named_modules()])[layer_id]\n",
        "            layer.register_forward_hook(self.save_outputs_hook(layer_id))\n",
        "\n",
        "    def save_outputs_hook(self, layer_id: str) -> Callable:\n",
        "        def fn(_, __, output):\n",
        "            self._features[layer_id] = output\n",
        "\n",
        "        return fn\n",
        "\n",
        "    def forward(self, x: torch.Tensor) -> Dict[str, torch.Tensor]:\n",
        "        _ = self.model(x)\n",
        "        return self._features"
      ],
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LILvfv3FZlT2"
      },
      "source": [
        "@torch.no_grad()\n",
        "def eval(model, val_dl):\n",
        "\n",
        "    device = torch.device(\"cuda\") if torch.cuda.is_available() else torch.device(\"cpu\")\n",
        "\n",
        "    model = model.eval()\n",
        "\n",
        "    model = FeatureExtractor(model, [\"identity\"]).to(device)\n",
        "\n",
        "    ys = []\n",
        "    outs = []\n",
        "    for x, y in tqdm(val_dl):\n",
        "        out = model(x.to(device)).get(\"identity\").detach()\n",
        "        outs.append(out.reshape(x.shape[0], -1))\n",
        "        ys.append(y)\n",
        "\n",
        "    outs = torch.cat(outs, dim=0).cpu()\n",
        "    ys = torch.cat(ys, dim=0).cpu()\n",
        "\n",
        "    decomposer = PCA(n_components=3)\n",
        "    decomposed = decomposer.fit_transform(outs.numpy())\n",
        "\n",
        "    colors = {\n",
        "        1: \"blue\",\n",
        "        2: \"orange\",\n",
        "        3: \"green\",\n",
        "        4: \"red\",\n",
        "        5: \"purple\",\n",
        "        6: \"brown\",\n",
        "        7: \"pink\",\n",
        "        8: \"gray\",\n",
        "        9: \"olive\",\n",
        "        0: \"cyan\",\n",
        "    }\n",
        "\n",
        "    fig = plt.figure()\n",
        "    ax = fig.add_subplot(projection=\"3d\")\n",
        "\n",
        "    ax.scatter(\n",
        "        xs=decomposed[:, 0],\n",
        "        ys=decomposed[:, 1],\n",
        "        zs=decomposed[:, 2],\n",
        "        c=list(map(lambda x: colors[int(x)], list(ys))),\n",
        "    )\n",
        "    plt.show()"
      ],
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "afQhgWVVobTs"
      },
      "source": [
        "# Execution"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cp5EwS-rocGl",
        "outputId": "7ab86c7a-1cb2-4cbd-ca19-1b44e8f61543"
      },
      "source": [
        "model = MLPMixer(\n",
        "    IMG_SZ,\n",
        "    img_channels=IMG_CHANNELS,\n",
        "    num_classes=NUM_CLASSES,\n",
        "    depth=DEPTH,\n",
        "    num_patches=NUM_PATCHES,\n",
        "    num_channels=NUM_CHANNELS,\n",
        "    expansion=EXPANSION,\n",
        "    dropout=DROPOUT,\n",
        ")\n",
        "\n",
        "print(summary(model, input_size=(1, IMG_CHANNELS, IMG_SZ, IMG_SZ)))"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "==========================================================================================\n",
            "Layer (type:depth-idx)                   Output Shape              Param #\n",
            "==========================================================================================\n",
            "MLPMixer                                 --                        --\n",
            "├─Linear: 1-1                            [1, 64, 256]              12,544\n",
            "├─ModuleList: 1                          --                        --\n",
            "│    └─MixerLayer: 2-1                   [1, 64, 256]              --\n",
            "│    │    └─LayerNorm: 3-1               [1, 64, 256]              512\n",
            "│    │    └─MLP: 3-2                     [1, 256, 64]              66,112\n",
            "│    │    └─LayerNorm: 3-3               [1, 64, 256]              512\n",
            "│    │    └─MLP: 3-4                     [1, 64, 256]              1,050,880\n",
            "│    └─MixerLayer: 2-2                   [1, 64, 256]              --\n",
            "│    │    └─LayerNorm: 3-5               [1, 64, 256]              512\n",
            "│    │    └─MLP: 3-6                     [1, 256, 64]              66,112\n",
            "│    │    └─LayerNorm: 3-7               [1, 64, 256]              512\n",
            "│    │    └─MLP: 3-8                     [1, 64, 256]              1,050,880\n",
            "│    └─MixerLayer: 2-3                   [1, 64, 256]              --\n",
            "│    │    └─LayerNorm: 3-9               [1, 64, 256]              512\n",
            "│    │    └─MLP: 3-10                    [1, 256, 64]              66,112\n",
            "│    │    └─LayerNorm: 3-11              [1, 64, 256]              512\n",
            "│    │    └─MLP: 3-12                    [1, 64, 256]              1,050,880\n",
            "│    └─MixerLayer: 2-4                   [1, 64, 256]              --\n",
            "│    │    └─LayerNorm: 3-13              [1, 64, 256]              512\n",
            "│    │    └─MLP: 3-14                    [1, 256, 64]              66,112\n",
            "│    │    └─LayerNorm: 3-15              [1, 64, 256]              512\n",
            "│    │    └─MLP: 3-16                    [1, 64, 256]              1,050,880\n",
            "│    └─MixerLayer: 2-5                   [1, 64, 256]              --\n",
            "│    │    └─LayerNorm: 3-17              [1, 64, 256]              512\n",
            "│    │    └─MLP: 3-18                    [1, 256, 64]              66,112\n",
            "│    │    └─LayerNorm: 3-19              [1, 64, 256]              512\n",
            "│    │    └─MLP: 3-20                    [1, 64, 256]              1,050,880\n",
            "│    └─MixerLayer: 2-6                   [1, 64, 256]              --\n",
            "│    │    └─LayerNorm: 3-21              [1, 64, 256]              512\n",
            "│    │    └─MLP: 3-22                    [1, 256, 64]              66,112\n",
            "│    │    └─LayerNorm: 3-23              [1, 64, 256]              512\n",
            "│    │    └─MLP: 3-24                    [1, 64, 256]              1,050,880\n",
            "│    └─MixerLayer: 2-7                   [1, 64, 256]              --\n",
            "│    │    └─LayerNorm: 3-25              [1, 64, 256]              512\n",
            "│    │    └─MLP: 3-26                    [1, 256, 64]              66,112\n",
            "│    │    └─LayerNorm: 3-27              [1, 64, 256]              512\n",
            "│    │    └─MLP: 3-28                    [1, 64, 256]              1,050,880\n",
            "│    └─MixerLayer: 2-8                   [1, 64, 256]              --\n",
            "│    │    └─LayerNorm: 3-29              [1, 64, 256]              512\n",
            "│    │    └─MLP: 3-30                    [1, 256, 64]              66,112\n",
            "│    │    └─LayerNorm: 3-31              [1, 64, 256]              512\n",
            "│    │    └─MLP: 3-32                    [1, 64, 256]              1,050,880\n",
            "│    └─MixerLayer: 2-9                   [1, 64, 256]              --\n",
            "│    │    └─LayerNorm: 3-33              [1, 64, 256]              512\n",
            "│    │    └─MLP: 3-34                    [1, 256, 64]              66,112\n",
            "│    │    └─LayerNorm: 3-35              [1, 64, 256]              512\n",
            "│    │    └─MLP: 3-36                    [1, 64, 256]              1,050,880\n",
            "│    └─MixerLayer: 2-10                  [1, 64, 256]              --\n",
            "│    │    └─LayerNorm: 3-37              [1, 64, 256]              512\n",
            "│    │    └─MLP: 3-38                    [1, 256, 64]              66,112\n",
            "│    │    └─LayerNorm: 3-39              [1, 64, 256]              512\n",
            "│    │    └─MLP: 3-40                    [1, 64, 256]              1,050,880\n",
            "│    └─MixerLayer: 2-11                  [1, 64, 256]              --\n",
            "│    │    └─LayerNorm: 3-41              [1, 64, 256]              512\n",
            "│    │    └─MLP: 3-42                    [1, 256, 64]              66,112\n",
            "│    │    └─LayerNorm: 3-43              [1, 64, 256]              512\n",
            "│    │    └─MLP: 3-44                    [1, 64, 256]              1,050,880\n",
            "│    └─MixerLayer: 2-12                  [1, 64, 256]              --\n",
            "│    │    └─LayerNorm: 3-45              [1, 64, 256]              512\n",
            "│    │    └─MLP: 3-46                    [1, 256, 64]              66,112\n",
            "│    │    └─LayerNorm: 3-47              [1, 64, 256]              512\n",
            "│    │    └─MLP: 3-48                    [1, 64, 256]              1,050,880\n",
            "│    └─MixerLayer: 2-13                  [1, 64, 256]              --\n",
            "│    │    └─LayerNorm: 3-49              [1, 64, 256]              512\n",
            "│    │    └─MLP: 3-50                    [1, 256, 64]              66,112\n",
            "│    │    └─LayerNorm: 3-51              [1, 64, 256]              512\n",
            "│    │    └─MLP: 3-52                    [1, 64, 256]              1,050,880\n",
            "│    └─MixerLayer: 2-14                  [1, 64, 256]              --\n",
            "│    │    └─LayerNorm: 3-53              [1, 64, 256]              512\n",
            "│    │    └─MLP: 3-54                    [1, 256, 64]              66,112\n",
            "│    │    └─LayerNorm: 3-55              [1, 64, 256]              512\n",
            "│    │    └─MLP: 3-56                    [1, 64, 256]              1,050,880\n",
            "│    └─MixerLayer: 2-15                  [1, 64, 256]              --\n",
            "│    │    └─LayerNorm: 3-57              [1, 64, 256]              512\n",
            "│    │    └─MLP: 3-58                    [1, 256, 64]              66,112\n",
            "│    │    └─LayerNorm: 3-59              [1, 64, 256]              512\n",
            "│    │    └─MLP: 3-60                    [1, 64, 256]              1,050,880\n",
            "│    └─MixerLayer: 2-16                  [1, 64, 256]              --\n",
            "│    │    └─LayerNorm: 3-61              [1, 64, 256]              512\n",
            "│    │    └─MLP: 3-62                    [1, 256, 64]              66,112\n",
            "│    │    └─LayerNorm: 3-63              [1, 64, 256]              512\n",
            "│    │    └─MLP: 3-64                    [1, 64, 256]              1,050,880\n",
            "│    └─MixerLayer: 2-17                  [1, 64, 256]              --\n",
            "│    │    └─LayerNorm: 3-65              [1, 64, 256]              512\n",
            "│    │    └─MLP: 3-66                    [1, 256, 64]              66,112\n",
            "│    │    └─LayerNorm: 3-67              [1, 64, 256]              512\n",
            "│    │    └─MLP: 3-68                    [1, 64, 256]              1,050,880\n",
            "│    └─MixerLayer: 2-18                  [1, 64, 256]              --\n",
            "│    │    └─LayerNorm: 3-69              [1, 64, 256]              512\n",
            "│    │    └─MLP: 3-70                    [1, 256, 64]              66,112\n",
            "│    │    └─LayerNorm: 3-71              [1, 64, 256]              512\n",
            "│    │    └─MLP: 3-72                    [1, 64, 256]              1,050,880\n",
            "│    └─MixerLayer: 2-19                  [1, 64, 256]              --\n",
            "│    │    └─LayerNorm: 3-73              [1, 64, 256]              512\n",
            "│    │    └─MLP: 3-74                    [1, 256, 64]              66,112\n",
            "│    │    └─LayerNorm: 3-75              [1, 64, 256]              512\n",
            "│    │    └─MLP: 3-76                    [1, 64, 256]              1,050,880\n",
            "│    └─MixerLayer: 2-20                  [1, 64, 256]              --\n",
            "│    │    └─LayerNorm: 3-77              [1, 64, 256]              512\n",
            "│    │    └─MLP: 3-78                    [1, 256, 64]              66,112\n",
            "│    │    └─LayerNorm: 3-79              [1, 64, 256]              512\n",
            "│    │    └─MLP: 3-80                    [1, 64, 256]              1,050,880\n",
            "├─Identity: 1-2                          [1, 256]                  --\n",
            "├─Linear: 1-3                            [1, 10]                   2,570\n",
            "==========================================================================================\n",
            "Total params: 22,375,434\n",
            "Trainable params: 22,375,434\n",
            "Non-trainable params: 0\n",
            "Total mult-adds (M): 22.38\n",
            "==========================================================================================\n",
            "Input size (MB): 0.01\n",
            "Forward/backward pass size (MB): 52.56\n",
            "Params size (MB): 89.50\n",
            "Estimated Total Size (MB): 142.07\n",
            "==========================================================================================\n"
          ],
          "name": "stdout"
        }
      ]
    },
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      "metadata": {
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            "9dbb1f4569d6446894948e433379b89a",
            "7be64fbd0ee14085b40a58d814c74424",
            "e8e3a9a59bfc4ca19e72631eed16633b"
          ]
        },
        "id": "DrCSYOVFqmw4",
        "outputId": "e674e1e8-2777-4c1e-8913-4242f7154a21"
      },
      "source": [
        "trainer = pl.Trainer(\n",
        "    default_root_dir=\"logs\",\n",
        "    gpus=(1 if torch.cuda.is_available() else 0),\n",
        "    max_epochs=EPOCHS,\n",
        "    logger=pl.loggers.TensorBoardLogger(\"logs/\", name=\"mlp_mixer_cifar_10\", version=0),\n",
        "    precision=16,\n",
        ")\n",
        "\n",
        "trainer.fit(model, train_dataloader=train_dl, val_dataloaders=val_dl)"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "GPU available: True, used: True\n",
            "TPU available: False, using: 0 TPU cores\n",
            "Using native 16bit precision.\n",
            "LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0]\n",
            "\n",
            "  | Name         | Type             | Params\n",
            "--------------------------------------------------\n",
            "0 | per_patch    | Linear           | 12.5 K\n",
            "1 | mixer_layers | ModuleList       | 22.4 M\n",
            "2 | identity     | Identity         | 0     \n",
            "3 | classifier   | Linear           | 2.6 K \n",
            "4 | loss         | CrossEntropyLoss | 0     \n",
            "--------------------------------------------------\n",
            "22.4 M    Trainable params\n",
            "0         Non-trainable params\n",
            "22.4 M    Total params\n",
            "89.502    Total estimated model params size (MB)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "cdd441555d0b4a5b96f6be44a6a341fd",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(FloatProgress(value=1.0, bar_style='info', description='Validation sanity check', layout=Layout…"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\r"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "30dc049ad99445bbbe551a7c76395805",
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            },
            "text/plain": [
              "HBox(children=(FloatProgress(value=1.0, bar_style='info', description='Training', layout=Layout(flex='2'), max…"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
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            },
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              "HBox(children=(FloatProgress(value=1.0, bar_style='info', description='Validating', layout=Layout(flex='2'), m…"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
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            },
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              "HBox(children=(FloatProgress(value=1.0, bar_style='info', description='Validating', layout=Layout(flex='2'), m…"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
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            },
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              "HBox(children=(FloatProgress(value=1.0, bar_style='info', description='Validating', layout=Layout(flex='2'), m…"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
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            },
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              "HBox(children=(FloatProgress(value=1.0, bar_style='info', description='Validating', layout=Layout(flex='2'), m…"
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          }
        },
        {
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              "HBox(children=(FloatProgress(value=1.0, bar_style='info', description='Validating', layout=Layout(flex='2'), m…"
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          }
        },
        {
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              "HBox(children=(FloatProgress(value=1.0, bar_style='info', description='Validating', layout=Layout(flex='2'), m…"
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          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
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              "HBox(children=(FloatProgress(value=1.0, bar_style='info', description='Validating', layout=Layout(flex='2'), m…"
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          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
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            "text/plain": [
              "HBox(children=(FloatProgress(value=1.0, bar_style='info', description='Validating', layout=Layout(flex='2'), m…"
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          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "442c48e3aef54f0fbad3c53b96cc13a4",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(FloatProgress(value=1.0, bar_style='info', description='Validating', layout=Layout(flex='2'), m…"
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          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "94735a4b0e6d4b15859beada0e00998c",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(FloatProgress(value=1.0, bar_style='info', description='Validating', layout=Layout(flex='2'), m…"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 100,
          "referenced_widgets": [
            "356a906a61d342c48bdd075f7c647277",
            "8c031b7048e04a569aa1834d22a4e017",
            "a5872d14d4684b9bbb4f70b5d5051d94",
            "ac6339453bce4bc288597e66a96c1d6b",
            "e7f6154a0c174525954391a55ec3f7e6",
            "7631304c7d8e4d4e8ceb3853fd1f74c3",
            "e9dc6c7883c146c8823b7720dc664f9c",
            "42dc9fa95d044ba2b9f14f3ccdf45ecf"
          ]
        },
        "id": "dpMWwe_LsC8D",
        "outputId": "50c4879e-8354-41c3-9e3c-4ddef75a991b"
      },
      "source": [
        "model = model.cuda().eval()\n",
        "ys = []\n",
        "outs = []\n",
        "\n",
        "with torch.no_grad():\n",
        "    for x, y in tqdm(val_dl): \n",
        "        out = model(x.cuda()).detach()\n",
        "        outs.append(out.reshape(x.shape[0], -1))\n",
        "        ys.append(y)\n",
        "\n",
        "outs = torch.cat(outs, dim=0).cpu()\n",
        "labels = torch.cat(ys, dim=0).cpu()\n",
        "\n",
        "labels_hat = torch.argmax(outs, dim=1)\n",
        "val_acc = torch.sum(labels == labels_hat).item() / (len(labels) * 1.0)\n",
        "\n",
        "print(\"\\nValidation Accuracy: \", round(100*val_acc, 4))"
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "356a906a61d342c48bdd075f7c647277",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(FloatProgress(value=0.0, max=157.0), HTML(value='')))"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "\n",
            "Validation Accuracy:  73.42\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "v3D5yPuB_BOq",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 297,
          "referenced_widgets": [
            "4d48f94f43b6449c80132c9d84290fd0",
            "7810529d40d847c1ae3b3f64c2e1be7a",
            "54f7c902b4a545949d428347b88fbc8b",
            "f866507f1a614a03bf08652ad22eca98",
            "1d1f0c776b234d24ba36e98d50a96bd0",
            "892642c5614a4580b0747faaa47332ca",
            "fccebe9fc40f4c6bbdd5f59fba7f4cc7",
            "a3c5de30c5284f14a62a0af5c3dae348"
          ]
        },
        "outputId": "a5647441-dbf0-4cc6-e990-3da5559a87ba"
      },
      "source": [
        "eval(model, val_dl)"
      ],
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "4d48f94f43b6449c80132c9d84290fd0",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(FloatProgress(value=0.0, max=157.0), HTML(value='')))"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RvPXcNmybAma"
      },
      "source": [
        ""
      ],
      "execution_count": 14,
      "outputs": []
    }
  ]
}