Keras_30_Example.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/jonbaer/googlecolab/blob/master/Keras_30_Example.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Bwjup8oq_M6v",
        "outputId": "530d42bd-665a-4479-ad1d-fb9cbf65ae45"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m2.0/2.0 MB\u001b[0m \u001b[31m12.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m44.6/44.6 kB\u001b[0m \u001b[31m5.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m806.1/806.1 kB\u001b[0m \u001b[31m18.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m776.9/776.9 kB\u001b[0m \u001b[31m22.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h"
          ]
        }
      ],
      "source": [
        "%pip install lightning einops --quiet"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "NNEj5hXFE1-A"
      },
      "outputs": [],
      "source": [
        "import torch, torchvision\n",
        "from torch import nn"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "ce0oqhkEQ0Q2"
      },
      "outputs": [],
      "source": [
        "from torchvision.transforms import v2 as T"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "ywJsG-27Wj6S"
      },
      "outputs": [],
      "source": [
        "import pytorch_lightning as ptl"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "ZVpa79agWqj7"
      },
      "outputs": [],
      "source": [
        "import torchmetrics"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "LIFzj9B7QiVf"
      },
      "outputs": [],
      "source": [
        "from einops.layers.torch import Rearrange"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "cZFTSFxYZJa2"
      },
      "outputs": [],
      "source": [
        "import matplotlib.pyplot as plt\n",
        "from mpl_toolkits.axes_grid1 import ImageGrid"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "9x4GQayVFpyT"
      },
      "outputs": [],
      "source": [
        "train_transforms = T.Compose([\n",
        "    T.RandomRotation(10),\n",
        "    T.ToImage(),\n",
        "    T.ToDtype(torch.float32, scale=True)\n",
        "])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "ofF4cs5RIsUB"
      },
      "outputs": [],
      "source": [
        "test_transforms = T.Compose([\n",
        "    T.ToImage(),\n",
        "    T.ToDtype(torch.float32, scale=True)\n",
        "])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "yxPH3aRuF_FL",
        "outputId": "9948b04f-6557-4b12-be31-80a94e675916"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Downloading https://www.cs.toronto.edu/~kriz/cifar-10-python.tar.gz to /cifar10/cifar-10-python.tar.gz\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "100%|██████████| 170498071/170498071 [00:04<00:00, 38868204.47it/s]\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Extracting /cifar10/cifar-10-python.tar.gz to /cifar10/\n",
            "Files already downloaded and verified\n"
          ]
        }
      ],
      "source": [
        "train_data = torchvision.datasets.CIFAR10('/cifar10/', download=True, transform=train_transforms)\n",
        "test_data = torchvision.datasets.CIFAR10('/cifar10/', download=True, transform=test_transforms)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Hzq99GmZFyis"
      },
      "outputs": [],
      "source": [
        "train_loader = torch.utils.data.DataLoader(train_data, batch_size=32, shuffle=True)\n",
        "test_loader = torch.utils.data.DataLoader(test_data, batch_size=32, shuffle=False)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "82AAcnPqHz4t"
      },
      "outputs": [],
      "source": [
        "from einops import rearrange"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "uwK9H064bpio"
      },
      "outputs": [],
      "source": [
        "class InputEmbedding(nn.Module):\n",
        "    def __init__(self, patch_size=4, input_shape=32, embedding_dim=32):\n",
        "        super().__init__()\n",
        "        self.patch_size = patch_size\n",
        "        self.embedding_dim = embedding_dim\n",
        "        num_patches = (input_shape // self.patch_size) * (input_shape // self.patch_size)\n",
        "        self.patchifier = Rearrange('b c (h p1) (w p2) -> b (h w) (p1 p2 c)', p1=patch_size, p2=patch_size)\n",
        "        self.proj = nn.Linear(self.patch_size*self.patch_size*3, self.embedding_dim)\n",
        "        self.pos_embeddings = nn.Parameter(torch.randn((1, num_patches  + 1, self.embedding_dim)))\n",
        "        self.cls_token = nn.Parameter(torch.randn((1, 1, self.embedding_dim)))\n",
        "\n",
        "    def forward(self, x):\n",
        "        x = self.proj(self.patchifier(x))\n",
        "        cls_token = self.cls_token.repeat(x.shape[0], 1, 1)\n",
        "        x = torch.concat([cls_token, x], axis=1)\n",
        "        return x + self.pos_embeddings"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "1oNP7IO_SGpm"
      },
      "outputs": [],
      "source": [
        "class RecurrentMHA(nn.Module):\n",
        "  def __init__(self, num_heads=4, embedding_dim=32, n_latents=8):\n",
        "    super().__init__()\n",
        "    self.Q = nn.Parameter(torch.randn((1, n_latents, embedding_dim)))\n",
        "    self.keys = nn.Linear(embedding_dim, embedding_dim)\n",
        "    self.values = nn.Linear(embedding_dim, embedding_dim)\n",
        "    self.mha = nn.MultiheadAttention(embedding_dim, num_heads, batch_first=True)\n",
        "\n",
        "  def forward(self, x):\n",
        "    K = self.keys(x)\n",
        "    V = self.values(x)\n",
        "    Q_t = self.Q.repeat(x.shape[0], 1, 1)\n",
        "    y = torch.zeros_like(x, device=x.device)\n",
        "    for i in range(x.shape[1]):\n",
        "      Q_t = self.mha(Q_t, K[:, i:i+1], V[:, i:i+1], need_weights=False)[0]\n",
        "      y[:, i] = Q_t[:, 0]\n",
        "    return y"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "ahVHKxeVcdhe"
      },
      "outputs": [],
      "source": [
        "class TransformerBlock(nn.Module):\n",
        "    def __init__(self, embedding_dim=32):\n",
        "      super().__init__()\n",
        "      self.mha = RecurrentMHA()\n",
        "      self.mlp = nn.Sequential(\n",
        "          nn.Linear(embedding_dim, embedding_dim * 3),\n",
        "          nn.GELU(),\n",
        "          nn.Linear(embedding_dim*3, embedding_dim)\n",
        "      )\n",
        "      self.ln1 = nn.LayerNorm(embedding_dim)\n",
        "      self.ln2 = nn.LayerNorm(embedding_dim)\n",
        "\n",
        "    def forward(self, x):\n",
        "      h = self.mha(self.ln1(x)) + x\n",
        "      h = self.mlp(self.ln1(x)) + x\n",
        "      return x"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "8MwSYUbydIrv"
      },
      "outputs": [],
      "source": [
        "class VisionTransformer(nn.Module):\n",
        "  def __init__(self, embedding_dim=32):\n",
        "    super().__init__()\n",
        "    self.inp_embedding = InputEmbedding()\n",
        "    self.block1 = TransformerBlock()\n",
        "    self.block2 = TransformerBlock()\n",
        "    self.block3 = TransformerBlock()\n",
        "    self.classifier = nn.Linear(embedding_dim, 10)\n",
        "\n",
        "  def forward(self, x):\n",
        "    x = self.inp_embedding(x)\n",
        "    x = self.block1(x)\n",
        "    x = self.block2(x)\n",
        "    x = self.block3(x)\n",
        "    return self.classifier(x[:, 0, :])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "SK60oxgsdLKn"
      },
      "outputs": [],
      "source": [
        "vit = VisionTransformer()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "u7z3MbuVV-3r"
      },
      "outputs": [],
      "source": [
        "class PTLWrapper(ptl.LightningModule):\n",
        "    def __init__(self, cnn):\n",
        "      super().__init__()\n",
        "      self.cnn = cnn\n",
        "      self.acc = torchmetrics.Accuracy(task='multiclass', num_classes=10)\n",
        "      self.loss = torch.nn.CrossEntropyLoss()\n",
        "\n",
        "    def training_step(self, batch, batch_idx):\n",
        "      xb, yb = batch\n",
        "      ypred = self.cnn(xb)\n",
        "      loss = self.loss(ypred, yb)\n",
        "      self.log(\"train_loss\", loss, prog_bar=True, logger=False)\n",
        "      return loss\n",
        "\n",
        "    def validation_step(self, batch, batch_idx):\n",
        "      xb, yb = batch\n",
        "      ypred = self.cnn(xb)\n",
        "      self.acc(ypred, yb)\n",
        "      self.log('val_accuracy', self.acc, on_step=False, on_epoch=True, prog_bar=True, logger=False)\n",
        "\n",
        "    def configure_optimizers(self):\n",
        "      return torch.optim.Adam(self.cnn.parameters())"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "PIAmJsxnWvNx"
      },
      "outputs": [],
      "source": [
        "#cnn = torch.compile(VisionTransformer())\n",
        "cnn = VisionTransformer()\n",
        "ptl_wrapper = PTLWrapper(cnn)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "w0HEo0e1WyPc",
        "outputId": "8d31a126-8c01-4c15-b0f2-a8b682dc51e8"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "INFO:pytorch_lightning.utilities.rank_zero:GPU available: True (cuda), used: True\n",
            "INFO:pytorch_lightning.utilities.rank_zero:TPU available: False, using: 0 TPU cores\n",
            "INFO:pytorch_lightning.utilities.rank_zero:IPU available: False, using: 0 IPUs\n",
            "INFO:pytorch_lightning.utilities.rank_zero:HPU available: False, using: 0 HPUs\n"
          ]
        }
      ],
      "source": [
        "trainer = ptl.Trainer(accelerator='gpu', devices=1,  max_epochs=5)"
      ]
    },
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        "id": "EL8hB1NvW1qt",
        "outputId": "7c43471a-1799-4cf9-d256-38af98172e67"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "WARNING:pytorch_lightning.loggers.tensorboard:Missing logger folder: /content/lightning_logs\n",
            "INFO:pytorch_lightning.accelerators.cuda:LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0]\n",
            "INFO:pytorch_lightning.callbacks.model_summary:\n",
            "  | Name | Type               | Params\n",
            "--------------------------------------------\n",
            "0 | cnn  | VisionTransformer  | 43.0 K\n",
            "1 | acc  | MulticlassAccuracy | 0     \n",
            "2 | loss | CrossEntropyLoss   | 0     \n",
            "--------------------------------------------\n",
            "43.0 K    Trainable params\n",
            "0         Non-trainable params\n",
            "43.0 K    Total params\n",
            "0.172     Total estimated model params size (MB)\n"
          ]
        },
        {
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            "text/plain": [
              "Sanity Checking: |          | 0/? [00:00<?, ?it/s]"
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          },
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        },
        {
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          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/pytorch_lightning/trainer/connectors/data_connector.py:441: The 'val_dataloader' does not have many workers which may be a bottleneck. Consider increasing the value of the `num_workers` argument` to `num_workers=1` in the `DataLoader` to improve performance.\n",
            "/usr/local/lib/python3.10/dist-packages/pytorch_lightning/trainer/connectors/data_connector.py:441: The 'train_dataloader' does not have many workers which may be a bottleneck. Consider increasing the value of the `num_workers` argument` to `num_workers=1` in the `DataLoader` to improve performance.\n"
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