train.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch\n",
    "from torch import nn\n",
    "from tqdm import tqdm\n",
    "import lightning as L\n",
    "from lightning.pytorch.loggers import TensorBoardLogger\n",
    "from lightning.pytorch.callbacks import ModelCheckpoint\n",
    "\n",
    "import torchmetrics\n",
    "from torch.utils import data\n",
    "from torchvision.transforms import v2\n",
    "import numpy as np\n",
    "import os\n",
    "from matplotlib import pyplot as plt\n",
    "from torch.nn import functional as F\n",
    "\n",
    "from pathlib import Path"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "config = {\n",
    "    \"batch_size\": 32,\n",
    "    \"learning_rate\": 0.001,\n",
    "    \"num_workers\": 5,\n",
    "    \"epochs\": 100,\n",
    "\n",
    "    \"checkpoint_dir\": \"~/ml/models/badapple_in_nn\",\n",
    "    \"checkpoint_filename\": \"badapple_in_nn-{epoch:02d}-{valid_acc:.2f}\",\n",
    "    \"onnx_path\": \"~/ml/models/badapple_in_nn/badapple_in_nn.onnx\",\n",
    "\n",
    "    \"log_dir\": \"~/ml/logs/badapple_in_nn\",\n",
    "    \"log_name\": \"bad_apple_nn\",\n",
    "\n",
    "    \"badapple_video\": \"./assets/Bad Apple.mp4\",\n",
    "    \"badapple_frames\": \"./assets/frames\",\n",
    "    \"display_size\": (360//3, 480//3),\n",
    "    \"interpreter_layers\": 10,\n",
    "}\n",
    "\n",
    "def replace_home():\n",
    "    global config\n",
    "    home_dir = Path.home()\n",
    "\n",
    "    for key, value in config.items():\n",
    "        if isinstance(value, str):\n",
    "            config[key] = value.replace(\"~\", str(home_dir.absolute()))\n",
    "\n",
    "replace_home()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "labels.dtype=torch.int64\n",
      "images.shape=torch.Size([9, 1, 81, 81])\n",
      "images[0].view(-1).max(dim=0).values=tensor(1.)\n",
      "images[0].view(-1).min(dim=0).values=tensor(0.)\n",
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",
      "text/plain": [
       "<Figure size 640x480 with 9 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "class HWDPlus(data.Dataset):\n",
    "    def __init__(self, image_file, writer_info_file, transforms=None):\n",
    "        self.images = np.expand_dims(np.load(image_file), axis=-1)\n",
    "        self.writer_info = np.load(writer_info_file).astype(np.int64)\n",
    "        self.labels = self.writer_info[:, 0]\n",
    "        self.transforms = transforms\n",
    "\n",
    "        self.len = self.images.shape[0]\n",
    "\n",
    "    def __getitem__(self, index):\n",
    "        if self.transforms is not None:\n",
    "            image = self.transforms(self.images[index])\n",
    "        else:\n",
    "            image = self.images[index]\n",
    "        return image, self.labels[index]\n",
    "\n",
    "    def __len__(self):\n",
    "        return self.len\n",
    "\n",
    "def get_loaders(batch_size=32):\n",
    "    transforms = v2.Compose([\n",
    "        v2.ToImage(),\n",
    "        v2.ToDtype(torch.float32, scale=True),\n",
    "        v2.RandomZoomOut(1, (1, 2)),\n",
    "        v2.Resize((81, 81), antialias=True),\n",
    "        v2.Lambda(lambda x: (-x + 1).clip(0, 1)),\n",
    "        v2.RandomRotation(30, fill=0),\n",
    "        v2.RandomAffine(0, translate=(0.3, 0.2)),\n",
    "        #v2.Lambda(lambda x: (x + torch.rand_like(x) * 0.2).clamp(-1, 1)),\n",
    "    ])\n",
    "\n",
    "    dataset = HWDPlus(\n",
    "        # https://link.springer.com/article/10.1007/s42979-022-01494-2#article-info\n",
    "        # https://drive.google.com/drive/folders/1f2o1kjXLvcxRgtmMMuDkA2PQ5Zato4Or\n",
    "        \"/home/kanna/ml/datasets/Images(500x500)-001.npy\",\n",
    "        \"/home/kanna/ml/datasets/WriterInfo.npy\",\n",
    "        transforms,\n",
    "    )\n",
    "\n",
    "    train, val = data.random_split(dataset, [11_000, 2_580])\n",
    "\n",
    "    train_loader = data.DataLoader(\n",
    "        train,\n",
    "        batch_size=batch_size,\n",
    "        shuffle=True,\n",
    "        num_workers=config[\"num_workers\"],\n",
    "        pin_memory=True,\n",
    "    )\n",
    "\n",
    "    val_loader = data.DataLoader(\n",
    "        val,\n",
    "        batch_size=batch_size,\n",
    "        num_workers=config[\"num_workers\"],\n",
    "        pin_memory=True\n",
    "    )\n",
    "\n",
    "    return train_loader, val_loader\n",
    "\n",
    "\n",
    "def test():\n",
    "    train_loader, val_loader = get_loaders(9)\n",
    "\n",
    "    images, labels = next(iter(train_loader))\n",
    "    \n",
    "    print(f\"{images.dtype=}\")\n",
    "    print(f\"{labels.dtype=}\")\n",
    "\n",
    "    print(f\"{images.shape=}\")\n",
    "    print(f\"{images[0].view(-1).max(dim=0).values=}\")\n",
    "    print(f\"{images[0].view(-1).min(dim=0).values=}\")\n",
    "    torch.set_printoptions(profile=\"full\")\n",
    "    print(f\"{images[0]=}\")\n",
    "\n",
    "    assert len(images) == 9, f\"{len(image)} should be 9\"\n",
    "    for i, (image, label) in enumerate(zip(images, labels)):\n",
    "        plt.subplot(3, 3, i + 1)\n",
    "        plt.axis(\"off\")\n",
    "        plt.title(label.item())\n",
    "        plt.imshow(image.reshape(81, 81), cmap=\"gray\")\n",
    "\n",
    "\n",
    "test()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Extracting Frames...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "6573it [00:04, 1619.84it/s]                          "
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ms_per_frame=33.333333333333336\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "\n",
    "import cv2 \n",
    "from PIL import Image\n",
    "\n",
    "def convert(vidfile, startms, idoffset):\n",
    "    frames = []\n",
    "\n",
    "    cam = cv2.VideoCapture(vidfile)\n",
    "    fps = cam.get(cv2.CAP_PROP_FPS)\n",
    "    ms_per_frame = 1000 / fps\n",
    "\n",
    "    cam.set(cv2.CAP_PROP_POS_MSEC, startms + idoffset * ms_per_frame)\n",
    "\n",
    "    pos_after_offset = int(cam.get(cv2.CAP_PROP_POS_FRAMES))\n",
    "    total_frames = int(cam.get(cv2.CAP_PROP_FRAME_COUNT)) - pos_after_offset\n",
    "\n",
    "    print('Extracting Frames...')\n",
    "    with tqdm(total=total_frames) as progress:\n",
    "        while True:\n",
    "            frame_num = int(cam.get(cv2.CAP_PROP_POS_FRAMES)) - pos_after_offset\n",
    "            progress.update(1)\n",
    "\n",
    "            ret, frame = cam.read()\n",
    "            if not ret: break\n",
    "            frames.append(Image.fromarray(cv2.cvtColor(frame, cv2.COLOR_RGB2GRAY)))\n",
    "\n",
    "    cam.release()\n",
    "\n",
    "    print(f\"{ms_per_frame=}\")\n",
    "\n",
    "    return frames\n",
    "\n",
    "frames = convert(config[\"badapple_video\"], 0, 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "class LinearLayer(nn.Module):\n",
    "    def __init__(self, input_size: int, output_size: int, bias=False):\n",
    "        super().__init__()\n",
    "        self.linear = nn.Linear(input_size, output_size, bias=bias)\n",
    "        self.relu = nn.ReLU(inplace=True)\n",
    "    \n",
    "    def forward(self, x):\n",
    "        return self.relu(self.linear(x))\n",
    "\n",
    "class BadAppleInNN(L.LightningModule):\n",
    "    def __init__(\n",
    "            self, \n",
    "            input_size: int, \n",
    "            badapple_display_size: tuple[int, int], \n",
    "            interpreter_layers: int,\n",
    "            frames,\n",
    "    ):\n",
    "        super().__init__()\n",
    "\n",
    "        self.input_size = input_size**2\n",
    "        self.badapple_display_size = badapple_display_size\n",
    "        self.interpreter_layers = interpreter_layers\n",
    "\n",
    "        self.display_width, self.display_height = badapple_display_size\n",
    "        \"\"\"\n",
    "        # = interpreter_layers\n",
    "        the inner box = display used for playing badapple\n",
    "\n",
    "        ########################\n",
    "        #|--------------------|#\n",
    "        #|                    |# \n",
    "        #| bad apple display  |#\n",
    "        #|                    |#\n",
    "        #|--------------------|#\n",
    "        ########################\n",
    "        \"\"\"\n",
    "        self.full_display_width = self.display_width + 2 * self.interpreter_layers\n",
    "        self.full_display_height = self.display_height + 2 * self.interpreter_layers\n",
    "        self.full_display_size = self.full_display_width * self.full_display_height\n",
    "        self.input_to_display = nn.Linear(self.input_size, self.full_display_size, bias=False)\n",
    "\n",
    "        self.hidden_layers = nn.Sequential(\n",
    "            LinearLayer(self.full_display_size, 512),\n",
    "            LinearLayer(512, 256),\n",
    "            LinearLayer(256, 128),\n",
    "        )\n",
    "\n",
    "        self.classifier = nn.Sequential(\n",
    "            nn.Linear(128, 10),\n",
    "            nn.LogSoftmax(dim=1),\n",
    "        )\n",
    "\n",
    "        self.embed_badapple(frames)\n",
    "\n",
    "        # training stuff\n",
    "        device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
    "        self.loss = nn.NLLLoss().to(device)\n",
    "        self.train_acc = torchmetrics.classification.Accuracy(task=\"multiclass\", num_classes=10)\n",
    "        self.valid_acc = torchmetrics.classification.Accuracy(task=\"multiclass\", num_classes=10)\n",
    "\n",
    "    def embed_badapple(self, frames):\n",
    "        self.transforms = v2.Compose([\n",
    "            v2.ToImage(),\n",
    "            v2.ToDtype(torch.float32, scale=True),\n",
    "            v2.Resize(self.badapple_display_size, antialias=True),\n",
    "        ])\n",
    "\n",
    "        display_model = zip(\n",
    "            frames,\n",
    "            # cuts out the interpreter layers\n",
    "            self.input_to_display.weight\n",
    "                .transpose(0, 1)\n",
    "                .view(self.input_size, self.full_display_width, self.full_display_height)\n",
    "                [:, self.interpreter_layers:self.full_display_width - self.interpreter_layers, self.interpreter_layers:self.full_display_height - self.interpreter_layers]\n",
    "        )\n",
    "\n",
    "        for frame, input_pixel in display_model:\n",
    "            image = self.transforms(frame).squeeze(0)\n",
    "            # print(image)\n",
    "            # print(input_pixel.data.shape)\n",
    "            # print(image.view(-1).shape)\n",
    "            input_pixel.data[:, :] = image\n",
    "\n",
    "        (\n",
    "            next(self.hidden_layers.children()).linear\n",
    "                .weight\n",
    "                .view(512, self.full_display_width, self.full_display_height)\n",
    "                .data\n",
    "                [:, self.interpreter_layers:self.full_display_width - self.interpreter_layers, self.interpreter_layers:self.full_display_height - self.interpreter_layers]\n",
    "        ) = torch.zeros(self.badapple_display_size)\n",
    "        \n",
    "\n",
    "    def forward(self, x):\n",
    "        x = x.view(-1, 81**2)\n",
    "        x = self.input_to_display(x)\n",
    "        x = self.hidden_layers(x)\n",
    "        x = self.classifier(x)\n",
    "        return x\n",
    "\n",
    "    def training_step(self, batch, batch_idx):\n",
    "        x, y = batch\n",
    "        y_hat = self(x)\n",
    "        loss = self.loss(y_hat, y)\n",
    "\n",
    "        self.log(\"train_loss\", loss)\n",
    "        self.train_acc(y_hat, y)\n",
    "        self.log(\"train_acc\", self.train_acc)\n",
    "\n",
    "        return loss\n",
    "\n",
    "    def validation_step(self, batch, batch_idx):\n",
    "        x, y = batch\n",
    "        y_hat = self(x)\n",
    "        loss = self.loss(y_hat, y)\n",
    "\n",
    "        self.valid_acc(y_hat, y)\n",
    "        self.log(\"valid_acc\", self.valid_acc)\n",
    "\n",
    "    def on_before_optimizer_step(self, optimizer):\n",
    "        # clear the badapple display gradients\n",
    "        (\n",
    "            next(self.input_to_display.parameters())\n",
    "                .grad\n",
    "                .transpose(0, 1)\n",
    "                .view(self.input_size, self.full_display_width, self.full_display_height)\n",
    "                [:, self.interpreter_layers:self.full_display_width - self.interpreter_layers, self.interpreter_layers:self.full_display_height - self.interpreter_layers]\n",
    "        ) = torch.zeros(self.badapple_display_size)\n",
    "\n",
    "        (\n",
    "            next(self.hidden_layers.parameters())\n",
    "                .grad\n",
    "                .view(512, self.full_display_width, self.full_display_height)\n",
    "                [:, self.interpreter_layers:self.full_display_width - self.interpreter_layers, self.interpreter_layers:self.full_display_height - self.interpreter_layers]\n",
    "        ) = torch.zeros(self.badapple_display_size)\n",
    "\n",
    "    def configure_optimizers(self):\n",
    "        return torch.optim.Adam(self.parameters(), lr=config[\"learning_rate\"], )\n",
    "\n",
    "def test_model():\n",
    "    test_frame_number = 2000\n",
    "    display_size = config[\"display_size\"]\n",
    "    interpreter_layers = 10\n",
    "\n",
    "    plt.subplot(2, 2, 1)\n",
    "    plt.axis(\"off\")\n",
    "    plt.title(f\"actual frame {test_frame_number}\")\n",
    "    plt.imshow(frames[test_frame_number], cmap=\"gray\")\n",
    "\n",
    "    model = BadAppleInNN(\n",
    "        input_size=81,\n",
    "        badapple_display_size=display_size,\n",
    "        interpreter_layers=interpreter_layers,\n",
    "        frames=frames,\n",
    "    )\n",
    "    \n",
    "    with torch.no_grad():\n",
    "        test_input = torch.zeros(1, 81**2)\n",
    "        test_input[0][test_frame_number] = 1\n",
    "        out = model.input_to_display(test_input)\n",
    "\n",
    "    total_display_size = (display_size[0] + 2 * interpreter_layers, display_size[1] + 2 * interpreter_layers)\n",
    "\n",
    "    plt.subplot(2, 2, 2)\n",
    "    plt.axis(\"off\")\n",
    "    plt.title(f\"model frame {test_frame_number}\")\n",
    "    plt.imshow(out[0].reshape(*total_display_size), cmap=\"gray\")\n",
    "\n",
    "    plt.subplot(2, 2, 3)\n",
    "    plt.title(f\"input {test_frame_number}\")\n",
    "    plt.imshow(test_input[0].reshape(81, 81), cmap=\"gray\")\n",
    "\n",
    "\n",
    "test_model()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using 16bit Automatic Mixed Precision (AMP)\n",
      "GPU available: True (cuda), used: True\n",
      "TPU available: False, using: 0 TPU cores\n",
      "IPU available: False, using: 0 IPUs\n",
      "HPU available: False, using: 0 HPUs\n",
      "/home/kanna/.conda/envs/torch/lib/python3.11/site-packages/lightning/pytorch/callbacks/model_checkpoint.py:639: Checkpoint directory /home/kanna/ml/models/badapple_in_nn exists and is not empty.\n",
      "LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0]\n",
      "\n",
      "  | Name             | Type               | Params\n",
      "--------------------------------------------------------\n",
      "0 | input_to_display | Linear             | 165 M \n",
      "1 | hidden_layers    | Sequential         | 13.1 M\n",
      "2 | classifier       | Sequential         | 1.3 K \n",
      "3 | transforms       | Compose            | 0     \n",
      "4 | loss             | NLLLoss            | 0     \n",
      "5 | train_acc        | MulticlassAccuracy | 0     \n",
      "6 | valid_acc        | MulticlassAccuracy | 0     \n",
      "--------------------------------------------------------\n",
      "178 M     Trainable params\n",
      "0         Non-trainable params\n",
      "178 M     Total params\n",
      "713.619   Total estimated model params size (MB)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 99: 100%|██████████| 344/344 [01:09<00:00,  4.96it/s, v_num=5]       "
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "`Trainer.fit` stopped: `max_epochs=100` reached.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 99: 100%|██████████| 344/344 [01:09<00:00,  4.96it/s, v_num=5]\n"
     ]
    }
   ],
   "source": [
    "def main():\n",
    "    global model, logger\n",
    "    model = BadAppleInNN(\n",
    "        input_size=81,\n",
    "        badapple_display_size=config[\"display_size\"],\n",
    "        interpreter_layers=config[\"interpreter_layers\"],\n",
    "        frames=frames,\n",
    "    )\n",
    "\n",
    "    train_loader, val_loader = get_loaders()\n",
    "\n",
    "    logger = TensorBoardLogger(\n",
    "        save_dir=config[\"log_dir\"],\n",
    "        name=config[\"log_name\"],\n",
    "    )\n",
    "\n",
    "    checkpoints = ModelCheckpoint(\n",
    "        dirpath=config[\"checkpoint_dir\"],\n",
    "        filename=config[\"checkpoint_filename\"],\n",
    "        monitor=\"valid_acc\",\n",
    "        mode=\"max\",\n",
    "        save_top_k=1,\n",
    "    )\n",
    "\n",
    "    trainer = L.Trainer(\n",
    "        max_epochs=config[\"epochs\"],\n",
    "        logger=logger,\n",
    "        accelerator=\"gpu\",\n",
    "        devices=1,\n",
    "        callbacks=[checkpoints],\n",
    "        precision=\"16-mixed\",\n",
    "        #profiler=\"simple\",\n",
    "    )\n",
    "\n",
    "    trainer.fit(model, train_loader, val_loader)\n",
    "\n",
    "main()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "%reload_ext tensorboard\n",
    "%tensorboard --logdir=/home/kanna/ml/logs/badapple_in_nn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "def load_model():\n",
    "    global model\n",
    "    model = BadAppleInNN.load_from_checkpoint(\n",
    "        checkpoint_path=\"/home/kanna/ml/models/badapple_in_nn/badapple_in_nn-epoch=86-valid_acc=0.61.ckpt\",\n",
    "        input_size=81,\n",
    "        badapple_display_size=config[\"display_size\"],\n",
    "        interpreter_layers=config[\"interpreter_layers\"],\n",
    "        frames=frames,\n",
    "    ).cpu()\n",
    "load_model()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.embed_badapple(frames)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor([[ -5.4074,  -3.1345,  -2.6930,  -5.1474,  -1.5366,  -2.5299,  -0.6521,\n",
      "          -4.9773,  -3.0365,  -4.8416],\n",
      "        [-10.1086,  -8.5862,  -7.3803,  -7.8564,  -0.0403,  -3.8912,  -5.7697,\n",
      "          -6.8468,  -6.7778,  -4.3788],\n",
      "        [ -1.7329,  -9.5819,  -6.0327,  -3.8273,  -7.5670,  -3.0550,  -3.8493,\n",
      "          -5.5886,  -0.3256,  -5.4746],\n",
      "        [ -5.0978,  -0.0582,  -6.8784,  -5.4569,  -6.3507,  -4.7535,  -4.9223,\n",
      "          -5.2473,  -3.8849,  -6.3672],\n",
      "        [ -3.7774,  -8.1789,  -4.7144,  -5.1617,  -5.1161,  -2.2965,  -0.4062,\n",
      "          -7.5226,  -1.6721,  -6.9450],\n",
      "        [ -3.3392,  -1.4014,  -1.5723,  -2.4360,  -3.6437,  -2.3825,  -2.0264,\n",
      "          -2.3545,  -2.8447,  -3.9209],\n",
      "        [-10.2313,  -8.4091,  -1.5956,  -6.8640,  -7.7500,  -5.7271,  -8.2679,\n",
      "          -0.2343,  -8.9918,  -7.2061],\n",
      "        [ -3.7797,  -5.8367,  -4.8916,  -1.0298,  -5.8390,  -0.8956,  -3.9981,\n",
      "          -4.6200,  -1.9727,  -3.4703],\n",
      "        [ -5.5497,  -9.7007,  -6.7088,  -0.4078, -10.2559,  -1.4286, -10.5032,\n",
      "          -4.9662,  -5.9071,  -2.5220]], grad_fn=<LogSoftmaxBackward0>)\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 640x480 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 2000x2000 with 9 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def test_model():\n",
    "    global model\n",
    "    test_frame_number = 1000\n",
    "    display_size = config[\"display_size\"]\n",
    "    interpreter_layers = config[\"interpreter_layers\"]\n",
    "\n",
    "    plt.subplot(2, 2, 1)\n",
    "    plt.axis(\"off\")\n",
    "    plt.title(f\"actual frame {test_frame_number}\")\n",
    "    plt.imshow(frames[test_frame_number], cmap=\"gray\")\n",
    "    \n",
    "    with torch.no_grad():\n",
    "        test_input = torch.zeros(1, 81**2)\n",
    "        test_input[0][test_frame_number] = 1\n",
    "        out = model.input_to_display(test_input)\n",
    "\n",
    "    total_display_size = (display_size[0] + 2 * interpreter_layers, display_size[1] + 2 * interpreter_layers)\n",
    "\n",
    "    plt.subplot(2, 2, 2)\n",
    "    plt.axis(\"off\")\n",
    "    plt.title(f\"model frame {test_frame_number}\")\n",
    "    plt.imshow(out[0].reshape(*total_display_size), cmap=\"gray\")\n",
    "\n",
    "    plt.subplot(2, 2, 3)\n",
    "    plt.title(f\"input {test_frame_number}\")\n",
    "    plt.imshow(test_input[0].reshape(81, 81), cmap=\"gray\")\n",
    "\n",
    "    plt.figure()\n",
    "\n",
    "    # test accuracy\n",
    "    _, val_loader = get_loaders(9)\n",
    "    images, labels = next(iter(val_loader))\n",
    "\n",
    "    out = model(images)\n",
    "\n",
    "    print(out)\n",
    "    \n",
    "    plt.figure(figsize=(20, 20))\n",
    "    confidences = out.softmax(dim=1).max(dim=1).values\n",
    "    predictions = out.argmax(dim=1)\n",
    "    \n",
    "    for i, (image, label, prediction, confidence) in enumerate(zip(images, labels, predictions, confidences)):\n",
    "        plt.subplot(3, 3, i + 1)\n",
    "        plt.axis(\"off\")\n",
    "        plt.title(f\"{label.item()} | predicted: {prediction.item()} | confidence: {confidence.item():.2%}\")\n",
    "        plt.imshow(image.reshape(81, 81), cmap=\"gray\")\n",
    "\n",
    "\n",
    "test_model()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Parameter containing:\n",
       "tensor([[ 0.1464,  0.0915,  0.0752,  ..., -0.2786, -0.3615, -0.4208],\n",
       "        [-0.2932, -0.2496, -0.1608,  ..., -0.1072, -0.2240, -0.1735],\n",
       "        [-0.0615,  0.0419,  0.0907,  ..., -0.0051, -0.0445, -0.0818],\n",
       "        ...,\n",
       "        [ 0.1886,  0.1483,  0.1031,  ...,  0.2433,  0.3455,  0.3783],\n",
       "        [ 0.1487,  0.0874,  0.1093,  ...,  0.2321,  0.1887,  0.1283],\n",
       "        [-0.1336, -0.0669, -0.0126,  ..., -0.2491, -0.2211, -0.1570]],\n",
       "       requires_grad=True)"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "torch.set_printoptions(profile=\"default\") # do not change to full (it uses a lot of memory)\n",
    "model.input_to_display.weight"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/kanna/.conda/envs/torch/lib/python3.11/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
      "  from .autonotebook import tqdm as notebook_tqdm\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Running on local URL:  http://127.0.0.1:7860\n",
      "\n",
      "To create a public link, set `share=True` in `launch()`.\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div><iframe src=\"http://127.0.0.1:7860/\" width=\"100%\" height=\"500\" allow=\"autoplay; camera; microphone; clipboard-read; clipboard-write;\" frameborder=\"0\" allowfullscreen></iframe></div>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "image.keys()=dict_keys(['background', 'layers', 'composite'])\n"
     ]
    }
   ],
   "source": [
    "import gradio as gr\n",
    "\n",
    "def launch_gradio():\n",
    "    transforms = v2.Compose([\n",
    "        v2.ToImage(),\n",
    "        v2.ToDtype(torch.float32, scale=True),\n",
    "        v2.Resize((81, 81), antialias=True),\n",
    "    ])\n",
    "\n",
    "    def recognize_digit(image):\n",
    "        with torch.no_grad():\n",
    "            print(f\"{image.keys()=}\")\n",
    "            image = transforms(np.expand_dims(image[\"composite\"], axis=2))\n",
    "            predictions = model(image).squeeze(dim=0).softmax(dim=0)\n",
    "            return {str(i): prediction.item() for i, prediction in enumerate(predictions)}\n",
    "\n",
    "    brush = gr.Brush(\n",
    "        default_size=10,\n",
    "        default_color=\"#FFFFFF\",\n",
    "        #colors=[\"#000000\", \"#FFFFFF\"],\n",
    "        color_mode=\"fixed\",\n",
    "    )\n",
    "\n",
    "    # \"https://zbase-global.zingfront.com/saasbox/resources/webp/loli4-1__3e4832ae6981121b935bfcb3db599b68.webp\"\n",
    "    sketchpad = gr.Sketchpad(brush=brush, image_mode=\"L\")\n",
    "    output_component = gr.Textbox()\n",
    "\n",
    "    gr.Interface(fn=recognize_digit, \n",
    "                inputs=sketchpad, \n",
    "                outputs=\"label\",\n",
    "                title=\"MNIST Sketchpad\",\n",
    "                description=\"Draw a number 0 through 9 on the sketchpad, and click submit to see the model's predictions. Model trained on the MNIST dataset.\",\n",
    "                thumbnail=\"https://raw.githubusercontent.com/gradio-app/real-time-mnist/master/thumbnail2.png\").launch()\n",
    "\n",
    "launch_gradio()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "def export_model():\n",
    "    dynamic_axes = {'input' : {0 : 'batch_size'}, \n",
    "                            'output' : {0 : 'batch_size'}}\n",
    "    torch.onnx.export(\n",
    "        model=model, \n",
    "        args=torch.zeros(1, 81, 81), \n",
    "        f=config[\"onnx_path\"],\n",
    "        dynamic_axes=dynamic_axes,\n",
    "        input_names=[\"input\"],\n",
    "        output_names=[\"output\"],\n",
    "    )\n",
    "export_model()"
   ]
  }
 ],
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