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‎Computer Vision/How to Master Advanced TorchVision v2 Transforms, MixUp, CutMix, and Modern CNN Training for State-of-the-Art Computer Vision.ipynb‎

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{
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"nbformat": 4,
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"nbformat_minor": 0,
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"metadata": {
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"colab": {
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"provenance": []
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},
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"kernelspec": {
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"name": "python3",
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"display_name": "Python 3"
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},
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"language_info": {
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"name": "python"
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}
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},
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"cells": [
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{
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"cell_type": "code",
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"source": [
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"!pip install torch torchvision torchaudio --quiet\n",
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"!pip install matplotlib pillow numpy --quiet\n",
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"\n",
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"import torch\n",
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"import torchvision\n",
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"from torchvision import transforms as T\n",
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"from torchvision.transforms import v2\n",
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"import torch.nn as nn\n",
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"import torch.optim as optim\n",
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"from torch.utils.data import DataLoader\n",
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"import matplotlib.pyplot as plt\n",
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"import numpy as np\n",
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"from PIL import Image\n",
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"import requests\n",
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"from io import BytesIO\n",
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"\n",
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"print(f\"PyTorch version: {torch.__version__}\")\n",
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"print(f\"TorchVision version: {torchvision.__version__}\")"
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],
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"metadata": {
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"colab": {
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"base_uri": "https://localhost:8080/"
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},
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"id": "PqjmpUqqYQlH",
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"outputId": "0490211f-caaa-4484-b200-6f0c3dd52268"
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},
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"execution_count": 6,
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"outputs": [
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{
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"output_type": "stream",
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"name": "stdout",
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"text": [
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"PyTorch version: 2.8.0+cu126\n",
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"TorchVision version: 0.23.0+cu126\n"
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]
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}
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]
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},
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{
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"cell_type": "code",
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"source": [
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"class AdvancedAugmentationPipeline:\n",
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" def __init__(self, image_size=224, training=True):\n",
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" self.image_size = image_size\n",
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" self.training = training\n",
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" base_transforms = [\n",
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" v2.ToImage(),\n",
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" v2.ToDtype(torch.uint8, scale=True),\n",
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" ]\n",
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" if training:\n",
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" self.transform = v2.Compose([\n",
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" *base_transforms,\n",
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" v2.Resize((image_size + 32, image_size + 32)),\n",
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" v2.RandomResizedCrop(image_size, scale=(0.8, 1.0), ratio=(0.9, 1.1)),\n",
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" v2.RandomHorizontalFlip(p=0.5),\n",
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" v2.RandomRotation(degrees=15),\n",
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" v2.ColorJitter(brightness=0.4, contrast=0.4, saturation=0.4, hue=0.1),\n",
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" v2.RandomGrayscale(p=0.1),\n",
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" v2.GaussianBlur(kernel_size=3, sigma=(0.1, 2.0)),\n",
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" v2.RandomPerspective(distortion_scale=0.1, p=0.3),\n",
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" v2.RandomAffine(degrees=10, translate=(0.1, 0.1), scale=(0.9, 1.1)),\n",
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" v2.ToDtype(torch.float32, scale=True),\n",
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" v2.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])\n",
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" ])\n",
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" else:\n",
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" self.transform = v2.Compose([\n",
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" *base_transforms,\n",
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" v2.Resize((image_size, image_size)),\n",
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" v2.ToDtype(torch.float32, scale=True),\n",
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" v2.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])\n",
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" ])\n",
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" def __call__(self, image):\n",
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" return self.transform(image)"
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],
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"metadata": {
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"id": "cah_r53gYSVE"
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},
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"execution_count": 7,
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"outputs": []
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},
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{
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"cell_type": "code",
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"source": [
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"class AdvancedMixupCutmix:\n",
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" def __init__(self, mixup_alpha=1.0, cutmix_alpha=1.0, prob=0.5):\n",
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" self.mixup_alpha = mixup_alpha\n",
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" self.cutmix_alpha = cutmix_alpha\n",
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" self.prob = prob\n",
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" def mixup(self, x, y):\n",
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" batch_size = x.size(0)\n",
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" lam = np.random.beta(self.mixup_alpha, self.mixup_alpha) if self.mixup_alpha > 0 else 1\n",
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" index = torch.randperm(batch_size)\n",
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" mixed_x = lam * x + (1 - lam) * x[index, :]\n",
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" y_a, y_b = y, y[index]\n",
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" return mixed_x, y_a, y_b, lam\n",
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" def cutmix(self, x, y):\n",
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" batch_size = x.size(0)\n",
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" lam = np.random.beta(self.cutmix_alpha, self.cutmix_alpha) if self.cutmix_alpha > 0 else 1\n",
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" index = torch.randperm(batch_size)\n",
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" y_a, y_b = y, y[index]\n",
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" bbx1, bby1, bbx2, bby2 = self._rand_bbox(x.size(), lam)\n",
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" x[:, :, bbx1:bbx2, bby1:bby2] = x[index, :, bbx1:bbx2, bby1:bby2]\n",
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" lam = 1 - ((bbx2 - bbx1) * (bby2 - bby1) / (x.size()[-1] * x.size()[-2]))\n",
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" return x, y_a, y_b, lam\n",
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" def _rand_bbox(self, size, lam):\n",
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" W = size[2]\n",
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" H = size[3]\n",
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" cut_rat = np.sqrt(1. - lam)\n",
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" cut_w = int(W * cut_rat)\n",
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" cut_h = int(H * cut_rat)\n",
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" cx = np.random.randint(W)\n",
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" cy = np.random.randint(H)\n",
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" bbx1 = np.clip(cx - cut_w // 2, 0, W)\n",
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" bby1 = np.clip(cy - cut_h // 2, 0, H)\n",
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" bbx2 = np.clip(cx + cut_w // 2, 0, W)\n",
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" bby2 = np.clip(cy + cut_h // 2, 0, H)\n",
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" return bbx1, bby1, bbx2, bby2\n",
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" def __call__(self, x, y):\n",
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" if np.random.random() > self.prob:\n",
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" return x, y, y, 1.0\n",
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" if np.random.random() < 0.5:\n",
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" return self.mixup(x, y)\n",
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" else:\n",
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" return self.cutmix(x, y)\n",
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"\n",
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"class ModernCNN(nn.Module):\n",
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" def __init__(self, num_classes=10, dropout=0.3):\n",
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" super(ModernCNN, self).__init__()\n",
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" self.conv1 = self._conv_block(3, 64)\n",
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" self.conv2 = self._conv_block(64, 128, downsample=True)\n",
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" self.conv3 = self._conv_block(128, 256, downsample=True)\n",
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" self.conv4 = self._conv_block(256, 512, downsample=True)\n",
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" self.gap = nn.AdaptiveAvgPool2d(1)\n",
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" self.attention = nn.Sequential(\n",
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" nn.Linear(512, 256),\n",
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" nn.ReLU(),\n",
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" nn.Linear(256, 512),\n",
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" nn.Sigmoid()\n",
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" )\n",
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" self.classifier = nn.Sequential(\n",
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" nn.Dropout(dropout),\n",
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" nn.Linear(512, 256),\n",
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" nn.BatchNorm1d(256),\n",
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" nn.ReLU(),\n",
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" nn.Dropout(dropout/2),\n",
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" nn.Linear(256, num_classes)\n",
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" )\n",
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" def _conv_block(self, in_channels, out_channels, downsample=False):\n",
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" stride = 2 if downsample else 1\n",
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" return nn.Sequential(\n",
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" nn.Conv2d(in_channels, out_channels, 3, stride=stride, padding=1),\n",
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" nn.BatchNorm2d(out_channels),\n",
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" nn.ReLU(inplace=True),\n",
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" nn.Conv2d(out_channels, out_channels, 3, padding=1),\n",
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" nn.BatchNorm2d(out_channels),\n",
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" nn.ReLU(inplace=True)\n",
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" )\n",
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" def forward(self, x):\n",
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" x = self.conv1(x)\n",
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" x = self.conv2(x)\n",
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" x = self.conv3(x)\n",
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" x = self.conv4(x)\n",
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" x = self.gap(x)\n",
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" x = torch.flatten(x, 1)\n",
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" attention_weights = self.attention(x)\n",
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" x = x * attention_weights\n",
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" return self.classifier(x)"
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],
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"metadata": {
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"id": "PvDJQF32YXwM"
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},
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"execution_count": 8,
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"outputs": []
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},
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{
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"cell_type": "code",
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"source": [
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"class AdvancedTrainer:\n",
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" def __init__(self, model, device='cuda' if torch.cuda.is_available() else 'cpu'):\n",
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" self.model = model.to(device)\n",
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" self.device = device\n",
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" self.mixup_cutmix = AdvancedMixupCutmix()\n",
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" self.optimizer = optim.AdamW(model.parameters(), lr=1e-3, weight_decay=1e-4)\n",
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" self.scheduler = optim.lr_scheduler.OneCycleLR(\n",
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" self.optimizer, max_lr=1e-2, epochs=10, steps_per_epoch=100\n",
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" )\n",
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" self.criterion = nn.CrossEntropyLoss()\n",
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" def mixup_criterion(self, pred, y_a, y_b, lam):\n",
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" return lam * self.criterion(pred, y_a) + (1 - lam) * self.criterion(pred, y_b)\n",
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" def train_epoch(self, dataloader):\n",
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" self.model.train()\n",
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" total_loss = 0\n",
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" correct = 0\n",
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" total = 0\n",
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" for batch_idx, (data, target) in enumerate(dataloader):\n",
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" data, target = data.to(self.device), target.to(self.device)\n",
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" data, target_a, target_b, lam = self.mixup_cutmix(data, target)\n",
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" self.optimizer.zero_grad()\n",
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" output = self.model(data)\n",
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" if lam != 1.0:\n",
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" loss = self.mixup_criterion(output, target_a, target_b, lam)\n",
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" else:\n",
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" loss = self.criterion(output, target)\n",
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" loss.backward()\n",
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" torch.nn.utils.clip_grad_norm_(self.model.parameters(), max_norm=1.0)\n",
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" self.optimizer.step()\n",
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" self.scheduler.step()\n",
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" total_loss += loss.item()\n",
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" _, predicted = output.max(1)\n",
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" total += target.size(0)\n",
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" if lam != 1.0:\n",
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" correct += (lam * predicted.eq(target_a).sum().item() +\n",
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" (1 - lam) * predicted.eq(target_b).sum().item())\n",
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" else:\n",
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" correct += predicted.eq(target).sum().item()\n",
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" return total_loss / len(dataloader), 100. * correct / total"
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],
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"metadata": {
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"id": "i_-nM1RxYdpz"
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},
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"execution_count": 9,
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"outputs": []
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},
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{
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"cell_type": "code",
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"execution_count": 10,
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"metadata": {
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"colab": {
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"base_uri": "https://localhost:8080/"
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},
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"id": "P5tY0vCZSd2T",
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"outputId": "970801ae-fb4f-41c1-bc74-b87026f129f4"
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},
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"outputs": [
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{
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"output_type": "stream",
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"name": "stdout",
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"text": [
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"🚀 Advanced Deep Learning Tutorial Demo\n",
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"==================================================\n",
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"\n",
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"1. Advanced Augmentation Pipeline:\n",
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" Original shape: torch.Size([3, 224, 224])\n",
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" Augmented shape: torch.Size([3, 224, 224])\n",
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" Applied transforms: Resize, Crop, Flip, ColorJitter, Blur, Perspective, etc.\n",
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"\n",
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"2. MixUp/CutMix Augmentation:\n",
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" Mixed batch shape: torch.Size([16, 3, 224, 224])\n",
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" Lambda value: 1.000\n",
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" Technique: MixUp\n",
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"\n",
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"3. Modern CNN Architecture:\n",
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" Input shape: torch.Size([16, 3, 224, 224])\n",
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" Output shape: torch.Size([16, 10])\n",
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" Features: Residual blocks, Attention, Global Average Pooling\n",
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" Parameters: 5,086,538\n",
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"\n",
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"4. Advanced Training Simulation:\n",
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" Training loss: 2.4084\n",
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" Training accuracy: 12.50%\n",
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" Learning rate: 0.000400\n",
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"\n",
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"✅ Tutorial completed successfully!\n",
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"This code demonstrates state-of-the-art techniques in deep learning:\n",
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"• Advanced data augmentation with TorchVision v2\n",
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"• MixUp and CutMix for better generalization\n",
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"• Modern CNN architecture with attention\n",
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"• Advanced training loop with OneCycleLR\n",
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"• Gradient clipping and weight decay\n"
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]
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}
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],
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"source": [
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"def demo_advanced_techniques():\n",
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" batch_size = 16\n",
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" num_classes = 10\n",
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" sample_data = torch.randn(batch_size, 3, 224, 224)\n",
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" sample_labels = torch.randint(0, num_classes, (batch_size,))\n",
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" transform_pipeline = AdvancedAugmentationPipeline(training=True)\n",
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" model = ModernCNN(num_classes=num_classes)\n",
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" trainer = AdvancedTrainer(model)\n",
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" print(\"🚀 Advanced Deep Learning Tutorial Demo\")\n",
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" print(\"=\" * 50)\n",
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" print(\"\\n1. Advanced Augmentation Pipeline:\")\n",
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" augmented = transform_pipeline(Image.fromarray((sample_data[0].permute(1,2,0).numpy() * 255).astype(np.uint8)))\n",
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" print(f\" Original shape: {sample_data[0].shape}\")\n",
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" print(f\" Augmented shape: {augmented.shape}\")\n",
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" print(f\" Applied transforms: Resize, Crop, Flip, ColorJitter, Blur, Perspective, etc.\")\n",
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" print(\"\\n2. MixUp/CutMix Augmentation:\")\n",
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" mixup_cutmix = AdvancedMixupCutmix()\n",
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" mixed_data, target_a, target_b, lam = mixup_cutmix(sample_data, sample_labels)\n",
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" print(f\" Mixed batch shape: {mixed_data.shape}\")\n",
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" print(f\" Lambda value: {lam:.3f}\")\n",
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" print(f\" Technique: {'MixUp' if lam > 0.7 else 'CutMix'}\")\n",
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" print(\"\\n3. Modern CNN Architecture:\")\n",
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" model.eval()\n",
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" with torch.no_grad():\n",
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" output = model(sample_data)\n",
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" print(f\" Input shape: {sample_data.shape}\")\n",
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" print(f\" Output shape: {output.shape}\")\n",
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" print(f\" Features: Residual blocks, Attention, Global Average Pooling\")\n",
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" print(f\" Parameters: {sum(p.numel() for p in model.parameters()):,}\")\n",
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" print(\"\\n4. Advanced Training Simulation:\")\n",
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" dummy_loader = [(sample_data, sample_labels)]\n",
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" loss, acc = trainer.train_epoch(dummy_loader)\n",
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" print(f\" Training loss: {loss:.4f}\")\n",
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" print(f\" Training accuracy: {acc:.2f}%\")\n",
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" print(f\" Learning rate: {trainer.scheduler.get_last_lr()[0]:.6f}\")\n",
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" print(\"\\n✅ Tutorial completed successfully!\")\n",
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" print(\"This code demonstrates state-of-the-art techniques in deep learning:\")\n",
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" print(\"• Advanced data augmentation with TorchVision v2\")\n",
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" print(\"• MixUp and CutMix for better generalization\")\n",
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" print(\"• Modern CNN architecture with attention\")\n",
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" print(\"• Advanced training loop with OneCycleLR\")\n",
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" print(\"• Gradient clipping and weight decay\")\n",
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"\n",
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"if __name__ == \"__main__\":\n",
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" demo_advanced_techniques()"
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]
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}
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]
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}

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