How can you properly plan Torchvision V2 developed, mixing, cuts, and today's training of the ART-OF-THE ART computer vision?
In this lesson, we examine advanced computer strategies using Torchvising Torchvision's V2 Transforms, modern import strategies, and powerful training developers. We are traveling through the Agmentation Pipeline process, using Mexup and Cutmix, designing today's CNN with attention, and using strong training. Using everything without seams in Google Colab, we put our attention and use Kingdom habits by learning and clarification and efficiency. Look Full codes here.
!pip install torch torchvision torchaudio --quiet
!pip install matplotlib pillow numpy --quiet
import torch
import torchvision
from torchvision import transforms as T
from torchvision.transforms import v2
import torch.nn as nn
import torch.optim as optim
from torch.utils.data import DataLoader
import matplotlib.pyplot as plt
import numpy as np
from PIL import Image
import requests
from io import BytesIO
print(f"PyTorch version: {torch.__version__}")
print(f"TorchVision version: {torchvision.__version__}")We start by installing libraries and invites all important modules to the travel of our work. We have organized the pytro, Torchvervation V2 converts, and supporting tools such as NUMPEL, life, and Matatplib, so we are ready to build and test computerized computer pipelines. Look Full codes here.
class AdvancedAugmentationPipeline:
def __init__(self, image_size=224, training=True):
self.image_size = image_size
self.training = training
base_transforms = [
v2.ToImage(),
v2.ToDtype(torch.uint8, scale=True),
]
if training:
self.transform = v2.Compose([
*base_transforms,
v2.Resize((image_size + 32, image_size + 32)),
v2.RandomResizedCrop(image_size, scale=(0.8, 1.0), ratio=(0.9, 1.1)),
v2.RandomHorizontalFlip(p=0.5),
v2.RandomRotation(degrees=15),
v2.ColorJitter(brights=0.4, contst=0.4, sation=0.4, hue=0.1),
v2.RandomGrayscale(p=0.1),
v2.GaussianBlur(kernel_size=3, sigma=(0.1, 2.0)),
v2.RandomPerspective(distortion_scale=0.1, p=0.3),
v2.RandomAffine(degrees=10, translate=(0.1, 0.1), scale=(0.9, 1.1)),
v2.ToDtype(torch.float32, scale=True),
v2.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
])
else:
self.transform = v2.Compose([
*base_transforms,
v2.Resize((image_size, image_size)),
v2.ToDtype(torch.float32, scale=True),
v2.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
])
def __call__(self, image):
return self.transform(image)We define the AGIGULIAL AUGPEMENTation pipeline that is consistent with both training and verification methods. We use strong v2 Torchvision V2 We are changing, like planting, color, color, vibration, viewing, and storage, while storing simple exchange and regular exchange. In this way, we ensure that we advertise the best training data while storing consistent and stable. Look Full codes here.
class AdvancedMixupCutmix:
def __init__(self, mixup_alpha=1.0, cutmix_alpha=1.0, prob=0.5):
self.mixup_alpha = mixup_alpha
self.cutmix_alpha = cutmix_alpha
self.prob = prob
def mixup(self, x, y):
batch_size = x.size(0)
lam = np.random.beta(self.mixup_alpha, self.mixup_alpha) if self.mixup_alpha > 0 else 1
index = torch.randperm(batch_size)
mixed_x = lam * x + (1 - lam) * x[index, :]
y_a, y_b = y, y[index]
return mixed_x, y_a, y_b, lam
def cutmix(self, x, y):
batch_size = x.size(0)
lam = np.random.beta(self.cutmix_alpha, self.cutmix_alpha) if self.cutmix_alpha > 0 else 1
index = torch.randperm(batch_size)
y_a, y_b = y, y[index]
bbx1, bby1, bbx2, bby2 = self._rand_bbox(x.size(), lam)
x[:, :, bbx1:bbx2, bby1:bby2] = x[index, :, bbx1:bbx2, bby1:bby2]
lam = 1 - ((bbx2 - bbx1) * (bby2 - bby1) / (x.size()[-1] * x.size()[-2]))
return x, y_a, y_b, lam
def _rand_bbox(self, size, lam):
W = size[2]
H = size[3]
cut_rat = np.sqrt(1. - lam)
cut_w = int(W * cut_rat)
cut_h = int(H * cut_rat)
cx = np.random.randint(W)
cy = np.random.randint(H)
bbx1 = np.clip(cx - cut_w // 2, 0, W)
bby1 = np.clip(cy - cut_h // 2, 0, H)
bbx2 = np.clip(cx + cut_w // 2, 0, W)
bby2 = np.clip(cy + cut_h // 2, 0, H)
return bbx1, bby1, bbx2, bby2
def __call__(self, x, y):
if np.random.random() > self.prob:
return x, y, y, 1.0
if np.random.random() < 0.5:
return self.mixup(x, y)
else:
return self.cutmix(x, y)
class ModernCNN(nn.Module):
def __init__(self, num_classes=10, dropout=0.3):
super(ModernCNN, self).__init__()
self.conv1 = self._conv_block(3, 64)
self.conv2 = self._conv_block(64, 128, downsample=True)
self.conv3 = self._conv_block(128, 256, downsample=True)
self.conv4 = self._conv_block(256, 512, downsample=True)
self.gap = nn.AdaptiveAvgPool2d(1)
self.attention = nn.Sequential(
nn.Linear(512, 256),
nn.ReLU(),
nn.Linear(256, 512),
nn.Sigmoid()
)
self.classifier = nn.Sequential(
nn.Dropout(dropout),
nn.Linear(512, 256),
nn.BatchNorm1d(256),
nn.ReLU(),
nn.Dropout(dropout/2),
nn.Linear(256, num_classes)
)
def _conv_block(self, in_channels, out_channels, downsample=False):
stride = 2 if downsample else 1
return nn.Sequential(
nn.Conv2d(in_channels, out_channels, 3, stride=stride, padding=1),
nn.BatchNorm2d(out_channels),
nn.ReLU(inplace=True),
nn.Conv2d(out_channels, out_channels, 3, padding=1),
nn.BatchNorm2d(out_channels),
nn.ReLU(inplace=True)
)
def forward(self, x):
x = self.conv1(x)
x = self.conv2(x)
x = self.conv3(x)
x = self.conv4(x)
x = self.gap(x)
x = torch.flatten(x, 1)
attention_weights = self.attention(x)
x = x * attention_weights
return self.classifier(x)We strengthen our training in united / cutmix modplies, where we combine pics or patch-Swap districts and compute label rendering pixel ratio. We are pairing this with a modern CNN filling with developing developing blocks, operating between the publications, and uses the Gate of Learned Having Learned Before the Disposable Divorce, so we promote the variable while keeping the integrity. Look Full codes here.
class AdvancedTrainer:
def __init__(self, model, device="cuda" if torch.cuda.is_available() else 'cpu'):
self.model = model.to(device)
self.device = device
self.mixup_cutmix = AdvancedMixupCutmix()
self.optimizer = optim.AdamW(model.parameters(), lr=1e-3, weight_decay=1e-4)
self.scheduler = optim.lr_scheduler.OneCycleLR(
self.optimizer, max_lr=1e-2, epochs=10, steps_per_epoch=100
)
self.criterion = nn.CrossEntropyLoss()
def mixup_criterion(self, pred, y_a, y_b, lam):
return lam * self.criterion(pred, y_a) + (1 - lam) * self.criterion(pred, y_b)
def train_epoch(self, dataloader):
self.model.train()
total_loss = 0
correct = 0
total = 0
for batch_idx, (data, target) in enumerate(dataloader):
data, target = data.to(self.device), target.to(self.device)
data, target_a, target_b, lam = self.mixup_cutmix(data, target)
self.optimizer.zero_grad()
output = self.model(data)
if lam != 1.0:
loss = self.mixup_criterion(output, target_a, target_b, lam)
else:
loss = self.criterion(output, target)
loss.backward()
torch.nn.utils.clip_grad_norm_(self.model.parameters(), max_norm=1.0)
self.optimizer.step()
self.scheduler.step()
total_loss += loss.item()
_, predicted = output.max(1)
total += target.size(0)
if lam != 1.0:
correct += (lam * predicted.eq(target_a).sum().item() +
(1 - lam) * predicted.eq(target_b).sum().item())
else:
correct += predicted.eq(target).sum().item()
return total_loss / len(dataloader), 100. * correct / totalWe plan to train Adamw, Arececlelr, and Dynamic Mexup / cutmix so we strengthen well work and encourage regular development. It includes the dropouts that we mix, combine gradientants to be safe, and step on each schedule is considered, so we track loss / accuracy with one epoch. Look Full codes here.
def demo_advanced_techniques():
batch_size = 16
num_classes = 10
sample_data = torch.randn(batch_size, 3, 224, 224)
sample_labels = torch.randint(0, num_classes, (batch_size,))
transform_pipeline = AdvancedAugmentationPipeline(training=True)
model = ModernCNN(num_classes=num_classes)
trainer = AdvancedTrainer(model)
print("🚀 Advanced Deep Learning Tutorial Demo")
print("=" * 50)
print("n1. Advanced Augmentation Pipeline:")
augmented = transform_pipeline(Image.fromarray((sample_data[0].permute(1,2,0).numpy() * 255).astype(np.uint8)))
print(f" Original shape: {sample_data[0].shape}")
print(f" Augmented shape: {augmented.shape}")
print(f" Applied transforms: Resize, Crop, Flip, ColorJitter, Blur, Perspective, etc.")
print("n2. MixUp/CutMix Augmentation:")
mixup_cutmix = AdvancedMixupCutmix()
mixed_data, target_a, target_b, lam = mixup_cutmix(sample_data, sample_labels)
print(f" Mixed batch shape: {mixed_data.shape}")
print(f" Lambda value: {lam:.3f}")
print(f" Technique: {'MixUp' if lam > 0.7 else 'CutMix'}")
print("n3. Modern CNN Architecture:")
model.eval()
with torch.no_grad():
output = model(sample_data)
print(f" Input shape: {sample_data.shape}")
print(f" Output shape: {output.shape}")
print(f" Features: Residual blocks, Attention, Global Average Pooling")
print(f" Parameters: {sum(p.numel() for p in model.parameters()):,}")
print("n4. Advanced Training Simulation:")
dummy_loader = [(sample_data, sample_labels)]
loss, acc = trainer.train_epoch(dummy_loader)
print(f" Training loss: {loss:.4f}")
print(f" Training accuracy: {acc:.2f}%")
print(f" Learning rate: {trainer.scheduler.get_last_lr()[0]:.6f}")
print("n✅ Tutorial completed successfully!")
print("This code demonstrates state-of-the-art techniques in deep learning:")
print("• Advanced data augmentation with TorchVision v2")
print("• MixUp and CutMix for better generalization")
print("• Modern CNN architecture with attention")
print("• Advanced training loop with OneCycleLR")
print("• Gradient clipping and weight decay")
if __name__ == "__main__":
demo_advanced_techniques()We use Compact END-TOD Domo when we see the Agregament Agregament Augmentation, enter MUXUP / Cutmix, and double check Modencnn for the forward. We then imitate one epoch in the Dummy data to ensure loss, accuracy, and planning a full stakes work before measuring real dataset.
In conclusion, we were successfully developed and explored that the total workouts of the work that included the increasing environment, the increase in new CNN, and modern training strategies. By trying with Torchvision V2, MUXUS, CUTMIX, TAXINGS, AND ACRECYCLELR, we do not confirm the performance of models but also deepen our understanding of cutters.
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