mirror of
https://github.com/MLSysBook/TinyTorch.git
synced 2026-05-05 14:24:46 -05:00
297 lines
9.2 KiB
Python
297 lines
9.2 KiB
Python
#!/usr/bin/env python3
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"""
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CNN Integration Test - After Module 11
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======================================
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This test validates that modules 1-11 work together for CNN image classification.
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Required modules:
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- Module 01-08: Core MLP functionality (from MNIST test)
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- Module 09: Spatial operations (Conv2d, MaxPool2d)
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- Module 10: DataLoader for efficient batch processing
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- Module 11: CNN training capabilities
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This demonstrates the milestone: "Can train CNNs on CIFAR-10"
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"""
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import sys
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import os
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sys.path.append(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
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import numpy as np
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from tinytorch.core.tensor import Tensor
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from tinytorch.core.layers import Linear
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from tinytorch.core.activations import ReLU
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from tinytorch.core.training import CrossEntropyLoss
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# Try to import spatial operations
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try:
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from tinytorch.core.spatial import Conv2d, MaxPool2d, Flatten
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SPATIAL_AVAILABLE = True
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except ImportError:
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print("⚠️ Spatial operations not available - using placeholder tests")
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SPATIAL_AVAILABLE = False
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class SimpleCNN:
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"""Simple CNN for CIFAR-10 style classification."""
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def __init__(self, num_classes=10):
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if SPATIAL_AVAILABLE:
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# Convolutional layers
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self.conv1 = Conv2d(3, 32, kernel_size=3) # 3 channels -> 32 filters
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self.conv2 = Conv2d(32, 64, kernel_size=3) # 32 -> 64 filters
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self.pool = MaxPool2d(kernel_size=2)
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self.flatten = Flatten()
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# Dense layers
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self.fc1 = Linear(64 * 5 * 5, 256) # Assuming 32x32 input -> 5x5 after conv+pool
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self.fc2 = Linear(256, num_classes)
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else:
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# Fallback: treat as flattened MLP
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self.fc1 = Linear(32*32*3, 256)
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self.fc2 = Linear(256, num_classes)
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self.relu = ReLU()
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def forward(self, x):
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"""Forward pass."""
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if SPATIAL_AVAILABLE:
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# CNN path
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x = self.conv1(x)
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x = self.relu(x)
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x = self.pool(x)
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x = self.conv2(x)
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x = self.relu(x)
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x = self.pool(x)
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x = self.flatten(x)
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else:
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# MLP path - flatten input
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if len(x.shape) == 4: # (batch, channels, height, width)
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batch_size = x.shape[0]
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x = Tensor(x.data.reshape(batch_size, -1))
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x = self.fc1(x)
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x = self.relu(x)
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x = self.fc2(x)
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return x
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def __call__(self, x):
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return self.forward(x)
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def parameters(self):
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"""Get all trainable parameters."""
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params = []
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if SPATIAL_AVAILABLE:
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if hasattr(self.conv1, 'parameters'):
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params.extend(self.conv1.parameters())
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if hasattr(self.conv2, 'parameters'):
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params.extend(self.conv2.parameters())
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params.extend([
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self.fc1.weights, self.fc1.bias,
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self.fc2.weights, self.fc2.bias
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])
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return params
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def generate_fake_cifar(num_samples=32, num_classes=10):
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"""Generate fake CIFAR-10 like data for testing."""
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np.random.seed(42)
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# Generate random 32x32x3 images
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X = np.random.randn(num_samples, 3, 32, 32).astype(np.float32)
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# Generate random labels
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y = np.random.randint(0, num_classes, size=(num_samples,)).astype(np.int64)
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return X, y
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def test_cnn_architecture():
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"""Test CNN architecture can handle image data."""
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print("🏗️ Testing CNN Architecture...")
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try:
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model = SimpleCNN(num_classes=10)
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# Create fake image batch: (batch_size, channels, height, width)
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batch_size = 8
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x = Tensor(np.random.randn(batch_size, 3, 32, 32).astype(np.float32))
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print(f" ✓ Created model and image batch")
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print(f" Input shape: {x.shape} (batch, channels, height, width)")
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# Forward pass
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output = model(x)
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print(f" ✓ Forward pass successful")
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print(f" Output shape: {output.shape}")
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expected_shape = (batch_size, 10)
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assert output.shape == expected_shape, f"Expected {expected_shape}, got {output.shape}"
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print("✅ CNN architecture working!")
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return True
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except Exception as e:
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print(f"❌ CNN architecture test failed: {e}")
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import traceback
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traceback.print_exc()
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return False
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def test_spatial_operations():
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"""Test spatial operations if available."""
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print("🔍 Testing Spatial Operations...")
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if not SPATIAL_AVAILABLE:
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print(" ⚠️ Spatial operations not available - skipping")
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return True
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try:
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# Test Conv2d
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conv = Conv2d(3, 16, kernel_size=3)
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x = Tensor(np.random.randn(1, 3, 8, 8).astype(np.float32))
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conv_out = conv(x)
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print(f" ✓ Conv2d: {x.shape} -> {conv_out.shape}")
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# Test MaxPool2d
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pool = MaxPool2d(kernel_size=2)
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pool_out = pool(conv_out)
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print(f" ✓ MaxPool2d: {conv_out.shape} -> {pool_out.shape}")
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# Test Flatten
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flatten = Flatten()
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flat_out = flatten(pool_out)
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print(f" ✓ Flatten: {pool_out.shape} -> {flat_out.shape}")
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print("✅ Spatial operations working!")
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return True
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except Exception as e:
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print(f"❌ Spatial operations test failed: {e}")
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import traceback
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traceback.print_exc()
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return False
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def test_cnn_training_step():
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"""Test CNN training step."""
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print("🏋️ Testing CNN Training Step...")
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try:
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# Create small CNN and fake CIFAR data
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model = SimpleCNN(num_classes=5)
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# Small batch
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x = Tensor(np.random.randn(4, 3, 16, 16).astype(np.float32)) # Smaller images
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y = Tensor(np.array([0, 1, 2, 3]))
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print(f" ✓ Created CNN model and data")
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print(f" Image batch shape: {x.shape}")
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print(f" Labels shape: {y.shape}")
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# Forward pass
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outputs = model(x)
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print(f" ✓ CNN forward pass: {x.shape} -> {outputs.shape}")
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# Loss computation
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criterion = CrossEntropyLoss()
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loss = criterion(outputs, y)
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print(f" ✓ Loss computation successful")
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print("✅ CNN training step working!")
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return True
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except Exception as e:
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print(f"❌ CNN training step failed: {e}")
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import traceback
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traceback.print_exc()
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return False
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def test_image_data_pipeline():
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"""Test image data processing pipeline."""
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print("📸 Testing Image Data Pipeline...")
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try:
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# Generate batch of fake CIFAR images
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X, y = generate_fake_cifar(num_samples=16)
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print(f" ✓ Generated fake image data")
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print(f" Images shape: {X.shape}")
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print(f" Labels shape: {y.shape}")
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# Convert to tensors
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X_tensor = Tensor(X)
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y_tensor = Tensor(y)
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print(f" ✓ Converted to tensors")
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# Test CNN can process this data
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model = SimpleCNN(num_classes=10)
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outputs = model(X_tensor)
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print(f" ✓ CNN processed image batch: {X_tensor.shape} -> {outputs.shape}")
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# Test loss computation
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criterion = CrossEntropyLoss()
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loss = criterion(outputs, y_tensor)
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print(f" ✓ Loss computation on image batch successful")
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print("✅ Image data pipeline working!")
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return True
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except Exception as e:
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print(f"❌ Image data pipeline failed: {e}")
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import traceback
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traceback.print_exc()
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return False
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def run_cnn_integration_test():
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"""Run complete CNN integration test."""
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print("=" * 60)
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print("🔥 CNN INTEGRATION TEST - Modules 1-11")
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print("=" * 60)
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print()
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success = True
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tests = [
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test_cnn_architecture,
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test_spatial_operations,
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test_cnn_training_step,
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test_image_data_pipeline
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]
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for test in tests:
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try:
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if not test():
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success = False
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print()
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except Exception as e:
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print(f"❌ Test failed with error: {e}")
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import traceback
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traceback.print_exc()
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success = False
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print()
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if success:
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print("🎉 CNN INTEGRATION TEST PASSED!")
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print()
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print("✅ Milestone Achieved: Can build CNNs for image classification")
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print(" • CNN architecture handles 4D image tensors")
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if SPATIAL_AVAILABLE:
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print(" • Spatial operations (Conv2d, MaxPool2d) work")
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else:
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print(" • Fallback MLP architecture works for images")
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print(" • Training pipeline supports image data")
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print(" • End-to-end image classification pipeline functional")
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print()
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print("🚀 Ready for Module 12+: Attention and Transformers!")
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else:
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print("❌ CNN INTEGRATION TEST FAILED!")
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print(" Check spatial and training modules before proceeding")
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print("=" * 60)
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return success
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if __name__ == "__main__":
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run_cnn_integration_test() |