mirror of
https://github.com/MLSysBook/TinyTorch.git
synced 2026-05-03 21:13:45 -05:00
Fix module file naming and tensor assignment issues
- Updated module.yaml files for 05_dense and 06_spatial to reference correct dev file names - Fixed #| default_exp directives in dense_dev.py and spatial_dev.py to export to correct module names - Fixed tensor assignment issues in 12_compression module by creating new Tensor objects instead of trying to assign to .data property - Removed missing function imports from autograd integration test - All individual module tests now pass (01_setup through 14_benchmarking) - Generated correct module files: dense.py, spatial.py, attention.py
This commit is contained in:
Vijay Janapa Reddi
860
modules/source/06_spatial/spatial_dev.py
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860
modules/source/06_spatial/spatial_dev.py
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# ---
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# jupyter:
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# jupytext:
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# text_representation:
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# extension: .py
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# format_name: percent
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# format_version: '1.3'
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# jupytext_version: 1.17.1
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# ---
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# %% [markdown]
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"""
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# CNN - Convolutional Neural Networks
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Welcome to the CNN module! Here you'll implement the core building block of modern computer vision: the convolutional layer.
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## Learning Goals
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- Understand the convolution operation and its importance in computer vision
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- Implement Conv2D with explicit for-loops to understand the sliding window mechanism
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- Build convolutional layers that can detect spatial patterns in images
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- Compose Conv2D with other layers to build complete convolutional networks
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- See how convolution enables parameter sharing and translation invariance
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## Build → Use → Reflect
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1. **Build**: Conv2D layer using sliding window convolution from scratch
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2. **Use**: Transform images and see feature maps emerge
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3. **Reflect**: How CNNs learn hierarchical spatial patterns
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## What You'll Learn
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By the end of this module, you'll understand:
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- How convolution works as a sliding window operation
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- Why convolution is perfect for spatial data like images
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- How to build learnable convolutional layers
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- The CNN pipeline: Conv2D → Activation → Flatten → Dense
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- How parameter sharing makes CNNs efficient
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"""
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# %% nbgrader={"grade": false, "grade_id": "cnn-imports", "locked": false, "schema_version": 3, "solution": false, "task": false}
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#| default_exp core.spatial
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#| export
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import numpy as np
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import os
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import sys
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from typing import List, Tuple, Optional
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import matplotlib.pyplot as plt
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# Import from the main package - try package first, then local modules
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try:
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from tinytorch.core.tensor import Tensor
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from tinytorch.core.layers import Dense
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from tinytorch.core.activations import ReLU
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except ImportError:
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# For development, import from local modules
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sys.path.append(os.path.join(os.path.dirname(__file__), '..', '01_tensor'))
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sys.path.append(os.path.join(os.path.dirname(__file__), '..', '02_activations'))
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sys.path.append(os.path.join(os.path.dirname(__file__), '..', '03_layers'))
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from tensor_dev import Tensor
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from activations_dev import ReLU
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from layers_dev import Dense
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# %% nbgrader={"grade": false, "grade_id": "cnn-setup", "locked": false, "schema_version": 3, "solution": false, "task": false}
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#| hide
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#| export
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def _should_show_plots():
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"""Check if we should show plots (disable during testing)"""
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# Check multiple conditions that indicate we're in test mode
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is_pytest = (
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'pytest' in sys.modules or
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'test' in sys.argv or
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os.environ.get('PYTEST_CURRENT_TEST') is not None or
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any('test' in arg for arg in sys.argv) or
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any('pytest' in arg for arg in sys.argv)
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)
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# Show plots in development mode (when not in test mode)
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return not is_pytest
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# %% nbgrader={"grade": false, "grade_id": "cnn-welcome", "locked": false, "schema_version": 3, "solution": false, "task": false}
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print("🔥 TinyTorch CNN Module")
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print(f"NumPy version: {np.__version__}")
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print(f"Python version: {sys.version_info.major}.{sys.version_info.minor}")
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print("Ready to build convolutional neural networks!")
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# %% [markdown]
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"""
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## 📦 Where This Code Lives in the Final Package
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**Learning Side:** You work in `modules/source/05_cnn/cnn_dev.py`
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**Building Side:** Code exports to `tinytorch.core.cnn`
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```python
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# Final package structure:
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from tinytorch.core.cnn import Conv2D, conv2d_naive, flatten # CNN operations!
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from tinytorch.core.layers import Dense # Fully connected layers
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from tinytorch.core.activations import ReLU # Nonlinearity
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from tinytorch.core.tensor import Tensor # Foundation
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```
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**Why this matters:**
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- **Learning:** Focused modules for deep understanding of convolution
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- **Production:** Proper organization like PyTorch's `torch.nn.Conv2d`
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- **Consistency:** All CNN operations live together in `core.cnn`
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- **Integration:** Works seamlessly with other TinyTorch components
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"""
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# %% [markdown]
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"""
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## Step 1: Understanding Convolution
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### What is Convolution?
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**Convolution** is a mathematical operation that slides a small filter (kernel) across an input, computing dot products at each position.
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### Why Convolution is Perfect for Images
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- **Local patterns**: Images have local structure (edges, textures)
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- **Translation invariance**: Same pattern can appear anywhere
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- **Parameter sharing**: One filter detects the pattern everywhere
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- **Spatial hierarchy**: Multiple layers build increasingly complex features
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### The Fundamental Insight
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**Convolution is pattern matching!** The kernel learns to detect specific patterns:
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- **Edge detectors**: Find boundaries between objects
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- **Texture detectors**: Recognize surface patterns
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- **Shape detectors**: Identify geometric forms
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- **Feature detectors**: Combine simple patterns into complex features
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### Real-World Applications
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- **Image processing**: Detect edges, blur, sharpen
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- **Computer vision**: Recognize objects, faces, text
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- **Medical imaging**: Detect tumors, analyze scans
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- **Autonomous driving**: Identify traffic signs, pedestrians
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### Visual Intuition
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```
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Input Image: Kernel: Output Feature Map:
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[1, 2, 3] [1, 0] [1*1+2*0+4*0+5*(-1), 2*1+3*0+5*0+6*(-1)]
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[4, 5, 6] [0, -1] [4*1+5*0+7*0+8*(-1), 5*1+6*0+8*0+9*(-1)]
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[7, 8, 9]
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```
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The kernel slides across the input, computing dot products at each position.
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Let's implement this step by step!
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"""
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# %% nbgrader={"grade": false, "grade_id": "conv2d-naive", "locked": false, "schema_version": 3, "solution": true, "task": false}
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#| export
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def conv2d_naive(input: np.ndarray, kernel: np.ndarray) -> np.ndarray:
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"""
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Naive 2D convolution (single channel, no stride, no padding).
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Args:
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input: 2D input array (H, W)
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kernel: 2D filter (kH, kW)
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Returns:
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2D output array (H-kH+1, W-kW+1)
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TODO: Implement the sliding window convolution using for-loops.
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APPROACH:
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1. Get input dimensions: H, W = input.shape
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2. Get kernel dimensions: kH, kW = kernel.shape
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3. Calculate output dimensions: out_H = H - kH + 1, out_W = W - kW + 1
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4. Create output array: np.zeros((out_H, out_W))
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5. Use nested loops to slide the kernel:
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- i loop: output rows (0 to out_H-1)
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- j loop: output columns (0 to out_W-1)
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- di loop: kernel rows (0 to kH-1)
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- dj loop: kernel columns (0 to kW-1)
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6. For each (i,j), compute: output[i,j] += input[i+di, j+dj] * kernel[di, dj]
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EXAMPLE:
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Input: [[1, 2, 3], Kernel: [[1, 0],
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[4, 5, 6], [0, -1]]
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[7, 8, 9]]
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Output[0,0] = 1*1 + 2*0 + 4*0 + 5*(-1) = 1 - 5 = -4
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Output[0,1] = 2*1 + 3*0 + 5*0 + 6*(-1) = 2 - 6 = -4
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Output[1,0] = 4*1 + 5*0 + 7*0 + 8*(-1) = 4 - 8 = -4
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Output[1,1] = 5*1 + 6*0 + 8*0 + 9*(-1) = 5 - 9 = -4
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HINTS:
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- Start with output = np.zeros((out_H, out_W))
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- Use four nested loops: for i in range(out_H): for j in range(out_W): for di in range(kH): for dj in range(kW):
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- Accumulate the sum: output[i,j] += input[i+di, j+dj] * kernel[di, dj]
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"""
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### BEGIN SOLUTION
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# Get input and kernel dimensions
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H, W = input.shape
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kH, kW = kernel.shape
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# Calculate output dimensions
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out_H, out_W = H - kH + 1, W - kW + 1
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# Initialize output array
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output = np.zeros((out_H, out_W), dtype=input.dtype)
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# Sliding window convolution with four nested loops
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for i in range(out_H):
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for j in range(out_W):
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for di in range(kH):
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for dj in range(kW):
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output[i, j] += input[i + di, j + dj] * kernel[di, dj]
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return output
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### END SOLUTION
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# %% [markdown]
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"""
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### 🧪 Unit Test: Convolution Operation
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Let's test your convolution implementation right away! This is the core operation that powers computer vision.
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**This is a unit test** - it tests one specific function (conv2d_naive) in isolation.
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"""
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# %% nbgrader={"grade": true, "grade_id": "test-conv2d-naive-immediate", "locked": true, "points": 10, "schema_version": 3, "solution": false, "task": false}
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# Test conv2d_naive function immediately after implementation
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print("🔬 Unit Test: Convolution Operation...")
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# Test simple 3x3 input with 2x2 kernel
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try:
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input_array = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]], dtype=np.float32)
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kernel_array = np.array([[1, 0], [0, 1]], dtype=np.float32) # Identity-like kernel
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result = conv2d_naive(input_array, kernel_array)
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expected = np.array([[6, 8], [12, 14]], dtype=np.float32) # 1+5, 2+6, 4+8, 5+9
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print(f"Input:\n{input_array}")
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print(f"Kernel:\n{kernel_array}")
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print(f"Result:\n{result}")
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print(f"Expected:\n{expected}")
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assert np.allclose(result, expected), f"Convolution failed: expected {expected}, got {result}"
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print("✅ Simple convolution test passed")
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except Exception as e:
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print(f"❌ Simple convolution test failed: {e}")
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raise
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# Test edge detection kernel
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try:
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input_array = np.array([[1, 1, 1], [1, 1, 1], [1, 1, 1]], dtype=np.float32)
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edge_kernel = np.array([[-1, -1], [-1, 3]], dtype=np.float32) # Edge detection
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result = conv2d_naive(input_array, edge_kernel)
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expected = np.array([[0, 0], [0, 0]], dtype=np.float32) # Uniform region = no edges
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assert np.allclose(result, expected), f"Edge detection failed: expected {expected}, got {result}"
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print("✅ Edge detection test passed")
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except Exception as e:
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print(f"❌ Edge detection test failed: {e}")
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raise
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# Test output shape
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try:
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input_5x5 = np.random.randn(5, 5).astype(np.float32)
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kernel_3x3 = np.random.randn(3, 3).astype(np.float32)
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result = conv2d_naive(input_5x5, kernel_3x3)
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expected_shape = (3, 3) # 5-3+1 = 3
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assert result.shape == expected_shape, f"Output shape wrong: expected {expected_shape}, got {result.shape}"
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print("✅ Output shape test passed")
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except Exception as e:
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print(f"❌ Output shape test failed: {e}")
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raise
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# Show the convolution process
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print("🎯 Convolution behavior:")
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print(" Slides kernel across input")
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print(" Computes dot product at each position")
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print(" Output size = Input size - Kernel size + 1")
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print("📈 Progress: Convolution operation ✓")
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# %% [markdown]
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"""
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## Step 2: Building the Conv2D Layer
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### What is a Conv2D Layer?
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A **Conv2D layer** is a learnable convolutional layer that:
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- Has learnable kernel weights (initialized randomly)
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- Applies convolution to input tensors
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- Integrates with the rest of the neural network
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### Why Conv2D Layers Matter
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- **Feature learning**: Kernels learn to detect useful patterns
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- **Composability**: Can be stacked with other layers
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- **Efficiency**: Shared weights reduce parameters dramatically
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- **Translation invariance**: Same patterns detected anywhere in the image
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|
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### Real-World Applications
|
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- **Image classification**: Recognize objects in photos
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- **Object detection**: Find and locate objects
|
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- **Medical imaging**: Detect anomalies in scans
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- **Autonomous driving**: Identify road features
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### Design Decisions
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- **Kernel size**: Typically 3×3 or 5×5 for balance of locality and capacity
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- **Initialization**: Small random values to break symmetry
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- **Integration**: Works with Tensor class and other layers
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"""
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# %% nbgrader={"grade": false, "grade_id": "conv2d-class", "locked": false, "schema_version": 3, "solution": true, "task": false}
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#| export
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class Conv2D:
|
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"""
|
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2D Convolutional Layer (single channel, single filter, no stride/pad).
|
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|
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A learnable convolutional layer that applies a kernel to detect spatial patterns.
|
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Perfect for building the foundation of convolutional neural networks.
|
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"""
|
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|
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def __init__(self, kernel_size: Tuple[int, int]):
|
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"""
|
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Initialize Conv2D layer with random kernel.
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|
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Args:
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kernel_size: (kH, kW) - size of the convolution kernel
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|
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TODO: Initialize a random kernel with small values.
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|
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APPROACH:
|
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1. Store kernel_size as instance variable
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2. Initialize random kernel with small values
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3. Use proper initialization for stable training
|
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|
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EXAMPLE:
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Conv2D((2, 2)) creates:
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- kernel: shape (2, 2) with small random values
|
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|
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HINTS:
|
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- Store kernel_size as self.kernel_size
|
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- Initialize kernel: np.random.randn(kH, kW) * 0.1 (small values)
|
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- Convert to float32 for consistency
|
||||
"""
|
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### BEGIN SOLUTION
|
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# Store kernel size
|
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self.kernel_size = kernel_size
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kH, kW = kernel_size
|
||||
|
||||
# Initialize random kernel with small values
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self.kernel = np.random.randn(kH, kW).astype(np.float32) * 0.1
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### END SOLUTION
|
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|
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def forward(self, x):
|
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"""
|
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Forward pass: apply convolution to input tensor.
|
||||
|
||||
Args:
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x: Input tensor (2D for simplicity)
|
||||
|
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Returns:
|
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Output tensor after convolution
|
||||
|
||||
TODO: Implement forward pass using conv2d_naive function.
|
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|
||||
APPROACH:
|
||||
1. Extract numpy array from input tensor
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2. Apply conv2d_naive with stored kernel
|
||||
3. Return result wrapped in Tensor
|
||||
|
||||
EXAMPLE:
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x = Tensor([[1, 2, 3], [4, 5, 6], [7, 8, 9]]) # shape (3, 3)
|
||||
layer = Conv2D((2, 2))
|
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y = layer(x) # shape (2, 2)
|
||||
|
||||
HINTS:
|
||||
- Use x.data to get numpy array
|
||||
- Use conv2d_naive(x.data, self.kernel)
|
||||
- Return Tensor(result) to wrap the result
|
||||
"""
|
||||
### BEGIN SOLUTION
|
||||
# Apply convolution using naive implementation
|
||||
result = conv2d_naive(x.data, self.kernel)
|
||||
return type(x)(result)
|
||||
### END SOLUTION
|
||||
|
||||
def __call__(self, x):
|
||||
"""Make layer callable: layer(x) same as layer.forward(x)"""
|
||||
return self.forward(x)
|
||||
|
||||
# %% [markdown]
|
||||
"""
|
||||
### 🧪 Unit Test: Conv2D Layer
|
||||
|
||||
Let's test your Conv2D layer implementation! This is a learnable convolutional layer that can be trained.
|
||||
|
||||
**This is a unit test** - it tests one specific class (Conv2D) in isolation.
|
||||
"""
|
||||
|
||||
# %% nbgrader={"grade": true, "grade_id": "test-conv2d-layer-immediate", "locked": true, "points": 10, "schema_version": 3, "solution": false, "task": false}
|
||||
# Test Conv2D layer immediately after implementation
|
||||
print("🔬 Unit Test: Conv2D Layer...")
|
||||
|
||||
# Create a Conv2D layer
|
||||
try:
|
||||
layer = Conv2D(kernel_size=(2, 2))
|
||||
print(f"Conv2D layer created with kernel size: {layer.kernel_size}")
|
||||
print(f"Kernel shape: {layer.kernel.shape}")
|
||||
|
||||
# Test that kernel is initialized properly
|
||||
assert layer.kernel.shape == (2, 2), f"Kernel shape should be (2, 2), got {layer.kernel.shape}"
|
||||
assert not np.allclose(layer.kernel, 0), "Kernel should not be all zeros"
|
||||
print("✅ Conv2D layer initialization successful")
|
||||
|
||||
# Test with sample input
|
||||
x = Tensor([[1, 2, 3], [4, 5, 6], [7, 8, 9]])
|
||||
print(f"Input shape: {x.shape}")
|
||||
|
||||
y = layer(x)
|
||||
print(f"Output shape: {y.shape}")
|
||||
print(f"Output: {y}")
|
||||
|
||||
# Verify shapes
|
||||
assert y.shape == (2, 2), f"Output shape should be (2, 2), got {y.shape}"
|
||||
assert isinstance(y, Tensor), "Output should be a Tensor"
|
||||
print("✅ Conv2D layer forward pass successful")
|
||||
|
||||
except Exception as e:
|
||||
print(f"❌ Conv2D layer test failed: {e}")
|
||||
raise
|
||||
|
||||
# Test different kernel sizes
|
||||
try:
|
||||
layer_3x3 = Conv2D(kernel_size=(3, 3))
|
||||
x_5x5 = Tensor([[1, 2, 3, 4, 5], [6, 7, 8, 9, 10], [11, 12, 13, 14, 15], [16, 17, 18, 19, 20], [21, 22, 23, 24, 25]])
|
||||
y_3x3 = layer_3x3(x_5x5)
|
||||
|
||||
assert y_3x3.shape == (3, 3), f"3x3 kernel output should be (3, 3), got {y_3x3.shape}"
|
||||
print("✅ Different kernel sizes work correctly")
|
||||
|
||||
except Exception as e:
|
||||
print(f"❌ Different kernel sizes test failed: {e}")
|
||||
raise
|
||||
|
||||
# Show the layer behavior
|
||||
print("🎯 Conv2D layer behavior:")
|
||||
print(" Learnable kernel weights")
|
||||
print(" Applies convolution to detect patterns")
|
||||
print(" Can be trained end-to-end")
|
||||
print("📈 Progress: Convolution operation ✓, Conv2D layer ✓")
|
||||
|
||||
# %% [markdown]
|
||||
"""
|
||||
## Step 3: Flattening for Dense Layers
|
||||
|
||||
### What is Flattening?
|
||||
**Flattening** converts multi-dimensional tensors to 1D vectors, enabling connection between convolutional and dense layers.
|
||||
|
||||
### Why Flattening is Needed
|
||||
- **Interface compatibility**: Conv2D outputs 2D, Dense expects 1D
|
||||
- **Network composition**: Connect spatial features to classification
|
||||
- **Standard practice**: Almost all CNNs use this pattern
|
||||
- **Dimension management**: Preserve information while changing shape
|
||||
|
||||
### The Pattern
|
||||
```
|
||||
Conv2D → ReLU → Conv2D → ReLU → Flatten → Dense → Output
|
||||
```
|
||||
|
||||
### Real-World Usage
|
||||
- **Classification**: Final layers need 1D input for class probabilities
|
||||
- **Feature extraction**: Convert spatial features to vector representations
|
||||
- **Transfer learning**: Extract features from pre-trained CNNs
|
||||
"""
|
||||
|
||||
# %% nbgrader={"grade": false, "grade_id": "flatten-function", "locked": false, "schema_version": 3, "solution": true, "task": false}
|
||||
#| export
|
||||
def flatten(x):
|
||||
"""
|
||||
Flatten a 2D tensor to 1D (for connecting to Dense layers).
|
||||
|
||||
Args:
|
||||
x: Input tensor to flatten
|
||||
|
||||
Returns:
|
||||
Flattened tensor with batch dimension preserved
|
||||
|
||||
TODO: Implement flattening operation.
|
||||
|
||||
APPROACH:
|
||||
1. Get the numpy array from the tensor
|
||||
2. Use .flatten() to convert to 1D
|
||||
3. Add batch dimension with [None, :]
|
||||
4. Return Tensor wrapped around the result
|
||||
|
||||
EXAMPLE:
|
||||
Input: Tensor([[1, 2], [3, 4]]) # shape (2, 2)
|
||||
Output: Tensor([[1, 2, 3, 4]]) # shape (1, 4)
|
||||
|
||||
HINTS:
|
||||
- Use x.data.flatten() to get 1D array
|
||||
- Add batch dimension: result[None, :]
|
||||
- Return Tensor(result)
|
||||
"""
|
||||
### BEGIN SOLUTION
|
||||
# Flatten the tensor and add batch dimension
|
||||
flattened = x.data.flatten()
|
||||
result = flattened[None, :] # Add batch dimension
|
||||
return type(x)(result)
|
||||
### END SOLUTION
|
||||
|
||||
# %% [markdown]
|
||||
"""
|
||||
### 🧪 Unit Test: Flatten Function
|
||||
|
||||
Let's test your flatten function! This connects convolutional layers to dense layers.
|
||||
|
||||
**This is a unit test** - it tests one specific function (flatten) in isolation.
|
||||
"""
|
||||
|
||||
# %% nbgrader={"grade": true, "grade_id": "test-flatten-immediate", "locked": true, "points": 10, "schema_version": 3, "solution": false, "task": false}
|
||||
# Test flatten function immediately after implementation
|
||||
print("🔬 Unit Test: Flatten Function...")
|
||||
|
||||
# Test case 1: 2x2 tensor
|
||||
try:
|
||||
x = Tensor([[1, 2], [3, 4]])
|
||||
flattened = flatten(x)
|
||||
|
||||
print(f"Input: {x}")
|
||||
print(f"Flattened: {flattened}")
|
||||
print(f"Flattened shape: {flattened.shape}")
|
||||
|
||||
# Verify shape and content
|
||||
assert flattened.shape == (1, 4), f"Flattened shape should be (1, 4), got {flattened.shape}"
|
||||
expected_data = np.array([[1, 2, 3, 4]])
|
||||
assert np.array_equal(flattened.data, expected_data), f"Flattened data should be {expected_data}, got {flattened.data}"
|
||||
print("✅ 2x2 flatten test passed")
|
||||
|
||||
except Exception as e:
|
||||
print(f"❌ 2x2 flatten test failed: {e}")
|
||||
raise
|
||||
|
||||
# Test case 2: 3x3 tensor
|
||||
try:
|
||||
x2 = Tensor([[1, 2, 3], [4, 5, 6], [7, 8, 9]])
|
||||
flattened2 = flatten(x2)
|
||||
|
||||
assert flattened2.shape == (1, 9), f"Flattened shape should be (1, 9), got {flattened2.shape}"
|
||||
expected_data2 = np.array([[1, 2, 3, 4, 5, 6, 7, 8, 9]])
|
||||
assert np.array_equal(flattened2.data, expected_data2), f"Flattened data should be {expected_data2}, got {flattened2.data}"
|
||||
print("✅ 3x3 flatten test passed")
|
||||
|
||||
except Exception as e:
|
||||
print(f"❌ 3x3 flatten test failed: {e}")
|
||||
raise
|
||||
|
||||
# Test case 3: Different shapes
|
||||
try:
|
||||
x3 = Tensor([[1, 2, 3, 4], [5, 6, 7, 8]]) # 2x4
|
||||
flattened3 = flatten(x3)
|
||||
|
||||
assert flattened3.shape == (1, 8), f"Flattened shape should be (1, 8), got {flattened3.shape}"
|
||||
expected_data3 = np.array([[1, 2, 3, 4, 5, 6, 7, 8]])
|
||||
assert np.array_equal(flattened3.data, expected_data3), f"Flattened data should be {expected_data3}, got {flattened3.data}"
|
||||
print("✅ Different shapes flatten test passed")
|
||||
|
||||
except Exception as e:
|
||||
print(f"❌ Different shapes flatten test failed: {e}")
|
||||
raise
|
||||
|
||||
# Show the flattening behavior
|
||||
print("🎯 Flatten behavior:")
|
||||
print(" Converts 2D tensor to 1D")
|
||||
print(" Preserves batch dimension")
|
||||
print(" Enables connection to Dense layers")
|
||||
print("📈 Progress: Convolution operation ✓, Conv2D layer ✓, Flatten ✓")
|
||||
|
||||
# %% [markdown]
|
||||
"""
|
||||
## Step 4: Comprehensive Test - Complete CNN Pipeline
|
||||
|
||||
### Real-World CNN Applications
|
||||
Let's test our CNN components in realistic scenarios:
|
||||
|
||||
#### **Image Classification Pipeline**
|
||||
```python
|
||||
# The standard CNN pattern
|
||||
Conv2D → ReLU → Flatten → Dense → Output
|
||||
```
|
||||
|
||||
#### **Multi-layer CNN**
|
||||
```python
|
||||
# Deeper pattern for complex features
|
||||
Conv2D → ReLU → Conv2D → ReLU → Flatten → Dense → Output
|
||||
```
|
||||
|
||||
#### **Feature Extraction**
|
||||
```python
|
||||
# Extract spatial features then classify
|
||||
image → CNN features → dense classifier → predictions
|
||||
```
|
||||
|
||||
This comprehensive test ensures our CNN components work together for real computer vision applications!
|
||||
"""
|
||||
|
||||
# %% nbgrader={"grade": true, "grade_id": "test-comprehensive", "locked": true, "points": 15, "schema_version": 3, "solution": false, "task": false}
|
||||
# Comprehensive test - complete CNN applications
|
||||
print("🔬 Comprehensive Test: Complete CNN Applications...")
|
||||
|
||||
try:
|
||||
# Test 1: Simple CNN Pipeline
|
||||
print("\n1. Simple CNN Pipeline Test:")
|
||||
|
||||
# Create pipeline: Conv2D → ReLU → Flatten → Dense
|
||||
conv = Conv2D(kernel_size=(2, 2))
|
||||
relu = ReLU()
|
||||
dense = Dense(input_size=4, output_size=3)
|
||||
|
||||
# Input image
|
||||
image = Tensor([[1, 2, 3], [4, 5, 6], [7, 8, 9]])
|
||||
|
||||
# Forward pass
|
||||
features = conv(image) # (3,3) → (2,2)
|
||||
activated = relu(features) # (2,2) → (2,2)
|
||||
flattened = flatten(activated) # (2,2) → (1,4)
|
||||
output = dense(flattened) # (1,4) → (1,3)
|
||||
|
||||
assert features.shape == (2, 2), f"Conv output shape wrong: {features.shape}"
|
||||
assert activated.shape == (2, 2), f"ReLU output shape wrong: {activated.shape}"
|
||||
assert flattened.shape == (1, 4), f"Flatten output shape wrong: {flattened.shape}"
|
||||
assert output.shape == (1, 3), f"Dense output shape wrong: {output.shape}"
|
||||
|
||||
print("✅ Simple CNN pipeline works correctly")
|
||||
|
||||
# Test 2: Multi-layer CNN
|
||||
print("\n2. Multi-layer CNN Test:")
|
||||
|
||||
# Create deeper pipeline: Conv2D → ReLU → Conv2D → ReLU → Flatten → Dense
|
||||
conv1 = Conv2D(kernel_size=(2, 2))
|
||||
relu1 = ReLU()
|
||||
conv2 = Conv2D(kernel_size=(2, 2))
|
||||
relu2 = ReLU()
|
||||
dense_multi = Dense(input_size=9, output_size=2)
|
||||
|
||||
# Larger input for multi-layer processing
|
||||
large_image = Tensor([[1, 2, 3, 4, 5], [6, 7, 8, 9, 10], [11, 12, 13, 14, 15], [16, 17, 18, 19, 20], [21, 22, 23, 24, 25]])
|
||||
|
||||
# Forward pass
|
||||
h1 = conv1(large_image) # (5,5) → (4,4)
|
||||
h2 = relu1(h1) # (4,4) → (4,4)
|
||||
h3 = conv2(h2) # (4,4) → (3,3)
|
||||
h4 = relu2(h3) # (3,3) → (3,3)
|
||||
h5 = flatten(h4) # (3,3) → (1,9)
|
||||
output_multi = dense_multi(h5) # (1,9) → (1,2)
|
||||
|
||||
assert h1.shape == (4, 4), f"Conv1 output wrong: {h1.shape}"
|
||||
assert h3.shape == (3, 3), f"Conv2 output wrong: {h3.shape}"
|
||||
assert h5.shape == (1, 9), f"Flatten output wrong: {h5.shape}"
|
||||
assert output_multi.shape == (1, 2), f"Final output wrong: {output_multi.shape}"
|
||||
|
||||
print("✅ Multi-layer CNN works correctly")
|
||||
|
||||
# Test 3: Image Classification Scenario
|
||||
print("\n3. Image Classification Test:")
|
||||
|
||||
# Simulate digit classification with 8x8 image
|
||||
digit_image = Tensor([[1, 0, 0, 1, 1, 0, 0, 1],
|
||||
[0, 1, 0, 1, 1, 0, 1, 0],
|
||||
[0, 0, 1, 1, 1, 1, 0, 0],
|
||||
[1, 1, 1, 0, 0, 1, 1, 1],
|
||||
[1, 0, 0, 1, 1, 0, 0, 1],
|
||||
[0, 1, 1, 0, 0, 1, 1, 0],
|
||||
[0, 0, 1, 1, 1, 1, 0, 0],
|
||||
[1, 1, 0, 0, 0, 0, 1, 1]])
|
||||
|
||||
# CNN for digit classification
|
||||
feature_extractor = Conv2D(kernel_size=(3, 3)) # (8,8) → (6,6)
|
||||
activation = ReLU()
|
||||
classifier = Dense(input_size=36, output_size=10) # 10 digit classes
|
||||
|
||||
# Forward pass
|
||||
features = feature_extractor(digit_image)
|
||||
activated_features = activation(features)
|
||||
feature_vector = flatten(activated_features)
|
||||
digit_scores = classifier(feature_vector)
|
||||
|
||||
assert features.shape == (6, 6), f"Feature extraction shape wrong: {features.shape}"
|
||||
assert feature_vector.shape == (1, 36), f"Feature vector shape wrong: {feature_vector.shape}"
|
||||
assert digit_scores.shape == (1, 10), f"Digit scores shape wrong: {digit_scores.shape}"
|
||||
|
||||
print("✅ Image classification scenario works correctly")
|
||||
|
||||
# Test 4: Feature Extraction and Composition
|
||||
print("\n4. Feature Extraction Test:")
|
||||
|
||||
# Create modular feature extractor
|
||||
feature_conv = Conv2D(kernel_size=(2, 2))
|
||||
feature_activation = ReLU()
|
||||
|
||||
# Create classifier head
|
||||
classifier_head = Dense(input_size=4, output_size=3)
|
||||
|
||||
# Test composition
|
||||
test_image = Tensor([[1, 2, 3], [4, 5, 6], [7, 8, 9]])
|
||||
|
||||
# Extract features
|
||||
extracted_features = feature_conv(test_image)
|
||||
activated_features = feature_activation(extracted_features)
|
||||
feature_representation = flatten(activated_features)
|
||||
|
||||
# Classify
|
||||
predictions = classifier_head(feature_representation)
|
||||
|
||||
assert extracted_features.shape == (2, 2), f"Feature extraction wrong: {extracted_features.shape}"
|
||||
assert feature_representation.shape == (1, 4), f"Feature representation wrong: {feature_representation.shape}"
|
||||
assert predictions.shape == (1, 3), f"Predictions wrong: {predictions.shape}"
|
||||
|
||||
print("✅ Feature extraction and composition works correctly")
|
||||
|
||||
print("\n🎉 Comprehensive test passed! Your CNN components work correctly for:")
|
||||
print(" • Image classification pipelines")
|
||||
print(" • Multi-layer feature extraction")
|
||||
print(" • Spatial pattern recognition")
|
||||
print(" • End-to-end CNN workflows")
|
||||
print("📈 Progress: Complete CNN architecture ready for computer vision!")
|
||||
|
||||
except Exception as e:
|
||||
print(f"❌ Comprehensive test failed: {e}")
|
||||
raise
|
||||
|
||||
print("📈 Final Progress: Complete CNN system ready for computer vision!")
|
||||
|
||||
def test_convolution_operation():
|
||||
"""Test convolution operation implementation comprehensively."""
|
||||
print("🔬 Unit Test: Convolution Operation...")
|
||||
|
||||
# Test basic convolution
|
||||
input_data = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]])
|
||||
kernel = np.array([[1, 0], [0, 1]])
|
||||
result = conv2d_naive(input_data, kernel)
|
||||
|
||||
assert result.shape == (2, 2), "Convolution should produce correct output shape"
|
||||
expected = np.array([[6, 8], [12, 14]])
|
||||
assert np.array_equal(result, expected), "Convolution should produce correct values"
|
||||
|
||||
print("✅ Convolution operation works correctly")
|
||||
|
||||
def test_conv2d_layer():
|
||||
"""Test Conv2D layer implementation comprehensively."""
|
||||
print("🔬 Unit Test: Conv2D Layer...")
|
||||
|
||||
# Test Conv2D layer
|
||||
conv = Conv2D(kernel_size=(3, 3))
|
||||
input_tensor = Tensor(np.random.randn(6, 6))
|
||||
output = conv(input_tensor)
|
||||
|
||||
assert output.shape == (4, 4), "Conv2D should produce correct output shape"
|
||||
assert hasattr(conv, 'kernel'), "Conv2D should have kernel attribute"
|
||||
assert conv.kernel.shape == (3, 3), "Kernel should have correct shape"
|
||||
|
||||
print("✅ Conv2D layer works correctly")
|
||||
|
||||
def test_flatten_function():
|
||||
"""Test flatten function implementation comprehensively."""
|
||||
print("🔬 Unit Test: Flatten Function...")
|
||||
|
||||
# Test flatten function
|
||||
input_2d = Tensor([[1, 2], [3, 4]])
|
||||
flattened = flatten(input_2d)
|
||||
|
||||
assert flattened.shape == (1, 4), "Flatten should produce output with batch dimension"
|
||||
expected = np.array([[1, 2, 3, 4]])
|
||||
assert np.array_equal(flattened.data, expected), "Flatten should preserve values"
|
||||
|
||||
print("✅ Flatten function works correctly")
|
||||
|
||||
# CNN pipeline integration test moved to tests/integration/test_cnn_pipeline.py
|
||||
|
||||
# %% [markdown]
|
||||
"""
|
||||
## 🧪 Module Testing
|
||||
|
||||
Time to test your implementation! This section uses TinyTorch's standardized testing framework to ensure your implementation works correctly.
|
||||
|
||||
**This testing section is locked** - it provides consistent feedback across all modules and cannot be modified.
|
||||
"""
|
||||
|
||||
# %% nbgrader={"grade": false, "grade_id": "standardized-testing", "locked": true, "schema_version": 3, "solution": false, "task": false}
|
||||
# =============================================================================
|
||||
# STANDARDIZED MODULE TESTING - DO NOT MODIFY
|
||||
# This cell is locked to ensure consistent testing across all TinyTorch modules
|
||||
# =============================================================================
|
||||
|
||||
if __name__ == "__main__":
|
||||
from tito.tools.testing import run_module_tests_auto
|
||||
|
||||
# Automatically discover and run all tests in this module
|
||||
success = run_module_tests_auto("CNN")
|
||||
|
||||
# %% [markdown]
|
||||
"""
|
||||
## 🎯 Module Summary
|
||||
|
||||
Congratulations! You've successfully implemented the core components of convolutional neural networks:
|
||||
|
||||
### What You've Accomplished
|
||||
✅ **Convolution Operation**: Implemented the sliding window mechanism from scratch
|
||||
✅ **Conv2D Layer**: Built learnable convolutional layers with random initialization
|
||||
✅ **Flatten Function**: Created the bridge between convolutional and dense layers
|
||||
✅ **CNN Pipelines**: Composed complete systems for image processing
|
||||
✅ **Real Applications**: Tested on image classification and feature extraction
|
||||
|
||||
### Key Concepts You've Learned
|
||||
- **Convolution as pattern matching**: Kernels detect specific features
|
||||
- **Sliding window mechanism**: How convolution processes spatial data
|
||||
- **Parameter sharing**: Same kernel applied across the entire image
|
||||
- **Spatial hierarchy**: Multiple layers build complex features
|
||||
- **CNN architecture**: Conv2D → Activation → Flatten → Dense pattern
|
||||
|
||||
### Mathematical Foundations
|
||||
- **Convolution operation**: dot product of kernel and image patches
|
||||
- **Output size calculation**: (input_size - kernel_size + 1)
|
||||
- **Translation invariance**: Same pattern detected anywhere in input
|
||||
- **Feature maps**: Spatial representations of detected patterns
|
||||
|
||||
### Real-World Applications
|
||||
- **Image classification**: Object recognition, medical imaging
|
||||
- **Computer vision**: Face detection, autonomous driving
|
||||
- **Pattern recognition**: Texture analysis, edge detection
|
||||
- **Feature extraction**: Transfer learning, representation learning
|
||||
|
||||
### CNN Architecture Insights
|
||||
- **Kernel size**: 3×3 most common, balances locality and capacity
|
||||
- **Stacking layers**: Builds hierarchical feature representations
|
||||
- **Spatial reduction**: Each layer reduces spatial dimensions
|
||||
- **Channel progression**: Typically increase channels while reducing spatial size
|
||||
|
||||
### Performance Characteristics
|
||||
- **Parameter efficiency**: Dramatic reduction vs. fully connected
|
||||
- **Translation invariance**: Robust to object location changes
|
||||
- **Computational efficiency**: Parallel processing of spatial regions
|
||||
- **Memory considerations**: Feature maps require storage during forward pass
|
||||
|
||||
### Next Steps
|
||||
1. **Export your code**: Use NBDev to export to the `tinytorch` package
|
||||
2. **Test your implementation**: Run the complete test suite
|
||||
3. **Build CNN architectures**:
|
||||
```python
|
||||
from tinytorch.core.cnn import Conv2D, flatten
|
||||
from tinytorch.core.layers import Dense
|
||||
from tinytorch.core.activations import ReLU
|
||||
|
||||
# Create CNN
|
||||
conv = Conv2D(kernel_size=(3, 3))
|
||||
relu = ReLU()
|
||||
dense = Dense(input_size=36, output_size=10)
|
||||
|
||||
# Process image
|
||||
features = relu(conv(image))
|
||||
predictions = dense(flatten(features))
|
||||
```
|
||||
4. **Explore advanced CNNs**: Pooling, multiple channels, modern architectures!
|
||||
|
||||
**Ready for the next challenge?** Let's build data loaders to handle real datasets efficiently!
|
||||
"""
|
||||
Reference in New Issue
Block a user