003 Name Skill D695a2ca

---
name: pytorch-model-cli
description: Guidance for creating standalone CLI tools that perform neural network inference by extracting PyTorch model weights and reimplementing inference in C/C++. This skill applies when tasks involve converting PyTorch models to standalone executables, extracting model weights to portable formats (JSON), implementing neural network forward passes in C/C++, or creating CLI tools that load images and run inference without Python dependencies.
---

# PyTorch Model to CLI Tool Conversion

This skill provides guidance for tasks that require converting PyTorch models into standalone command-line tools, typically implemented in C/C++ for portability and independence from Python runtime.

## Task Recognition

This skill applies when the task involves:
- Converting a PyTorch model to a standalone executable
- Extracting model weights to a portable format (JSON, binary)
- Implementing neural network inference in C/C++
- Creating CLI tools that perform image classification or prediction
- Building inference tools using libraries like cJSON and lodepng

## Recommended Approach

### Phase 1: Environment Analysis

Before writing any code, thoroughly analyze the available resources:

1. **Identify the model architecture**
   - Read the model definition file (e.g., `model.py`) completely
   - Document all layer types, dimensions, and activation functions
   - Note any default parameters (hidden dimensions, number of classes)

2. **Examine available libraries**
   - Check for image loading libraries (lodepng, stb_image)
   - Check for JSON parsing libraries (cJSON, nlohmann/json)
   - Identify compilation requirements (headers, source files)

3. **Understand input requirements**
   - Determine expected image dimensions (e.g., 28x28 for MNIST)
   - Identify color format (grayscale, RGB, RGBA)
   - Document normalization requirements (divide by 255, mean/std normalization)

4. **Verify preprocessing pipeline**
   - If training code is available, examine data transformations
   - Match inference preprocessing exactly to training preprocessing
   - Common transformations: resize, grayscale conversion, normalization

### Phase 2: Weight Extraction

Extract model weights from PyTorch format to a portable format:

1. **Load the model checkpoint**
   ```python
   import torch
   import json

   # Load state dict
   state_dict = torch.load('model.pth', map_location='cpu')
   ```

2. **Convert tensors to lists**
   ```python
   weights = {}
   for key, tensor in state_dict.items():
       weights[key] = tensor.numpy().tolist()
   ```

3. **Save to JSON**
   ```python
   with open('weights.json', 'w') as f:
       json.dump(weights, f)
   ```

4. **Verify extraction**
   - Check that all expected layer weights are present
   - Verify dimensions match the model architecture
   - For a model with layers fc1, fc2, fc3: expect fc1.weight, fc1.bias, etc.

### Phase 3: Reference Implementation

Before implementing in C/C++, create a reference output:

1. **Run inference in PyTorch**
   ```python
   model.eval()
   with torch.no_grad():
       output = model(input_tensor)
       prediction = output.argmax().item()
   ```

2. **Save reference outputs**
   - Store intermediate layer outputs for debugging
   - Record the final prediction for verification
   - This allows validating the C/C++ implementation

### Phase 4: C/C++ Implementation

Implement the inference logic in C/C++:

1. **Image loading and preprocessing**
   - Load image using the available library (lodepng for PNG)
   - Handle color channel conversion (RGBA to grayscale if needed)
   - Apply normalization (typically divide by 255.0)
   - Flatten to 1D array in correct order (row-major)

2. **Weight loading**
   - Parse JSON file containing weights
   - Store weights in appropriate data structures
   - Verify dimensions during loading

3. **Forward pass implementation**
   - Implement matrix-vector multiplication for linear layers
   - Implement activation functions (ReLU, softmax, etc.)
   - Process layers in correct order

4. **Output handling**
   - Find argmax for classification tasks
   - Write prediction to output file
   - Ensure only prediction goes to stdout (not progress/debug info)

### Phase 5: Compilation and Testing

1. **Compile with appropriate flags**
   ```bash
   g++ -o cli_tool main.cpp lodepng.cpp cJSON.c -std=c++11 -lm
   ```
   - Double-check flag syntax (avoid concatenation errors like `-std=c++11-lm`)

2. **Test against reference**
   - Run the CLI tool on the same input used for reference
   - Compare output to PyTorch reference
   - Debug any discrepancies by checking intermediate values

## Verification Strategies

### Before Implementation
- [ ] Model architecture fully documented
- [ ] All layer dimensions verified
- [ ] Preprocessing requirements identified
- [ ] Reference output generated from PyTorch

### After Weight Extraction
- [ ] All expected keys present in JSON
- [ ] Weight dimensions match architecture
- [ ] Bias terms included for all layers

### After C/C++ Implementation
- [ ] Compilation succeeds without warnings
- [ ] Output matches PyTorch reference exactly
- [ ] CLI tool handles missing files gracefully
- [ ] Only prediction output goes to stdout

### Final Validation
- [ ] All test cases pass
- [ ] Memory properly managed (no leaks)
- [ ] Error messages go to stderr, not stdout

## Common Pitfalls

### Weight Extraction
- **Forgetting to use `map_location='cpu'`** when loading on CPU-only systems
- **Missing bias terms** - ensure both weights and biases are extracted
- **Incorrect tensor ordering** - PyTorch uses different conventions than some C libraries

### Preprocessing Mismatches
- **Wrong normalization** - training might use mean/std normalization, not just /255
- **Color channel issues** - PNG might be RGBA while model expects grayscale
- **Dimension ordering** - ensure row-major vs column-major consistency

### C/C++ Implementation
- **Matrix multiplication order** - verify (input × weights^T) vs (weights × input)
- **Activation function placement** - apply after linear layer, before next layer
- **Integer vs float division** - use 255.0, not 255, for normalization

### Compilation Issues
- **Flag concatenation** - ensure spaces between compiler flags
- **Missing libraries** - include all required source files (lodepng.cpp, cJSON.c)
- **Header dependencies** - verify all headers are in include path

### Output Handling
- **Verbose library output** - suppress or redirect debug/progress output
- **Newline handling** - ensure consistent line endings in output files
- **Buffering issues** - flush stdout before program exit

## Efficiency Guidelines

- Avoid repeatedly checking package managers; identify available tools first
- Create reference outputs early to catch implementation bugs quickly
- Review complete code before compilation attempts
- Minimize status-only updates; batch related operations
- Test with multiple inputs when possible, not just the provided test case