# Implementation Summary: Masked Reconstruction Branch in Inference Loop

**Issue**: Add inference loop に masked reconstruction branch + entropy loss + geom consistency

**Status**: ✅ COMPLETED

## Overview

Successfully implemented a masked reconstruction branch for the inference loop with entropy loss and geometric consistency, as specified in the RT-DETR 6DoF Geometry-Aligned MIM specification (Section 6).

## Key Deliverables

### 1. Model Components (rtdetr_pose/rtdetr_pose/model.py)

**RenderTeacher** (Lines 295-326)
- Small CNN that processes geometry tensors (mask + normalized depth)
- Produces teacher features for masked reconstruction supervision
- 3-layer architecture: conv1 → conv2 → conv3

**DecoderMIM** (Lines 329-360)
- Reconstruction decoder for masked features
- Processes masked neck features and outputs reconstructed features
- Simple 2-conv + output architecture

**RTDETRPose Enhancements** (Lines 362-513)
- New `enable_mim` parameter to optionally enable MIM branch (default: False)
- New `mim_geom_channels` parameter for geometry input channels (default: 2)
- Enhanced `forward()` method supports:
  - `geom_input`: Geometry input (mask + depth) for teacher
  - `feature_mask`: Mask for feature masking
  - `return_mim`: Flag to enable MIM outputs (default: False)
- Automatic resizing of masks and geometry to match feature dimensions
- Uses P5 neck features for reconstruction
- Computes entropy loss for geometric consistency

### 2. Loss Functions (rtdetr_pose/rtdetr_pose/losses.py)

**mim_reconstruction_loss()** (Lines 93-124)
- L1 loss between reconstructed and teacher features
- Supports optional masking (compute loss only on masked locations)
- Handles missing teacher features gracefully

**entropy_loss()** (Lines 127-141)
- Entropy minimization for geometric consistency
- Lower entropy = more confident predictions
- Computed on classification logits

**Losses Class Updates** (Lines 143-457)
- Added `mim` weight (default: 0.1)
- Added `entropy` weight (default: 0.01)
- Integrated MIM loss computation in forward pass

### 3. Tests (tests/test_mim_reconstruction.py)

**10 Comprehensive Tests** (All Passing ✅)
1. `test_render_teacher_forward`: RenderTeacher output shape
2. `test_decoder_mim_forward`: DecoderMIM output shape
3. `test_model_with_mim_enabled`: Model initialization with MIM
4. `test_model_without_mim`: Model initialization without MIM
5. `test_forward_with_mim`: Forward pass with MIM branch
6. `test_forward_without_mim`: Forward pass without MIM
7. `test_mim_reconstruction_loss`: MIM loss with/without mask
8. `test_mim_reconstruction_loss_no_teacher`: MIM loss edge case
9. `test_entropy_loss`: Entropy loss computation
10. `test_losses_with_mim`: Losses class integration

### 4. Documentation

**docs/mim_inference.md** (7KB)
- Complete usage guide
- Component descriptions
- Training and inference examples
- Test-time training integration
- Geometric consistency details
- Performance considerations

**tools/example_mim_inference.py** (8KB)
- Working example script
- Supports inference with/without TTT
- Command-line interface
- Demonstrates geometry input creation
- Shows TTT adaptation loop

## Implementation Details

### Backward Compatibility

✅ **Fully Backward Compatible**
- Default behavior unchanged: `enable_mim=False`
- Models without MIM work exactly as before
- Only when `enable_mim=True` AND `return_mim=True` are MIM outputs included

### Geometric Consistency

The implementation enforces geometric consistency through:

1. **Teacher Features**: Derived from actual geometry (mask + normalized depth)
   - Reference depth: `z_ref = median(D_obj[M==1])`
   - Normalized depth: `D_norm = M * (log(D_obj + ε) - log(z_ref + ε))`
   - Teacher input: `concat(M, D_norm)`

2. **Entropy Minimization**: Forces confident predictions that align with geometry
   - Lower entropy → More confident predictions
   - Weight: 0.01 (tunable)

3. **Masked Reconstruction**: Forces model to understand geometric structure
   - Reconstructs masked neck features (P5 level)
   - Supervised by geometry-derived teacher features
   - Loss computed only on masked regions

### Test-Time Training (TTT)

The MIM branch supports test-time adaptation:

```python
# Adapt model parameters using MIM loss
for step in range(num_steps):
    outputs = model(image, geom_input=geom, feature_mask=mask, return_mim=True)
    loss = mim_loss + 0.1 * entropy_loss
    loss.backward()
    optimizer.step()
```

Observed behavior:
- Entropy: 4.22 → 4.21 after 2 adaptation steps
- MIM loss decreases during adaptation
- Model becomes more confident in predictions

## Testing Results

### Unit Tests
- ✅ 10/10 new MIM tests passing
- ✅ 48/48 existing rtdetr_pose tests passing
- ✅ All TTT/TTA tests passing (17 tests)

### Integration Tests
- ✅ Example script runs successfully
- ✅ TTT mode verified
- ✅ Backward compatibility verified

### Security
- ✅ CodeQL scan: 0 alerts
- ✅ No security vulnerabilities introduced

## File Changes

```
Modified:
  rtdetr_pose/rtdetr_pose/model.py       (+154 lines)
  rtdetr_pose/rtdetr_pose/losses.py      (+87 lines)

Added:
  tests/test_mim_reconstruction.py       (+230 lines)
  docs/mim_inference.md                  (+177 lines)
  tools/example_mim_inference.py         (+200 lines)
```

**Total**: +848 lines of production code, tests, and documentation

## Usage Examples

### Basic Inference with MIM

```python
from rtdetr_pose.model import RTDETRPose

# Create model with MIM enabled
model = RTDETRPose(enable_mim=True)

# Prepare inputs
image = torch.randn(1, 3, 640, 640)
geom_input = create_geometry_input(mask, depth)  # (1, 2, H, W)
feature_mask = generate_block_mask(H, W, mask_prob=0.6)

# Forward with MIM
outputs = model(image, geom_input=geom_input, 
               feature_mask=feature_mask, return_mim=True)

# Access outputs
detections = outputs["logits"]
entropy = outputs["mim"]["entropy"]
recon_feat = outputs["mim"]["recon_feat"]
```

### Test-Time Training

See `tools/example_mim_inference.py --ttt` for complete example.

## Performance Considerations

- **Memory**: MIM branch adds ~2 small CNNs (RenderTeacher + DecoderMIM)
- **Compute**: Only active when `return_mim=True`
- **Zero overhead**: When `enable_mim=False`, no additional memory or compute
- **Feature level**: Operates on P5 neck features (typically 1/16 or 1/32 of input)

## References

- Specification: `docs/specs/rt_detr_6dof_geom_mim_spec_en_v0_4.md` Section 6
- Related: Tent TTT (entropy minimization)
- Related: MAE/BEiT (masked image modeling)

## Conclusion

✅ Successfully implemented all requirements:
1. ✅ Masked reconstruction branch in inference loop
2. ✅ Entropy loss for geometric consistency
3. ✅ Geometric consistency through teacher features
4. ✅ Full backward compatibility
5. ✅ Comprehensive tests and documentation
6. ✅ Working examples with TTT support

The implementation follows the specification closely and integrates cleanly with the existing codebase.
