Skip to content

PIPELINE_COVERAGE.md

Latest commit

 

History

History
304 lines (244 loc) · 10.9 KB

PIPELINE_COVERAGE.md

File metadata and controls

304 lines (244 loc) · 10.9 KB
Raw

Original Pipeline Coverage Analysis

This document analyzes the coverage of the original LineamentLearning pipeline features in the modernized version.

Component-by-Component Comparison

✅ Fully Covered Components

1. Model Architecture (MODEL.py)

  • Original: get_RotateNet() - Single architecture
  • Modern: model_modern.py with three architectures:
    • RotateNet (enhanced with batch norm, dropout)
    • U-Net (encoder-decoder with skip connections)
    • ResNet (residual blocks)
  • Status: ✅ Enhanced - Original functionality preserved and extended

2. Post-Processing (Prob2Line.py)

  • Original: prob2map class with DBSCAN clustering and line fitting
  • Modern: postprocessing.py with PostProcessor class:
    • DBSCAN clustering (same algorithm)
    • Linear fitting (RANSAC)
    • Curve fitting (polynomial)
    • BestCurve fitting (auto-select degree)
    • Statistics and visualization
  • Status: ✅ Enhanced - All original methods available plus improvements

3. Configuration (globalVariables.py)

  • Original: Global variables for settings
  • Modern: config.py with dataclass-based configuration:
    • ModelConfig (window_size, layers, etc.)
    • DataConfig (directories, ratios, etc.)
    • InferenceConfig (threshold, clustering params)
    • JSON save/load support
  • Status: ✅ Enhanced - More flexible and maintainable

4. CLI Interface (RotateLearning.py partial)

  • Original: Command-line arguments via argparse
  • Modern: cli.py with multiple commands:
    • lineament-train
    • lineament-predict
    • lineament-evaluate
    • lineament-convert
    • lineament-export
  • Status: ✅ Enhanced - More comprehensive interface

⚠️ Partially Covered Components

5. Training Workflows (RotateLearning.py)

  • Original Workflows:

    • train-choosy: Train on fault areas with angle detection
    • test-choosy: Test with angle models
    • train-fault-all: Train on all areas
    • test-fault-all: Test on all areas
    • prepare-datasets-ang: Prepare angle datasets
    • prepare-datasets-flt: Prepare fault datasets
    • train-prepared: Train from prepared datasets

  • Modern Implementation:

    • ✅ Training infrastructure: ModelTrainer class
    • ✅ Callbacks and checkpointing
    • ⚠️ Data loading: Placeholder, needs DATASET integration
    • ⚠️ Rotation workflows: Not implemented yet
    • ⚠️ Dataset preparation: Not implemented yet

  • Status: ⚠️ Infrastructure ready, data integration needed

6. Data Loading (DATASET.py)

  • Original:

    • Load from .mat files
    • Generate training samples with rotation
    • Mask handling
    • Data augmentation

  • Modern Implementation:

    • ✅ Original DATASET.py still available (backward compatible)
    • ⚠️ Not integrated with modern ModelTrainer
    • ⚠️ No modern data pipeline (tf.data)

  • Status: ⚠️ Available but not modernized

7. Rotation Filters (FILTER.py)

  • Original:

    • Load rotation matrices from .mat files
    • Apply rotations for augmentation

  • Modern Implementation:

    • ✅ Original FILTER.py still available
    • ⚠️ Not integrated with modern training
    • ⚠️ Could be replaced with tf.keras augmentation

  • Status: ⚠️ Available but not modernized

✅ Preserved Legacy Components

8. GUI Applet (PmapViewer.py, Demo.py)

  • Original: TKinter-based GUI for visualization
  • Modern: Original files preserved
  • Status: ✅ Preserved - Still fully functional

9. Utilities (Utility.py)

  • Original: Visualization and helper functions
  • Modern: Original file preserved
  • Status: ✅ Preserved - Still available

10. Logging (Logger.py, LogParser.py)

  • Original: Custom logging system
  • Modern:
    • Original files preserved
    • CSV logging in ModelTrainer
    • TensorBoard integration
  • Status: ✅ Preserved + modern alternatives

Functionality Matrix

Feature Original Modern Status
Model architecture RotateNet RotateNet + U-Net + ResNet ✅ Enhanced
Model training Via RotateLearning.py Via ModelTrainer ✅ Enhanced
Data loading DATASET.py DATASET.py (not integrated) ⚠️ Available
Rotation augmentation FILTER.py Not integrated ⚠️ Available
Post-processing Prob2Line.py postprocessing.py ✅ Enhanced
Clustering DBSCAN DBSCAN ✅ Same
Line fitting Linear, Curve Linear, Curve, BestCurve ✅ Enhanced
Configuration Global variables config.py (JSON) ✅ Enhanced
CLI argparse (basic) cli.py (comprehensive) ✅ Enhanced
GUI PmapViewer PmapViewer (preserved) ✅ Preserved
Visualization Utility.py Utility.py + matplotlib ✅ Enhanced
Logging Logger.py Logger.py + CSV + TensorBoard ✅ Enhanced
Package management None setup.py + requirements.txt ✅ New
Documentation Basic README 11,500+ lines ✅ Enhanced
Examples None 4 working examples ✅ New

Missing Integration Points

📖 For detailed improvement specifications, see DATA_LOADING_ROTATION_IMPROVEMENTS.md

1. Data Loading Pipeline

What's Missing: Integration of DATASET.py with ModelTrainer

Specific Issues:

  • ❌ No tf.data.Dataset pipeline for efficient data loading
  • ❌ No batch prefetching and parallel loading
  • ❌ No integration with ModelTrainer's fit() method
  • ❌ CLI commands assume data integration but it doesn't work out-of-the-box
  • ❌ No streaming for large datasets

Impact: Cannot run actual training without manual integration

Workaround: Use original DATASET.py directly:

from DATASET import DATASET
from model_modern import build_model

ds = DATASET('path/to/data.mat')
X, Y, IDX = ds.generateDS(ds.OUTPUT, ds.trainMask)
model = build_model(config)
model.fit(X, Y)

What Needs to Be Done:

  1. Create DataGenerator class that wraps DATASET and provides tf.data.Dataset
  2. Integrate DataGenerator with ModelTrainer
  3. Update CLI to use DataGenerator automatically
  4. Add examples and documentation

Estimated Effort: 1-2 days (see detailed specification in DATA_LOADING_ROTATION_IMPROVEMENTS.md)

2. Rotation-Based Augmentation

What's Missing: Integration of FILTER.py rotation matrices

Specific Issues:

  • ❌ No integration with tf.keras data augmentation layers
  • ❌ No automatic rotation during training
  • ❌ No configuration option to enable/disable rotation augmentation
  • ❌ Cannot use rotation augmentation with modern ModelTrainer
  • ❌ No random rotation angle generation using modern TensorFlow operations

Impact: Original rotation augmentation not available in modern training

Workaround: Use original FILTER.py:

from FILTER import FILTER
flt = FILTER('path/to/filters.mat')
# Apply rotations manually

What Needs to Be Done:

  1. Create RotationAugmentation tf.keras layer
  2. Add AugmentationConfig to config.py with rotation settings
  3. Integrate augmentation layers in model building
  4. Support both FILTER.py matrices and TensorFlow rotation
  5. Add configuration examples and documentation

Estimated Effort: 1 day (see detailed specification in DATA_LOADING_ROTATION_IMPROVEMENTS.md)

3. Workflow Scripts

What's Missing: Direct equivalents of train-choosy, test-choosy, etc.

Specific Issues:

  • ❌ No preset workflows for common training scenarios
  • ❌ No angle detection workflow implementation
  • ❌ No dataset preparation commands
  • ❌ Users need to write custom scripts for specialized workflows

Impact: Need to manually implement workflows

Workaround: Use CLI with custom scripts:

# Instead of: python RotateLearning.py train-choosy
# Use: Custom script with DATASET + ModelTrainer

What Needs to Be Done:

  1. Add workflow presets to CLI (e.g., --workflow choosy)
  2. Implement angle detection workflow
  3. Add dataset preparation commands
  4. Document workflow options

Estimated Effort: 1-2 days

Note: This is lower priority than data loading and rotation integration.

Backward Compatibility

Everything Still Works

All original files are preserved and functional:

  • Run original GUI: python Demo.py
  • Use original training: python RotateLearning.py train-choosy
  • Use original classes: from MODEL import MODEL

Modern Alternative Usage

# Original way (still works)
from MODEL import MODEL
from DATASET import DATASET
model = MODEL()
ds = DATASET('data.mat')
X, Y, _ = ds.generateDS(ds.OUTPUT, ds.trainMask)
model.train(X, Y)

# Modern way
from config import Config
from model_modern import build_model
config = Config()
model = build_model(config)
# Data loading needs integration

Summary

✅ What's Complete (Core Modernization)

  1. Model architectures: 3 modern architectures
  2. Post-processing: Complete clustering and line fitting
  3. Configuration: Modern JSON-based system
  4. CLI: Comprehensive command-line interface
  5. Documentation: 11,500+ lines
  6. Examples: 4 working demonstrations
  7. Package structure: Professional setup.py

⚠️ What Needs Integration (For Full Training)

  1. Data loading: DATASET.py → ModelTrainer integration
  2. Rotation filters: FILTER.py → modern augmentation
  3. Training workflows: Specific workflow implementations
  4. Full pipeline: End-to-end training → inference

📖 Detailed Improvement Specifications: See DATA_LOADING_ROTATION_IMPROVEMENTS.md for:

  • Specific technical requirements for each improvement
  • Implementation roadmap with time estimates
  • Code examples and API specifications
  • Testing strategy and success criteria

✅ What's Preserved (Backward Compatibility)

  1. All original files work as before
  2. Original GUI (PmapViewer, Demo.py)
  3. Original utilities (Utility.py)
  4. Original training (RotateLearning.py)

Recommendation

The modernization provides:

  • ✅ Modern ML stack (TensorFlow 2.x, multiple architectures)
  • ✅ Better UX (CLI, config, docs)
  • ✅ Enhanced features (post-processing, visualization)
  • ✅ 100% backward compatibility

To make it production-ready for training:

  1. Create DataGenerator class wrapping DATASET.py
  2. Add rotation augmentation to ModelTrainer
  3. Implement workflow presets in CLI
  4. Add integration examples

Current state: Excellent for inference and post-processing, needs data integration for training.

Time to complete:

  • Data integration: ~1-2 days (HIGH priority)
  • Rotation augmentation: ~1 day (MEDIUM priority)
  • Workflow presets: ~1-2 days (LOW priority)

📖 See DATA_LOADING_ROTATION_IMPROVEMENTS.md for complete implementation specifications, including:

  • Detailed technical requirements
  • Code examples and API designs
  • Testing strategy
  • Performance considerations
  • Common issues and solutions