From 07cbd146e5fdef2612b36d31b812ed045da7a17c Mon Sep 17 00:00:00 2001 From: Masonmason Date: Wed, 23 Jul 2025 22:10:03 +0800 Subject: [PATCH] update .md files --- cluster4npu_ui/CLAUDE.md | 228 ++++----------- cluster4npu_ui/DEVELOPMENT_ROADMAP.md | 332 ++++++++++++++++++++++ cluster4npu_ui/INTEGRATION_SUMMARY.md | 175 ------------ cluster4npu_ui/NODE_CREATION_FIX.md | 98 ------- cluster4npu_ui/PROJECT_SUMMARY.md | 217 ++++++++++++++ cluster4npu_ui/PROPERTIES_FIX_COMPLETE.md | 116 -------- cluster4npu_ui/REFACTORING_RECORD.md | 294 ------------------- cluster4npu_ui/REFACTORING_SUMMARY.md | 266 ----------------- cluster4npu_ui/TODO.md | 292 ------------------- 9 files changed, 605 insertions(+), 1413 deletions(-) create mode 100644 cluster4npu_ui/DEVELOPMENT_ROADMAP.md delete mode 100644 cluster4npu_ui/INTEGRATION_SUMMARY.md delete mode 100644 cluster4npu_ui/NODE_CREATION_FIX.md create mode 100644 cluster4npu_ui/PROJECT_SUMMARY.md delete mode 100644 cluster4npu_ui/PROPERTIES_FIX_COMPLETE.md delete mode 100644 cluster4npu_ui/REFACTORING_RECORD.md delete mode 100644 cluster4npu_ui/REFACTORING_SUMMARY.md delete mode 100644 cluster4npu_ui/TODO.md diff --git a/cluster4npu_ui/CLAUDE.md b/cluster4npu_ui/CLAUDE.md index 113263e..2502efd 100644 --- a/cluster4npu_ui/CLAUDE.md +++ b/cluster4npu_ui/CLAUDE.md @@ -1,191 +1,75 @@ -# CLAUDE.md +Always follow the instructions in plan.md. When I say "go", find the next unmarked test in plan.md, implement the test, then implement only enough code to make that test pass. -This file provides guidance to Claude Code (claude.ai/code) when working with code in this repository. +# ROLE AND EXPERTISE -## Project Overview +You are a senior software engineer who follows Mason Huang's Test-Driven Development (TDD) and Tidy First principles. Your purpose is to guide development following these methodologies precisely. -**cluster4npu** is a high-performance multi-stage inference pipeline system for Kneron NPU dongles. The project enables flexible single-stage and cascaded multi-stage AI inference workflows optimized for real-time video processing and high-throughput scenarios. +# CORE DEVELOPMENT PRINCIPLES -### Core Architecture +- Always follow the TDD cycle: Red → Green → Refactor +- Write the simplest failing test first +- Implement the minimum code needed to make tests pass +- Refactor only after tests are passing +- Follow Beck's "Tidy First" approach by separating structural changes from behavioral changes +- Maintain high code quality throughout development -- **InferencePipeline**: Main orchestrator managing multi-stage workflows with automatic queue management and thread coordination -- **MultiDongle**: Hardware abstraction layer for Kneron NPU devices (KL520, KL720, etc.) -- **StageConfig**: Configuration system for individual pipeline stages -- **PipelineData**: Data structure that flows through pipeline stages, accumulating results -- **PreProcessor/PostProcessor**: Flexible data transformation components for inter-stage processing +# TDD METHODOLOGY GUIDANCE -### Key Design Patterns +- Start by writing a failing test that defines a small increment of functionality +- Use meaningful test names that describe behavior (e.g., "shouldSumTwoPositiveNumbers") +- Make test failures clear and informative +- Write just enough code to make the test pass - no more +- Once tests pass, consider if refactoring is needed +- Repeat the cycle for new functionality +- When fixing a defect, first write an API-level failing test then write the smallest possible test that replicates the problem then get both tests to pass. -- **Producer-Consumer**: Each stage runs in separate threads with input/output queues -- **Pipeline Architecture**: Linear data flow through configurable stages with result accumulation -- **Hardware Abstraction**: MultiDongle encapsulates Kneron SDK complexity -- **Callback-Based**: Asynchronous result handling via configurable callbacks +# TIDY FIRST APPROACH -## Development Commands +- Separate all changes into two distinct types: + 1. STRUCTURAL CHANGES: Rearranging code without changing behavior (renaming, extracting methods, moving code) + 2. BEHAVIORAL CHANGES: Adding or modifying actual functionality +- Never mix structural and behavioral changes in the same commit +- Always make structural changes first when both are needed +- Validate structural changes do not alter behavior by running tests before and after -### Environment Setup -```bash -# Setup virtual environment with uv -uv venv -source .venv/bin/activate # Windows: .venv\Scripts\activate +# COMMIT DISCIPLINE -# Install dependencies -uv pip install -r requirements.txt -``` +- Only commit when: + 1. ALL tests are passing + 2. ALL compiler/linter warnings have been resolved + 3. The change represents a single logical unit of work + 4. Commit messages clearly state whether the commit contains structural or behavioral changes +- Use small, frequent commits rather than large, infrequent ones -### Running Examples -```bash -# Single-stage pipeline -uv run python src/cluster4npu/test.py --example single +# CODE QUALITY STANDARDS -# Two-stage cascade pipeline -uv run python src/cluster4npu/test.py --example cascade +- Eliminate duplication ruthlessly +- Express intent clearly through naming and structure +- Make dependencies explicit +- Keep methods small and focused on a single responsibility +- Minimize state and side effects +- Use the simplest solution that could possibly work -# Complex multi-stage pipeline -uv run python src/cluster4npu/test.py --example complex +# REFACTORING GUIDELINES -# Basic MultiDongle usage -uv run python src/cluster4npu/Multidongle.py +- Refactor only when tests are passing (in the "Green" phase) +- Use established refactoring patterns with their proper names +- Make one refactoring change at a time +- Run tests after each refactoring step +- Prioritize refactorings that remove duplication or improve clarity -# Complete UI application with full workflow -uv run python UI.py +# EXAMPLE WORKFLOW -# UI integration examples -uv run python ui_integration_example.py +When approaching a new feature: -# Test UI configuration system -uv run python ui_config.py -``` +1. Write a simple failing test for a small part of the feature +2. Implement the bare minimum to make it pass +3. Run tests to confirm they pass (Green) +4. Make any necessary structural changes (Tidy First), running tests after each change +5. Commit structural changes separately +6. Add another test for the next small increment of functionality +7. Repeat until the feature is complete, committing behavioral changes separately from structural ones -### UI Application Workflow -The UI.py provides a complete visual workflow: +Follow this process precisely, always prioritizing clean, well-tested code over quick implementation. -1. **Dashboard/Home** - Main entry point with recent files -2. **Pipeline Editor** - Visual node-based pipeline design -3. **Stage Configuration** - Dongle allocation and hardware setup -4. **Performance Estimation** - FPS calculations and optimization -5. **Save & Deploy** - Export configurations and cost estimation -6. **Monitoring & Management** - Real-time pipeline monitoring - -```bash -# Access different workflow stages directly: -# 1. Create new pipeline → Pipeline Editor -# 2. Configure Stages & Deploy → Stage Configuration -# 3. Pipeline menu → Performance Analysis → Performance Panel -# 4. Pipeline menu → Deploy Pipeline → Save & Deploy Dialog -``` - -### Testing -```bash -# Run pipeline tests -uv run python test_pipeline.py - -# Test MultiDongle functionality -uv run python src/cluster4npu/test.py -``` - -## Hardware Requirements - -- **Kneron NPU dongles**: KL520, KL720, etc. -- **Firmware files**: `fw_scpu.bin`, `fw_ncpu.bin` -- **Models**: `.nef` format files -- **USB ports**: Multiple ports required for multi-dongle setups - -## Critical Implementation Notes - -### Pipeline Configuration -- Each stage requires unique `stage_id` and dedicated `port_ids` -- Queue sizes (`max_queue_size`) must be balanced between memory usage and throughput -- Stages process sequentially - output from stage N becomes input to stage N+1 - -### Thread Safety -- All pipeline operations are thread-safe -- Each stage runs in isolated worker threads -- Use callbacks for result handling, not direct queue access - -### Data Flow -``` -Input → Stage1 → Stage2 → ... → StageN → Output - ↓ ↓ ↓ ↓ - Queue Process Process Result - + Results + Results Callback -``` - -### Hardware Management -- Always call `initialize()` before `start()` -- Always call `stop()` for clean shutdown -- Firmware upload (`upload_fw=True`) only needed once per session -- Port IDs must match actual USB connections - -### Error Handling -- Pipeline continues on individual stage errors -- Failed stages return error results rather than blocking -- Comprehensive statistics available via `get_pipeline_statistics()` - -## UI Application Architecture - -### Complete Workflow Components - -- **DashboardLogin**: Main entry point with project management -- **PipelineEditor**: Node-based visual pipeline design using NodeGraphQt -- **StageConfigurationDialog**: Hardware allocation and dongle assignment -- **PerformanceEstimationPanel**: Real-time performance analysis and optimization -- **SaveDeployDialog**: Export configurations and deployment cost estimation -- **MonitoringDashboard**: Live pipeline monitoring and cluster management - -### UI Integration System - -- **ui_config.py**: Configuration management and UI/core integration -- **ui_integration_example.py**: Demonstrates conversion from UI to core tools -- **UIIntegration class**: Bridges UI configurations to InferencePipeline - -### Key UI Features - -- **Auto-dongle allocation**: Smart assignment of dongles to pipeline stages -- **Performance estimation**: Real-time FPS and latency calculations -- **Cost analysis**: Hardware and operational cost projections -- **Export formats**: Python scripts, JSON configs, YAML, Docker containers -- **Live monitoring**: Real-time metrics and cluster scaling controls - -## Code Patterns - -### Basic Pipeline Setup -```python -config = StageConfig( - stage_id="unique_name", - port_ids=[28, 32], - scpu_fw_path="fw_scpu.bin", - ncpu_fw_path="fw_ncpu.bin", - model_path="model.nef", - upload_fw=True -) - -pipeline = InferencePipeline([config]) -pipeline.initialize() -pipeline.start() -pipeline.set_result_callback(callback_func) -# ... processing ... -pipeline.stop() -``` - -### Inter-Stage Processing -```python -# Custom preprocessing for stage input -preprocessor = PreProcessor(resize_fn=custom_resize_func) - -# Custom postprocessing for stage output -postprocessor = PostProcessor(process_fn=custom_process_func) - -config = StageConfig( - # ... basic config ... - input_preprocessor=preprocessor, - output_postprocessor=postprocessor -) -``` - -## Performance Considerations - -- **Queue Sizing**: Smaller queues = lower latency, larger queues = higher throughput -- **Dongle Distribution**: Spread dongles across stages for optimal parallelization -- **Processing Functions**: Keep preprocessors/postprocessors lightweight -- **Memory Management**: Monitor queue sizes to prevent memory buildup \ No newline at end of file +Always write one test at a time, make it run, then improve structure. Always run all the tests (except long-running tests) each time. \ No newline at end of file diff --git a/cluster4npu_ui/DEVELOPMENT_ROADMAP.md b/cluster4npu_ui/DEVELOPMENT_ROADMAP.md new file mode 100644 index 0000000..28fe00f --- /dev/null +++ b/cluster4npu_ui/DEVELOPMENT_ROADMAP.md @@ -0,0 +1,332 @@ +# Development Roadmap: Visual Parallel Inference Pipeline Designer + +## 🎯 Mission Statement + +Transform Cluster4NPU into an intuitive visual tool that enables users to create parallel AI inference pipelines without coding knowledge, with clear visualization of speedup benefits and performance optimization. + +## 🚨 Critical Missing Features Analysis + +### 1. **Parallel Processing Visualization** (CRITICAL) +**Current Gap**: Users can't see how parallel processing improves performance +**Impact**: Core value proposition not visible to users + +**Missing Components**: +- Visual representation of parallel execution paths +- Real-time speedup metrics (2x, 3x, 4x faster) +- Before/after performance comparison +- Parallel device utilization visualization + +### 2. **Performance Benchmarking System** (CRITICAL) +**Current Gap**: No systematic way to measure and compare performance +**Impact**: Users can't quantify benefits of parallel processing + +**Missing Components**: +- Automated benchmark execution +- Single vs multi-device comparison +- Throughput and latency measurement +- Performance regression testing + +### 3. **Device Management Dashboard** (HIGH) +**Current Gap**: Limited visibility into hardware resources +**Impact**: Users can't optimize device allocation + +**Missing Components**: +- Visual device status monitoring +- Device health and temperature tracking +- Manual device assignment interface +- Load balancing visualization + +### 4. **Real-time Performance Monitoring** (HIGH) +**Current Gap**: Basic status bar insufficient for performance analysis +**Impact**: Users can't monitor and optimize running pipelines + +**Missing Components**: +- Live performance graphs (FPS, latency) +- Resource utilization charts +- Bottleneck identification +- Performance alerts + +## 📋 Detailed Implementation Plan + +### Phase 1: Performance Visualization Foundation (Weeks 1-2) + +#### 1.1 Performance Benchmarking Engine +**Location**: `core/functions/performance_benchmarker.py` +```python +class PerformanceBenchmarker: + def run_single_device_benchmark(pipeline_config, test_data) + def run_multi_device_benchmark(pipeline_config, test_data, device_count) + def calculate_speedup_metrics(single_results, multi_results) + def generate_performance_report(benchmark_results) +``` + +**Features**: +- Automated test execution with standardized datasets +- Precise timing measurements (inference time, throughput) +- Statistical analysis (mean, std, percentiles) +- Speedup calculation: `speedup = single_device_time / parallel_time` + +#### 1.2 Performance Dashboard Widget +**Location**: `ui/components/performance_dashboard.py` +```python +class PerformanceDashboard(QWidget): + def __init__(self): + # Real-time charts using matplotlib or pyqtgraph + self.fps_chart = LiveChart("FPS") + self.latency_chart = LiveChart("Latency (ms)") + self.speedup_display = SpeedupWidget() + self.device_utilization = DeviceUtilizationChart() +``` + +**UI Elements**: +- **Speedup Indicator**: Large, prominent display (e.g., "3.2x FASTER") +- **Live Charts**: FPS, latency, throughput over time +- **Device Utilization**: Bar charts showing per-device usage +- **Performance Comparison**: Side-by-side single vs parallel metrics + +#### 1.3 Benchmark Integration in Dashboard +**Location**: `ui/windows/dashboard.py` (enhancement) +```python +class IntegratedPipelineDashboard: + def create_performance_panel(self): + # Add performance dashboard to right panel + self.performance_dashboard = PerformanceDashboard() + + def run_benchmark_test(self): + # Automated benchmark execution + # Show progress dialog + # Display results in performance dashboard +``` + +### Phase 2: Device Management Enhancement (Weeks 3-4) + +#### 2.1 Advanced Device Manager +**Location**: `core/functions/device_manager.py` +```python +class AdvancedDeviceManager: + def detect_all_devices(self) -> List[DeviceInfo] + def get_device_health(self, device_id) -> DeviceHealth + def monitor_device_performance(self, device_id) -> DeviceMetrics + def assign_devices_to_stages(self, pipeline, device_allocation) + def optimize_device_allocation(self, pipeline) -> DeviceAllocation +``` + +**Features**: +- Real-time device health monitoring (temperature, utilization) +- Automatic device allocation optimization +- Device performance profiling and history +- Load balancing across available devices + +#### 2.2 Device Management Panel +**Location**: `ui/components/device_management_panel.py` +```python +class DeviceManagementPanel(QWidget): + def __init__(self): + self.device_list = DeviceListWidget() + self.device_details = DeviceDetailsWidget() + self.allocation_visualizer = DeviceAllocationWidget() + self.health_monitor = DeviceHealthWidget() +``` + +**UI Features**: +- **Device Grid**: Visual representation of all detected devices +- **Health Indicators**: Color-coded status (green/yellow/red) +- **Assignment Interface**: Drag-and-drop device allocation to pipeline stages +- **Performance History**: Charts showing device performance over time + +#### 2.3 Parallel Execution Visualizer +**Location**: `ui/components/parallel_visualizer.py` +```python +class ParallelExecutionVisualizer(QWidget): + def show_execution_flow(self, pipeline, device_allocation) + def animate_data_flow(self, pipeline_data) + def highlight_bottlenecks(self, performance_metrics) + def show_load_balancing(self, device_utilization) +``` + +**Visual Elements**: +- **Execution Timeline**: Show parallel processing stages +- **Data Flow Animation**: Visual representation of data moving through pipeline +- **Bottleneck Highlighting**: Red indicators for performance bottlenecks +- **Load Distribution**: Visual representation of work distribution + +### Phase 3: Pipeline Optimization Assistant (Weeks 5-6) + +#### 3.1 Optimization Engine +**Location**: `core/functions/optimization_engine.py` +```python +class PipelineOptimizationEngine: + def analyze_pipeline_bottlenecks(self, pipeline, metrics) + def suggest_device_allocation(self, pipeline, available_devices) + def predict_performance(self, pipeline, device_allocation) + def generate_optimization_recommendations(self, analysis) +``` + +**Optimization Strategies**: +- **Bottleneck Analysis**: Identify slowest stages in pipeline +- **Device Allocation**: Optimal distribution of devices across stages +- **Queue Size Tuning**: Optimize buffer sizes for throughput +- **Preprocessing Optimization**: Suggest efficient preprocessing strategies + +#### 3.2 Optimization Assistant UI +**Location**: `ui/dialogs/optimization_assistant.py` +```python +class OptimizationAssistant(QDialog): + def __init__(self, pipeline): + self.analysis_results = OptimizationAnalysisWidget() + self.recommendations = RecommendationListWidget() + self.performance_prediction = PerformancePredictionWidget() + self.apply_optimizations = OptimizationApplyWidget() +``` + +**Features**: +- **Automatic Analysis**: One-click pipeline optimization analysis +- **Recommendation List**: Prioritized list of optimization suggestions +- **Performance Prediction**: Estimated speedup from each optimization +- **One-Click Apply**: Easy application of recommended optimizations + +#### 3.3 Configuration Templates +**Location**: `core/templates/pipeline_templates.py` +```python +class PipelineTemplates: + def get_fire_detection_template(self, device_count) + def get_object_detection_template(self, device_count) + def get_classification_template(self, device_count) + def create_custom_template(self, pipeline_config) +``` + +**Template Categories**: +- **Common Use Cases**: Fire detection, object detection, classification +- **Device-Optimized**: Templates for 2, 4, 8 device configurations +- **Performance-Focused**: High-throughput vs low-latency configurations +- **Custom Templates**: User-created and shared templates + +### Phase 4: Advanced Monitoring and Analytics (Weeks 7-8) + +#### 4.1 Real-time Analytics Engine +**Location**: `core/functions/analytics_engine.py` +```python +class AnalyticsEngine: + def collect_performance_metrics(self, pipeline) + def analyze_performance_trends(self, historical_data) + def detect_performance_anomalies(self, current_metrics) + def generate_performance_insights(self, analytics_data) +``` + +**Analytics Features**: +- **Performance Trending**: Track performance over time +- **Anomaly Detection**: Identify unusual performance patterns +- **Predictive Analytics**: Forecast performance degradation +- **Comparative Analysis**: Compare different pipeline configurations + +#### 4.2 Advanced Visualization Components +**Location**: `ui/components/advanced_charts.py` +```python +class AdvancedChartComponents: + class ParallelTimelineChart: # Show parallel execution timeline + class SpeedupComparisonChart: # Compare different configurations + class ResourceUtilizationHeatmap: # Device usage over time + class PerformanceTrendChart: # Long-term performance trends +``` + +**Chart Types**: +- **Timeline Charts**: Show parallel execution stages over time +- **Heatmaps**: Device utilization and performance hotspots +- **Comparison Charts**: Side-by-side performance comparisons +- **Trend Analysis**: Long-term performance patterns + +#### 4.3 Reporting and Export +**Location**: `core/functions/report_generator.py` +```python +class ReportGenerator: + def generate_performance_report(self, benchmark_results) + def create_optimization_report(self, before_after_metrics) + def export_configuration_summary(self, pipeline_config) + def generate_executive_summary(self, project_metrics) +``` + +**Report Types**: +- **Performance Reports**: Detailed benchmark results and analysis +- **Optimization Reports**: Before/after optimization comparisons +- **Configuration Documentation**: Pipeline setup and device allocation +- **Executive Summaries**: High-level performance and ROI metrics + +## 🎨 User Experience Enhancements + +### Enhanced Pipeline Editor +**Location**: `ui/windows/pipeline_editor.py` (new) +```python +class EnhancedPipelineEditor(QMainWindow): + def __init__(self): + self.node_graph = NodeGraphWidget() + self.performance_overlay = PerformanceOverlayWidget() + self.device_allocation_panel = DeviceAllocationPanel() + self.optimization_assistant = OptimizationAssistantPanel() +``` + +**New Features**: +- **Performance Overlay**: Show performance metrics directly on pipeline nodes +- **Device Allocation Visualization**: Color-coded nodes showing device assignments +- **Real-time Feedback**: Live performance updates during pipeline execution +- **Optimization Hints**: Visual suggestions for pipeline improvements + +### Guided Setup Wizard +**Location**: `ui/dialogs/setup_wizard.py` +```python +class PipelineSetupWizard(QWizard): + def __init__(self): + self.use_case_selection = UseCaseSelectionPage() + self.device_configuration = DeviceConfigurationPage() + self.performance_targets = PerformanceTargetsPage() + self.optimization_preferences = OptimizationPreferencesPage() +``` + +**Wizard Steps**: +1. **Use Case Selection**: Choose from common pipeline templates +2. **Device Configuration**: Automatic device detection and allocation +3. **Performance Targets**: Set FPS, latency, and throughput goals +4. **Optimization Preferences**: Choose between speed vs accuracy tradeoffs + +## 📊 Success Metrics and Validation + +### Key Performance Indicators +1. **Time to First Pipeline**: < 5 minutes from launch to working pipeline +2. **Speedup Visibility**: Clear display of performance improvements (2x, 3x, etc.) +3. **Optimization Impact**: Measurable performance gains from suggestions +4. **User Satisfaction**: Intuitive interface requiring minimal training + +### Validation Approach +1. **Automated Testing**: Comprehensive test suite for all new components +2. **Performance Benchmarking**: Systematic testing across different hardware configurations +3. **User Testing**: Feedback from non-technical users on ease of use +4. **Performance Validation**: Verify actual speedup matches predicted improvements + +## 🛠 Technical Implementation Notes + +### Architecture Principles +- **Modular Design**: Each component should be independently testable +- **Performance First**: All visualizations must not impact inference performance +- **User-Centric**: Every feature should directly benefit the end user experience +- **Scalable**: Design for future expansion to more device types and use cases + +### Integration Strategy +- **Extend Existing**: Build on current InferencePipeline and dashboard architecture +- **Backward Compatible**: Maintain compatibility with existing pipeline configurations +- **Progressive Enhancement**: Add features incrementally without breaking existing functionality +- **Clean Interfaces**: Well-defined APIs between components for maintainability + +## 🎯 Expected Outcomes + +### For End Users +- **Dramatic Productivity Increase**: Create parallel pipelines in minutes instead of hours +- **Clear ROI Demonstration**: Visual proof of performance improvements and cost savings +- **Optimized Performance**: Automatic suggestions leading to better hardware utilization +- **Professional Results**: Production-ready pipelines without deep technical knowledge + +### For the Platform +- **Market Differentiation**: Unique visual approach to parallel AI inference +- **Reduced Support Burden**: Self-service optimization reduces need for expert consultation +- **Scalable Business Model**: Platform enables users to handle larger, more complex projects +- **Community Growth**: Easy-to-use tools attract broader user base + +This roadmap transforms Cluster4NPU from a functional tool into an intuitive platform that makes parallel AI inference accessible to non-technical users while providing clear visualization of performance benefits. \ No newline at end of file diff --git a/cluster4npu_ui/INTEGRATION_SUMMARY.md b/cluster4npu_ui/INTEGRATION_SUMMARY.md deleted file mode 100644 index fa571ad..0000000 --- a/cluster4npu_ui/INTEGRATION_SUMMARY.md +++ /dev/null @@ -1,175 +0,0 @@ -# Pipeline Editor Integration Summary - -## 概述 - -成功將 `pipeline_editor.py` 中的核心功能整合到 `dashboard.py` 中，提供統一的管道編輯和監控體驗。 - -## 整合的功能 - -### 1. StageCountWidget -- **功能**: 實時顯示管道階段計數和狀態 -- **位置**: 管道編輯器面板右上角 -- **特性**: - - 顯示當前階段數量 - - 顯示管道狀態（就緒/無效/錯誤） - - 錯誤信息顯示 - - 動態樣式更新 - -### 2. 管道分析和驗證 -- **功能**: 實時分析管道結構和驗證完整性 -- **方法**: - - `analyze_pipeline()`: 分析當前管道 - - `print_pipeline_analysis()`: 詳細輸出分析結果 - - `validate_pipeline()`: 驗證管道完整性 - - `update_info_panel()`: 更新信息面板 - -### 3. 工具欄節點創建 -- **功能**: 快速添加節點到管道 -- **包含按鈕**: - - Add Input Node - - Add Model Node - - Add Preprocess Node - - Add Postprocess Node - - Add Output Node - - Validate Pipeline - - Clear Pipeline - -### 4. 實時統計和監控 -- **功能**: 自動監控管道變化並更新統計 -- **特性**: - - 500ms 延遲的定時器分析 - - 節點變化時自動觸發分析 - - 連接變化時自動更新 - - 詳細的終端輸出日誌 - -### 5. 管道信息面板 -- **功能**: 在配置面板中顯示管道分析結果 -- **位置**: 右側面板 → Stages 標籤 -- **顯示內容**: - - 階段計數 - - 驗證狀態 - - 節點統計 - - 錯誤信息 - -## 技術實現 - -### 新增類 -```python -class StageCountWidget(QWidget): - """階段計數顯示組件""" - - update_stage_count() - - setup_ui() -``` - -### 新增方法 -```python -class IntegratedPipelineDashboard: - # 分析相關 - - setup_analysis_timer() - - schedule_analysis() - - analyze_pipeline() - - print_pipeline_analysis() - - update_info_panel() - - # 工具欄相關 - - create_pipeline_toolbar() - - clear_pipeline() - - # 增強的驗證 - - validate_pipeline() # 更新版本 -``` - -### 新增信號 -```python -pipeline_changed = pyqtSignal() -stage_count_changed = pyqtSignal(int) -``` - -### 新增屬性 -```python -self.stage_count_widget = None -self.analysis_timer = None -self.previous_stage_count = 0 -self.info_text = None # 管道信息瀏覽器 -``` - -## 界面變化 - -### 管道編輯器面板 -1. **頭部**: 添加了 StageCountWidget 顯示當前狀態 -2. **工具欄**: 新增節點創建和管道操作按鈕 -3. **樣式**: 與主題保持一致的深色風格 - -### 配置面板 -1. **Stages 標籤**: 添加了管道分析信息顯示 -2. **實時更新**: 節點變化時自動更新信息 - -## 導入處理 - -### 管道分析函數 -```python -try: - from cluster4npu_ui.core.pipeline import get_stage_count, analyze_pipeline_stages, get_pipeline_summary -except ImportError: - # 提供後備函數以保證系統穩定性 - def get_stage_count(graph): return 0 - def analyze_pipeline_stages(graph): return {} - def get_pipeline_summary(graph): return {...} -``` - -## 測試驗證 - -### 集成測試 -- ✅ 所有導入正常 -- ✅ StageCountWidget 功能完整 -- ✅ Dashboard 方法存在且可調用 -- ✅ 管道分析函數工作正常 - -### 運行時測試 -- ✅ 創建 StageCountWidget 成功 -- ✅ 階段計數更新正常 -- ✅ 錯誤狀態處理正確 -- ✅ 所有新增方法可調用 - -## 向後兼容性 - -1. **保留原有功能**: 所有原有的 dashboard 功能保持不變 -2. **漸進式增強**: 新功能作為附加特性，不影響核心功能 -3. **錯誤處理**: 導入失敗時提供後備方案 - -## 使用指南 - -### 啟動應用 -```bash -python main.py -``` - -### 使用新功能 -1. **查看階段計數**: 右上角的 StageCountWidget 實時顯示 -2. **快速添加節點**: 使用工具欄按鈕 -3. **驗證管道**: 點擊 "Validate Pipeline" 按鈕 -4. **清除管道**: 點擊 "Clear Pipeline" 按鈕 -5. **查看詳細分析**: 在 Stages 標籤查看管道信息 - -### 監控輸出 -- 終端會輸出詳細的管道分析信息 -- 每次節點變化都會觸發新的分析 -- 使用表情符號標記不同類型的操作 - -## 未來增強 - -1. **性能優化**: 可以添加更復雜的性能分析 -2. **可視化**: 可以添加圖表顯示管道流程 -3. **導出功能**: 可以導出管道分析報告 -4. **自動建議**: 可以添加管道優化建議 - -## 結論 - -成功將 pipeline_editor.py 的核心功能完全整合到 dashboard.py 中，提供了： -- 🎯 實時階段計數和狀態監控 -- 🔧 便捷的節點創建工具欄 -- 📊 詳細的管道分析和驗證 -- 🔄 自動化的實時更新機制 -- 📋 完整的信息顯示面板 - -整合保持了代碼的清潔性和可維護性，同時提供了豐富的用戶體驗。 \ No newline at end of file diff --git a/cluster4npu_ui/NODE_CREATION_FIX.md b/cluster4npu_ui/NODE_CREATION_FIX.md deleted file mode 100644 index 85fbe68..0000000 --- a/cluster4npu_ui/NODE_CREATION_FIX.md +++ /dev/null @@ -1,98 +0,0 @@ -# Node Creation Fix Guide - -## ✅ Problem Resolved! (Updated Fix) - -**Issue Found:** NodeGraphQt automatically appends the class name to the identifier during registration, so the actual registered identifier becomes `com.cluster.input_node.SimpleInputNode` instead of just `com.cluster.input_node`. - -**Solution Applied:** Updated the node creation logic to try multiple identifier formats automatically. - -The "Can't find node: com.cluster.input_node" error has been fixed. Here's what was implemented: - -### 🔧 **Solution Applied** - -1. **Created Simple Node Classes** (`simple_input_node.py`) - - Direct inheritance from NodeGraphQt BaseNode - - Proper identifier registration - - Compatible with NodeGraphQt system - -2. **Fixed Dashboard Registration** - - Updated node registration process - - Added debugging output for registration - - Better error handling for node creation - -3. **Enhanced Error Messages** - - Clear feedback when node creation fails - - Troubleshooting suggestions in error dialogs - - Console debugging information - -### 🚀 **How to Test the Fix** - -1. **Test the final fix:** - ```bash - python test_fixed_creation.py - ``` - Should show: ✅ ALL NODES CREATED SUCCESSFULLY! - -2. **Launch the application:** - ```bash - python -m cluster4npu_ui.main - ``` - -3. **Test in the UI:** - - Open the application - - Click any "Add" button in the Node Templates panel - - You should see nodes appear in the pipeline editor - -### 📋 **What Should Work Now** - -- ✅ All 5 node types can be created (Input, Model, Preprocess, Postprocess, Output) -- ✅ Nodes appear in the pipeline editor -- ✅ Node properties can be edited -- ✅ Pipeline validation works -- ✅ Save/load functionality preserved - -### 🐛 **If Still Having Issues** - -**Check NodeGraphQt Version:** -```bash -pip show NodeGraphQt -``` - -**Reinstall if needed:** -```bash -pip uninstall NodeGraphQt -pip install NodeGraphQt -``` - -**Verify Qt Installation:** -```bash -python -c "from PyQt5.QtWidgets import QApplication; print('PyQt5 OK')" -``` - -### 🔍 **Debug Information** - -When you click "Add" buttons in the dashboard, you should now see: -``` -Attempting to create node with identifier: com.cluster.input_node -✓ Successfully created node: Input Node -``` - -### 📝 **Technical Details** - -**Root Cause:** The original nodes inherited from a custom `BaseNodeWithProperties` class that wasn't fully compatible with NodeGraphQt's registration system. - -**Solution:** Created simplified nodes that inherit directly from `NodeGraphQt.BaseNode` with proper identifiers and registration. - -**Files Modified:** -- `cluster4npu_ui/core/nodes/simple_input_node.py` (NEW) -- `cluster4npu_ui/ui/windows/dashboard.py` (UPDATED) - -### ✨ **Result** - -You should now be able to: -1. Click any "Add" button in the node template panel -2. See the node appear in the pipeline editor -3. Select and configure node properties -4. Build complete pipelines without errors - -The modular refactoring is now **98% complete** with full node creation functionality! 🎉 \ No newline at end of file diff --git a/cluster4npu_ui/PROJECT_SUMMARY.md b/cluster4npu_ui/PROJECT_SUMMARY.md new file mode 100644 index 0000000..a564faf --- /dev/null +++ b/cluster4npu_ui/PROJECT_SUMMARY.md @@ -0,0 +1,217 @@ +# Cluster4NPU - Visual Pipeline Designer for Parallel AI Inference + +## Project Overview + +Cluster4NPU is a visual pipeline designer that enables users to create parallel AI inference workflows for Kneron NPU dongles without extensive coding knowledge. The system provides drag-and-drop pipeline construction with real-time performance analysis and speedup visualization. + +## Current System Status + +### ✅ Completed Core Features + +#### 1. **Visual Pipeline Designer** +- **Node-based interface**: Drag-and-drop pipeline construction using NodeGraphQt +- **5 node types**: Input, Model, Preprocess, Postprocess, Output nodes +- **Real-time validation**: Instant pipeline structure analysis and error detection +- **Property editing**: Type-aware configuration widgets for each node +- **Save/Load**: Pipeline persistence in .mflow format + +#### 2. **High-Performance Inference Engine** +- **Multi-stage pipelines**: Chain multiple AI models for complex workflows +- **Hardware integration**: Kneron NPU dongles (KL520, KL720, KL1080) with auto-detection +- **Thread-safe processing**: Concurrent execution with automatic queue management +- **Flexible preprocessing**: Custom data transformation between stages +- **Comprehensive statistics**: Built-in performance monitoring and metrics + +#### 3. **Professional UI Architecture** +- **Modular design**: Refactored from 3,345-line monolithic file to focused modules +- **3-panel layout**: Node templates, pipeline editor, configuration panels +- **Real-time status**: Global status bar with stage count and connection monitoring +- **Clean interface**: Removed unnecessary UI elements for professional appearance + +#### 4. **Recent Major Improvements** +- **Enhanced stage calculation**: Only connected model nodes count as stages +- **Improved connection detection**: Accurate pipeline connectivity analysis +- **Node creation fixes**: Resolved instantiation and property editing issues +- **UI cleanup**: Professional interface with consistent styling + +### 🔄 Current Capabilities + +#### Pipeline Construction +``` +Input Node → Preprocess Node → Model Node → Postprocess Node → Output Node + ↓ ↓ ↓ ↓ ↓ + Camera/File Resize/Norm AI Inference Format/Filter File/Display +``` + +#### Supported Hardware +- **Kneron NPU dongles**: KL520, KL720, KL1080 series +- **Multi-device support**: Automatic detection and load balancing +- **USB connectivity**: Port-based device management + +#### Input Sources +- **Camera input**: USB cameras with configurable resolution/FPS +- **Video files**: MP4, AVI, MOV with frame-by-frame processing +- **Image files**: JPG, PNG, BMP with batch processing +- **RTSP streams**: Live video streaming (basic support) + +## 🎯 Main Goal: Parallel Pipeline Speedup Visualization + +### User Requirements +1. **No-code pipeline development**: Visual interface for non-technical users +2. **Parallel processing setup**: Easy configuration of multi-device inference +3. **Speedup visualization**: Clear metrics showing performance improvements +4. **Real-time monitoring**: Live performance feedback during execution + +## 🚨 Critical Missing Features + +### 1. **Parallel Processing Visualization** (HIGH PRIORITY) +**Current State**: Basic multi-device support exists but no visual representation +**Missing**: +- Visual representation of parallel execution paths +- Device allocation visualization +- Load balancing indicators +- Parallel vs sequential comparison charts + +### 2. **Performance Benchmarking System** (HIGH PRIORITY) +**Current State**: Basic statistics collection exists +**Missing**: +- Automated benchmark execution +- Speedup calculation (parallel vs single device) +- Performance comparison charts +- Bottleneck identification +- Throughput optimization suggestions + +### 3. **Device Management Interface** (MEDIUM PRIORITY) +**Current State**: Auto-detection works but limited UI +**Missing**: +- Visual device status dashboard +- Device health monitoring +- Manual device assignment interface +- Device performance profiling + +### 4. **Pipeline Optimization Assistant** (MEDIUM PRIORITY) +**Current State**: Manual configuration only +**Missing**: +- Automatic pipeline optimization suggestions +- Device allocation recommendations +- Performance prediction before execution +- Configuration templates for common use cases + +### 5. **Real-time Performance Dashboard** (HIGH PRIORITY) +**Current State**: Basic status bar with limited info +**Missing**: +- Live performance graphs (FPS, latency, throughput) +- Resource utilization charts (CPU, memory, device usage) +- Parallel execution timeline visualization +- Performance alerts and warnings + +## 📊 Detailed Gap Analysis + +### Core Engine Gaps +| Feature | Current Status | Missing Components | +|---------|---------------|-------------------| +| **Parallel Execution** | ✅ Multi-device support | ❌ Visual parallel flow representation | +| **Performance Metrics** | ✅ Basic statistics | ❌ Speedup calculation & comparison | +| **Device Management** | ✅ Auto-detection | ❌ Visual device dashboard | +| **Optimization** | ✅ Manual tuning | ❌ Automatic optimization suggestions | +| **Real-time Monitoring** | ✅ Status updates | ❌ Live performance visualization | + +### UI/UX Gaps +| Component | Current Status | Missing Elements | +|-----------|---------------|------------------| +| **Pipeline Visualization** | ✅ Node graph | ❌ Parallel execution paths | +| **Performance Dashboard** | ✅ Status bar | ❌ Charts and graphs | +| **Device Interface** | ✅ Basic detection | ❌ Management dashboard | +| **Benchmarking** | ❌ Not implemented | ❌ Complete benchmarking system | +| **Optimization UI** | ❌ Not implemented | ❌ Suggestion interface | + +## 🛠 Technical Architecture Needs + +### Missing Core Components +1. **ParallelExecutionEngine**: Coordinate multiple inference paths +2. **PerformanceBenchmarker**: Automated testing and comparison +3. **DeviceManager**: Advanced device control and monitoring +4. **OptimizationEngine**: Automatic pipeline optimization +5. **VisualizationEngine**: Real-time charts and graphs + +### Missing UI Components +1. **PerformanceDashboard**: Live monitoring interface +2. **DeviceManagementPanel**: Device status and control +3. **BenchmarkingDialog**: Performance testing interface +4. **OptimizationAssistant**: Suggestion and recommendation UI +5. **ParallelVisualizationWidget**: Parallel execution display + +## 🎯 Development Priorities + +### Phase 1: Performance Visualization (Weeks 1-2) +**Goal**: Show users the speedup benefits of parallel processing +- Implement performance benchmarking system +- Create speedup calculation and comparison +- Build basic performance dashboard with charts +- Add parallel vs sequential execution comparison + +### Phase 2: Device Management Enhancement (Weeks 3-4) +**Goal**: Better control over hardware resources +- Enhanced device detection and status monitoring +- Visual device allocation interface +- Device health and performance profiling +- Manual device assignment capabilities + +### Phase 3: Pipeline Optimization (Weeks 5-6) +**Goal**: Automatic optimization suggestions +- Pipeline analysis and bottleneck detection +- Automatic device allocation recommendations +- Performance prediction before execution +- Configuration templates and presets + +### Phase 4: Advanced Visualization (Weeks 7-8) +**Goal**: Professional monitoring and analysis tools +- Real-time performance graphs and charts +- Resource utilization monitoring +- Parallel execution timeline visualization +- Advanced analytics and reporting + +## 🎨 User Experience Vision + +### Target User Journey +1. **Pipeline Design**: Drag-and-drop nodes to create inference pipeline +2. **Device Setup**: Visual device detection and allocation +3. **Performance Preview**: See predicted speedup before execution +4. **Execution Monitoring**: Real-time performance dashboard +5. **Results Analysis**: Speedup comparison and optimization suggestions + +### Key Success Metrics +- **Time to create pipeline**: < 5 minutes for typical use case +- **Speedup visibility**: Clear before/after performance comparison +- **Device utilization**: Visual feedback on hardware usage +- **Optimization impact**: Measurable performance improvements + +## 🔧 Implementation Strategy + +### Immediate Next Steps +1. **Create performance benchmarking spec** for automated testing +2. **Design parallel visualization interface** for execution monitoring +3. **Implement device management dashboard** for hardware control +4. **Build speedup calculation engine** for performance comparison + +### Technical Approach +- **Extend existing InferencePipeline**: Add parallel execution coordination +- **Enhance UI with new panels**: Performance dashboard and device management +- **Integrate visualization libraries**: Charts.js or similar for real-time graphs +- **Add benchmarking automation**: Systematic performance testing + +## 📈 Expected Outcomes + +### For End Users +- **Simplified parallel processing**: No coding required for multi-device setup +- **Clear performance benefits**: Visual proof of speedup improvements +- **Optimized configurations**: Automatic suggestions for best performance +- **Professional monitoring**: Real-time insights into system performance + +### For the Platform +- **Competitive advantage**: Unique visual approach to parallel AI inference +- **User adoption**: Lower barrier to entry for non-technical users +- **Performance optimization**: Systematic approach to hardware utilization +- **Scalability**: Foundation for advanced features and enterprise use + +This consolidated summary focuses on your main goal of creating an intuitive GUI for parallel inference pipeline development with clear speedup visualization. The missing components are prioritized based on their impact on user experience and the core value proposition. \ No newline at end of file diff --git a/cluster4npu_ui/PROPERTIES_FIX_COMPLETE.md b/cluster4npu_ui/PROPERTIES_FIX_COMPLETE.md deleted file mode 100644 index c851b48..0000000 --- a/cluster4npu_ui/PROPERTIES_FIX_COMPLETE.md +++ /dev/null @@ -1,116 +0,0 @@ -# Properties Editor - Complete Fix - -## ✅ **Both Issues Fixed!** - -### 🔧 **Issue 1: CSS Warnings - FIXED** -- Removed unsupported CSS properties (`transform`, `box-shadow`) from theme -- Fixed HiDPI warning by setting attributes before QApplication creation -- Cleaner console output with fewer warnings - -### 🔧 **Issue 2: Node Properties Not Editable - FIXED** -- Enhanced property detection system with multiple fallback methods -- Created smart property widgets based on property names and types -- Added proper event handlers for property changes - -## 🚀 **What's Working Now** - -### **Node Creation** -- ✅ All 5 node types create successfully -- ✅ Nodes appear in pipeline editor with proper positioning -- ✅ Console shows successful creation messages - -### **Property Editing** -- ✅ **Select any node** → Properties panel updates automatically -- ✅ **Smart widgets** based on property type: - - 📁 **File paths**: Browse button for model_path, destination - - 📋 **Dropdowns**: source_type, dongle_series, output_format - - ☑️ **Checkboxes**: Boolean properties like normalize - - 🔢 **Spinboxes**: Numbers with appropriate ranges - - 📝 **Text fields**: Strings with helpful placeholders - -### **Property Types by Node** - -**🎯 Input Node:** -- Source Type: Camera, File, RTSP Stream, HTTP Stream -- Device ID: 0-10 range -- Resolution: Text field (e.g., 1920x1080) -- FPS: 1-120 range - -**🧠 Model Node:** -- Model Path: File browser button -- Dongle Series: 520, 720, 1080, Custom -- Num Dongles: 1-16 range - -**⚙️ Preprocess Node:** -- Resize Width/Height: 64-4096 range -- Normalize: True/False checkbox - -**🔧 Postprocess Node:** -- Output Format: JSON, XML, CSV, Binary -- Confidence Threshold: 0.0-1.0 with 0.01 steps - -**📤 Output Node:** -- Output Type: File, API Endpoint, Database, Display -- Destination: File browser button -- Format: JSON, XML, CSV, Binary - -## 🎯 **How to Test** - -1. **Launch the application:** - ```bash - python -m cluster4npu_ui.main - ``` - -2. **Create nodes:** - - Click any "Add" button in Node Templates panel - - Nodes will appear in the pipeline editor - -3. **Edit properties:** - - Click on any node to select it - - Properties panel will show editable controls - - Change values and they'll be saved to the node - -4. **Verify changes:** - - Select different nodes and come back - - Your changes should be preserved - -## 📋 **Expected Console Output** - -**Clean startup (minimal warnings):** -``` -Registering nodes with NodeGraphQt... -✓ Registered SimpleInputNode with identifier com.cluster.input_node -✓ Registered SimpleModelNode with identifier com.cluster.model_node -... -Node graph setup completed successfully -``` - -**Node creation:** -``` -Attempting to create node with identifier: com.cluster.input_node -Trying identifier: com.cluster.input_node -✗ Failed with com.cluster.input_node: Can't find node: "com.cluster.input_node" -Trying identifier: com.cluster.input_node.SimpleInputNode -✓ Success with identifier: com.cluster.input_node.SimpleInputNode -✓ Successfully created node: Input Node -``` - -## 🎉 **Final Status** - -### **✅ Complete Functionality:** -- Node creation: **Working** -- Property editing: **Working** -- UI responsiveness: **Working** -- Pipeline building: **Working** -- Clean console output: **Working** - -### **🏆 Refactoring Complete: 100%** - -The modular Cluster4NPU UI application is now **fully functional** with: -- ✅ Complete separation of concerns -- ✅ Professional modular architecture -- ✅ Enhanced node property system -- ✅ Clean, maintainable codebase -- ✅ Full pipeline editing capabilities - -**You can now build complete ML inference pipelines with a professional, modular UI!** 🚀 \ No newline at end of file diff --git a/cluster4npu_ui/REFACTORING_RECORD.md b/cluster4npu_ui/REFACTORING_RECORD.md deleted file mode 100644 index 83c3f7d..0000000 --- a/cluster4npu_ui/REFACTORING_RECORD.md +++ /dev/null @@ -1,294 +0,0 @@ -# UI.py Refactoring Record - -## Overview -This document tracks the complete refactoring process of the monolithic UI.py file (3,345 lines) into a modular, maintainable project structure. - -## Project Analysis - -### Original Structure -- **File**: `UI.py` (3,345 lines) -- **Total Classes**: 15 major classes -- **Main Components**: Styling, Node definitions, UI components, Main windows, Dialogs, Application entry - -### Identified Issues -1. **Monolithic Structure**: All code in single file -2. **Mixed Concerns**: Business logic, UI, and styling intermingled -3. **Poor Maintainability**: Difficult to navigate and modify -4. **No Separation**: Hard to test individual components -5. **Collaboration Challenges**: Multiple developers working on same file - -## Refactoring Plan - -### Target Structure -``` -cluster4npu_ui/ -├── __init__.py -├── main.py # Application entry point -├── config/ -│ ├── __init__.py -│ ├── theme.py # Theme and styling constants -│ └── settings.py # Application settings -├── core/ -│ ├── __init__.py -│ ├── nodes/ -│ │ ├── __init__.py -│ │ ├── base_node.py # Base node functionality -│ │ ├── input_node.py # Input node implementation -│ │ ├── model_node.py # Model node implementation -│ │ ├── preprocess_node.py # Preprocessing node -│ │ ├── postprocess_node.py # Postprocessing node -│ │ └── output_node.py # Output node implementation -│ └── pipeline.py # Pipeline logic and management -├── ui/ -│ ├── __init__.py -│ ├── components/ -│ │ ├── __init__.py -│ │ ├── node_palette.py # Node template selector -│ │ ├── properties_widget.py # Property editor -│ │ └── common_widgets.py # Shared UI components -│ ├── dialogs/ -│ │ ├── __init__.py -│ │ ├── create_pipeline.py # Pipeline creation dialog -│ │ ├── stage_config.py # Stage configuration dialog -│ │ ├── performance.py # Performance estimation -│ │ ├── save_deploy.py # Save and deploy dialog -│ │ └── properties.py # Simple properties dialog -│ └── windows/ -│ ├── __init__.py -│ ├── dashboard.py # Main dashboard window -│ ├── login.py # Login/startup window -│ └── pipeline_editor.py # Pipeline editor window -├── utils/ -│ ├── __init__.py -│ ├── file_utils.py # File operations -│ └── ui_utils.py # UI utility functions -└── resources/ - ├── __init__.py - ├── icons/ # Icon files - └── styles/ # Additional stylesheets -``` - -## Migration Steps - -### Phase 1: Directory Structure Creation -- [x] Create main directory structure -- [x] Add __init__.py files for Python packages -- [x] Create placeholder files for all modules - -### Phase 2: Core Module Extraction -- [ ] Extract base node functionality -- [ ] Separate individual node implementations -- [ ] Create pipeline management module - -### Phase 3: Configuration Module -- [ ] Extract theme and styling constants -- [ ] Create settings management system - -### Phase 4: UI Components -- [ ] Extract property editor widget -- [ ] Create node palette component -- [ ] Separate common UI widgets - -### Phase 5: Dialog Extraction -- [ ] Extract all dialog implementations -- [ ] Ensure proper parent-child relationships -- [ ] Test dialog functionality - -### Phase 6: Main Windows -- [ ] Extract dashboard window -- [ ] Create login/startup window -- [ ] Separate pipeline editor window - -### Phase 7: Utilities and Resources -- [ ] Create file utility functions -- [ ] Add UI helper functions -- [ ] Organize resources and assets - -### Phase 8: Integration and Testing -- [ ] Update imports across all modules -- [ ] Test individual components -- [ ] Verify complete application functionality -- [ ] Performance testing - -## Detailed Migration Log - -### 2024-07-04 - Project Analysis Complete -- Analyzed original UI.py structure (3,345 lines) -- Identified 15 major classes and 6 functional sections -- Created comprehensive refactoring plan -- Established target modular structure - -### 2024-07-04 - Migration Documentation Created -- Created REFACTORING_RECORD.md for tracking progress -- Documented all classes and their target modules -- Established migration phases and checkpoints - -## Class Migration Map - -| Original Class | Target Module | Status | -|---------------|---------------|---------| -| ModelNode | core/nodes/model_node.py | Pending | -| PreprocessNode | core/nodes/preprocess_node.py | Pending | -| PostprocessNode | core/nodes/postprocess_node.py | Pending | -| InputNode | core/nodes/input_node.py | Pending | -| OutputNode | core/nodes/output_node.py | Pending | -| CustomPropertiesWidget | ui/components/properties_widget.py | Pending | -| CreatePipelineDialog | ui/dialogs/create_pipeline.py | Pending | -| SimplePropertiesDialog | ui/dialogs/properties.py | Pending | -| NodePalette | ui/components/node_palette.py | Pending | -| IntegratedPipelineDashboard | ui/windows/dashboard.py | Pending | -| PipelineEditor | ui/windows/pipeline_editor.py | Pending | -| DashboardLogin | ui/windows/login.py | Pending | -| StageConfigurationDialog | ui/dialogs/stage_config.py | Pending | -| PerformanceEstimationPanel | ui/dialogs/performance.py | Pending | -| SaveDeployDialog | ui/dialogs/save_deploy.py | Pending | - -## Code Quality Improvements - -### Type Hints -- [ ] Add type annotations to all functions and methods -- [ ] Import typing modules where needed -- [ ] Use proper generic types for containers - -### Error Handling -- [ ] Implement comprehensive exception handling -- [ ] Add logging framework integration -- [ ] Create error recovery mechanisms - -### Documentation -- [ ] Add docstrings to all classes and methods -- [ ] Create module-level documentation -- [ ] Generate API documentation - -### Testing -- [ ] Create unit tests for core functionality -- [ ] Add integration tests for UI components -- [ ] Implement automated testing pipeline - -## Notes and Considerations - -### Dependencies -- PyQt5: Main UI framework -- NodeGraphQt: Node graph visualization -- Standard library: json, os, sys - -### Backward Compatibility -- Ensure .mflow file format remains compatible -- Maintain existing API contracts -- Preserve user workflow and experience - -### Performance Considerations -- Monitor import times with modular structure -- Optimize heavy UI components -- Consider lazy loading for large modules - -### Future Enhancements -- Plugin system for custom nodes -- Theme switching capability -- Internationalization support -- Advanced debugging tools - -## Validation Checklist - -### Functional Testing -- [ ] All dialogs open and close properly -- [ ] Node creation and connection works -- [ ] Property editing functions correctly -- [ ] Pipeline save/load operations work -- [ ] All menu items and buttons function - -### Code Quality -- [ ] No circular imports -- [ ] Consistent naming conventions -- [ ] Proper error handling -- [ ] Complete documentation -- [ ] Type hints throughout - -### Performance -- [ ] Application startup time acceptable -- [ ] UI responsiveness maintained -- [ ] Memory usage optimized -- [ ] No resource leaks - -## Completion Status -- **Current Phase**: Phase 8 - Integration and Testing -- **Overall Progress**: 95% (Major refactoring complete) -- **Completed**: All core components and main dashboard extracted and modularized -- **Next Steps**: Final UI component extraction and testing validation - -## Implementation Summary - -### Successfully Refactored Components ✅ - -#### Configuration Module -- ✅ **config/theme.py**: Complete QSS theme extraction with color constants -- ✅ **config/settings.py**: Comprehensive settings management system - -#### Core Module -- ✅ **core/nodes/base_node.py**: Enhanced base node with business properties -- ✅ **core/nodes/model_node.py**: Complete model inference node implementation -- ✅ **core/nodes/preprocess_node.py**: Full preprocessing node with validation -- ✅ **core/nodes/postprocess_node.py**: Comprehensive postprocessing node -- ✅ **core/nodes/input_node.py**: Complete input source node implementation -- ✅ **core/nodes/output_node.py**: Full output destination node -- ✅ **core/nodes/__init__.py**: Package initialization with node registry - -#### UI Module Foundation -- ✅ **ui/windows/login.py**: Complete startup/dashboard login window -- ✅ **ui/windows/dashboard.py**: Complete integrated pipeline dashboard (1,100+ lines) -- ✅ **main.py**: Application entry point with theme integration - -#### Project Structure -- ✅ **REFACTORING_RECORD.md**: Comprehensive documentation -- ✅ Complete modular directory structure created -- ✅ All package __init__.py files in place - -### Completed Migration Tasks - -| Component | Original Lines | Status | New Location | -|-----------|---------------|---------|--------------| -| Theme/Styling | 26-234 | ✅ Complete | config/theme.py | -| Node Definitions | 239-369 | ✅ Complete | core/nodes/ | -| Settings Management | N/A | ✅ New | config/settings.py | -| Application Entry | 3299-3345 | ✅ Complete | main.py | -| Base Node Framework | N/A | ✅ Enhanced | core/nodes/base_node.py | -| Login/Startup Window | 2450-2800 | ✅ Complete | ui/windows/login.py | -| Main Dashboard | 945-2044 | ✅ Complete | ui/windows/dashboard.py | - -### Key Achievements - -1. **Complete Separation of Concerns** - - Business logic isolated in core modules - - UI components properly separated - - Configuration externalized - -2. **Enhanced Node System** - - Type-safe property management - - Comprehensive validation framework - - Extensible base architecture - -3. **Professional Configuration Management** - - Persistent settings system - - Recent files management - - Theme and preference handling - -4. **Improved Maintainability** - - Clear module boundaries - - Comprehensive documentation - - Consistent coding patterns - -### Benefits Achieved - -- **94% Code Size Reduction** per module (from 3,345 lines to focused modules) -- **Enhanced Testability** with isolated components -- **Better Collaboration** support with clear module ownership -- **Improved Performance** through optimized imports -- **Future-Proof Architecture** for easy extension - -### Remaining Work (5%) - -- UI component dialogs (create_pipeline, stage_config, etc.) -- ~~Main dashboard window extraction~~ ✅ COMPLETED -- Additional UI widgets and components -- Final integration testing -- Migration validation and cleanup \ No newline at end of file diff --git a/cluster4npu_ui/REFACTORING_SUMMARY.md b/cluster4npu_ui/REFACTORING_SUMMARY.md deleted file mode 100644 index 3beb2dd..0000000 --- a/cluster4npu_ui/REFACTORING_SUMMARY.md +++ /dev/null @@ -1,266 +0,0 @@ -# Cluster4NPU UI - Modular Refactoring Complete - -## Executive Summary - -Successfully refactored the monolithic PyQt5 application (`UI.py` - 3,345 lines) into a comprehensive modular architecture with **94% complexity reduction per module**, enhanced maintainability, and professional-grade organization. - -## 🎯 Key Achievements - -### 1. Complete Architecture Transformation -- **From**: Single 3,345-line monolithic file -- **To**: 15+ focused, modular components (200-400 lines each) -- **Result**: 94% per-module complexity reduction - -### 2. Successful Component Extraction - -| Component | Original Lines | Status | New Location | -|-----------|---------------|---------|--------------| -| **Theme System** | 26-234 (209 lines) | ✅ Complete | `config/theme.py` | -| **Node Definitions** | 239-369 (130 lines) | ✅ Complete | `core/nodes/` | -| **Application Entry** | 3299-3345 (46 lines) | ✅ Complete | `main.py` | -| **Login Window** | 2450-2800 (350 lines) | ✅ Complete | `ui/windows/login.py` | -| **Settings System** | N/A | ✅ New | `config/settings.py` | -| **Base Node Framework** | N/A | ✅ Enhanced | `core/nodes/base_node.py` | - -### 3. Enhanced Business Logic - -#### Node System Improvements -- **Type-safe property management** with validation -- **Comprehensive configuration validation** -- **Hardware requirement estimation** -- **Performance metrics calculation** -- **Extensible plugin architecture** - -#### Configuration Management -- **Persistent settings system** with JSON storage -- **Recent files management** with validation -- **Window state preservation** -- **Theme and preference handling** -- **Import/export functionality** - -### 4. Professional Code Quality - -#### Documentation -- **100% documented modules** with comprehensive docstrings -- **Clear API interfaces** and usage examples -- **Migration tracking** with detailed records -- **Type hints throughout** for better IDE support - -#### Architecture Benefits -- **Separation of concerns** (business logic, UI, configuration) -- **Modular imports** for improved performance -- **Clear dependency management** -- **Enhanced testability** with isolated components - -## 📁 Final Modular Structure - -``` -cluster4npu_ui/ # Main package -├── __init__.py # ✅ Package initialization -├── main.py # ✅ Application entry point -│ -├── config/ # Configuration management -│ ├── __init__.py # ✅ Config package -│ ├── theme.py # ✅ QSS themes and colors -│ └── settings.py # ✅ Settings and preferences -│ -├── core/ # Business logic -│ ├── __init__.py # ✅ Core package -│ ├── nodes/ # Node implementations -│ │ ├── __init__.py # ✅ Node registry -│ │ ├── base_node.py # ✅ Enhanced base functionality -│ │ ├── input_node.py # ✅ Input sources -│ │ ├── model_node.py # ✅ Model inference -│ │ ├── preprocess_node.py # ✅ Data preprocessing -│ │ ├── postprocess_node.py # ✅ Result postprocessing -│ │ └── output_node.py # ✅ Output destinations -│ └── pipeline.py # 🔄 Future: Pipeline orchestration -│ -├── ui/ # User interface -│ ├── __init__.py # ✅ UI package -│ ├── components/ # Reusable components -│ │ ├── __init__.py # ✅ Component package -│ │ ├── node_palette.py # 🔄 Node template selector -│ │ ├── properties_widget.py # 🔄 Property editor -│ │ └── common_widgets.py # 🔄 Shared widgets -│ ├── dialogs/ # Dialog boxes -│ │ ├── __init__.py # ✅ Dialog package -│ │ ├── create_pipeline.py # 🔄 Pipeline creation -│ │ ├── stage_config.py # 🔄 Stage configuration -│ │ ├── performance.py # 🔄 Performance analysis -│ │ ├── save_deploy.py # 🔄 Export and deploy -│ │ └── properties.py # 🔄 Property dialogs -│ └── windows/ # Main windows -│ ├── __init__.py # ✅ Windows package -│ ├── dashboard.py # 🔄 Main dashboard -│ ├── login.py # ✅ Startup window -│ └── pipeline_editor.py # 🔄 Pipeline editor -│ -├── utils/ # Utility functions -│ ├── __init__.py # ✅ Utils package -│ ├── file_utils.py # 🔄 File operations -│ └── ui_utils.py # 🔄 UI helpers -│ -└── resources/ # Static resources - ├── __init__.py # ✅ Resources package - ├── icons/ # 📁 Icon files - └── styles/ # 📁 Additional styles - -Legend: -✅ Implemented and tested -🔄 Structure ready for implementation -📁 Directory structure created -``` - -## 🚀 Usage Examples - -### Basic Node System -```python -from cluster4npu_ui.core.nodes import ModelNode, InputNode - -# Create and configure nodes -input_node = InputNode() -input_node.set_property('source_type', 'Camera') -input_node.set_property('resolution', '1920x1080') - -model_node = ModelNode() -model_node.set_property('dongle_series', '720') -model_node.set_property('num_dongles', 2) - -# Validate configuration -valid, error = model_node.validate_configuration() -if valid: - config = model_node.get_inference_config() -``` - -### Configuration Management -```python -from cluster4npu_ui.config import get_settings, apply_theme - -# Manage settings -settings = get_settings() -settings.add_recent_file('/path/to/pipeline.mflow') -recent_files = settings.get_recent_files() - -# Apply theme -apply_theme(app) -``` - -### Application Launch -```python -from cluster4npu_ui.main import main - -# Launch the complete application -main() -``` - -## 📊 Performance Metrics - -### Code Organization -- **Original**: 1 file, 3,345 lines -- **Modular**: 15+ files, ~200-400 lines each -- **Reduction**: 94% complexity per module - -### Development Benefits -- **Faster Navigation**: Jump directly to relevant modules -- **Parallel Development**: Multiple developers can work simultaneously -- **Easier Testing**: Isolated components for unit testing -- **Better Debugging**: Clear module boundaries for issue isolation - -### Maintenance Improvements -- **Clear Ownership**: Each module has specific responsibilities -- **Easier Updates**: Modify one aspect without affecting others -- **Better Documentation**: Focused, comprehensive docstrings -- **Future Extensions**: Plugin architecture for new node types - -## 🔄 Implementation Status - -### Completed (85%) -- ✅ **Core Architecture**: Complete modular foundation -- ✅ **Node System**: All 5 node types with enhanced capabilities -- ✅ **Configuration**: Theme and settings management -- ✅ **Application Entry**: Modern startup system -- ✅ **Documentation**: Comprehensive migration tracking - -### Remaining (15%) -- 🔄 **UI Components**: Property editor, node palette -- 🔄 **Dialog Extraction**: Pipeline creation, stage config -- 🔄 **Main Windows**: Dashboard and pipeline editor -- 🔄 **Integration Testing**: Complete workflow validation -- 🔄 **Migration Cleanup**: Final optimization and polish - -## 🎉 Benefits Realized - -### For Developers -1. **Faster Development**: Clear module structure reduces search time -2. **Better Collaboration**: Multiple developers can work in parallel -3. **Easier Debugging**: Isolated components simplify issue tracking -4. **Enhanced Testing**: Unit test individual components -5. **Cleaner Git History**: Focused commits to specific modules - -### For Maintainers -1. **Reduced Complexity**: Each module handles one concern -2. **Improved Documentation**: Clear interfaces and usage examples -3. **Better Performance**: Optimized imports and lazy loading -4. **Future-Proof**: Plugin architecture for extensibility -5. **Professional Quality**: Industry-standard organization - -### For Users -1. **Better Performance**: Optimized application startup -2. **Enhanced Stability**: Isolated components reduce crash propagation -3. **Improved Features**: Enhanced node validation and configuration -4. **Future Updates**: Easier to add new features and node types - -## 🎯 Next Steps - -1. **Complete UI Extraction** (1-2 days) - - Extract remaining dialog implementations - - Complete main dashboard window - - Implement property editor component - -2. **Integration Testing** (1 day) - - Test complete workflow end-to-end - - Validate all import dependencies - - Performance testing and optimization - -3. **Documentation Finalization** (0.5 days) - - API documentation generation - - User migration guide - - Developer contribution guidelines - -4. **Production Deployment** (0.5 days) - - Package structure optimization - - Distribution preparation - - Final validation and release - -## 📋 Validation Checklist - -### ✅ Architecture -- [x] Monolithic file successfully decomposed -- [x] Clear separation of concerns achieved -- [x] All major components extracted and modularized -- [x] Professional package structure implemented - -### ✅ Functionality -- [x] Core node system working with enhanced features -- [x] Configuration management fully operational -- [x] Theme system properly extracted and functional -- [x] Application entry point successfully modularized - -### ✅ Quality -- [x] Comprehensive documentation throughout -- [x] Type hints and validation implemented -- [x] Error handling and edge cases covered -- [x] Migration process fully documented - -### 🔄 Remaining -- [ ] Complete UI component extraction -- [ ] Final integration testing -- [ ] Performance optimization -- [ ] Production deployment preparation - ---- - -**🎉 Refactoring Mission: 85% COMPLETE** - -The core modular architecture is fully functional, tested, and ready for the remaining UI component extraction. The foundation provides a solid, maintainable, and extensible platform for the Cluster4NPU pipeline designer application. \ No newline at end of file diff --git a/cluster4npu_ui/TODO.md b/cluster4npu_ui/TODO.md deleted file mode 100644 index b9142fa..0000000 --- a/cluster4npu_ui/TODO.md +++ /dev/null @@ -1,292 +0,0 @@ -# Cluster4NPU Pipeline TODO - -## Current Status -✅ **Pipeline Core**: Multi-stage pipeline with device auto-detection working -✅ **Hardware Integration**: Kneron NPU dongles connecting and initializing successfully -✅ **Auto-resize Preprocessing**: Model input shape detection and automatic preprocessing implemented -✅ **Data Input Sources**: Camera and video file inputs implemented -✅ **Result Persistence**: Result saving to file implemented -✅ **End-to-End Workflow**: UI configuration now connects to core pipeline execution -✅ **Bug Fixes**: Addressed file path and data processing issues -✅ **Real-time Viewer**: Implemented a live view for real-time inference visualization - ---- - -## Priority 1: Essential Components for Complete Inference Workflow - -### 1. Data Source Implementation -**Status**: 🔴 Critical Missing Components -**Location**: Need to create new classes in `core/functions/` or extend existing ones - -#### 1.1 Camera Input Source -- **File**: `core/functions/camera_source.py` (new) -- **Class**: `CameraSource` -- **Purpose**: Wrapper around cv2.VideoCapture for camera input -- **Integration**: Connect to InferencePipeline.put_data() -- **Features**: - - Multiple camera index support - - Resolution and FPS configuration - - Format conversion (BGR → model input format) - - Error handling for camera disconnection - -#### 1.2 Video File Input Source -- **File**: `core/functions/video_source.py` (new) -- **Class**: `VideoFileSource` -- **Purpose**: Process video files frame by frame -- **Integration**: Feed frames to InferencePipeline -- **Features**: - - Support common video formats (MP4, AVI, MOV) - - Frame rate control and seeking - - Batch processing capabilities - - Progress tracking - -#### 1.3 Image File Input Source -- **File**: `core/functions/image_source.py` (new) -- **Class**: `ImageFileSource` -- **Purpose**: Process single images or image directories -- **Integration**: Single-shot inference through pipeline -- **Features**: - - Support common image formats (JPG, PNG, BMP) - - Batch directory processing - - Image validation and error handling - -#### 1.4 RTSP/HTTP Stream Source -- **File**: `core/functions/stream_source.py` (new) -- **Class**: `RTSPSource`, `HTTPStreamSource` -- **Purpose**: Process live video streams -- **Integration**: Real-time streaming to pipeline -- **Features**: - - Stream connection management - - Reconnection on failure - - Buffer management and frame dropping - -### 2. Result Persistence System -**Status**: 🔴 Critical Missing Components -**Location**: `core/functions/result_handler.py` (new) - -#### 2.1 Result Serialization -- **Class**: `ResultSerializer` -- **Purpose**: Convert inference results to standard formats -- **Features**: - - JSON export with timestamps - - CSV export for analytics - - Binary format for performance - - Configurable fields and formatting - -#### 2.2 File Output Manager -- **Class**: `FileOutputManager` -- **Purpose**: Handle result file writing and organization -- **Features**: - - Timestamped file naming - - Directory organization by date/pipeline - - File rotation and cleanup - - Output format configuration - -#### 2.3 Real-time Result Streaming -- **Class**: `ResultStreamer` -- **Purpose**: Stream results to external systems -- **Features**: - - WebSocket result broadcasting - - REST API endpoints - - Message queue integration (Redis, RabbitMQ) - - Custom callback system - -### 3. Input/Output Integration Bridge -**Status**: 🔴 Critical Missing Components -**Location**: `core/functions/pipeline_manager.py` (new) - -#### 3.1 Pipeline Configuration Manager -- **Class**: `PipelineConfigManager` -- **Purpose**: Convert UI configurations to executable pipelines -- **Integration**: Bridge between UI and core pipeline -- **Features**: - - Parse UI node configurations - - Instantiate appropriate data sources - - Configure result handlers - - Manage pipeline lifecycle - -#### 3.2 Unified Workflow Orchestrator -- **Class**: `WorkflowOrchestrator` -- **Purpose**: Coordinate complete data flow from input to output -- **Features**: - - Input source management - - Pipeline execution control - - Result handling and persistence - - Error recovery and logging - ---- - -## Priority 2: Enhanced Preprocessing and Auto-resize - -### 4. Enhanced Preprocessing System -**Status**: 🟡 Partially Implemented -**Location**: `core/functions/Multidongle.py` (existing) + new preprocessing modules - -#### 4.1 Current Auto-resize Implementation -- **Location**: `Multidongle.py:354-371` (preprocess_frame method) -- **Features**: ✅ Already implemented - - Automatic model input shape detection - - Dynamic resizing based on model requirements - - Format conversion (BGR565, RGB8888, YUYV, RAW8) - - Aspect ratio handling - -#### 4.2 Enhanced Preprocessing Pipeline -- **File**: `core/functions/preprocessor.py` (new) -- **Class**: `AdvancedPreprocessor` -- **Purpose**: Extended preprocessing capabilities -- **Features**: - - **Smart cropping**: Maintain aspect ratio with intelligent cropping - - **Normalization**: Configurable pixel value normalization - - **Augmentation**: Real-time data augmentation for training - - **Multi-model support**: Different preprocessing for different models - - **Caching**: Preprocessed frame caching for performance - -#### 4.3 Model-Aware Preprocessing -- **Enhancement**: Extend existing `Multidongle` class -- **Location**: `core/functions/Multidongle.py:188-199` (model_input_shape detection) -- **Features**: - - **Dynamic preprocessing**: Adjust preprocessing based on model metadata - - **Model-specific optimization**: Tailored preprocessing for different model types - - **Preprocessing profiles**: Saved preprocessing configurations per model - ---- - -## Priority 3: UI Integration and User Experience - -### 5. Dashboard Integration -**Status**: 🟡 Partially Implemented -**Location**: `ui/windows/dashboard.py` (existing) - -#### 5.1 Real-time Pipeline Monitoring -- **Enhancement**: Extend existing Dashboard class -- **Features**: - - Live inference statistics - - Real-time result visualization - - Performance metrics dashboard - - Error monitoring and alerts - -#### 5.2 Input Source Configuration -- **Integration**: Connect UI input nodes to actual data sources -- **Features**: - - Camera selection and preview - - File browser integration - - Stream URL validation - - Input source testing - -### 6. Result Visualization -**Status**: 🔴 Not Implemented -**Location**: `ui/widgets/result_viewer.py` (new) - -#### 6.1 Result Display Widget -- **Class**: `ResultViewer` -- **Purpose**: Display inference results in UI -- **Features**: - - Real-time result streaming - - Result history and filtering - - Export capabilities - - Customizable display formats - ---- - -## Priority 4: Advanced Features and Optimization - -### 7. Performance Optimization -**Status**: 🟡 Basic Implementation -**Location**: Multiple files - -#### 7.1 Memory Management -- **Enhancement**: Optimize existing queue systems -- **Files**: `InferencePipeline.py`, `Multidongle.py` -- **Features**: - - Smart queue sizing based on available memory - - Frame dropping under load - - Memory leak detection and prevention - - Garbage collection optimization - -#### 7.2 Multi-device Load Balancing -- **Enhancement**: Extend existing multi-dongle support -- **Location**: `core/functions/Multidongle.py` (existing auto-detection) -- **Features**: - - Intelligent device allocation - - Load balancing across devices - - Device health monitoring - - Automatic failover - -### 8. Error Handling and Recovery -**Status**: 🟡 Basic Implementation -**Location**: Throughout codebase - -#### 8.1 Comprehensive Error Recovery -- **Enhancement**: Extend existing error handling -- **Features**: - - Automatic device reconnection - - Pipeline restart on critical errors - - Input source recovery - - Result persistence on failure - ---- - -## Implementation Roadmap - -### Phase 1: Core Data Flow (Weeks 1-2) -1. ✅ **Complete**: Pipeline deployment and device initialization -2. 🔄 **In Progress**: Auto-resize preprocessing (mostly implemented) -3. **Next**: Implement basic camera input source -4. **Next**: Add simple result file output -5. **Next**: Create basic pipeline manager - -### Phase 2: Complete Workflow (Weeks 3-4) -1. Add video file input support -2. Implement comprehensive result persistence -3. Create UI integration bridge -4. Add real-time monitoring - -### Phase 3: Advanced Features (Weeks 5-6) -1. Enhanced preprocessing pipeline -2. Performance optimization -3. Advanced error handling -4. Result visualization - -### Phase 4: Production Features (Weeks 7-8) -1. Multi-device load balancing -2. Advanced stream input support -3. Analytics and reporting -4. Configuration management - ---- - -## Key Code Locations for Current Auto-resize Implementation - -### Model Input Shape Detection -- **File**: `core/functions/Multidongle.py` -- **Lines**: 188-199 (model_input_shape property) -- **Status**: ✅ Working - detects model input dimensions from NEF files - -### Automatic Preprocessing -- **File**: `core/functions/Multidongle.py` -- **Lines**: 354-371 (preprocess_frame method) -- **Status**: ✅ Working - auto-resizes based on model input shape -- **Features**: Format conversion, aspect ratio handling - -### Pipeline Data Processing -- **File**: `core/functions/InferencePipeline.py` -- **Lines**: 165-240 (_process_data method) -- **Status**: ✅ Working - integrates preprocessing with inference -- **Features**: Inter-stage processing, result accumulation - -### Format Conversion -- **File**: `core/functions/Multidongle.py` -- **Lines**: 382-396 (_convert_format method) -- **Status**: ✅ Working - supports BGR565, RGB8888, YUYV, RAW8 - ---- - -## Notes for Development - -1. **Auto-resize is already implemented** ✅ - The system automatically detects model input shape and resizes accordingly -2. **Priority should be on input sources** - Camera and file input are the critical missing pieces -3. **Result persistence is essential** - Current system only provides callbacks, need file output -4. **UI integration gap** - UI configuration doesn't connect to core pipeline execution -5. **Performance is good** - Multi-threading and device management are solid foundations - -The core pipeline and preprocessing are working well - the focus should be on completing the input/output ecosystem around the existing robust inference engine. \ No newline at end of file