GitLab

- Update batch processing logic for improved efficiency
- Refactor image analysis and inference methods
- Optimize dataset handling and image retrieval
- Improve error handling and logging in batch processes

- Refactor VRAM detection logic for better readability and efficiency
- Add fallback mechanism for unknown VRAM sizes
- Improve device checking in get_vram function

- Remove unused imports and comments
- Increase MIN_BATCH_INFERENCE_SIZE from 100 to 200
- Comment out VRAM cleaning and logging in batch_analyze.py
- Simplify code in doc_analyze_by_custom_model.py- Add tqdm progress bar in pdf_parse_union_core_v2.py
- Enable tqdm in OCR processing

- Remove unused imports for concurrent.futures, multiprocessing, and paddle
- Delete commented-out code
- Update numpy dependency to remove upper version limit
- Remove InferenceResult import that was commented out

- Split OCR process into detection and recognition stages
- Update batch analysis and document analysis pipelines
- Modify OCR result formatting and handling
- Remove unused imports and optimize code structure

- Remove separate condition for GPU memory >= 24GB
- Simplify logic to use a single threshold of 16GB

- Increase batch ratio to 32 for GPU memory >= 24GB
- Set batch ratio to 16 for GPU memory >= 16GB
- Reduce batch ratio to 8 for GPU memory >= 12GB
- Lower batch ratio to 4 for GPU memory >= 8GB
- Set batch ratio to 2 for GPU memory >= 6GB
- Keep batch ratio at 1 for lower GPU memory sizes

- Remove torchtext version check and deprecation warning handling from multiple files
- This code was unnecessary and potentially caused issues when torchtext was not installed

- Add support for Apple M1 chips (mps device)
- Refactor image processing for better performance and compatibility
- Update model loading and inference for various devices
- Adjust batch processing and memory management

- Set NPUDTCompile to false for better performance on NPU
- Adjust batch ratio

- Increase batch ratio to 8 for GPU memory >=16GB
- Improve inference performance on systems with higher GPU memory

- Simplify batch ratio logic for GPU memory >= 16GB
- Remove unnecessary conditions for 20GB and 40GB memory

- Simplify batch ratio logic for GPU memory >= 16GB
- Remove unnecessary conditions for 20GB and 40GB memory

- Move batch model initialization outside the loop
- Collect page dimensions before analyzing- Update page info dictionary structure
- Add null dimensions for non-analyzed pages

- Update the logic for determining `end_page_id` to handle negative values
- This change ensures proper behavior when `end_page_id` is set to -1 or other negative values

- Move environment variable settings for NPU, MPS, and other configurations to the global scope in doc_analyze_by_custom_model.py
- Remove redundant environment variable settings in pdf_extract_kit.py
- This change ensures consistent configuration across the application and avoids potential conflicts or duplicate settings

- Remove unused utility functions
- Update import statements for better readability
- Add conditional imports for Ascend plugin
- Refactor table model initialization to support NPU

- Update batch ratio calculation logic to better utilize available GPU memory
- Improve logging for all GPU memory sizes

- Update batch ratio calculation for GPU memory range
- Increase upper limit for batch ratio 16 from 24 to 32 GB

- Update conditions for batch ratio assignment:
  -8 <= gpu_memory < 10: batch_ratio = 2 - 10 <= gpu_memory <= 12: batch_ratio =4
- This fix ensures proper batch ratio selection for GPU memory sizes

- Improve batch ratio calculation based on GPU memory
- Enhance performance for devices with 8GB or more VRAM

- Reduce batch_ratio by 1 for better performance and stability
- This change ensures more consistent memory usage when processing documents

refactor(magic_pdf): adjust VRAM allocation and MFR batch size- Update VRAM allocation logic to use 'VIRTUAL_VRAM_SIZE' environment variable
- Reduce MFR (Math Formula Recognition) batch size from 64 to 32

- Update GPU memory check and batch ratio calculation logic
- Add support for virtual VRAM size environment variable
- Improve logging for GPU memory and batch ratio

- Reduce YOLO_LAYOUT_BASE_BATCH_SIZE from 4 to 1
- Simplify batch ratio calculation for formula detection
- Remove unused conditional logic in batch ratio determination

- Commented out the original batch ratio calculation
- Set a fixed batch ratio of 2 for GPUs with less than 8 GB memory
- Increased batch ratio to 4 for GPUs with 8 GB or more memory

- Adjust end_page_id calculation to prevent IndexError when accessing pages
- Enhance error handling in LLM post-processing by specifically catching JSONDecodeError

- Add support for NPU (Neural Processing Unit) when available
- Implement batch analysis for GPU and NPU devices
- Optimize memory usage and improve performance
- Update logging and error handling

- Update clean_memory function to support both CUDA and NPU devices
- Implement get_device function to centralize device selection logic
- Modify model initialization and memory cleaning to use the selected device
- Update RapidTableModel to support both RapidOCR and PaddleOCR engines