feat：Add NPU support #1419
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feat：Add NPU support #1419
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- Add NPU support for memory cleaning and model initialization - Optimize table model initialization and prediction process - Update memory utils to support NPU - Add language parameter for table model
- 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
- Add openai package to requirements-docker.txt
- Remove upper version limit for pydantic dependency - This change allows for the use of the latest pydantic version
- Update `clean_memory.py` to use `torch_npu.npu` instead of `torch.npu` - Update `model_utils.py` to use `torch_npu.npu` instead of `torch.npu` - Simplify NPU availability check and bfloat16 support in `pdf_parse_union_core_v2.py`
- Remove use_npu variable initialization - Comment out device assignment and npu check - Comment out use_npu parameter in ModifiedPaddleOCR constructor
…xtraction - Update the logic for inserting spaces between characters- Consider the next character's position instead of the previous one - Adjust the spacing threshold to 25% of the average character width - Ignore spaces at the end of lines to prevent double spaces
- Implement ONNXModelSingleton to manage ONNX models - Modify ModifiedPaddleOCR to use ONNX models on ARM CPUs without CUDA - Update RapidTableModel to use RapidOCR with ONNXRuntime on CPU - Add rapidocr_onnxruntime dependency in setup.py
- Remove logger.info() call for additional_ocr_params to reduce log verbosity
- Add missing 'else' statement in OCR model selection logic - Ensure consistent formatting of 'if' statements for better readability - Remove unnecessary empty line in the 'app.py' file
… code formatting- Remove extra space in conditional statement for character spacing logic - Adjust spacing in trigonometric checks for line direction- Improve overall code readability and consistency
- Add `draw_char_bbox` function to `draw_bbox.py` for drawing character bounding boxes - Integrate `draw_char_bbox` into `common.py` for use in PDF processing pipeline - Include option to draw character bounding boxes in debug mode
- Add section for using NPU acceleration in both English and Chinese README files - Update system requirements to include CANN environment for NPU support - Enhance the "Quick Start" guide with NPU-related information- Modify hardware requirements to specify "Ascend 910b" for NPU acceleration
- Add Dockerfile for global setup with Ubuntu base image - Add Dockerfile for Huawei NPU setup with Ascend base image - Update requirements file structure: - Rename requirements-docker.txt to docker/china/requirements.txt - Add new requirements files for global and Huawei NPU setups - Install necessary packages and dependencies in both Dockerfiles- Set up virtual environment and install Python packages - Download models and configure magic-pdf for both setups
- Update Dockerfiles in china, global, and huawei_npu directories - Improve wget commands by specifying output file names - Update READMEs to reflect new Dockerfile locations
- Update package sources to use Aliyun mirrors for faster downloads - Upgrade pip and install Python packages in virtual environment - Add python3.10-dev package to Huawei NPU Dockerfile - Update requirements file URLs to master branch- Install specific version of torch_npu in Huawei NPU Dockerfile - Update magic-pdf installation method - Improve modelscope installation process - Optimize model download and configuration update steps
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