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locviettriandCursor
b132a915e7
* feat(inference_engine): add TensorRT engine support - Add TensorRT inference session with dynamic shape support - Implement automatic engine building and caching per GPU architecture - Add pre-allocated buffers with pinned memory optimization - Support FP16/FP32/INT8 precision modes - Add optimization profiles for det/rec/cls models - Include comprehensive error handling and resource cleanup - Add unit tests for TensorRT engine (skipped when unavailable) - Add TensorRT configuration section in config.yaml * fix(tensorrt): dynamically allocate output buffers to prevent overflow * Update test_engine.py with TensorRT support final version * feat(tensorrt): add model_type to engine cache key - Include model_type in TensorRT engine cache key generation - Ensures proper cache differentiation based on model_type * fix(tensorrt): update maximum shape configurations for input and output tensors - Increased max_shape values in config.yaml and engine_builder.py to 2048 for both detection and recognition tasks. - Updated default maximum shapes in memory_utils.py to reflect the new limits for input and output buffers. * feat(tensorrt): implement square input handling for MULTI models - Added functionality to check if the model requires square input based on configuration. - Implemented padding of input to square shape and cropping of output back to original dimensions. - Introduced methods to determine maximum profile size and handle square input requirements for TensorRT inference. --------- Co-authored-by: Cursor <cursoragent@cursor.com>