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1d1faaabef
Added int32, int64 support and type inference to dnn #24411 **Added a type inference to dnn similar to the shape inference, added int32 and int64 support.** - Added getTypes method for layers that calculates layer outputs types and internals types from inputs types (Similar to getMemoryShapes). By default outputs and internals types = input[0] type - Added type inference pipeline similar to shape inference pipeline. LayersShapes struct (that is used in shape inference pipeline) now contains both shapes and types - All layers output blobs are now allocated using the calculated types from the type inference. - Inputs and constants with int32 and int64 types are not automatically converted into float32 now. - Added int32 and int64 support for all the layers with indexing and for all the layers required in tests. Added int32 and int64 support for CUDA: - Added host<->device data moving for int32 and int64 - Added int32 and int64 support for several layers (just slightly modified CUDA C++ templates) Passed all the accuracy tests on CPU, OCL, OCL_FP16, CUDA, CUDA_FP16. (except RAFT model) **CURRENT PROBLEMS**: - ONNX parser always converts int64 constants and layers attributes to int32, so some models with int64 constants doesn't work (e.g. RAFT). The solution is to disable int64->int32 conversion and fix attributes reading in a lot of ONNX layers parsers (https://github.com/opencv/opencv/issues/25102) - I didn't add type inference and int support to VULCAN, so it doesn't work at all now. - Some layers don't support int yet, so some unknown models may not work. **CURRENT WORKAROUNDS**: - CPU arg_layer indides are implemented in int32 followed by a int32->int64 conversion (the master branch has the same workaround with int32->float conversion) - CPU and OCL pooling_layer indices are implemented in float followed by a float->int64 conversion - CPU gather_layer indices are implemented in int32, so int64 indices are converted to int32 (the master branch has the same workaround with float->int32 conversion) **DISABLED TESTS**: - RAFT model **REMOVED TESTS**: - Greater_input_dtype_int64 (because it doesn't fit ONNX rules, the whole test is just comparing float tensor with int constant) **TODO IN NEXT PULL REQUESTS**: - Add int64 support for ONNX parser - Add int support for more layers - Add int support for OCL (currently int layers just run on CPU) - Add int tests - Add int support for other backends |
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| .. | ||
| imagenet_cls_test_alexnet.py | ||
| imagenet_cls_test_googlenet.py | ||
| imagenet_cls_test_inception.py | ||
| npy_blob.cpp | ||
| npy_blob.hpp | ||
| pascal_semsegm_test_fcn.py | ||
| test_backends.cpp | ||
| test_caffe_importer.cpp | ||
| test_common.cpp | ||
| test_common.hpp | ||
| test_common.impl.hpp | ||
| test_darknet_importer.cpp | ||
| test_googlenet.cpp | ||
| test_graph_simplifier.cpp | ||
| test_ie_models.cpp | ||
| test_int8_layers.cpp | ||
| test_int.cpp | ||
| test_layers_1d.cpp | ||
| test_layers.cpp | ||
| test_main.cpp | ||
| test_misc.cpp | ||
| test_model.cpp | ||
| test_nms.cpp | ||
| test_onnx_conformance_layer_filter__cuda_denylist.inl.hpp | ||
| test_onnx_conformance_layer_filter__openvino.inl.hpp | ||
| test_onnx_conformance_layer_filter__vulkan_denylist.inl.hpp | ||
| test_onnx_conformance_layer_filter_opencv_all_denylist.inl.hpp | ||
| test_onnx_conformance_layer_filter_opencv_cpu_denylist.inl.hpp | ||
| test_onnx_conformance_layer_filter_opencv_denylist.inl.hpp | ||
| test_onnx_conformance_layer_filter_opencv_ocl_fp16_denylist.inl.hpp | ||
| test_onnx_conformance_layer_filter_opencv_ocl_fp32_denylist.inl.hpp | ||
| test_onnx_conformance_layer_parser_denylist.inl.hpp | ||
| test_onnx_conformance.cpp | ||
| test_onnx_importer.cpp | ||
| test_precomp.hpp | ||
| test_tf_importer.cpp | ||
| test_tflite_importer.cpp | ||