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reimplement resize cpu kernel for image processing (#768)
* reimplement resize cpu kernel for image processing * accuracy fixing and code refinement * fix the build issues * fix Linux build issue * more fixings * Fix the pipeline issue * fix the ci script * try to fix CUDA machine pool
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,13 @@ | ||
| # Copyright (c) Microsoft Corporation. All rights reserved. | ||
| # Licensed under the MIT License. See License.txt in the project root for | ||
| # license information. | ||
| ############################################################################### | ||
|
|
||
| from . import _extensions_pydll as _C | ||
| if not hasattr(_C, "create_processor"): | ||
| raise ImportError("onnxruntime_extensions is not built with pre-processing API") | ||
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|
||
| create_processor = _C.create_processor | ||
| load_images = _C.load_images | ||
| image_pre_process = _C.image_pre_process | ||
| tensor_result_get_at = _C.tensor_result_get_at |
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,114 @@ | ||
| // Copyright (c) Microsoft Corporation. All rights reserved. | ||
| // Licensed under the MIT License. | ||
|
|
||
| #include <pybind11/iostream.h> | ||
| #include <pybind11/pybind11.h> | ||
| #include <pybind11/stl.h> | ||
| #include <pybind11/functional.h> | ||
| #include <pybind11/numpy.h> | ||
| #include <thread> | ||
|
|
||
| #include "ortx_utils.h" | ||
| #include "ortx_processor.h" | ||
| #include "pykernel.h" | ||
|
|
||
| namespace py = pybind11; | ||
|
|
||
| template <typename T> | ||
| int64_t NumOfElement(const T& sp) { | ||
| size_t c = 1; | ||
| for (auto v : sp) { | ||
| c *= v; | ||
| } | ||
| return c; | ||
| } | ||
|
|
||
| void AddGlobalMethodsCApi(pybind11::module& m) { | ||
| m.def( | ||
| "create_processor", | ||
| [](const char* processor_def_json) { | ||
| OrtxProcessor* processor = nullptr; | ||
| auto err = OrtxCreateProcessor(&processor, processor_def_json); | ||
| if (err != kOrtxOK) { | ||
| throw std::runtime_error(std::string("Failed to create processor") + OrtxGetLastErrorMessage()); | ||
| } | ||
| return reinterpret_cast<std::uintptr_t>(processor); | ||
| }, | ||
| "Create a processor."); | ||
|
|
||
| m.def( | ||
| "load_images", | ||
| [](const std::vector<std::string>& image_paths) { | ||
| OrtxRawImages* images = nullptr; | ||
| size_t num_images = image_paths.size(); | ||
| auto image_ptrs = std::make_unique<const char*[]>(num_images); | ||
| for (size_t i = 0; i < num_images; ++i) { | ||
| image_ptrs[i] = image_paths[i].c_str(); | ||
| } | ||
|
|
||
| auto err = OrtxLoadImages(&images, image_ptrs.get(), num_images, nullptr); | ||
| if (err != kOrtxOK) { | ||
| throw std::runtime_error(std::string("Failed to load images") + OrtxGetLastErrorMessage()); | ||
| } | ||
| return reinterpret_cast<std::uintptr_t>(images); | ||
| }, | ||
| "Load images."); | ||
|
|
||
| m.def( | ||
| "image_pre_process", | ||
| [](std::uintptr_t processor_h, std::uintptr_t images_h) { | ||
| OrtxProcessor* processor = reinterpret_cast<OrtxProcessor*>(processor_h); | ||
| OrtxRawImages* images = reinterpret_cast<OrtxRawImages*>(images_h); | ||
| OrtxTensorResult* result{}; | ||
| auto err = OrtxImagePreProcess(processor, images, &result); | ||
| if (err != kOrtxOK) { | ||
| throw std::runtime_error(std::string("Failed to preprocess images") + OrtxGetLastErrorMessage()); | ||
| } | ||
| return reinterpret_cast<std::uintptr_t>(result); | ||
| }, | ||
| "Preprocess images."); | ||
|
|
||
| m.def("tensor_result_get_at", [](std::uintptr_t result_h, size_t index) { | ||
| OrtxTensorResult* result = reinterpret_cast<OrtxTensorResult*>(result_h); | ||
| OrtxTensor* tensor{}; | ||
| auto err = OrtxTensorResultGetAt(result, index, &tensor); | ||
| if (err != kOrtxOK) { | ||
| throw std::runtime_error(std::string("Failed to get tensor") + OrtxGetLastErrorMessage()); | ||
| } | ||
|
|
||
| extDataType_t tensor_type; | ||
|
|
||
| OrtxGetTensorType(tensor, &tensor_type); | ||
| const int64_t* shape{}; | ||
| size_t num_dims; | ||
| const void* data{}; | ||
| size_t elem_size = 0; | ||
| if (tensor_type == extDataType_t::kOrtxInt64 || tensor_type == extDataType_t::kOrtxFloat) { | ||
| OrtxGetTensorData(tensor, reinterpret_cast<const void**>(&data), &shape, &num_dims); | ||
| elem_size = 4; | ||
| if (tensor_type == extDataType_t::kOrtxInt64) { | ||
| elem_size = 8; | ||
| } | ||
| } else if (tensor_type == extDataType_t::kOrtxUnknownType) { | ||
| throw std::runtime_error("Failed to get tensor type"); | ||
| } else if (tensor_type == extDataType_t::kOrtxUnknownType) { | ||
| throw std::runtime_error("unsupported tensor type"); | ||
| } | ||
|
|
||
| std::vector<std::size_t> npy_dims; | ||
| for (auto n = num_dims - num_dims; n < num_dims; ++n) { | ||
| npy_dims.push_back(shape[n]); | ||
| } | ||
| py::array obj{}; | ||
|
|
||
| if (tensor_type == extDataType_t::kOrtxFloat) { | ||
| obj = py::array_t<float>(npy_dims); | ||
| } else if (tensor_type == extDataType_t::kOrtxInt64) { | ||
| obj = py::array_t<int64_t>(npy_dims); | ||
| } | ||
|
|
||
| void* out_ptr = obj.mutable_data(); | ||
| memcpy(out_ptr, data, NumOfElement(npy_dims) * elem_size); | ||
| return obj; | ||
| }, "Get tensor at index."); | ||
| } |
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