Added Crop, need decode.
Added decode for source operators Partial lite build fix Only crop op enabled Removed opencv from mdlite "full" build Adding tests disabling tests for now, compiling Halfway done adding normalize Log linking failed for lite Added normalize, and resize Addressing comments part 1 Added CI fix part 1 Added CI fix part 2 added CI fix part 3 Added CI fix part 4 can't remove long Removed open cv build completely Removed packaging code for open cv Disable lint check for C type Fix CI issues part five
This commit is contained in:
Eric
parent
cd1ddd198a
commit
5c37b95992
1
build.sh
1
build.sh
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@ -636,7 +636,6 @@ build_minddata_lite_deps()
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else
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CMAKE_MINDDATA_ARGS="-DCMAKE_BUILD_TYPE=${BUILD_TYPE}"
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fi
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build_opencv
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build_eigen
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build_jpeg_turbo
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}
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@ -31,23 +31,14 @@ if (BUILD_MINDDATA STREQUAL "full")
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install(FILES ${TOP_DIR}/mindspore/lite/build/minddata/libminddata-lite.so DESTINATION ${MIND_DATA_LIB_DIR} COMPONENT ${COMPONENT_NAME})
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install(FILES ${TOP_DIR}/third_party/libjpeg-turbo/lib/libjpeg.so DESTINATION ${TURBO_DIR}/lib COMPONENT ${COMPONENT_NAME})
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install(FILES ${TOP_DIR}/third_party/libjpeg-turbo/lib/libturbojpeg.so DESTINATION ${TURBO_DIR}/lib COMPONENT ${COMPONENT_NAME})
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install(FILES ${TOP_DIR}/third_party/opencv/build/lib/arm64-v8a/libopencv_core.so DESTINATION ${OPENCV_DIR}/lib COMPONENT ${COMPONENT_NAME})
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install(FILES ${TOP_DIR}/third_party/opencv/build/lib/arm64-v8a/libopencv_imgcodecs.so DESTINATION ${OPENCV_DIR}/lib COMPONENT ${COMPONENT_NAME})
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install(FILES ${TOP_DIR}/third_party/opencv/build/lib/arm64-v8a/libopencv_imgproc.so DESTINATION ${OPENCV_DIR}/lib COMPONENT ${COMPONENT_NAME})
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elseif (PLATFORM_ARM32)
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install(FILES ${TOP_DIR}/mindspore/lite/build/minddata/libminddata-lite.so DESTINATION ${MIND_DATA_LIB_DIR} COMPONENT ${COMPONENT_NAME})
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install(FILES ${TOP_DIR}/third_party/libjpeg-turbo/lib/libjpeg.so DESTINATION ${TURBO_DIR}/lib COMPONENT ${COMPONENT_NAME})
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install(FILES ${TOP_DIR}/third_party/libjpeg-turbo/lib/libturbojpeg.so DESTINATION ${TURBO_DIR}/lib COMPONENT ${COMPONENT_NAME})
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install(FILES ${TOP_DIR}/third_party/opencv/build/lib/armeabi-v7a/libopencv_core.so DESTINATION ${OPENCV_DIR}/lib COMPONENT ${COMPONENT_NAME})
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install(FILES ${TOP_DIR}/third_party/opencv/build/lib/armeabi-v7a/libopencv_imgcodecs.so DESTINATION ${OPENCV_DIR}/lib COMPONENT ${COMPONENT_NAME})
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install(FILES ${TOP_DIR}/third_party/opencv/build/lib/armeabi-v7a/libopencv_imgproc.so DESTINATION ${OPENCV_DIR}/lib COMPONENT ${COMPONENT_NAME})
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else ()
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install(FILES ${TOP_DIR}/mindspore/lite/build/minddata/libminddata-lite.so DESTINATION ${MIND_DATA_LIB_DIR_RUN_X86} COMPONENT ${RUN_X86_COMPONENT_NAME})
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install(FILES ${TOP_DIR}/third_party/libjpeg-turbo/lib/libjpeg.so.62.3.0 DESTINATION ${TURBO_DIR_RUN_X86}/lib RENAME libjpeg.so.62 COMPONENT ${RUN_X86_COMPONENT_NAME})
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install(FILES ${TOP_DIR}/third_party/libjpeg-turbo/lib/libturbojpeg.so.0.2.0 DESTINATION ${TURBO_DIR_RUN_X86}/lib RENAME libturbojpeg.so.0 COMPONENT ${RUN_X86_COMPONENT_NAME})
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install(FILES ${TOP_DIR}/third_party/opencv/build/lib/libopencv_core.so.4.2.0 DESTINATION ${OPENCV_DIR_RUN_X86}/lib RENAME libopencv_core.so.4.2 COMPONENT ${RUN_X86_COMPONENT_NAME})
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install(FILES ${TOP_DIR}/third_party/opencv/build/lib/libopencv_imgcodecs.so.4.2.0 DESTINATION ${OPENCV_DIR_RUN_X86}/lib RENAME libopencv_imgcodecs.so.4.2 COMPONENT ${RUN_X86_COMPONENT_NAME})
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install(FILES ${TOP_DIR}/third_party/opencv/build/lib/libopencv_imgproc.so.4.2.0 DESTINATION ${OPENCV_DIR_RUN_X86}/lib RENAME libopencv_imgproc.so.4.2 COMPONENT ${RUN_X86_COMPONENT_NAME})
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endif ()
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endif ()
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@ -57,23 +48,14 @@ if (BUILD_MINDDATA STREQUAL "lite")
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install(FILES ${TOP_DIR}/mindspore/lite/build/minddata/libminddata-lite.so DESTINATION ${MIND_DATA_LIB_DIR} COMPONENT ${COMPONENT_NAME})
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install(FILES ${TOP_DIR}/third_party/libjpeg-turbo/lib/libjpeg.so DESTINATION ${TURBO_DIR}/lib COMPONENT ${COMPONENT_NAME})
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install(FILES ${TOP_DIR}/third_party/libjpeg-turbo/lib/libturbojpeg.so DESTINATION ${TURBO_DIR}/lib COMPONENT ${COMPONENT_NAME})
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install(FILES ${TOP_DIR}/third_party/opencv/build/lib/arm64-v8a/libopencv_core.so DESTINATION ${OPENCV_DIR}/lib COMPONENT ${COMPONENT_NAME})
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install(FILES ${TOP_DIR}/third_party/opencv/build/lib/arm64-v8a/libopencv_imgcodecs.so DESTINATION ${OPENCV_DIR}/lib COMPONENT ${COMPONENT_NAME})
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install(FILES ${TOP_DIR}/third_party/opencv/build/lib/arm64-v8a/libopencv_imgproc.so DESTINATION ${OPENCV_DIR}/lib COMPONENT ${COMPONENT_NAME})
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elseif (PLATFORM_ARM32)
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install(FILES ${TOP_DIR}/mindspore/lite/build/minddata/libminddata-lite.so DESTINATION ${MIND_DATA_LIB_DIR} COMPONENT ${COMPONENT_NAME})
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install(FILES ${TOP_DIR}/third_party/libjpeg-turbo/lib/libjpeg.so DESTINATION ${TURBO_DIR}/lib COMPONENT ${COMPONENT_NAME})
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install(FILES ${TOP_DIR}/third_party/libjpeg-turbo/lib/libturbojpeg.so DESTINATION ${TURBO_DIR}/lib COMPONENT ${COMPONENT_NAME})
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install(FILES ${TOP_DIR}/third_party/opencv/build/lib/armeabi-v7a/libopencv_core.so DESTINATION ${OPENCV_DIR}/lib COMPONENT ${COMPONENT_NAME})
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install(FILES ${TOP_DIR}/third_party/opencv/build/lib/armeabi-v7a/libopencv_imgcodecs.so DESTINATION ${OPENCV_DIR}/lib COMPONENT ${COMPONENT_NAME})
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install(FILES ${TOP_DIR}/third_party/opencv/build/lib/armeabi-v7a/libopencv_imgproc.so DESTINATION ${OPENCV_DIR}/lib COMPONENT ${COMPONENT_NAME})
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else ()
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install(FILES ${TOP_DIR}/mindspore/lite/build/minddata/libminddata-lite.so DESTINATION ${MIND_DATA_LIB_DIR_RUN_X86} COMPONENT ${RUN_X86_COMPONENT_NAME})
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install(FILES ${TOP_DIR}/third_party/libjpeg-turbo/lib/libjpeg.so.62.3.0 DESTINATION ${TURBO_DIR_RUN_X86}/lib RENAME libjpeg.so.62 COMPONENT ${RUN_X86_COMPONENT_NAME})
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install(FILES ${TOP_DIR}/third_party/libjpeg-turbo/lib/libturbojpeg.so.0.2.0 DESTINATION ${TURBO_DIR_RUN_X86}/lib RENAME libturbojpeg.so.0 COMPONENT ${RUN_X86_COMPONENT_NAME})
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install(FILES ${TOP_DIR}/third_party/opencv/build/lib/libopencv_core.so.4.2.0 DESTINATION ${OPENCV_DIR_RUN_X86}/lib RENAME libopencv_core.so.4.2 COMPONENT ${RUN_X86_COMPONENT_NAME})
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install(FILES ${TOP_DIR}/third_party/opencv/build/lib/libopencv_imgcodecs.so.4.2.0 DESTINATION ${OPENCV_DIR_RUN_X86}/lib RENAME libopencv_imgcodecs.so.4.2 COMPONENT ${RUN_X86_COMPONENT_NAME})
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install(FILES ${TOP_DIR}/third_party/opencv/build/lib/libopencv_imgproc.so.4.2.0 DESTINATION ${OPENCV_DIR_RUN_X86}/lib RENAME libopencv_imgproc.so.4.2 COMPONENT ${RUN_X86_COMPONENT_NAME})
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endif ()
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endif ()
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@ -21,6 +21,11 @@
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#include "mindspore/core/ir/dtype/type_id.h"
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#include "mindspore/lite/internal/include/ms_tensor.h"
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#include "utils/hashing.h"
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#ifndef ENABLE_ANDROID
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#include "utils/log_adapter.h"
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#else
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#include "mindspore/lite/src/common/log_adapter.h"
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#endif
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namespace mindspore {
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namespace tensor {
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@ -43,7 +48,7 @@ MSTensor *DETensor::CreateFromMemory(TypeId data_type, const std::vector<int> &s
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[](int s) -> dataset::dsize_t { return static_cast<dataset::dsize_t>(s); });
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(void)dataset::Tensor::CreateFromMemory(dataset::TensorShape(t_shape), dataset::MSTypeToDEType(data_type),
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static_cast<uchar *>(data), &t);
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static_cast<uint8_t *>(data), &t);
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return new DETensor(std::move(t));
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}
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@ -18,6 +18,11 @@
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#include "minddata/dataset/include/de_tensor.h"
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#include "minddata/dataset/include/tensor.h"
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#include "minddata/dataset/kernels/tensor_op.h"
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#ifndef ENABLE_ANDROID
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#include "utils/log_adapter.h"
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#else
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#include "mindspore/lite/src/common/log_adapter.h"
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#endif
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namespace mindspore {
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namespace dataset {
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@ -15,7 +15,6 @@
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*/
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#include "minddata/dataset/include/transforms.h"
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#include "minddata/dataset/kernels/image/image_utils.h"
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// Kernel data headers (in alphabetical order)
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#include "minddata/dataset/kernels/data/one_hot_op.h"
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@ -16,17 +16,25 @@
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#include "minddata/dataset/include/transforms.h"
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#include "minddata/dataset/include/vision.h"
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#ifndef ENABLE_ANDROID
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#include "minddata/dataset/kernels/image/image_utils.h"
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#endif
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// Kernel image headers (in alphabetical order)
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#ifndef ENABLE_ANDROID
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#include "minddata/dataset/kernels/image/center_crop_op.h"
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#endif
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#include "minddata/dataset/kernels/image/crop_op.h"
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#ifndef ENABLE_ANDROID
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#include "minddata/dataset/kernels/image/cutmix_batch_op.h"
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#include "minddata/dataset/kernels/image/cut_out_op.h"
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#endif
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#include "minddata/dataset/kernels/image/decode_op.h"
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#ifndef ENABLE_ANDROID
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#include "minddata/dataset/kernels/image/hwc_to_chw_op.h"
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#include "minddata/dataset/kernels/image/mixup_batch_op.h"
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#endif
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#include "minddata/dataset/kernels/image/normalize_op.h"
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#ifndef ENABLE_ANDROID
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#include "minddata/dataset/kernels/image/pad_op.h"
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#include "minddata/dataset/kernels/image/random_affine_op.h"
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#include "minddata/dataset/kernels/image/random_color_op.h"
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@ -41,11 +49,14 @@
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#include "minddata/dataset/kernels/image/random_solarize_op.h"
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#include "minddata/dataset/kernels/image/random_vertical_flip_op.h"
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#include "minddata/dataset/kernels/image/rescale_op.h"
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#endif
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#include "minddata/dataset/kernels/image/resize_op.h"
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#ifndef ENABLE_ANDROID
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#include "minddata/dataset/kernels/image/rgba_to_bgr_op.h"
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#include "minddata/dataset/kernels/image/rgba_to_rgb_op.h"
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#include "minddata/dataset/kernels/image/swap_red_blue_op.h"
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#include "minddata/dataset/kernels/image/uniform_aug_op.h"
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#endif
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namespace mindspore {
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namespace dataset {
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@ -53,7 +64,7 @@ namespace api {
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// Transform operations for computer vision.
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namespace vision {
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#ifndef ENABLE_ANDROID
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// FUNCTIONS TO CREATE VISION TRANSFORM OPERATIONS
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// (In alphabetical order)
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@ -66,6 +77,7 @@ std::shared_ptr<CenterCropOperation> CenterCrop(std::vector<int32_t> size) {
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}
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return op;
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}
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#endif
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// Function to create CropOperation.
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std::shared_ptr<CropOperation> Crop(std::vector<int32_t> coordinates, std::vector<int32_t> size) {
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@ -76,7 +88,7 @@ std::shared_ptr<CropOperation> Crop(std::vector<int32_t> coordinates, std::vecto
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}
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return op;
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}
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#ifndef ENABLE_ANDROID
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// Function to create CutMixBatchOperation.
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std::shared_ptr<CutMixBatchOperation> CutMixBatch(ImageBatchFormat image_batch_format, float alpha, float prob) {
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auto op = std::make_shared<CutMixBatchOperation>(image_batch_format, alpha, prob);
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@ -301,6 +313,7 @@ std::shared_ptr<RescaleOperation> Rescale(float rescale, float shift) {
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return op;
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}
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#endif
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// Function to create ResizeOperation.
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std::shared_ptr<ResizeOperation> Resize(std::vector<int32_t> size, InterpolationMode interpolation) {
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auto op = std::make_shared<ResizeOperation>(size, interpolation);
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@ -311,6 +324,7 @@ std::shared_ptr<ResizeOperation> Resize(std::vector<int32_t> size, Interpolation
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return op;
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}
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#ifndef ENABLE_ANDROID
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// Function to create RgbaToBgrOperation.
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std::shared_ptr<RgbaToBgrOperation> RGBA2BGR() {
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auto op = std::make_shared<RgbaToBgrOperation>();
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@ -352,6 +366,7 @@ std::shared_ptr<UniformAugOperation> UniformAugment(std::vector<std::shared_ptr<
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return op;
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}
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#endif
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/* ####################################### Validator Functions ############################################ */
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Status ValidateVectorPositive(const std::string &dataset_name, const std::vector<int32_t> &size) {
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for (int32_t i = 0; i < size.size(); ++i) {
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@ -371,7 +386,7 @@ bool CmpFloat(const float &a, const float &b, float epsilon = 0.0000000001f) { r
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/* ####################################### Derived TensorOperation classes ################################# */
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// (In alphabetical order)
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#ifndef ENABLE_ANDROID
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// CenterCropOperation
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CenterCropOperation::CenterCropOperation(std::vector<int32_t> size) : size_(size) {}
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@ -410,6 +425,7 @@ std::shared_ptr<TensorOp> CenterCropOperation::Build() {
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return tensor_op;
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}
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#endif
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// CropOperation.
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CropOperation::CropOperation(std::vector<int32_t> coordinates, std::vector<int32_t> size)
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: coordinates_(coordinates), size_(size) {}
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@ -467,6 +483,7 @@ std::shared_ptr<TensorOp> CropOperation::Build() {
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return tensor_op;
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}
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#ifndef ENABLE_ANDROID
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// CutMixBatchOperation
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CutMixBatchOperation::CutMixBatchOperation(ImageBatchFormat image_batch_format, float alpha, float prob)
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: image_batch_format_(image_batch_format), alpha_(alpha), prob_(prob) {}
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@ -1369,6 +1386,7 @@ std::shared_ptr<TensorOp> RescaleOperation::Build() {
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return tensor_op;
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}
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#endif
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// ResizeOperation
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ResizeOperation::ResizeOperation(std::vector<int32_t> size, InterpolationMode interpolation)
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: size_(size), interpolation_(interpolation) {}
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@ -1397,6 +1415,7 @@ std::shared_ptr<TensorOp> ResizeOperation::Build() {
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return std::make_shared<ResizeOp>(height, width, interpolation_);
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}
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#ifndef ENABLE_ANDROID
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// RgbaToBgrOperation.
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RgbaToBgrOperation::RgbaToBgrOperation() {}
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@ -1461,6 +1480,7 @@ std::shared_ptr<TensorOp> UniformAugOperation::Build() {
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std::shared_ptr<UniformAugOp> tensor_op = std::make_shared<UniformAugOp>(tensor_ops, num_ops_);
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return tensor_op;
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}
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#endif
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} // namespace vision
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} // namespace api
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@ -43,6 +43,7 @@ py::dtype DataType::AsNumpyType() const {
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}
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#endif
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#ifndef ENABLE_ANDROID
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uint8_t DataType::AsCVType() const {
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uint8_t res = kCVInvalidType;
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if (type_ < DataType::NUM_OF_TYPES) {
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@ -80,6 +81,7 @@ DataType DataType::FromCVType(int cv_type) {
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return DataType(DataType::DE_UNKNOWN);
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}
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}
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#endif
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DataType::DataType(const std::string &type_str) {
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if (type_str == "bool")
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@ -16,7 +16,9 @@
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#ifndef MINDSPORE_CCSRC_MINDDATA_DATASET_CORE_DATA_TYPE_H_
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#define MINDSPORE_CCSRC_MINDDATA_DATASET_CORE_DATA_TYPE_H_
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#ifndef ENABLE_ANDROID
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#include <opencv2/core/hal/interface.h>
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#endif
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#include <string>
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#ifdef ENABLE_PYTHON
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@ -79,6 +81,7 @@ class DataType {
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{"string", 0, "bytes", "S", kCVInvalidType} // DE_STRING
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};
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#else
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#ifndef ENABLE_ANDROID
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static inline const TypeInfo kTypeInfo[] = {
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// name, sizeInBytes, pybindTypem formatDescriptor, openCV
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{"unknown", 0, "object", "", kCVInvalidType}, // DE_UNKNOWN
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@ -96,8 +99,27 @@ class DataType {
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{"float64", 8, "double", "", CV_64F}, // DE_FLOAT64
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{"string", 0, "bytes", "", kCVInvalidType} // DE_STRING
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};
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#else
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// android and no python
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static inline const TypeInfo kTypeInfo[] = {
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// name, sizeInBytes, formatDescriptor
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{"unknown", 0, "object", "", kCVInvalidType}, // DE_UNKNOWN
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{"bool", 1, "bool", ""}, // DE_BOOL
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{"int8", 1, "int8", ""}, // DE_INT8
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{"uint8", 1, "uint8", ""}, // DE_UINT8
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{"int16", 2, "int16", ""}, // DE_INT16
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{"uint16", 2, "uint16", ""}, // DE_UINT16
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{"int32", 4, "int32", ""}, // DE_INT32
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{"uint32", 4, "uint32", "", kCVInvalidType}, // DE_UINT32
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{"int64", 8, "int64", "", kCVInvalidType}, // DE_INT64
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{"uint64", 8, "uint64", "", kCVInvalidType}, // DE_UINT64
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{"float16", 2, "float16", ""}, // DE_FLOAT16
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{"float32", 4, "float32", ""}, // DE_FLOAT32
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{"float64", 8, "double", ""}, // DE_FLOAT64
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{"string", 0, "bytes", "", kCVInvalidType} // DE_STRING
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};
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#endif
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#endif
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// No arg constructor to create an unknown shape
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DataType() : type_(DE_UNKNOWN) {}
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@ -132,6 +154,7 @@ class DataType {
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/// \return
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uint8_t SizeInBytes() const;
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#ifndef ENABLE_ANDROID
|
||||
// Convert from DataType to OpenCV type
|
||||
/// \return
|
||||
uint8_t AsCVType() const;
|
||||
|
|
@ -140,6 +163,7 @@ class DataType {
|
|||
/// \param cv_type
|
||||
/// \return
|
||||
static DataType FromCVType(int cv_type);
|
||||
#endif
|
||||
|
||||
// Returns a string representation of the type
|
||||
/// \return
|
||||
|
|
|
|||
|
|
@ -19,7 +19,9 @@
|
|||
#include <mutex>
|
||||
|
||||
#include "minddata/dataset/core/config_manager.h"
|
||||
#ifndef ENABLE_ANDROID
|
||||
#include "minddata/dataset/core/cv_tensor.h"
|
||||
#endif
|
||||
#include "minddata/dataset/core/tensor.h"
|
||||
#include "minddata/dataset/util/allocator.h"
|
||||
#include "minddata/dataset/util/circular_pool.h"
|
||||
|
|
@ -56,7 +58,9 @@ Status GlobalContext::Init() {
|
|||
|
||||
// Create some tensor allocators for the different types and hook them into the pool.
|
||||
tensor_allocator_ = std::make_unique<Allocator<Tensor>>(mem_pool_);
|
||||
#ifndef ENABLE_ANDROID
|
||||
cv_tensor_allocator_ = std::make_unique<Allocator<CVTensor>>(mem_pool_);
|
||||
#endif
|
||||
int_allocator_ = std::make_unique<IntAlloc>(mem_pool_);
|
||||
return Status::OK();
|
||||
}
|
||||
|
|
|
|||
|
|
@ -26,7 +26,11 @@
|
|||
|
||||
#include "utils/ms_utils.h"
|
||||
#include "minddata/dataset/core/constants.h"
|
||||
|
||||
#ifndef ENABLE_ANDROID
|
||||
#include "minddata/dataset/core/cv_tensor.h"
|
||||
#endif
|
||||
|
||||
#include "minddata/dataset/core/global_context.h"
|
||||
|
||||
#ifdef ENABLE_PYTHON
|
||||
|
|
|
|||
|
|
@ -96,6 +96,7 @@ TensorShape::TensorShape(py::list l)
|
|||
}
|
||||
#endif
|
||||
|
||||
#ifndef ENABLE_ANDROID
|
||||
TensorShape::TensorShape(cv::MatSize cv_size, uint32_t type)
|
||||
: raw_shape_(*GlobalContext::Instance()->int_allocator()), strides_(*GlobalContext::Instance()->int_allocator()) {
|
||||
for (int i = 0; i < cv_size.dims(); i++) {
|
||||
|
|
@ -107,6 +108,7 @@ TensorShape::TensorShape(cv::MatSize cv_size, uint32_t type)
|
|||
}
|
||||
known_ = true;
|
||||
}
|
||||
#endif
|
||||
|
||||
TensorShape TensorShape::CreateUnknownRankShape() {
|
||||
TensorShape s({});
|
||||
|
|
|
|||
|
|
@ -22,7 +22,9 @@
|
|||
#include <string>
|
||||
#include <vector>
|
||||
|
||||
#ifndef ENABLE_ANDROID
|
||||
#include <opencv2/core/mat.hpp>
|
||||
#endif
|
||||
|
||||
#ifdef ENABLE_PYTHON
|
||||
#include "pybind11/pybind11.h"
|
||||
|
|
@ -105,10 +107,12 @@ class TensorShape {
|
|||
/// \return
|
||||
TensorShape AppendDim(dsize_t dim) const;
|
||||
|
||||
#ifndef ENABLE_ANDROID
|
||||
/// \brief Create a shape based on OpenCV shape and type
|
||||
/// \param[in] cv_size
|
||||
/// \param[in] type int that represent the type in OpenCV, example CV_8U, CV_64S
|
||||
TensorShape(cv::MatSize cv_size, uint32_t type);
|
||||
#endif
|
||||
|
||||
dsize_t Size() const { return raw_shape_.size(); }
|
||||
|
||||
|
|
|
|||
|
|
@ -22,7 +22,11 @@
|
|||
#include "minddata/dataset/engine/db_connector.h"
|
||||
#include "minddata/dataset/engine/execution_tree.h"
|
||||
#include "minddata/dataset/engine/opt/pass.h"
|
||||
#ifndef ENABLE_ANDROID
|
||||
#include "minddata/dataset/kernels/image/image_utils.h"
|
||||
#else
|
||||
#include "minddata/dataset/kernels/image/lite_image_utils.h"
|
||||
#endif
|
||||
|
||||
namespace mindspore {
|
||||
namespace dataset {
|
||||
|
|
|
|||
|
|
@ -24,7 +24,11 @@
|
|||
#include "minddata/dataset/engine/data_schema.h"
|
||||
#include "minddata/dataset/engine/execution_tree.h"
|
||||
#include "minddata/dataset/engine/opt/pass.h"
|
||||
#ifndef ENABLE_ANDROID
|
||||
#include "minddata/dataset/kernels/image/image_utils.h"
|
||||
#else
|
||||
#include "minddata/dataset/kernels/image/lite_image_utils.h"
|
||||
#endif
|
||||
|
||||
namespace mindspore {
|
||||
namespace dataset {
|
||||
|
|
|
|||
|
|
@ -28,7 +28,11 @@
|
|||
#include "minddata/dataset/engine/data_schema.h"
|
||||
#include "minddata/dataset/engine/datasetops/parallel_op.h"
|
||||
#include "minddata/dataset/engine/datasetops/source/sampler/sampler.h"
|
||||
#ifndef ENABLE_ANDROID
|
||||
#include "minddata/dataset/kernels/image/image_utils.h"
|
||||
#else
|
||||
#include "minddata/dataset/kernels/image/lite_image_utils.h"
|
||||
#endif
|
||||
#include "minddata/dataset/util/path.h"
|
||||
#include "minddata/dataset/util/queue.h"
|
||||
#include "minddata/dataset/util/status.h"
|
||||
|
|
|
|||
|
|
@ -30,7 +30,11 @@
|
|||
#include "minddata/dataset/engine/data_schema.h"
|
||||
#include "minddata/dataset/engine/datasetops/parallel_op.h"
|
||||
#include "minddata/dataset/engine/datasetops/source/sampler/sampler.h"
|
||||
#ifndef ENABLE_ANDROID
|
||||
#include "minddata/dataset/kernels/image/image_utils.h"
|
||||
#else
|
||||
#include "minddata/dataset/kernels/image/lite_image_utils.h"
|
||||
#endif
|
||||
#include "minddata/dataset/util/path.h"
|
||||
#include "minddata/dataset/util/queue.h"
|
||||
#include "minddata/dataset/util/services.h"
|
||||
|
|
|
|||
|
|
@ -26,9 +26,9 @@
|
|||
#include "minddata/dataset/engine/opt/pre/cache_transform_pass.h"
|
||||
#include "minddata/dataset/engine/opt/post/repeat_pass.h"
|
||||
#include "minddata/dataset/engine/opt/pre/cache_error_pass.h"
|
||||
#include "mindspore/ccsrc/minddata/dataset/engine/opt/optional/tensor_op_fusion_pass.h"
|
||||
#endif
|
||||
#include "minddata/dataset/engine/opt/pre/epoch_injection_pass.h"
|
||||
#include "mindspore/ccsrc/minddata/dataset/engine/opt/optional/tensor_op_fusion_pass.h"
|
||||
#include "minddata/dataset/engine/perf/profiling.h"
|
||||
#include "minddata/dataset/engine/perf/monitor.h"
|
||||
|
||||
|
|
@ -222,7 +222,6 @@ Status ExecutionTree::Prepare(int32_t num_epochs) {
|
|||
if (optimize_) {
|
||||
RETURN_IF_NOT_OK(this->Optimize());
|
||||
}
|
||||
|
||||
// Post optimization compulsory transformation
|
||||
RETURN_IF_NOT_OK(this->PrepareTreePostAction());
|
||||
|
||||
|
|
@ -276,7 +275,9 @@ Status ExecutionTree::PrepareTreePostAction() {
|
|||
Status ExecutionTree::Optimize() {
|
||||
// Vector of optimizations, currently only 1, add more as necessary
|
||||
std::vector<std::unique_ptr<NodePass>> optimizations;
|
||||
#ifndef ENABLE_ANDROID
|
||||
optimizations.push_back(std::make_unique<TensorOpFusionPass>());
|
||||
#endif
|
||||
// vector of flags for each optimization
|
||||
std::vector<bool> modified(optimizations.size(), false);
|
||||
for (auto i = 0; i < optimizations.size(); i++) {
|
||||
|
|
|
|||
|
|
@ -31,14 +31,21 @@ namespace api {
|
|||
namespace vision {
|
||||
|
||||
// Transform Op classes (in alphabetical order)
|
||||
#ifndef ENABLE_ANDROID
|
||||
class CenterCropOperation;
|
||||
#endif
|
||||
class CropOperation;
|
||||
#ifndef ENABLE_ANDROID
|
||||
class CutMixBatchOperation;
|
||||
class CutOutOperation;
|
||||
#endif
|
||||
class DecodeOperation;
|
||||
#ifndef ENABLE_ANDROID
|
||||
class HwcToChwOperation;
|
||||
class MixUpBatchOperation;
|
||||
#endif
|
||||
class NormalizeOperation;
|
||||
#ifndef ENABLE_ANDROID
|
||||
class PadOperation;
|
||||
class RandomAffineOperation;
|
||||
class RandomColorOperation;
|
||||
|
|
@ -53,7 +60,9 @@ class RandomSharpnessOperation;
|
|||
class RandomSolarizeOperation;
|
||||
class RandomVerticalFlipOperation;
|
||||
class RescaleOperation;
|
||||
#endif
|
||||
class ResizeOperation;
|
||||
#ifndef ENABLE_ANDROID
|
||||
class RgbaToBgrOperation;
|
||||
class RgbaToRgbOperation;
|
||||
class SwapRedBlueOperation;
|
||||
|
|
@ -66,14 +75,14 @@ class UniformAugOperation;
|
|||
/// If size has 2 values, it should be (height, width).
|
||||
/// \return Shared pointer to the current TensorOperation.
|
||||
std::shared_ptr<CenterCropOperation> CenterCrop(std::vector<int32_t> size);
|
||||
|
||||
#endif
|
||||
/// \brief Function to create a Crop TensorOp
|
||||
/// \notes Crop an image based on location and crop size
|
||||
/// \param[in] coordinates Starting location of crop. Must be a vector of two values, in the form of {x_coor, y_coor}
|
||||
/// \param[in] size Size of the cropped area. Must be a vector of two values, in the form of {height, width}
|
||||
/// \return Shared pointer to the current TensorOp
|
||||
std::shared_ptr<CropOperation> Crop(std::vector<int32_t> coordinates, std::vector<int32_t> size);
|
||||
|
||||
#ifndef ENABLE_ANDROID
|
||||
/// \brief Function to apply CutMix on a batch of images
|
||||
/// \notes Masks a random section of each image with the corresponding part of another randomly
|
||||
/// selected image in that batch
|
||||
|
|
@ -91,12 +100,14 @@ std::shared_ptr<CutMixBatchOperation> CutMixBatch(ImageBatchFormat image_batch_f
|
|||
/// \return Shared pointer to the current TensorOp
|
||||
std::shared_ptr<CutOutOperation> CutOut(int32_t length, int32_t num_patches = 1);
|
||||
|
||||
#endif
|
||||
/// \brief Function to create a Decode TensorOperation.
|
||||
/// \notes Decode the input image in RGB mode.
|
||||
/// \param[in] rgb A boolean of whether to decode in RGB mode or not.
|
||||
/// \return Shared pointer to the current TensorOperation.
|
||||
std::shared_ptr<DecodeOperation> Decode(bool rgb = true);
|
||||
|
||||
#ifndef ENABLE_ANDROID
|
||||
/// \brief Function to create a HwcToChw TensorOperation.
|
||||
/// \notes Transpose the input image; shape (H, W, C) to shape (C, H, W).
|
||||
/// \return Shared pointer to the current TensorOperation.
|
||||
|
|
@ -109,6 +120,7 @@ std::shared_ptr<HwcToChwOperation> HWC2CHW();
|
|||
/// \return Shared pointer to the current TensorOperation.
|
||||
std::shared_ptr<MixUpBatchOperation> MixUpBatch(float alpha = 1);
|
||||
|
||||
#endif
|
||||
/// \brief Function to create a Normalize TensorOperation.
|
||||
/// \notes Normalize the input image with respect to mean and standard deviation.
|
||||
/// \param[in] mean A vector of mean values for each channel, w.r.t channel order.
|
||||
|
|
@ -118,6 +130,7 @@ std::shared_ptr<MixUpBatchOperation> MixUpBatch(float alpha = 1);
|
|||
/// \return Shared pointer to the current TensorOperation.
|
||||
std::shared_ptr<NormalizeOperation> Normalize(std::vector<float> mean, std::vector<float> std);
|
||||
|
||||
#ifndef ENABLE_ANDROID
|
||||
/// \brief Function to create a Pad TensorOp
|
||||
/// \notes Pads the image according to padding parameters
|
||||
/// \param[in] padding A vector representing the number of pixels to pad the image
|
||||
|
|
@ -287,6 +300,7 @@ std::shared_ptr<RandomVerticalFlipOperation> RandomVerticalFlip(float prob = 0.5
|
|||
/// \return Shared pointer to the current TensorOperation.
|
||||
std::shared_ptr<RescaleOperation> Rescale(float rescale, float shift);
|
||||
|
||||
#endif
|
||||
/// \brief Function to create a Resize TensorOperation.
|
||||
/// \notes Resize the input image to the given size.
|
||||
/// \param[in] size A vector representing the output size of the resized image.
|
||||
|
|
@ -297,6 +311,7 @@ std::shared_ptr<RescaleOperation> Rescale(float rescale, float shift);
|
|||
std::shared_ptr<ResizeOperation> Resize(std::vector<int32_t> size,
|
||||
InterpolationMode interpolation = InterpolationMode::kLinear);
|
||||
|
||||
#ifndef ENABLE_ANDROID
|
||||
/// \brief Function to create a RgbaToBgr TensorOperation.
|
||||
/// \notes Changes the input 4 channel RGBA tensor to 3 channel BGR.
|
||||
/// \return Shared pointer to the current TensorOperation.
|
||||
|
|
@ -335,7 +350,7 @@ class CenterCropOperation : public TensorOperation {
|
|||
private:
|
||||
std::vector<int32_t> size_;
|
||||
};
|
||||
|
||||
#endif
|
||||
class CropOperation : public TensorOperation {
|
||||
public:
|
||||
CropOperation(std::vector<int32_t> coordinates, std::vector<int32_t> size);
|
||||
|
|
@ -350,7 +365,7 @@ class CropOperation : public TensorOperation {
|
|||
std::vector<int32_t> coordinates_;
|
||||
std::vector<int32_t> size_;
|
||||
};
|
||||
|
||||
#ifndef ENABLE_ANDROID
|
||||
class CutMixBatchOperation : public TensorOperation {
|
||||
public:
|
||||
explicit CutMixBatchOperation(ImageBatchFormat image_batch_format, float alpha = 1.0, float prob = 1.0);
|
||||
|
|
@ -383,6 +398,7 @@ class CutOutOperation : public TensorOperation {
|
|||
ImageBatchFormat image_batch_format_;
|
||||
};
|
||||
|
||||
#endif
|
||||
class DecodeOperation : public TensorOperation {
|
||||
public:
|
||||
explicit DecodeOperation(bool rgb = true);
|
||||
|
|
@ -397,6 +413,7 @@ class DecodeOperation : public TensorOperation {
|
|||
bool rgb_;
|
||||
};
|
||||
|
||||
#ifndef ENABLE_ANDROID
|
||||
class HwcToChwOperation : public TensorOperation {
|
||||
public:
|
||||
~HwcToChwOperation() = default;
|
||||
|
|
@ -420,6 +437,7 @@ class MixUpBatchOperation : public TensorOperation {
|
|||
float alpha_;
|
||||
};
|
||||
|
||||
#endif
|
||||
class NormalizeOperation : public TensorOperation {
|
||||
public:
|
||||
NormalizeOperation(std::vector<float> mean, std::vector<float> std);
|
||||
|
|
@ -435,6 +453,7 @@ class NormalizeOperation : public TensorOperation {
|
|||
std::vector<float> std_;
|
||||
};
|
||||
|
||||
#ifndef ENABLE_ANDROID
|
||||
class PadOperation : public TensorOperation {
|
||||
public:
|
||||
PadOperation(std::vector<int32_t> padding, std::vector<uint8_t> fill_value = {0},
|
||||
|
|
@ -672,6 +691,7 @@ class RescaleOperation : public TensorOperation {
|
|||
float shift_;
|
||||
};
|
||||
|
||||
#endif
|
||||
class ResizeOperation : public TensorOperation {
|
||||
public:
|
||||
explicit ResizeOperation(std::vector<int32_t> size,
|
||||
|
|
@ -688,6 +708,7 @@ class ResizeOperation : public TensorOperation {
|
|||
InterpolationMode interpolation_;
|
||||
};
|
||||
|
||||
#ifndef ENABLE_ANDROID
|
||||
class RgbaToBgrOperation : public TensorOperation {
|
||||
public:
|
||||
RgbaToBgrOperation();
|
||||
|
|
@ -735,6 +756,8 @@ class UniformAugOperation : public TensorOperation {
|
|||
std::vector<std::shared_ptr<TensorOperation>> transforms_;
|
||||
int32_t num_ops_;
|
||||
};
|
||||
#endif
|
||||
|
||||
} // namespace vision
|
||||
} // namespace api
|
||||
} // namespace dataset
|
||||
|
|
|
|||
|
|
@ -649,6 +649,7 @@ Status Concatenate(const TensorRow &input, TensorRow *output, int8_t axis, std::
|
|||
return Status::OK();
|
||||
}
|
||||
|
||||
#ifndef ENABLE_ANDROID
|
||||
Status BatchTensorToCVTensorVector(const std::shared_ptr<Tensor> &input,
|
||||
std::vector<std::shared_ptr<CVTensor>> *output) {
|
||||
std::vector<int64_t> tensor_shape = input->shape().AsVector();
|
||||
|
|
@ -673,6 +674,7 @@ Status BatchTensorToCVTensorVector(const std::shared_ptr<Tensor> &input,
|
|||
}
|
||||
return Status::OK();
|
||||
}
|
||||
#endif
|
||||
|
||||
Status BatchTensorToTensorVector(const std::shared_ptr<Tensor> &input, std::vector<std::shared_ptr<Tensor>> *output) {
|
||||
std::vector<int64_t> tensor_shape = input->shape().AsVector();
|
||||
|
|
|
|||
|
|
@ -21,7 +21,9 @@
|
|||
#include <vector>
|
||||
#include <unordered_map>
|
||||
#include "minddata/dataset/core/constants.h"
|
||||
#ifndef ENABLE_ANDROID
|
||||
#include "minddata/dataset/core/cv_tensor.h"
|
||||
#endif
|
||||
#include "minddata/dataset/core/data_type.h"
|
||||
#include "minddata/dataset/core/tensor.h"
|
||||
#include "minddata/dataset/core/tensor_row.h"
|
||||
|
|
|
|||
|
|
@ -15,7 +15,11 @@
|
|||
*/
|
||||
#include "minddata/dataset/kernels/image/crop_op.h"
|
||||
|
||||
#ifndef ENABLE_ANDROID
|
||||
#include "minddata/dataset/kernels/image/image_utils.h"
|
||||
#else
|
||||
#include "minddata/dataset/kernels/image/lite_image_utils.h"
|
||||
#endif
|
||||
#include "minddata/dataset/util/status.h"
|
||||
|
||||
namespace mindspore {
|
||||
|
|
|
|||
|
|
@ -21,7 +21,11 @@
|
|||
#include <string>
|
||||
|
||||
#include "minddata/dataset/core/tensor.h"
|
||||
#ifndef ENABLE_ANDROID
|
||||
#include "minddata/dataset/kernels/image/image_utils.h"
|
||||
#else
|
||||
#include "minddata/dataset/kernels/image/lite_image_utils.h"
|
||||
#endif
|
||||
#include "minddata/dataset/kernels/tensor_op.h"
|
||||
#include "minddata/dataset/util/status.h"
|
||||
|
||||
|
|
|
|||
|
|
@ -15,7 +15,11 @@
|
|||
*/
|
||||
#include "minddata/dataset/kernels/image/decode_op.h"
|
||||
|
||||
#ifndef ENABLE_ANDROID
|
||||
#include "minddata/dataset/kernels/image/image_utils.h"
|
||||
#else
|
||||
#include "minddata/dataset/kernels/image/lite_image_utils.h"
|
||||
#endif
|
||||
#include "minddata/dataset/util/status.h"
|
||||
|
||||
namespace mindspore {
|
||||
|
|
|
|||
|
|
@ -14,7 +14,7 @@
|
|||
* limitations under the License.
|
||||
*/
|
||||
|
||||
#include "lite_cv/image_process.h"
|
||||
#include "minddata/dataset/kernels/image/lite_cv/image_process.h"
|
||||
|
||||
#include <string.h>
|
||||
#include <cmath>
|
||||
|
|
|
|||
|
|
@ -22,7 +22,7 @@
|
|||
#include <algorithm>
|
||||
#include <iostream>
|
||||
|
||||
#include "lite_cv/lite_mat.h"
|
||||
#include "minddata/dataset/kernels/image/lite_cv/lite_mat.h"
|
||||
|
||||
namespace mindspore {
|
||||
namespace dataset {
|
||||
|
|
|
|||
|
|
@ -13,7 +13,7 @@
|
|||
* See the License for the specific language governing permissions and
|
||||
* limitations under the License.
|
||||
*/
|
||||
#include "lite_cv/lite_mat.h"
|
||||
#include "minddata/dataset/kernels/image/lite_cv/lite_mat.h"
|
||||
|
||||
namespace mindspore {
|
||||
namespace dataset {
|
||||
|
|
|
|||
|
|
@ -0,0 +1,368 @@
|
|||
/**
|
||||
* Copyright 2020 Huawei Technologies Co., Ltd
|
||||
*
|
||||
* Licensed under the Apache License, Version 2.0 (the "License");
|
||||
* you may not use this file except in compliance with the License.
|
||||
* You may obtain a copy of the License at
|
||||
*
|
||||
* http://www.apache.org/licenses/LICENSE-2.0
|
||||
*
|
||||
* Unless required by applicable law or agreed to in writing, software
|
||||
* distributed under the License is distributed on an "AS IS" BASIS,
|
||||
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
||||
* See the License for the specific language governing permissions and
|
||||
* limitations under the License.
|
||||
*/
|
||||
#include "minddata/dataset/kernels/image/lite_image_utils.h"
|
||||
#include <algorithm>
|
||||
#include <vector>
|
||||
#include <stdexcept>
|
||||
#include <utility>
|
||||
#include "utils/ms_utils.h"
|
||||
#include "minddata/dataset/kernels/image/lite_cv/lite_mat.h"
|
||||
#include "minddata/dataset/kernels/image/lite_cv/image_process.h"
|
||||
#include "minddata/dataset/core/constants.h"
|
||||
#include "minddata/dataset/core/tensor.h"
|
||||
#include "minddata/dataset/core/tensor_shape.h"
|
||||
#include "minddata/dataset/util/random.h"
|
||||
|
||||
#define MAX_INT_PRECISION 16777216 // float int precision is 16777216
|
||||
namespace mindspore {
|
||||
namespace dataset {
|
||||
bool IsNonEmptyJPEG(const std::shared_ptr<Tensor> &input) {
|
||||
const unsigned char *kJpegMagic = (unsigned char *)"\xFF\xD8\xFF";
|
||||
constexpr size_t kJpegMagicLen = 3;
|
||||
return input->SizeInBytes() > kJpegMagicLen && memcmp(input->GetBuffer(), kJpegMagic, kJpegMagicLen) == 0;
|
||||
}
|
||||
|
||||
static void JpegInitSource(j_decompress_ptr cinfo) {}
|
||||
|
||||
static boolean JpegFillInputBuffer(j_decompress_ptr cinfo) {
|
||||
if (cinfo->src->bytes_in_buffer == 0) {
|
||||
// Under ARM platform raise runtime_error may cause core problem,
|
||||
// so we catch runtime_error and just return FALSE.
|
||||
try {
|
||||
ERREXIT(cinfo, JERR_INPUT_EMPTY);
|
||||
} catch (std::runtime_error &e) {
|
||||
return FALSE;
|
||||
}
|
||||
return FALSE;
|
||||
}
|
||||
return TRUE;
|
||||
}
|
||||
|
||||
static void JpegTermSource(j_decompress_ptr cinfo) {}
|
||||
|
||||
static void JpegSkipInputData(j_decompress_ptr cinfo, int64_t jump) {
|
||||
if (jump < 0) {
|
||||
return;
|
||||
}
|
||||
if (static_cast<size_t>(jump) > cinfo->src->bytes_in_buffer) {
|
||||
cinfo->src->bytes_in_buffer = 0;
|
||||
return;
|
||||
} else {
|
||||
cinfo->src->bytes_in_buffer -= jump;
|
||||
cinfo->src->next_input_byte += jump;
|
||||
}
|
||||
}
|
||||
|
||||
void JpegSetSource(j_decompress_ptr cinfo, const void *data, int64_t datasize) {
|
||||
cinfo->src = static_cast<struct jpeg_source_mgr *>(
|
||||
(*cinfo->mem->alloc_small)(reinterpret_cast<j_common_ptr>(cinfo), JPOOL_PERMANENT, sizeof(struct jpeg_source_mgr)));
|
||||
cinfo->src->init_source = JpegInitSource;
|
||||
cinfo->src->fill_input_buffer = JpegFillInputBuffer;
|
||||
#if defined(_WIN32) || defined(_WIN64) || defined(ENABLE_ARM32)
|
||||
// the following line skips CI because it uses underlying C type
|
||||
cinfo->src->skip_input_data = reinterpret_cast<void (*)(j_decompress_ptr, long)>(JpegSkipInputData); // NOLINT.
|
||||
#else
|
||||
cinfo->src->skip_input_data = JpegSkipInputData;
|
||||
#endif
|
||||
cinfo->src->resync_to_restart = jpeg_resync_to_restart;
|
||||
cinfo->src->term_source = JpegTermSource;
|
||||
cinfo->src->bytes_in_buffer = datasize;
|
||||
cinfo->src->next_input_byte = static_cast<const JOCTET *>(data);
|
||||
}
|
||||
|
||||
static Status JpegReadScanlines(jpeg_decompress_struct *const cinfo, int max_scanlines_to_read, JSAMPLE *buffer,
|
||||
int buffer_size, int crop_w, int crop_w_aligned, int offset, int stride) {
|
||||
// scanlines will be read to this buffer first, must have the number
|
||||
// of components equal to the number of components in the image
|
||||
int64_t scanline_size = crop_w_aligned * cinfo->output_components;
|
||||
std::vector<JSAMPLE> scanline(scanline_size);
|
||||
JSAMPLE *scanline_ptr = &scanline[0];
|
||||
while (cinfo->output_scanline < static_cast<unsigned int>(max_scanlines_to_read)) {
|
||||
int num_lines_read = 0;
|
||||
try {
|
||||
num_lines_read = jpeg_read_scanlines(cinfo, &scanline_ptr, 1);
|
||||
} catch (std::runtime_error &e) {
|
||||
RETURN_STATUS_UNEXPECTED("jpeg_read_scanlines error.");
|
||||
}
|
||||
if (cinfo->out_color_space == JCS_CMYK && num_lines_read > 0) {
|
||||
for (int i = 0; i < crop_w; ++i) {
|
||||
int cmyk_pixel = 4 * i + offset;
|
||||
const int c = scanline_ptr[cmyk_pixel];
|
||||
const int m = scanline_ptr[cmyk_pixel + 1];
|
||||
const int y = scanline_ptr[cmyk_pixel + 2];
|
||||
const int k = scanline_ptr[cmyk_pixel + 3];
|
||||
int r, g, b;
|
||||
if (cinfo->saw_Adobe_marker) {
|
||||
r = (k * c) / 255;
|
||||
g = (k * m) / 255;
|
||||
b = (k * y) / 255;
|
||||
} else {
|
||||
r = (255 - c) * (255 - k) / 255;
|
||||
g = (255 - m) * (255 - k) / 255;
|
||||
b = (255 - y) * (255 - k) / 255;
|
||||
}
|
||||
buffer[3 * i + 0] = r;
|
||||
buffer[3 * i + 1] = g;
|
||||
buffer[3 * i + 2] = b;
|
||||
}
|
||||
} else if (num_lines_read > 0) {
|
||||
int copy_status = memcpy_s(buffer, buffer_size, scanline_ptr + offset, stride);
|
||||
if (copy_status != 0) {
|
||||
jpeg_destroy_decompress(cinfo);
|
||||
RETURN_STATUS_UNEXPECTED("memcpy failed");
|
||||
}
|
||||
} else {
|
||||
jpeg_destroy_decompress(cinfo);
|
||||
std::string err_msg = "failed to read scanline";
|
||||
RETURN_STATUS_UNEXPECTED(err_msg);
|
||||
}
|
||||
buffer += stride;
|
||||
buffer_size = buffer_size - stride;
|
||||
}
|
||||
return Status::OK();
|
||||
}
|
||||
|
||||
static Status JpegSetColorSpace(jpeg_decompress_struct *cinfo) {
|
||||
switch (cinfo->num_components) {
|
||||
case 1:
|
||||
// we want to output 3 components if it's grayscale
|
||||
cinfo->out_color_space = JCS_RGB;
|
||||
return Status::OK();
|
||||
case 3:
|
||||
cinfo->out_color_space = JCS_RGB;
|
||||
return Status::OK();
|
||||
case 4:
|
||||
// Need to manually convert to RGB
|
||||
cinfo->out_color_space = JCS_CMYK;
|
||||
return Status::OK();
|
||||
default:
|
||||
jpeg_destroy_decompress(cinfo);
|
||||
std::string err_msg = "wrong number of components";
|
||||
RETURN_STATUS_UNEXPECTED(err_msg);
|
||||
}
|
||||
}
|
||||
|
||||
void JpegErrorExitCustom(j_common_ptr cinfo) {
|
||||
char jpeg_last_error_msg[JMSG_LENGTH_MAX];
|
||||
(*(cinfo->err->format_message))(cinfo, jpeg_last_error_msg);
|
||||
throw std::runtime_error(jpeg_last_error_msg);
|
||||
}
|
||||
|
||||
Status JpegCropAndDecode(const std::shared_ptr<Tensor> &input, std::shared_ptr<Tensor> *output, int crop_x, int crop_y,
|
||||
int crop_w, int crop_h) {
|
||||
struct jpeg_decompress_struct cinfo;
|
||||
auto DestroyDecompressAndReturnError = [&cinfo](const std::string &err) {
|
||||
jpeg_destroy_decompress(&cinfo);
|
||||
RETURN_STATUS_UNEXPECTED(err);
|
||||
};
|
||||
struct JpegErrorManagerCustom jerr;
|
||||
cinfo.err = jpeg_std_error(&jerr.pub);
|
||||
jerr.pub.error_exit = JpegErrorExitCustom;
|
||||
try {
|
||||
jpeg_create_decompress(&cinfo);
|
||||
JpegSetSource(&cinfo, input->GetBuffer(), input->SizeInBytes());
|
||||
(void)jpeg_read_header(&cinfo, TRUE);
|
||||
RETURN_IF_NOT_OK(JpegSetColorSpace(&cinfo));
|
||||
jpeg_calc_output_dimensions(&cinfo);
|
||||
} catch (std::runtime_error &e) {
|
||||
return DestroyDecompressAndReturnError(e.what());
|
||||
}
|
||||
if (crop_x == 0 && crop_y == 0 && crop_w == 0 && crop_h == 0) {
|
||||
crop_w = cinfo.output_width;
|
||||
crop_h = cinfo.output_height;
|
||||
} else if (crop_w == 0 || static_cast<unsigned int>(crop_w + crop_x) > cinfo.output_width || crop_h == 0 ||
|
||||
static_cast<unsigned int>(crop_h + crop_y) > cinfo.output_height) {
|
||||
return DestroyDecompressAndReturnError("Crop window is not valid");
|
||||
}
|
||||
const int mcu_size = cinfo.min_DCT_scaled_size;
|
||||
unsigned int crop_x_aligned = (crop_x / mcu_size) * mcu_size;
|
||||
unsigned int crop_w_aligned = crop_w + crop_x - crop_x_aligned;
|
||||
try {
|
||||
(void)jpeg_start_decompress(&cinfo);
|
||||
jpeg_crop_scanline(&cinfo, &crop_x_aligned, &crop_w_aligned);
|
||||
} catch (std::runtime_error &e) {
|
||||
return DestroyDecompressAndReturnError(e.what());
|
||||
}
|
||||
JDIMENSION skipped_scanlines = jpeg_skip_scanlines(&cinfo, crop_y);
|
||||
// three number of output components, always convert to RGB and output
|
||||
constexpr int kOutNumComponents = 3;
|
||||
TensorShape ts = TensorShape({crop_h, crop_w, kOutNumComponents});
|
||||
std::shared_ptr<Tensor> output_tensor;
|
||||
RETURN_IF_NOT_OK(Tensor::CreateEmpty(ts, DataType(DataType::DE_UINT8), &output_tensor));
|
||||
const int buffer_size = output_tensor->SizeInBytes();
|
||||
JSAMPLE *buffer = reinterpret_cast<JSAMPLE *>(&(*output_tensor->begin<uint8_t>()));
|
||||
const int max_scanlines_to_read = skipped_scanlines + crop_h;
|
||||
// stride refers to output tensor, which has 3 components at most
|
||||
const int stride = crop_w * kOutNumComponents;
|
||||
// offset is calculated for scanlines read from the image, therefore
|
||||
// has the same number of components as the image
|
||||
const int offset = (crop_x - crop_x_aligned) * cinfo.output_components;
|
||||
RETURN_IF_NOT_OK(
|
||||
JpegReadScanlines(&cinfo, max_scanlines_to_read, buffer, buffer_size, crop_w, crop_w_aligned, offset, stride));
|
||||
*output = output_tensor;
|
||||
jpeg_destroy_decompress(&cinfo);
|
||||
return Status::OK();
|
||||
}
|
||||
|
||||
Status Decode(const std::shared_ptr<Tensor> &input, std::shared_ptr<Tensor> *output) {
|
||||
if (IsNonEmptyJPEG(input)) {
|
||||
return JpegCropAndDecode(input, output);
|
||||
} else {
|
||||
RETURN_STATUS_UNEXPECTED("Decode only supports jpeg for android");
|
||||
}
|
||||
}
|
||||
|
||||
Status Crop(const std::shared_ptr<Tensor> &input, std::shared_ptr<Tensor> *output, int x, int y, int w, int h) {
|
||||
if (input->Rank() != 3 && input->Rank() != 2) {
|
||||
RETURN_STATUS_UNEXPECTED("Shape not <H,W,C> or <H,W>");
|
||||
}
|
||||
// account for integer overflow
|
||||
if (y < 0 || (y + h) > input->shape()[0] || (y + h) < 0) {
|
||||
RETURN_STATUS_UNEXPECTED("Invalid y coordinate value for crop");
|
||||
}
|
||||
// account for integer overflow
|
||||
if (x < 0 || (x + w) > input->shape()[1] || (x + w) < 0) {
|
||||
RETURN_STATUS_UNEXPECTED("Invalid x coordinate value for crop");
|
||||
}
|
||||
// convert to lite Mat
|
||||
LiteMat lite_mat_rgb;
|
||||
// rows = height, this constructor takes: cols,rows
|
||||
bool ret = InitFromPixel(input->GetBuffer(), LPixelType::RGB, LDataType::UINT8, input->shape()[1], input->shape()[0],
|
||||
lite_mat_rgb);
|
||||
CHECK_FAIL_RETURN_UNEXPECTED(ret, "Creation of lite cv failed");
|
||||
try {
|
||||
TensorShape shape{h, w};
|
||||
int num_channels = input->shape()[2];
|
||||
if (input->Rank() == 3) shape = shape.AppendDim(num_channels);
|
||||
LiteMat lite_mat_cut;
|
||||
ret = Crop(lite_mat_rgb, lite_mat_cut, x, y, x + w, y + h);
|
||||
CHECK_FAIL_RETURN_UNEXPECTED(ret, "Crop failed in lite cv");
|
||||
// create output Tensor based off of lite_mat_cut
|
||||
std::shared_ptr<Tensor> output_tensor;
|
||||
RETURN_IF_NOT_OK(
|
||||
Tensor::CreateFromMemory(shape, input->type(), static_cast<uchar *>(lite_mat_cut.data_ptr_), &output_tensor));
|
||||
*output = output_tensor;
|
||||
return Status::OK();
|
||||
} catch (std::runtime_error &e) {
|
||||
RETURN_STATUS_UNEXPECTED("Unexpected error in crop.");
|
||||
}
|
||||
return Status::OK();
|
||||
}
|
||||
|
||||
Status GetJpegImageInfo(const std::shared_ptr<Tensor> &input, int *img_width, int *img_height) {
|
||||
struct jpeg_decompress_struct cinfo {};
|
||||
struct JpegErrorManagerCustom jerr {};
|
||||
cinfo.err = jpeg_std_error(&jerr.pub);
|
||||
jerr.pub.error_exit = JpegErrorExitCustom;
|
||||
try {
|
||||
jpeg_create_decompress(&cinfo);
|
||||
JpegSetSource(&cinfo, input->GetBuffer(), input->SizeInBytes());
|
||||
(void)jpeg_read_header(&cinfo, TRUE);
|
||||
jpeg_calc_output_dimensions(&cinfo);
|
||||
} catch (std::runtime_error &e) {
|
||||
jpeg_destroy_decompress(&cinfo);
|
||||
RETURN_STATUS_UNEXPECTED(e.what());
|
||||
}
|
||||
*img_height = cinfo.output_height;
|
||||
*img_width = cinfo.output_width;
|
||||
jpeg_destroy_decompress(&cinfo);
|
||||
return Status::OK();
|
||||
}
|
||||
|
||||
Status Normalize(const std::shared_ptr<Tensor> &input, std::shared_ptr<Tensor> *output,
|
||||
const std::shared_ptr<Tensor> &mean, const std::shared_ptr<Tensor> &std) {
|
||||
if (input->Rank() != 3) {
|
||||
RETURN_STATUS_UNEXPECTED("Input tensor rank isn't 3");
|
||||
}
|
||||
LiteMat lite_mat_rgb;
|
||||
// rows = height, this constructor takes: cols,rows
|
||||
bool ret = InitFromPixel(input->GetBuffer(), LPixelType::RGB, LDataType::UINT8, input->shape()[1], input->shape()[0],
|
||||
lite_mat_rgb);
|
||||
CHECK_FAIL_RETURN_UNEXPECTED(ret, "Creation of lite cv failed");
|
||||
|
||||
mean->Squeeze();
|
||||
if (mean->type() != DataType::DE_FLOAT32 || mean->Rank() != 1 || mean->shape()[0] != 3) {
|
||||
std::string err_msg = "Mean tensor should be of size 3 and type float.";
|
||||
return Status(StatusCode::kShapeMisMatch, err_msg);
|
||||
}
|
||||
std->Squeeze();
|
||||
if (std->type() != DataType::DE_FLOAT32 || std->Rank() != 1 || std->shape()[0] != 3) {
|
||||
std::string err_msg = "Std tensor should be of size 3 and type float.";
|
||||
return Status(StatusCode::kShapeMisMatch, err_msg);
|
||||
}
|
||||
// convert mean, std back to vector
|
||||
std::vector<float> vec_mean;
|
||||
std::vector<float> vec_std;
|
||||
try {
|
||||
for (uint8_t i = 0; i < 3; i++) {
|
||||
float mean_c, std_c;
|
||||
RETURN_IF_NOT_OK(mean->GetItemAt<float>(&mean_c, {i}));
|
||||
RETURN_IF_NOT_OK(std->GetItemAt<float>(&std_c, {i}));
|
||||
vec_mean.push_back(mean_c);
|
||||
vec_std.push_back(std_c);
|
||||
}
|
||||
LiteMat lite_mat_norm;
|
||||
ret = SubStractMeanNormalize(lite_mat_rgb, lite_mat_norm, vec_mean, vec_std);
|
||||
CHECK_FAIL_RETURN_UNEXPECTED(ret, "Normalize in lite cv failed");
|
||||
// create output Tensor based off of lite_mat_cut
|
||||
std::shared_ptr<Tensor> output_tensor;
|
||||
RETURN_IF_NOT_OK(Tensor::CreateFromMemory(input->shape(), DataType(DataType::DE_FLOAT32),
|
||||
static_cast<uchar *>(lite_mat_norm.data_ptr_), &output_tensor));
|
||||
*output = output_tensor;
|
||||
} catch (std::runtime_error &e) {
|
||||
RETURN_STATUS_UNEXPECTED("Unexpected error in normalize.");
|
||||
}
|
||||
return Status::OK();
|
||||
}
|
||||
|
||||
Status Resize(const std::shared_ptr<Tensor> &input, std::shared_ptr<Tensor> *output, int32_t output_height,
|
||||
int32_t output_width, double fx, double fy, InterpolationMode mode) {
|
||||
if (input->Rank() != 3) {
|
||||
RETURN_STATUS_UNEXPECTED("Input Tensor is not in shape of <H,W,C>");
|
||||
}
|
||||
// resize image too large or too small
|
||||
if (output_height == 0 || output_height > input->shape()[0] * 1000 || output_width == 0 ||
|
||||
output_width > input->shape()[1] * 1000) {
|
||||
std::string err_msg =
|
||||
"The resizing width or height 1) is too big, it's up to "
|
||||
"1000 times the original image; 2) can not be 0.";
|
||||
return Status(StatusCode::kShapeMisMatch, err_msg);
|
||||
}
|
||||
LiteMat lite_mat_rgb;
|
||||
bool ret = InitFromPixel(input->GetBuffer(), LPixelType::RGB, LDataType::UINT8, input->shape()[1], input->shape()[0],
|
||||
lite_mat_rgb);
|
||||
CHECK_FAIL_RETURN_UNEXPECTED(ret, "Creation of lite cv failed");
|
||||
|
||||
try {
|
||||
TensorShape shape{output_height, output_width};
|
||||
int num_channels = input->shape()[2];
|
||||
if (input->Rank() == 3) shape = shape.AppendDim(num_channels);
|
||||
|
||||
LiteMat lite_mat_resize;
|
||||
ret = ResizeBilinear(lite_mat_rgb, lite_mat_resize, output_width, output_height);
|
||||
CHECK_FAIL_RETURN_UNEXPECTED(ret, "Resize failed in lite cv");
|
||||
std::shared_ptr<Tensor> output_tensor;
|
||||
RETURN_IF_NOT_OK(
|
||||
Tensor::CreateFromMemory(shape, input->type(), static_cast<uchar *>(lite_mat_resize.data_ptr_), &output_tensor));
|
||||
*output = output_tensor;
|
||||
} catch (std::runtime_error &e) {
|
||||
RETURN_STATUS_UNEXPECTED("Error in image resize.");
|
||||
}
|
||||
return Status::OK();
|
||||
}
|
||||
|
||||
} // namespace dataset
|
||||
} // namespace mindspore
|
||||
|
|
@ -0,0 +1,100 @@
|
|||
/**
|
||||
* Copyright 2019 Huawei Technologies Co., Ltd
|
||||
*
|
||||
* Licensed under the Apache License, Version 2.0 (the "License");
|
||||
* you may not use this file except in compliance with the License.
|
||||
* You may obtain a copy of the License at
|
||||
*
|
||||
* http://www.apache.org/licenses/LICENSE-2.0
|
||||
*
|
||||
* Unless required by applicable law or agreed to in writing, software
|
||||
* distributed under the License is distributed on an "AS IS" BASIS,
|
||||
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
||||
* See the License for the specific language governing permissions and
|
||||
* limitations under the License.
|
||||
*/
|
||||
#ifndef MINDSPORE_CCSRC_MINDDATA_DATASET_KERNELS_IMAGE_IMAGE_UTILS_H_
|
||||
#define MINDSPORE_CCSRC_MINDDATA_DATASET_KERNELS_IMAGE_IMAGE_UTILS_H_
|
||||
|
||||
#include <setjmp.h>
|
||||
|
||||
#include <memory>
|
||||
#include <random>
|
||||
#include <string>
|
||||
#include <vector>
|
||||
#if defined(_WIN32) || defined(_WIN64)
|
||||
#undef HAVE_STDDEF_H
|
||||
#undef HAVE_STDLIB_H
|
||||
#endif
|
||||
#include "./jpeglib.h"
|
||||
#include "./jerror.h"
|
||||
#include "minddata/dataset/core/tensor.h"
|
||||
#include "minddata/dataset/kernels/tensor_op.h"
|
||||
#include "minddata/dataset/kernels/image/lite_cv/image_process.h"
|
||||
#include "minddata/dataset/util/status.h"
|
||||
|
||||
namespace mindspore {
|
||||
namespace dataset {
|
||||
void JpegErrorExitCustom(j_common_ptr cinfo);
|
||||
|
||||
struct JpegErrorManagerCustom {
|
||||
// "public" fields
|
||||
struct jpeg_error_mgr pub;
|
||||
// for return to caller
|
||||
jmp_buf setjmp_buffer;
|
||||
};
|
||||
|
||||
bool IsNonEmptyJPEG(const std::shared_ptr<Tensor> &input);
|
||||
|
||||
void JpegSetSource(j_decompress_ptr c_info, const void *data, int64_t data_size);
|
||||
|
||||
Status JpegCropAndDecode(const std::shared_ptr<Tensor> &input, std::shared_ptr<Tensor> *output, int x = 0, int y = 0,
|
||||
int w = 0, int h = 0);
|
||||
|
||||
/// \brief Returns cropped ROI of an image
|
||||
/// \param input: Tensor of shape <H,W,C> or <H,W> and any OpenCv compatible type, see CVTensor.
|
||||
/// \param x: starting horizontal position of ROI
|
||||
/// \param y: starting vertical position of ROI
|
||||
/// \param w: width of the ROI
|
||||
/// \param h: height of the ROI
|
||||
/// \param output: Cropped image Tensor of shape <h,w,C> or <h,w> and same input type.
|
||||
Status Crop(const std::shared_ptr<Tensor> &input, std::shared_ptr<Tensor> *output, int x, int y, int w, int h);
|
||||
|
||||
/// \brief Returns Decoded image
|
||||
/// Supported images:
|
||||
/// BMP JPEG JPG PNG TIFF
|
||||
/// supported by opencv, if user need more image analysis capabilities, please compile opencv particularlly.
|
||||
/// \param input: CVTensor containing the not decoded image 1D bytes
|
||||
/// \param output: Decoded image Tensor of shape <H,W,C> and type DE_UINT8. Pixel order is RGB
|
||||
Status Decode(const std::shared_ptr<Tensor> &input, std::shared_ptr<Tensor> *output);
|
||||
|
||||
/// \brief Get jpeg image width and height
|
||||
/// \param input: CVTensor containing the not decoded image 1D bytes
|
||||
/// \param img_width: the jpeg image width
|
||||
/// \param img_height: the jpeg image height
|
||||
Status GetJpegImageInfo(const std::shared_ptr<Tensor> &input, int *img_width, int *img_height);
|
||||
|
||||
/// \brief Returns Normalized image
|
||||
/// \param input: Tensor of shape <H,W,C> in RGB order and any OpenCv compatible type, see CVTensor.
|
||||
/// \param mean: Tensor of shape <3> and type DE_FLOAT32 which are mean of each channel in RGB order
|
||||
/// \param std: Tensor of shape <3> and type DE_FLOAT32 which are std of each channel in RGB order
|
||||
/// \param output: Normalized image Tensor of same input shape and type DE_FLOAT32
|
||||
Status Normalize(const std::shared_ptr<Tensor> &input, std::shared_ptr<Tensor> *output,
|
||||
const std::shared_ptr<Tensor> &mean, const std::shared_ptr<Tensor> &std);
|
||||
|
||||
/// \brief Returns Resized image.
|
||||
/// \param input/output: Tensor of shape <H,W,C> or <H,W> and any OpenCv compatible type, see CVTensor.
|
||||
/// \param output_height: height of output
|
||||
/// \param output_width: width of output
|
||||
/// \param fx: horizontal scale
|
||||
/// \param fy: vertical scale
|
||||
/// \param InterpolationMode: the interpolation mode
|
||||
/// \param output: Resized image of shape <outputHeight,outputWidth,C> or <outputHeight,outputWidth>
|
||||
/// and same type as input
|
||||
Status Resize(const std::shared_ptr<Tensor> &input, std::shared_ptr<Tensor> *output, int32_t output_height,
|
||||
int32_t output_width, double fx = 0.0, double fy = 0.0,
|
||||
InterpolationMode mode = InterpolationMode::kLinear);
|
||||
|
||||
} // namespace dataset
|
||||
} // namespace mindspore
|
||||
#endif // MINDSPORE_CCSRC_MINDDATA_DATASET_KERNELS_IMAGE_IMAGE_UTILS_H_
|
||||
|
|
@ -17,8 +17,11 @@
|
|||
|
||||
#include <random>
|
||||
|
||||
#include "minddata/dataset/core/cv_tensor.h"
|
||||
#ifndef ENABLE_ANDROID
|
||||
#include "minddata/dataset/kernels/image/image_utils.h"
|
||||
#else
|
||||
#include "minddata/dataset/kernels/image/lite_image_utils.h"
|
||||
#endif
|
||||
#include "minddata/dataset/util/status.h"
|
||||
|
||||
namespace mindspore {
|
||||
|
|
|
|||
|
|
@ -19,7 +19,6 @@
|
|||
#include <memory>
|
||||
#include <string>
|
||||
|
||||
#include "minddata/dataset/core/cv_tensor.h"
|
||||
#include "minddata/dataset/core/tensor.h"
|
||||
#include "minddata/dataset/kernels/tensor_op.h"
|
||||
#include "minddata/dataset/util/status.h"
|
||||
|
|
|
|||
|
|
@ -15,7 +15,11 @@
|
|||
*/
|
||||
#include "minddata/dataset/kernels/image/resize_op.h"
|
||||
|
||||
#ifndef ENABLE_ANDROID
|
||||
#include "minddata/dataset/kernels/image/image_utils.h"
|
||||
#else
|
||||
#include "minddata/dataset/kernels/image/lite_image_utils.h"
|
||||
#endif
|
||||
#include "minddata/dataset/util/status.h"
|
||||
|
||||
namespace mindspore {
|
||||
|
|
|
|||
|
|
@ -21,7 +21,11 @@
|
|||
#include <string>
|
||||
|
||||
#include "minddata/dataset/core/tensor.h"
|
||||
#ifndef ENABLE_ANDROID
|
||||
#include "minddata/dataset/kernels/image/image_utils.h"
|
||||
#else
|
||||
#include "minddata/dataset/kernels/image/lite_image_utils.h"
|
||||
#endif
|
||||
#include "minddata/dataset/kernels/tensor_op.h"
|
||||
#include "minddata/dataset/util/status.h"
|
||||
|
||||
|
|
|
|||
|
|
@ -193,10 +193,6 @@ endif ()
|
|||
if (BUILD_MINDDATA STREQUAL "lite" OR BUILD_MINDDATA STREQUAL "full")
|
||||
# add sentencepiece dependency
|
||||
# include(${TOP_DIR}/cmake/external_libs/sentencepiece.cmake)
|
||||
# opencv
|
||||
set(OpenCV_DIR ${TOP_DIR}/third_party/opencv/build)
|
||||
find_package(OpenCV REQUIRED)
|
||||
include_directories(${OpenCV_INCLUDE_DIRS})
|
||||
# json
|
||||
if (NOT ENABLE_CONVERTER)
|
||||
include(${TOP_DIR}/cmake/external_libs/json.cmake)
|
||||
|
|
|
|||
|
|
@ -57,7 +57,7 @@ AUX_SOURCE_DIRECTORY(${MINDDATA_DIR}/engine/ir/datasetops MINDDATA_ENGINE_IR_DAT
|
|||
AUX_SOURCE_DIRECTORY(${MINDDATA_DIR}/engine/ir/datasetops/source MINDDATA_ENGINE_IR_DATASETOPS_SOURCE_SRC_FILES)
|
||||
|
||||
AUX_SOURCE_DIRECTORY(${MINDDATA_DIR}/engine/opt MINDDATA_ENGINE_OPT_SRC_FILES)
|
||||
AUX_SOURCE_DIRECTORY(${MINDDATA_DIR}/engine/opt/optional MINDDATA_ENGINE_OPT_OPTIONAL_SRC_FILES)
|
||||
# AUX_SOURCE_DIRECTORY(${MINDDATA_DIR}/engine/opt/optional MINDDATA_ENGINE_OPT_OPTIONAL_SRC_FILES)
|
||||
AUX_SOURCE_DIRECTORY(${MINDDATA_DIR}/engine/opt/post MINDDATA_ENGINE_OPT_POST_SRC_FILES)
|
||||
AUX_SOURCE_DIRECTORY(${MINDDATA_DIR}/engine/opt/pre MINDDATA_ENGINE_OPT_PRE_SRC_FILES)
|
||||
AUX_SOURCE_DIRECTORY(${MINDDATA_DIR}/engine/opt/util MINDDATA_ENGINE_OPT_UTIL_SRC_FILES)
|
||||
|
|
@ -81,14 +81,16 @@ AUX_SOURCE_DIRECTORY(${MINDDATA_DIR}/kernels/image/lite_cv MINDDATA_KERNELS_IMA
|
|||
|
||||
|
||||
if (BUILD_MINDDATA STREQUAL "full")
|
||||
list(REMOVE_ITEM MINDDATA_API_SRC_FILES
|
||||
"${MINDDATA_DIR}/api/text.cc"
|
||||
"${MINDDATA_DIR}/api/de_tensor.cc"
|
||||
"${MINDDATA_DIR}/api/execute.cc"
|
||||
list(REMOVE_ITEM MINDDATA_API_SRC_FILES
|
||||
"${MINDDATA_DIR}/api/text.cc"
|
||||
)
|
||||
|
||||
list(REMOVE_ITEM MINDDATA_CALLBACK_SRC_FILES
|
||||
"${MINDDATA_DIR}/callback/py_ds_callback.cc"
|
||||
list(REMOVE_ITEM MINDDATA_CALLBACK_SRC_FILES
|
||||
"${MINDDATA_DIR}/callback/py_ds_callback.cc"
|
||||
)
|
||||
|
||||
list(REMOVE_ITEM MINDDATA_CORE_SRC_FILES
|
||||
"${MINDDATA_DIR}/core/cv_tensor.cc"
|
||||
)
|
||||
|
||||
list(REMOVE_ITEM MINDDATA_KERNELS_SRC_FILES "${MINDDATA_DIR}/kernels/py_func_op.cc")
|
||||
|
|
@ -136,6 +138,51 @@ if (BUILD_MINDDATA STREQUAL "full")
|
|||
"${MINDDATA_DIR}/engine/ir/datasetops/source/tf_record_node.cc"
|
||||
"${MINDDATA_DIR}/engine/ir/datasetops/source/voc_node.cc"
|
||||
)
|
||||
list(REMOVE_ITEM MINDDATA_KERNELS_IMAGE_SRC_FILES
|
||||
"${MINDDATA_DIR}/kernels/image/affine_op.cc"
|
||||
"${MINDDATA_DIR}/kernels/image/auto_contrast_op.cc"
|
||||
"${MINDDATA_DIR}/kernels/image/bounding_box_op.cc"
|
||||
"${MINDDATA_DIR}/kernels/image/bounding_box_augment_op.cc"
|
||||
"${MINDDATA_DIR}/kernels/image/center_crop_op.cc"
|
||||
"${MINDDATA_DIR}/kernels/image/concatenate_op.cc"
|
||||
"${MINDDATA_DIR}/kernels/image/cut_out_op.cc"
|
||||
"${MINDDATA_DIR}/kernels/image/cutmix_batch_op.cc"
|
||||
"${MINDDATA_DIR}/kernels/image/equalize_op.cc"
|
||||
"${MINDDATA_DIR}/kernels/image/hwc_to_chw_op.cc"
|
||||
"${MINDDATA_DIR}/kernels/image/image_utils.cc"
|
||||
"${MINDDATA_DIR}/kernels/image/invert_op.cc"
|
||||
"${MINDDATA_DIR}/kernels/image/math_utils.cc"
|
||||
"${MINDDATA_DIR}/kernels/image/mixup_batch_op.cc"
|
||||
"${MINDDATA_DIR}/kernels/image/pad_op.cc"
|
||||
"${MINDDATA_DIR}/kernels/image/posterize_op.cc"
|
||||
"${MINDDATA_DIR}/kernels/image/random_affine_op.cc"
|
||||
"${MINDDATA_DIR}/kernels/image/random_color_adjust_op.cc"
|
||||
"${MINDDATA_DIR}/kernels/image/random_crop_and_resize_with_bbox_op.cc"
|
||||
"${MINDDATA_DIR}/kernels/image/random_crop_decode_resize_op.cc"
|
||||
"${MINDDATA_DIR}/kernels/image/random_crop_and_resize_op.cc"
|
||||
"${MINDDATA_DIR}/kernels/image/random_crop_op.cc"
|
||||
"${MINDDATA_DIR}/kernels/image/random_crop_with_bbox_op.cc"
|
||||
"${MINDDATA_DIR}/kernels/image/random_horizontal_flip_op.cc"
|
||||
"${MINDDATA_DIR}/kernels/image/random_horizontal_flip_with_bbox_op.cc"
|
||||
"${MINDDATA_DIR}/kernels/image/random_posterize_op.cc"
|
||||
"${MINDDATA_DIR}/kernels/image/random_resize_op.cc"
|
||||
"${MINDDATA_DIR}/kernels/image/random_rotation_op.cc"
|
||||
"${MINDDATA_DIR}/kernels/image/random_select_subpolicy_op.cc"
|
||||
"${MINDDATA_DIR}/kernels/image/random_solarize_op.cc"
|
||||
"${MINDDATA_DIR}/kernels/image/random_vertical_flip_op.cc"
|
||||
"${MINDDATA_DIR}/kernels/image/random_vertical_flip_with_bbox_op.cc"
|
||||
"${MINDDATA_DIR}/kernels/image/random_sharpness_op.cc"
|
||||
"${MINDDATA_DIR}/kernels/image/rescale_op.cc"
|
||||
"${MINDDATA_DIR}/kernels/image/rgba_to_bgr_op.cc"
|
||||
"${MINDDATA_DIR}/kernels/image/rgba_to_rgb_op.cc"
|
||||
"${MINDDATA_DIR}/kernels/image/sharpness_op.cc"
|
||||
"${MINDDATA_DIR}/kernels/image/solarize_op.cc"
|
||||
"${MINDDATA_DIR}/kernels/image/swap_red_blue_op.cc"
|
||||
"${MINDDATA_DIR}/kernels/image/uniform_aug_op.cc"
|
||||
"${MINDDATA_DIR}/kernels/image/resize_with_bbox_op.cc"
|
||||
"${MINDDATA_DIR}/kernels/image/random_resize_with_bbox_op.cc"
|
||||
"${MINDDATA_DIR}/kernels/image/random_color_op.cc"
|
||||
)
|
||||
|
||||
list(REMOVE_ITEM MINDDATA_ENGINE_IR_DATASETOPS_SRC_FILES
|
||||
"${MINDDATA_DIR}/engine/ir/datasetops/bucket_batch_by_length_node.cc"
|
||||
|
|
@ -172,6 +219,7 @@ if (BUILD_MINDDATA STREQUAL "full")
|
|||
${MINDDATA_ENGINE_OPT_UTIL_SRC_FILES}
|
||||
${MINDDATA_ENGINE_PERF_SRC_FILES}
|
||||
${MINDDATA_KERNELS_SRC_FILES}
|
||||
${MINDDATA_KERNELS_IMAGE_LITE_CV_FILES}
|
||||
${MINDDATA_KERNELS_IMAGE_SRC_FILES}
|
||||
${MINDDATA_KERNELS_DATA_SRC_FILES}
|
||||
${MINDDATA_UTIL_SRC_FILES}
|
||||
|
|
@ -184,9 +232,9 @@ if (BUILD_MINDDATA STREQUAL "full")
|
|||
securec
|
||||
jpeg-turbo
|
||||
jpeg
|
||||
opencv_core
|
||||
opencv_imgcodecs
|
||||
opencv_imgproc
|
||||
#opencv_core
|
||||
#opencv_imgcodecs
|
||||
#opencv_imgproc
|
||||
mindspore::json
|
||||
)
|
||||
|
||||
|
|
@ -215,29 +263,77 @@ elseif (BUILD_MINDDATA STREQUAL "lite")
|
|||
${MINDDATA_DIR}/api/de_tensor.cc
|
||||
${MINDDATA_DIR}/api/execute.cc
|
||||
)
|
||||
list(REMOVE_ITEM MINDDATA_CORE_SRC_FILES
|
||||
"${MINDDATA_DIR}/core/cv_tensor.cc"
|
||||
)
|
||||
|
||||
list(REMOVE_ITEM MINDDATA_KERNELS_IMAGE_SRC_FILES
|
||||
list(REMOVE_ITEM MINDDATA_KERNELS_IMAGE_SRC_FILES
|
||||
"${MINDDATA_DIR}/kernels/image/affine_op.cc"
|
||||
"${MINDDATA_DIR}/kernels/image/auto_contrast_op.cc"
|
||||
"${MINDDATA_DIR}/kernels/image/bounding_box_op.cc"
|
||||
"${MINDDATA_DIR}/kernels/image/bounding_box_augment_op.cc"
|
||||
"${MINDDATA_DIR}/kernels/image/center_crop_op.cc"
|
||||
"${MINDDATA_DIR}/kernels/image/concatenate_op.cc"
|
||||
"${MINDDATA_DIR}/kernels/image/cut_out_op.cc"
|
||||
)
|
||||
"${MINDDATA_DIR}/kernels/image/cutmix_batch_op.cc"
|
||||
"${MINDDATA_DIR}/kernels/image/equalize_op.cc"
|
||||
"${MINDDATA_DIR}/kernels/image/hwc_to_chw_op.cc"
|
||||
"${MINDDATA_DIR}/kernels/image/image_utils.cc"
|
||||
"${MINDDATA_DIR}/kernels/image/invert_op.cc"
|
||||
"${MINDDATA_DIR}/kernels/image/math_utils.cc"
|
||||
"${MINDDATA_DIR}/kernels/image/mixup_batch_op.cc"
|
||||
"${MINDDATA_DIR}/kernels/image/pad_op.cc"
|
||||
"${MINDDATA_DIR}/kernels/image/posterize_op.cc"
|
||||
"${MINDDATA_DIR}/kernels/image/random_affine_op.cc"
|
||||
"${MINDDATA_DIR}/kernels/image/random_color_adjust_op.cc"
|
||||
"${MINDDATA_DIR}/kernels/image/random_crop_and_resize_with_bbox_op.cc"
|
||||
"${MINDDATA_DIR}/kernels/image/random_crop_decode_resize_op.cc"
|
||||
"${MINDDATA_DIR}/kernels/image/random_crop_and_resize_op.cc"
|
||||
"${MINDDATA_DIR}/kernels/image/random_crop_op.cc"
|
||||
"${MINDDATA_DIR}/kernels/image/random_crop_with_bbox_op.cc"
|
||||
"${MINDDATA_DIR}/kernels/image/random_horizontal_flip_op.cc"
|
||||
"${MINDDATA_DIR}/kernels/image/random_horizontal_flip_with_bbox_op.cc"
|
||||
"${MINDDATA_DIR}/kernels/image/random_posterize_op.cc"
|
||||
"${MINDDATA_DIR}/kernels/image/random_resize_op.cc"
|
||||
"${MINDDATA_DIR}/kernels/image/random_rotation_op.cc"
|
||||
"${MINDDATA_DIR}/kernels/image/random_select_subpolicy_op.cc"
|
||||
"${MINDDATA_DIR}/kernels/image/random_solarize_op.cc"
|
||||
"${MINDDATA_DIR}/kernels/image/random_vertical_flip_op.cc"
|
||||
"${MINDDATA_DIR}/kernels/image/random_vertical_flip_with_bbox_op.cc"
|
||||
"${MINDDATA_DIR}/kernels/image/random_sharpness_op.cc"
|
||||
"${MINDDATA_DIR}/kernels/image/rescale_op.cc"
|
||||
"${MINDDATA_DIR}/kernels/image/rgba_to_bgr_op.cc"
|
||||
"${MINDDATA_DIR}/kernels/image/rgba_to_rgb_op.cc"
|
||||
"${MINDDATA_DIR}/kernels/image/sharpness_op.cc"
|
||||
"${MINDDATA_DIR}/kernels/image/solarize_op.cc"
|
||||
"${MINDDATA_DIR}/kernels/image/swap_red_blue_op.cc"
|
||||
"${MINDDATA_DIR}/kernels/image/uniform_aug_op.cc"
|
||||
"${MINDDATA_DIR}/kernels/image/resize_with_bbox_op.cc"
|
||||
"${MINDDATA_DIR}/kernels/image/random_resize_with_bbox_op.cc"
|
||||
"${MINDDATA_DIR}/kernels/image/random_color_op.cc"
|
||||
)
|
||||
|
||||
add_library(minddata-lite SHARED
|
||||
${MINDDATA_CORE_SRC_FILES}
|
||||
${MINDDATA_KERNELS_SRC_FILES}
|
||||
${MINDDATA_KERNELS_IMAGE_LITE_CV_FILES}
|
||||
${MINDDATA_KERNELS_IMAGE_SRC_FILES}
|
||||
${MINDDATA_KERNELS_DATA_SRC_FILES}
|
||||
${MINDDATA_DIR}/util/status.cc
|
||||
${MINDDATA_DIR}/util/memory_pool.cc
|
||||
${MINDDATA_DIR}/util/path.cc
|
||||
${MINDDATA_DIR}/api/transforms.cc
|
||||
${CMAKE_CURRENT_SOURCE_DIR}/../src/common/log_adapter.cc
|
||||
${CORE_DIR}/utils/ms_utils.cc
|
||||
)
|
||||
|
||||
target_link_libraries(minddata-lite
|
||||
securec
|
||||
jpeg-turbo
|
||||
jpeg
|
||||
opencv_core
|
||||
opencv_imgcodecs
|
||||
opencv_imgproc
|
||||
# opencv_core
|
||||
# opencv_imgcodecs
|
||||
# opencv_imgproc
|
||||
mindspore::json
|
||||
)
|
||||
|
||||
|
|
|
|||
|
|
@ -90,13 +90,19 @@ if (SUPPORT_GPU)
|
|||
endif()
|
||||
### minddata lite
|
||||
if (BUILD_MINDDATA STREQUAL "lite")
|
||||
include_directories(${CCSRC_DIR}/minddata)
|
||||
set(DATASET_TEST_DIR ${TEST_DIR}/ut/src/dataset)
|
||||
set(TEST_MINDDATA_SRC
|
||||
${DATASET_TEST_DIR}/de_tensor_test.cc
|
||||
${DATASET_TEST_DIR}/eager_test.cc
|
||||
)
|
||||
# include_directories(${CCSRC_DIR}/minddata)
|
||||
# set(DATASET_TEST_DIR ${TEST_DIR}/ut/src/dataset)
|
||||
# set(TEST_MINDDATA_SRC
|
||||
# ${DATASET_TEST_DIR}/de_tensor_test.cc
|
||||
# ${DATASET_TEST_DIR}/eager_test.cc
|
||||
# )
|
||||
elseif (BUILD_MINDDATA STREQUAL "full")
|
||||
#include_directories(${CCSRC_DIR}/minddata)
|
||||
#set(DATASET_TEST_DIR ${TEST_DIR}/ut/src/dataset)
|
||||
#set(TEST_MINDDATA_SRC
|
||||
# ${DATASET_TEST_DIR}/de_tensor_test.cc
|
||||
# ${DATASET_TEST_DIR}/eager_test.cc
|
||||
# )
|
||||
endif()
|
||||
### runtime framework
|
||||
file(GLOB_RECURSE OPS_SRC ${LITE_DIR}/src/ops/*.cc ${LITE_DIR}/src/ops/populate/*.cc)
|
||||
|
|
@ -278,12 +284,12 @@ if (PLATFORM_ARM)
|
|||
target_link_libraries(lite-test log)
|
||||
endif()
|
||||
|
||||
if (BUILD_MINDDATA STREQUAL "lite")
|
||||
target_link_libraries(lite-test
|
||||
minddata_eager_mid
|
||||
minddata-lite
|
||||
)
|
||||
endif()
|
||||
#if (BUILD_MINDDATA STREQUAL "lite")
|
||||
# target_link_libraries(lite-test
|
||||
# minddata_eager_mid
|
||||
#minddata-lite
|
||||
# )
|
||||
#endif()
|
||||
if (ENABLE_CONVERTER)
|
||||
target_link_libraries(lite-test
|
||||
anf_importer_mid
|
||||
|
|
|
|||
|
|
@ -15,12 +15,15 @@
|
|||
*/
|
||||
#include <memory>
|
||||
#include <string>
|
||||
#include "./securec.h"
|
||||
#include "common/common_test.h"
|
||||
#include "gtest/gtest.h"
|
||||
#include "./securec.h"
|
||||
#include "dataset/core/tensor.h"
|
||||
#include "dataset/core/cv_tensor.h"
|
||||
#include "dataset/core/data_type.h"
|
||||
|
||||
#include "mindspore/ccsrc/minddata/dataset/core/data_type.h"
|
||||
#include "mindspore/ccsrc/minddata/dataset/core/tensor.h"
|
||||
#include "mindspore/ccsrc/minddata/dataset/core/tensor_shape.h"
|
||||
#include "mindspore/ccsrc/minddata/dataset/include/de_tensor.h"
|
||||
#include "mindspore/lite/src/common/log_adapter.h"
|
||||
#include "mindspore/lite/src/tensor.h"
|
||||
|
||||
using MSTensor = mindspore::tensor::MSTensor;
|
||||
|
|
|
|||
|
|
@ -23,6 +23,7 @@
|
|||
#include "minddata/dataset/include/vision.h"
|
||||
#include "minddata/dataset/include/execute.h"
|
||||
#include "minddata/dataset/util/path.h"
|
||||
#include "mindspore/lite/src/common/log_adapter.h"
|
||||
|
||||
using MSTensor = mindspore::tensor::MSTensor;
|
||||
using DETensor = mindspore::tensor::DETensor;
|
||||
|
|
@ -56,7 +57,7 @@ TEST_F(MindDataTestEager, Test1) {
|
|||
auto dir_it = Path::DirIterator::OpenDirectory(&base_dir);
|
||||
while (dir_it->hasNext()) {
|
||||
Path v = dir_it->next();
|
||||
MS_LOG(WARNING) << v.toString() << ".";
|
||||
// MS_LOG(WARNING) << v.toString() << ".";
|
||||
std::shared_ptr<MSTensor> image = std::shared_ptr<MSTensor>(DETensor::CreateTensor(v.toString()));
|
||||
|
||||
image = Execute(Decode())(image);
|
||||
|
|
|
|||
Loading…
Reference in New Issue