forked from huawei/mindspore2022
1235 lines
51 KiB
C++
1235 lines
51 KiB
C++
/**
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* Copyright 2020-2021 Huawei Technologies Co., Ltd
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*
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* Licensed under the Apache License, Version 2.0 (the "License");
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* you may not use this file except in compliance with the License.
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* You may obtain a copy of the License at
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*
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* http://www.apache.org/licenses/LICENSE-2.0
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*
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* Unless required by applicable law or agreed to in writing, software
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* distributed under the License is distributed on an "AS IS" BASIS,
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* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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* See the License for the specific language governing permissions and
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* limitations under the License.
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*/
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#ifndef MINDSPORE_CCSRC_MINDDATA_DATASET_INCLUDE_VISION_H_
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#define MINDSPORE_CCSRC_MINDDATA_DATASET_INCLUDE_VISION_H_
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#include <map>
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#include <memory>
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#include <string>
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#include <utility>
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#include <vector>
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#include "include/api/status.h"
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#include "minddata/dataset/include/constants.h"
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#include "minddata/dataset/include/transforms.h"
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#include "minddata/dataset/include/vision_lite.h"
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namespace mindspore {
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namespace dataset {
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// Transform operations for performing computer vision.
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namespace vision {
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// Char arrays storing name of corresponding classes (in alphabetical order)
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constexpr char kAutoContrastOperation[] = "AutoContrast";
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constexpr char kBoundingBoxAugmentOperation[] = "BoundingBoxAugment";
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constexpr char kCutMixBatchOperation[] = "CutMixBatch";
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constexpr char kCutOutOperation[] = "CutOut";
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constexpr char kDvppDecodeResizeCropOperation[] = "DvppDecodeResizeCrop";
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constexpr char kEqualizeOperation[] = "Equalize";
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constexpr char kHwcToChwOperation[] = "HwcToChw";
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constexpr char kInvertOperation[] = "Invert";
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constexpr char kMixUpBatchOperation[] = "MixUpBatch";
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constexpr char kNormalizePadOperation[] = "NormalizePad";
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constexpr char kPadOperation[] = "Pad";
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constexpr char kRandomAffineOperation[] = "RandomAffine";
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constexpr char kRandomColorAdjustOperation[] = "RandomColorAdjust";
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constexpr char kRandomColorOperation[] = "RandomColor";
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constexpr char kRandomCropDecodeResizeOperation[] = "RandomCropDecodeResize";
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constexpr char kRandomCropOperation[] = "RandomCrop";
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constexpr char kRandomCropWithBBoxOperation[] = "RandomCropWithBBox";
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constexpr char kRandomHorizontalFlipWithBBoxOperation[] = "RandomHorizontalFlipWithBBox";
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constexpr char kRandomHorizontalFlipOperation[] = "RandomHorizontalFlip";
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constexpr char kRandomPosterizeOperation[] = "RandomPosterize";
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constexpr char kRandomResizedCropOperation[] = "RandomResizedCrop";
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constexpr char kRandomResizedCropWithBBoxOperation[] = "RandomResizedCropWithBBox";
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constexpr char kRandomResizeOperation[] = "RandomResize";
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constexpr char kRandomResizeWithBBoxOperation[] = "RandomResizeWithBBox";
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constexpr char kRandomRotationOperation[] = "RandomRotation";
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constexpr char kRandomSolarizeOperation[] = "RandomSolarize";
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constexpr char kRandomSharpnessOperation[] = "RandomSharpness";
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constexpr char kRandomVerticalFlipOperation[] = "RandomVerticalFlip";
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constexpr char kRandomVerticalFlipWithBBoxOperation[] = "RandomVerticalFlipWithBBox";
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constexpr char kRescaleOperation[] = "Rescale";
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constexpr char kResizeWithBBoxOperation[] = "ResizeWithBBox";
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constexpr char kRgbaToBgrOperation[] = "RgbaToBgr";
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constexpr char kRgbaToRgbOperation[] = "RgbaToRgb";
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constexpr char kSoftDvppDecodeRandomCropResizeJpegOperation[] = "SoftDvppDecodeRandomCropResizeJpeg";
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constexpr char kSoftDvppDecodeResizeJpegOperation[] = "SoftDvppDecodeResizeJpeg";
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constexpr char kSwapRedBlueOperation[] = "SwapRedBlue";
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constexpr char kUniformAugOperation[] = "UniformAug";
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// Transform Op classes (in alphabetical order)
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class AutoContrastOperation;
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class BoundingBoxAugmentOperation;
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class CutMixBatchOperation;
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class CutOutOperation;
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class DvppDecodeResizeCropOperation;
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class EqualizeOperation;
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class HwcToChwOperation;
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class InvertOperation;
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class MixUpBatchOperation;
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class NormalizePadOperation;
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class PadOperation;
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class RandomAffineOperation;
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class RandomColorOperation;
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class RandomColorAdjustOperation;
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class RandomCropOperation;
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class RandomCropDecodeResizeOperation;
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class RandomCropWithBBoxOperation;
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class RandomHorizontalFlipOperation;
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class RandomHorizontalFlipWithBBoxOperation;
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class RandomPosterizeOperation;
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class RandomResizeOperation;
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class RandomResizeWithBBoxOperation;
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class RandomResizedCropOperation;
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class RandomResizedCropWithBBoxOperation;
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class RandomRotationOperation;
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class RandomSelectSubpolicyOperation;
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class RandomSharpnessOperation;
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class RandomSolarizeOperation;
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class RandomVerticalFlipOperation;
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class RandomVerticalFlipWithBBoxOperation;
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class RescaleOperation;
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class ResizeWithBBoxOperation;
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class RgbaToBgrOperation;
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class RgbaToRgbOperation;
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class SoftDvppDecodeRandomCropResizeJpegOperation;
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class SoftDvppDecodeResizeJpegOperation;
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class SwapRedBlueOperation;
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class UniformAugOperation;
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/// \brief Function to create a AutoContrast TensorOperation.
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/// \notes Apply automatic contrast on input image.
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/// \param[in] cutoff Percent of pixels to cut off from the histogram, the valid range of cutoff value is 0 to 100.
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/// \param[in] ignore Pixel values to ignore.
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/// \return Shared pointer to the current TensorOperation.
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std::shared_ptr<AutoContrastOperation> AutoContrast(float cutoff = 0.0, std::vector<uint32_t> ignore = {});
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/// \brief Function to create a BoundingBoxAugment TensorOperation.
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/// \notes Apply a given image transform on a random selection of bounding box regions of a given image.
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/// \param[in] transform A TensorOperation transform.
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/// \param[in] ratio Ratio of bounding boxes to apply augmentation on. Range: [0, 1] (default=0.3).
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/// \return Shared pointer to the current TensorOperation.
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std::shared_ptr<BoundingBoxAugmentOperation> BoundingBoxAugment(std::shared_ptr<TensorOperation> transform,
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float ratio = 0.3);
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/// \brief Function to apply CutMix on a batch of images
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/// \notes Masks a random section of each image with the corresponding part of another randomly
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/// selected image in that batch
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/// \param[in] image_batch_format The format of the batch
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/// \param[in] alpha The hyperparameter of beta distribution (default = 1.0)
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/// \param[in] prob The probability by which CutMix is applied to each image (default = 1.0)
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/// \return Shared pointer to the current TensorOp
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std::shared_ptr<CutMixBatchOperation> CutMixBatch(ImageBatchFormat image_batch_format, float alpha = 1.0,
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float prob = 1.0);
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/// \brief Function to create a CutOut TensorOp
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/// \notes Randomly cut (mask) out a given number of square patches from the input image
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/// \param[in] length Integer representing the side length of each square patch
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/// \param[in] num_patches Integer representing the number of patches to be cut out of an image
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/// \return Shared pointer to the current TensorOp
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std::shared_ptr<CutOutOperation> CutOut(int32_t length, int32_t num_patches = 1);
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/// \brief Function to create a DvppDecodeResizeCropJpeg TensorOperation.
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/// \notes Tensor operation to decode and resize JPEG image using the simulation algorithm of Ascend series
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/// chip DVPP module. It is recommended to use this algorithm in the following scenarios:
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/// When training, the DVPP of the Ascend chip is not used,
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/// and the DVPP of the Ascend chip is used during inference,
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/// and the accuracy of inference is lower than the accuracy of training;
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/// and the input image size should be in range [16*16, 4096*4096].
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/// Only images with an even resolution can be output. The output of odd resolution is not supported.
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/// \param[in] crop vector representing the output size of the final crop image.
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/// \param[in] size A vector representing the output size of the intermediate resized image.
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/// If size is a single value, smaller edge of the image will be resized to this value with
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/// the same image aspect ratio. If size has 2 values, it should be (height, width).
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/// \return Shared pointer to the current TensorOperation.
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std::shared_ptr<DvppDecodeResizeCropOperation> DvppDecodeResizeCropJpeg(std::vector<uint32_t> crop = {224, 224},
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std::vector<uint32_t> resize = {256, 256});
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/// \brief Function to create a Equalize TensorOperation.
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/// \notes Apply histogram equalization on input image.
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/// \return Shared pointer to the current TensorOperation.
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std::shared_ptr<EqualizeOperation> Equalize();
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/// \brief Function to create a HwcToChw TensorOperation.
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/// \notes Transpose the input image; shape (H, W, C) to shape (C, H, W).
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/// \return Shared pointer to the current TensorOperation.
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std::shared_ptr<HwcToChwOperation> HWC2CHW();
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/// \brief Function to create a Invert TensorOperation.
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/// \notes Apply invert on input image in RGB mode.
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/// \return Shared pointer to the current TensorOperation.
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std::shared_ptr<InvertOperation> Invert();
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/// \brief Function to create a MixUpBatch TensorOperation.
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/// \notes Apply MixUp transformation on an input batch of images and labels. The labels must be in
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/// one-hot format and Batch must be called before calling this function.
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/// \param[in] alpha hyperparameter of beta distribution (default = 1.0)
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/// \return Shared pointer to the current TensorOperation.
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std::shared_ptr<MixUpBatchOperation> MixUpBatch(float alpha = 1);
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/// \brief Function to create a NormalizePad TensorOperation.
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/// \notes Normalize the input image with respect to mean and standard deviation and pad an extra
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/// channel with value zero.
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/// \param[in] mean A vector of mean values for each channel, w.r.t channel order.
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/// The mean values must be in range [0.0, 255.0].
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/// \param[in] std A vector of standard deviations for each channel, w.r.t. channel order.
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/// The standard deviation values must be in range (0.0, 255.0]
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/// \param[in] dtype The output datatype of Tensor.
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/// The standard deviation values must be "float32" or "float16"（default = "float32"）
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/// \return Shared pointer to the current TensorOperation.
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std::shared_ptr<NormalizePadOperation> NormalizePad(const std::vector<float> &mean, const std::vector<float> &std,
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const std::string &dtype = "float32");
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/// \brief Function to create a Pad TensorOp
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/// \notes Pads the image according to padding parameters
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/// \param[in] padding A vector representing the number of pixels to pad the image
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/// If vector has one value, it pads all sides of the image with that value.
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/// If vector has two values, it pads left and top with the first and
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/// right and bottom with the second value.
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/// If vector has four values, it pads left, top, right, and bottom with
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/// those values respectively.
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/// \param[in] fill_value A vector representing the pixel intensity of the borders if the padding_mode is
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/// BorderType.kConstant. If 1 value is provided, it is used for all RGB channels. If 3 values are provided,
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/// it is used to fill R, G, B channels respectively.
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/// \param[in] padding_mode The method of padding (default=BorderType.kConstant)
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/// Can be any of
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/// [BorderType.kConstant, BorderType.kEdge, BorderType.kReflect, BorderType.kSymmetric]
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/// - BorderType.kConstant, means it fills the border with constant values
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/// - BorderType.kEdge, means it pads with the last value on the edge
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/// - BorderType.kReflect, means it reflects the values on the edge omitting the last value of edge
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/// - BorderType.kSymmetric, means it reflects the values on the edge repeating the last value of edge
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/// \return Shared pointer to the current TensorOp
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std::shared_ptr<PadOperation> Pad(std::vector<int32_t> padding, std::vector<uint8_t> fill_value = {0},
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BorderType padding_mode = BorderType::kConstant);
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/// \brief Function to create a RandomAffine TensorOperation.
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/// \notes Applies a Random Affine transformation on input image in RGB or Greyscale mode.
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/// \param[in] degrees A float vector of size 2, representing the starting and ending degree
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/// \param[in] translate_range A float vector of size 2 or 4, representing percentages of translation on x and y axes.
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/// if size is 2, (min_dx, max_dx, 0, 0)
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/// if size is 4, (min_dx, max_dx, min_dy, max_dy)
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/// all values are in range [-1, 1]
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/// \param[in] scale_range A float vector of size 2, representing the starting and ending scales in the range.
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/// \param[in] shear_ranges A float vector of size 2 or 4, representing the starting and ending shear degrees
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/// vertically and horizontally.
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/// if size is 2, (min_shear_x, max_shear_x, 0, 0)
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/// if size is 4, (min_shear_x, max_shear_x, min_shear_y, max_shear_y)
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/// \param[in] interpolation An enum for the mode of interpolation
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/// \param[in] fill_value A vector representing the value to fill the area outside the transform
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/// in the output image. If 1 value is provided, it is used for all RGB channels.
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/// If 3 values are provided, it is used to fill R, G, B channels respectively.
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/// \return Shared pointer to the current TensorOperation.
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std::shared_ptr<RandomAffineOperation> RandomAffine(
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const std::vector<float_t> °rees, const std::vector<float_t> &translate_range = {0.0, 0.0, 0.0, 0.0},
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const std::vector<float_t> &scale_range = {1.0, 1.0}, const std::vector<float_t> &shear_ranges = {0.0, 0.0, 0.0, 0.0},
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InterpolationMode interpolation = InterpolationMode::kNearestNeighbour,
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const std::vector<uint8_t> &fill_value = {0, 0, 0});
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/// \brief Blends an image with its grayscale version with random weights
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/// t and 1 - t generated from a given range. If the range is trivial
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/// then the weights are determinate and t equals the bound of the interval
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/// \param[in] t_lb Lower bound on the range of random weights
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/// \param[in] t_lb Upper bound on the range of random weights
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/// \return Shared pointer to the current TensorOp
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std::shared_ptr<RandomColorOperation> RandomColor(float t_lb, float t_ub);
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/// \brief Randomly adjust the brightness, contrast, saturation, and hue of the input image
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/// \param[in] brightness Brightness adjustment factor. Must be a vector of one or two values
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/// if it's a vector of two values it needs to be in the form of [min, max]. Default value is {1, 1}
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/// \param[in] contrast Contrast adjustment factor. Must be a vector of one or two values
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/// if it's a vector of two values it needs to be in the form of [min, max]. Default value is {1, 1}
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/// \param[in] saturation Saturation adjustment factor. Must be a vector of one or two values
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/// if it's a vector of two values it needs to be in the form of [min, max]. Default value is {1, 1}
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/// \param[in] hue Brightness adjustment factor. Must be a vector of one or two values
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/// if it's a vector of two values it must be in the form of [min, max] where -0.5 <= min <= max <= 0.5
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/// Default value is {0, 0}
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/// \return Shared pointer to the current TensorOp
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std::shared_ptr<RandomColorAdjustOperation> RandomColorAdjust(std::vector<float> brightness = {1.0, 1.0},
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std::vector<float> contrast = {1.0, 1.0},
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std::vector<float> saturation = {1.0, 1.0},
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std::vector<float> hue = {0.0, 0.0});
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/// \brief Function to create a RandomCrop TensorOperation.
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/// \notes Crop the input image at a random location.
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/// \param[in] size A vector representing the output size of the cropped image.
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/// If size is a single value, a square crop of size (size, size) is returned.
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/// If size has 2 values, it should be (height, width).
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/// \param[in] padding A vector representing the number of pixels to pad the image
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/// If vector has one value, it pads all sides of the image with that value.
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/// If vector has two values, it pads left and top with the first and
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/// right and bottom with the second value.
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/// If vector has four values, it pads left, top, right, and bottom with
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/// those values respectively.
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/// \param[in] pad_if_needed A boolean whether to pad the image if either side is smaller than
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/// the given output size.
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/// \param[in] fill_value A vector representing the pixel intensity of the borders if the padding_mode is
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/// BorderType.kConstant. If 1 value is provided, it is used for all RGB channels.
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/// If 3 values are provided, it is used to fill R, G, B channels respectively.
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/// \return Shared pointer to the current TensorOperation.
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std::shared_ptr<RandomCropOperation> RandomCrop(std::vector<int32_t> size, std::vector<int32_t> padding = {0, 0, 0, 0},
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bool pad_if_needed = false, std::vector<uint8_t> fill_value = {0, 0, 0},
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BorderType padding_mode = BorderType::kConstant);
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/// \brief Function to create a RandomCropDecodeResize TensorOperation.
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/// \notes Equivalent to RandomResizedCrop, but crops before decodes.
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/// \param[in] size A vector representing the output size of the cropped image.
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/// If size is a single value, a square crop of size (size, size) is returned.
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/// If size has 2 values, it should be (height, width).
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/// \param[in] scale Range [min, max) of respective size of the
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/// original size to be cropped (default=(0.08, 1.0))
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/// \param[in] ratio Range [min, max) of aspect ratio to be
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/// cropped (default=(3. / 4., 4. / 3.))
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/// \param[in] interpolation An enum for the mode of interpolation
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/// \param[in] The maximum number of attempts to propose a valid crop_area (default=10).
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/// If exceeded, fall back to use center_crop instead.
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/// \return Shared pointer to the current TensorOperation.
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std::shared_ptr<RandomCropDecodeResizeOperation> RandomCropDecodeResize(
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std::vector<int32_t> size, std::vector<float> scale = {0.08, 1.0}, std::vector<float> ratio = {3. / 4, 4. / 3},
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InterpolationMode interpolation = InterpolationMode::kLinear, int32_t max_attempts = 10);
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/// \brief Function to create a RandomCropWithBBox TensorOperation.
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/// \Crop the input image at a random location and adjust bounding boxes accordingly.
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/// \param[in] size A vector representing the output size of the cropped image.
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/// If size is a single value, a square crop of size (size, size) is returned.
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/// If size has 2 values, it should be (height, width).
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/// \param[in] padding A vector representing the number of pixels to pad the image
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/// If vector has one value, it pads all sides of the image with that value.
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/// If vector has two values, it pads left and top with the first and
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/// right and bottom with the second value.
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/// If vector has four values, it pads left, top, right, and bottom with
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/// those values respectively.
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/// \param[in] pad_if_needed A boolean whether to pad the image if either side is smaller than
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/// the given output size.
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/// \param[in] fill_value A vector representing the pixel intensity of the borders if the padding_mode is
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/// BorderType.kConstant. If 1 value is provided, it is used for all RGB channels.
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/// If 3 values are provided, it is used to fill R, G, B channels respectively.
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/// \param[in] padding_mode The method of padding (default=BorderType::kConstant).It can be any of
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/// [BorderType::kConstant, BorderType::kEdge, BorderType::kReflect, BorderType::kSymmetric].
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/// \return Shared pointer to the current TensorOperation.
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std::shared_ptr<RandomCropWithBBoxOperation> RandomCropWithBBox(std::vector<int32_t> size,
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std::vector<int32_t> padding = {0, 0, 0, 0},
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bool pad_if_needed = false,
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std::vector<uint8_t> fill_value = {0, 0, 0},
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BorderType padding_mode = BorderType::kConstant);
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/// \brief Function to create a RandomHorizontalFlip TensorOperation.
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/// \notes Tensor operation to perform random horizontal flip.
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/// \param[in] prob A float representing the probability of flip.
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/// \return Shared pointer to the current TensorOperation.
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std::shared_ptr<RandomHorizontalFlipOperation> RandomHorizontalFlip(float prob = 0.5);
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/// \brief Function to create a RandomHorizontalFlipWithBBox TensorOperation.
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/// \notes Flip the input image horizontally, randomly with a given probability and adjust bounding boxes accordingly.
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/// \param[in] prob A float representing the probability of flip.
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/// \return Shared pointer to the current TensorOperation.
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std::shared_ptr<RandomHorizontalFlipWithBBoxOperation> RandomHorizontalFlipWithBBox(float prob = 0.5);
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/// \brief Function to create a RandomPosterize TensorOperation.
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/// \notes Tensor operation to perform random posterize.
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/// \param[in] bit_range - uint8_t vector representing the minimum and maximum bit in range. (Default={4, 8})
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/// \return Shared pointer to the current TensorOperation.
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std::shared_ptr<RandomPosterizeOperation> RandomPosterize(const std::vector<uint8_t> &bit_range = {4, 8});
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/// \brief Function to create a RandomResize TensorOperation.
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/// \notes Resize the input image using a randomly selected interpolation mode.
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/// \param[in] size A vector representing the output size of the resized image.
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/// If size is a single value, the smaller edge of the image will be resized to this value with
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// the same image aspect ratio. If size has 2 values, it should be (height, width).
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std::shared_ptr<RandomResizeOperation> RandomResize(std::vector<int32_t> size);
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/// \brief Function to create a RandomResizeWithBBox TensorOperation.
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/// \notes Resize the input image using a randomly selected interpolation mode and adjust
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/// bounding boxes accordingly.
|
||
/// \param[in] size A vector representing the output size of the resized image.
|
||
/// If size is a single value, the smaller edge of the image will be resized to this value with
|
||
// the same image aspect ratio. If size has 2 values, it should be (height, width).
|
||
std::shared_ptr<RandomResizeWithBBoxOperation> RandomResizeWithBBox(std::vector<int32_t> size);
|
||
|
||
/// \brief Function to create a RandomResizedCrop TensorOperation.
|
||
/// \notes Crop the input image to a random size and aspect ratio.
|
||
/// \param[in] size A vector representing the output size of the cropped image.
|
||
/// If size is a single value, a square crop of size (size, size) is returned.
|
||
/// If size has 2 values, it should be (height, width).
|
||
/// \param[in] scale Range [min, max) of respective size of the original
|
||
/// size to be cropped (default=(0.08, 1.0))
|
||
/// \param[in] ratio Range [min, max) of aspect ratio to be cropped
|
||
/// (default=(3. / 4., 4. / 3.)).
|
||
/// \param[in] interpolation Image interpolation mode (default=InterpolationMode::kLinear)
|
||
/// \param[in] max_attempts The maximum number of attempts to propose a valid
|
||
/// crop_area (default=10). If exceeded, fall back to use center_crop instead.
|
||
/// \return Shared pointer to the current TensorOperation.
|
||
std::shared_ptr<RandomResizedCropOperation> RandomResizedCrop(
|
||
std::vector<int32_t> size, std::vector<float> scale = {0.08, 1.0}, std::vector<float> ratio = {3. / 4., 4. / 3.},
|
||
InterpolationMode interpolation = InterpolationMode::kLinear, int32_t max_attempts = 10);
|
||
|
||
/// \brief Function to create a RandomResizedCropWithBBox TensorOperation.
|
||
/// \notes Crop the input image to a random size and aspect ratio.
|
||
/// \param[in] size A vector representing the output size of the cropped image.
|
||
/// If size is a single value, a square crop of size (size, size) is returned.
|
||
/// If size has 2 values, it should be (height, width).
|
||
/// \param[in] scale Range [min, max) of respective size of the original
|
||
/// size to be cropped (default=(0.08, 1.0))
|
||
/// \param[in] ratio Range [min, max) of aspect ratio to be cropped
|
||
/// (default=(3. / 4., 4. / 3.)).
|
||
/// \param[in] interpolation Image interpolation mode (default=InterpolationMode::kLinear)
|
||
/// \param[in] max_attempts The maximum number of attempts to propose a valid
|
||
/// crop_area (default=10). If exceeded, fall back to use center_crop instead.
|
||
/// \return Shared pointer to the current TensorOperation.
|
||
std::shared_ptr<RandomResizedCropWithBBoxOperation> RandomResizedCropWithBBox(
|
||
std::vector<int32_t> size, std::vector<float> scale = {0.08, 1.0}, std::vector<float> ratio = {3. / 4., 4. / 3.},
|
||
InterpolationMode interpolation = InterpolationMode::kLinear, int32_t max_attempts = 10);
|
||
|
||
/// \brief Function to create a RandomRotation TensorOp
|
||
/// \notes Rotates the image according to parameters
|
||
/// \param[in] degrees A float vector of size, representing the starting and ending degree
|
||
/// \param[in] resample An enum for the mode of interpolation
|
||
/// \param[in] expand A boolean representing whether the image is expanded after rotation
|
||
/// \param[in] center A float vector of size 2, representing the x and y center of rotation.
|
||
/// \param[in] fill_value A vector representing the value to fill the area outside the transform
|
||
/// in the output image. If 1 value is provided, it is used for all RGB channels.
|
||
/// If 3 values are provided, it is used to fill R, G, B channels respectively.
|
||
/// \return Shared pointer to the current TensorOp
|
||
std::shared_ptr<RandomRotationOperation> RandomRotation(
|
||
std::vector<float> degrees, InterpolationMode resample = InterpolationMode::kNearestNeighbour, bool expand = false,
|
||
std::vector<float> center = {-1, -1}, std::vector<uint8_t> fill_value = {0, 0, 0});
|
||
|
||
/// \brief Function to create a RandomSelectSubpolicy TensorOperation.
|
||
/// \notes Choose a random sub-policy from a list to be applied on the input image. A sub-policy is a list of tuples
|
||
/// (op, prob), where op is a TensorOp operation and prob is the probability that this op will be applied. Once
|
||
/// a sub-policy is selected, each op within the subpolicy with be applied in sequence according to its probability.
|
||
/// \param[in] policy Vector of sub-policies to choose from.
|
||
/// \return Shared pointer to the current TensorOperation.
|
||
std::shared_ptr<RandomSelectSubpolicyOperation> RandomSelectSubpolicy(
|
||
std::vector<std::vector<std::pair<std::shared_ptr<TensorOperation>, double>>> policy);
|
||
|
||
/// \brief Function to create a RandomSharpness TensorOperation.
|
||
/// \notes Tensor operation to perform random sharpness.
|
||
/// \param[in] degrees A float vector of size 2, representing the starting and ending degree to uniformly
|
||
/// sample from, to select a degree to adjust sharpness.
|
||
/// \return Shared pointer to the current TensorOperation.
|
||
std::shared_ptr<RandomSharpnessOperation> RandomSharpness(std::vector<float> degrees = {0.1, 1.9});
|
||
|
||
/// \brief Function to create a RandomSolarize TensorOperation.
|
||
/// \notes Invert pixels randomly within specified range. If min=max, it is a single fixed magnitude operation
|
||
/// to inverts all pixel above that threshold
|
||
/// \param[in] threshold A vector with two elements specifying the pixel range to invert.
|
||
/// \return Shared pointer to the current TensorOperation.
|
||
std::shared_ptr<RandomSolarizeOperation> RandomSolarize(std::vector<uint8_t> threshold = {0, 255});
|
||
|
||
/// \brief Function to create a RandomVerticalFlip TensorOperation.
|
||
/// \notes Tensor operation to perform random vertical flip.
|
||
/// \param[in] prob A float representing the probability of flip.
|
||
/// \return Shared pointer to the current TensorOperation.
|
||
std::shared_ptr<RandomVerticalFlipOperation> RandomVerticalFlip(float prob = 0.5);
|
||
|
||
/// \brief Function to create a RandomVerticalFlipWithBBox TensorOperation.
|
||
/// \notes Flip the input image vertically, randomly with a given probability and adjust bounding boxes accordingly.
|
||
/// \param[in] prob A float representing the probability of flip.
|
||
/// \return Shared pointer to the current TensorOperation.
|
||
std::shared_ptr<RandomVerticalFlipWithBBoxOperation> RandomVerticalFlipWithBBox(float prob = 0.5);
|
||
|
||
/// \brief Function to create a RescaleOperation TensorOperation.
|
||
/// \notes Tensor operation to rescale the input image.
|
||
/// \param[in] rescale Rescale factor.
|
||
/// \param[in] shift Shift factor.
|
||
/// \return Shared pointer to the current TensorOperation.
|
||
std::shared_ptr<RescaleOperation> Rescale(float rescale, float shift);
|
||
|
||
/// \brief Function to create a ResizeWithBBox TensorOperation.
|
||
/// \notes Resize the input image to the given size and adjust bounding boxes accordingly.
|
||
/// \param[in] size The output size of the resized image.
|
||
/// If size is an integer, smaller edge of the image will be resized to this value with the same image aspect ratio.
|
||
/// If size is a sequence of length 2, it should be (height, width).
|
||
/// \param[in] interpolation An enum for the mode of interpolation (default=InterpolationMode::kLinear).
|
||
/// \return Shared pointer to the current TensorOperation.
|
||
std::shared_ptr<ResizeWithBBoxOperation> ResizeWithBBox(std::vector<int32_t> size,
|
||
InterpolationMode interpolation = InterpolationMode::kLinear);
|
||
|
||
/// \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.
|
||
std::shared_ptr<RgbaToBgrOperation> RGBA2BGR();
|
||
|
||
/// \brief Function to create a RgbaToRgb TensorOperation.
|
||
/// \notes Changes the input 4 channel RGBA tensor to 3 channel RGB.
|
||
/// \return Shared pointer to the current TensorOperation.
|
||
std::shared_ptr<RgbaToRgbOperation> RGBA2RGB();
|
||
|
||
/// \brief Function to create a SoftDvppDecodeRandomCropResizeJpeg TensorOperation.
|
||
/// \notes Tensor operation to decode, random crop and resize JPEG image using the simulation algorithm of
|
||
/// Ascend series chip DVPP module. The usage scenario is consistent with SoftDvppDecodeResizeJpeg.
|
||
/// The input image size should be in range [32*32, 8192*8192].
|
||
/// The zoom-out and zoom-in multiples of the image length and width should in the range [1/32, 16].
|
||
/// Only images with an even resolution can be output. The output of odd resolution is not supported.
|
||
/// \param[in] size A vector representing the output size of the resized image.
|
||
/// If size is a single value, smaller edge of the image will be resized to this value with
|
||
/// the same image aspect ratio. If size has 2 values, it should be (height, width).
|
||
/// \return Shared pointer to the current TensorOperation.
|
||
std::shared_ptr<SoftDvppDecodeRandomCropResizeJpegOperation> SoftDvppDecodeRandomCropResizeJpeg(
|
||
std::vector<int32_t> size, std::vector<float> scale = {0.08, 1.0}, std::vector<float> ratio = {3. / 4., 4. / 3.},
|
||
int32_t max_attempts = 10);
|
||
|
||
/// \brief Function to create a SoftDvppDecodeResizeJpeg TensorOperation.
|
||
/// \notes Tensor operation to decode and resize JPEG image using the simulation algorithm of Ascend series
|
||
/// chip DVPP module. It is recommended to use this algorithm in the following scenarios:
|
||
/// When training, the DVPP of the Ascend chip is not used,
|
||
/// and the DVPP of the Ascend chip is used during inference,
|
||
/// and the accuracy of inference is lower than the accuracy of training;
|
||
/// and the input image size should be in range [32*32, 8192*8192].
|
||
/// The zoom-out and zoom-in multiples of the image length and width should in the range [1/32, 16].
|
||
/// Only images with an even resolution can be output. The output of odd resolution is not supported.
|
||
/// \param[in] size A vector representing the output size of the resized image.
|
||
/// If size is a single value, smaller edge of the image will be resized to this value with
|
||
/// the same image aspect ratio. If size has 2 values, it should be (height, width).
|
||
/// \return Shared pointer to the current TensorOperation.
|
||
std::shared_ptr<SoftDvppDecodeResizeJpegOperation> SoftDvppDecodeResizeJpeg(std::vector<int32_t> size);
|
||
|
||
/// \brief Function to create a SwapRedBlue TensorOp
|
||
/// \notes Swaps the red and blue channels in image
|
||
/// \return Shared pointer to the current TensorOp
|
||
std::shared_ptr<SwapRedBlueOperation> SwapRedBlue();
|
||
|
||
/// \brief Function to create a UniformAugment TensorOperation.
|
||
/// \notes Tensor operation to perform randomly selected augmentation.
|
||
/// \param[in] transforms A vector of TensorOperation transforms.
|
||
/// \param[in] num_ops An integer representing the number of OPs to be selected and applied.
|
||
/// \return Shared pointer to the current TensorOperation.
|
||
std::shared_ptr<UniformAugOperation> UniformAugment(std::vector<std::shared_ptr<TensorOperation>> transforms,
|
||
int32_t num_ops = 2);
|
||
|
||
/* ####################################### Derived TensorOperation classes ################################# */
|
||
|
||
class AutoContrastOperation : public TensorOperation {
|
||
public:
|
||
explicit AutoContrastOperation(float cutoff = 0.0, std::vector<uint32_t> ignore = {});
|
||
|
||
~AutoContrastOperation() = default;
|
||
|
||
std::shared_ptr<TensorOp> Build() override;
|
||
|
||
Status ValidateParams() override;
|
||
|
||
std::string Name() const override { return kAutoContrastOperation; }
|
||
|
||
Status to_json(nlohmann::json *out_json) override;
|
||
|
||
private:
|
||
float cutoff_;
|
||
std::vector<uint32_t> ignore_;
|
||
};
|
||
|
||
class BoundingBoxAugmentOperation : public TensorOperation {
|
||
public:
|
||
explicit BoundingBoxAugmentOperation(std::shared_ptr<TensorOperation> transform, float ratio = 0.3);
|
||
|
||
~BoundingBoxAugmentOperation() = default;
|
||
|
||
std::shared_ptr<TensorOp> Build() override;
|
||
|
||
Status ValidateParams() override;
|
||
|
||
std::string Name() const override { return kBoundingBoxAugmentOperation; }
|
||
|
||
Status to_json(nlohmann::json *out_json) override;
|
||
|
||
private:
|
||
std::shared_ptr<TensorOperation> transform_;
|
||
float ratio_;
|
||
};
|
||
|
||
class CutMixBatchOperation : public TensorOperation {
|
||
public:
|
||
explicit CutMixBatchOperation(ImageBatchFormat image_batch_format, float alpha = 1.0, float prob = 1.0);
|
||
|
||
~CutMixBatchOperation() = default;
|
||
|
||
std::shared_ptr<TensorOp> Build() override;
|
||
|
||
Status ValidateParams() override;
|
||
|
||
std::string Name() const override { return kCutMixBatchOperation; }
|
||
|
||
Status to_json(nlohmann::json *out_json) override;
|
||
|
||
private:
|
||
float alpha_;
|
||
float prob_;
|
||
ImageBatchFormat image_batch_format_;
|
||
};
|
||
|
||
class CutOutOperation : public TensorOperation {
|
||
public:
|
||
explicit CutOutOperation(int32_t length, int32_t num_patches = 1);
|
||
|
||
~CutOutOperation() = default;
|
||
|
||
std::shared_ptr<TensorOp> Build() override;
|
||
|
||
Status ValidateParams() override;
|
||
|
||
std::string Name() const override { return kCutOutOperation; }
|
||
|
||
Status to_json(nlohmann::json *out_json) override;
|
||
|
||
private:
|
||
int32_t length_;
|
||
int32_t num_patches_;
|
||
};
|
||
|
||
class DvppDecodeResizeCropOperation : public TensorOperation {
|
||
public:
|
||
explicit DvppDecodeResizeCropOperation(const std::vector<uint32_t> &crop, const std::vector<uint32_t> &resize);
|
||
|
||
~DvppDecodeResizeCropOperation() = default;
|
||
|
||
std::shared_ptr<TensorOp> Build() override;
|
||
|
||
Status ValidateParams() override;
|
||
|
||
std::string Name() const override { return kDvppDecodeResizeCropOperation; }
|
||
|
||
private:
|
||
std::vector<uint32_t> crop_;
|
||
std::vector<uint32_t> resize_;
|
||
};
|
||
|
||
class EqualizeOperation : public TensorOperation {
|
||
public:
|
||
~EqualizeOperation() = default;
|
||
|
||
std::shared_ptr<TensorOp> Build() override;
|
||
|
||
Status ValidateParams() override;
|
||
|
||
std::string Name() const override { return kEqualizeOperation; }
|
||
};
|
||
|
||
class HwcToChwOperation : public TensorOperation {
|
||
public:
|
||
~HwcToChwOperation() = default;
|
||
|
||
std::shared_ptr<TensorOp> Build() override;
|
||
|
||
Status ValidateParams() override;
|
||
|
||
std::string Name() const override { return kHwcToChwOperation; }
|
||
};
|
||
|
||
class InvertOperation : public TensorOperation {
|
||
public:
|
||
~InvertOperation() = default;
|
||
|
||
std::shared_ptr<TensorOp> Build() override;
|
||
|
||
Status ValidateParams() override;
|
||
|
||
std::string Name() const override { return kInvertOperation; }
|
||
};
|
||
|
||
class MixUpBatchOperation : public TensorOperation {
|
||
public:
|
||
explicit MixUpBatchOperation(float alpha = 1);
|
||
|
||
~MixUpBatchOperation() = default;
|
||
|
||
std::shared_ptr<TensorOp> Build() override;
|
||
|
||
Status ValidateParams() override;
|
||
|
||
std::string Name() const override { return kMixUpBatchOperation; }
|
||
|
||
Status to_json(nlohmann::json *out_json) override;
|
||
|
||
private:
|
||
float alpha_;
|
||
};
|
||
|
||
class NormalizePadOperation : public TensorOperation {
|
||
public:
|
||
NormalizePadOperation(const std::vector<float> &mean, const std::vector<float> &std,
|
||
const std::string &dtype = "float32");
|
||
|
||
~NormalizePadOperation() = default;
|
||
|
||
std::shared_ptr<TensorOp> Build() override;
|
||
|
||
Status ValidateParams() override;
|
||
|
||
std::string Name() const override { return kNormalizePadOperation; }
|
||
|
||
Status to_json(nlohmann::json *out_json) override;
|
||
|
||
private:
|
||
std::vector<float> mean_;
|
||
std::vector<float> std_;
|
||
std::string dtype_;
|
||
};
|
||
|
||
class PadOperation : public TensorOperation {
|
||
public:
|
||
PadOperation(std::vector<int32_t> padding, std::vector<uint8_t> fill_value = {0},
|
||
BorderType padding_mode = BorderType::kConstant);
|
||
|
||
~PadOperation() = default;
|
||
|
||
std::shared_ptr<TensorOp> Build() override;
|
||
|
||
Status ValidateParams() override;
|
||
|
||
std::string Name() const override { return kPadOperation; }
|
||
|
||
Status to_json(nlohmann::json *out_json) override;
|
||
|
||
private:
|
||
std::vector<int32_t> padding_;
|
||
std::vector<uint8_t> fill_value_;
|
||
BorderType padding_mode_;
|
||
};
|
||
|
||
class RandomAffineOperation : public TensorOperation {
|
||
public:
|
||
RandomAffineOperation(const std::vector<float_t> °rees, const std::vector<float_t> &translate_range = {0.0, 0.0},
|
||
const std::vector<float_t> &scale_range = {1.0, 1.0},
|
||
const std::vector<float_t> &shear_ranges = {0.0, 0.0, 0.0, 0.0},
|
||
InterpolationMode interpolation = InterpolationMode::kNearestNeighbour,
|
||
const std::vector<uint8_t> &fill_value = {0, 0, 0});
|
||
|
||
~RandomAffineOperation() = default;
|
||
|
||
std::shared_ptr<TensorOp> Build() override;
|
||
|
||
Status ValidateParams() override;
|
||
|
||
std::string Name() const override { return kRandomAffineOperation; }
|
||
|
||
Status to_json(nlohmann::json *out_json) override;
|
||
|
||
private:
|
||
std::vector<float_t> degrees_; // min_degree, max_degree
|
||
std::vector<float_t> translate_range_; // maximum x translation percentage, maximum y translation percentage
|
||
std::vector<float_t> scale_range_; // min_scale, max_scale
|
||
std::vector<float_t> shear_ranges_; // min_x_shear, max_x_shear, min_y_shear, max_y_shear
|
||
InterpolationMode interpolation_;
|
||
std::vector<uint8_t> fill_value_;
|
||
};
|
||
|
||
class RandomColorOperation : public TensorOperation {
|
||
public:
|
||
RandomColorOperation(float t_lb, float t_ub);
|
||
|
||
~RandomColorOperation() = default;
|
||
|
||
std::shared_ptr<TensorOp> Build() override;
|
||
|
||
Status ValidateParams() override;
|
||
|
||
std::string Name() const override { return kRandomColorOperation; }
|
||
|
||
Status to_json(nlohmann::json *out_json) override;
|
||
|
||
private:
|
||
float t_lb_;
|
||
float t_ub_;
|
||
};
|
||
|
||
class RandomColorAdjustOperation : public TensorOperation {
|
||
public:
|
||
RandomColorAdjustOperation(std::vector<float> brightness = {1.0, 1.0}, std::vector<float> contrast = {1.0, 1.0},
|
||
std::vector<float> saturation = {1.0, 1.0}, std::vector<float> hue = {0.0, 0.0});
|
||
|
||
~RandomColorAdjustOperation() = default;
|
||
|
||
std::shared_ptr<TensorOp> Build() override;
|
||
|
||
Status ValidateParams() override;
|
||
|
||
std::string Name() const override { return kRandomColorAdjustOperation; }
|
||
|
||
Status to_json(nlohmann::json *out_json) override;
|
||
|
||
private:
|
||
std::vector<float> brightness_;
|
||
std::vector<float> contrast_;
|
||
std::vector<float> saturation_;
|
||
std::vector<float> hue_;
|
||
};
|
||
|
||
class RandomCropOperation : public TensorOperation {
|
||
public:
|
||
RandomCropOperation(std::vector<int32_t> size, std::vector<int32_t> padding = {0, 0, 0, 0},
|
||
bool pad_if_needed = false, std::vector<uint8_t> fill_value = {0, 0, 0},
|
||
BorderType padding_mode = BorderType::kConstant);
|
||
|
||
~RandomCropOperation() = default;
|
||
|
||
std::shared_ptr<TensorOp> Build() override;
|
||
|
||
Status ValidateParams() override;
|
||
|
||
std::string Name() const override { return kRandomCropOperation; }
|
||
|
||
Status to_json(nlohmann::json *out_json) override;
|
||
|
||
private:
|
||
std::vector<int32_t> size_;
|
||
std::vector<int32_t> padding_;
|
||
bool pad_if_needed_;
|
||
std::vector<uint8_t> fill_value_;
|
||
BorderType padding_mode_;
|
||
};
|
||
|
||
class RandomResizedCropOperation : public TensorOperation {
|
||
public:
|
||
RandomResizedCropOperation(std::vector<int32_t> size, std::vector<float> scale = {0.08, 1.0},
|
||
std::vector<float> ratio = {3. / 4., 4. / 3.},
|
||
InterpolationMode interpolation = InterpolationMode::kNearestNeighbour,
|
||
int32_t max_attempts = 10);
|
||
|
||
/// \brief default copy constructor
|
||
explicit RandomResizedCropOperation(const RandomResizedCropOperation &) = default;
|
||
|
||
~RandomResizedCropOperation() = default;
|
||
|
||
std::shared_ptr<TensorOp> Build() override;
|
||
|
||
Status ValidateParams() override;
|
||
|
||
std::string Name() const override { return kRandomResizedCropOperation; }
|
||
|
||
Status to_json(nlohmann::json *out_json) override;
|
||
|
||
protected:
|
||
std::vector<int32_t> size_;
|
||
std::vector<float> scale_;
|
||
std::vector<float> ratio_;
|
||
InterpolationMode interpolation_;
|
||
int32_t max_attempts_;
|
||
};
|
||
|
||
class RandomCropDecodeResizeOperation : public RandomResizedCropOperation {
|
||
public:
|
||
RandomCropDecodeResizeOperation(std::vector<int32_t> size, std::vector<float> scale, std::vector<float> ratio,
|
||
InterpolationMode interpolation, int32_t max_attempts);
|
||
|
||
explicit RandomCropDecodeResizeOperation(const RandomResizedCropOperation &base);
|
||
|
||
~RandomCropDecodeResizeOperation() = default;
|
||
|
||
std::shared_ptr<TensorOp> Build() override;
|
||
|
||
std::string Name() const override { return kRandomCropDecodeResizeOperation; }
|
||
|
||
Status to_json(nlohmann::json *out_json) override;
|
||
};
|
||
|
||
class RandomCropWithBBoxOperation : public TensorOperation {
|
||
public:
|
||
RandomCropWithBBoxOperation(std::vector<int32_t> size, std::vector<int32_t> padding = {0, 0, 0, 0},
|
||
bool pad_if_needed = false, std::vector<uint8_t> fill_value = {0, 0, 0},
|
||
BorderType padding_mode = BorderType::kConstant);
|
||
|
||
~RandomCropWithBBoxOperation() = default;
|
||
|
||
std::shared_ptr<TensorOp> Build() override;
|
||
|
||
Status ValidateParams() override;
|
||
|
||
std::string Name() const override { return kRandomCropWithBBoxOperation; }
|
||
|
||
Status to_json(nlohmann::json *out_json) override;
|
||
|
||
private:
|
||
std::vector<int32_t> size_;
|
||
std::vector<int32_t> padding_;
|
||
bool pad_if_needed_;
|
||
std::vector<uint8_t> fill_value_;
|
||
BorderType padding_mode_;
|
||
};
|
||
|
||
class RandomHorizontalFlipOperation : public TensorOperation {
|
||
public:
|
||
explicit RandomHorizontalFlipOperation(float probability = 0.5);
|
||
|
||
~RandomHorizontalFlipOperation() = default;
|
||
|
||
std::shared_ptr<TensorOp> Build() override;
|
||
|
||
Status ValidateParams() override;
|
||
|
||
std::string Name() const override { return kRandomHorizontalFlipOperation; }
|
||
|
||
Status to_json(nlohmann::json *out_json) override;
|
||
|
||
private:
|
||
float probability_;
|
||
};
|
||
|
||
class RandomHorizontalFlipWithBBoxOperation : public TensorOperation {
|
||
public:
|
||
explicit RandomHorizontalFlipWithBBoxOperation(float probability = 0.5);
|
||
|
||
~RandomHorizontalFlipWithBBoxOperation() = default;
|
||
|
||
std::shared_ptr<TensorOp> Build() override;
|
||
|
||
Status ValidateParams() override;
|
||
|
||
std::string Name() const override { return kRandomHorizontalFlipWithBBoxOperation; }
|
||
|
||
Status to_json(nlohmann::json *out_json) override;
|
||
|
||
private:
|
||
float probability_;
|
||
};
|
||
|
||
class RandomPosterizeOperation : public TensorOperation {
|
||
public:
|
||
explicit RandomPosterizeOperation(const std::vector<uint8_t> &bit_range = {4, 8});
|
||
|
||
~RandomPosterizeOperation() = default;
|
||
|
||
std::shared_ptr<TensorOp> Build() override;
|
||
|
||
Status ValidateParams() override;
|
||
|
||
std::string Name() const override { return kRandomPosterizeOperation; }
|
||
|
||
Status to_json(nlohmann::json *out_json) override;
|
||
|
||
private:
|
||
std::vector<uint8_t> bit_range_;
|
||
};
|
||
|
||
class RandomResizeOperation : public TensorOperation {
|
||
public:
|
||
explicit RandomResizeOperation(std::vector<int32_t> size);
|
||
|
||
~RandomResizeOperation() = default;
|
||
|
||
std::shared_ptr<TensorOp> Build() override;
|
||
|
||
Status ValidateParams() override;
|
||
|
||
std::string Name() const override { return kRandomResizeOperation; }
|
||
|
||
Status to_json(nlohmann::json *out_json) override;
|
||
|
||
private:
|
||
std::vector<int32_t> size_;
|
||
};
|
||
|
||
class RandomResizeWithBBoxOperation : public TensorOperation {
|
||
public:
|
||
explicit RandomResizeWithBBoxOperation(std::vector<int32_t> size);
|
||
|
||
~RandomResizeWithBBoxOperation() = default;
|
||
|
||
std::shared_ptr<TensorOp> Build() override;
|
||
|
||
Status ValidateParams() override;
|
||
|
||
std::string Name() const override { return kRandomResizeWithBBoxOperation; }
|
||
|
||
Status to_json(nlohmann::json *out_json) override;
|
||
|
||
private:
|
||
std::vector<int32_t> size_;
|
||
};
|
||
|
||
class RandomResizedCropWithBBoxOperation : public TensorOperation {
|
||
public:
|
||
explicit RandomResizedCropWithBBoxOperation(std::vector<int32_t> size, std::vector<float> scale = {0.08, 1.0},
|
||
std::vector<float> ratio = {3. / 4., 4. / 3.},
|
||
InterpolationMode interpolation = InterpolationMode::kNearestNeighbour,
|
||
int32_t max_attempts = 10);
|
||
|
||
~RandomResizedCropWithBBoxOperation() = default;
|
||
|
||
std::shared_ptr<TensorOp> Build() override;
|
||
|
||
Status ValidateParams() override;
|
||
|
||
std::string Name() const override { return kRandomResizedCropWithBBoxOperation; }
|
||
|
||
Status to_json(nlohmann::json *out_json) override;
|
||
|
||
private:
|
||
std::vector<int32_t> size_;
|
||
std::vector<float> scale_;
|
||
std::vector<float> ratio_;
|
||
InterpolationMode interpolation_;
|
||
int32_t max_attempts_;
|
||
};
|
||
|
||
class RandomRotationOperation : public TensorOperation {
|
||
public:
|
||
RandomRotationOperation(std::vector<float> degrees, InterpolationMode interpolation_mode, bool expand,
|
||
std::vector<float> center, std::vector<uint8_t> fill_value);
|
||
|
||
~RandomRotationOperation() = default;
|
||
|
||
std::shared_ptr<TensorOp> Build() override;
|
||
|
||
Status ValidateParams() override;
|
||
|
||
std::string Name() const override { return kRandomRotationOperation; }
|
||
|
||
Status to_json(nlohmann::json *out_json) override;
|
||
|
||
private:
|
||
std::vector<float> degrees_;
|
||
InterpolationMode interpolation_mode_;
|
||
std::vector<float> center_;
|
||
bool expand_;
|
||
std::vector<uint8_t> fill_value_;
|
||
};
|
||
|
||
class RandomSelectSubpolicyOperation : public TensorOperation {
|
||
public:
|
||
explicit RandomSelectSubpolicyOperation(
|
||
std::vector<std::vector<std::pair<std::shared_ptr<TensorOperation>, double>>> policy);
|
||
|
||
~RandomSelectSubpolicyOperation() = default;
|
||
|
||
std::shared_ptr<TensorOp> Build() override;
|
||
|
||
Status ValidateParams() override;
|
||
|
||
std::string Name() const override { return kRandomSelectSubpolicyOperation; }
|
||
|
||
Status to_json(nlohmann::json *out_json) override;
|
||
|
||
private:
|
||
std::vector<std::vector<std::pair<std::shared_ptr<TensorOperation>, double>>> policy_;
|
||
};
|
||
|
||
class RandomSharpnessOperation : public TensorOperation {
|
||
public:
|
||
explicit RandomSharpnessOperation(std::vector<float> degrees = {0.1, 1.9});
|
||
|
||
~RandomSharpnessOperation() = default;
|
||
|
||
std::shared_ptr<TensorOp> Build() override;
|
||
|
||
Status ValidateParams() override;
|
||
|
||
std::string Name() const override { return kRandomSharpnessOperation; }
|
||
|
||
Status to_json(nlohmann::json *out_json) override;
|
||
|
||
private:
|
||
std::vector<float> degrees_;
|
||
};
|
||
|
||
class RandomSolarizeOperation : public TensorOperation {
|
||
public:
|
||
explicit RandomSolarizeOperation(std::vector<uint8_t> threshold);
|
||
|
||
~RandomSolarizeOperation() = default;
|
||
|
||
std::shared_ptr<TensorOp> Build() override;
|
||
|
||
Status ValidateParams() override;
|
||
|
||
std::string Name() const override { return kRandomSolarizeOperation; }
|
||
|
||
Status to_json(nlohmann::json *out_json) override;
|
||
|
||
private:
|
||
std::vector<uint8_t> threshold_;
|
||
};
|
||
|
||
class RandomVerticalFlipOperation : public TensorOperation {
|
||
public:
|
||
explicit RandomVerticalFlipOperation(float probability = 0.5);
|
||
|
||
~RandomVerticalFlipOperation() = default;
|
||
|
||
std::shared_ptr<TensorOp> Build() override;
|
||
|
||
Status ValidateParams() override;
|
||
|
||
std::string Name() const override { return kRandomVerticalFlipOperation; }
|
||
|
||
Status to_json(nlohmann::json *out_json) override;
|
||
|
||
private:
|
||
float probability_;
|
||
};
|
||
|
||
class RandomVerticalFlipWithBBoxOperation : public TensorOperation {
|
||
public:
|
||
explicit RandomVerticalFlipWithBBoxOperation(float probability = 0.5);
|
||
|
||
~RandomVerticalFlipWithBBoxOperation() = default;
|
||
|
||
std::shared_ptr<TensorOp> Build() override;
|
||
|
||
Status ValidateParams() override;
|
||
|
||
std::string Name() const override { return kRandomVerticalFlipWithBBoxOperation; }
|
||
|
||
Status to_json(nlohmann::json *out_json) override;
|
||
|
||
private:
|
||
float probability_;
|
||
};
|
||
|
||
class RescaleOperation : public TensorOperation {
|
||
public:
|
||
explicit RescaleOperation(float rescale, float shift);
|
||
|
||
~RescaleOperation() = default;
|
||
|
||
std::shared_ptr<TensorOp> Build() override;
|
||
|
||
Status ValidateParams() override;
|
||
|
||
std::string Name() const override { return kRescaleOperation; }
|
||
|
||
Status to_json(nlohmann::json *out_json) override;
|
||
|
||
private:
|
||
float rescale_;
|
||
float shift_;
|
||
};
|
||
|
||
class ResizeWithBBoxOperation : public TensorOperation {
|
||
public:
|
||
explicit ResizeWithBBoxOperation(std::vector<int32_t> size,
|
||
InterpolationMode interpolation_mode = InterpolationMode::kLinear);
|
||
|
||
~ResizeWithBBoxOperation() = default;
|
||
|
||
std::shared_ptr<TensorOp> Build() override;
|
||
|
||
Status ValidateParams() override;
|
||
|
||
std::string Name() const override { return kResizeWithBBoxOperation; }
|
||
|
||
Status to_json(nlohmann::json *out_json) override;
|
||
|
||
private:
|
||
std::vector<int32_t> size_;
|
||
InterpolationMode interpolation_;
|
||
};
|
||
|
||
class RgbaToBgrOperation : public TensorOperation {
|
||
public:
|
||
RgbaToBgrOperation();
|
||
|
||
~RgbaToBgrOperation() = default;
|
||
|
||
std::shared_ptr<TensorOp> Build() override;
|
||
|
||
Status ValidateParams() override;
|
||
|
||
std::string Name() const override { return kRgbaToBgrOperation; }
|
||
};
|
||
|
||
class RgbaToRgbOperation : public TensorOperation {
|
||
public:
|
||
RgbaToRgbOperation();
|
||
|
||
~RgbaToRgbOperation() = default;
|
||
|
||
std::shared_ptr<TensorOp> Build() override;
|
||
|
||
Status ValidateParams() override;
|
||
|
||
std::string Name() const override { return kRgbaToRgbOperation; }
|
||
};
|
||
|
||
class SoftDvppDecodeRandomCropResizeJpegOperation : public TensorOperation {
|
||
public:
|
||
explicit SoftDvppDecodeRandomCropResizeJpegOperation(std::vector<int32_t> size, std::vector<float> scale,
|
||
std::vector<float> ratio, int32_t max_attempts);
|
||
|
||
~SoftDvppDecodeRandomCropResizeJpegOperation() = default;
|
||
|
||
std::shared_ptr<TensorOp> Build() override;
|
||
|
||
Status ValidateParams() override;
|
||
|
||
std::string Name() const override { return kSoftDvppDecodeRandomCropResizeJpegOperation; }
|
||
|
||
Status to_json(nlohmann::json *out_json) override;
|
||
|
||
private:
|
||
std::vector<int32_t> size_;
|
||
std::vector<float> scale_;
|
||
std::vector<float> ratio_;
|
||
int32_t max_attempts_;
|
||
};
|
||
|
||
class SoftDvppDecodeResizeJpegOperation : public TensorOperation {
|
||
public:
|
||
explicit SoftDvppDecodeResizeJpegOperation(std::vector<int32_t> size);
|
||
|
||
~SoftDvppDecodeResizeJpegOperation() = default;
|
||
|
||
std::shared_ptr<TensorOp> Build() override;
|
||
|
||
Status ValidateParams() override;
|
||
|
||
std::string Name() const override { return kSoftDvppDecodeResizeJpegOperation; }
|
||
|
||
Status to_json(nlohmann::json *out_json) override;
|
||
|
||
private:
|
||
std::vector<int32_t> size_;
|
||
};
|
||
|
||
class SwapRedBlueOperation : public TensorOperation {
|
||
public:
|
||
SwapRedBlueOperation();
|
||
|
||
~SwapRedBlueOperation() = default;
|
||
|
||
std::shared_ptr<TensorOp> Build() override;
|
||
|
||
Status ValidateParams() override;
|
||
|
||
std::string Name() const override { return kSwapRedBlueOperation; }
|
||
};
|
||
|
||
class UniformAugOperation : public TensorOperation {
|
||
public:
|
||
explicit UniformAugOperation(std::vector<std::shared_ptr<TensorOperation>> transforms, int32_t num_ops = 2);
|
||
|
||
~UniformAugOperation() = default;
|
||
|
||
std::shared_ptr<TensorOp> Build() override;
|
||
|
||
Status ValidateParams() override;
|
||
|
||
std::string Name() const override { return kUniformAugOperation; }
|
||
|
||
Status to_json(nlohmann::json *out_json) override;
|
||
|
||
private:
|
||
std::vector<std::shared_ptr<TensorOperation>> transforms_;
|
||
int32_t num_ops_;
|
||
};
|
||
|
||
} // namespace vision
|
||
} // namespace dataset
|
||
} // namespace mindspore
|
||
#endif // MINDSPORE_CCSRC_MINDDATA_DATASET_INCLUDE_VISION_H_
|