forked from huawei/mindspore2022
675 lines
25 KiB
C++
675 lines
25 KiB
C++
/**
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* Copyright 2020 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_API_TRANSFORMS_H_
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#define MINDSPORE_CCSRC_MINDDATA_DATASET_API_TRANSFORMS_H_
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#include <vector>
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#include <memory>
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#include "minddata/dataset/core/constants.h"
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namespace mindspore {
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namespace dataset {
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class TensorOp;
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namespace api {
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// Abstract class to represent a dataset in the data pipeline.
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class TensorOperation : public std::enable_shared_from_this<TensorOperation> {
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public:
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/// \brief Constructor
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TensorOperation();
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/// \brief Destructor
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~TensorOperation() = default;
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/// \brief Pure virtual function to convert a TensorOperation class into a runtime TensorOp object.
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/// \return shared pointer to the newly created TensorOp.
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virtual std::shared_ptr<TensorOp> Build() = 0;
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virtual bool ValidateParams() = 0;
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};
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// Transform operations for performing computer vision.
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namespace vision {
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// Transform Op classes (in alphabetical order)
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class CenterCropOperation;
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class CropOperation;
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class CutMixBatchOperation;
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class CutOutOperation;
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class DecodeOperation;
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class HwcToChwOperation;
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class MixUpBatchOperation;
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class NormalizeOperation;
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class OneHotOperation;
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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 RandomHorizontalFlipOperation;
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class RandomPosterizeOperation;
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class RandomRotationOperation;
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class RandomSharpnessOperation;
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class RandomSolarizeOperation;
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class RandomVerticalFlipOperation;
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class ResizeOperation;
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class RgbaToBgrOperation;
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class RgbaToRgbOperation;
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class SwapRedBlueOperation;
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class UniformAugOperation;
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/// \brief Function to create a CenterCrop TensorOperation.
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/// \notes Crops the input image at the center to the given size.
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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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/// \return Shared pointer to the current TensorOperation.
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std::shared_ptr<CenterCropOperation> CenterCrop(std::vector<int32_t> size);
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/// \brief Function to create a Crop TensorOp
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/// \notes Crop an image based on location and crop size
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/// \param[in] coordinates Starting location of crop. Must be a vector of two values, in the form of {x_coor, y_coor}
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/// \param[in] size Size of the cropped area. Must be a vector of two values, in the form of {height, width}
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/// \return Shared pointer to the current TensorOp
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std::shared_ptr<CropOperation> Crop(std::vector<int32_t> coordinates, std::vector<int32_t> size);
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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 selected image in
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/// 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 Decode TensorOperation.
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/// \notes Decode the input image in RGB mode.
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/// \param[in] rgb - a boolean of whether to decode in RGB mode or not.
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/// \return Shared pointer to the current TensorOperation.
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std::shared_ptr<DecodeOperation> Decode(bool rgb = true);
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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 MixUpBatch TensorOperation.
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/// \notes Apply MixUp transformation on an input batch of images and labels. The labels must be in one-hot format and
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/// 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 Normalize TensorOperation.
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/// \notes Normalize the input image with respect to mean and standard deviation.
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/// \param[in] mean A vector of mean values for each channel, w.r.t channel order.
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/// \param[in] std A vector of standard deviations for each channel, w.r.t. channel order.
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/// \return Shared pointer to the current TensorOperation.
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std::shared_ptr<NormalizeOperation> Normalize(std::vector<float> mean, std::vector<float> std);
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/// \brief Function to create a OneHot TensorOperation.
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/// \notes Convert the labels into OneHot format.
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/// \param[in] num_classes number of classes.
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/// \return Shared pointer to the current TensorOperation.
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std::shared_ptr<OneHotOperation> OneHot(int32_t num_classes);
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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 right with the first and
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/// top 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 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 size 2, representing the starting and ending degree
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/// \param[in] translate_range A float vector size 2, representing percentages of translation on x and y axes.
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/// \param[in] scale_range A float vector size 2, representing the starting and ending scales in the range.
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/// \param[in] shear_ranges A float vector size 4, representing the starting and ending shear degrees vertically and
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/// horizontally.
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/// \param[in] interpolation An enum for the mode of interpolation
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/// \param[in] fill_value A uint8_t vector size 3, representing the pixel intensity of the borders, it is used to
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/// 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},
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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 with the value of pixels to pad the image. If 4 values are provided,
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/// it pads the left, top, right and bottom 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, it is used to
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/// 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 RandomHorizontalFlip TensorOperation.
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/// \notes Tensor operation to perform random horizontal flip.
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/// \param[in] prob - 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 RandomPosterize TensorOperation.
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/// \notes Tensor operation to perform random posterize.
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/// \param[in] min_bit - uint8_t representing the minimum bit in range. (Default=8)
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/// \param[in] max_bit - uint8_t representing the maximum bit in range. (Default=8)
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/// \return Shared pointer to the current TensorOperation.
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std::shared_ptr<RandomPosterizeOperation> RandomPosterize(uint8_t min_bit = 8, uint8_t max_bit = 8);
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/// \brief Function to create a RandomRotation TensorOp
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/// \notes Rotates the image according to parameters
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/// \param[in] degrees A float vector size 2, representing the starting and ending degree
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/// \param[in] resample An enum for the mode of interpolation
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/// \param[in] expand A boolean representing whether the image is expanded after rotation
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/// \param[in] center A float vector size 2, representing the x and y center of rotation.
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/// \param[in] fill_value A uint8_t vector size 3, representing the rgb value of the fill color
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/// \return Shared pointer to the current TensorOp
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std::shared_ptr<RandomRotationOperation> RandomRotation(
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std::vector<float> degrees, InterpolationMode resample = InterpolationMode::kNearestNeighbour, bool expand = false,
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std::vector<float> center = {-1, -1}, std::vector<uint8_t> fill_value = {0, 0, 0});
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/// \brief Function to create a RandomSharpness TensorOperation.
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/// \notes Tensor operation to perform random sharpness.
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/// \param[in] start_degree - float representing the start of the range to uniformly sample the factor from it.
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/// \param[in] end_degree - float representing the end of the range.
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/// \return Shared pointer to the current TensorOperation.
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std::shared_ptr<RandomSharpnessOperation> RandomSharpness(std::vector<float> degrees = {0.1, 1.9});
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/// \brief Function to create a RandomSolarize TensorOperation.
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/// \notes Invert pixels within specified range. If min=max, then it inverts all pixel above that threshold
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/// \param[in] threshold - a vector with two elements specifying the pixel range to invert.
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/// \return Shared pointer to the current TensorOperation.
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std::shared_ptr<RandomSolarizeOperation> RandomSolarize(std::vector<uint8_t> threshold = {0, 255});
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/// \brief Function to create a RandomVerticalFlip TensorOperation.
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/// \notes Tensor operation to perform random vertical flip.
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/// \param[in] prob - float representing the probability of flip.
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/// \return Shared pointer to the current TensorOperation.
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std::shared_ptr<RandomVerticalFlipOperation> RandomVerticalFlip(float prob = 0.5);
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/// \brief Function to create a RgbaToBgr TensorOperation.
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/// \notes Changes the input 4 channel RGBA tensor to 3 channel BGR.
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/// \return Shared pointer to the current TensorOperation.
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std::shared_ptr<RgbaToBgrOperation> RGBA2BGR();
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/// \brief Function to create a RgbaToRgb TensorOperation.
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/// \notes Changes the input 4 channel RGBA tensor to 3 channel RGB.
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/// \return Shared pointer to the current TensorOperation.
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std::shared_ptr<RgbaToRgbOperation> RGBA2RGB();
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/// \brief Function to create a Resize TensorOperation.
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/// \notes Resize the input image to the given size.
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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 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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/// \param[in] interpolation An enum for the mode of interpolation
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/// \return Shared pointer to the current TensorOperation.
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std::shared_ptr<ResizeOperation> Resize(std::vector<int32_t> size,
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InterpolationMode interpolation = InterpolationMode::kLinear);
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/// \brief Function to create a SwapRedBlue TensorOp
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/// \notes Swaps the red and blue channels in image
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/// \return Shared pointer to the current TensorOp
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std::shared_ptr<SwapRedBlueOperation> SwapRedBlue();
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/// \brief Function to create a UniformAugment TensorOperation.
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/// \notes Tensor operation to perform randomly selected augmentation.
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/// \param[in] transforms - a vector of TensorOperation transforms.
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/// \param[in] num_ops - integer representing the number of OPs to be selected and applied.
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/// \return Shared pointer to the current TensorOperation.
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std::shared_ptr<UniformAugOperation> UniformAugment(std::vector<std::shared_ptr<TensorOperation>> transforms,
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int32_t num_ops = 2);
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/* ####################################### Derived TensorOperation classes ################################# */
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class CenterCropOperation : public TensorOperation {
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public:
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explicit CenterCropOperation(std::vector<int32_t> size);
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~CenterCropOperation() = default;
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std::shared_ptr<TensorOp> Build() override;
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bool ValidateParams() override;
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private:
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std::vector<int32_t> size_;
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};
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class CropOperation : public TensorOperation {
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public:
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CropOperation(std::vector<int32_t> coordinates, std::vector<int32_t> size);
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~CropOperation() = default;
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std::shared_ptr<TensorOp> Build() override;
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bool ValidateParams() override;
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private:
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std::vector<int32_t> coordinates_;
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std::vector<int32_t> size_;
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};
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class CutMixBatchOperation : public TensorOperation {
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public:
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explicit CutMixBatchOperation(ImageBatchFormat image_batch_format, float alpha = 1.0, float prob = 1.0);
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~CutMixBatchOperation() = default;
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std::shared_ptr<TensorOp> Build() override;
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bool ValidateParams() override;
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private:
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float alpha_;
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float prob_;
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ImageBatchFormat image_batch_format_;
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};
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class CutOutOperation : public TensorOperation {
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public:
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explicit CutOutOperation(int32_t length, int32_t num_patches = 1);
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~CutOutOperation() = default;
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std::shared_ptr<TensorOp> Build() override;
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bool ValidateParams() override;
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private:
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int32_t length_;
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int32_t num_patches_;
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ImageBatchFormat image_batch_format_;
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};
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class DecodeOperation : public TensorOperation {
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public:
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explicit DecodeOperation(bool rgb = true);
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~DecodeOperation() = default;
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std::shared_ptr<TensorOp> Build() override;
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bool ValidateParams() override;
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private:
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bool rgb_;
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};
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class HwcToChwOperation : public TensorOperation {
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public:
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~HwcToChwOperation() = default;
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std::shared_ptr<TensorOp> Build() override;
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bool ValidateParams() override;
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};
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class MixUpBatchOperation : public TensorOperation {
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public:
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explicit MixUpBatchOperation(float alpha = 1);
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~MixUpBatchOperation() = default;
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std::shared_ptr<TensorOp> Build() override;
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bool ValidateParams() override;
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private:
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float alpha_;
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};
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class NormalizeOperation : public TensorOperation {
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public:
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NormalizeOperation(std::vector<float> mean, std::vector<float> std);
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~NormalizeOperation() = default;
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std::shared_ptr<TensorOp> Build() override;
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bool ValidateParams() override;
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private:
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std::vector<float> mean_;
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std::vector<float> std_;
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};
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class OneHotOperation : public TensorOperation {
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public:
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explicit OneHotOperation(int32_t num_classes_);
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~OneHotOperation() = default;
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std::shared_ptr<TensorOp> Build() override;
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bool ValidateParams() override;
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private:
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float num_classes_;
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};
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class PadOperation : public TensorOperation {
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public:
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PadOperation(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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~PadOperation() = default;
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std::shared_ptr<TensorOp> Build() override;
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bool ValidateParams() override;
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private:
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std::vector<int32_t> padding_;
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std::vector<uint8_t> fill_value_;
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BorderType padding_mode_;
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};
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class RandomAffineOperation : public TensorOperation {
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public:
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RandomAffineOperation(const std::vector<float_t> °rees, const std::vector<float_t> &translate_range = {0.0, 0.0},
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const std::vector<float_t> &scale_range = {1.0, 1.0},
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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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~RandomAffineOperation() = default;
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std::shared_ptr<TensorOp> Build() override;
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bool ValidateParams() override;
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private:
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std::vector<float_t> degrees_; // min_degree, max_degree
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std::vector<float_t> translate_range_; // maximum x translation percentage, maximum y translation percentage
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std::vector<float_t> scale_range_; // min_scale, max_scale
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std::vector<float_t> shear_ranges_; // min_x_shear, max_x_shear, min_y_shear, max_y_shear
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InterpolationMode interpolation_;
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std::vector<uint8_t> fill_value_;
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};
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class RandomColorOperation : public TensorOperation {
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public:
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RandomColorOperation(float t_lb, float t_ub);
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~RandomColorOperation() = default;
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std::shared_ptr<TensorOp> Build() override;
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bool ValidateParams() override;
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private:
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float t_lb_;
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float t_ub_;
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};
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class RandomColorAdjustOperation : public TensorOperation {
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public:
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RandomColorAdjustOperation(std::vector<float> brightness = {1.0, 1.0}, std::vector<float> contrast = {1.0, 1.0},
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std::vector<float> saturation = {1.0, 1.0}, std::vector<float> hue = {0.0, 0.0});
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~RandomColorAdjustOperation() = default;
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std::shared_ptr<TensorOp> Build() override;
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bool ValidateParams() override;
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private:
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std::vector<float> brightness_;
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std::vector<float> contrast_;
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std::vector<float> saturation_;
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std::vector<float> hue_;
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};
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class RandomCropOperation : public TensorOperation {
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public:
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|
RandomCropOperation(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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~RandomCropOperation() = default;
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|
|
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std::shared_ptr<TensorOp> Build() override;
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|
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|
bool ValidateParams() override;
|
|
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private:
|
|
std::vector<int32_t> size_;
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|
std::vector<int32_t> padding_;
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|
bool pad_if_needed_;
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|
std::vector<uint8_t> fill_value_;
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|
BorderType padding_mode_;
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|
};
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|
|
|
class RandomHorizontalFlipOperation : public TensorOperation {
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|
public:
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|
explicit RandomHorizontalFlipOperation(float probability = 0.5);
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|
|
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~RandomHorizontalFlipOperation() = default;
|
|
|
|
std::shared_ptr<TensorOp> Build() override;
|
|
|
|
bool ValidateParams() override;
|
|
|
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private:
|
|
float probability_;
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|
};
|
|
|
|
class RandomPosterizeOperation : public TensorOperation {
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|
public:
|
|
explicit RandomPosterizeOperation(uint8_t min_bit = 8, uint8_t max_bit = 8);
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|
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|
~RandomPosterizeOperation() = default;
|
|
|
|
std::shared_ptr<TensorOp> Build() override;
|
|
|
|
bool ValidateParams() override;
|
|
|
|
private:
|
|
uint8_t min_bit_;
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|
uint8_t max_bit_;
|
|
};
|
|
|
|
class RandomRotationOperation : public TensorOperation {
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|
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;
|
|
|
|
bool ValidateParams() override;
|
|
|
|
private:
|
|
std::vector<float> degrees_;
|
|
InterpolationMode interpolation_mode_;
|
|
std::vector<float> center_;
|
|
bool expand_;
|
|
std::vector<uint8_t> fill_value_;
|
|
};
|
|
|
|
class RandomSharpnessOperation : public TensorOperation {
|
|
public:
|
|
explicit RandomSharpnessOperation(std::vector<float> degrees = {0.1, 1.9});
|
|
|
|
~RandomSharpnessOperation() = default;
|
|
|
|
std::shared_ptr<TensorOp> Build() override;
|
|
|
|
bool ValidateParams() override;
|
|
|
|
private:
|
|
std::vector<float> degrees_;
|
|
};
|
|
|
|
class RandomVerticalFlipOperation : public TensorOperation {
|
|
public:
|
|
explicit RandomVerticalFlipOperation(float probability = 0.5);
|
|
|
|
~RandomVerticalFlipOperation() = default;
|
|
|
|
std::shared_ptr<TensorOp> Build() override;
|
|
|
|
bool ValidateParams() override;
|
|
|
|
private:
|
|
float probability_;
|
|
};
|
|
|
|
class ResizeOperation : public TensorOperation {
|
|
public:
|
|
explicit ResizeOperation(std::vector<int32_t> size,
|
|
InterpolationMode interpolation_mode = InterpolationMode::kLinear);
|
|
|
|
~ResizeOperation() = default;
|
|
|
|
std::shared_ptr<TensorOp> Build() override;
|
|
|
|
bool ValidateParams() override;
|
|
|
|
private:
|
|
std::vector<int32_t> size_;
|
|
InterpolationMode interpolation_;
|
|
};
|
|
|
|
class RgbaToBgrOperation : public TensorOperation {
|
|
public:
|
|
RgbaToBgrOperation();
|
|
|
|
~RgbaToBgrOperation() = default;
|
|
|
|
std::shared_ptr<TensorOp> Build() override;
|
|
|
|
bool ValidateParams() override;
|
|
};
|
|
|
|
class RgbaToRgbOperation : public TensorOperation {
|
|
public:
|
|
RgbaToRgbOperation();
|
|
|
|
~RgbaToRgbOperation() = default;
|
|
|
|
std::shared_ptr<TensorOp> Build() override;
|
|
|
|
bool ValidateParams() override;
|
|
};
|
|
|
|
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;
|
|
|
|
bool ValidateParams() override;
|
|
|
|
private:
|
|
std::vector<std::shared_ptr<TensorOperation>> transforms_;
|
|
int32_t num_ops_;
|
|
};
|
|
|
|
class SwapRedBlueOperation : public TensorOperation {
|
|
public:
|
|
SwapRedBlueOperation();
|
|
|
|
~SwapRedBlueOperation() = default;
|
|
|
|
std::shared_ptr<TensorOp> Build() override;
|
|
|
|
bool ValidateParams() override;
|
|
};
|
|
|
|
class RandomSolarizeOperation : public TensorOperation {
|
|
public:
|
|
explicit RandomSolarizeOperation(std::vector<uint8_t> threshold);
|
|
|
|
~RandomSolarizeOperation() = default;
|
|
|
|
std::shared_ptr<TensorOp> Build() override;
|
|
|
|
bool ValidateParams() override;
|
|
|
|
private:
|
|
std::vector<uint8_t> threshold_;
|
|
};
|
|
} // namespace vision
|
|
} // namespace api
|
|
} // namespace dataset
|
|
} // namespace mindspore
|
|
#endif // MINDSPORE_CCSRC_MINDDATA_DATASET_API_TRANSFORMS_H_
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