chore: import upstream snapshot with attribution
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wehub-resource-sync
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//
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// CPUCropAndResize.cpp
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// MNN
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//
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// Created by MNN on 2018/08/23.
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// Copyright © 2018, Alibaba Group Holding Limited
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//
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#include "backend/cpu/CPUCropAndResize.hpp"
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#include <math.h>
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#include "backend/cpu/CPUBackend.hpp"
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namespace MNN {
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template <typename T>
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CPUCropAndResize<T>::CPUCropAndResize(Backend* backend, const Op* op) : Execution(backend) {
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auto cr = op->main_as_CropAndResize();
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mMethod = cr->method();
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mExtrapolationValue = cr->extrapolationValue();
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}
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template <typename T>
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const ErrorCode CPUCropAndResize<T>::CropAndResize(const Tensor* image, const Tensor* boxes, const Tensor* boxIndex,
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Tensor* crops) {
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const int batchSize = image->buffer().dim[0].extent;
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const int imageHeight = image->buffer().dim[1].extent;
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const int imageWidth = image->buffer().dim[2].extent;
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const int imageDepth = image->buffer().dim[3].extent;
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MNN_ASSERT(imageWidth > 0 && imageHeight > 0);
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const int numBoxes = crops->buffer().dim[0].extent;
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const int cropHeight = crops->buffer().dim[1].extent;
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const int cropWidth = crops->buffer().dim[2].extent;
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const int depth = crops->buffer().dim[3].extent;
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// init
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memset(crops->host<float>(), 0, crops->size());
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// Sharding across boxes.
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auto CropAndResizePerBox = [&](int startBox, int limitBox) {
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for (int b = startBox; b < limitBox; ++b) {
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const float y1 = boxes->host<float>()[b * 4];
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const float x1 = boxes->host<float>()[b * 4 + 1];
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const float y2 = boxes->host<float>()[b * 4 + 2];
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const float x2 = boxes->host<float>()[b * 4 + 3];
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const int32_t bIn = boxIndex->host<int32_t>()[b];
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if (0 > bIn || bIn >= batchSize) {
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continue;
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}
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const float heightScale = (cropHeight > 1) ? (y2 - y1) * (imageHeight - 1) / (cropHeight - 1) : 0;
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const float widthScale = (cropWidth > 1) ? (x2 - x1) * (imageWidth - 1) / (cropWidth - 1) : 0;
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int32_t cropsHeight = crops->buffer().dim[1].extent;
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int32_t cropsWidth = crops->buffer().dim[2].extent;
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int32_t cropsDepth = crops->buffer().dim[3].extent;
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for (int y = 0; y < cropHeight; ++y) {
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const float inY =
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(cropHeight > 1) ? y1 * (imageHeight - 1) + y * heightScale : 0.5 * (y1 + y2) * (imageHeight - 1);
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if (inY < 0 || inY > imageHeight - 1) {
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for (int x = 0; x < cropWidth; ++x) {
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for (int d = 0; d < depth; ++d) {
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crops->host<float>()[b * cropsHeight * cropsWidth * cropsDepth +
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y * cropsWidth * cropsDepth + x * cropsDepth + d] =
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mExtrapolationValue;
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}
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}
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continue;
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}
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if (mMethod == CropAndResizeMethod_BILINEAR) {
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const int topYIndex = floorf(inY);
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const int bottomYIndex = ceilf(inY);
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const float yLerp = inY - topYIndex;
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for (int x = 0; x < cropWidth; ++x) {
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const float inX = (cropWidth > 1) ? x1 * (imageWidth - 1) + x * widthScale
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: 0.5 * (x1 + x2) * (imageWidth - 1);
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if (inX < 0 || inX > imageWidth - 1) {
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for (int d = 0; d < depth; ++d) {
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crops->host<float>()[b * cropsHeight * cropsWidth * cropsDepth +
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y * cropsWidth * cropsDepth + x * cropsDepth + d] =
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mExtrapolationValue;
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}
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continue;
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}
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const int leftXIndex = floorf(inX);
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const int rightXIndex = ceilf(inX);
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const float xLerp = inX - leftXIndex;
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for (int d = 0; d < depth; ++d) {
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const float topLeft(
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static_cast<float>(image->host<float>()[bIn * imageHeight * imageWidth * imageDepth +
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topYIndex * imageWidth * imageDepth +
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leftXIndex * imageDepth + d]));
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const float topRight(
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static_cast<float>(image->host<float>()[bIn * imageHeight * imageWidth * imageDepth +
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topYIndex * imageWidth * imageDepth +
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rightXIndex * imageDepth + d]));
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const float bottomLeft(
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static_cast<float>(image->host<float>()[bIn * imageHeight * imageWidth * imageDepth +
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bottomYIndex * imageWidth * imageDepth +
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leftXIndex * imageDepth + d]));
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const float bottomRight(
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static_cast<float>(image->host<float>()[bIn * imageHeight * imageWidth * imageDepth +
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bottomYIndex * imageWidth * imageDepth +
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rightXIndex * imageDepth + d]));
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const float top = topLeft + (topRight - topLeft) * xLerp;
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const float bottom = bottomLeft + (bottomRight - bottomLeft) * xLerp;
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crops->host<float>()[b * cropsHeight * cropsWidth * cropsDepth +
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y * cropsWidth * cropsDepth + x * cropsDepth + d] =
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top + (bottom - top) * yLerp;
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}
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}
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} else if (mMethod == CropAndResizeMethod_NEAREST) { // method == "nearest"
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for (int x = 0; x < cropWidth; ++x) {
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const float inX = (cropWidth > 1) ? x1 * (imageWidth - 1) + x * widthScale
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: 0.5 * (x1 + x2) * (imageWidth - 1);
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if (inX < 0 || inX > imageWidth - 1) {
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for (int d = 0; d < depth; ++d) {
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crops->host<float>()[b * cropsHeight * cropsWidth * cropsDepth +
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y * cropsWidth * cropsDepth + x * cropsDepth + d] =
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mExtrapolationValue;
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}
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continue;
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}
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const int closestXIndex = roundf(inX);
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const int closestYIndex = roundf(inY);
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for (int d = 0; d < depth; ++d) {
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crops->host<float>()[b * cropsHeight * cropsWidth * cropsDepth +
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y * cropsWidth * cropsDepth + x * cropsDepth + d] =
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static_cast<float>(image->host<float>()[bIn * imageHeight * imageWidth * imageDepth +
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closestYIndex * imageWidth * imageDepth +
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closestXIndex * imageDepth + d]);
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}
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}
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} else {
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MNN_ASSERT(false);
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}
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}
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}
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};
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for (int i = 0; i < numBoxes; i++) {
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CropAndResizePerBox(i, i + 1);
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}
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return NO_ERROR;
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}
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template <typename T>
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ErrorCode CPUCropAndResize<T>::onExecute(const std::vector<Tensor*>& inputs, const std::vector<Tensor*>& outputs) {
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// The shape of 'image' is [batch_size, image_height, image_width,
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// channels].
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const Tensor* image = inputs[0];
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// The shape of 'boxes' is [num_boxes, 4].
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const Tensor* boxes = inputs[1];
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// The shape of 'box_index' is [num_boxes].
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const Tensor* boxIndex = inputs[2];
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const ErrorCode status = CropAndResize(image, boxes, boxIndex, outputs[0]);
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return status;
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}
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class CPUCropAndResizeCreator : public CPUBackend::Creator {
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public:
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virtual Execution* onCreate(const std::vector<Tensor*>& inputs, const std::vector<Tensor*>& outputs,
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const MNN::Op* op, Backend* backend) const {
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return new CPUCropAndResize<int32_t>(backend, op);
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}
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};
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REGISTER_CPU_OP_CREATOR(CPUCropAndResizeCreator, OpType_CropAndResize);
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} // namespace MNN
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