MNN/source/shape/ShapeCrop.cpp

44 lines
1.4 KiB
C++
Raw Normal View History

2019-04-17 10:49:11 +08:00
//
// ShapeCrop.cpp
// MNN
//
// Created by MNN on 2019/01/10.
// Copyright © 2018, Alibaba Group Holding Limited
//
2020-11-05 16:41:56 +08:00
#include "shape/SizeComputer.hpp"
2019-12-27 22:16:57 +08:00
#include "core/Macro.h"
2019-04-17 10:49:11 +08:00
namespace MNN {
class CropSizeComputer : public SizeComputer {
virtual bool onComputeSize(const MNN::Op* op, const std::vector<Tensor*>& inputs,
const std::vector<Tensor*>& outputs) const override {
MNN_ASSERT(2 == inputs.size());
MNN_ASSERT(1 == outputs.size());
MNN_ASSERT(4 == inputs[0]->buffer().dimensions && 4 == inputs[1]->buffer().dimensions);
MNN_ASSERT(inputs[0]->buffer().dimensions == inputs[1]->buffer().dimensions);
auto& ibInput0 = inputs[0]->buffer();
auto& ibInput1 = inputs[1]->buffer();
auto& ob = outputs[0]->buffer();
ob.dimensions = ibInput1.dimensions;
::memcpy(ob.dim, ibInput1.dim, ibInput1.dimensions * sizeof(halide_dimension_t));
auto cropParam = op->main_as_Crop();
for (int i = 0; i < ibInput1.dimensions; ++i) {
if (i < cropParam->axis()) {
ob.dim[i].extent = ibInput0.dim[i].extent;
}
}
2020-02-26 09:57:17 +08:00
ob.type = ibInput0.type;
2020-11-05 16:41:56 +08:00
TensorUtils::getDescribe(outputs[0])->dimensionFormat = TensorUtils::getDescribe(inputs[0])->dimensionFormat;
2019-04-17 10:49:11 +08:00
return true;
}
};
REGISTER_SHAPE(CropSizeComputer, OpType_Crop);
} // namespace MNN