mirror of https://github.com/alibaba/MNN.git
125 lines
4.6 KiB
C++
125 lines
4.6 KiB
C++
//
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// ShapeInterp.cpp
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// MNN
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//
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// Created by MNN on 2019/01/10.
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// Copyright © 2018, Alibaba Group Holding Limited
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//
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#include "shape/SizeComputer.hpp"
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#include "core/Macro.h"
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namespace MNN {
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// Size Computer
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class InterpComputer : public SizeComputer {
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virtual bool onComputeSize(const MNN::Op* op, const std::vector<Tensor*>& inputs,
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const std::vector<Tensor*>& outputs) const override {
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MNN_ASSERT(1 == inputs.size() || 2 == inputs.size());
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MNN_ASSERT(1 == outputs.size());
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auto& input = inputs[0]->buffer(); // input tensor(data)
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auto& output = outputs[0]->buffer();
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int w = 0;
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int h = 0;
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const int inputSize = (int)inputs.size();
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auto iw = inputs[0]->width();
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auto ih = inputs[0]->height();
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// copy dims
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memcpy(output.dim, input.dim, sizeof(halide_dimension_t) * input.dimensions);
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outputs[0]->buffer().dimensions = inputs[0]->dimensions();
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outputs[0]->buffer().type = inputs[0]->getType();
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auto format = TensorUtils::getDescribe(inputs[0])->dimensionFormat;
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TensorUtils::getDescribe(outputs[0])->dimensionFormat = format;
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if (2 == inputSize) {
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auto shape = inputs[1]; // input shape(shape)
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if(shape->length(0) == input.dimensions) {
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// For Onnx's Resize
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// Don't support batch / channel resize
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for (int i=0; i<2; ++i) {
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output.dim[i].extent = input.dim[i].extent;
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}
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if (shape->getType().code == halide_type_int) {
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// Width / Height
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auto shapePtr = shape->host<int>();
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for (int i=2; i<input.dimensions; ++i) {
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output.dim[i].extent = shapePtr[i];
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}
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} else {
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// Scale
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auto scalePtr = shape->host<float>();
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for (int i=2; i<input.dimensions; ++i) {
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output.dim[i].extent = (scalePtr[i] * (float)input.dim[i].extent);
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}
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}
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return true;
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}
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}
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if (1 == inputSize) {
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// For old mnn model from onnx
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auto interp = op->main_as_Interp();
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// get output dims
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w = interp->outputWidth();
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h = interp->outputHeight();
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if (w == 0 || h == 0) {
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w = iw * interp->widthScale();
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h = ih * interp->heightScale();
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}
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} else {
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// For mnn model from tensorflow
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auto shape = inputs[1]; // input shape(shape)
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// Tensorflow's interp: h, w
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if(2 != shape->buffer().dim[0].extent) {
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MNN_ERROR("Tensorflow's interp's shape should be length two\n");
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return false;
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}
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if (shape->getType().code == halide_type_float) {
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const float *shapeData = shape->host<float>();
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w = shapeData[1];
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h = shapeData[0];
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} else {
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const int32_t *shapeData = shape->host<int32_t>();
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w = shapeData[1];
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h = shapeData[0];
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}
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}
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if (0 == w && 0 == h) {
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return false;
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}
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if (MNN_DATA_FORMAT_NHWC == format) {
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output.dim[2].extent = w;
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output.dim[1].extent = h;
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} else {
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output.dim[3].extent = w;
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output.dim[2].extent = h;
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}
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return true;
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}
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virtual float onComputeFlops(const MNN::Op* op, const std::vector<Tensor*>& inputs,
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const std::vector<Tensor*>& outputs) const override {
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auto elementInM = (float)outputs[0]->elementSize() / 1024.0f / 1024.0f;
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auto interp = op->main_as_Interp();
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auto unit = 0;
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int dimensions = inputs[0]->dimensions();
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int interpDims = dimensions - 2;
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switch (interp->resizeType()) {
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case 1:
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case 4:
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unit = 1;
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break;
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case 2:
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unit = (1 << interpDims);
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break;
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case 3:
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unit = (4 << interpDims);
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break;
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default:
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break;
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}
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return unit * elementInM;
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}
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};
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REGISTER_SHAPE_INPUTS(InterpComputer, OpType_Interp, {1});
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} // namespace MNN
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