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chore: import upstream snapshot with attribution

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wehub-resource-sync
2026-07-13 13:33:03 +08:00
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source/shape/ShapeReshape.cpp
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//
// ShapeReshape.cpp
// MNN
//
// Created by MNN on 2019/01/10.
// Copyright © 2018, Alibaba Group Holding Limited
//
#include "shape/SizeComputer.hpp"
#include "core/Macro.h"
#include "core/TensorUtils.hpp"
namespace MNN {
class FlattenComputer : public SizeComputer {
public:
// Ref: https://github.com/onnx/onnx/blob/master/docs/Operators.md#Flatten
virtual bool onComputeSize(const MNN::Op* op, const std::vector<Tensor*>& inputs,
const std::vector<Tensor*>& outputs) const override {
auto flatten = op->main_as_Flatten();
if (nullptr == flatten || inputs.empty() || outputs.empty()) {
return false;
}
auto axis = flatten->axis();
auto endAxis = flatten->endAxis();
auto dim = inputs[0]->dimensions();
if (axis < 0) {
axis += dim;
}
if (endAxis < 0) {
endAxis += dim;
}
int inside = 1;
int middle = 1;
int outside = 1;
if (endAxis == 0) {
for (int i=0; i<axis; ++i) {
outside *= inputs[0]->length(i);
}
for (int i=axis; i<dim; ++i) {
inside *= inputs[0]->length(i);
}
outputs[0]->buffer().dimensions = 2;
outputs[0]->setLength(0, outside);
outputs[0]->setLength(1, inside);
} else {
// [ 0 - axis, 1, endAxis - lastDim]
outputs[0]->buffer().dimensions = dim - endAxis + axis;
for (int i = 0; i < axis; ++i) {
outputs[0]->setLength(i, inputs[0]->length(i));
}
for (int i = axis; i <= endAxis; ++i) {
outside *= inputs[0]->length(i);
}
outputs[0]->setLength(axis, outside);
if (dim > endAxis + 1) {
for (int i = endAxis + 1; i < dim; ++i) {
outputs[0]->setLength(i, inputs[0]->length(i));
}
}
}
outputs[0]->buffer().type = inputs[0]->getType();
TensorUtils::getDescribe(outputs[0])->dimensionFormat = TensorUtils::getDescribe(inputs[0])->dimensionFormat;
return true;
}
};
class ReshapeComputer : public SizeComputer {
public:
virtual bool onComputeSize(const MNN::Op* op, const std::vector<Tensor*>& inputs,
const std::vector<Tensor*>& outputs) const override {
MNN_ASSERT(1 == inputs.size() || 2 == inputs.size());
MNN_ASSERT(1 == outputs.size());
auto input = inputs[0];
auto output = outputs[0];
outputs[0]->buffer().type = inputs[0]->buffer().type;
int dimSize = 0;
int shapes[MNN_MAX_TENSOR_DIM];
auto inputFormat = TensorUtils::getDescribe(inputs[0])->dimensionFormat;
bool fromTf = false;
auto mainType = op->main_type();
if (1 == inputs.size()) {
// Const shape
if (OpParameter_Reshape == mainType) {
auto shape = op->main_as_Reshape()->dims();
dimSize = shape->size();
for (int i = 0; i < dimSize; ++i) {
shapes[i] = shape->data()[i];
}
} else {
// For old model compability
auto shape = op->main_as_QuantizedReshape()->dims();
dimSize = shape->size();
for (int i = 0; i < dimSize; ++i) {
shapes[i] = shape->data()[i];
}
}
} else {
// shape which is getted at the runtime
auto inputShape = inputs[1];
// For the model convert from tensorflow, the format is NHWC, otherwise NCHW
fromTf = TensorUtils::getDescribe(inputShape)->dimensionFormat == MNN_DATA_FORMAT_NHWC;
dimSize = inputShape->elementSize();
auto dimType = MNN_DATA_FORMAT_NHWC;
if (OpParameter_Reshape == mainType) {
dimType = op->main_as_Reshape()->dimType();
}
if (inputShape->buffer().type == halide_type_of<int32_t>()) {
auto dim = inputShape->host<int32_t>();
if ((inputFormat == MNN_DATA_FORMAT_NC4HW4) && dimType == MNN_DATA_FORMAT_NHWC) {
//NCHW / NC4HW4
//NHWC -> NCHW
shapes[0] = dim[0];
shapes[1] = dim[3];
shapes[2] = dim[1];
shapes[3] = dim[2];
} else {
for (int i = 0; i < dimSize; ++i) {
shapes[i] = dim[i];
}
}
} else if (inputShape->buffer().type == halide_type_of<int64_t>()) {
auto dim = inputShape->host<int64_t>();
if ((inputFormat == MNN_DATA_FORMAT_NC4HW4) && dimType == MNN_DATA_FORMAT_NHWC) {
//NCHW / NC4HW4
//NHWC -> NCHW
shapes[0] = (int)dim[0];
shapes[1] = (int)dim[3];
shapes[2] = (int)dim[1];
shapes[3] = (int)dim[2];
} else {
for (int i = 0; i < dimSize; ++i) {
shapes[i] = (int)dim[i];
}
}
} else {
MNN_ERROR("Invalid reshape shape type: code=%d bits=%d\n", inputShape->buffer().type.code, inputShape->buffer().type.bits);
return false;
}
}
output->buffer().dimensions = dimSize;
int totalSizeInput = 1;
for (int i = 0; i < input->buffer().dimensions; ++i) {
auto l = input->length(i);
totalSizeInput *= l;
}
int determinAxis = -1;
for (int i = 0; i < dimSize; ++i) {
int reshapeDim = shapes[i];
if (reshapeDim == -1) {
determinAxis = i;
output->buffer().dim[i].extent = 1;
continue;
}
// Keep input dimension if reshape dimension is 0 and the element
// count of the input does not equal to 0.
// TODO: Reshape 0 is not allowed if the input element count is not
// 0 for TensorFlow.
if (reshapeDim == 0 && (!fromTf)) {
output->buffer().dim[i].extent = input->buffer().dim[i].extent;
} else {
output->buffer().dim[i].extent = reshapeDim;
}
}
int totalSizeOutput = 1;
for (int i = 0; i < dimSize; ++i) {
totalSizeOutput *= output->buffer().dim[i].extent;
}
if (determinAxis >= 0) {
output->buffer().dim[determinAxis].extent = totalSizeOutput ? totalSizeInput / totalSizeOutput : 0;
totalSizeOutput *= output->buffer().dim[determinAxis].extent;
}
if (totalSizeInput != totalSizeOutput) {
MNN_PRINT("Reshape error: %d -> %d\n", totalSizeInput, totalSizeOutput);
return false;
}
TensorUtils::getDescribe(output)->dimensionFormat = TensorUtils::getDescribe(input)->dimensionFormat;
return true;
}
};
REGISTER_SHAPE_INPUTS(ReshapeComputer, OpType_Reshape, {1});
REGISTER_SHAPE_INPUTS(ReshapeComputer, OpType_QuantizedReshape, {1});
REGISTER_SHAPE(FlattenComputer, OpType_Flatten);
} // namespace MNN