MNN/source/shape/ShapeQuantizedAvgPool.cpp

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2019-04-17 10:49:11 +08:00
//
// ShapeQuantizedAvgPool.cpp
// MNN
//
// Created by MNN on 2019/01/10.
// Copyright © 2018, Alibaba Group Holding Limited
//
#include <math.h>
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#include "shape/SizeComputer.hpp"
#ifdef MNN_SUPPORT_DEPRECATED_OP
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#include "core/Macro.h"
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namespace MNN {
class QuantizedAvgPoolComputer : public SizeComputer {
virtual bool onComputeSize(const MNN::Op* op, const std::vector<Tensor*>& inputs,
const std::vector<Tensor*>& outputs) const override {
auto layer = op->main_as_QuantizedAvgPool();
MNN_ASSERT(layer->strideX() == layer->strideY());
int kernel_width = layer->kernelX();
int kernel_height = layer->kernelY();
int output_width = 1;
int output_height = 1;
auto input = inputs[0];
if (layer->padType() == PoolPadType_SAME) { // Tensorflow padding mode SAME
output_width = ceil((float)input->width() / (float)layer->strideX()); // NHWC for tensorflow
output_height = ceil((float)input->height() / (float)layer->strideY()); // the default layout is NCHW
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} else if (layer->padType() == PoolPadType_VALID) { // Tensorflow padding mode VALID
output_width = ceil((float)(input->width() - kernel_width + 1) / (float)layer->strideX());
output_height = ceil((float)(input->height() - kernel_height + 1) / (float)layer->strideY());
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} else {
MNN_ASSERT(false); // unsupported type
}
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// outputNHWC MNN: nchw
auto& outputBuffer = outputs[0]->buffer();
outputBuffer.dimensions = input->buffer().dimensions;
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outputs[0]->setType(DataType_DT_UINT8);
auto format = TensorUtils::getDescribe(inputs[0])->dimensionFormat;
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outputBuffer.dim[0].extent = input->buffer().dim[0].extent;
outputBuffer.dim[2].extent = output_height;
outputBuffer.dim[3].extent = output_width;
outputBuffer.dim[1].extent = input->buffer().dim[1].extent;
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if (format == MNN_DATA_FORMAT_NHWC) {
outputBuffer.dim[1].extent = output_height;
outputBuffer.dim[2].extent = output_width;
outputBuffer.dim[3].extent = input->channel();
}
TensorUtils::getDescribe(outputs[0])->dimensionFormat = format;
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return true;
}
};
} // namespace MNN
- build: - unify schema building in core and converter; - add more build script for android; - add linux build script for python; - ops impl: - add floor mod support in binary; - use eltwise impl in add/max/sub/mul binary for optimization; - remove fake double support in cast; - fix 5d support for concat; - add adjX and adjY support for batch matmul; - optimize conv2d back prop filter; - add pad mode support for conv3d; - fix bug in conv2d & conv depthwise with very small feature map; - optimize binary without broacast; - add data types support for gather; - add gather ND support; - use uint8 data type in gather v2; - add transpose support for matmul; - add matrix band part; - add dim != 4 support for padding, reshape & tensor convert; - add pad type support for pool3d; - make ops based on TensorFlow Lite quantization optional; - add all & any support for reduction; - use type in parameter as output type in reduction; - add int support for unary; - add variable weight support for conv2d; - fix conv2d depthwise weights initialization; - fix type support for transpose; - fix grad outputs count for reduce grad and reshape grad; - fix priorbox & detection output; - fix metal softmax error; - python: - add runSessionWithCallBackInfo interface; - add max nodes limit (1400) for visualization tool; - fix save error in python3; - align default dim; - convert: - add extra design for optimization; - add more post converting optimizers; - add caffe v1 weights blob support; - add cast, unary, conv transpose support for onnx model; - optimize batchnorm, conv with variable weights, prelu, reshape, slice, upsample for onnx model; - add cos/sin/atan/tan support for unary for tensorflow model; - add any/all support for reduction for tensorflow model; - add elu, conv3d, pool3d support for tensorflow model; - optimize argmax, batchnorm, concat, batch to space, conv with variable weights, prelu, slice for tensorflow model; - others: - fix size computer lock; - fix thread pool deadlock; - add express & parameters in express; - rewrite blitter chooser without static map; - add tests for expr;
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#endif
namespace MNN {
REGISTER_SHAPE_OLD(QuantizedAvgPoolComputer, OpType_QuantizedAvgPool);
};