2019-04-17 10:49:11 +08:00
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//
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// ShapeQuantizedAvgPool.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 <math.h>
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2020-11-05 16:41:56 +08:00
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#include "shape/SizeComputer.hpp"
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2022-07-19 13:52:07 +08:00
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#ifdef MNN_SUPPORT_DEPRECATED_OP
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2019-12-27 22:16:57 +08:00
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#include "core/Macro.h"
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2019-04-17 10:49:11 +08:00
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namespace MNN {
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class QuantizedAvgPoolComputer : 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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auto layer = op->main_as_QuantizedAvgPool();
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MNN_ASSERT(layer->strideX() == layer->strideY());
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int kernel_width = layer->kernelX();
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int kernel_height = layer->kernelY();
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int output_width = 1;
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int output_height = 1;
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auto input = inputs[0];
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if (layer->padType() == PoolPadType_SAME) { // Tensorflow padding mode SAME
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2019-05-05 20:27:57 +08:00
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output_width = ceil((float)input->width() / (float)layer->strideX()); // NHWC for tensorflow
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output_height = ceil((float)input->height() / (float)layer->strideY()); // the default layout is NCHW
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2019-04-17 10:49:11 +08:00
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} else if (layer->padType() == PoolPadType_VALID) { // Tensorflow padding mode VALID
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2019-05-05 20:27:57 +08:00
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output_width = ceil((float)(input->width() - kernel_width + 1) / (float)layer->strideX());
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output_height = ceil((float)(input->height() - kernel_height + 1) / (float)layer->strideY());
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2019-04-17 10:49:11 +08:00
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} else {
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MNN_ASSERT(false); // unsupported type
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}
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2019-05-05 20:27:57 +08:00
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2019-04-17 10:49:11 +08:00
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// output:NHWC MNN: nchw
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auto& outputBuffer = outputs[0]->buffer();
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outputBuffer.dimensions = input->buffer().dimensions;
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2020-11-05 16:41:56 +08:00
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outputs[0]->setType(DataType_DT_UINT8);
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auto format = TensorUtils::getDescribe(inputs[0])->dimensionFormat;
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2019-04-17 10:49:11 +08:00
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outputBuffer.dim[0].extent = input->buffer().dim[0].extent;
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2019-05-05 20:27:57 +08:00
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outputBuffer.dim[2].extent = output_height;
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outputBuffer.dim[3].extent = output_width;
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outputBuffer.dim[1].extent = input->buffer().dim[1].extent;
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2020-11-05 16:41:56 +08:00
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if (format == MNN_DATA_FORMAT_NHWC) {
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outputBuffer.dim[1].extent = output_height;
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outputBuffer.dim[2].extent = output_width;
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outputBuffer.dim[3].extent = input->channel();
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}
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TensorUtils::getDescribe(outputs[0])->dimensionFormat = format;
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2019-08-22 20:13:46 +08:00
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2019-04-17 10:49:11 +08:00
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return true;
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}
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};
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} // namespace MNN
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- 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;
2019-10-29 13:37:26 +08:00
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#endif
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2022-07-19 13:52:07 +08:00
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namespace MNN {
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REGISTER_SHAPE_OLD(QuantizedAvgPoolComputer, OpType_QuantizedAvgPool);
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};
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