cjolivier01 commented on a change in pull request #9688: [MXNET-108] Adding BilinearResize2D and AdaptiveAvgPool2d operators URL: https://github.com/apache/incubator-mxnet/pull/9688#discussion_r175571737
########## File path: src/operator/adaptive_avg_pooling-inl.h ########## @@ -0,0 +1,179 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one + * or more contributor license agreements. See the NOTICE file + * distributed with this work for additional information + * regarding copyright ownership. The ASF licenses this file + * to you under the Apache License, Version 2.0 (the + * "License"); you may not use this file except in compliance + * with the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, + * software distributed under the License is distributed on an + * "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY + * KIND, either express or implied. See the License for the + * specific language governing permissions and limitations + * under the License. + */ +/*! + * Copyright (c) 2018 by Contributors + * \file adaptive_avg_pooling-inl.h + * \brief adaptive average pooling operator + * \author Hang Zhang +*/ +#ifndef MXNET_OPERATOR_ADAPTIVE_AVG_POOLING_INL_H_ +#define MXNET_OPERATOR_ADAPTIVE_AVG_POOLING_INL_H_ + +#include <dmlc/logging.h> +#include <dmlc/parameter.h> +#include <mxnet/operator.h> +#include <mxnet/ndarray.h> +#include <map> +#include <vector> +#include <string> +#include <utility> +#include "../ndarray/ndarray_function.h" +#include "./operator_common.h" +#include "./mxnet_op.h" +#include "./mshadow_op.h" + +namespace mxnet { +namespace op { + +struct AdaptiveAvgPoolParam : public dmlc::Parameter<AdaptiveAvgPoolParam> { + int output_size; + DMLC_DECLARE_PARAMETER(AdaptiveAvgPoolParam) { + DMLC_DECLARE_FIELD(output_size).set_range(1, 1000) + .describe("output size"); + } +}; + +static inline bool IsWriting(const OpReqType ort) { + return ort == kWriteTo || ort == kWriteInplace; +} + +template<typename xpu, typename DType, typename AccReal> +void AdaptiveAvgPoolUpdateOutput(mshadow::Stream<cpu> *s, + const std::vector<TBlob> &input, + const std::vector<TBlob> &output); + +template<typename xpu, typename DType, typename AccReal> +void AdaptiveAvgPoolUpdateGradInput(mshadow::Stream<cpu> *s, + const std::vector<TBlob> &input, + const std::vector<TBlob> &output); + +#if MXNET_USE_CUDA +template<typename xpu, typename DType, typename AccReal> +void AdaptiveAvgPoolUpdateOutput(mshadow::Stream<gpu> *s, + const std::vector<TBlob> &input, + const std::vector<TBlob> &output); + +template<typename xpu, typename DType, typename AccReal> +void AdaptiveAvgPoolUpdateGradInput(mshadow::Stream<gpu> *s, + const std::vector<TBlob> &input, + const std::vector<TBlob> &output); +#endif // MXNET_USE_CUDA + +template <typename xpu> +inline void AdaptiveAvgPoolOpForward(const nnvm::NodeAttrs& attrs, + const OpContext &ctx, + const std::vector<TBlob> &inputs, + const std::vector<OpReqType> &req, + const std::vector<TBlob> &outputs) { + CHECK_EQ(inputs.size(), 1U); + CHECK_EQ(outputs.size(), 1U); + mshadow::Stream<xpu> *s = ctx.get_stream<xpu>(); + MSHADOW_REAL_TYPE_SWITCH_EX(inputs[0].type_flag_, DType, AccReal, { + AdaptiveAvgPoolUpdateOutput<xpu, DType, AccReal>(s, inputs, outputs); + }); +} + + +template <typename xpu> +inline void AdaptiveAvgPoolOpBackward(const nnvm::NodeAttrs& attrs, + const OpContext &ctx, + const std::vector<TBlob> &inputs, + const std::vector<OpReqType> &req, + const std::vector<TBlob> &outputs) { + CHECK_EQ(inputs.size(), 1U); + CHECK_EQ(outputs.size(), 1U); + mshadow::Stream<xpu> *s = ctx.get_stream<xpu>(); + if (IsWriting(req[0])) { + // zero grad before backwarding + MSHADOW_TYPE_SWITCH(inputs[0].type_flag_, DType, { + Fill<false>(s, outputs[0], kWriteTo, 0); + }) + } + MSHADOW_REAL_TYPE_SWITCH_EX(inputs[0].type_flag_, DType, AccReal, { + AdaptiveAvgPoolUpdateGradInput<xpu, DType, AccReal>(s, inputs, outputs); + }); +} + + +static bool AdaptiveAvgPoolOpInferShape(const nnvm::NodeAttrs& attrs, + std::vector<TShape> *in_shape, + std::vector<TShape> *out_shape) { + using namespace mshadow; + CHECK_EQ(in_shape->size(), 1U) << "Input:[data]"; + CHECK_EQ(out_shape->size(), 1U) << "Output:[data]"; + const AdaptiveAvgPoolParam& param = nnvm::get<AdaptiveAvgPoolParam>(attrs.parsed); + TShape dshape(in_shape->at(0)); + if (dshape.ndim() == 0) return false; + dshape[2] = param.output_size; + dshape[3] = param.output_size; + out_shape->clear(); + out_shape->push_back(dshape); + return true; +} + +static bool AdaptiveAvgPoolOpInferType(const nnvm::NodeAttrs& attrs, + std::vector<int> *in_type, + std::vector<int> *out_type) { + using namespace mshadow; + CHECK_EQ(in_type->size(), 1U); + int dtype = (*in_type)[0]; + CHECK_NE(dtype, -1) << "First input must have specified type"; + // For float16 input type beta, gamma, mean, and average are stored in float32. + // For other input types, these parameters have the same type as input + // NOTE: This requirement is from cuDNN (v. 4 and 5) + int dtype_param = 0; + MSHADOW_REAL_TYPE_SWITCH_EX(dtype, DTypeX, AccRealX, { + dtype_param = mshadow::DataType<AccRealX>::kFlag; }); + out_type->clear(); + out_type->push_back(dtype_param); + return true; +} + +static inline bool AdaptiveAvgPoolOpStorageType(const nnvm::NodeAttrs &attrs, + const int dev_mask, + DispatchMode *dispatch_mode, + std::vector<int> *in_attrs, + std::vector<int> *out_attrs) { + CHECK_EQ(in_attrs->size(), 1); + CHECK_EQ(out_attrs->size(), 1); + *dispatch_mode = DispatchMode::kFCompute; + for (int& v : *in_attrs) { + if (v == - 1) v = kDefaultStorage; + } + for (size_t i = 0; i < out_attrs->size(); i++) { + (*out_attrs)[i] = kDefaultStorage; + } + return true; +} + +using namespace mshadow; +template<typename xpu, int Dim, typename DType> +MSHADOW_XINLINE int get_stride(Tensor<xpu, Dim, DType> tensor, int idx) { + int stride; + stride = 1; + for (int i = Dim-2; i >= idx; --i) { + stride = tensor.size(i+1) * stride; Review comment: nit: stride *= tensor.size(i+1) ---------------------------------------------------------------- This is an automated message from the Apache Git Service. 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