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368 lines
14 KiB
368 lines
14 KiB
/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserved.
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Licensed under the Apache License, Version 2.0 (the "License");
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you may not use this file except in compliance with the License.
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You may obtain a copy of the License at
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http://www.apache.org/licenses/LICENSE-2.0
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Unless required by applicable law or agreed to in writing, software
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distributed under the License is distributed on an "AS IS" BASIS,
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WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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See the License for the specific language governing permissions and
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limitations under the License. */
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#include "paddle/fluid/operators/pool_op.h"
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#ifdef PADDLE_WITH_CUDA
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#include "paddle/fluid/platform/cudnn_helper.h"
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#endif
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#ifdef PADDLE_WITH_MKLDNN
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#include "paddle/fluid/platform/mkldnn_helper.h"
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#endif
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namespace paddle {
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namespace operators {
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int PoolOutputSize(int input_size, int filter_size, int padding, int stride,
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bool ceil_mode) {
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int output_size;
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if (!ceil_mode) {
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output_size = (input_size - filter_size + 2 * padding) / stride + 1;
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} else {
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output_size =
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(input_size - filter_size + 2 * padding + stride - 1) / stride + 1;
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}
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PADDLE_ENFORCE(output_size > 0,
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"Due to the settings of padding(%d), filter_size(%d) and "
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"stride(%d), the output size is less than 0, please check "
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"again. Input_size:%d",
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padding, filter_size, stride, input_size);
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return output_size;
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}
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void PoolOp::InferShape(framework::InferShapeContext *ctx) const {
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PADDLE_ENFORCE(ctx->HasInput("X"), "X(Input) of Pooling should not be null.");
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PADDLE_ENFORCE(ctx->HasOutput("Out"),
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"Out(Output) of Pooling should not be null.");
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auto in_x_dims = ctx->GetInputDim("X");
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std::string pooling_type = ctx->Attrs().Get<std::string>("pooling_type");
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std::vector<int> ksize = ctx->Attrs().Get<std::vector<int>>("ksize");
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std::vector<int> strides = ctx->Attrs().Get<std::vector<int>>("strides");
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std::vector<int> paddings = ctx->Attrs().Get<std::vector<int>>("paddings");
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bool ceil_mode = ctx->Attrs().Get<bool>("ceil_mode");
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PADDLE_ENFORCE(in_x_dims.size() == 4 || in_x_dims.size() == 5,
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"Pooling intput should be 4-D or 5-D tensor.");
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if (ctx->Attrs().Get<bool>("global_pooling")) {
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ksize.resize(static_cast<size_t>(in_x_dims.size()) - 2);
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for (size_t i = 0; i < ksize.size(); ++i) {
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paddings[i] = 0;
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ksize[i] = static_cast<int>(in_x_dims[i + 2]);
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}
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}
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PADDLE_ENFORCE(in_x_dims.size() - ksize.size() == 2U,
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"Input size and pooling size should be consistent.");
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PADDLE_ENFORCE_EQ(ksize.size(), strides.size(),
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"Strides size and pooling size should be the same.");
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PADDLE_ENFORCE_EQ(ksize.size(), paddings.size(),
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"Paddings size and pooling size should be the same.");
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std::vector<int64_t> output_shape({in_x_dims[0], in_x_dims[1]});
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for (size_t i = 0; i < ksize.size(); ++i) {
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output_shape.push_back(PoolOutputSize(in_x_dims[i + 2], ksize[i],
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paddings[i], strides[i], ceil_mode));
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}
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ctx->SetOutputDim("Out", framework::make_ddim(output_shape));
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ctx->ShareLoD("X", "Out");
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}
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framework::OpKernelType PoolOp::GetExpectedKernelType(
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const framework::ExecutionContext &ctx) const {
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framework::LibraryType library_{framework::LibraryType::kPlain};
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std::string data_format = ctx.Attr<std::string>("data_format");
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framework::DataLayout layout_ = framework::StringToDataLayout(data_format);
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#ifdef PADDLE_WITH_CUDA
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if (platform::CanCUDNNBeUsed(ctx)) {
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library_ = framework::LibraryType::kCUDNN;
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}
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#endif
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#ifdef PADDLE_WITH_MKLDNN
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if (library_ == framework::LibraryType::kPlain &&
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platform::CanMKLDNNBeUsed(ctx)) {
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library_ = framework::LibraryType::kMKLDNN;
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layout_ = framework::DataLayout::kMKLDNN;
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}
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#endif
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return framework::OpKernelType(
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framework::ToDataType(ctx.Input<Tensor>("X")->type()), ctx.GetPlace(),
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layout_, library_);
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}
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void PoolOpGrad::InferShape(framework::InferShapeContext *ctx) const {
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PADDLE_ENFORCE(ctx->HasInput("X"), "Input(X) must not be null.");
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PADDLE_ENFORCE(ctx->HasOutput(framework::GradVarName("X")),
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"Input(X@GRAD) should not be null.");
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ctx->SetOutputDim(framework::GradVarName("X"), ctx->GetInputDim("X"));
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}
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framework::OpKernelType PoolOpGrad::GetExpectedKernelType(
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const framework::ExecutionContext &ctx) const {
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framework::LibraryType library_{framework::LibraryType::kPlain};
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std::string data_format = ctx.Attr<std::string>("data_format");
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framework::DataLayout layout_ = framework::StringToDataLayout(data_format);
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#ifdef PADDLE_WITH_CUDA
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if (platform::CanCUDNNBeUsed(ctx)) {
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library_ = framework::LibraryType::kCUDNN;
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}
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#endif
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#ifdef PADDLE_WITH_MKLDNN
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if (library_ == framework::LibraryType::kPlain &&
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platform::CanMKLDNNBeUsed(ctx)) {
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library_ = framework::LibraryType::kMKLDNN;
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layout_ = framework::DataLayout::kMKLDNN;
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}
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#endif
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auto input_data_type = framework::ToDataType(ctx.Input<Tensor>("X")->type());
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if (input_data_type == framework::proto::VarType::FP16) {
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PADDLE_ENFORCE_EQ(library_, framework::LibraryType::kCUDNN,
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"float16 can only be used when CUDNN is used");
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}
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return framework::OpKernelType(input_data_type, ctx.GetPlace(), layout_,
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library_);
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}
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void Pool2dOpMaker::Make() {
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AddInput(
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"X",
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"(Tensor) The input tensor of pooling operator. "
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"The format of input tensor is NCHW, where N is batch size, C is the "
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"number of channels, H is the height of the feature, "
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"and W is the width of the feature.");
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AddOutput("Out",
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"(Tensor) The output tensor of pooling operator. "
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"The format of output tensor is also NCHW, "
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"where N is batch size, C is the number of channels, "
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"H is the height of the feature, "
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"and W is the width of the feature.")
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.Reuse("X");
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AddAttr<std::string>("pooling_type",
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"(string), pooling type, can be \"max\" for max-pooling "
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"and \"avg\" for average-pooling.")
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.InEnum({"max", "avg"});
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AddAttr<std::vector<int>>("ksize",
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"(vector<int>) The pooling window "
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"size(height, width) of the pooling operator. "
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"If global_pooling = true, ksize and paddings will "
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"be ignored."); // TODO(Chengduo): Add checker.
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// (Currently,
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// TypedAttrChecker don't support vector type.)
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AddAttr<bool>("global_pooling",
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"(bool, default false) Whether to use the global pooling. "
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"If global_pooling = true, ksize and paddings will be ignored.")
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.SetDefault(false);
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AddAttr<std::vector<int>>("strides",
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"(vector<int>, default {1, 1}), strides(height, "
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"width) of pooling operator.")
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.SetDefault({1, 1});
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// TODO(Chengduo): Add checker. (Currently,
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// TypedAttrChecker don't support vector type.)
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AddAttr<std::vector<int>>(
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"paddings",
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"(vector<int>, default {0,0}), paddings(height, width) of pooling "
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"operator."
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"If global_pooling = true, paddings and ksize will be ignored.")
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.SetDefault({0, 0});
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AddAttr<bool>(
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"use_cudnn",
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"(bool, default false) Only used in cudnn kernel, need install cudnn")
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.SetDefault(false);
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AddAttr<bool>(
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"ceil_mode",
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"(bool, default false) Wether to use the ceil function to calculate "
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"output height and width. False is the default. If it is set to False, "
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"the floor function will be used.")
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.SetDefault(false);
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AddAttr<bool>("use_mkldnn",
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"(bool, default false) Only used in mkldnn kernel")
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.SetDefault(false);
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AddAttr<std::string>(
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"data_format",
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"(string, default NCHW) Only used in "
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"An optional string from: \"NHWC\", \"NCHW\". "
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"Defaults to \"NHWC\". Specify the data format of the output data, "
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"the input will be transformed automatically. ")
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.SetDefault("AnyLayout");
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// TODO(dzhwinter): need to registered layout transform function
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AddComment(R"DOC(
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The pooling2d operation calculates the output based on
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the input, pooling_type and ksize, strides, paddings parameters.
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Input(X) and output(Out) are in NCHW format, where N is batch size, C is the
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number of channels, H is the height of the feature, and W is the width of the feature.
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Parameters(ksize, strides, paddings) are two elements.
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These two elements represent height and width, respectively.
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The input(X) size and output(Out) size may be different.
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Example:
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Input:
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X shape: $(N, C, H_{in}, W_{in})$
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Output:
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Out shape: $(N, C, H_{out}, W_{out})$
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For ceil_mode = false:
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$$
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H_{out} = \\frac{(H_{in} - ksize[0] + 2 * paddings[0])}{strides[0]} + 1
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$$
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$$
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W_{out} = \\frac{(W_{in} - ksize[1] + 2 * paddings[1])}{strides[1]} + 1
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$$
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For ceil_mode = true:
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$$
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H_{out} = \\frac{(H_{in} - ksize[0] + 2 * paddings[0] + strides[0] - 1)}{strides[0]} + 1
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$$
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$$
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W_{out} = \\frac{(W_{in} - ksize[1] + 2 * paddings[1] + strides[1] - 1)}{strides[1]} + 1
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$$
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)DOC");
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}
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void Pool3dOpMaker::Make() {
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AddInput("X",
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"(Tensor) The input tensor of pooling operator. "
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"The format of input tensor is NCDHW, where N is batch size, C is "
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"the number of channels, and D, H and W is the depth, height and "
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"width of "
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"the feature, respectively.");
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AddOutput("Out",
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"(Tensor) The output tensor of pooling operator."
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"The format of output tensor is also NCDHW, "
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"where N is batch size, C is "
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"the number of channels, and D, H and W is the depth, height and "
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"width of the feature, respectively.")
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.Reuse("X");
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AddAttr<std::string>("pooling_type",
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"(string) Pooling type, can be \"max\" for max-pooling "
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"and \"avg\" for average-pooling.")
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.InEnum({"max", "avg"});
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AddAttr<std::vector<int>>(
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"ksize",
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"(vector<int>) The pooling window size(depth, height, "
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"width) of pooling operator. "
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"If global_pooling = true, ksize and paddings will "
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"be ignored."); // TODO(Chengduo): Add checker.
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// (Currently,
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// TypedAttrChecker don't support vector type.)
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AddAttr<bool>(
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"global_pooling",
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"(bool, default false) Whether to use the global pooling. "
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"If global_pooling = true, ksize and paddings wille be ignored.")
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.SetDefault(false);
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AddAttr<std::vector<int>>(
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"strides",
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"(vector<int>, default {1,1,1}) Strides(depth, height, "
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"width) of the pooling operator.")
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.SetDefault({1, 1, 1}); // TODO(Chengduo): Add checker. (Currently,
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// TypedAttrChecker don't support vector type.)
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AddAttr<std::vector<int>>(
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"paddings",
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"(vector<int>, default {0,0,0}), paddings(depth, height, "
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"width) of pooling operator. "
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"If global_pooling = true, ksize and paddings will be ignored.")
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.SetDefault({0, 0, 0}); // TODO(Chengduo): Add checker. (Currently,
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// TypedAttrChecker don't support vector type.)
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AddAttr<bool>(
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"use_cudnn",
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"(bool, default false) Only used in cudnn kernel, need install cudnn")
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.SetDefault(false);
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AddAttr<bool>(
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"ceil_mode",
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"(bool, default false) Wether to use the ceil function to calculate "
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"output height and width. False is the default. If it is set to False, "
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"the floor function will be used.")
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.SetDefault(false);
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AddAttr<bool>("use_mkldnn",
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"(bool, default false) Only used in mkldnn kernel")
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.SetDefault(false);
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AddAttr<std::string>(
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"data_format",
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"(string, default NCHW) Only used in "
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"An optional string from: \"NHWC\", \"NCHW\". "
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"Defaults to \"NHWC\". Specify the data format of the output data, "
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"the input will be transformed automatically. ")
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.SetDefault("AnyLayout");
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// TODO(dzhwinter): need to registered layout transform function
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AddComment(R"DOC(
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Pool3d Operator.
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The pooling3d operation calculates the output based on
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the input, pooling_type, ksize, strides, and paddings parameters.
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Input(X) and output(Out) are in NCDHW format, where N is batch
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size, C is the number of channels, and D, H and W are the depth, height and
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width of the feature, respectively. Parameters(ksize, strides, paddings)
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are three elements. These three elements represent depth, height and
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width, respectively. The input(X) size and output(Out) size may be different.
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Example:
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Input:
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X shape: $(N, C, D_{in}, H_{in}, W_{in})$
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Output:
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Out shape: $(N, C, D_{out}, H_{out}, W_{out})$
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For ceil_mode = false:
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$$
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D_{out} = \frac{(D_{in} - ksize[0] + 2 * paddings[0])}{strides[0]} + 1 \\
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H_{out} = \frac{(H_{in} - ksize[1] + 2 * paddings[1])}{strides[1]} + 1 \\
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W_{out} = \frac{(W_{in} - ksize[2] + 2 * paddings[2])}{strides[2]} + 1
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$$
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For ceil_mode = true:
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$$
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D_{out} = \frac{(D_{in} - ksize[0] + 2 * paddings[0] + strides[0] -1)}{strides[0]} + 1 \\
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H_{out} = \frac{(H_{in} - ksize[1] + 2 * paddings[1] + strides[1] -1)}{strides[1]} + 1 \\
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W_{out} = \frac{(W_{in} - ksize[2] + 2 * paddings[2] + strides[2] -1)}{strides[2]} + 1
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$$
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)DOC");
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}
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} // namespace operators
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} // namespace paddle
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namespace ops = paddle::operators;
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REGISTER_OPERATOR(pool2d, ops::PoolOp, ops::Pool2dOpMaker,
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paddle::framework::DefaultGradOpDescMaker<true>);
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REGISTER_OPERATOR(pool2d_grad, ops::PoolOpGrad);
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REGISTER_OP_CPU_KERNEL(
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pool2d, ops::PoolKernel<paddle::platform::CPUDeviceContext, float>,
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ops::PoolKernel<paddle::platform::CPUDeviceContext, double>);
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REGISTER_OP_CPU_KERNEL(
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pool2d_grad, ops::PoolGradKernel<paddle::platform::CPUDeviceContext, float>,
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ops::PoolGradKernel<paddle::platform::CPUDeviceContext, double>);
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REGISTER_OPERATOR(pool3d, ops::PoolOp, ops::Pool3dOpMaker,
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paddle::framework::DefaultGradOpDescMaker<true>);
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REGISTER_OPERATOR(pool3d_grad, ops::PoolOpGrad);
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REGISTER_OP_CPU_KERNEL(
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pool3d, ops::PoolKernel<paddle::platform::CPUDeviceContext, float>,
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ops::PoolKernel<paddle::platform::CPUDeviceContext, double>);
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REGISTER_OP_CPU_KERNEL(
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pool3d_grad, ops::PoolGradKernel<paddle::platform::CPUDeviceContext, float>,
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ops::PoolGradKernel<paddle::platform::CPUDeviceContext, double>);
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