Merge branch 'develop' of https://github.com/PaddlePaddle/Paddle into fix-6590
yangyaming
7 years ago
commit
984e4ca530
@ -0,0 +1,80 @@
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/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
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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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||||
|
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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
|
||||
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 <algorithm>
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#include <string>
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#include "paddle/framework/executor.h"
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#include "paddle/framework/init.h"
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#include "paddle/platform/place.h"
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#include "paddle/string/piece.h"
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namespace paddle {
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namespace framework {
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std::once_flag gflags_init_flag;
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// TODO(qijun) move init gflags to init.cc
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void InitGflags(std::vector<std::string> &argv) {
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std::call_once(gflags_init_flag, [&]() {
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int argc = argv.size();
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char **arr = new char *[argv.size()];
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std::string line;
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for (size_t i = 0; i < argv.size(); i++) {
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arr[i] = &argv[i][0];
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line += argv[i];
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line += ' ';
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}
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google::ParseCommandLineFlags(&argc, &arr, true);
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VLOG(1) << "Init commandline: " << line;
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});
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}
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bool InitDevices(const std::vector<std::string> &devices) {
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// device format
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// CPU
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// GPU:1
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// TODO(dzhwinter) : add device format annotation for users.
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std::vector<platform::Place> places;
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for (auto &device : devices) {
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auto p = string::Piece(device);
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if (string::Find(p, ':', 0) == string::Piece::npos) {
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places.emplace_back(platform::CPUPlace());
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} else if (string::HasPrefix(p, "GPU")) {
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#ifdef PADDLE_WITH_CUDA
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auto pos = string::RFind(p, ':', string::Piece::npos);
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auto number = device.substr(pos + 1);
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places.emplace_back(platform::GPUPlace(std::stoi(number)));
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#else
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LOG(WARNING)
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<< "'GPU' is not supported, Please re-compile with WITH_GPU option";
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#endif
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} else {
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return false;
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}
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}
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if (std::find_if(places.begin(), places.end(),
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[&](const platform::Place &place) {
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return platform::is_cpu_place(place);
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}) == places.end()) {
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places.emplace_back(platform::CPUPlace());
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LOG(WARNING) << "Not specified any device, use CPU by Default.";
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}
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DeviceContextPool::Create(places);
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return true;
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return true;
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}
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} // namespace framework
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} // namespace paddle
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@ -0,0 +1,28 @@
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/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
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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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|
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http://www.apache.org/licenses/LICENSE-2.0
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|
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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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#pragma once
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#include <mutex>
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#include "gflags/gflags.h"
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#include "glog/logging.h"
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namespace paddle {
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namespace framework {
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void InitGflags(std::vector<std::string> &argv);
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bool InitDevices(const std::vector<std::string> &devices);
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} // namespace framework
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} // namespace paddle
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@ -0,0 +1,27 @@
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/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
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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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|
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http://www.apache.org/licenses/LICENSE-2.0
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|
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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 "gtest/gtest.h"
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#include "paddle/framework/init.h"
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TEST(Init, InitDevices) {
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using paddle::framework::InitDevices;
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std::vector<std::string> ds1 = {"CPU"};
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ASSERT_EQ(InitDevices(ds1), true);
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#ifdef PADDLE_WITH_CUDA
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std::vector<std::string> ds2 = {"CPU", "GPU:0", "GPU:1"};
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ASSERT_EQ(InitDevices(ds2), true);
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#endif
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}
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@ -0,0 +1,99 @@
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/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
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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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Indicesou may obtain a copy of the License at
|
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|
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http://www.apache.org/licenses/LICENSE-2.0
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|
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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,
|
||||
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/operators/spp_op.h"
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namespace paddle {
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namespace operators {
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class SppOpMaker : public framework::OpProtoAndCheckerMaker {
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public:
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SppOpMaker(framework::OpProto* proto, framework::OpAttrChecker* op_checker)
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: OpProtoAndCheckerMaker(proto, op_checker) {
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AddInput(
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"X",
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"(Tensor) The input tensor of spp 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 and W is the height and width of feature.");
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AddOutput("Out",
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"(Tensor) The output tensor of spp operator."
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"N * M."
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"M = C * H * W");
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AddAttr<int>("pyramid_height", "(int), multi level pooling");
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AddAttr<std::string>(
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"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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AddComment(R"DOC(
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"With spatial pyramid pooling, the input image can
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be of any sizes. This not only allows arbitrary aspect
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ratios, but also allows arbitrary scales. We can resize
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the input image to any scale (e.g., min(w, h)=180, 224,
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...) and apply the same deep network. When the
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input image is at different scales, the network (with
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the same filter sizes) will extract features at different
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scales. The scales play important roles in traditional
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methods.
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Input shape: $(N, C_{in}, H_{in}, W_{in})$
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Output shape: $(H_{out}, W_{out})$
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Where
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$$
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H_{out} = N \\
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W_{out} = (((4^pyramid_height) - 1) / (4 - 1))$ * C_{in}
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$$
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paper https://arxiv.org/pdf/1406.4729v4.pdf
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)DOC");
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}
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};
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class SppOp : public framework::OperatorWithKernel {
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public:
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using framework::OperatorWithKernel::OperatorWithKernel;
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void InferShape(framework::InferShapeContext* ctx) const override {
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PADDLE_ENFORCE(ctx->HasInput("X"),
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"Input(X) of SppOp"
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"should not be null.");
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PADDLE_ENFORCE(ctx->HasOutput("Out"),
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"Output(Out) of SppOp should not be null.");
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auto in_x_dims = ctx->GetInputDim("X");
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int pyramid_height = ctx->Attrs().Get<int>("pyramid_height");
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PADDLE_ENFORCE(in_x_dims.size() == 4,
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"Spping intput must be of 4-dimensional.");
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int outlen = ((std::pow(4, pyramid_height) - 1) / (4 - 1)) * in_x_dims[1];
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std::vector<int64_t> output_shape({in_x_dims[0], outlen});
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ctx->SetOutputDim("Out", framework::make_ddim(output_shape));
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}
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};
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class SppOpGrad : public framework::OperatorWithKernel {
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public:
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using framework::OperatorWithKernel::OperatorWithKernel;
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void InferShape(framework::InferShapeContext* ctx) const override {
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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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};
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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_OP(spp, ops::SppOp, ops::SppOpMaker, spp_grad, ops::SppOpGrad);
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REGISTER_OP_CPU_KERNEL(
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spp, ops::SppKernel<paddle::platform::CPUDeviceContext, float>,
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ops::SppKernel<paddle::platform::CPUDeviceContext, double>);
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REGISTER_OP_CPU_KERNEL(
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spp_grad, ops::SppGradKernel<paddle::platform::CPUDeviceContext, float>,
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ops::SppGradKernel<paddle::platform::CPUDeviceContext, double>);
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@ -0,0 +1,23 @@
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/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
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|
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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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Indicesou may obtain a copy of the License at
|
||||
|
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http://www.apache.org/licenses/LICENSE-2.0
|
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|
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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/operators/spp_op.h"
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namespace ops = paddle::operators;
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REGISTER_OP_CUDA_KERNEL(
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spp, ops::SppKernel<paddle::platform::CUDADeviceContext, float>,
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ops::SppKernel<paddle::platform::CUDADeviceContext, double>);
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REGISTER_OP_CUDA_KERNEL(
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spp_grad, ops::SppGradKernel<paddle::platform::CUDADeviceContext, float>,
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ops::SppGradKernel<paddle::platform::CUDADeviceContext, double>);
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@ -0,0 +1,161 @@
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/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
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|
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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.
|
||||
Indicesou may obtain a copy of the License at
|
||||
|
||||
http://www.apache.org/licenses/LICENSE-2.0
|
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|
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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.
|
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See the License for the specific language governing permissions and
|
||||
limitations under the License. */
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#pragma once
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#include "paddle/framework/op_registry.h"
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#include "paddle/operators/math/math_function.h"
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#include "paddle/operators/math/pooling.h"
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#include "paddle/operators/strided_memcpy.h"
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namespace paddle {
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namespace operators {
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template <typename DeviceContext, typename T>
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class SppKernel : public framework::OpKernel<T> {
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public:
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void Compute(const framework::ExecutionContext& context) const override {
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const framework::Tensor* in_x = context.Input<framework::Tensor>("X");
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auto* out = context.Output<framework::Tensor>("Out");
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int pyramid_height = context.template Attr<int>("pyramid_height");
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std::string pooling_type =
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context.template Attr<std::string>("pooling_type");
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out->mutable_data<T>(context.GetPlace());
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auto out_stride = framework::stride(out->dims());
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int input_h = in_x->dims()[2];
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int input_w = in_x->dims()[3];
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size_t output_offset = 0;
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for (int p = 0; p < pyramid_height; ++p) {
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int bins = std::pow(2, p);
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int kernel_size_h = std::ceil(input_h / static_cast<double>(bins));
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int kernel_size_w = std::ceil(input_w / static_cast<double>(bins));
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int padding_h = (kernel_size_h * bins - input_h + 1) / 2;
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int padding_w = (kernel_size_w * bins - input_w + 1) / 2;
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std::vector<int> kernel_size({kernel_size_h, kernel_size_w});
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std::vector<int> strides({kernel_size_h, kernel_size_w});
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std::vector<int> paddings({padding_h, padding_w});
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// pooling output shape
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framework::Tensor out_level;
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std::vector<int64_t> output_shape_vec(
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{in_x->dims()[0], in_x->dims()[1], bins, bins});
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framework::DDim output_shape(framework::make_ddim(output_shape_vec));
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out_level.mutable_data<T>(output_shape, context.GetPlace());
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// pooling
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if (pooling_type == "max") {
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math::Pool2dFunctor<DeviceContext, math::MaxPool<T>, T> pool_forward;
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math::MaxPool<T> max_process;
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pool_forward(context.template device_context<DeviceContext>(), *in_x,
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kernel_size, strides, paddings, max_process, &out_level);
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} else if (pooling_type == "avg") {
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math::Pool2dFunctor<DeviceContext, math::AvgPool<T>, T> pool_forward;
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math::AvgPool<T> avg_process;
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pool_forward(context.template device_context<DeviceContext>(), *in_x,
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kernel_size, strides, paddings, avg_process, &out_level);
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}
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// flatten pooling output shape
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int output_flatten_w = in_x->dims()[1] * bins * bins;
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std::vector<int64_t> output_flatten_shape_vec(
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{in_x->dims()[0], output_flatten_w});
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framework::DDim output_flatten_shape(
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framework::make_ddim(output_flatten_shape_vec));
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out_level.Resize(output_flatten_shape);
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// concat
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auto out_level_stride = framework::stride(out_level.dims());
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StridedMemcpy<T>(context.template device_context<DeviceContext>(),
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out_level.data<T>(), out_level_stride, out_level.dims(),
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out_stride, out->data<T>() + output_offset);
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output_offset += out_level.dims()[1] * out_level_stride[1];
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}
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}
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};
|
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template <typename DeviceContext, typename T>
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class SppGradKernel : public framework::OpKernel<T> {
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public:
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void Compute(const framework::ExecutionContext& context) const override {
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const framework::Tensor* in_x = context.Input<framework::Tensor>("X");
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const framework::Tensor* out = context.Input<framework::Tensor>("Out");
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const framework::Tensor* out_grad =
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context.Input<framework::Tensor>(framework::GradVarName("Out"));
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framework::Tensor* in_x_grad =
|
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context.Output<framework::Tensor>(framework::GradVarName("X"));
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int pyramid_height = context.template Attr<int>("pyramid_height");
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std::string pooling_type =
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context.template Attr<std::string>("pooling_type");
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auto& device_ctx = context.template device_context<DeviceContext>();
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math::SetConstant<DeviceContext, T> zero;
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in_x_grad->mutable_data<T>(context.GetPlace());
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zero(device_ctx, in_x_grad, static_cast<T>(0));
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auto out_stride = framework::stride(out->dims());
|
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int input_h = in_x->dims()[2];
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int input_w = in_x->dims()[3];
|
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size_t out_offset = 0;
|
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for (int p = 0; p < pyramid_height; ++p) {
|
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int bins = std::pow(2, p);
|
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int kernel_size_h = std::ceil(input_h / static_cast<double>(bins));
|
||||
int kernel_size_w = std::ceil(input_w / static_cast<double>(bins));
|
||||
int padding_h = (kernel_size_h * bins - input_h + 1) / 2;
|
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int padding_w = (kernel_size_w * bins - input_w + 1) / 2;
|
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std::vector<int> kernel_size({kernel_size_h, kernel_size_w});
|
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std::vector<int> strides({kernel_size_h, kernel_size_w});
|
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std::vector<int> paddings({padding_h, padding_w});
|
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// split out and outgrad ... to flatten
|
||||
framework::Tensor out_level;
|
||||
framework::Tensor outgrad_level;
|
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int out_flatten_w = in_x->dims()[1] * bins * bins;
|
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std::vector<int64_t> out_flatten_shape_vec(
|
||||
{in_x->dims()[0], out_flatten_w});
|
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framework::DDim out_flatten_shape(
|
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framework::make_ddim(out_flatten_shape_vec));
|
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out_level.mutable_data<T>(out_flatten_shape, context.GetPlace());
|
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outgrad_level.mutable_data<T>(out_flatten_shape, context.GetPlace());
|
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auto flatten_stride = framework::stride(out_level.dims());
|
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// memcpy
|
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StridedMemcpy<T>(context.template device_context<DeviceContext>(),
|
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out->data<T>() + out_offset, out_stride,
|
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out_level.dims(), flatten_stride, out_level.data<T>());
|
||||
|
||||
StridedMemcpy<T>(context.template device_context<DeviceContext>(),
|
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out_grad->data<T>() + out_offset, out_stride,
|
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outgrad_level.dims(), flatten_stride,
|
||||
outgrad_level.data<T>());
|
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out_offset += out_level.dims()[1] * out_stride[1];
|
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// flatten backward to nchw
|
||||
|
||||
std::vector<int64_t> out_shape_vec({in_x->dims()[0], in_x->dims()[1]});
|
||||
out_shape_vec.push_back(
|
||||
(input_h - kernel_size_h + 2 * padding_h) / kernel_size_h + 1);
|
||||
out_shape_vec.push_back(
|
||||
(input_w - kernel_size_w + 2 * padding_w) / kernel_size_w + 1);
|
||||
framework::DDim out_shape(framework::make_ddim(out_shape_vec));
|
||||
out_level.ShareDataWith(out_level);
|
||||
out_level.Resize(out_shape);
|
||||
outgrad_level.ShareDataWith(outgrad_level);
|
||||
outgrad_level.Resize(out_shape);
|
||||
// pooling backward
|
||||
if (pooling_type == "max") {
|
||||
math::MaxPool2dGradFunctor<DeviceContext, T> pool2d_backward;
|
||||
pool2d_backward(context.template device_context<DeviceContext>(), *in_x,
|
||||
*&out_level, *&outgrad_level, kernel_size, strides,
|
||||
paddings, in_x_grad);
|
||||
} else if (pooling_type == "avg") {
|
||||
math::Pool2dGradFunctor<DeviceContext, math::AvgPoolGrad<T>, T>
|
||||
pool_backward;
|
||||
math::AvgPoolGrad<T> avg_process;
|
||||
pool_backward(context.template device_context<DeviceContext>(), *in_x,
|
||||
*&out_level, *&outgrad_level, kernel_size, strides,
|
||||
paddings, avg_process, in_x_grad);
|
||||
}
|
||||
}
|
||||
}
|
||||
};
|
||||
} // namespace operators
|
||||
} // namespace paddle
|
||||
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Reference in new issue