Jiabin Yang
7 years ago
committed by
GitHub
commit
9f65b616b2
@ -0,0 +1,31 @@
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set(INFERENCE_URL "http://paddle-inference-dist.cdn.bcebos.com" CACHE STRING "inference download url")
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set(INFERENCE_DEMO_INSTALL_DIR "${THIRD_PARTY_PATH}/inference_demo" CACHE STRING
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"A path setting inference demo download directories.")
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function (inference_download install_dir url filename)
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message(STATUS "Download inference test stuff from ${url}/${filename}")
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execute_process(COMMAND bash -c "mkdir -p ${install_dir}")
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execute_process(COMMAND bash -c "cd ${install_dir} && wget -q ${url}/${filename}")
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message(STATUS "finish downloading ${filename}")
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endfunction()
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function (inference_download_and_uncompress install_dir url filename)
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inference_download(${install_dir} ${url} ${filename})
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execute_process(COMMAND bash -c "cd ${install_dir} && tar xzf ${filename}")
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endfunction()
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set(WORD2VEC_INSTALL_DIR "${INFERENCE_DEMO_INSTALL_DIR}/word2vec")
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if (NOT EXISTS ${WORD2VEC_INSTALL_DIR})
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inference_download_and_uncompress(${WORD2VEC_INSTALL_DIR} ${INFERENCE_URL} "word2vec.inference.model.tar.gz")
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endif()
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set(WORD2VEC_MODEL_DIR "${WORD2VEC_INSTALL_DIR}/word2vec.inference.model")
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function (inference_base_test TARGET)
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set(options "")
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set(oneValueArgs "")
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set(multiValueArgs SRCS ARGS DEPS)
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cmake_parse_arguments(base_test "${options}" "${oneValueArgs}" "${multiValueArgs}" ${ARGN})
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if(WITH_GPU)
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set(mem_opt "--fraction_of_gpu_memory_to_use=0.5")
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endif()
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cc_test(${TARGET} SRCS ${base_test_SRCS} DEPS ${base_test_DEPS} ARGS ${mem_opt} ${base_test_ARGS})
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endfunction()
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@ -0,0 +1,112 @@
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/* Copyright (c) 2018 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/framework/op_registry.h"
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#include "paddle/fluid/platform/cudnn_helper.h"
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namespace paddle {
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namespace operators {
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using Tensor = framework::Tensor;
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using ScopedSpatialTransformerDescriptor =
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platform::ScopedSpatialTransformerDescriptor;
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template <typename T>
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class CUDNNAffineGridOpKernel : public framework::OpKernel<T> {
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public:
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void Compute(const framework::ExecutionContext& ctx) const override {
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PADDLE_ENFORCE(platform::is_gpu_place(ctx.GetPlace()),
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"It must use CUDAPlace.");
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auto& dev_ctx = ctx.template device_context<platform::CUDADeviceContext>();
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auto handle = dev_ctx.cudnn_handle();
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auto* theta = ctx.Input<Tensor>("Theta");
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auto* output = ctx.Output<Tensor>("Output");
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const T* theta_data = theta->data<T>();
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int n = theta->dims()[0];
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auto size_attr = ctx.Attr<std::vector<int>>("output_shape");
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Tensor h_sizes;
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int* h_size_data;
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if (size_attr.size() == 0) {
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auto* output_shape = ctx.Input<Tensor>("OutputShape");
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framework::TensorCopy(*output_shape, platform::CPUPlace(), &h_sizes);
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h_size_data = h_sizes.data<int>();
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} else {
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h_size_data = h_sizes.mutable_data<int>({4}, platform::CPUPlace());
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h_size_data[0] = n;
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h_size_data[1] = size_attr[1];
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h_size_data[2] = size_attr[2];
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h_size_data[3] = size_attr[3];
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}
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T* output_data = output->mutable_data<T>(
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{n, h_size_data[2], h_size_data[3], 2}, ctx.GetPlace());
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ScopedSpatialTransformerDescriptor st_desc;
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cudnnSpatialTransformerDescriptor_t cudnn_st_desc =
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st_desc.descriptor<T>(4, h_size_data);
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PADDLE_ENFORCE(platform::dynload::cudnnSpatialTfGridGeneratorForward(
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handle, cudnn_st_desc, theta_data, output_data));
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}
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};
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template <typename T>
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class CUDNNAffineGridGradOpKernel : public framework::OpKernel<T> {
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public:
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void Compute(const framework::ExecutionContext& ctx) const override {
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PADDLE_ENFORCE(platform::is_gpu_place(ctx.GetPlace()),
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"It must use CUDAPlace.");
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auto& dev_ctx = ctx.template device_context<platform::CUDADeviceContext>();
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auto handle = dev_ctx.cudnn_handle();
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auto output_grad = ctx.Input<Tensor>(framework::GradVarName("Output"));
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auto theta_grad = ctx.Output<Tensor>(framework::GradVarName("Theta"));
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int n = output_grad->dims()[0];
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auto size_attr = ctx.Attr<std::vector<int>>("output_shape");
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Tensor h_sizes;
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int* h_size_data;
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if (size_attr.size() == 0) {
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auto* output_shape = ctx.Input<Tensor>("OutputShape");
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framework::TensorCopy(*output_shape, platform::CPUPlace(), &h_sizes);
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h_size_data = h_sizes.data<int>();
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} else {
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h_size_data = h_sizes.mutable_data<int>({4}, platform::CPUPlace());
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h_size_data[0] = n;
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h_size_data[1] = size_attr[1];
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h_size_data[2] = size_attr[2];
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h_size_data[3] = size_attr[3];
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}
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ScopedSpatialTransformerDescriptor st_desc;
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cudnnSpatialTransformerDescriptor_t cudnn_st_desc =
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st_desc.descriptor<T>(4, h_size_data);
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const T* output_grad_data = output_grad->data<T>();
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T* theta_grad_data = theta_grad->mutable_data<T>(ctx.GetPlace());
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PADDLE_ENFORCE(platform::dynload::cudnnSpatialTfGridGeneratorBackward(
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handle, cudnn_st_desc, output_grad_data, theta_grad_data));
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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 plat = paddle::platform;
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REGISTER_OP_KERNEL(affine_grid, CUDNN, plat::CUDAPlace,
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paddle::operators::CUDNNAffineGridOpKernel<float>,
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paddle::operators::CUDNNAffineGridOpKernel<double>);
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REGISTER_OP_KERNEL(affine_grid_grad, CUDNN, plat::CUDAPlace,
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paddle::operators::CUDNNAffineGridGradOpKernel<float>,
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paddle::operators::CUDNNAffineGridGradOpKernel<double>);
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@ -0,0 +1,233 @@
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/* Copyright (c) 2018 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/affine_grid_op.h"
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#include <string>
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#include "paddle/fluid/framework/op_registry.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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namespace paddle {
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namespace operators {
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using Tensor = framework::Tensor;
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template <typename T>
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struct Linspace<paddle::platform::CPUDeviceContext, T> {
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framework::Tensor operator()(T start, T end, int count,
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const framework::ExecutionContext& ctx) {
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Tensor numbers;
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T* number_data = numbers.mutable_data<T>({count}, platform::CPUPlace());
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T slice = (end - start) / (T)(count - 1);
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for (int i = 0; i < count; ++i) {
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number_data[i] = start + (T)i * slice;
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}
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return numbers;
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}
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};
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class AffineGridOp : 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("Theta"),
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"Input(Theta) of AffineGridOp should not be null.");
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PADDLE_ENFORCE(ctx->HasOutput("Output"),
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"Output(Output) of AffineGridOp should not be null.");
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auto theta_dims = ctx->GetInputDim("Theta");
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PADDLE_ENFORCE(theta_dims.size() == 3,
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"AffineGrid's Input(Theta) should be 3-D tensor.");
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auto output_shape = ctx->Attrs().Get<std::vector<int>>("output_shape");
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if (output_shape.size() == 0) {
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PADDLE_ENFORCE(ctx->HasInput("OutputShape"),
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"Input(OutputShape) of AffineGridOp should not be null if "
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"attr(output_shape) is not configured.");
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auto output_shape_dims = ctx->GetInputDim("OutputShape");
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PADDLE_ENFORCE(output_shape_dims.size() == 1,
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"AffineGrid's Input(OutputShape) should be 1-D tensor.");
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} else {
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PADDLE_ENFORCE(output_shape.size() == 4,
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"The size of attr(output_shape) should be 4.");
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}
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PADDLE_ENFORCE(theta_dims[1] == 2, "Input(theta) dims[1] should be 2.");
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PADDLE_ENFORCE(theta_dims[2] == 3, "Input(theta) dims[2] should be 3.");
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// N * H * W * 2
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ctx->SetOutputDim("Output",
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framework::make_ddim({theta_dims[0], -1, -1, 2}));
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ctx->ShareLoD("Theta", "Output");
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}
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protected:
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framework::OpKernelType GetExpectedKernelType(
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const framework::ExecutionContext& ctx) const override {
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framework::LibraryType library{framework::LibraryType::kPlain};
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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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auto data_type = framework::ToDataType(ctx.Input<Tensor>("Theta")->type());
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return framework::OpKernelType(data_type, ctx.GetPlace(),
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framework::DataLayout::kAnyLayout, library);
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}
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};
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class AffineGridOpMaker : public framework::OpProtoAndCheckerMaker {
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public:
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void Make() override {
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AddInput(
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"Theta",
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"(Tensor) A batch of affine transform parameters with shape [N, 2, 3]. "
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"It is used to transform coordinate (x_0, y_0) to coordinate (x_1, "
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"y_1).");
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AddInput("OutputShape",
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"(Tensor) The shape of target image with format [N, C, H, W].")
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.AsDispensable();
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AddOutput("Output", "(Tensor) Output Tensor with shape [N, H, W, 2].");
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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(true);
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AddAttr<std::vector<int>>(
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"output_shape",
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"The target output image shape with format [N, C, H, W].")
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.SetDefault(std::vector<int>());
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AddComment(R"DOC(
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It generates a grid of (x,y) coordinates using the parameters of the
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affine transformation that correspond to a set of points where the input
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feature map should be sampled to produce the transformed output feature map.
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Given:
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Theta = [[[x_11, x_12, x_13]
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[x_14, x_15, x_16]]
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[[x_21, x_22, x_23]
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[x_24, x_25, x_26]]]
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OutputShape = [2, 3, 5, 5]
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Step 1:
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Generate relative coordinates according to OutputShape.
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The values of relative coordinates are in the interval between -1 and 1.
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The shape of the relative coordinates is [2, H, W] as below:
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C = [[[-1. -1. -1. -1. -1. ]
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[-0.5 -0.5 -0.5 -0.5 -0.5]
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[ 0. 0. 0. 0. 0. ]
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[ 0.5 0.5 0.5 0.5 0.5]
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[ 1. 1. 1. 1. 1. ]]
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[[-1. -0.5 0. 0.5 1. ]
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[-1. -0.5 0. 0.5 1. ]
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[-1. -0.5 0. 0.5 1. ]
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[-1. -0.5 0. 0.5 1. ]
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[-1. -0.5 0. 0.5 1. ]]]
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C[0] is the coordinates in height axis and C[1] is the coordinates in width axis.
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Step2:
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Tanspose and reshape C to shape [H * W, 2] and append ones to last dimension. The we get:
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C_ = [[-1. -1. 1. ]
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[-0.5 -1. 1. ]
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[ 0. -1. 1. ]
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[ 0.5 -1. 1. ]
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[ 1. -1. 1. ]
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[-1. -0.5 1. ]
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[-0.5 -0.5 1. ]
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[ 0. -0.5 1. ]
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[ 0.5 -0.5 1. ]
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[ 1. -0.5 1. ]
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[-1. 0. 1. ]
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[-0.5 0. 1. ]
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[ 0. 0. 1. ]
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[ 0.5 0. 1. ]
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[ 1. 0. 1. ]
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[-1. 0.5 1. ]
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[-0.5 0.5 1. ]
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[ 0. 0.5 1. ]
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[ 0.5 0.5 1. ]
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[ 1. 0.5 1. ]
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[-1. 1. 1. ]
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[-0.5 1. 1. ]
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[ 0. 1. 1. ]
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[ 0.5 1. 1. ]
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[ 1. 1. 1. ]]
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Step3:
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Compute output by equation $$Output[i] = C_ * Theta[i]^T$$
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)DOC");
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}
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};
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class AffineGridOpGrad : 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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auto theta_dims = ctx->GetInputDim("Theta");
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if (ctx->HasOutput(framework::GradVarName("Theta"))) {
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ctx->SetOutputDim(framework::GradVarName("Theta"), theta_dims);
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}
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}
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protected:
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framework::OpKernelType GetExpectedKernelType(
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const framework::ExecutionContext& ctx) const override {
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framework::LibraryType library_{framework::LibraryType::kPlain};
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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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return framework::OpKernelType(
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framework::ToDataType(ctx.Input<Tensor>("Theta")->type()),
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ctx.GetPlace(), framework::DataLayout::kAnyLayout, library_);
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}
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};
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||||||
|
class AffineGridGradMaker : public framework::SingleGradOpDescMaker {
|
||||||
|
public:
|
||||||
|
using framework::SingleGradOpDescMaker::SingleGradOpDescMaker;
|
||||||
|
|
||||||
|
protected:
|
||||||
|
std::unique_ptr<framework::OpDesc> Apply() const override {
|
||||||
|
auto* op = new framework::OpDesc();
|
||||||
|
op->SetType("affine_grid_grad");
|
||||||
|
op->SetInput("Theta", Input("Theta"));
|
||||||
|
op->SetInput("OutputShape", Input("OutputShape"));
|
||||||
|
op->SetInput(framework::GradVarName("Output"), OutputGrad("Output"));
|
||||||
|
|
||||||
|
op->SetAttrMap(Attrs());
|
||||||
|
|
||||||
|
op->SetOutput(framework::GradVarName("Theta"), InputGrad("Theta"));
|
||||||
|
return std::unique_ptr<framework::OpDesc>(op);
|
||||||
|
}
|
||||||
|
};
|
||||||
|
|
||||||
|
} // namespace operators
|
||||||
|
} // namespace paddle
|
||||||
|
|
||||||
|
namespace ops = paddle::operators;
|
||||||
|
REGISTER_OPERATOR(affine_grid, ops::AffineGridOp, ops::AffineGridOpMaker,
|
||||||
|
ops::AffineGridGradMaker);
|
||||||
|
REGISTER_OPERATOR(affine_grid_grad, ops::AffineGridOpGrad);
|
||||||
|
|
||||||
|
REGISTER_OP_CPU_KERNEL(
|
||||||
|
affine_grid,
|
||||||
|
ops::AffineGridOpKernel<paddle::platform::CPUDeviceContext, float>,
|
||||||
|
ops::AffineGridOpKernel<paddle::platform::CPUDeviceContext, double>);
|
||||||
|
REGISTER_OP_CPU_KERNEL(
|
||||||
|
affine_grid_grad,
|
||||||
|
ops::AffineGridGradOpKernel<paddle::platform::CPUDeviceContext, float>,
|
||||||
|
ops::AffineGridGradOpKernel<paddle::platform::CPUDeviceContext, double>);
|
||||||
@ -0,0 +1,190 @@
|
|||||||
|
/* Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
|
||||||
|
|
||||||
|
Licensed 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. */
|
||||||
|
|
||||||
|
#pragma once
|
||||||
|
#include <vector>
|
||||||
|
#include "paddle/fluid/framework/eigen.h"
|
||||||
|
#include "paddle/fluid/framework/op_registry.h"
|
||||||
|
#include "paddle/fluid/operators/math/blas.h"
|
||||||
|
#include "paddle/fluid/operators/math/math_function.h"
|
||||||
|
|
||||||
|
namespace paddle {
|
||||||
|
namespace operators {
|
||||||
|
|
||||||
|
using Tensor = framework::Tensor;
|
||||||
|
template <typename T, size_t D, int MajorType = Eigen::RowMajor,
|
||||||
|
typename IndexType = Eigen::DenseIndex>
|
||||||
|
using EigenTensor = framework::EigenTensor<T, D, MajorType, IndexType>;
|
||||||
|
|
||||||
|
using Array1 = Eigen::DSizes<int64_t, 1>;
|
||||||
|
using Array2 = Eigen::DSizes<int64_t, 2>;
|
||||||
|
using Array3 = Eigen::DSizes<int64_t, 3>;
|
||||||
|
using Array4 = Eigen::DSizes<int64_t, 4>;
|
||||||
|
|
||||||
|
/**
|
||||||
|
*Return a tensor with evenly spaced numbers over a specified interval.
|
||||||
|
*/
|
||||||
|
template <typename DeviceContext, typename T>
|
||||||
|
struct Linspace {
|
||||||
|
framework::Tensor operator()(T start, T end, int count,
|
||||||
|
const framework::ExecutionContext& ctx);
|
||||||
|
};
|
||||||
|
|
||||||
|
template <typename DeviceContext, typename T>
|
||||||
|
class AffineGridOpKernel : public framework::OpKernel<T> {
|
||||||
|
public:
|
||||||
|
void Compute(const framework::ExecutionContext& ctx) const override {
|
||||||
|
auto& place = *ctx.template device_context<DeviceContext>().eigen_device();
|
||||||
|
auto* theta = ctx.Input<Tensor>("Theta");
|
||||||
|
int n = theta->dims()[0];
|
||||||
|
|
||||||
|
auto size_attr = ctx.Attr<std::vector<int>>("output_shape");
|
||||||
|
int h = 0;
|
||||||
|
int w = 0;
|
||||||
|
if (size_attr.size() == 0) {
|
||||||
|
auto* output_shape = ctx.Input<Tensor>("OutputShape");
|
||||||
|
Tensor h_sizes;
|
||||||
|
framework::TensorCopy(*output_shape, platform::CPUPlace(), &h_sizes);
|
||||||
|
const int* h_size_data = h_sizes.data<int>();
|
||||||
|
h = h_size_data[2];
|
||||||
|
w = h_size_data[3];
|
||||||
|
} else {
|
||||||
|
h = size_attr[2];
|
||||||
|
w = size_attr[3];
|
||||||
|
}
|
||||||
|
|
||||||
|
auto* output = ctx.Output<Tensor>("Output");
|
||||||
|
output->mutable_data<T>({n, h, w, 2}, ctx.GetPlace());
|
||||||
|
|
||||||
|
math::SetConstant<DeviceContext, T>()(
|
||||||
|
ctx.template device_context<DeviceContext>(), output,
|
||||||
|
static_cast<T>(0));
|
||||||
|
|
||||||
|
Linspace<DeviceContext, T> linspace;
|
||||||
|
// Get indexes of height with shape [height, width, 1]
|
||||||
|
auto h_idx = linspace((T)-1, (T)1, h, ctx);
|
||||||
|
auto h_idx_t = EigenTensor<T, 1>::From(h_idx);
|
||||||
|
// Get indexes of width with shape [height, width, 1]
|
||||||
|
auto w_idx = linspace((T)-1, (T)1, w, ctx);
|
||||||
|
auto w_idx_t = EigenTensor<T, 1>::From(w_idx);
|
||||||
|
// Get constant ones tensor with shape [height, width, 1]
|
||||||
|
Tensor ones;
|
||||||
|
ones.mutable_data<T>({h, w, 1}, ctx.GetPlace());
|
||||||
|
auto ones_t = EigenTensor<T, 3>::From(ones).setConstant((T)1);
|
||||||
|
// Get grid tensor with shape [n, h, w, 3] by concatenating h_idx, w_idx and
|
||||||
|
// ones
|
||||||
|
Tensor grid;
|
||||||
|
grid.mutable_data<T>({n, h, w, 3}, ctx.GetPlace());
|
||||||
|
auto grid_t = EigenTensor<T, 4>::From(grid);
|
||||||
|
|
||||||
|
grid_t.device(place) = w_idx_t.reshape(Array2(1, w))
|
||||||
|
.broadcast(Array2(h, 1))
|
||||||
|
.reshape(Array3(h, w, 1))
|
||||||
|
.concatenate(h_idx_t.reshape(Array2(1, h))
|
||||||
|
.broadcast(Array2(w, 1))
|
||||||
|
.shuffle(Array2(1, 0))
|
||||||
|
.reshape(Array3(h, w, 1)),
|
||||||
|
2)
|
||||||
|
.eval()
|
||||||
|
.concatenate(ones_t, 2)
|
||||||
|
.reshape(Array4(1, h, w, 3))
|
||||||
|
.broadcast(Array4(n, 1, 1, 1));
|
||||||
|
|
||||||
|
// output = grid * theta.T
|
||||||
|
// TODO(wanghaoshuang): Refine batched matrix multiply
|
||||||
|
auto blas = math::GetBlas<DeviceContext, T>(ctx);
|
||||||
|
for (int i = 0; i < n; ++i) {
|
||||||
|
Tensor sliced_grid = grid.Slice(i, i + 1).Resize({h * w, 3});
|
||||||
|
Tensor sliced_theta = theta->Slice(i, i + 1).Resize({2, 3});
|
||||||
|
Tensor sliced_out = output->Slice(i, i + 1).Resize({h * w, 2});
|
||||||
|
blas.MatMul(sliced_grid, false, sliced_theta, true, T(1), &sliced_out,
|
||||||
|
T(0));
|
||||||
|
}
|
||||||
|
}
|
||||||
|
};
|
||||||
|
|
||||||
|
template <typename DeviceContext, typename T>
|
||||||
|
class AffineGridGradOpKernel : public framework::OpKernel<T> {
|
||||||
|
public:
|
||||||
|
void Compute(const framework::ExecutionContext& ctx) const override {
|
||||||
|
auto& place = *ctx.template device_context<DeviceContext>().eigen_device();
|
||||||
|
auto output_grad = ctx.Input<Tensor>(framework::GradVarName("Output"));
|
||||||
|
auto theta_grad = ctx.Output<Tensor>(framework::GradVarName("Theta"));
|
||||||
|
|
||||||
|
int n = output_grad->dims()[0];
|
||||||
|
auto size_attr = ctx.Attr<std::vector<int>>("output_shape");
|
||||||
|
int h = 0;
|
||||||
|
int w = 0;
|
||||||
|
if (size_attr.size() == 0) {
|
||||||
|
auto* output_shape = ctx.Input<Tensor>("OutputShape");
|
||||||
|
Tensor h_sizes;
|
||||||
|
framework::TensorCopy(*output_shape, platform::CPUPlace(), &h_sizes);
|
||||||
|
const int* h_size_data = h_sizes.data<int>();
|
||||||
|
h = h_size_data[2];
|
||||||
|
w = h_size_data[3];
|
||||||
|
} else {
|
||||||
|
h = size_attr[2];
|
||||||
|
w = size_attr[3];
|
||||||
|
}
|
||||||
|
|
||||||
|
theta_grad->mutable_data<T>({n, 2, 3}, ctx.GetPlace());
|
||||||
|
|
||||||
|
math::SetConstant<DeviceContext, T>()(
|
||||||
|
ctx.template device_context<DeviceContext>(), theta_grad,
|
||||||
|
static_cast<T>(0));
|
||||||
|
|
||||||
|
Linspace<DeviceContext, T> linspace;
|
||||||
|
|
||||||
|
// Get indexes of height with shape [height, width, 1]
|
||||||
|
auto h_idx = linspace((T)-1, (T)1, h, ctx);
|
||||||
|
auto h_idx_t = EigenTensor<T, 1>::From(h_idx);
|
||||||
|
// Get indexes of width with shape [height, width, 1]
|
||||||
|
auto w_idx = linspace((T)-1, (T)1, w, ctx);
|
||||||
|
auto w_idx_t = EigenTensor<T, 1>::From(w_idx);
|
||||||
|
// Get constant ones tensor with shape [height, width, 1]
|
||||||
|
Tensor ones;
|
||||||
|
ones.mutable_data<T>({h, w, 1}, ctx.GetPlace());
|
||||||
|
auto ones_t = EigenTensor<T, 3>::From(ones).setConstant((T)1);
|
||||||
|
// Get grid tensor with shape [n, h, w, 3] by concatenating h_idx, w_idx and
|
||||||
|
// ones
|
||||||
|
Tensor grid;
|
||||||
|
grid.mutable_data<T>({n, h, w, 3}, ctx.GetPlace());
|
||||||
|
auto grid_t = EigenTensor<T, 4>::From(grid);
|
||||||
|
grid_t.device(place) = w_idx_t.reshape(Array2(1, w))
|
||||||
|
.broadcast(Array2(h, 1))
|
||||||
|
.reshape(Array3(h, w, 1))
|
||||||
|
.concatenate(h_idx_t.reshape(Array2(1, h))
|
||||||
|
.broadcast(Array2(w, 1))
|
||||||
|
.shuffle(Array2(1, 0))
|
||||||
|
.reshape(Array3(h, w, 1)),
|
||||||
|
2)
|
||||||
|
.eval()
|
||||||
|
.concatenate(ones_t, 2)
|
||||||
|
.reshape(Array4(1, h, w, 3))
|
||||||
|
.broadcast(Array4(n, 1, 1, 1));
|
||||||
|
// output = grid * theta.T
|
||||||
|
// TODO(wanghaoshuang): Refine batched matrix multiply
|
||||||
|
auto blas = math::GetBlas<DeviceContext, T>(ctx);
|
||||||
|
for (int i = 0; i < n; ++i) {
|
||||||
|
Tensor sliced_grid = grid.Slice(i, i + 1).Resize({h * w, 3});
|
||||||
|
Tensor sliced_out_grad = output_grad->Slice(i, i + 1).Resize({h * w, 2});
|
||||||
|
Tensor sliced_theta_grad = theta_grad->Slice(i, i + 1).Resize({2, 3});
|
||||||
|
blas.MatMul(sliced_out_grad, true, sliced_grid, false, T(1),
|
||||||
|
&sliced_theta_grad, T(0));
|
||||||
|
}
|
||||||
|
}
|
||||||
|
};
|
||||||
|
|
||||||
|
} // namespace operators
|
||||||
|
} // namespace paddle
|
||||||
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Reference in new issue