add unpool

paddle/operators/CMakeLists.txt

@@ -139,6 +139,7 @@ set(DEPS_OPS
     sum_op
     pool_op
     maxout_op
+    unpool_op
     pool_with_index_op
     nccl_op
     sequence_conv_op
@@ -151,6 +152,7 @@ op_library(softmax_with_cross_entropy_op DEPS cross_entropy softmax)
 op_library(sum_op DEPS net_op selected_rows_functor)
 op_library(pool_op DEPS pooling)
 op_library(maxout_op DEPS maxouting)
+op_library(unpool_op DEPS unpooling)
 op_library(pool_with_index_op DEPS pooling)
 op_library(lod_rank_table_op SRCS lod_rank_table_op.cc DEPS lod_rank_table)
 if(WITH_GPU)

paddle/operators/math/CMakeLists.txt

@@ -14,6 +14,7 @@ if(WITH_GPU)
     nv_library(sequence2batch SRCS sequence2batch.cc sequence2batch.cu DEPS device_context)
     nv_library(lstm_compute SRCS lstm_compute.cc lstm_compute.cu DEPS device_context activation_functions)
     nv_library(maxouting SRCS maxouting.cc maxouting.cu DEPS device_context)
+    nv_library(unpooling SRCS unpooling.cc unpooling.cu DEPS device_context)
 else()
     cc_library(math_function SRCS math_function.cc im2col.cc DEPS cblas device_context operator)
     cc_library(selected_rows_functor SRCS selected_rows_functor.cc DEPS selected_rows math_function)
@@ -26,6 +27,7 @@ else()
     cc_library(sequence2batch SRCS sequence2batch.cc DEPS device_context)
     cc_library(lstm_compute SRCS lstm_compute.cc DEPS device_context activation_functions)
     cc_library(maxouting SRCS maxouting.cc DEPS device_context)
+    cc_library(unpooling SRCS unpooling.cc DEPS device_context)
 endif()
 
 cc_test(math_function_test SRCS math_function_test.cc DEPS math_function tensor)

paddle/operators/math/unpooling.cc

+/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
+
+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. */
+
+#include "paddle/operators/math/maxouting.h"
+
+namespace paddle {
+namespace operators {
+namespace math {
+
+// All tensors are in NCHW format
+template <typename T>
+class Unpool2d_Max_Functor<platform::CPUPlace, T> {
+ public:
+  void operator()(const platform::DeviceContext& context,
+                  const framework::Tensor& input,
+                  const framework::Tensor& indices,
+                  framework::Tensor * output) {
+    const int batch_size = input.dims()[0];
+    const int input_height = input.dims()[2];
+    const int input_width = input.dims()[3];
+    const int output_channels = output->dims()[1];
+    const int output_height = output->dims()[2];
+    const int output_width = output->dims()[3];
+
+    int input_feasize = input_height * input_width;
+    int output_feasize = output_height * output_width;
+    const T* input_data = input.data<T>();
+    const T* indices_data = indices.data<T>();
+    T* output_data = output->mutable_data<T>(context.GetPlace());
+
+    for (int b = 0; b < batch_size; ++b) {
+      for (int c = 0; c < output_channels; ++c) {
+        for (int i = 0; i < input_feasize; ++i) {
+          int index =  indices_data[i];
+          if(index > output_feasize) {
+            //抛一个异常！
+          }
+          output_data[index] = input_data[i];
+        }
+        input_data += input_feasize;
+        indices_data += input_feasize;
+        output_data += output_feasize;
+      }
+    }
+  }
+};
+
+
+
+template <class T>
+class Unpool2d_MaxGradFunctor<platform::CPUPlace, T> {
+public:
+  void operator()(const platform::DeviceContext& context,
+                  const framework::Tensor& input,
+                  const framework::Tensor& indices,
+                  framework::Tensor * input_grad,
+                  const framework::Tensor& output,
+                  const framework::Tensor& output_grad) {
+    const int batch_size = input.dims()[0];
+    const int input_height = input.dims()[2];
+    const int input_width = input.dims()[3];
+    const int output_channels = output->dims()[1];
+    const int output_height = output->dims()[2];
+    const int output_width = output->dims()[3];
+
+    int input_feasize = input_height * input_width;
+    int output_feasize = output_height * output_width;
+    const T* input_data = input.data<T>();
+    const T* indices_data = indices.data<T>();
+    const T* output_data = output.data<T>();
+    const T* output_grad_data = output_grad.data<T>();
+
+    T* input_grad_data = input_grad->mutable_data<T>(context.GetPlace());
+
+    for (int b = 0; b < batch_size; ++b) {
+      for (int c = 0; c < output_channels; ++c) {
+        for (int f = 0; f < input_feasize; ++f) {
+          int index = indices_data[i];
+          if(index > output_feasize) {
+            //抛一个异常！
+          }
+          input_grad_data[i] = output_grad_data[index];
+        }
+        input_grad_data += input_feasize;
+        indices_data += input_feasize;
+        output_grad_data += output_feasize;
+      }
+    }
+  }
+};
+
+template class Unpool2d_MaxGradFunctor<platform::CPUPlace, float>;
+template class Unpool2d_MaxGradFunctor<platform::CPUPlace, double>;
+template class Unpool2d_MaxFunctor<platform::CPUPlace, float>;
+template class Unpool2d_MaxFunctor<platform::CPUPlace, double>;
+
+}  // namespace math
+}  // namespace operators
+}  // namespace paddle

paddle/operators/math/unpooling.cu

+/* Copyright (c) 2016 paddlepaddle Authors. All Rights Reserve.
+
+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. */
+
+#include "paddle/operators/math/maxouting.h"
+#include "paddle/platform/cuda_helper.h"
+
+namespace paddle {
+namespace operators {
+namespace math {
+
+template <typename T>
+__global__ void KernelUnpool2dMax(const int nthreads,
+                                  const T* input_data,
+                                  const T* indices_data,
+                                  const int input_height,
+                                  const int input_width,
+                                  T* output_data,
+                                  const int output_height,
+                                  const int output_width) {
+  int index = blockIdx.x * blockDim.x + threadIdx.x;
+  int offset = blockDim.x * gridDim.x;
+  for (int i = index; i < nthreads; i += offset) {
+    int out_offset =  i / (input_height * input_width) \
+                      * output_height * output_width;
+    int out_index = indices_data[i];
+    output_data[out_offset + out_index] = input_data[i];
+  }
+}
+template <typename T>
+__global__ void KernelUnpool2dMaxGrad(const int nthreads,
+                                      const T* input_data,
+                                      const int input_height,
+                                      const int input_width,
+                                      const T* output_data,
+                                      const T* output_grad,
+                                      const int output_height,
+                                      const int output_width,
+                                      T* input_grad) {
+    int index = blockIdx.x * blockDim.x + threadIdx.x;
+    int offset = blockDim.x * gridDim.x;
+    for (int i = index; i < nthreads; i += offset) {
+        int out_offset =  i / (input_height * input_width) \
+                          * output_height * output_width;
+        int out_index = indices_data[i];
+        input_grad[i] = output_grad[out_offset + out_index];
+    }
+}
+/*
+ * All tensors are in NCHW format.
+ */
+template <typename T>
+class Unpool2d_MaxFunctor<platform::GPUPlace, T> {
+ public:
+  void operator()(const platform::DeviceContext& context,
+                  const framework::Tensor& input,
+                  const framework::Tensor& indices,
+                  framework::Tensor * output) {
+    const int batch_size = input.dims()[0];
+    const int input_height = input.dims()[2];
+    const int input_width = input.dims()[3];
+    const int output_channels = output->dims()[1];
+    const int output_height = output->dims()[2];
+    const int output_width = output->dims()[3];
+    int input_feasize = input_height * input_width;
+    int output_feasize = output_height * output_width;
+    const T* input_data = input.data<T>();
+    const T* indices_data = indices.data<T>();
+    T* output_data = output->mutable_data<T>(context.GetPlace());
+
+    int nthreads =  output->numel();
+    int blocks = (nthreads + 1024 - 1) / 1024;
+    dim3 threads(1024, 1);
+    dim3 grid(blocks, 1);
+
+    KernelUnpool2dMax<
+        T><<<grid, threads, 0,
+             reinterpret_cast<const platform::CUDADeviceContext&>(context)
+                 .stream()>>>(nthreads, input_data, indices_data,
+                              input_height, input_width,
+                              output_data, output_height, output_width);
+  }
+};
+/*
+ * All tensors are in NCHW format.
+ */
+template <typename T>
+class Unpool2d_MaxGradFunctor<platform::GPUPlace, T> {
+ public:
+  void operator()(const platform::DeviceContext& context,
+                  const framework::Tensor& input,
+                  framework::Tensor * input_grad,
+                  const framework::Tensor& output,
+                  const framework::Tensor& output_grad,
+                  int groups) {
+    const int batch_size = input.dims()[0];
+    const int input_height = input.dims()[2];
+    const int input_width = input.dims()[3];
+    const int output_channels = output.dims()[1];
+    const int output_height = output.dims()[2];
+    const int output_width = output.dims()[3];
+
+    const T* input_data = input.data<T>();
+    const T* indices_data = indices.data<T>();
+    const T* output_data = output.data<T>();
+    const T* output_grad_data = output_grad.data<T>();
+    T* input_grad_data = input_grad->mutable_data<T>(context.GetPlace());
+    int nthreads =  output.numel();
+    int blocks = (nthreads + 1024 - 1) / 1024;
+    dim3 threads(1024, 1);
+    dim3 grid(blocks, 1);
+
+    KernelUnpool2dMaxGrad<
+        T><<<grid, threads, 0,
+             reinterpret_cast<const platform::CUDADeviceContext&>(context)
+                 .stream()>>>(
+                              nthreads, input_data, indices_data,
+                              input_height, input_width,
+                              output_data, output_grad_data,
+                              output_height, output_width,
+                              input_grad_data);
+  }
+};
+
+template class Unpool2d_MaxGradFunctor<platform::GPUPlace, float>;
+template class Unpool2d_MaxGradFunctor<platform::GPUPlace, double>;
+
+template class Unpool2d_MaxFunctor<platform::GPUPlace, float>;
+template class Unpool2d_MaxFunctor<platform::GPUPlace, double>;
+
+}  // namespace math
+}  // namespace operators
+}  // namespace paddle

paddle/operators/math/unpooling.h

+/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
+
+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 "paddle/framework/tensor.h"
+#include "paddle/platform/device_context.h"
+#include "paddle/platform/hostdevice.h"
+
+namespace paddle {
+namespace operators {
+namespace math {
+
+#define FLT_MAX \
+    __FLT_MAX__
+
+template <typename Place, typename T>
+
+class Unpool2d_Max_Functor {
+ public:
+  void operator()(const platform::DeviceContext& context,
+                  const framework::Tensor& input,
+                  const framework::Tensor& indices,
+                  framework::Tensor * output);
+};
+
+template <typename Place, class T>
+class Unpool2d_Max_GradFunctor {
+ public:
+  void operator()(const platform::DeviceContext& context,
+                  const framework::Tensor& input,
+                  framework::Tensor * input_grad,
+                  const framework::Tensor& output,
+                  const framework::Tensor& output_grad);
+};
+}  // namespace math
+}  // namespace operators
+}  // namespace paddle

paddle/operators/unpool_op.cc

+/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
+ *
+ * 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. */
+
+#include "paddle/operators/unpool_op.h"
+namespace paddle {
+namespace operators {
+
+using framework::Tensor;
+
+class Unpool2dOpMaker : public framework::OpProtoAndCheckerMaker {
+ public:
+  UnpoolOpMaker(framework::OpProto* proto, framework::OpAttrChecker* op_checker)
+      : OpProtoAndCheckerMaker(proto, op_checker) {
+    AddInput("X",
+        "(Tensor) The input tensor of unpool operator. "
+        "The format of input tensor is NCHW. Where N is batch size, C is the "
+        "number of channels, H and W is the height and width of feature.");
+    AddInput("Y",
+        "(Tensor) The input tensor of the indices given out by MaxPool2d. "
+        "The format of input tensor is NCHW. Where N is batch size, C is the "
+        "number of channels, H and W is the height and width of feature.");
+    AddOutput("Out",
+        "(Tensor) The output tensor of unpool operator."
+        "The format of output tensor is also NCHW."
+        "Where N is batch size, C is "
+        "the number of channels, H and W is the height and "
+        "width of feature.");
+    AddAttr<std::vector<int>>("ksize",
+        "(vector ), the unpooling window size(height, width) "
+        "of unpooling operator.");
+    AddAttr<std::vector<int>>("strides",                                                                        "(vector, default:{1, 1}), "
+        "strides(height, width) of unpooling operator.")
+        .SetDefault({1, 1});
+    AddAttr<std::vector<int>>("paddings",                                                                       "(vector defalut:{0,0}), "
+        "paddings(height, width) of unpooling operator.")
+        .SetDefault({0, 0});
+    AddAttr<std::string>("unpoolingType",
+        "(string), unpooling type, can be \"max\" for max-unpooling "
+        "and \"avg\" for average-unpooling.")
+        .InEnum({"max", "avg"});
+    AddComment(R"DOC(
+
+        )DOC");
+  }
+};
+
+int OutputSize(int input_size, int ksize, int padding, int stride) {
+  int output_size = (input_size -1) * stride - 2 * padding + ksize;
+  return output_size;
+}
+
+class UnpoolOp : public framework::OperatorWithKernel {
+ public:
+  using framework::OperatorWithKernel::OperatorWithKernel;
+  void InferShape(framework::InferShapeContext* ctx) const override {
+    PADDLE_ENFORCE(ctx->HasInput("X"), "Input(X) of UnpoolOp"
+                   "should not be null.");
+    PADDLE_ENFORCE(ctx->HasInput("Y"), "Input(Y) of UnpoolOp"
+                   "should not be null.");
+    PADDLE_ENFORCE(ctx->HasOutput("Out"),
+                   "Output(Out) of UnpoolOp should not be null.");
+
+    auto in_x_dims = ctx->GetInputDim("X");
+    auto in_y_dims = ctx->GetInputDim("Y");
+    std::string unpooling_type = ctx->Attrs().Get<std::string>("unpooling_type");
+    std::vector<int> ksize = ctx->Attrs().Get<std::vector<int>>("ksize");
+    std::vector<int> strides = ctx->Attrs().Get<std::vector<int>>("strides");
+    std::vector<int> paddings = ctx->Attrs().Get<std::vector<int>>("paddings");
+
+    PADDLE_ENFORCE(in_x_dims.size() == 4 || in_x_dims.size() == 5,
+                    "Unpooling intput should be 4-D or 5-D tensor.");
+
+    std::vector<int64_t> output_shape({in_x_dims[0], in_x_dims[1]});
+    for (size_t i = 0; i < ksize.size(); ++i) {
+      output_shape.push_back(
+        OutputSize(in_x_dims[i + 2], ksize[i], paddings[i], strides[i]));
+    }
+    ctx->SetOutputDim("Out", framework::make_ddim(output_shape));
+  }
+};
+
+class UnpoolOpGrad : public framework::OperatorWithKernel {
+ public:
+  using framework::OperatorWithKernel::OperatorWithKernel;
+  void InferShape(framework::InferShapeContext* ctx) const override {
+    PADDLE_ENFORCE(ctx->HasInput("X"), "Input(X) must not be null.");
+    PADDLE_ENFORCE(ctx->HasInput("Y"), "Input(X) must not be null.");
+    PADDLE_ENFORCE(ctx->HasInput(framework::GradVarName("Out")),
+                                  "Input(Out@GRAD) should not be null");
+    PADDLE_ENFORCE(ctx->HasOutput(framework::GradVarName("X")),
+                                  "Input(X@GRAD) should not be null.");
+    ctx->SetOutputDim(framework::GradVarName("X"), ctx->GetInputDim("X"));
+  }
+};
+}    // namespace operators
+}    // namespace paddle
+
+namespace ops = paddle::operators;
+REGISTER_OP(unpool2d, ops::UnpoolOp, ops::Unpool2dOpMaker, unpool2d_grad,
+            ops::UnpoolOpGrad);
+REGISTER_OP_CPU_KERNEL(unpool2d, ops::UnpoolKernel<paddle::platform::CPUPlace,
+                        float>);
+REGISTER_OP_CPU_KERNEL(unpool2d_grad,
+                        ops::UnpoolGradKernel<paddle::platform::CPUPlace,
+                        float>);

paddle/operators/unpool_op.cu.cc

+/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
+
+   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. */
+
+#include "paddle/operators/unpool_op.h"
+
+namespace ops = paddle::operators;
+REGISTER_OP_GPU_KERNEL(unpool2d,
+                       ops::UnpoolKernel<paddle::platform::GPUPlace, float>);
+REGISTER_OP_GPU_KERNEL(unpool2d_grad,
+                       ops::UnpoolGradKernel<paddle::platform::GPUPlace,
+                        float>);

paddle/operators/unpool_op.h

+/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
+
+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 "paddle/framework/op_registry.h"
+#include "paddle/operators/math/math_function.h"
+#include "paddle/operators/math/unpooling.h"
+
+namespace paddle {
+namespace operators {
+
+using Tensor = framework::Tensor;
+
+template <typename Place, typename T>
+class UnpoolKernel : public framework::OpKernel<T> {
+ public:
+  void Compute(const framework::ExecutionContext& context) const override {
+    const Tensor* in_x = context.Input<Tensor>("X");
+    const Tensor* in_y = context.Input<Tensor>("Y");
+    Tensor* out = context.Output<Tensor>("Out");
+    std::string pooling_type = context.Attr<std::string>("unpooling_type");
+    std::vector<int> ksize = context.Attr<std::vector<int>>("ksize");
+    std::vector<int> strides = context.Attr<std::vector<int>>("strides");
+    std::vector<int> paddings = context.Attr<std::vector<int>>("paddings");
+    switch (ksize.size()) {
+    case 2: {
+      if (pooling_type == "max") {
+        math::Unpool2d_Max_Functor<Place, T> unpool2d_max_forward;
+        unpool2d_max_forward(context.device_context(), *in_x, *in_y,
+                             ksize, strides, paddings, out);
+      }
+    } break;
+    default: { PADDLE_THROW("Pool op only supports 2D input."); }
+    }
+  }
+};
+
+template <typename Place, typename T>
+class UnpoolGradKernel : public framework::OpKernel<T> {
+ public:
+  void Compute(const framework::ExecutionContext& context) const override {
+    const Tensor* in_x = context.Input<Tensor>("X");
+    const Tensor* in_y = context.Input<Tensor>("Y");
+    const Tensor* out = context.Input<Tensor>("Out");
+    const Tensor* out_grad =
+        context.Input<Tensor>(framework::GradVarName("Out"));
+    Tensor* in_x_grad = context.Output<Tensor>(framework::GradVarName("X"));
+    std::string pooling_type = context.Attr<std::string>("unpooling_type");
+    std::vector<int> ksize = context.Attr<std::vector<int>>("ksize");
+    std::vector<int> strides = context.Attr<std::vector<int>>("strides");
+    std::vector<int> paddings = context.Attr<std::vector<int>>("paddings");
+
+    auto& device_ctx = context.device_context();
+    math::SetConstant<Place, T> zero;
+    if (in_x_grad) {
+      in_x_grad->mutable_data<T>(context.GetPlace());
+      zero(device_ctx, in_x_grad, static_cast<T>(0.0));
+          }
+    switch (ksize.size()) {
+    case 2: {
+    if (pooling_type == "max") {
+      math::UnpoolGradFunctor<Place, T> maxout_backward;
+      maxout_backward(context.device_context(), *in_x, *in_y, in_x_grad, *out,
+                      *out_grad, ksize, strides, paddings);
+      }
+    } break;
+    default: { PADDLE_THROW("Pool op only supports 2D input."); }
+    }
+  }
+};
+
+}  // namespace operators
+}  // namespace paddle