deconv op implementing ...

paddle/operators/deconv2d_op.cc

@@ -0,0 +1,100 @@
+/* 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/deconv2d_op.h"
+#include "paddle/operators/conv2d_op.h"
+
+namespace paddle {
+namespace operators {
+
+void Deconv2DOp::InferShape(framework::InferShapeContext* ctx) const {
+  PADDLE_ENFORCE(ctx->HasInput("Input"),
+                 "Input(Input) of Deconv2DOp should not be null.");
+  PADDLE_ENFORCE(ctx->HasInput("Filter"),
+                 "Input(Filter) of Deconv2DOp should not be null.");
+  PADDLE_ENFORCE(ctx->HasOutput("Output"),
+                 "Output(Output) of Deconv2DOp should not be null.");
+
+  auto in_dims = ctx->GetInputDim("Input");
+  auto filter_dims = ctx->GetInputDim("Filter");
+  std::vector<int> strides = ctx->Attrs().Get<std::vector<int>>("strides");
+  std::vector<int> paddings = ctx->Attrs().Get<std::vector<int>>("paddings");
+
+  for (int i = 0; i < paddings.size(); ++i) {
+    PADDLE_ENFORCE_EQ(paddings[i], 0, "No Padding allowed in deconv op.");
+  }
+
+  PADDLE_ENFORCE_EQ(in_dims.size(), 4, "Deconv2DOp input should be 4-D.");
+  PADDLE_ENFORCE_EQ(filter_dims.size(), 4, "Deconv2DOp filter should be 4-D.");
+  PADDLE_ENFORCE_EQ(in_dims[1], filter_dims[0],
+                    "input and kernel input dimension should be equal.");
+
+  auto output_height = (in_dims[2] - 1) * strides[0] + filter_dims[2];
+  auto output_width = (in_dims[3] - 1) * strides[1] + filter_dims[3];
+  ctx->SetOutputDim("Output",
+                    {in_dims[0], filter_dims[1], output_height, output_width});
+}
+
+Deconv2DOpMaker::Deconv2DOpMaker(framework::OpProto* proto,
+                                 framework::OpAttrChecker* op_checker)
+    : OpProtoAndCheckerMaker(proto, op_checker) {
+  AddInput(
+      "Input",
+      "The input tensor of deconvolution operator. "
+      "The format of input tensor is NMHW. Where N is batch size, M is the "
+      "number of input channels, H and W is the height and width of image.");
+  AddInput("Filter",
+           "The filter tensor of deconvolution operator."
+           "The format of the filter tensor is MCHW, where M is the number of "
+           "input image channels, C is the number of output image channels, "
+           "H and W is height and width of filter. "
+           "We enforce groups number == 1 and padding == 0 in our "
+           "deconvolution Scenario.");
+  AddOutput("Output",
+            "The output tensor of deconvolution operator."
+            "The format of output tensor is also NCHW.");
+  AddAttr<std::vector<int>>("strides", "strides of deconvolution operator.")
+      .SetDefault({1, 1});
+  AddAttr<std::vector<int>>("paddings", "paddings of deconvolution operator.")
+      .SetDefault({0, 0});
+  AddComment(R"DOC(
+The deconvolution operation calculates the output based on the input, filter
+and strides, paddings, groups parameters. The size of each dimension of the
+parameters is checked in the infer-shape.
+)DOC");
+}
+
+void Deconv2DOpGrad::InferShape(framework::InferShapeContext* ctx) const {
+  auto in_dims = ctx->GetInputDim("Input");
+  auto filter_dims = ctx->GetInputDim("Filter");
+  if (ctx->HasOutput(framework::GradVarName("Input"))) {
+    ctx->SetOutputDim(framework::GradVarName("Input"), in_dims);
+  }
+  if (ctx->HasOutput(framework::GradVarName("Filter"))) {
+    ctx->SetOutputDim(framework::GradVarName("Filter"), filter_dims);
+  }
+}
+
+}  // namespace operators
+}  // namespace paddle
+
+namespace ops = paddle::operators;
+REGISTER_OP(deconv2d, ops::Deconv2DOp, ops::Deconv2DOpMaker, deconv2d_grad,
+            ops::Deconv2DOpGrad);
+
+REGISTER_OP_CPU_KERNEL(
+    deconv2d, ops::GemmDeconv2DKernel<paddle::platform::CPUPlace, float>);
+REGISTER_OP_CPU_KERNEL(
+    deconv2d_grad,
+    ops::GemmDeconvGrad2DKernel<paddle::platform::CPUPlace, float>);

paddle/operators/deconv2d_op.cu

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+/* 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/deconv2d_op.h"
+
+namespace ops = paddle::operators;
+
+REGISTER_OP_GPU_KERNEL(
+    deconv2d, ops::GemmDeconv2DKernel<paddle::platform::GPUPlace, float>);
+REGISTER_OP_GPU_KERNEL(
+    deconv2d_grad,
+    ops::GemmDeconvGrad2DKernel<paddle::platform::GPUPlace, float>);

paddle/operators/deconv2d_op.h

@@ -0,0 +1,251 @@
+/* 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 "glog/logging.h"
+#include "paddle/framework/eigen.h"
+#include "paddle/framework/op_registry.h"
+#include "paddle/operators/math/im2col.h"
+#include "paddle/operators/math/math_function.h"
+
+namespace paddle {
+namespace operators {
+
+using Tensor = framework::Tensor;
+using DDim = framework::DDim;
+
+// Define Op classes in .h file so that other deconv
+// operator implementations can reuse the code.
+class Deconv2DOpMaker : public framework::OpProtoAndCheckerMaker {
+ public:
+  Deconv2DOpMaker(framework::OpProto* proto,
+                  framework::OpAttrChecker* op_checker);
+};
+
+class Deconv2DOp : public framework::OperatorWithKernel {
+ public:
+  using framework::OperatorWithKernel::OperatorWithKernel;
+
+ protected:
+  void InferShape(framework::InferShapeContext* ctx) const override;
+};
+
+class Deconv2DOpGrad : public framework::OperatorWithKernel {
+ public:
+  using framework::OperatorWithKernel::OperatorWithKernel;
+
+ protected:
+  void InferShape(framework::InferShapeContext* ctx) const override;
+};
+
+template <typename Place, typename T>
+class GemmDeconv2DKernel : public framework::OpKernel<T> {
+ public:
+  void Compute(const framework::ExecutionContext& context) const override {
+    const Tensor* input = context.Input<Tensor>("Input");
+    // filter will be reshaped, so we do not use constant pointer here
+    Tensor filter = *context.Input<Tensor>("Filter");
+
+    Tensor* output = context.Output<Tensor>("Output");
+
+    std::vector<int> strides = context.Attr<std::vector<int>>("strides");
+
+    // no paddings and groups allowed in deconv
+
+    int N = input->dims()[0];
+    int M = input->dims()[1];
+    int H = input->dims()[2];
+    int W = input->dims()[3];
+
+    int K_H = filter.dims()[2];
+    int K_W = filter.dims()[3];
+
+    int C = output->dims()[1];  // output channels
+    int O_H = output->dims()[2];
+    int O_W = output->dims()[3];
+
+    paddle::operators::math::Col2ImFunctor<
+        paddle::operators::math::ColFormat::kCFO, Place, T>
+        col2im;
+
+    // use col_shape in the im2col and col2im calculation
+    DDim col_shape = {C, K_H, K_W, H, W};
+
+    // use col_matrix_shape in the gemm calculation
+    DDim col_matrix_shape = {C * K_H * K_W, H * W};
+
+    Tensor col;
+    col.mutable_data<T>(col_shape, context.GetPlace());
+    // col_matrix shares the same piece of data with col,
+    // but will be reshaped into a two-dimensional matrix shape
+    // to call the matrix multiplication interface.
+    Tensor col_matrix = col;
+    col_matrix.Resize(col_matrix_shape);
+
+    DDim output_shape = {C, O_H, O_W};
+    DDim input_matrix_shape = {M, H * W};
+
+    DDim filter_matrix_shape = {M, C * K_H * K_W};
+    filter.Resize(filter_matrix_shape);
+
+    // deconvolution: gemm + col2im (similar to conv-backward on input)
+
+    output->mutable_data<T>(context.GetPlace());
+    auto t = framework::EigenVector<T>::Flatten(*output);
+    t.device(context.GetEigenDevice<Place>()) = t.constant(static_cast<T>(0));
+
+    for (int i = 0; i < N; i++) {
+      // batch with size (M, H * W)
+      Tensor input_batch = input->Slice<T>(i, i + 1).Resize(input_matrix_shape);
+      // filter size: (M, C * K_H * K_W)
+
+      // output size: (C, O_H, O_W)
+      Tensor output_batch = output->Slice<T>(i, i + 1).Resize(output_shape);
+
+      // col_matrix = filter * input_batch
+      // of shape (C * K_H * K_W, H * W)
+      math::matmul<Place, T>(context.device_context(), filter, true,
+                             input_batch, false, T(1.0), &col_matrix, T(0.0));
+      col2im(context.device_context(), output_batch, col, strides[0],
+             strides[1], 0, 0);
+    }
+  }
+};
+
+template <typename Place, typename T>
+class GemmDeconvGrad2DKernel : public framework::OpKernel<T> {
+ public:
+  void Compute(const framework::ExecutionContext& context) const override {
+    const Tensor* input = context.Input<Tensor>("Input");
+    const Tensor* output_grad =
+        context.Input<Tensor>(framework::GradVarName("Output"));
+
+    // For filter, we do not use const pointer b/c we will do reshape
+    // but we should avoid modifying its value
+    Tensor filter = *context.Input<Tensor>("Filter");
+
+    Tensor* input_grad =
+        context.Output<Tensor>(framework::GradVarName("Input"));
+    Tensor* filter_grad =
+        context.Output<Tensor>(framework::GradVarName("Filter"));
+
+    std::vector<int> strides = context.Attr<std::vector<int>>("strides");
+    // Actually, no paddings and groups allowed in deconv
+    std::vector<int> paddings = context.Attr<std::vector<int>>("paddings");
+
+    int N = input->dims()[0];
+    int M = input->dims()[1];
+    int H = input->dims()[2];
+    int W = input->dims()[3];
+
+    int K_H = filter.dims()[2];
+    int K_W = filter.dims()[3];
+
+    int C = output_grad->dims()[1];  // output channels
+    int O_H = output_grad->dims()[2];
+    int O_W = output_grad->dims()[3];
+
+    // Only im2col functor required for bp to get to the right shape
+    paddle::operators::math::Im2ColFunctor<
+        paddle::operators::math::ColFormat::kCFO, Place, T>
+        im2col;
+
+    // use col_shape in the im2col and col2im calculation
+    DDim col_shape = {C, K_H, K_W, H, W};
+
+    // use col_matrix_shape in the gemm calculation
+    DDim col_matrix_shape_f = {C * H * W, K_H * K_W};
+
+    Tensor col;
+    col.mutable_data<T>(col_shape, context.GetPlace());
+    // col_matrix shares the same piece of data with col,
+    // but will be reshaped into a two-dimensional matrix shape
+    // to call the matrix multiplication interface.
+
+    DDim output_shape = {C, O_H, O_W};
+    DDim input_matrix_shape = {M, H * W};
+
+    DDim filter_matrix_shape = {M, C * K_H * K_W};
+    filter.Resize(filter_matrix_shape);
+
+    // deconvolution grad on input:
+    // im2col + gemm (similar to conv-forward)
+    // input need to compute gradient
+    if (input_grad) {
+      Tensor col_matrix = col;
+      DDim col_matrix_shape = {C * K_H * K_W, H * W};
+      col_matrix.Resize(col_matrix_shape);
+
+      input_grad->mutable_data<T>(context.GetPlace());
+      auto t = framework::EigenVector<T>::Flatten(*input_grad);
+      t.device(context.GetEigenDevice<Place>()) = t.constant(static_cast<T>(0));
+
+      for (int i = 0; i < N; i++) {
+        // batch with size (C, O_H * O_W)
+        Tensor output_grad_batch =
+            output_grad->Slice<T>(i, i + 1).Resize(output_shape);
+        // filter of size (M, C * K_H * K_W)
+
+        // batch with size (M, H, W)
+        Tensor input_grad_batch =
+            input_grad->Slice<T>(i, i + 1).Resize(input_matrix_shape);
+
+        // im2col: dy from (C, O_H, O_W) -> (C * K_H * K_W, H * W)
+        im2col(context.device_context(), output_grad_batch, col, strides[0],
+               strides[1], paddings[0], paddings[1]);
+
+        // gemm: dx = filter * dy
+        // (M, C * K_H * K_W) * (C * K_H * K_W, H * W) -> (M, C, H)
+        math::matmul<Place, T>(context.device_context(), filter, false,
+                               col_matrix, false, T(1.0), &input_grad_batch,
+                               T(0.0));
+      }
+    }
+
+    // filter gradient required
+    if (filter_grad) {
+      Tensor col_matrix_f = col;
+      DDim col_matrix_shape_f = {C * H * W, K_H * K_W};
+      col_matrix_f.Resize(col_matrix_shape_f);
+
+      filter_grad->mutable_data<T>(context.GetPlace());
+      Tensor filter_grad_ = *filter_grad;
+      filter_grad_.Resize(filter_matrix_shape);
+      auto t = framework::EigenVector<T>::Flatten(filter_grad_);
+      t.device(context.GetEigenDevice<Place>()) = t.constant(static_cast<T>(0));
+
+      for (int i = 0; i < N; ++i) {
+        // batch with size (C, O_H, O_W)
+        Tensor output_grad_batch =
+            output_grad->Slice<T>(i, i + 1).Resize(output_shape);
+        // input batch
+        Tensor in_batch = input->Slice<T>(i, i + 1).Resize(input_matrix_shape);
+
+        // im2col: (C * H * W, K_H * K_W)
+        im2col(context.device_context(), output_grad_batch, col, strides[0],
+               strides[1], paddings[0], paddings[1]);
+
+        // gemm: d_filter = x * y_grad^T
+        // (M, C * H * W) * (K_H * K_W, C * H * W) -> (M, C, H)
+        math::matmul<Place, T>(context.device_context(), in_batch, false,
+                               col_matrix_f, true, T(1.0), &filter_grad_,
+                               T(1.0));
+      }
+    }
+  }
+};
+
+}  // namespace operators
+}  // namespace paddle