deconv

paddle/operators/deconv2d_op.cc

@@ -0,0 +1,117 @@
+/* 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/gemm_conv2d_op.h"
+
+namespace paddle {
+namespace operators {
+
+class Deconv2DOp : public framework::OperatorWithKernel {
+ public:
+  using framework::OperatorWithKernel::OperatorWithKernel;
+
+ protected:
+  void InferShape(framework::InferShapeContext* ctx) const override {
+    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");
+    int groups = ctx->Attrs().Get<int>("groups");
+    int input_channels = in_dims[1];
+    int output_channels = filter_dims[0];
+
+    PADDLE_ENFORCE_EQ(in_dims.size(), 4, "Conv2DOp input should be 4-D.");
+    PADDLE_ENFORCE_EQ(filter_dims.size(), 4, "Conv2DOp filter should be 4-D.");
+    PADDLE_ENFORCE_EQ(input_channels, filter_dims[1] * groups,
+                      "The number of input channels should be equal to filter "
+                      "channels * groups.");
+    PADDLE_ENFORCE_EQ(
+        output_channels % groups, 0,
+        "The number of output channels should be divided by groups.");
+
+    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[0], output_height, output_width});
+  }
+};
+
+class Deconv2DOpMaker : public framework::OpProtoAndCheckerMaker {
+ public:
+  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 NCHW. Where N is batch size, C is the "
+        "number of 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 "
+        "output image channels, C is the number of input 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");
+  }
+};
+
+class Deconv2DOpGrad : public framework::OperatorWithKernel {
+ public:
+  using framework::OperatorWithKernel::OperatorWithKernel;
+
+ protected:
+  void InferShape(framework::InferShapeContext* ctx) const override {
+    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::GemmConvGrad2DKernel<paddle::platform::CPUPlace, float>);
+REGISTER_OP_CPU_KERNEL(
+    deconv2d_grad, ops::GemmConv2DKernel<paddle::platform::CPUPlace, float>);