add expand comment

paddle/operators/block_expand_op.cc

@@ -23,12 +23,18 @@ class BlockExpandOp : public framework::OperatorWithKernel {
 
  protected:
   void InferShape(framework::InferShapeContext* ctx) const override {
-    PADDLE_ENFORCE(ctx->HasInput("block"),
-                   "Input(block) of BlockExpandOp should not be null.");
-    PADDLE_ENFORCE(ctx->HasInput("padding"),
-                   "Input(padding) of BlockExpandOp should not be null.");
-    PADDLE_ENFORCE(ctx->HasInput("stride"),
-                   "Input(stride) of BlockExpandOp should not be null.");
+    using namespace framework;
+    PADDLE_ENFORCE(ctx->HasInput("input"),
+                   "Input of BlockExpandOp should not be null.");
+    PADDLE_ENFORCE(ctx->HasOutput("Out"),
+                   "Output(Out) of BlockExpandOp op should not be null.");
+
+    auto in_dim = ctx->GetInputDim("input");
+    PADDLE_ENFORCE_EQ(in_dim.size(), 4, "Input format  must be NCHW.");
+    PADDLE_ENFORCE_GE(in_dim[0], 1, "Input batchsize must >= 1.");
+
+    ctx->ShareLoD("X", /*->*/ "Out");
+
     // ctx->SetOutputDim("Out", {1});
   }
 };
@@ -38,8 +44,26 @@ class BlockExpandOpMaker : public framework::OpProtoAndCheckerMaker {
   BlockExpandOpMaker(framework::OpProto* proto,
                      framework::OpAttrChecker* op_checker)
       : OpProtoAndCheckerMaker(proto, op_checker) {
-    AddInput("block", "The input of block_expand op");
-    AddOutput("stride", "The output of block_expand op");
+    AddInput("input", "The input of block_expand op");
+    AddOutput("out", "The output of block_expand op");
+    AddAttr<int>("block_height",
+                 R"DOC(
+        )DOC");
+    AddAttr<int>("block_width",
+                 R"DOC(
+        )DOC");
+    AddAttr<int>("stride_height",
+                 R"DOC(
+        )DOC");
+    AddAttr<int>("stride_width",
+                 R"DOC(
+        )DOC");
+    AddAttr<int>("padding_height",
+                 R"DOC(
+        )DOC");
+    AddAttr<int>("padding_width",
+                 R"DOC(
+        )DOC");
     AddComment(R"DOC(
 Expand feature map to minibatch matrix.
 - matrix width is: blockH_ * blockW_ * channels_

paddle/operators/block_expand_op.h

@@ -25,34 +25,34 @@ namespace operators {
 template <typename Place, typename T>
 class BlockExpandKernel : public framework::OpKernel<T> {
  public:
-  void Compute(const framework::ExecutionContext& context) const override {
+  void Compute(const framework::ExecutionContext& ctx) const override {
     using namespace framework;
-    const Tensor* input = context.Input<Tensor>("input");
-    const Tensor* filter = context.Input<Tensor>("filter");
-    const Tensor* stride = context.Input<Tensor>("stride");
-    const Tensor* padding = context.Input<Tensor>("padding");
-    Tensor* out = context.Output<Tensor>("Out");
-
-    auto input_dim = input->dims();
-    size_t N = input_dim[0];
-    size_t C = input_dim[1];
-    PADDLE_ENFORCE_GE(N, 1, "Input batchsize must >= 1.");
-    PADDLE_ENFORCE_EQ(input_dim.size(), 4, "Input format  must be NCHW.");
-
-    size_t input_height = input_dim[2];
-    size_t input_height = input_dim[3];
-
-    size_t filter_height = filter[0];
-    size_t filter_width = filter[1];
-
-    size_t output_height = 1 +
-                           (input_height + 2 * padding_height - block_height() +
-                            stride_height - 1) /
+    const Tensor* in = ctx.Input<Tensor>("input");
+    Tensor* out = ctx.Output<Tensor>("Out");
+    out->mutable_data<T>(ctx.GetPlace());
+
+    auto in_dim = in->dims();
+    int N = in_dim[0];
+    int C = in_dim[1];
+
+    int in_height = in_dim[2];
+    int in_width = in_dim[3];
+
+    int block_height = ctx.Attr<int>("block_height");
+    int block_width = ctx.Attr<int>("block_width");
+    int stride_height = ctx.Attr<int>("stride_height");
+    int stride_width = ctx.Attr<int>("stride_width");
+    int padding_height = ctx.Attr<int>("padding_height");
+    int padding_width = ctx.Attr<int>("padding_width");
+
+    int output_height =
+        1 +
+        (in_height + 2 * padding_height - block_height + stride_height - 1) /
             stride_height;
 
-    size_t output_width =
+    int output_width =
         1 +
-        (input_width + 2 * padding_width - block_width() + stride_width - 1) /
+        (in_width + 2 * padding_width - block_width + stride_width - 1) /
             stride_width;
 
     Tensor col;