deconv2d

paddle/operators/deconv2d_op.cc

@@ -31,12 +31,14 @@ void Deconv2DOp::InferShape(framework::InferShapeContext* ctx) const {
   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) {
+  for (size_t 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.size(), 4,
+                    "Deconv2DOp input should be 4-D tensor.");
+  PADDLE_ENFORCE_EQ(filter_dims.size(), 4,
+                    "Deconv2DOp filter should be 4-D tensor.");
   PADDLE_ENFORCE_EQ(in_dims[1], filter_dims[0],
                     "input and kernel input dimension should be equal.");
 
@@ -52,14 +54,14 @@ Deconv2DOpMaker::Deconv2DOpMaker(framework::OpProto* proto,
   AddInput(
       "Input",
       "The input tensor of deconvolution operator. "
-      "The format of input tensor is NMHW. Where N is batch size, M is the "
+      "The format of input tensor is NCHW. Where N is batch size, C 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, "
+           "The format of the filter tensor is MCHW, where C is the number of "
+           "output image channels, M 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 "
+           "We enforce groups number == 1 and padding == 0 in "
            "deconvolution Scenario.");
   AddOutput("Output",
             "The output tensor of deconvolution operator."

paddle/operators/deconv2d_op.h

@@ -55,7 +55,7 @@ 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
+    // The filter will be reshaped, so it should not be constant pointer
     Tensor filter = *context.Input<Tensor>("Filter");
 
     Tensor* output = context.Output<Tensor>("Output");
@@ -132,8 +132,8 @@ class GemmDeconvGrad2DKernel : public framework::OpKernel<T> {
     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
+    // 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 =
@@ -142,7 +142,7 @@ class GemmDeconvGrad2DKernel : public framework::OpKernel<T> {
         context.Output<Tensor>(framework::GradVarName("Filter"));
 
     std::vector<int> strides = context.Attr<std::vector<int>>("strides");
-    // Actually, no paddings and groups allowed in deconv
+    // Actually, no paddings and groups allowed in deconv.
     std::vector<int> paddings = context.Attr<std::vector<int>>("paddings");
 
     int N = input->dims()[0];