deconv -> conv transpose

paddle/operators/conv2dtranspose_op.cc

@@ -12,8 +12,7 @@
    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"
+#include "paddle/operators/conv2dtranspose_op.h"
 
 namespace paddle {
 namespace operators {
@@ -54,18 +53,18 @@ Conv2DTransposeOpMaker::Conv2DTransposeOpMaker(
     : OpProtoAndCheckerMaker(proto, op_checker) {
   AddInput(
       "Input",
-      "The input tensor of convolution transpose operator. "
+      "(Tensor) The input tensor of convolution transpose operator. "
       "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 convolution transpose operator."
+           "(Tensor) The filter tensor of convolution transpose operator."
            "The format of the filter tensor is CMHW, 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 "
            "convolution transpose Scenario.");
   AddOutput("Output",
-            "The output tensor of convolution transpose operator."
+            "(Tensor) The output tensor of convolution transpose operator."
             "The format of output tensor is also NCHW.");
   AddAttr<std::vector<int>>("strides",
                             "strides of convolution transpose operator.")

paddle/operators/conv2dtranspose_op.cu

@@ -12,7 +12,7 @@
    See the License for the specific language governing permissions and
    limitations under the License. */
 
-#include "paddle/operators/deconv2d_op.h"
+#include "paddle/operators/conv2dtranspose_op.h"
 
 namespace ops = paddle::operators;
 

paddle/operators/conv2dtranspose_op.h

@@ -14,7 +14,6 @@ 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"
@@ -62,7 +61,8 @@ class GemmConv2DTransposeKernel : public framework::OpKernel<T> {
 
     std::vector<int> strides = context.Attr<std::vector<int>>("strides");
 
-    // no paddings and groups allowed in deconv
+    // TODO(Zhuoyuan): Paddings can be added in future.
+    // groups will alway be disabled in conv2dtranspose.
 
     const int batch_size = input->dims()[0];
     const int m = input->dims()[1];
@@ -91,7 +91,8 @@ class GemmConv2DTransposeKernel : public framework::OpKernel<T> {
     // 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;
+    Tensor col_matrix;
+    col_matrix.ShareDataWith(col);
     col_matrix.Resize(col_matrix_shape);
 
     DDim output_shape = {c, o_h, o_w};
@@ -100,7 +101,7 @@ class GemmConv2DTransposeKernel : public framework::OpKernel<T> {
     DDim filter_matrix_shape = {m, c * k_h * k_w};
     filter.Resize(filter_matrix_shape);
 
-    // deconvolution: gemm + col2im (similar to conv-backward on input)
+    // convolution transpose: gemm + col2im (similar to conv-backward on input)
 
     output->mutable_data<T>(context.GetPlace());
     auto t = framework::EigenVector<T>::Flatten(*output);
@@ -142,7 +143,7 @@ class GemmConv2DTransposeGradKernel : 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 conv transpose.
     std::vector<int> paddings = context.Attr<std::vector<int>>("paddings");
 
     const int batch_size = input->dims()[0];
@@ -180,11 +181,12 @@ class GemmConv2DTransposeGradKernel : public framework::OpKernel<T> {
     DDim filter_matrix_shape = {m, c * k_h * k_w};
     filter.Resize(filter_matrix_shape);
 
-    // deconvolution grad on input:
+    // convolution transpose grad on input:
     // im2col + gemm (similar to conv-forward)
     // input need to compute gradient
     if (input_grad) {
-      Tensor col_matrix = col;
+      Tensor col_matrix;
+      col_matrix.ShareDataWith(col);
       DDim col_matrix_shape = {c * k_h * k_w, h * w};
       col_matrix.Resize(col_matrix_shape);
 
@@ -216,7 +218,8 @@ class GemmConv2DTransposeGradKernel : public framework::OpKernel<T> {
 
     // filter gradient required
     if (filter_grad) {
-      Tensor col_matrix_f = col;
+      Tensor col_matrix_f;
+      col_matrix_f.ShareDataWith(col);
       DDim col_matrix_shape_f = {c * h * w, k_h * k_w};
       col_matrix_f.Resize(col_matrix_shape_f);
 

python/paddle/v2/framework/tests/test_conv2dtranspose_op.py

@@ -3,14 +3,14 @@ import numpy as np
 from op_test import OpTest
 
 
-def deconv2d_forward_naive(input_, filter_, deconv_param):
+def conv2dtranspose_forward_naive(input_, filter_, conv2dtranspose_param):
     # [2, 3, 5, 5]
     in_n, in_c, in_h, in_w = input_.shape
     # [3, 6, 3, 3]
     f_c, out_c, f_h, f_w = filter_.shape
     assert in_c == f_c
 
-    stride, pad = deconv_param['stride'], deconv_param['pad']
+    stride, pad = conv2dtranspose_param['stride'], conv2dtranspose_param['pad']
     out_h = (in_h - 1) * stride[0] + f_h
     out_w = (in_w - 1) * stride[1] + f_w
 
@@ -32,18 +32,19 @@ def deconv2d_forward_naive(input_, filter_, deconv_param):
     return out
 
 
-class TestDeconv2dOp(OpTest):
+class TestConv2dTransposeOp(OpTest):
     def setUp(self):
-        # init as deconv
+        # init as conv transpose
         self.init_op_type()
 
         # [2, 3, 5, 5] -> kernel [3, 6, 3, 3] -> output [2, 6, 7, 7]
         self.init_test_case()
 
-        deconv2d_param = {'stride': self.stride, 'pad': self.pad}
+        conv2dtranspose_param = {'stride': self.stride, 'pad': self.pad}
         input_ = np.random.random(self.input_size).astype("float32")
         filter_ = np.random.random(self.filter_size).astype("float32")
-        output = deconv2d_forward_naive(input_, filter_, deconv2d_param)
+        output = conv2dtranspose_forward_naive(input_, filter_,
+                                               conv2dtranspose_param)
         # print 'deconv output py', output, output.shape
 
         self.inputs = {'Input': input_, 'Filter': filter_}
@@ -85,7 +86,7 @@ class TestDeconv2dOp(OpTest):
         self.filter_size = [f_c, 6, 3, 3]
 
     def init_op_type(self):
-        self.op_type = "deconv2d"
+        self.op_type = "conv2dtranspose"
 
 
 """