rename conv_transposeXd-->convXd_transpose

python/paddle/fluid/tests/unittests/test_conv1d_transpose_layer.py

@@ -92,7 +92,7 @@ def functional(self, place):
                     "weight", self.weight_shape, dtype=self.dtype)
                 b_var = fluid.data(
                     "bias", (self.out_channels, ), dtype=self.dtype)
-                y_var = F.conv_transpose1d(
+                y_var = F.conv1d_transpose(
                     x_var,
                     w_var,
                     None if self.no_bias else b_var,

python/paddle/fluid/tests/unittests/test_conv2d_transpose_layer.py

@@ -128,7 +128,7 @@ def functional(self, place):
                 else:
                     output_size = self.output_size
 
-                y_var = F.conv_transpose2d(
+                y_var = F.conv2d_transpose(
                     x_var,
                     w_var,
                     None if self.no_bias else b_var,

python/paddle/fluid/tests/unittests/test_conv3d_transpose_layer.py

@@ -119,7 +119,7 @@ def functional(self, place):
                     "weight", self.weight_shape, dtype=self.dtype)
                 b_var = fluid.data(
                     "bias", (self.num_filters, ), dtype=self.dtype)
-                y_var = F.conv_transpose3d(
+                y_var = F.conv3d_transpose(
                     x_var,
                     w_var,
                     None if self.no_bias else b_var,

python/paddle/fluid/tests/unittests/test_functional_conv2d_transpose.py

@@ -111,7 +111,7 @@ def static_graph_case_2(self):
                     "weight", self.weight.shape, dtype=self.dtype)
                 if not self.no_bias:
                     bias = fluid.data("bias", self.bias.shape, dtype=self.dtype)
-                y = F.conv_transpose2d(
+                y = F.conv2d_transpose(
                     x,
                     weight,
                     None if self.no_bias else bias,
@@ -134,7 +134,7 @@ def dygraph_case(self):
             x = dg.to_variable(self.input)
             weight = dg.to_variable(self.weight)
             bias = None if self.no_bias else dg.to_variable(self.bias)
-            y = F.conv_transpose2d(
+            y = F.conv2d_transpose(
                 x,
                 weight,
                 bias,
@@ -215,7 +215,7 @@ def static_graph_case(self):
                     "weight", self.weight_shape, dtype=self.dtype)
                 if not self.no_bias:
                     bias = fluid.data("bias", self.bias_shape, dtype=self.dtype)
-                y = F.conv_transpose2d(
+                y = F.conv2d_transpose(
                     x,
                     weight,
                     None if self.no_bias else bias,

python/paddle/fluid/tests/unittests/test_functional_conv3d_transpose.py

@@ -113,7 +113,7 @@ def static_graph_case_2(self):
                     "weight", self.weight.shape, dtype=self.dtype)
                 if not self.no_bias:
                     bias = fluid.data("bias", self.bias.shape, dtype=self.dtype)
-                y = F.conv_transpose3d(
+                y = F.conv3d_transpose(
                     x,
                     weight,
                     None if self.no_bias else bias,
@@ -138,7 +138,7 @@ def dygraph_case(self):
             x = dg.to_variable(self.input)
             weight = dg.to_variable(self.weight)
             bias = None if self.no_bias else dg.to_variable(self.bias)
-            y = F.conv_transpose3d(
+            y = F.conv3d_transpose(
                 x,
                 weight,
                 bias,
@@ -222,7 +222,7 @@ def static_graph_case(self):
                     "weight", self.weight_shape, dtype=self.dtype)
                 if not self.no_bias:
                     bias = fluid.data("bias", self.bias_shape, dtype=self.dtype)
-                y = F.conv_transpose3d(
+                y = F.conv3d_transpose(
                     x,
                     weight,
                     None if self.no_bias else bias,

python/paddle/nn/functional/__init__.py

@@ -73,12 +73,12 @@
 from .common import upsample  #DEFINE_ALIAS
 from .common import bilinear  #DEFINE_ALIAS
 from .conv import conv1d  #DEFINE_ALIAS
-from .conv import conv_transpose1d  #DEFINE_ALIAS
+from .conv import conv1d_transpose  #DEFINE_ALIAS
 from .common import linear  #DEFINE_ALIAS
 from .conv import conv2d  #DEFINE_ALIAS
-from .conv import conv_transpose2d  #DEFINE_ALIAS
+from .conv import conv2d_transpose  #DEFINE_ALIAS
 from .conv import conv3d  #DEFINE_ALIAS
-from .conv import conv_transpose3d  #DEFINE_ALIAS
+from .conv import conv3d_transpose  #DEFINE_ALIAS
 # from .extension import add_position_encoding  #DEFINE_ALIAS
 # from .extension import autoincreased_step_counter        #DEFINE_ALIAS
 # from .extension import continuous_value_model  #DEFINE_ALIAS

python/paddle/nn/functional/conv.py

@@ -15,11 +15,11 @@
 
 __all__ = [
     'conv1d',
-    'conv_transpose1d',
+    'conv1d_transpose',
     'conv2d',
-    'conv_transpose2d',
+    'conv2d_transpose',
     'conv3d',
-    'conv_transpose3d',
+    'conv3d_transpose',
 ]
 
 import numpy as np
@@ -541,7 +541,7 @@ def conv2d(x,
     return out
 
 
-def conv_transpose1d(x,
+def conv1d_transpose(x,
                      weight,
                      bias=None,
                      stride=1,
@@ -682,7 +682,7 @@ def conv_transpose1d(x,
                       [[4, 2]]]).astype(np.float32)
           x_var = paddle.to_tensor(x)
           w_var = paddle.to_tensor(w)
-          y_var = F.conv_transpose1d(x_var, w_var)
+          y_var = F.conv1d_transpose(x_var, w_var)
           y_np = y_var.numpy()
           print y_np
 
@@ -802,7 +802,7 @@ def conv_transpose1d(x,
     return out
 
 
-def conv_transpose2d(x,
+def conv2d_transpose(x,
                      weight,
                      bias=None,
                      stride=1,
@@ -920,7 +920,7 @@ def conv_transpose2d(x,
            None by default.
 
     Returns:
-        A Tensor representing the conv_transpose2d, whose 
+        A Tensor representing the conv2d_transpose, whose
         data type is the same with input and shape is (num_batches, channels, out_h, 
         out_w) or (num_batches, out_h, out_w, channels). The tensor variable storing 
         transposed convolution result.
@@ -946,7 +946,7 @@ def conv_transpose2d(x,
           x_var = paddle.randn((2, 3, 8, 8), dtype='float32')
           w_var = paddle.randn((3, 6, 3, 3), dtype='float32')
 
-          y_var = F.conv_transpose2d(x_var, w_var)
+          y_var = F.conv2d_transpose(x_var, w_var)
           y_np = y_var.numpy()
 
           print(y_np.shape)
@@ -1242,7 +1242,7 @@ def conv3d(x,
     return out
 
 
-def conv_transpose3d(x,
+def conv3d_transpose(x,
                      weight,
                      bias=None,
                      stride=1,
@@ -1364,7 +1364,7 @@ def conv_transpose3d(x,
            None by default.
 
     Returns:
-        A Tensor representing the conv_transpose3d, whose data 
+        A Tensor representing the conv3d_transpose, whose data
         type is the same with input and shape is (num_batches, channels, out_d, out_h, 
         out_w) or (num_batches, out_d, out_h, out_w, channels). If act is None, the tensor 
         variable storing the transposed convolution result, and if act is not None, the tensor 
@@ -1391,7 +1391,7 @@ def conv_transpose3d(x,
           x_var = paddle.randn((2, 3, 8, 8, 8), dtype='float32')
           w_var = paddle.randn((3, 6, 3, 3, 3), dtype='float32')
 
-          y_var = F.conv_transpose3d(x_var, w_var)
+          y_var = F.conv3d_transpose(x_var, w_var)
           y_np = y_var.numpy()
 
           print(y_np.shape)

python/paddle/nn/layer/conv.py

@@ -427,7 +427,7 @@ def __init__(self,
             data_format=data_format)
 
     def forward(self, x, output_size=None):
-        out = F.conv_transpose1d(
+        out = F.conv1d_transpose(
             x,
             self.weight,
             bias=self.bias,
@@ -744,7 +744,7 @@ def forward(self, x, output_size=None):
         else:
             output_padding = 0
 
-        out = F.conv_transpose2d(
+        out = F.conv2d_transpose(
             x,
             self.weight,
             bias=self.bias,
@@ -948,16 +948,16 @@ class ConvTranspose3d(_ConvNd):
 
     **Note**:
 
-          The conv_transpose3d can be seen as the backward of the conv3d. For conv3d, 
+          The conv3d_transpose can be seen as the backward of the conv3d. For conv3d,
           when stride > 1, conv3d maps multiple input shape to the same output shape, 
-          so for conv_transpose3d, when stride > 1, input shape maps multiple output shape.
+          so for conv3d_transpose, when stride > 1, input shape maps multiple output shape.
           If output_size is None, :math:`H_{out} = H^\prime_{out}, :math:`H_{out} = \
           H^\prime_{out}, W_{out} = W^\prime_{out}`; else, the :math:`D_{out}` of the output 
           size must between :math:`D^\prime_{out}` and :math:`D^\prime_{out} + strides[0]`, 
           the :math:`H_{out}` of the output size must between :math:`H^\prime_{out}` 
           and :math:`H^\prime_{out} + strides[1]`, and the :math:`W_{out}` of the output size must 
           between :math:`W^\prime_{out}` and :math:`W^\prime_{out} + strides[2]`, 
-          conv_transpose3d can compute the kernel size automatically.
+          conv3d_transpose can compute the kernel size automatically.
 
     Parameters:
         in_channels(int): The number of channels in the input image.
@@ -1078,7 +1078,7 @@ def forward(self, x, output_size=None):
         else:
             output_padding = 0
 
-        out = F.conv_transpose3d(
+        out = F.conv3d_transpose(
             x,
             self.weight,
             bias=self.bias,