refine conv2d_transpose layer doc

paddle/operators/conv_transpose_op.cc

@@ -117,8 +117,8 @@ The input(X) size and output(Out) size may be different.
  Output shape: $(N, C_{out}, H_{out}, W_{out})$
  Where
  $$
- H_{out} = (H_{in} - 1) * strides[0] - 2 * paddings[0] + H_f \\
- W_{out} = (W_{in} - 1) * strides[1] - 2 * paddings[1] + W_f
+ H_{out} = (H_{in} - 1) * strides[0] - 2 * paddings[0] + dilations[0] * (H_f - 1) + 1 \\
+ W_{out} = (W_{in} - 1) * strides[1] - 2 * paddings[1] + dilations[1] * (W_f - 1) + 1
  $$
 )DOC");
 }
@@ -187,9 +187,9 @@ The input(X) size and output(Out) size may be different.
  Output shape: $(N, C_{out}, D_{out}, H_{out}, W_{out})$
  Where
  $$
- D_{out} = (D_{in} - 1) * strides[0] - 2 * paddings[0] + D_f \\
- H_{out} = (H_{in} - 1) * strides[1] - 2 * paddings[1] + H_f \\
- W_{out} = (W_{in} - 1) * strides[2] - 2 * paddings[2] + W_f
+ D_{out} = (D_{in} - 1) * strides[0] - 2 * paddings[0] + dilations[0] * (D_f - 1) + 1 \\
+ H_{out} = (H_{in} - 1) * strides[1] - 2 * paddings[1] + dilations[1] * (H_f - 1) + 1 \\
+ W_{out} = (W_{in} - 1) * strides[2] - 2 * paddings[2] + dilations[2] * (W_f - 1) + 1
  $$
 )DOC");
 }

paddle/operators/conv_transpose_op.h

@@ -133,9 +133,9 @@ class GemmConvTransposeKernel : public framework::OpKernel<T> {
  if (data_dim == 2U) {
  // col2im: col_matrix -> dy
  // from (c * k_h * k_w, h * w) to (c, o_h, o_w)
- col2im(dev_ctx, col, std::vector<int>{dilations[0], dilations[1]},
- strides, std::vector<int>{paddings[0], paddings[1], paddings[0],
-  paddings[1]},
+ col2im(dev_ctx, col, dilations, strides,
+ std::vector<int>{paddings[0], paddings[1], paddings[0],
+ paddings[1]},
  &output_batch);
  } else if (data_dim == 3U) {
  // col2vol: col_matrix -> dy
@@ -239,8 +239,7 @@ class GemmConvTransposeGradKernel : public framework::OpKernel<T> {
  if (data_dim == 2U) {
  // im2col: dy -> col matrix
  // from (c, o_h, o_w) to (c * k_h * k_w, h * w)
- im2col(dev_ctx, output_grad_batch,
- std::vector<int>{dilations[0], dilations[1]}, strides,
+ im2col(dev_ctx, output_grad_batch, dilations, strides,
  std::vector<int>{paddings[0], paddings[1], paddings[0],
  paddings[1]},
  &col);

python/paddle/v2/fluid/layers/nn.py

@@ -851,9 +851,51 @@ def conv2d_transpose(input,
  dilation=None,
  param_attr=None):
  """
- The transpose of conv2d layer.
+ **Convlution2D transpose layer**
 
- This layer is also known as deconvolution layer.
+ The convolution2D layer calculates the output based on the input, filter
+ and strides, paddings, dilations, groups parameters. Input(Input) and Output(Output)
+ are in NCHW format. Where N is batch size, C is the number of channels, H is the height
+ of the feature, and W is the width of the feature.
+ The details of convolution transpose layer, please refer to the following explanation
+ and references therein
+ <http://datascience.stackexchange.com/questions/6107/what-are-deconvolutional-layers/>`_ .
+
+ For each input :math:`X`, the equation is:
+
+
+ .. math::
+
+ Out = W \\ast X
+
+ In the above equation:
+
+ * :math:`X`: Input value, a tensor with NCHW format.
+ * :math:`W`: Filter value, a tensor with MCHW format.
+ * :math: \\ast : Convolution transpose operation.
+ * :math:`Out`: Output value, the shape of :math:`Out` and :math:`X` may be different.
+
+ Example:
+
+ - Input:
+
+ Input shape: $(N, C_{in}, H_{in}, W_{in})$
+
+ Filter shape: $(C_{in}, C_{out}, H_f, W_f)$
+
+ - Output:
+
+ Output shape: $(N, C_{out}, H_{out}, W_{out})$
+
+ Where
+
+ .. math::
+
+ H_{out} = (H_{in} - 1) * strides[0] - 2 * paddings[0] + dilations[0] * (H_f - 1) + 1 \\\\
+ W_{out} = (W_{in} - 1) * strides[1] - 2 * paddings[1] + dilations[1] * (W_f - 1) + 1
+
+ All the input variables are passed in as local variables to the LayerHelper
+ constructor.
 
  Args:
  input(Variable): The input image with [N, C, H, W] format.
@@ -876,11 +918,19 @@ def conv2d_transpose(input,
  contain two integers, (dilation_H, dilation_W). Otherwise, the
  dilation_H = dilation_W = dilation.
  param_attr: Parameter Attribute.
- main_program(Program): the main program
- startup_program(Program): the startup program
 
  Returns:
- Variable: Output image.
+ Variable: The tensor variable storing the convolution transpose result.
+
+ Raises:
+ ValueError: If the shapes of input, filter_size, stride, padding and groups mismatch.
+
+ Examples:
+ .. code-block:: python
+
+ data = fluid.layers.data(name='data', shape=[3, 32, 32], dtype='float32')
+ conv2d = fluid.layers.conv2d_transpose(input=data, num_filters=2, filter_size=3)
+
  """
  helper = LayerHelper("conv2d_transpose", **locals())
  if not isinstance(input, Variable):