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fix doc and unit test

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mobile_baidu
chengduoZH 7 years ago
parent 10bd9f6831
commit 0f1b30ef86
4 changed files with 55 additions and 39 deletions
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47
paddle/operators/conv_transpose_op.cc
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@ -65,16 +65,17 @@ Conv2DTransposeOpMaker::Conv2DTransposeOpMaker(
"Input",
"(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.");
"number of input channels, H is the height of the feature, and "
"W is the width of the feature.");
AddInput("Filter",
"(Tensor) 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. "
"H is the height of the filter, and W is the width of the filter. "
"We enforce groups number == 1 and padding == 0 in "
"convolution transpose Scenario.");
"the convolution transpose scenario.");
AddOutput("Output",
"(Tensor) 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",
@ -85,13 +86,15 @@ Conv2DTransposeOpMaker::Conv2DTransposeOpMaker(
"(vector defalut:{0, 0}), paddings of convolution transpose operator.")
.SetDefault({0, 0});
AddComment(R"DOC(
Convolution2D Transpose Operator.
The convolution transpose operation calculates the output based on the input, filter
and strides, paddings, groups parameters. The size of each dimension of the
parameters is checked in the infer-shape.
Input(Input, Filter) and output(Output) are in NCHW format. Where N is batch
size, C is the number of channels, H and W is the height and
width of feature. Parameters(ksize, strides, paddings) are two elements.
size, C is the number of channels, H is the height of the feature, and
W is the width of the feature. Parameters(ksize, strides, paddings) are two elements.
These two elements represent height and width, respectively.
The input(X) size and output(Out) size may be different.
Example:
@ -109,25 +112,26 @@ Example:
Conv3DTransposeOpMaker::Conv3DTransposeOpMaker(
framework::OpProto* proto, framework::OpAttrChecker* op_checker)
: OpProtoAndCheckerMaker(proto, op_checker) {
AddInput(
"Input",
"(Tensor) The input tensor of convolution transpose operator."
"The format of input tensor is NCDHW. Where N is batch size, C is "
"the number of channels, D, H and W is the depth, height and width of "
"feature.");
AddInput("Input",
"(Tensor) The input tensor of convolution transpose operator."
"The format of input tensor is NCDHW. Where N is batch size, C is "
"the number of channels, D is the depth of the feature, H is the "
"height of the feature, and "
"W is the width of the feature.");
AddInput("Filter",
"(Tensor) The filter tensor of convolution transpose operator."
"The format of the filter tensor is CMDHW, where C is the number of "
"output image channels, M is the number of input image channels, "
"D, H and W is depth, height and width of filter. "
"output image channels, M is the number of input image channels, D "
"is the depth of the filter, H is the height of the filter, and "
"W is the width of the filter."
"We enforce groups number == 1 and padding == 0 in "
"convolution transpose Scenario.");
"the convolution3d transpose scenario.");
AddOutput("Output",
"(Tensor) The output tensor of convolution transpose operator."
"The format of output tensor is also NCDHW."
"Where N is batch size, C is "
"the number of channels, D, H and W is the depth, height and "
"width of feature.");
"the number of channels, D is the depth of the feature, H is the "
"height of the feature, and W is the width of the feature.");
AddAttr<std::vector<int>>(
"strides",
"(vector defalut:{1, 1, 1}), strides of convolution transpose operator.")
@ -137,13 +141,16 @@ Conv3DTransposeOpMaker::Conv3DTransposeOpMaker(
"(vector defalut:{0, 0, 0}), paddings of convolution transpose operator.")
.SetDefault({0, 0, 0});
AddComment(R"DOC(
Convolution3D Transpose Operator.
The convolution transpose operation calculates the output based on the input, filter
and strides, paddings, groups parameters. The size of each dimension of the
parameters is checked in the infer-shape.
Input(Input, Filter) and output(Output) are in NCDHW format. Where N is batch
size, C is the number of channels, d, H and W is the depth, height and
width of feature. Parameters(ksize, strides, paddings) are three elements.
size, C is the number of channels, D is the depth of the feature,
H is the height of the feature, and W is the width of the feature.
Parameters(ksize, strides, paddings) are three elements.
These three elements represent depth, height and width, respectively.
The input(X) size and output(Out) size may be different.
Example:

12
paddle/operators/conv_transpose_op.h
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@ -175,6 +175,10 @@ class GemmConv2DTransposeGradKernel : public framework::OpKernel<T> {
DDim filter_matrix_shape = {m, c * k_h * k_w};
filter.Resize(filter_matrix_shape);
if ((!input_grad) && (!filter_grad)) {
return;
}
// convolution transpose grad on input:
// im2col + gemm (similar to conv-forward)
// input need to compute gradient
@ -265,7 +269,7 @@ class GemmConv3DTransposeKernel : public framework::OpKernel<T> {
const int64_t o_h = output->dims()[3];
const int64_t o_w = output->dims()[4];
paddle::operators::math::Col2VolFunctor<Place, T> col2vol;
math::Col2VolFunctor<Place, T> col2vol;
// use col_shape in the vol2col and col2vol calculation
DDim col_shape = {c, k_d, k_h, k_w, d, h, w};
@ -349,7 +353,7 @@ class GemmConv3DTransposeGradKernel : public framework::OpKernel<T> {
const int64_t o_w = output_grad->dims()[4];
// Only vol2col functor required for bp to get to the right shape
paddle::operators::math::Vol2ColFunctor<Place, T> vol2col;
math::Vol2ColFunctor<Place, T> vol2col;
// use col_shape in the vol2col and col2vol calculation
DDim col_shape = {c, k_d, k_h, k_w, d, h, w};
@ -363,6 +367,10 @@ class GemmConv3DTransposeGradKernel : public framework::OpKernel<T> {
DDim filter_matrix_shape = {m, c * k_d * k_h * k_w};
filter.Resize(filter_matrix_shape);
if ((!input_grad) && (!filter_grad)) {
return;
}
// convolution transpose grad on input:
// vol2col + gemm (similar to conv-forward)
// input need to compute gradient

29
python/paddle/v2/framework/tests/test_conv2d_transpose_op.py
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@ -58,36 +58,37 @@ class TestConv2dTransposeOp(OpTest):
print 'check output here for', self.op_type
self.check_output()
def init_test_case(self):
self.pad = [0, 0]
self.stride = [1, 1]
self.dilations = [1, 1]
self.input_size = [2, 3, 5, 5] # NCHW
f_c = self.input_size[1]
self.filter_size = [f_c, 6, 3, 3]
def init_op_type(self):
self.op_type = "conv2d_transpose"
def test_check_grad_no_input(self):
self.check_grad(
['Filter'],
'Output',
max_relative_error=0.05,
max_relative_error=0.02,
no_grad_set=set(['Input']))
def test_check_grad_no_filter(self):
self.check_grad(
['Input'],
'Output',
max_relative_error=0.05,
max_relative_error=0.02,
no_grad_set=set(['Filter']))
def test_check_grad(self):
self.check_grad(
set(['Input', 'Filter']), 'Output', max_relative_error=0.05)
set(['Input', 'Filter']), 'Output', max_relative_error=0.02)
def init_test_case(self):
self.pad = [0, 0]
self.stride = [1, 1]
self.dilations = [1, 1]
self.input_size = [2, 3, 5, 5] # NCHW
f_c = self.input_size[1]
self.filter_size = [f_c, 6, 3, 3]
def init_op_type(self):
self.op_type = "conv2d_transpose"
# ------------ test_cudnn ------------
class TestCudnn(TestConv2dTransposeOp):
def init_op_type(self):
self.op_type = "conv2d_transpose_cudnn"

6
python/paddle/v2/framework/tests/test_conv3d_transpose_op.py
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@ -65,20 +65,20 @@ class TestConv3dTransposeOp(OpTest):
def test_check_grad(self):
self.check_grad(
set(['Input', 'Filter']), 'Output', max_relative_error=0.05)
set(['Input', 'Filter']), 'Output', max_relative_error=0.02)
def test_check_grad_no_filter(self):
self.check_grad(
['Input'],
'Output',
max_relative_error=0.05,
max_relative_error=0.02,
no_grad_set=set(['Filter']))
def test_check_grad_no_input(self):
self.check_grad(
['Filter'],
'Output',
max_relative_error=0.05,
max_relative_error=0.02,
no_grad_set=set(['Input']))
def init_test_case(self):

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