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Merge pull request #5951 from chengduoZH/fix_conv_doc

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fix conv and conv_trans op doc
release/0.11.0
chengduo 8 years ago committed by GitHub
parent 1b6dcc2fe8 c339e1b786
commit 5a3d1362f7
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GPG Key ID: 4AEE18F83AFDEB23
5 changed files with 104 additions and 82 deletions
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61
paddle/operators/conv_op.cc
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@ -97,7 +97,7 @@ Conv2DOpMaker::Conv2DOpMaker(framework::OpProto* proto,
.SetDefault({0, 0});
AddAttr<int>(
"groups",
"(int default:1), the group size of convolution operator. "
"(int default:1), the groups number of the convolution operator. "
"According to grouped convolution in Alex Krizhevsky's Deep CNN paper: "
"when group=2, the first half of the filters is only connected to the "
"first half of the input channels, while the second half of the filters "
@ -112,23 +112,29 @@ Conv2DOpMaker::Conv2DOpMaker(framework::OpProto* proto,
Convolution Operator.
The convolution operation calculates the output based on the input, filter
and strides, paddings, groups, dilations parameters. The size of each dimension of the
and strides, paddings, dilations, 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
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. Parameters(ksize, strides, paddings, dilations) are two elements.
These two elements represent height and width, respectively.
the width of the feature.
Filters(Input) is MCHW format. Where M is the number of output image channels, C is
the number of input image channels, H is the height of the filter, and W
is the width of the filter.
Parameters(strides, paddings, dilations) are two elements. These two elements represent
height and width, respectively.
The input(X) size and output(Out) size may be different.
Example:
Input:
Input shape: (N, C_in, H_in, W_in)
Filter shape: (C_out, C_in, H_f, W_f)
Input shape: $(N, C_{in}, H_{in}, W_{in})$
Filter shape: $(C_{out}, C_{in}, H_f, W_f)$
Output:
Output shape: (N, C_out, H_out, W_out)
where
H_out = (H_in + 2 * paddings[0] - (dilations[0]*(filter_size[0] - 1) + 1)) / strides[0] + 1;
W_out = (W_in + 2 * paddings[1] - (dilations[1]*(filter_size[1] - 1) + 1)) / strides[1] + 1;
Output shape: $(N, C_{out}, H_{out}, W_{out})$
Where
$$
H_{out}= \frac{(H_{in} + 2 * paddings[0] - (dilations[0] * (H_f - 1) + 1))}{strides[0]}+ 1 \\
W_{out}= \frac{(W_{in} + 2 * paddings[1] - (dilations[1] * (W_f - 1) + 1))}{strides[1]}+ 1
$$
)DOC");
}
@ -165,7 +171,7 @@ Conv3DOpMaker::Conv3DOpMaker(framework::OpProto* proto,
.SetDefault({0, 0, 0});
AddAttr<int>(
"groups",
"(int default:1), the group size of convolution operator. "
"(int default:1), the groups number of the convolution operator. "
"According to grouped convolution in Alex Krizhevsky's Deep CNN paper: "
"when group=2, the first half of the filters is only connected to the "
"first half of the input channels, while the second half of the filters "
@ -174,32 +180,37 @@ Conv3DOpMaker::Conv3DOpMaker(framework::OpProto* proto,
AddAttr<std::vector<int>>("dilations",
"(vector<int> default:{1, 1, 1}), the "
"dilations(d_dilation, h_dilation, w_dilation) of "
"convolution operator. Currently, conv3d doesn't "
"support dilation.")
"convolution operator.")
.SetDefault({1, 1, 1});
AddComment(R"DOC(
Convolution3D Operator.
The convolution operation calculates the output based on the input, filter
and strides, paddings, groups parameters. The size of each dimension of the
and strides, paddings, dilations, 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
Input(Input) and output(Output) are in NCDHW format, 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. Parameters(ksize, strides, paddings)
are three elements. These three elements represent depth, height and width, respectively.
the feature, and W is the width of the feature.
Filters(Input) is MCDHW format, where M is the number of output image channels,
C 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.
Parameters(strides, paddings, dilations) are three elements. These three elements
represent depth, height and width, respectively.
The input(X) size and output(Out) size may be different.
Example:
Input:
Input shape: (N, C_in, D_in, H_in, W_in)
Filter shape: (C_out, C_in, D_f, H_f, W_f)
Input shape: $(N, C_{in}, D_{in}, H_{in}, W_{in})$
Filter shape: $(C_{out}, C_{in}, D_f, H_f, W_f)$
Output:
Output shape: (N, C_out, D_out, H_out, W_out)
where
D_out = (D_in - filter_size[0] + 2 * paddings[0]) / strides[0] + 1;
H_out = (H_in - filter_size[1] + 2 * paddings[1]) / strides[1] + 1;
W_out = (W_in - filter_size[2] + 2 * paddings[2]) / strides[2] + 1;
Output shape: $(N, C_{out}, D_{out}, H_{out}, W_{out})$
Where
$$
D_{out}= \frac{(D_{in} + 2 * paddings[0] - (dilations[0] * (D_f - 1) + 1))}{ strides[0]}+ 1 \\
H_{out}= \frac{(H_{in} + 2 * paddings[1] - (dilations[1] * (H_f - 1) + 1))}{ strides[1]}+ 1 \\
W_{out}= \frac{(W_{in} + 2 * paddings[2] - (dilations[2] * (W_f - 1) + 1))}{ strides[2]}+ 1
$$
)DOC");
}

82
paddle/operators/conv_transpose_op.cc
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@ -39,7 +39,7 @@ void ConvTransposeOp::InferShape(framework::InferShapeContext* ctx) const {
"ConvTransposeOp input dimension and strides dimension should "
"be consistent.");
PADDLE_ENFORCE_EQ(paddings.size(), strides.size(),
"ConvTransposeOp paddings dimension and Conv strides "
"ConvTransposeOp paddings dimension and strides "
"dimension should be the same.");
PADDLE_ENFORCE_EQ(in_dims[1], filter_dims[0],
"In ConvTransposeOp, The input channel should be the same "
@ -62,13 +62,14 @@ Conv2DTransposeOpMaker::Conv2DTransposeOpMaker(
"The format of input tensor is NCHW. Where N is batch size, C is the "
"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. "
"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 is the height of the filter, and W is the width of the filter. "
"We enforce groups number == 1 and padding == 0 in "
"the convolution transpose scenario.");
AddInput(
"Filter",
"(Tensor) The filter tensor of convolution transpose operator. "
"The format of the filter tensor is MCHW, where M is the number of "
"input feature channels, C is the number of "
"output feature channels,"
"H is the height of the filter, and W is the width of the filter. "
"We enforce groups number == 1 in the convolution transpose scenario.");
AddOutput("Output",
"(Tensor) The output tensor of convolution transpose operator. "
"The format of output tensor is also NCHW.");
@ -88,21 +89,26 @@ 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 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.
Input(Input) and output(Output) are in NCHW format. Where N is batchsize, C is the
number of channels, H is the height of the feature, and W is the width of the feature.
Filter(Input) is in MCHW format. Where M is the number of input feature channels,
C is the number of output feature channels, H is the height of the filter,
and W is the width of the filter.
Parameters(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:
Input:
Input shape: (N, C_in, H_in, W_in)
Filter shape: (C_in, C_out, H_f, W_f)
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
H_out = (H_in - 1) * strides[0] - 2 * paddings[0] + H_f;
W_out = (W_in - 1) * strides[1] - 2 * paddings[1] + W_f;
Output shape: $(N, C_{out}, H_{out}, W_{out})$
Where
$$
H_{out} = (H_{in} - 1) * strides[0] - 2 * paddings[0] + H_f \\
W_{out} = (W_{in} - 1) * strides[1] - 2 * paddings[1] + W_f
$$
)DOC");
}
@ -117,8 +123,9 @@ Conv3DTransposeOpMaker::Conv3DTransposeOpMaker(
"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 "
"The format of the filter tensor is MCDHW, where M is the number of "
"input feature channels, C is the number of "
"output feature 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 "
@ -144,23 +151,28 @@ 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 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.
Input(Input) and output(Output) are in NCDHW format. 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.
Filter(Input) is in MCDHW format. Where M is the number of input feature channels,
C is the number of output feature channels, D is the depth of the filter,H is the
height of the filter, and W is the width of the filter.
Parameters(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:
Example:
Input:
Input shape: (N, C_in, D_in, H_in, W_in)
Filter shape: (C_in, C_out, D_f, H_f, W_f)
Input shape: $(N, C_{in}, D_{in}, H_{in}, W_{in})$
Filter shape: $(C_{in}, C_{out}, D_f, H_f, W_f)$
Output:
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;
Output shape: $(N, C_{out}, D_{out}, H_{out}, W_{out})$
Where
$$
D_{out} = (D_{in} - 1) * strides[0] - 2 * paddings[0] + D_f \\
H_{out} = (H_{in} - 1) * strides[1] - 2 * paddings[1] + H_f \\
W_{out} = (W_{in} - 1) * strides[2] - 2 * paddings[2] + W_f
$$
)DOC");
}

1
paddle/operators/conv_transpose_op.h
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@ -63,7 +63,6 @@ class GemmConvTransposeKernel : public framework::OpKernel<T> {
std::vector<int> strides = context.Attr<std::vector<int>>("strides");
std::vector<int> paddings = context.Attr<std::vector<int>>("paddings");
// TODO(Zhuoyuan): Paddings can be added in future.
// groups will alway be disabled in conv2dtranspose.
const int batch_size = static_cast<int>(input->dims()[0]);

24
paddle/operators/pool_op.cc
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@ -105,7 +105,7 @@ Pool2dOpMaker::Pool2dOpMaker(framework::OpProto *proto,
// TypedAttrChecker don't support vector type.)
AddAttr<std::vector<int>>(
"paddings",
"(vector<int>, defalut {0,0}), paddings(height, width) of pooling "
"(vector<int>, default {0,0}), paddings(height, width) of pooling "
"operator."
"If global_pooling = true, paddings and ksize will be ignored.")
.SetDefault({0, 0}); // TODO(Chengduo): Add checker. (Currently,
@ -122,15 +122,15 @@ 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:
Example:
Input:
X shape: $(N, C, H_{in}, W_{in})$
Output:
Out shape: $(N, C, H_{out}, W_{out})$
where
Where
$$
H_{out} = (H_{in} - ksize[0] + 2 * paddings[0]) / strides[0] + 1 \\
W_{out} = (W_{in} - ksize[1] + 2 * paddings[1]) / strides[1] + 1
H_{out} = \frac{(H_{in} - ksize[0] + 2 * paddings[0])}{strides[0]} + 1 \\
W_{out} = \frac{(W_{in} - ksize[1] + 2 * paddings[1])}{strides[1]} + 1
$$
)DOC");
@ -177,7 +177,7 @@ Pool3dOpMaker::Pool3dOpMaker(framework::OpProto *proto,
// TypedAttrChecker don't support vector type.)
AddAttr<std::vector<int>>(
"paddings",
"(vector<int>, defalut {0,0,0}), paddings(depth, height, "
"(vector<int>, default {0,0,0}), paddings(depth, height, "
"width) of pooling operator. "
"If global_pooling = true, ksize and paddings will be ignored.")
.SetDefault({0, 0, 0}); // TODO(Chengduo): Add checker. (Currently,
@ -199,12 +199,12 @@ Example:
X shape: $(N, C, D_{in}, H_{in}, W_{in})$
Output:
Out shape: $(N, C, D_{out}, H_{out}, W_{out})$
where
$$
D_{out} = (D_{in} - ksize[0] + 2 * paddings[0]) / strides[0] + 1 \\
H_{out} = (H_{in} - ksize[1] + 2 * paddings[1]) / strides[1] + 1 \\
W_{out} = (W_{in} - ksize[2] + 2 * paddings[2]) / strides[2] + 1
$$
Where
$$
D_{out} = \frac{(D_{in} - ksize[0] + 2 * paddings[0])}{strides[0]} + 1 \\
H_{out} = \frac{(H_{in} - ksize[1] + 2 * paddings[1])}{strides[1]} + 1 \\
W_{out} = \frac{(W_{in} - ksize[2] + 2 * paddings[2])}{strides[2]} + 1
$$
)DOC");
}

18
paddle/operators/pool_with_index_op.cc
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@ -142,7 +142,7 @@ class MaxPool2dWithIndexOpMaker : public framework::OpProtoAndCheckerMaker {
// TypedAttrChecker don't support vector type.)
AddAttr<std::vector<int>>(
"paddings",
"(vector<int>, defalut:{0, 0}), paddings(height, width) of pooling "
"(vector<int>, default:{0, 0}), paddings(height, width) of pooling "
"operator. "
"If global_pooling = true, paddings and will be ignored.")
.SetDefault({0, 0}); // TODO(Chengduo): Add checker. (Currently,
@ -166,10 +166,10 @@ Example:
Output:
Out shape: $(N, C, H_{out}, W_{out})$
Mask shape: $(N, C, H_{out}, W_{out})$
where
Where
$$
H_{out} = (H_{in} - ksize[0] + 2 * paddings[0]) / strides[0] + 1 \\
W_{out} = (W_{in} - ksize[1] + 2 * paddings[1]) / strides[1] + 1
H_{out} = \frac{(H_{in} - ksize[0] + 2 * paddings[0])}{strides[0]} + 1 \\
W_{out} = \frac{(W_{in} - ksize[1] + 2 * paddings[1])}{strides[1]} + 1
$$
)DOC");
@ -220,7 +220,7 @@ class MaxPool3dWithIndexOpMaker : public framework::OpProtoAndCheckerMaker {
// TypedAttrChecker don't support vector type.)
AddAttr<std::vector<int>>(
"paddings",
"(vector, defalut {0,0,0}), paddings(depth, "
"(vector, default {0,0,0}), paddings(depth, "
"height, width) of pooling operator. "
"If global_pooling = true, paddings and ksize will be ignored.")
.SetDefault({0, 0, 0}); // TODO(Chengduo): Add checker. (Currently,
@ -244,11 +244,11 @@ Example:
Output:
Out shape: $(N, C, D_{out}, H_{out}, W_{out})$
Mask shape: $(N, C, D_{out}, H_{out}, W_{out})$
where
Where
$$
D_{out} = (D_{in} - ksize[0] + 2 * paddings[0]) / strides[0] + 1 \\
H_{out} = (H_{in} - ksize[1] + 2 * paddings[1]) / strides[1] + 1 \\
W_{out} = (W_{in} - ksize[2] + 2 * paddings[2]) / strides[2] + 1
D_{out} = \frac{(D_{in} - ksize[0] + 2 * paddings[0])}{strides[0]} + 1 \\
H_{out} = \frac{(H_{in} - ksize[1] + 2 * paddings[1])}{strides[1]} + 1 \\
W_{out} = \frac{(W_{in} - ksize[2] + 2 * paddings[2])}{strides[2]} + 1
$$
)DOC");

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