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修复paddle.nn.functinal包和paddle.nn包下API文档 #48581

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Merged
Ligoml merged 2 commits into from
Dec 7, 2022
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修复paddle.nn.functinal包和paddle.nn包下API文档 #48581

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修复paddle.nn.functinal包和paddle.nn包下API文档
Fancy-hjyp Nov 30, 2022
c97a59a
Merge branch 'develop' of https://github.com/PaddlePaddle/Paddle into…
Fancy-hjyp Nov 30, 2022
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23 changes: 10 additions & 13 deletions python/paddle/nn/functional/conv.py
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Original file line number Diff line number Diff line change
Expand Up @@ -608,10 +608,10 @@ def conv2d(
the number of output channels, g is the number of groups, kH is the filter's
height, kW is the filter's width.
bias (Tensor, optional): The bias with shape [M,].
stride (int|list|tuple): The stride size. It means the stride in convolution.
stride (int|list|tuple, optional): The stride size. It means the stride in convolution.
If stride is a list/tuple, it must contain two integers, (stride_height, stride_width).
Otherwise, stride_height = stride_width = stride. Default: stride = 1.
padding (string|int|list|tuple): The padding size. It means the number of zero-paddings
padding (string|int|list|tuple, optional): The padding size. It means the number of zero-paddings
on both sides for each dimension.If `padding` is a string, either 'VALID' or
'SAME' which is the padding algorithm. If padding size is a tuple or list,
it could be in three forms: `[pad_height, pad_width]` or
Expand All @@ -621,11 +621,11 @@ def conv2d(
when `data_format` is `"NHWC"`, `padding` can be in the form
`[[0,0], [pad_height_top, pad_height_bottom], [pad_width_left, pad_width_right], [0,0]]`.
Default: padding = 0.
dilation (int|list|tuple): The dilation size. It means the spacing between the kernel
dilation (int|list|tuple, optional): The dilation size. It means the spacing between the kernel
points. If dilation is a list/tuple, it must contain two integers, (dilation_height,
dilation_width). Otherwise, dilation_height = dilation_width = dilation.
Default: dilation = 1.
groups (int): The groups number of the Conv2D Layer. According to grouped
groups (int, optional): The groups number of the Conv2D Layer. 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 is only
Expand All @@ -651,10 +651,9 @@ def conv2d(
w_var = paddle.randn((6, 3, 3, 3), dtype='float32')

y_var = F.conv2d(x_var, w_var)
y_np = y_var.numpy()

print(y_np.shape)
# (2, 6, 6, 6)
print(y_var.shape)
# [2, 6, 6, 6]
"""
# entry checks
if data_format not in ["NCHW", "NHWC"]:
Expand Down Expand Up @@ -1224,10 +1223,9 @@ def conv2d_transpose(
w_var = paddle.randn((3, 6, 3, 3), dtype='float32')

y_var = F.conv2d_transpose(x_var, w_var)
y_np = y_var.numpy()

print(y_np.shape)
# (2, 6, 10, 10)
print(y_var.shape)
# [2, 6, 10, 10]
"""

if data_format not in ['NCHW', 'NHWC']:
Expand Down Expand Up @@ -1513,10 +1511,9 @@ def conv3d(
w_var = paddle.randn((6, 3, 3, 3, 3), dtype='float32')

y_var = F.conv3d(x_var, w_var)
y_np = y_var.numpy()

print(y_np.shape)
# (2, 6, 6, 6, 6)
print(y_var.shape)
# [2, 6, 6, 6, 6]
"""
# entry check
if data_format not in ["NCDHW", "NDHWC"]:
Expand Down
21 changes: 9 additions & 12 deletions python/paddle/nn/layer/conv.py
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Original file line number Diff line number Diff line change
Expand Up @@ -449,7 +449,7 @@ class Conv1DTranspose(_ConvNd):
in_channels(int): The number of channels in the input image.
out_channels(int): The number of the filter. It is as same as the output
feature map.
kernel_size(int|tuple|list, optional): The filter size. If kernel_size is a tuple/list,
kernel_size(int|tuple|list): The filter size. If kernel_size is a tuple/list,
it must contain one integers, (kernel_size). None if
use output size to calculate kernel_size. Default: None. kernel_size and
output_size should not be None at the same time.
Expand Down Expand Up @@ -598,7 +598,7 @@ class Conv2D(_ConvNd):
Parameters:
in_channels(int): The number of input channels in the input image.
out_channels(int): The number of output channels produced by the convolution.
kernel_size(int|list|tuple, optional): The size of the convolving kernel.
kernel_size(int|list|tuple): The size of the convolving kernel.
stride(int|list|tuple, optional): The stride size. If stride is a list/tuple, it must
contain three integers, (stride_H, stride_W). Otherwise, the
stride_H = stride_W = stride. The default value is 1.
Expand Down Expand Up @@ -668,9 +668,8 @@ class Conv2D(_ConvNd):

conv = nn.Conv2D(4, 6, (3, 3))
y_var = conv(x_var)
y_np = y_var.numpy()
print(y_np.shape)
# (2, 6, 6, 6)
print(y_var.shape)
# [2, 6, 6, 6]
"""

def __init__(
Expand Down Expand Up @@ -841,9 +840,8 @@ class Conv2DTranspose(_ConvNd):

conv = nn.Conv2DTranspose(4, 6, (3, 3))
y_var = conv(x_var)
y_np = y_var.numpy()
print(y_np.shape)
# (2, 6, 10, 10)
print(y_var.shape)
# [2, 6, 10, 10]
"""

def __init__(
Expand Down Expand Up @@ -927,7 +925,7 @@ class Conv3D(_ConvNd):
Parameters:
in_channels(int): The number of input channels in the input image.
out_channels(int): The number of output channels produced by the convolution.
kernel_size(int|list|tuple, optional): The size of the convolving kernel.
kernel_size(int|list|tuple): The size of the convolving kernel.
stride(int|list|tuple, optional): The stride size. If stride is a list/tuple, it must
contain three integers, (stride_D, stride_H, stride_W). Otherwise, the
stride_D = stride_H = stride_W = stride. The default value is 1.
Expand Down Expand Up @@ -999,9 +997,8 @@ class Conv3D(_ConvNd):

conv = nn.Conv3D(4, 6, (3, 3, 3))
y_var = conv(x_var)
y_np = y_var.numpy()
print(y_np.shape)
# (2, 6, 6, 6, 6)
print(y_var.shape)
# [2, 6, 6, 6, 6]
"""

def __init__(
Expand Down