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use standard docstr for basic and bottleneck block

pull/2/head
yanglei 2020-06-14 13:29:47 +08:00
parent 335047083e
commit a5b53ada94
1 changed files with 40 additions and 4 deletions
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44
mmcls/models/backbones/resnet.py
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@ -9,6 +9,26 @@ from .base_backbone import BaseBackbone
class BasicBlock(nn.Module): class BasicBlock(nn.Module):
"""BasicBlock for ResNet.
Args:
inplanes (int): inplanes of block.
planes (int): planes of block.
stride (int): stride of the block. Default: 1
dilation (int): dilation of convolution. Default: 1
downsample (nn.Module): downsample operation on identity branch.
Default: None
style (str): `pytorch` or `caffe`. If set to "pytorch", the stride-two
layer is the 3x3 conv layer, otherwise the stride-two layer is
the first 1x1 conv layer.
with_cp (bool): Use checkpoint or not. Using checkpoint will save some
memory while slowing down the training speed.
conv_cfg (dict): dictionary to construct and config conv layer.
Default: None
norm_cfg (dict): dictionary to construct and config norm layer.
Default: dict(type='BN')
"""
expansion = 1 expansion = 1
def __init__(self, def __init__(self,
@ -84,6 +104,26 @@ class BasicBlock(nn.Module):
class Bottleneck(nn.Module): class Bottleneck(nn.Module):
"""Bottleneck block for ResNet.
Args:
inplanes (int): inplanes of block.
planes (int): planes of block.
stride (int): stride of the block. Default: 1
dilation (int): dilation of convolution. Default: 1
downsample (nn.Module): downsample operation on identity branch.
Default: None
style (str): `pytorch` or `caffe`. If set to "pytorch", the stride-two
layer is the 3x3 conv layer, otherwise the stride-two layer is
the first 1x1 conv layer.
with_cp (bool): Use checkpoint or not. Using checkpoint will save some
memory while slowing down the training speed.
conv_cfg (dict): dictionary to construct and config conv layer.
Default: None
norm_cfg (dict): dictionary to construct and config norm layer.
Default: dict(type='BN')
"""
expansion = 4 expansion = 4
def __init__(self, def __init__(self,
@ -96,10 +136,6 @@ class Bottleneck(nn.Module):
with_cp=False, with_cp=False,
conv_cfg=None, conv_cfg=None,
norm_cfg=dict(type='BN')): norm_cfg=dict(type='BN')):
"""Bottleneck block for ResNet.
If style is "pytorch", the stride-two layer is the 3x3 conv layer,
if it is "caffe", the stride-two layer is the first 1x1 conv layer.
"""
super(Bottleneck, self).__init__() super(Bottleneck, self).__init__()
assert style in ['pytorch', 'caffe'] assert style in ['pytorch', 'caffe']