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[Enhancement] Refine the docstring of ResNet (#723)

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* refine docstring of resnet

* refine docstring
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Junjun2016 2021-07-28 17:33:15 +08:00 committed by GitHub
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1 changed files with 28 additions and 14 deletions
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mmseg/models/backbones/resnet.py
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@ -312,25 +312,38 @@ class ResNet(BaseModule):
Args: Args:
depth (int): Depth of resnet, from {18, 34, 50, 101, 152}. depth (int): Depth of resnet, from {18, 34, 50, 101, 152}.
in_channels (int): Number of input image channels. Default" 3. in_channels (int): Number of input image channels. Default: 3.
stem_channels (int): Number of stem channels. Default: 64. stem_channels (int): Number of stem channels. Default: 64.
base_channels (int): Number of base channels of res layer. Default: 64. base_channels (int): Number of base channels of res layer. Default: 64.
num_stages (int): Resnet stages, normally 4. num_stages (int): Resnet stages, normally 4. Default: 4.
strides (Sequence[int]): Strides of the first block of each stage. strides (Sequence[int]): Strides of the first block of each stage.
Default: (1, 2, 2, 2).
dilations (Sequence[int]): Dilation of each stage. dilations (Sequence[int]): Dilation of each stage.
Default: (1, 1, 1, 1).
out_indices (Sequence[int]): Output from which stages. out_indices (Sequence[int]): Output from which stages.
Default: (0, 1, 2, 3).
style (str): `pytorch` or `caffe`. If set to "pytorch", the stride-two style (str): `pytorch` or `caffe`. If set to "pytorch", the stride-two
layer is the 3x3 conv layer, otherwise the stride-two layer is layer is the 3x3 conv layer, otherwise the stride-two layer is
the first 1x1 conv layer. the first 1x1 conv layer. Default: 'pytorch'.
deep_stem (bool): Replace 7x7 conv in input stem with 3 3x3 conv deep_stem (bool): Replace 7x7 conv in input stem with 3 3x3 conv.
Default: False.
avg_down (bool): Use AvgPool instead of stride conv when avg_down (bool): Use AvgPool instead of stride conv when
downsampling in the bottleneck. downsampling in the bottleneck. Default: False.
frozen_stages (int): Stages to be frozen (stop grad and set eval mode). frozen_stages (int): Stages to be frozen (stop grad and set eval mode).
-1 means not freezing any parameters. -1 means not freezing any parameters. Default: -1.
conv_cfg (dict | None): Dictionary to construct and config conv layer.
When conv_cfg is None, cfg will be set to dict(type='Conv2d').
Default: None.
norm_cfg (dict): Dictionary to construct and config norm layer. norm_cfg (dict): Dictionary to construct and config norm layer.
Default: dict(type='BN', requires_grad=True).
norm_eval (bool): Whether to set norm layers to eval mode, namely, norm_eval (bool): Whether to set norm layers to eval mode, namely,
freeze running stats (mean and var). Note: Effect on Batch Norm freeze running stats (mean and var). Note: Effect on Batch Norm
and its variants only. and its variants only. Default: False.
dcn (dict | None): Dictionary to construct and config DCN conv layer.
When dcn is not None, conv_cfg must be None. Default: None.
stage_with_dcn (Sequence[bool]): Whether to set DCN conv for each
stage. The length of stage_with_dcn is equal to num_stages.
Default: (False, False, False, False).
plugins (list[dict]): List of plugins for stages, each dict contains: plugins (list[dict]): List of plugins for stages, each dict contains:
- cfg (dict, required): Cfg dict to build plugin. - cfg (dict, required): Cfg dict to build plugin.
@ -339,18 +352,19 @@ class ResNet(BaseModule):
options: 'after_conv1', 'after_conv2', 'after_conv3'. options: 'after_conv1', 'after_conv2', 'after_conv3'.
- stages (tuple[bool], optional): Stages to apply plugin, length - stages (tuple[bool], optional): Stages to apply plugin, length
should be same as 'num_stages' should be same as 'num_stages'.
Default: None.
multi_grid (Sequence[int]|None): Multi grid dilation rates of last multi_grid (Sequence[int]|None): Multi grid dilation rates of last
stage. Default: None stage. Default: None.
contract_dilation (bool): Whether contract first dilation of each layer contract_dilation (bool): Whether contract first dilation of each layer
Default: False Default: False.
with_cp (bool): Use checkpoint or not. Using checkpoint will save some with_cp (bool): Use checkpoint or not. Using checkpoint will save some
memory while slowing down the training speed. memory while slowing down the training speed. Default: False.
zero_init_residual (bool): Whether to use zero init for last norm layer zero_init_residual (bool): Whether to use zero init for last norm layer
in resblocks to let them behave as identity. in resblocks to let them behave as identity. Default: True.
pretrained (str, optional): model pretrained path. Default: None pretrained (str, optional): model pretrained path. Default: None.
init_cfg (dict or list[dict], optional): Initialization config dict. init_cfg (dict or list[dict], optional): Initialization config dict.
Default: None Default: None.
Example: Example:
>>> from mmseg.models import ResNet >>> from mmseg.models import ResNet