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add docstrings to component modules of fast-scnn

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johnzja 2020-08-10 13:58:11 +08:00
parent e35f9acde1
commit d8cba3d6a9
1 changed files with 71 additions and 14 deletions
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85
mmseg/models/backbones/fast_scnn.py
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@ -10,7 +10,24 @@ from ..builder import BACKBONES
class LearningToDownsample(nn.Module):
"""Learning to downsample module."""
"""Learning to downsample module.
Args:
in_channels (int): Number of input channels.
dw_channels1 (int): Number of output channels of the first
depthwise conv (dwconv) layer.
dw_channels2 (int): Number of output channels of the second
dwconv layer.
out_channels (int): Number of output channels of the whole
'learning to downsample' module.
conv_cfg (dict|None): Config of conv layers.
norm_cfg (dict|None): Config of norm layers.
act_cfg (dict): Config of activation layers.
"""
def __init__(self,
in_channels,
@ -53,7 +70,29 @@ class LearningToDownsample(nn.Module):
class GlobalFeatureExtractor(nn.Module):
"""Global feature extractor module."""
"""Global feature extractor module.
Args:
in_channels (int): Number of input channels of the GFE module.
block_channels (tuple): Tuple of ints. Each int specifies the
number of output channels of each Inverted Residual module.
out_channels(int): Number of output channels of the GFE module.
t (int): t parameter (upsampling factor) of each Inverted Residual
module.
num_blocks (tuple): Tuple of ints. Each int specifies the number of
times each Inverted Residual module is repeated.
pool_scales (tuple): Tuple of ints. Each int specifies the parameter
required in 'global average pooling' within PPM.
conv_cfg (dict|None): Config of conv layers.
norm_cfg (dict|None): Config of norm layers.
act_cfg (dict): Config of activation layers.
align_corners (bool): align_corners argument of F.interpolate.
"""
def __init__(self,
in_channels=64,
@ -115,7 +154,25 @@ class GlobalFeatureExtractor(nn.Module):
class FeatureFusionModule(nn.Module):
"""Feature fusion module."""
"""Feature fusion module.
Args:
higher_in_channels (int): Number of input channels of the
higher-resolution branch.
lower_in_channels (int): Number of input channels of the
lower-resolution branch.
out_channels (int): Number of output channels.
scale_factor (int): Scale factor applied to the lower-res input.
Should be coherent with the downsampling factor determined
by the GFE module.
conv_cfg (dict|None): Config of conv layers.
norm_cfg (dict|None): Config of norm layers.
act_cfg (dict): Config of activation layers.
align_corners (bool): align_corners argument of F.interpolate.
"""
def __init__(self,
higher_in_channels,
@ -190,39 +247,39 @@ class FastSCNN(nn.Module):
align_corners=False):
"""Fast-SCNN Backbone.
Args:
in_channels(int): Number of input image channels. Default=3 (RGB)
in_channels (int): Number of input image channels. Default=3 (RGB)
downsample_dw_channels1(int): Number of output channels after
downsample_dw_channels1 (int): Number of output channels after
the first conv layer in Learning-To-Downsample (LTD) module.
downsample_dw_channels2(int): Number of output channels
downsample_dw_channels2 (int): Number of output channels
after the second conv layer in LTD.
global_in_channels(int): Number of input channels of
global_in_channels (int): Number of input channels of
Global Feature Extractor(GFE).
Equal to number of output channels of LTD.
global_block_channels(tuple): Tuple of integers that describe
global_block_channels (tuple): Tuple of integers that describe
the output channels for each of the MobileNet-v2 bottleneck
residual blocks in GFE.
global_out_channels(int): Number of output channels of GFE.
global_out_channels (int): Number of output channels of GFE.
higher_in_channels(int): Number of input channels of the higher
higher_in_channels (int): Number of input channels of the higher
resolution branch in FFM.
Equal to global_in_channels.
lower_in_channels(int): Number of input channels of the lower
lower_in_channels (int): Number of input channels of the lower
resolution branch in FFM.
Equal to global_out_channels.
fusion_out_channels(int): Number of output channels of FFM.
fusion_out_channels (int): Number of output channels of FFM.
scale_factor(int): The upsampling factor of the higher resolution
scale_factor (int): The upsampling factor of the higher resolution
branch in FFM.
Equal to the downsampling factor in GFE.
out_indices(tuple): Tuple of indices of list
out_indices (tuple): Tuple of indices of list
[higher_res_features, lower_res_features, fusion_output].
Often set to (0,1,2) to enable aux. heads.