diff --git a/mmseg/models/backbones/fast_scnn.py b/mmseg/models/backbones/fast_scnn.py
index d69bc5bed..522411ac7 100644
--- a/mmseg/models/backbones/fast_scnn.py
+++ b/mmseg/models/backbones/fast_scnn.py
@@ -10,7 +10,7 @@ from ..builder import BACKBONES
class LearningToDownsample(nn.Module):
- """Learning to downsample module"""
+ """Learning to downsample module."""
def __init__(self,
in_channels,
@@ -53,7 +53,7 @@ class LearningToDownsample(nn.Module):
class GlobalFeatureExtractor(nn.Module):
- """Global feature extractor module"""
+ """Global feature extractor module."""
def __init__(self,
in_channels=64,
@@ -115,7 +115,7 @@ class GlobalFeatureExtractor(nn.Module):
class FeatureFusionModule(nn.Module):
- """Feature fusion module"""
+ """Feature fusion module."""
def __init__(self,
higher_in_channels,
@@ -188,7 +188,6 @@ class FastSCNN(nn.Module):
norm_cfg=dict(type='BN'),
act_cfg=dict(type='ReLU'),
align_corners=False):
-
"""Fast-SCNN Backbone.
Args:
in_channels(int): Number of input image channels. Default=3 (RGB)
@@ -196,28 +195,35 @@ class FastSCNN(nn.Module):
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 after the second conv layer in LTD.
+ 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 Feature Extractor(GFE).
+ 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 the output channels for
- each of the MobileNet-v2 bottleneck residual blocks in GFE.
+ 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.
- higher_in_channels(int): Number of input channels of the higher resolution branch in FFM.
+ 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 resolution branch in FFM.
+ 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.
- scale_factor(int): The upsampling factor of the higher resolution branch in FFM.
+ 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 [higher_res_features, lower_res_features, fusion_output].
+ 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.
conv_cfg (dict|None): Config of conv layers.
@@ -228,11 +234,14 @@ class FastSCNN(nn.Module):
super(FastSCNN, self).__init__()
if global_in_channels != higher_in_channels:
- raise AssertionError('Global Input Channels must be the same with Higher Input Channels!')
+ raise AssertionError('Global Input Channels must be the same \
+ with Higher Input Channels!')
elif global_out_channels != lower_in_channels:
- raise AssertionError('Global Output Channels must be the same with Lower Input Channels!')
+ raise AssertionError('Global Output Channels must be the same \
+ with Lower Input Channels!')
if scale_factor != 4:
- raise AssertionError('Scale-factor must compensate the downsampling factor in the GFE module!')
+ raise AssertionError('Scale-factor must compensate the \
+ downsampling factor in the GFE module!')
self.in_channels = in_channels
self.downsample_dw_channels1 = downsample_dw_channels1
diff --git a/mmseg/models/decode_heads/sep_fcn_head.py b/mmseg/models/decode_heads/sep_fcn_head.py
index d93c246f7..c7030b2cb 100644
--- a/mmseg/models/decode_heads/sep_fcn_head.py
+++ b/mmseg/models/decode_heads/sep_fcn_head.py
@@ -5,7 +5,8 @@ from .fcn_head import FCNHead
@HEADS.register_module()
class SepFCNHead(FCNHead):
- """Depthwise-Separable Fully Convolutional Network for Semantic Segmentation
+ """Depthwise-Separable Fully Convolutional Network for Semantic
+ Segmentation.
This head is implemented according to Fast-SCNN.
Args:
@@ -16,7 +17,8 @@ class SepFCNHead(FCNHead):
concat_input(bool): Whether to concatenate original decode input into
the result of consecutive convolution layers.
- num_classes(int): Used to determine the dimension of final prediction tensor.
+ num_classes(int): Used to determine the dimension of
+ final prediction tensor.
in_index(int): Correspond with 'out_indices' in FastSCNN backbone.
@@ -24,7 +26,8 @@ class SepFCNHead(FCNHead):
align_corners (bool): align_corners argument of F.interpolate.
- loss_decode(dict): Config of loss type and some relevant additional options.
+ loss_decode(dict): Config of loss type and some
+ relevant additional options.
"""
def __init__(self, **kwargs):
diff --git a/tests/test_models/test_backbone.py b/tests/test_models/test_backbone.py
index 282550179..c030464f0 100644
--- a/tests/test_models/test_backbone.py
+++ b/tests/test_models/test_backbone.py
@@ -4,7 +4,7 @@ from mmcv.ops import DeformConv2dPack
from mmcv.utils.parrots_wrapper import _BatchNorm
from torch.nn.modules import AvgPool2d, GroupNorm
-from mmseg.models.backbones import ResNet, ResNetV1d, ResNeXt, FastSCNN
+from mmseg.models.backbones import FastSCNN, ResNet, ResNetV1d, ResNeXt
from mmseg.models.backbones.resnet import BasicBlock, Bottleneck
from mmseg.models.backbones.resnext import Bottleneck as BottleneckX
from mmseg.models.utils import ResLayer
@@ -680,11 +680,9 @@ def test_fastscnn_backbone():
feat = model(imgs)
assert len(feat) == 3
- assert feat[0].shape == torch.Size([num_batch_picts, 64, 128, 256]) # higher-res
- assert feat[1].shape == torch.Size([num_batch_picts, 128, 32, 64]) # lower-res
- assert feat[2].shape == torch.Size([num_batch_picts, 128, 128, 256]) # FFM output
-
-
-
-
-
+ # higher-res
+ assert feat[0].shape == torch.Size([num_batch_picts, 64, 128, 256])
+ # lower-res
+ assert feat[1].shape == torch.Size([num_batch_picts, 128, 32, 64])
+ # FFM output
+ assert feat[2].shape == torch.Size([num_batch_picts, 128, 128, 256])