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[Fix] fix fast scnn (#606)

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* [Refactor] Match paddle seg weight

* Match inference

* fix exp setting

* delete comment and rename config files

* replace hard code with config parameters

* fix ppm concat order

* remove hardcode

* update result

* fix typo

* complement docstring

* complement FutureFusionModule docstring

* modify log link
This commit is contained in:
谢昕辰 2021-07-02 17:58:35 +08:00 committed by GitHub
parent 7abd73a80f
commit 7e1d853f2b
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7 changed files with 70 additions and 32 deletions
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2
configs/_base_/models/fast_scnn.py
View File

@ -25,7 +25,7 @@ model = dict(
norm_cfg=norm_cfg,
align_corners=False,
loss_decode=dict(
type='CrossEntropyLoss', use_sigmoid=True, loss_weight=0.4)),
type='CrossEntropyLoss', use_sigmoid=True, loss_weight=1)),
auxiliary_head=[
dict(
type='FCNHead',

2
configs/fastscnn/README.md
View File

@ -19,4 +19,4 @@
| Method | Backbone | Crop Size | Lr schd | Mem (GB) | Inf time (fps) | mIoU | mIoU(ms+flip) | config | download |
| --------- | --------- | --------- | ------: | -------- | -------------- | ----: | ------------- | --------------------------------------------------------------------------------------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| Fast-SCNN | Fast-SCNN | 512x1024 | 80000 | 8.4 | 63.61 | 69.06 | - | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/fast_scnn.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/fast_scnn/fast_scnn_4x8_80k_lr0.12_cityscapes-f5096c79.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/fast_scnn/fast_scnn_4x8_80k_lr0.12_cityscapes-20200807_165744.log.json) |
| Fast-SCNN | Fast-SCNN | 512x1024 | 160000 | 3.3 | 56.45 | 70.96 | 72.65 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/fast_scnn.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/fast_scnn/fast_scnn_8x4_160k_lr0.12_cityscapes-0cec9937.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/fast_scnn/fast_scnn_8x4_160k_lr0.12_cityscapes-20210630_164853.log.json) |

4
configs/fastscnn/fast_scnn_4x8_80k_lr0.12_cityscapes.py → configs/fastscnn/fast_scnn_lr0.12_8x4_160k_cityscapes.py
View File

@ -1,10 +1,10 @@
_base_ = [
'../_base_/models/fast_scnn.py', '../_base_/datasets/cityscapes.py',
'../_base_/default_runtime.py', '../_base_/schedules/schedule_80k.py'
'../_base_/default_runtime.py', '../_base_/schedules/schedule_160k.py'
]
# Re-config the data sampler.
data = dict(samples_per_gpu=2, workers_per_gpu=4)
data = dict(samples_per_gpu=4, workers_per_gpu=4)
# Re-config the optimizer.
optimizer = dict(type='SGD', lr=0.12, momentum=0.9, weight_decay=4e-5)

63
mmseg/models/backbones/fast_scnn.py
View File

@ -6,7 +6,7 @@ from mmcv.runner import BaseModule
from mmseg.models.decode_heads.psp_head import PPM
from mmseg.ops import resize
from ..builder import BACKBONES
from ..utils.inverted_residual import InvertedResidual
from ..utils import InvertedResidual
class LearningToDownsample(nn.Module):
@ -23,6 +23,9 @@ class LearningToDownsample(nn.Module):
dict(type='BN')
act_cfg (dict): Config of activation layers. Default:
dict(type='ReLU')
dw_act_cfg (dict): In DepthwiseSeparableConvModule, activation config
of depthwise ConvModule. If it is 'default', it will be the same
as `act_cfg`. Default: None.
"""
def __init__(self,
@ -31,11 +34,13 @@ class LearningToDownsample(nn.Module):
out_channels,
conv_cfg=None,
norm_cfg=dict(type='BN'),
act_cfg=dict(type='ReLU')):
act_cfg=dict(type='ReLU'),
dw_act_cfg=None):
super(LearningToDownsample, self).__init__()
self.conv_cfg = conv_cfg
self.norm_cfg = norm_cfg
self.act_cfg = act_cfg
self.dw_act_cfg = dw_act_cfg
dw_channels1 = dw_channels[0]
dw_channels2 = dw_channels[1]
@ -44,23 +49,28 @@ class LearningToDownsample(nn.Module):
dw_channels1,
3,
stride=2,
padding=1,
conv_cfg=self.conv_cfg,
norm_cfg=self.norm_cfg,
act_cfg=self.act_cfg)
self.dsconv1 = DepthwiseSeparableConvModule(
dw_channels1,
dw_channels2,
kernel_size=3,
stride=2,
padding=1,
norm_cfg=self.norm_cfg)
norm_cfg=self.norm_cfg,
dw_act_cfg=self.dw_act_cfg)
self.dsconv2 = DepthwiseSeparableConvModule(
dw_channels2,
out_channels,
kernel_size=3,
stride=2,
padding=1,
norm_cfg=self.norm_cfg)
norm_cfg=self.norm_cfg,
dw_act_cfg=self.dw_act_cfg)
def forward(self, x):
x = self.conv(x)
@ -136,10 +146,12 @@ class GlobalFeatureExtractor(nn.Module):
norm_cfg=self.norm_cfg,
act_cfg=self.act_cfg,
align_corners=align_corners)
self.out = ConvModule(
block_channels[2] * 2,
out_channels,
1,
3,
padding=1,
conv_cfg=self.conv_cfg,
norm_cfg=self.norm_cfg,
act_cfg=self.act_cfg)
@ -156,7 +168,8 @@ class GlobalFeatureExtractor(nn.Module):
out_channels,
stride,
expand_ratio,
norm_cfg=self.norm_cfg)
norm_cfg=self.norm_cfg,
act_cfg=self.act_cfg)
]
for i in range(1, blocks):
layers.append(
@ -165,7 +178,8 @@ class GlobalFeatureExtractor(nn.Module):
out_channels,
1,
expand_ratio,
norm_cfg=self.norm_cfg))
norm_cfg=self.norm_cfg,
act_cfg=self.act_cfg))
return nn.Sequential(*layers)
def forward(self, x):
@ -189,10 +203,12 @@ class FeatureFusionModule(nn.Module):
conv_cfg (dict | None): Config of conv layers. Default: None
norm_cfg (dict | None): Config of norm layers. Default:
dict(type='BN')
act_cfg (dict): Config of activation layers. Default:
dict(type='ReLU')
dwconv_act_cfg (dict): Config of activation layers in 3x3 conv.
Default: dict(type='ReLU').
conv_act_cfg (dict): Config of activation layers in the two 1x1 conv.
Default: None.
align_corners (bool): align_corners argument of F.interpolate.
Default: False
Default: False.
"""
def __init__(self,
@ -201,34 +217,40 @@ class FeatureFusionModule(nn.Module):
out_channels,
conv_cfg=None,
norm_cfg=dict(type='BN'),
act_cfg=dict(type='ReLU'),
dwconv_act_cfg=dict(type='ReLU'),
conv_act_cfg=None,
align_corners=False):
super(FeatureFusionModule, self).__init__()
self.conv_cfg = conv_cfg
self.norm_cfg = norm_cfg
self.act_cfg = act_cfg
self.dwconv_act_cfg = dwconv_act_cfg
self.conv_act_cfg = conv_act_cfg
self.align_corners = align_corners
self.dwconv = ConvModule(
lower_in_channels,
out_channels,
1,
3,
padding=1,
groups=out_channels,
conv_cfg=self.conv_cfg,
norm_cfg=self.norm_cfg,
act_cfg=self.act_cfg)
act_cfg=self.dwconv_act_cfg)
self.conv_lower_res = ConvModule(
out_channels,
out_channels,
1,
conv_cfg=self.conv_cfg,
norm_cfg=self.norm_cfg,
act_cfg=None)
act_cfg=self.conv_act_cfg)
self.conv_higher_res = ConvModule(
higher_in_channels,
out_channels,
1,
conv_cfg=self.conv_cfg,
norm_cfg=self.norm_cfg,
act_cfg=None)
act_cfg=self.conv_act_cfg)
self.relu = nn.ReLU(True)
def forward(self, higher_res_feature, lower_res_feature):
@ -290,6 +312,9 @@ class FastSCNN(BaseModule):
dict(type='ReLU')
align_corners (bool): align_corners argument of F.interpolate.
Default: False
dw_act_cfg (dict): In DepthwiseSeparableConvModule, activation config
of depthwise ConvModule. If it is 'default', it will be the same
as `act_cfg`. Default: None.
init_cfg (dict or list[dict], optional): Initialization config dict.
Default: None
"""
@ -309,6 +334,7 @@ class FastSCNN(BaseModule):
norm_cfg=dict(type='BN'),
act_cfg=dict(type='ReLU'),
align_corners=False,
dw_act_cfg=None,
init_cfg=None):
super(FastSCNN, self).__init__(init_cfg)
@ -348,7 +374,8 @@ class FastSCNN(BaseModule):
global_in_channels,
conv_cfg=self.conv_cfg,
norm_cfg=self.norm_cfg,
act_cfg=self.act_cfg)
act_cfg=self.act_cfg,
dw_act_cfg=dw_act_cfg)
self.global_feature_extractor = GlobalFeatureExtractor(
global_in_channels,
global_block_channels,
@ -364,7 +391,7 @@ class FastSCNN(BaseModule):
fusion_out_channels,
conv_cfg=self.conv_cfg,
norm_cfg=self.norm_cfg,
act_cfg=self.act_cfg,
dwconv_act_cfg=self.act_cfg,
align_corners=self.align_corners)
def forward(self, x):

5
mmseg/models/decode_heads/psp_head.py
View File

@ -22,7 +22,7 @@ class PPM(nn.ModuleList):
"""
def __init__(self, pool_scales, in_channels, channels, conv_cfg, norm_cfg,
act_cfg, align_corners):
act_cfg, align_corners, **kwargs):
super(PPM, self).__init__()
self.pool_scales = pool_scales
self.align_corners = align_corners
@ -41,7 +41,8 @@ class PPM(nn.ModuleList):
1,
conv_cfg=self.conv_cfg,
norm_cfg=self.norm_cfg,
act_cfg=self.act_cfg)))
act_cfg=self.act_cfg,
**kwargs)))
def forward(self, x):
"""Forward function."""

14
mmseg/models/decode_heads/sep_fcn_head.py
View File

@ -24,23 +24,28 @@ class DepthwiseSeparableFCNHead(FCNHead):
Default: False.
loss_decode(dict): Config of loss type and some
relevant additional options.
dw_act_cfg (dict):Activation config of depthwise ConvModule. If it is
'default', it will be the same as `act_cfg`. Default: None.
"""
def __init__(self, **kwargs):
def __init__(self, dw_act_cfg=None, **kwargs):
super(DepthwiseSeparableFCNHead, self).__init__(**kwargs)
self.convs[0] = DepthwiseSeparableConvModule(
self.in_channels,
self.channels,
kernel_size=self.kernel_size,
padding=self.kernel_size // 2,
norm_cfg=self.norm_cfg)
norm_cfg=self.norm_cfg,
dw_act_cfg=dw_act_cfg)
for i in range(1, self.num_convs):
self.convs[i] = DepthwiseSeparableConvModule(
self.channels,
self.channels,
kernel_size=self.kernel_size,
padding=self.kernel_size // 2,
norm_cfg=self.norm_cfg)
norm_cfg=self.norm_cfg,
dw_act_cfg=dw_act_cfg)
if self.concat_input:
self.conv_cat = DepthwiseSeparableConvModule(
@ -48,4 +53,5 @@ class DepthwiseSeparableFCNHead(FCNHead):
self.channels,
kernel_size=self.kernel_size,
padding=self.kernel_size // 2,
norm_cfg=self.norm_cfg)
norm_cfg=self.norm_cfg,
dw_act_cfg=dw_act_cfg)

12
mmseg/models/utils/inverted_residual.py
View File

@ -37,7 +37,8 @@ class InvertedResidual(nn.Module):
conv_cfg=None,
norm_cfg=dict(type='BN'),
act_cfg=dict(type='ReLU6'),
with_cp=False):
with_cp=False,
**kwargs):
super(InvertedResidual, self).__init__()
self.stride = stride
assert stride in [1, 2], f'stride must in [1, 2]. ' \
@ -55,7 +56,8 @@ class InvertedResidual(nn.Module):
kernel_size=1,
conv_cfg=conv_cfg,
norm_cfg=norm_cfg,
act_cfg=act_cfg))
act_cfg=act_cfg,
**kwargs))
layers.extend([
ConvModule(
in_channels=hidden_dim,
@ -67,14 +69,16 @@ class InvertedResidual(nn.Module):
groups=hidden_dim,
conv_cfg=conv_cfg,
norm_cfg=norm_cfg,
act_cfg=act_cfg),
act_cfg=act_cfg,
**kwargs),
ConvModule(
in_channels=hidden_dim,
out_channels=out_channels,
kernel_size=1,
conv_cfg=conv_cfg,
norm_cfg=norm_cfg,
act_cfg=None)
act_cfg=None,
**kwargs)
])
self.conv = nn.Sequential(*layers)