Fix fastscnn resize problems.

configs/fastscnn/fast_scnn_4x8_80k_lr0.12_pascal.py

@@ -0,0 +1,70 @@
+_base_ = [
+ '../_base_/models/fast_scnn.py', '../_base_/datasets/pascal_voc12.py',
+ '../_base_/default_runtime.py', '../_base_/schedules/schedule_80k.py'
+]
+
+# Re-config the data sampler.
+data = dict(samples_per_gpu=8, workers_per_gpu=4)
+
+# Re-config the optimizer.
+optimizer = dict(type='SGD', lr=0.12, momentum=0.9, weight_decay=4e-5)
+
+# update num_classes of the segmentor.
+# model settings
+norm_cfg = dict(type='SyncBN', requires_grad=True, momentum=0.01)
+model = dict(
+ type='EncoderDecoder',
+ backbone=dict(
+ type='FastSCNN',
+ downsample_dw_channels=(32, 48),
+ global_in_channels=64,
+ global_block_channels=(64, 96, 128),
+ global_block_strides=(2, 2, 1),
+ global_out_channels=128,
+ higher_in_channels=64,
+ lower_in_channels=128,
+ fusion_out_channels=128,
+ out_indices=(0, 1, 2),
+ norm_cfg=norm_cfg,
+ align_corners=False),
+ decode_head=dict(
+ type='DepthwiseSeparableFCNHead',
+ in_channels=128,
+ channels=128,
+ concat_input=False,
+ num_classes=21,
+ in_index=-1,
+ norm_cfg=norm_cfg,
+ align_corners=False,
+ loss_decode=dict(
+ type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.)),
+ auxiliary_head=[
+ dict(
+ type='FCNHead',
+ in_channels=128,
+ channels=32,
+ num_convs=1,
+ num_classes=21,
+ in_index=-2,
+ norm_cfg=norm_cfg,
+ concat_input=False,
+ align_corners=False,
+ loss_decode=dict(
+ type='CrossEntropyLoss', use_sigmoid=False, loss_weight=0.4)),
+ dict(
+ type='FCNHead',
+ in_channels=64,
+ channels=32,
+ num_convs=1,
+ num_classes=21,
+ in_index=-3,
+ norm_cfg=norm_cfg,
+ concat_input=False,
+ align_corners=False,
+ loss_decode=dict(
+ type='CrossEntropyLoss', use_sigmoid=False, loss_weight=0.4)),
+ ])
+
+# model training and testing settings
+train_cfg = dict()
+test_cfg = dict(mode='whole')

mmseg/__init__.py

@@ -3,7 +3,7 @@
 from .version import __version__, version_info
 
 MMCV_MIN = '1.0.5'
-MMCV_MAX = '1.0.5'
+MMCV_MAX = '1.1.0'
 
 
 def digit_version(version_str):

mmseg/models/backbones/fast_scnn.py

@@ -186,9 +186,6 @@ class FeatureFusionModule(nn.Module):
  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. Default: None
  norm_cfg (dict | None): Config of norm layers. Default:
  dict(type='BN')
@@ -202,13 +199,11 @@ def __init__(self,
  higher_in_channels,
  lower_in_channels,
  out_channels,
- scale_factor,
  conv_cfg=None,
  norm_cfg=dict(type='BN'),
  act_cfg=dict(type='ReLU'),
  align_corners=False):
  super(FeatureFusionModule, self).__init__()
- self.scale_factor = scale_factor
  self.conv_cfg = conv_cfg
  self.norm_cfg = norm_cfg
  self.act_cfg = act_cfg
@@ -239,7 +234,7 @@ def __init__(self,
  def forward(self, higher_res_feature, lower_res_feature):
  lower_res_feature = resize(
  lower_res_feature,
- scale_factor=self.scale_factor,
+ size=higher_res_feature.size()[2:],
  mode='bilinear',
  align_corners=self.align_corners)
  lower_res_feature = self.dwconv(lower_res_feature)
@@ -321,11 +316,6 @@ def __init__(self,
  raise AssertionError('Global Output Channels must be the same \
  with Lower Input Channels!')
 
- # Calculate scale factor used in FFM.
- self.scale_factor = 1
- for factor in global_block_strides:
- self.scale_factor *= factor
-
  self.in_channels = in_channels
  self.downsample_dw_channels1 = downsample_dw_channels[0]
  self.downsample_dw_channels2 = downsample_dw_channels[1]
@@ -361,7 +351,6 @@ def __init__(self,
  higher_in_channels,
  lower_in_channels,
  fusion_out_channels,
- scale_factor=self.scale_factor,
  conv_cfg=self.conv_cfg,
  norm_cfg=self.norm_cfg,
  act_cfg=self.act_cfg,