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Arg scale_factor deleted.

pull/58/head
johnzja 2020-08-14 12:09:22 +08:00
parent a8a5ff80b3
commit 88a123d16f
3 changed files with 31 additions and 16 deletions
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2
configs/_base_/models/fast_scnn.py
View File

@ -7,11 +7,11 @@ model = dict(
downsample_dw_channels=(32, 48),
global_in_channels=64,
global_block_channels=(64, 96, 128),
global_block_downsample_factors=(2, 2, 1),
global_out_channels=128,
higher_in_channels=64,
lower_in_channels=128,
fusion_out_channels=128,
scale_factor=4,
out_indices=(0, 1, 2),
norm_cfg=norm_cfg,
align_corners=False),

36
mmseg/models/backbones/fast_scnn.py
View File

@ -83,7 +83,11 @@ class GlobalFeatureExtractor(nn.Module):
module. Default: 6
num_blocks (tuple[int]): Tuple of ints. Each int specifies the
number of times each Inverted Residual module is repeated.
The repeated Inverted Residual modules are called a 'group'.
Default: (3, 3, 3)
downsample_factors (tuple[int]): Tuple of ints. Each int specifies
the downsampling factor of each 'group'.
Default: (2, 2, 1)
pool_scales (tuple[int]): Tuple of ints. Each int specifies
the parameter required in 'global average pooling' within PPM.
Default: (1, 2, 3, 6)
@ -102,6 +106,7 @@ class GlobalFeatureExtractor(nn.Module):
out_channels=128,
expand_ratio=6,
num_blocks=(3, 3, 3),
downsample_factors=(2, 2, 1),
pool_scales=(1, 2, 3, 6),
conv_cfg=None,
norm_cfg=dict(type='BN'),
@ -113,13 +118,17 @@ class GlobalFeatureExtractor(nn.Module):
self.act_cfg = act_cfg
assert len(block_channels) == len(num_blocks) == 3
self.bottleneck1 = self._make_layer(in_channels, block_channels[0],
num_blocks[0], 2, expand_ratio)
num_blocks[0],
downsample_factors[0],
expand_ratio)
self.bottleneck2 = self._make_layer(block_channels[0],
block_channels[1], num_blocks[1],
2, expand_ratio)
downsample_factors[1],
expand_ratio)
self.bottleneck3 = self._make_layer(block_channels[1],
block_channels[2], num_blocks[2],
1, expand_ratio)
downsample_factors[2],
expand_ratio)
self.ppm = PPM(
pool_scales,
block_channels[2],
@ -260,6 +269,10 @@ class FastSCNN(nn.Module):
the output channels for each of the MobileNet-v2 bottleneck
residual blocks in GFE.
Default: (64, 96, 128).
global_block_downsample_factors (tuple[int]): Tuple of integers
that describe the downsampling factors for each of the
MobileNet-v2 bottleneck residual blocks in GFE.
Default: (2, 2, 1).
global_out_channels (int): Number of output channels of GFE.
Default: 128.
higher_in_channels (int): Number of input channels of the higher
@ -272,10 +285,6 @@ class FastSCNN(nn.Module):
Default: 128.
fusion_out_channels (int): Number of output channels of FFM.
Default: 128.
scale_factor (int): The upsampling factor of the higher resolution
branch in FFM.
Equal to the downsampling factor in GFE.
Default: 4.
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.
@ -294,11 +303,11 @@ class FastSCNN(nn.Module):
downsample_dw_channels=(32, 48),
global_in_channels=64,
global_block_channels=(64, 96, 128),
global_block_downsample_factors=(2, 2, 1),
global_out_channels=128,
higher_in_channels=64,
lower_in_channels=128,
fusion_out_channels=128,
scale_factor=4,
out_indices=(0, 1, 2),
conv_cfg=None,
norm_cfg=dict(type='BN'),
@ -312,20 +321,22 @@ class FastSCNN(nn.Module):
elif global_out_channels != lower_in_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!')
# Calculate scale factor used in FFM.
self.scale_factor = 1
for factor in global_block_downsample_factors:
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]
self.global_in_channels = global_in_channels
self.global_block_channels = global_block_channels
self.global_block_downsample_factors = global_block_downsample_factors
self.global_out_channels = global_out_channels
self.higher_in_channels = higher_in_channels
self.lower_in_channels = lower_in_channels
self.fusion_out_channels = fusion_out_channels
self.scale_factor = scale_factor
self.out_indices = out_indices
self.conv_cfg = conv_cfg
self.norm_cfg = norm_cfg
@ -342,6 +353,7 @@ class FastSCNN(nn.Module):
global_in_channels,
global_block_channels,
global_out_channels,
downsample_factors=self.global_block_downsample_factors,
conv_cfg=self.conv_cfg,
norm_cfg=self.norm_cfg,
act_cfg=self.act_cfg,

9
tests/test_models/test_backbone.py
View File

@ -47,7 +47,6 @@ def check_norm_state(modules, train_state):
def test_resnet_basic_block():
with pytest.raises(AssertionError):
# Not implemented yet.
dcn = dict(type='DCN', deform_groups=1, fallback_on_stride=False)
@ -97,7 +96,6 @@ def test_resnet_basic_block():
def test_resnet_bottleneck():
with pytest.raises(AssertionError):
# Style must be in ['pytorch', 'caffe']
Bottleneck(64, 64, style='tensorflow')
@ -669,7 +667,12 @@ def test_resnext_backbone():
def test_fastscnn_backbone():
with pytest.raises(AssertionError):
# Fast-SCNN channel constraints.
FastSCNN(3, (32, 48), 64, (64, 96, 128), 127, 64, 128)
FastSCNN(
3, (32, 48),
64, (64, 96, 128), (2, 2, 1),
global_out_channels=127,
higher_in_channels=64,
lower_in_channels=128)
# Test FastSCNN Standard Forward
model = FastSCNN()