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Rename cs2->cs3 for darknets. Fix features_only for cs3 darknets.

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pull/1327/head
Ross Wightman 3 years ago
parent d765305821
commit d0c5bd5722
1 changed files with 32 additions and 30 deletions
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60
timm/models/cspnet.py
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@ -58,13 +58,13 @@ default_cfgs = {
), ),
'darknetaa53': _cfg(url=''), 'darknetaa53': _cfg(url=''),
'cs2darknet_m': _cfg( 'cs3darknet_m': _cfg(
url=''), url=''),
'cs2darknet_l': _cfg( 'cs3darknet_l': _cfg(
url=''), url=''),
'cs2darknet_f_m': _cfg( 'cs3darknet_focus_m': _cfg(
url=''), url=''),
'cs2darknet_f_l': _cfg( 'cs3darknet_focus_l': _cfg(
url=''), url=''),
} }
@ -185,7 +185,7 @@ model_cfgs = dict(
), ),
), ),
cs2darknet_m=dict( cs3darknet_m=dict(
stem=dict(out_chs=(24, 48), kernel_size=3, stride=2, pool=''), stem=dict(out_chs=(24, 48), kernel_size=3, stride=2, pool=''),
stage=dict( stage=dict(
out_chs=(96, 192, 384, 768), out_chs=(96, 192, 384, 768),
@ -196,12 +196,11 @@ model_cfgs = dict(
avg_down=False, avg_down=False,
), ),
), ),
cs3darknet_l=dict(
cs2darknet_f_m=dict( stem=dict(out_chs=(32, 64), kernel_size=3, stride=2, pool=''),
stem=dict(out_chs=48, kernel_size=6, stride=2, padding=2, pool=''),
stage=dict( stage=dict(
out_chs=(96, 192, 384, 768), out_chs=(128, 256, 512, 1024),
depth=(2, 4, 6, 2), depth=(3, 6, 9, 3),
stride=(2,) * 4, stride=(2,) * 4,
bottle_ratio=(1.,) * 4, bottle_ratio=(1.,) * 4,
block_ratio=(0.5,) * 4, block_ratio=(0.5,) * 4,
@ -209,19 +208,18 @@ model_cfgs = dict(
), ),
), ),
cs2darknet_l=dict( cs3darknet_focus_m=dict(
stem=dict(out_chs=(32, 64), kernel_size=3, stride=2, pool=''), stem=dict(out_chs=48, kernel_size=6, stride=2, padding=2, pool=''),
stage=dict( stage=dict(
out_chs=(128, 256, 512, 1024), out_chs=(96, 192, 384, 768),
depth=(3, 6, 9, 3), depth=(2, 4, 6, 2),
stride=(2,) * 4, stride=(2,) * 4,
bottle_ratio=(1.,) * 4, bottle_ratio=(1.,) * 4,
block_ratio=(0.5,) * 4, block_ratio=(0.5,) * 4,
avg_down=False, avg_down=False,
), ),
), ),
cs3darknet_focus_l=dict(
cs2darknet_f_l=dict(
stem=dict(out_chs=64, kernel_size=6, stride=2, padding=2, pool=''), stem=dict(out_chs=64, kernel_size=6, stride=2, padding=2, pool=''),
stage=dict( stage=dict(
out_chs=(128, 256, 512, 1024), out_chs=(128, 256, 512, 1024),
@ -438,9 +436,9 @@ class CrossStage(nn.Module):
return out return out
class CrossStage2(nn.Module): class CrossStage3(nn.Module):
"""Cross Stage v2. """Cross Stage 3.
Similar to CrossStage, but with one transition conv for the concat output. Similar to CrossStage, but with only one transition conv for the output.
""" """
def __init__( def __init__(
self, self,
@ -461,7 +459,7 @@ class CrossStage2(nn.Module):
block_fn=ResBottleneck, block_fn=ResBottleneck,
**block_kwargs **block_kwargs
): ):
super(CrossStage2, self).__init__() super(CrossStage3, self).__init__()
first_dilation = first_dilation or dilation first_dilation = first_dilation or dilation
down_chs = out_chs if down_growth else in_chs # grow downsample channels to output channels down_chs = out_chs if down_growth else in_chs # grow downsample channels to output channels
self.exp_chs = exp_chs = int(round(out_chs * exp_ratio)) self.exp_chs = exp_chs = int(round(out_chs * exp_ratio))
@ -696,8 +694,12 @@ def _init_weights(module, name, zero_init_last=False):
def _create_cspnet(variant, pretrained=False, **kwargs): def _create_cspnet(variant, pretrained=False, **kwargs):
if variant.startswith('darknet') or variant.startswith('cspdarknet'):
# NOTE: DarkNet is one of few models with stride==1 features w/ 6 out_indices [0..5] # NOTE: DarkNet is one of few models with stride==1 features w/ 6 out_indices [0..5]
out_indices = kwargs.pop('out_indices', (0, 1, 2, 3, 4, 5) if 'darknet' in variant else (0, 1, 2, 3, 4)) default_out_indices = (0, 1, 2, 3, 4, 5)
else:
default_out_indices = (0, 1, 2, 3, 4)
out_indices = kwargs.pop('out_indices', default_out_indices)
return build_model_with_cfg( return build_model_with_cfg(
CspNet, variant, pretrained, CspNet, variant, pretrained,
model_cfg=model_cfgs[variant], model_cfg=model_cfgs[variant],
@ -757,24 +759,24 @@ def darknetaa53(pretrained=False, **kwargs):
@register_model @register_model
def cs2darknet_m(pretrained=False, **kwargs): def cs3darknet_m(pretrained=False, **kwargs):
return _create_cspnet( return _create_cspnet(
'cs2darknet_m', pretrained=pretrained, block_fn=DarkBlock, stage_fn=CrossStage2, act_layer='silu', **kwargs) 'cs3darknet_m', pretrained=pretrained, block_fn=DarkBlock, stage_fn=CrossStage3, act_layer='silu', **kwargs)
@register_model @register_model
def cs2darknet_l(pretrained=False, **kwargs): def cs3darknet_l(pretrained=False, **kwargs):
return _create_cspnet( return _create_cspnet(
'cs2darknet_l', pretrained=pretrained, block_fn=DarkBlock, stage_fn=CrossStage2, act_layer='silu', **kwargs) 'cs3darknet_l', pretrained=pretrained, block_fn=DarkBlock, stage_fn=CrossStage3, act_layer='silu', **kwargs)
@register_model @register_model
def cs2darknet_f_m(pretrained=False, **kwargs): def cs3darknet_focus_m(pretrained=False, **kwargs):
return _create_cspnet( return _create_cspnet(
'cs2darknet_f_m', pretrained=pretrained, block_fn=DarkBlock, stage_fn=CrossStage2, act_layer='silu', **kwargs) 'cs3darknet_focus_m', pretrained=pretrained, block_fn=DarkBlock, stage_fn=CrossStage3, act_layer='silu', **kwargs)
@register_model @register_model
def cs2darknet_f_l(pretrained=False, **kwargs): def cs3darknet_focus_l(pretrained=False, **kwargs):
return _create_cspnet( return _create_cspnet(
'cs2darknet_f_l', pretrained=pretrained, block_fn=DarkBlock, stage_fn=CrossStage2, act_layer='silu', **kwargs) 'cs3darknet_focus_l', pretrained=pretrained, block_fn=DarkBlock, stage_fn=CrossStage3, act_layer='silu', **kwargs)
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