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Add experimental RegNetZ(ish) models for training / perf trials.

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Ross Wightman 2021-09-23 15:42:57 -07:00
parent 3d9c23af87
commit 8e11da0ce3
1 changed files with 77 additions and 0 deletions
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77
timm/models/byobnet.py
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@ -137,6 +137,17 @@ default_cfgs = {
'gcresnext50ts': _cfg(
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-attn-weights/gcresnext50ts_256-3e0f515e.pth',
first_conv='stem.conv1.conv', input_size=(3, 256, 256), pool_size=(8, 8), interpolation='bicubic'),
# experimental models
'regnetz_b': _cfg(
url='',
input_size=(3, 256, 256), pool_size=(8, 8), interpolation='bicubic'),
'regnetz_c': _cfg(
url='',
input_size=(3, 256, 256), pool_size=(8, 8), interpolation='bicubic'),
'regnetz_d': _cfg(
url='',
input_size=(3, 256, 256), pool_size=(8, 8), interpolation='bicubic'),
}
@ -489,6 +500,51 @@ model_cfgs = dict(
act_layer='silu',
attn_layer='gca',
),
# experimental models, closer to a RegNetZ than a ResNet. Similar to EfficientNets but w/ groups instead of DW
regnetz_b=ByoModelCfg(
blocks=(
ByoBlockCfg(type='bottle', d=2, c=192, s=2, gs=24, br=0.25, block_kwargs=dict(linear_out=True)),
ByoBlockCfg(type='bottle', d=6, c=384, s=2, gs=24, br=0.25, block_kwargs=dict(linear_out=True)),
ByoBlockCfg(type='bottle', d=12, c=768, s=2, gs=24, br=0.25, block_kwargs=dict(linear_out=True)),
ByoBlockCfg(type='bottle', d=2, c=1536, s=2, gs=24, br=0.25, block_kwargs=dict(linear_out=True)),
),
stem_chs=32,
stem_pool='',
num_features=1792,
act_layer='silu',
attn_layer='se',
attn_kwargs=dict(rd_ratio=0.25),
),
regnetz_c=ByoModelCfg(
blocks=(
ByoBlockCfg(type='bottle', d=2, c=128, s=2, gs=16, br=0.5, block_kwargs=dict(linear_out=True)),
ByoBlockCfg(type='bottle', d=6, c=512, s=2, gs=32, br=0.25, block_kwargs=dict(linear_out=True)),
ByoBlockCfg(type='bottle', d=12, c=768, s=2, gs=32, br=0.25, block_kwargs=dict(linear_out=True)),
ByoBlockCfg(type='bottle', d=2, c=1536, s=2, gs=64, br=0.25, block_kwargs=dict(linear_out=True)),
),
stem_chs=32,
stem_pool='',
num_features=1792,
act_layer='silu',
attn_layer='se',
attn_kwargs=dict(rd_ratio=0.25),
),
regnetz_d=ByoModelCfg(
blocks=(
ByoBlockCfg(type='bottle', d=3, c=256, s=1, gs=64, br=0.25, block_kwargs=dict(linear_out=True)),
ByoBlockCfg(type='bottle', d=6, c=512, s=2, gs=64, br=0.25, block_kwargs=dict(linear_out=True)),
ByoBlockCfg(type='bottle', d=12, c=768, s=2, gs=64, br=0.25, block_kwargs=dict(linear_out=True)),
ByoBlockCfg(type='bottle', d=3, c=1536, s=2, gs=64, br=0.25, block_kwargs=dict(linear_out=True)),
),
stem_chs=128,
stem_type='quad',
stem_pool='',
num_features=1792,
act_layer='silu',
attn_layer='se',
attn_kwargs=dict(rd_ratio=0.25),
),
)
@ -678,6 +734,27 @@ def gcresnext50ts(pretrained=False, **kwargs):
return _create_byobnet('gcresnext50ts', pretrained=pretrained, **kwargs)
@register_model
def regnetz_b(pretrained=False, **kwargs):
"""
"""
return _create_byobnet('regnetz_b', pretrained=pretrained, **kwargs)
@register_model
def regnetz_c(pretrained=False, **kwargs):
"""
"""
return _create_byobnet('regnetz_c', pretrained=pretrained, **kwargs)
@register_model
def regnetz_d(pretrained=False, **kwargs):
"""
"""
return _create_byobnet('regnetz_d', pretrained=pretrained, **kwargs)
def expand_blocks_cfg(stage_blocks_cfg: Union[ByoBlockCfg, Sequence[ByoBlockCfg]]) -> List[ByoBlockCfg]:
if not isinstance(stage_blocks_cfg, Sequence):
stage_blocks_cfg = (stage_blocks_cfg,)