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Merge pull request #875 from alexander-soare/effnets-norm-layer

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make it possible to provide norm_layer via create_model
pull/882/head
Ross Wightman 3 years ago committed by GitHub
parent a6e8598aaf 6bbc50beb4
commit 3d9c23af87
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GPG Key ID: 4AEE18F83AFDEB23
1 changed files with 17 additions and 17 deletions
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timm/models/efficientnet.py
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@ -600,7 +600,7 @@ def _gen_mnasnet_a1(variant, channel_multiplier=1.0, pretrained=False, **kwargs)
block_args=decode_arch_def(arch_def),
stem_size=32,
round_chs_fn=partial(round_channels, multiplier=channel_multiplier),
norm_layer=partial(nn.BatchNorm2d, **resolve_bn_args(kwargs)),
norm_layer=kwargs.pop('norm_layer', None) or partial(nn.BatchNorm2d, **resolve_bn_args(kwargs)),
**kwargs
)
model = _create_effnet(variant, pretrained, **model_kwargs)
@ -636,7 +636,7 @@ def _gen_mnasnet_b1(variant, channel_multiplier=1.0, pretrained=False, **kwargs)
block_args=decode_arch_def(arch_def),
stem_size=32,
round_chs_fn=partial(round_channels, multiplier=channel_multiplier),
norm_layer=partial(nn.BatchNorm2d, **resolve_bn_args(kwargs)),
norm_layer=kwargs.pop('norm_layer', None) or partial(nn.BatchNorm2d, **resolve_bn_args(kwargs)),
**kwargs
)
model = _create_effnet(variant, pretrained, **model_kwargs)
@ -665,7 +665,7 @@ def _gen_mnasnet_small(variant, channel_multiplier=1.0, pretrained=False, **kwar
block_args=decode_arch_def(arch_def),
stem_size=8,
round_chs_fn=partial(round_channels, multiplier=channel_multiplier),
norm_layer=partial(nn.BatchNorm2d, **resolve_bn_args(kwargs)),
norm_layer=kwargs.pop('norm_layer', None) or partial(nn.BatchNorm2d, **resolve_bn_args(kwargs)),
**kwargs
)
model = _create_effnet(variant, pretrained, **model_kwargs)
@ -694,7 +694,7 @@ def _gen_mobilenet_v2(
stem_size=32,
fix_stem=fix_stem_head,
round_chs_fn=round_chs_fn,
norm_layer=partial(nn.BatchNorm2d, **resolve_bn_args(kwargs)),
norm_layer=kwargs.pop('norm_layer', None) or partial(nn.BatchNorm2d, **resolve_bn_args(kwargs)),
act_layer=resolve_act_layer(kwargs, 'relu6'),
**kwargs
)
@ -725,7 +725,7 @@ def _gen_fbnetc(variant, channel_multiplier=1.0, pretrained=False, **kwargs):
stem_size=16,
num_features=1984, # paper suggests this, but is not 100% clear
round_chs_fn=partial(round_channels, multiplier=channel_multiplier),
norm_layer=partial(nn.BatchNorm2d, **resolve_bn_args(kwargs)),
norm_layer=kwargs.pop('norm_layer', None) or partial(nn.BatchNorm2d, **resolve_bn_args(kwargs)),
**kwargs
)
model = _create_effnet(variant, pretrained, **model_kwargs)
@ -760,7 +760,7 @@ def _gen_spnasnet(variant, channel_multiplier=1.0, pretrained=False, **kwargs):
block_args=decode_arch_def(arch_def),
stem_size=32,
round_chs_fn=partial(round_channels, multiplier=channel_multiplier),
norm_layer=partial(nn.BatchNorm2d, **resolve_bn_args(kwargs)),
norm_layer=kwargs.pop('norm_layer', None) or partial(nn.BatchNorm2d, **resolve_bn_args(kwargs)),
**kwargs
)
model = _create_effnet(variant, pretrained, **model_kwargs)
@ -807,7 +807,7 @@ def _gen_efficientnet(variant, channel_multiplier=1.0, depth_multiplier=1.0, pre
stem_size=32,
round_chs_fn=round_chs_fn,
act_layer=resolve_act_layer(kwargs, 'swish'),
norm_layer=partial(nn.BatchNorm2d, **resolve_bn_args(kwargs)),
norm_layer=kwargs.pop('norm_layer', None) or partial(nn.BatchNorm2d, **resolve_bn_args(kwargs)),
**kwargs,
)
model = _create_effnet(variant, pretrained, **model_kwargs)
@ -836,7 +836,7 @@ def _gen_efficientnet_edge(variant, channel_multiplier=1.0, depth_multiplier=1.0
num_features=round_chs_fn(1280),
stem_size=32,
round_chs_fn=round_chs_fn,
norm_layer=partial(nn.BatchNorm2d, **resolve_bn_args(kwargs)),
norm_layer=kwargs.pop('norm_layer', None) or partial(nn.BatchNorm2d, **resolve_bn_args(kwargs)),
act_layer=resolve_act_layer(kwargs, 'relu'),
**kwargs,
)
@ -867,7 +867,7 @@ def _gen_efficientnet_condconv(
num_features=round_chs_fn(1280),
stem_size=32,
round_chs_fn=round_chs_fn,
norm_layer=partial(nn.BatchNorm2d, **resolve_bn_args(kwargs)),
norm_layer=kwargs.pop('norm_layer', None) or partial(nn.BatchNorm2d, **resolve_bn_args(kwargs)),
act_layer=resolve_act_layer(kwargs, 'swish'),
**kwargs,
)
@ -909,7 +909,7 @@ def _gen_efficientnet_lite(variant, channel_multiplier=1.0, depth_multiplier=1.0
fix_stem=True,
round_chs_fn=partial(round_channels, multiplier=channel_multiplier),
act_layer=resolve_act_layer(kwargs, 'relu6'),
norm_layer=partial(nn.BatchNorm2d, **resolve_bn_args(kwargs)),
norm_layer=kwargs.pop('norm_layer', None) or partial(nn.BatchNorm2d, **resolve_bn_args(kwargs)),
**kwargs,
)
model = _create_effnet(variant, pretrained, **model_kwargs)
@ -937,7 +937,7 @@ def _gen_efficientnetv2_base(
num_features=round_chs_fn(1280),
stem_size=32,
round_chs_fn=round_chs_fn,
norm_layer=partial(nn.BatchNorm2d, **resolve_bn_args(kwargs)),
norm_layer=kwargs.pop('norm_layer', None) or partial(nn.BatchNorm2d, **resolve_bn_args(kwargs)),
act_layer=resolve_act_layer(kwargs, 'silu'),
**kwargs,
)
@ -976,7 +976,7 @@ def _gen_efficientnetv2_s(
num_features=round_chs_fn(num_features),
stem_size=24,
round_chs_fn=round_chs_fn,
norm_layer=partial(nn.BatchNorm2d, **resolve_bn_args(kwargs)),
norm_layer=kwargs.pop('norm_layer', None) or partial(nn.BatchNorm2d, **resolve_bn_args(kwargs)),
act_layer=resolve_act_layer(kwargs, 'silu'),
**kwargs,
)
@ -1006,7 +1006,7 @@ def _gen_efficientnetv2_m(variant, channel_multiplier=1.0, depth_multiplier=1.0,
num_features=1280,
stem_size=24,
round_chs_fn=partial(round_channels, multiplier=channel_multiplier),
norm_layer=partial(nn.BatchNorm2d, **resolve_bn_args(kwargs)),
norm_layer=kwargs.pop('norm_layer', None) or partial(nn.BatchNorm2d, **resolve_bn_args(kwargs)),
act_layer=resolve_act_layer(kwargs, 'silu'),
**kwargs,
)
@ -1036,7 +1036,7 @@ def _gen_efficientnetv2_l(variant, channel_multiplier=1.0, depth_multiplier=1.0,
num_features=1280,
stem_size=32,
round_chs_fn=partial(round_channels, multiplier=channel_multiplier),
norm_layer=partial(nn.BatchNorm2d, **resolve_bn_args(kwargs)),
norm_layer=kwargs.pop('norm_layer', None) or partial(nn.BatchNorm2d, **resolve_bn_args(kwargs)),
act_layer=resolve_act_layer(kwargs, 'silu'),
**kwargs,
)
@ -1066,7 +1066,7 @@ def _gen_efficientnetv2_xl(variant, channel_multiplier=1.0, depth_multiplier=1.0
num_features=1280,
stem_size=32,
round_chs_fn=partial(round_channels, multiplier=channel_multiplier),
norm_layer=partial(nn.BatchNorm2d, **resolve_bn_args(kwargs)),
norm_layer=kwargs.pop('norm_layer', None) or partial(nn.BatchNorm2d, **resolve_bn_args(kwargs)),
act_layer=resolve_act_layer(kwargs, 'silu'),
**kwargs,
)
@ -1100,7 +1100,7 @@ def _gen_mixnet_s(variant, channel_multiplier=1.0, pretrained=False, **kwargs):
num_features=1536,
stem_size=16,
round_chs_fn=partial(round_channels, multiplier=channel_multiplier),
norm_layer=partial(nn.BatchNorm2d, **resolve_bn_args(kwargs)),
norm_layer=kwargs.pop('norm_layer', None) or partial(nn.BatchNorm2d, **resolve_bn_args(kwargs)),
**kwargs
)
model = _create_effnet(variant, pretrained, **model_kwargs)
@ -1133,7 +1133,7 @@ def _gen_mixnet_m(variant, channel_multiplier=1.0, depth_multiplier=1.0, pretrai
num_features=1536,
stem_size=24,
round_chs_fn=partial(round_channels, multiplier=channel_multiplier),
norm_layer=partial(nn.BatchNorm2d, **resolve_bn_args(kwargs)),
norm_layer=kwargs.pop('norm_layer', None) or partial(nn.BatchNorm2d, **resolve_bn_args(kwargs)),
**kwargs
)
model = _create_effnet(variant, pretrained, **model_kwargs)

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