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Merge pull request #2264 from huggingface/group_size_eff

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Allow group_size override for more efficientnet and mobilenetv3 based…
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Ross Wightman 2024-08-22 15:24:02 -07:00 committed by GitHub
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2 changed files with 55 additions and 27 deletions
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66
timm/models/efficientnet.py
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@ -488,7 +488,8 @@ def _gen_mnasnet_small(variant, channel_multiplier=1.0, pretrained=False, **kwar
def _gen_mobilenet_v1( def _gen_mobilenet_v1(
variant, channel_multiplier=1.0, depth_multiplier=1.0, variant, channel_multiplier=1.0, depth_multiplier=1.0,
fix_stem_head=False, head_conv=False, pretrained=False, **kwargs): group_size=None, fix_stem_head=False, head_conv=False, pretrained=False, **kwargs
):
""" """
Ref impl: https://github.com/tensorflow/models/blob/master/research/slim/nets/mobilenet/mobilenet_v2.py Ref impl: https://github.com/tensorflow/models/blob/master/research/slim/nets/mobilenet/mobilenet_v2.py
Paper: https://arxiv.org/abs/1801.04381 Paper: https://arxiv.org/abs/1801.04381
@ -503,7 +504,12 @@ def _gen_mobilenet_v1(
round_chs_fn = partial(round_channels, multiplier=channel_multiplier) round_chs_fn = partial(round_channels, multiplier=channel_multiplier)
head_features = (1024 if fix_stem_head else max(1024, round_chs_fn(1024))) if head_conv else 0 head_features = (1024 if fix_stem_head else max(1024, round_chs_fn(1024))) if head_conv else 0
model_kwargs = dict( model_kwargs = dict(
block_args=decode_arch_def(arch_def, depth_multiplier=depth_multiplier, fix_first_last=fix_stem_head), block_args=decode_arch_def(
arch_def,
depth_multiplier=depth_multiplier,
fix_first_last=fix_stem_head,
group_size=group_size,
),
num_features=head_features, num_features=head_features,
stem_size=32, stem_size=32,
fix_stem=fix_stem_head, fix_stem=fix_stem_head,
@ -517,7 +523,9 @@ def _gen_mobilenet_v1(
def _gen_mobilenet_v2( def _gen_mobilenet_v2(
variant, channel_multiplier=1.0, depth_multiplier=1.0, fix_stem_head=False, pretrained=False, **kwargs): variant, channel_multiplier=1.0, depth_multiplier=1.0,
group_size=None, fix_stem_head=False, pretrained=False, **kwargs
):
""" Generate MobileNet-V2 network """ Generate MobileNet-V2 network
Ref impl: https://github.com/tensorflow/models/blob/master/research/slim/nets/mobilenet/mobilenet_v2.py Ref impl: https://github.com/tensorflow/models/blob/master/research/slim/nets/mobilenet/mobilenet_v2.py
Paper: https://arxiv.org/abs/1801.04381 Paper: https://arxiv.org/abs/1801.04381
@ -533,7 +541,12 @@ def _gen_mobilenet_v2(
] ]
round_chs_fn = partial(round_channels, multiplier=channel_multiplier) round_chs_fn = partial(round_channels, multiplier=channel_multiplier)
model_kwargs = dict( model_kwargs = dict(
block_args=decode_arch_def(arch_def, depth_multiplier=depth_multiplier, fix_first_last=fix_stem_head), block_args=decode_arch_def(
arch_def,
depth_multiplier=depth_multiplier,
fix_first_last=fix_stem_head,
group_size=group_size,
),
num_features=1280 if fix_stem_head else max(1280, round_chs_fn(1280)), num_features=1280 if fix_stem_head else max(1280, round_chs_fn(1280)),
stem_size=32, stem_size=32,
fix_stem=fix_stem_head, fix_stem=fix_stem_head,
@ -613,7 +626,8 @@ def _gen_spnasnet(variant, channel_multiplier=1.0, pretrained=False, **kwargs):
def _gen_efficientnet( def _gen_efficientnet(
variant, channel_multiplier=1.0, depth_multiplier=1.0, channel_divisor=8, variant, channel_multiplier=1.0, depth_multiplier=1.0, channel_divisor=8,
group_size=None, pretrained=False, **kwargs): group_size=None, pretrained=False, **kwargs
):
"""Creates an EfficientNet model. """Creates an EfficientNet model.
Ref impl: https://github.com/tensorflow/tpu/blob/master/models/official/efficientnet/efficientnet_model.py Ref impl: https://github.com/tensorflow/tpu/blob/master/models/official/efficientnet/efficientnet_model.py
@ -661,7 +675,8 @@ def _gen_efficientnet(
def _gen_efficientnet_edge( def _gen_efficientnet_edge(
variant, channel_multiplier=1.0, depth_multiplier=1.0, group_size=None, pretrained=False, **kwargs): variant, channel_multiplier=1.0, depth_multiplier=1.0, group_size=None, pretrained=False, **kwargs
):
""" Creates an EfficientNet-EdgeTPU model """ Creates an EfficientNet-EdgeTPU model
Ref impl: https://github.com/tensorflow/tpu/tree/master/models/official/efficientnet/edgetpu Ref impl: https://github.com/tensorflow/tpu/tree/master/models/official/efficientnet/edgetpu
@ -692,7 +707,8 @@ def _gen_efficientnet_edge(
def _gen_efficientnet_condconv( def _gen_efficientnet_condconv(
variant, channel_multiplier=1.0, depth_multiplier=1.0, experts_multiplier=1, pretrained=False, **kwargs): variant, channel_multiplier=1.0, depth_multiplier=1.0, experts_multiplier=1, pretrained=False, **kwargs
):
"""Creates an EfficientNet-CondConv model. """Creates an EfficientNet-CondConv model.
Ref impl: https://github.com/tensorflow/tpu/tree/master/models/official/efficientnet/condconv Ref impl: https://github.com/tensorflow/tpu/tree/master/models/official/efficientnet/condconv
@ -764,7 +780,8 @@ def _gen_efficientnet_lite(variant, channel_multiplier=1.0, depth_multiplier=1.0
def _gen_efficientnetv2_base( def _gen_efficientnetv2_base(
variant, channel_multiplier=1.0, depth_multiplier=1.0, pretrained=False, **kwargs): variant, channel_multiplier=1.0, depth_multiplier=1.0, group_size=None, pretrained=False, **kwargs
):
""" Creates an EfficientNet-V2 base model """ Creates an EfficientNet-V2 base model
Ref impl: https://github.com/google/automl/tree/master/efficientnetv2 Ref impl: https://github.com/google/automl/tree/master/efficientnetv2
@ -780,7 +797,7 @@ def _gen_efficientnetv2_base(
] ]
round_chs_fn = partial(round_channels, multiplier=channel_multiplier, round_limit=0.) round_chs_fn = partial(round_channels, multiplier=channel_multiplier, round_limit=0.)
model_kwargs = dict( model_kwargs = dict(
block_args=decode_arch_def(arch_def, depth_multiplier), block_args=decode_arch_def(arch_def, depth_multiplier, group_size=group_size),
num_features=round_chs_fn(1280), num_features=round_chs_fn(1280),
stem_size=32, stem_size=32,
round_chs_fn=round_chs_fn, round_chs_fn=round_chs_fn,
@ -793,7 +810,8 @@ def _gen_efficientnetv2_base(
def _gen_efficientnetv2_s( def _gen_efficientnetv2_s(
variant, channel_multiplier=1.0, depth_multiplier=1.0, group_size=None, rw=False, pretrained=False, **kwargs): variant, channel_multiplier=1.0, depth_multiplier=1.0, group_size=None, rw=False, pretrained=False, **kwargs
):
""" Creates an EfficientNet-V2 Small model """ Creates an EfficientNet-V2 Small model
Ref impl: https://github.com/google/automl/tree/master/efficientnetv2 Ref impl: https://github.com/google/automl/tree/master/efficientnetv2
@ -831,7 +849,9 @@ def _gen_efficientnetv2_s(
return model return model
def _gen_efficientnetv2_m(variant, channel_multiplier=1.0, depth_multiplier=1.0, pretrained=False, **kwargs): def _gen_efficientnetv2_m(
variant, channel_multiplier=1.0, depth_multiplier=1.0, group_size=None, pretrained=False, **kwargs
):
""" Creates an EfficientNet-V2 Medium model """ Creates an EfficientNet-V2 Medium model
Ref impl: https://github.com/google/automl/tree/master/efficientnetv2 Ref impl: https://github.com/google/automl/tree/master/efficientnetv2
@ -849,7 +869,7 @@ def _gen_efficientnetv2_m(variant, channel_multiplier=1.0, depth_multiplier=1.0,
] ]
model_kwargs = dict( model_kwargs = dict(
block_args=decode_arch_def(arch_def, depth_multiplier), block_args=decode_arch_def(arch_def, depth_multiplier, group_size=group_size),
num_features=1280, num_features=1280,
stem_size=24, stem_size=24,
round_chs_fn=partial(round_channels, multiplier=channel_multiplier), round_chs_fn=partial(round_channels, multiplier=channel_multiplier),
@ -861,7 +881,9 @@ def _gen_efficientnetv2_m(variant, channel_multiplier=1.0, depth_multiplier=1.0,
return model return model
def _gen_efficientnetv2_l(variant, channel_multiplier=1.0, depth_multiplier=1.0, pretrained=False, **kwargs): def _gen_efficientnetv2_l(
variant, channel_multiplier=1.0, depth_multiplier=1.0, group_size=None, pretrained=False, **kwargs
):
""" Creates an EfficientNet-V2 Large model """ Creates an EfficientNet-V2 Large model
Ref impl: https://github.com/google/automl/tree/master/efficientnetv2 Ref impl: https://github.com/google/automl/tree/master/efficientnetv2
@ -879,7 +901,7 @@ def _gen_efficientnetv2_l(variant, channel_multiplier=1.0, depth_multiplier=1.0,
] ]
model_kwargs = dict( model_kwargs = dict(
block_args=decode_arch_def(arch_def, depth_multiplier), block_args=decode_arch_def(arch_def, depth_multiplier, group_size=group_size),
num_features=1280, num_features=1280,
stem_size=32, stem_size=32,
round_chs_fn=partial(round_channels, multiplier=channel_multiplier), round_chs_fn=partial(round_channels, multiplier=channel_multiplier),
@ -891,7 +913,9 @@ def _gen_efficientnetv2_l(variant, channel_multiplier=1.0, depth_multiplier=1.0,
return model return model
def _gen_efficientnetv2_xl(variant, channel_multiplier=1.0, depth_multiplier=1.0, pretrained=False, **kwargs): def _gen_efficientnetv2_xl(
variant, channel_multiplier=1.0, depth_multiplier=1.0, group_size=None, pretrained=False, **kwargs
):
""" Creates an EfficientNet-V2 Xtra-Large model """ Creates an EfficientNet-V2 Xtra-Large model
Ref impl: https://github.com/google/automl/tree/master/efficientnetv2 Ref impl: https://github.com/google/automl/tree/master/efficientnetv2
@ -909,7 +933,7 @@ def _gen_efficientnetv2_xl(variant, channel_multiplier=1.0, depth_multiplier=1.0
] ]
model_kwargs = dict( model_kwargs = dict(
block_args=decode_arch_def(arch_def, depth_multiplier), block_args=decode_arch_def(arch_def, depth_multiplier, group_size=group_size),
num_features=1280, num_features=1280,
stem_size=32, stem_size=32,
round_chs_fn=partial(round_channels, multiplier=channel_multiplier), round_chs_fn=partial(round_channels, multiplier=channel_multiplier),
@ -923,7 +947,8 @@ def _gen_efficientnetv2_xl(variant, channel_multiplier=1.0, depth_multiplier=1.0
def _gen_efficientnet_x( def _gen_efficientnet_x(
variant, channel_multiplier=1.0, depth_multiplier=1.0, channel_divisor=8, variant, channel_multiplier=1.0, depth_multiplier=1.0, channel_divisor=8,
group_size=None, version=1, pretrained=False, **kwargs): group_size=None, version=1, pretrained=False, **kwargs
):
"""Creates an EfficientNet model. """Creates an EfficientNet model.
Ref impl: https://github.com/tensorflow/tpu/blob/master/models/official/efficientnet/efficientnet_model.py Ref impl: https://github.com/tensorflow/tpu/blob/master/models/official/efficientnet/efficientnet_model.py
@ -1069,9 +1094,7 @@ def _gen_mixnet_m(variant, channel_multiplier=1.0, depth_multiplier=1.0, pretrai
return model return model
def _gen_tinynet( def _gen_tinynet(variant, model_width=1.0, depth_multiplier=1.0, pretrained=False, **kwargs):
variant, model_width=1.0, depth_multiplier=1.0, pretrained=False, **kwargs
):
"""Creates a TinyNet model. """Creates a TinyNet model.
""" """
arch_def = [ arch_def = [
@ -1183,8 +1206,7 @@ def _gen_mobilenet_edgetpu(variant, channel_multiplier=1.0, depth_multiplier=1.0
return model return model
def _gen_test_efficientnet( def _gen_test_efficientnet(variant, channel_multiplier=1.0, depth_multiplier=1.0, pretrained=False, **kwargs):
variant, channel_multiplier=1.0, depth_multiplier=1.0, pretrained=False, **kwargs):
""" Minimal test EfficientNet generator. """ Minimal test EfficientNet generator.
""" """
arch_def = [ arch_def = [

16
timm/models/mobilenetv3.py
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@ -412,7 +412,9 @@ def _create_mnv3(variant: str, pretrained: bool = False, **kwargs) -> MobileNetV
return model return model
def _gen_mobilenet_v3_rw(variant: str, channel_multiplier: float = 1.0, pretrained: bool = False, **kwargs) -> MobileNetV3: def _gen_mobilenet_v3_rw(
variant: str, channel_multiplier: float = 1.0, pretrained: bool = False, **kwargs
) -> MobileNetV3:
"""Creates a MobileNet-V3 model. """Creates a MobileNet-V3 model.
Ref impl: ? Ref impl: ?
@ -450,7 +452,9 @@ def _gen_mobilenet_v3_rw(variant: str, channel_multiplier: float = 1.0, pretrain
return model return model
def _gen_mobilenet_v3(variant: str, channel_multiplier: float = 1.0, pretrained: bool = False, **kwargs) -> MobileNetV3: def _gen_mobilenet_v3(
variant: str, channel_multiplier: float = 1.0, group_size=None, pretrained: bool = False, **kwargs
) -> MobileNetV3:
"""Creates a MobileNet-V3 model. """Creates a MobileNet-V3 model.
Ref impl: ? Ref impl: ?
@ -533,7 +537,7 @@ def _gen_mobilenet_v3(variant: str, channel_multiplier: float = 1.0, pretrained:
] ]
se_layer = partial(SqueezeExcite, gate_layer='hard_sigmoid', force_act_layer=nn.ReLU, rd_round_fn=round_channels) se_layer = partial(SqueezeExcite, gate_layer='hard_sigmoid', force_act_layer=nn.ReLU, rd_round_fn=round_channels)
model_kwargs = dict( model_kwargs = dict(
block_args=decode_arch_def(arch_def), block_args=decode_arch_def(arch_def, group_size=group_size),
num_features=num_features, num_features=num_features,
stem_size=16, stem_size=16,
fix_stem=channel_multiplier < 0.75, fix_stem=channel_multiplier < 0.75,
@ -646,7 +650,9 @@ def _gen_lcnet(variant: str, channel_multiplier: float = 1.0, pretrained: bool =
return model return model
def _gen_mobilenet_v4(variant: str, channel_multiplier: float = 1.0, pretrained: bool = False, **kwargs) -> MobileNetV3: def _gen_mobilenet_v4(
variant: str, channel_multiplier: float = 1.0, group_size=None, pretrained: bool = False, **kwargs,
) -> MobileNetV3:
"""Creates a MobileNet-V4 model. """Creates a MobileNet-V4 model.
Ref impl: ? Ref impl: ?
@ -877,7 +883,7 @@ def _gen_mobilenet_v4(variant: str, channel_multiplier: float = 1.0, pretrained:
assert False, f'Unknown variant {variant}.' assert False, f'Unknown variant {variant}.'
model_kwargs = dict( model_kwargs = dict(
block_args=decode_arch_def(arch_def), block_args=decode_arch_def(arch_def, group_size=group_size),
head_bias=False, head_bias=False,
head_norm=True, head_norm=True,
num_features=num_features, num_features=num_features,