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https://github.com/huggingface/pytorch-image-models.git
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Merge pull request #2264 from huggingface/group_size_eff
Allow group_size override for more efficientnet and mobilenetv3 based…
This commit is contained in:
Ross Wightman
GitHub
commit
ed7aaf8d6d
@ -488,7 +488,8 @@ def _gen_mnasnet_small(variant, channel_multiplier=1.0, pretrained=False, **kwar
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def _gen_mobilenet_v1(
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variant, channel_multiplier=1.0, depth_multiplier=1.0,
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fix_stem_head=False, head_conv=False, pretrained=False, **kwargs):
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group_size=None, fix_stem_head=False, head_conv=False, pretrained=False, **kwargs
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):
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"""
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Ref impl: https://github.com/tensorflow/models/blob/master/research/slim/nets/mobilenet/mobilenet_v2.py
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Paper: https://arxiv.org/abs/1801.04381
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@ -503,7 +504,12 @@ def _gen_mobilenet_v1(
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round_chs_fn = partial(round_channels, multiplier=channel_multiplier)
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head_features = (1024 if fix_stem_head else max(1024, round_chs_fn(1024))) if head_conv else 0
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model_kwargs = dict(
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block_args=decode_arch_def(arch_def, depth_multiplier=depth_multiplier, fix_first_last=fix_stem_head),
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block_args=decode_arch_def(
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arch_def,
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depth_multiplier=depth_multiplier,
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fix_first_last=fix_stem_head,
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group_size=group_size,
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),
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num_features=head_features,
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stem_size=32,
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fix_stem=fix_stem_head,
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@ -517,7 +523,9 @@ def _gen_mobilenet_v1(
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def _gen_mobilenet_v2(
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variant, channel_multiplier=1.0, depth_multiplier=1.0, fix_stem_head=False, pretrained=False, **kwargs):
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variant, channel_multiplier=1.0, depth_multiplier=1.0,
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group_size=None, fix_stem_head=False, pretrained=False, **kwargs
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):
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""" Generate MobileNet-V2 network
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Ref impl: https://github.com/tensorflow/models/blob/master/research/slim/nets/mobilenet/mobilenet_v2.py
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Paper: https://arxiv.org/abs/1801.04381
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@ -533,7 +541,12 @@ def _gen_mobilenet_v2(
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]
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round_chs_fn = partial(round_channels, multiplier=channel_multiplier)
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model_kwargs = dict(
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block_args=decode_arch_def(arch_def, depth_multiplier=depth_multiplier, fix_first_last=fix_stem_head),
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block_args=decode_arch_def(
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arch_def,
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depth_multiplier=depth_multiplier,
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fix_first_last=fix_stem_head,
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group_size=group_size,
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),
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num_features=1280 if fix_stem_head else max(1280, round_chs_fn(1280)),
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stem_size=32,
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fix_stem=fix_stem_head,
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@ -613,7 +626,8 @@ def _gen_spnasnet(variant, channel_multiplier=1.0, pretrained=False, **kwargs):
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def _gen_efficientnet(
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variant, channel_multiplier=1.0, depth_multiplier=1.0, channel_divisor=8,
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group_size=None, pretrained=False, **kwargs):
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group_size=None, pretrained=False, **kwargs
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):
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"""Creates an EfficientNet model.
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Ref impl: https://github.com/tensorflow/tpu/blob/master/models/official/efficientnet/efficientnet_model.py
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@ -661,7 +675,8 @@ def _gen_efficientnet(
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def _gen_efficientnet_edge(
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variant, channel_multiplier=1.0, depth_multiplier=1.0, group_size=None, pretrained=False, **kwargs):
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variant, channel_multiplier=1.0, depth_multiplier=1.0, group_size=None, pretrained=False, **kwargs
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):
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""" Creates an EfficientNet-EdgeTPU model
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Ref impl: https://github.com/tensorflow/tpu/tree/master/models/official/efficientnet/edgetpu
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@ -692,7 +707,8 @@ def _gen_efficientnet_edge(
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def _gen_efficientnet_condconv(
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variant, channel_multiplier=1.0, depth_multiplier=1.0, experts_multiplier=1, pretrained=False, **kwargs):
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variant, channel_multiplier=1.0, depth_multiplier=1.0, experts_multiplier=1, pretrained=False, **kwargs
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):
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"""Creates an EfficientNet-CondConv model.
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Ref impl: https://github.com/tensorflow/tpu/tree/master/models/official/efficientnet/condconv
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@ -764,7 +780,8 @@ def _gen_efficientnet_lite(variant, channel_multiplier=1.0, depth_multiplier=1.0
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def _gen_efficientnetv2_base(
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variant, channel_multiplier=1.0, depth_multiplier=1.0, pretrained=False, **kwargs):
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variant, channel_multiplier=1.0, depth_multiplier=1.0, group_size=None, pretrained=False, **kwargs
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):
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""" Creates an EfficientNet-V2 base model
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Ref impl: https://github.com/google/automl/tree/master/efficientnetv2
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@ -780,7 +797,7 @@ def _gen_efficientnetv2_base(
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]
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round_chs_fn = partial(round_channels, multiplier=channel_multiplier, round_limit=0.)
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model_kwargs = dict(
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block_args=decode_arch_def(arch_def, depth_multiplier),
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block_args=decode_arch_def(arch_def, depth_multiplier, group_size=group_size),
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num_features=round_chs_fn(1280),
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stem_size=32,
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round_chs_fn=round_chs_fn,
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@ -793,7 +810,8 @@ def _gen_efficientnetv2_base(
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def _gen_efficientnetv2_s(
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variant, channel_multiplier=1.0, depth_multiplier=1.0, group_size=None, rw=False, pretrained=False, **kwargs):
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variant, channel_multiplier=1.0, depth_multiplier=1.0, group_size=None, rw=False, pretrained=False, **kwargs
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):
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""" Creates an EfficientNet-V2 Small model
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Ref impl: https://github.com/google/automl/tree/master/efficientnetv2
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@ -831,7 +849,9 @@ def _gen_efficientnetv2_s(
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return model
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def _gen_efficientnetv2_m(variant, channel_multiplier=1.0, depth_multiplier=1.0, pretrained=False, **kwargs):
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def _gen_efficientnetv2_m(
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variant, channel_multiplier=1.0, depth_multiplier=1.0, group_size=None, pretrained=False, **kwargs
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):
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""" Creates an EfficientNet-V2 Medium model
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Ref impl: https://github.com/google/automl/tree/master/efficientnetv2
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@ -849,7 +869,7 @@ def _gen_efficientnetv2_m(variant, channel_multiplier=1.0, depth_multiplier=1.0,
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]
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model_kwargs = dict(
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block_args=decode_arch_def(arch_def, depth_multiplier),
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block_args=decode_arch_def(arch_def, depth_multiplier, group_size=group_size),
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num_features=1280,
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stem_size=24,
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round_chs_fn=partial(round_channels, multiplier=channel_multiplier),
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@ -861,7 +881,9 @@ def _gen_efficientnetv2_m(variant, channel_multiplier=1.0, depth_multiplier=1.0,
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return model
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def _gen_efficientnetv2_l(variant, channel_multiplier=1.0, depth_multiplier=1.0, pretrained=False, **kwargs):
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def _gen_efficientnetv2_l(
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variant, channel_multiplier=1.0, depth_multiplier=1.0, group_size=None, pretrained=False, **kwargs
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):
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""" Creates an EfficientNet-V2 Large model
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Ref impl: https://github.com/google/automl/tree/master/efficientnetv2
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@ -879,7 +901,7 @@ def _gen_efficientnetv2_l(variant, channel_multiplier=1.0, depth_multiplier=1.0,
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]
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model_kwargs = dict(
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block_args=decode_arch_def(arch_def, depth_multiplier),
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block_args=decode_arch_def(arch_def, depth_multiplier, group_size=group_size),
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num_features=1280,
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stem_size=32,
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round_chs_fn=partial(round_channels, multiplier=channel_multiplier),
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@ -891,7 +913,9 @@ def _gen_efficientnetv2_l(variant, channel_multiplier=1.0, depth_multiplier=1.0,
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return model
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def _gen_efficientnetv2_xl(variant, channel_multiplier=1.0, depth_multiplier=1.0, pretrained=False, **kwargs):
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def _gen_efficientnetv2_xl(
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variant, channel_multiplier=1.0, depth_multiplier=1.0, group_size=None, pretrained=False, **kwargs
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):
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""" Creates an EfficientNet-V2 Xtra-Large model
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Ref impl: https://github.com/google/automl/tree/master/efficientnetv2
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@ -909,7 +933,7 @@ def _gen_efficientnetv2_xl(variant, channel_multiplier=1.0, depth_multiplier=1.0
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]
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model_kwargs = dict(
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block_args=decode_arch_def(arch_def, depth_multiplier),
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block_args=decode_arch_def(arch_def, depth_multiplier, group_size=group_size),
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num_features=1280,
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stem_size=32,
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round_chs_fn=partial(round_channels, multiplier=channel_multiplier),
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@ -923,7 +947,8 @@ def _gen_efficientnetv2_xl(variant, channel_multiplier=1.0, depth_multiplier=1.0
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def _gen_efficientnet_x(
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variant, channel_multiplier=1.0, depth_multiplier=1.0, channel_divisor=8,
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group_size=None, version=1, pretrained=False, **kwargs):
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group_size=None, version=1, pretrained=False, **kwargs
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):
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"""Creates an EfficientNet model.
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Ref impl: https://github.com/tensorflow/tpu/blob/master/models/official/efficientnet/efficientnet_model.py
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@ -1069,9 +1094,7 @@ def _gen_mixnet_m(variant, channel_multiplier=1.0, depth_multiplier=1.0, pretrai
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return model
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def _gen_tinynet(
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variant, model_width=1.0, depth_multiplier=1.0, pretrained=False, **kwargs
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):
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def _gen_tinynet(variant, model_width=1.0, depth_multiplier=1.0, pretrained=False, **kwargs):
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"""Creates a TinyNet model.
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"""
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arch_def = [
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@ -1183,8 +1206,7 @@ def _gen_mobilenet_edgetpu(variant, channel_multiplier=1.0, depth_multiplier=1.0
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return model
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def _gen_test_efficientnet(
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variant, channel_multiplier=1.0, depth_multiplier=1.0, pretrained=False, **kwargs):
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def _gen_test_efficientnet(variant, channel_multiplier=1.0, depth_multiplier=1.0, pretrained=False, **kwargs):
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""" Minimal test EfficientNet generator.
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"""
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arch_def = [
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@ -412,7 +412,9 @@ def _create_mnv3(variant: str, pretrained: bool = False, **kwargs) -> MobileNetV
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return model
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def _gen_mobilenet_v3_rw(variant: str, channel_multiplier: float = 1.0, pretrained: bool = False, **kwargs) -> MobileNetV3:
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def _gen_mobilenet_v3_rw(
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variant: str, channel_multiplier: float = 1.0, pretrained: bool = False, **kwargs
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) -> MobileNetV3:
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"""Creates a MobileNet-V3 model.
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Ref impl: ?
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@ -450,7 +452,9 @@ def _gen_mobilenet_v3_rw(variant: str, channel_multiplier: float = 1.0, pretrain
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return model
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def _gen_mobilenet_v3(variant: str, channel_multiplier: float = 1.0, pretrained: bool = False, **kwargs) -> MobileNetV3:
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def _gen_mobilenet_v3(
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variant: str, channel_multiplier: float = 1.0, group_size=None, pretrained: bool = False, **kwargs
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) -> MobileNetV3:
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"""Creates a MobileNet-V3 model.
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Ref impl: ?
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@ -533,7 +537,7 @@ def _gen_mobilenet_v3(variant: str, channel_multiplier: float = 1.0, pretrained:
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]
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se_layer = partial(SqueezeExcite, gate_layer='hard_sigmoid', force_act_layer=nn.ReLU, rd_round_fn=round_channels)
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model_kwargs = dict(
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block_args=decode_arch_def(arch_def),
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block_args=decode_arch_def(arch_def, group_size=group_size),
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num_features=num_features,
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stem_size=16,
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fix_stem=channel_multiplier < 0.75,
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@ -646,7 +650,9 @@ def _gen_lcnet(variant: str, channel_multiplier: float = 1.0, pretrained: bool =
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return model
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def _gen_mobilenet_v4(variant: str, channel_multiplier: float = 1.0, pretrained: bool = False, **kwargs) -> MobileNetV3:
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def _gen_mobilenet_v4(
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variant: str, channel_multiplier: float = 1.0, group_size=None, pretrained: bool = False, **kwargs,
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) -> MobileNetV3:
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"""Creates a MobileNet-V4 model.
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Ref impl: ?
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@ -877,7 +883,7 @@ def _gen_mobilenet_v4(variant: str, channel_multiplier: float = 1.0, pretrained:
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assert False, f'Unknown variant {variant}.'
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model_kwargs = dict(
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block_args=decode_arch_def(arch_def),
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block_args=decode_arch_def(arch_def, group_size=group_size),
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head_bias=False,
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head_norm=True,
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num_features=num_features,
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