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EfficientNet-V2S preliminary model def (for experimentation)

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Ross Wightman 2021-04-02 09:35:03 -07:00
parent d5ed58d623
commit de9dff933a
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timm/models/efficientnet.py
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@ -154,6 +154,9 @@ default_cfgs = {
url='https://imvl-automl-sh.oss-cn-shanghai.aliyuncs.com/darts/hyperml/hyperml/job_45403/outputs/effnetb3_pruned_5abcc29f.pth', url='https://imvl-automl-sh.oss-cn-shanghai.aliyuncs.com/darts/hyperml/hyperml/job_45403/outputs/effnetb3_pruned_5abcc29f.pth',
input_size=(3, 300, 300), pool_size=(10, 10), crop_pct=0.904, mean=IMAGENET_INCEPTION_MEAN, std=IMAGENET_INCEPTION_STD), input_size=(3, 300, 300), pool_size=(10, 10), crop_pct=0.904, mean=IMAGENET_INCEPTION_MEAN, std=IMAGENET_INCEPTION_STD),
'efficientnet_v2s': _cfg(
url='', input_size=(3, 224, 224), test_size=(3, 300, 300), pool_size=(7, 7)), # FIXME WIP
'tf_efficientnet_b0': _cfg( 'tf_efficientnet_b0': _cfg(
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/tf_efficientnet_b0_aa-827b6e33.pth', url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/tf_efficientnet_b0_aa-827b6e33.pth',
input_size=(3, 224, 224)), input_size=(3, 224, 224)),
@ -819,6 +822,37 @@ def _gen_efficientnet_lite(variant, channel_multiplier=1.0, depth_multiplier=1.0
return model return model
def _gen_efficientnet_v2s(variant, channel_multiplier=1.0, depth_multiplier=1.0, pretrained=False, **kwargs):
""" Creates an EfficientNet-V2s model
NOTE: this is a preliminary definition based on paper, awaiting official code release for details
and weights
Ref impl:
Paper: https://arxiv.org/abs/2104.00298
"""
arch_def = [
['er_r2_k3_s1_e1_c24_noskip'],
['er_r4_k3_s2_e4_c48'],
['er_r4_k3_s2_e4_c64'],
['ir_r6_k3_s2_e4_c128_se0.25'],
['ir_r9_k3_s1_e6_c160_se0.25'],
['ir_r15_k3_s2_e6_c272_se0.25'],
]
model_kwargs = dict(
block_args=decode_arch_def(arch_def, depth_multiplier),
num_features=round_channels(1792, channel_multiplier, 8, None),
stem_size=24,
channel_multiplier=channel_multiplier,
norm_kwargs=resolve_bn_args(kwargs),
act_layer=resolve_act_layer(kwargs, 'silu'),
**kwargs,
)
model = _create_effnet(variant, pretrained, **model_kwargs)
return model
def _gen_mixnet_s(variant, channel_multiplier=1.0, pretrained=False, **kwargs): def _gen_mixnet_s(variant, channel_multiplier=1.0, pretrained=False, **kwargs):
"""Creates a MixNet Small model. """Creates a MixNet Small model.
@ -1258,6 +1292,15 @@ def efficientnet_b3_pruned(pretrained=False, **kwargs):
return model return model
@register_model
def efficientnet_v2s(pretrained=False, **kwargs):
""" EfficientNet-V2 Small. """
model = _gen_efficientnet_v2s(
'efficientnet_v2s', channel_multiplier=1.0, depth_multiplier=1.0, pretrained=pretrained, **kwargs)
return model
@register_model @register_model
def tf_efficientnet_b0(pretrained=False, **kwargs): def tf_efficientnet_b0(pretrained=False, **kwargs):
""" EfficientNet-B0. Tensorflow compatible variant """ """ EfficientNet-B0. Tensorflow compatible variant """