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added efficientnet pruned weights #136

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Merged
rwightman merged 1 commit into from
May 3, 2020
Merged

added efficientnet pruned weights #136

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52 changes: 50 additions & 2 deletions timm/models/efficientnet.py
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Original file line number Diff line number Diff line change
Expand Up @@ -27,7 +27,7 @@
from .efficientnet_builder import *
from .feature_hooks import FeatureHooks
from .registry import register_model
from .helpers import load_pretrained
from .helpers import load_pretrained, adapt_model_from_file
from .layers import SelectAdaptivePool2d
from timm.models.layers import create_conv2d
from timm.data import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD, IMAGENET_INCEPTION_MEAN, IMAGENET_INCEPTION_STD
Expand Down Expand Up @@ -131,6 +131,16 @@ def _cfg(url='', **kwargs):
'efficientnet_lite4': _cfg(
url='', input_size=(3, 380, 380), pool_size=(12, 12), crop_pct=0.922),

'efficientnet_b1_pruned': _cfg(
url='https://imvl-automl-sh.oss-cn-shanghai.aliyuncs.com/darts/hyperml/hyperml/job_45403/outputs/effnetb1_pruned_9ebb3fe6.pth',
input_size=(3, 240, 240), pool_size=(8, 8), crop_pct=0.882, mean=IMAGENET_INCEPTION_MEAN, std=IMAGENET_INCEPTION_STD),
'efficientnet_b2_pruned': _cfg(
url='https://imvl-automl-sh.oss-cn-shanghai.aliyuncs.com/darts/hyperml/hyperml/job_45403/outputs/effnetb2_pruned_203f55bc.pth',
input_size=(3, 260, 260), pool_size=(9, 9), crop_pct=0.890, mean=IMAGENET_INCEPTION_MEAN, std=IMAGENET_INCEPTION_STD),
'efficientnet_b3_pruned': _cfg(
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),

'tf_efficientnet_b0': _cfg(
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/tf_efficientnet_b0_aa-827b6e33.pth',
input_size=(3, 224, 224)),
Expand Down Expand Up @@ -482,9 +492,11 @@ def _create_model(model_kwargs, default_cfg, pretrained=False):
else:
load_strict = True
model_class = EfficientNet

variant = model_kwargs.pop('variant', '')
model = model_class(**model_kwargs)
model.default_cfg = default_cfg
if '_pruned' in variant:
model = adapt_model_from_file(model, variant)
if pretrained:
load_pretrained(
model,
Expand Down Expand Up @@ -730,6 +742,7 @@ def _gen_efficientnet(variant, channel_multiplier=1.0, depth_multiplier=1.0, pre
channel_multiplier=channel_multiplier,
act_layer=Swish,
norm_kwargs=resolve_bn_args(kwargs),
variant=variant,
**kwargs,
)
model = _create_model(model_kwargs, default_cfgs[variant], pretrained)
Expand Down Expand Up @@ -1229,6 +1242,41 @@ def efficientnet_lite4(pretrained=False, **kwargs):
return model




@register_model
def efficientnet_b1_pruned(pretrained=False, **kwargs):
""" EfficientNet-B1 Pruned. The pruning has been obtained using https://arxiv.org/pdf/2002.08258.pdf """
kwargs['bn_eps'] = BN_EPS_TF_DEFAULT
kwargs['pad_type'] = 'same'
variant = 'efficientnet_b1_pruned'
model = _gen_efficientnet(
variant, channel_multiplier=1.0, depth_multiplier=1.1, pretrained=pretrained, **kwargs)
return model


@register_model
def efficientnet_b2_pruned(pretrained=False, **kwargs):
""" EfficientNet-B2 Pruned. The pruning has been obtained using https://arxiv.org/pdf/2002.08258.pdf """
kwargs['bn_eps'] = BN_EPS_TF_DEFAULT
kwargs['pad_type'] = 'same'
model = _gen_efficientnet(
'efficientnet_b2_pruned', channel_multiplier=1.1, depth_multiplier=1.2, pretrained=pretrained, **kwargs)
return model


@register_model
def efficientnet_b3_pruned(pretrained=False, **kwargs):
""" EfficientNet-B3 Pruned. The pruning has been obtained using https://arxiv.org/pdf/2002.08258.pdf """
kwargs['bn_eps'] = BN_EPS_TF_DEFAULT
kwargs['pad_type'] = 'same'
model = _gen_efficientnet(
'efficientnet_b3_pruned', channel_multiplier=1.2, depth_multiplier=1.4, pretrained=pretrained, **kwargs)
return model




@register_model
def tf_efficientnet_b0(pretrained=False, **kwargs):
""" EfficientNet-B0. Tensorflow compatible variant """
Expand Down
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