added efficientnet pruned weights

timm/models/efficientnet.py

@@ -27,7 +27,7 @@ Hacked together by Ross Wightman
 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
@@ -131,6 +131,16 @@ default_cfgs = {
     '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)),
@@ -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,
@@ -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)
@@ -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  """