mobilenet_edgetpu can use group_size override, more consistency in arg wrap/sadface w/ extra group_size arg

Author: rwightman

timm/models/efficientnet.py

@@ -488,7 +488,8 @@ def _gen_mnasnet_small(variant, channel_multiplier=1.0, pretrained=False, **kwar
 
 def _gen_mobilenet_v1(
         variant, channel_multiplier=1.0, depth_multiplier=1.0,
-        group_size=None, 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
     Paper: https://arxiv.org/abs/1801.04381
@@ -625,7 +626,8 @@ def _gen_spnasnet(variant, channel_multiplier=1.0, pretrained=False, **kwargs):
 
 def _gen_efficientnet(
         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.
 
     Ref impl: https://github.com/tensorflow/tpu/blob/master/models/official/efficientnet/efficientnet_model.py
@@ -673,7 +675,8 @@ def _gen_efficientnet(
 
 
 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
 
     Ref impl: https://github.com/tensorflow/tpu/tree/master/models/official/efficientnet/edgetpu
@@ -704,7 +707,8 @@ def _gen_efficientnet_edge(
 
 
 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.
 
     Ref impl: https://github.com/tensorflow/tpu/tree/master/models/official/efficientnet/condconv
@@ -776,7 +780,8 @@ def _gen_efficientnet_lite(variant, channel_multiplier=1.0, depth_multiplier=1.0
 
 
 def _gen_efficientnetv2_base(
-        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-V2 base model
 
     Ref impl: https://github.com/google/automl/tree/master/efficientnetv2
@@ -805,7 +810,8 @@ def _gen_efficientnetv2_base(
 
 
 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
 
     Ref impl: https://github.com/google/automl/tree/master/efficientnetv2
@@ -844,7 +850,8 @@ def _gen_efficientnetv2_s(
 
 
 def _gen_efficientnetv2_m(
-        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-V2 Medium model
 
     Ref impl: https://github.com/google/automl/tree/master/efficientnetv2
@@ -875,7 +882,8 @@ def _gen_efficientnetv2_m(
 
 
 def _gen_efficientnetv2_l(
-        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-V2 Large model
 
     Ref impl: https://github.com/google/automl/tree/master/efficientnetv2
@@ -906,7 +914,8 @@ def _gen_efficientnetv2_l(
 
 
 def _gen_efficientnetv2_xl(
-        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-V2 Xtra-Large model
 
     Ref impl: https://github.com/google/automl/tree/master/efficientnetv2
@@ -938,7 +947,8 @@ def _gen_efficientnetv2_xl(
 
 def _gen_efficientnet_x(
         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.
 
     Ref impl: https://github.com/tensorflow/tpu/blob/master/models/official/efficientnet/efficientnet_model.py
@@ -1084,9 +1094,7 @@ def _gen_mixnet_m(variant, channel_multiplier=1.0, depth_multiplier=1.0, pretrai
     return model
 
 
-def _gen_tinynet(
-    variant, model_width=1.0, depth_multiplier=1.0, pretrained=False, **kwargs
-):
+def _gen_tinynet(variant, model_width=1.0, depth_multiplier=1.0, pretrained=False, **kwargs):
     """Creates a TinyNet model.
     """
     arch_def = [
@@ -1109,8 +1117,7 @@ def _gen_tinynet(
     return model
 
 
-def _gen_mobilenet_edgetpu(
-        variant, channel_multiplier=1.0, depth_multiplier=1.0, group_size=None, pretrained=False, **kwargs):
+def _gen_mobilenet_edgetpu(variant, channel_multiplier=1.0, depth_multiplier=1.0, pretrained=False, **kwargs):
     """
     Based on definitions in: https://github.com/tensorflow/models/tree/d2427a562f401c9af118e47af2f030a0a5599f55/official/projects/edgetpu/vision
     """
@@ -1186,7 +1193,7 @@ def _arch_def(chs: List[int], group_size: int):
         ]
 
     model_kwargs = dict(
-        block_args=decode_arch_def(arch_def, depth_multiplier, group_size=group_size),
+        block_args=decode_arch_def(arch_def, depth_multiplier),
         num_features=num_features,
         stem_size=stem_size,
         stem_kernel_size=stem_kernel_size,
@@ -1199,8 +1206,7 @@ def _arch_def(chs: List[int], group_size: int):
     return model
 
 
-def _gen_test_efficientnet(
-        variant, channel_multiplier=1.0, depth_multiplier=1.0, pretrained=False, **kwargs):
+def _gen_test_efficientnet(variant, channel_multiplier=1.0, depth_multiplier=1.0, pretrained=False, **kwargs):
     """ Minimal test EfficientNet generator.
     """
     arch_def = [

timm/models/mobilenetv3.py

@@ -412,7 +412,9 @@ def _create_mnv3(variant: str, pretrained: bool = False, **kwargs) -> MobileNetV
     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.
 
     Ref impl: ?