Simplify efficientnet code by removing _efficientnet_conf (#4690)

* adding efficientnet to prototype

* Removing _efficientnet_conf from prototype and main efficientnet classification models

* fixing merge conflicts

* fixing merge conflicts

* fixing lint errors

* fixing lint errors

* fixing lint errors

torchvision/models/efficientnet.py

 import copy
 import math
 from functools import partial
-from typing import Any, Callable, List, Optional, Sequence
+from typing import Any, Callable, Optional, List, Sequence
 
 import torch
 from torch import nn, Tensor
@@ -263,7 +263,15 @@ class EfficientNet(nn.Module):
         return self._forward_impl(x)
 
 
-def _efficientnet_conf(width_mult: float, depth_mult: float, **kwargs: Any) -> List[MBConvConfig]:
+def _efficientnet(
+    arch: str,
+    width_mult: float,
+    depth_mult: float,
+    dropout: float,
+    pretrained: bool,
+    progress: bool,
+    **kwargs: Any,
+) -> EfficientNet:
     bneck_conf = partial(MBConvConfig, width_mult=width_mult, depth_mult=depth_mult)
     inverted_residual_setting = [
         bneck_conf(1, 3, 1, 32, 16, 1),
@@ -274,17 +282,6 @@ def _efficientnet_conf(width_mult: float, depth_mult: float, **kwargs: Any) -> L
         bneck_conf(6, 5, 2, 112, 192, 4),
         bneck_conf(6, 3, 1, 192, 320, 1),
     ]
-    return inverted_residual_setting
-
-
-def _efficientnet(
-    arch: str,
-    inverted_residual_setting: List[MBConvConfig],
-    dropout: float,
-    pretrained: bool,
-    progress: bool,
-    **kwargs: Any,
-) -> EfficientNet:
     model = EfficientNet(inverted_residual_setting, dropout, **kwargs)
     if pretrained:
         if model_urls.get(arch, None) is None:
@@ -303,8 +300,7 @@ def efficientnet_b0(pretrained: bool = False, progress: bool = True, **kwargs: A
         pretrained (bool): If True, returns a model pre-trained on ImageNet
         progress (bool): If True, displays a progress bar of the download to stderr
     """
-    inverted_residual_setting = _efficientnet_conf(width_mult=1.0, depth_mult=1.0, **kwargs)
-    return _efficientnet("efficientnet_b0", inverted_residual_setting, 0.2, pretrained, progress, **kwargs)
+    return _efficientnet("efficientnet_b0", 1.0, 1.0, 0.2, pretrained, progress, **kwargs)
 
 
 def efficientnet_b1(pretrained: bool = False, progress: bool = True, **kwargs: Any) -> EfficientNet:
@@ -316,8 +312,7 @@ def efficientnet_b1(pretrained: bool = False, progress: bool = True, **kwargs: A
         pretrained (bool): If True, returns a model pre-trained on ImageNet
         progress (bool): If True, displays a progress bar of the download to stderr
     """
-    inverted_residual_setting = _efficientnet_conf(width_mult=1.0, depth_mult=1.1, **kwargs)
-    return _efficientnet("efficientnet_b1", inverted_residual_setting, 0.2, pretrained, progress, **kwargs)
+    return _efficientnet("efficientnet_b1", 1.0, 1.1, 0.2, pretrained, progress, **kwargs)
 
 
 def efficientnet_b2(pretrained: bool = False, progress: bool = True, **kwargs: Any) -> EfficientNet:
@@ -329,8 +324,7 @@ def efficientnet_b2(pretrained: bool = False, progress: bool = True, **kwargs: A
         pretrained (bool): If True, returns a model pre-trained on ImageNet
         progress (bool): If True, displays a progress bar of the download to stderr
     """
-    inverted_residual_setting = _efficientnet_conf(width_mult=1.1, depth_mult=1.2, **kwargs)
-    return _efficientnet("efficientnet_b2", inverted_residual_setting, 0.3, pretrained, progress, **kwargs)
+    return _efficientnet("efficientnet_b2", 1.1, 1.2, 0.3, pretrained, progress, **kwargs)
 
 
 def efficientnet_b3(pretrained: bool = False, progress: bool = True, **kwargs: Any) -> EfficientNet:
@@ -342,8 +336,7 @@ def efficientnet_b3(pretrained: bool = False, progress: bool = True, **kwargs: A
         pretrained (bool): If True, returns a model pre-trained on ImageNet
         progress (bool): If True, displays a progress bar of the download to stderr
     """
-    inverted_residual_setting = _efficientnet_conf(width_mult=1.2, depth_mult=1.4, **kwargs)
-    return _efficientnet("efficientnet_b3", inverted_residual_setting, 0.3, pretrained, progress, **kwargs)
+    return _efficientnet("efficientnet_b3", 1.2, 1.4, 0.3, pretrained, progress, **kwargs)
 
 
 def efficientnet_b4(pretrained: bool = False, progress: bool = True, **kwargs: Any) -> EfficientNet:
@@ -355,8 +348,7 @@ def efficientnet_b4(pretrained: bool = False, progress: bool = True, **kwargs: A
         pretrained (bool): If True, returns a model pre-trained on ImageNet
         progress (bool): If True, displays a progress bar of the download to stderr
     """
-    inverted_residual_setting = _efficientnet_conf(width_mult=1.4, depth_mult=1.8, **kwargs)
-    return _efficientnet("efficientnet_b4", inverted_residual_setting, 0.4, pretrained, progress, **kwargs)
+    return _efficientnet("efficientnet_b4", 1.4, 1.8, 0.4, pretrained, progress, **kwargs)
 
 
 def efficientnet_b5(pretrained: bool = False, progress: bool = True, **kwargs: Any) -> EfficientNet:
@@ -368,10 +360,10 @@ def efficientnet_b5(pretrained: bool = False, progress: bool = True, **kwargs: A
         pretrained (bool): If True, returns a model pre-trained on ImageNet
         progress (bool): If True, displays a progress bar of the download to stderr
     """
-    inverted_residual_setting = _efficientnet_conf(width_mult=1.6, depth_mult=2.2, **kwargs)
     return _efficientnet(
         "efficientnet_b5",
-        inverted_residual_setting,
+        1.6,
+        2.2,
         0.4,
         pretrained,
         progress,
@@ -389,10 +381,10 @@ def efficientnet_b6(pretrained: bool = False, progress: bool = True, **kwargs: A
         pretrained (bool): If True, returns a model pre-trained on ImageNet
         progress (bool): If True, displays a progress bar of the download to stderr
     """
-    inverted_residual_setting = _efficientnet_conf(width_mult=1.8, depth_mult=2.6, **kwargs)
     return _efficientnet(
         "efficientnet_b6",
-        inverted_residual_setting,
+        1.8,
+        2.6,
         0.5,
         pretrained,
         progress,
@@ -410,10 +402,10 @@ def efficientnet_b7(pretrained: bool = False, progress: bool = True, **kwargs: A
         pretrained (bool): If True, returns a model pre-trained on ImageNet
         progress (bool): If True, displays a progress bar of the download to stderr
     """
-    inverted_residual_setting = _efficientnet_conf(width_mult=2.0, depth_mult=3.1, **kwargs)
     return _efficientnet(
         "efficientnet_b7",
-        inverted_residual_setting,
+        2.0,
+        3.1,
         0.5,
         pretrained,
         progress,

torchvision/prototype/models/efficientnet.py

 import warnings
 from functools import partial
-from typing import Any, List, Optional
+from typing import Any, Optional
 
 from torch import nn
 from torchvision.transforms.functional import InterpolationMode
 
-from ...models.efficientnet import EfficientNet, MBConvConfig, _efficientnet_conf
+from ...models.efficientnet import EfficientNet, MBConvConfig
 from ..transforms.presets import ImageNetEval
 from ._api import Weights, WeightEntry
 from ._meta import _IMAGENET_CATEGORIES
@@ -33,7 +33,8 @@ __all__ = [
 
 
 def _efficientnet(
-    inverted_residual_setting: List[MBConvConfig],
+    width_mult: float,
+    depth_mult: float,
     dropout: float,
     weights: Optional[Weights],
     progress: bool,
@@ -42,6 +43,17 @@ def _efficientnet(
     if weights is not None:
         kwargs["num_classes"] = len(weights.meta["categories"])
 
+    bneck_conf = partial(MBConvConfig, width_mult=width_mult, depth_mult=depth_mult)
+    inverted_residual_setting = [
+        bneck_conf(1, 3, 1, 32, 16, 1),
+        bneck_conf(6, 3, 2, 16, 24, 2),
+        bneck_conf(6, 5, 2, 24, 40, 2),
+        bneck_conf(6, 3, 2, 40, 80, 3),
+        bneck_conf(6, 5, 1, 80, 112, 3),
+        bneck_conf(6, 5, 2, 112, 192, 4),
+        bneck_conf(6, 3, 1, 192, 320, 1),
+    ]
+
     model = EfficientNet(inverted_residual_setting, dropout, **kwargs)
 
     if weights is not None:
@@ -172,8 +184,7 @@ def efficientnet_b0(
         warnings.warn("The argument pretrained is deprecated, please use weights instead.")
         weights = EfficientNetB0Weights.ImageNet1K_TimmV1 if kwargs.pop("pretrained") else None
     weights = EfficientNetB0Weights.verify(weights)
-    inverted_residual_setting = _efficientnet_conf(width_mult=1.0, depth_mult=1.0, **kwargs)
-    return _efficientnet(inverted_residual_setting, dropout=0.2, weights=weights, progress=progress, **kwargs)
+    return _efficientnet(width_mult=1.0, depth_mult=1.0, dropout=0.2, weights=weights, progress=progress, **kwargs)
 
 
 def efficientnet_b1(
@@ -183,8 +194,7 @@ def efficientnet_b1(
         warnings.warn("The argument pretrained is deprecated, please use weights instead.")
         weights = EfficientNetB1Weights.ImageNet1K_TimmV1 if kwargs.pop("pretrained") else None
     weights = EfficientNetB1Weights.verify(weights)
-    inverted_residual_setting = _efficientnet_conf(width_mult=1.0, depth_mult=1.1, **kwargs)
-    return _efficientnet(inverted_residual_setting, dropout=0.2, weights=weights, progress=progress, **kwargs)
+    return _efficientnet(width_mult=1.0, depth_mult=1.1, dropout=0.2, weights=weights, progress=progress, **kwargs)
 
 
 def efficientnet_b2(
@@ -194,8 +204,7 @@ def efficientnet_b2(
         warnings.warn("The argument pretrained is deprecated, please use weights instead.")
         weights = EfficientNetB2Weights.ImageNet1K_TimmV1 if kwargs.pop("pretrained") else None
     weights = EfficientNetB2Weights.verify(weights)
-    inverted_residual_setting = _efficientnet_conf(width_mult=1.1, depth_mult=1.2, **kwargs)
-    return _efficientnet(inverted_residual_setting, dropout=0.3, weights=weights, progress=progress, **kwargs)
+    return _efficientnet(width_mult=1.1, depth_mult=1.2, dropout=0.3, weights=weights, progress=progress, **kwargs)
 
 
 def efficientnet_b3(
@@ -205,8 +214,7 @@ def efficientnet_b3(
         warnings.warn("The argument pretrained is deprecated, please use weights instead.")
         weights = EfficientNetB3Weights.ImageNet1K_TimmV1 if kwargs.pop("pretrained") else None
     weights = EfficientNetB3Weights.verify(weights)
-    inverted_residual_setting = _efficientnet_conf(width_mult=1.2, depth_mult=1.4, **kwargs)
-    return _efficientnet(inverted_residual_setting, dropout=0.3, weights=weights, progress=progress, **kwargs)
+    return _efficientnet(width_mult=1.2, depth_mult=1.4, dropout=0.3, weights=weights, progress=progress, **kwargs)
 
 
 def efficientnet_b4(
@@ -216,8 +224,7 @@ def efficientnet_b4(
         warnings.warn("The argument pretrained is deprecated, please use weights instead.")
         weights = EfficientNetB4Weights.ImageNet1K_TimmV1 if kwargs.pop("pretrained") else None
     weights = EfficientNetB4Weights.verify(weights)
-    inverted_residual_setting = _efficientnet_conf(width_mult=1.4, depth_mult=1.8, **kwargs)
-    return _efficientnet(inverted_residual_setting, dropout=0.4, weights=weights, progress=progress, **kwargs)
+    return _efficientnet(width_mult=1.4, depth_mult=1.8, dropout=0.4, weights=weights, progress=progress, **kwargs)
 
 
 def efficientnet_b5(
@@ -227,9 +234,9 @@ def efficientnet_b5(
         warnings.warn("The argument pretrained is deprecated, please use weights instead.")
         weights = EfficientNetB5Weights.ImageNet1K_TFV1 if kwargs.pop("pretrained") else None
     weights = EfficientNetB5Weights.verify(weights)
-    inverted_residual_setting = _efficientnet_conf(width_mult=1.6, depth_mult=2.2, **kwargs)
     return _efficientnet(
-        inverted_residual_setting,
+        width_mult=1.6,
+        depth_mult=2.2,
         dropout=0.4,
         weights=weights,
         progress=progress,
@@ -245,9 +252,9 @@ def efficientnet_b6(
         warnings.warn("The argument pretrained is deprecated, please use weights instead.")
         weights = EfficientNetB6Weights.ImageNet1K_TFV1 if kwargs.pop("pretrained") else None
     weights = EfficientNetB6Weights.verify(weights)
-    inverted_residual_setting = _efficientnet_conf(width_mult=1.8, depth_mult=2.6, **kwargs)
     return _efficientnet(
-        inverted_residual_setting,
+        width_mult=1.8,
+        depth_mult=2.6,
         dropout=0.5,
         weights=weights,
         progress=progress,
@@ -263,9 +270,9 @@ def efficientnet_b7(
         warnings.warn("The argument pretrained is deprecated, please use weights instead.")
         weights = EfficientNetB7Weights.ImageNet1K_TFV1 if kwargs.pop("pretrained") else None
     weights = EfficientNetB7Weights.verify(weights)
-    inverted_residual_setting = _efficientnet_conf(width_mult=2.0, depth_mult=3.1, **kwargs)
     return _efficientnet(
-        inverted_residual_setting,
+        width_mult=2.0,
+        depth_mult=3.1,
         dropout=0.5,
         weights=weights,
         progress=progress,