Develop (#303)

* refactor workflows * refactor SEs att * formatter action * formatter action * formatter action * formatter action * formatter action * reformatted ✨ * formatter action * Attention refactor (#284) * se * fixed se models * reformatted ✨ * tests for se models Co-authored-by: FrancescoSaverioZuppichini <[email protected]> * Feature/#285 pretrained (#287) * pretrained weights refactor + logger * reformatted ✨ * runners * minor changes * minor changes * minor changes * minor changes * better tests * fixtures * minor changes Co-authored-by: FrancescoSaverioZuppichini <[email protected]> * Feature/refactor (#295) * types * types * types * broken images * general refactor - swith to torchvision StocasticDepth\n- more blocks in nn.blocks\n- full model list in hf store * raise python version to 3.8 * minor changes * hf token in action * hf token in action * refactor * refactor * hf storage models now uses the hidden api to get the model names * minor changes * minor changes * minor changes * minor changes * minor changes * done (#298) * better version of drop path (#302) * reformatted ✨ Co-authored-by: nektos/act <nektos/[email protected]> Co-authored-by: FrancescoSaverioZuppichini <[email protected]>
FrancescoSaverioZuppichini · Feb 22, 2022 · 75871bd · 75871bd
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Showing 15 changed files with 106 additions and 86 deletions.
diff --git a/glasses/models/classification/deit/__init__.py b/glasses/models/classification/deit/__init__.py
@@ -69,7 +69,7 @@ def __init__(
         *args,
         head: nn.Module = DeiTClassificationHead,
         tokens: nn.Module = DeiTTokens,
-        **kwargs
+        **kwargs,
     ):
         super().__init__(*args, head=head, tokens=tokens, **kwargs)
 
@@ -105,6 +105,6 @@ def deit_base_patch16_384(cls, **kwargs):
             depth=12,
             num_heads=12,
             qkv_bias=True,
-            **kwargs
+            **kwargs,
         )
         return model
diff --git a/glasses/models/classification/densenet/__init__.py b/glasses/models/classification/densenet/__init__.py
@@ -29,7 +29,7 @@ def __init__(
         out_features: int,
         activation: nn.Module = ReLUInPlace,
         *args,
-        **kwargs
+        **kwargs,
     ):
         super().__init__()
         self.block = nn.Sequential(
@@ -72,7 +72,7 @@ def __init__(
         out_features: int,
         activation: nn.Module = ReLUInPlace,
         expansion: int = 4,
-        **kwargs
+        **kwargs,
     ):
         super().__init__(in_features, out_features, activation, **kwargs)
         self.expansion = expansion
@@ -88,7 +88,7 @@ def __init__(
                         self.expanded_features,
                         kernel_size=1,
                         bias=False,
-                        **kwargs
+                        **kwargs,
                     ),
                     "bn2": nn.BatchNorm2d(self.expanded_features),
                     "act2": activation(),
@@ -97,7 +97,7 @@ def __init__(
                         out_features,
                         kernel_size=3,
                         bias=False,
-                        **kwargs
+                        **kwargs,
                     ),
                 }
             )
@@ -154,7 +154,7 @@ def __init__(
         block: nn.Module = DenseBottleNeckBlock,
         transition_block: nn.Module = TransitionBlock,
         *args,
-        **kwargs
+        **kwargs,
     ):
         self.out_features = grow_rate * n + in_features
         super().__init__(
@@ -166,7 +166,7 @@ def __init__(
             # reduce the output features by a factor of 2
             transition_block(self.out_features, *args, **kwargs)
             if transition_block
-            else nn.Identity()
+            else nn.Identity(),
         )
 
 
@@ -191,7 +191,7 @@ def __init__(
         activation: nn.Module = ReLUInPlace,
         block: nn.Module = DenseBottleNeckBlock,
         *args,
-        **kwargs
+        **kwargs,
     ):
         super().__init__(in_channels)
         self.layers = nn.ModuleList([])
@@ -223,7 +223,7 @@ def __init__(
                 transition_block=lambda x: nn.Sequential(
                     nn.BatchNorm2d(self.widths[-1]), activation()
                 ),
-                **kwargs
+                **kwargs,
             )
         )
 
@@ -275,7 +275,7 @@ def __init__(
         encoder: nn.Module = DenseNetEncoder,
         head: nn.Module = ResNetHead,
         *args,
-        **kwargs
+        **kwargs,
     ):
         super().__init__(encoder, head, *args, **kwargs)
 

diff --git a/glasses/models/classification/efficientnet/__init__.py b/glasses/models/classification/efficientnet/__init__.py
@@ -44,7 +44,7 @@ def __init__(
         drop_rate: float = 0.2,
         se: bool = True,
         kernel_size: int = 3,
-        **kwargs
+        **kwargs,
     ):
         super().__init__()
 
@@ -138,7 +138,7 @@ def __init__(
         drop_rate: float = 0.2,
         stem: nn.Module = EfficientNetStem,
         activation: nn.Module = partial(nn.SiLU, inplace=True),
-        **kwargs
+        **kwargs,
     ):
         super().__init__()
 
@@ -314,7 +314,7 @@ def __init__(
         encoder: nn.Module = EfficientNetEncoder,
         head: nn.Module = EfficientNetHead,
         *args,
-        **kwargs
+        **kwargs,
     ):
         super().__init__(
             encoder, partial(head, drop_rate=kwargs["drop_rate"]), *args, **kwargs

diff --git a/glasses/models/classification/fishnet/__init__.py b/glasses/models/classification/fishnet/__init__.py
@@ -65,7 +65,7 @@ def __init__(
         depth: int = 1,
         trans_depth: int = 1,
         *args,
-        **kwargs
+        **kwargs,
     ):
         super().__init__()
 
@@ -79,13 +79,13 @@ def __init__(
                 out_features,
                 shortcut=FishNetChannelReductionShortcut,
                 *args,
-                **kwargs
+                **kwargs,
             ),
             *[
                 block(out_features, out_features, *args, **kwargs)
                 for _ in range(depth - 1)
             ],
-            nn.Upsample(scale_factor=2)
+            nn.Upsample(scale_factor=2),
         )
 
     def forward(self, x: Tensor, res: Tensor) -> Tensor:
@@ -115,7 +115,7 @@ def __init__(
         block: nn.Module = FishNetBottleNeck,
         depth: int = 1,
         trans_depth: int = 1,
-        **kwargs
+        **kwargs,
     ):
         super().__init__(
             in_features,
@@ -124,13 +124,13 @@ def __init__(
             block,
             depth,
             trans_depth,
-            **kwargs
+            **kwargs,
         )
 
         self.block = nn.Sequential(
             block(in_features, out_features, shortcut=ResNetShorcut, **kwargs),
             *[block(out_features, out_features, **kwargs) for _ in range(depth - 1)],
-            nn.MaxPool2d(kernel_size=2, stride=2)
+            nn.MaxPool2d(kernel_size=2, stride=2),
         )
 
 
@@ -170,7 +170,7 @@ def __init__(
             *[
                 FishNetBottleNeck(out_features, out_features, activation=activation)
                 for _ in range(depth - 1)
-            ]
+            ],
         )
         # very wrong SE implementation and application -> I have contacted the authors and he confirmed they got it wrong.
         self.att = nn.Sequential(
@@ -208,12 +208,12 @@ def __init__(
         depth: int = 1,
         block: nn.Module = FishNetBottleNeck,
         *args,
-        **kwargs
+        **kwargs,
     ):
         super().__init__(
             block(in_features, out_features, **kwargs),
             *[block(out_features, out_features, **kwargs) for _ in range(depth - 1)],
-            nn.MaxPool2d(kernel_size=2, stride=2)
+            nn.MaxPool2d(kernel_size=2, stride=2),
         )
 
 
@@ -251,7 +251,7 @@ def __init__(
         block: nn.Module = FishNetBottleNeck,
         stem: nn.Module = ResNetStemC,
         activation: nn.Module = ReLUInPlace,
-        **kwargs
+        **kwargs,
     ):
         super().__init__()
 
@@ -269,7 +269,7 @@ def __init__(
                     depth=depth,
                     block=block,
                     activation=activation,
-                    **kwargs
+                    **kwargs,
                 )
                 for (in_features, out_features), depth in zip(
                     self.tail_widths, tail_depths
@@ -301,8 +301,8 @@ def __init__(
                     trans_depth=trans_depth,
                     block=block,
                     activation=activation,
-                    dilation=2 ** i,
-                    padding=2 ** i,
+                    dilation=2**i,
+                    padding=2**i,
                 )
             )
 
@@ -444,7 +444,7 @@ def __init__(
         encoder: nn.Module = FishNetEncoder,
         head: nn.Module = FishNetHead,
         *args,
-        **kwargs
+        **kwargs,
     ):
         super().__init__(encoder, head, *args, **kwargs)
 
@@ -483,7 +483,7 @@ def fishnet99(self, *args, **kwargs) -> FishNet:
             body_trans_depths=body_trans_depths,
             head_depths=head_depths,
             head_trans_depths=head_trans_depths,
-            **kwargs
+            **kwargs,
         )
 
     @classmethod
@@ -513,5 +513,5 @@ def fishnet150(self, *args, **kwargs) -> FishNet:
             body_trans_depths=body_trans_depths,
             head_depths=head_depths,
             head_trans_depths=head_trans_depths,
-            **kwargs
+            **kwargs,
         )
diff --git a/glasses/models/classification/mobilenet/__init__.py b/glasses/models/classification/mobilenet/__init__.py
@@ -29,5 +29,5 @@ def mobilenet_v2(cls, *args, **kwargs) -> EfficientNet:
             drop_rate=0,
             activation=nn.ReLU6,
             *args,
-            **kwargs
+            **kwargs,
         )
diff --git a/glasses/models/classification/regnet/__init__.py b/glasses/models/classification/regnet/__init__.py
@@ -61,7 +61,7 @@ def __init__(
             out_features,
             reduction=1,
             groups=out_features // groups_width,
-            **kwargs
+            **kwargs,
         )
 
 
@@ -291,7 +291,7 @@ def regnety_002(cls, *args, **kwargs):
             widths=widths,
             block=RegNetYBotteneckBlock,
             groups_width=groups_width,
-            **kwargs
+            **kwargs,
         )
 
     @classmethod
@@ -303,7 +303,7 @@ def regnety_004(cls, *args, **kwargs):
             widths=widths,
             block=RegNetYBotteneckBlock,
             groups_width=groups_width,
-            **kwargs
+            **kwargs,
         )
 
     @classmethod
@@ -315,7 +315,7 @@ def regnety_006(cls, *args, **kwargs):
             widths=widths,
             block=RegNetYBotteneckBlock,
             groups_width=groups_width,
-            **kwargs
+            **kwargs,
         )
 
     @classmethod
@@ -327,7 +327,7 @@ def regnety_008(cls, *args, **kwargs):
             widths=widths,
             block=RegNetYBotteneckBlock,
             groups_width=groups_width,
-            **kwargs
+            **kwargs,
         )
 
     @classmethod
@@ -339,7 +339,7 @@ def regnety_016(cls, *args, **kwargs):
             widths=widths,
             block=RegNetYBotteneckBlock,
             groups_width=groups_width,
-            **kwargs
+            **kwargs,
         )
 
     @classmethod
@@ -351,7 +351,7 @@ def regnety_032(cls, *args, **kwargs):
             widths=widths,
             block=RegNetYBotteneckBlock,
             groups_width=groups_width,
-            **kwargs
+            **kwargs,
         )
 
     @classmethod
@@ -363,7 +363,7 @@ def regnety_040(cls, *args, **kwargs):
             widths=widths,
             block=RegNetYBotteneckBlock,
             groups_width=groups_width,
-            **kwargs
+            **kwargs,
         )
 
     @classmethod
@@ -375,7 +375,7 @@ def regnety_064(cls, *args, **kwargs):
             widths=widths,
             block=RegNetYBotteneckBlock,
             groups_width=groups_width,
-            **kwargs
+            **kwargs,
         )
 
     @classmethod
@@ -387,7 +387,7 @@ def regnety_080(cls, *args, **kwargs):
             widths=widths,
             block=RegNetYBotteneckBlock,
             groups_width=groups_width,
-            **kwargs
+            **kwargs,
         )
 
     @classmethod
@@ -399,7 +399,7 @@ def regnety_120(cls, *args, **kwargs):
             widths=widths,
             block=RegNetYBotteneckBlock,
             groups_width=groups_width,
-            **kwargs
+            **kwargs,
         )
 
     @classmethod
@@ -411,7 +411,7 @@ def regnety_160(cls, *args, **kwargs):
             widths=widths,
             block=RegNetYBotteneckBlock,
             groups_width=groups_width,
-            **kwargs
+            **kwargs,
         )
 
     @classmethod
@@ -423,5 +423,5 @@ def regnety_320(cls, *args, **kwargs):
             widths=widths,
             block=RegNetYBotteneckBlock,
             groups_width=groups_width,
-            **kwargs
+            **kwargs,
         )