[Vision] fix small nit on BeitDropPath layers (#20587)

* fix small nit * add last file
huggingface · Dec 5, 2022 · 0911057 · 0911057
1 parent e135a6c
commit 0911057
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Showing 15 changed files with 30 additions and 30 deletions.
diff --git a/src/transformers/models/beit/modeling_beit.py b/src/transformers/models/beit/modeling_beit.py
@@ -118,8 +118,8 @@ def __init__(self, drop_prob: Optional[float] = None) -> None:
         super().__init__()
         self.drop_prob = drop_prob
 
-    def forward(self, x: torch.Tensor) -> torch.Tensor:
-        return drop_path(x, self.drop_prob, self.training)
+    def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
+        return drop_path(hidden_states, self.drop_prob, self.training)
 
     def extra_repr(self) -> str:
         return "p={}".format(self.drop_prob)

diff --git a/src/transformers/models/convnext/modeling_convnext.py b/src/transformers/models/convnext/modeling_convnext.py
@@ -82,8 +82,8 @@ def __init__(self, drop_prob: Optional[float] = None) -> None:
         super().__init__()
         self.drop_prob = drop_prob
 
-    def forward(self, x: torch.Tensor) -> torch.Tensor:
-        return drop_path(x, self.drop_prob, self.training)
+    def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
+        return drop_path(hidden_states, self.drop_prob, self.training)
 
     def extra_repr(self) -> str:
         return "p={}".format(self.drop_prob)

diff --git a/src/transformers/models/cvt/modeling_cvt.py b/src/transformers/models/cvt/modeling_cvt.py
@@ -107,8 +107,8 @@ def __init__(self, drop_prob: Optional[float] = None) -> None:
         super().__init__()
         self.drop_prob = drop_prob
 
-    def forward(self, x: torch.Tensor) -> torch.Tensor:
-        return drop_path(x, self.drop_prob, self.training)
+    def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
+        return drop_path(hidden_states, self.drop_prob, self.training)
 
     def extra_repr(self) -> str:
         return "p={}".format(self.drop_prob)

diff --git a/src/transformers/models/data2vec/modeling_data2vec_vision.py b/src/transformers/models/data2vec/modeling_data2vec_vision.py
@@ -120,8 +120,8 @@ def __init__(self, drop_prob: Optional[float] = None) -> None:
         super().__init__()
         self.drop_prob = drop_prob
 
-    def forward(self, x: torch.Tensor) -> torch.Tensor:
-        return drop_path(x, self.drop_prob, self.training)
+    def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
+        return drop_path(hidden_states, self.drop_prob, self.training)
 
     def extra_repr(self) -> str:
         return "p={}".format(self.drop_prob)

diff --git a/src/transformers/models/dinat/modeling_dinat.py b/src/transformers/models/dinat/modeling_dinat.py
@@ -295,8 +295,8 @@ def __init__(self, drop_prob: Optional[float] = None) -> None:
         super().__init__()
         self.drop_prob = drop_prob
 
-    def forward(self, x: torch.Tensor) -> torch.Tensor:
-        return drop_path(x, self.drop_prob, self.training)
+    def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
+        return drop_path(hidden_states, self.drop_prob, self.training)
 
     def extra_repr(self) -> str:
         return "p={}".format(self.drop_prob)

diff --git a/src/transformers/models/donut/modeling_donut_swin.py b/src/transformers/models/donut/modeling_donut_swin.py
@@ -325,8 +325,8 @@ def __init__(self, drop_prob: Optional[float] = None) -> None:
         super().__init__()
         self.drop_prob = drop_prob
 
-    def forward(self, x: torch.Tensor) -> torch.Tensor:
-        return drop_path(x, self.drop_prob, self.training)
+    def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
+        return drop_path(hidden_states, self.drop_prob, self.training)
 
     def extra_repr(self) -> str:
         return "p={}".format(self.drop_prob)

diff --git a/src/transformers/models/glpn/modeling_glpn.py b/src/transformers/models/glpn/modeling_glpn.py
@@ -82,8 +82,8 @@ def __init__(self, drop_prob: Optional[float] = None) -> None:
         super().__init__()
         self.drop_prob = drop_prob
 
-    def forward(self, x: torch.Tensor) -> torch.Tensor:
-        return drop_path(x, self.drop_prob, self.training)
+    def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
+        return drop_path(hidden_states, self.drop_prob, self.training)
 
     def extra_repr(self) -> str:
         return "p={}".format(self.drop_prob)

diff --git a/src/transformers/models/maskformer/modeling_maskformer_swin.py b/src/transformers/models/maskformer/modeling_maskformer_swin.py
@@ -285,8 +285,8 @@ def __init__(self, drop_prob: Optional[float] = None) -> None:
         super().__init__()
         self.drop_prob = drop_prob
 
-    def forward(self, x: torch.Tensor) -> torch.Tensor:
-        return drop_path(x, self.drop_prob, self.training)
+    def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
+        return drop_path(hidden_states, self.drop_prob, self.training)
 
     def extra_repr(self) -> str:
         return "p={}".format(self.drop_prob)

diff --git a/src/transformers/models/nat/modeling_nat.py b/src/transformers/models/nat/modeling_nat.py
@@ -289,8 +289,8 @@ def __init__(self, drop_prob: Optional[float] = None) -> None:
         super().__init__()
         self.drop_prob = drop_prob
 
-    def forward(self, x: torch.Tensor) -> torch.Tensor:
-        return drop_path(x, self.drop_prob, self.training)
+    def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
+        return drop_path(hidden_states, self.drop_prob, self.training)
 
     def extra_repr(self) -> str:
         return "p={}".format(self.drop_prob)

diff --git a/src/transformers/models/poolformer/modeling_poolformer.py b/src/transformers/models/poolformer/modeling_poolformer.py
@@ -79,8 +79,8 @@ def __init__(self, drop_prob: Optional[float] = None) -> None:
         super().__init__()
         self.drop_prob = drop_prob
 
-    def forward(self, x: torch.Tensor) -> torch.Tensor:
-        return drop_path(x, self.drop_prob, self.training)
+    def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
+        return drop_path(hidden_states, self.drop_prob, self.training)
 
     def extra_repr(self) -> str:
         return "p={}".format(self.drop_prob)

diff --git a/src/transformers/models/segformer/modeling_segformer.py b/src/transformers/models/segformer/modeling_segformer.py
@@ -114,8 +114,8 @@ def __init__(self, drop_prob: Optional[float] = None) -> None:
         super().__init__()
         self.drop_prob = drop_prob
 
-    def forward(self, x: torch.Tensor) -> torch.Tensor:
-        return drop_path(x, self.drop_prob, self.training)
+    def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
+        return drop_path(hidden_states, self.drop_prob, self.training)
 
     def extra_repr(self) -> str:
         return "p={}".format(self.drop_prob)

diff --git a/src/transformers/models/swin/modeling_swin.py b/src/transformers/models/swin/modeling_swin.py
@@ -397,8 +397,8 @@ def __init__(self, drop_prob: Optional[float] = None) -> None:
         super().__init__()
         self.drop_prob = drop_prob
 
-    def forward(self, x: torch.Tensor) -> torch.Tensor:
-        return drop_path(x, self.drop_prob, self.training)
+    def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
+        return drop_path(hidden_states, self.drop_prob, self.training)
 
     def extra_repr(self) -> str:
         return "p={}".format(self.drop_prob)

diff --git a/src/transformers/models/swinv2/modeling_swinv2.py b/src/transformers/models/swinv2/modeling_swinv2.py
@@ -262,8 +262,8 @@ def __init__(self, drop_prob: Optional[float] = None) -> None:
         super().__init__()
         self.drop_prob = drop_prob
 
-    def forward(self, x: torch.Tensor) -> torch.Tensor:
-        return drop_path(x, self.drop_prob, self.training)
+    def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
+        return drop_path(hidden_states, self.drop_prob, self.training)
 
     def extra_repr(self) -> str:
         return "p={}".format(self.drop_prob)

diff --git a/src/transformers/models/van/modeling_van.py b/src/transformers/models/van/modeling_van.py
@@ -83,8 +83,8 @@ def __init__(self, drop_prob: Optional[float] = None) -> None:
         super().__init__()
         self.drop_prob = drop_prob
 
-    def forward(self, x: torch.Tensor) -> torch.Tensor:
-        return drop_path(x, self.drop_prob, self.training)
+    def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
+        return drop_path(hidden_states, self.drop_prob, self.training)
 
     def extra_repr(self) -> str:
         return "p={}".format(self.drop_prob)

diff --git a/src/transformers/models/x_clip/modeling_x_clip.py b/src/transformers/models/x_clip/modeling_x_clip.py
@@ -381,8 +381,8 @@ def __init__(self, drop_prob: Optional[float] = None) -> None:
         super().__init__()
         self.drop_prob = drop_prob
 
-    def forward(self, x: torch.Tensor) -> torch.Tensor:
-        return drop_path(x, self.drop_prob, self.training)
+    def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
+        return drop_path(hidden_states, self.drop_prob, self.training)
 
     def extra_repr(self) -> str:
         return "p={}".format(self.drop_prob)