include suggestions from review

Co-Authored-By: Ross Wightman <[email protected]>

timm/layers/typing.py

@@ -1,9 +1,7 @@
-import functools
-import types
-from typing import Tuple, Union
+from typing import Callable, Tuple, Type, Union
 
-import torch.nn
+import torch
 
 
-LayerType = Union[type, str, types.FunctionType, functools.partial, torch.nn.Module]
+LayerType = Union[str, Callable, Type[torch.nn.Module]]
 PadType = Union[str, int, Tuple[int, int]]

timm/models/mobilenetv3.py

@@ -12,7 +12,6 @@
 import torch
 import torch.nn as nn
 import torch.nn.functional as F
-from torch import Tensor
 from torch.utils.checkpoint import checkpoint
 
 from timm.data import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD, IMAGENET_INCEPTION_MEAN, IMAGENET_INCEPTION_STD
@@ -151,7 +150,7 @@ def reset_classifier(self, num_classes: int, global_pool: str = 'avg'):
         self.flatten = nn.Flatten(1) if global_pool else nn.Identity()  # don't flatten if pooling disabled
         self.classifier = Linear(self.num_features, num_classes) if num_classes > 0 else nn.Identity()
 
-    def forward_features(self, x: Tensor) -> Tensor:
+    def forward_features(self, x: torch.Tensor) -> torch.Tensor:
         x = self.conv_stem(x)
         x = self.bn1(x)
         if self.grad_checkpointing and not torch.jit.is_scripting():
@@ -160,7 +159,7 @@ def forward_features(self, x: Tensor) -> Tensor:
             x = self.blocks(x)
         return x
 
-    def forward_head(self, x: Tensor, pre_logits: bool = False) -> Tensor:
+    def forward_head(self, x: torch.Tensor, pre_logits: bool = False) -> torch.Tensor:
         x = self.global_pool(x)
         x = self.conv_head(x)
         x = self.act2(x)
@@ -171,7 +170,7 @@ def forward_head(self, x: Tensor, pre_logits: bool = False) -> Tensor:
             x = F.dropout(x, p=self.drop_rate, training=self.training)
         return self.classifier(x)
 
-    def forward(self, x: Tensor) -> Tensor:
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
         x = self.forward_features(x)
         x = self.forward_head(x)
         return x
@@ -262,7 +261,7 @@ def __init__(
     def set_grad_checkpointing(self, enable: bool = True):
         self.grad_checkpointing = enable
 
-    def forward(self, x: Tensor) -> List[Tensor]:
+    def forward(self, x: torch.Tensor) -> List[torch.Tensor]:
         x = self.conv_stem(x)
         x = self.bn1(x)
         x = self.act1(x)

timm/models/resnet.py

@@ -14,7 +14,6 @@
 import torch
 import torch.nn as nn
 import torch.nn.functional as F
-from torch import Tensor
 
 from timm.data import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD
 from timm.layers import DropBlock2d, DropPath, AvgPool2dSame, BlurPool2d, GroupNorm, LayerType, create_attn, \
@@ -112,7 +111,7 @@ def zero_init_last(self):
         if getattr(self.bn2, 'weight', None) is not None:
             nn.init.zeros_(self.bn2.weight)
 
-    def forward(self, x: Tensor) -> Tensor:
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
         shortcut = x
 
         x = self.conv1(x)
@@ -212,7 +211,7 @@ def zero_init_last(self):
         if getattr(self.bn3, 'weight', None) is not None:
             nn.init.zeros_(self.bn3.weight)
 
-    def forward(self, x: Tensor) -> Tensor:
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
         shortcut = x
 
         x = self.conv1(x)
@@ -554,7 +553,7 @@ def reset_classifier(self, num_classes, global_pool='avg'):
         self.num_classes = num_classes
         self.global_pool, self.fc = create_classifier(self.num_features, self.num_classes, pool_type=global_pool)
 
-    def forward_features(self, x: Tensor) -> Tensor:
+    def forward_features(self, x: torch.Tensor) -> torch.Tensor:
         x = self.conv1(x)
         x = self.bn1(x)
         x = self.act1(x)
@@ -569,13 +568,13 @@ def forward_features(self, x: Tensor) -> Tensor:
             x = self.layer4(x)
         return x
 
-    def forward_head(self, x: Tensor, pre_logits: bool = False) -> Tensor:
+    def forward_head(self, x: torch.Tensor, pre_logits: bool = False) -> torch.Tensor:
         x = self.global_pool(x)
         if self.drop_rate:
             x = F.dropout(x, p=float(self.drop_rate), training=self.training)
         return x if pre_logits else self.fc(x)
 
-    def forward(self, x: Tensor) -> Tensor:
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
         x = self.forward_features(x)
         x = self.forward_head(x)
         return x