Move Permute layer to ops. (#6055)

docs/source/ops.rst

@@ -87,8 +87,9 @@ TorchVision provides commonly used building blocks as layers:
     DeformConv2d
     DropBlock2d
     DropBlock3d
-    MLP
     FrozenBatchNorm2d
+    MLP
+    Permute
     SqueezeExcitation
     StochasticDepth
 

torchvision/models/convnext.py

@@ -5,7 +5,7 @@ import torch
 from torch import nn, Tensor
 from torch.nn import functional as F
 
-from ..ops.misc import Conv2dNormActivation
+from ..ops.misc import Conv2dNormActivation, Permute
 from ..ops.stochastic_depth import StochasticDepth
 from ..transforms._presets import ImageClassification
 from ..utils import _log_api_usage_once
@@ -35,15 +35,6 @@ class LayerNorm2d(nn.LayerNorm):
         return x
 
 
-class Permute(nn.Module):
-    def __init__(self, dims: List[int]):
-        super().__init__()
-        self.dims = dims
-
-    def forward(self, x):
-        return torch.permute(x, self.dims)
-
-
 class CNBlock(nn.Module):
     def __init__(
         self,

torchvision/models/swin_transformer.py

@@ -5,14 +5,13 @@ import torch
 import torch.nn.functional as F
 from torch import nn, Tensor
 
-from ..ops.misc import MLP
+from ..ops.misc import MLP, Permute
 from ..ops.stochastic_depth import StochasticDepth
 from ..transforms._presets import ImageClassification, InterpolationMode
 from ..utils import _log_api_usage_once
 from ._api import WeightsEnum, Weights
 from ._meta import _IMAGENET_CATEGORIES
 from ._utils import _ovewrite_named_param
-from .convnext import Permute  # TODO: move Permute on ops
 
 
 __all__ = [

torchvision/ops/__init__.py

@@ -19,7 +19,7 @@ from .drop_block import drop_block2d, DropBlock2d, drop_block3d, DropBlock3d
 from .feature_pyramid_network import FeaturePyramidNetwork
 from .focal_loss import sigmoid_focal_loss
 from .giou_loss import generalized_box_iou_loss
-from .misc import FrozenBatchNorm2d, Conv2dNormActivation, Conv3dNormActivation, SqueezeExcitation, MLP
+from .misc import FrozenBatchNorm2d, Conv2dNormActivation, Conv3dNormActivation, SqueezeExcitation, MLP, Permute
 from .poolers import MultiScaleRoIAlign
 from .ps_roi_align import ps_roi_align, PSRoIAlign
 from .ps_roi_pool import ps_roi_pool, PSRoIPool
@@ -62,6 +62,7 @@ __all__ = [
     "Conv3dNormActivation",
     "SqueezeExcitation",
     "MLP",
+    "Permute",
     "generalized_box_iou_loss",
     "distance_box_iou_loss",
     "complete_box_iou_loss",

torchvision/ops/misc.py

@@ -10,7 +10,6 @@ from ..utils import _log_api_usage_once
 interpolate = torch.nn.functional.interpolate
 
 
-# This is not in nn
 class FrozenBatchNorm2d(torch.nn.Module):
     """
     BatchNorm2d where the batch statistics and the affine parameters are fixed
@@ -297,3 +296,18 @@ class MLP(torch.nn.Sequential):
 
         super().__init__(*layers)
         _log_api_usage_once(self)
+
+
+class Permute(torch.nn.Module):
+    """This module returns a view of the tensor input with its dimensions permuted.
+
+    Args:
+        dims (List[int]): The desired ordering of dimensions
+    """
+
+    def __init__(self, dims: List[int]):
+        super().__init__()
+        self.dims = dims
+
+    def forward(self, x: Tensor) -> Tensor:
+        return torch.permute(x, self.dims)