Expose misc ops at package level (#4812)

* Expose misc ops at package level

* Adding documentation to the ops exposed

docs/source/ops.rst

@@ -43,3 +43,6 @@ Operators
     MultiScaleRoIAlign
     FeaturePyramidNetwork
     StochasticDepth
+    FrozenBatchNorm2d
+    ConvNormActivation
+    SqueezeExcitation

torchvision/ops/__init__.py

@@ -13,6 +13,7 @@ from .boxes import box_convert
 from .deform_conv import deform_conv2d, DeformConv2d
 from .feature_pyramid_network import FeaturePyramidNetwork
 from .focal_loss import sigmoid_focal_loss
+from .misc import FrozenBatchNorm2d, ConvNormActivation, SqueezeExcitation
 from .poolers import MultiScaleRoIAlign
 from .ps_roi_align import ps_roi_align, PSRoIAlign
 from .ps_roi_pool import ps_roi_pool, PSRoIPool
@@ -48,4 +49,7 @@ __all__ = [
     "sigmoid_focal_loss",
     "stochastic_depth",
     "StochasticDepth",
+    "FrozenBatchNorm2d",
+    "ConvNormActivation",
+    "SqueezeExcitation",
 ]

torchvision/ops/misc.py

-"""
-helper class that supports empty tensors on some nn functions.
-
-Ideally, add support directly in PyTorch to empty tensors in
-those functions.
-
-This can be removed once https://github.com/pytorch/pytorch/issues/12013
-is implemented
-"""
-
 import warnings
 from typing import Callable, List, Optional
 
@@ -53,8 +43,11 @@ 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
+    BatchNorm2d where the batch statistics and the affine parameters are fixed
+
+    Args:
+        num_features (int): Number of features ``C`` from an expected input of size ``(N, C, H, W)``
+        eps (float): a value added to the denominator for numerical stability. Default: 1e-5
     """
 
     def __init__(
@@ -109,6 +102,23 @@ class FrozenBatchNorm2d(torch.nn.Module):
 
 
 class ConvNormActivation(torch.nn.Sequential):
+    """
+    Configurable block used for Convolution-Normalzation-Activation blocks.
+
+    Args:
+        in_channels (int): Number of channels in the input image
+        out_channels (int): Number of channels produced by the Convolution-Normalzation-Activation block
+        kernel_size: (int, optional): Size of the convolving kernel. Default: 3
+        stride (int, optional): Stride of the convolution. Default: 1
+        padding (int, tuple or str, optional): Padding added to all four sides of the input. Default: None, in wich case it will calculated as ``padding = (kernel_size - 1) // 2 * dilation``
+        groups (int, optional): Number of blocked connections from input channels to output channels. Default: 1
+        norm_layer (Callable[..., torch.nn.Module], optional): Norm layer that will be stacked on top of the convolutiuon layer. If ``None`` this layer wont be used. Default: ``torch.nn.BatchNorm2d``
+        activation_layer (Callable[..., torch.nn.Module], optinal): Activation function which will be stacked on top of the normalization layer (if not None), otherwise on top of the conv layer. If ``None`` this layer wont be used. Default: ``torch.nn.ReLU``
+        dilation (int): Spacing between kernel elements. Default: 1
+        inplace (bool): Parameter for the activation layer, which can optionally do the operation in-place. Default ``True``
+
+    """
+
     def __init__(
         self,
         in_channels: int,
@@ -146,6 +156,17 @@ class ConvNormActivation(torch.nn.Sequential):
 
 
 class SqueezeExcitation(torch.nn.Module):
+    """
+    This block implements the Squeeze-and-Excitation block from https://arxiv.org/abs/1709.01507 (see Fig. 1).
+    Parameters ``activation``, and ``scale_activation`` correspond to ``delta`` and ``sigma`` in in eq. 3.
+
+    Args:
+        input_channels (int): Number of channels in the input image
+        squeeze_channels (int): Number of squeeze channels
+        activation (Callable[..., torch.nn.Module], optional): ``delta`` activation. Default: ``torch.nn.ReLU``
+        scale_activation (Callable[..., torch.nn.Module]): ``sigma`` activation. Default: ``torch.nn.Sigmoid``
+    """
+
     def __init__(
         self,
         input_channels: int,