From 10a12bf3ba17f2b0c8256553f3987681d05fa048 Mon Sep 17 00:00:00 2001
From: Nicolas Hug <nicolashug@fb.com>
Date: Tue, 30 Nov 2021 15:33:40 +0000
Subject: [PATCH 1/2] Add bias parameter to ConvNormActivation

---
 torchvision/ops/misc.py | 6 +++++-
 1 file changed, 5 insertions(+), 1 deletion(-)

diff --git a/torchvision/ops/misc.py b/torchvision/ops/misc.py
index fac9a3570d6..857148e4ab2 100644
--- a/torchvision/ops/misc.py
+++ b/torchvision/ops/misc.py
@@ -116,6 +116,7 @@ class ConvNormActivation(torch.nn.Sequential):
         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``
+        bias (bool): Whether to use bias in the ``norm_layer``. By default, biases are included if ``norm_layer is None``.
 
     """
 
@@ -131,9 +132,12 @@ def __init__(
         activation_layer: Optional[Callable[..., torch.nn.Module]] = torch.nn.ReLU,
         dilation: int = 1,
         inplace: bool = True,
+        bias: Optional[bool] = None,
     ) -> None:
         if padding is None:
             padding = (kernel_size - 1) // 2 * dilation
+        if bias is None:
+            bias = norm_layer is None
         layers = [
             torch.nn.Conv2d(
                 in_channels,
@@ -143,7 +147,7 @@ def __init__(
                 padding,
                 dilation=dilation,
                 groups=groups,
-                bias=norm_layer is None,
+                bias=bias,
             )
         ]
         if norm_layer is not None:

From 1622d8849ddd09a2068ab3472a93eb40a9678957 Mon Sep 17 00:00:00 2001
From: Nicolas Hug <contact@nicolas-hug.com>
Date: Tue, 30 Nov 2021 16:32:18 +0000
Subject: [PATCH 2/2] Update torchvision/ops/misc.py

Co-authored-by: Vasilis Vryniotis <datumbox@users.noreply.github.com>
---
 torchvision/ops/misc.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/torchvision/ops/misc.py b/torchvision/ops/misc.py
index 857148e4ab2..392517cb772 100644
--- a/torchvision/ops/misc.py
+++ b/torchvision/ops/misc.py
@@ -116,7 +116,7 @@ class ConvNormActivation(torch.nn.Sequential):
         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``
-        bias (bool): Whether to use bias in the ``norm_layer``. By default, biases are included if ``norm_layer is None``.
+        bias (bool, optional): Whether to use bias in the convolution layer. By default, biases are included if ``norm_layer is None``.
 
     """