Add Conv2dNormActivation and Conv3dNormActivation Blocks (#5445)

* Add ops.conv3d

* Refactor for conv2d and 3d

* Refactor

* Fix bug

* Addres review

* Fix bug

* nit fix

* Fix flake

* Final fix

* remove documentation

* fix linter

* Update all the implementations to use new Conv

* Small doc fix
Co-authored-by: Vasilis Vryniotis <datumbox@users.noreply.github.com>
Co-authored-by: Joao Gomes <jdsgomes@fb.com>

docs/source/ops.rst

@@ -45,4 +45,6 @@ Operators
     FeaturePyramidNetwork
     StochasticDepth
     FrozenBatchNorm2d
+    Conv2dNormActivation
+    Conv3dNormActivation
     SqueezeExcitation

torchvision/models/convnext.py

@@ -6,7 +6,7 @@ from torch import nn, Tensor
 from torch.nn import functional as F
 
 from .._internally_replaced_utils import load_state_dict_from_url
-from ..ops.misc import ConvNormActivation
+from ..ops.misc import Conv2dNormActivation
 from ..ops.stochastic_depth import StochasticDepth
 from ..utils import _log_api_usage_once
 
@@ -127,7 +127,7 @@ class ConvNeXt(nn.Module):
         # Stem
         firstconv_output_channels = block_setting[0].input_channels
         layers.append(
-            ConvNormActivation(
+            Conv2dNormActivation(
                 3,
                 firstconv_output_channels,
                 kernel_size=4,

torchvision/models/detection/ssdlite.py

@@ -7,7 +7,7 @@ import torch
 from torch import nn, Tensor
 
 from ..._internally_replaced_utils import load_state_dict_from_url
-from ...ops.misc import ConvNormActivation
+from ...ops.misc import Conv2dNormActivation
 from ...utils import _log_api_usage_once
 from .. import mobilenet
 from . import _utils as det_utils
@@ -29,7 +29,7 @@ def _prediction_block(
 ) -> nn.Sequential:
     return nn.Sequential(
         # 3x3 depthwise with stride 1 and padding 1
-        ConvNormActivation(
+        Conv2dNormActivation(
             in_channels,
             in_channels,
             kernel_size=kernel_size,
@@ -47,11 +47,11 @@ def _extra_block(in_channels: int, out_channels: int, norm_layer: Callable[...,
     intermediate_channels = out_channels // 2
     return nn.Sequential(
         # 1x1 projection to half output channels
-        ConvNormActivation(
+        Conv2dNormActivation(
             in_channels, intermediate_channels, kernel_size=1, norm_layer=norm_layer, activation_layer=activation
         ),
         # 3x3 depthwise with stride 2 and padding 1
-        ConvNormActivation(
+        Conv2dNormActivation(
             intermediate_channels,
             intermediate_channels,
             kernel_size=3,
@@ -61,7 +61,7 @@ def _extra_block(in_channels: int, out_channels: int, norm_layer: Callable[...,
             activation_layer=activation,
         ),
         # 1x1 projetion to output channels
-        ConvNormActivation(
+        Conv2dNormActivation(
             intermediate_channels, out_channels, kernel_size=1, norm_layer=norm_layer, activation_layer=activation
         ),
     )

torchvision/models/efficientnet.py

@@ -8,7 +8,7 @@ from torch import nn, Tensor
 from torchvision.ops import StochasticDepth
 
 from .._internally_replaced_utils import load_state_dict_from_url
-from ..ops.misc import ConvNormActivation, SqueezeExcitation
+from ..ops.misc import Conv2dNormActivation, SqueezeExcitation
 from ..utils import _log_api_usage_once
 from ._utils import _make_divisible
 
@@ -104,7 +104,7 @@ class MBConv(nn.Module):
         expanded_channels = cnf.adjust_channels(cnf.input_channels, cnf.expand_ratio)
         if expanded_channels != cnf.input_channels:
             layers.append(
-                ConvNormActivation(
+                Conv2dNormActivation(
                     cnf.input_channels,
                     expanded_channels,
                     kernel_size=1,
@@ -115,7 +115,7 @@ class MBConv(nn.Module):
 
         # depthwise
         layers.append(
-            ConvNormActivation(
+            Conv2dNormActivation(
                 expanded_channels,
                 expanded_channels,
                 kernel_size=cnf.kernel,
@@ -132,7 +132,7 @@ class MBConv(nn.Module):
 
         # project
         layers.append(
-            ConvNormActivation(
+            Conv2dNormActivation(
                 expanded_channels, cnf.out_channels, kernel_size=1, norm_layer=norm_layer, activation_layer=None
             )
         )
@@ -193,7 +193,7 @@ class EfficientNet(nn.Module):
         # building first layer
         firstconv_output_channels = inverted_residual_setting[0].input_channels
         layers.append(
-            ConvNormActivation(
+            Conv2dNormActivation(
                 3, firstconv_output_channels, kernel_size=3, stride=2, norm_layer=norm_layer, activation_layer=nn.SiLU
             )
         )
@@ -224,7 +224,7 @@ class EfficientNet(nn.Module):
         lastconv_input_channels = inverted_residual_setting[-1].out_channels
         lastconv_output_channels = 4 * lastconv_input_channels
         layers.append(
-            ConvNormActivation(
+            Conv2dNormActivation(
                 lastconv_input_channels,
                 lastconv_output_channels,
                 kernel_size=1,

torchvision/models/mobilenetv2.py

@@ -6,7 +6,7 @@ from torch import Tensor
 from torch import nn
 
 from .._internally_replaced_utils import load_state_dict_from_url
-from ..ops.misc import ConvNormActivation
+from ..ops.misc import Conv2dNormActivation
 from ..utils import _log_api_usage_once
 from ._utils import _make_divisible
 
@@ -20,11 +20,11 @@ model_urls = {
 
 
 # necessary for backwards compatibility
-class _DeprecatedConvBNAct(ConvNormActivation):
+class _DeprecatedConvBNAct(Conv2dNormActivation):
     def __init__(self, *args, **kwargs):
         warnings.warn(
             "The ConvBNReLU/ConvBNActivation classes are deprecated since 0.12 and will be removed in 0.14. "
-            "Use torchvision.ops.misc.ConvNormActivation instead.",
+            "Use torchvision.ops.misc.Conv2dNormActivation instead.",
             FutureWarning,
         )
         if kwargs.get("norm_layer", None) is None:
@@ -56,12 +56,12 @@ class InvertedResidual(nn.Module):
         if expand_ratio != 1:
             # pw
             layers.append(
-                ConvNormActivation(inp, hidden_dim, kernel_size=1, norm_layer=norm_layer, activation_layer=nn.ReLU6)
+                Conv2dNormActivation(inp, hidden_dim, kernel_size=1, norm_layer=norm_layer, activation_layer=nn.ReLU6)
             )
         layers.extend(
             [
                 # dw
-                ConvNormActivation(
+                Conv2dNormActivation(
                     hidden_dim,
                     hidden_dim,
                     stride=stride,
@@ -144,7 +144,7 @@ class MobileNetV2(nn.Module):
         input_channel = _make_divisible(input_channel * width_mult, round_nearest)
         self.last_channel = _make_divisible(last_channel * max(1.0, width_mult), round_nearest)
         features: List[nn.Module] = [
-            ConvNormActivation(3, input_channel, stride=2, norm_layer=norm_layer, activation_layer=nn.ReLU6)
+            Conv2dNormActivation(3, input_channel, stride=2, norm_layer=norm_layer, activation_layer=nn.ReLU6)
         ]
         # building inverted residual blocks
         for t, c, n, s in inverted_residual_setting:
@@ -155,7 +155,7 @@ class MobileNetV2(nn.Module):
                 input_channel = output_channel
         # building last several layers
         features.append(
-            ConvNormActivation(
+            Conv2dNormActivation(
                 input_channel, self.last_channel, kernel_size=1, norm_layer=norm_layer, activation_layer=nn.ReLU6
             )
         )

torchvision/models/mobilenetv3.py

@@ -6,7 +6,7 @@ import torch
 from torch import nn, Tensor
 
 from .._internally_replaced_utils import load_state_dict_from_url
-from ..ops.misc import ConvNormActivation, SqueezeExcitation as SElayer
+from ..ops.misc import Conv2dNormActivation, SqueezeExcitation as SElayer
 from ..utils import _log_api_usage_once
 from ._utils import _make_divisible
 
@@ -83,7 +83,7 @@ class InvertedResidual(nn.Module):
         # expand
         if cnf.expanded_channels != cnf.input_channels:
             layers.append(
-                ConvNormActivation(
+                Conv2dNormActivation(
                     cnf.input_channels,
                     cnf.expanded_channels,
                     kernel_size=1,
@@ -95,7 +95,7 @@ class InvertedResidual(nn.Module):
         # depthwise
         stride = 1 if cnf.dilation > 1 else cnf.stride
         layers.append(
-            ConvNormActivation(
+            Conv2dNormActivation(
                 cnf.expanded_channels,
                 cnf.expanded_channels,
                 kernel_size=cnf.kernel,
@@ -112,7 +112,7 @@ class InvertedResidual(nn.Module):
 
         # project
         layers.append(
-            ConvNormActivation(
+            Conv2dNormActivation(
                 cnf.expanded_channels, cnf.out_channels, kernel_size=1, norm_layer=norm_layer, activation_layer=None
             )
         )
@@ -172,7 +172,7 @@ class MobileNetV3(nn.Module):
         # building first layer
         firstconv_output_channels = inverted_residual_setting[0].input_channels
         layers.append(
-            ConvNormActivation(
+            Conv2dNormActivation(
                 3,
                 firstconv_output_channels,
                 kernel_size=3,
@@ -190,7 +190,7 @@ class MobileNetV3(nn.Module):
         lastconv_input_channels = inverted_residual_setting[-1].out_channels
         lastconv_output_channels = 6 * lastconv_input_channels
         layers.append(
-            ConvNormActivation(
+            Conv2dNormActivation(
                 lastconv_input_channels,
                 lastconv_output_channels,
                 kernel_size=1,

torchvision/models/optical_flow/raft.py

@@ -6,7 +6,7 @@ import torch.nn.functional as F
 from torch import Tensor
 from torch.nn.modules.batchnorm import BatchNorm2d
 from torch.nn.modules.instancenorm import InstanceNorm2d
-from torchvision.ops.misc import ConvNormActivation
+from torchvision.ops import Conv2dNormActivation
 
 from ..._internally_replaced_utils import load_state_dict_from_url
 from ...utils import _log_api_usage_once
@@ -38,17 +38,17 @@ class ResidualBlock(nn.Module):
         # and frozen for the rest of the training process (i.e. set as eval()). The bias term is thus still useful
         # for the rest of the datasets. Technically, we could remove the bias for other norm layers like Instance norm
         # because these aren't frozen, but we don't bother (also, we woudn't be able to load the original weights).
-        self.convnormrelu1 = ConvNormActivation(
+        self.convnormrelu1 = Conv2dNormActivation(
             in_channels, out_channels, norm_layer=norm_layer, kernel_size=3, stride=stride, bias=True
         )
-        self.convnormrelu2 = ConvNormActivation(
+        self.convnormrelu2 = Conv2dNormActivation(
             out_channels, out_channels, norm_layer=norm_layer, kernel_size=3, bias=True
         )
 
         if stride == 1:
             self.downsample = nn.Identity()
         else:
-            self.downsample = ConvNormActivation(
+            self.downsample = Conv2dNormActivation(
                 in_channels,
                 out_channels,
                 norm_layer=norm_layer,
@@ -77,13 +77,13 @@ class BottleneckBlock(nn.Module):
         super().__init__()
 
         # See note in ResidualBlock for the reason behind bias=True
-        self.convnormrelu1 = ConvNormActivation(
+        self.convnormrelu1 = Conv2dNormActivation(
             in_channels, out_channels // 4, norm_layer=norm_layer, kernel_size=1, bias=True
         )
-        self.convnormrelu2 = ConvNormActivation(
+        self.convnormrelu2 = Conv2dNormActivation(
             out_channels // 4, out_channels // 4, norm_layer=norm_layer, kernel_size=3, stride=stride, bias=True
         )
-        self.convnormrelu3 = ConvNormActivation(
+        self.convnormrelu3 = Conv2dNormActivation(
             out_channels // 4, out_channels, norm_layer=norm_layer, kernel_size=1, bias=True
         )
         self.relu = nn.ReLU(inplace=True)
@@ -91,7 +91,7 @@ class BottleneckBlock(nn.Module):
         if stride == 1:
             self.downsample = nn.Identity()
         else:
-            self.downsample = ConvNormActivation(
+            self.downsample = Conv2dNormActivation(
                 in_channels,
                 out_channels,
                 norm_layer=norm_layer,
@@ -124,7 +124,9 @@ class FeatureEncoder(nn.Module):
         assert len(layers) == 5
 
         # See note in ResidualBlock for the reason behind bias=True
-        self.convnormrelu = ConvNormActivation(3, layers[0], norm_layer=norm_layer, kernel_size=7, stride=2, bias=True)
+        self.convnormrelu = Conv2dNormActivation(
+            3, layers[0], norm_layer=norm_layer, kernel_size=7, stride=2, bias=True
+        )
 
         self.layer1 = self._make_2_blocks(block, layers[0], layers[1], norm_layer=norm_layer, first_stride=1)
         self.layer2 = self._make_2_blocks(block, layers[1], layers[2], norm_layer=norm_layer, first_stride=2)
@@ -170,17 +172,17 @@ class MotionEncoder(nn.Module):
         assert len(flow_layers) == 2
         assert len(corr_layers) in (1, 2)
 
-        self.convcorr1 = ConvNormActivation(in_channels_corr, corr_layers[0], norm_layer=None, kernel_size=1)
+        self.convcorr1 = Conv2dNormActivation(in_channels_corr, corr_layers[0], norm_layer=None, kernel_size=1)
         if len(corr_layers) == 2:
-            self.convcorr2 = ConvNormActivation(corr_layers[0], corr_layers[1], norm_layer=None, kernel_size=3)
+            self.convcorr2 = Conv2dNormActivation(corr_layers[0], corr_layers[1], norm_layer=None, kernel_size=3)
         else:
             self.convcorr2 = nn.Identity()
 
-        self.convflow1 = ConvNormActivation(2, flow_layers[0], norm_layer=None, kernel_size=7)
-        self.convflow2 = ConvNormActivation(flow_layers[0], flow_layers[1], norm_layer=None, kernel_size=3)
+        self.convflow1 = Conv2dNormActivation(2, flow_layers[0], norm_layer=None, kernel_size=7)
+        self.convflow2 = Conv2dNormActivation(flow_layers[0], flow_layers[1], norm_layer=None, kernel_size=3)
 
         # out_channels - 2 because we cat the flow (2 channels) at the end
-        self.conv = ConvNormActivation(
+        self.conv = Conv2dNormActivation(
             corr_layers[-1] + flow_layers[-1], out_channels - 2, norm_layer=None, kernel_size=3
         )
 
@@ -301,7 +303,7 @@ class MaskPredictor(nn.Module):
 
     def __init__(self, *, in_channels, hidden_size, multiplier=0.25):
         super().__init__()
-        self.convrelu = ConvNormActivation(in_channels, hidden_size, norm_layer=None, kernel_size=3)
+        self.convrelu = Conv2dNormActivation(in_channels, hidden_size, norm_layer=None, kernel_size=3)
         # 8 * 8 * 9 because the predicted flow is downsampled by 8, from the downsampling of the initial FeatureEncoder
         # and we interpolate with all 9 surrounding neighbors. See paper and appendix B.
         self.conv = nn.Conv2d(hidden_size, 8 * 8 * 9, 1, padding=0)

torchvision/models/quantization/mobilenetv2.py

@@ -6,7 +6,7 @@ from torch.ao.quantization import QuantStub, DeQuantStub
 from torchvision.models.mobilenetv2 import InvertedResidual, MobileNetV2, model_urls
 
 from ..._internally_replaced_utils import load_state_dict_from_url
-from ...ops.misc import ConvNormActivation
+from ...ops.misc import Conv2dNormActivation
 from .utils import _fuse_modules, _replace_relu, quantize_model
 
 
@@ -54,7 +54,7 @@ class QuantizableMobileNetV2(MobileNetV2):
 
     def fuse_model(self, is_qat: Optional[bool] = None) -> None:
         for m in self.modules():
-            if type(m) is ConvNormActivation:
+            if type(m) is Conv2dNormActivation:
                 _fuse_modules(m, ["0", "1", "2"], is_qat, inplace=True)
             if type(m) is QuantizableInvertedResidual:
                 m.fuse_model(is_qat)

torchvision/models/quantization/mobilenetv3.py

@@ -5,7 +5,7 @@ from torch import nn, Tensor
 from torch.ao.quantization import QuantStub, DeQuantStub
 
 from ..._internally_replaced_utils import load_state_dict_from_url
-from ...ops.misc import ConvNormActivation, SqueezeExcitation
+from ...ops.misc import Conv2dNormActivation, SqueezeExcitation
 from ..mobilenetv3 import InvertedResidual, InvertedResidualConfig, MobileNetV3, model_urls, _mobilenet_v3_conf
 from .utils import _fuse_modules, _replace_relu
 
@@ -103,7 +103,7 @@ class QuantizableMobileNetV3(MobileNetV3):
 
     def fuse_model(self, is_qat: Optional[bool] = None) -> None:
         for m in self.modules():
-            if type(m) is ConvNormActivation:
+            if type(m) is Conv2dNormActivation:
                 modules_to_fuse = ["0", "1"]
                 if len(m) == 3 and type(m[2]) is nn.ReLU:
                     modules_to_fuse.append("2")

torchvision/models/regnet.py

@@ -12,7 +12,7 @@ import torch
 from torch import nn, Tensor
 
 from .._internally_replaced_utils import load_state_dict_from_url
-from ..ops.misc import ConvNormActivation, SqueezeExcitation
+from ..ops.misc import Conv2dNormActivation, SqueezeExcitation
 from ..utils import _log_api_usage_once
 from ._utils import _make_divisible
 
@@ -55,7 +55,7 @@ model_urls = {
 }
 
 
-class SimpleStemIN(ConvNormActivation):
+class SimpleStemIN(Conv2dNormActivation):
     """Simple stem for ImageNet: 3x3, BN, ReLU."""
 
     def __init__(
@@ -88,10 +88,10 @@ class BottleneckTransform(nn.Sequential):
         w_b = int(round(width_out * bottleneck_multiplier))
         g = w_b // group_width
 
-        layers["a"] = ConvNormActivation(
+        layers["a"] = Conv2dNormActivation(
             width_in, w_b, kernel_size=1, stride=1, norm_layer=norm_layer, activation_layer=activation_layer
         )
-        layers["b"] = ConvNormActivation(
+        layers["b"] = Conv2dNormActivation(
             w_b, w_b, kernel_size=3, stride=stride, groups=g, norm_layer=norm_layer, activation_layer=activation_layer
         )
 
@@ -105,7 +105,7 @@ class BottleneckTransform(nn.Sequential):
                 activation=activation_layer,
             )
 
-        layers["c"] = ConvNormActivation(
+        layers["c"] = Conv2dNormActivation(
             w_b, width_out, kernel_size=1, stride=1, norm_layer=norm_layer, activation_layer=None
         )
         super().__init__(layers)
@@ -131,7 +131,7 @@ class ResBottleneckBlock(nn.Module):
         self.proj = None
         should_proj = (width_in != width_out) or (stride != 1)
         if should_proj:
-            self.proj = ConvNormActivation(
+            self.proj = Conv2dNormActivation(
                 width_in, width_out, kernel_size=1, stride=stride, norm_layer=norm_layer, activation_layer=None
             )
         self.f = BottleneckTransform(

torchvision/models/vision_transformer.py

@@ -7,7 +7,7 @@ import torch
 import torch.nn as nn
 
 from .._internally_replaced_utils import load_state_dict_from_url
-from ..ops.misc import ConvNormActivation
+from ..ops.misc import Conv2dNormActivation
 from ..utils import _log_api_usage_once
 
 __all__ = [
@@ -163,7 +163,7 @@ class VisionTransformer(nn.Module):
             for i, conv_stem_layer_config in enumerate(conv_stem_configs):
                 seq_proj.add_module(
                     f"conv_bn_relu_{i}",
-                    ConvNormActivation(
+                    Conv2dNormActivation(
                         in_channels=prev_channels,
                         out_channels=conv_stem_layer_config.out_channels,
                         kernel_size=conv_stem_layer_config.kernel_size,

torchvision/ops/__init__.py

@@ -14,7 +14,7 @@ from .deform_conv import deform_conv2d, DeformConv2d
 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, SqueezeExcitation
+from .misc import FrozenBatchNorm2d, Conv2dNormActivation, Conv3dNormActivation, SqueezeExcitation
 from .poolers import MultiScaleRoIAlign
 from .ps_roi_align import ps_roi_align, PSRoIAlign
 from .ps_roi_pool import ps_roi_pool, PSRoIPool
@@ -51,6 +51,8 @@ __all__ = [
     "stochastic_depth",
     "StochasticDepth",
     "FrozenBatchNorm2d",
+    "Conv2dNormActivation",
+    "Conv3dNormActivation",
     "SqueezeExcitation",
     "generalized_box_iou_loss",
 ]

torchvision/ops/misc.py

+import warnings
 from typing import Callable, List, Optional
 
 import torch
@@ -66,24 +67,6 @@ 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``
-        bias (bool, optional): Whether to use bias in the convolution layer. By default, biases are included if ``norm_layer is None``.
-
-    """
-
     def __init__(
         self,
         in_channels: int,
@@ -97,13 +80,16 @@ class ConvNormActivation(torch.nn.Sequential):
         dilation: int = 1,
         inplace: Optional[bool] = True,
         bias: Optional[bool] = None,
+        conv_layer: Callable[..., torch.nn.Module] = torch.nn.Conv2d,
     ) -> None:
+
         if padding is None:
             padding = (kernel_size - 1) // 2 * dilation
         if bias is None:
             bias = norm_layer is None
+
         layers = [
-            torch.nn.Conv2d(
+            conv_layer(
                 in_channels,
                 out_channels,
                 kernel_size,
@@ -114,8 +100,10 @@ class ConvNormActivation(torch.nn.Sequential):
                 bias=bias,
             )
         ]
+
         if norm_layer is not None:
             layers.append(norm_layer(out_channels))
+
         if activation_layer is not None:
             params = {} if inplace is None else {"inplace": inplace}
             layers.append(activation_layer(**params))
@@ -123,6 +111,110 @@ class ConvNormActivation(torch.nn.Sequential):
         _log_api_usage_once(self)
         self.out_channels = out_channels
 
+        if self.__class__ == ConvNormActivation:
+            warnings.warn(
+                "Don't use ConvNormActivation directly, please use Conv2dNormActivation and Conv3dNormActivation instead."
+            )
+
+
+class Conv2dNormActivation(ConvNormActivation):
+    """
+    Configurable block used for Convolution2d-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 convolution 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``
+        bias (bool, optional): Whether to use bias in the convolution layer. By default, biases are included if ``norm_layer is None``.
+
+    """
+
+    def __init__(
+        self,
+        in_channels: int,
+        out_channels: int,
+        kernel_size: int = 3,
+        stride: int = 1,
+        padding: Optional[int] = None,
+        groups: int = 1,
+        norm_layer: Optional[Callable[..., torch.nn.Module]] = torch.nn.BatchNorm2d,
+        activation_layer: Optional[Callable[..., torch.nn.Module]] = torch.nn.ReLU,
+        dilation: int = 1,
+        inplace: Optional[bool] = True,
+        bias: Optional[bool] = None,
+    ) -> None:
+
+        super().__init__(
+            in_channels,
+            out_channels,
+            kernel_size,
+            stride,
+            padding,
+            groups,
+            norm_layer,
+            activation_layer,
+            dilation,
+            inplace,
+            bias,
+            torch.nn.Conv2d,
+        )
+
+
+class Conv3dNormActivation(ConvNormActivation):
+    """
+    Configurable block used for Convolution3d-Normalzation-Activation blocks.
+
+    Args:
+        in_channels (int): Number of channels in the input video.
+        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 convolution layer. If ``None`` this layer wont be used. Default: ``torch.nn.BatchNorm3d``
+        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, optional): Whether to use bias in the convolution layer. By default, biases are included if ``norm_layer is None``.
+    """
+
+    def __init__(
+        self,
+        in_channels: int,
+        out_channels: int,
+        kernel_size: int = 3,
+        stride: int = 1,
+        padding: Optional[int] = None,
+        groups: int = 1,
+        norm_layer: Optional[Callable[..., torch.nn.Module]] = torch.nn.BatchNorm3d,
+        activation_layer: Optional[Callable[..., torch.nn.Module]] = torch.nn.ReLU,
+        dilation: int = 1,
+        inplace: Optional[bool] = True,
+        bias: Optional[bool] = None,
+    ) -> None:
+
+        super().__init__(
+            in_channels,
+            out_channels,
+            kernel_size,
+            stride,
+            padding,
+            groups,
+            norm_layer,
+            activation_layer,
+            dilation,
+            inplace,
+            bias,
+            torch.nn.Conv3d,
+        )
+
 
 class SqueezeExcitation(torch.nn.Module):
     """