Conv2dNormActivation

class torchvision.ops.Conv2dNormActivation(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.modules.module.Module]] = <class 'torch.nn.modules.batchnorm.BatchNorm2d'>, activation_layer: Optional[Callable[[...], torch.nn.modules.module.Module]] = <class 'torch.nn.modules.activation.ReLU'>, dilation: int = 1, inplace: Optional[bool] = True, bias: Optional[bool] = None)

Configurable block used for Convolution2d-Normalization-Activation blocks.

Parameters

• in_channels (int) – Number of channels in the input image

• out_channels (int) – Number of channels produced by the Convolution-Normalization-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 which 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], optional) – 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.