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Conv bn relu block

ConvBNReLU

Bases: ConvBNAct

Class for Convolution2d-Batchnorm2d-Relu layer. To exclude Batchnorm module use use_normalization=False, to exclude Relu activation use use_activation=False.

It exists to keep backward compatibility and will be superseeded by ConvBNAct in future releases. For new classes please use ConvBNAct instead.

For convolution arguments documentation see nn.Conv2d. For batchnorm arguments documentation see nn.BatchNorm2d.

Source code in V3_2/src/super_gradients/modules/conv_bn_relu_block.py 
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class ConvBNReLU(ConvBNAct):
    """
    Class for Convolution2d-Batchnorm2d-Relu layer. To exclude Batchnorm module use
        `use_normalization=False`, to exclude Relu activation use `use_activation=False`.

    It exists to keep backward compatibility and will be superseeded by ConvBNAct in future releases.
    For new classes please use ConvBNAct instead.

    For convolution arguments documentation see `nn.Conv2d`.
    For batchnorm arguments documentation see `nn.BatchNorm2d`.
    """

    def __init__(
        self,
        in_channels: int,
        out_channels: int,
        kernel_size: Union[int, Tuple[int, int]],
        stride: Union[int, Tuple[int, int]] = 1,
        padding: Union[int, Tuple[int, int]] = 0,
        dilation: Union[int, Tuple[int, int]] = 1,
        groups: int = 1,
        bias: bool = True,
        padding_mode: str = "zeros",
        use_normalization: bool = True,
        eps: float = 1e-5,
        momentum: float = 0.1,
        affine: bool = True,
        track_running_stats: bool = True,
        device=None,
        dtype=None,
        use_activation: bool = True,
        inplace: bool = False,
    ):
        super().__init__(
            in_channels=in_channels,
            out_channels=out_channels,
            kernel_size=kernel_size,
            padding=padding,
            activation_type=nn.ReLU if use_activation else None,
            activation_kwargs=dict(inplace=inplace) if inplace else None,
            stride=stride,
            dilation=dilation,
            groups=groups,
            bias=bias,
            padding_mode=padding_mode,
            use_normalization=use_normalization,
            eps=eps,
            momentum=momentum,
            affine=affine,
            track_running_stats=track_running_stats,
            device=device,
            dtype=dtype,
        )