59ce4322d7b774394099f8af0381d1020623d919,pytorch/pytorchcv/models/common.py,SEBlock,__init__,#SEBlock#Any#Any#Any#Any#,764

Before Change

                 approx_sigmoid=False,
                 activation=(lambda: nn.ReLU(inplace=True))):
        super(SEBlock, self).__init__()
        mid_cannels = channels // reduction

        self.pool = nn.AdaptiveAvgPool2d(output_size=1)
        self.conv1 = conv1x1(
            in_channels=channels,
            out_channels=mid_cannels,
            bias=True)
        self.activ = get_activation_layer(activation)
        self.conv2 = conv1x1(
            in_channels=mid_cannels,
            out_channels=channels,
            bias=True)

After Change

        Padding value for convolution layer.
    groups : int, default 1
        Number of groups.
    bias : bool, default False
        Whether the layer uses a bias vector.
    
    return nn.Conv2d(
        in_channels=in_channels,
        out_channels=out_channels,
        kernel_size=3,
        stride=stride,
        padding=padding,
        dilation=dilation,
        groups=groups,
        bias=bias)


def depthwise_conv3x3(channels,
                      stride):
    
    Depthwise convolution 3x3 layer.

    Parameters:
    ----------
    channels : int
        Number of input/output channels.
    strides : int or tuple/list of 2 int
        Strides of the convolution.
    

Instances