def make_deconv_layers()

in common/nets/layer.py [0:0]

• 16 lines of code
• 7 McCabe index (conditional complexity)

def make_deconv_layers(feat_dims, bnrelu_final=True):
    layers = []
    for i in range(len(feat_dims)-1):
        layers.append(
            nn.ConvTranspose2d(
                in_channels=feat_dims[i],
                out_channels=feat_dims[i+1],
                kernel_size=4,
                stride=2,
                padding=1,
                output_padding=0,
                bias=False))

        # Do not use BN and ReLU for final estimation
        if i < len(feat_dims)-2 or (i == len(feat_dims)-2 and bnrelu_final):
            layers.append(nn.BatchNorm2d(feat_dims[i+1]))
            layers.append(nn.ReLU(inplace=True))

    return nn.Sequential(*layers)