def __init__()

in src/controlnet_aux/zoe/zoedepth/models/base_models/midas_repo/midas/midas_net_custom.py [0:0]

• 37 lines of code
• 6 McCabe index (conditional complexity)

    def __init__(self, path=None, features=64, backbone="efficientnet_lite3", non_negative=True, exportable=True, channels_last=False, align_corners=True,
        blocks={'expand': True}):
        """Init.

        Args:
            path (str, optional): Path to saved model. Defaults to None.
            features (int, optional): Number of features. Defaults to 256.
            backbone (str, optional): Backbone network for encoder. Defaults to resnet50
        """
        print("Loading weights: ", path)

        super(MidasNet_small, self).__init__()

        use_pretrained = False if path else True
                
        self.channels_last = channels_last
        self.blocks = blocks
        self.backbone = backbone

        self.groups = 1

        features1=features
        features2=features
        features3=features
        features4=features
        self.expand = False
        if "expand" in self.blocks and self.blocks['expand'] == True:
            self.expand = True
            features1=features
            features2=features*2
            features3=features*4
            features4=features*8

        self.pretrained, self.scratch = _make_encoder(self.backbone, features, use_pretrained, groups=self.groups, expand=self.expand, exportable=exportable)
  
        self.scratch.activation = nn.ReLU(False)    

        self.scratch.refinenet4 = FeatureFusionBlock_custom(features4, self.scratch.activation, deconv=False, bn=False, expand=self.expand, align_corners=align_corners)
        self.scratch.refinenet3 = FeatureFusionBlock_custom(features3, self.scratch.activation, deconv=False, bn=False, expand=self.expand, align_corners=align_corners)
        self.scratch.refinenet2 = FeatureFusionBlock_custom(features2, self.scratch.activation, deconv=False, bn=False, expand=self.expand, align_corners=align_corners)
        self.scratch.refinenet1 = FeatureFusionBlock_custom(features1, self.scratch.activation, deconv=False, bn=False, align_corners=align_corners)

        
        self.scratch.output_conv = nn.Sequential(
            nn.Conv2d(features, features//2, kernel_size=3, stride=1, padding=1, groups=self.groups),
            Interpolate(scale_factor=2, mode="bilinear"),
            nn.Conv2d(features//2, 32, kernel_size=3, stride=1, padding=1),
            self.scratch.activation,
            nn.Conv2d(32, 1, kernel_size=1, stride=1, padding=0),
            nn.ReLU(True) if non_negative else nn.Identity(),
            nn.Identity(),
        )
        
        if path:
            self.load(path)