Class D2FocalNet

semantic_sam/backbone/focal.py:598–679

class D2FocalNet(FocalNet, Backbone):
    def __init__(self, cfg, input_shape):

        pretrain_img_size = cfg['BACKBONE']['FOCAL']['PRETRAIN_IMG_SIZE']
        patch_size = cfg['BACKBONE']['FOCAL']['PATCH_SIZE']
        in_chans = 3
        embed_dim = cfg['BACKBONE']['FOCAL']['EMBED_DIM']
        depths = cfg['BACKBONE']['FOCAL']['DEPTHS']
        mlp_ratio = cfg['BACKBONE']['FOCAL']['MLP_RATIO']
        drop_rate = cfg['BACKBONE']['FOCAL']['DROP_RATE']
        drop_path_rate = cfg['BACKBONE']['FOCAL']['DROP_PATH_RATE']
        norm_layer = nn.LayerNorm
        patch_norm = cfg['BACKBONE']['FOCAL']['PATCH_NORM']
        use_checkpoint = cfg['BACKBONE']['FOCAL']['USE_CHECKPOINT']
        out_indices = cfg['BACKBONE']['FOCAL']['OUT_INDICES']
        scaling_modulator = cfg['BACKBONE']['FOCAL'].get('SCALING_MODULATOR', False)

        super().__init__(
            pretrain_img_size,
            patch_size,
            in_chans,
            embed_dim,
            depths,
            mlp_ratio,
            drop_rate,
            drop_path_rate,
            norm_layer,
            patch_norm,
            out_indices,
            focal_levels=cfg['BACKBONE']['FOCAL']['FOCAL_LEVELS'],
            focal_windows=cfg['BACKBONE']['FOCAL']['FOCAL_WINDOWS'],   
            use_conv_embed=cfg['BACKBONE']['FOCAL']['USE_CONV_EMBED'],    
            use_postln=cfg['BACKBONE']['FOCAL']['USE_POSTLN'],       
            use_postln_in_modulation=cfg['BACKBONE']['FOCAL']['USE_POSTLN_IN_MODULATION'], 
            scaling_modulator=scaling_modulator,
            use_layerscale=cfg['BACKBONE']['FOCAL']['USE_LAYERSCALE'], 
            use_checkpoint=use_checkpoint,
        )

        self._out_features = cfg['BACKBONE']['FOCAL']['OUT_FEATURES']

        self._out_feature_strides = {
            "res2": 4,
            "res3": 8,
            "res4": 16,
            "res5": 32,
        }
        self._out_feature_channels = {
            "res2": self.num_features[0],
            "res3": self.num_features[1],
            "res4": self.num_features[2],
            "res5": self.num_features[3],
        }

    def forward(self, x):
        """
        Args:
            x: Tensor of shape (N,C,H,W). H, W must be a multiple of ``self.size_divisibility``.