def __init__()

in levit.py [0:0]

• 74 lines of code
• 9 McCabe index (conditional complexity)

    def __init__(self, img_size=224,
                 patch_size=16,
                 in_chans=3,
                 num_classes=1000,
                 embed_dim=[192],
                 key_dim=[64],
                 depth=[12],
                 num_heads=[3],
                 attn_ratio=[2],
                 mlp_ratio=[2],
                 hybrid_backbone=None,
                 down_ops=[],
                 attention_activation=torch.nn.Hardswish,
                 mlp_activation=torch.nn.Hardswish,
                 distillation=True,
                 drop_path=0):
        super().__init__()
        global FLOPS_COUNTER

        self.num_classes = num_classes
        self.num_features = embed_dim[-1]
        self.embed_dim = embed_dim
        self.distillation = distillation

        self.patch_embed = hybrid_backbone

        self.blocks = []
        down_ops.append([''])
        resolution = img_size // patch_size
        for i, (ed, kd, dpth, nh, ar, mr, do) in enumerate(
                zip(embed_dim, key_dim, depth, num_heads, attn_ratio, mlp_ratio, down_ops)):
            for _ in range(dpth):
                self.blocks.append(
                    Residual(Attention(
                        ed, kd, nh,
                        attn_ratio=ar,
                        activation=attention_activation,
                        resolution=resolution,
                    ), drop_path))
                if mr > 0:
                    h = int(ed * mr)
                    self.blocks.append(
                        Residual(torch.nn.Sequential(
                            Linear_BN(ed, h, resolution=resolution),
                            mlp_activation(),
                            Linear_BN(h, ed, bn_weight_init=0,
                                      resolution=resolution),
                        ), drop_path))
            if do[0] == 'Subsample':
                #('Subsample',key_dim, num_heads, attn_ratio, mlp_ratio, stride)
                resolution_ = (resolution - 1) // do[5] + 1
                self.blocks.append(
                    AttentionSubsample(
                        *embed_dim[i:i + 2], key_dim=do[1], num_heads=do[2],
                        attn_ratio=do[3],
                        activation=attention_activation,
                        stride=do[5],
                        resolution=resolution,
                        resolution_=resolution_))
                resolution = resolution_
                if do[4] > 0:  # mlp_ratio
                    h = int(embed_dim[i + 1] * do[4])
                    self.blocks.append(
                        Residual(torch.nn.Sequential(
                            Linear_BN(embed_dim[i + 1], h,
                                      resolution=resolution),
                            mlp_activation(),
                            Linear_BN(
                                h, embed_dim[i + 1], bn_weight_init=0, resolution=resolution),
                        ), drop_path))
        self.blocks = torch.nn.Sequential(*self.blocks)

        # Classifier head
        self.head = BN_Linear(
            embed_dim[-1], num_classes) if num_classes > 0 else torch.nn.Identity()
        if distillation:
            self.head_dist = BN_Linear(
                embed_dim[-1], num_classes) if num_classes > 0 else torch.nn.Identity()

        self.FLOPS = FLOPS_COUNTER
        FLOPS_COUNTER = 0