timm/models/mvitv2.py [199:223]:
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
    def __init__(
            self,
            dim,
            dim_out,
            feat_size,
            num_heads=8,
            qkv_bias=True,
            mode="conv",
            kernel_q=(1, 1),
            kernel_kv=(1, 1),
            stride_q=(1, 1),
            stride_kv=(1, 1),
            has_cls_token=True,
            rel_pos_type='spatial',
            residual_pooling=True,
            norm_layer=nn.LayerNorm,
    ):
        super().__init__()
        self.num_heads = num_heads
        self.dim_out = dim_out
        self.head_dim = dim_out // num_heads
        self.scale = self.head_dim ** -0.5
        self.has_cls_token = has_cls_token
        padding_q = tuple([int(q // 2) for q in kernel_q])
        padding_kv = tuple([int(kv // 2) for kv in kernel_kv])
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -



timm/models/mvitv2.py [369:393]:
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
    def __init__(
            self,
            dim,
            dim_out,
            feat_size,
            num_heads=8,
            qkv_bias=True,
            mode="conv",
            kernel_q=(1, 1),
            kernel_kv=(1, 1),
            stride_q=(1, 1),
            stride_kv=(1, 1),
            has_cls_token=True,
            rel_pos_type='spatial',
            residual_pooling=True,
            norm_layer=nn.LayerNorm,
    ):
        super().__init__()
        self.num_heads = num_heads
        self.dim_out = dim_out
        self.head_dim = dim_out // num_heads
        self.scale = self.head_dim ** -0.5
        self.has_cls_token = has_cls_token
        padding_q = tuple([int(q // 2) for q in kernel_q])
        padding_kv = tuple([int(kv // 2) for kv in kernel_kv])
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -