Method
__init__
(self,
embed_dims,
num_heads,
attn_drop=0.,
proj_drop=0.,
dropout_layer=None,
init_cfg=None,
batch_first=True,
qkv_bias=False,
norm_cfg=dict(type='LN'),
sr_ratio=1)
Source from the content-addressed store, hash-verified
| 116 | """ |
| 117 | |
| 118 | def __init__(self, |
| 119 | embed_dims, |
| 120 | num_heads, |
| 121 | attn_drop=0., |
| 122 | proj_drop=0., |
| 123 | dropout_layer=None, |
| 124 | init_cfg=None, |
| 125 | batch_first=True, |
| 126 | qkv_bias=False, |
| 127 | norm_cfg=dict(type='LN'), |
| 128 | sr_ratio=1): |
| 129 | super().__init__( |
| 130 | embed_dims, |
| 131 | num_heads, |
| 132 | attn_drop, |
| 133 | proj_drop, |
| 134 | dropout_layer=dropout_layer, |
| 135 | init_cfg=init_cfg, |
| 136 | batch_first=batch_first, |
| 137 | bias=qkv_bias) |
| 138 | |
| 139 | self.sr_ratio = sr_ratio |
| 140 | if sr_ratio > 1: |
| 141 | self.sr = Conv2d( |
| 142 | in_channels=embed_dims, |
| 143 | out_channels=embed_dims, |
| 144 | kernel_size=sr_ratio, |
| 145 | stride=sr_ratio) |
| 146 | # The ret[0] of build_norm_layer is norm name. |
| 147 | self.norm = build_norm_layer(norm_cfg, embed_dims)[1] |
| 148 | |
| 149 | def forward(self, x, hw_shape, identity=None): |
| 150 | |
Callers
nothing calls this directly
Tested by
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