(
self,
dim,
num_heads,
window_size=7,
shift_size=0,
mlp_ratio=4.0,
qkv_bias=True,
qk_scale=None,
drop=0.0,
attn_drop=0.0,
drop_path=0.0,
act_layer=nn.GELU,
norm_layer=nn.LayerNorm,
)
| 195 | """ |
| 196 | |
| 197 | def __init__( |
| 198 | self, |
| 199 | dim, |
| 200 | num_heads, |
| 201 | window_size=7, |
| 202 | shift_size=0, |
| 203 | mlp_ratio=4.0, |
| 204 | qkv_bias=True, |
| 205 | qk_scale=None, |
| 206 | drop=0.0, |
| 207 | attn_drop=0.0, |
| 208 | drop_path=0.0, |
| 209 | act_layer=nn.GELU, |
| 210 | norm_layer=nn.LayerNorm, |
| 211 | ): |
| 212 | super().__init__() |
| 213 | self.dim = dim |
| 214 | self.num_heads = num_heads |
| 215 | self.window_size = window_size |
| 216 | self.shift_size = shift_size |
| 217 | self.mlp_ratio = mlp_ratio |
| 218 | assert 0 <= self.shift_size < self.window_size, "shift_size must in 0-window_size" |
| 219 | |
| 220 | self.norm1 = norm_layer(dim) |
| 221 | self.attn = WindowAttention( |
| 222 | dim, |
| 223 | window_size=to_2tuple(self.window_size), |
| 224 | num_heads=num_heads, |
| 225 | qkv_bias=qkv_bias, |
| 226 | qk_scale=qk_scale, |
| 227 | attn_drop=attn_drop, |
| 228 | proj_drop=drop, |
| 229 | ) |
| 230 | |
| 231 | self.drop_path = DropPath(drop_path) if drop_path > 0.0 else nn.Identity() |
| 232 | self.norm2 = norm_layer(dim) |
| 233 | mlp_hidden_dim = int(dim * mlp_ratio) |
| 234 | self.mlp = Mlp( |
| 235 | in_features=dim, hidden_features=mlp_hidden_dim, act_layer=act_layer, drop=drop |
| 236 | ) |
| 237 | |
| 238 | self.H = None |
| 239 | self.W = None |
| 240 | |
| 241 | def forward(self, x, mask_matrix): |
| 242 | """Forward function. |
nothing calls this directly
no test coverage detected