(self,
dim,
depth,
mlp_ratio=4.,
drop=0.,
drop_path=0.,
norm_layer=nn.LayerNorm,
downsample=None,
focal_window=9,
focal_level=2,
use_conv_embed=False,
use_postln=False,
use_postln_in_modulation=False,
scaling_modulator=False,
use_layerscale=False,
use_checkpoint=False,
use_pre_norm=False,
)
| 221 | """ |
| 222 | |
| 223 | def __init__(self, |
| 224 | dim, |
| 225 | depth, |
| 226 | mlp_ratio=4., |
| 227 | drop=0., |
| 228 | drop_path=0., |
| 229 | norm_layer=nn.LayerNorm, |
| 230 | downsample=None, |
| 231 | focal_window=9, |
| 232 | focal_level=2, |
| 233 | use_conv_embed=False, |
| 234 | use_postln=False, |
| 235 | use_postln_in_modulation=False, |
| 236 | scaling_modulator=False, |
| 237 | use_layerscale=False, |
| 238 | use_checkpoint=False, |
| 239 | use_pre_norm=False, |
| 240 | ): |
| 241 | super().__init__() |
| 242 | self.depth = depth |
| 243 | self.use_checkpoint = use_checkpoint |
| 244 | |
| 245 | # build blocks |
| 246 | self.blocks = nn.ModuleList([ |
| 247 | FocalModulationBlock( |
| 248 | dim=dim, |
| 249 | mlp_ratio=mlp_ratio, |
| 250 | drop=drop, |
| 251 | drop_path=drop_path[i] if isinstance(drop_path, list) else drop_path, |
| 252 | focal_window=focal_window, |
| 253 | focal_level=focal_level, |
| 254 | use_postln=use_postln, |
| 255 | use_postln_in_modulation=use_postln_in_modulation, |
| 256 | scaling_modulator=scaling_modulator, |
| 257 | use_layerscale=use_layerscale, |
| 258 | norm_layer=norm_layer) |
| 259 | for i in range(depth)]) |
| 260 | |
| 261 | # patch merging layer |
| 262 | if downsample is not None: |
| 263 | self.downsample = downsample( |
| 264 | patch_size=2, |
| 265 | in_chans=dim, embed_dim=2*dim, |
| 266 | use_conv_embed=use_conv_embed, |
| 267 | norm_layer=norm_layer, |
| 268 | is_stem=False, |
| 269 | use_pre_norm=use_pre_norm |
| 270 | ) |
| 271 | |
| 272 | else: |
| 273 | self.downsample = None |
| 274 | |
| 275 | def forward(self, x, H, W): |
| 276 | """ Forward function. |
nothing calls this directly
no test coverage detected