(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
)
| 212 | """ |
| 213 | |
| 214 | def __init__(self, |
| 215 | dim, |
| 216 | depth, |
| 217 | mlp_ratio=4., |
| 218 | drop=0., |
| 219 | drop_path=0., |
| 220 | norm_layer=nn.LayerNorm, |
| 221 | downsample=None, |
| 222 | focal_window=9, |
| 223 | focal_level=2, |
| 224 | use_conv_embed=False, |
| 225 | use_postln=False, |
| 226 | use_postln_in_modulation=False, |
| 227 | scaling_modulator=False, |
| 228 | use_layerscale=False, |
| 229 | use_checkpoint=False |
| 230 | ): |
| 231 | super().__init__() |
| 232 | self.depth = depth |
| 233 | self.use_checkpoint = use_checkpoint |
| 234 | |
| 235 | # build blocks |
| 236 | self.blocks = nn.ModuleList([ |
| 237 | FocalModulationBlock( |
| 238 | dim=dim, |
| 239 | mlp_ratio=mlp_ratio, |
| 240 | drop=drop, |
| 241 | drop_path=drop_path[i] if isinstance(drop_path, list) else drop_path, |
| 242 | focal_window=focal_window, |
| 243 | focal_level=focal_level, |
| 244 | use_postln=use_postln, |
| 245 | use_postln_in_modulation=use_postln_in_modulation, |
| 246 | scaling_modulator=scaling_modulator, |
| 247 | use_layerscale=use_layerscale, |
| 248 | norm_layer=norm_layer) |
| 249 | for i in range(depth)]) |
| 250 | |
| 251 | # patch merging layer |
| 252 | if downsample is not None: |
| 253 | self.downsample = downsample( |
| 254 | patch_size=2, |
| 255 | in_chans=dim, embed_dim=2*dim, |
| 256 | use_conv_embed=use_conv_embed, |
| 257 | norm_layer=norm_layer, |
| 258 | is_stem=False |
| 259 | ) |
| 260 | |
| 261 | else: |
| 262 | self.downsample = None |
| 263 | |
| 264 | def forward(self, x, H, W): |
| 265 | """ Forward function. |
nothing calls this directly
no test coverage detected