Image to Patch Embedding Args: patch_size (int): Patch token size. Default: 4. in_chans (int): Number of input image channels. Default: 3. embed_dim (int): Number of linear projection output channels. Default: 96. norm_layer (nn.Module, optional): Normalization
| 289 | |
| 290 | |
| 291 | class PatchEmbed(nn.Module): |
| 292 | """ Image to Patch Embedding |
| 293 | |
| 294 | Args: |
| 295 | patch_size (int): Patch token size. Default: 4. |
| 296 | in_chans (int): Number of input image channels. Default: 3. |
| 297 | embed_dim (int): Number of linear projection output channels. Default: 96. |
| 298 | norm_layer (nn.Module, optional): Normalization layer. Default: None |
| 299 | """ |
| 300 | |
| 301 | def __init__(self, patch_size=4, in_chans=3, embed_dim=96, norm_layer=None): |
| 302 | super().__init__() |
| 303 | patch_size = to_2tuple(patch_size) |
| 304 | self.patch_size = patch_size |
| 305 | |
| 306 | self.in_chans = in_chans |
| 307 | self.embed_dim = embed_dim |
| 308 | |
| 309 | self.proj = nn.Conv2d(in_chans, embed_dim, kernel_size=patch_size, stride=patch_size) |
| 310 | if norm_layer is not None: |
| 311 | self.norm = norm_layer(embed_dim) |
| 312 | else: |
| 313 | self.norm = None |
| 314 | |
| 315 | def forward(self, x): |
| 316 | """Forward function.""" |
| 317 | # padding |
| 318 | _, _, H, W = x.size() |
| 319 | if W % self.patch_size[1] != 0: |
| 320 | x = F.pad(x, (0, self.patch_size[1] - W % self.patch_size[1])) |
| 321 | if H % self.patch_size[0] != 0: |
| 322 | x = F.pad(x, (0, 0, 0, self.patch_size[0] - H % self.patch_size[0])) |
| 323 | |
| 324 | x = self.proj(x) # B C Wh Ww |
| 325 | if self.norm is not None: |
| 326 | Wh, Ww = x.size(2), x.size(3) |
| 327 | x = x.flatten(2).transpose(1, 2) |
| 328 | x = self.norm(x) |
| 329 | x = x.transpose(1, 2).view(-1, self.embed_dim, Wh, Ww) |
| 330 | |
| 331 | return x |
| 332 | |
| 333 | |
| 334 | class MultiModalSwinTransformer(nn.Module): |