| 378 | """ |
| 379 | |
| 380 | def __init__(self, patch_size=4, in_chans=3, embed_dim=96, norm_layer=None, use_conv_embed=False, is_stem=False, use_pre_norm=False): |
| 381 | super().__init__() |
| 382 | patch_size = to_2tuple(patch_size) |
| 383 | self.patch_size = patch_size |
| 384 | |
| 385 | self.in_chans = in_chans |
| 386 | self.embed_dim = embed_dim |
| 387 | self.use_pre_norm = use_pre_norm |
| 388 | |
| 389 | if use_conv_embed: |
| 390 | # if we choose to use conv embedding, then we treat the stem and non-stem differently |
| 391 | if is_stem: |
| 392 | kernel_size = 7; padding = 3; stride = 4 |
| 393 | else: |
| 394 | kernel_size = 3; padding = 1; stride = 2 |
| 395 | self.proj = nn.Conv2d(in_chans, embed_dim, kernel_size=kernel_size, stride=stride, padding=padding) |
| 396 | else: |
| 397 | self.proj = nn.Conv2d(in_chans, embed_dim, kernel_size=patch_size, stride=patch_size) |
| 398 | |
| 399 | if self.use_pre_norm: |
| 400 | if norm_layer is not None: |
| 401 | self.norm = norm_layer(in_chans) |
| 402 | else: |
| 403 | self.norm = None |
| 404 | else: |
| 405 | if norm_layer is not None: |
| 406 | self.norm = norm_layer(embed_dim) |
| 407 | else: |
| 408 | self.norm = None |
| 409 | |
| 410 | def forward(self, x): |
| 411 | """Forward function.""" |