(
self,
pretrain_img_size=224,
patch_size=4,
in_chans=3,
embed_dim=96,
depths=[2, 2, 6, 2],
num_heads=[3, 6, 12, 24],
window_size=7,
mlp_ratio=4.0,
qkv_bias=True,
qk_scale=None,
drop_rate=0.0,
attn_drop_rate=0.0,
drop_path_rate=0.2,
norm_layer=nn.LayerNorm,
ape=False,
patch_norm=True,
out_indices=(0, 1, 2, 3),
frozen_stages=-1,
use_checkpoint=False,
)
| 524 | """ |
| 525 | |
| 526 | def __init__( |
| 527 | self, |
| 528 | pretrain_img_size=224, |
| 529 | patch_size=4, |
| 530 | in_chans=3, |
| 531 | embed_dim=96, |
| 532 | depths=[2, 2, 6, 2], |
| 533 | num_heads=[3, 6, 12, 24], |
| 534 | window_size=7, |
| 535 | mlp_ratio=4.0, |
| 536 | qkv_bias=True, |
| 537 | qk_scale=None, |
| 538 | drop_rate=0.0, |
| 539 | attn_drop_rate=0.0, |
| 540 | drop_path_rate=0.2, |
| 541 | norm_layer=nn.LayerNorm, |
| 542 | ape=False, |
| 543 | patch_norm=True, |
| 544 | out_indices=(0, 1, 2, 3), |
| 545 | frozen_stages=-1, |
| 546 | use_checkpoint=False, |
| 547 | ): |
| 548 | super().__init__() |
| 549 | |
| 550 | self.pretrain_img_size = pretrain_img_size |
| 551 | self.num_layers = len(depths) |
| 552 | self.embed_dim = embed_dim |
| 553 | self.ape = ape |
| 554 | self.patch_norm = patch_norm |
| 555 | self.out_indices = out_indices |
| 556 | self.frozen_stages = frozen_stages |
| 557 | |
| 558 | # split image into non-overlapping patches |
| 559 | self.patch_embed = PatchEmbed( |
| 560 | patch_size=patch_size, |
| 561 | in_chans=in_chans, |
| 562 | embed_dim=embed_dim, |
| 563 | norm_layer=norm_layer if self.patch_norm else None, |
| 564 | ) |
| 565 | |
| 566 | # absolute position embedding |
| 567 | if self.ape: |
| 568 | pretrain_img_size = to_2tuple(pretrain_img_size) |
| 569 | patch_size = to_2tuple(patch_size) |
| 570 | patches_resolution = [ |
| 571 | pretrain_img_size[0] // patch_size[0], |
| 572 | pretrain_img_size[1] // patch_size[1], |
| 573 | ] |
| 574 | |
| 575 | self.absolute_pos_embed = nn.Parameter( |
| 576 | torch.zeros(1, embed_dim, patches_resolution[0], patches_resolution[1]) |
| 577 | ) |
| 578 | trunc_normal_(self.absolute_pos_embed, std=0.02) |
| 579 | |
| 580 | self.pos_drop = nn.Dropout(p=drop_rate) |
| 581 | |
| 582 | # stochastic depth |
| 583 | dpr = [ |
nothing calls this directly
no test coverage detected