2D image to patch embedding: (B,C,H,W) -> (B,N,D) Args: img_size: Image size. patch_size: Patch token size. in_chans: Number of input image channels. embed_dim: Number of linear projection output channels. norm_layer: Normalization layer.
| 16 | |
| 17 | |
| 18 | class PatchEmbed(nn.Module): |
| 19 | """ |
| 20 | 2D image to patch embedding: (B,C,H,W) -> (B,N,D) |
| 21 | |
| 22 | Args: |
| 23 | img_size: Image size. |
| 24 | patch_size: Patch token size. |
| 25 | in_chans: Number of input image channels. |
| 26 | embed_dim: Number of linear projection output channels. |
| 27 | norm_layer: Normalization layer. |
| 28 | """ |
| 29 | |
| 30 | def __init__( |
| 31 | self, |
| 32 | img_size: Union[int, Tuple[int, int]] = 224, |
| 33 | patch_size: Union[int, Tuple[int, int]] = 16, |
| 34 | in_chans: int = 3, |
| 35 | embed_dim: int = 768, |
| 36 | norm_layer: Optional[Callable] = None, |
| 37 | flatten_embedding: bool = True, |
| 38 | ) -> None: |
| 39 | super().__init__() |
| 40 | |
| 41 | image_HW = make_2tuple(img_size) |
| 42 | patch_HW = make_2tuple(patch_size) |
| 43 | patch_grid_size = ( |
| 44 | image_HW[0] // patch_HW[0], |
| 45 | image_HW[1] // patch_HW[1], |
| 46 | ) |
| 47 | |
| 48 | self.img_size = image_HW |
| 49 | self.patch_size = patch_HW |
| 50 | self.patches_resolution = patch_grid_size |
| 51 | self.num_patches = patch_grid_size[0] * patch_grid_size[1] |
| 52 | |
| 53 | self.in_chans = in_chans |
| 54 | self.embed_dim = embed_dim |
| 55 | |
| 56 | self.flatten_embedding = flatten_embedding |
| 57 | |
| 58 | self.proj = nn.Conv2d(in_chans, embed_dim, kernel_size=patch_HW, stride=patch_HW) |
| 59 | self.norm = norm_layer(embed_dim) if norm_layer else nn.Identity() |
| 60 | |
| 61 | def forward(self, x: Tensor) -> Tensor: |
| 62 | _, _, H, W = x.shape |
| 63 | |
| 64 | x = self.proj(x) # B C H W |
| 65 | H, W = x.size(2), x.size(3) |
| 66 | x = x.flatten(2).transpose(1, 2) # B HW C |
| 67 | x = self.norm(x) |
| 68 | if not self.flatten_embedding: |
| 69 | x = x.reshape(-1, H, W, self.embed_dim) # B H W C |
| 70 | return x |
| 71 | |
| 72 | def flops(self) -> float: |
| 73 | Ho, Wo = self.patches_resolution |
| 74 | flops = Ho * Wo * self.embed_dim * self.in_chans * (self.patch_size[0] * self.patch_size[1]) |
| 75 | if self.norm is not None: |