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Running on Zero
| from typing import Callable, Optional, Tuple, Union | |
| from torch import Tensor | |
| import torch.nn as nn | |
| def make_2tuple(x): | |
| if isinstance(x, tuple): | |
| assert len(x) == 2 | |
| return x | |
| assert isinstance(x, int) | |
| return (x, x) | |
| class PatchEmbed(nn.Module): | |
| """ | |
| 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. | |
| """ | |
| def __init__( | |
| self, | |
| img_size: Union[int, Tuple[int, int]] = 224, | |
| patch_size: Union[int, Tuple[int, int]] = 16, | |
| in_chans: int = 3, | |
| embed_dim: int = 768, | |
| norm_layer: Optional[Callable] = None, | |
| flatten_embedding: bool = True, | |
| ) -> None: | |
| super().__init__() | |
| image_HW = make_2tuple(img_size) | |
| patch_HW = make_2tuple(patch_size) | |
| patch_grid_size = ( | |
| image_HW[0] // patch_HW[0], | |
| image_HW[1] // patch_HW[1], | |
| ) | |
| self.img_size = image_HW | |
| self.patch_size = patch_HW | |
| self.patches_resolution = patch_grid_size | |
| self.num_patches = patch_grid_size[0] * patch_grid_size[1] | |
| self.in_chans = in_chans | |
| self.embed_dim = embed_dim | |
| self.flatten_embedding = flatten_embedding | |
| self.proj = nn.Conv2d(in_chans, embed_dim, kernel_size=patch_HW, stride=patch_HW) | |
| self.norm = norm_layer(embed_dim) if norm_layer else nn.Identity() | |
| def forward(self, x: Tensor) -> Tensor: | |
| _, _, H, W = x.shape | |
| patch_H, patch_W = self.patch_size | |
| assert H % patch_H == 0, f"Input image height {H} is not a multiple of patch height {patch_H}" | |
| assert W % patch_W == 0, f"Input image width {W} is not a multiple of patch width: {patch_W}" | |
| x = self.proj(x) # B C H W | |
| H, W = x.size(2), x.size(3) | |
| x = x.flatten(2).transpose(1, 2) # B HW C | |
| x = self.norm(x) | |
| if not self.flatten_embedding: | |
| x = x.reshape(-1, H, W, self.embed_dim) # B H W C | |
| return x | |
| def flops(self) -> float: | |
| Ho, Wo = self.patches_resolution | |
| flops = Ho * Wo * self.embed_dim * self.in_chans * (self.patch_size[0] * self.patch_size[1]) | |
| if self.norm is not None: | |
| flops += Ho * Wo * self.embed_dim | |
| return flops | |