| |
| |
|
|
| from functools import partial |
|
|
| import torch.nn as nn |
| from einops import rearrange |
| from torch import _assert |
| from torch.nn.modules.utils import _pair |
|
|
| try: |
| from flash_attn.ops.fused_dense import FusedDense |
| except ImportError: |
| FusedDense = None |
|
|
|
|
| class PatchEmbed(nn.Module): |
| """2D Image to Patch Embedding""" |
|
|
| def __init__( |
| self, |
| img_size=224, |
| patch_size=16, |
| in_chans=3, |
| embed_dim=768, |
| norm_layer=None, |
| flatten=True, |
| bias=True, |
| fused_bias_fc=False, |
| ): |
| super().__init__() |
| img_size = _pair(img_size) |
| patch_size = _pair(patch_size) |
| self.img_size = img_size |
| self.patch_size = patch_size |
| self.grid_size = (img_size[0] // patch_size[0], img_size[1] // patch_size[1]) |
| self.num_patches = self.grid_size[0] * self.grid_size[1] |
| self.flatten = flatten |
| if fused_bias_fc and FusedDense is None: |
| raise ImportError("fused_dense is not installed") |
|
|
| linear_cls = nn.Linear if not fused_bias_fc or not bias else FusedDense |
| self.proj = linear_cls(in_chans * patch_size[0] * patch_size[1], embed_dim, bias=bias) |
| self.norm = norm_layer(embed_dim) if norm_layer else nn.Identity() |
|
|
| def forward(self, x): |
| _, _, H, W = x.shape |
| _assert( |
| H == self.img_size[0], |
| f"Input image height ({H}) doesn't match model ({self.img_size[0]}).", |
| ) |
| _assert( |
| W == self.img_size[1], |
| f"Input image width ({W}) doesn't match model ({self.img_size[1]}).", |
| ) |
| x = self.proj( |
| rearrange( |
| x, |
| "b c (h p1) (w p2) -> b h w (c p1 p2)", |
| p1=self.patch_size[0], |
| p2=self.patch_size[1], |
| ) |
| ) |
| if self.flatten: |
| x = rearrange(x, "b h w c -> b (h w) c") |
| x = self.norm(x) |
| return x |
|
|
