| | |
| | |
| |
|
| | 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 |
| |
|
