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eca55dc | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 | import torch
import torch.nn as nn
from timm.layers import PatchEmbed as TimmPatchEmbed
class PatchEmbed(nn.Module):
"""
2D Image to Patch Embedding.
Args:
img_size (tuple[int, int]): Input image size (H, W).
patch_size (tuple[int, int]): Patch size (H, W).
in_chans (int): Number of input channels.
embed_dim (int): Embedding dimension.
"""
def __init__(
self,
img_size: tuple[int, int] = (128, 256),
patch_size: tuple[int, int] = (16, 16),
in_chans: int = 1,
embed_dim: int = 768,
):
super().__init__()
self.img_size = img_size
self.patch_size = patch_size
self.in_chans = in_chans
self.embed_dim = embed_dim
self.patch_embed = TimmPatchEmbed(
img_size=img_size,
patch_size=patch_size,
in_chans=in_chans,
embed_dim=embed_dim,
flatten=True,
bias=True,
strict_img_size=False,
)
self.num_patches = self.patch_embed.num_patches
def forward(self, x: torch.Tensor) -> torch.Tensor:
"""
Forward pass.
Args:
x (torch.Tensor): Input tensor [B, C, H, W].
Returns:
torch.Tensor: Patch embeddings [B, N, D].
"""
return self.patch_embed(x)
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