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import torch
from einops import rearrange
from einops.layers.torch import Rearrange
from torch import Tensor, nn
from .embedding.sincos import sincos_2d
from .encoder import Encoder
class PatchDropout(nn.Module):
def __init__(self, prob):
super().__init__()
assert 0 <= prob < 1.
self.prob = prob
def forward(self, x):
if not self.training or self.prob == 0.:
return x
b, n, _, device = *x.shape, x.device
batch_indices = torch.arange(b, device = device)
batch_indices = rearrange(batch_indices, '... -> ... 1')
num_patches_keep = max(1, int(n * (1 - self.prob)))
patch_indices_keep = torch.randn(b, n, device = device).topk(num_patches_keep, dim = -1).indices
return x[batch_indices, patch_indices_keep]
class Vit(nn.Module):
def __init__(
self,
image_height: int,
image_width: int,
patch_height: int,
patch_width: int,
dim: int,
encoder: Encoder,
channels: int = 1,
patch_dropout: float = 0.0,
) -> None:
super().__init__()
patch_dim = patch_height * patch_width * channels
self.to_patch_embedding = nn.Sequential(
Rearrange(
"b c (h ph) (w pw) -> b (h w) (ph pw c)",
ph=patch_height,
pw=patch_width,
),
nn.LayerNorm(patch_dim),
nn.Linear(patch_dim, dim),
nn.LayerNorm(dim),
)
self.patch_embedding = sincos_2d(
h=image_height // patch_height, w=image_width // patch_width, dim=dim
)
self.encoder = encoder
self.patch_dropout = PatchDropout(patch_dropout)
def forward(self, images: Tensor) -> Tensor:
x = self.to_patch_embedding(images)
x = x + self.patch_embedding.to(images.device, dtype=images.dtype)
x = self.patch_dropout(x)
return self.encoder(x)
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