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Running on Zero
Running on Zero
File size: 1,568 Bytes
04012a7 | 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 53 54 55 56 57 | from typing import Tuple
import torch
import torch.nn as nn
import torch.nn.functional as F
def window_partition(x, window_size):
B, H, W, C = x.shape
pad_h = (window_size - H % window_size) % window_size
pad_w = (window_size - W % window_size) % window_size
if pad_h > 0 or pad_w > 0:
x = F.pad(x, (0, 0, 0, pad_w, 0, pad_h))
Hp, Wp = H + pad_h, W + pad_w
x = x.view(B, Hp // window_size, window_size, Wp // window_size, window_size, C)
windows = x.permute(0, 1, 3, 2, 4, 5).reshape(-1, window_size, window_size, C)
return windows, (Hp, Wp)
def window_unpartition(windows, window_size, pad_hw, hw):
Hp, Wp = pad_hw
H, W = hw
B = windows.shape[0] // (Hp * Wp // window_size // window_size)
x = windows.reshape(
B, Hp // window_size, Wp // window_size, window_size, window_size, -1
)
x = x.permute(0, 1, 3, 2, 4, 5).reshape(B, Hp, Wp, -1)
if Hp > H or Wp > W:
x = x[:, :H, :W, :]
return x
class PatchEmbed(nn.Module):
def __init__(
self,
kernel_size: Tuple[int, ...] = (7, 7),
stride: Tuple[int, ...] = (4, 4),
padding: Tuple[int, ...] = (3, 3),
in_chans: int = 3,
embed_dim: int = 768,
):
super().__init__()
self.proj = nn.Conv2d(
in_chans, embed_dim, kernel_size=kernel_size, stride=stride, padding=padding
)
def forward(self, x: torch.Tensor) -> torch.Tensor:
x = self.proj(x)
x = x.permute(0, 2, 3, 1)
return x
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