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| | """Some utilities for backbones, in particular for windowing"""
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| |
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| | from typing import Tuple
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| |
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| | import torch
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| | import torch.nn as nn
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| | import torch.nn.functional as F
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| | def window_partition(x, window_size):
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| | """
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| | Partition into non-overlapping windows with padding if needed.
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| | Args:
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| | x (tensor): input tokens with [B, H, W, C].
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| | window_size (int): window size.
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| | Returns:
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| | windows: windows after partition with [B * num_windows, window_size, window_size, C].
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| | (Hp, Wp): padded height and width before partition
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| | """
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| | B, H, W, C = x.shape
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| |
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| | pad_h = (window_size - H % window_size) % window_size
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| | pad_w = (window_size - W % window_size) % window_size
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| | if pad_h > 0 or pad_w > 0:
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| | x = F.pad(x, (0, 0, 0, pad_w, 0, pad_h))
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| | Hp, Wp = H + pad_h, W + pad_w
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| |
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| | x = x.view(B, Hp // window_size, window_size, Wp // window_size, window_size, C)
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| | windows = (
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| | x.permute(0, 1, 3, 2, 4, 5).contiguous().view(-1, window_size, window_size, C)
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| | )
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| | return windows, (Hp, Wp)
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| | def window_unpartition(windows, window_size, pad_hw, hw):
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| | """
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| | Window unpartition into original sequences and removing padding.
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| | Args:
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| | x (tensor): input tokens with [B * num_windows, window_size, window_size, C].
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| | window_size (int): window size.
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| | pad_hw (Tuple): padded height and width (Hp, Wp).
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| | hw (Tuple): original height and width (H, W) before padding.
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| | Returns:
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| | x: unpartitioned sequences with [B, H, W, C].
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| | """
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| | Hp, Wp = pad_hw
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| | H, W = hw
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| | B = windows.shape[0] // (Hp * Wp // window_size // window_size)
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| | x = windows.view(
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| | B, Hp // window_size, Wp // window_size, window_size, window_size, -1
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| | )
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| | x = x.permute(0, 1, 3, 2, 4, 5).contiguous().view(B, Hp, Wp, -1)
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| | if Hp > H or Wp > W:
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| | x = x[:, :H, :W, :].contiguous()
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| | return x
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| |
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| | class PatchEmbed(nn.Module):
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| | """
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| | Image to Patch Embedding.
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| | """
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| | def __init__(
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| | self,
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| | kernel_size: Tuple[int, ...] = (7, 7),
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| | stride: Tuple[int, ...] = (4, 4),
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| | padding: Tuple[int, ...] = (3, 3),
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| | in_chans: int = 3,
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| | embed_dim: int = 768,
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| | ):
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| | """
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| | Args:
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| | kernel_size (Tuple): kernel size of the projection layer.
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| | stride (Tuple): stride of the projection layer.
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| | padding (Tuple): padding size of the projection layer.
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| | in_chans (int): Number of input image channels.
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| | embed_dim (int): embed_dim (int): Patch embedding dimension.
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| | """
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| | super().__init__()
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| | self.proj = nn.Conv2d(
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| | in_chans, embed_dim, kernel_size=kernel_size, stride=stride, padding=padding
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| | )
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| | def forward(self, x: torch.Tensor) -> torch.Tensor:
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| | x = self.proj(x)
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| | x = x.permute(0, 2, 3, 1)
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| | return x
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