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"""Some utilities for backbones, in particular for windowing""" |
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from typing import Tuple |
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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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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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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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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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