mirror of https://github.com/alibaba/EasyCV.git
734 lines
28 KiB
Python
734 lines
28 KiB
Python
# modified from https://github.com/SwinTransformer/Video-Swin-Transformer
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from functools import lru_cache, reduce
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from operator import mul
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import numpy as np
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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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import torch.utils.checkpoint as checkpoint
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from einops import rearrange
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from mmcv.runner import load_checkpoint
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from timm.models.layers import DropPath, trunc_normal_
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from easycv.models.utils import Mlp
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from easycv.utils.checkpoint import get_checkpoint
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from easycv.utils.logger import get_root_logger
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from ..registry import BACKBONES
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def window_partition(x, window_size):
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"""
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Args:
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x: (B, D, H, W, C)
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window_size (tuple[int]): window size
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Returns:
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windows: (B*num_windows, window_size*window_size, C)
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"""
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B, D, H, W, C = x.shape
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x = x.view(B, D // window_size[0], window_size[0], H // window_size[1],
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window_size[1], W // window_size[2], window_size[2], C)
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windows = x.permute(0, 1, 3, 5, 2, 4, 6,
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7).contiguous().view(-1, reduce(mul, window_size), C)
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return windows
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def window_reverse(windows, window_size, B, D, H, W):
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"""
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Args:
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windows: (B*num_windows, window_size, window_size, C)
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window_size (tuple[int]): Window size
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H (int): Height of image
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W (int): Width of image
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Returns:
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x: (B, D, H, W, C)
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"""
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x = windows.view(B, D // window_size[0], H // window_size[1],
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W // window_size[2], window_size[0], window_size[1],
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window_size[2], -1)
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x = x.permute(0, 1, 4, 2, 5, 3, 6, 7).contiguous().view(B, D, H, W, -1)
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return x
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def get_window_size(x_size, window_size, shift_size=None):
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use_window_size = list(window_size)
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if shift_size is not None:
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use_shift_size = list(shift_size)
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for i in range(len(x_size)):
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if x_size[i] <= window_size[i]:
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use_window_size[i] = x_size[i]
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if shift_size is not None:
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use_shift_size[i] = 0
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if shift_size is None:
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return tuple(use_window_size)
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else:
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return tuple(use_window_size), tuple(use_shift_size)
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class WindowAttention3D(nn.Module):
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""" Window based multi-head self attention (W-MSA) module with relative position bias.
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It supports both of shifted and non-shifted window.
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Args:
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dim (int): Number of input channels.
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window_size (tuple[int]): The temporal length, height and width of the window.
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num_heads (int): Number of attention heads.
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qkv_bias (bool, optional): If True, add a learnable bias to query, key, value. Default: True
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qk_scale (float | None, optional): Override default qk scale of head_dim ** -0.5 if set
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attn_drop (float, optional): Dropout ratio of attention weight. Default: 0.0
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proj_drop (float, optional): Dropout ratio of output. Default: 0.0
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"""
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def __init__(self,
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dim,
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window_size,
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num_heads,
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qkv_bias=False,
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qk_scale=None,
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attn_drop=0.,
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proj_drop=0.):
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super().__init__()
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self.dim = dim
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self.window_size = window_size # Wd, Wh, Ww
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self.num_heads = num_heads
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head_dim = dim // num_heads
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self.scale = qk_scale or head_dim**-0.5
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# define a parameter table of relative position bias
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self.relative_position_bias_table = nn.Parameter(
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torch.zeros((2 * window_size[0] - 1) * (2 * window_size[1] - 1) *
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(2 * window_size[2] - 1),
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num_heads)) # 2*Wd-1 * 2*Wh-1 * 2*Ww-1, nH
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# get pair-wise relative position index for each token inside the window
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coords_d = torch.arange(self.window_size[0])
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coords_h = torch.arange(self.window_size[1])
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coords_w = torch.arange(self.window_size[2])
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coords = torch.stack(torch.meshgrid(coords_d, coords_h,
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coords_w)) # 3, Wd, Wh, Ww
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coords_flatten = torch.flatten(coords, 1) # 3, Wd*Wh*Ww
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relative_coords = coords_flatten[:, :,
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None] - coords_flatten[:,
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None, :] # 3, Wd*Wh*Ww, Wd*Wh*Ww
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relative_coords = relative_coords.permute(
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1, 2, 0).contiguous() # Wd*Wh*Ww, Wd*Wh*Ww, 3
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relative_coords[:, :,
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0] += self.window_size[0] - 1 # shift to start from 0
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relative_coords[:, :, 1] += self.window_size[1] - 1
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relative_coords[:, :, 2] += self.window_size[2] - 1
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relative_coords[:, :, 0] *= (2 * self.window_size[1] -
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1) * (2 * self.window_size[2] - 1)
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relative_coords[:, :, 1] *= (2 * self.window_size[2] - 1)
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relative_position_index = relative_coords.sum(-1) # Wd*Wh*Ww, Wd*Wh*Ww
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self.register_buffer('relative_position_index',
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relative_position_index)
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self.qkv = nn.Linear(dim, dim * 3, bias=qkv_bias)
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self.attn_drop = nn.Dropout(attn_drop)
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self.proj = nn.Linear(dim, dim)
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self.proj_drop = nn.Dropout(proj_drop)
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trunc_normal_(self.relative_position_bias_table, std=.02)
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self.softmax = nn.Softmax(dim=-1)
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def forward(self, x, mask=None):
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""" Forward function.
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Args:
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x: input features with shape of (num_windows*B, N, C)
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mask: (0/-inf) mask with shape of (num_windows, N, N) or None
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"""
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B_, N, C = x.shape
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qkv = self.qkv(x).reshape(B_, N, 3, self.num_heads,
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C // self.num_heads).permute(2, 0, 3, 1, 4)
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q, k, v = qkv[0], qkv[1], qkv[2] # B_, nH, N, C
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q = q * self.scale
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attn = q @ k.transpose(-2, -1)
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relative_position_bias = self.relative_position_bias_table[
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self.relative_position_index[:N, :N].reshape(-1)].reshape(
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N, N, -1) # Wd*Wh*Ww,Wd*Wh*Ww,nH
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relative_position_bias = relative_position_bias.permute(
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2, 0, 1).contiguous() # nH, Wd*Wh*Ww, Wd*Wh*Ww
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attn = attn + relative_position_bias.unsqueeze(0) # B_, nH, N, N
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if mask is not None:
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nW = mask.shape[0]
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attn = attn.view(B_ // nW, nW, self.num_heads, N,
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N) + mask.unsqueeze(1).unsqueeze(0)
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attn = attn.view(-1, self.num_heads, N, N)
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attn = self.softmax(attn)
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else:
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attn = self.softmax(attn)
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attn = self.attn_drop(attn)
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x = (attn @ v).transpose(1, 2).reshape(B_, N, C)
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x = self.proj(x)
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x = self.proj_drop(x)
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return x
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class SwinTransformerBlock3D(nn.Module):
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""" Swin Transformer Block.
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Args:
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dim (int): Number of input channels.
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num_heads (int): Number of attention heads.
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window_size (tuple[int]): Window size.
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shift_size (tuple[int]): Shift size for SW-MSA.
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mlp_ratio (float): Ratio of mlp hidden dim to embedding dim.
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qkv_bias (bool, optional): If True, add a learnable bias to query, key, value. Default: True
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qk_scale (float | None, optional): Override default qk scale of head_dim ** -0.5 if set.
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drop (float, optional): Dropout rate. Default: 0.0
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attn_drop (float, optional): Attention dropout rate. Default: 0.0
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drop_path (float, optional): Stochastic depth rate. Default: 0.0
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act_layer (nn.Module, optional): Activation layer. Default: nn.GELU
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norm_layer (nn.Module, optional): Normalization layer. Default: nn.LayerNorm
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"""
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def __init__(self,
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dim,
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num_heads,
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window_size=(2, 7, 7),
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shift_size=(0, 0, 0),
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mlp_ratio=4.,
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qkv_bias=True,
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qk_scale=None,
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drop=0.,
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attn_drop=0.,
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drop_path=0.,
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act_layer=nn.GELU,
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norm_layer=nn.LayerNorm,
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use_checkpoint=False):
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super().__init__()
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self.dim = dim
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self.num_heads = num_heads
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self.window_size = window_size
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self.shift_size = shift_size
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self.mlp_ratio = mlp_ratio
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self.use_checkpoint = use_checkpoint
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assert 0 <= self.shift_size[0] < self.window_size[
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0], 'shift_size must in 0-window_size'
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assert 0 <= self.shift_size[1] < self.window_size[
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1], 'shift_size must in 0-window_size'
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assert 0 <= self.shift_size[2] < self.window_size[
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2], 'shift_size must in 0-window_size'
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self.norm1 = norm_layer(dim)
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self.attn = WindowAttention3D(
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dim,
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window_size=self.window_size,
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num_heads=num_heads,
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qkv_bias=qkv_bias,
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qk_scale=qk_scale,
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attn_drop=attn_drop,
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proj_drop=drop)
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self.drop_path = DropPath(
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drop_path) if drop_path > 0. else nn.Identity()
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self.norm2 = norm_layer(dim)
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mlp_hidden_dim = int(dim * mlp_ratio)
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self.mlp = Mlp(
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in_features=dim,
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hidden_features=mlp_hidden_dim,
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act_layer=act_layer,
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drop=drop)
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def forward_part1(self, x, mask_matrix):
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B, D, H, W, C = x.shape
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window_size, shift_size = get_window_size((D, H, W), self.window_size,
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self.shift_size)
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x = self.norm1(x)
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# pad feature maps to multiples of window size
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pad_l = pad_t = pad_d0 = 0
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pad_d1 = (window_size[0] - D % window_size[0]) % window_size[0]
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pad_b = (window_size[1] - H % window_size[1]) % window_size[1]
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pad_r = (window_size[2] - W % window_size[2]) % window_size[2]
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x = F.pad(x, (0, 0, pad_l, pad_r, pad_t, pad_b, pad_d0, pad_d1))
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_, Dp, Hp, Wp, _ = x.shape
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# cyclic shift
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if any(i > 0 for i in shift_size):
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shifted_x = torch.roll(
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x,
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shifts=(-shift_size[0], -shift_size[1], -shift_size[2]),
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dims=(1, 2, 3))
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attn_mask = mask_matrix
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else:
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shifted_x = x
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attn_mask = None
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# partition windows
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x_windows = window_partition(shifted_x,
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window_size) # B*nW, Wd*Wh*Ww, C
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# W-MSA/SW-MSA
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attn_windows = self.attn(
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x_windows, mask=attn_mask) # B*nW, Wd*Wh*Ww, C
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# merge windows
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attn_windows = attn_windows.view(-1, *(window_size + (C, )))
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shifted_x = window_reverse(attn_windows, window_size, B, Dp, Hp,
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Wp) # B D' H' W' C
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# reverse cyclic shift
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if any(i > 0 for i in shift_size):
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x = torch.roll(
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shifted_x,
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shifts=(shift_size[0], shift_size[1], shift_size[2]),
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dims=(1, 2, 3))
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else:
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x = shifted_x
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if pad_d1 > 0 or pad_r > 0 or pad_b > 0:
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x = x[:, :D, :H, :W, :].contiguous()
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return x
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def forward_part2(self, x):
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return self.drop_path(self.mlp(self.norm2(x)))
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def forward(self, x, mask_matrix):
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""" Forward function.
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Args:
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x: Input feature, tensor size (B, D, H, W, C).
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mask_matrix: Attention mask for cyclic shift.
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"""
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shortcut = x
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if self.use_checkpoint:
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x = checkpoint.checkpoint(self.forward_part1, x, mask_matrix)
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else:
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x = self.forward_part1(x, mask_matrix)
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x = shortcut + self.drop_path(x)
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if self.use_checkpoint:
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x = x + checkpoint.checkpoint(self.forward_part2, x)
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else:
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x = x + self.forward_part2(x)
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return x
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class PatchMerging(nn.Module):
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""" Patch Merging Layer
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Args:
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dim (int): Number of input channels.
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norm_layer (nn.Module, optional): Normalization layer. Default: nn.LayerNorm
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"""
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def __init__(self, dim, norm_layer=nn.LayerNorm):
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super().__init__()
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self.dim = dim
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self.reduction = nn.Linear(4 * dim, 2 * dim, bias=False)
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self.norm = norm_layer(4 * dim)
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def forward(self, x):
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""" Forward function.
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Args:
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x: Input feature, tensor size (B, D, H, W, C).
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"""
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B, D, H, W, C = x.shape
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# padding
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pad_input = (H % 2 == 1) or (W % 2 == 1)
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if pad_input:
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x = F.pad(x, (0, 0, 0, W % 2, 0, H % 2))
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x0 = x[:, :, 0::2, 0::2, :] # B D H/2 W/2 C
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x1 = x[:, :, 1::2, 0::2, :] # B D H/2 W/2 C
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x2 = x[:, :, 0::2, 1::2, :] # B D H/2 W/2 C
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x3 = x[:, :, 1::2, 1::2, :] # B D H/2 W/2 C
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x = torch.cat([x0, x1, x2, x3], -1) # B D H/2 W/2 4*C
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x = self.norm(x)
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x = self.reduction(x)
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return x
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# cache each stage results
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@lru_cache()
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def compute_mask(D, H, W, window_size, shift_size, device):
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img_mask = torch.zeros((1, D, H, W, 1), device=device) # 1 Dp Hp Wp 1
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cnt = 0
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for d in slice(-window_size[0]), slice(-window_size[0],
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-shift_size[0]), slice(
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-shift_size[0], None):
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for h in slice(-window_size[1]), slice(-window_size[1],
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-shift_size[1]), slice(
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-shift_size[1], None):
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for w in slice(-window_size[2]), slice(-window_size[2],
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-shift_size[2]), slice(
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-shift_size[2], None):
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img_mask[:, d, h, w, :] = cnt
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cnt += 1
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mask_windows = window_partition(img_mask,
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window_size) # nW, ws[0]*ws[1]*ws[2], 1
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mask_windows = mask_windows.squeeze(-1) # nW, ws[0]*ws[1]*ws[2]
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attn_mask = mask_windows.unsqueeze(1) - mask_windows.unsqueeze(2)
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attn_mask = attn_mask.masked_fill(attn_mask != 0,
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float(-100.0)).masked_fill(
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attn_mask == 0, float(0.0))
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return attn_mask
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class BasicLayer(nn.Module):
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""" A basic Swin Transformer layer for one stage.
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Args:
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dim (int): Number of feature channels
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depth (int): Depths of this stage.
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num_heads (int): Number of attention head.
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window_size (tuple[int]): Local window size. Default: (1,7,7).
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mlp_ratio (float): Ratio of mlp hidden dim to embedding dim. Default: 4.
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qkv_bias (bool, optional): If True, add a learnable bias to query, key, value. Default: True
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qk_scale (float | None, optional): Override default qk scale of head_dim ** -0.5 if set.
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drop (float, optional): Dropout rate. Default: 0.0
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attn_drop (float, optional): Attention dropout rate. Default: 0.0
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drop_path (float | tuple[float], optional): Stochastic depth rate. Default: 0.0
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norm_layer (nn.Module, optional): Normalization layer. Default: nn.LayerNorm
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downsample (nn.Module | None, optional): Downsample layer at the end of the layer. Default: None
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"""
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def __init__(self,
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dim,
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depth,
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num_heads,
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window_size=(1, 7, 7),
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mlp_ratio=4.,
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qkv_bias=False,
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qk_scale=None,
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drop=0.,
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attn_drop=0.,
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drop_path=0.,
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norm_layer=nn.LayerNorm,
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downsample=None,
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use_checkpoint=False):
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super().__init__()
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self.window_size = window_size
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self.shift_size = tuple(i // 2 for i in window_size)
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self.depth = depth
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self.use_checkpoint = use_checkpoint
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# build blocks
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self.blocks = nn.ModuleList([
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SwinTransformerBlock3D(
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dim=dim,
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num_heads=num_heads,
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window_size=window_size,
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shift_size=(0, 0, 0) if (i % 2 == 0) else self.shift_size,
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mlp_ratio=mlp_ratio,
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qkv_bias=qkv_bias,
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qk_scale=qk_scale,
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drop=drop,
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attn_drop=attn_drop,
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drop_path=drop_path[i]
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if isinstance(drop_path, list) else drop_path,
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norm_layer=norm_layer,
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use_checkpoint=use_checkpoint,
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) for i in range(depth)
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])
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self.downsample = downsample
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if self.downsample is not None:
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self.downsample = downsample(dim=dim, norm_layer=norm_layer)
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def forward(self, x):
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""" Forward function.
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Args:
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x: Input feature, tensor size (B, C, D, H, W).
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"""
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# calculate attention mask for SW-MSA
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B, C, D, H, W = x.shape
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window_size, shift_size = get_window_size((D, H, W), self.window_size,
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self.shift_size)
|
|
x = rearrange(x, 'b c d h w -> b d h w c')
|
|
Dp = int(np.ceil(D / window_size[0])) * window_size[0]
|
|
Hp = int(np.ceil(H / window_size[1])) * window_size[1]
|
|
Wp = int(np.ceil(W / window_size[2])) * window_size[2]
|
|
attn_mask = compute_mask(Dp, Hp, Wp, window_size, shift_size, x.device)
|
|
for blk in self.blocks:
|
|
x = blk(x, attn_mask)
|
|
x = x.view(B, D, H, W, -1)
|
|
|
|
if self.downsample is not None:
|
|
x = self.downsample(x)
|
|
x = rearrange(x, 'b d h w c -> b c d h w')
|
|
return x
|
|
|
|
|
|
class PatchEmbed3D(nn.Module):
|
|
""" Video to Patch Embedding.
|
|
Args:
|
|
patch_size (int): Patch token size. Default: (2,4,4).
|
|
in_chans (int): Number of input video channels. Default: 3.
|
|
embed_dim (int): Number of linear projection output channels. Default: 96.
|
|
norm_layer (nn.Module, optional): Normalization layer. Default: None
|
|
"""
|
|
|
|
def __init__(self,
|
|
patch_size=(2, 4, 4),
|
|
in_chans=3,
|
|
embed_dim=96,
|
|
norm_layer=None):
|
|
super().__init__()
|
|
self.patch_size = patch_size
|
|
|
|
self.in_chans = in_chans
|
|
self.embed_dim = embed_dim
|
|
|
|
self.proj = nn.Conv3d(
|
|
in_chans, embed_dim, kernel_size=patch_size, stride=patch_size)
|
|
if norm_layer is not None:
|
|
self.norm = norm_layer(embed_dim)
|
|
else:
|
|
self.norm = None
|
|
|
|
def forward(self, x):
|
|
"""Forward function."""
|
|
# padding
|
|
_, _, D, H, W = x.size()
|
|
if W % self.patch_size[2] != 0:
|
|
x = F.pad(x, (0, self.patch_size[2] - W % self.patch_size[2]))
|
|
if H % self.patch_size[1] != 0:
|
|
x = F.pad(x,
|
|
(0, 0, 0, self.patch_size[1] - H % self.patch_size[1]))
|
|
if D % self.patch_size[0] != 0:
|
|
x = F.pad(
|
|
x,
|
|
(0, 0, 0, 0, 0, self.patch_size[0] - D % self.patch_size[0]))
|
|
|
|
x = self.proj(x) # B C D Wh Ww
|
|
if self.norm is not None:
|
|
D, Wh, Ww = x.size(2), x.size(3), x.size(4)
|
|
x = x.flatten(2).transpose(1, 2)
|
|
x = self.norm(x)
|
|
x = x.transpose(1, 2).view(-1, self.embed_dim, D, Wh, Ww)
|
|
|
|
return x
|
|
|
|
|
|
@BACKBONES.register_module()
|
|
class SwinTransformer3D(nn.Module):
|
|
""" Swin Transformer backbone.
|
|
A PyTorch impl of : `Swin Transformer: Hierarchical Vision Transformer using Shifted Windows` -
|
|
https://arxiv.org/pdf/2103.14030
|
|
Args:
|
|
patch_size (int | tuple(int)): Patch size. Default: (4,4,4).
|
|
in_chans (int): Number of input image channels. Default: 3.
|
|
embed_dim (int): Number of linear projection output channels. Default: 96.
|
|
depths (tuple[int]): Depths of each Swin Transformer stage.
|
|
num_heads (tuple[int]): Number of attention head of each stage.
|
|
window_size (int): Window size. Default: 7.
|
|
mlp_ratio (float): Ratio of mlp hidden dim to embedding dim. Default: 4.
|
|
qkv_bias (bool): If True, add a learnable bias to query, key, value. Default: Truee
|
|
qk_scale (float): Override default qk scale of head_dim ** -0.5 if set.
|
|
drop_rate (float): Dropout rate.
|
|
attn_drop_rate (float): Attention dropout rate. Default: 0.
|
|
drop_path_rate (float): Stochastic depth rate. Default: 0.2.
|
|
norm_layer: Normalization layer. Default: nn.LayerNorm.
|
|
patch_norm (bool): If True, add normalization after patch embedding. Default: False.
|
|
frozen_stages (int): Stages to be frozen (stop grad and set eval mode).
|
|
-1 means not freezing any parameters.
|
|
"""
|
|
|
|
def __init__(self,
|
|
pretrained=None,
|
|
pretrained2d=True,
|
|
patch_size=(4, 4, 4),
|
|
in_chans=3,
|
|
embed_dim=96,
|
|
depths=[2, 2, 6, 2],
|
|
num_heads=[3, 6, 12, 24],
|
|
window_size=(2, 7, 7),
|
|
mlp_ratio=4.,
|
|
qkv_bias=True,
|
|
qk_scale=None,
|
|
drop_rate=0.,
|
|
attn_drop_rate=0.,
|
|
drop_path_rate=0.2,
|
|
norm_layer=nn.LayerNorm,
|
|
patch_norm=False,
|
|
frozen_stages=-1,
|
|
use_checkpoint=False):
|
|
super().__init__()
|
|
|
|
self.pretrained = pretrained
|
|
self.pretrained2d = pretrained2d
|
|
self.num_layers = len(depths)
|
|
self.embed_dim = embed_dim
|
|
self.patch_norm = patch_norm
|
|
self.frozen_stages = frozen_stages
|
|
self.window_size = window_size
|
|
self.patch_size = patch_size
|
|
|
|
# split image into non-overlapping patches
|
|
self.patch_embed = PatchEmbed3D(
|
|
patch_size=patch_size,
|
|
in_chans=in_chans,
|
|
embed_dim=embed_dim,
|
|
norm_layer=norm_layer if self.patch_norm else None)
|
|
|
|
self.pos_drop = nn.Dropout(p=drop_rate)
|
|
|
|
# stochastic depth
|
|
dpr = [
|
|
x.item() for x in torch.linspace(0, drop_path_rate, sum(depths))
|
|
] # stochastic depth decay rule
|
|
|
|
# build layers
|
|
self.layers = nn.ModuleList()
|
|
for i_layer in range(self.num_layers):
|
|
layer = BasicLayer(
|
|
dim=int(embed_dim * 2**i_layer),
|
|
depth=depths[i_layer],
|
|
num_heads=num_heads[i_layer],
|
|
window_size=window_size,
|
|
mlp_ratio=mlp_ratio,
|
|
qkv_bias=qkv_bias,
|
|
qk_scale=qk_scale,
|
|
drop=drop_rate,
|
|
attn_drop=attn_drop_rate,
|
|
drop_path=dpr[sum(depths[:i_layer]):sum(depths[:i_layer + 1])],
|
|
norm_layer=norm_layer,
|
|
downsample=PatchMerging
|
|
if i_layer < self.num_layers - 1 else None,
|
|
use_checkpoint=use_checkpoint)
|
|
self.layers.append(layer)
|
|
|
|
self.num_features = int(embed_dim * 2**(self.num_layers - 1))
|
|
|
|
# add a norm layer for each output
|
|
self.norm = norm_layer(self.num_features)
|
|
|
|
self._freeze_stages()
|
|
|
|
def _freeze_stages(self):
|
|
if self.frozen_stages >= 0:
|
|
self.patch_embed.eval()
|
|
for param in self.patch_embed.parameters():
|
|
param.requires_grad = False
|
|
|
|
if self.frozen_stages >= 1:
|
|
self.pos_drop.eval()
|
|
for i in range(0, self.frozen_stages):
|
|
m = self.layers[i]
|
|
m.eval()
|
|
for param in m.parameters():
|
|
param.requires_grad = False
|
|
|
|
def inflate_weights(self, logger):
|
|
"""Inflate the swin2d parameters to swin3d.
|
|
The differences between swin3d and swin2d mainly lie in an extra
|
|
axis. To utilize the pretrained parameters in 2d model,
|
|
the weight of swin2d models should be inflated to fit in the shapes of
|
|
the 3d counterpart.
|
|
Args:
|
|
logger (logging.Logger): The logger used to print
|
|
debugging infomation.
|
|
"""
|
|
checkpoint = torch.load(self.pretrained, map_location='cpu')
|
|
state_dict = checkpoint['model']
|
|
|
|
# delete relative_position_index since we always re-init it
|
|
relative_position_index_keys = [
|
|
k for k in state_dict.keys() if 'relative_position_index' in k
|
|
]
|
|
for k in relative_position_index_keys:
|
|
del state_dict[k]
|
|
|
|
# delete attn_mask since we always re-init it
|
|
attn_mask_keys = [k for k in state_dict.keys() if 'attn_mask' in k]
|
|
for k in attn_mask_keys:
|
|
del state_dict[k]
|
|
|
|
state_dict['patch_embed.proj.weight'] = state_dict[
|
|
'patch_embed.proj.weight'].unsqueeze(2).repeat(
|
|
1, 1, self.patch_size[0], 1, 1) / self.patch_size[0]
|
|
|
|
# bicubic interpolate relative_position_bias_table if not match
|
|
relative_position_bias_table_keys = [
|
|
k for k in state_dict.keys() if 'relative_position_bias_table' in k
|
|
]
|
|
for k in relative_position_bias_table_keys:
|
|
relative_position_bias_table_pretrained = state_dict[k]
|
|
relative_position_bias_table_current = self.state_dict()[k]
|
|
L1, nH1 = relative_position_bias_table_pretrained.size()
|
|
L2, nH2 = relative_position_bias_table_current.size()
|
|
L2 = (2 * self.window_size[1] - 1) * (2 * self.window_size[2] - 1)
|
|
wd = self.window_size[0]
|
|
if nH1 != nH2:
|
|
logger.warning(f'Error in loading {k}, passing')
|
|
else:
|
|
if L1 != L2:
|
|
S1 = int(L1**0.5)
|
|
relative_position_bias_table_pretrained_resized = torch.nn.functional.interpolate(
|
|
relative_position_bias_table_pretrained.permute(
|
|
1, 0).view(1, nH1, S1, S1),
|
|
size=(2 * self.window_size[1] - 1,
|
|
2 * self.window_size[2] - 1),
|
|
mode='bicubic')
|
|
relative_position_bias_table_pretrained = relative_position_bias_table_pretrained_resized.view(
|
|
nH2, L2).permute(1, 0)
|
|
state_dict[k] = relative_position_bias_table_pretrained.repeat(
|
|
2 * wd - 1, 1)
|
|
|
|
msg = self.load_state_dict(state_dict, strict=False)
|
|
logger.info(msg)
|
|
logger.info(f"=> loaded successfully '{self.pretrained}'")
|
|
del checkpoint
|
|
torch.cuda.empty_cache()
|
|
|
|
def init_weights(self, pretrained=None):
|
|
"""Initialize the weights in backbone.
|
|
Args:
|
|
pretrained (str, optional): Path to pre-trained weights.
|
|
Defaults to None.
|
|
"""
|
|
|
|
def _init_weights(m):
|
|
if isinstance(m, nn.Linear):
|
|
trunc_normal_(m.weight, std=.02)
|
|
if isinstance(m, nn.Linear) and m.bias is not None:
|
|
nn.init.constant_(m.bias, 0)
|
|
elif isinstance(m, nn.LayerNorm):
|
|
nn.init.constant_(m.bias, 0)
|
|
nn.init.constant_(m.weight, 1.0)
|
|
|
|
if pretrained:
|
|
self.pretrained = pretrained
|
|
if isinstance(self.pretrained, str):
|
|
self.apply(_init_weights)
|
|
logger = get_root_logger()
|
|
logger.info(f'load model from: {self.pretrained}')
|
|
self.pretrained = get_checkpoint(self.pretrained)
|
|
if self.pretrained2d:
|
|
# Inflate 2D model into 3D model.
|
|
self.inflate_weights(logger)
|
|
else:
|
|
# Directly load 3D model.
|
|
load_checkpoint(
|
|
self, self.pretrained, strict=False, logger=logger)
|
|
elif self.pretrained is None:
|
|
self.apply(_init_weights)
|
|
else:
|
|
raise TypeError('pretrained must be a str or None')
|
|
|
|
def forward(self, x):
|
|
"""Forward function."""
|
|
x = self.patch_embed(x)
|
|
|
|
x = self.pos_drop(x)
|
|
|
|
for layer in self.layers:
|
|
x = layer(x.contiguous())
|
|
|
|
x = rearrange(x, 'n c d h w -> n d h w c')
|
|
x = self.norm(x)
|
|
x = rearrange(x, 'n d h w c -> n c d h w')
|
|
|
|
return x
|
|
|
|
def train(self, mode=True):
|
|
"""Convert the model into training mode while keep layers freezed."""
|
|
super(SwinTransformer3D, self).train(mode)
|
|
self._freeze_stages()
|