video_swin_transformer.py

'''
Credit to the official implementation: https://github.com/SwinTransformer/Video-Swin-Transformer
Adapted from https://github.com/haofanwang/video-swin-transformer-pytorch
'''

import torch
import torch.nn.functional as F
from torch import nn
from torch.utils import checkpoint
import numpy as np
from timm.layers import DropPath, trunc_normal_
import transfuser_utils as t_u

from functools import reduce, lru_cache
from operator import mul
from einops import rearrange


class Mlp(nn.Module):
  """ Multilayer perceptron."""

  def __init__(self, in_features, hidden_features=None, out_features=None, act_layer=nn.GELU, drop=0.):
    super().__init__()
    out_features = out_features or in_features
    hidden_features = hidden_features or in_features
    self.fc1 = nn.Linear(in_features, hidden_features)
    self.act = act_layer()
    self.fc2 = nn.Linear(hidden_features, out_features)
    self.drop = nn.Dropout(drop)

  def forward(self, x):
    x = self.fc1(x)
    x = self.act(x)
    x = self.drop(x)
    x = self.fc2(x)
    x = self.drop(x)
    return x


def window_partition(x, window_size):
  """
    Args:
        x: (B, D, H, W, C)
        window_size (tuple[int]): window size
    Returns:
        windows: (B*num_windows, window_size*window_size, C)
    """
  b, d, h, w, c = x.shape
  x = x.view(b, d // window_size[0], window_size[0], h // window_size[1], window_size[1], w // window_size[2],
             window_size[2], c)
  windows = x.permute(0, 1, 3, 5, 2, 4, 6, 7).contiguous().view(-1, reduce(mul, window_size), c)
  return windows


def window_reverse(windows, window_size, b, d, h, w):
  """
    Args:
        windows: (B*num_windows, window_size, window_size, C)
        window_size (tuple[int]): Window size
        h (int): Height of image
        w (int): Width of image
    Returns:
        x: (b, d, h, w, c)
    """
  x = windows.view(b, d // window_size[0], h // window_size[1], w // window_size[2], window_size[0], window_size[1],
                   window_size[2], -1)
  x = x.permute(0, 1, 4, 2, 5, 3, 6, 7).contiguous().view(b, d, h, w, -1)
  return x


def get_window_size(x_size, window_size, shift_size=None):
  use_window_size = list(window_size)
  if shift_size is not None:
    use_shift_size = list(shift_size)
  for i in range(len(x_size)):
    if x_size[i] <= window_size[i]:
      use_window_size[i] = x_size[i]
      if shift_size is not None:
        use_shift_size[i] = 0

  if shift_size is None:
    return tuple(use_window_size)
  else:
    return tuple(use_window_size), tuple(use_shift_size)


class WindowAttention3D(nn.Module):
  """ Window based multi-head self attention (W-MSA) module with relative position bias.
    It supports both of shifted and non-shifted window.
    Args:
        dim (int): Number of input channels.
        window_size (tuple[int]): The temporal length, height and width of the window.
        num_heads (int): Number of attention heads.
        qkv_bias (bool, optional):  If True, add a learnable bias to query, key, value. Default: True
        qk_scale (float | None, optional): Override default qk scale of head_dim ** -0.5 if set
        attn_drop (float, optional): Dropout ratio of attention weight. Default: 0.0
        proj_drop (float, optional): Dropout ratio of output. Default: 0.0
    """

  def __init__(self, dim, window_size, num_heads, qkv_bias=False, qk_scale=None, attn_drop=0., proj_drop=0.):

    super().__init__()
    self.dim = dim
    self.window_size = window_size  # Wd, Wh, Ww
    self.num_heads = num_heads
    head_dim = dim // num_heads
    self.scale = qk_scale or head_dim**-0.5

    # define a parameter table of relative position bias
    self.relative_position_bias_table = nn.Parameter(
        torch.zeros((2 * window_size[0] - 1) * (2 * window_size[1] - 1) * (2 * window_size[2] - 1),
                    num_heads))  # 2*Wd-1 * 2*Wh-1 * 2*Ww-1, nH

    # get pair-wise relative position index for each token inside the window
    coords_d = torch.arange(self.window_size[0])
    coords_h = torch.arange(self.window_size[1])
    coords_w = torch.arange(self.window_size[2])
    coords = torch.stack(torch.meshgrid(coords_d, coords_h, coords_w))  # 3, Wd, Wh, Ww
    coords_flatten = torch.flatten(coords, 1)  # 3, Wd*Wh*Ww
    relative_coords = coords_flatten[:, :, None] - coords_flatten[:, None, :]  # 3, Wd*Wh*Ww, Wd*Wh*Ww
    relative_coords = relative_coords.permute(1, 2, 0).contiguous()  # Wd*Wh*Ww, Wd*Wh*Ww, 3
    relative_coords[:, :, 0] += self.window_size[0] - 1  # shift to start from 0
    relative_coords[:, :, 1] += self.window_size[1] - 1
    relative_coords[:, :, 2] += self.window_size[2] - 1

    relative_coords[:, :, 0] *= (2 * self.window_size[1] - 1) * (2 * self.window_size[2] - 1)
    relative_coords[:, :, 1] *= (2 * self.window_size[2] - 1)
    relative_position_index = relative_coords.sum(-1)  # Wd*Wh*Ww, Wd*Wh*Ww
    self.register_buffer('relative_position_index', relative_position_index)

    self.qkv = nn.Linear(dim, dim * 3, bias=qkv_bias)
    self.attn_drop = nn.Dropout(attn_drop)
    self.proj = nn.Linear(dim, dim)
    self.proj_drop = nn.Dropout(proj_drop)

    trunc_normal_(self.relative_position_bias_table, std=.02)
    self.softmax = nn.Softmax(dim=-1)

  def forward(self, x, mask=None):
    """ Forward function.
        Args:
            x: input features with shape of (num_windows*B, N, C)
            mask: (0/-inf) mask with shape of (num_windows, N, N) or None
        """
    b_, n, c = x.shape
    qkv = self.qkv(x).reshape(b_, n, 3, self.num_heads, c // self.num_heads).permute(2, 0, 3, 1, 4)
    q, k, v = qkv[0], qkv[1], qkv[2]  # B_, nH, N, C

    q = q * self.scale
    attn = q @ k.transpose(-2, -1)

    relative_position_bias = self.relative_position_bias_table[self.relative_position_index[:n, :n].reshape(
        -1)].reshape(n, n, -1)  # Wd*Wh*Ww,Wd*Wh*Ww,nH
    relative_position_bias = relative_position_bias.permute(2, 0, 1).contiguous()  # nH, Wd*Wh*Ww, Wd*Wh*Ww
    attn = attn + relative_position_bias.unsqueeze(0)  # B_, nH, N, N

    if mask is not None:
      n_w = mask.shape[0]
      attn = attn.view(b_ // n_w, n_w, self.num_heads, n, n) + mask.unsqueeze(1).unsqueeze(0)
      attn = attn.view(-1, self.num_heads, n, n)
      attn = self.softmax(attn)
    else:
      attn = self.softmax(attn)

    attn = self.attn_drop(attn)

    x = (attn @ v).transpose(1, 2).reshape(b_, n, c)
    x = self.proj(x)
    x = self.proj_drop(x)
    return x


class SwinTransformerBlock3D(nn.Module):
  """ Swin Transformer Block.
    Args:
        dim (int): Number of input channels.
        num_heads (int): Number of attention heads.
        window_size (tuple[int]): Window size.
        shift_size (tuple[int]): Shift size for SW-MSA.
        mlp_ratio (float): Ratio of mlp hidden dim to embedding dim.
        qkv_bias (bool, optional): If True, add a learnable bias to query, key, value. Default: True
        qk_scale (float | None, optional): Override default qk scale of head_dim ** -0.5 if set.
        drop (float, optional): Dropout rate. Default: 0.0
        attn_drop (float, optional): Attention dropout rate. Default: 0.0
        drop_path (float, optional): Stochastic depth rate. Default: 0.0
        act_layer (nn.Module, optional): Activation layer. Default: nn.GELU
        norm_layer (nn.Module, optional): Normalization layer.  Default: nn.LayerNorm
    """

  def __init__(self,
               dim,
               num_heads,
               window_size=(2, 7, 7),
               shift_size=(0, 0, 0),
               mlp_ratio=4.,
               qkv_bias=True,
               qk_scale=None,
               drop=0.,
               attn_drop=0.,
               drop_path=0.,
               act_layer=nn.GELU,
               norm_layer=nn.LayerNorm,
               use_checkpoint=False):
    super().__init__()
    self.dim = dim
    self.num_heads = num_heads
    self.window_size = window_size
    self.shift_size = shift_size
    self.mlp_ratio = mlp_ratio
    self.use_checkpoint = use_checkpoint

    assert 0 <= self.shift_size[0] < self.window_size[0], 'shift_size must in 0-window_size'
    assert 0 <= self.shift_size[1] < self.window_size[1], 'shift_size must in 0-window_size'
    assert 0 <= self.shift_size[2] < self.window_size[2], 'shift_size must in 0-window_size'

    self.norm1 = norm_layer(dim)
    self.attn = WindowAttention3D(dim,
                                  window_size=self.window_size,
                                  num_heads=num_heads,
                                  qkv_bias=qkv_bias,
                                  qk_scale=qk_scale,
                                  attn_drop=attn_drop,
                                  proj_drop=drop)

    self.drop_path = DropPath(drop_path) if drop_path > 0. else nn.Identity()
    self.norm2 = norm_layer(dim)
    mlp_hidden_dim = int(dim * mlp_ratio)
    self.mlp = Mlp(in_features=dim, hidden_features=mlp_hidden_dim, act_layer=act_layer, drop=drop)

  def forward_part1(self, x, mask_matrix):
    b, d, h, w, c = x.shape
    window_size, shift_size = get_window_size((d, h, w), self.window_size, self.shift_size)

    x = self.norm1(x)
    # pad feature maps to multiples of window size
    pad_l = pad_t = pad_d0 = 0
    pad_d1 = (window_size[0] - d % window_size[0]) % window_size[0]
    pad_b = (window_size[1] - h % window_size[1]) % window_size[1]
    pad_r = (window_size[2] - w % window_size[2]) % window_size[2]
    x = F.pad(x, (0, 0, pad_l, pad_r, pad_t, pad_b, pad_d0, pad_d1))
    _, dp, hp, wp, _ = x.shape
    # cyclic shift
    if any(i > 0 for i in shift_size):
      shifted_x = torch.roll(x, shifts=(-shift_size[0], -shift_size[1], -shift_size[2]), dims=(1, 2, 3))
      attn_mask = mask_matrix
    else:
      shifted_x = x
      attn_mask = None
    # partition windows
    x_windows = window_partition(shifted_x, window_size)  # B*nW, Wd*Wh*Ww, C
    # W-MSA/SW-MSA
    attn_windows = self.attn(x_windows, mask=attn_mask)  # B*nW, Wd*Wh*Ww, C
    # merge windows
    attn_windows = attn_windows.view(-1, *(window_size + (c,)))
    shifted_x = window_reverse(attn_windows, window_size, b, dp, hp, wp)  # B D' H' W' C
    # reverse cyclic shift
    if any(i > 0 for i in shift_size):
      x = torch.roll(shifted_x, shifts=(shift_size[0], shift_size[1], shift_size[2]), dims=(1, 2, 3))
    else:
      x = shifted_x

    if pad_d1 > 0 or pad_r > 0 or pad_b > 0:
      x = x[:, :d, :h, :w, :].contiguous()
    return x

  def forward_part2(self, x):
    return self.drop_path(self.mlp(self.norm2(x)))

  def forward(self, x, mask_matrix):
    """ Forward function.
        Args:
            x: Input feature, tensor size (B, D, H, W, C).
            mask_matrix: Attention mask for cyclic shift.
        """

    shortcut = x
    if self.use_checkpoint:
      x = checkpoint.checkpoint(self.forward_part1, x, mask_matrix)
    else:
      x = self.forward_part1(x, mask_matrix)
    x = shortcut + self.drop_path(x)

    if self.use_checkpoint:
      x = x + checkpoint.checkpoint(self.forward_part2, x)
    else:
      x = x + self.forward_part2(x)

    return x


class PatchMerging(nn.Module):
  """ Patch Merging Layer
    Args:
        dim (int): Number of input channels.
        norm_layer (nn.Module, optional): Normalization layer.  Default: nn.LayerNorm
    """

  def __init__(self, dim, norm_layer=nn.LayerNorm):
    super().__init__()
    self.dim = dim
    self.reduction = nn.Linear(4 * dim, 2 * dim, bias=False)
    self.norm = norm_layer(4 * dim)

  def forward(self, x):
    """ Forward function.
        Args:
            x: Input feature, tensor size (B, D, H, W, C).
        """
    _, _, h, w, _ = x.shape

    # padding
    pad_input = (h % 2 == 1) or (w % 2 == 1)
    if pad_input:
      x = F.pad(x, (0, 0, 0, w % 2, 0, h % 2))

    x0 = x[:, :, 0::2, 0::2, :]  # B D H/2 W/2 C
    x1 = x[:, :, 1::2, 0::2, :]  # B D H/2 W/2 C
    x2 = x[:, :, 0::2, 1::2, :]  # B D H/2 W/2 C
    x3 = x[:, :, 1::2, 1::2, :]  # B D H/2 W/2 C
    x = torch.cat([x0, x1, x2, x3], -1)  # B D H/2 W/2 4*C

    x = self.norm(x)
    x = self.reduction(x)

    return x


# cache each stage results
@lru_cache()
def compute_mask(depth, height, width, window_size, shift_size, device):
  img_mask = torch.zeros((1, depth, height, width, 1), device=device)  # 1 Dp Hp Wp 1
  cnt = 0
  for d in slice(-window_size[0]), slice(-window_size[0], -shift_size[0]), slice(-shift_size[0], None):
    for h in slice(-window_size[1]), slice(-window_size[1], -shift_size[1]), slice(-shift_size[1], None):
      for w in slice(-window_size[2]), slice(-window_size[2], -shift_size[2]), slice(-shift_size[2], None):
        img_mask[:, d, h, w, :] = cnt
        cnt += 1
  mask_windows = window_partition(img_mask, window_size)  # nW, ws[0]*ws[1]*ws[2], 1
  mask_windows = mask_windows.squeeze(-1)  # nW, ws[0]*ws[1]*ws[2]
  attn_mask = mask_windows.unsqueeze(1) - mask_windows.unsqueeze(2)
  attn_mask = attn_mask.masked_fill(attn_mask != 0, float(-100.0)).masked_fill(attn_mask == 0, float(0.0))
  return attn_mask


class BasicLayer(nn.Module):
  """ A basic Swin Transformer layer for one stage.
    Args:
        dim (int): Number of feature channels
        depth (int): Depths of this stage.
        num_heads (int): Number of attention head.
        window_size (tuple[int]): Local window size. Default: (1,7,7).
        mlp_ratio (float): Ratio of mlp hidden dim to embedding dim. Default: 4.
        qkv_bias (bool, optional): If True, add a learnable bias to query, key, value. Default: True
        qk_scale (float | None, optional): Override default qk scale of head_dim ** -0.5 if set.
        drop (float, optional): Dropout rate. Default: 0.0
        attn_drop (float, optional): Attention dropout rate. Default: 0.0
        drop_path (float | tuple[float], optional): Stochastic depth rate. Default: 0.0
        norm_layer (nn.Module, optional): Normalization layer. Default: nn.LayerNorm
        downsample (nn.Module | None, optional): Downsample layer at the end of the layer. Default: None
    """

  def __init__(self,
               dim,
               depth,
               num_heads,
               window_size=(1, 7, 7),
               mlp_ratio=4.,
               qkv_bias=False,
               qk_scale=None,
               drop=0.,
               attn_drop=0.,
               drop_path=0.,
               norm_layer=nn.LayerNorm,
               downsample=None,
               use_checkpoint=False):
    super().__init__()
    self.window_size = window_size
    self.shift_size = tuple(i // 2 for i in window_size)
    self.depth = depth
    self.use_checkpoint = use_checkpoint

    # build blocks
    self.blocks = nn.ModuleList([
        SwinTransformerBlock3D(
            dim=dim,
            num_heads=num_heads,
            window_size=window_size,
            shift_size=(0, 0, 0) if (i % 2 == 0) else self.shift_size,
            mlp_ratio=mlp_ratio,
            qkv_bias=qkv_bias,
            qk_scale=qk_scale,
            drop=drop,
            attn_drop=attn_drop,
            drop_path=drop_path[i] if isinstance(drop_path, list) else drop_path,
            norm_layer=norm_layer,
            use_checkpoint=use_checkpoint,
        ) for i in range(depth)
    ])

    self.downsample = downsample
    if self.downsample is not None:
      self.downsample = downsample(dim=dim, norm_layer=norm_layer)

  def forward(self, x):
    """ Forward function.
        Args:
            x: Input feature, tensor size (B, C, D, H, W).
        """
    # calculate attention mask for SW-MSA
    b, _, d, h, w = x.shape
    window_size, shift_size = get_window_size((d, h, w), self.window_size, 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


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=(2, 4, 4),
               in_chans=3,
               embed_dim=96,
               depths=(2, 2, 6, 2),
               num_heads=(3, 6, 12, 24),
               window_size=(8, 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=True,
               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.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.ModuleDict()
    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[f'layer{i_layer}'] = layer

    self.num_features = int(embed_dim * 2**(self.num_layers - 1))

    # add a norm layer for each output
    self.norm = CustomNorm(norm_layer, self.num_features)

    self.return_layers = {'pos_drop': 0, 'layer0': 1, 'layer1': 2, 'layer2': 3, 'norm': 4}
    self.feature_info = t_u.InfoDummy([
        dict(num_chs=96, reduction=4, module='pos_drop'),
        dict(num_chs=192, reduction=8, module='layer0'),
        dict(num_chs=384, reduction=16, module='layer1'),
        dict(num_chs=768, reduction=32, module='layer2'),
        dict(num_chs=768, reduction=32, module='norm')
    ])

  def items(self):
    return (('patch_embed', self.patch_embed), ('pos_drop', self.pos_drop), ('layer0', self.layers['layer0']),
            ('layer1', self.layers['layer1']), ('layer2', self.layers['layer2']), ('layer3', self.layers['layer3']),
            ('norm', self.norm))

  def init_weights(self, pretrained=None):  # pylint: disable=locally-disabled, unused-argument
    """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)

    self.apply(_init_weights)


class CustomNorm(nn.Module):
  """
  Changes the channel dimensions before applying a norm and reverts it back afterwards.
  """

  def __init__(self, norm_layer, num_features):
    super().__init__()
    self.norm = norm_layer(num_features)

  def forward(self, x):
    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