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- # --------------------------------------------------------
- # Swin Transformer
- # Copyright (c) 2021 Microsoft
- # Licensed under The MIT License [see LICENSE for details]
- # Written by Ze Liu
- # --------------------------------------------------------
- import torch
- import torch.nn as nn
- import torch.nn.functional as F
- import torch.utils.checkpoint as checkpoint
- from timm.models.layers import DropPath, to_2tuple, trunc_normal_
- class Mlp(nn.Module):
- 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, H, W, C)
- window_size (int): window size
- Returns:
- windows: (num_windows*B, window_size, window_size, C)
- """
- B, H, W, C = x.shape
- x = x.view(B, H // window_size, window_size, W // window_size, window_size, C)
- windows = x.permute(0, 1, 3, 2, 4, 5).contiguous().view(-1, window_size, window_size, C)
- return windows
- def window_reverse(windows, window_size, H, W):
- """
- Args:
- windows: (num_windows*B, window_size, window_size, C)
- window_size (int): Window size
- H (int): Height of image
- W (int): Width of image
- Returns:
- x: (B, H, W, C)
- """
- B = int(windows.shape[0] / (H * W / window_size / window_size))
- x = windows.view(B, H // window_size, W // window_size, window_size, window_size, -1)
- x = x.permute(0, 1, 3, 2, 4, 5).contiguous().view(B, H, W, -1)
- return x
- class SwinMLPBlock(nn.Module):
- r""" Swin MLP Block.
- Args:
- dim (int): Number of input channels.
- input_resolution (tuple[int]): Input resulotion.
- num_heads (int): Number of attention heads.
- window_size (int): Window size.
- shift_size (int): Shift size for SW-MSA.
- mlp_ratio (float): Ratio of mlp hidden dim to embedding dim.
- drop (float, optional): 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, input_resolution, num_heads, window_size=7, shift_size=0,
- mlp_ratio=4., drop=0., drop_path=0.,
- act_layer=nn.GELU, norm_layer=nn.LayerNorm):
- super().__init__()
- self.dim = dim
- self.input_resolution = input_resolution
- self.num_heads = num_heads
- self.window_size = window_size
- self.shift_size = shift_size
- self.mlp_ratio = mlp_ratio
- if min(self.input_resolution) <= self.window_size:
- # if window size is larger than input resolution, we don't partition windows
- self.shift_size = 0
- self.window_size = min(self.input_resolution)
- assert 0 <= self.shift_size < self.window_size, "shift_size must in 0-window_size"
- self.padding = [self.window_size - self.shift_size, self.shift_size,
- self.window_size - self.shift_size, self.shift_size] # P_l,P_r,P_t,P_b
- self.norm1 = norm_layer(dim)
- # use group convolution to implement multi-head MLP
- self.spatial_mlp = nn.Conv1d(self.num_heads * self.window_size ** 2,
- self.num_heads * self.window_size ** 2,
- kernel_size=1,
- groups=self.num_heads)
- 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(self, x):
- H, W = self.input_resolution
- B, L, C = x.shape
- assert L == H * W, "input feature has wrong size"
- shortcut = x
- x = self.norm1(x)
- x = x.view(B, H, W, C)
- # shift
- if self.shift_size > 0:
- P_l, P_r, P_t, P_b = self.padding
- shifted_x = F.pad(x, [0, 0, P_l, P_r, P_t, P_b], "constant", 0)
- else:
- shifted_x = x
- _, _H, _W, _ = shifted_x.shape
- # partition windows
- x_windows = window_partition(shifted_x, self.window_size) # nW*B, window_size, window_size, C
- x_windows = x_windows.view(-1, self.window_size * self.window_size, C) # nW*B, window_size*window_size, C
- # Window/Shifted-Window Spatial MLP
- x_windows_heads = x_windows.view(-1, self.window_size * self.window_size, self.num_heads, C // self.num_heads)
- x_windows_heads = x_windows_heads.transpose(1, 2) # nW*B, nH, window_size*window_size, C//nH
- x_windows_heads = x_windows_heads.reshape(-1, self.num_heads * self.window_size * self.window_size,
- C // self.num_heads)
- spatial_mlp_windows = self.spatial_mlp(x_windows_heads) # nW*B, nH*window_size*window_size, C//nH
- spatial_mlp_windows = spatial_mlp_windows.view(-1, self.num_heads, self.window_size * self.window_size,
- C // self.num_heads).transpose(1, 2)
- spatial_mlp_windows = spatial_mlp_windows.reshape(-1, self.window_size * self.window_size, C)
- # merge windows
- spatial_mlp_windows = spatial_mlp_windows.reshape(-1, self.window_size, self.window_size, C)
- shifted_x = window_reverse(spatial_mlp_windows, self.window_size, _H, _W) # B H' W' C
- # reverse shift
- if self.shift_size > 0:
- P_l, P_r, P_t, P_b = self.padding
- x = shifted_x[:, P_t:-P_b, P_l:-P_r, :].contiguous()
- else:
- x = shifted_x
- x = x.view(B, H * W, C)
- # FFN
- x = shortcut + self.drop_path(x)
- x = x + self.drop_path(self.mlp(self.norm2(x)))
- return x
- def extra_repr(self) -> str:
- return f"dim={self.dim}, input_resolution={self.input_resolution}, num_heads={self.num_heads}, " \
- f"window_size={self.window_size}, shift_size={self.shift_size}, mlp_ratio={self.mlp_ratio}"
- def flops(self):
- flops = 0
- H, W = self.input_resolution
- # norm1
- flops += self.dim * H * W
- # Window/Shifted-Window Spatial MLP
- if self.shift_size > 0:
- nW = (H / self.window_size + 1) * (W / self.window_size + 1)
- else:
- nW = H * W / self.window_size / self.window_size
- flops += nW * self.dim * (self.window_size * self.window_size) * (self.window_size * self.window_size)
- # mlp
- flops += 2 * H * W * self.dim * self.dim * self.mlp_ratio
- # norm2
- flops += self.dim * H * W
- return flops
- class PatchMerging(nn.Module):
- r""" Patch Merging Layer.
- Args:
- input_resolution (tuple[int]): Resolution of input feature.
- dim (int): Number of input channels.
- norm_layer (nn.Module, optional): Normalization layer. Default: nn.LayerNorm
- """
- def __init__(self, input_resolution, dim, norm_layer=nn.LayerNorm):
- super().__init__()
- self.input_resolution = input_resolution
- self.dim = dim
- self.reduction = nn.Linear(4 * dim, 2 * dim, bias=False)
- self.norm = norm_layer(4 * dim)
- def forward(self, x):
- """
- x: B, H*W, C
- """
- H, W = self.input_resolution
- B, L, C = x.shape
- assert L == H * W, "input feature has wrong size"
- assert H % 2 == 0 and W % 2 == 0, f"x size ({H}*{W}) are not even."
- x = x.view(B, H, W, C)
- x0 = x[:, 0::2, 0::2, :] # B H/2 W/2 C
- x1 = x[:, 1::2, 0::2, :] # B H/2 W/2 C
- x2 = x[:, 0::2, 1::2, :] # B H/2 W/2 C
- x3 = x[:, 1::2, 1::2, :] # B H/2 W/2 C
- x = torch.cat([x0, x1, x2, x3], -1) # B H/2 W/2 4*C
- x = x.view(B, -1, 4 * C) # B H/2*W/2 4*C
- x = self.norm(x)
- x = self.reduction(x)
- return x
- def extra_repr(self) -> str:
- return f"input_resolution={self.input_resolution}, dim={self.dim}"
- def flops(self):
- H, W = self.input_resolution
- flops = H * W * self.dim
- flops += (H // 2) * (W // 2) * 4 * self.dim * 2 * self.dim
- return flops
- class BasicLayer(nn.Module):
- """ A basic Swin MLP layer for one stage.
- Args:
- dim (int): Number of input channels.
- input_resolution (tuple[int]): Input resolution.
- depth (int): Number of blocks.
- num_heads (int): Number of attention heads.
- window_size (int): Local window size.
- mlp_ratio (float): Ratio of mlp hidden dim to embedding dim.
- drop (float, optional): 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
- use_checkpoint (bool): Whether to use checkpointing to save memory. Default: False.
- """
- def __init__(self, dim, input_resolution, depth, num_heads, window_size,
- mlp_ratio=4., drop=0., drop_path=0.,
- norm_layer=nn.LayerNorm, downsample=None, use_checkpoint=False):
- super().__init__()
- self.dim = dim
- self.input_resolution = input_resolution
- self.depth = depth
- self.use_checkpoint = use_checkpoint
- # build blocks
- self.blocks = nn.ModuleList([
- SwinMLPBlock(dim=dim, input_resolution=input_resolution,
- num_heads=num_heads, window_size=window_size,
- shift_size=0 if (i % 2 == 0) else window_size // 2,
- mlp_ratio=mlp_ratio,
- drop=drop,
- drop_path=drop_path[i] if isinstance(drop_path, list) else drop_path,
- norm_layer=norm_layer)
- for i in range(depth)])
- # patch merging layer
- if downsample is not None:
- self.downsample = downsample(input_resolution, dim=dim, norm_layer=norm_layer)
- else:
- self.downsample = None
- def forward(self, x):
- for blk in self.blocks:
- if self.use_checkpoint:
- x = checkpoint.checkpoint(blk, x)
- else:
- x = blk(x)
- if self.downsample is not None:
- x = self.downsample(x)
- return x
- def extra_repr(self) -> str:
- return f"dim={self.dim}, input_resolution={self.input_resolution}, depth={self.depth}"
- def flops(self):
- flops = 0
- for blk in self.blocks:
- flops += blk.flops()
- if self.downsample is not None:
- flops += self.downsample.flops()
- return flops
- class PatchEmbed(nn.Module):
- r""" Image to Patch Embedding
- Args:
- img_size (int): Image size. Default: 224.
- patch_size (int): Patch token size. Default: 4.
- in_chans (int): Number of input image 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, img_size=224, patch_size=4, in_chans=3, embed_dim=96, norm_layer=None):
- super().__init__()
- img_size = to_2tuple(img_size)
- patch_size = to_2tuple(patch_size)
- patches_resolution = [img_size[0] // patch_size[0], img_size[1] // patch_size[1]]
- self.img_size = img_size
- self.patch_size = patch_size
- self.patches_resolution = patches_resolution
- self.num_patches = patches_resolution[0] * patches_resolution[1]
- self.in_chans = in_chans
- self.embed_dim = embed_dim
- self.proj = nn.Conv2d(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):
- B, C, H, W = x.shape
- # FIXME look at relaxing size constraints
- assert H == self.img_size[0] and W == self.img_size[1], \
- f"Input image size ({H}*{W}) doesn't match model ({self.img_size[0]}*{self.img_size[1]})."
- x = self.proj(x).flatten(2).transpose(1, 2) # B Ph*Pw C
- if self.norm is not None:
- x = self.norm(x)
- return x
- def flops(self):
- Ho, Wo = self.patches_resolution
- flops = Ho * Wo * self.embed_dim * self.in_chans * (self.patch_size[0] * self.patch_size[1])
- if self.norm is not None:
- flops += Ho * Wo * self.embed_dim
- return flops
- class SwinMLP(nn.Module):
- r""" Swin MLP
- Args:
- img_size (int | tuple(int)): Input image size. Default 224
- patch_size (int | tuple(int)): Patch size. Default: 4
- in_chans (int): Number of input image channels. Default: 3
- num_classes (int): Number of classes for classification head. Default: 1000
- embed_dim (int): Patch embedding dimension. Default: 96
- depths (tuple(int)): Depth of each Swin MLP layer.
- num_heads (tuple(int)): Number of attention heads in different layers.
- window_size (int): Window size. Default: 7
- mlp_ratio (float): Ratio of mlp hidden dim to embedding dim. Default: 4
- drop_rate (float): Dropout rate. Default: 0
- drop_path_rate (float): Stochastic depth rate. Default: 0.1
- norm_layer (nn.Module): Normalization layer. Default: nn.LayerNorm.
- ape (bool): If True, add absolute position embedding to the patch embedding. Default: False
- patch_norm (bool): If True, add normalization after patch embedding. Default: True
- use_checkpoint (bool): Whether to use checkpointing to save memory. Default: False
- """
- def __init__(self, img_size=224, patch_size=4, in_chans=3, num_classes=1000,
- embed_dim=96, depths=[2, 2, 6, 2], num_heads=[3, 6, 12, 24],
- window_size=7, mlp_ratio=4., drop_rate=0., drop_path_rate=0.1,
- norm_layer=nn.LayerNorm, ape=False, patch_norm=True,
- use_checkpoint=False, **kwargs):
- super().__init__()
- self.num_classes = num_classes
- self.num_layers = len(depths)
- self.embed_dim = embed_dim
- self.ape = ape
- self.patch_norm = patch_norm
- self.num_features = int(embed_dim * 2 ** (self.num_layers - 1))
- self.mlp_ratio = mlp_ratio
- # split image into non-overlapping patches
- self.patch_embed = PatchEmbed(
- img_size=img_size, patch_size=patch_size, in_chans=in_chans, embed_dim=embed_dim,
- norm_layer=norm_layer if self.patch_norm else None)
- num_patches = self.patch_embed.num_patches
- patches_resolution = self.patch_embed.patches_resolution
- self.patches_resolution = patches_resolution
- # absolute position embedding
- if self.ape:
- self.absolute_pos_embed = nn.Parameter(torch.zeros(1, num_patches, embed_dim))
- trunc_normal_(self.absolute_pos_embed, std=.02)
- 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),
- input_resolution=(patches_resolution[0] // (2 ** i_layer),
- patches_resolution[1] // (2 ** i_layer)),
- depth=depths[i_layer],
- num_heads=num_heads[i_layer],
- window_size=window_size,
- mlp_ratio=self.mlp_ratio,
- drop=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.norm = norm_layer(self.num_features)
- self.avgpool = nn.AdaptiveAvgPool1d(1)
- self.head = nn.Linear(self.num_features, num_classes) if num_classes > 0 else nn.Identity()
- self.apply(self._init_weights)
- def _init_weights(self, m):
- if isinstance(m, (nn.Linear, nn.Conv1d)):
- trunc_normal_(m.weight, std=.02)
- if 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)
- @torch.jit.ignore
- def no_weight_decay(self):
- return {'absolute_pos_embed'}
- @torch.jit.ignore
- def no_weight_decay_keywords(self):
- return {'relative_position_bias_table'}
- def forward_features(self, x):
- x = self.patch_embed(x)
- if self.ape:
- x = x + self.absolute_pos_embed
- x = self.pos_drop(x)
- for layer in self.layers:
- x = layer(x)
- x = self.norm(x) # B L C
- x = self.avgpool(x.transpose(1, 2)) # B C 1
- x = torch.flatten(x, 1)
- return x
- def forward(self, x):
- x = self.forward_features(x)
- x = self.head(x)
- return x
- def flops(self):
- flops = 0
- flops += self.patch_embed.flops()
- for i, layer in enumerate(self.layers):
- flops += layer.flops()
- flops += self.num_features * self.patches_resolution[0] * self.patches_resolution[1] // (2 ** self.num_layers)
- flops += self.num_features * self.num_classes
- return flops
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