|
- """ Swin Transformer
- A PyTorch impl of : `Swin Transformer: Hierarchical Vision Transformer using Shifted Windows`
- - https://arxiv.org/pdf/2103.14030
-
- Code/weights from https://github.com/microsoft/Swin-Transformer
-
- """
-
- import torch
- import torch.nn as nn
- import torch.nn.functional as F
- import torch.utils.checkpoint as checkpoint
- import numpy as np
- from typing import Optional
-
-
- def drop_path_f(x, drop_prob: float = 0., training: bool = False):
- """Drop paths (Stochastic Depth) per sample (when applied in main path of residual blocks).
-
- This is the same as the DropConnect impl I created for EfficientNet, etc networks, however,
- the original name is misleading as 'Drop Connect' is a different form of dropout in a separate paper...
- See discussion: https://github.com/tensorflow/tpu/issues/494#issuecomment-532968956 ... I've opted for
- changing the layer and argument names to 'drop path' rather than mix DropConnect as a layer name and use
- 'survival rate' as the argument.
-
- """
- if drop_prob == 0. or not training:
- return x
- keep_prob = 1 - drop_prob
- shape = (x.shape[0],) + (1,) * (x.ndim - 1) # work with diff dim tensors, not just 2D ConvNets
- random_tensor = keep_prob + torch.rand(shape, dtype=x.dtype, device=x.device)
- random_tensor.floor_() # binarize
- output = x.div(keep_prob) * random_tensor
- return output
-
-
- class DropPath(nn.Module):
- """Drop paths (Stochastic Depth) per sample (when applied in main path of residual blocks).
- """
- def __init__(self, drop_prob=None):
- super(DropPath, self).__init__()
- self.drop_prob = drop_prob
-
- def forward(self, x):
- return drop_path_f(x, self.drop_prob, self.training)
-
-
- def window_partition(x, window_size: int):
- """
- 将feature map按照window_size划分成一个个没有重叠的window
- Args:
- x: (B, H, W, C)
- window_size (int): window size(M)
-
- 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)
- # permute: [B, H//Mh, Mh, W//Mw, Mw, C] -> [B, H//Mh, W//Mh, Mw, Mw, C]
- # view: [B, H//Mh, W//Mw, Mh, Mw, C] -> [B*num_windows, Mh, Mw, 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: int, H: int, W: int):
- """
- 将一个个window还原成一个feature map
- Args:
- windows: (num_windows*B, window_size, window_size, C)
- window_size (int): Window size(M)
- 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))
- # view: [B*num_windows, Mh, Mw, C] -> [B, H//Mh, W//Mw, Mh, Mw, C]
- x = windows.view(B, H // window_size, W // window_size, window_size, window_size, -1)
- # permute: [B, H//Mh, W//Mw, Mh, Mw, C] -> [B, H//Mh, Mh, W//Mw, Mw, C]
- # view: [B, H//Mh, Mh, W//Mw, Mw, C] -> [B, H, W, C]
- x = x.permute(0, 1, 3, 2, 4, 5).contiguous().view(B, H, W, -1)
- return x
-
- # from torch.nn.modules import Module
- class PatchEmbed(nn.Module):
- """
- 2D Image to Patch Embedding
- """
- def __init__(self, patch_size=4, in_c=3, embed_dim=96, norm_layer=None):
- super().__init__()
- patch_size = (patch_size, patch_size)
- self.patch_size = patch_size
- self.in_chans = in_c
- self.embed_dim = embed_dim
- self.proj = nn.Conv2d(in_c, embed_dim, kernel_size=patch_size, stride=patch_size)
- self.norm = norm_layer(embed_dim) if norm_layer else nn.Identity()
-
- def forward(self, x):
- _, _, H, W = x.shape
-
- # padding
- # 如果输入图片的H,W不是patch_size的整数倍,需要进行padding
- pad_input = (H % self.patch_size[0] != 0) or (W % self.patch_size[1] != 0)
- if pad_input:
- # to pad the last 3 dimensions,
- # (W_left, W_right, H_top,H_bottom, C_front, C_back)
- x = F.pad(x, (0, self.patch_size[1] - W % self.patch_size[1],
- 0, self.patch_size[0] - H % self.patch_size[0],
- 0, 0))
-
- # 下采样patch_size倍
- x = self.proj(x)
- _, _, H, W = x.shape
- # flatten: [B, C, H, W] -> [B, C, HW]
- # transpose: [B, C, HW] -> [B, HW, C]
- x = x.flatten(2).transpose(1, 2)
- x = self.norm(x)
- return x, H, W
-
-
- class PatchMerging(nn.Module):
- r""" 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, H, W):
- """
- x: B, H*W, C
- """
- B, L, C = x.shape
- assert L == H * W, "input feature has wrong size"
-
- x = x.view(B, H, W, C)
-
- # padding
- # 如果输入feature map的H,W不是2的整数倍,需要进行padding
- pad_input = (H % 2 == 1) or (W % 2 == 1)
- if pad_input:
- # to pad the last 3 dimensions, starting from the last dimension and moving forward.
- # (C_front, C_back, W_left, W_right, H_top, H_bottom)
- # 注意这里的Tensor通道是[B, H, W, C],所以会和官方文档有些不同
- x = F.pad(x, (0, 0, 0, W % 2, 0, H % 2))
-
- 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) # [B, H/2*W/2, 2*C]
-
- return x
-
-
- class Mlp(nn.Module):
- """ MLP as used in Vision Transformer, MLP-Mixer and related networks
- """
- 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.drop1 = nn.Dropout(drop)
- self.fc2 = nn.Linear(hidden_features, out_features)
- self.drop2 = nn.Dropout(drop)
-
- def forward(self, x):
- x = self.fc1(x)
- x = self.act(x)
- x = self.drop1(x)
- x = self.fc2(x)
- x = self.drop2(x)
- return x
-
-
- class WindowAttention(nn.Module):
- r""" 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 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
- 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=True, attn_drop=0., proj_drop=0.):
-
- super().__init__()
- self.dim = dim
- self.window_size = window_size # [Mh, Mw]
- self.num_heads = num_heads
- head_dim = dim // num_heads
- self.scale = 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), num_heads)) # [2*Mh-1 * 2*Mw-1, nH]
-
- # get pair-wise relative position index for each token inside the window
- coords_h = torch.arange(self.window_size[0])
- coords_w = torch.arange(self.window_size[1])
- input = torch.meshgrid([coords_h, coords_w], indexing="ij")
- coords = torch.stack(input) # [2, Mh, Mw]
- coords_flatten = torch.flatten(coords, 1) # [2, Mh*Mw]
- # [2, Mh*Mw, 1] - [2, 1, Mh*Mw]
- relative_coords = coords_flatten[:, :, None] - coords_flatten[:, None, :] # [2, Mh*Mw, Mh*Mw]
- relative_coords = relative_coords.permute(1, 2, 0).contiguous() # [Mh*Mw, Mh*Mw, 2]
- relative_coords[:, :, 0] += self.window_size[0] - 1 # shift to start from 0
- relative_coords[:, :, 1] += self.window_size[1] - 1
- relative_coords[:, :, 0] *= 2 * self.window_size[1] - 1
- relative_position_index = relative_coords.sum(-1) # [Mh*Mw, Mh*Mw]
- 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)
-
- nn.init.trunc_normal_(self.relative_position_bias_table, std=.02)
- self.softmax = nn.Softmax(dim=-1)
-
- def forward(self, x, mask: Optional[torch.Tensor] = None):
- """
- Args:
- x: input features with shape of (num_windows*B, Mh*Mw, C)
- mask: (0/-inf) mask with shape of (num_windows, Wh*Ww, Wh*Ww) or None
- """
- # [batch_size*num_windows, Mh*Mw, total_embed_dim]
- B_, N, C = x.shape
- # qkv(): -> [batch_size*num_windows, Mh*Mw, 3 * total_embed_dim]
- # reshape: -> [batch_size*num_windows, Mh*Mw, 3, num_heads, embed_dim_per_head]
- # permute: -> [3, batch_size*num_windows, num_heads, Mh*Mw, embed_dim_per_head]
- qkv = self.qkv(x).reshape(B_, N, 3, self.num_heads, C // self.num_heads).permute(2, 0, 3, 1, 4)
- # [batch_size*num_windows, num_heads, Mh*Mw, embed_dim_per_head]
- q, k, v = qkv.unbind(0) # make torchscript happy (cannot use tensor as tuple)
-
- # transpose: -> [batch_size*num_windows, num_heads, embed_dim_per_head, Mh*Mw]
- # @: multiply -> [batch_size*num_windows, num_heads, Mh*Mw, Mh*Mw]
- q = q * self.scale
- attn = (q @ k.transpose(-2, -1))
-
- # relative_position_bias_table.view: [Mh*Mw*Mh*Mw,nH] -> [Mh*Mw,Mh*Mw,nH]
- relative_position_bias = self.relative_position_bias_table[self.relative_position_index.view(-1)].view(
- self.window_size[0] * self.window_size[1], self.window_size[0] * self.window_size[1], -1)
- relative_position_bias = relative_position_bias.permute(2, 0, 1).contiguous() # [nH, Mh*Mw, Mh*Mw]
- attn = attn + relative_position_bias.unsqueeze(0)
-
- if mask is not None:
- # mask: [nW, Mh*Mw, Mh*Mw]
- nW = mask.shape[0] # num_windows
- # attn.view: [batch_size, num_windows, num_heads, Mh*Mw, Mh*Mw]
- # mask.unsqueeze: [1, nW, 1, Mh*Mw, Mh*Mw]
- attn = attn.view(B_ // nW, nW, 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)
-
- # @: multiply -> [batch_size*num_windows, num_heads, Mh*Mw, embed_dim_per_head]
- # transpose: -> [batch_size*num_windows, Mh*Mw, num_heads, embed_dim_per_head]
- # reshape: -> [batch_size*num_windows, Mh*Mw, total_embed_dim]
- x = (attn @ v).transpose(1, 2).reshape(B_, N, C)
- x = self.proj(x)
- x = self.proj_drop(x)
- return x
-
-
- class SwinTransformerBlock(nn.Module):
- r""" Swin Transformer Block.
-
- Args:
- dim (int): Number of input channels.
- 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.
- qkv_bias (bool, optional): If True, add a learnable bias to query, key, value. Default: True
- 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=7, shift_size=0,
- mlp_ratio=4., qkv_bias=True, drop=0., attn_drop=0., drop_path=0.,
- act_layer=nn.GELU, norm_layer=nn.LayerNorm):
- 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
- assert 0 <= self.shift_size < self.window_size, "shift_size must in 0-window_size"
-
- self.norm1 = norm_layer(dim)
- self.attn = WindowAttention(
- dim, window_size=(self.window_size, self.window_size), num_heads=num_heads, qkv_bias=qkv_bias,
- 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(self, x, attn_mask):
- H, W = self.H, self.W
- 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)
-
- # pad feature maps to multiples of window size
- # 把feature map给pad到window size的整数倍
- pad_l = pad_t = 0
- pad_r = (self.window_size - W % self.window_size) % self.window_size
- pad_b = (self.window_size - H % self.window_size) % self.window_size
- x = F.pad(x, (0, 0, pad_l, pad_r, pad_t, pad_b))
- _, Hp, Wp, _ = x.shape
-
- # cyclic shift
- if self.shift_size > 0:
- shifted_x = torch.roll(x, shifts=(-self.shift_size, -self.shift_size), dims=(1, 2))
- else:
- shifted_x = x
- attn_mask = None
-
- # partition windows
- x_windows = window_partition(shifted_x, self.window_size) # [nW*B, Mh, Mw, C]
- x_windows = x_windows.view(-1, self.window_size * self.window_size, C) # [nW*B, Mh*Mw, C]
-
- # W-MSA/SW-MSA
- attn_windows = self.attn(x_windows, mask=attn_mask) # [nW*B, Mh*Mw, C]
-
- # merge windows
- attn_windows = attn_windows.view(-1, self.window_size, self.window_size, C) # [nW*B, Mh, Mw, C]
- shifted_x = window_reverse(attn_windows, self.window_size, Hp, Wp) # [B, H', W', C]
-
- # reverse cyclic shift
- if self.shift_size > 0:
- x = torch.roll(shifted_x, shifts=(self.shift_size, self.shift_size), dims=(1, 2))
- else:
- x = shifted_x
-
- if pad_r > 0 or pad_b > 0:
- # 把前面pad的数据移除掉
- x = x[:, :H, :W, :].contiguous()
-
- 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
-
-
- class BasicLayer(nn.Module):
- """
- A basic Swin Transformer layer for one stage.
-
- Args:
- dim (int): Number of input channels.
- 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.
- qkv_bias (bool, optional): If True, add a learnable bias to query, key, value. Default: True
- 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
- use_checkpoint (bool): Whether to use checkpointing to save memory. Default: False.
- """
-
- def __init__(self, dim, depth, num_heads, window_size,
- mlp_ratio=4., qkv_bias=True, drop=0., attn_drop=0.,
- drop_path=0., norm_layer=nn.LayerNorm, downsample=None, use_checkpoint=False):
- super().__init__()
- self.dim = dim
- self.depth = depth
- self.window_size = window_size
- self.use_checkpoint = use_checkpoint
- self.shift_size = window_size // 2
-
- # build blocks
- self.blocks = nn.ModuleList([
- SwinTransformerBlock(
- dim=dim,
- num_heads=num_heads,
- window_size=window_size,
- shift_size=0 if (i % 2 == 0) else self.shift_size,
- mlp_ratio=mlp_ratio,
- qkv_bias=qkv_bias,
- drop=drop,
- attn_drop=attn_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(dim=dim, norm_layer=norm_layer)
- else:
- self.downsample = None
-
- def create_mask(self, x, H, W):
- # calculate attention mask for SW-MSA
- # 保证Hp和Wp是window_size的整数倍
- Hp = int(np.ceil(H / self.window_size)) * self.window_size
- Wp = int(np.ceil(W / self.window_size)) * self.window_size
- # 拥有和feature map一样的通道排列顺序,方便后续window_partition
- img_mask = torch.zeros((1, Hp, Wp, 1), device=x.device) # [1, Hp, Wp, 1]
- h_slices = (slice(0, -self.window_size),
- slice(-self.window_size, -self.shift_size),
- slice(-self.shift_size, None))
- w_slices = (slice(0, -self.window_size),
- slice(-self.window_size, -self.shift_size),
- slice(-self.shift_size, None))
- cnt = 0
- for h in h_slices:
- for w in w_slices:
- img_mask[:, h, w, :] = cnt
- cnt += 1
-
- mask_windows = window_partition(img_mask, self.window_size) # [nW, Mh, Mw, 1]
- mask_windows = mask_windows.view(-1, self.window_size * self.window_size) # [nW, Mh*Mw]
- attn_mask = mask_windows.unsqueeze(1) - mask_windows.unsqueeze(2) # [nW, 1, Mh*Mw] - [nW, Mh*Mw, 1]
- # [nW, Mh*Mw, Mh*Mw]
- attn_mask = attn_mask.masked_fill(attn_mask != 0, float(-100.0)).masked_fill(attn_mask == 0, float(0.0))
- return attn_mask
-
- def forward(self, x, H, W):
- attn_mask = self.create_mask(x, H, W) # [nW, Mh*Mw, Mh*Mw]
- for blk in self.blocks:
- blk.H, blk.W = H, W
- if not torch.jit.is_scripting() and self.use_checkpoint:
- x = checkpoint.checkpoint(blk, x, attn_mask)
- else:
- x = blk(x, attn_mask)
- if self.downsample is not None:
- x = self.downsample(x, H, W)
- H, W = (H + 1) // 2, (W + 1) // 2
-
- return x, H, W
-
-
- class SwinTransformer(nn.Module):
- r""" Swin Transformer
- 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
- 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 Transformer 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
- qkv_bias (bool): If True, add a learnable bias to query, key, value. Default: True
- drop_rate (float): Dropout rate. Default: 0
- attn_drop_rate (float): Attention dropout rate. Default: 0
- drop_path_rate (float): Stochastic depth rate. Default: 0.1
- norm_layer (nn.Module): Normalization layer. Default: nn.LayerNorm.
- 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, 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., qkv_bias=True,
- drop_rate=0., attn_drop_rate=0., drop_path_rate=0.1,
- norm_layer=nn.LayerNorm, 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.patch_norm = patch_norm
- # stage4输出特征矩阵的channels
- 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(
- patch_size=patch_size, in_c=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):
- # 注意这里构建的stage和论文图中有些差异
- # 这里的stage不包含该stage的patch_merging层,包含的是下个stage的
- layers = BasicLayer(dim=int(embed_dim * 2 ** i_layer),
- depth=depths[i_layer],
- num_heads=num_heads[i_layer],
- window_size=window_size,
- mlp_ratio=self.mlp_ratio,
- qkv_bias=qkv_bias,
- 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(layers)
-
- 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.init.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)
-
- def forward(self, x):
- # x: [B, L, C]
- x, H, W = self.patch_embed(x)
- x = self.pos_drop(x)
-
- for layer in self.layers:
- x, H, W = layer(x, H, W)
-
- x = self.norm(x) # [B, L, C]
- x = self.avgpool(x.transpose(1, 2)) # [B, C, 1]
- x = torch.flatten(x, 1)
- x = self.head(x)
- return x
-
-
- def swin_tiny_patch4_window7_224(num_classes: int = 1000, **kwargs):
- # trained ImageNet-1K
- # https://github.com/SwinTransformer/storage/releases/download/v1.0.0/swin_tiny_patch4_window7_224.pth
- model = SwinTransformer(in_chans=3,
- patch_size=4,
- window_size=7,
- embed_dim=96,
- depths=(2, 2, 6, 2),
- num_heads=(3, 6, 12, 24),
- num_classes=num_classes,
- **kwargs)
- return model
-
-
- def swin_small_patch4_window7_224(num_classes: int = 1000, **kwargs):
- # trained ImageNet-1K
- # https://github.com/SwinTransformer/storage/releases/download/v1.0.0/swin_small_patch4_window7_224.pth
- model = SwinTransformer(in_chans=3,
- patch_size=4,
- window_size=7,
- embed_dim=96,
- depths=(2, 2, 18, 2),
- num_heads=(3, 6, 12, 24),
- num_classes=num_classes,
- **kwargs)
- return model
-
-
- def swin_base_patch4_window7_224(num_classes: int = 1000, **kwargs):
- # trained ImageNet-1K
- # https://github.com/SwinTransformer/storage/releases/download/v1.0.0/swin_base_patch4_window7_224.pth
- model = SwinTransformer(in_chans=3,
- patch_size=4,
- window_size=7,
- embed_dim=128,
- depths=(2, 2, 18, 2),
- num_heads=(4, 8, 16, 32),
- num_classes=num_classes,
- **kwargs)
- return model
-
-
- def swin_base_patch4_window12_384(num_classes: int = 1000, **kwargs):
- # trained ImageNet-1K
- # https://github.com/SwinTransformer/storage/releases/download/v1.0.0/swin_base_patch4_window12_384.pth
- model = SwinTransformer(in_chans=3,
- patch_size=4,
- window_size=12,
- embed_dim=128,
- depths=(2, 2, 18, 2),
- num_heads=(4, 8, 16, 32),
- num_classes=num_classes,
- **kwargs)
- return model
-
-
- def swin_base_patch4_window7_224_in22k(num_classes: int = 21841, **kwargs):
- # trained ImageNet-22K
- # https://github.com/SwinTransformer/storage/releases/download/v1.0.0/swin_base_patch4_window7_224_22k.pth
- model = SwinTransformer(in_chans=3,
- patch_size=4,
- window_size=7,
- embed_dim=128,
- depths=(2, 2, 18, 2),
- num_heads=(4, 8, 16, 32),
- num_classes=num_classes,
- **kwargs)
- return model
-
-
- def swin_base_patch4_window12_384_in22k(num_classes: int = 21841, **kwargs):
- # trained ImageNet-22K
- # https://github.com/SwinTransformer/storage/releases/download/v1.0.0/swin_base_patch4_window12_384_22k.pth
- model = SwinTransformer(in_chans=3,
- patch_size=4,
- window_size=12,
- embed_dim=128,
- depths=(2, 2, 18, 2),
- num_heads=(4, 8, 16, 32),
- num_classes=num_classes,
- **kwargs)
- return model
-
-
- def swin_large_patch4_window7_224_in22k(num_classes: int = 21841, **kwargs):
- # trained ImageNet-22K
- # https://github.com/SwinTransformer/storage/releases/download/v1.0.0/swin_large_patch4_window7_224_22k.pth
- model = SwinTransformer(in_chans=3,
- patch_size=4,
- window_size=7,
- embed_dim=192,
- depths=(2, 2, 18, 2),
- num_heads=(6, 12, 24, 48),
- num_classes=num_classes,
- **kwargs)
- return model
-
-
- def swin_large_patch4_window12_384_in22k(num_classes: int = 21841, **kwargs):
- # trained ImageNet-22K
- # https://github.com/SwinTransformer/storage/releases/download/v1.0.0/swin_large_patch4_window12_384_22k.pth
- model = SwinTransformer(in_chans=3,
- patch_size=4,
- window_size=12,
- embed_dim=192,
- depths=(2, 2, 18, 2),
- num_heads=(6, 12, 24, 48),
- num_classes=num_classes,
- **kwargs)
- return model
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