Spaces:
Runtime error
Runtime error
| # -------------------------------------------------------- | |
| # Swin Transformer | |
| # Copyright (c) 2021 Microsoft | |
| # Licensed under The MIT License [see LICENSE for details] | |
| # Written by Ze Liu, Yutong Lin, Yixuan Wei | |
| # -------------------------------------------------------- | |
| # Copyright (c) Facebook, Inc. and its affiliates. | |
| # Modified by Bowen Cheng from https://github.com/SwinTransformer/Swin-Transformer-Semantic-Segmentation/blob/main/mmseg/models/backbones/swin_transformer.py | |
| # Copyright (c) Meta Platforms, Inc. All Rights Reserved | |
| import numpy as np | |
| 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_ | |
| from detectron2.modeling import BACKBONE_REGISTRY, Backbone, ShapeSpec | |
| class Mlp(nn.Module): | |
| """Multilayer perceptron.""" | |
| def __init__( | |
| self, | |
| in_features, | |
| hidden_features=None, | |
| out_features=None, | |
| act_layer=nn.GELU, | |
| drop=0.0, | |
| ): | |
| 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 WindowAttention(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 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=True, | |
| qk_scale=None, | |
| attn_drop=0.0, | |
| proj_drop=0.0, | |
| ): | |
| super().__init__() | |
| self.dim = dim | |
| self.window_size = window_size # 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), num_heads) | |
| ) # 2*Wh-1 * 2*Ww-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]) | |
| coords = torch.stack(torch.meshgrid([coords_h, coords_w])) # 2, Wh, Ww | |
| coords_flatten = torch.flatten(coords, 1) # 2, Wh*Ww | |
| relative_coords = ( | |
| coords_flatten[:, :, None] - coords_flatten[:, None, :] | |
| ) # 2, Wh*Ww, Wh*Ww | |
| relative_coords = relative_coords.permute( | |
| 1, 2, 0 | |
| ).contiguous() # Wh*Ww, Wh*Ww, 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) # Wh*Ww, 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=0.02) | |
| 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, Wh*Ww, Wh*Ww) 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], | |
| ) # make torchscript happy (cannot use tensor as tuple) | |
| q = q * self.scale | |
| attn = q @ k.transpose(-2, -1) | |
| 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, | |
| ) # Wh*Ww,Wh*Ww,nH | |
| relative_position_bias = relative_position_bias.permute( | |
| 2, 0, 1 | |
| ).contiguous() # nH, Wh*Ww, Wh*Ww | |
| attn = attn + relative_position_bias.unsqueeze(0) | |
| if mask is not None: | |
| nW = mask.shape[0] | |
| 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) | |
| 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): | |
| """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 | |
| 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=7, | |
| shift_size=0, | |
| mlp_ratio=4.0, | |
| qkv_bias=True, | |
| qk_scale=None, | |
| drop=0.0, | |
| attn_drop=0.0, | |
| drop_path=0.0, | |
| 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=to_2tuple(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 > 0.0 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, | |
| ) | |
| self.H = None | |
| self.W = None | |
| def forward(self, x, mask_matrix): | |
| """Forward function. | |
| Args: | |
| x: Input feature, tensor size (B, H*W, C). | |
| H, W: Spatial resolution of the input feature. | |
| mask_matrix: Attention mask for cyclic shift. | |
| """ | |
| B, L, C = x.shape | |
| H, W = self.H, self.W | |
| 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 | |
| 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) | |
| ) | |
| attn_mask = mask_matrix | |
| else: | |
| shifted_x = x | |
| attn_mask = None | |
| # 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 | |
| # W-MSA/SW-MSA | |
| attn_windows = self.attn( | |
| x_windows, mask=attn_mask | |
| ) # nW*B, window_size*window_size, C | |
| # merge windows | |
| attn_windows = attn_windows.view(-1, self.window_size, self.window_size, 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: | |
| 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 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, H, W): | |
| """Forward function. | |
| Args: | |
| x: Input feature, tensor size (B, H*W, C). | |
| H, W: Spatial resolution of the input feature. | |
| """ | |
| B, L, C = x.shape | |
| assert L == H * W, "input feature has wrong size" | |
| x = x.view(B, H, W, C) | |
| # 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 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 | |
| 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 (int): Local window size. Default: 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 | |
| use_checkpoint (bool): Whether to use checkpointing to save memory. Default: False. | |
| """ | |
| def __init__( | |
| self, | |
| dim, | |
| depth, | |
| num_heads, | |
| window_size=7, | |
| mlp_ratio=4.0, | |
| qkv_bias=True, | |
| qk_scale=None, | |
| drop=0.0, | |
| attn_drop=0.0, | |
| drop_path=0.0, | |
| norm_layer=nn.LayerNorm, | |
| downsample=None, | |
| use_checkpoint=False, | |
| ): | |
| super().__init__() | |
| self.window_size = window_size | |
| self.shift_size = window_size // 2 | |
| self.depth = depth | |
| self.use_checkpoint = use_checkpoint | |
| # 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 window_size // 2, | |
| 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, | |
| ) | |
| 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 forward(self, x, H, W): | |
| """Forward function. | |
| Args: | |
| x: Input feature, tensor size (B, H*W, C). | |
| H, W: Spatial resolution of the input feature. | |
| """ | |
| # calculate attention mask for SW-MSA | |
| Hp = int(np.ceil(H / self.window_size)) * self.window_size | |
| Wp = int(np.ceil(W / self.window_size)) * self.window_size | |
| 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, window_size, window_size, 1 | |
| mask_windows = mask_windows.view(-1, self.window_size * self.window_size) | |
| 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) | |
| ) | |
| for blk in self.blocks: | |
| blk.H, blk.W = H, W | |
| if self.use_checkpoint: | |
| x = checkpoint.checkpoint(blk, x, attn_mask) | |
| else: | |
| x = blk(x, attn_mask) | |
| if self.downsample is not None: | |
| x_down = self.downsample(x, H, W) | |
| Wh, Ww = (H + 1) // 2, (W + 1) // 2 | |
| return x, H, W, x_down, Wh, Ww | |
| else: | |
| return x, H, W, x, H, W | |
| class PatchEmbed(nn.Module): | |
| """Image to Patch Embedding | |
| Args: | |
| 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, patch_size=4, in_chans=3, embed_dim=96, norm_layer=None): | |
| super().__init__() | |
| patch_size = to_2tuple(patch_size) | |
| self.patch_size = patch_size | |
| 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): | |
| """Forward function.""" | |
| # padding | |
| _, _, H, W = x.size() | |
| if W % self.patch_size[1] != 0: | |
| x = F.pad(x, (0, self.patch_size[1] - W % self.patch_size[1])) | |
| if H % self.patch_size[0] != 0: | |
| x = F.pad(x, (0, 0, 0, self.patch_size[0] - H % self.patch_size[0])) | |
| x = self.proj(x) # B C Wh Ww | |
| if self.norm is not None: | |
| Wh, Ww = x.size(2), x.size(3) | |
| x = x.flatten(2).transpose(1, 2) | |
| x = self.norm(x) | |
| x = x.transpose(1, 2).view(-1, self.embed_dim, Wh, Ww) | |
| return x | |
| class SwinTransformer(nn.Module): | |
| """Swin Transformer backbone. | |
| A PyTorch impl of : `Swin Transformer: Hierarchical Vision Transformer using Shifted Windows` - | |
| https://arxiv.org/pdf/2103.14030 | |
| Args: | |
| pretrain_img_size (int): Input image size for training the pretrained model, | |
| used in absolute postion embedding. Default 224. | |
| patch_size (int | tuple(int)): Patch size. Default: 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: True | |
| 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 (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. | |
| out_indices (Sequence[int]): Output from which stages. | |
| frozen_stages (int): Stages to be frozen (stop grad and set eval mode). | |
| -1 means not freezing any parameters. | |
| use_checkpoint (bool): Whether to use checkpointing to save memory. Default: False. | |
| """ | |
| def __init__( | |
| self, | |
| pretrain_img_size=224, | |
| patch_size=4, | |
| in_chans=3, | |
| embed_dim=96, | |
| depths=[2, 2, 6, 2], | |
| num_heads=[3, 6, 12, 24], | |
| window_size=7, | |
| mlp_ratio=4.0, | |
| qkv_bias=True, | |
| qk_scale=None, | |
| drop_rate=0.0, | |
| attn_drop_rate=0.0, | |
| drop_path_rate=0.2, | |
| norm_layer=nn.LayerNorm, | |
| ape=False, | |
| patch_norm=True, | |
| out_indices=(0, 1, 2, 3), | |
| norm_indices=None, | |
| frozen_stages=-1, | |
| use_checkpoint=False, | |
| projection=False, | |
| project_dim=256, | |
| ): | |
| super().__init__() | |
| self.pretrain_img_size = pretrain_img_size | |
| self.num_layers = len(depths) | |
| self.embed_dim = embed_dim | |
| self.ape = ape | |
| self.patch_norm = patch_norm | |
| self.out_indices = out_indices | |
| self.norm_indices = norm_indices if norm_indices is not None else out_indices | |
| self.frozen_stages = frozen_stages | |
| # split image into non-overlapping patches | |
| self.patch_embed = PatchEmbed( | |
| patch_size=patch_size, | |
| in_chans=in_chans, | |
| embed_dim=embed_dim, | |
| norm_layer=norm_layer if self.patch_norm else None, | |
| ) | |
| # absolute position embedding | |
| if self.ape: | |
| pretrain_img_size = to_2tuple(pretrain_img_size) | |
| patch_size = to_2tuple(patch_size) | |
| patches_resolution = [ | |
| pretrain_img_size[0] // patch_size[0], | |
| pretrain_img_size[1] // patch_size[1], | |
| ] | |
| self.absolute_pos_embed = nn.Parameter( | |
| torch.zeros(1, embed_dim, patches_resolution[0], patches_resolution[1]) | |
| ) | |
| trunc_normal_(self.absolute_pos_embed, std=0.02) | |
| 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), | |
| 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) | |
| num_features = [int(embed_dim * 2 ** i) for i in range(self.num_layers)] | |
| self.num_features = num_features | |
| # add a norm layer for each output | |
| for i_layer in self.norm_indices: | |
| if i_layer >= len(self.num_features): | |
| continue | |
| layer = norm_layer(num_features[i_layer]) | |
| layer_name = f"norm{i_layer}" | |
| self.add_module(layer_name, layer) | |
| # add projector head | |
| self.projection = projection | |
| if projection: | |
| self.project_dim = project_dim | |
| self.norm = norm_layer(self.num_features[-1]) | |
| self.projector = nn.Linear(self.num_features[-1], project_dim, bias=False) | |
| 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 and self.ape: | |
| self.absolute_pos_embed.requires_grad = False | |
| if self.frozen_stages >= 2: | |
| self.pos_drop.eval() | |
| for i in range(0, self.frozen_stages - 1): | |
| m = self.layers[i] | |
| m.eval() | |
| for param in m.parameters(): | |
| param.requires_grad = False | |
| 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=0.02) | |
| 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): | |
| """Forward function.""" | |
| x = self.patch_embed(x) | |
| Wh, Ww = x.size(2), x.size(3) | |
| if self.ape: | |
| # interpolate the position embedding to the corresponding size | |
| absolute_pos_embed = F.interpolate( | |
| self.absolute_pos_embed, size=(Wh, Ww), mode="bicubic" | |
| ) | |
| x = (x + absolute_pos_embed).flatten(2).transpose(1, 2) # B Wh*Ww C | |
| else: | |
| x = x.flatten(2).transpose(1, 2) | |
| x = self.pos_drop(x) | |
| outs = {} | |
| for i in range(self.num_layers): | |
| layer = self.layers[i] | |
| x_out, H, W, x, Wh, Ww = layer(x, Wh, Ww) | |
| if i in self.out_indices: | |
| if i in self.norm_indices: | |
| norm_layer = getattr(self, f"norm{i}") | |
| x_out = norm_layer(x_out) | |
| out = ( | |
| x_out.view(-1, H, W, self.num_features[i]) | |
| .permute(0, 3, 1, 2) | |
| .contiguous() | |
| ) | |
| outs["res{}".format(i + 2)] = out | |
| if self.projection: | |
| x_out = self.norm(x_out) | |
| x_out = x_out.view(-1, H, W, self.num_features[-1]).contiguous() | |
| outs["fc"] = self.projector(x_out).permute(0, 3, 1, 2) | |
| return outs | |
| def train(self, mode=True): | |
| """Convert the model into training mode while keep layers freezed.""" | |
| super(SwinTransformer, self).train(mode) | |
| self._freeze_stages() | |
| class D2SwinTransformer(SwinTransformer, Backbone): | |
| def __init__(self, cfg, input_shape): | |
| pretrain_img_size = cfg.MODEL.SWIN.PRETRAIN_IMG_SIZE | |
| patch_size = cfg.MODEL.SWIN.PATCH_SIZE | |
| in_chans = 3 | |
| embed_dim = cfg.MODEL.SWIN.EMBED_DIM | |
| depths = cfg.MODEL.SWIN.DEPTHS | |
| num_heads = cfg.MODEL.SWIN.NUM_HEADS | |
| window_size = cfg.MODEL.SWIN.WINDOW_SIZE | |
| mlp_ratio = cfg.MODEL.SWIN.MLP_RATIO | |
| qkv_bias = cfg.MODEL.SWIN.QKV_BIAS | |
| qk_scale = cfg.MODEL.SWIN.QK_SCALE | |
| drop_rate = cfg.MODEL.SWIN.DROP_RATE | |
| attn_drop_rate = cfg.MODEL.SWIN.ATTN_DROP_RATE | |
| drop_path_rate = cfg.MODEL.SWIN.DROP_PATH_RATE | |
| norm_layer = nn.LayerNorm | |
| ape = cfg.MODEL.SWIN.APE | |
| patch_norm = cfg.MODEL.SWIN.PATCH_NORM | |
| norm_indices = cfg.MODEL.SWIN.NORM_INDICES | |
| projection = cfg.MODEL.SWIN.PROJECTION | |
| project_dim = cfg.MODEL.SWIN.PROJECT_DIM | |
| super().__init__( | |
| pretrain_img_size, | |
| patch_size, | |
| in_chans, | |
| embed_dim, | |
| depths, | |
| num_heads, | |
| window_size, | |
| mlp_ratio, | |
| qkv_bias, | |
| qk_scale, | |
| drop_rate, | |
| attn_drop_rate, | |
| drop_path_rate, | |
| norm_layer, | |
| ape, | |
| patch_norm, | |
| norm_indices=norm_indices, | |
| projection=projection, | |
| project_dim=project_dim, | |
| ) | |
| self._out_features = cfg.MODEL.SWIN.OUT_FEATURES | |
| self._out_feature_strides = { | |
| "res2": 4, | |
| "res3": 8, | |
| "res4": 16, | |
| "res5": 32, | |
| "fc": 32, | |
| } | |
| self._out_feature_channels = { | |
| "res2": self.num_features[0], | |
| "res3": self.num_features[1], | |
| "res4": self.num_features[2], | |
| "res5": self.num_features[3], | |
| "fc": self.num_features[3], | |
| } | |
| def forward(self, x): | |
| """ | |
| Args: | |
| x: Tensor of shape (N,C,H,W). H, W must be a multiple of ``self.size_divisibility``. | |
| Returns: | |
| dict[str->Tensor]: names and the corresponding features | |
| """ | |
| assert ( | |
| x.dim() == 4 | |
| ), f"SwinTransformer takes an input of shape (N, C, H, W). Got {x.shape} instead!" | |
| outputs = {} | |
| y = super().forward(x) | |
| for k in y.keys(): | |
| if k in self._out_features: | |
| outputs[k] = y[k] | |
| return outputs | |
| def output_shape(self): | |
| return { | |
| name: ShapeSpec( | |
| channels=self._out_feature_channels[name], | |
| stride=self._out_feature_strides[name], | |
| ) | |
| for name in self._out_features | |
| } | |
| def size_divisibility(self): | |
| return 32 | |