import torch import torch.nn as nn import math from functools import partial from .utils import trunc_normal_ def drop_path(x, drop_prob: float = 0., training: bool = False): 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(x, self.drop_prob, self.training) 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 class Attention(nn.Module): def __init__(self, dim, num_heads=8, qkv_bias=False, qk_scale=None, attn_drop=0., proj_drop=0.): super().__init__() self.num_heads = num_heads head_dim = dim // num_heads self.scale = qk_scale or head_dim ** -0.5 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) def forward(self, x): 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] attn = (q @ k.transpose(-2, -1)) * self.scale attn = attn.softmax(dim=-1) 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, attn class Block(nn.Module): def __init__(self, dim, num_heads, mlp_ratio=4., qkv_bias=False, qk_scale=None, drop=0., attn_drop=0., drop_path=0., act_layer=nn.GELU, norm_layer=nn.LayerNorm): super().__init__() self.norm1 = norm_layer(dim) self.attn = Attention( dim, 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(self, x, return_attention=False): y, attn = self.attn(self.norm1(x)) if return_attention: return attn x = x + self.drop_path(y) x = x + self.drop_path(self.mlp(self.norm2(x))) return x class PatchEmbed(nn.Module): """ Image to Patch Embedding """ def __init__(self, img_size=224, patch_size=16, in_chans=3, embed_dim=768): super().__init__() num_patches = (img_size // patch_size) * (img_size // patch_size) self.img_size = img_size self.patch_size = patch_size self.num_patches = num_patches self.proj = nn.Conv2d(in_chans, embed_dim, kernel_size=patch_size, stride=patch_size) def forward(self, x): B, C, H, W = x.shape x = self.proj(x).flatten(2).transpose(1, 2) return x class VisionTransformer(nn.Module): """ Vision Transformer """ def __init__(self, img_size=[224], patch_size=16, in_chans=3, num_classes=0, embed_dim=768, depth=12, num_heads=12, mlp_ratio=4., qkv_bias=False, qk_scale=None, drop_rate=0., attn_drop_rate=0., drop_path_rate=0., norm_layer=nn.LayerNorm, **kwargs): super().__init__() self.num_features = self.embed_dim = embed_dim self.patch_embed = PatchEmbed( img_size=img_size[0], patch_size=patch_size, in_chans=in_chans, embed_dim=embed_dim) num_patches = self.patch_embed.num_patches self.cls_token = nn.Parameter(torch.zeros(1, 1, embed_dim)) self.pos_embed = nn.Parameter(torch.zeros(1, num_patches + 1, embed_dim)) self.pos_drop = nn.Dropout(p=drop_rate) dpr = [x.item() for x in torch.linspace(0, drop_path_rate, depth)] # stochastic depth decay rule self.blocks = nn.ModuleList([ Block( dim=embed_dim, num_heads=num_heads, mlp_ratio=mlp_ratio, qkv_bias=qkv_bias, qk_scale=qk_scale, drop=drop_rate, attn_drop=attn_drop_rate, drop_path=dpr[i], norm_layer=norm_layer) for i in range(depth)]) self.norm = norm_layer(embed_dim) # Classifier head self.head = nn.Linear(embed_dim, num_classes) if num_classes > 0 else nn.Identity() trunc_normal_(self.pos_embed, std=.02) trunc_normal_(self.cls_token, std=.02) self.apply(self._init_weights) def _init_weights(self, 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) def interpolate_pos_encoding(self, x, w, h): npatch = x.shape[1] - 1 N = self.pos_embed.shape[1] - 1 if npatch == N and w == h: return self.pos_embed class_pos_embed = self.pos_embed[:, 0] patch_pos_embed = self.pos_embed[:, 1:] dim = x.shape[-1] w0 = w // self.patch_embed.patch_size h0 = h // self.patch_embed.patch_size # we add a small number to avoid floating point error in the interpolation # see discussion at https://github.com/facebookresearch/dino/issues/8 w0, h0 = w0 + 0.1, h0 + 0.1 patch_pos_embed = nn.functional.interpolate( patch_pos_embed.reshape(1, int(math.sqrt(N)), int(math.sqrt(N)), dim).permute(0, 3, 1, 2), scale_factor=(w0 / math.sqrt(N), h0 / math.sqrt(N)), mode='bicubic', align_corners=False, recompute_scale_factor=False ) assert int(w0) == patch_pos_embed.shape[-2] and int(h0) == patch_pos_embed.shape[-1] patch_pos_embed = patch_pos_embed.permute(0, 2, 3, 1).view(1, -1, dim) return torch.cat((class_pos_embed.unsqueeze(0), patch_pos_embed), dim=1) def prepare_tokens(self, x, ada_token=None): B, nc, w, h = x.shape x = self.patch_embed(x) # patch linear embedding # add the [CLS] token to the embed patch tokens cls_tokens = self.cls_token.expand(B, -1, -1) x = torch.cat((cls_tokens, x), dim=1) # add positional encoding to each token x = x + self.interpolate_pos_encoding(x, w, h) if ada_token is not None: ada_tokens = ada_token.expand(B, -1, -1) # B, p, d x = torch.cat((x, ada_tokens), dim=1) return self.pos_drop(x) def forward(self, x, ada_token=None, use_patches=False): x = self.prepare_tokens(x, ada_token) for blk in self.blocks: x = blk(x) x = self.norm(x) if use_patches: return x[:, 1:] else: return x[:, 0] def get_last_selfattention(self, x): x = self.prepare_tokens(x) for i, blk in enumerate(self.blocks): if i < len(self.blocks) - 1: x = blk(x) else: # return attention of the last block return blk(x, return_attention=True) def get_intermediate_layers(self, x, n=1): x = self.prepare_tokens(x) # we return the output tokens from the `n` last blocks output = [] for i, blk in enumerate(self.blocks): x = blk(x) if len(self.blocks) - i <= n: output.append(self.norm(x)) return output def vit_tiny(patch_size=16, **kwargs): model = VisionTransformer( patch_size=patch_size, embed_dim=192, depth=12, num_heads=3, mlp_ratio=4, qkv_bias=True, norm_layer=partial(nn.LayerNorm, eps=1e-6), **kwargs) return model def vit_small(patch_size=16, **kwargs): model = VisionTransformer( patch_size=patch_size, embed_dim=384, depth=12, num_heads=6, mlp_ratio=4, qkv_bias=True, norm_layer=partial(nn.LayerNorm, eps=1e-6), **kwargs) return model def vit_base(patch_size=16, **kwargs): model = VisionTransformer( patch_size=patch_size, embed_dim=768, depth=12, num_heads=12, mlp_ratio=4, qkv_bias=True, norm_layer=partial(nn.LayerNorm, eps=1e-6), **kwargs) return model