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| # Copyright (c) Facebook, Inc. and its affiliates. | |
| # | |
| # Licensed under the Apache License, Version 2.0 (the "License"); | |
| # you may not use this file except in compliance with the License. | |
| # You may obtain a copy of the License at | |
| # | |
| # http://www.apache.org/licenses/LICENSE-2.0 | |
| # | |
| # Unless required by applicable law or agreed to in writing, software | |
| # distributed under the License is distributed on an "AS IS" BASIS, | |
| # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. | |
| # See the License for the specific language governing permissions and | |
| # limitations under the License. | |
| """ | |
| Mostly copy-paste from timm library. | |
| https://github.com/rwightman/pytorch-image-models/blob/master/timm/models/vision_transformer.py | |
| """ | |
| import math | |
| from functools import partial | |
| import torch | |
| import torch.nn as nn | |
| 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', | |
| ) | |
| 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): | |
| 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) | |
| return self.pos_drop(x) | |
| def forward(self, x): | |
| x = self.prepare_tokens(x) | |
| for blk in self.blocks: | |
| x = blk(x) | |
| x = self.norm(x) | |
| 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 | |
| class DINOHead(nn.Module): | |
| def __init__(self, in_dim, out_dim, use_bn=False, norm_last_layer=True, nlayers=3, hidden_dim=2048, bottleneck_dim=256): | |
| super().__init__() | |
| nlayers = max(nlayers, 1) | |
| if nlayers == 1: | |
| self.mlp = nn.Linear(in_dim, bottleneck_dim) | |
| else: | |
| layers = [nn.Linear(in_dim, hidden_dim)] | |
| if use_bn: | |
| layers.append(nn.BatchNorm1d(hidden_dim)) | |
| layers.append(nn.GELU()) | |
| for _ in range(nlayers - 2): | |
| layers.append(nn.Linear(hidden_dim, hidden_dim)) | |
| if use_bn: | |
| layers.append(nn.BatchNorm1d(hidden_dim)) | |
| layers.append(nn.GELU()) | |
| layers.append(nn.Linear(hidden_dim, bottleneck_dim)) | |
| self.mlp = nn.Sequential(*layers) | |
| self.apply(self._init_weights) | |
| self.last_layer = nn.utils.weight_norm(nn.Linear(bottleneck_dim, out_dim, bias=False)) | |
| self.last_layer.weight_g.data.fill_(1) | |
| if norm_last_layer: | |
| self.last_layer.weight_g.requires_grad = False | |
| 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) | |
| def forward(self, x): | |
| x = self.mlp(x) | |
| x = nn.functional.normalize(x, dim=-1, p=2) | |
| x = self.last_layer(x) | |
| return x | |