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| import math | |
| import warnings | |
| from functools import partial | |
| import timm | |
| import torch | |
| import torch.nn as nn | |
| eps = 1e-4 | |
| def _no_grad_trunc_normal_(tensor, mean, std, a, b): | |
| # Cut & paste from PyTorch official master until it's in a few official releases - RW | |
| # Method based on https://people.sc.fsu.edu/~jburkardt/presentations/truncated_normal.pdf | |
| def norm_cdf(x): | |
| # Computes standard normal cumulative distribution function | |
| return (1. + math.erf(x / math.sqrt(2.))) / 2. | |
| if (mean < a - 2 * std) or (mean > b + 2 * std): | |
| warnings.warn("mean is more than 2 std from [a, b] in nn.init.trunc_normal_. " | |
| "The distribution of values may be incorrect.", | |
| stacklevel=2) | |
| with torch.no_grad(): | |
| # Values are generated by using a truncated uniform distribution and | |
| # then using the inverse CDF for the normal distribution. | |
| # Get upper and lower cdf values | |
| l = norm_cdf((a - mean) / std) | |
| u = norm_cdf((b - mean) / std) | |
| # Uniformly fill tensor with values from [l, u], then translate to | |
| # [2l-1, 2u-1]. | |
| tensor.uniform_(2 * l - 1, 2 * u - 1) | |
| # Use inverse cdf transform for normal distribution to get truncated | |
| # standard normal | |
| tensor.erfinv_() | |
| # Transform to proper mean, std | |
| tensor.mul_(std * math.sqrt(2.)) | |
| tensor.add_(mean) | |
| # Clamp to ensure it's in the proper range | |
| tensor.clamp_(min=a, max=b) | |
| return tensor | |
| def trunc_normal_(tensor, mean=0., std=1., a=-2., b=2.): | |
| # type: (Tensor, float, float, float, float) -> Tensor | |
| return _no_grad_trunc_normal_(tensor, mean, std, a, b) | |
| 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, return_qkv=False): | |
| 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, qkv | |
| 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, return_qkv=False): | |
| y, attn, qkv = 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))) | |
| if return_qkv: | |
| return x, attn, qkv | |
| 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).reshape(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 forward_feats(self, x): | |
| x = self.prepare_tokens(x) | |
| for blk in self.blocks: | |
| x = blk(x) | |
| x = self.norm(x) | |
| return x | |
| def get_intermediate_feat(self, x, n=1, norm=True): | |
| x = self.prepare_tokens(x) | |
| # we return the output tokens from the `n` last blocks | |
| feat = [] | |
| attns = [] | |
| qkvs = [] | |
| for i, blk in enumerate(self.blocks): | |
| x, attn, qkv = blk(x, return_qkv=True) | |
| if len(self.blocks) - i <= n: | |
| if norm: | |
| feat.append(self.norm(x)) | |
| else: | |
| feat.append(x) | |
| qkvs.append(qkv) | |
| attns.append(attn) | |
| return feat, attns, qkvs | |
| 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=eps), **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=eps), **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=eps), **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 | |
| class DINOFeaturizer(nn.Module): | |
| def __init__(self, arch, patch_size, feat_type): | |
| super().__init__() | |
| self.arch = arch | |
| self.patch_size = patch_size | |
| self.feat_type = feat_type | |
| self.config = { | |
| "arch": arch, | |
| "patch_size": patch_size, | |
| "feat_type": feat_type | |
| } | |
| self.model = vit_small( | |
| patch_size=patch_size, | |
| num_classes=0) | |
| if "3d-dino" in arch: | |
| state_dict = torch.load("../models/3d-dino-co3d.pth")["teacher"] | |
| state_dict = {k.replace("module.", "").replace("backbone.", ""): v for k, v in state_dict.items()} | |
| state_dict = {k: v for k, v in state_dict.items() if "head." not in k} | |
| elif "iarpa-dino" in arch: | |
| state_dict = torch.load("../models/dino_iarpa.pth")["teacher"] | |
| state_dict = {k.replace("module.", "").replace("backbone.", ""): v for k, v in state_dict.items()} | |
| state_dict = {k: v for k, v in state_dict.items() if "head." not in k} | |
| elif "chk-dino" in arch: | |
| state_dict = torch.load("../models/dino_deitsmall16_pretrain_full_checkpoint.pth")["teacher"] | |
| state_dict = {k.replace("module.", "").replace("backbone.", ""): v for k, v in state_dict.items()} | |
| state_dict = {k: v for k, v in state_dict.items() if "head." not in k} | |
| elif "ft_dino" in arch: | |
| arch = "_".join(arch.split("_")[:-1]) | |
| state_dict = torch.load("../models/{}.pth".format(arch))["teacher"] | |
| state_dict = {k.replace("module.", "").replace("backbone.", ""): v for k, v in state_dict.items()} | |
| state_dict = {k: v for k, v in state_dict.items() if "head." not in k} | |
| elif "dino" in arch: | |
| state_dict = torch.hub.load('facebookresearch/dino:main', self.arch).state_dict() | |
| else: # model from timm -- load weights from timm to dino model (enables working on arbitrary size images). | |
| temp_model = timm.create_model(self.arch, pretrained=True) | |
| state_dict = temp_model.state_dict() | |
| del state_dict['head.weight'] | |
| del state_dict['head.bias'] | |
| self.model.load_state_dict(state_dict, strict=True) | |
| if arch == "vit_small": | |
| self.n_feats = 384 | |
| else: | |
| self.n_feats = 768 | |
| def get_cls_token(self, img): | |
| return self.model.forward(img) | |
| def forward(self, img, n=1, include_cls=False): | |
| assert (img.shape[2] % self.patch_size == 0) | |
| assert (img.shape[3] % self.patch_size == 0) | |
| feat, attn, qkv = self.model.get_intermediate_feat(img, n=n) | |
| feat, attn, qkv = feat[0], attn[0], qkv[0] | |
| feat_h = img.shape[2] // self.patch_size | |
| feat_w = img.shape[3] // self.patch_size | |
| if self.feat_type == "token": | |
| image_feat = feat[:, 1:, :].reshape(feat.shape[0], feat_h, feat_w, -1).permute(0, 3, 1, 2) | |
| cls_feat = feat[:, 0, :] | |
| elif self.feat_type == "key": | |
| x = qkv[1, :, :, 1:, :] # remove cls token | |
| desc = x.permute(0, 2, 3, 1).flatten(start_dim=-2, end_dim=-1) | |
| image_feat = desc.reshape(desc.shape[0], feat_h, feat_w, desc.shape[2]) \ | |
| .permute(0, 3, 1, 2) | |
| cls_feat = None | |
| else: | |
| raise ValueError("Unknown feat type:{}".format(self.feat_type)) | |
| if include_cls: | |
| return image_feat, cls_feat | |
| return image_feat |