import torch
import torch.nn as nn
from functools import partial
import math
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)
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=1000, 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.num_classes = num_classes
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 forward(self, x):
# convert to list
if not isinstance(x, list):
x = [x]
# Perform forward pass separately on each resolution input.
# The inputs corresponding to a single resolution are clubbed and single
# forward is run on the same resolution inputs. Hence we do several
# forward passes = number of different resolutions used. We then
# concatenate all the output features.
idx_crops = torch.cumsum(torch.unique_consecutive(
torch.tensor([inp.shape[-1] for inp in x]),
return_counts=True,
)[1], 0)
start_idx = 0
for end_idx in idx_crops:
_out = self.forward_features(torch.cat(x[start_idx: end_idx]))
if start_idx == 0:
output = _out
else:
output = torch.cat((output, _out))
start_idx = end_idx
# Run the head forward on the concatenated features.
return self.head(output)
def forward_features(self, x):
B = x.shape[0]
x = self.patch_embed(x)
cls_tokens = self.cls_token.expand(B, -1, -1)
x = torch.cat((cls_tokens, x), dim=1)
pos_embed = self.interpolate_pos_encoding(x, self.pos_embed)
x = x + pos_embed
x = self.pos_drop(x)
for blk in self.blocks:
x = blk(x)
if self.norm is not None:
x = self.norm(x)
return x[:, 0]
def interpolate_pos_encoding(self, x, pos_embed):
npatch = x.shape[1] - 1
N = pos_embed.shape[1] - 1
if npatch == N:
return pos_embed
class_emb = pos_embed[:, 0]
pos_embed = pos_embed[:, 1:]
dim = x.shape[-1]
pos_embed = nn.functional.interpolate(
pos_embed.reshape(1, int(math.sqrt(N)), int(math.sqrt(N)), dim).permute(0, 3, 1, 2),
scale_factor=math.sqrt(npatch / N),
mode='bicubic',
)
pos_embed = pos_embed.permute(0, 2, 3, 1).view(1, -1, dim)
return torch.cat((class_emb.unsqueeze(0), pos_embed), dim=1)
def forward_selfattention(self, x):
B, nc, w, h = x.shape
x = self.patch_embed(x)
# interpolate patch embeddings
dim = x.shape[-1]
w0 = w // self.patch_embed.patch_size
h0 = h // self.patch_embed.patch_size
class_pos_embed = self.pos_embed[:, 0]
if self.pos_embed.shape[1] == 198:
N = self.pos_embed.shape[1] - 2
dist_pos_embed = self.pos_embed[:, 1]
patch_pos_embed = self.pos_embed[:, 2:]
else:
N = self.pos_embed.shape[1] - 1
patch_pos_embed = self.pos_embed[:, 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',
)
if w0 != patch_pos_embed.shape[-2]:
helper = torch.zeros(h0)[None, None, None, :].repeat(1, dim, w0 - patch_pos_embed.shape[-2], 1).to(x.device)
patch_pos_embed = torch.cat((patch_pos_embed, helper), dim=-2)
if h0 != patch_pos_embed.shape[-1]:
helper = torch.zeros(w0)[None, None, :, None].repeat(1, dim, 1, h0 - patch_pos_embed.shape[-1]).to(x.device)
patch_pos_embed = torch.cat((patch_pos_embed, helper), dim=-1)
patch_pos_embed = patch_pos_embed.permute(0, 2, 3, 1).view(1, -1, dim)
if self.pos_embed.shape[1] == 198:
pos_embed = torch.cat((class_pos_embed.unsqueeze(0), dist_pos_embed.unsqueeze(0), patch_pos_embed), dim=1)
else:
pos_embed = torch.cat((class_pos_embed.unsqueeze(0), patch_pos_embed), dim=1)
cls_tokens = self.cls_token.expand(B, -1, -1)
if self.pos_embed.shape[1] == 198:
dist_token = self.dist_token.expand(B, -1, -1)
x = torch.cat((cls_tokens, dist_token, x), dim=1)
else:
x = torch.cat((cls_tokens, x), dim=1)
x = x + pos_embed
x = self.pos_drop(x)
for i, blk in enumerate(self.blocks):
if i < len(self.blocks) - 1:
x = blk(x)
else:
return blk(x, return_attention=True)
def forward_return_n_last_blocks(self, x, n=1, return_patch_avgpool=False):
B = x.shape[0]
x = self.patch_embed(x)
cls_tokens = self.cls_token.expand(B, -1, -1)
x = torch.cat((cls_tokens, x), dim=1)
pos_embed = self.interpolate_pos_encoding(x, self.pos_embed)
x = x + pos_embed
x = self.pos_drop(x)
# we will return the [CLS] 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)[:, 0])
if return_patch_avgpool:
x = self.norm(x)
# In addition to the [CLS] tokens from the `n` last blocks, we also return
# the patch tokens from the last block. This is useful for linear eval.
output.append(torch.mean(x[:, 1:], dim=1))
return torch.cat(output, dim=-1)
def dino_small(patch_size=16, pretrained=False, **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)
model_url = {
16: "https://dl.fbaipublicfiles.com/dino/dino_deitsmall16_pretrain/dino_deitsmall16_pretrain.pth",
8: "https://dl.fbaipublicfiles.com/dino/dino_deitsmall8_300ep_pretrain/dino_deitsmall8_300ep_pretrain.pth"
}
if pretrained:
state_dict = torch.hub.load_state_dict_from_url(model_url[patch_size])
model.load_state_dict(state_dict, strict=False)
return model