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bbc0514 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 | import torch
from torch import nn, Tensor
import torch.nn.functional as F
from transformers import Dinov2Model, Dinov2Config
from torchvision.transforms import v2
from code import interact
import json
import os
from PIL import Image
import numpy as np
from typing import Union
transforms = v2.Compose([
v2.ToImage(),
v2.ToDtype(torch.float32, scale=True),
v2.Resize((224, 224)),
v2.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
])
class CrossAttention(nn.Module):
def __init__(self, d_model:int, *args, **kwargs):
super().__init__(*args, **kwargs)
self.Wq = nn.Linear(d_model, d_model)
self.Wk = nn.Linear(d_model, d_model)
self.Wv = nn.Linear(d_model, d_model)
def forward(self, queries, candidates):
Q = self.Wk(candidates) # (B, num_candidates, d_model)
K = self.Wq(queries) # (B, num_queries, d_model)
V = self.Wv(queries) # (B, num_queries, d_model)
attn_out = F.scaled_dot_product_attention(Q, K, V) # (B, num_candidates, d_model)
return attn_out
class JointTransformer(nn.Module):
def __init__(
self,
d_model=384,
nhead=4,
num_layers=4,
*args, **kwargs
):
super().__init__(*args, **kwargs)
# Transformer encoder
encoder_layer = nn.TransformerEncoderLayer(
d_model=d_model,
nhead=nhead,
dim_feedforward=4 * d_model,
batch_first=True,
dropout=0.0
)
self.transformer = nn.TransformerEncoder(encoder_layer, num_layers)
def forward(self, query: Tensor, candidates: Tensor) -> Tensor :
Q = query.size(1)
assert Q == 1
x = torch.cat((query, candidates), dim=1) # (B, Q+C, D)
x = self.transformer(x) # (B, Q+C, D)
query = x[:,:Q,:] # (B, Q, D)
candidates = x[:, Q:, :] # (B, C, D)
return query, candidates
class MLP(nn.Module):
def __init__(self, emb_dim, expand_factor, *args, **kwargs):
super().__init__(*args, **kwargs)
self.lin1 = nn.Linear(emb_dim, emb_dim*expand_factor)
self.gelu = nn.GELU("tanh")
self.lin2 = nn.Linear(emb_dim*expand_factor, emb_dim)
def forward(self, x:Tensor) -> Tensor:
x = self.lin1(x)
x = self.gelu(x)
x = self.lin2(x)
return x
class Matcher(nn.Module):
def __init__(self, max_candidates, num_layers, dino_dir, *args, **kwargs):
super().__init__(*args, **kwargs)
# -------------- Pre-trained Encoder (frozen) -----------------
assert isinstance(dino_dir, str)
with open(os.path.join(dino_dir, "config.json"), "r") as f:
dino_cfg = json.load(f)
self.encoder = Dinov2Model.from_pretrained(dino_dir, config = Dinov2Config(**dino_cfg))
self.freeze_encoder()
# ----------------- Embeddings to distinguish queries and candidates ---------------------
self.query_image_embed = nn.Parameter(torch.randn(1, 1, dino_cfg["hidden_size"]))
self.candidates_image_embed = nn.Embedding(max_candidates, dino_cfg["hidden_size"])
self.null_candidate = nn.Parameter(torch.randn(1, 1, dino_cfg["hidden_size"])) # null candidate embedding
# ---------------- Joint transformer (trained) ----------------------
self.max_candidates = max_candidates
self.num_layers = num_layers
self.joint_transformer = JointTransformer(
d_model = dino_cfg["hidden_size"],
nhead = dino_cfg["num_attention_heads"],
num_layers = num_layers,
)
self.lnormq = nn.LayerNorm(dino_cfg["hidden_size"], )
self.lnormc = nn.LayerNorm(dino_cfg["hidden_size"], )
# ------------------------ Final operation ---------------------------
self.cross_attn = CrossAttention(dino_cfg["hidden_size"])
self.lnormc2 = nn.LayerNorm(dino_cfg["hidden_size"])
self.classification_layer = nn.Linear(dino_cfg["hidden_size"], 1)
def freeze_encoder(self) -> None:
for p in self.encoder.parameters():
p.requires_grad_(False)
def pre_process_img(self, image:Union[Image.Image, np.ndarray, str]):
if isinstance(image, str):
image = Image.open(image)
return transforms(image)
@torch.inference_mode()
def predict(self, query_crop: np.ndarray, candidate_crops: list[np.ndarray]):
query = transforms(query_crop)[None, None, ...]
candidates = torch.stack([transforms(candidate_crop) for candidate_crop in candidate_crops]).unsqueeze(0)
probs = self.forward(query, candidates).softmax(dim=-1)
return probs.numpy()
def forward(self, query: Tensor, candidates: Tensor) -> Tensor :
# query (B,1,3,H,W), candidates (B,C,3,H,W)
B, C, _, H, W = candidates.shape
query = self.encoder(
query.view(B, 3, H, W)
)['last_hidden_state'] # (B, T, D)
# pick the CLS_TOKEN
query = query[:,0,:].view(B, 1, -1) # (B, 1, D)
candidates = self.encoder(
candidates.view(B*C, 3, H, W)
)['last_hidden_state'] # (B*C, T, D)
# pick the CLS_TOKEN
candidates = candidates[:,0,:].view(B, C, -1) # (B, C, D)
# Add embeddings
query = query + self.query_image_embed.repeat(B, 1, 1) # (B, 1, D)
candidate_ids = torch.arange(C, device=query.device).view(1, C)
candidates = candidates + self.candidates_image_embed(candidate_ids) # (B, C, D)
candidates = torch.cat(
(
candidates,
self.null_candidate.repeat(B, 1, 1)
),
dim=1) # (B, C+1, D)
# Joint transformer, candidate and query tokens attend to each other
q, c = self.joint_transformer(query, candidates)
# skip connections
query = self.lnormq(query + q)
candidates = self.lnormc(candidates + c)
# Cross attention, query attends to candidates
c = self.cross_attn(query, candidates) # (B, C+1, D)
candidates = self.lnormc2(candidates + c)
candidates = candidates + c
logits = self.classification_layer(candidates) # (B, C+1, 1)
return logits.squeeze(-1)
if __name__ == "__main__":
import random
B, H, W = 1, 224, 224
max_candidates = 10
num_layers = 4
query = torch.randn((B, 1, 3, H, W))
candidates = torch.randn((B, random.randint(2, max_candidates), 3, H, W))
matcher = Matcher(max_candidates, num_layers, "DINOv2_base")
out = matcher(query, candidates)
interact(local=locals())
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