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c881b77 | 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 | import torch
import torch.nn as nn
import torch.distributed as dist
import torch.nn.functional as F
import torch.optim as optim
from .kmeans_pytorch import kmeans
from datamodules import RefTable
from PIL import Image
import os
from utils import Dict
class PrototypeLearner(nn.Module):
def __init__(self, num_prototypes=64, prototype_dim=1024, lambda_reg=0.1, lr=0.01, iter_steps=50, verbose=True):
super().__init__()
self.num_prototypes = num_prototypes
self.prototype_dim = prototype_dim
self.lambda_reg = lambda_reg
self.lr = lr
self.iter_steps = iter_steps
self.verbose = verbose
def get_initial_prototypes(self, features, device):
# features: [N_total, D]
feats_norm = F.normalize(features, p=2, dim=-1)
_, centers = kmeans(
X=feats_norm,
num_clusters=self.num_prototypes,
distance='cosine',
device=device,
tqdm_flag=False
)
return centers.to(device) # [K, D]
def calculate_reconstruction_loss(self, targets, protos):
protos_norm = F.normalize(protos, p=2, dim=-1)
# Formula: W = T * P^T * (P * P^T + lambda * I)^(-1)
# Ref: Eq. (2) in paper
# P * P^T: Gram Matrix [K, K], (P * P^T + lambda * I)^(-1)
p_gram = torch.matmul(protos_norm, protos_norm.t())
identity = torch.eye(self.num_prototypes, device=targets.device)
inverse_term = torch.inverse(p_gram + self.lambda_reg * identity)
# Mapping weights [N, K]
# W = T * P^T * Inverse
mapping_weights = torch.matmul(targets, protos_norm.t())
mapping_weights = torch.matmul(mapping_weights, inverse_term)
# Reconstruct: T_hat = W * P
reconstructed = torch.matmul(mapping_weights, protos_norm)
return F.mse_loss(reconstructed, targets)
def forward(self, features):
"""
features: [N_total, D]
Return: Optimized Prototypes [K, D]
"""
device = features.device
N, D = features.shape
targets = F.normalize(features, p=2, dim=-1).detach()
initial_protos = self.get_initial_prototypes(features, device)
initial_protos_static = initial_protos.clone().detach()
with torch.no_grad():
baseline_loss = self.calculate_reconstruction_loss(targets, initial_protos_static)
if self.verbose:
print(f"\n[ProtoLearner] Start. Baseline (K-Means) Reconstruction MSE: {baseline_loss.item():.6f}")
prototypes = nn.Parameter(initial_protos.clone())
optimizer = optim.Adam([prototypes], lr=self.lr)
for i in range(self.iter_steps):
optimizer.zero_grad()
loss = self.calculate_reconstruction_loss(targets, prototypes)
loss.backward()
optimizer.step()
with torch.no_grad():
prototypes.data.copy_(F.normalize(prototypes.data, p=2, dim=-1))
if self.verbose and (i == 0 or (i + 1) % 10 == 0 or i == self.iter_steps - 1):
with torch.no_grad():
curr_protos_norm = F.normalize(prototypes, p=2, dim=-1)
init_protos_norm = F.normalize(initial_protos_static, p=2, dim=-1)
shift_dist = torch.norm(curr_protos_norm - init_protos_norm, dim=-1).mean().item()
cosine_sim = F.cosine_similarity(curr_protos_norm, init_protos_norm, dim=-1).mean().item()
gain_pct = (baseline_loss.item() - loss.item()) / baseline_loss.item() * 100
print(f"Iter {i+1:02d}/{self.iter_steps} | "
f"Loss: {loss.item():.6f} | "
f"Gain: {gain_pct:.2f}% | "
f"Shift(L2): {shift_dist:.4f} | "
f"Sim(Cos): {cosine_sim:.4f}")
return F.normalize(prototypes, p=2, dim=-1).detach()
class PrototypeBank(nn.Module):
def __init__(self, config, image_encoder, image_processor, num_prototypes=128, prototype_dim=1024):
super().__init__()
self.config = config
self.num_prototypes = num_prototypes
self.prototype_dim = prototype_dim
self.image_patch_path = config.dataset.image_patch_path
self.categories = config.dataset.categories[config.phase]
self.aux = Dict(image_processor=image_processor, image_encoder=image_encoder)
shape = (len(self.categories) + 1, num_prototypes, prototype_dim)
self.register_buffer('prototypes', torch.zeros(shape))
self.register_buffer('prototype_flag', torch.tensor(False))
self.learner = PrototypeLearner(
num_prototypes=num_prototypes,
prototype_dim=prototype_dim,
lambda_reg=0.1,
lr=0.05,
iter_steps=50
)
@property
def image_processor(self):
return self.aux.image_processor
@property
def image_encoder(self):
return self.aux.image_encoder
def build_prototypes(self):
self.ref_table = RefTable()()
self.cate_to_id = {cate: idx for (idx, cate) in enumerate(self.categories)}
self.cate_to_id[''] = -1
if self.prototype_flag.item():
if (dist.is_initialized() and dist.get_rank() == 0) or (not dist.is_initialized()):
print(f"[PrototypeBank] Prototypes loaded from checkpoint (Frozen). Skip calculation.")
return
is_dist = dist.is_initialized()
rank = dist.get_rank() if is_dist else 0
device = next(iter(self.image_encoder.parameters())).device
temp_prototypes = None
if rank == 0:
print("[PrototypeBank] Calculating prototypes online...")
self.dense_features = {}
for category in self.categories:
files = list(self.ref_table[category].keys())
ref_images = []
for file in files:
img = Image.open(os.path.join(self.image_patch_path, category, file)).convert('RGB')
img = self.image_processor(images=img, return_tensors="pt", do_normalize=False)['pixel_values'].squeeze(0)
ref_images.append(img)
self.dense_features[category] = self.image_encoder(torch.stack(ref_images).to(device), mode='x_norm_patchtokens')
prototype_list = []
for category in self.categories:
flat_feats = self.dense_features[category].reshape(-1, self.prototype_dim)
optimized_protos = self.learner(flat_feats)
prototype_list.append(optimized_protos)
prototype_list.append(torch.zeros_like(prototype_list[-1]))
temp_prototypes = torch.stack(prototype_list)
self.prototypes.copy_(temp_prototypes)
self.prototype_flag.fill_(True)
if is_dist:
dist.broadcast(self.prototypes, src=0)
dist.broadcast(self.prototype_flag, src=0)
def get_prototypes(self, captions):
return torch.stack(list(map(lambda caption: self.prototypes[self.cate_to_id[caption]], captions))) |