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utils.py
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utils.py
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from tqdm import tqdm
import torch
import torch.nn.functional as F
import torch.nn as nn
import clip
def cls_acc(output, target, topk=1):
pred = output.topk(topk, 1, True, True)[1].t()
correct = pred.eq(target.view(1, -1).expand_as(pred))
acc = float(correct[: topk].reshape(-1).float().sum(0, keepdim=True).cpu().numpy())
acc = 100 * acc / target.shape[0]
return acc
def gpt_clip_classifier(classnames, gpt_prompts, clip_model, template):
with torch.no_grad():
clip_weights = []
for classname in classnames:
# Tokenize the prompts
classname = classname.replace('_', ' ')
texts = []
for t in gpt_prompts[classname]:
texts.append(t)
texts = clip.tokenize(texts).cuda()
# prompt ensemble for ImageNet
class_embeddings = clip_model.encode_text(texts)
class_embeddings /= class_embeddings.norm(dim=-1, keepdim=True)
class_embedding = class_embeddings.mean(dim=0)
class_embedding /= class_embedding.norm()
clip_weights.append(class_embedding)
clip_weights = torch.stack(clip_weights, dim=1).cuda()
return clip_weights
def build_clip_cache_model(cfg, clip_model, train_loader_cache):
if cfg['load_cache'] == False:
cache_keys = []
cache_values = []
with torch.no_grad():
# Data augmentation for the cache model
for augment_idx in range(cfg['augment_epoch']):
train_features = []
print('Augment Epoch: {:} / {:}'.format(augment_idx, cfg['augment_epoch']))
for i, (images, target) in enumerate(tqdm(train_loader_cache)):
images = images.cuda()
image_features = clip_model.encode_image(images)
train_features.append(image_features)
if augment_idx == 0:
target = target.cuda()
cache_values.append(target)
cache_keys.append(torch.cat(train_features, dim=0).unsqueeze(0))
cache_keys = torch.cat(cache_keys, dim=0).mean(dim=0)
cache_keys /= cache_keys.norm(dim=-1, keepdim=True)
cache_keys = cache_keys.permute(1, 0)
cache_values = F.one_hot(torch.cat(cache_values, dim=0)).half()
torch.save(cache_keys, cfg['cache_dir'] + '/clip_keys_' + str(cfg['shots']) + "shots.pt")
torch.save(cache_values, cfg['cache_dir'] + '/clip_values_' + str(cfg['shots']) + "shots.pt")
else:
cache_keys = torch.load(cfg['cache_dir'] + '/clip_keys_' + str(cfg['shots']) + "shots.pt")
cache_values = torch.load(cfg['cache_dir'] + '/clip_values_' + str(cfg['shots']) + "shots.pt")
return cache_keys, cache_values
def build_dino_cache_model(cfg, dino_model, train_loader_cache):
if cfg['load_cache'] == False:
cache_keys = []
cache_values = []
with torch.no_grad():
# Data augmentation for the cache model
for augment_idx in range(cfg['augment_epoch']):
train_features = []
print('Augment Epoch: {:} / {:}'.format(augment_idx, cfg['augment_epoch']))
for i, (images, target) in enumerate(tqdm(train_loader_cache)):
images = images.cuda()
image_features = dino_model(images)
train_features.append(image_features)
if augment_idx == 0:
target = target.cuda()
cache_values.append(target)
cache_keys.append(torch.cat(train_features, dim=0).unsqueeze(0))
cache_keys = torch.cat(cache_keys, dim=0).mean(dim=0)
cache_keys /= cache_keys.norm(dim=-1, keepdim=True)
cache_keys = cache_keys.permute(1, 0)
cache_values = F.one_hot(torch.cat(cache_values, dim=0)).half()
torch.save(cache_keys, cfg['cache_dir'] + '/dino_keys_' + str(cfg['shots']) + "shots.pt")
torch.save(cache_values, cfg['cache_dir'] + '/dino_values_' + str(cfg['shots']) + "shots.pt")
else:
cache_keys = torch.load(cfg['cache_dir'] + '/dino_keys_' + str(cfg['shots']) + "shots.pt")
cache_values = torch.load(cfg['cache_dir'] + '/dino_values_' + str(cfg['shots']) + "shots.pt")
return cache_keys, cache_values
def build_clip_dalle_cache_model(cfg, clip_model, train_loader_cache):
if cfg['load_cache'] == False:
cache_keys = []
cache_values = []
with torch.no_grad():
# Data augmentation for the cache model
for augment_idx in range(cfg['augment_epoch']):
train_features = []
print('Augment Epoch: {:} / {:}'.format(augment_idx, cfg['augment_epoch']))
for i, (images, target) in enumerate(tqdm(train_loader_cache)):
images = images.cuda()
image_features = clip_model.encode_image(images)
train_features.append(image_features)
if augment_idx == 0:
target = target.cuda()
cache_values.append(target)
cache_keys.append(torch.cat(train_features, dim=0).unsqueeze(0))
cache_keys = torch.cat(cache_keys, dim=0).mean(dim=0)
cache_keys /= cache_keys.norm(dim=-1, keepdim=True)
cache_keys = cache_keys.permute(1, 0)
cache_values = F.one_hot(torch.cat(cache_values, dim=0)).half()
torch.save(cache_keys, cfg['cache_dir'] + '/clip_dalle_keys_' + str(cfg['dalle_shots']) + "shots.pt")
torch.save(cache_values, cfg['cache_dir'] + '/clip_dalle_values_' + str(cfg['dalle_shots']) + "shots.pt")
else:
cache_keys = torch.load(cfg['cache_dir'] + '/clip_dalle_keys_' + str(cfg['dalle_shots']) + "shots.pt")
cache_values = torch.load(cfg['cache_dir'] + '/clip_dalle_values_' + str(cfg['dalle_shots']) + "shots.pt")
return cache_keys, cache_values
def build_dino_dalle_cache_model(cfg, dino_model, train_loader_cache):
if cfg['load_cache'] == False:
cache_keys = []
cache_values = []
with torch.no_grad():
# Data augmentation for the cache model
for augment_idx in range(cfg['augment_epoch']):
train_features = []
print('Augment Epoch: {:} / {:}'.format(augment_idx, cfg['augment_epoch']))
for i, (images, target) in enumerate(tqdm(train_loader_cache)):
images = images.cuda()
image_features = dino_model(images)
train_features.append(image_features)
if augment_idx == 0:
target = target.cuda()
cache_values.append(target)
cache_keys.append(torch.cat(train_features, dim=0).unsqueeze(0))
cache_keys = torch.cat(cache_keys, dim=0).mean(dim=0)
cache_keys /= cache_keys.norm(dim=-1, keepdim=True)
cache_keys = cache_keys.permute(1, 0)
cache_values = F.one_hot(torch.cat(cache_values, dim=0)).half()
torch.save(cache_keys, cfg['cache_dir'] + '/dino_dalle_keys_' + str(cfg['dalle_shots']) + "shots.pt")
torch.save(cache_values, cfg['cache_dir'] + '/dino_dalle_values_' + str(cfg['dalle_shots']) + "shots.pt")
else:
cache_keys = torch.load(cfg['cache_dir'] + '/dino_dalle_keys_' + str(cfg['dalle_shots']) + "shots.pt")
cache_values = torch.load(cfg['cache_dir'] + '/dino_dalle_values_' + str(cfg['dalle_shots']) + "shots.pt")
return cache_keys, cache_values
def pre_CLIP_load_features(cfg, split, clip_model, loader):
if cfg['load_pre_feat'] == False:
features, labels = [], []
with torch.no_grad():
for i, (images, target) in enumerate(tqdm(loader)):
images, target = images.cuda(), target.cuda()
image_features = clip_model.encode_image(images)
image_features /= image_features.norm(dim=-1, keepdim=True)
features.append(image_features)
labels.append(target)
features, labels = torch.cat(features), torch.cat(labels)
torch.save(features, cfg['cache_dir'] + "/" + split + "_clip_f.pt")
torch.save(labels, cfg['cache_dir'] + "/" + split + "_clip_l.pt")
else:
features = torch.load(cfg['cache_dir'] + "/" + split + "_clip_f.pt")
labels = torch.load(cfg['cache_dir'] + "/" + split + "_clip_l.pt")
return features, labels
def pre_DINO_load_features(cfg, split, dino_model, loader):
if cfg['load_pre_feat'] == False:
features, labels = [], []
with torch.no_grad():
for i, (images, target) in enumerate(tqdm(loader)):
images, target = images.cuda(), target.cuda()
image_features = dino_model(images)
image_features /= image_features.norm(dim=-1, keepdim=True)
features.append(image_features)
labels.append(target)
features, labels = torch.cat(features), torch.cat(labels)
torch.save(features, cfg['cache_dir'] + "/" + split + "_dino_f.pt")
torch.save(labels, cfg['cache_dir'] + "/" + split + "_dino_l.pt")
else:
features = torch.load(cfg['cache_dir'] + "/" + split + "_dino_f.pt")
labels = torch.load(cfg['cache_dir'] + "/" + split + "_dino_l.pt")
return features, labels
def search_hp(cfg, cache_keys, cache_values, features, labels, clip_weights, adapter=None):
if cfg['search_hp'] == True:
beta_list = [i * (cfg['search_scale'][0] - 0.1) / cfg['search_step'][0] + 0.1 for i in range(cfg['search_step'][0])]
alpha_list = [i * (cfg['search_scale'][1] - 0.1) / cfg['search_step'][1] + 0.1 for i in range(cfg['search_step'][1])]
best_acc = 0
best_beta, best_alpha = 0, 0
for beta in beta_list:
for alpha in alpha_list:
if adapter:
affinity = adapter(features)
else:
affinity = features @ cache_keys
cache_logits = ((-1) * (beta - beta * affinity)).exp() @ cache_values
clip_logits = 100. * features @ clip_weights
tip_logits = clip_logits + cache_logits * alpha
acc = cls_acc(tip_logits, labels)
if acc > best_acc:
print("New best setting, beta: {:.2f}, alpha: {:.2f}; accuracy: {:.2f}".format(beta, alpha, acc))
best_acc = acc
best_beta = beta
best_alpha = alpha
print("\nAfter searching, the best accuarcy: {:.2f}.\n".format(best_acc))
return best_beta, best_alpha
def search_no_clip_hp(cfg, cache_keys, cache_values, features, labels, adapter=None):
if cfg['search_hp'] == True:
beta_list = [i * (cfg['search_scale'][0] - 0.1) / cfg['search_step'][0] + 0.1 for i in range(cfg['search_step'][0])]
alpha_list = [i * (cfg['search_scale'][1] - 0.1) / cfg['search_step'][1] + 0.1 for i in range(cfg['search_step'][1])]
best_acc = 0
best_beta, best_alpha = 0, 0
for beta in beta_list:
for alpha in alpha_list:
if adapter:
affinity = adapter(features).to(torch.float16)
else:
affinity = features @ cache_keys
cache_logits = ((-1) * (beta - beta * affinity)).exp() @ cache_values
# clip_logits = 100. * features @ clip_weights
# tip_logits = clip_logits + cache_logits * alpha
tip_logits = cache_logits
acc = cls_acc(tip_logits, labels)
if acc > best_acc:
print("New best setting, beta: {:.2f}, alpha: {:.2f}; accuracy: {:.2f}".format(beta, alpha, acc))
best_acc = acc
best_beta = beta
best_alpha = alpha
print("\nAfter searching, the best accuarcy: {:.2f}.\n".format(best_acc))
return best_beta, best_alpha
def search_ensemble_hp(cfg,
clip_cache_keys,
clip_cache_values,
clip_features,
dino_cache_keys,
dino_cache_values,
dino_features,
labels,
clip_weights,
clip_adapter=None,
dino_adapter=None):
if cfg['search_hp'] == True:
beta_list = [i * (cfg['search_scale'][0] - 0.1) / cfg['search_step'][0] + 0.1 for i in range(cfg['search_step'][0])]
alpha_list = [i * (cfg['search_scale'][1] - 0.1) / cfg['search_step'][1] + 0.1 for i in range(cfg['search_step'][1])]
best_acc = 0
best_beta, best_alpha = 0, 0
for beta in beta_list:
for alpha in alpha_list:
if clip_adapter:
clip_affinity = clip_adapter(clip_features)
dino_affinity = dino_adapter(dino_features).to(dino_cache_values)
else:
clip_affinity = clip_features @ clip_cache_keys
dino_affinity = (dino_features @ dino_cache_keys).to(dino_cache_values)
clip_cache_logits = ((-1) * (beta - beta * clip_affinity)).exp() @ clip_cache_values
dino_cache_logits = ((-1) * (beta - beta * dino_affinity)).exp() @ dino_cache_values
clip_logits = 100. * clip_features @ clip_weights
cache_logits = logits_fuse(clip_logits, [clip_cache_logits, dino_cache_logits])
tip_logits = clip_logits + cache_logits * alpha
acc = cls_acc(tip_logits, labels)
if acc > best_acc:
print("New best setting, beta: {:.2f}, alpha: {:.2f}; accuracy: {:.2f}".format(beta, alpha, acc))
best_acc = acc
best_beta = beta
best_alpha = alpha
print("\nAfter searching, the best accuarcy: {:.2f}.\n".format(best_acc))
with open("best.txt","w") as f:
f.write("After searching, the best accuarcy: {:.2f}.\n".format(best_acc))
return best_beta, best_alpha
# clip zero_shot as baseline
def logits_fuse(zero_logtis, logits, normalize='mean'):
# normalize logits
softmax_fun = nn.Softmax(dim=1)
if normalize == 'softmax':
zero_logtis = softmax_fun(zero_logtis)
elif normalize =='linear':
zero_logtis /= torch.norm(zero_logtis, p=2, dim=1, keepdim=True)
elif normalize == 'mean':
logits_std = torch.std(zero_logtis, dim=1, keepdim=True)
logits_mean = torch.mean(zero_logtis, dim=1, keepdim=True)
zero_logtis = (zero_logtis - logits_mean) / logits_std
else:
raise("error normalize!")
similarity_matrix = []
normalize_logits = []
for logit in logits:
if normalize == 'softmax':
current_normalize_logits = softmax_fun(logit)
elif normalize =='linear':
current_normalize_logits = logit / torch.norm(logit, p=2, dim=1, keepdim=True)
elif normalize == 'mean':
logits_std = torch.std(logit, dim=1, keepdim=True)
logits_mean = torch.mean(logit, dim=1, keepdim=True)
current_normalize_logits = (logit - logits_mean) / logits_std
else:
raise("error normalize!")
current_similarity = current_normalize_logits * zero_logtis
current_similarity = torch.sum(current_similarity, dim=1, keepdim=True)
similarity_matrix.append(current_similarity)
normalize_logits.append(current_normalize_logits)
similarity_matrix = torch.stack(similarity_matrix, dim=-2)
similarity_matrix = softmax_fun(similarity_matrix)
normalize_logits = torch.stack(normalize_logits, dim=-2)
result_logits = torch.sum(normalize_logits * similarity_matrix, dim=1)
return result_logits
def logits_fuse_s(zero_logtis, logits, normalize='mean'):
# normalize logits
softmax_fun = nn.Softmax(dim=1)
if normalize == 'softmax':
zero_logtis = softmax_fun(zero_logtis)
elif normalize =='linear':
zero_logtis /= torch.norm(zero_logtis, p=2, dim=1, keepdim=True)
elif normalize == 'mean':
logits_std = torch.std(zero_logtis, dim=1, keepdim=True)
logits_mean = torch.mean(zero_logtis, dim=1, keepdim=True)
zero_logtis = (zero_logtis - logits_mean) / logits_std
else:
raise("error normalize!")
similarity_matrix = []
normalize_logits = []
for logit in logits:
if normalize == 'softmax':
current_normalize_logits = softmax_fun(logit)
elif normalize =='linear':
current_normalize_logits = logit / torch.norm(logit, p=2, dim=1, keepdim=True)
elif normalize == 'mean':
logits_std = torch.std(logit, dim=1, keepdim=True)
logits_mean = torch.mean(logit, dim=1, keepdim=True)
current_normalize_logits = (logit - logits_mean) / logits_std
else:
raise("error normalize!")
current_similarity = current_normalize_logits * zero_logtis
current_similarity = torch.sum(current_similarity, dim=1, keepdim=True)
similarity_matrix.append(current_similarity)
normalize_logits.append(current_normalize_logits)
similarity_matrix = torch.stack(similarity_matrix, dim=-2)
similarity_matrix = softmax_fun(similarity_matrix)
count = 0
for i in similarity_matrix:
if i[0]>0.4 and i[0]<0.6:
count += 1
normalize_logits = torch.stack(normalize_logits, dim=-2)
result_logits = torch.sum(normalize_logits * similarity_matrix, dim=1)
return result_logits, count