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V2 changed #11

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d4ea2b0
cub test
ParisZhang11 Jan 30, 2023
f3cf638
remove print line
ParisZhang11 Jan 30, 2023
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62 changes: 33 additions & 29 deletions main.py
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Original file line number Diff line number Diff line change
Expand Up @@ -80,38 +80,42 @@ def flatten_config(dic, running_key=None, flattened_dict={}):
model = model.to(device)

# # load data
# if args.DATA.LOAD_CACHED:
# cache_file = f"{args.DATA.SAVE_PATH}/{args.DATA.DATASET}/{args.EXP.IMAGE_FEATURES}_{args.EXP.CLIP_PRETRAINED_DATASET}_{args.EXP.CLIP_MODEL.replace('/','_')}.pt"
# dataset_classes, dataset_domains = dh.DATASET_CLASSES[args.DATA.DATASET], dh.DATASET_DOMAINS[args.DATA.DATASET]
# assert os.path.exists(cache_file), f"{cache_file} does not exist. To compute embeddings, set DATA.LOAD_CACHED=False"
# print(f"Loading cached embeddings from {cache_file}")
# train_features, train_labels, train_groups, train_domains, train_filenames, val_features, val_labels, val_groups, val_domains, val_filenames, test_features, test_labels, test_groups, test_domains, test_filenames = load_embeddings(cache_file, args.DATA.DATASET)
cache_file = f"{args.DATA.SAVE_PATH}/{args.DATA.DATASET}/{args.EXP.IMAGE_FEATURES}_{args.EXP.CLIP_PRETRAINED_DATASET}_{args.EXP.CLIP_MODEL.replace('/','_')}.pt"
dataset_classes, dataset_domains = dh.DATASET_CLASSES[args.DATA.DATASET], dh.DATASET_DOMAINS[args.DATA.DATASET]
if os.path.exists(cache_file):
if args.DATA.LOAD_CACHED:
cache_file = "data/CUB/vit14_new.pth"
dataset_classes, dataset_domains = dh.DATASET_CLASSES[args.DATA.DATASET], dh.DATASET_DOMAINS[args.DATA.DATASET]
assert os.path.exists(cache_file), f"{cache_file} does not exist. To compute embeddings, set DATA.LOAD_CACHED=False"
print(f"Loading cached embeddings from {cache_file}")
train_features, train_labels, train_groups, train_domains, train_filenames, val_features, val_labels, val_groups, val_domains, val_filenames, test_features, test_labels, test_groups, test_domains, test_filenames = load_embeddings(cache_file, args.DATA.DATASET)
else:
print(f"Computing embeddings and saving to {cache_file}")
trainset, valset, testset = dh.get_dataset(DATASET_NAME, preprocess, biased_val=args.EXP.BIASED_VAL)
dataset_classes, dataset_domains = dh.get_class(DATASET_NAME), dh.get_domain(DATASET_NAME)
train_loader = torch.utils.data.DataLoader(trainset, batch_size=cfg.DATA.BATCH_SIZE, shuffle=True)
val_loader = torch.utils.data.DataLoader(valset, batch_size=cfg.DATA.BATCH_SIZE, shuffle=False)
test_loader = torch.utils.data.DataLoader(testset, batch_size=cfg.DATA.BATCH_SIZE, shuffle=False)
train_features, train_labels, train_groups, train_domains, train_filenames = get_features(train_loader, model, device, model_type=args.EXP.IMAGE_FEATURES)
val_features, val_labels, val_groups, val_domains, val_filenames = get_features(val_loader, model, device, model_type=args.EXP.IMAGE_FEATURES)
test_features, test_labels, test_groups, test_domains, test_filenames = get_features(test_loader, model, device, model_type=args.EXP.IMAGE_FEATURES)
save_dict = {
"train_features": train_features, "train_labels": train_labels, "train_groups": train_groups, "train_domains": train_domains, "train_filenames": train_filenames,
"val_features": val_features, "val_labels": val_labels, "val_groups": val_groups, "val_domains": val_domains, "val_filenames": val_filenames,
"test_features": test_features, "test_labels": test_labels, "test_groups": test_groups, "test_domains": test_domains, "test_filenames": test_filenames,
"seed": args.EXP.SEED
}
if not os.path.exists(f"{args.DATA.SAVE_PATH}/{args.DATA.DATASET}"):
os.makedirs(f"{args.DATA.SAVE_PATH}/{args.DATA.DATASET}")
cache_file = f"{args.DATA.SAVE_PATH}/{args.DATA.DATASET}/{args.EXP.IMAGE_FEATURES}_{args.EXP.CLIP_PRETRAINED_DATASET}_{args.EXP.CLIP_MODEL.replace('/','_')}.pt"
torch.save(save_dict, cache_file)
print(f"Saved CLIP embeddings to {cache_file}")
cache_file = "data/CUB/vit14_new.pth"
dataset_classes, dataset_domains = dh.DATASET_CLASSES[args.DATA.DATASET], dh.DATASET_DOMAINS[args.DATA.DATASET]
if os.path.exists(cache_file):
print(f"Loading cached embeddings from {cache_file}")
train_features, train_labels, train_groups, train_domains, train_filenames, val_features, val_labels, val_groups, val_domains, val_filenames, test_features, test_labels, test_groups, test_domains, test_filenames = load_embeddings(cache_file, args.DATA.DATASET)
else:
print(f"Computing embeddings and saving to {cache_file}")
trainset, valset, testset = dh.get_dataset(DATASET_NAME, preprocess, biased_val=args.EXP.BIASED_VAL)
dataset_classes, dataset_domains = dh.get_class(DATASET_NAME), dh.get_domain(DATASET_NAME)
train_loader = torch.utils.data.DataLoader(trainset, batch_size=cfg.DATA.BATCH_SIZE, shuffle=True)
val_loader = torch.utils.data.DataLoader(valset, batch_size=cfg.DATA.BATCH_SIZE, shuffle=False)
test_loader = torch.utils.data.DataLoader(testset, batch_size=cfg.DATA.BATCH_SIZE, shuffle=False)
train_features, train_labels, train_groups, train_domains, train_filenames = get_features(train_loader, model, device, model_type=args.EXP.IMAGE_FEATURES)
val_features, val_labels, val_groups, val_domains, val_filenames = get_features(val_loader, model, device, model_type=args.EXP.IMAGE_FEATURES)
test_features, test_labels, test_groups, test_domains, test_filenames = get_features(test_loader, model, device, model_type=args.EXP.IMAGE_FEATURES)
save_dict = {
"train_features": train_features, "train_labels": train_labels, "train_groups": train_groups, "train_domains": train_domains, "train_filenames": train_filenames,
"val_features": val_features, "val_labels": val_labels, "val_groups": val_groups, "val_domains": val_domains, "val_filenames": val_filenames,
"test_features": test_features, "test_labels": test_labels, "test_groups": test_groups, "test_domains": test_domains, "test_filenames": test_filenames,
"seed": args.EXP.SEED
}
if not os.path.exists(f"{args.DATA.SAVE_PATH}/{args.DATA.DATASET}"):
os.makedirs(f"{args.DATA.SAVE_PATH}/{args.DATA.DATASET}")
cache_file = "data/CUB/vit14_new.pth"
torch.save(save_dict, cache_file)
print(f"Saved CLIP embeddings to {cache_file}")
old_val_features, old_val_labels, old_val_groups, old_val_domains, old_val_filenames = val_features, val_labels, val_groups, val_domains, val_filenames
val_features, val_labels, val_groups, val_domains, val_filenames = val_features[::2], val_labels[::2], val_groups[::2], val_domains[::2], val_filenames[::2]
test_features, test_labels, test_groups, test_domains, test_filenames = np.concatenate((test_features, old_val_features[1::2])), np.concatenate((test_labels, old_val_labels[1::2])), np.concatenate((test_groups, old_val_groups[1::2])), np.concatenate((test_domains, old_val_domains[1::2])), np.concatenate((test_filenames, old_val_filenames[1::2]))

if args.METHOD.NORMALIZE:
train_features /= np.linalg.norm(train_features, axis=-1, keepdims=True)
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2 changes: 1 addition & 1 deletion methods/predictors.py
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Expand Up @@ -518,7 +518,7 @@ def predict(self, image_feature):
# def predict(self, img_embeddings, label=None):
# return self.prompt_learner.predict(img_embeddings.cuda())

from CLIP.clip.simple_tokenizer import SimpleTokenizer as _Tokenizer
from clip.simple_tokenizer import SimpleTokenizer as _Tokenizer
from collections import OrderedDict

_tokenizer = _Tokenizer()
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