from utils.data_reader import prepare_data_for_feature from utils.utils import getMetrics from utils.features import get_feature from utils import constant from models.classifier import get_classifier import numpy as np ''' Try python main_classifier.py --emb_dim 300 ''' train, val, dev_no_lab, vocab = prepare_data_for_feature() ## feature_list: glove emoji elmo bert deepmoji emo2vec ## if you want twitter glove or common glove use ty='twitter' and ty='common' X_train, y_train = get_feature(train, vocab, feature_list=['glove'], mode=['sum'],split="train",ty='common') ## [29010,3,emb_size] 3 is number of sentence X_test, y_test = get_feature(val, vocab, feature_list=['glove'], mode=['sum'],split="valid",ty='common') ## [1150,3,emb_size] model = get_classifier(ty='LR') ## train aval and predict model.fit(X_train.reshape(X_train.shape[0], -1), y_train) ## [29010,3,emb_size] --> [29010, 3 * emb_size] ## validate to validation set y_pred = model.predict(X_test.reshape(X_test.shape[0], -1)) ## covert output to one hot one_hot = np.zeros((y_pred.shape[0], 4)) one_hot[np.arange(y_pred.shape[0]), y_pred] = 1 ## call the scorer getMetrics(one_hot,y_test,verbose=True)
else:
ood_test_set = get_dataset(P, dataset=ood, test_only=True, image_size=P.image_size)
if P.multi_gpu:
ood_sampler = DistributedSampler(ood_test_set, num_replicas=P.n_gpus, rank=P.local_rank)
ood_test_loader[ood] = DataLoader(ood_test_set, sampler=ood_sampler, batch_size=P.test_batch_size, **kwargs)
else:
ood_test_loader[ood] = DataLoader(ood_test_set, shuffle=False, batch_size=P.test_batch_size, **kwargs)
### Initialize model ###
simclr_aug = C.get_simclr_augmentation(P, image_size=P.image_size).to(device)
P.shift_trans, P.K_shift = C.get_shift_module(P, eval=True)
P.shift_trans = P.shift_trans.to(device)
model = C.get_classifier(P.model, n_classes=P.n_classes).to(device)
model = C.get_shift_classifer(model, P.K_shift).to(device)
criterion = nn.CrossEntropyLoss().to(device)
if P.optimizer == 'sgd':
optimizer = optim.SGD(model.parameters(), lr=P.lr_init, momentum=0.9, weight_decay=P.weight_decay)
lr_decay_gamma = 0.1
elif P.optimizer == 'lars':
from torchlars import LARS
base_optimizer = optim.SGD(model.parameters(), lr=P.lr_init, momentum=0.9, weight_decay=P.weight_decay)
optimizer = LARS(base_optimizer, eps=1e-8, trust_coef=0.001)
lr_decay_gamma = 0.1
else:
raise NotImplementedError()
def main(P):
P.no_strict = False
P.shift_trans_type = 'rotation'
P.mode = 'ood_pre'
P.n_classes = 2
P.model = 'resnet18_imagenet'
P.image_size = (224, 224, 3)
P.resize_factor = 0.54
P.resize_fix = True
P.layers = ['simclr', 'shift']
device = torch.device(f"cuda" if P.use_cuda else "cpu")
P.shift_trans, P.K_shift = C.get_shift_module(P, eval=True)
P.axis = pickle.load(open(P.axis_path, "rb"))
if P.w_sim_path:
P.weight_sim = pickle.load(open(P.w_sim_path, "rb"))
P.weight_shi = pickle.load(open(P.w_shi_path, "rb"))
else:
P.weight_sim = [
0.007519226599080519, 0.007939391391667395, 0.008598049328054363,
0.015014530319964874
]
P.weight_shi = [
0.04909334419285857, 0.052858438675397496, 0.05840793893796496,
0.11790745570891596
]
hflip = TL.HorizontalFlipLayer().to(device)
test_transform = transforms.Compose([
transforms.Resize(256),
transforms.CenterCrop(224),
transforms.ToTensor(),
])
simclr_aug = get_simclr_augmentation(P, P.image_size).to(device)
model = C.get_classifier(P.model, n_classes=P.n_classes).to(device)
model = C.get_shift_classifer(model, P.K_shift).to(device)
if P.use_cuda:
checkpoint = torch.load(P.load_path)
else:
checkpoint = torch.load(P.load_path, map_location='cpu')
model.load_state_dict(checkpoint, strict=not P.no_strict)
kwargs = {
'simclr_aug': simclr_aug,
'layers': P.layers,
'hflip': hflip,
'device': device
}
image_files = glob.glob(os.path.join(P.image_dir, "*"))
total_scores = []
for i, image_file in enumerate(image_files):
print(i, len(image_files), image_file)
start = time.time()
try:
img = Image.open(image_file).convert("RGB")
except:
continue
img = test_transform(img)
features = get_features(P, model, img, **kwargs)
scores = get_scores(P, features, device).numpy()
print(time.time() - start)
print(scores)
total_scores += list(scores)
print(total_scores)
total_scores = np.array(total_scores)
for i in range(20, 100):
if P.is_positive:
print('true accuracy', i,
(total_scores >= i / 100).sum() / len(total_scores))
else:
print('false accuracy', i,
(total_scores < i / 100).sum() / len(total_scores))
if P.is_positive:
print('true accuracy thres', P.score_thres,
(total_scores >= P.score_thres).sum() / len(total_scores))
else:
print('false accuracy thres', P.score_thres,
(total_scores < P.score_thres).sum() / len(total_scores))