q_dir = QUESTIONS_DIR
v_dir = VIDEOS_DIR
labels_file = LABELS_FILE
split_file = SPLIT_FILE
with open(split_file, 'r') as f:
split = json.load(f)
train_file_ids = split['train']
val_file_ids = split['val']
with open(labels_file, 'r') as f:
labels = json.load(f)
# Initialize datasets for training and validation
train_data = VNQADataset(q_dir=q_dir, v_dir=v_dir, filenames=train_file_ids, labels=labels)
val_data = VNQADataset(q_dir=q_dir, v_dir=v_dir, filenames=val_file_ids, labels=labels)
print('%d train examples, %d validation examples' % (len(train_data), len(val_data)))
# Create DataLoader objects for training and validation sets
train_loader = DataLoader(dataset=train_data, batch_size=args.batch_size, shuffle=True,
num_workers=args.num_workers)
val_loader = DataLoader(dataset=val_data, batch_size=args.batch_size, shuffle=True,
num_workers=args.num_workers)
# Initialize the model
num_classes = args.num_classes
if args.model == 'concat2d':
model = QConcatCNN2DLSTM(batch_size=args.batch_size,
q_embedding_size=args.embed_size,
nb_classes=num_classes,
q_dir = QUESTIONS_DIR
v_dir = VIDEOS_DIR
labels_file = LABELS_FILE
split_file = SPLIT_FILE
with open(split_file, 'r') as f:
split = json.load(f)
train_file_ids = split['train']
test_file_ids = split['test']
max_num_frames = MAX_ALLOWED_NUM_FRAMES_DROPPING
with open(labels_file, 'r') as f:
labels = json.load(f)
# Initialize datasets
train_data = VNQADataset(q_dir=q_dir, v_dir=v_dir, filenames=train_file_ids, labels=labels)
test_data = VNQADataset(q_dir=q_dir, v_dir=v_dir, filenames=test_file_ids, labels=labels,
q_metadata=True)
# Create DataLoader objects
train_loader = DataLoader(dataset=train_data, batch_size=args.batch_size, shuffle=True,
num_workers=args.num_workers)
test_loader = DataLoader(dataset=test_data, batch_size=args.batch_size, shuffle=True,
num_workers=args.num_workers)
# Initialize the model
num_classes = args.num_classes
if args.model == 'concat2d':
model = QConcatCNN2DLSTM(batch_size=args.batch_size,
q_embedding_size=args.embed_size,
nb_classes=num_classes,
if __name__ == '__main__':
with open(SPLIT_FILE, 'r') as f:
split = json.load(f)
train_file_ids = split['train']
test_file_ids = split['test']
print('%d train examples, %d test examples' %
(len(train_file_ids), len(test_file_ids)))
with open(LABELS_FILE, 'r') as f:
labels = json.load(f)
# Initialize datasets for training and testing
train_data = VNQADataset(q_dir=QUESTIONS_DIR,
v_dir=VIDEOS_DIR,
v_only=True,
filenames=train_file_ids,
labels=labels)
test_data = VNQADataset(q_dir=QUESTIONS_DIR,
v_dir=VIDEOS_DIR,
v_only=True,
filenames=test_file_ids,
labels=labels)
# Create DataLoader objects for training and test datasets
train_loader = DataLoader(dataset=train_data,
batch_size=args.batch_size,
shuffle=True,
num_workers=args.num_workers)
test_loader = DataLoader(dataset=test_data,
batch_size=args.batch_size,
if __name__=='__main__':
with open(SPLIT_FILE, 'r') as f:
split = json.load(f)
train_file_ids = split['train']
val_file_ids = split['val']
print('%d train examples, %d validation examples' % (len(train_file_ids), len(val_file_ids)))
with open(LABELS_FILE, 'r') as f:
labels = json.load(f)
# Initialize datasets for training and testing
num_classes = args.num_classes
train_data = VNQADataset(q_dir=QUESTIONS_DIR, v_dir=VIDEOS_DIR, v_only=True,
num_classes=num_classes, filenames=train_file_ids, labels=labels)
val_data = VNQADataset(q_dir=QUESTIONS_DIR, v_dir=VIDEOS_DIR, v_only=True,
num_classes=num_classes, filenames=val_file_ids, labels=labels)
# Create DataLoader objects for training and test datasets
train_loader = DataLoader(dataset=train_data,
batch_size=args.batch_size,
shuffle=True,
num_workers=args.num_workers)
val_loader = DataLoader(dataset=val_data,
batch_size=args.batch_size,
shuffle=True,
num_workers=args.num_workers)
# Initialize the model
model = VideoOnlyCNN3D(nb_classes=num_classes)
if __name__ == '__main__':
with open(args.split_file, 'r') as f:
split = json.load(f)
train_file_ids = split['train']
test_file_ids = split['test']
print('%d test examples' % len(test_file_ids))
with open(args.labels_file, 'r') as f:
labels = json.load(f)
num_classes = args.num_classes
# Initialize datasets
train_data = VNQADataset(q_dir=args.q_dir,
v_dir=args.v_dir,
q_only=True,
filenames=train_file_ids,
labels=labels,
num_classes=num_classes)
test_data = VNQADataset(q_dir=args.q_dir,
v_dir=args.v_dir,
q_only=True,
filenames=test_file_ids,
labels=labels,
num_classes=num_classes)
# Create DataLoader objects
test_loader = DataLoader(dataset=test_data,
batch_size=args.batch_size,
shuffle=True,
num_workers=args.num_workers)