def main():
# Load config yaml file
parser = argparse.ArgumentParser()
parser.add_argument('--path_config',
default='config/default.yaml',
type=str,
help='path to a yaml config file')
args = parser.parse_args()
if args.path_config is not None:
with open(args.path_config, 'r') as handle:
config = yaml.load(handle)
cudnn.benchmark = True
# Generate dataset and loader
print("Loading samples to predict from %s" % os.path.join(
config['annotations']['dir'], config['prediction']['split'] + '.json'))
# Load annotations
path_annotations = os.path.join(config['annotations']['dir'],
config['prediction']['split'] + '.json')
input_annotations = json.load(open(path_annotations, 'r'))
# Data loader and dataset
input_loader = vqa_dataset.get_loader(config,
split=config['prediction']['split'])
# Load model weights
print("Loading Model from %s" % config['prediction']['model_path'])
log = torch.load(config['prediction']['model_path'])
# Num tokens seen during training
num_tokens = len(log['vocabs']['question']) + 1
# Use the same configuration used during training
train_config = log['config']
model = models.Model(train_config, num_tokens).to(dev)
dict_weights = log['weights']
model.load_state_dict(dict_weights)
predicted, samples_ids = predict_answers(
model, input_loader, split=config['prediction']['split'])
submission = create_submission(input_annotations, predicted, samples_ids,
input_loader.dataset.vocabs)
with open(config['prediction']['submission_file'], 'w') as fd:
json.dump(submission, fd)
print("Submission file saved in %s" %
config['prediction']['submission_file'])
def main():
# Load config yaml file
parser = argparse.ArgumentParser()
parser.add_argument('--path_config',
default='config/default.yaml',
type=str,
help='path to a yaml config file')
args = parser.parse_args()
if args.path_config is not None:
with open(args.path_config, 'r') as handle:
config = yaml.load(handle)
# generate log directory
dir_name = datetime.now().strftime("%Y-%m-%d_%H:%M:%S")
path_log_dir = os.path.join(config['logs']['dir_logs'], dir_name)
if not os.path.exists(path_log_dir):
os.makedirs(path_log_dir)
print('Model logs will be saved in {}'.format(path_log_dir))
cudnn.benchmark = True
# Generate datasets and loaders
train_loader = vqa_dataset.get_loader(config, split='train')
val_loader = vqa_dataset.get_loader(config, split='val')
model = models.Model(config, train_loader.dataset.num_tokens).to(dev)
optimizer = torch.optim.Adam(
filter(lambda p: p.requires_grad, model.parameters()),
config['training']['lr'])
# Load model weights if necessary
if config['model']['pretrained_model'] is not None:
print("Loading Model from %s" % config['model']['pretrained_model'])
log = torch.load(config['model']['pretrained_model'])
dict_weights = log['weights']
model.load_state_dict(dict_weights)
tracker = utils.Tracker()
min_loss = 10
max_accuracy = 0
path_best_accuracy = os.path.join(path_log_dir, 'best_accuracy_log.pth')
path_best_loss = os.path.join(path_log_dir, 'best_loss_log.pth')
for i in range(config['training']['epochs']):
train(model,
train_loader,
optimizer,
tracker,
epoch=i,
split=config['training']['train_split'])
# If we are training on the train split (and not on train+val) we can evaluate on val
if config['training']['train_split'] == 'train':
eval_results = evaluate(model,
val_loader,
tracker,
epoch=i,
split='val')
# save all the information in the log file
log_data = {
'epoch': i,
'tracker': tracker.to_dict(),
'config': config,
'weights': model.state_dict(),
'eval_results': eval_results,
'vocabs': train_loader.dataset.vocabs,
}
# save logs for min validation loss and max validation accuracy
if eval_results['avg_loss'] < min_loss:
# torch.save(log_data, path_best_loss) # save model
min_loss = eval_results['avg_loss'] # update min loss value
if eval_results['avg_accuracy'] > max_accuracy:
# torch.save(log_data, path_best_accuracy) # save model
max_accuracy = eval_results[
'avg_accuracy'] # update max accuracy value
# Save final model
log_data = {
'tracker': tracker.to_dict(),
'config': config,
'weights': model.state_dict(),
'vocabs': train_loader.dataset.vocabs,
}
path_final_log = os.path.join(path_log_dir, 'final_log.pth')
torch.save(log_data, path_final_log)