def main():
opt = parse_args()
if not os.path.exists(opt.dataset):
os.mkdir(opt.dataset)
local_path = '.'
output_directory = os.path.join(opt.dataset, 'checkpoint/')
filehandler = logging.FileHandler(os.path.join(opt.dataset, 'summary.log'))
streamhandler = logging.StreamHandler()
logger = logging.getLogger('')
logger.setLevel(logging.INFO)
logger.addHandler(filehandler)
logger.addHandler(streamhandler)
logging.info(opt)
target_hyperparams = config_choice(opt.data_dir, opt.dataset)
dataset_dir = target_hyperparams.pop('dataset')
train_dataset, val_dataset, test_dataset = ImagePredictor.Dataset.from_folders(
dataset_dir)
if isinstance(val_dataset, pd.DataFrame) and len(val_dataset) < 1:
val_dataset = None
predictor = ImagePredictor(log_dir=output_directory)
num_classes = target_hyperparams.pop('classes')
assert num_classes == len(
train_dataset.classes
), f'num_class mismatch, {num_classes} vs {len(train_dataset.classes)}'
# overwriting default by command line:
if opt.batch_size > 0:
target_hyperparams['batch_size'] = opt.batch_size
num_trials = target_hyperparams.pop('num_trials')
ngpus_per_trial = target_hyperparams.pop('ngpus_per_trial')
if opt.num_epochs > 0:
target_hyperparams['epochs'] = opt.num_epochs
num_trials = opt.num_trials if opt.num_trials > 0 else num_trials
if opt.ngpus_per_trial >= 0:
ngpus_per_trial = opt.ngpus_per_trial
predictor.fit(train_data=train_dataset,
val_data=val_dataset,
hyperparameters=target_hyperparams,
ngpus_per_trial=ngpus_per_trial,
num_trials=num_trials,
verbosity=2)
summary = predictor.fit_summary()
logging.info('Top-1 val acc: %.3f' % summary['train_acc'])
logger.info(summary)
def main():
opt = parse_args()
if not os.path.exists(opt.dataset):
os.mkdir(opt.dataset)
dataset_path = os.path.join(opt.data_dir, opt.dataset)
local_path = os.path.dirname(__file__)
output_directory = os.path.join(opt.dataset, 'checkpoint/')
filehandler = logging.FileHandler(os.path.join(opt.dataset, 'summary.log'))
streamhandler = logging.StreamHandler()
logger = logging.getLogger('')
logger.setLevel(logging.INFO)
logger.addHandler(filehandler)
logger.addHandler(streamhandler)
logging.info(opt)
target = config_choice(opt.data_dir, opt.dataset)
load_dataset = task.Dataset(target['dataset'])
classifier = task.fit(dataset=load_dataset,
output_directory=output_directory,
net=target['net'],
optimizer=target['optimizer'],
tricks=target['tricks'],
lr_config=target['lr_config'],
resume=opt.resume,
epochs=opt.num_epochs,
ngpus_per_trial=opt.ngpus_per_trial,
num_trials=opt.num_trials,
batch_size=opt.batch_size,
verbose=True,
plot_results=True)
summary = classifier.fit_summary(output_directory=opt.dataset, verbosity=4)
logging.info('Top-1 val acc: %.3f' % classifier.results['best_reward'])
logger.info(summary)
if opt.submission:
test_dataset = task.Dataset(os.path.join(opt.data_dir, opt.dataset,
'test'),
train=False)
inds, probs, probs_all, value = predict_details(
test_dataset, classifier, load_dataset)
ag.utils.generate_csv_submission(dataset_path, opt.dataset, local_path,
inds, probs_all, value, opt.custom)