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test.py
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test.py
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import argparse
import torch
from auglib.augmentation import Augmentations
from auglib.dataset_loader import CSVDatasetWithName
from auglib.test import test_with_augmentation
def parse_args():
parser = argparse.ArgumentParser()
parser.add_argument('model', help='Path to the model')
parser.add_argument('dataset_root', help='Path to dataset root')
parser.add_argument('dataset_csv', help='Path to dataset csv')
parser.add_argument('-n', type=int, default=1,
help='Number of image copies')
parser.add_argument('--print-predictions', '-p', action='store_true',
help='Print the predicted value for each image')
parser.add_argument('--output', '-o',
help='Path to output CSV file')
return parser.parse_args()
def main(args):
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
model = torch.load(args.model)
model.eval()
model.to(device)
augs = Augmentations(**model.aug_params)
dataset = CSVDatasetWithName(args.dataset_root,
args.dataset_csv,
'image_id',
'melanoma',
transform=augs.tf_transform,
add_extension='.jpg')
score, preds = test_with_augmentation(model, dataset, device, 8, args.n)
print(score)
if args.print_predictions:
for _, row in preds.iterrows():
print("{},{}".format(row['image'], row['score']))
if args.output:
preds.to_csv(args.output, index=False)
if __name__ == '__main__':
args = parse_args()
main(args)