def main():
params.print_params()
parser = argparse.ArgumentParser()
parser.add_argument('--local_test',
type=str2bool,
default=False,
help='local test verbose')
parser.add_argument('--aug',
type=str2bool,
default=False,
help='source domain id')
args = parser.parse_args()
X_train, Y_train, X_val, Y_val, X_test, Y_test = loadData(
params.data_folder)
print(X_train.shape, Y_train.shape)
print(X_val.shape, Y_val.shape)
print(X_test.shape, Y_test.shape)
model, avg_layer = ResNet(X_train, Y_train, X_test, Y_test, args)
pred = model.predict(X_test)
acc = evaluate(pred, Y_test)
print('\nAccuracy: {:.4f}'.format(acc))
else:
from VGG_predict import make_predictions
from models.VGG_16 import VGG_16_test, VGG_19_test
if args.version == '16':
make_predictions(args.img_dim, VGG_16_test)
elif args.version =='19':
make_predictions(args.img_dim, VGG_19_test)
else:
sys.exit('cannot find model you have specified')
elif args.model == 'ResNet':
import models.ResNet as ResNet
train_data, train_labels, valid_data, valid_labels, test_data, test_ids = preprocess.get_roof_data(augmented=True, shape=(64, 64))
if args.train:
ResNet.train(train_data, train_labels, valid_data, valid_labels, dropout=0.62, num_blocks=3, lr=0.007, weight_decay=0.004)
else:
model_vargs = dict(dropout=0.62, num_blocks=3)
fn = 'results/best.model'
valid_predictions = ResNet.predict(fn, model_vargs, valid_data)
test_predictions = ResNet.predict(fn, model_vargs, test_data)
make_prediction_file.make_prediction_file(test_ids, test_predictions, 'Resnet805_64_64', valid_labels=valid_labels, valid_predictions=valid_predictions)
