def main():
label_names = D.get_tox21_label_names()
parser = argparse.ArgumentParser(
description='Inference with a trained model.')
parser.add_argument('--in-dir',
'-i',
type=str,
default='result',
help='path result directory of training')
parser.add_argument('--batchsize',
'-b',
type=int,
default=128,
help='batch size')
parser.add_argument('--gpu',
'-g',
type=int,
default=-1,
help='GPU ID to use. Negative value indicates '
'not to use GPU and to run the code in CPU.')
args = parser.parse_args()
with open(os.path.join(args.in_dir, 'config.json'), 'r') as i:
config = json.loads(i.read())
method = config['method']
if method == 'schnet':
raise ValueError('Currently SchNet does not support prediction.')
labels = config['labels']
if labels:
class_num = len(labels) if isinstance(labels, list) else 1
else:
class_num = len(label_names)
_, _, test = data.load_dataset(method, labels)
predictor_ = predictor.build_predictor(method, config['unit_num'],
config['conv_layers'], class_num)
if args.gpu >= 0:
chainer.cuda.get_device_from_id(args.gpu).use()
predictor_.to_gpu()
inference_loop = predictor.InferenceLoop(predictor_)
y_pred = inference_loop.inference(test)
numpy.save('prediction.npy', y_pred)
def main():
label_names = D.get_tox21_label_names()
parser = argparse.ArgumentParser(
description='Inference with a trained model.')
parser.add_argument('--in-dir', '-i', type=str, default='result',
help='Path to the result directory of the training '
'script.')
parser.add_argument('--trainer-snapshot', '-s', type=str, default='',
help='Path to the snapshot file of the Chainer '
'trainer from which serialized model parameters '
'are extracted. If it is not specified, this '
'script searches the training result directory '
'for the latest snapshot, assuming that '
'the naming convension of snapshot files is '
'`snapshot_iter_N` where N is the number of '
'iterations, which is the default configuration '
'of Chainer.')
parser.add_argument('--batchsize', '-b', type=int, default=128,
help='batch size')
parser.add_argument('--gpu', '-g', type=int, default=-1,
help='GPU ID to use. Negative value indicates '
'not to use GPU and to run the code in CPU.')
args = parser.parse_args()
with open(os.path.join(args.in_dir, 'config.json'), 'r') as i:
config = json.loads(i.read())
method = config['method']
if method == 'schnet':
raise ValueError('Currently SchNet does not support prediction.')
labels = config['labels']
if labels:
class_num = len(labels) if isinstance(labels, list) else 1
else:
class_num = len(label_names)
_, test, _ = data.load_dataset(method, labels)
test = test.get_datasets()
X_test = D.NumpyTupleDataset(*test[:-1])
y_test = test[-1]
# Load pretrained model
predictor_ = predictor.build_predictor(
method, config['unit_num'], config['conv_layers'], class_num)
snapshot_file = args.trainer_snapshot
if not snapshot_file:
snapshot_file = _find_latest_snapshot(args.in_dir)
print('Loading pretrained model parameters from {}'.format(snapshot_file))
chainer.serializers.load_npz(snapshot_file,
predictor_, 'updater/model:main/predictor/')
if args.gpu >= 0:
chainer.cuda.get_device_from_id(args.gpu).use()
predictor_.to_gpu()
inference_loop = predictor.InferenceLoop(predictor_)
y_pred = inference_loop.inference(X_test)
if y_pred.shape != y_test.shape:
raise RuntimeError('The shape of the prediction result array and '
'that of the ground truth array do not match. '
'Contents of the input directory may be corrupted '
'or modified.')
if y_pred.ndim == 1:
y_pred = y_pred[:, None]
y_test = y_test[:, None]
statistics = []
for t, p in six.moves.zip(y_test.T, y_pred.T):
idx = t != -1
n_correct = (t[idx] == p[idx]).sum()
n_total = len(t[idx])
accuracy = float(n_correct) / n_total
statistics.append([n_correct, n_total, accuracy])
print('{:>6} {:>8} {:>8} {:>8}'
.format('TaskID', 'Correct', 'Total', 'Accuracy'))
for idx, (n_correct, n_total, accuracy) in enumerate(statistics):
print('task{:>2} {:>8} {:>8} {:>8.4f}'
.format(idx, n_correct, n_total, accuracy))
prediction_result_file = 'prediction.npz'
print('Save prediction result to {}'.format(prediction_result_file))
numpy.savez_compressed(prediction_result_file, y_pred)