| """ |
| test pretrained models |
| """ |
| from __future__ import print_function |
| import mxnet as mx |
| from common import find_mxnet, modelzoo |
| from score import score |
| |
| VAL_DATA='data/val-5k-256.rec' |
| def download_data(): |
| return mx.test_utils.download( |
| 'http://data.mxnet.io/data/val-5k-256.rec', VAL_DATA) |
| |
| def test_imagenet1k_resnet(**kwargs): |
| models = ['imagenet1k-resnet-50', 'imagenet1k-resnet-152'] |
| accs = [.77, .78] |
| for (m, g) in zip(models, accs): |
| acc = mx.metric.create('acc') |
| (speed,) = score(model=m, data_val=VAL_DATA, |
| rgb_mean='0,0,0', metrics=acc, **kwargs) |
| r = acc.get()[1] |
| print('Tested %s, acc = %f, speed = %f img/sec' % (m, r, speed)) |
| assert r > g and r < g + .1 |
| |
| def test_imagenet1k_inception_bn(**kwargs): |
| acc = mx.metric.create('acc') |
| m = 'imagenet1k-inception-bn' |
| g = 0.75 |
| (speed,) = score(model=m, |
| data_val=VAL_DATA, |
| rgb_mean='123.68,116.779,103.939', metrics=acc, **kwargs) |
| r = acc.get()[1] |
| print('Tested %s acc = %f, speed = %f img/sec' % (m, r, speed)) |
| assert r > g and r < g + .1 |
| |
| if __name__ == '__main__': |
| gpus = mx.test_utils.list_gpus() |
| assert len(gpus) > 0 |
| batch_size = 16 * len(gpus) |
| gpus = ','.join([str(i) for i in gpus]) |
| |
| kwargs = {'gpus':gpus, 'batch_size':batch_size, 'max_num_examples':500} |
| download_data() |
| test_imagenet1k_resnet(**kwargs) |
| test_imagenet1k_inception_bn(**kwargs) |