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"""
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)