def test_basic(self):
""" test basic apis 'next/size/drained'
"""
roi_source = build_source(self.config)
for i, sample in enumerate(roi_source):
self.assertTrue('image' in sample)
self.assertGreater(len(sample['image']), 0)
self.assertTrue(roi_source.drained())
self.assertEqual(i + 1, roi_source.size())
def test_batch(self):
""" test batched dataset
"""
batchsize = 2
mapper = tf.build_mapper(self.ops)
ds = build_source(self.sc_config)
mapped_ds = tf.map(ds, mapper)
batched_ds = tf.batch(mapped_ds, batchsize, True)
for sample in batched_ds:
out = sample
self.assertEqual(len(out), batchsize)
def test_reset(self):
""" test functions 'reset/epoch_id'
"""
roi_source = build_source(self.config)
self.assertTrue(roi_source.next() is not None)
self.assertEqual(roi_source.epoch_id(), 0)
roi_source.reset()
self.assertEqual(roi_source.epoch_id(), 1)
self.assertTrue(roi_source.next() is not None)
def test_map(self):
""" test transformer.map
"""
mapper = tf.build_mapper(self.ops)
ds = build_source(self.sc_config)
mapped_ds = tf.map(ds, mapper)
ct = 0
for sample in mapped_ds:
self.assertTrue(type(sample[0]) is np.ndarray)
ct += 1
self.assertEqual(ct, mapped_ds.size())
def test_parallel_map(self):
""" test transformer.map with concurrent workers
"""
mapper = tf.build_mapper(self.ops)
ds = build_source(self.sc_config)
worker_conf = {'WORKER_NUM': 2, 'use_process': True}
mapped_ds = tf.map(ds, mapper, worker_conf)
ct = 0
for sample in mapped_ds:
self.assertTrue(type(sample[0]) is np.ndarray)
ct += 1
self.assertTrue(mapped_ds.drained())
self.assertEqual(ct, mapped_ds.size())
mapped_ds.reset()
ct = 0
for sample in mapped_ds:
self.assertTrue(type(sample[0]) is np.ndarray)
ct += 1
self.assertEqual(ct, mapped_ds.size())
def _my_data_reader():
mydata = build_source(self.rcnn_conf['DATA']['TRAIN'])
for i, sample in enumerate(mydata):
yield sample