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_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_ops_all(self):
""" test operators
"""
# ResizeImage
ops_conf = [{
'op': 'DecodeImage'
}, {
'op': 'ResizeImage',
'target_size': 300,
'max_size': 1333
}]
mapper = tf.build_mapper(ops_conf)
self.assertTrue(mapper is not None)
data = self.sample.copy()
result0 = mapper(data)
self.assertIsNotNone(result0['image'])
self.assertEqual(len(result0['image'].shape), 3)
# RandFlipImage
ops_conf = [{'op': 'RandomFlipImage'}]
mapper = tf.build_mapper(ops_conf)
self.assertTrue(mapper is not None)
result1 = mapper(result0)
self.assertEqual(result1['image'].shape, result0['image'].shape)
self.assertEqual(result1['gt_bbox'].shape, result0['gt_bbox'].shape)
# NormalizeImage
ops_conf = [{'op': 'NormalizeImage', 'is_channel_first': False}]
mapper = tf.build_mapper(ops_conf)
self.assertTrue(mapper is not None)
result2 = mapper(result1)
im1 = result1['image']
count = np.where(im1 <= 1)[0]
if im1.dtype == 'float64':
self.assertEqual(count, im1.shape[0] * im1.shape[1], im1.shape[2])
# ArrangeSample
ops_conf = [{'op': 'ArrangeRCNN'}]
mapper = tf.build_mapper(ops_conf)
self.assertTrue(mapper is not None)
result3 = mapper(result2)
self.assertEqual(type(result3), tuple)
def test_ops_part3(self):
"""test Mixup and RandomInterp
"""
ops_conf = [{
'op': 'DecodeImage',
'with_mixup': True,
}, {
'op': 'MixupImage',
}, {
'op': 'RandomInterpImage',
'target_size': 608
}]
mapper = tf.build_mapper(ops_conf)
self.assertTrue(mapper is not None)
data = self.sample.copy()
result = mapper(data)
self.assertEqual(len(result['image'].shape), 3)
self.assertGreater(result['gt_bbox'].shape[0], 0)
def test_ops_part2(self):
"""test Expand and RandomDistort
"""
ops_conf = [{
'op': 'DecodeImage'
}, {
'op': 'NormalizeBox'
}, {
'op': 'ExpandImage',
'max_ratio': 1.5,
'prob': 1
}]
mapper = tf.build_mapper(ops_conf)
self.assertTrue(mapper is not None)
data = self.sample.copy()
result = mapper(data)
self.assertEqual(len(result['image'].shape), 3)
self.assertGreater(result['gt_bbox'].shape[0], 0)
def test_ops_part1(self):
"""test Crop and Resize
"""
ops_conf = [{
'op': 'DecodeImage'
}, {
'op': 'NormalizeBox'
}, {
'op': 'CropImage',
'batch_sampler': [[1, 1, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0],
[1, 50, 0.3, 1.0, 0.5, 2.0, 0.1, 0.0],
[1, 50, 0.3, 1.0, 0.5, 2.0, 0.3, 0.0],
[1, 50, 0.3, 1.0, 0.5, 2.0, 0.5, 0.0],
[1, 50, 0.3, 1.0, 0.5, 2.0, 0.7, 0.0],
[1, 50, 0.3, 1.0, 0.5, 2.0, 0.9, 0.0],
[1, 50, 0.3, 1.0, 0.5, 2.0, 0.0, 1.0]]
}]
mapper = tf.build_mapper(ops_conf)
self.assertTrue(mapper is not None)
data = self.sample.copy()
result = mapper(data)
self.assertEqual(len(result['image'].shape), 3)
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())
