def test(self):
pack_lt = ops.pack([self.original_lt, self.original_lt], 'batch')
golden_lt = core.LabeledTensor(
array_ops.stack([self.original_lt.tensor, self.original_lt.tensor]),
['batch', self.a0, self.a1, self.a2, self.a3])
self.assertLabeledTensorsEqual(pack_lt, golden_lt)
def test(self):
pack_lt = ops.pack([self.original_lt, self.original_lt], 'batch')
golden_lt = core.LabeledTensor(
array_ops.stack([self.original_lt.tensor, self.original_lt.tensor]),
['batch', self.a0, self.a1, self.a2, self.a3])
self.assertLabeledTensorsEqual(pack_lt, golden_lt)
def setUp(self):
super(ShuffleBatchTest, self).setUp()
tensors = []
for i in range(10):
offset_lt = core.LabeledTensor(constant_op.constant(i), [])
tensors.append(core.add(self.original_lt, offset_lt))
self.pack_lt = ops.pack(tensors, 'batch')
def setUp(self):
super(ShuffleBatchTest, self).setUp()
tensors = []
for i in range(10):
offset_lt = core.LabeledTensor(constant_op.constant(i), [])
tensors.append(core.add(self.original_lt, offset_lt))
self.pack_lt = ops.pack(tensors, 'batch')
def test_no_enqueue_many(self):
[batch_2_op] = ops.batch([self.original_lt], batch_size=2)
self.assertEqual(len(batch_2_op.axes['batch']), 2)
[batch_10_op] = ops.batch([batch_2_op], batch_size=10, enqueue_many=True)
self.assertLabeledTensorsEqual(
ops.pack(10 * [self.original_lt], 'batch'), batch_10_op)
def test_no_enqueue_many(self):
[batch_2_op] = ops.batch([self.original_lt], batch_size=2)
self.assertEqual(len(batch_2_op.axes['batch']), 2)
[batch_10_op] = ops.batch([batch_2_op], batch_size=10, enqueue_many=True)
self.assertLabeledTensorsEqual(
ops.pack(10 * [self.original_lt], 'batch'), batch_10_op)
def test_axis(self):
pack_lt = ops.pack(
[self.original_lt, self.original_lt], new_axis='batch', axis_position=4)
golden_lt = core.LabeledTensor(
array_ops.stack(
[self.original_lt.tensor, self.original_lt.tensor], axis=4),
[self.a0, self.a1, self.a2, self.a3, 'batch'])
self.assertLabeledTensorsEqual(pack_lt, golden_lt)
def test_axis(self):
pack_lt = ops.pack(
[self.original_lt, self.original_lt], new_axis='batch', axis_position=4)
golden_lt = core.LabeledTensor(
array_ops.stack(
[self.original_lt.tensor, self.original_lt.tensor], axis=4),
[self.a0, self.a1, self.a2, self.a3, 'batch'])
self.assertLabeledTensorsEqual(pack_lt, golden_lt)
def test_invalid_input(self):
with self.assertRaises(ValueError):
ops.pack([self.original_lt, self.original_lt], 'channel')
def test_name(self):
pack_lt = ops.pack([self.original_lt, self.original_lt], 'batch')
self.assertIn('lt_pack', pack_lt.name)
def test_invalid_input(self):
with self.assertRaises(ValueError):
ops.pack([self.original_lt, self.original_lt], 'channel')
def test_name(self):
pack_lt = ops.pack([self.original_lt, self.original_lt], 'batch')
self.assertIn('lt_pack', pack_lt.name)
