def setUp(self):
super(Base, self).setUp()
self.x_size = 7
self.channel_size = 3
self.z_size = 4
self.probs_size = 11
tensor = math_ops.range(
0, self.x_size * self.channel_size * self.z_size * self.probs_size)
tensor = array_ops.reshape(
tensor,
[self.x_size, self.channel_size, self.z_size, self.probs_size])
a0 = ('x', range(self.x_size))
a1 = ('channel', ['red', 'green', 'blue'])
a2 = 'z'
a3 = ('probs', np.linspace(0.0, 1.0, self.probs_size))
self.tensor = tensor
self.a0 = a0
self.a1 = a1
self.a2 = a2
self.a2_resolved = ('z', self.z_size)
self.a3 = a3
self.original_lt = core.LabeledTensor(tensor, [a0, a1, a2, a3])
self.x_probs_lt = core.slice_function(self.original_lt, {'z': 0})
self.x_probs_lt = ops.select(self.x_probs_lt, {'channel': 'red'})
self.channel_probs_lt = core.slice_function(self.original_lt, {
'x': 3,
'z': 0
})
def test_slice(self):
select_lt = ops.select(self.original_lt,
{'channel': slice('red', 'green')})
a1_sliced = ('channel', ['red', 'green'])
golden_lt = core.LabeledTensor(self.tensor[:, :2, :, :],
[self.a0, a1_sliced, self.a2, self.a3])
self.assertLabeledTensorsEqual(select_lt, golden_lt)
def test_invalid_input(self):
with self.assertRaises(ValueError):
rc = sugar.ReshapeCoder(['channel', 'mask'], ['depth'])
rc.decode(self.masked_image_lt)
with self.assertRaises(ValueError):
rc = sugar.ReshapeCoder(['channel', 'mask'], ['depth'])
rc.encode(self.masked_image_lt)
rc.encode(ops.select(self.masked_image_lt, {'channel': 'red'}))
def test_slices(self):
select_lt = ops.select(self.original_lt, {
'x': slice(1, 4),
'channel': slice('green', None)
})
a0_sliced = ('x', range(1, 5))
a1_sliced = ('channel', ['green', 'blue'])
golden_lt = core.LabeledTensor(
self.tensor[1:5,
1:, :, :], [a0_sliced, a1_sliced, self.a2, self.a3])
self.assertLabeledTensorsEqual(select_lt, golden_lt)
def test_scalar(self):
select_lt = ops.select(self.original_lt, {'channel': 'green'})
golden_lt = core.LabeledTensor(self.tensor[:, 1, :, :],
[self.a0, self.a2, self.a3])
self.assertLabeledTensorsEqual(select_lt, golden_lt)
def test_name(self):
select_lt = ops.select(self.original_lt, {'channel': 'green'})
self.assertIn('lt_select', select_lt.name)
def test(self):
concat_lt = ops.concat([self.red_lt, self.green_lt], 'channel')
golden_lt = ops.select(self.original_lt, {'channel': ['red', 'green']})
self.assertLabeledTensorsEqual(concat_lt, golden_lt)
def setUp(self):
super(ConcatTest, self).setUp()
self.red_lt = ops.select(self.original_lt, {'channel': ['red']})
self.green_lt = ops.select(self.original_lt, {'channel': ['green']})
self.blue_lt = ops.select(self.original_lt, {'channel': ['blue']})
def test_invalid_input(self):
with self.assertRaises(ValueError):
ops.select(self.original_lt, {'foo': 1})
with self.assertRaises(ValueError):
ops.select(self.original_lt, {'z': 1})
with self.assertRaises(KeyError):
ops.select(self.original_lt, {'channel': 'purple'})
with self.assertRaises(KeyError):
ops.select(self.original_lt, {'channel': ['red', 'purple']})
with self.assertRaises(NotImplementedError):
ops.select(self.original_lt, {'channel': ['red'], 'x': [1]})
with self.assertRaises(NotImplementedError):
ops.select(self.original_lt, {'channel': ['red'], 'x': 1})
with self.assertRaises(NotImplementedError):
ops.select(self.original_lt, {'channel': slice('red', 'green', 2)})
def test_tuple(self):
original_lt = core.LabeledTensor(constant_op.constant([5, 6]),
[('x', [(1, 2), (3, 4)])])
select_lt = ops.select(original_lt, {'x': (1, 2)})
golden_lt = core.LabeledTensor(constant_op.constant(5), [])
self.assertLabeledTensorsEqual(select_lt, golden_lt)
def test_list_zero_items(self):
select_lt = ops.select(self.original_lt, {'channel': []})
golden_lt = core.LabeledTensor(self.tensor[:, :0, :, :],
[self.a0, 'channel', self.a2, self.a3])
self.assertLabeledTensorsEqual(select_lt, golden_lt)
def test_list_one_item(self):
select_lt = ops.select(self.original_lt, {'channel': ['red']})
a1_sliced = ('channel', ['red'])
golden_lt = core.LabeledTensor(self.tensor[:, :1, :, :],
[self.a0, a1_sliced, self.a2, self.a3])
self.assertLabeledTensorsEqual(select_lt, golden_lt)