def test_iterator(self):
"""
Tests that batches returned by an iterator with topological
data_specs are the same as the ones returned by calling
get_topological_view on the dataset with the corresponding order
"""
batch_size = 100
b01c_X = self.test.X[0:batch_size, :]
b01c_topo = self.test.get_topological_view(b01c_X)
b01c_b01c_it = self.test.iterator(
mode='sequential',
batch_size=batch_size,
data_specs=(Conv2DSpace(shape=(16, 8),
num_channels=1,
axes=('b', 0, 1, 'c')),
'features'))
b01c_b01c = b01c_b01c_it.next()
assert np.all(b01c_topo == b01c_b01c)
c01b_test = OCR(which_set='test', axes=('c', 0, 1, 'b'))
c01b_X = c01b_test.X[0:batch_size, :]
c01b_topo = c01b_test.get_topological_view(c01b_X)
c01b_c01b_it = c01b_test.iterator(
mode='sequential',
batch_size=batch_size,
data_specs=(Conv2DSpace(shape=(16, 8),
num_channels=1,
axes=('c', 0, 1, 'b')),
'features'))
c01b_c01b = c01b_c01b_it.next()
assert np.all(c01b_topo == c01b_c01b)
# Also check that samples from iterators with the same data_specs
# with Conv2DSpace do not depend on the axes of the dataset
b01c_c01b_it = self.test.iterator(
mode='sequential',
batch_size=batch_size,
data_specs=(Conv2DSpace(shape=(16, 8),
num_channels=1,
axes=('c', 0, 1, 'b')),
'features'))
b01c_c01b = b01c_c01b_it.next()
assert np.all(b01c_c01b == c01b_c01b)
c01b_b01c_it = c01b_test.iterator(
mode='sequential',
batch_size=batch_size,
data_specs=(Conv2DSpace(shape=(16, 8),
num_channels=1,
axes=('b', 0, 1, 'c')),
'features'))
c01b_b01c = c01b_b01c_it.next()
assert np.all(c01b_b01c == b01c_b01c)
def test_iterator(self):
"""
Tests that batches returned by an iterator with topological
data_specs are the same as the ones returned by calling
get_topological_view on the dataset with the corresponding order
"""
batch_size = 100
b01c_X = self.test.X[0:batch_size, :]
b01c_topo = self.test.get_topological_view(b01c_X)
b01c_b01c_it = self.test.iterator(
mode='sequential',
batch_size=batch_size,
data_specs=(Conv2DSpace(shape=(16, 8),
num_channels=1,
axes=('b', 0, 1, 'c')), 'features'))
b01c_b01c = b01c_b01c_it.next()
assert np.all(b01c_topo == b01c_b01c)
c01b_test = OCR(which_set='test', axes=('c', 0, 1, 'b'))
c01b_X = c01b_test.X[0:batch_size, :]
c01b_topo = c01b_test.get_topological_view(c01b_X)
c01b_c01b_it = c01b_test.iterator(
mode='sequential',
batch_size=batch_size,
data_specs=(Conv2DSpace(shape=(16, 8),
num_channels=1,
axes=('c', 0, 1, 'b')), 'features'))
c01b_c01b = c01b_c01b_it.next()
assert np.all(c01b_topo == c01b_c01b)
# Also check that samples from iterators with the same data_specs
# with Conv2DSpace do not depend on the axes of the dataset
b01c_c01b_it = self.test.iterator(
mode='sequential',
batch_size=batch_size,
data_specs=(Conv2DSpace(shape=(16, 8),
num_channels=1,
axes=('c', 0, 1, 'b')), 'features'))
b01c_c01b = b01c_c01b_it.next()
assert np.all(b01c_c01b == c01b_c01b)
c01b_b01c_it = c01b_test.iterator(
mode='sequential',
batch_size=batch_size,
data_specs=(Conv2DSpace(shape=(16, 8),
num_channels=1,
axes=('b', 0, 1, 'c')), 'features'))
c01b_b01c = c01b_b01c_it.next()
assert np.all(c01b_b01c == b01c_b01c)
class TestOCR(unittest.TestCase):
"""
Unit test of OCR dataset
Parameters
----------
None
"""
def setUp(self):
"""Load train, test, valid sets"""
skip_if_no_data()
self.train = OCR(which_set='train')
self.valid = OCR(which_set='valid')
self.test = OCR(which_set='test')
def test_topo(self):
"""Tests that a topological batch has 4 dimensions"""
topo = self.train.get_batch_topo(1)
assert topo.ndim == 4
def test_topo_c01b(self):
"""
Tests that a topological batch with axes ('c',0,1,'b')
can be dimshuffled back to match the standard ('b',0,1,'c')
format.
"""
batch_size = 100
c01b_test = OCR(which_set='test', axes=('c', 0, 1, 'b'))
c01b_X = c01b_test.X[0:batch_size, :]
c01b = c01b_test.get_topological_view(c01b_X)
assert c01b.shape == (1, 16, 8, batch_size)
b01c = c01b.transpose(3, 1, 2, 0)
b01c_X = self.test.X[0:batch_size, :]
assert c01b_X.shape == b01c_X.shape
assert np.all(c01b_X == b01c_X)
b01c_direct = self.test.get_topological_view(b01c_X)
assert b01c_direct.shape == b01c.shape
assert np.all(b01c_direct == b01c)
def test_iterator(self):
"""
Tests that batches returned by an iterator with topological
data_specs are the same as the ones returned by calling
get_topological_view on the dataset with the corresponding order
"""
batch_size = 100
b01c_X = self.test.X[0:batch_size, :]
b01c_topo = self.test.get_topological_view(b01c_X)
b01c_b01c_it = self.test.iterator(
mode='sequential',
batch_size=batch_size,
data_specs=(Conv2DSpace(shape=(16, 8),
num_channels=1,
axes=('b', 0, 1, 'c')),
'features'))
b01c_b01c = b01c_b01c_it.next()
assert np.all(b01c_topo == b01c_b01c)
c01b_test = OCR(which_set='test', axes=('c', 0, 1, 'b'))
c01b_X = c01b_test.X[0:batch_size, :]
c01b_topo = c01b_test.get_topological_view(c01b_X)
c01b_c01b_it = c01b_test.iterator(
mode='sequential',
batch_size=batch_size,
data_specs=(Conv2DSpace(shape=(16, 8),
num_channels=1,
axes=('c', 0, 1, 'b')),
'features'))
c01b_c01b = c01b_c01b_it.next()
assert np.all(c01b_topo == c01b_c01b)
# Also check that samples from iterators with the same data_specs
# with Conv2DSpace do not depend on the axes of the dataset
b01c_c01b_it = self.test.iterator(
mode='sequential',
batch_size=batch_size,
data_specs=(Conv2DSpace(shape=(16, 8),
num_channels=1,
axes=('c', 0, 1, 'b')),
'features'))
b01c_c01b = b01c_c01b_it.next()
assert np.all(b01c_c01b == c01b_c01b)
c01b_b01c_it = c01b_test.iterator(
mode='sequential',
batch_size=batch_size,
data_specs=(Conv2DSpace(shape=(16, 8),
num_channels=1,
axes=('b', 0, 1, 'c')),
'features'))
c01b_b01c = c01b_b01c_it.next()
assert np.all(c01b_b01c == b01c_b01c)
class TestOCR(unittest.TestCase):
"""
Unit test of OCR dataset
Parameters
----------
None
"""
def setUp(self):
"""Load train, test, valid sets"""
skip_if_no_data()
self.train = OCR(which_set='train')
self.valid = OCR(which_set='valid')
self.test = OCR(which_set='test')
def test_topo(self):
"""Tests that a topological batch has 4 dimensions"""
topo = self.train.get_batch_topo(1)
assert topo.ndim == 4
def test_topo_c01b(self):
"""
Tests that a topological batch with axes ('c',0,1,'b')
can be dimshuffled back to match the standard ('b',0,1,'c')
format.
"""
batch_size = 100
c01b_test = OCR(which_set='test', axes=('c', 0, 1, 'b'))
c01b_X = c01b_test.X[0:batch_size, :]
c01b = c01b_test.get_topological_view(c01b_X)
assert c01b.shape == (1, 16, 8, batch_size)
b01c = c01b.transpose(3, 1, 2, 0)
b01c_X = self.test.X[0:batch_size, :]
assert c01b_X.shape == b01c_X.shape
assert np.all(c01b_X == b01c_X)
b01c_direct = self.test.get_topological_view(b01c_X)
assert b01c_direct.shape == b01c.shape
assert np.all(b01c_direct == b01c)
def test_iterator(self):
"""
Tests that batches returned by an iterator with topological
data_specs are the same as the ones returned by calling
get_topological_view on the dataset with the corresponding order
"""
batch_size = 100
b01c_X = self.test.X[0:batch_size, :]
b01c_topo = self.test.get_topological_view(b01c_X)
b01c_b01c_it = self.test.iterator(
mode='sequential',
batch_size=batch_size,
data_specs=(Conv2DSpace(shape=(16, 8),
num_channels=1,
axes=('b', 0, 1, 'c')), 'features'))
b01c_b01c = b01c_b01c_it.next()
assert np.all(b01c_topo == b01c_b01c)
c01b_test = OCR(which_set='test', axes=('c', 0, 1, 'b'))
c01b_X = c01b_test.X[0:batch_size, :]
c01b_topo = c01b_test.get_topological_view(c01b_X)
c01b_c01b_it = c01b_test.iterator(
mode='sequential',
batch_size=batch_size,
data_specs=(Conv2DSpace(shape=(16, 8),
num_channels=1,
axes=('c', 0, 1, 'b')), 'features'))
c01b_c01b = c01b_c01b_it.next()
assert np.all(c01b_topo == c01b_c01b)
# Also check that samples from iterators with the same data_specs
# with Conv2DSpace do not depend on the axes of the dataset
b01c_c01b_it = self.test.iterator(
mode='sequential',
batch_size=batch_size,
data_specs=(Conv2DSpace(shape=(16, 8),
num_channels=1,
axes=('c', 0, 1, 'b')), 'features'))
b01c_c01b = b01c_c01b_it.next()
assert np.all(b01c_c01b == c01b_c01b)
c01b_b01c_it = c01b_test.iterator(
mode='sequential',
batch_size=batch_size,
data_specs=(Conv2DSpace(shape=(16, 8),
num_channels=1,
axes=('b', 0, 1, 'c')), 'features'))
c01b_b01c = c01b_b01c_it.next()
assert np.all(c01b_b01c == b01c_b01c)