def setUp(self):
source1 = numpy.zeros((2, 3, 4, 5), dtype='uint8')
source1[:] = numpy.arange(3 * 4 * 5, dtype='uint8').reshape((3, 4, 5))
source2 = numpy.empty(2, dtype=object)
source2[0] = numpy.arange(3 * 4 * 5, dtype='uint8').reshape((3, 4, 5))
source2[1] = numpy.arange(3 * 4 * 6, dtype='uint8').reshape((3, 4, 6))
source3 = [source2[0], source2[1]]
self.source1 = source1
self.source2 = source2
self.source3 = source3
axis_labels = {
'source1': ('batch', 'channel', 'height', 'width'),
'source2': ('batch', 'channel', 'height', 'width'),
'source3': ('batch', 'channel', 'height', 'width')
}
self.dataset = \
IndexableDataset(OrderedDict([('source1', source1),
('source2', source2),
('source3', source3)]),
axis_labels=axis_labels)
self.common_setup()
def setUp(self):
rng = numpy.random.RandomState(config.default_seed)
source1 = []
source2 = []
source3 = []
self.shapes = [(5, 9), (4, 6), (3, 6), (6, 4), (2, 5), (4, 8), (8, 3)]
for i, shape in enumerate(self.shapes):
source1.append(rng.normal(size=shape))
source2.append(rng.normal(size=shape[::-1]))
source3.append(i)
self.dataset = IndexableDataset(
OrderedDict([('source1', source1), ('source2', source2),
('source3', source3)]),
axis_labels={'source1': ('batch', 'channel', 'height', 'width')})
self.common_setup()
def setUp(self):
rng = numpy.random.RandomState(config.default_seed)
self.shapes = [(10, 12, 3), (9, 8, 4), (12, 14, 3), (4, 7), (9, 8, 4),
(7, 9, 3)]
pil1 = Image.fromarray(rng.random_integers(
0, 255, size=self.shapes[0]).astype('uint8'),
mode='RGB')
pil2 = Image.fromarray(rng.random_integers(
0, 255, size=self.shapes[1]).astype('uint8'),
mode='CMYK')
pil3 = Image.fromarray(rng.random_integers(
0, 255, size=self.shapes[2]).astype('uint8'),
mode='RGB')
pil4 = Image.fromarray(rng.random_integers(
0, 255, size=self.shapes[3]).astype('uint8'),
mode='L')
pil5 = Image.fromarray(rng.random_integers(
0, 255, size=self.shapes[4]).astype('uint8'),
mode='RGBA')
pil6 = Image.fromarray(rng.random_integers(
0, 255, size=self.shapes[5]).astype('uint8'),
mode='YCbCr')
source1 = [pil1, pil2, pil3]
source2 = [pil4, pil5, pil6]
bytesio1 = [BytesIO() for _ in range(3)]
bytesio2 = [BytesIO() for _ in range(3)]
formats1 = ['PNG', 'JPEG', 'BMP']
formats2 = ['GIF', 'PNG', 'JPEG']
for s, b, f in zip(source1, bytesio1, formats1):
s.save(b, format=f)
for s, b, f in zip(source2, bytesio2, formats2):
s.save(b, format=f)
self.dataset = IndexableDataset(OrderedDict([
('source1', [b.getvalue() for b in bytesio1]),
('source2', [b.getvalue() for b in bytesio2])
]),
axis_labels={
'source1': ('batch', 'bytes'),
'source2': ('batch', 'bytes')
})
self.common_setup()
def setUp(self):
source1 = numpy.zeros((9, 3, 7, 5), dtype='uint8')
source1[:] = numpy.arange(3 * 7 * 5, dtype='uint8').reshape(3, 7, 5)
shapes = [(5, 9), (6, 8), (5, 6), (5, 5), (6, 4), (7, 4), (9, 4),
(5, 6), (6, 5)]
source2 = []
biggest = 0
num_channels = 2
for shp in shapes:
biggest = max(biggest, shp[0] * shp[1] * 2)
ex = numpy.arange(shp[0] * shp[1] *
num_channels).reshape((num_channels, ) +
shp).astype('uint8')
source2.append(ex)
self.source2_biggest = biggest
axis_labels = {
'source1': ('batch', 'channel', 'height', 'width'),
'source2': ('batch', 'channel', 'height', 'width')
}
self.dataset = IndexableDataset(OrderedDict([('source1', source1),
('source2', source2)]),
axis_labels=axis_labels)
self.common_setup()