def test_generator_datashape(self, n, bs):
ds = SVHNDataset("test")
ds._images = np.random.randint(low=0, high=255, size=(n, 32, 32, 3))
ds.labels = np.random.randint(low=1, high=10, size=(n, 1))
ds_gen = ds.generator(batch_size=bs, flatten=False, ae=False)
for i in range(len(ds_gen)):
if i == len(ds_gen) - 1:
if n % bs == 0:
assert (bs, 32, 32, 3) == ds_gen[i][0].shape
assert (bs, 1) == ds_gen[i][1].shape
else:
assert (n % bs, 32, 32, 3) == ds_gen[i][0].shape
assert (n % bs, 1) == ds_gen[i][1].shape
else:
assert (bs, 32, 32, 3) == ds_gen[i][0].shape
assert (bs, 1) == ds_gen[i][1].shape
def test_generator_batch(self, n, bs):
ds = SVHNDataset("test")
ds._images = np.random.randint(low=0, high=255, size=(n, 32, 32, 3))
ds.labels = np.random.randint(low=1, high=10, size=(n, 1))
assert np.ceil(n / bs) == len(ds.generator(batch_size=bs))