def get_squad(tmpdir_factory):
# Generate a synthetic features cache file to avoid using the real dataset
tmpdir = tmpdir_factory.mktemp("ndr_test_tmp_data")
input_file = str(tmpdir) + "/inputfile"
dataset_size = 3333
features = generate_random_features(128, 30, dataset_size)
cache_file = input_file + f".{128}.cache"
with open(cache_file, "wb") as f:
pickle.dump(features, f)
print(cache_file)
return tmpdir
def test_generated_data_squad():
sequence_length = 128
batch_size = 2
vocab_length = 4864
features = generate_random_features(sequence_length, vocab_length,
batch_size)
dl = SquadDataLoader(features, batch_size=batch_size)
assert (len(dl) == 1)
sizes = [sequence_length, sequence_length, sequence_length, 1, 1, 1]
ranges = [
vocab_length, sequence_length + 1, 2, sequence_length + 1,
sequence_length, sequence_length
]
dl_itr = iter(dl)
for data, size, max_value in zip(next(dl_itr), sizes, ranges):
assert (np.all(data < max_value))
assert (data.shape == (batch_size, size))
