def pipe_iterator(path, size=1):
count = 0
while True:
with open("{}_{}".format(path, count), 'rb') as f:
for rec in chunk_iterable(read_recordio(f), size, last='error'):
yield rec
count += 1
def test_serializer():
s = RecordSerializer()
array_data = [[1.0, 2.0, 3.0], [10.0, 20.0, 30.0]]
buf = s.serialize(np.array(array_data))
for record_data, expected in zip(read_recordio(buf), array_data):
record = Record()
record.ParseFromString(record_data)
assert record.features["values"].float64_tensor.values == expected
def test_serializer_accepts_one_dimensional_array():
s = RecordSerializer()
array_data = [1.0, 2.0, 3.0]
buf = s.serialize(np.array(array_data))
record_data = next(read_recordio(buf))
record = Record()
record.ParseFromString(record_data)
assert record.features["values"].float64_tensor.values == array_data
def read_pipe(pipe):
with open(pipe,'rb') as f:
for rec in read_recordio(f):
print("read record")
print(rec)
record = Record()
record.ParseFromString(rec)
print("record parsed")
print(record)
def test_int_write_numpy_to_dense_tensor():
array_data = [[1, 2, 3], [10, 20, 3]]
array = np.array(array_data)
with tempfile.TemporaryFile() as f:
write_numpy_to_dense_tensor(f, array)
f.seek(0)
for record_data, expected in zip(read_recordio(f), array_data):
record = Record()
record.ParseFromString(record_data)
assert record.features["values"].int32_tensor.values == expected
def test_float32_write_numpy_to_dense_tensor():
array_data = [[1.0, 2.0, 3.0], [10.0, 20.0, 30.0]]
array = np.array(array_data).astype(np.dtype("float32"))
with tempfile.TemporaryFile() as f:
write_numpy_to_dense_tensor(f, array)
f.seek(0)
for record_data, expected in zip(read_recordio(f), array_data):
record = Record()
record.ParseFromString(record_data)
assert record.features["values"].float32_tensor.values == expected
def test_float_label():
array_data = [[1, 2, 3], [10, 20, 3]]
array = np.array(array_data)
label_data = np.array([99, 98, 97]).astype(np.dtype("float64"))
with tempfile.TemporaryFile() as f:
write_numpy_to_dense_tensor(f, array, label_data)
f.seek(0)
for record_data, expected, label in zip(read_recordio(f), array_data, label_data):
record = Record()
record.ParseFromString(record_data)
assert record.features["values"].int32_tensor.values == expected
assert record.label["values"].float64_tensor.values == [label]
def read_pipe(pipe):
for i in range(5):
with open(pipe + "_{}".format(i), 'rb') as f:
print("opened pipe {}".format(i))
count = 0
for label, f1, f2 in chunk_iterable(read_recordio(f), 3):
label = int(label.decode('utf-8'))
fs1, aud1 = wavfile.read(BytesIO(f1))
fs2, aud2 = wavfile.read(BytesIO(f2))
print("{} label: {}".format(count, label))
print("audio1: {},{}".format(fs1, aud1.shape))
print("audio2: {},{}".format(fs2, aud2.shape))
count += 1
def test_dense_int_write_spmatrix_to_sparse_tensor():
array_data = [[1.0, 2.0, 3.0], [10.0, 20.0, 30.0]]
keys_data = [[0, 1, 2], [0, 1, 2]]
array = coo_matrix(np.array(array_data).astype(np.dtype('int')))
with tempfile.TemporaryFile() as f:
write_spmatrix_to_sparse_tensor(f, array)
f.seek(0)
for record_data, expected_data, expected_keys in zip(read_recordio(f), array_data, keys_data):
record = Record()
record.ParseFromString(record_data)
assert record.features["values"].int32_tensor.values == expected_data
assert record.features["values"].int32_tensor.keys == expected_keys
assert record.features["values"].int32_tensor.shape == [len(expected_data)]
def test_dense_float64_spmatrix_to_sparse_label():
array_data = [[1, 2, 3], [10, 20, 3]]
keys_data = [[0, 1, 2], [0, 1, 2]]
array = coo_matrix(np.array(array_data).astype("float64"))
label_data = np.array([99, 98, 97])
with tempfile.TemporaryFile() as f:
write_spmatrix_to_sparse_tensor(f, array, label_data)
f.seek(0)
for record_data, expected_data, expected_keys, label in zip(
read_recordio(f), array_data, keys_data, label_data
):
record = Record()
record.ParseFromString(record_data)
assert record.features["values"].float64_tensor.values == expected_data
assert record.features["values"].float64_tensor.keys == expected_keys
assert record.label["values"].int32_tensor.values == [label]
assert record.features["values"].float64_tensor.shape == [len(expected_data)]
def __iter__(self):
info = data.get_worker_info()
if not info:
worker = None
else:
worker = info.id
if worker is None:
assert len(self.paths) == 1
path = self.paths[0]
else:
assert worker >= 0
assert worker < len(self.paths)
path = self.paths[worker]
if path not in self.counts:
self.counts[path] = 0
with open("{}_{}".format(path, self.counts[path]),'rb') as f:
self.counts[path] += 1
for rec in chunk_iterable(read_recordio(f), self.size, last='error'):
yield rec
def test_sparse_int_write_spmatrix_to_sparse_tensor():
n = 4
array_data = [[1.0, 2.0], [10.0, 30.0], [100.0, 200.0, 300.0, 400.0], [1000.0, 2000.0, 3000.0]]
keys_data = [[0, 1], [1, 2], [0, 1, 2, 3], [0, 2, 3]]
flatten_data = list(itertools.chain.from_iterable(array_data))
y_indices = list(itertools.chain.from_iterable(keys_data))
x_indices = [[i] * len(keys_data[i]) for i in range(len(keys_data))]
x_indices = list(itertools.chain.from_iterable(x_indices))
array = coo_matrix((flatten_data, (x_indices, y_indices)), dtype='int')
with tempfile.TemporaryFile() as f:
write_spmatrix_to_sparse_tensor(f, array)
f.seek(0)
for record_data, expected_data, expected_keys in zip(read_recordio(f), array_data, keys_data):
record = Record()
record.ParseFromString(record_data)
assert record.features["values"].int32_tensor.values == expected_data
assert record.features["values"].int32_tensor.keys == expected_keys
assert record.features["values"].int32_tensor.shape == [n]
