| import json |
| import os |
| import tempfile |
|
|
| import datasets |
| from datasets.arrow_writer import ArrowWriter |
| from datasets.features import Array2D |
| from utils import generate_examples, get_duration |
|
|
|
|
| SHAPE_TEST_1 = (30, 487) |
| SHAPE_TEST_2 = (36, 1024) |
| SPEED_TEST_SHAPE = (100, 100) |
| SPEED_TEST_N_EXAMPLES = 100 |
|
|
| DEFAULT_FEATURES = datasets.Features( |
| {"text": Array2D(SHAPE_TEST_1, dtype="float32"), "image": Array2D(SHAPE_TEST_2, dtype="float32")} |
| ) |
|
|
| RESULTS_BASEPATH, RESULTS_FILENAME = os.path.split(__file__) |
| RESULTS_FILE_PATH = os.path.join(RESULTS_BASEPATH, "results", RESULTS_FILENAME.replace(".py", ".json")) |
|
|
|
|
| @get_duration |
| def write(my_features, dummy_data, tmp_dir): |
| with ArrowWriter(features=my_features, path=os.path.join(tmp_dir, "beta.arrow")) as writer: |
| for key, record in dummy_data: |
| example = my_features.encode_example(record) |
| writer.write(example) |
| num_examples, num_bytes = writer.finalize() |
|
|
|
|
| @get_duration |
| def read_unformated(feats, tmp_dir): |
| dataset = datasets.Dataset.from_file( |
| filename=os.path.join(tmp_dir, "beta.arrow"), info=datasets.DatasetInfo(features=feats) |
| ) |
| for _ in dataset: |
| pass |
|
|
|
|
| @get_duration |
| def read_formatted_as_numpy(feats, tmp_dir): |
| dataset = datasets.Dataset.from_file( |
| filename=os.path.join(tmp_dir, "beta.arrow"), info=datasets.DatasetInfo(features=feats) |
| ) |
| dataset.set_format("numpy") |
| for _ in dataset: |
| pass |
|
|
|
|
| @get_duration |
| def read_batch_unformated(feats, tmp_dir): |
| batch_size = 10 |
| dataset = datasets.Dataset.from_file( |
| filename=os.path.join(tmp_dir, "beta.arrow"), info=datasets.DatasetInfo(features=feats) |
| ) |
| for i in range(0, len(dataset), batch_size): |
| _ = dataset[i : i + batch_size] |
|
|
|
|
| @get_duration |
| def read_batch_formatted_as_numpy(feats, tmp_dir): |
| batch_size = 10 |
| dataset = datasets.Dataset.from_file( |
| filename=os.path.join(tmp_dir, "beta.arrow"), info=datasets.DatasetInfo(features=feats) |
| ) |
| dataset.set_format("numpy") |
| for i in range(0, len(dataset), batch_size): |
| _ = dataset[i : i + batch_size] |
|
|
|
|
| @get_duration |
| def read_col_unformated(feats, tmp_dir): |
| dataset = datasets.Dataset.from_file( |
| filename=os.path.join(tmp_dir, "beta.arrow"), info=datasets.DatasetInfo(features=feats) |
| ) |
| for col in feats: |
| _ = dataset[col] |
|
|
|
|
| @get_duration |
| def read_col_formatted_as_numpy(feats, tmp_dir): |
| dataset = datasets.Dataset.from_file( |
| filename=os.path.join(tmp_dir, "beta.arrow"), info=datasets.DatasetInfo(features=feats) |
| ) |
| dataset.set_format("numpy") |
| for col in feats: |
| _ = dataset[col] |
|
|
|
|
| def benchmark_array_xd(): |
| times = {} |
| read_functions = ( |
| read_unformated, |
| read_formatted_as_numpy, |
| read_batch_unformated, |
| read_batch_formatted_as_numpy, |
| read_col_unformated, |
| read_col_formatted_as_numpy, |
| ) |
| with tempfile.TemporaryDirectory() as tmp_dir: |
| feats = datasets.Features({"image": Array2D(SPEED_TEST_SHAPE, dtype="float32")}) |
| data = generate_examples(features=feats, num_examples=SPEED_TEST_N_EXAMPLES) |
| times["write_array2d"] = write(feats, data, tmp_dir) |
| for read_func in read_functions: |
| times[read_func.__name__ + " after write_array2d"] = read_func(feats, tmp_dir) |
|
|
| with tempfile.TemporaryDirectory() as tmp_dir: |
| |
| |
| |
| |
| feats = datasets.Features({"image": datasets.Sequence(datasets.Sequence(datasets.Value("float32")))}) |
| data = generate_examples( |
| features=feats, num_examples=SPEED_TEST_N_EXAMPLES, seq_shapes={"image": SPEED_TEST_SHAPE} |
| ) |
| times["write_nested_sequence"] = write(feats, data, tmp_dir) |
| for read_func in read_functions: |
| times[read_func.__name__ + " after write_nested_sequence"] = read_func(feats, tmp_dir) |
|
|
| with tempfile.TemporaryDirectory() as tmp_dir: |
| |
| |
| |
| |
| feats = datasets.Features({"image": datasets.Sequence(datasets.Value("float32"))}) |
| data = generate_examples( |
| features=feats, |
| num_examples=SPEED_TEST_N_EXAMPLES, |
| seq_shapes={"image": [SPEED_TEST_SHAPE[0] * SPEED_TEST_SHAPE[1]]}, |
| ) |
| times["write_flattened_sequence"] = write(feats, data, tmp_dir) |
| for read_func in read_functions: |
| times[read_func.__name__ + " after write_flattened_sequence"] = read_func(feats, tmp_dir) |
|
|
| with open(RESULTS_FILE_PATH, "wb") as f: |
| f.write(json.dumps(times).encode("utf-8")) |
|
|
|
|
| if __name__ == "__main__": |
| benchmark_array_xd() |
|
|
