| @@ -1,8 +0,0 @@ | |||
| { | |||
| "deviceNum":4, | |||
| "deviceId": 2, | |||
| "shardConfig":"ALL", | |||
| "shuffle":"ON", | |||
| "seed": 0, | |||
| "epoch": 2 | |||
| } | |||
| @@ -1,8 +0,0 @@ | |||
| { | |||
| "deviceNum":4, | |||
| "deviceId": 2, | |||
| "shardConfig":"RANDOM", | |||
| "shuffle":"ON", | |||
| "seed": 0, | |||
| "epoch": 1 | |||
| } | |||
| @@ -1,8 +0,0 @@ | |||
| { | |||
| "deviceNum":4, | |||
| "deviceId": 2, | |||
| "shardConfig":"UNIQUE", | |||
| "shuffle":"ON", | |||
| "seed": 0, | |||
| "epoch": 3 | |||
| } | |||
| @@ -1,7 +0,0 @@ | |||
| { | |||
| "deviceNum":1, | |||
| "deviceId": 0, | |||
| "shardConfig":"RANDOM", | |||
| "shuffle":"OFF", | |||
| "seed": 0 | |||
| } | |||
| @@ -12,15 +12,12 @@ | |||
| # See the License for the specific language governing permissions and | |||
| # limitations under the License. | |||
| # ============================================================================== | |||
| from util import save_and_check | |||
| import mindspore.dataset as ds | |||
| from mindspore import log as logger | |||
| from util import save_and_check_dict | |||
| DATA_DIR = ["../data/dataset/testTFTestAllTypes/test.data"] | |||
| SCHEMA_DIR = "../data/dataset/testTFTestAllTypes/datasetSchema.json" | |||
| COLUMNS = ["col_1d", "col_2d", "col_3d", "col_binary", "col_float", | |||
| "col_sint16", "col_sint32", "col_sint64"] | |||
| GENERATE_GOLDEN = False | |||
| @@ -33,9 +30,6 @@ def test_2ops_repeat_shuffle(): | |||
| repeat_count = 2 | |||
| buffer_size = 5 | |||
| seed = 0 | |||
| parameters = {"params": {'repeat_count': repeat_count, | |||
| 'buffer_size': buffer_size, | |||
| 'seed': seed}} | |||
| # apply dataset operations | |||
| data1 = ds.TFRecordDataset(DATA_DIR, SCHEMA_DIR, shuffle=False) | |||
| @@ -44,7 +38,7 @@ def test_2ops_repeat_shuffle(): | |||
| data1 = data1.shuffle(buffer_size=buffer_size) | |||
| filename = "test_2ops_repeat_shuffle.npz" | |||
| save_and_check(data1, parameters, filename, generate_golden=GENERATE_GOLDEN) | |||
| save_and_check_dict(data1, filename, generate_golden=GENERATE_GOLDEN) | |||
| def test_2ops_shuffle_repeat(): | |||
| @@ -56,10 +50,6 @@ def test_2ops_shuffle_repeat(): | |||
| repeat_count = 2 | |||
| buffer_size = 5 | |||
| seed = 0 | |||
| parameters = {"params": {'repeat_count': repeat_count, | |||
| 'buffer_size': buffer_size, | |||
| 'reshuffle_each_iteration': False, | |||
| 'seed': seed}} | |||
| # apply dataset operations | |||
| data1 = ds.TFRecordDataset(DATA_DIR, SCHEMA_DIR, shuffle=False) | |||
| @@ -68,7 +58,7 @@ def test_2ops_shuffle_repeat(): | |||
| data1 = data1.repeat(repeat_count) | |||
| filename = "test_2ops_shuffle_repeat.npz" | |||
| save_and_check(data1, parameters, filename, generate_golden=GENERATE_GOLDEN) | |||
| save_and_check_dict(data1, filename, generate_golden=GENERATE_GOLDEN) | |||
| def test_2ops_repeat_batch(): | |||
| @@ -79,8 +69,6 @@ def test_2ops_repeat_batch(): | |||
| # define parameters | |||
| repeat_count = 2 | |||
| batch_size = 5 | |||
| parameters = {"params": {'repeat_count': repeat_count, | |||
| 'batch_size': batch_size}} | |||
| # apply dataset operations | |||
| data1 = ds.TFRecordDataset(DATA_DIR, SCHEMA_DIR, shuffle=False) | |||
| @@ -88,7 +76,7 @@ def test_2ops_repeat_batch(): | |||
| data1 = data1.batch(batch_size, drop_remainder=True) | |||
| filename = "test_2ops_repeat_batch.npz" | |||
| save_and_check(data1, parameters, filename, generate_golden=GENERATE_GOLDEN) | |||
| save_and_check_dict(data1, filename, generate_golden=GENERATE_GOLDEN) | |||
| def test_2ops_batch_repeat(): | |||
| @@ -99,8 +87,6 @@ def test_2ops_batch_repeat(): | |||
| # define parameters | |||
| repeat_count = 2 | |||
| batch_size = 5 | |||
| parameters = {"params": {'repeat_count': repeat_count, | |||
| 'batch_size': batch_size}} | |||
| # apply dataset operations | |||
| data1 = ds.TFRecordDataset(DATA_DIR, SCHEMA_DIR, shuffle=False) | |||
| @@ -108,7 +94,7 @@ def test_2ops_batch_repeat(): | |||
| data1 = data1.repeat(repeat_count) | |||
| filename = "test_2ops_batch_repeat.npz" | |||
| save_and_check(data1, parameters, filename, generate_golden=GENERATE_GOLDEN) | |||
| save_and_check_dict(data1, filename, generate_golden=GENERATE_GOLDEN) | |||
| def test_2ops_batch_shuffle(): | |||
| @@ -120,9 +106,6 @@ def test_2ops_batch_shuffle(): | |||
| buffer_size = 5 | |||
| seed = 0 | |||
| batch_size = 2 | |||
| parameters = {"params": {'buffer_size': buffer_size, | |||
| 'seed': seed, | |||
| 'batch_size': batch_size}} | |||
| # apply dataset operations | |||
| data1 = ds.TFRecordDataset(DATA_DIR, SCHEMA_DIR, shuffle=False) | |||
| @@ -131,7 +114,7 @@ def test_2ops_batch_shuffle(): | |||
| data1 = data1.shuffle(buffer_size=buffer_size) | |||
| filename = "test_2ops_batch_shuffle.npz" | |||
| save_and_check(data1, parameters, filename, generate_golden=GENERATE_GOLDEN) | |||
| save_and_check_dict(data1, filename, generate_golden=GENERATE_GOLDEN) | |||
| def test_2ops_shuffle_batch(): | |||
| @@ -143,9 +126,6 @@ def test_2ops_shuffle_batch(): | |||
| buffer_size = 5 | |||
| seed = 0 | |||
| batch_size = 2 | |||
| parameters = {"params": {'buffer_size': buffer_size, | |||
| 'seed': seed, | |||
| 'batch_size': batch_size}} | |||
| # apply dataset operations | |||
| data1 = ds.TFRecordDataset(DATA_DIR, SCHEMA_DIR, shuffle=False) | |||
| @@ -154,7 +134,7 @@ def test_2ops_shuffle_batch(): | |||
| data1 = data1.batch(batch_size, drop_remainder=True) | |||
| filename = "test_2ops_shuffle_batch.npz" | |||
| save_and_check(data1, parameters, filename, generate_golden=GENERATE_GOLDEN) | |||
| save_and_check_dict(data1, filename, generate_golden=GENERATE_GOLDEN) | |||
| if __name__ == '__main__': | |||
| @@ -14,7 +14,7 @@ | |||
| # ============================================================================== | |||
| import mindspore.dataset as ds | |||
| from mindspore import log as logger | |||
| from util import save_and_check | |||
| from util import save_and_check_dict | |||
| # Note: Number of rows in test.data dataset: 12 | |||
| DATA_DIR = ["../data/dataset/testTFTestAllTypes/test.data"] | |||
| @@ -29,8 +29,6 @@ def test_batch_01(): | |||
| # define parameters | |||
| batch_size = 2 | |||
| drop_remainder = True | |||
| parameters = {"params": {'batch_size': batch_size, | |||
| 'drop_remainder': drop_remainder}} | |||
| # apply dataset operations | |||
| data1 = ds.TFRecordDataset(DATA_DIR, shuffle=ds.Shuffle.FILES) | |||
| @@ -38,7 +36,7 @@ def test_batch_01(): | |||
| assert sum([1 for _ in data1]) == 6 | |||
| filename = "batch_01_result.npz" | |||
| save_and_check(data1, parameters, filename, generate_golden=GENERATE_GOLDEN) | |||
| save_and_check_dict(data1, filename, generate_golden=GENERATE_GOLDEN) | |||
| def test_batch_02(): | |||
| @@ -49,8 +47,6 @@ def test_batch_02(): | |||
| # define parameters | |||
| batch_size = 5 | |||
| drop_remainder = True | |||
| parameters = {"params": {'batch_size': batch_size, | |||
| 'drop_remainder': drop_remainder}} | |||
| # apply dataset operations | |||
| data1 = ds.TFRecordDataset(DATA_DIR, shuffle=ds.Shuffle.FILES) | |||
| @@ -58,7 +54,7 @@ def test_batch_02(): | |||
| assert sum([1 for _ in data1]) == 2 | |||
| filename = "batch_02_result.npz" | |||
| save_and_check(data1, parameters, filename, generate_golden=GENERATE_GOLDEN) | |||
| save_and_check_dict(data1, filename, generate_golden=GENERATE_GOLDEN) | |||
| def test_batch_03(): | |||
| @@ -69,8 +65,6 @@ def test_batch_03(): | |||
| # define parameters | |||
| batch_size = 3 | |||
| drop_remainder = False | |||
| parameters = {"params": {'batch_size': batch_size, | |||
| 'drop_remainder': drop_remainder}} | |||
| # apply dataset operations | |||
| data1 = ds.TFRecordDataset(DATA_DIR, shuffle=ds.Shuffle.FILES) | |||
| @@ -78,7 +72,7 @@ def test_batch_03(): | |||
| assert sum([1 for _ in data1]) == 4 | |||
| filename = "batch_03_result.npz" | |||
| save_and_check(data1, parameters, filename, generate_golden=GENERATE_GOLDEN) | |||
| save_and_check_dict(data1, filename, generate_golden=GENERATE_GOLDEN) | |||
| def test_batch_04(): | |||
| @@ -89,8 +83,6 @@ def test_batch_04(): | |||
| # define parameters | |||
| batch_size = 7 | |||
| drop_remainder = False | |||
| parameters = {"params": {'batch_size': batch_size, | |||
| 'drop_remainder': drop_remainder}} | |||
| # apply dataset operations | |||
| data1 = ds.TFRecordDataset(DATA_DIR, shuffle=ds.Shuffle.FILES) | |||
| @@ -98,7 +90,7 @@ def test_batch_04(): | |||
| assert sum([1 for _ in data1]) == 2 | |||
| filename = "batch_04_result.npz" | |||
| save_and_check(data1, parameters, filename, generate_golden=GENERATE_GOLDEN) | |||
| save_and_check_dict(data1, filename, generate_golden=GENERATE_GOLDEN) | |||
| def test_batch_05(): | |||
| @@ -108,7 +100,6 @@ def test_batch_05(): | |||
| logger.info("test_batch_05") | |||
| # define parameters | |||
| batch_size = 1 | |||
| parameters = {"params": {'batch_size': batch_size}} | |||
| # apply dataset operations | |||
| data1 = ds.TFRecordDataset(DATA_DIR, shuffle=ds.Shuffle.FILES) | |||
| @@ -116,7 +107,7 @@ def test_batch_05(): | |||
| assert sum([1 for _ in data1]) == 12 | |||
| filename = "batch_05_result.npz" | |||
| save_and_check(data1, parameters, filename, generate_golden=GENERATE_GOLDEN) | |||
| save_and_check_dict(data1, filename, generate_golden=GENERATE_GOLDEN) | |||
| def test_batch_06(): | |||
| @@ -127,8 +118,6 @@ def test_batch_06(): | |||
| # define parameters | |||
| batch_size = 12 | |||
| drop_remainder = False | |||
| parameters = {"params": {'batch_size': batch_size, | |||
| 'drop_remainder': drop_remainder}} | |||
| # apply dataset operations | |||
| data1 = ds.TFRecordDataset(DATA_DIR, shuffle=ds.Shuffle.FILES) | |||
| @@ -136,7 +125,7 @@ def test_batch_06(): | |||
| assert sum([1 for _ in data1]) == 1 | |||
| filename = "batch_06_result.npz" | |||
| save_and_check(data1, parameters, filename, generate_golden=GENERATE_GOLDEN) | |||
| save_and_check_dict(data1, filename, generate_golden=GENERATE_GOLDEN) | |||
| def test_batch_07(): | |||
| @@ -148,9 +137,6 @@ def test_batch_07(): | |||
| batch_size = 4 | |||
| drop_remainder = False | |||
| num_parallel_workers = 2 | |||
| parameters = {"params": {'batch_size': batch_size, | |||
| 'drop_remainder': drop_remainder, | |||
| 'num_parallel_workers': num_parallel_workers}} | |||
| # apply dataset operations | |||
| data1 = ds.TFRecordDataset(DATA_DIR, shuffle=ds.Shuffle.FILES) | |||
| @@ -159,7 +145,7 @@ def test_batch_07(): | |||
| assert sum([1 for _ in data1]) == 3 | |||
| filename = "batch_07_result.npz" | |||
| save_and_check(data1, parameters, filename, generate_golden=GENERATE_GOLDEN) | |||
| save_and_check_dict(data1, filename, generate_golden=GENERATE_GOLDEN) | |||
| def test_batch_08(): | |||
| @@ -170,8 +156,6 @@ def test_batch_08(): | |||
| # define parameters | |||
| batch_size = 6 | |||
| num_parallel_workers = 1 | |||
| parameters = {"params": {'batch_size': batch_size, | |||
| 'num_parallel_workers': num_parallel_workers}} | |||
| # apply dataset operations | |||
| data1 = ds.TFRecordDataset(DATA_DIR, shuffle=ds.Shuffle.FILES) | |||
| @@ -179,7 +163,7 @@ def test_batch_08(): | |||
| assert sum([1 for _ in data1]) == 2 | |||
| filename = "batch_08_result.npz" | |||
| save_and_check(data1, parameters, filename, generate_golden=GENERATE_GOLDEN) | |||
| save_and_check_dict(data1, filename, generate_golden=GENERATE_GOLDEN) | |||
| def test_batch_09(): | |||
| @@ -190,8 +174,6 @@ def test_batch_09(): | |||
| # define parameters | |||
| batch_size = 13 | |||
| drop_remainder = False | |||
| parameters = {"params": {'batch_size': batch_size, | |||
| 'drop_remainder': drop_remainder}} | |||
| # apply dataset operations | |||
| data1 = ds.TFRecordDataset(DATA_DIR, shuffle=ds.Shuffle.FILES) | |||
| @@ -199,7 +181,7 @@ def test_batch_09(): | |||
| assert sum([1 for _ in data1]) == 1 | |||
| filename = "batch_09_result.npz" | |||
| save_and_check(data1, parameters, filename, generate_golden=GENERATE_GOLDEN) | |||
| save_and_check_dict(data1, filename, generate_golden=GENERATE_GOLDEN) | |||
| def test_batch_10(): | |||
| @@ -210,8 +192,6 @@ def test_batch_10(): | |||
| # define parameters | |||
| batch_size = 99 | |||
| drop_remainder = True | |||
| parameters = {"params": {'batch_size': batch_size, | |||
| 'drop_remainder': drop_remainder}} | |||
| # apply dataset operations | |||
| data1 = ds.TFRecordDataset(DATA_DIR, shuffle=ds.Shuffle.FILES) | |||
| @@ -219,7 +199,7 @@ def test_batch_10(): | |||
| assert sum([1 for _ in data1]) == 0 | |||
| filename = "batch_10_result.npz" | |||
| save_and_check(data1, parameters, filename, generate_golden=GENERATE_GOLDEN) | |||
| save_and_check_dict(data1, filename, generate_golden=GENERATE_GOLDEN) | |||
| def test_batch_11(): | |||
| @@ -229,7 +209,6 @@ def test_batch_11(): | |||
| logger.info("test_batch_11") | |||
| # define parameters | |||
| batch_size = 1 | |||
| parameters = {"params": {'batch_size': batch_size}} | |||
| # apply dataset operations | |||
| # Use schema file with 1 row | |||
| @@ -239,7 +218,7 @@ def test_batch_11(): | |||
| assert sum([1 for _ in data1]) == 1 | |||
| filename = "batch_11_result.npz" | |||
| save_and_check(data1, parameters, filename, generate_golden=GENERATE_GOLDEN) | |||
| save_and_check_dict(data1, filename, generate_golden=GENERATE_GOLDEN) | |||
| def test_batch_12(): | |||
| @@ -249,7 +228,6 @@ def test_batch_12(): | |||
| logger.info("test_batch_12") | |||
| # define parameters | |||
| batch_size = True | |||
| parameters = {"params": {'batch_size': batch_size}} | |||
| # apply dataset operations | |||
| data1 = ds.TFRecordDataset(DATA_DIR, shuffle=ds.Shuffle.FILES) | |||
| @@ -257,7 +235,7 @@ def test_batch_12(): | |||
| assert sum([1 for _ in data1]) == 12 | |||
| filename = "batch_12_result.npz" | |||
| save_and_check(data1, parameters, filename, generate_golden=GENERATE_GOLDEN) | |||
| save_and_check_dict(data1, filename, generate_golden=GENERATE_GOLDEN) | |||
| def test_batch_exception_01(): | |||
| @@ -356,9 +356,13 @@ def test_clue_to_device(): | |||
| if __name__ == "__main__": | |||
| test_clue() | |||
| test_clue_num_shards() | |||
| test_clue_num_samples() | |||
| test_textline_dataset_get_datasetsize() | |||
| test_clue_afqmc() | |||
| test_clue_cmnli() | |||
| test_clue_csl() | |||
| test_clue_iflytek() | |||
| test_clue_tnews() | |||
| test_clue_wsc() | |||
| test_clue_to_device() | |||
| @@ -26,7 +26,7 @@ def generator_1d(): | |||
| yield (np.array([i]),) | |||
| def test_case_0(): | |||
| def test_generator_0(): | |||
| """ | |||
| Test 1D Generator | |||
| """ | |||
| @@ -48,7 +48,7 @@ def generator_md(): | |||
| yield (np.array([[i, i + 1], [i + 2, i + 3]]),) | |||
| def test_case_1(): | |||
| def test_generator_1(): | |||
| """ | |||
| Test MD Generator | |||
| """ | |||
| @@ -70,7 +70,7 @@ def generator_mc(maxid=64): | |||
| yield (np.array([i]), np.array([[i, i + 1], [i + 2, i + 3]])) | |||
| def test_case_2(): | |||
| def test_generator_2(): | |||
| """ | |||
| Test multi column generator | |||
| """ | |||
| @@ -88,7 +88,7 @@ def test_case_2(): | |||
| i = i + 1 | |||
| def test_case_3(): | |||
| def test_generator_3(): | |||
| """ | |||
| Test 1D Generator + repeat(4) | |||
| """ | |||
| @@ -108,7 +108,7 @@ def test_case_3(): | |||
| i = 0 | |||
| def test_case_4(): | |||
| def test_generator_4(): | |||
| """ | |||
| Test fixed size 1D Generator + batch | |||
| """ | |||
| @@ -146,7 +146,7 @@ def type_tester(t): | |||
| i = i + 4 | |||
| def test_case_5(): | |||
| def test_generator_5(): | |||
| """ | |||
| Test 1D Generator on different data type | |||
| """ | |||
| @@ -173,7 +173,7 @@ def type_tester_with_type_check(t, c): | |||
| i = i + 4 | |||
| def test_case_6(): | |||
| def test_generator_6(): | |||
| """ | |||
| Test 1D Generator on different data type with type check | |||
| """ | |||
| @@ -208,7 +208,7 @@ def type_tester_with_type_check_2c(t, c): | |||
| i = i + 4 | |||
| def test_case_7(): | |||
| def test_generator_7(): | |||
| """ | |||
| Test 2 column Generator on different data type with type check | |||
| """ | |||
| @@ -223,7 +223,7 @@ def test_case_7(): | |||
| type_tester_with_type_check_2c(np_types[i], [None, de_types[i]]) | |||
| def test_case_8(): | |||
| def test_generator_8(): | |||
| """ | |||
| Test multi column generator with few mapops | |||
| """ | |||
| @@ -249,7 +249,7 @@ def test_case_8(): | |||
| i = i + 1 | |||
| def test_case_9(): | |||
| def test_generator_9(): | |||
| """ | |||
| Test map column order when len(input_columns) == len(output_columns). | |||
| """ | |||
| @@ -280,7 +280,7 @@ def test_case_9(): | |||
| i = i + 1 | |||
| def test_case_10(): | |||
| def test_generator_10(): | |||
| """ | |||
| Test map column order when len(input_columns) != len(output_columns). | |||
| """ | |||
| @@ -303,7 +303,7 @@ def test_case_10(): | |||
| i = i + 1 | |||
| def test_case_11(): | |||
| def test_generator_11(): | |||
| """ | |||
| Test map column order when len(input_columns) != len(output_columns). | |||
| """ | |||
| @@ -327,7 +327,7 @@ def test_case_11(): | |||
| i = i + 1 | |||
| def test_case_12(): | |||
| def test_generator_12(): | |||
| """ | |||
| Test map column order when input_columns and output_columns are None. | |||
| """ | |||
| @@ -361,7 +361,7 @@ def test_case_12(): | |||
| i = i + 1 | |||
| def test_case_13(): | |||
| def test_generator_13(): | |||
| """ | |||
| Test map column order when input_columns is None. | |||
| """ | |||
| @@ -391,7 +391,7 @@ def test_case_13(): | |||
| i = i + 1 | |||
| def test_case_14(): | |||
| def test_generator_14(): | |||
| """ | |||
| Test 1D Generator MP + CPP sampler | |||
| """ | |||
| @@ -408,7 +408,7 @@ def test_case_14(): | |||
| i = 0 | |||
| def test_case_15(): | |||
| def test_generator_15(): | |||
| """ | |||
| Test 1D Generator MP + Python sampler | |||
| """ | |||
| @@ -426,7 +426,7 @@ def test_case_15(): | |||
| i = 0 | |||
| def test_case_16(): | |||
| def test_generator_16(): | |||
| """ | |||
| Test multi column generator Mp + CPP sampler | |||
| """ | |||
| @@ -445,7 +445,7 @@ def test_case_16(): | |||
| i = i + 1 | |||
| def test_case_17(): | |||
| def test_generator_17(): | |||
| """ | |||
| Test multi column generator Mp + Python sampler | |||
| """ | |||
| @@ -465,7 +465,7 @@ def test_case_17(): | |||
| i = i + 1 | |||
| def test_case_error_1(): | |||
| def test_generator_error_1(): | |||
| def generator_np(): | |||
| for i in range(64): | |||
| yield (np.array([{i}]),) | |||
| @@ -477,7 +477,7 @@ def test_case_error_1(): | |||
| assert "Invalid data type" in str(info.value) | |||
| def test_case_error_2(): | |||
| def test_generator_error_2(): | |||
| def generator_np(): | |||
| for i in range(64): | |||
| yield ({i},) | |||
| @@ -489,7 +489,7 @@ def test_case_error_2(): | |||
| assert "Generator should return a tuple of numpy arrays" in str(info.value) | |||
| def test_case_error_3(): | |||
| def test_generator_error_3(): | |||
| with pytest.raises(ValueError) as info: | |||
| # apply dataset operations | |||
| data1 = ds.GeneratorDataset(generator_mc(2048), ["label", "image"]) | |||
| @@ -501,7 +501,7 @@ def test_case_error_3(): | |||
| assert "When (len(input_columns) != len(output_columns)), columns_order must be specified." in str(info.value) | |||
| def test_case_error_4(): | |||
| def test_generator_error_4(): | |||
| with pytest.raises(RuntimeError) as info: | |||
| # apply dataset operations | |||
| data1 = ds.GeneratorDataset(generator_mc(2048), ["label", "image"]) | |||
| @@ -513,7 +513,7 @@ def test_case_error_4(): | |||
| assert "Unexpected error. Result of a tensorOp doesn't match output column names" in str(info.value) | |||
| def test_sequential_sampler(): | |||
| def test_generator_sequential_sampler(): | |||
| source = [(np.array([x]),) for x in range(64)] | |||
| ds1 = ds.GeneratorDataset(source, ["data"], sampler=ds.SequentialSampler()) | |||
| i = 0 | |||
| @@ -523,14 +523,14 @@ def test_sequential_sampler(): | |||
| i = i + 1 | |||
| def test_random_sampler(): | |||
| def test_generator_random_sampler(): | |||
| source = [(np.array([x]),) for x in range(64)] | |||
| ds1 = ds.GeneratorDataset(source, ["data"], shuffle=True) | |||
| for _ in ds1.create_dict_iterator(): # each data is a dictionary | |||
| pass | |||
| def test_distributed_sampler(): | |||
| def test_generator_distributed_sampler(): | |||
| source = [(np.array([x]),) for x in range(64)] | |||
| for sid in range(8): | |||
| ds1 = ds.GeneratorDataset(source, ["data"], shuffle=False, num_shards=8, shard_id=sid) | |||
| @@ -541,7 +541,7 @@ def test_distributed_sampler(): | |||
| i = i + 8 | |||
| def test_num_samples(): | |||
| def test_generator_num_samples(): | |||
| source = [(np.array([x]),) for x in range(64)] | |||
| num_samples = 32 | |||
| ds1 = ds.GeneratorDataset(source, ["data"], sampler=ds.SequentialSampler(num_samples=num_samples)) | |||
| @@ -564,7 +564,7 @@ def test_num_samples(): | |||
| assert count == num_samples | |||
| def test_num_samples_underflow(): | |||
| def test_generator_num_samples_underflow(): | |||
| source = [(np.array([x]),) for x in range(64)] | |||
| num_samples = 256 | |||
| ds2 = ds.GeneratorDataset(source, ["data"], sampler=[i for i in range(64)], num_samples=num_samples) | |||
| @@ -600,7 +600,7 @@ def type_tester_with_type_check_2c_schema(t, c): | |||
| i = i + 4 | |||
| def test_schema(): | |||
| def test_generator_schema(): | |||
| """ | |||
| Test 2 column Generator on different data type with type check with schema input | |||
| """ | |||
| @@ -615,9 +615,9 @@ def test_schema(): | |||
| type_tester_with_type_check_2c_schema(np_types[i], [de_types[i], de_types[i]]) | |||
| def manual_test_keyborad_interrupt(): | |||
| def manual_test_generator_keyboard_interrupt(): | |||
| """ | |||
| Test keyborad_interrupt | |||
| Test keyboard_interrupt | |||
| """ | |||
| logger.info("Test 1D Generator MP : 0 - 63") | |||
| @@ -635,31 +635,31 @@ def manual_test_keyborad_interrupt(): | |||
| if __name__ == "__main__": | |||
| test_case_0() | |||
| test_case_1() | |||
| test_case_2() | |||
| test_case_3() | |||
| test_case_4() | |||
| test_case_5() | |||
| test_case_6() | |||
| test_case_7() | |||
| test_case_8() | |||
| test_case_9() | |||
| test_case_10() | |||
| test_case_11() | |||
| test_case_12() | |||
| test_case_13() | |||
| test_case_14() | |||
| test_case_15() | |||
| test_case_16() | |||
| test_case_17() | |||
| test_case_error_1() | |||
| test_case_error_2() | |||
| test_case_error_3() | |||
| test_case_error_4() | |||
| test_sequential_sampler() | |||
| test_distributed_sampler() | |||
| test_random_sampler() | |||
| test_num_samples() | |||
| test_num_samples_underflow() | |||
| test_schema() | |||
| test_generator_0() | |||
| test_generator_1() | |||
| test_generator_2() | |||
| test_generator_3() | |||
| test_generator_4() | |||
| test_generator_5() | |||
| test_generator_6() | |||
| test_generator_7() | |||
| test_generator_8() | |||
| test_generator_9() | |||
| test_generator_10() | |||
| test_generator_11() | |||
| test_generator_12() | |||
| test_generator_13() | |||
| test_generator_14() | |||
| test_generator_15() | |||
| test_generator_16() | |||
| test_generator_17() | |||
| test_generator_error_1() | |||
| test_generator_error_2() | |||
| test_generator_error_3() | |||
| test_generator_error_4() | |||
| test_generator_sequential_sampler() | |||
| test_generator_distributed_sampler() | |||
| test_generator_random_sampler() | |||
| test_generator_num_samples() | |||
| test_generator_num_samples_underflow() | |||
| test_generator_schema() | |||
| @@ -33,7 +33,7 @@ def check(project_columns): | |||
| assert all([np.array_equal(d1, d2) for d1, d2 in zip(data_actual, data_expected)]) | |||
| def test_case_iterator(): | |||
| def test_iterator_create_tuple(): | |||
| """ | |||
| Test creating tuple iterator | |||
| """ | |||
| @@ -95,7 +95,9 @@ class MyDict(dict): | |||
| def test_tree_copy(): | |||
| # Testing copying the tree with a pyfunc that cannot be pickled | |||
| """ | |||
| Testing copying the tree with a pyfunc that cannot be pickled | |||
| """ | |||
| data = ds.TFRecordDataset(DATA_DIR, SCHEMA_DIR, columns_list=COLUMNS) | |||
| data1 = data.map(operations=[MyDict()]) | |||
| @@ -110,4 +112,6 @@ def test_tree_copy(): | |||
| if __name__ == '__main__': | |||
| test_iterator_create_tuple() | |||
| test_iterator_weak_ref() | |||
| test_tree_copy() | |||
| @@ -13,10 +13,9 @@ | |||
| # limitations under the License. | |||
| # ============================================================================== | |||
| import numpy as np | |||
| from util import save_and_check | |||
| import mindspore.dataset as ds | |||
| from mindspore import log as logger | |||
| from util import save_and_check_dict | |||
| # Note: Number of rows in test.data dataset: 12 | |||
| DATA_DIR = ["../data/dataset/testTFTestAllTypes/test.data"] | |||
| @@ -31,7 +30,6 @@ def test_shuffle_01(): | |||
| # define parameters | |||
| buffer_size = 5 | |||
| seed = 1 | |||
| parameters = {"params": {'buffer_size': buffer_size, "seed": seed}} | |||
| # apply dataset operations | |||
| data1 = ds.TFRecordDataset(DATA_DIR, shuffle=ds.Shuffle.FILES) | |||
| @@ -39,7 +37,7 @@ def test_shuffle_01(): | |||
| data1 = data1.shuffle(buffer_size=buffer_size) | |||
| filename = "shuffle_01_result.npz" | |||
| save_and_check(data1, parameters, filename, generate_golden=GENERATE_GOLDEN) | |||
| save_and_check_dict(data1, filename, generate_golden=GENERATE_GOLDEN) | |||
| def test_shuffle_02(): | |||
| @@ -50,7 +48,6 @@ def test_shuffle_02(): | |||
| # define parameters | |||
| buffer_size = 12 | |||
| seed = 1 | |||
| parameters = {"params": {'buffer_size': buffer_size, "seed": seed}} | |||
| # apply dataset operations | |||
| data1 = ds.TFRecordDataset(DATA_DIR, shuffle=ds.Shuffle.FILES) | |||
| @@ -58,7 +55,7 @@ def test_shuffle_02(): | |||
| data1 = data1.shuffle(buffer_size=buffer_size) | |||
| filename = "shuffle_02_result.npz" | |||
| save_and_check(data1, parameters, filename, generate_golden=GENERATE_GOLDEN) | |||
| save_and_check_dict(data1, filename, generate_golden=GENERATE_GOLDEN) | |||
| def test_shuffle_03(): | |||
| @@ -69,7 +66,6 @@ def test_shuffle_03(): | |||
| # define parameters | |||
| buffer_size = 2 | |||
| seed = 1 | |||
| parameters = {"params": {'buffer_size': buffer_size, "seed": seed}} | |||
| # apply dataset operations | |||
| data1 = ds.TFRecordDataset(DATA_DIR, shuffle=ds.Shuffle.FILES) | |||
| @@ -77,7 +73,7 @@ def test_shuffle_03(): | |||
| data1 = data1.shuffle(buffer_size) | |||
| filename = "shuffle_03_result.npz" | |||
| save_and_check(data1, parameters, filename, generate_golden=GENERATE_GOLDEN) | |||
| save_and_check_dict(data1, filename, generate_golden=GENERATE_GOLDEN) | |||
| def test_shuffle_04(): | |||
| @@ -88,7 +84,6 @@ def test_shuffle_04(): | |||
| # define parameters | |||
| buffer_size = 2 | |||
| seed = 1 | |||
| parameters = {"params": {'buffer_size': buffer_size, "seed": seed}} | |||
| # apply dataset operations | |||
| data1 = ds.TFRecordDataset(DATA_DIR, num_samples=2) | |||
| @@ -96,7 +91,7 @@ def test_shuffle_04(): | |||
| data1 = data1.shuffle(buffer_size=buffer_size) | |||
| filename = "shuffle_04_result.npz" | |||
| save_and_check(data1, parameters, filename, generate_golden=GENERATE_GOLDEN) | |||
| save_and_check_dict(data1, filename, generate_golden=GENERATE_GOLDEN) | |||
| def test_shuffle_05(): | |||
| @@ -107,7 +102,6 @@ def test_shuffle_05(): | |||
| # define parameters | |||
| buffer_size = 13 | |||
| seed = 1 | |||
| parameters = {"params": {'buffer_size': buffer_size, "seed": seed}} | |||
| # apply dataset operations | |||
| data1 = ds.TFRecordDataset(DATA_DIR, shuffle=ds.Shuffle.FILES) | |||
| @@ -115,7 +109,7 @@ def test_shuffle_05(): | |||
| data1 = data1.shuffle(buffer_size=buffer_size) | |||
| filename = "shuffle_05_result.npz" | |||
| save_and_check(data1, parameters, filename, generate_golden=GENERATE_GOLDEN) | |||
| save_and_check_dict(data1, filename, generate_golden=GENERATE_GOLDEN) | |||
| def test_shuffle_06(): | |||
| @@ -24,9 +24,6 @@ import numpy as np | |||
| import mindspore.dataset as ds | |||
| from mindspore import log as logger | |||
| # These are the column names defined in the testTFTestAllTypes dataset | |||
| COLUMNS = ["col_1d", "col_2d", "col_3d", "col_binary", "col_float", | |||
| "col_sint16", "col_sint32", "col_sint64"] | |||
| # These are list of plot title in different visualize modes | |||
| PLOT_TITLE_DICT = { | |||
| 1: ["Original image", "Transformed image"], | |||
| @@ -82,39 +79,6 @@ def _save_json(filename, parameters, result_dict): | |||
| fout.write(jsbeautifier.beautify(json.dumps(out_dict), options)) | |||
| def save_and_check(data, parameters, filename, generate_golden=False): | |||
| """ | |||
| Save the dataset dictionary and compare (as numpy array) with golden file. | |||
| Use create_dict_iterator to access the dataset. | |||
| Note: save_and_check() is deprecated; use save_and_check_dict(). | |||
| """ | |||
| num_iter = 0 | |||
| result_dict = {} | |||
| for column_name in COLUMNS: | |||
| result_dict[column_name] = [] | |||
| for item in data.create_dict_iterator(): # each data is a dictionary | |||
| for data_key in list(item.keys()): | |||
| if data_key not in result_dict: | |||
| result_dict[data_key] = [] | |||
| result_dict[data_key].append(item[data_key].tolist()) | |||
| num_iter += 1 | |||
| logger.info("Number of data in data1: {}".format(num_iter)) | |||
| cur_dir = os.path.dirname(os.path.realpath(__file__)) | |||
| golden_ref_dir = os.path.join(cur_dir, "../../data/dataset", 'golden', filename) | |||
| if generate_golden: | |||
| # Save as the golden result | |||
| _save_golden(cur_dir, golden_ref_dir, result_dict) | |||
| _compare_to_golden(golden_ref_dir, result_dict) | |||
| if SAVE_JSON: | |||
| # Save result to a json file for inspection | |||
| _save_json(filename, parameters, result_dict) | |||
| def save_and_check_dict(data, filename, generate_golden=False): | |||
| """ | |||
| Save the dataset dictionary and compare (as dictionary) with golden file. | |||
| @@ -203,6 +167,29 @@ def save_and_check_tuple(data, parameters, filename, generate_golden=False): | |||
| _save_json(filename, parameters, result_dict) | |||
| def config_get_set_seed(seed_new): | |||
| """ | |||
| Get and return the original configuration seed value. | |||
| Set the new configuration seed value. | |||
| """ | |||
| seed_original = ds.config.get_seed() | |||
| ds.config.set_seed(seed_new) | |||
| logger.info("seed: original = {} new = {} ".format(seed_original, seed_new)) | |||
| return seed_original | |||
| def config_get_set_num_parallel_workers(num_parallel_workers_new): | |||
| """ | |||
| Get and return the original configuration num_parallel_workers value. | |||
| Set the new configuration num_parallel_workers value. | |||
| """ | |||
| num_parallel_workers_original = ds.config.get_num_parallel_workers() | |||
| ds.config.set_num_parallel_workers(num_parallel_workers_new) | |||
| logger.info("num_parallel_workers: original = {} new = {} ".format(num_parallel_workers_original, | |||
| num_parallel_workers_new)) | |||
| return num_parallel_workers_original | |||
| def diff_mse(in1, in2): | |||
| mse = (np.square(in1.astype(float) / 255 - in2.astype(float) / 255)).mean() | |||
| return mse * 100 | |||
| @@ -265,29 +252,6 @@ def visualize_image(image_original, image_de, mse=None, image_lib=None): | |||
| plt.show() | |||
| def config_get_set_seed(seed_new): | |||
| """ | |||
| Get and return the original configuration seed value. | |||
| Set the new configuration seed value. | |||
| """ | |||
| seed_original = ds.config.get_seed() | |||
| ds.config.set_seed(seed_new) | |||
| logger.info("seed: original = {} new = {} ".format(seed_original, seed_new)) | |||
| return seed_original | |||
| def config_get_set_num_parallel_workers(num_parallel_workers_new): | |||
| """ | |||
| Get and return the original configuration num_parallel_workers value. | |||
| Set the new configuration num_parallel_workers value. | |||
| """ | |||
| num_parallel_workers_original = ds.config.get_num_parallel_workers() | |||
| ds.config.set_num_parallel_workers(num_parallel_workers_new) | |||
| logger.info("num_parallel_workers: original = {} new = {} ".format(num_parallel_workers_original, | |||
| num_parallel_workers_new)) | |||
| return num_parallel_workers_original | |||
| def visualize_with_bounding_boxes(orig, aug, annot_name="annotation", plot_rows=3): | |||
| """ | |||
| Take a list of un-augmented and augmented images with "annotation" bounding boxes | |||