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@@ -21,7 +21,7 @@ import glob |
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import numpy as np |
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import mindspore.dataset as ds |
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import mindspore.dataset.transforms.vision.c_transforms as vision |
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import mindspore.dataset.transforms.vision.c_transforms as c_vision |
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import mindspore.dataset.transforms.vision.py_transforms as py_vision |
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from mindspore import log as logger |
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@@ -85,12 +85,12 @@ def test_pipeline(): |
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data1 = ds.TFRecordDataset(DATA_DIR, SCHEMA_DIR, shuffle=False) |
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ds.config.set_num_parallel_workers(2) |
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data1 = data1.map(input_columns=["image"], operations=[vision.Decode(True)]) |
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data1 = data1.map(input_columns=["image"], operations=[c_vision.Decode(True)]) |
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ds.serialize(data1, "testpipeline.json") |
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data2 = ds.TFRecordDataset(DATA_DIR, SCHEMA_DIR, shuffle=False) |
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ds.config.set_num_parallel_workers(4) |
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data2 = data2.map(input_columns=["image"], operations=[vision.Decode(True)]) |
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data2 = data2.map(input_columns=["image"], operations=[c_vision.Decode(True)]) |
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ds.serialize(data2, "testpipeline2.json") |
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# check that the generated output is different |
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@@ -128,8 +128,8 @@ def test_deterministic_run_fail(): |
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# Assuming we get the same seed on calling constructor, if this op is re-used then result won't be |
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# the same in between the two datasets. For example, RandomCrop constructor takes seed (0) |
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# outputs a deterministic series of numbers, e,g "a" = [1, 2, 3, 4, 5, 6] <- pretend these are random |
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random_crop_op = vision.RandomCrop([512, 512], [200, 200, 200, 200]) |
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decode_op = vision.Decode() |
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random_crop_op = c_vision.RandomCrop([512, 512], [200, 200, 200, 200]) |
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decode_op = c_vision.Decode() |
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data1 = data1.map(input_columns=["image"], operations=decode_op) |
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data1 = data1.map(input_columns=["image"], operations=random_crop_op) |
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@@ -153,24 +153,24 @@ def test_deterministic_run_fail(): |
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ds.config.set_seed(seed_original) |
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def test_deterministic_run_pass(): |
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def test_seed_undeterministic(): |
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""" |
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Test deterministic run with setting the seed |
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Test seed with num parallel workers in c, this test is expected to fail some of the time |
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""" |
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logger.info("test_deterministic_run_pass") |
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logger.info("test_seed_undeterministic") |
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# Save original configuration values |
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num_parallel_workers_original = ds.config.get_num_parallel_workers() |
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seed_original = ds.config.get_seed() |
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ds.config.set_seed(0) |
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ds.config.set_num_parallel_workers(1) |
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ds.config.set_num_parallel_workers(3) |
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# First dataset |
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data1 = ds.TFRecordDataset(DATA_DIR, SCHEMA_DIR, columns_list=["image"], shuffle=False) |
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# We get the seed when constructor is called |
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random_crop_op = vision.RandomCrop([512, 512], [200, 200, 200, 200]) |
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decode_op = vision.Decode() |
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random_crop_op = c_vision.RandomCrop([512, 512], [200, 200, 200, 200]) |
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decode_op = c_vision.Decode() |
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data1 = data1.map(input_columns=["image"], operations=decode_op) |
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data1 = data1.map(input_columns=["image"], operations=random_crop_op) |
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@@ -179,7 +179,7 @@ def test_deterministic_run_pass(): |
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data2 = data2.map(input_columns=["image"], operations=decode_op) |
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# Since seed is set up on constructor, so the two ops output deterministic sequence. |
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# Assume the generated random sequence "a" = [1, 2, 3, 4, 5, 6] <- pretend these are random |
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random_crop_op2 = vision.RandomCrop([512, 512], [200, 200, 200, 200]) |
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random_crop_op2 = c_vision.RandomCrop([512, 512], [200, 200, 200, 200]) |
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data2 = data2.map(input_columns=["image"], operations=random_crop_op2) |
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try: |
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for item1, item2 in zip(data1.create_dict_iterator(), data2.create_dict_iterator()): |
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@@ -194,11 +194,11 @@ def test_deterministic_run_pass(): |
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ds.config.set_seed(seed_original) |
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def test_seed_undeterministic(): |
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def test_seed_deterministic(): |
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""" |
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Test seed with num parallel workers in c, this test is expected to fail some of the time |
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Test deterministic run with setting the seed, only works with num_parallel worker = 1 |
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""" |
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logger.info("test_seed_undeterministic") |
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logger.info("test_seed_deterministic") |
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# Save original configuration values |
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num_parallel_workers_original = ds.config.get_num_parallel_workers() |
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@@ -210,8 +210,8 @@ def test_seed_undeterministic(): |
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# First dataset |
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data1 = ds.TFRecordDataset(DATA_DIR, SCHEMA_DIR, columns_list=["image"], shuffle=False) |
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# seed will be read in during constructor call |
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random_crop_op = vision.RandomCrop([512, 512], [200, 200, 200, 200]) |
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decode_op = vision.Decode() |
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random_crop_op = c_vision.RandomCrop([512, 512], [200, 200, 200, 200]) |
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decode_op = c_vision.Decode() |
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data1 = data1.map(input_columns=["image"], operations=decode_op) |
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data1 = data1.map(input_columns=["image"], operations=random_crop_op) |
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@@ -219,7 +219,7 @@ def test_seed_undeterministic(): |
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data2 = ds.TFRecordDataset(DATA_DIR, SCHEMA_DIR, columns_list=["image"], shuffle=False) |
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data2 = data2.map(input_columns=["image"], operations=decode_op) |
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# If seed is set up on constructor, so the two ops output deterministic sequence |
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random_crop_op2 = vision.RandomCrop([512, 512], [200, 200, 200, 200]) |
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random_crop_op2 = c_vision.RandomCrop([512, 512], [200, 200, 200, 200]) |
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data2 = data2.map(input_columns=["image"], operations=random_crop_op2) |
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for item1, item2 in zip(data1.create_dict_iterator(), data2.create_dict_iterator()): |
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@@ -246,8 +246,8 @@ def test_deterministic_run_distribution(): |
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# First dataset |
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data1 = ds.TFRecordDataset(DATA_DIR, SCHEMA_DIR, columns_list=["image"], shuffle=False) |
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random_crop_op = vision.RandomHorizontalFlip(0.1) |
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decode_op = vision.Decode() |
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random_crop_op = c_vision.RandomHorizontalFlip(0.1) |
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decode_op = c_vision.Decode() |
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data1 = data1.map(input_columns=["image"], operations=decode_op) |
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data1 = data1.map(input_columns=["image"], operations=random_crop_op) |
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@@ -255,7 +255,7 @@ def test_deterministic_run_distribution(): |
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data2 = ds.TFRecordDataset(DATA_DIR, SCHEMA_DIR, columns_list=["image"], shuffle=False) |
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data2 = data2.map(input_columns=["image"], operations=decode_op) |
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# If seed is set up on constructor, so the two ops output deterministic sequence |
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random_crop_op2 = vision.RandomHorizontalFlip(0.1) |
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random_crop_op2 = c_vision.RandomHorizontalFlip(0.1) |
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data2 = data2.map(input_columns=["image"], operations=random_crop_op2) |
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for item1, item2 in zip(data1.create_dict_iterator(), data2.create_dict_iterator()): |
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@@ -270,7 +270,7 @@ def test_deterministic_python_seed(): |
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""" |
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Test deterministic execution with seed in python |
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""" |
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logger.info("deterministic_random_crop_op_python_2") |
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logger.info("test_deterministic_python_seed") |
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# Save original configuration values |
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num_parallel_workers_original = ds.config.get_num_parallel_workers() |
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@@ -315,11 +315,12 @@ def test_deterministic_python_seed_multi_thread(): |
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""" |
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Test deterministic execution with seed in python, this fails with multi-thread pyfunc run |
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""" |
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logger.info("deterministic_random_crop_op_python_2") |
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logger.info("test_deterministic_python_seed_multi_thread") |
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# Save original configuration values |
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num_parallel_workers_original = ds.config.get_num_parallel_workers() |
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seed_original = ds.config.get_seed() |
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ds.config.set_num_parallel_workers(3) |
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ds.config.set_seed(0) |
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# when we set the seed all operations within our dataset should be deterministic |
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# First dataset |
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@@ -355,15 +356,17 @@ def test_deterministic_python_seed_multi_thread(): |
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assert "Array" in str(e) |
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# Restore original configuration values |
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ds.config.set_num_parallel_workers(num_parallel_workers_original) |
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ds.config.set_seed(seed_original) |
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if __name__ == '__main__': |
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test_basic() |
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test_get_seed() |
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test_pipeline() |
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test_deterministic_run_pass() |
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test_deterministic_run_distribution() |
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test_deterministic_run_fail() |
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test_deterministic_python_seed() |
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test_seed_undeterministic() |
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test_get_seed() |
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test_seed_deterministic() |
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test_deterministic_run_distribution() |
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test_deterministic_python_seed() |
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test_deterministic_python_seed_multi_thread() |