forked from mindspore-Ecosystem/mindspore
!8714 dataset Python UT: Add Sampler Chain Tests, with get_dataset_size
From: @cathwong Reviewed-by: @nsyca,@mikef Signed-off-by: @nsyca
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@ -273,6 +273,7 @@ def test_sequential_sampler():
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result.append(item["label"])
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num_iter += 1
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assert num_iter == 44
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logger.info("Result: {}".format(result))
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assert result == golden
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@ -383,6 +384,7 @@ def test_weighted_random_sampler():
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logger.info("Number of data in data1: {}".format(num_iter))
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assert num_iter == 11
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def test_weighted_random_sampler_exception():
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"""
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Test error cases for WeightedRandomSampler
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@ -413,6 +415,250 @@ def test_weighted_random_sampler_exception():
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weights = [0, 0, 0, 0, 0]
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ds.WeightedRandomSampler(weights)
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def test_chained_sampler_01():
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logger.info("Test Case Chained Sampler - Random and Sequential, with repeat")
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# Create chained sampler, random and sequential
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sampler = ds.RandomSampler()
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child_sampler = ds.SequentialSampler()
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sampler.add_child(child_sampler)
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# Create ImageFolderDataset with sampler
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data1 = ds.ImageFolderDataset(DATA_DIR, sampler=sampler)
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data1 = data1.repeat(count=3)
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# Verify dataset size
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data1_size = data1.get_dataset_size()
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logger.info("dataset size is: {}".format(data1_size))
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assert data1_size == 132
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# Verify number of iterations
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num_iter = 0
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for item in data1.create_dict_iterator(num_epochs=1): # each data is a dictionary
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# in this example, each dictionary has keys "image" and "label"
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logger.info("image is {}".format(item["image"]))
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logger.info("label is {}".format(item["label"]))
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num_iter += 1
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logger.info("Number of data in data1: {}".format(num_iter))
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assert num_iter == 132
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def test_chained_sampler_02():
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logger.info("Test Case Chained Sampler - Random and Sequential, with batch then repeat")
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# Create chained sampler, random and sequential
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sampler = ds.RandomSampler()
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child_sampler = ds.SequentialSampler()
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sampler.add_child(child_sampler)
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# Create ImageFolderDataset with sampler
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data1 = ds.ImageFolderDataset(DATA_DIR, sampler=sampler)
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data1 = data1.batch(batch_size=5, drop_remainder=True)
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data1 = data1.repeat(count=2)
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# Verify dataset size
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data1_size = data1.get_dataset_size()
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logger.info("dataset size is: {}".format(data1_size))
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assert data1_size == 16
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# Verify number of iterations
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num_iter = 0
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for item in data1.create_dict_iterator(num_epochs=1): # each data is a dictionary
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# in this example, each dictionary has keys "image" and "label"
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logger.info("image is {}".format(item["image"]))
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logger.info("label is {}".format(item["label"]))
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num_iter += 1
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logger.info("Number of data in data1: {}".format(num_iter))
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assert num_iter == 16
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def test_chained_sampler_03():
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logger.info("Test Case Chained Sampler - Random and Sequential, with repeat then batch")
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# Create chained sampler, random and sequential
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sampler = ds.RandomSampler()
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child_sampler = ds.SequentialSampler()
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sampler.add_child(child_sampler)
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# Create ImageFolderDataset with sampler
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data1 = ds.ImageFolderDataset(DATA_DIR, sampler=sampler)
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data1 = data1.repeat(count=2)
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data1 = data1.batch(batch_size=5, drop_remainder=False)
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# Verify dataset size
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data1_size = data1.get_dataset_size()
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logger.info("dataset size is: {}".format(data1_size))
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assert data1_size == 18
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# Verify number of iterations
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num_iter = 0
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for item in data1.create_dict_iterator(num_epochs=1): # each data is a dictionary
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# in this example, each dictionary has keys "image" and "label"
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logger.info("image is {}".format(item["image"]))
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logger.info("label is {}".format(item["label"]))
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num_iter += 1
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logger.info("Number of data in data1: {}".format(num_iter))
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assert num_iter == 18
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def test_chained_sampler_04():
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logger.info("Test Case Chained Sampler - Distributed and Random, with batch then repeat")
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# Create chained sampler, distributed and random
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sampler = ds.DistributedSampler(num_shards=4, shard_id=3)
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child_sampler = ds.RandomSampler()
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sampler.add_child(child_sampler)
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# Create ImageFolderDataset with sampler
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data1 = ds.ImageFolderDataset(DATA_DIR, sampler=sampler)
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data1 = data1.batch(batch_size=5, drop_remainder=True)
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data1 = data1.repeat(count=3)
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# Verify dataset size
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data1_size = data1.get_dataset_size()
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logger.info("dataset size is: {}".format(data1_size))
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assert data1_size == 24
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# Verify number of iterations
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num_iter = 0
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for item in data1.create_dict_iterator(num_epochs=1): # each data is a dictionary
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# in this example, each dictionary has keys "image" and "label"
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logger.info("image is {}".format(item["image"]))
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logger.info("label is {}".format(item["label"]))
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num_iter += 1
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logger.info("Number of data in data1: {}".format(num_iter))
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# Note: Each of the 4 shards has 44/4=11 samples
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# Note: Number of iterations is (11/5 = 2) * 3 = 6
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assert num_iter == 6
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def skip_test_chained_sampler_05():
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logger.info("Test Case Chained Sampler - PKSampler and WeightedRandom")
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# Create chained sampler, PKSampler and WeightedRandom
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sampler = ds.PKSampler(num_val=3) # Number of elements per class is 3 (and there are 4 classes)
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weights = [1.0, 0.1, 0.02, 0.3, 0.4, 0.05, 1.2, 0.13, 0.14, 0.015, 0.16, 0.5]
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child_sampler = ds.WeightedRandomSampler(weights, num_samples=12)
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sampler.add_child(child_sampler)
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# Create ImageFolderDataset with sampler
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data1 = ds.ImageFolderDataset(DATA_DIR, sampler=sampler)
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# Verify dataset size
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data1_size = data1.get_dataset_size()
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logger.info("dataset size is: {}".format(data1_size))
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assert data1_size == 12
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# Verify number of iterations
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num_iter = 0
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for item in data1.create_dict_iterator(num_epochs=1): # each data is a dictionary
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# in this example, each dictionary has keys "image" and "label"
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logger.info("image is {}".format(item["image"]))
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logger.info("label is {}".format(item["label"]))
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num_iter += 1
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logger.info("Number of data in data1: {}".format(num_iter))
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# Note: PKSampler produces 4x3=12 samples
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# Note: Child WeightedRandomSampler produces 12 samples
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assert num_iter == 12
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def test_chained_sampler_06():
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logger.info("Test Case Chained Sampler - WeightedRandom and PKSampler")
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# Create chained sampler, WeightedRandom and PKSampler
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weights = [1.0, 0.1, 0.02, 0.3, 0.4, 0.05, 1.2, 0.13, 0.14, 0.015, 0.16, 0.5]
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sampler = ds.WeightedRandomSampler(weights=weights, num_samples=12)
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child_sampler = ds.PKSampler(num_val=3) # Number of elements per class is 3 (and there are 4 classes)
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sampler.add_child(child_sampler)
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# Create ImageFolderDataset with sampler
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data1 = ds.ImageFolderDataset(DATA_DIR, sampler=sampler)
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# Verify dataset size
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data1_size = data1.get_dataset_size()
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logger.info("dataset size is: {}".format(data1_size))
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assert data1_size == 12
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# Verify number of iterations
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num_iter = 0
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for item in data1.create_dict_iterator(num_epochs=1): # each data is a dictionary
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# in this example, each dictionary has keys "image" and "label"
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logger.info("image is {}".format(item["image"]))
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logger.info("label is {}".format(item["label"]))
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num_iter += 1
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logger.info("Number of data in data1: {}".format(num_iter))
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# Note: WeightedRandomSampler produces 12 samples
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# Note: Child PKSampler produces 12 samples
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assert num_iter == 12
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def test_chained_sampler_07():
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logger.info("Test Case Chained Sampler - SubsetRandom and Distributed, 2 shards")
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# Create chained sampler, subset random and distributed
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indices = [0, 1, 2, 3, 4, 5, 12, 13, 14, 15, 16, 11]
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sampler = ds.SubsetRandomSampler(indices, num_samples=12)
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child_sampler = ds.DistributedSampler(num_shards=2, shard_id=1)
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sampler.add_child(child_sampler)
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# Create ImageFolderDataset with sampler
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data1 = ds.ImageFolderDataset(DATA_DIR, sampler=sampler)
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# Verify dataset size
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data1_size = data1.get_dataset_size()
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logger.info("dataset size is: {}".format(data1_size))
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assert data1_size == 12
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# Verify number of iterations
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num_iter = 0
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for item in data1.create_dict_iterator(num_epochs=1): # each data is a dictionary
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# in this example, each dictionary has keys "image" and "label"
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logger.info("image is {}".format(item["image"]))
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logger.info("label is {}".format(item["label"]))
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num_iter += 1
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logger.info("Number of data in data1: {}".format(num_iter))
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# Note: SubsetRandomSampler produces 12 samples
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# Note: Each of 2 shards has 6 samples
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# FIXME: Uncomment the following assert when code issue is resolved; at runtime, number of samples is 12 not 6
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# assert num_iter == 6
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def skip_test_chained_sampler_08():
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logger.info("Test Case Chained Sampler - SubsetRandom and Distributed, 4 shards")
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# Create chained sampler, subset random and distributed
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indices = [0, 1, 2, 3, 4, 5, 12, 13, 14, 15, 16, 11]
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sampler = ds.SubsetRandomSampler(indices, num_samples=12)
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child_sampler = ds.DistributedSampler(num_shards=4, shard_id=1)
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sampler.add_child(child_sampler)
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# Create ImageFolderDataset with sampler
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data1 = ds.ImageFolderDataset(DATA_DIR, sampler=sampler)
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# Verify dataset size
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data1_size = data1.get_dataset_size()
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logger.info("dataset size is: {}".format(data1_size))
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assert data1_size == 3
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# Verify number of iterations
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num_iter = 0
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for item in data1.create_dict_iterator(num_epochs=1): # each data is a dictionary
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# in this example, each dictionary has keys "image" and "label"
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logger.info("image is {}".format(item["image"]))
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logger.info("label is {}".format(item["label"]))
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num_iter += 1
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logger.info("Number of data in data1: {}".format(num_iter))
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# Note: SubsetRandomSampler returns 12 samples
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# Note: Each of 4 shards has 3 samples
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assert num_iter == 3
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def test_imagefolder_rename():
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logger.info("Test Case rename")
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# define parameters
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@ -498,6 +744,30 @@ if __name__ == '__main__':
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test_weighted_random_sampler_exception()
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logger.info('test_weighted_random_sampler_exception Ended.\n')
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test_chained_sampler_01()
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logger.info('test_chained_sampler_01 Ended.\n')
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test_chained_sampler_02()
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logger.info('test_chained_sampler_02 Ended.\n')
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test_chained_sampler_03()
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logger.info('test_chained_sampler_03 Ended.\n')
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test_chained_sampler_04()
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logger.info('test_chained_sampler_04 Ended.\n')
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# test_chained_sampler_05()
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# logger.info('test_chained_sampler_05 Ended.\n')
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test_chained_sampler_06()
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logger.info('test_chained_sampler_06 Ended.\n')
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test_chained_sampler_07()
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logger.info('test_chained_sampler_07 Ended.\n')
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# test_chained_sampler_08()
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# logger.info('test_chained_sampler_07 Ended.\n')
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test_imagefolder_numshards()
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logger.info('test_imagefolder_numshards Ended.\n')
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@ -0,0 +1,250 @@
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# Copyright 2020 Huawei Technologies Co., Ltd
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# ==============================================================================
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import pytest
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import mindspore.dataset as ds
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import mindspore.dataset.transforms.c_transforms as c_transforms
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from mindspore import log as logger
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from util import save_and_check_md5
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GENERATE_GOLDEN = False
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def test_numpyslices_sampler_no_chain():
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"""
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Test NumpySlicesDataset with sampler, no chain
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"""
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logger.info("test_numpyslices_sampler_no_chain")
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# Create NumpySlicesDataset with sampler, no chain
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np_data = [1, 2, 3, 4]
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sampler = ds.SequentialSampler(start_index=1, num_samples=2)
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data1 = ds.NumpySlicesDataset(np_data, sampler=sampler)
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# Verify dataset size
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data1_size = data1.get_dataset_size()
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logger.info("dataset size is: {}".format(data1_size))
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assert data1_size == 2
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# Verify number of rows
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assert sum([1 for _ in data1]) == 2
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# Verify dataset contents
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res = []
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for item in data1.create_tuple_iterator(num_epochs=1, output_numpy=True):
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logger.info("item: {}".format(item))
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res.append(item)
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logger.info("dataset: {}".format(res))
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def test_numpyslices_sampler_chain():
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"""
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Test NumpySlicesDataset sampler chain
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"""
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logger.info("test_numpyslices_sampler_chain")
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# Create NumpySlicesDataset with sampler chain
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# Use 1 statement to add child sampler
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np_data = [1, 2, 3, 4]
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sampler = ds.SequentialSampler(start_index=1, num_samples=2)
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sampler = sampler.add_child(ds.SequentialSampler(start_index=1, num_samples=2))
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data1 = ds.NumpySlicesDataset(np_data, sampler=sampler)
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# Verify dataset size
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data1_size = data1.get_dataset_size()
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logger.info("dataset size is: {}".format(data1_size))
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assert data1_size == 4
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# Verify number of rows
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assert sum([1 for _ in data1]) == 4
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# Verify dataset contents
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res = []
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for item in data1.create_tuple_iterator(num_epochs=1, output_numpy=True):
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logger.info("item: {}".format(item))
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res.append(item)
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logger.info("dataset: {}".format(res))
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def test_numpyslices_sampler_chain2():
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"""
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Test NumpySlicesDataset sampler chain
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"""
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logger.info("test_numpyslices_sampler_chain2")
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# Create NumpySlicesDataset with sampler chain
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# Use 2 statements to add child sampler
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np_data = [1, 2, 3, 4]
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sampler = ds.SequentialSampler(start_index=1, num_samples=2)
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child_sampler = ds.SequentialSampler(start_index=1, num_samples=2)
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sampler.add_child(child_sampler)
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data1 = ds.NumpySlicesDataset(np_data, sampler=sampler)
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# Verify dataset size
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data1_size = data1.get_dataset_size()
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logger.info("dataset size is: {}".format(data1_size))
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# FIXME: Uncomment the following assert when code issue is resolved; at runtime, data1_size is 2 not 4
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# assert data1_size == 4
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# Verify number of rows
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# FIXME: Uncomment the following assert when code issue is resolved; at runtime, number of rows is 2 not 4
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# assert sum([1 for _ in data1]) == 4
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# Verify dataset contents
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# FIXME: Uncomment the following test code when runtime code issue is resolved
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# res = []
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# for item in data1.create_tuple_iterator(num_epochs=1, output_numpy=True):
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# logger.info("item: {}".format(item))
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# res.append(item)
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# logger.info("dataset: {}".format(res))
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def test_numpyslices_sampler_chain_batch():
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"""
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Test NumpySlicesDataset sampler chaining, with batch
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"""
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logger.info("test_numpyslices_sampler_chain_batch")
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# Create NumpySlicesDataset with sampler chain
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np_data = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]
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sampler = ds.SequentialSampler(start_index=1, num_samples=3)
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sampler = sampler.add_child(ds.SequentialSampler(start_index=1, num_samples=2))
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data1 = ds.NumpySlicesDataset(np_data, sampler=sampler)
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data1 = data1.batch(batch_size=3, drop_remainder=False)
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# Verify dataset size
|
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data1_size = data1.get_dataset_size()
|
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logger.info("dataset size is: {}".format(data1_size))
|
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assert data1_size == 4
|
||||
|
||||
# Verify number of rows
|
||||
assert sum([1 for _ in data1]) == 4
|
||||
|
||||
# Verify dataset contents
|
||||
res = []
|
||||
for item in data1.create_tuple_iterator(num_epochs=1, output_numpy=True):
|
||||
logger.info("item: {}".format(item))
|
||||
res.append(item)
|
||||
logger.info("dataset: {}".format(res))
|
||||
|
||||
|
||||
def test_sampler_chain_errors():
|
||||
"""
|
||||
Test error cases for sampler chains
|
||||
"""
|
||||
logger.info("test_sampler_chain_errors")
|
||||
|
||||
error_msg_1 = "'NoneType' object has no attribute 'add_child'"
|
||||
# Test add child sampler within child sampler
|
||||
sampler = ds.SequentialSampler(start_index=1, num_samples=2)
|
||||
sampler = sampler.add_child(ds.SequentialSampler(start_index=1, num_samples=2))
|
||||
with pytest.raises(AttributeError, match=error_msg_1):
|
||||
sampler.add_child(ds.SequentialSampler(start_index=1, num_samples=2))
|
||||
|
||||
# error_msg_2 = "'NoneType' object has no attribute 'add_child'"
|
||||
# Test add second and nested child sampler
|
||||
sampler = ds.SequentialSampler(start_index=1, num_samples=2)
|
||||
child_sampler = ds.SequentialSampler(start_index=1, num_samples=2)
|
||||
sampler.add_child(child_sampler)
|
||||
child_sampler2 = ds.SequentialSampler(start_index=1, num_samples=2)
|
||||
sampler.add_child(child_sampler2)
|
||||
# FIXME - no error is raised; uncomment after code issue is resolved
|
||||
# with pytest.raises(AttributeError, match=error_msg_2):
|
||||
# sampler.add_child(child_sampler2)
|
||||
# np_data = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]
|
||||
# data1 = ds.NumpySlicesDataset(np_data, sampler=sampler)
|
||||
|
||||
error_msg_3 = "Conflicting arguments during sampler assignments."
|
||||
# Test conflicting arguments (sampler and shuffle=False) for sampler (no chain)
|
||||
np_data = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]
|
||||
sampler = ds.SequentialSampler(start_index=1, num_samples=3)
|
||||
with pytest.raises(ValueError, match=error_msg_3):
|
||||
ds.NumpySlicesDataset(np_data, shuffle=False, sampler=sampler)
|
||||
|
||||
# error_msg_4 = "Conflicting arguments during sampler assignments."
|
||||
# Test conflicting arguments (sampler and shuffle=False) for sampler chaining
|
||||
np_data = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]
|
||||
sampler = ds.SequentialSampler(start_index=1, num_samples=3)
|
||||
sampler = sampler.add_child(ds.SequentialSampler(start_index=1, num_samples=2))
|
||||
# FIXME - no error is raised; uncomment after code issue is resolved
|
||||
# with pytest.raises(ValueError, match=error_msg_4):
|
||||
# ds.NumpySlicesDataset(np_data, shuffle=False, sampler=sampler)
|
||||
|
||||
|
||||
def test_manifest_sampler_chain_repeat():
|
||||
"""
|
||||
Test ManifestDataset sampler chain DistributedSampler->SequentialSampler, with repeat
|
||||
"""
|
||||
logger.info("test_manifest_sampler_chain_batch")
|
||||
manifest_file = "../data/dataset/testManifestData/test5trainimgs.json"
|
||||
|
||||
# Create sampler chain DistributedSampler->SequentialSampler
|
||||
sampler = ds.DistributedSampler(num_shards=1, shard_id=0, shuffle=False, num_samples=5)
|
||||
child_sampler = ds.SequentialSampler()
|
||||
sampler.add_child(child_sampler)
|
||||
|
||||
# Create ManifestDataset with sampler chain
|
||||
data1 = ds.ManifestDataset(manifest_file, sampler=sampler)
|
||||
data1 = data1.repeat(count=2)
|
||||
|
||||
# Verify dataset size
|
||||
data1_size = data1.get_dataset_size()
|
||||
logger.info("dataset size is: {}".format(data1_size))
|
||||
assert data1_size == 10
|
||||
|
||||
# Verify number of rows
|
||||
assert sum([1 for _ in data1]) == 10
|
||||
|
||||
# Verify dataset contents
|
||||
filename = "sampler_chain_manifest_repeat_result.npz"
|
||||
save_and_check_md5(data1, filename, generate_golden=GENERATE_GOLDEN)
|
||||
|
||||
|
||||
def test_manifest_sampler_chain_batch_repeat():
|
||||
"""
|
||||
Test ManifestDataset sampler chain DistributedSampler->SequentialSampler, with batch then repeat
|
||||
"""
|
||||
logger.info("test_manifest_sampler_chain_batch_repeat")
|
||||
manifest_file = "../data/dataset/testManifestData/test5trainimgs.json"
|
||||
|
||||
# Create sampler chain DistributedSampler->SequentialSampler
|
||||
sampler = ds.DistributedSampler(num_shards=1, shard_id=0, shuffle=False, num_samples=5)
|
||||
child_sampler = ds.SequentialSampler()
|
||||
sampler.add_child(child_sampler)
|
||||
|
||||
# Create ManifestDataset with sampler chain
|
||||
data1 = ds.ManifestDataset(manifest_file, decode=True, sampler=sampler)
|
||||
one_hot_encode = c_transforms.OneHot(3)
|
||||
data1 = data1.map(operations=one_hot_encode, input_columns=["label"])
|
||||
data1 = data1.batch(batch_size=5, drop_remainder=False)
|
||||
data1 = data1.repeat(count=2)
|
||||
|
||||
# Verify dataset size
|
||||
data1_size = data1.get_dataset_size()
|
||||
logger.info("dataset size is: {}".format(data1_size))
|
||||
assert data1_size == 2
|
||||
|
||||
# Verify number of rows
|
||||
# FIXME: Uncomment the following assert when code issue is resolved
|
||||
# assert sum([1 for _ in data1]) == 2
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
test_numpyslices_sampler_no_chain()
|
||||
test_numpyslices_sampler_chain()
|
||||
test_numpyslices_sampler_chain2()
|
||||
test_numpyslices_sampler_chain_batch()
|
||||
test_sampler_chain_errors()
|
||||
test_manifest_sampler_chain_repeat()
|
||||
test_manifest_sampler_chain_batch_repeat()
|
||||
Loading…
Reference in New Issue