forked from huawei/mindspore2022
230 lines
8.2 KiB
Python
230 lines
8.2 KiB
Python
# Copyright 2022 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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"""
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This is the test module for two level pipeline.
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"""
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import os
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import pytest
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import numpy as np
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import mindspore.dataset as ds
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from mindspore import log as logger
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from util_minddataset import add_and_remove_cv_file, add_and_remove_file # pylint: disable=unused-import
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# pylint: disable=redefined-outer-name
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def test_minddtaset_generatordataset_01(add_and_remove_cv_file):
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"""
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Feature: Test basic two level pipeline.
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Description: MindDataset + GeneratorDataset
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Expectation: Data Iteration Successfully.
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"""
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columns_list = ["data", "file_name", "label"]
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num_readers = 1
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file_name = os.environ.get('PYTEST_CURRENT_TEST').split(':')[-1].split(' ')[0]
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data_set = ds.MindDataset(file_name + "0", columns_list, num_parallel_workers=num_readers, shuffle=None)
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dataset_size = data_set.get_dataset_size()
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class MyIterable:
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""" custom iteration """
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def __init__(self, dataset, dataset_size):
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self._iter = None
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self._index = 0
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self._dataset = dataset
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self._dataset_size = dataset_size
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def __next__(self):
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if self._index >= self._dataset_size:
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raise StopIteration
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if self._iter:
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item = next(self._iter)
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self._index += 1
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return item
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self._iter = self._dataset.create_tuple_iterator(num_epochs=1, output_numpy=True)
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return next(self)
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def __iter__(self):
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self._index = 0
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self._iter = None
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return self
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def __len__(self):
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return self._dataset_size
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dataset = ds.GeneratorDataset(source=MyIterable(data_set, dataset_size),
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column_names=["data", "file_name", "label"], num_parallel_workers=1)
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num_epochs = 3
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iter_ = dataset.create_dict_iterator(num_epochs=num_epochs, output_numpy=True)
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num_iter = 0
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for _ in range(num_epochs):
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for _ in iter_:
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num_iter += 1
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assert num_iter == num_epochs * dataset_size
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# pylint: disable=redefined-outer-name
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def test_minddtaset_generatordataset_exception_01(add_and_remove_cv_file):
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"""
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Feature: Test basic two level pipeline.
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Description: invalid column name in MindDataset
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Expectation: throw expected exception.
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"""
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err_columns_list = ["data", "filename", "label"]
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num_readers = 1
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file_name = os.environ.get('PYTEST_CURRENT_TEST').split(':')[-1].split(' ')[0]
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data_set = ds.MindDataset(file_name + "0", err_columns_list, num_parallel_workers=num_readers, shuffle=None)
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dataset_size = data_set.get_dataset_size()
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class MyIterable:
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""" custom iteration """
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def __init__(self, dataset, dataset_size):
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self._iter = None
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self._index = 0
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self._dataset = dataset
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self._dataset_size = dataset_size
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def __next__(self):
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if self._index >= self._dataset_size:
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raise StopIteration
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if self._iter:
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item = next(self._iter)
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self._index += 1
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return item
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self._iter = self._dataset.create_tuple_iterator(num_epochs=1, output_numpy=True)
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return next(self)
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def __iter__(self):
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self._index = 0
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self._iter = None
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return self
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def __len__(self):
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return self._dataset_size
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dataset = ds.GeneratorDataset(source=MyIterable(data_set, dataset_size),
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column_names=["data", "file_name", "label"], num_parallel_workers=1)
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num_epochs = 3
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iter_ = dataset.create_dict_iterator(num_epochs=num_epochs, output_numpy=True)
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num_iter = 0
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with pytest.raises(RuntimeError) as error_info:
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for _ in range(num_epochs):
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for _ in iter_:
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num_iter += 1
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assert 'Unexpected error. Invalid data, column name:' in str(error_info.value)
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# pylint: disable=redefined-outer-name
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def test_minddtaset_generatordataset_exception_02(add_and_remove_file):
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"""
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Feature: Test basic two level pipeline for mixed dataset.
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Description: Invalid column name in MindDataset
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Expectation: Throw expected exception.
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"""
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columns_list = ["data", "file_name", "label"]
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num_readers = 1
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file_name = os.environ.get('PYTEST_CURRENT_TEST').split(':')[-1].split(' ')[0]
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file_paths = [file_name + "_cv" + str(i) for i in range(4)]
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file_paths += [file_name + "_nlp" + str(i) for i in range(4)]
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class MyIterable:
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""" custom iteration """
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def __init__(self, file_paths):
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self._iter = None
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self._index = 0
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self._idx = 0
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self._file_paths = file_paths
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def __next__(self):
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if self._index >= len(self._file_paths) * 10:
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raise StopIteration
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if self._iter:
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try:
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item = next(self._iter)
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self._index += 1
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except StopIteration:
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if self._idx >= len(self._file_paths):
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raise StopIteration
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self._iter = None
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return next(self)
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return item
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logger.info("load <<< {}.".format(self._file_paths[self._idx]))
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self._iter = ds.MindDataset(self._file_paths[self._idx],
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columns_list, num_parallel_workers=num_readers,
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shuffle=None).create_tuple_iterator(num_epochs=1, output_numpy=True)
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self._idx += 1
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return next(self)
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def __iter__(self):
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self._index = 0
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self._idx = 0
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self._iter = None
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return self
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def __len__(self):
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return len(self._file_paths) * 10
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dataset = ds.GeneratorDataset(source=MyIterable(file_paths),
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column_names=["data", "file_name", "label"], num_parallel_workers=1)
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num_epochs = 1
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iter_ = dataset.create_dict_iterator(num_epochs=num_epochs, output_numpy=True)
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num_iter = 0
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with pytest.raises(RuntimeError) as error_info:
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for _ in range(num_epochs):
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for item in iter_:
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print("item: ", item)
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num_iter += 1
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assert 'Unexpected error. Invalid data, column name:' in str(error_info.value)
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def test_two_level_pipeline_with_multiprocessing():
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"""
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Feature: Test basic two level pipeline with multiprocessing testcases.
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Description: Test basic feature on two level pipeline with multiprocessing scenario.
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Expectation: Basic feature work fine.
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"""
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file_name = "../data/dataset/testPK/data"
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class DatasetGenerator:
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def __init__(self):
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data1 = ds.ImageFolderDataset(file_name)
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data1 = data1.map(DatasetGenerator.pyfunc, input_columns=["image"], python_multiprocessing=True)
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self.iter = data1.create_tuple_iterator(output_numpy=True)
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def __getitem__(self, item):
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return next(self.iter)
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def __len__(self):
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return 10
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@staticmethod
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def pyfunc(x):
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return x
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source = DatasetGenerator()
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data2 = ds.GeneratorDataset(source, ["data", "label"])
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assert data2.output_shapes() == [[159109], []]
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data3 = ds.GeneratorDataset(source, ["data", "label"])
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assert data3.output_types() == [np.uint8, np.int32]
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data4 = ds.GeneratorDataset(source, ["data", "label"])
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assert data4.get_dataset_size() == 10
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nums = 0
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for _ in data4.create_dict_iterator(output_numpy=True):
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nums += 1
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assert nums == 10
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