forked from mindspore-Ecosystem/mindspore
97 lines
4.0 KiB
Python
97 lines
4.0 KiB
Python
# 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 numpy as np
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import mindspore.dataset as ds
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# tests the construction of multiple ops from a single dataset.
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# map dataset with columns order arguments should produce a ProjectOp over MapOp
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# This test does not utilize the compiling passes at this time.
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def test_map_reorder0():
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def generator_mc(maxid=1):
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for _ in range(maxid):
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yield (np.array([0]), np.array([1]))
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# Generator -> Map
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data0 = ds.GeneratorDataset(generator_mc, ["col0", "col1"])
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data0 = data0.map(input_columns="col0", output_columns="out", columns_order=["col1", "out"],
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operations=(lambda x: x))
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for item in data0.create_tuple_iterator(num_epochs=1): # each data is a dictionary
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assert item == [np.array(1), np.array(0)]
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# tests the construction of multiple ops from a single dataset.
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# map dataset with columns order arguments should produce a ProjectOp over MapOp
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# This test does not utilize the compiling passes at this time.
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def test_map_reorder1():
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def generator_mc(maxid=1):
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for _ in range(maxid):
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yield (np.array([0]), np.array([1]), np.array([2]))
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# Three map and zip
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data0 = ds.GeneratorDataset(generator_mc, ["a0", "a1", "a2"])
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data0 = data0.map(input_columns="a0", columns_order=["a2", "a1", "a0"], operations=(lambda x: x))
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data1 = ds.GeneratorDataset(generator_mc, ["b0", "b1", "b2"])
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data1 = data1.map(input_columns="b0", columns_order=["b1", "b2", "b0"], operations=(lambda x: x))
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data2 = ds.zip((data0, data1))
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data2 = data2.map(input_columns="a0", columns_order=["b2", "a2", "b1", "a1", "b0", "a0"], operations=(lambda x: x))
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for item in data2.create_tuple_iterator(num_epochs=1):
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assert item == [np.array(2), np.array(2), np.array(1), np.array(1), np.array(0), np.array(0)]
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# tests the construction of multiple ops from a single dataset.
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# TFRecordDataset with global shuffle should produce a ShuffleOp over TfReaderOp.
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# This test does not utilize the compiling passes at this time.
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def test_shuffle():
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FILES = ["../data/dataset/testTFTestAllTypes/test.data"]
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SCHEMA_FILE = "../data/dataset/testTFTestAllTypes/datasetSchema.json"
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ds.config.set_seed(1)
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data1 = ds.TFRecordDataset(FILES, schema=SCHEMA_FILE, shuffle=ds.Shuffle.GLOBAL)
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data2 = ds.TFRecordDataset(FILES, schema=SCHEMA_FILE, shuffle=ds.Shuffle.FILES)
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data2 = data2.shuffle(10000)
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for d1, d2 in zip(data1, data2):
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for t1, t2 in zip(d1, d2):
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np.testing.assert_array_equal(t1, t2)
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ds.config.set_seed(1)
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DATA_ALL_FILE = "../data/dataset/testTextFileDataset/*"
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data1 = ds.TextFileDataset(DATA_ALL_FILE, shuffle=ds.Shuffle.GLOBAL)
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data2 = ds.TextFileDataset(DATA_ALL_FILE, shuffle=ds.Shuffle.FILES)
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data2 = data2.shuffle(10000)
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for d1, d2 in zip(data1, data2):
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for t1, t2 in zip(d1, d2):
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np.testing.assert_array_equal(t1, t2)
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ds.config.set_seed(1)
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TRAIN_FILE = '../data/dataset/testCLUE/afqmc/train.json'
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data1 = ds.CLUEDataset(TRAIN_FILE, task='AFQMC', usage='train', shuffle=ds.Shuffle.GLOBAL)
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data2 = ds.CLUEDataset(TRAIN_FILE, task='AFQMC', usage='train', shuffle=ds.Shuffle.FILES)
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data2 = data2.shuffle(10000)
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for d1, d2 in zip(data1, data2):
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for t1, t2 in zip(d1, d2):
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np.testing.assert_array_equal(t1, t2)
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if __name__ == "__main__":
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test_map_reorder0()
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test_map_reorder1()
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test_global_shuffle()
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