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148 lines
4.0 KiB
148 lines
4.0 KiB
# Copyright 2019 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 time
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import mindspore.dataset as ds
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import mindspore.dataset.vision.c_transforms as vision
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from mindspore import log as logger
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DATA_DIR = ["../data/dataset/test_tf_file_3_images/train-0000-of-0001.data"]
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SCHEMA_DIR = "../data/dataset/test_tf_file_3_images/datasetSchema.json"
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TF_FILES = ["../data/dataset/testTFTestAllTypes/test.data"]
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TF_SCHEMA_FILE = "../data/dataset/testTFTestAllTypes/datasetSchema.json"
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def test_case_0():
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"""
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Test Repeat
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"""
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# apply dataset operations
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data = ds.TFRecordDataset(DATA_DIR, SCHEMA_DIR, shuffle=False)
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# define parameters
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repeat_count = 2
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data = data.repeat(repeat_count)
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data = data.device_que()
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data.send()
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time.sleep(0.1)
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data.stop_send()
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def test_case_1():
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"""
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Test Batch
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"""
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data = ds.TFRecordDataset(DATA_DIR, SCHEMA_DIR, shuffle=False)
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# define data augmentation parameters
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resize_height, resize_width = 224, 224
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# define map operations
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decode_op = vision.Decode()
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resize_op = vision.Resize((resize_height, resize_width))
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# apply map operations on images
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data = data.map(operations=decode_op, input_columns=["image"])
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data = data.map(operations=resize_op, input_columns=["image"])
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batch_size = 3
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data = data.batch(batch_size, drop_remainder=True)
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data = data.device_que()
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data.send()
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time.sleep(0.1)
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data.stop_send()
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def test_case_2():
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"""
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Test Batch & Repeat
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"""
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data = ds.TFRecordDataset(DATA_DIR, SCHEMA_DIR, shuffle=False)
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# define data augmentation parameters
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resize_height, resize_width = 224, 224
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# define map operations
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decode_op = vision.Decode()
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resize_op = vision.Resize((resize_height, resize_width))
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# apply map operations on images
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data = data.map(operations=decode_op, input_columns=["image"])
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data = data.map(operations=resize_op, input_columns=["image"])
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batch_size = 2
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data = data.batch(batch_size, drop_remainder=True)
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data = data.repeat(2)
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data = data.device_que()
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assert data.get_repeat_count() == 2
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data.send()
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time.sleep(0.1)
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data.stop_send()
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def test_case_3():
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"""
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Test Repeat & Batch
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"""
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data = ds.TFRecordDataset(DATA_DIR, SCHEMA_DIR, shuffle=False)
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# define data augmentation parameters
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resize_height, resize_width = 224, 224
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# define map operations
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decode_op = vision.Decode()
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resize_op = vision.Resize((resize_height, resize_width))
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# apply map operations on images
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data = data.map(operations=decode_op, input_columns=["image"])
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data = data.map(operations=resize_op, input_columns=["image"])
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data = data.repeat(2)
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batch_size = 2
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data = data.batch(batch_size, drop_remainder=True)
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data = data.device_que()
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data.send()
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time.sleep(0.1)
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data.stop_send()
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def test_case_tf_file():
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data = ds.TFRecordDataset(TF_FILES, TF_SCHEMA_FILE, shuffle=ds.Shuffle.FILES)
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data = data.to_device()
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data.send()
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time.sleep(0.1)
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data.stop_send()
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if __name__ == '__main__':
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logger.info('===========now test Repeat============')
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test_case_0()
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logger.info('===========now test Batch============')
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test_case_1()
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logger.info('===========now test Batch & Repeat============')
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test_case_2()
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logger.info('===========now test Repeat & Batch============')
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test_case_3()
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logger.info('===========now test tf file============')
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test_case_tf_file()
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