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548 lines
22 KiB
548 lines
22 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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"""
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This is the test module for mindrecord
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"""
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import collections
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import json
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import os
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import re
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import string
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import mindspore.dataset.transforms.vision.c_transforms as vision
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import numpy as np
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import pytest
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from mindspore.dataset.transforms.vision import Inter
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from mindspore import log as logger
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import mindspore.dataset as ds
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from mindspore.mindrecord import FileWriter
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FILES_NUM = 4
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CV_FILE_NAME = "../data/mindrecord/imagenet.mindrecord"
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CV_DIR_NAME = "../data/mindrecord/testImageNetData"
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NLP_FILE_NAME = "../data/mindrecord/aclImdb.mindrecord"
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NLP_FILE_POS = "../data/mindrecord/testAclImdbData/pos"
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NLP_FILE_VOCAB= "../data/mindrecord/testAclImdbData/vocab.txt"
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@pytest.fixture
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def add_and_remove_cv_file():
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"""add/remove cv file"""
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paths = ["{}{}".format(CV_FILE_NAME, str(x).rjust(1, '0'))
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for x in range(FILES_NUM)]
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for x in paths:
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os.remove("{}".format(x)) if os.path.exists("{}".format(x)) else None
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os.remove("{}.db".format(x)) if os.path.exists("{}.db".format(x)) else None
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writer = FileWriter(CV_FILE_NAME, FILES_NUM)
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data = get_data(CV_DIR_NAME)
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cv_schema_json = {"id": {"type": "int32"},
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"file_name": {"type": "string"},
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"label": {"type": "int32"},
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"data": {"type": "bytes"}}
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writer.add_schema(cv_schema_json, "img_schema")
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writer.add_index(["file_name", "label"])
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writer.write_raw_data(data)
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writer.commit()
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yield "yield_cv_data"
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for x in paths:
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os.remove("{}".format(x))
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os.remove("{}.db".format(x))
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@pytest.fixture
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def add_and_remove_nlp_file():
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"""add/remove nlp file"""
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paths = ["{}{}".format(NLP_FILE_NAME, str(x).rjust(1, '0'))
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for x in range(FILES_NUM)]
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for x in paths:
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if os.path.exists("{}".format(x)):
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os.remove("{}".format(x))
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if os.path.exists("{}.db".format(x)):
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os.remove("{}.db".format(x))
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writer = FileWriter(NLP_FILE_NAME, FILES_NUM)
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data = [x for x in get_nlp_data(NLP_FILE_POS, NLP_FILE_VOCAB, 10)]
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nlp_schema_json = {"id": {"type": "string"}, "label": {"type": "int32"},
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"rating": {"type": "float32"},
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"input_ids": {"type": "int64",
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"shape": [-1]},
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"input_mask": {"type": "int64",
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"shape": [1, -1]},
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"segment_ids": {"type": "int64",
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"shape": [2,-1]}
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}
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writer.set_header_size(1 << 14)
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writer.set_page_size(1 << 15)
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writer.add_schema(nlp_schema_json, "nlp_schema")
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writer.add_index(["id", "rating"])
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writer.write_raw_data(data)
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writer.commit()
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yield "yield_nlp_data"
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for x in paths:
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os.remove("{}".format(x))
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os.remove("{}.db".format(x))
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def test_cv_minddataset_writer_tutorial():
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"""tutorial for cv dataset writer."""
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paths = ["{}{}".format(CV_FILE_NAME, str(x).rjust(1, '0'))
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for x in range(FILES_NUM)]
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for x in paths:
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os.remove("{}".format(x)) if os.path.exists("{}".format(x)) else None
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os.remove("{}.db".format(x)) if os.path.exists("{}.db".format(x)) else None
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writer = FileWriter(CV_FILE_NAME, FILES_NUM)
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data = get_data(CV_DIR_NAME)
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cv_schema_json = {"file_name": {"type": "string"}, "label": {"type": "int32"},
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"data": {"type": "bytes"}}
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writer.add_schema(cv_schema_json, "img_schema")
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writer.add_index(["file_name", "label"])
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writer.write_raw_data(data)
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writer.commit()
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for x in paths:
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os.remove("{}".format(x))
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os.remove("{}.db".format(x))
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def test_cv_minddataset_partition_tutorial(add_and_remove_cv_file):
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"""tutorial for cv minddataset."""
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columns_list = ["data", "file_name", "label"]
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num_readers = 4
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def partitions(num_shards):
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for partition_id in range(num_shards):
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data_set = ds.MindDataset(CV_FILE_NAME + "0", columns_list, num_readers,
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num_shards=num_shards, shard_id=partition_id)
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num_iter = 0
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for item in data_set.create_dict_iterator():
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logger.info("-------------- partition : {} ------------------------".format(partition_id))
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logger.info("-------------- item[label]: {} -----------------------".format(item["label"]))
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num_iter += 1
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return num_iter
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assert partitions(4) == 3
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assert partitions(5) == 2
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assert partitions(9) == 2
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def test_cv_minddataset_dataset_size(add_and_remove_cv_file):
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"""tutorial for cv minddataset."""
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columns_list = ["data", "file_name", "label"]
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num_readers = 4
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data_set = ds.MindDataset(CV_FILE_NAME + "0", columns_list, num_readers)
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assert data_set.get_dataset_size() == 10
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repeat_num = 2
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data_set = data_set.repeat(repeat_num)
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num_iter = 0
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for item in data_set.create_dict_iterator():
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logger.info("-------------- get dataset size {} -----------------".format(num_iter))
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logger.info("-------------- item[label]: {} ---------------------".format(item["label"]))
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logger.info("-------------- item[data]: {} ----------------------".format(item["data"]))
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num_iter += 1
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assert num_iter == 20
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data_set = ds.MindDataset(CV_FILE_NAME + "0", columns_list, num_readers,
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num_shards=4, shard_id=3)
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assert data_set.get_dataset_size() == 3
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def test_cv_minddataset_repeat_reshuffle(add_and_remove_cv_file):
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"""tutorial for cv minddataset."""
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columns_list = ["data", "label"]
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num_readers = 4
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data_set = ds.MindDataset(CV_FILE_NAME + "0", columns_list, num_readers)
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decode_op = vision.Decode()
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data_set = data_set.map(input_columns=["data"], operations=decode_op, num_parallel_workers=2)
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resize_op = vision.Resize((32, 32), interpolation=Inter.LINEAR)
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data_set = data_set.map(input_columns="data", operations=resize_op, num_parallel_workers=2)
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data_set = data_set.batch(2)
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data_set = data_set.repeat(2)
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num_iter = 0
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labels = []
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for item in data_set.create_dict_iterator():
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logger.info("-------------- get dataset size {} -----------------".format(num_iter))
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logger.info("-------------- item[label]: {} ---------------------".format(item["label"]))
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logger.info("-------------- item[data]: {} ----------------------".format(item["data"]))
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num_iter += 1
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labels.append(item["label"])
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assert num_iter == 10
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logger.info("repeat shuffle: {}".format(labels))
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assert len(labels) == 10
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assert labels[0:5] == labels[0:5]
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assert labels[0:5] != labels[5:5]
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def test_cv_minddataset_batch_size_larger_than_records(add_and_remove_cv_file):
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"""tutorial for cv minddataset."""
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columns_list = ["data", "label"]
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num_readers = 4
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data_set = ds.MindDataset(CV_FILE_NAME + "0", columns_list, num_readers)
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decode_op = vision.Decode()
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data_set = data_set.map(input_columns=["data"], operations=decode_op, num_parallel_workers=2)
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resize_op = vision.Resize((32, 32), interpolation=Inter.LINEAR)
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data_set = data_set.map(input_columns="data", operations=resize_op, num_parallel_workers=2)
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data_set = data_set.batch(32, drop_remainder=True)
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num_iter = 0
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for item in data_set.create_dict_iterator():
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logger.info("-------------- get dataset size {} -----------------".format(num_iter))
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logger.info("-------------- item[label]: {} ---------------------".format(item["label"]))
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logger.info("-------------- item[data]: {} ----------------------".format(item["data"]))
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num_iter += 1
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assert num_iter == 0
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def test_cv_minddataset_issue_888(add_and_remove_cv_file):
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"""issue 888 test."""
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columns_list = ["data", "label"]
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num_readers = 2
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data = ds.MindDataset(CV_FILE_NAME + "0", columns_list, num_readers, shuffle=False, num_shards=5, shard_id=1)
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data = data.shuffle(2)
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data = data.repeat(9)
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num_iter = 0
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for item in data.create_dict_iterator():
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num_iter += 1
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assert num_iter == 18
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def test_cv_minddataset_blockreader_tutorial(add_and_remove_cv_file):
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"""tutorial for cv minddataset."""
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columns_list = ["data", "label"]
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num_readers = 4
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data_set = ds.MindDataset(CV_FILE_NAME + "0", columns_list, num_readers,
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block_reader=True)
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assert data_set.get_dataset_size() == 10
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repeat_num = 2
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data_set = data_set.repeat(repeat_num)
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num_iter = 0
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for item in data_set.create_dict_iterator():
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logger.info("-------------- block reader repeat tow {} -----------------".format(num_iter))
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logger.info("-------------- item[label]: {} ----------------------------".format(item["label"]))
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logger.info("-------------- item[data]: {} -----------------------------".format(item["data"]))
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num_iter += 1
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assert num_iter == 20
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def test_cv_minddataset_blockreader_some_field_not_in_index_tutorial(add_and_remove_cv_file):
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"""tutorial for cv minddataset."""
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columns_list = ["id", "data", "label"]
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num_readers = 4
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data_set = ds.MindDataset(CV_FILE_NAME + "0", columns_list, num_readers, shuffle=False,
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block_reader=True)
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assert data_set.get_dataset_size() == 10
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repeat_num = 2
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data_set = data_set.repeat(repeat_num)
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num_iter = 0
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for item in data_set.create_dict_iterator():
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logger.info("-------------- block reader repeat tow {} -----------------".format(num_iter))
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logger.info("-------------- item[id]: {} ----------------------------".format(item["id"]))
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logger.info("-------------- item[label]: {} ----------------------------".format(item["label"]))
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logger.info("-------------- item[data]: {} -----------------------------".format(item["data"]))
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num_iter += 1
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assert num_iter == 20
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def test_cv_minddataset_reader_basic_tutorial(add_and_remove_cv_file):
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"""tutorial for cv minderdataset."""
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columns_list = ["data", "file_name", "label"]
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num_readers = 4
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data_set = ds.MindDataset(CV_FILE_NAME + "0", columns_list, num_readers)
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assert data_set.get_dataset_size() == 10
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num_iter = 0
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for item in data_set.create_dict_iterator():
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logger.info("-------------- cv reader basic: {} ------------------------".format(num_iter))
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logger.info("-------------- len(item[data]): {} ------------------------".format(len(item["data"])))
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logger.info("-------------- item[data]: {} -----------------------------".format(item["data"]))
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logger.info("-------------- item[file_name]: {} ------------------------".format(item["file_name"]))
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logger.info("-------------- item[label]: {} ----------------------------".format(item["label"]))
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num_iter += 1
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assert num_iter == 10
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def test_nlp_minddataset_reader_basic_tutorial(add_and_remove_nlp_file):
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"""tutorial for nlp minderdataset."""
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num_readers = 4
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data_set = ds.MindDataset(NLP_FILE_NAME + "0", None, num_readers)
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assert data_set.get_dataset_size() == 10
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num_iter = 0
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for item in data_set.create_dict_iterator():
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logger.info("-------------- cv reader basic: {} ------------------------".format(num_iter))
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logger.info("-------------- num_iter: {} ------------------------".format(num_iter))
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logger.info("-------------- item[id]: {} ------------------------".format(item["id"]))
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logger.info("-------------- item[rating]: {} --------------------".format(item["rating"]))
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logger.info("-------------- item[input_ids]: {}, shape: {} -----------------".format(
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item["input_ids"], item["input_ids"].shape))
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logger.info("-------------- item[input_mask]: {}, shape: {} -----------------".format(
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item["input_mask"], item["input_mask"].shape))
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logger.info("-------------- item[segment_ids]: {}, shape: {} -----------------".format(
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item["segment_ids"], item["segment_ids"].shape))
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assert item["input_ids"].shape == (50,)
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assert item["input_mask"].shape == (1, 50)
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assert item["segment_ids"].shape == (2, 25)
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num_iter += 1
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assert num_iter == 10
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def test_cv_minddataset_reader_basic_tutorial_5_epoch(add_and_remove_cv_file):
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"""tutorial for cv minderdataset."""
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columns_list = ["data", "file_name", "label"]
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num_readers = 4
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data_set = ds.MindDataset(CV_FILE_NAME + "0", columns_list, num_readers)
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assert data_set.get_dataset_size() == 10
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for epoch in range(5):
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num_iter = 0
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for data in data_set:
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logger.info("data is {}".format(data))
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num_iter += 1
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assert num_iter == 10
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data_set.reset()
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def test_cv_minddataset_reader_basic_tutorial_5_epoch_with_batch(add_and_remove_cv_file):
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"""tutorial for cv minderdataset."""
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columns_list = ["data", "file_name", "label"]
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num_readers = 4
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data_set = ds.MindDataset(CV_FILE_NAME + "0", columns_list, num_readers)
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resize_height = 32
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resize_width = 32
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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), ds.transforms.vision.Inter.LINEAR)
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data_set = data_set.map(input_columns=["data"], operations=decode_op, num_parallel_workers=4)
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data_set = data_set.map(input_columns=["data"], operations=resize_op, num_parallel_workers=4)
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data_set = data_set.batch(2)
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assert data_set.get_dataset_size() == 5
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for epoch in range(5):
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num_iter = 0
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for data in data_set:
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logger.info("data is {}".format(data))
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num_iter += 1
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assert num_iter == 5
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data_set.reset()
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def test_cv_minddataset_reader_no_columns(add_and_remove_cv_file):
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"""tutorial for cv minderdataset."""
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data_set = ds.MindDataset(CV_FILE_NAME + "0")
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assert data_set.get_dataset_size() == 10
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num_iter = 0
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for item in data_set.create_dict_iterator():
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logger.info("-------------- cv reader basic: {} ------------------------".format(num_iter))
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logger.info("-------------- len(item[data]): {} ------------------------".format(len(item["data"])))
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logger.info("-------------- item[data]: {} -----------------------------".format(item["data"]))
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logger.info("-------------- item[file_name]: {} ------------------------".format(item["file_name"]))
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logger.info("-------------- item[label]: {} ----------------------------".format(item["label"]))
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num_iter += 1
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assert num_iter == 10
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def test_cv_minddataset_reader_repeat_tutorial(add_and_remove_cv_file):
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"""tutorial for cv minderdataset."""
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columns_list = ["data", "file_name", "label"]
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num_readers = 4
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data_set = ds.MindDataset(CV_FILE_NAME + "0", columns_list, num_readers)
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repeat_num = 2
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data_set = data_set.repeat(repeat_num)
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num_iter = 0
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for item in data_set.create_dict_iterator():
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logger.info("-------------- repeat two test {} ------------------------".format(num_iter))
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logger.info("-------------- len(item[data]): {} -----------------------".format(len(item["data"])))
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logger.info("-------------- item[data]: {} ----------------------------".format(item["data"]))
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logger.info("-------------- item[file_name]: {} -----------------------".format(item["file_name"]))
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logger.info("-------------- item[label]: {} ---------------------------".format(item["label"]))
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num_iter += 1
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assert num_iter == 20
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def get_data(dir_name):
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"""
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usage: get data from imagenet dataset
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params:
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dir_name: directory containing folder images and annotation information
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"""
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if not os.path.isdir(dir_name):
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raise IOError("Directory {} not exists".format(dir_name))
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img_dir = os.path.join(dir_name, "images")
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ann_file = os.path.join(dir_name, "annotation.txt")
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with open(ann_file, "r") as file_reader:
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lines = file_reader.readlines()
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data_list = []
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for i, line in enumerate(lines):
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try:
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filename, label = line.split(",")
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label = label.strip("\n")
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with open(os.path.join(img_dir, filename), "rb") as file_reader:
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img = file_reader.read()
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data_json = {"id": i,
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"file_name": filename,
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"data": img,
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"label": int(label)}
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data_list.append(data_json)
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except FileNotFoundError:
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continue
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return data_list
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def get_multi_bytes_data(file_name, bytes_num=3):
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"""
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Return raw data of multi-bytes dataset.
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Args:
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file_name (str): String of multi-bytes dataset's path.
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bytes_num (int): Number of bytes fields.
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Returns:
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List
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"""
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if not os.path.exists(file_name):
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raise IOError("map file {} not exists".format(file_name))
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dir_name = os.path.dirname(file_name)
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with open(file_name, "r") as file_reader:
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lines = file_reader.readlines()
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data_list = []
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row_num = 0
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for line in lines:
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try:
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img10_path = line.strip('\n').split(" ")
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img5 = []
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for path in img10_path[:bytes_num]:
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with open(os.path.join(dir_name, path), "rb") as file_reader:
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img5 += [file_reader.read()]
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data_json = {"image_{}".format(i): img5[i]
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for i in range(len(img5))}
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data_json.update({"id": row_num})
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row_num += 1
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data_list.append(data_json)
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except FileNotFoundError:
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continue
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return data_list
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def get_mkv_data(dir_name):
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"""
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Return raw data of Vehicle_and_Person dataset.
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Args:
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dir_name (str): String of Vehicle_and_Person dataset's path.
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Returns:
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List
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"""
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if not os.path.isdir(dir_name):
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raise IOError("Directory {} not exists".format(dir_name))
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img_dir = os.path.join(dir_name, "Image")
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label_dir = os.path.join(dir_name, "prelabel")
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data_list = []
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file_list = os.listdir(label_dir)
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index = 1
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for item in file_list:
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if os.path.splitext(item)[1] == '.json':
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file_path = os.path.join(label_dir, item)
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image_name = ''.join([os.path.splitext(item)[0], ".jpg"])
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image_path = os.path.join(img_dir, image_name)
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|
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with open(file_path, "r") as load_f:
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load_dict = json.load(load_f)
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if os.path.exists(image_path):
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with open(image_path, "rb") as file_reader:
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img = file_reader.read()
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data_json = {"file_name": image_name,
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"prelabel": str(load_dict),
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"data": img,
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"id": index}
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data_list.append(data_json)
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index += 1
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logger.info('{} images are missing'.format(len(file_list)-len(data_list)))
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return data_list
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|
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def get_nlp_data(dir_name, vocab_file, num):
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"""
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|
Return raw data of aclImdb dataset.
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|
|
|
Args:
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|
dir_name (str): String of aclImdb dataset's path.
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|
vocab_file (str): String of dictionary's path.
|
|
num (int): Number of sample.
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|
|
|
Returns:
|
|
List
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|
"""
|
|
if not os.path.isdir(dir_name):
|
|
raise IOError("Directory {} not exists".format(dir_name))
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|
for root, dirs, files in os.walk(dir_name):
|
|
for index, file_name_extension in enumerate(files):
|
|
if index < num:
|
|
file_path = os.path.join(root, file_name_extension)
|
|
file_name, _ = file_name_extension.split('.', 1)
|
|
id_, rating = file_name.split('_', 1)
|
|
with open(file_path, 'r') as f:
|
|
raw_content = f.read()
|
|
|
|
dictionary = load_vocab(vocab_file)
|
|
vectors = [dictionary.get('[CLS]')]
|
|
vectors += [dictionary.get(i) if i in dictionary
|
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else dictionary.get('[UNK]')
|
|
for i in re.findall(r"[\w']+|[{}]"
|
|
.format(string.punctuation),
|
|
raw_content)]
|
|
vectors += [dictionary.get('[SEP]')]
|
|
input_, mask, segment = inputs(vectors)
|
|
input_ids = np.reshape(np.array(input_), [-1])
|
|
input_mask = np.reshape(np.array(mask), [1, -1])
|
|
segment_ids = np.reshape(np.array(segment), [2, -1])
|
|
data = {
|
|
"label": 1,
|
|
"id": id_,
|
|
"rating": float(rating),
|
|
"input_ids": input_ids,
|
|
"input_mask": input_mask,
|
|
"segment_ids": segment_ids
|
|
}
|
|
yield data
|
|
|
|
def convert_to_uni(text):
|
|
if isinstance(text, str):
|
|
return text
|
|
if isinstance(text, bytes):
|
|
return text.decode('utf-8', 'ignore')
|
|
raise Exception("The type %s does not convert!" % type(text))
|
|
|
|
def load_vocab(vocab_file):
|
|
"""load vocabulary to translate statement."""
|
|
vocab = collections.OrderedDict()
|
|
vocab.setdefault('blank', 2)
|
|
index = 0
|
|
with open(vocab_file) as reader:
|
|
while True:
|
|
tmp = reader.readline()
|
|
if not tmp:
|
|
break
|
|
token = convert_to_uni(tmp)
|
|
token = token.strip()
|
|
vocab[token] = index
|
|
index += 1
|
|
return vocab
|
|
|
|
def inputs(vectors, maxlen=50):
|
|
length = len(vectors)
|
|
if length > maxlen:
|
|
return vectors[0:maxlen], [1]*maxlen, [0]*maxlen
|
|
input_ = vectors+[0]*(maxlen-length)
|
|
mask = [1]*length + [0]*(maxlen-length)
|
|
segment = [0]*maxlen
|
|
return input_, mask, segment
|