213 lines
8.2 KiB
Python
213 lines
8.2 KiB
Python
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# 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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@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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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(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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def test_cv_minddataset_subset_random_sample_basic(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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indices = [1, 2, 3, 5, 7]
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sampler = ds.SubsetRandomSampler(indices)
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data_set = ds.MindDataset(CV_FILE_NAME + "0", columns_list, num_readers,
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sampler=sampler)
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data = get_data(CV_DIR_NAME)
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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(
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"-------------- cv reader basic: {} ------------------------".format(num_iter))
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logger.info(
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"-------------- item[data]: {} -----------------------------".format(item["data"]))
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logger.info(
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"-------------- item[file_name]: {} ------------------------".format(item["file_name"]))
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logger.info(
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"-------------- item[label]: {} ----------------------------".format(item["label"]))
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num_iter += 1
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assert num_iter == 5
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def test_cv_minddataset_subset_random_sample_replica(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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indices = [1, 2, 2, 5, 7, 9]
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sampler = ds.SubsetRandomSampler(indices)
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data_set = ds.MindDataset(CV_FILE_NAME + "0", columns_list, num_readers,
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sampler=sampler)
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data = get_data(CV_DIR_NAME)
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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(
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"-------------- cv reader basic: {} ------------------------".format(num_iter))
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logger.info(
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"-------------- item[data]: {} -----------------------------".format(item["data"]))
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logger.info(
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"-------------- item[file_name]: {} ------------------------".format(item["file_name"]))
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logger.info(
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"-------------- item[label]: {} ----------------------------".format(item["label"]))
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num_iter += 1
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assert num_iter == 6
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def test_cv_minddataset_subset_random_sample_empty(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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indices = []
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sampler = ds.SubsetRandomSampler(indices)
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data_set = ds.MindDataset(CV_FILE_NAME + "0", columns_list, num_readers,
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sampler=sampler)
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data = get_data(CV_DIR_NAME)
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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(
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"-------------- cv reader basic: {} ------------------------".format(num_iter))
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logger.info(
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"-------------- item[data]: {} -----------------------------".format(item["data"]))
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logger.info(
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"-------------- item[file_name]: {} ------------------------".format(item["file_name"]))
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logger.info(
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"-------------- item[label]: {} ----------------------------".format(item["label"]))
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num_iter += 1
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assert num_iter == 0
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def test_cv_minddataset_subset_random_sample_out_range(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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indices = [1, 2, 4, 11, 13]
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sampler = ds.SubsetRandomSampler(indices)
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data_set = ds.MindDataset(CV_FILE_NAME + "0", columns_list, num_readers,
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sampler=sampler)
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data = get_data(CV_DIR_NAME)
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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(
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"-------------- cv reader basic: {} ------------------------".format(num_iter))
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logger.info(
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"-------------- item[data]: {} -----------------------------".format(item["data"]))
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logger.info(
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"-------------- item[file_name]: {} ------------------------".format(item["file_name"]))
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logger.info(
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"-------------- item[label]: {} ----------------------------".format(item["label"]))
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num_iter += 1
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assert num_iter == 5
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def test_cv_minddataset_subset_random_sample_negative(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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indices = [1, 2, 4, -1, -2]
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sampler = ds.SubsetRandomSampler(indices)
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data_set = ds.MindDataset(CV_FILE_NAME + "0", columns_list, num_readers,
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sampler=sampler)
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data = get_data(CV_DIR_NAME)
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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(
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"-------------- cv reader basic: {} ------------------------".format(num_iter))
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logger.info(
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"-------------- item[data]: {} -----------------------------".format(item["data"]))
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logger.info(
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"-------------- item[file_name]: {} ------------------------".format(item["file_name"]))
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logger.info(
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"-------------- item[label]: {} ----------------------------".format(item["label"]))
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num_iter += 1
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assert num_iter == 5
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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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