def parse_args(): #参数解析器
parser = argparse.ArgumentParser()
parser.add_argument('-t', '--tensorflow-data-dir', default='pic/')
parser.add_argument('--train-shards', default=2, type=int)
parser.add_argument('--validation-shards', default=2, type=int)
parser.add_argument('--num-threads', default=2, type=int)
parser.add_argument('--dataset-name', default='sitposrec', type=str)
return parser.parse_args()
if __name__ == '__main__':
logging.basicConfig(level=logging.INFO)
args = parse_args()
args.tensorflow_dir = args.tensorflow_data_dir
args.train_directory = os.path.join(args.tensorflow_dir, 'train')
args.validation_directory = os.path.join(args.tensorflow_dir, 'validation')
args.output_directory = args.tensorflow_dir
args.labels_file = os.path.join(args.tensorflow_dir, 'label.txt')
if os.path.exists(args.labels_file) is False:
logging.warning('Can\'t find label.txt. Now create it.')
all_entries = os.listdir(args.train_directory)
dirnames = []
for entry in all_entries:
if os.path.isdir(os.path.join(args.train_directory, entry)):
dirnames.append(entry)
with open(args.labels_file, 'w') as f:
for dirname in dirnames:
f.write(dirname + '\n')
main(args)
from src.tfrecord import main
def parse_args():
parser = argparse.ArgumentParser()
parser.add_argument('-t', '--tensorflow-data-dir', default='pic/')
parser.add_argument('--train-shards', default=2, type=int)
parser.add_argument('--validation-shards', default=2, type=int)
parser.add_argument('--num-threads', default=2, type=int)
parser.add_argument('--dataset-name', default='satellite', type=str)
return parser.parse_args()
if __name__ == '__main__':
logging.basicConfig(level=logging.INFO)
args = parse_args()
args.tensorflow_dir = args.tensorflow_data_dir
args.train_directory = os.path.join(args.tensorflow_dir, 'train')
args.validation_directory = os.path.join(args.tensorflow_dir, 'validation')
args.output_directory = args.tensorflow_dir
args.labels_file = os.path.join(args.tensorflow_dir, 'label.txt')
if os.path.exists(args.labels_file) is False:
logging.warning('Can\'t find label.txt. Now create it.')
all_entries = os.listdir(args.train_directory)
dirnames = []
for entry in all_entries:
if os.path.isdir(os.path.join(args.train_directory, entry)):
dirnames.append(entry)
with open(args.labels_file, 'w') as f:
for dirname in dirnames:
f.write(dirname + '\n')
main(args)
parser.add_argument('--num-threads', default=2, type=int)
parser.add_argument('--dataset-name', default='satellite', type=str)
return parser.parser_args() #返回定义的各个参数的NameSpace
if __name__ == '__main__': #如果现在执行的.py文件为主程序，就执行下列代码
logging.basicConfig(level=logging.INFO) #配置日志的输出信息，格式等
args = parse_args() #将函数parse_args()返回的namespace赋值给args
args.tensorflow_dir = args.tensorflow_data_dir #将tensorflow_data_dir的参数值赋给变量args.tensorflow_dir(args.tensorflow_dir相当于又给args增添了一个参数，其参数值与tensorflow_data_dir参数的相同）
args.train_directory = os.path.join(
args.tensorflow_dir, 'train') #参数train_dirctory的值为trainding data的存储路径
args.validation_directory = os.path.join(
args.tensorflow_dir,
'validation') #参数validation_directory的值为validation data的存储路径
args.output_directory = args.tensorflow_dir #将新生成的文件存储于pic/中
args.labels_file = os.path.join(
args.tensorflow_dir, 'label.txt') #参数labels_file的值为label file的存储路径
if os.path.exists(args.labels_file) is False: #如果label file路径不存在
logging.warning('Can\'t find label.txt.Now creat it.')
all_entries = os.listdir(
args.train_directory) #将train文件夹中所有文件名称以列表的形式展开
dirnames = []
for entry in all_entries:
if os.path.isdir(os.path.join(args.train_directory,
entry)): #如果entry为文件夹
dirnames.append(entry) #将文件夹名称添加到dirnames
with open(args.labels_file, 'w') as f:
for dirname in dirnames:
f.write(dirname + '\n') #将各个数据的类别名写入label file
main(args) #作者自己编写的程序main()，在src.tfrecord脚本中