protobuf python 使用proto3 为什么生成_pb2.py

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不用担心,因为proto2和proto3生成的代码都是_pb2.py这个后缀。这个后缀只是为了区分开proto1(只在Google内部使用)。

生成python代码的命令行:

protoc --proto_path=src --python_out=build/gen src/foo.proto src/bar/baz.proto

参见谷歌官方文档
Currently both proto2 and proto3 use _pb2.py for their generated files.

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File "c:/Users/lianlian/Downloads/yolov5-6.0/yolov5-6.0/train.py", line 48, in <module> from utils.loggers.wandb.wandb_utils import check_wandb_resume File "c:\Users\lianlian\Downloads\yolov5-6.0\yolov5-6.0\utils\loggers\__init__.py", line 10, in <module> from torch.utils.tensorboard import SummaryWriter File "D:\anaconda3\envs\yolo_v5\lib\site-packages\torch\utils\tensorboard\__init__.py", line 8, in <module> from .writer import FileWriter, SummaryWriter # noqa F401 File "D:\anaconda3\envs\yolo_v5\lib\site-packages\torch\utils\tensorboard\writer.py", line 9, in <module> from tensorboard.compat.proto.event_pb2 import SessionLog File "D:\anaconda3\envs\yolo_v5\lib\site-packages\tensorboard\compat\proto\event_pb2.py", line 17, in <module> from tensorboard.compat.proto import summary_pb2 as tensorboard_dot_compat_dot_proto_dot_summary__pb2 File "D:\anaconda3\envs\yolo_v5\lib\site-packages\tensorboard\compat\proto\summary_pb2.py", line 17, in <module> from tensorboard.compat.proto import tensor_pb2 as tensorboard_dot_compat_dot_proto_dot_tensor__pb2 File "D:\anaconda3\envs\yolo_v5\lib\site-packages\tensorboard\compat\proto\tensor_pb2.py", line 16, in <module> from tensorboard.compat.proto import resource_handle_pb2 as tensorboard_dot_compat_dot_proto_dot_resource__handle__pb2 File "D:\anaconda3\envs\yolo_v5\lib\site-packages\tensorboard\compat\proto\resource_handle_pb2.py", line 16, in <module> from tensorboard.compat.proto import tensor_shape_pb2 as tensorboard_dot_compat_dot_proto_dot_tensor__shape__pb2 File "D:\anaconda3\envs\yolo_v5\lib\site-packages\tensorboard\compat\proto\tensor_shape_pb2.py", line 36, in <module> _descriptor.FieldDescriptor( File "D:\anaconda3\envs\yolo_v5\lib\site-packages\google\protobuf\descriptor.py", line 621, in __new__ _message.Message._CheckCalledFromGeneratedFile() TypeError: Descriptors cannot be created directly. If this call came from a _pb2.py file, your generated code is out of date and must be regenerated with protoc >= 3.19.0. If you cannot immediately regenerate your protos, some other possible workarounds are: 1. Downgrade the protobuf package to 3.20.x or lower. 2. Set PROTOCOL_BUFFERS_PYTHON_IMPLEMENTATION=python (but this will use pure-Python parsing and will be much slower). More information: https://developers.google.com/protocol-buffers/docs/news/2022-05-06#python-updates这个报错是为什么
03-28
D:\anaconda3\envs\pytorch-tf-gpu-py310\python.exe D:\PycharmProjects\pythonProject2\tb.py Traceback (most recent call last): File "D:\PycharmProjects\pythonProject2\tb.py", line 1, in <module> from torch.utils.tensorboard import SummaryWriter File "D:\anaconda3\envs\pytorch-tf-gpu-py310\lib\site-packages\torch\utils\tensorboard\__init__.py", line 12, in <module> from .writer import FileWriter, SummaryWriter File "D:\anaconda3\envs\pytorch-tf-gpu-py310\lib\site-packages\torch\utils\tensorboard\writer.py", line 13, in <module> from tensorboard.compat.proto import event_pb2 File "D:\anaconda3\envs\pytorch-tf-gpu-py310\lib\site-packages\tensorboard\compat\proto\event_pb2.py", line 17, in <module> from tensorboard.compat.proto import summary_pb2 as tensorboard_dot_compat_dot_proto_dot_summary__pb2 File "D:\anaconda3\envs\pytorch-tf-gpu-py310\lib\site-packages\tensorboard\compat\proto\summary_pb2.py", line 17, in <module> from tensorboard.compat.proto import tensor_pb2 as tensorboard_dot_compat_dot_proto_dot_tensor__pb2 File "D:\anaconda3\envs\pytorch-tf-gpu-py310\lib\site-packages\tensorboard\compat\proto\tensor_pb2.py", line 16, in <module> from tensorboard.compat.proto import resource_handle_pb2 as tensorboard_dot_compat_dot_proto_dot_resource__handle__pb2 File "D:\anaconda3\envs\pytorch-tf-gpu-py310\lib\site-packages\tensorboard\compat\proto\resource_handle_pb2.py", line 16, in <module> from tensorboard.compat.proto import tensor_shape_pb2 as tensorboard_dot_compat_dot_proto_dot_tensor__shape__pb2 File "D:\anaconda3\envs\pytorch-tf-gpu-py310\lib\site-packages\tensorboard\compat\proto\tensor_shape_pb2.py", line 36, in <module> _descriptor.FieldDescriptor( File "D:\anaconda3\envs\pytorch-tf-gpu-py310\lib\site-packages\google\protobuf\descriptor.py", line 621, in __new__ _message.Message._CheckCalledFromGeneratedFile() TypeError: Descriptors cannot be created directly. If this call came from a _pb2.py file, your generated code is out of date and must be regenerated with protoc >= 3.19.0. If you cannot immediately regenerate your protos, some other possible workarounds are: 1. Downgrade the protobuf package to 3.20.x or lower. 2. Set PROTOCOL_BUFFERS_PYTHON_IMPLEMENTATION=python (but this will use pure-Python parsing and will be much slower). More information: https://developers.google.com/protocol-buffers/docs/news/2022-05-06#python-updates 进程已结束,退出代码为 1怎么回事
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08-06
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