- 我正在通用集成学习范例中训练多个模型, 目前我正在使用一些探测器,每次训练时我 必须编辑每个检测器的配置文件,这显然会导致 困惑,有几次我开始使用错误的配置进行训练 文件。
- 作为解决方案,我正在尝试为 Google 对象检测 API 构建一个编辑器 配置文件。配置文件适用于 Google Protocol Buffer .
- 链接到我使用的文件:pipeline.proto , object_detection/protos ,例如.config file
我尝试过以下代码:
from object_detection.protos import input_reader_pb2
with open('/models/research/object_detection/samples/configs/ssd_resnet50_v1_fpn_shared_box_predictor_640x640_coco14_sync.config', 'rb') as f:
config = f.read()
read = input_reader_pb2.InputReader().ParseFromString(config)
我收到以下错误:
exec(code_obj, self.user_global_ns, self.user_ns)
File "<ipython-input-19-8043e6bb108f>", line 1, in <module>
input_reader_pb2.InputReader().ParseFromString(txt)
google.protobuf.message.DecodeError: Error parsing message
我在这里缺少什么?解析和编辑配置文件的适当方法是什么?
谢谢
霍德
最佳答案
使用以下代码我能够解析配置文件。
import tensorflow as tf
from google.protobuf import text_format
from object_detection.protos import pipeline_pb2
def get_configs_from_pipeline_file(pipeline_config_path, config_override=None):
'''
read .config and convert it to proto_buffer_object
'''
pipeline_config = pipeline_pb2.TrainEvalPipelineConfig()
with tf.gfile.GFile(pipeline_config_path, "r") as f:
proto_str = f.read()
text_format.Merge(proto_str, pipeline_config)
if config_override:
text_format.Merge(config_override, pipeline_config)
#print(pipeline_config)
return pipeline_config
def create_configs_from_pipeline_proto(pipeline_config):
'''
Returns the configurations as dictionary
'''
configs = {}
configs["model"] = pipeline_config.model
configs["train_config"] = pipeline_config.train_config
configs["train_input_config"] = pipeline_config.train_input_reader
configs["eval_config"] = pipeline_config.eval_config
configs["eval_input_configs"] = pipeline_config.eval_input_reader
# Keeps eval_input_config only for backwards compatibility. All clients should
# read eval_input_configs instead.
if configs["eval_input_configs"]:
configs["eval_input_config"] = configs["eval_input_configs"][0]
if pipeline_config.HasField("graph_rewriter"):
configs["graph_rewriter_config"] = pipeline_config.graph_rewriter
return configs
configs = get_configs_from_pipeline_file('faster_rcnn_resnet101_pets.config')
config_as_dict = create_configs_from_pipeline_proto(configs)
引用自here
关于python - 如何使用 Google Protobuf 解析、编辑和生成 object_detection/pipeline.config 文件,我们在Stack Overflow上找到一个类似的问题: https://stackoverflow.com/questions/54615940/