manipulate_tf_pipeline_config.py

manipulate_tf_pipeline_config.py

# -*- coding: utf-8 -*-
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# %%
# %run ../ml-workspace-common/ml-workspace-common.py
cuda_select_best_memfree()

# %%
import sys
sys.path.insert(0,expanduser('~/Programs/VDLServer/VDL_InferenceProxy3/engines/tf/lib'))

# %%
from google.protobuf import text_format
from object_detection.protos import pipeline_pb2

## 어떻게 pipeline.config 를 읽어 오는가
config_file = 'cliprojects/107/Default_Net/data/pipeline.config'
config = pipeline_pb2.TrainEvalPipelineConfig()
with open(config_file,encoding='utf-8') as f:
    text_format.Merge(str(f.read()),config)

# %%
## 초기 내용 확인
config.train_config

# %%
## 단순값 설정은 그냥 assignment 로 충분
config.train_config.num_steps = 100

# %%
config.train_config

# %%
## optimizer message를 먼저 정하고, learning_rate 및 manual_step_learning_rate 변경 방법
opt_cfg = config.train_config.optimizer.momentum_optimizer  
## 기존에 initial_learning_rate 및 schedule 값이 있었을 수 있으니 Clear() 필요
opt_cfg.learning_rate.manual_step_learning_rate.Clear()
## 이제 opt_cfg 에 뭔가 하면 기존 optimizre 선택은 날아감. 처음부터 momentum_optimizer 였으면 상관없음.
opt_cfg.learning_rate.manual_step_learning_rate.initial_learning_rate = 0.001
## 값 1개 추가
opt_cfg.learning_rate.manual_step_learning_rate.schedule.add(step=500,learning_rate=1e-6)

# %%
opt_cfg

# %%
## 다른 방법으로 repeat message 추가
new_schedule = opt_cfg.learning_rate.manual_step_learning_rate.schedule.add()
new_schedule.step = 1000
new_schedule.learning_rate = 1e-5
new_schedule = opt_cfg.learning_rate.manual_step_learning_rate.schedule.add()
new_schedule.step = 5000
new_schedule.learning_rate = 1e-6

# %%
opt_cfg

# %%
## data_augmentation_options 의 repeat message 조작 방법
config.train_config.ClearField("data_augmentation_options")
## 새로운 data_augmentation_options 추가
config.train_config.data_augmentation_options.add().random_horizontal_flip.Clear()

# %%
config.train_config

# %%
config.train_config.data_augmentation_options.add().random_jitter_boxes.Clear()

# %%
config.train_config

# %%