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model_base.py
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model_base.py
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import abc
import tensorflow as tf
from box import Box
import os
class Model:
__metaclass__ = abc.ABCMeta
def __init__(self, params):
self.params = params
self.global_step = tf.Variable(0, tf.int32)
self.sub_models = dict()
self.obj_to_save = dict()
self.model = self.model_def()
self.initialize()
if len(self.obj_to_save.keys()) == 0:
print("please define obj_to_save")
raise
self.obj_to_save['global_step'] = self.global_step
self.ckpt = tf.train.Checkpoint(**self.obj_to_save)
self.ckpt_manager = tf.train.CheckpointManager(
self.ckpt, directory=self.params['ckpt_path'], max_to_keep=50
)
latest_checkpoint = tf.train.latest_checkpoint(self.params['ckpt_path'])
if latest_checkpoint is not None:
status = self.ckpt.restore(latest_checkpoint)
else:
print("No checkpoint found")
@abc.abstractmethod
def model_def(self):
return tf.keras.Model()
@abc.abstractmethod
def initialize(self):
pass
@abc.abstractmethod
def saver_config(self):
pass
@abc.abstractmethod
def train_and_validate(self):
pass
def validate(self):
pass
def evaluate(self, dataset):
pass
def save(self):
self.ckpt_manager.save(self.global_step)
def restore(self, ckpt_path=None):
if ckpt_path is None:
ckpt_path = self.ckpt_manager.latest_checkpoint
self.ckpt.restore(ckpt_path)