def convert(load_file, dest_file):
from tensorflow.python.framework import meta_graph
features, labels = dual_net.get_inference_input()
dual_net.model_fn(features, labels, tf.estimator.ModeKeys.PREDICT,
dual_net.get_default_hyperparams())
sess = tf.Session()
# retrieve the global step as a python value
ckpt = tf.train.load_checkpoint(load_file)
global_step_value = ckpt.get_tensor('global_step')
# restore all saved weights, except global_step
meta_graph_def = meta_graph.read_meta_graph_file(load_file + '.meta')
stored_var_names = set([
n.name for n in meta_graph_def.graph_def.node if n.op == 'VariableV2'
])
stored_var_names.remove('global_step')
var_list = [
v for v in tf.global_variables() if v.op.name in stored_var_names
]
tf.train.Saver(var_list=var_list).restore(sess, load_file)
# manually set the global step
global_step_tensor = tf.train.get_or_create_global_step()
assign_op = tf.assign(global_step_tensor, global_step_value)
sess.run(assign_op)
# export a new savedmodel that has the right global step type
tf.train.Saver().save(sess, dest_file)
sess.close()
tf.reset_default_graph()
def backfill():
models = [m[1] for m in fsdb.get_models()]
import dual_net
import tensorflow as tf
from tqdm import tqdm
features, labels = dual_net.get_inference_input()
dual_net.model_fn(features, labels, tf.estimator.ModeKeys.PREDICT,
dual_net.get_default_hyperparams())
for model_name in tqdm(models):
if model_name.endswith('-upgrade'):
continue
try:
load_file = os.path.join(fsdb.models_dir(), model_name)
dest_file = os.path.join(fsdb.models_dir(), model_name)
main.convert(load_file, dest_file)
except:
print('failed on', model_name)
continue
import tensorflow as tf
import dual_net
save_file = '../epoch_12_step_6924.data-00000-of-00001'
dest_file = '../epoch_12_step_6924.data-00000-of-00001-upgrade'
features, labels = dual_net.get_inference_input()
dual_net.model_fn(features, labels, tf.estimator.ModeKeys.PREDICT,
dual_net.get_default_hyperparams())
sess = tf.Session()
# retrieve the global step as a python value
ckpt = tf.train.load_checkpoint(save_file)
global_step_value = ckpt.get_tensor('global_step')
# restore all saved weights, except global_step
from tensorflow.python.framework import meta_graph
meta_graph_def = meta_graph.read_meta_graph_file(save_file '.meta')
stored_var_names = set([n.name
for n in meta_graph_def.graph_def.node
if n.op == 'VariableV2'])
print(stored_var_names)
stored_var_names.remove('global_step')
var_list = [v for v in tf.global_variables()
if v.op.name in stored_var_names]
tf.train.Saver(var_list=var_list).restore(sess, save_file)
# manually set the global step
global_step_tensor = tf.train.get_or_create_global_step()
assign_op = tf.assign(global_step_tensor, global_step_value)
sess.run(assign_op)
