def __init__(self,
emb=None,
fixed_emb=None,
name=None,
fixed_name=None,
model_dir=None,
model_name=None,
sess=None):
self._sess = sess or tf.InteractiveSession()
if os.path.isdir(emb):
model_dir = emb
if model_dir is None:
if isinstance(emb, str):
emb = np.load(emb)
emb = melt.load_constant(emb, name=name)
if isinstance(fixed_emb, str):
#fixed_emb is corpus embeddings, all sumed and normed already
fixed_emb = melt.load(fixed_emb, name=fixed_emb)
else:
model_path = melt.get_model_path(model_dir, model_name)
emb = tf.Variable(0., name=name, validate_shape=False)
#emb = tf.Variable(0., name=name)
#like word2vec the name is 'w_in'
embedding_saver = tf.train.Saver({name: emb})
embedding_saver.restore(self._sess, model_path)
#assume 0 index not used, 0 for PAD
mask = zero_first_row(emb)
emb = tf.multiply(emb, mask)
self._emb = emb
self._fixed_emb = fixed_emb
self._normed_emb = None
def get_imagenet_from_checkpoint(checkpoint_path):
"""
net = get_net_from_checkpoint(checkpoint)
net.func_name # like inception_v4
net.default_image_size # like 299
"""
checkpoint = melt.get_model_path(checkpoint_path)
if not checkpoint or \
(not os.path.exists(checkpoint) \
and not os.path.exists(checkpoint + '.index')):
return None
from tensorflow.python import pywrap_tensorflow
reader = pywrap_tensorflow.NewCheckpointReader(checkpoint)
var_to_shape_map = reader.get_variable_to_shape_map()
name = None
for key in var_to_shape_map.keys():
name = key.split('/')[0]
gnu_name = gezi.to_gnu_name(name)
if gnu_name in nets_factory.networks_map:
break
if name is None:
return None
else:
nets_factory.networks_map[gnu_name].name = name
return nets_factory.networks_map[gnu_name]
def load(self, model_dir, var_list=None, model_name=None): """ only load varaibels from checkpoint file, you need to create the graph before calling load """ self.model_path = melt.get_model_path(model_dir, model_name) saver = melt.restore_from_path(self.sess, self.model_path, var_list) return self.sess
def restore(self, model_dir, model_name=None): """ do not need to create graph restore graph from meta file then restore values from checkpoint file """ self.model_path = model_path = melt.get_model_path(model_dir, model_name) meta_filename = '%s.meta'%model_path saver = tf.train.import_meta_graph(meta_filename) self.restore_from_graph() saver.restore(self.sess, model_path) return self.sess
def load(self, model_dir, var_list=None, model_name=None, sess = None):
"""
only load varaibels from checkpoint file, you need to
create the graph before calling load
"""
if sess is not None:
self.sess = sess
self.model_path = melt.get_model_path(model_dir, model_name)
timer = gezi.Timer('load model ok %s' % self.model_path)
saver = melt.restore_from_path(self.sess, self.model_path, var_list)
timer.print()
return self.sess
def restore(self, model_dir, model_name=None, sess=None):
"""
do not need to create graph
restore graph from meta file then restore values from checkpoint file
"""
if sess is not None:
self.sess = sess
self.model_path = model_path = melt.get_model_path(model_dir, model_name)
timer = gezi.Timer('restore meta grpah and model ok %s' % model_path)
meta_filename = '%s.meta'%model_path
saver = tf.train.import_meta_graph(meta_filename)
self.restore_from_graph()
saver.restore(self.sess, model_path)
#---TODO not work remove can run but hang FIXME add predictor + exact_predictor during train will face
#@gauravsindhwani , can you still run the code successfully after you remove these two collections since they are actually part of the graph.
#I try your way but find the program is stuck after restoring."
#https://github.com/tensorflow/tensorflow/issues/9747
#tf.get_default_graph().clear_collection("queue_runners")
#tf.get_default_graph().clear_collection("local_variables")
#--for num_epochs not 0
#self.sess.run(tf.local_variables_initializer())
timer.print()
return self.sess
# ==============================================================================
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import sys, os
import melt, gezi
assert melt.get_num_gpus() == 1
try:
model_dir = sys.argv[1]
except Exception:
model_dir = './'
model_path = melt.get_model_path(model_dir)
input_file = sys.argv[2]
result_file = model_path + '.full-evaluate-inference.txt'
batch_size = 50
if len(sys.argv) > 3:
batch_size = int(sys.argv[3])
feature_name = 'attention'
if len(sys.argv) > 4:
feature_name = sys.argv[4]
if not gezi.non_empty(result_file) or len(
open(result_file).readlines()) != 30000:
#!/usr/bin/env python
# ==============================================================================
# \file show-var-of-model.py
# \author chenghuige
# \date 2017-09-06 07:52:34.258312
# \Description
# ==============================================================================
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import sys, os
import melt
from tensorflow.python import pywrap_tensorflow
model_dir = sys.argv[1]
var_name = sys.argv[2]
checkpoint_path = melt.get_model_path(model_dir)
reader = pywrap_tensorflow.NewCheckpointReader(checkpoint_path)
var_to_shape_map = reader.get_variable_to_shape_map()
for key in var_to_shape_map:
if var_name in key:
print("tensor_name: ", key)
print(reader.get_tensor(key))