如何冻结设备特定的已保存模型？
How to freeze a device specific saved model?
我需要冻结保存的模型以提供服务，但一些保存的模型是特定于设备的，如何解决这个问题？
with tf.Session(config=tf.ConfigProto(allow_soft_placement=True)) as sess:
sess.run(tf.tables_initializer())
tf.saved_model.loader.load(sess, [tag_constants.SERVING], saved_model_dir)
inference_graph_def=tf.get_default_graph().as_graph_def()
for node in inference_graph_def.node:
node.device = ''
frozen_graph_path = os.path.join(frozen_dir, 'frozen_inference_graph.pb')
output_keys = ['ToInt64', 'ToInt32', 'while/Exit_5']
output_node_names = ','.join(["%s/%s" % ('NmtModel', output_key) for output_key in output_keys])
_ = freeze_graph.freeze_graph(
input_graph=inference_graph_def,
input_saver=None,
input_binary=True,
input_saved_model_dir=saved_model_dir,
input_checkpoint=None,
output_node_names=output_node_names,
restore_op_name=None,
filename_tensor_name=None,
output_graph=frozen_graph_path,
clear_devices=True,
initializer_nodes='')
logging.info("export frozen_inference_graph.pb success!!!")
Cannot assign a device for operation NmtModel/transpose/Rank: Operation was explicitly assigned to /device:GPU:4 but available devices are [ /job:localhost/replica:0/task:0/device:CPU:0, /job:localhost/replica:0/task:0/device:GPU:0, /job:localhost/replica:0/task:0/device:GPU:1, /job:localhost/replica:0/task:0/device:XLA_CPU:0, /job:localhost/replica:0/task:0/device:XLA_GPU:0 ]. Make sure the device specification refers to a valid device.
[[node NmtModel/transpose/Rank (defined at /home/yongxian.zyx/alitranx4Corp/mtprime/transformer_sync/saved_model2frozen.py:16) = Rank[T=DT_INT64, _device="/device:GPU:4"](NmtModel/Placeholder)]]
Caused by op u'NmtModel/transpose/Rank', defined at:
File "/home/yongxian.zyx/alitranx4Corp/mtprime/transformer_sync/saved_model2frozen.py", line 55, in <module>
absl_app.run(main)
File "/home/yongxian.zyx/alitranx4Corp/.venv/lib/python2.7/site-packages/absl/app.py", line 300, in run
_run_main(main, args)
File "/home/yongxian.zyx/alitranx4Corp/.venv/lib/python2.7/site-packages/absl/app.py", line 251, in _run_main
sys.exit(main(argv))
File "/home/yongxian.zyx/alitranx4Corp/mtprime/transformer_sync/saved_model2frozen.py", line 50, in main
saved_model2frozen(FLAGS.saved_model_dir, FLAGS.frozen_dir)
File "/home/yongxian.zyx/alitranx4Corp/mtprime/transformer_sync/saved_model2frozen.py", line 16, in saved_model2frozen
tf.saved_model.loader.load(sess, [tag_constants.SERVING], saved_model_dir)
File "/home/yongxian.zyx/alitranx4Corp/.venv/lib/python2.7/site-packages/tensorflow/python/saved_model/loader_impl.py", line 197, in load
return loader.load(sess, tags, import_scope, **saver_kwargs)
File "/home/yongxian.zyx/alitranx4Corp/.venv/lib/python2.7/site-packages/tensorflow/python/saved_model/loader_impl.py", line 350, in load
**saver_kwargs)
File "/home/yongxian.zyx/alitranx4Corp/.venv/lib/python2.7/site-packages/tensorflow/python/saved_model/loader_impl.py", line 278, in load_graph
meta_graph_def, import_scope=import_scope, **saver_kwargs)
File "/home/yongxian.zyx/alitranx4Corp/.venv/lib/python2.7/site-packages/tensorflow/python/training/saver.py", line 1696, in _import_meta_graph_with_return_elements
**kwargs))
File "/home/yongxian.zyx/alitranx4Corp/.venv/lib/python2.7/site-packages/tensorflow/python/framework/meta_graph.py", line 806, in import_scoped_meta_graph_with_return_elements
return_elements=return_elements)
File "/home/yongxian.zyx/alitranx4Corp/.venv/lib/python2.7/site-packages/tensorflow/python/util/deprecation.py", line 488, in new_func
return func(*args, **kwargs)
File "/home/yongxian.zyx/alitranx4Corp/.venv/lib/python2.7/site-packages/tensorflow/python/framework/importer.py", line 442, in import_graph_def
_ProcessNewOps(graph)
File "/home/yongxian.zyx/alitranx4Corp/.venv/lib/python2.7/site-packages/tensorflow/python/framework/importer.py", line 234, in _ProcessNewOps
for new_op in graph._add_new_tf_operations(compute_devices=False): # pylint: disable=protected-access
File "/home/yongxian.zyx/alitranx4Corp/.venv/lib/python2.7/site-packages/tensorflow/python/framework/ops.py", line 3440, in _add_new_tf_operations
for c_op in c_api_util.new_tf_operations(self)
File "/home/yongxian.zyx/alitranx4Corp/.venv/lib/python2.7/site-packages/tensorflow/python/framework/ops.py", line 3299, in _create_op_from_tf_operation
ret = Operation(c_op, self)
File "/home/yongxian.zyx/alitranx4Corp/.venv/lib/python2.7/site-packages/tensorflow/python/framework/ops.py", line 1770, in __init__
self._traceback = tf_stack.extract_stack()
InvalidArgumentError (see above for traceback): Restoring from checkpoint failed. This is most likely due to a mismatch between the current graph and the graph from the checkpoint. Please ensure that you have not altered the graph expected based on the checkpoint. Original error:
Cannot assign a device for operation NmtModel/transpose/Rank: Operation was explicitly assigned to /device:GPU:4 but available devices are [ /job:localhost/replica:0/task:0/device:CPU:0, /job:localhost/replica:0/task:0/device:GPU:0, /job:localhost/replica:0/task:0/device:GPU:1, /job:localhost/replica:0/task:0/device:XLA_CPU:0, /job:localhost/replica:0/task:0/device:XLA_GPU:0 ]. Make sure the device specification refers to a valid device.
[[node NmtModel/transpose/Rank (defined at /home/yongxian.zyx/alitranx4Corp/mtprime/transformer_sync/saved_model2frozen.py:16) = Rank[T=DT_INT64, _device="/device:GPU:4"](NmtModel/Placeholder)]]
似乎有些模型是在多 GPU 上训练的，但是导出到保存的模型时没有明确的设备信息。
我不确定是否有更好的方法来解决这个问题，但一种可能性是简单地编辑保存的型号信息以删除设备规格。下面的代码片段应该可以做到，尽管您应该在使用之前备份您保存的模型以防万一。
from pathlib import Path
import tensorflow as tf
from tensorflow.core.protobuf.saved_model_pb2 import SavedModel
# Read the model file
model_path = saved_model_dir
graph_path = Path(model_path, 'saved_model.pb')
sm = SavedModel()
with graph_path.open('rb') as f:
sm.ParseFromString(f.read())
# Go through graph and functions to remove every device specification
for mg in sm.meta_graphs:
for node in mg.graph_def.node:
node.device = ''
for func in mg.graph_def.library.function:
for node in func.node_def:
node.device = ''
# Write over file
with graph_path.open('wb') as f:
f.write(sm.SerializeToString())
# Now load model as usual
# ...
我需要冻结保存的模型以提供服务，但一些保存的模型是特定于设备的，如何解决这个问题？
with tf.Session(config=tf.ConfigProto(allow_soft_placement=True)) as sess:
sess.run(tf.tables_initializer())
tf.saved_model.loader.load(sess, [tag_constants.SERVING], saved_model_dir)
inference_graph_def=tf.get_default_graph().as_graph_def()
for node in inference_graph_def.node:
node.device = ''
frozen_graph_path = os.path.join(frozen_dir, 'frozen_inference_graph.pb')
output_keys = ['ToInt64', 'ToInt32', 'while/Exit_5']
output_node_names = ','.join(["%s/%s" % ('NmtModel', output_key) for output_key in output_keys])
_ = freeze_graph.freeze_graph(
input_graph=inference_graph_def,
input_saver=None,
input_binary=True,
input_saved_model_dir=saved_model_dir,
input_checkpoint=None,
output_node_names=output_node_names,
restore_op_name=None,
filename_tensor_name=None,
output_graph=frozen_graph_path,
clear_devices=True,
initializer_nodes='')
logging.info("export frozen_inference_graph.pb success!!!")
Cannot assign a device for operation NmtModel/transpose/Rank: Operation was explicitly assigned to /device:GPU:4 but available devices are [ /job:localhost/replica:0/task:0/device:CPU:0, /job:localhost/replica:0/task:0/device:GPU:0, /job:localhost/replica:0/task:0/device:GPU:1, /job:localhost/replica:0/task:0/device:XLA_CPU:0, /job:localhost/replica:0/task:0/device:XLA_GPU:0 ]. Make sure the device specification refers to a valid device.
[[node NmtModel/transpose/Rank (defined at /home/yongxian.zyx/alitranx4Corp/mtprime/transformer_sync/saved_model2frozen.py:16) = Rank[T=DT_INT64, _device="/device:GPU:4"](NmtModel/Placeholder)]]
Caused by op u'NmtModel/transpose/Rank', defined at:
File "/home/yongxian.zyx/alitranx4Corp/mtprime/transformer_sync/saved_model2frozen.py", line 55, in <module>
absl_app.run(main)
File "/home/yongxian.zyx/alitranx4Corp/.venv/lib/python2.7/site-packages/absl/app.py", line 300, in run
_run_main(main, args)
File "/home/yongxian.zyx/alitranx4Corp/.venv/lib/python2.7/site-packages/absl/app.py", line 251, in _run_main
sys.exit(main(argv))
File "/home/yongxian.zyx/alitranx4Corp/mtprime/transformer_sync/saved_model2frozen.py", line 50, in main
saved_model2frozen(FLAGS.saved_model_dir, FLAGS.frozen_dir)
File "/home/yongxian.zyx/alitranx4Corp/mtprime/transformer_sync/saved_model2frozen.py", line 16, in saved_model2frozen
tf.saved_model.loader.load(sess, [tag_constants.SERVING], saved_model_dir)
File "/home/yongxian.zyx/alitranx4Corp/.venv/lib/python2.7/site-packages/tensorflow/python/saved_model/loader_impl.py", line 197, in load
return loader.load(sess, tags, import_scope, **saver_kwargs)
File "/home/yongxian.zyx/alitranx4Corp/.venv/lib/python2.7/site-packages/tensorflow/python/saved_model/loader_impl.py", line 350, in load
**saver_kwargs)
File "/home/yongxian.zyx/alitranx4Corp/.venv/lib/python2.7/site-packages/tensorflow/python/saved_model/loader_impl.py", line 278, in load_graph
meta_graph_def, import_scope=import_scope, **saver_kwargs)
File "/home/yongxian.zyx/alitranx4Corp/.venv/lib/python2.7/site-packages/tensorflow/python/training/saver.py", line 1696, in _import_meta_graph_with_return_elements
**kwargs))
File "/home/yongxian.zyx/alitranx4Corp/.venv/lib/python2.7/site-packages/tensorflow/python/framework/meta_graph.py", line 806, in import_scoped_meta_graph_with_return_elements
return_elements=return_elements)
File "/home/yongxian.zyx/alitranx4Corp/.venv/lib/python2.7/site-packages/tensorflow/python/util/deprecation.py", line 488, in new_func
return func(*args, **kwargs)
File "/home/yongxian.zyx/alitranx4Corp/.venv/lib/python2.7/site-packages/tensorflow/python/framework/importer.py", line 442, in import_graph_def
_ProcessNewOps(graph)
File "/home/yongxian.zyx/alitranx4Corp/.venv/lib/python2.7/site-packages/tensorflow/python/framework/importer.py", line 234, in _ProcessNewOps
for new_op in graph._add_new_tf_operations(compute_devices=False): # pylint: disable=protected-access
File "/home/yongxian.zyx/alitranx4Corp/.venv/lib/python2.7/site-packages/tensorflow/python/framework/ops.py", line 3440, in _add_new_tf_operations
for c_op in c_api_util.new_tf_operations(self)
File "/home/yongxian.zyx/alitranx4Corp/.venv/lib/python2.7/site-packages/tensorflow/python/framework/ops.py", line 3299, in _create_op_from_tf_operation
ret = Operation(c_op, self)
File "/home/yongxian.zyx/alitranx4Corp/.venv/lib/python2.7/site-packages/tensorflow/python/framework/ops.py", line 1770, in __init__
self._traceback = tf_stack.extract_stack()
InvalidArgumentError (see above for traceback): Restoring from checkpoint failed. This is most likely due to a mismatch between the current graph and the graph from the checkpoint. Please ensure that you have not altered the graph expected based on the checkpoint. Original error:
Cannot assign a device for operation NmtModel/transpose/Rank: Operation was explicitly assigned to /device:GPU:4 but available devices are [ /job:localhost/replica:0/task:0/device:CPU:0, /job:localhost/replica:0/task:0/device:GPU:0, /job:localhost/replica:0/task:0/device:GPU:1, /job:localhost/replica:0/task:0/device:XLA_CPU:0, /job:localhost/replica:0/task:0/device:XLA_GPU:0 ]. Make sure the device specification refers to a valid device.
[[node NmtModel/transpose/Rank (defined at /home/yongxian.zyx/alitranx4Corp/mtprime/transformer_sync/saved_model2frozen.py:16) = Rank[T=DT_INT64, _device="/device:GPU:4"](NmtModel/Placeholder)]]
似乎有些模型是在多 GPU 上训练的，但是导出到保存的模型时没有明确的设备信息。
我不确定是否有更好的方法来解决这个问题，但一种可能性是简单地编辑保存的型号信息以删除设备规格。下面的代码片段应该可以做到，尽管您应该在使用之前备份您保存的模型以防万一。
from pathlib import Path
import tensorflow as tf
from tensorflow.core.protobuf.saved_model_pb2 import SavedModel
# Read the model file
model_path = saved_model_dir
graph_path = Path(model_path, 'saved_model.pb')
sm = SavedModel()
with graph_path.open('rb') as f:
sm.ParseFromString(f.read())
# Go through graph and functions to remove every device specification
for mg in sm.meta_graphs:
for node in mg.graph_def.node:
node.device = ''
for func in mg.graph_def.library.function:
for node in func.node_def:
node.device = ''
# Write over file
with graph_path.open('wb') as f:
f.write(sm.SerializeToString())
# Now load model as usual
# ...