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224 lines
8.6 KiB
224 lines
8.6 KiB
# Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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from __future__ import print_function
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import os
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import collections
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from ..framework import Variable, default_main_program, in_dygraph_mode, dygraph_only, Parameter, ParamBase, _varbase_creator, _dygraph_tracer
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import pickle
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import six
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from . import learning_rate_scheduler
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import warnings
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from .. import core
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from paddle.fluid.dygraph.io import VARIABLE_FILENAME, EXTRA_VAR_INFO_FILENAME, _load_persistable_vars
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__all__ = [
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'save_dygraph',
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'load_dygraph',
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]
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@dygraph_only
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def save_dygraph(state_dict, model_path):
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'''
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:api_attr: imperative
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Save Layer's state_dict to disk. This will generate a file with suffix ".pdparams"
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The state_dict is get from Layers.state_dict function
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Args:
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state_dict(dict) : The state dict to be saved.
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model_path(str) : the file prefix to save the state_dict. The format is "dirname/file_prefix". If file_prefix is empty str. A exception will be raised
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Returns:
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None
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Examples:
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.. code-block:: python
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import paddle.fluid as fluid
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with fluid.dygraph.guard():
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emb = fluid.dygraph.Embedding([10, 10])
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state_dict = emb.state_dict()
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fluid.save_dygraph( state_dict, "paddle_dy")
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adam = fluid.optimizer.Adam( learning_rate = fluid.layers.noam_decay( 100, 10000),
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parameter_list = emb.parameters() )
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state_dict = adam.state_dict()
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fluid.save_dygraph( state_dict, "paddle_dy")
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'''
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base_name = os.path.basename(model_path)
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assert base_name != "", "The input model_path MUST be format of dirname/filename [dirname\\filename in Windows system], but received filename is empty string."
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suffix = ".pdparams"
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assert len(state_dict) > 0, "state_dict is empty, no need to save"
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param_num = 0
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for k, v in state_dict.items():
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if isinstance(v, ParamBase):
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param_num += 1
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if param_num == 0:
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suffix = ".pdopt"
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model_dict = {}
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name_table = {}
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for k, v in state_dict.items():
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if isinstance(v, (Variable, core.VarBase)):
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model_dict[k] = v.numpy()
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name_table[k] = v.name
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else:
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model_dict[k] = v
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model_dict["StructuredToParameterName@@"] = name_table
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file_name = model_path + suffix
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dir_name = os.path.dirname(file_name)
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if dir_name and not os.path.exists(dir_name):
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os.makedirs(dir_name)
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with open(file_name, 'wb') as f:
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pickle.dump(model_dict, f, protocol=2)
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# TODO(qingqing01): remove dygraph_only to support loading static model.
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# maybe need to unify the loading interface after 2.0 API is ready.
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#@dygraph_only
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def load_dygraph(model_path, keep_name_table=False):
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'''
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:api_attr: imperative
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Load parameter state_dict from disk.
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Args:
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model_path(str) : The file prefix store the state_dict. (The path should Not contain suffix '.pdparams')
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keep_name_table(bool, optional) : Whether keep structed name to parameter name conversion table in output dict.
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Default : False
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Returns:
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state_dict(dict) : the dict store the state_dict
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Examples:
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.. code-block:: python
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import paddle.fluid as fluid
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with fluid.dygraph.guard():
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emb = fluid.dygraph.Embedding([10, 10])
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state_dict = emb.state_dict()
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fluid.save_dygraph( state_dict, "paddle_dy")
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adam = fluid.optimizer.Adam( learning_rate = fluid.layers.noam_decay( 100, 10000),
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parameter_list = emb.parameters() )
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state_dict = adam.state_dict()
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fluid.save_dygraph( state_dict, "paddle_dy")
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para_state_dict, opti_state_dict = fluid.load_dygraph( "paddle_dy")
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'''
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model_prefix = model_path
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if model_prefix.endswith(".pdparams"):
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model_prefix = model_prefix[:-9]
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elif model_prefix.endswith(".pdopt"):
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model_prefix = model_prefix[:-6]
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para_dict = None
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opti_dict = None
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params_file_path = model_prefix + ".pdparams"
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opti_file_path = model_prefix + ".pdopt"
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if not os.path.exists(params_file_path) and not os.path.exists(
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opti_file_path):
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# Load state dict by `jit.save` save format
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# TODO(chenweihang): [Why not support `io.save_infernece_model` save format here]
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# The model saved by `save_inference_model` does not completely correspond to
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# the information required by the `state_dict` under the dygraph.
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# Although we reluctantly restore the `state_dict` in some scenarios,
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# this may not be complete and there are some limitations, so this function
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# will be considered later. The limitations include:
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# 1. `save_inference_model` not save structured name, we need to remind
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# the user to configure the `use_structured_name` argument when `set_dict`,
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# but this argument is currently not public
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# 2. if `save_inference_model` save all persistable variables in a single file,
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# user need to give the variable name list to load `state_dict`
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# 1. check model path
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if not os.path.isdir(model_prefix):
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raise ValueError("Model saved directory '%s' is not exists." %
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model_prefix)
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# 2. load `__variables.info__`
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var_info_path = os.path.join(model_prefix, EXTRA_VAR_INFO_FILENAME)
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if not os.path.exists(var_info_path):
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raise RuntimeError(
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"No target can be loaded. Now only supports loading `state_dict` from "
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"the result saved by `imperative.save` and `imperative.jit.save`."
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)
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with open(var_info_path, 'rb') as f:
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extra_var_info = pickle.load(f)
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# 3. load `__variables__`
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# TODO(chenweihang): now only supports loading from default save format:
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# - all persistable vars saved in one file named `__variables__`
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# for other case, we may need to modify the arguments of this API
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var_file_path = os.path.join(model_prefix, VARIABLE_FILENAME)
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if not os.path.exists(var_file_path):
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raise RuntimeError(
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"The parameter file to be loaded was not found. "
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"Now only supports loading from the default save format, "
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"and does not support custom params_filename and "
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"save parameters separately.")
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# 4. load all persistable vars
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load_var_list = []
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for name in sorted(extra_var_info):
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var = _varbase_creator(name=name, persistable=True)
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load_var_list.append(var)
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_dygraph_tracer().trace_op(
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type='load_combine',
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inputs={},
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outputs={'Out': load_var_list},
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attrs={'file_path': var_file_path})
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# 5. construct state_dict
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para_dict = dict()
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for var in load_var_list:
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structured_name = extra_var_info[var.name].get('structured_name',
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None)
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if structured_name is None:
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raise RuntimeError(
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"Cannot find saved variable (%s)'s structured name in saved model.",
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var.name)
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para_dict[structured_name] = var.numpy()
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# NOTE: `jit.save` doesn't save optimizer state
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else:
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# Load state dict by `save_dygraph` save format
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if os.path.exists(params_file_path):
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with open(params_file_path, 'rb') as f:
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para_dict = pickle.load(f) if six.PY2 else pickle.load(
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f, encoding='latin1')
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if not keep_name_table and "StructuredToParameterName@@" in para_dict:
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del para_dict["StructuredToParameterName@@"]
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if os.path.exists(opti_file_path):
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with open(opti_file_path, 'rb') as f:
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opti_dict = pickle.load(f) if six.PY2 else pickle.load(
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f, encoding='latin1')
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return para_dict, opti_dict
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