# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import paddle
import numpy as np
[文档]def static_params_to_dygraph(model, static_tensor_dict):
"""Simple tool for convert static paramters to dygraph paramters dict.
**NOTE** The model must both support static graph and dygraph mode.
Args:
model (nn.Layer): the model of a neural network.
static_tensor_dict (string): path of which locate the saved paramters in static mode.
Usualy load by `paddle.static.load_program_state`.
Returns:
[tensor dict]: a state dict the same as the dygraph mode.
"""
state_dict = model.state_dict()
# static_tensor_dict = paddle.static.load_program_state(static_params_path)
ret_dict = dict()
for n, p in state_dict.items():
ret_dict[n] = static_tensor_dict[p.name]
return ret_dict
[文档]def dygraph_params_to_static(model, dygraph_tensor_dict, topo=None):
"""Simple tool for convert dygraph paramters to static paramters dict.
**NOTE** The model must both support static graph and dygraph mode.
Args:
model (nn.Layer): the model of a neural network.
dygraph_tensor_dict (string): path of which locate the saved paramters in static mode.
Returns:
[tensor dict]: a state dict the same as the dygraph mode.
"""
state_dict = model.state_dict()
ret_dict = dict()
for name, parm in state_dict.items():
if name not in dygraph_tensor_dict:
print("Miss \t\t", name)
continue
tensor = dygraph_tensor_dict[name]
if parm.is_distributed:
assert topo is not None
for dim, v in enumerate(tensor.shape):
if parm.shape[dim] != v:
break
splited = np.split(
tensor, topo.mp_info.size, axis=dim)[topo.mp_info.rank]
ret_dict[parm.name] = splited
else:
ret_dict[parm.name] = tensor
return ret_dict
[文档]class TimeCostAverage(object):
"""
Simple tool for calcluating time average cost in the process of training and inferencing.
"""
def __init__(self):
self.reset()
[文档] def reset(self):
"""
Reset the recoder state, and reset the `cnt` to zero.
"""
self.cnt = 0
self.total_time = 0
[文档] def record(self, usetime):
"""
Recoding the time cost in current step and accumulating the `cnt`.
"""
self.cnt += 1
self.total_time += usetime
[文档] def get_average(self):
"""
Returning the average time cost after the start of training.
"""
if self.cnt == 0:
return 0
return self.total_time / self.cnt
[文档]def get_env_device():
"""
Return the device name of running enviroment.
"""
if paddle.is_compiled_with_cuda():
return 'gpu'
elif paddle.is_compiled_with_npu():
return 'npu'
elif paddle.is_compiled_with_rocm():
return 'rocm'
elif paddle.is_compiled_with_xpu():
return 'xpu'
return 'cpu'
[文档]def compare_version(version, pair_version):
"""
Args:
version (str): The first version string needed to be compared.
The format of version string should be as follow : "xxx.yyy.zzz".
pair_version (str): The second version string needed to be compared.
The format of version string should be as follow : "xxx.yyy.zzz".
Returns:
int: The result of comparasion. 1 means version > pair_version; 0 means
version = pair_version; -1 means version < pair_version.
Examples:
>>> compare_version("2.2.1", "2.2.0")
>>> 1
>>> compare_version("2.2.0", "2.2.0")
>>> 0
>>> compare_version("2.2.0-rc0", "2.2.0")
>>> -1
>>> compare_version("2.3.0-rc0", "2.2.0")
>>> 1
"""
version = version.strip()
pair_version = pair_version.strip()
if version == pair_version:
return 0
version_list = version.split(".")
pair_version_list = pair_version.split(".")
for version_code, pair_version_code in zip(version_list, pair_version_list):
if not version_code.isnumeric():
return -1
if not pair_version_code.isnumeric():
return 1
if int(version_code) > int(pair_version_code):
return 1
elif int(version_code) < int(pair_version_code):
return -1
return 0