186 lines
6.7 KiB
Python
186 lines
6.7 KiB
Python
# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserve.
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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 absolute_import
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from __future__ import division
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from __future__ import print_function
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import errno
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import os
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import re
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import shutil
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import tempfile
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import paddle
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from paddle.static import load_program_state
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from paddle.utils.download import get_weights_path_from_url
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from ppcls.utils import logger
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__all__ = ['init_model', 'save_model', 'load_dygraph_pretrain']
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def _mkdir_if_not_exist(path):
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"""
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mkdir if not exists, ignore the exception when multiprocess mkdir together
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"""
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if not os.path.exists(path):
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try:
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os.makedirs(path)
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except OSError as e:
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if e.errno == errno.EEXIST and os.path.isdir(path):
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logger.warning(
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'be happy if some process has already created {}'.format(
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path))
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else:
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raise OSError('Failed to mkdir {}'.format(path))
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def load_dygraph_pretrain(model, path=None, load_static_weights=False):
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if not (os.path.isdir(path) or os.path.exists(path + '.pdparams')):
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raise ValueError("Model pretrain path {} does not "
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"exists.".format(path))
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if load_static_weights:
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pre_state_dict = load_program_state(path)
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param_state_dict = {}
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model_dict = model.state_dict()
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for key in model_dict.keys():
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weight_name = model_dict[key].name
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if weight_name in pre_state_dict.keys():
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logger.info('Load weight: {}, shape: {}'.format(
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weight_name, pre_state_dict[weight_name].shape))
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param_state_dict[key] = pre_state_dict[weight_name]
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else:
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param_state_dict[key] = model_dict[key]
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model.set_dict(param_state_dict)
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return
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param_state_dict = paddle.load(path + ".pdparams")
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model.set_dict(param_state_dict)
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return
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def load_dygraph_pretrain_from_url(model,
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pretrained_url,
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use_ssld,
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load_static_weights=False):
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if use_ssld:
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pretrained_url = pretrained_url.replace("_pretrained",
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"_ssld_pretrained")
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local_weight_path = get_weights_path_from_url(pretrained_url).replace(
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".pdparams", "")
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load_dygraph_pretrain(
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model, path=local_weight_path, load_static_weights=load_static_weights)
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return
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def load_distillation_model(model, pretrained_model, load_static_weights):
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logger.info("In distillation mode, teacher model will be "
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"loaded firstly before student model.")
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if not isinstance(pretrained_model, list):
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pretrained_model = [pretrained_model]
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if not isinstance(load_static_weights, list):
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load_static_weights = [load_static_weights] * len(pretrained_model)
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teacher = model.teacher if hasattr(model,
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"teacher") else model._layers.teacher
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student = model.student if hasattr(model,
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"student") else model._layers.student
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load_dygraph_pretrain(
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teacher,
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path=pretrained_model[0],
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load_static_weights=load_static_weights[0])
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logger.info("Finish initing teacher model from {}".format(
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pretrained_model))
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# load student model
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if len(pretrained_model) >= 2:
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load_dygraph_pretrain(
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student,
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path=pretrained_model[1],
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load_static_weights=load_static_weights[1])
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logger.info("Finish initing student model from {}".format(
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pretrained_model))
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def init_model(config, net, optimizer=None):
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"""
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load model from checkpoint or pretrained_model
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"""
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checkpoints = config.get('checkpoints')
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if checkpoints and optimizer is not None:
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assert os.path.exists(checkpoints + ".pdparams"), \
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"Given dir {}.pdparams not exist.".format(checkpoints)
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assert os.path.exists(checkpoints + ".pdopt"), \
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"Given dir {}.pdopt not exist.".format(checkpoints)
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para_dict = paddle.load(checkpoints + ".pdparams")
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opti_dict = paddle.load(checkpoints + ".pdopt")
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metric_dict = paddle.load(checkpoints + ".pdstates")
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net.set_dict(para_dict)
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optimizer.set_state_dict(opti_dict)
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logger.info("Finish load checkpoints from {}".format(checkpoints))
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return metric_dict
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pretrained_model = config.get('pretrained_model')
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load_static_weights = config.get('load_static_weights', False)
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use_distillation = config.get('use_distillation', False)
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if pretrained_model:
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if use_distillation:
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load_distillation_model(net, pretrained_model, load_static_weights)
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else: # common load
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load_dygraph_pretrain(
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net,
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path=pretrained_model,
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load_static_weights=load_static_weights)
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logger.info(
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logger.coloring("Finish load pretrained model from {}".format(
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pretrained_model), "HEADER"))
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def _save_student_model(net, model_prefix):
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"""
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save student model if the net is the network contains student
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"""
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student_model_prefix = model_prefix + "_student.pdparams"
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if hasattr(net, "_layers"):
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net = net._layers
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if hasattr(net, "student"):
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paddle.save(net.student.state_dict(), student_model_prefix)
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logger.info("Already save student model in {}".format(
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student_model_prefix))
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def save_model(net,
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optimizer,
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metric_info,
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model_path,
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model_name="",
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prefix='ppcls'):
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"""
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save model to the target path
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"""
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if paddle.distributed.get_rank() != 0:
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return
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model_path = os.path.join(model_path, model_name)
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_mkdir_if_not_exist(model_path)
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model_prefix = os.path.join(model_path, prefix)
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_save_student_model(net, model_prefix)
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paddle.save(net.state_dict(), model_prefix + ".pdparams")
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paddle.save(optimizer.state_dict(), model_prefix + ".pdopt")
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paddle.save(metric_info, model_prefix + ".pdstates")
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logger.info("Already save model in {}".format(model_path))
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