202 lines
7.8 KiB
Python
202 lines
7.8 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 paddle
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from . import logger
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from .download import get_weights_path_from_url
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__all__ = ['init_model', 'save_model', 'load_dygraph_pretrain']
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def load_dygraph_pretrain(model, path=None):
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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 {}.pdparams does not "
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"exists.".format(path))
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param_state_dict = paddle.load(path + ".pdparams")
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if isinstance(model, list):
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for m in model:
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if hasattr(m, 'set_dict'):
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m.set_dict(param_state_dict)
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else:
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model.set_dict(param_state_dict)
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logger.info("Finish load pretrained model from {}".format(path))
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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=False,
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use_imagenet22k_pretrained=False,
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use_imagenet22kto1k_pretrained=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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if use_imagenet22k_pretrained:
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pretrained_url = pretrained_url.replace("_pretrained",
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"_22k_pretrained")
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if use_imagenet22kto1k_pretrained:
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pretrained_url = pretrained_url.replace("_pretrained",
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"_22kto1k_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(model, path=local_weight_path)
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return
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def load_distillation_model(model, pretrained_model):
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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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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(teacher, path=pretrained_model[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(student, path=pretrained_model[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,
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net,
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optimizer=None,
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loss: paddle.nn.Layer=None,
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model_ema=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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# load state dict
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opti_dict = paddle.load(checkpoints + ".pdopt")
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para_dict = paddle.load(checkpoints + ".pdparams")
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metric_dict = paddle.load(checkpoints + ".pdstates")
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# set state dict
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net.set_state_dict(para_dict)
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loss.set_state_dict(para_dict)
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for i in range(len(optimizer)):
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optimizer[i].set_state_dict(opti_dict[i] if isinstance(
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opti_dict, list) else opti_dict)
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if model_ema is not None:
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assert os.path.exists(checkpoints + ".ema.pdparams"), \
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"Given dir {}.ema.pdparams not exist.".format(checkpoints)
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para_ema_dict = paddle.load(checkpoints + ".ema.pdparams")
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model_ema.module.set_state_dict(para_ema_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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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)
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else: # common load
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load_dygraph_pretrain(net, path=pretrained_model)
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logger.info("Finish load pretrained model from {}".format(
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pretrained_model))
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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 _extract_student_weights(all_params, student_prefix="Student."):
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s_params = {
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key[len(student_prefix):]: all_params[key]
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for key in all_params if student_prefix in key
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}
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return s_params
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class ModelSaver(object):
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def __init__(self,
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engine,
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net_name="model",
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loss_name="train_loss_func",
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opt_name="optimizer",
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model_ema_name="model_ema"):
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# net, loss, opt, model_ema, output_dir,
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self.engine = engine
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self.net_name = net_name
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self.loss_name = loss_name
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self.opt_name = opt_name
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self.model_ema_name = model_ema_name
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arch_name = engine.config["Arch"]["name"]
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self.output_dir = os.path.join(engine.output_dir, arch_name)
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_mkdir_if_not_exist(self.output_dir)
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def save(self, metric_info, prefix='ppcls', save_student_model=False):
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if paddle.distributed.get_rank() != 0:
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return
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save_dir = os.path.join(self.output_dir, prefix)
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params_state_dict = getattr(self.engine, self.net_name).state_dict()
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loss = getattr(self.engine, self.loss_name)
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if loss is not None:
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loss_state_dict = loss.state_dict()
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keys_inter = set(params_state_dict.keys()) & set(
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loss_state_dict.keys())
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assert len(keys_inter) == 0, \
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f"keys in model and loss state_dict must be unique, but got intersection {keys_inter}"
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params_state_dict.update(loss_state_dict)
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if save_student_model:
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s_params = _extract_student_weights(params_state_dict)
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if len(s_params) > 0:
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paddle.save(s_params, save_dir + "_student.pdparams")
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paddle.save(params_state_dict, save_dir + ".pdparams")
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model_ema = getattr(self.engine, self.model_ema_name)
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if model_ema is not None:
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paddle.save(model_ema.module.state_dict(),
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save_dir + ".ema.pdparams")
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optimizer = getattr(self.engine, self.opt_name)
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paddle.save([opt.state_dict() for opt in optimizer],
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save_dir + ".pdopt")
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paddle.save(metric_info, save_dir + ".pdstates")
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logger.info("Already save model in {}".format(save_dir))
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