mirror of
https://github.com/PaddlePaddle/PaddleClas.git
synced 2025-06-03 21:55:06 +08:00
126 lines
5.0 KiB
Python
126 lines
5.0 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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