mirror of
https://github.com/PaddlePaddle/PaddleOCR.git
synced 2025-11-04 03:39:22 +00:00
132 lines
4.2 KiB
Python
Executable File
132 lines
4.2 KiB
Python
Executable File
# 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 shutil
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import tempfile
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import paddle
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import paddle.fluid as fluid
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from .utility import initial_logger
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import re
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logger = initial_logger()
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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_state(path):
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if os.path.exists(path + '.pdopt'):
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# XXX another hack to ignore the optimizer state
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tmp = tempfile.mkdtemp()
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dst = os.path.join(tmp, os.path.basename(os.path.normpath(path)))
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shutil.copy(path + '.pdparams', dst + '.pdparams')
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state = fluid.io.load_program_state(dst)
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shutil.rmtree(tmp)
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else:
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state = fluid.io.load_program_state(path)
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return state
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def load_params(exe, prog, path, ignore_params=[]):
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"""
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Load model from the given path.
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Args:
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exe (fluid.Executor): The fluid.Executor object.
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prog (fluid.Program): load weight to which Program object.
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path (string): URL string or loca model path.
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ignore_params (list): ignore variable to load when finetuning.
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It can be specified by finetune_exclude_pretrained_params
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and the usage can refer to docs/advanced_tutorials/TRANSFER_LEARNING.md
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"""
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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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logger.info('Loading parameters from {}...'.format(path))
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ignore_set = set()
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state = _load_state(path)
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# ignore the parameter which mismatch the shape
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# between the model and pretrain weight.
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all_var_shape = {}
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for block in prog.blocks:
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for param in block.all_parameters():
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all_var_shape[param.name] = param.shape
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ignore_set.update([
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name for name, shape in all_var_shape.items()
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if name in state and shape != state[name].shape
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])
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if ignore_params:
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all_var_names = [var.name for var in prog.list_vars()]
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ignore_list = filter(
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lambda var: any([re.match(name, var) for name in ignore_params]),
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all_var_names)
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ignore_set.update(list(ignore_list))
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if len(ignore_set) > 0:
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for k in ignore_set:
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if k in state:
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logger.warning('variable {} not used'.format(k))
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del state[k]
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fluid.io.set_program_state(prog, state)
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def init_model(config, program, exe):
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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['Global'].get('checkpoints')
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if checkpoints:
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path = checkpoints
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fluid.load(program, path, exe)
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logger.info("Finish initing model from {}".format(path))
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return
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pretrain_weights = config['Global'].get('pretrain_weights')
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if pretrain_weights:
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path = pretrain_weights
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load_params(exe, program, path)
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logger.info("Finish initing model from {}".format(path))
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return
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def save_model(program, model_path):
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"""
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save model to the target path
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"""
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fluid.save(program, model_path)
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logger.info("Already save model in {}".format(model_path))
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