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# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserve.
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# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
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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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@ -12,120 +12,78 @@
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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 logging
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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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def initial_logger():
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FORMAT = '%(asctime)s-%(levelname)s: %(message)s'
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logging.basicConfig(level=logging.INFO, format=FORMAT)
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logger = logging.getLogger(__name__)
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return logger
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import importlib
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def create_module(module_str):
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tmpss = module_str.split(",")
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assert len(tmpss) == 2, "Error formate\
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of the module path: {}".format(module_str)
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module_name, function_name = tmpss[0], tmpss[1]
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somemodule = importlib.import_module(module_name, __package__)
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function = getattr(somemodule, function_name)
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return function
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def get_check_global_params(mode):
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check_params = ['use_gpu', 'max_text_length', 'image_shape',\
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'image_shape', 'character_type', 'loss_type']
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if mode == "train_eval":
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check_params = check_params + [\
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'train_batch_size_per_card', 'test_batch_size_per_card']
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elif mode == "test":
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check_params = check_params + ['test_batch_size_per_card']
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return check_params
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def get_check_reader_params(mode):
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check_params = []
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if mode == "train_eval":
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check_params = ['TrainReader', 'EvalReader']
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elif mode == "test":
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check_params = ['TestReader']
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return check_params
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def get_image_file_list(img_file):
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imgs_lists = []
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if img_file is None or not os.path.exists(img_file):
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raise Exception("not found any img file in {}".format(img_file))
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img_end = ['jpg', 'png', 'jpeg', 'JPEG', 'JPG', 'bmp']
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if os.path.isfile(img_file) and img_file.split('.')[-1] in img_end:
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imgs_lists.append(img_file)
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elif os.path.isdir(img_file):
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for single_file in os.listdir(img_file):
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if single_file.split('.')[-1] in img_end:
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imgs_lists.append(os.path.join(img_file, single_file))
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if len(imgs_lists) == 0:
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raise Exception("not found any img file in {}".format(img_file))
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return imgs_lists
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from paddle import fluid
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def create_multi_devices_program(program, loss_var_name):
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build_strategy = fluid.BuildStrategy()
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build_strategy.memory_optimize = False
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build_strategy.enable_inplace = True
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exec_strategy = fluid.ExecutionStrategy()
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exec_strategy.num_iteration_per_drop_scope = 1
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compile_program = fluid.CompiledProgram(program).with_data_parallel(
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loss_name=loss_var_name,
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build_strategy=build_strategy,
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exec_strategy=exec_strategy)
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return compile_program
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shaohua.zhang
5 years ago
committed by
GitHub