72 lines
2.3 KiB
Python
72 lines
2.3 KiB
Python
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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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# 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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import logging
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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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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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