forked from mindspore-Ecosystem/mindspore
92 lines
3.9 KiB
Python
92 lines
3.9 KiB
Python
# Copyright 2020-2021 Huawei Technologies Co., Ltd
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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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# ============================================================================
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"""export checkpoint file into models"""
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import os
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import shutil
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import numpy as np
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import mindspore.common.dtype as mstype
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from mindspore import Tensor, context, load_checkpoint, export
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from src.finetune_eval_model import BertCLSModel, BertSquadModel, BertNERModel
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from src.bert_for_finetune import BertNER
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from src.utils import convert_labels_to_index
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from src.model_utils.config import config as args, bert_net_cfg
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from src.model_utils.moxing_adapter import moxing_wrapper
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from src.model_utils.device_adapter import get_device_id
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def modelarts_pre_process():
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'''modelarts pre process function.'''
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args.device_id = get_device_id()
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_file_dir = os.path.dirname(os.path.abspath(__file__))
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args.export_ckpt_file = os.path.join(_file_dir, args.export_ckpt_file)
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args.label_file_path = os.path.join(args.data_path, args.label_file_path)
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args.export_file_name = os.path.join(_file_dir, args.export_file_name)
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@moxing_wrapper(pre_process=modelarts_pre_process)
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def run_export():
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'''export function'''
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context.set_context(mode=context.GRAPH_MODE, device_target=args.device_target)
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if args.device_target == "Ascend":
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context.set_context(device_id=args.device_id)
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if args.description == "run_ner":
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label_list = []
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with open(args.label_file_path) as f:
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for label in f:
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label_list.append(label.strip())
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tag_to_index = convert_labels_to_index(label_list)
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if args.use_crf.lower() == "true":
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max_val = max(tag_to_index.values())
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tag_to_index["<START>"] = max_val + 1
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tag_to_index["<STOP>"] = max_val + 2
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number_labels = len(tag_to_index)
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net = BertNER(bert_net_cfg, args.export_batch_size, False, num_labels=number_labels,
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use_crf=True, tag_to_index=tag_to_index)
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else:
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number_labels = len(tag_to_index)
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net = BertNERModel(bert_net_cfg, False, number_labels, use_crf=(args.use_crf.lower() == "true"))
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elif args.description == "run_classifier":
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net = BertCLSModel(bert_net_cfg, False, num_labels=args.num_class)
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elif args.description == "run_squad":
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net = BertSquadModel(bert_net_cfg, False)
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else:
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raise ValueError("unsupported downstream task")
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load_checkpoint(args.export_ckpt_file, net=net)
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net.set_train(False)
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input_ids = Tensor(np.zeros([args.export_batch_size, bert_net_cfg.seq_length]), mstype.int32)
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input_mask = Tensor(np.zeros([args.export_batch_size, bert_net_cfg.seq_length]), mstype.int32)
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token_type_id = Tensor(np.zeros([args.export_batch_size, bert_net_cfg.seq_length]), mstype.int32)
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label_ids = Tensor(np.zeros([args.export_batch_size, bert_net_cfg.seq_length]), mstype.int32)
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if args.description == "run_ner" and args.use_crf.lower() == "true":
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input_data = [input_ids, input_mask, token_type_id, label_ids]
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else:
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input_data = [input_ids, input_mask, token_type_id]
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export(net, *input_data, file_name=args.export_file_name, file_format=args.file_format)
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if args.enable_modelarts:
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air_file = f"{args.export_file_name}.{args.file_format.lower()}"
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shutil.move(air_file, args.output_path)
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if __name__ == "__main__":
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run_export()
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