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export.py
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export.py
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# Copyright 2021 Huawei Technologies Co., Ltd
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ============================================================================
"""export checkpoint file into models"""
import argparse
import numpy as np
import mindspore.common.dtype as mstype
from mindspore import Tensor, context, load_checkpoint, export
from src.finetune_eval_config import ernie_net_cfg
from src.finetune_eval_model import ErnieCLSModel, ErnieNERModel
parser = argparse.ArgumentParser(description="ERNIE finetune export")
parser.add_argument("--task_type", type=str, choices=["chnsenticorp", "xnli", "dbqa"],
default="chnsenticorp", help="task type to export")
parser.add_argument("--device_id", type=int, default=0, help="Device id")
parser.add_argument("--batch_size", type=int, default=1, help="batch size")
parser.add_argument("--number_labels", type=int, default=3, help="number of labels ")
parser.add_argument("--ckpt_file", type=str, required=True, help="Ernie ckpt file.")
parser.add_argument("--file_name", type=str, default="ernie_finetune", help="Ernie output air name.")
parser.add_argument("--file_format", type=str, choices=["AIR", "ONNX", "MINDIR"],
default="AIR", help="file format")
parser.add_argument("--device_target", type=str, default="Ascend",
choices=["Ascend", "GPU", "CPU"], help="device target (default: Ascend)")
args = parser.parse_args()
context.set_context(mode=context.GRAPH_MODE, device_target=args.device_target)
if args.device_target == "Ascend":
context.set_context(device_id=args.device_id)
if __name__ == "__main__":
if args.task_type == 'msra_ner':
net = ErnieNERModel(ernie_net_cfg, False, num_labels=args.number_labels)
elif args.task_type == "chnsenticorp":
net = ErnieCLSModel(ernie_net_cfg, False, num_labels=args.number_labels)
elif args.task_type in ["xnli", "dbqa"]:
ernie_net_cfg.seq_length = 512
net = ErnieCLSModel(ernie_net_cfg, False, num_labels=args.number_labels)
else:
raise ValueError('unsupported task type')
load_checkpoint(args.ckpt_file, net=net)
net.set_train(False)
input_ids = Tensor(np.zeros([args.batch_size, ernie_net_cfg.seq_length]), mstype.int32)
input_mask = Tensor(np.zeros([args.batch_size, ernie_net_cfg.seq_length]), mstype.int32)
token_type_id = Tensor(np.zeros([args.batch_size, ernie_net_cfg.seq_length]), mstype.int32)
input_data = [input_ids, input_mask, token_type_id]
export(net, *input_data, file_name=args.file_name, file_format=args.file_format)