You can not select more than 25 topics
Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
51 lines
2.1 KiB
51 lines
2.1 KiB
# Copyright 2020 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 mindir model#################
|
|
python export.py
|
|
"""
|
|
import argparse
|
|
import os
|
|
|
|
import numpy as np
|
|
|
|
from mindspore import Tensor
|
|
from mindspore import export, load_checkpoint, load_param_into_net
|
|
from src.config import lstm_cfg as cfg
|
|
from src.lstm import SentimentNet
|
|
|
|
if __name__ == '__main__':
|
|
parser = argparse.ArgumentParser(description='MindSpore LSTM Exporter')
|
|
parser.add_argument('--preprocess_path', type=str, default='./preprocess',
|
|
help='path where the pre-process data is stored.')
|
|
parser.add_argument('--ckpt_file', type=str, required=True, help='lstm ckpt file.')
|
|
args = parser.parse_args()
|
|
|
|
embedding_table = np.loadtxt(os.path.join(args.preprocess_path, "weight.txt")).astype(np.float32)
|
|
network = SentimentNet(vocab_size=embedding_table.shape[0],
|
|
embed_size=cfg.embed_size,
|
|
num_hiddens=cfg.num_hiddens,
|
|
num_layers=cfg.num_layers,
|
|
bidirectional=cfg.bidirectional,
|
|
num_classes=cfg.num_classes,
|
|
weight=Tensor(embedding_table),
|
|
batch_size=cfg.batch_size)
|
|
|
|
param_dict = load_checkpoint(args.ckpt_file)
|
|
load_param_into_net(network, param_dict)
|
|
|
|
input_arr = Tensor(np.random.uniform(0.0, 1e5, size=[64, 500]).astype(np.int32))
|
|
export(network, input_arr, file_name="lstm", file_format="MINDIR")
|