lstm add export.py

model_zoo/official/nlp/lstm/export.py

@@ -0,0 +1,50 @@
+# 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")