add resnet_thor export file

model_zoo/official/cv/resnet_thor/export.py

@@ -0,0 +1,46 @@
+# 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"""
+import argparse
+import numpy as np
+
+from mindspore import context
+from mindspore import Tensor
+from mindspore.train.serialization import load_checkpoint, load_param_into_net, export
+from src.resnet_thor import resnet50 as resnet
+from src.config import config
+
+parser = argparse.ArgumentParser(description='checkpoint export')
+parser.add_argument('--checkpoint_path', type=str, default=None, help='Checkpoint file path')
+args_opt = parser.parse_args()
+
+if __name__ == '__main__':
+
+    context.set_context(mode=context.GRAPH_MODE, save_graphs=False)
+
+    # define net
+    net = resnet(class_num=config.class_num)
+    net.add_flags_recursive(thor=False)
+
+    # load checkpoint
+    param_dict = load_checkpoint(args_opt.checkpoint_path)
+    keys = list(param_dict.keys())
+    for key in keys:
+        if "damping" in key:
+            param_dict.pop(key)
+    load_param_into_net(net, param_dict)
+
+    inputs = np.random.uniform(0.0, 1.0, size=[1, 3, 224, 224]).astype(np.float32)
+    export(net, Tensor(inputs), file_name='resnet-42_5004.air', file_format='AIR')