add export.py to deeplabv3

model_zoo/official/cv/deeplabv3/export.py

@@ -0,0 +1,45 @@
+# 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 AIR file."""
+import argparse
+import numpy as np
+
+from mindspore import Tensor
+from mindspore import context
+from mindspore.train.serialization import load_checkpoint, load_param_into_net
+from mindspore.train.serialization import export
+
+from src.nets import net_factory
+
+context.set_context(mode=context.GRAPH_MODE, save_graphs=False)
+
+if __name__ == '__main__':
+    parser = argparse.ArgumentParser(description='checkpoint export')
+    parser.add_argument('--checkpoint', type=str.lower, default='', help='checkpoint of deeplabv3 (Default: None)')
+    parser.add_argument('--model', type=str.lower, default='deeplab_v3_s8', choices=['deeplab_v3_s16', 'deeplab_v3_s8'],
+                        help='Select model structure (Default: deeplab_v3_s8)')
+    parser.add_argument('--num_classes', type=int, default=21, help='the number of classes (Default: 21)')
+    args = parser.parse_args()
+
+    if args.model == 'deeplab_v3_s16':
+        network = net_factory.nets_map['deeplab_v3_s16']('eval', args.num_classes, 16, True)
+    else:
+        network = net_factory.nets_map['deeplab_v3_s8']('eval', args.num_classes, 8, True)
+    param_dict = load_checkpoint(args.checkpoint)
+
+    # load the parameter into net
+    load_param_into_net(network, param_dict)
+    input_data = np.random.uniform(0.0, 1.0, size=[32, 3, 513, 513]).astype(np.float32)
+    export(network, Tensor(input_data), file_name=args.model+'-300_11.air', file_format='AIR')