!10516 add export.py in autodis network

From: @shuzigood
Reviewed-by: @wuxuejian,@linqingke
Signed-off-by: @linqingke

model_zoo/research/recommend/autodis/export.py

@@ -0,0 +1,51 @@
+# 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 ckpt to model"""
+import argparse
+import numpy as np
+
+from mindspore import context, Tensor
+from mindspore.train.serialization import export, load_checkpoint
+
+from src.autodis import ModelBuilder
+from src.config import DataConfig, ModelConfig, TrainConfig
+
+parser = argparse.ArgumentParser(description="autodis export")
+parser.add_argument("--device_id", type=int, default=0, help="Device id")
+parser.add_argument("--batch_size", type=int, default=16000, help="batch size")
+parser.add_argument("--ckpt_file", type=str, required=True, help="Checkpoint file path.")
+parser.add_argument("--file_name", type=str, default="autodis", help="output file name.")
+parser.add_argument("--file_format", type=str, choices=["AIR", "ONNX", "MINDIR"], default="AIR", help="file format")
+parser.add_argument("--device_target", type=str, choices=["Ascend"], default="Ascend",
+                    help="device target")
+args = parser.parse_args()
+
+context.set_context(mode=context.GRAPH_MODE, device_target=args.device_target, device_id=args.device_id)
+
+if __name__ == "__main__":
+    data_config = DataConfig()
+
+    model_builder = ModelBuilder(ModelConfig, TrainConfig)
+    _, network = model_builder.get_train_eval_net()
+    network.set_train(False)
+
+    load_checkpoint(args.ckpt_file, net=network)
+
+    batch_ids = Tensor(np.zeros([data_config.batch_size, data_config.data_field_size]).astype(np.int32))
+    batch_wts = Tensor(np.zeros([data_config.batch_size, data_config.data_field_size]).astype(np.float32))
+    labels = Tensor(np.zeros([data_config.batch_size, 1]).astype(np.float32))
+
+    input_data = [batch_ids, batch_wts, labels]
+    export(network, *input_data, file_name=args.file_name, file_format=args.file_format)