add wide_and_deep export for 310 predict

model_zoo/official/recommend/wide_and_deep/README.md

@@ -112,6 +112,7 @@ python eval.py  --data_path=./data/mindrecord --data_type=mindrecord
     ├── train_and_eval_parameter_server.py
     ├── train_and_eval.py
     └── train.py
+    └── export.py
 ```
 
 ## [Script Parameters](#contents)

model_zoo/official/recommend/wide_and_deep/export.py

@@ -0,0 +1,60 @@
+# 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 air and onnx models#################
+"""
+import numpy as np
+
+from mindspore import Tensor, nn
+from mindspore.ops import operations as P
+from mindspore.train.serialization import load_checkpoint, load_param_into_net, export
+
+from src.wide_and_deep import WideDeepModel
+from src.config import WideDeepConfig
+
+class PredictWithSigmoid(nn.Cell):
+    """
+    PredictWithSigmoid
+    """
+    def __init__(self, network):
+        super(PredictWithSigmoid, self).__init__()
+        self.network = network
+        self.sigmoid = P.Sigmoid()
+
+    def construct(self, batch_ids, batch_wts):
+        logits, _, = self.network(batch_ids, batch_wts)
+        pred_probs = self.sigmoid(logits)
+        return pred_probs
+
+def get_WideDeep_net(config):
+    """
+    Get network of wide&deep predict model.
+    """
+    WideDeep_net = WideDeepModel(config)
+    eval_net = PredictWithSigmoid(WideDeep_net)
+    return eval_net
+
+if __name__ == '__main__':
+    widedeep_config = WideDeepConfig()
+    widedeep_config.argparse_init()
+    ckpt_path = widedeep_config.ckpt_path
+    net = get_WideDeep_net(widedeep_config)
+    param_dict = load_checkpoint(ckpt_path)
+    load_param_into_net(net, param_dict)
+    ids = Tensor(np.ones([widedeep_config.eval_batch_size, widedeep_config.field_size]).astype(np.int32))
+    wts = Tensor(np.ones([widedeep_config.eval_batch_size, widedeep_config.field_size]).astype(np.float32))
+    input_tensor_list = [ids, wts]
+    export(net, *input_tensor_list, file_name='wide_and_deep.onnx', file_format="ONNX")
+    export(net, *input_tensor_list, file_name='wide_and_deep.air', file_format="AIR")