add wide_and_deep net

model_zoo/wide_and_deep/metrics.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.
+# ============================================================================
+
+"""
+Area under cure metric
+"""
+
+from mindspore.nn.metrics import Metric
+from sklearn.metrics import roc_auc_score
+
+class AUCMetric(Metric):
+    """
+    Area under cure metric
+    """
+
+    def __init__(self):
+        super(AUCMetric, self).__init__()
+        self.clear()
+
+    def clear(self):
+        """Clear the internal evaluation result."""
+        self.true_labels = []
+        self.pred_probs = []
+
+    def update(self, *inputs): # inputs
+        all_predict = inputs[1].asnumpy() # predict
+        all_label = inputs[2].asnumpy() # label
+        self.true_labels.extend(all_label.flatten().tolist())
+        self.pred_probs.extend(all_predict.flatten().tolist())
+
+    def eval(self):
+        if len(self.true_labels) != len(self.pred_probs):
+            raise RuntimeError(
+                'true_labels.size is not equal to pred_probs.size()')
+
+        auc = roc_auc_score(self.true_labels, self.pred_probs)
+        print("====" * 20 + " auc_metric  end")
+        print("====" * 20 + " auc: {}".format(auc))
+        return auc

model_zoo/wide_and_deep/test.py

@@ -0,0 +1,94 @@
+# 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.
+# ============================================================================
+
+""" test_training """
+
+import os
+
+from mindspore import Model, context
+from mindspore.train.serialization import load_checkpoint, load_param_into_net
+
+from wide_deep.models.WideDeep import PredictWithSigmoid, TrainStepWrap, NetWithLossClass, WideDeepModel
+from wide_deep.utils.callbacks import LossCallBack, EvalCallBack
+from wide_deep.data.datasets import create_dataset
+from wide_deep.utils.metrics import AUCMetric
+from tools.config import Config_WideDeep
+
+context.set_context(mode=context.GRAPH_MODE, device_target="Davinci",
+                    save_graphs=True)
+
+
+def get_WideDeep_net(config):
+    WideDeep_net = WideDeepModel(config)
+
+    loss_net = NetWithLossClass(WideDeep_net, config)
+    train_net = TrainStepWrap(loss_net)
+    eval_net = PredictWithSigmoid(WideDeep_net)
+
+    return train_net, eval_net
+
+
+class ModelBuilder():
+    """
+    Wide and deep model builder
+    """
+    def __init__(self):
+        pass
+
+    def get_hook(self):
+        pass
+
+    def get_train_hook(self):
+        hooks = []
+        callback = LossCallBack()
+        hooks.append(callback)
+
+        if int(os.getenv('DEVICE_ID')) == 0:
+            pass
+        return hooks
+
+    def get_net(self, config):
+        return get_WideDeep_net(config)
+
+
+def test_eval(config):
+    """
+    test evaluate
+    """
+    data_path = config.data_path
+    batch_size = config.batch_size
+    ds_eval = create_dataset(data_path, train_mode=False, epochs=2,
+                             batch_size=batch_size)
+    print("ds_eval.size: {}".format(ds_eval.get_dataset_size()))
+
+    net_builder = ModelBuilder()
+    train_net, eval_net = net_builder.get_net(config)
+
+    param_dict = load_checkpoint(config.ckpt_path)
+    load_param_into_net(eval_net, param_dict)
+
+    auc_metric = AUCMetric()
+    model = Model(train_net, eval_network=eval_net, metrics={"auc": auc_metric})
+
+    eval_callback = EvalCallBack(model, ds_eval, auc_metric, config)
+
+    model.eval(ds_eval, callbacks=eval_callback)
+
+
+if __name__ == "__main__":
+    widedeep_config = Config_WideDeep()
+    widedeep_config.argparse_init()
+
+    test_eval(widedeep_config.widedeep)