Add evaluation

model_zoo/official/recommend/wide_and_deep_multitable/eval.py

+# 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.
+# ============================================================================
+""" training_and_evaluating """
+
+import os
+import sys
+from mindspore import Model, context
+from mindspore.train.serialization import load_checkpoint, load_param_into_net
+
+from src.wide_and_deep import PredictWithSigmoid, TrainStepWrap, NetWithLossClass, WideDeepModel
+from src.callbacks import LossCallBack, EvalCallBack
+from src.datasets import create_dataset, compute_emb_dim
+from src.metrics import AUCMetric
+from src.config import WideDeepConfig
+sys.path.append(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
+
+
+def get_WideDeep_net(config):
+    """
+    Get network of wide&deep model.
+    """
+    WideDeep_net = WideDeepModel(config)
+
+    loss_net = NetWithLossClass(WideDeep_net, config)
+    train_net = TrainStepWrap(loss_net, config)
+    eval_net = PredictWithSigmoid(WideDeep_net)
+
+    return train_net, eval_net
+
+
+class ModelBuilder():
+    """
+    ModelBuilder.
+    """
+    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 train_and_eval(config):
+    """
+    train_and_eval.
+    """
+    data_path = config.data_path
+    epochs = config.epochs
+    print("epochs is {}".format(epochs))
+
+    ds_eval = create_dataset(data_path, train_mode=False, epochs=1,
+                             batch_size=config.batch_size, is_tf_dataset=config.is_tf_dataset)
+
+    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__":
+    wide_and_deep_config = WideDeepConfig()
+    wide_and_deep_config.argparse_init()
+    compute_emb_dim(wide_and_deep_config)
+    context.set_context(mode=context.GRAPH_MODE, device_target="Davinci")
+    train_and_eval(wide_and_deep_config)