!1601 [Auto parallel] Add some wide&deep files

Merge pull request !1601 from Xiaoda/some-wide-deep-files

model_zoo/wide_and_deep/src/callbacks.py

@@ -0,0 +1,104 @@
+# 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.
+"""
+callbacks
+"""
+import time
+from mindspore.train.callback import Callback
+from mindspore import context
+
+def add_write(file_path, out_str):
+    """
+    add lines to the file
+    """
+    with open(file_path, 'a+', encoding="utf-8") as file_out:
+        file_out.write(out_str + "\n")
+
+
+    class LossCallBack(Callback):
+        """
+        Monitor the loss in training.
+
+        If the loss is NAN or INF, terminate the training.
+
+        Note:
+            If per_print_times is 0, do NOT print loss.
+
+        Args:
+            per_print_times (int): Print loss every times. Default: 1.
+        """
+        def __init__(self, config, per_print_times=1):
+            super(LossCallBack, self).__init__()
+            if not isinstance(per_print_times, int) or per_print_times < 0:
+                raise ValueError("per_print_times must be in and >= 0.")
+            self._per_print_times = per_print_times
+            self.config = config
+
+        def step_end(self, run_context):
+            cb_params = run_context.original_args()
+            wide_loss, deep_loss = cb_params.net_outputs[0].asnumpy(), cb_params.net_outputs[1].asnumpy()
+            cur_step_in_epoch = (cb_params.cur_step_num - 1) % cb_params.batch_num + 1
+            cur_num = cb_params.cur_step_num
+            print("===loss===", cb_params.cur_epoch_num, cur_step_in_epoch, wide_loss, deep_loss)
+
+            # raise ValueError
+            if self._per_print_times != 0 and cur_num % self._per_print_times == 0:
+                loss_file = open(self.config.loss_file_name, "a+")
+                loss_file.write("epoch: %s, step: %s, wide_loss: %s, deep_loss: %s" %
+                                (cb_params.cur_epoch_num, cur_step_in_epoch, wide_loss, deep_loss))
+                loss_file.write("\n")
+                loss_file.close()
+                print("epoch: %s, step: %s, wide_loss: %s, deep_loss: %s" %
+                      (cb_params.cur_epoch_num, cur_step_in_epoch, wide_loss, deep_loss))
+
+
+    class EvalCallBack(Callback):
+        """
+        Monitor the loss in evaluating.
+
+        If the loss is NAN or INF, terminate evaluating.
+
+        Note:
+            If per_print_times is 0, do NOT print loss.
+
+        Args:
+            print_per_step (int): Print loss every times. Default: 1.
+        """
+        def __init__(self, model, eval_dataset, auc_metric, config, print_per_step=1):
+            super(EvalCallBack, self).__init__()
+            if not isinstance(print_per_step, int) or print_per_step < 0:
+                raise ValueError("print_per_step must be int and >= 0.")
+            self.print_per_step = print_per_step
+            self.model = model
+            self.eval_dataset = eval_dataset
+            self.aucMetric = auc_metric
+            self.aucMetric.clear()
+            self.eval_file_name = config.eval_file_name
+
+        def epoch_name(self, run_context):
+            """
+            epoch name
+            """
+            self.aucMetric.clear()
+            context.set_auto_parallel_context(strategy_ckpt_save_file="",
+                                              strategy_ckpt_load_file="./strategy_train.ckpt")
+            start_time = time.time()
+            out = self.model.eval(self.eval_dataset)
+            end_time = time.time()
+            eval_time = int(end_time - start_time)
+
+            time_str = time.strftime("%Y-%m-%d %H:%M%S", time.localtime())
+            out_str = "{}==== EvalCallBack model.eval(): {}; eval_time: {}s".format(time_str, out.values(), eval_time)
+            print(out_str)
+            add_write(self.eval_file_name, out_str)

model_zoo/wide_and_deep/train.py

@@ -0,0 +1,85 @@
+# 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.callback import ModelCheckpoint, CheckpointConfig
+
+from wide_deep.models.WideDeep import PredictWithSigmoid, TrainStepWarp, NetWithLossClass, WideDeepModel
+from wide_deep.utils.callbacks import LossCallBack
+from wide_deep.data.datasets import create_dataset
+from tools.config import Config_WideDeep
+
+context.set_context(model=context.GRAPH_MODE, device_target="Ascend", save_graphs=True)
+
+
+def get_WideDeep_net(configure):
+    WideDeep_net = WideDeepModel(configure)
+
+    loss_net = NetWithLossClass(WideDeep_net, configure)
+    train_net = TrainStepWarp(loss_net)
+    eval_net = PredictWithSigmoid(WideDeep_net)
+
+    return train_net, eval_net
+
+
+class ModelBuilder():
+    """
+    Build the model.
+    """
+    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, configure):
+        return get_WideDeep_net(configure)
+
+
+def test_train(configure):
+    """
+    test_train
+    """
+    data_path = configure.data_path
+    batch_size = configure.batch_size
+    epochs = configure.epochs
+    ds_train = create_dataset(data_path, train_mode=True, epochs=epochs, batch_size=batch_size)
+    print("ds_train.size: {}".format(ds_train.get_dataset_size()))
+
+    net_builder = ModelBuilder()
+    train_net, _ = net_builder.get_net(configure)
+    train_net.set_train()
+
+    model = Model(train_net)
+    callback = LossCallBack(configure)
+    ckptconfig = CheckpointConfig(save_checkpoint_steps=1,
+                                  keep_checkpoint_max=5)
+    ckpoint_cb = ModelCheckpoint(prefix='widedeep_train', directory=configure.ckpt_path, config=ckptconfig)
+    model.train(epochs, ds_train, callbacks=[callback, ckpoint_cb])
+
+
+if __name__ == "__main__":
+    config = Config_WideDeep()
+    config.argparse_init()
+
+    test_train(config)