deepfm network use mindrecord dataset

tests/st/model_zoo_tests/DeepFM/src/config.py

@@ -27,7 +27,7 @@ class DataConfig:
     batch_size = 16000
     data_field_size = 39
     # dataset format, 1: mindrecord, 2: tfrecord, 3: h5
-    data_format = 3
+    data_format = 1
 
 
 class ModelConfig:

tests/st/model_zoo_tests/DeepFM/test_deepfm.py

@@ -14,7 +14,7 @@
 # ============================================================================
 """train_criteo."""
 import os
-# import pytest
+import pytest
 
 from mindspore import context
 from mindspore.train.model import Model
@@ -27,10 +27,10 @@ from src.callback import EvalCallBack, LossCallBack, TimeMonitor
 
 set_seed(1)
 
-# @pytest.mark.level0
-# @pytest.mark.platform_arm_ascend_training
-# @pytest.mark.platform_x86_ascend_training
-# @pytest.mark.env_onecard
+@pytest.mark.level0
+@pytest.mark.platform_arm_ascend_training
+@pytest.mark.platform_x86_ascend_training
+@pytest.mark.env_onecard
 def test_deepfm():
     data_config = DataConfig()
     train_config = TrainConfig()
@@ -39,7 +39,7 @@ def test_deepfm():
     rank_size = None
     rank_id = None
 
-    dataset_path = "/home/workspace/mindspore_dataset/criteo_data/criteo_h5/"
+    dataset_path = "/home/workspace/mindspore_dataset/criteo_data/mindrecord/"
     print("dataset_path:", dataset_path)
     ds_train = create_dataset(dataset_path,
                               train_mode=True,
@@ -71,10 +71,10 @@ def test_deepfm():
     print("train_config.train_epochs:", train_config.train_epochs)
     model.train(train_config.train_epochs, ds_train, callbacks=callback_list)
 
-    export_loss_value = 0.51
+    export_loss_value = 0.52
     print("loss_callback.loss:", loss_callback.loss)
     assert loss_callback.loss < export_loss_value
-    export_per_step_time = 40.0
+    export_per_step_time = 30.0
     print("time_callback:", time_callback.per_step_time)
     assert time_callback.per_step_time < export_per_step_time
     print("*******test case pass!********")