Fix batch size check

model_zoo/official/recommend/wide_and_deep/train_and_eval_auto_parallel.py

@@ -121,12 +121,12 @@ def train_and_eval(config):
     model = Model(train_net, eval_network=eval_net,
                   metrics={"auc": auc_metric})
 
-    eval_callback = EvalCallBack(
-        model, ds_eval, auc_metric, config)
-
     # Save strategy ckpts according to the rank id, this must be done before initializing the callbacks.
     config.stra_ckpt = os.path.join(config.stra_ckpt + "-{}".format(get_rank()), "strategy.ckpt")
 
+    eval_callback = EvalCallBack(
+        model, ds_eval, auc_metric, config)
+
     callback = LossCallBack(config=config, per_print_times=20)
     ckptconfig = CheckpointConfig(save_checkpoint_steps=ds_train.get_dataset_size()*epochs,
                                   keep_checkpoint_max=5, integrated_save=False)
@@ -146,10 +146,11 @@ if __name__ == "__main__":
     wide_deep_config = WideDeepConfig()
     wide_deep_config.argparse_init()
     context.set_context(mode=context.GRAPH_MODE,
-                        device_target=wide_deep_config.device_target, save_graphs=True)
+                        device_target=wide_deep_config.device_target)
     context.set_context(variable_memory_max_size="24GB")
     context.set_context(enable_sparse=True)
     init()
+    context.set_context(save_graphs_path='./graphs_of_device_id_' + str(get_rank()), save_graphs=True)
     if wide_deep_config.sparse:
         context.set_auto_parallel_context(
             parallel_mode=ParallelMode.SEMI_AUTO_PARALLEL, gradients_mean=True)

model_zoo/official/recommend/wide_and_deep_multitable/src/datasets.py

@@ -209,14 +209,20 @@ def _get_tf_dataset(data_dir,
                                       shuffle=shuffle,
                                       schema=schema,
                                       num_parallel_workers=8)
+    if batch_size <= 0:
+        raise ValueError("Batch size should be a positive int value, but found {}".format(str(batch_size)))
+    if batch_size % line_per_sample != 0:
+        raise ValueError(
+            "Batch size should be a multiple of {}, but found {}".format(str(line_per_sample), str(batch_size)))
+
     data_set = data_set.batch(int(batch_size / line_per_sample), drop_remainder=True)
 
     operations_list = []
     for key in columns_list:
         operations_list.append(lambda x: np.array(x).flatten().reshape(input_shape_dict[key]))
-    print("ssssssssssssssssssssss---------------------" * 10)
+    print("input_shape_dict start logging")
     print(input_shape_dict)
-    print("---------------------" * 10)
+    print("input_shape_dict end logging")
     print(schema_dict)
 
     def mixup(a, b, c, d, e, f, g, h, i, j, k, l, m, n, o, p, q, r, s, t, u):