!13517 add check input args for embedinglookup

From: @lianliguang
Reviewed-by: @ginfung,@zh_qh
Signed-off-by: @zh_qh

mindspore/ops/operations/array_ops.py

@@ -5017,7 +5017,7 @@ class Sort(PrimitiveWithInfer):
         return x_dtype, mstype.tensor_type(mstype.int32)
 
 
-class EmbeddingLookup(PrimitiveWithInfer):
+class EmbeddingLookup(PrimitiveWithCheck):
     """
     Returns a slice of input tensor based on the specified indices.
 
@@ -5063,28 +5063,13 @@ class EmbeddingLookup(PrimitiveWithInfer):
         self.init_prim_io_names(inputs=['params', 'indices', 'offset'],
                                 outputs=['output'])
 
-    def __infer__(self, params, indices, offset):
+    def __check__(self, params, indices, offset):
         validator.check_subclass("params", params['dtype'], mstype.tensor, self.name)
         validator.check_tensor_dtype_valid("indices", indices['dtype'], mstype.int_type, self.name)
         validator.check_subclass("offset", offset['dtype'], mstype.int_, self.name)
         params_shp = params['shape']
         if len(params_shp) > 2:
             raise ValueError("The dimension of 'params' in EmbeddingLookup must <= 2, but got %d." % len(params_shp))
-        out_shape = indices['shape'] + params_shp[1:]
-        if 'max_shape' in indices:
-            out_max_shape = indices['max_shape'] + params_shp[1:]
-        else:
-            out_max_shape = out_shape
-        if 'min_shape' in indices:
-            out_min_shape = indices['min_shape'] + params_shp[1:]
-        else:
-            out_min_shape = out_shape
-        out = {'shape': out_shape,
-               'dtype': params['dtype'],
-               'value': None,
-               'max_shape': out_max_shape,
-               'min_shape': out_min_shape}
-        return out
 
 
 class GatherD(PrimitiveWithInfer):

model_zoo/official/cv/yolov3_darknet53/eval.py

@@ -243,7 +243,7 @@ def test():
     args = parse_args()
 
     devid = int(os.getenv('DEVICE_ID')) if os.getenv('DEVICE_ID') else 0
-    context.set_context(mode=context.GRAPH_MODE, device_target=args.device_target, save_graphs=True, device_id=devid)
+    context.set_context(mode=context.GRAPH_MODE, device_target=args.device_target, save_graphs=False, device_id=devid)
 
     # logger
     args.outputs_dir = os.path.join(args.log_path,

model_zoo/official/cv/yolov3_darknet53/train.py

@@ -134,7 +134,7 @@ def conver_training_shape(args):
 def network_init(args):
     devid = int(os.getenv('DEVICE_ID', '0'))
     context.set_context(mode=context.GRAPH_MODE, enable_auto_mixed_precision=True,
-                        device_target=args.device_target, save_graphs=True, device_id=devid)
+                        device_target=args.device_target, save_graphs=False, device_id=devid)
     # init distributed
     if args.is_distributed:
         if args.device_target == "Ascend":