!15442 modify code method metrics

From: @changzherui
Reviewed-by: @kingxian,@zhoufeng54
Signed-off-by: @kingxian

mindspore/common/tensor.py

@@ -80,28 +80,10 @@ class Tensor(Tensor_):
         if isinstance(input_data, np_types):
             input_data = np.array(input_data)
 
-        if input_data is not None and shape is not None:
-            raise ValueError("If input_data is available, shape doesn't need to be set")
-
-        if init is not None and (shape is None or dtype is None):
-            raise ValueError("init, dtype and shape must have values at the same time.")
-
-        if ((input_data is not None and init is None) or (input_data is None and init is not None)) is False:
-            raise TypeError("input_data and init can not be None at the same time.")
-
         if isinstance(shape, numbers.Number):
             shape = (shape,)
 
-        if input_data is not None:
-            if isinstance(input_data, np.ndarray) and input_data.ndim > 1 and input_data.size == 0:
-                raise ValueError("input_data can not contain zero dimension.")
-            if isinstance(input_data, (tuple, list)) and np.array(input_data).ndim > 1 \
-                    and np.array(input_data).size == 0:
-                raise ValueError("input_data can not contain zero dimension.")
-
-        if shape is not None and not (hasattr(init, "__enable_zero_dim__") and init.__enable_zero_dim__):
-            if 0 in shape:
-                raise ValueError("Shape can not contain zero value.")
+        _check_tensor_input(input_data, dtype, shape, init)
 
         # If input_data is tuple/list/numpy.ndarray, it's support in check_type method.
         if init is None:
@@ -828,4 +810,26 @@ def _vm_compare(*args):
     return Tensor(np.array(fn(y)))
 
 
+def _check_tensor_input(input_data=None, dtype=None, shape=None, init=None):
+    """Check the tensor input."""
+    if input_data is not None and shape is not None:
+        raise ValueError("If input_data is available, shape doesn't need to be set")
+
+    if init is not None and (shape is None or dtype is None):
+        raise ValueError("init, dtype and shape must have values at the same time.")
+
+    if (int(input_data is None) + int(init is None)) != 1:
+        raise TypeError("input_data and init can not be None at the same time.")
+
+    if input_data is not None:
+        if isinstance(input_data, np.ndarray) and input_data.ndim > 1 and input_data.size == 0:
+            raise ValueError("input_data can not contain zero dimension.")
+        if isinstance(input_data, (tuple, list)) and np.array(input_data).ndim > 1 \
+                and np.array(input_data).size == 0:
+            raise ValueError("input_data can not contain zero dimension.")
+
+    if shape is not None and not (hasattr(init, "__enable_zero_dim__") and init.__enable_zero_dim__) and 0 in shape:
+        raise ValueError("Shape can not contain zero value.")
+
+
 tensor_operator_registry.register('vm_compare', _vm_compare)

mindspore/train/serialization.py

@@ -654,28 +654,17 @@ def _export(net, file_name, file_format, *inputs):
 def _save_mindir(net, file_name, *inputs):
     """Save MindIR format file."""
     model = mindir_model()
-    if net._auto_parallel_mode:
-        phase_name = "predict"
-    else:
-        phase_name = 'export.mindir'
+
+    phase_name = "predict" if net._auto_parallel_mode else "export.mindir"
+
     graph_id, _ = _executor.compile(net, *inputs, phase=phase_name,
                                     do_convert=False, auto_parallel_mode=net._auto_parallel_mode)
     mindir_stream = _executor._get_func_graph_proto(net, graph_id, 'mind_ir')
 
     net_dict = net.parameters_dict()
-    data_total = 0
-    save_together = True
-
     model.ParseFromString(mindir_stream)
-    for param_proto in model.graph.parameter:
-        name = param_proto.name[param_proto.name.find(":") + 1:]
-        if name in net_dict.keys():
-            data_total += sys.getsizeof(net_dict[name].data.asnumpy().tobytes()) / 1024
-        else:
-            raise RuntimeError('Graph parameter: {} Undefined in network.'.format(param_proto.name))
-        if data_total > TOTAL_SAVE:
-            save_together = False
-            break
+
+    save_together = _mindir_save_together(net_dict, model)
 
     if save_together:
         for param_proto in model.graph.parameter:
@@ -746,6 +735,20 @@ def _save_mindir(net, file_name, *inputs):
             f.write(model.SerializeToString())
 
 
+def _mindir_save_together(net_dict, model):
+    """Whether graph and parameter save together during save mindir model."""
+    data_total = 0
+    for param_proto in model.graph.parameter:
+        name = param_proto.name[param_proto.name.find(":") + 1:]
+        if name in net_dict.keys():
+            data_total += sys.getsizeof(net_dict[name].data.asnumpy().tobytes()) / 1024
+        else:
+            raise RuntimeError('Graph parameter: {} Undefined in network.'.format(param_proto.name))
+        if data_total > TOTAL_SAVE:
+            return False
+    return True
+
+
 def _quant_export(network, *inputs, file_format, **kwargs):
     """
     Exports MindSpore quantization predict model to deploy with AIR and MINDIR.

tests/ut/python/runtest.sh

@@ -13,9 +13,9 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 # ============================================================================
-CURRPATH=$(cd "$(dirname $0)"; pwd)
+CURRPATH=$(cd "$(dirname $0)" || exit; pwd)
 IGNORE_EXEC="--ignore=$CURRPATH/exec"
-PROJECT_PATH=$(cd ${CURRPATH}/../../..; pwd)
+PROJECT_PATH=$(cd ${CURRPATH}/../../.. || exit; pwd)
 
 if [ $BUILD_PATH ];then
     echo "BUILD_PATH = $BUILD_PATH"