add tensor.ndim and rename tensor.size() to tensor.size

mindspore/_checkparam.py

@@ -635,7 +635,7 @@ def check_input_data(*data, data_class):
                                  f' either a single'
                                  f' or a list of {data_class.__name__},'
                                  f' but got part data type is {str(type(item))}.')
-            if hasattr(item, "size") and item.size() == 0:
+            if hasattr(item, "size") and item.size == 0:
                 msg = "Please provide non-empty data."
                 logger.error(msg)
                 raise ValueError(msg)

mindspore/_extends/parse/standard_method.py

@@ -31,6 +31,8 @@ shape_ = P.Shape()
 reshape_ = P.Reshape()
 dtype_ = P.DType()
 abs_ = P.Abs()
+ndim_ = P.Rank()
+size_ = P.Size()
 
 def mean(x, axis=(), keep_dims=False):
     """

mindspore/ccsrc/pipeline/jit/resource.cc

@@ -192,6 +192,8 @@ BuiltInTypeMap &GetAttrMap() {
      {
        {"shape", std::string("shape_")},  // C.shape_
        {"dtype", std::string("dtype_")},  // C.dtype_
+       {"size", std::string("size_")},    // C.size_
+       {"ndim", std::string("ndim_")},    // C.ndim_
      }},
     {kObjectTypeRowTensorType,
      {

mindspore/ccsrc/pybind_api/ir/tensor_py.cc

@@ -370,6 +370,17 @@ REGISTER_PYBIND_DEFINE(Tensor, ([](const py::module *m) {
                                  >>> data.shape()
                                  (3, 3)
                              )mydelimiter")
+                           .def_property_readonly("_size", &Tensor::DataSize, R"mydelimiter(
+                             Get tensor's data size.
+
+                             Returns:
+                                 int, the size of tensor.
+
+                             Examples:
+                                 >>> data = mindspore.Tensor(np.ones((2, 3)))
+                                 >>> data.size
+                                 6
+                             )mydelimiter")
                            .def("from_numpy", TensorPy::MakeTensorNoCopy, R"mydelimiter(
                              Creates a Tensor from a numpy.ndarray without copy.
 
@@ -396,17 +407,6 @@ REGISTER_PYBIND_DEFINE(Tensor, ([](const py::module *m) {
                                  array([[1., 1., 1.],
                                         [1., 1., 1.]])
                              )mydelimiter")
-                           .def("size", &Tensor::DataSize, R"mydelimiter(
-                             Get tensor's data size.
-
-                             Returns:
-                                 int, the size of tensor.
-
-                             Examples:
-                                 >>> data = mindspore.Tensor(np.ones((2, 3)))
-                                 >>> data.size()
-                                 6
-                             )mydelimiter")
                            .def("is_init", &Tensor::is_init, R"mydelimiter(
                              Get tensor init_flag.
 

mindspore/common/tensor.py

@@ -237,6 +237,16 @@ class Tensor(Tensor_):
         """The dtype of tensor is a mindspore type."""
         return self._dtype
 
+    @property
+    def size(self):
+        """The size reflects the total number of elements in tensor."""
+        return self._size
+
+    @property
+    def ndim(self):
+        """The ndim of tensor is an integer."""
+        return len(self._shape)
+
     @property
     def virtual_flag(self):
         """Mark tensor is virtual."""

mindspore/nn/layer/basic.py

@@ -277,7 +277,7 @@ class Dense(Cell):
         self.shape_op = P.Shape()
 
         if isinstance(weight_init, Tensor):
-            if weight_init.dim() != 2 or weight_init.shape[0] != out_channels or \
+            if weight_init.ndim != 2 or weight_init.shape[0] != out_channels or \
                weight_init.shape[1] != in_channels:
                 raise ValueError("Weight init shape error.")
         self.weight = Parameter(initializer(weight_init, [out_channels, in_channels]), name="weight")
@@ -285,7 +285,7 @@ class Dense(Cell):
         self.bias = None
         if self.has_bias:
             if isinstance(bias_init, Tensor):
-                if bias_init.dim() != 1 or bias_init.shape[0] != out_channels:
+                if bias_init.ndim != 1 or bias_init.shape[0] != out_channels:
                     raise ValueError("Bias init shape error.")
             self.bias = Parameter(initializer(bias_init, [out_channels]), name="bias")
             self.bias_add = P.BiasAdd()

mindspore/nn/layer/normalization.py

@@ -318,7 +318,7 @@ class BatchNorm1d(_BatchNorm):
                                           input_dims='1d')
 
     def _check_data_dim(self, x):
-        if x.dim() != 2:
+        if x.ndim != 2:
             pass
 
 
@@ -415,7 +415,7 @@ class BatchNorm2d(_BatchNorm):
                                           data_format=data_format)
 
     def _check_data_dim(self, x):
-        if x.dim() != 4:
+        if x.ndim != 4:
             pass
 
 

mindspore/nn/layer/quant.py

@@ -1008,7 +1008,7 @@ class DenseQuant(Cell):
         self.has_bias = Validator.check_bool(has_bias)
 
         if isinstance(weight_init, Tensor):
-            if weight_init.dim() != 2 or weight_init.shape[0] != out_channels or \
+            if weight_init.ndim != 2 or weight_init.shape[0] != out_channels or \
                     weight_init.shape[1] != in_channels:
                 raise ValueError("weight_init shape error")
 
@@ -1017,7 +1017,7 @@ class DenseQuant(Cell):
 
         if self.has_bias:
             if isinstance(bias_init, Tensor):
-                if bias_init.dim() != 1 or bias_init.shape[0] != out_channels:
+                if bias_init.ndim != 1 or bias_init.shape[0] != out_channels:
                     raise ValueError("bias_init shape error")
 
             self.bias = Parameter(initializer(

mindspore/nn/optim/optimizer.py

@@ -276,17 +276,17 @@ class Optimizer(Cell):
             learning_rate = float(learning_rate)
             validator.check_non_negative_float(learning_rate, "learning rate", self.cls_name)
             return learning_rate
-        if isinstance(learning_rate, Tensor) and learning_rate.dim() == 0:
+        if isinstance(learning_rate, Tensor) and learning_rate.ndim == 0:
             return learning_rate
 
         self.dynamic_lr = True
         if isinstance(learning_rate, Iterable):
             return Tensor(np.array(list(learning_rate)).astype(np.float32))
         if isinstance(learning_rate, Tensor):
-            if learning_rate.dim() > 1:
+            if learning_rate.ndim > 1:
                 raise ValueError("The dim of `Tensor` type Learning rate should be a 0 or 1,"
-                                 f"but got {learning_rate.dim()}.")
-            if learning_rate.dim() == 1 and learning_rate.size() < 2:
+                                 f"but got {learning_rate.ndim}.")
+            if learning_rate.ndim == 1 and learning_rate.size < 2:
                 logger.warning("If use `Tensor` type dynamic learning rate, please make sure that the number"
                                "of elements in the tensor passed is greater than 1.")
             return learning_rate
@@ -301,12 +301,12 @@ class Optimizer(Cell):
             if self.is_group_lr and self.dynamic_lr:
                 learning_rate = _ConvertToCell(learning_rate)
             return learning_rate
-        if isinstance(learning_rate, Tensor) and learning_rate.dim() == 0:
+        if isinstance(learning_rate, Tensor) and learning_rate.ndim == 0:
             learning_rate = Parameter(learning_rate, name)
             if self.is_group_lr and self.dynamic_lr:
                 learning_rate = _ConvertToCell(learning_rate)
             return learning_rate
-        if isinstance(learning_rate, Tensor) and learning_rate.dim() == 1:
+        if isinstance(learning_rate, Tensor) and learning_rate.ndim == 1:
             return _IteratorLearningRate(learning_rate, name)
         return learning_rate
 
@@ -336,8 +336,8 @@ class Optimizer(Cell):
     def _parse_group_params(self, parameters, learning_rate):
         """Parse group params."""
         self._check_group_params(parameters)
-        if isinstance(learning_rate, Tensor) and learning_rate.dim() == 1:
-            tensor_lr_length = learning_rate.size()
+        if isinstance(learning_rate, Tensor) and learning_rate.ndim == 1:
+            tensor_lr_length = learning_rate.size
         else:
             tensor_lr_length = 0
 
@@ -355,8 +355,8 @@ class Optimizer(Cell):
                 self.is_group_lr = True
                 group_lr = self._preprocess_single_lr(group_param['lr'])
 
-                if isinstance(group_lr, Tensor) and group_lr.dim() == 1:
-                    group_lr_length = group_lr.size()
+                if isinstance(group_lr, Tensor) and group_lr.ndim == 1:
+                    group_lr_length = group_lr.size
                     if tensor_lr_length == 0:
                         tensor_lr_length = group_lr_length
                     elif group_lr_length != tensor_lr_length:
@@ -615,9 +615,9 @@ class _IteratorLearningRate(LearningRateSchedule):
     def __init__(self, learning_rate, name):
         super(_IteratorLearningRate, self).__init__()
         if isinstance(learning_rate, Tensor):
-            if learning_rate.dim() != 1:
+            if learning_rate.ndim != 1:
                 raise ValueError("The dim of `Tensor` type dynamic learning rate should be a 1,"
-                                 f"but got {learning_rate.dim()}.")
+                                 f"but got {learning_rate.ndim}.")
         else:
             raise TypeError("Learning rate should be Tensor.")
 

mindspore/ops/operations/array_ops.py

@@ -143,7 +143,7 @@ class ExpandDims(PrimitiveWithInfer):
         - **input_x** (Tensor) - The shape of tensor is :math:`(x_1, x_2, ..., x_R)`.
         - **axis** (int) - Specifies the dimension index at which to expand
           the shape of `input_x`. The value of axis must be in the range
-          `[-input_x.dim()-1, input_x.dim()]`. Only constant value is allowed.
+          `[-input_x.ndim-1, input_x.ndim]`. Only constant value is allowed.
 
     Outputs:
         Tensor, the shape of tensor is :math:`(1, x_1, x_2, ..., x_R)` if the
@@ -597,7 +597,7 @@ class Squeeze(PrimitiveWithInfer):
     Returns a tensor with the same type but dimensions of 1 are removed based on `axis`.
 
     Note:
-        The dimension index starts at 0 and must be in the range `[-input.dim(), input.dim())`.
+        The dimension index starts at 0 and must be in the range `[-input.ndim, input.ndim`.
 
     Raises:
         ValueError: If the corresponding dimension of the specified axis does not equal to 1.

mindspore/parallel/_tensor.py

@@ -238,7 +238,7 @@ def _load_tensor_by_layout(tensor, layout):
     group = layout[5]
     if uniform_split == 0:
         raise RuntimeError("The load tensor only support uniform split now")
-    if tensor.size() == 1:
+    if tensor.size == 1:
         return tensor
     tensor_slice = _load_tensor(tensor, dev_mat, tensor_map)
     if group:

model_zoo/official/cv/resnet_thor/src/thor_layer.py

@@ -294,7 +294,7 @@ class Dense_Thor_GPU(Cell):
         self.has_bias = Validator.check_bool(has_bias)
         self.thor = True
         if isinstance(weight_init, Tensor):
-            if weight_init.dim() != 2 or weight_init.shape[0] != out_channels or \
+            if weight_init.ndim != 2 or weight_init.shape[0] != out_channels or \
                     weight_init.shape[1] != in_channels:
                 raise ValueError("weight_init shape error")
 
@@ -302,7 +302,7 @@ class Dense_Thor_GPU(Cell):
 
         if self.has_bias:
             if isinstance(bias_init, Tensor):
-                if bias_init.dim() != 1 or bias_init.shape[0] != out_channels:
+                if bias_init.ndim != 1 or bias_init.shape[0] != out_channels:
                     raise ValueError("bias_init shape error")
 
             self.bias = Parameter(initializer(bias_init, [out_channels]))
@@ -639,7 +639,7 @@ class Dense_Thor(Cell):
         self.thor = True
         self.batch_size = batch_size
         if isinstance(weight_init, Tensor):
-            if weight_init.dim() != 2 or weight_init.shape[0] != out_channels or \
+            if weight_init.ndim != 2 or weight_init.shape[0] != out_channels or \
                     weight_init.shape[1] != in_channels:
                 raise ValueError("weight_init shape error")
 
@@ -647,7 +647,7 @@ class Dense_Thor(Cell):
 
         if self.has_bias:
             if isinstance(bias_init, Tensor):
-                if bias_init.dim() != 1 or bias_init.shape[0] != out_channels:
+                if bias_init.ndim != 1 or bias_init.shape[0] != out_channels:
                     raise ValueError("bias_init shape error")
 
             self.bias = Parameter(initializer(bias_init, [out_channels]))

model_zoo/official/cv/yolov3_darknet53/src/convert_weight.py

@@ -50,16 +50,16 @@ def convert(weights_file, output_file):
         var = params[i+2]
         gamma = params[i+3]
         beta = params[i+4]
-        beta_data = weights[index: index+beta.size()].reshape(beta.shape)
-        index += beta.size()
-        gamma_data = weights[index: index+gamma.size()].reshape(gamma.shape)
-        index += gamma.size()
-        mean_data = weights[index: index+mean.size()].reshape(mean.shape)
-        index += mean.size()
-        var_data = weights[index: index + var.size()].reshape(var.shape)
-        index += var.size()
-        weight_data = weights[index: index+weight.size()].reshape(weight.shape)
-        index += weight.size()
+        beta_data = weights[index: index+beta.size].reshape(beta.shape)
+        index += beta.size
+        gamma_data = weights[index: index+gamma.size].reshape(gamma.shape)
+        index += gamma.size
+        mean_data = weights[index: index+mean.size].reshape(mean.shape)
+        index += mean.size
+        var_data = weights[index: index + var.size].reshape(var.shape)
+        index += var.size
+        weight_data = weights[index: index+weight.size].reshape(weight.shape)
+        index += weight.size
 
         param_list.append({'name': weight.name, 'type': weight.dtype, 'shape': weight.shape,
                            'data': Tensor(weight_data)})

model_zoo/official/gnn/gat/src/gat.py

@@ -76,7 +76,7 @@ class GNNFeatureTransform(nn.Cell):
         self.has_bias = has_bias
 
         if isinstance(weight_init, Tensor):
-            if weight_init.dim() != 2 or weight_init.shape[0] != out_channels or \
+            if weight_init.ndim != 2 or weight_init.shape[0] != out_channels or \
                weight_init.shape[1] != in_channels:
                 raise ValueError("weight_init shape error")
 
@@ -84,7 +84,7 @@ class GNNFeatureTransform(nn.Cell):
 
         if self.has_bias:
             if isinstance(bias_init, Tensor):
-                if bias_init.dim() != 1 or bias_init.shape[0] != out_channels:
+                if bias_init.ndim != 1 or bias_init.shape[0] != out_channels:
                     raise ValueError("bias_init shape error")
 
             self.bias = Parameter(initializer(bias_init, [out_channels]))

model_zoo/official/nlp/bert_thor/src/thor_layer.py

@@ -162,7 +162,7 @@ class Dense_Thor(Cell):
         self.has_bias = Validator.check_bool(has_bias)
         self.thor = True
         if isinstance(weight_init, Tensor):
-            if weight_init.dim() != 2 or weight_init.shape()[0] != out_channels or \
+            if weight_init.ndim != 2 or weight_init.shape()[0] != out_channels or \
                     weight_init.shape()[1] != in_channels:
                 raise ValueError("weight_init shape error")
 
@@ -170,7 +170,7 @@ class Dense_Thor(Cell):
 
         if self.has_bias:
             if isinstance(bias_init, Tensor):
-                if bias_init.dim() != 1 or bias_init.shape()[0] != out_channels:
+                if bias_init.ndim != 1 or bias_init.shape()[0] != out_channels:
                     raise ValueError("bias_init shape error")
 
             self.bias = Parameter(initializer(bias_init, [out_channels]))

model_zoo/official/recommend/ncf/src/ncf.py

@@ -43,7 +43,7 @@ class DenseLayer(nn.Cell):
         self.has_bias = validator.check_bool(has_bias)
 
         if isinstance(weight_init, Tensor):
-            if weight_init.dim() != 2 or weight_init.shape()[0] != out_channels or \
+            if weight_init.ndim != 2 or weight_init.shape()[0] != out_channels or \
                     weight_init.shape()[1] != in_channels:
                 raise ValueError("weight_init shape error")
 
@@ -51,7 +51,7 @@ class DenseLayer(nn.Cell):
 
         if self.has_bias:
             if isinstance(bias_init, Tensor):
-                if bias_init.dim() != 1 or bias_init.shape()[0] != out_channels:
+                if bias_init.ndim != 1 or bias_init.shape()[0] != out_channels:
                     raise ValueError("bias_init shape error")
 
             self.bias = Parameter(initializer(bias_init, [out_channels]))

tests/st/gnn/aggregator.py

@@ -78,7 +78,7 @@ class GNNFeatureTransform(nn.Cell):
         self.has_bias = Validator.check_bool(has_bias)
 
         if isinstance(weight_init, Tensor):
-            if weight_init.dim() != 2 or weight_init.shape[0] != out_channels or \
+            if weight_init.ndim != 2 or weight_init.shape[0] != out_channels or \
                     weight_init.shape[1] != in_channels:
                 raise ValueError("weight_init shape error")
 
@@ -86,7 +86,7 @@ class GNNFeatureTransform(nn.Cell):
 
         if self.has_bias:
             if isinstance(bias_init, Tensor):
-                if bias_init.dim() != 1 or bias_init.shape[0] != out_channels:
+                if bias_init.ndim != 1 or bias_init.shape[0] != out_channels:
                     raise ValueError("bias_init shape error")
 
             self.bias = Parameter(initializer(bias_init, [out_channels]), name="bias")

tests/st/networks/models/resnet50/src_thor/thor_layer.py

@@ -342,7 +342,7 @@ class Dense_Thor(Cell):
         self.has_bias = Validator.check_bool(has_bias)
         self.thor = True
         if isinstance(weight_init, Tensor):
-            if weight_init.dim() != 2 or weight_init.shape[0] != out_channels or \
+            if weight_init.ndim != 2 or weight_init.shape[0] != out_channels or \
                     weight_init.shape[1] != in_channels:
                 raise ValueError("weight_init shape error")
 
@@ -350,7 +350,7 @@ class Dense_Thor(Cell):
 
         if self.has_bias:
             if isinstance(bias_init, Tensor):
-                if bias_init.dim() != 1 or bias_init.shape[0] != out_channels:
+                if bias_init.ndim != 1 or bias_init.shape[0] != out_channels:
                     raise ValueError("bias_init shape error")
 
             self.bias = Parameter(initializer(bias_init, [out_channels]), name="bias")

tests/st/ops/ascend/test_tbe_ops/test_bias_add.py

@@ -35,7 +35,7 @@ class Net(nn.Cell):
         self.biasAdd = P.BiasAdd()
 
         if isinstance(bias_init, Tensor):
-            if bias_init.dim() != 1 or bias_init.shape[0] != output_channels:
+            if bias_init.ndim != 1 or bias_init.shape[0] != output_channels:
                 raise ValueError("bias_init shape error")
 
         self.bias = Parameter(initializer(

tests/ut/python/exec/test_bias_add.py

@@ -33,7 +33,7 @@ class Net(nn.Cell):
         self.biasAdd = P.BiasAdd()
 
         if isinstance(bias_init, Tensor):
-            if bias_init.dim() != 1 or bias_init.shape[0] != output_channels:
+            if bias_init.ndim != 1 or bias_init.shape[0] != output_channels:
                 raise ValueError("bias_init shape error")
 
         self.bias = Parameter(initializer(

tests/ut/python/exec/test_train.py

@@ -65,7 +65,7 @@ def test_bias_add(test_with_simu):
             self.biasAdd = P.BiasAdd()
 
             if isinstance(bias_init, Tensor):
-                if bias_init.dim() != 1 or bias_init.shape[0] != output_channels:
+                if bias_init.ndim != 1 or bias_init.shape[0] != output_channels:
                     raise ValueError("bias_init shape error")
 
             self.bias = Parameter(initializer(

tests/ut/python/ir/test_tensor_py.py

@@ -24,7 +24,7 @@ from ..ut_filter import non_graph_engine
 
 def _attribute(tensor, shape_, size_, dtype_):
     result = (tensor.shape == shape_) and \
-             (tensor.size() == size_) and \
+             (tensor.size == size_) and \
              (tensor.dtype == dtype_)
     return result
 
@@ -60,13 +60,13 @@ def test_tensor_mul():
 def test_tensor_dim():
     arr = np.ones((1, 6))
     b = ms.Tensor(arr)
-    assert b.dim() == 2
+    assert b.ndim == 2
 
 
 def test_tensor_size():
     arr = np.ones((1, 6))
     b = ms.Tensor(arr)
-    assert arr.size == b.size()
+    assert arr.size == b.size
 
 
 def test_dtype():