Extension interface for dense

mindspore/nn/layer/basic.py

@@ -24,7 +24,7 @@ from mindspore.ops import operations as P
 from mindspore.ops import functional as F
 from mindspore.ops.functional import identity
 from mindspore.ops.operations import _inner_ops as inner
-from mindspore.ops.primitive import constexpr
+from mindspore.ops.primitive import constexpr, Primitive
 from mindspore.common.parameter import Parameter
 from mindspore._extends import cell_attr_register
 from mindspore._checkparam import Rel, Validator
@@ -175,8 +175,8 @@ class Dense(Cell):
         bias_init (Union[Tensor, str, Initializer, numbers.Number]): The trainable bias_init parameter. The dtype is
             same as input x. The values of str refer to the function `initializer`. Default: 'zeros'.
         has_bias (bool): Specifies whether the layer uses a bias vector. Default: True.
-        activation (str): activate function applied to the output of the fully connected layer, eg. 'ReLU'.
+        activation (Union[str, Cell, Primitive]): activate function applied to the output of the fully connected layer,
-            Default: None.
+            eg. 'ReLU'.Default: None.
 
     Raises:
         ValueError: If weight_init or bias_init shape is incorrect.
@@ -222,7 +222,9 @@ class Dense(Cell):
             self.bias_add = P.BiasAdd()
 
         self.matmul = P.MatMul(transpose_b=True)
-        self.activation = get_activation(activation)
+        self.activation = get_activation(activation) if isinstance(activation, str) else activation
+        if activation is not None and not isinstance(self.activation, (Cell, Primitive)):
+            raise TypeError("The activation must be str or Cell or Primitive,"" but got {}.".format(activation))
         self.activation_flag = self.activation is not None
 
     def construct(self, x):

mindspore/nn/layer/quant.py

@@ -19,6 +19,7 @@ from collections import namedtuple
 import numpy as np
 from mindspore import nn
 import mindspore.common.dtype as mstype
+from mindspore.ops.primitive import Primitive
 from mindspore.ops import operations as P
 from mindspore.ops import functional as F
 from mindspore.common.parameter import Parameter
@@ -85,7 +86,7 @@ class Conv2dBnAct(Cell):
         momentum (float): Momentum for moving average.Momentum value must be [0, 1].Default:0.9
         eps (float): Term added to the denominator to improve numerical stability. Should be greater than 0. Default:
                      1e-5.
-        activation (Cell): Specifies activation type. The optional values are as following:
+        activation (Union[str, Cell, Primitive]): Specifies activation type. The optional values are as following:
             'softmax', 'logsoftmax', 'relu', 'relu6', 'tanh', 'gelu', 'sigmoid',
             'prelu', 'leakyrelu', 'hswish', 'hsigmoid'. Default: None.
         alpha (float): Slope of the activation function at x < 0. Default: 0.2.
@@ -143,7 +144,9 @@ class Conv2dBnAct(Cell):
         if activation == "leakyrelu":
             self.activation = LeakyReLU(alpha)
         else:
-            self.activation = get_activation(activation)
+            self.activation = get_activation(activation) if isinstance(activation, str) else activation
+            if activation is not None and not isinstance(self.activation, (Cell, Primitive)):
+                raise TypeError("The activation must be str or Cell or Primitive,"" but got {}.".format(activation))
 
     def construct(self, x):
         x = self.conv(x)
@@ -170,7 +173,7 @@ class DenseBnAct(Cell):
         has_bias (bool): Specifies whether the layer uses a bias vector. Default: True.
         activation (Cell): The regularization function applied to the output of the layer, eg. 'ReLU'. Default: None.
         has_bn (bool): Specifies to use batchnorm or not. Default: False.
-        activation (string): Specifies activation type. The optional values are as following:
+        activation (Union[str, Cell, Primitive]): Specifies activation type. The optional values are as following:
             'Softmax', 'LogSoftmax', 'ReLU', 'ReLU6', 'Tanh', 'GELU', 'Sigmoid',
             'PReLU', 'LeakyReLU', 'h-Swish', and 'h-Sigmoid'. Default: None.
         after_fake(bool): Determin whether there must be a fake quantization operation after DenseBnAct.
@@ -208,7 +211,9 @@ class DenseBnAct(Cell):
         self.after_fake = after_fake
         if has_bn:
             self.batchnorm = BatchNorm1d(out_channels)
-        self.activation = get_activation(activation)
+        self.activation = get_activation(activation) if isinstance(activation, str) else activation
+        if activation is not None and not isinstance(self.activation, (Cell, Primitive)):
+            raise TypeError("The activation must be str or Cell or Primitive,"" but got {}.".format(activation))
 
     def construct(self, x):
         x = self.dense(x)
@@ -930,7 +935,8 @@ class DenseQuant(Cell):
         bias_init (Union[Tensor, str, Initializer, numbers.Number]): The trainable bias_init parameter. The dtype is
             same as input x. The values of str refer to the function `initializer`. Default: 'zeros'.
         has_bias (bool): Specifies whether the layer uses a bias vector. Default: True.
-        activation (str): The regularization function applied to the output of the layer, eg. 'relu'. Default: None.
+        activation (Union[str, Cell, Primitive]): The regularization function applied to the output of the layer,
+            eg. 'relu'. Default: None.
         quant_config (QuantConfig): Configs the oberser type of weight and activation. Default: quant_config_default.
         quant_dtype (QuantDtype): Specifies the FakeQuant datatype. Default: QuantDtype.INT8.
 
@@ -979,7 +985,9 @@ class DenseQuant(Cell):
         self.matmul = P.MatMul(transpose_b=True)
         self.bias_add = P.BiasAdd()
 
-        self.activation = get_activation(activation)
+        self.activation = get_activation(activation) if isinstance(activation, str) else activation
+        if activation is not None and not isinstance(self.activation, (Cell, Primitive)):
+            raise TypeError("The activation must be str or Cell or Primitive,"" but got {}.".format(activation))
         self.activation_flag = self.activation is not None
         self.fake_quant_weight = quant_config.weight(min_init=-6,
                                                      max_init=6,

tests/ut/python/nn/test_dense.py

@@ -19,6 +19,7 @@ import pytest
 import mindspore.context as context
 import mindspore.nn as nn
 from mindspore import Tensor
+from mindspore.ops import operations as P
 from mindspore.common.api import _executor
 from ..ut_filter import non_graph_engine
 
@@ -37,6 +38,24 @@ def test_dense_str_activation():
     dense(input_data)
 
 
+@non_graph_engine
+def test_dense_nn_activation_():
+    dense = nn.Dense(1, 1, activation=nn.ReLU())
+    assert isinstance(dense.activation, nn.ReLU)
+
+    input_data = Tensor(np.random.randint(0, 255, [1, 1]).astype(np.float32))
+    dense(input_data)
+
+
+@non_graph_engine
+def test_dense_ops_activation_():
+    dense = nn.Dense(1, 1, activation=P.ReLU())
+    assert isinstance(dense.activation, P.ReLU)
+
+    input_data = Tensor(np.random.randint(0, 255, [1, 1]).astype(np.float32))
+    dense(input_data)
+
+
 def test_dense_weight_error():
     dim_error = Tensor(np.array([[[0.1], [0.3], [0.6]], [[0.4], [0.5], [0.2]]]))
     with pytest.raises(ValueError):