change combined to nn

mindspore/nn/layer/combined.py

-# Copyright 2020 Huawei Technologies Co., Ltd
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-# http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-# ============================================================================
-"""Use combination of Conv, Dense, Relu, Batchnorm."""
-
-from .normalization import BatchNorm2d
-from .activation import get_activation
-from ..cell import Cell
-from . import conv, basic
-from ..._checkparam import ParamValidator as validator
-
-
-__all__ = ['Conv2d', 'Dense']
-
-class Conv2d(Cell):
-    r"""
-    A combination of convolution, Batchnorm, activation layer.
-
-    For a more Detailed overview of Conv2d op.
-
-    Args:
-        in_channels (int): The number of input channel :math:`C_{in}`.
-        out_channels (int): The number of output channel :math:`C_{out}`.
-        kernel_size (Union[int, tuple]): The data type is int or tuple with 2 integers. Specifies the height
-            and width of the 2D convolution window. Single int means the value if for both height and width of
-            the kernel. A tuple of 2 ints means the first value is for the height and the other is for the
-            width of the kernel.
-        stride (int): Specifies stride for all spatial dimensions with the same value. Value of stride should be
-            greater or equal to 1 but bounded by the height and width of the input. Default: 1.
-        pad_mode (str): Specifies padding mode. The optional values are "same", "valid", "pad". Default: "same".
-        padding (int): Implicit paddings on both sides of the input. Default: 0.
-        dilation (int): Specifying the dilation rate to use for dilated convolution. If set to be :math:`k > 1`,
-            there will be :math:`k - 1` pixels skipped for each sampling location. Its value should be greater
-            or equal to 1 and bounded by the height and width of the input. Default: 1.
-        group (int): Split filter into groups, `in_ channels` and `out_channels` should be
-            divisible by the number of groups. Default: 1.
-        has_bias (bool): Specifies whether the layer uses a bias vector. Default: False.
-        weight_init (Union[Tensor, str, Initializer, numbers.Number]): Initializer for the convolution kernel.
-            It can be a Tensor, a string, an Initializer or a numbers.Number. When a string is specified,
-            values from 'TruncatedNormal', 'Normal', 'Uniform', 'HeUniform' and 'XavierUniform' distributions as well
-            as constant 'One' and 'Zero' distributions are possible. Alias 'xavier_uniform', 'he_uniform', 'ones'
-            and 'zeros' are acceptable. Uppercase and lowercase are both acceptable. Refer to the values of
-            Initializer for more details. Default: 'normal'.
-        bias_init (Union[Tensor, str, Initializer, numbers.Number]): Initializer for the bias vector. Possible
-            Initializer and string are the same as 'weight_init'. Refer to the values of
-            Initializer for more details. Default: 'zeros'.
-        batchnorm (bool): Specifies to used batchnorm or not. Default: None.
-        activation (string): Specifies activation type. The optional values are as following:
-            'softmax', 'logsoftmax', 'relu', 'relu6', 'tanh', 'gelu', 'sigmoid',
-            'prelu', 'leakyrelu', 'hswish', 'hsigmoid'. Default: None.
-
-    Inputs:
-        - **input** (Tensor) - Tensor of shape :math:`(N, C_{in}, H_{in}, W_{in})`.
-
-    Outputs:
-        Tensor of shape :math:`(N, C_{out}, H_{out}, W_{out})`.
-
-    Examples:
-        >>> net = combined.Conv2d(120, 240, 4, batchnorm=True, activation='ReLU')
-        >>> input = Tensor(np.ones([1, 120, 1024, 640]), mindspore.float32)
-        >>> net(input).shape
-        (1, 240, 1024, 640)
-    """
-
-    def __init__(self,
-                 in_channels,
-                 out_channels,
-                 kernel_size,
-                 stride=1,
-                 pad_mode='same',
-                 padding=0,
-                 dilation=1,
-                 group=1,
-                 has_bias=False,
-                 weight_init='normal',
-                 bias_init='zeros',
-                 batchnorm=None,
-                 activation=None):
-        super(Conv2d, self).__init__()
-        self.conv = conv.Conv2d(
-            in_channels,
-            out_channels,
-            kernel_size,
-            stride,
-            pad_mode,
-            padding,
-            dilation,
-            group,
-            has_bias,
-            weight_init,
-            bias_init)
-        self.has_bn = batchnorm is not None
-        self.has_act = activation is not None
-        self.batchnorm = batchnorm
-        if batchnorm is True:
-            self.batchnorm = BatchNorm2d(out_channels)
-        elif batchnorm is not None:
-            validator.check_isinstance('batchnorm', batchnorm, (BatchNorm2d,))
-        self.activation = get_activation(activation)
-
-    def construct(self, x):
-        x = self.conv(x)
-        if self.has_bn:
-            x = self.batchnorm(x)
-        if self.has_act:
-            x = self.activation(x)
-        return x
-
-
-class Dense(Cell):
-    r"""
-    A combination of Dense, Batchnorm, activation layer.
-
-    For a more Detailed overview of Dense op.
-
-    Args:
-        in_channels (int): The number of channels in the input space.
-        out_channels (int): The number of channels in the output space.
-        weight_init (Union[Tensor, str, Initializer, numbers.Number]): The trainable weight_init parameter. The dtype
-            is same as input x. The values of str refer to the function `initializer`. Default: 'normal'.
-        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): Regularizer function applied to the output of the layer, eg. 'relu'. Default: None.
-        batchnorm (bool): Specifies to used batchnorm or not. Default: None.
-        activation (string): Specifies activation type. The optional values are as following:
-            'softmax', 'logsoftmax', 'relu', 'relu6', 'tanh', 'gelu', 'sigmoid',
-            'prelu', 'leakyrelu', 'hswish', 'hsigmoid'. Default: None.
-
-    Inputs:
-        - **input** (Tensor) - Tensor of shape :math:`(N, in\_channels)`.
-
-    Outputs:
-        Tensor of shape :math:`(N, out\_channels)`.
-
-    Examples:
-        >>> net = nn.Dense(3, 4)
-        >>> input = Tensor(np.random.randint(0, 255, [2, 3]), mindspore.float32)
-        >>> net(input)
-    """
-
-    def __init__(self,
-                 in_channels,
-                 out_channels,
-                 weight_init='normal',
-                 bias_init='zeros',
-                 has_bias=True,
-                 batchnorm=None,
-                 activation=None):
-        super(Dense, self).__init__()
-        self.dense = basic.Dense(
-            in_channels,
-            out_channels,
-            weight_init,
-            bias_init,
-            has_bias)
-        self.has_bn = batchnorm is not None
-        self.has_act = activation is not None
-        if batchnorm is True:
-            self.batchnorm = BatchNorm2d(out_channels)
-        elif batchnorm is not None:
-            validator.check_isinstance('batchnorm', batchnorm, (BatchNorm2d,))
-        self.activation = get_activation(activation)
-
-    def construct(self, x):
-        x = self.dense(x)
-        if self.has_bn:
-            x = self.batchnorm(x)
-        if self.has_act:
-            x = self.activation(x)
-        return x

mindspore/nn/layer/quant.py

@@ -27,8 +27,16 @@ from mindspore._checkparam import Validator as validator, Rel
 from mindspore.nn.cell import Cell
 from mindspore.nn.layer.activation import get_activation
 import mindspore.context as context
+from .normalization import BatchNorm2d
+from .activation import get_activation
+from ..cell import Cell
+from . import conv, basic
+from ..._checkparam import ParamValidator as validator
+
 
 __all__ = [
+    'Conv2dBnAct',
+    'DenseBnAct',
     'FakeQuantWithMinMax',
     'Conv2dBatchNormQuant',
     'Conv2dQuant',
@@ -42,6 +50,165 @@ __all__ = [
 ]
 
 
+class Conv2dBnAct(Cell):
+    r"""
+    A combination of convolution, Batchnorm, activation layer.
+
+    For a more Detailed overview of Conv2d op.
+
+    Args:
+        in_channels (int): The number of input channel :math:`C_{in}`.
+        out_channels (int): The number of output channel :math:`C_{out}`.
+        kernel_size (Union[int, tuple]): The data type is int or tuple with 2 integers. Specifies the height
+            and width of the 2D convolution window. Single int means the value if for both height and width of
+            the kernel. A tuple of 2 ints means the first value is for the height and the other is for the
+            width of the kernel.
+        stride (int): Specifies stride for all spatial dimensions with the same value. Value of stride should be
+            greater or equal to 1 but bounded by the height and width of the input. Default: 1.
+        pad_mode (str): Specifies padding mode. The optional values are "same", "valid", "pad". Default: "same".
+        padding (int): Implicit paddings on both sides of the input. Default: 0.
+        dilation (int): Specifying the dilation rate to use for dilated convolution. If set to be :math:`k > 1`,
+            there will be :math:`k - 1` pixels skipped for each sampling location. Its value should be greater
+            or equal to 1 and bounded by the height and width of the input. Default: 1.
+        group (int): Split filter into groups, `in_ channels` and `out_channels` should be
+            divisible by the number of groups. Default: 1.
+        has_bias (bool): Specifies whether the layer uses a bias vector. Default: False.
+        weight_init (Union[Tensor, str, Initializer, numbers.Number]): Initializer for the convolution kernel.
+            It can be a Tensor, a string, an Initializer or a numbers.Number. When a string is specified,
+            values from 'TruncatedNormal', 'Normal', 'Uniform', 'HeUniform' and 'XavierUniform' distributions as well
+            as constant 'One' and 'Zero' distributions are possible. Alias 'xavier_uniform', 'he_uniform', 'ones'
+            and 'zeros' are acceptable. Uppercase and lowercase are both acceptable. Refer to the values of
+            Initializer for more details. Default: 'normal'.
+        bias_init (Union[Tensor, str, Initializer, numbers.Number]): Initializer for the bias vector. Possible
+            Initializer and string are the same as 'weight_init'. Refer to the values of
+            Initializer for more details. Default: 'zeros'.
+        batchnorm (bool): Specifies to used batchnorm or not. Default: None.
+        activation (string): Specifies activation type. The optional values are as following:
+            'softmax', 'logsoftmax', 'relu', 'relu6', 'tanh', 'gelu', 'sigmoid',
+            'prelu', 'leakyrelu', 'hswish', 'hsigmoid'. Default: None.
+
+    Inputs:
+        - **input** (Tensor) - Tensor of shape :math:`(N, C_{in}, H_{in}, W_{in})`.
+
+    Outputs:
+        Tensor of shape :math:`(N, C_{out}, H_{out}, W_{out})`.
+
+    Examples:
+        >>> net = Conv2dBnAct(120, 240, 4, batchnorm=True, activation='ReLU')
+        >>> input = Tensor(np.ones([1, 120, 1024, 640]), mindspore.float32)
+        >>> net(input).shape
+        (1, 240, 1024, 640)
+    """
+
+    def __init__(self,
+                 in_channels,
+                 out_channels,
+                 kernel_size,
+                 stride=1,
+                 pad_mode='same',
+                 padding=0,
+                 dilation=1,
+                 group=1,
+                 has_bias=False,
+                 weight_init='normal',
+                 bias_init='zeros',
+                 batchnorm=None,
+                 activation=None):
+        super(Conv2dBnAct, self).__init__()
+        self.conv = conv.Conv2d(
+            in_channels,
+            out_channels,
+            kernel_size,
+            stride,
+            pad_mode,
+            padding,
+            dilation,
+            group,
+            has_bias,
+            weight_init,
+            bias_init)
+        self.has_bn = batchnorm is not None
+        self.has_act = activation is not None
+        self.batchnorm = batchnorm
+        if batchnorm is True:
+            self.batchnorm = BatchNorm2d(out_channels)
+        elif batchnorm is not None:
+            validator.check_isinstance('batchnorm', batchnorm, (BatchNorm2d,))
+        self.activation = get_activation(activation)
+
+    def construct(self, x):
+        x = self.conv(x)
+        if self.has_bn:
+            x = self.batchnorm(x)
+        if self.has_act:
+            x = self.activation(x)
+        return x
+
+
+class DenseBnAct(Cell):
+    r"""
+    A combination of Dense, Batchnorm, activation layer.
+
+    For a more Detailed overview of Dense op.
+
+    Args:
+        in_channels (int): The number of channels in the input space.
+        out_channels (int): The number of channels in the output space.
+        weight_init (Union[Tensor, str, Initializer, numbers.Number]): The trainable weight_init parameter. The dtype
+            is same as input x. The values of str refer to the function `initializer`. Default: 'normal'.
+        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): Regularizer function applied to the output of the layer, eg. 'relu'. Default: None.
+        batchnorm (bool): Specifies to used batchnorm or not. Default: None.
+        activation (string): Specifies activation type. The optional values are as following:
+            'softmax', 'logsoftmax', 'relu', 'relu6', 'tanh', 'gelu', 'sigmoid',
+            'prelu', 'leakyrelu', 'hswish', 'hsigmoid'. Default: None.
+
+    Inputs:
+        - **input** (Tensor) - Tensor of shape :math:`(N, in\_channels)`.
+
+    Outputs:
+        Tensor of shape :math:`(N, out\_channels)`.
+
+    Examples:
+        >>> net = nn.Dense(3, 4)
+        >>> input = Tensor(np.random.randint(0, 255, [2, 3]), mindspore.float32)
+        >>> net(input)
+    """
+
+    def __init__(self,
+                 in_channels,
+                 out_channels,
+                 weight_init='normal',
+                 bias_init='zeros',
+                 has_bias=True,
+                 batchnorm=None,
+                 activation=None):
+        super(DenseBnAct, self).__init__()
+        self.dense = basic.Dense(
+            in_channels,
+            out_channels,
+            weight_init,
+            bias_init,
+            has_bias)
+        self.has_bn = batchnorm is not None
+        self.has_act = activation is not None
+        if batchnorm is True:
+            self.batchnorm = BatchNorm2d(out_channels)
+        elif batchnorm is not None:
+            validator.check_isinstance('batchnorm', batchnorm, (BatchNorm2d,))
+        self.activation = get_activation(activation)
+
+    def construct(self, x):
+        x = self.dense(x)
+        if self.has_bn:
+            x = self.batchnorm(x)
+        if self.has_act:
+            x = self.activation(x)
+        return x
+
+
 class BatchNormFoldCell(Cell):
     """
     Batch normalization folded.
@@ -302,8 +469,8 @@ class Conv2dBatchNormQuant(Cell):
 
         # initialize convolution op and Parameter
         if context.get_context('device_target') == "Ascend" and group > 1:
-            validator.check_integer('group', group, in_channels, Rel.EQ, 'Conv2dBatchNormQuant')
-            validator.check_integer('group', group, out_channels, Rel.EQ, 'Conv2dBatchNormQuant')
+            validator.check_integer('group', group, in_channels, Rel.EQ)
+            validator.check_integer('group', group, out_channels, Rel.EQ)
             self.conv = P.DepthwiseConv2dNative(channel_multiplier=1,
                                                 kernel_size=self.kernel_size,
                                                 pad_mode=pad_mode,

mindspore/train/quant/quant.py

@@ -19,7 +19,6 @@ from ... import nn
 from ... import ops
 from ..._checkparam import ParamValidator as validator
 from ..._checkparam import Rel
-from ...nn.layer import combined
 from ...nn.layer import quant
 
 _ACTIVATION_MAP = {nn.ReLU: quant.ReLUQuant,
@@ -123,13 +122,13 @@ class ConvertToQuantNetwork:
             subcell = cells[name]
             if subcell == network:
                 continue
-            elif isinstance(subcell, combined.Conv2d):
+            elif isinstance(subcell, quant.Conv2dBnAct):
                 prefix = subcell.param_prefix
                 new_subcell = self._convert_conv(subcell)
                 new_subcell.update_parameters_name(prefix + '.')
                 network.insert_child_to_cell(name, new_subcell)
                 change = True
-            elif isinstance(subcell, combined.Dense):
+            elif isinstance(subcell, quant.DenseBnAct):
                 prefix = subcell.param_prefix
                 new_subcell = self._convert_dense(subcell)
                 new_subcell.update_parameters_name(prefix + '.')
@@ -159,7 +158,7 @@ class ConvertToQuantNetwork:
 
     def _convert_conv(self, subcell):
         """
-        convet conv cell to combine cell
+        convet conv cell to quant cell
         """
         conv_inner = subcell.conv
         bn_inner = subcell.batchnorm

tests/ut/python/train/quant/mobilenetv2_combined.py

 """mobile net v2"""
 from mindspore import nn
-from mindspore.nn.layer import combined
 from mindspore.ops import operations as P
 
 
@@ -14,11 +13,11 @@ def _conv_bn(in_channel,
              stride=1):
     """Get a conv2d batchnorm and relu layer."""
     return nn.SequentialCell(
-        [combined.Conv2d(in_channel,
-                         out_channel,
-                         kernel_size=ksize,
-                         stride=stride,
-                         batchnorm=True)])
+        [nn.Conv2dBnAct(in_channel,
+                        out_channel,
+                        kernel_size=ksize,
+                        stride=stride,
+                        batchnorm=True)])
 
 
 class InvertedResidual(nn.Cell):
@@ -31,30 +30,30 @@ class InvertedResidual(nn.Cell):
         self.use_res_connect = self.stride == 1 and inp == oup
         if expend_ratio == 1:
             self.conv = nn.SequentialCell([
-                combined.Conv2d(hidden_dim,
-                                hidden_dim,
-                                3,
-                                stride,
-                                group=hidden_dim,
-                                batchnorm=True,
-                                activation='relu6'),
-                combined.Conv2d(hidden_dim, oup, 1, 1,
-                                batchnorm=True)
+                nn.Conv2dBnAct(hidden_dim,
+                               hidden_dim,
+                               3,
+                               stride,
+                               group=hidden_dim,
+                               batchnorm=True,
+                               activation='relu6'),
+                nn.Conv2dBnAct(hidden_dim, oup, 1, 1,
+                               batchnorm=True)
             ])
         else:
             self.conv = nn.SequentialCell([
-                combined.Conv2d(inp, hidden_dim, 1, 1,
-                                batchnorm=True,
-                                activation='relu6'),
-                combined.Conv2d(hidden_dim,
-                                hidden_dim,
-                                3,
-                                stride,
-                                group=hidden_dim,
-                                batchnorm=True,
-                                activation='relu6'),
-                combined.Conv2d(hidden_dim, oup, 1, 1,
-                                batchnorm=True)
+                nn.Conv2dBnAct(inp, hidden_dim, 1, 1,
+                               batchnorm=True,
+                               activation='relu6'),
+                nn.Conv2dBnAct(hidden_dim,
+                               hidden_dim,
+                               3,
+                               stride,
+                               group=hidden_dim,
+                               batchnorm=True,
+                               activation='relu6'),
+                nn.Conv2dBnAct(hidden_dim, oup, 1, 1,
+                               batchnorm=True)
             ])
         self.add = P.TensorAdd()
 
@@ -99,7 +98,7 @@ class MobileNetV2(nn.Cell):
 
         self.features = nn.SequentialCell(features)
         self.mean = P.ReduceMean(keep_dims=False)
-        self.classifier = combined.Dense(self.last_channel, num_class)
+        self.classifier = nn.DenseBnAct(self.last_channel, num_class)
 
     def construct(self, input_x):
         out = input_x

tests/ut/python/train/quant/test_quant.py

@@ -15,7 +15,7 @@
 """ tests for quant """
 import mindspore.context as context
 from mindspore import nn
-from mindspore.nn.layer import combined
+
 
 context.set_context(mode=context.GRAPH_MODE, device_target="GPU")
 
@@ -37,12 +37,11 @@ class LeNet5(nn.Cell):
     def __init__(self, num_class=10):
         super(LeNet5, self).__init__()
         self.num_class = num_class
-        self.conv1 = combined.Conv2d(
-            1, 6, kernel_size=5, batchnorm=True, activation='relu6')
-        self.conv2 = combined.Conv2d(6, 16, kernel_size=5, activation='relu')
-        self.fc1 = combined.Dense(16 * 5 * 5, 120, activation='relu')
-        self.fc2 = combined.Dense(120, 84, activation='relu')
-        self.fc3 = combined.Dense(84, self.num_class)
+        self.conv1 = nn.Conv2dBnAct(1, 6, kernel_size=5, batchnorm=True, activation='relu6')
+        self.conv2 = nn.Conv2dBnAct(6, 16, kernel_size=5, activation='relu')
+        self.fc1 = nn.DenseBnAct(16 * 5 * 5, 120, activation='relu')
+        self.fc2 = nn.DenseBnAct(120, 84, activation='relu')
+        self.fc3 = nn.DenseBnAct(84, self.num_class)
         self.max_pool2d = nn.MaxPool2d(kernel_size=2, stride=2)
         self.flattern = nn.Flatten()