add quant combined

mindspore/nn/cell.py

@@ -97,7 +97,7 @@ class Cell:
 
         After invoked, can get all the cell's children's name perfix by '_param_perfix'.
         """
-        cells = self.cells_and_names
+        cells = self.cells_and_names()
 
         for cell_name, cell in cells:
             cell._param_perfix = cell_name

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/train/quant/__init__.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.
+# ============================================================================
+"""
+quantization.
+
+User can use aware quantization to train a model. Mindspore supports quantization aware training,
+which models quantization errors in both the forward and backward passes using fake-quantization
+ops. Note that the entire computation is carried out in floating point. At the end of quantization
+aware training, Mindspore provides conversion functions to convert the trained model into lower precision.
+"""
+
+from .quant import convert_quant_network
+
+__all__ = ["convert_quant_network"]

mindspore/train/quant/quant.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.
+# ============================================================================
+"""aware quantization."""
+
+import re
+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,
+                   nn.ReLU6: quant.ReLU6Quant,
+                   nn.HSigmoid: quant.HSigmoidQuant,
+                   nn.HSwish: quant.HSwishQuant}
+
+
+class _AddFakeQuantInputOutput(nn.Cell):
+    """
+    Add FakeQuant at input and output of the Network. Only support one input and one output case.
+    """
+
+    def __init__(self, network, quant_delay=0):
+        super(_AddFakeQuantInputOutput, self).__init__(auto_prefix=False)
+        self.network = network
+        self.fake_quant_input = quant.FakeQuantWithMinMax(
+            min_init=-6, max_init=6, quant_delay=quant_delay, ema=True)
+        self.fake_quant_input.update_parameters_name('fake_quant_input')
+        self.fake_quant_output = quant.FakeQuantWithMinMax(
+            min_init=-6, max_init=6, quant_delay=quant_delay, ema=True)
+        self.fake_quant_output.update_parameters_name('fake_quant_output')
+
+    def construct(self, data):
+        data = self.fake_quant_input(data)
+        output = self.network(data)
+        output = self.fake_quant_output(output)
+        return output
+
+
+class _AddFakeQuantAfterSubCell(nn.Cell):
+    """
+    Add FakeQuant after of the sub Cell.
+    """
+
+    def __init__(self, subcell, quant_delay=0, num_bits=8):
+        super(_AddFakeQuantAfterSubCell, self).__init__(auto_prefix=False)
+        self.subcell = subcell
+        self.fake_quant_act = quant.FakeQuantWithMinMax(min_init=-6,
+                                                        max_init=6,
+                                                        num_bits=num_bits,
+                                                        quant_delay=quant_delay,
+                                                        ema=True)
+
+    def construct(self, *data):
+        output = self.subcell(*data)
+        output = self.fake_quant_act(output)
+        return output
+
+
+class ConvertToQuantNetwork:
+    """
+    Convert network to quantization aware network
+    """
+    __quant_op_name__ = ["TensorAdd", "Sub", "Mul", "RealDiv"]
+
+    def __init__(self,
+                 network,
+                 quant_delay=0,
+                 bn_fold=False,
+                 freeze_bn=0,
+                 weight_bits=8,
+                 act_bits=8,
+                 per_channel=False,
+                 symmetric=False,
+                 narrow_range=False):
+        self.network = validator.check_isinstance(
+            'network', network, (nn.Cell,))
+        self.quant_delay = validator.check_integer(
+            "quant delay", quant_delay, 0, Rel.GE)
+        self.freeze_bn = validator.check_integer(
+            "freeze bn", freeze_bn, 0, Rel.GE)
+        self.weight_bits = validator.check_integer(
+            "weights bit", weight_bits, 0, Rel.GE)
+        self.act_bits = validator.check_integer(
+            "activations bit", act_bits, 0, Rel.GE)
+        self.bn_fold = validator.check_bool("bn fold", bn_fold)
+        self.per_channel = validator.check_bool("per channel", per_channel)
+        self.symmetric = validator.check_bool("symmetric", symmetric)
+        self.narrow_range = validator.check_bool("narrow range", narrow_range)
+
+    def _convert_op_name(self, name):
+        pattern = re.compile(r'([A-Z]{1})')
+        name_new = re.sub(pattern, r'_\1', name).lower()
+        if name_new[0] == '_':
+            name_new = name_new[1:]
+        return name_new
+
+    def run(self):
+        self.network.update_cell_prefix()
+        network = self._convert_subcells2quant(self.network)
+        return network
+
+    def _convert_subcells2quant(self, network):
+        """
+        convet sub cell to quant cell
+        """
+        cells = network.name_cells()
+        change = False
+        for name in cells:
+            subcell = cells[name]
+            if subcell == network:
+                continue
+            elif isinstance(subcell, combined.Conv2d):
+                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):
+                prefix = subcell.param_prefix
+                new_subcell = self._convert_dense(subcell)
+                new_subcell.update_parameters_name(prefix + '.')
+                network.insert_child_to_cell(name, new_subcell)
+                change = True
+            else:
+                self._convert_subcells2quant(subcell)
+        if isinstance(network, nn.SequentialCell) and change:
+            network.cell_list = list(network.cells())
+
+        # tensoradd to tensoradd quant
+        add_list = []
+        for name in network.__dict__:
+            if name[0] == '_':
+                continue
+            attr = network.__dict__[name]
+            if isinstance(attr, ops.Primitive) and attr.name in ConvertToQuantNetwork.__quant_op_name__:
+                add_list.append((name, attr))
+        for name, prim_op in add_list:
+            prefix = name
+            add_quant = _AddFakeQuantAfterSubCell(prim_op)  # quant.TensorAddQuant()
+            prefix = '.'.join([network.param_prefix, self._convert_op_name(prim_op.name)])
+            add_quant.update_parameters_name(prefix + '.')
+            del network.__dict__[name]
+            network.insert_child_to_cell(name, add_quant)
+        return network
+
+    def _convert_conv(self, subcell):
+        """
+        convet conv cell to combine cell
+        """
+        conv_inner = subcell.conv
+        bn_inner = subcell.batchnorm
+        if subcell.batchnorm is not None and self.bn_fold:
+            conv_inner = quant.Conv2dBatchNormQuant(conv_inner.in_channels,
+                                                    conv_inner.out_channels,
+                                                    kernel_size=conv_inner.kernel_size,
+                                                    stride=conv_inner.stride,
+                                                    pad_mode=conv_inner.pad_mode,
+                                                    padding=conv_inner.padding,
+                                                    dilation=conv_inner.dilation,
+                                                    group=conv_inner.group,
+                                                    eps=bn_inner.eps,
+                                                    momentum=bn_inner.momentum,
+                                                    quant_delay=self.quant_delay,
+                                                    freeze_bn=self.freeze_bn,
+                                                    per_channel=self.per_channel,
+                                                    num_bits=self.weight_bits,
+                                                    fake=True,
+                                                    symmetric=self.symmetric,
+                                                    narrow_range=self.narrow_range)
+            del subcell.batchnorm
+            subcell.batchnorm = None
+            subcell.has_bn = False
+        else:
+            conv_inner = quant.Conv2dQuant(conv_inner.in_channels,
+                                           conv_inner.out_channels,
+                                           kernel_size=conv_inner.kernel_size,
+                                           stride=conv_inner.stride,
+                                           pad_mode=conv_inner.pad_mode,
+                                           padding=conv_inner.padding,
+                                           dilation=conv_inner.dilation,
+                                           group=conv_inner.group,
+                                           has_bias=conv_inner.has_bias,
+                                           quant_delay=self.quant_delay,
+                                           per_channel=self.per_channel,
+                                           num_bits=self.weight_bits,
+                                           symmetric=self.symmetric,
+                                           narrow_range=self.narrow_range)
+        subcell.conv = conv_inner
+        if subcell.activation is not None:
+            subcell.activation = self._convert_activation(subcell.activation)
+        else:
+            subcell = _AddFakeQuantAfterSubCell(subcell)
+        return subcell
+
+    def _convert_dense(self, subcell):
+        """
+        convert dense cell to combine dense cell
+        """
+        dense_inner = subcell.dense
+        dense_inner = quant.DenseQuant(dense_inner.in_channels,
+                                       dense_inner.out_channels,
+                                       has_bias=dense_inner.has_bias,
+                                       quant_delay=self.quant_delay,
+                                       per_channel=self.per_channel,
+                                       num_bits=self.weight_bits)
+        subcell.dense = dense_inner
+        if subcell.activation is not None:
+            subcell.activation = self._convert_activation(subcell.activation)
+        return subcell
+
+    def _convert_activation(self, activation):
+        act_class = activation.__class__
+        if act_class not in _ACTIVATION_MAP:
+            raise ValueError(
+                "Unsupported activation in auto Quant: ", act_class)
+        return _ACTIVATION_MAP[act_class](num_bits=self.act_bits, quant_delay=self.quant_delay)
+
+
+def convert_quant_network(network,
+                          quant_delay=0,
+                          bn_fold=False,
+                          freeze_bn=0,
+                          weight_bits=8,
+                          act_bits=8,
+                          per_channel=False,
+                          symmetric=False,
+                          narrow_range=False
+                          ):
+    r"""
+    Create aware quantizaiton training network.
+
+    Args:
+        network (Cell): Obtain a pipeline through network for saving graph summary.
+        quant_delay (int): Number of steps after which weights and activations are quantized during eval. Default: 0.
+        bn_fold (bool): Flag to used bn fold ops for simulation inference operation. Default: False.
+        freeze_bn (bool): Number of steps after which BN parameters used total mean and variance. Default: 0.
+        weight_bits (int): Number of bits to use for quantizing weights. Default: 8.
+        act_bits (int): Number of bits to use for quantizing activations. Default: 8.
+        per_channel (bool):  Quantization granularity based on layer or on channel. Default: False.
+        symmetric (bool): Quantization algorithm use symmetric or not. Default: False.
+        narrow_range (bool): Quantization algorithm use narrow range or not. Default: False.
+
+    returns:
+        Cell, Network which has change to aware quantization training network.
+    """
+    net = ConvertToQuantNetwork(
+        network, quant_delay, bn_fold, freeze_bn, weight_bits, act_bits, per_channel, symmetric, narrow_range)
+    return net.run()

tests/ut/python/train/quant/mobilenetv2.py

+"""MobileNetV2"""
+from mindspore import nn
+from mindspore.ops import operations as P
+
+
+def make_divisible(input_x, div_by=8):
+    return int((input_x + div_by) // div_by)
+
+
+def _conv_bn(in_channel,
+             out_channel,
+             ksize,
+             stride=1):
+    """Get a conv2d batchnorm and relu layer."""
+    return nn.SequentialCell(
+        [nn.Conv2d(in_channel,
+                   out_channel,
+                   kernel_size=ksize,
+                   stride=stride),
+         nn.BatchNorm2d(out_channel)])
+
+
+class InvertedResidual(nn.Cell):
+    def __init__(self, inp, oup, stride, expend_ratio):
+        super(InvertedResidual, self).__init__()
+        self.stride = stride
+        assert stride in [1, 2]
+
+        hidden_dim = int(inp * expend_ratio)
+        self.use_res_connect = self.stride == 1 and inp == oup
+        if expend_ratio == 1:
+            self.conv = nn.SequentialCell([
+                nn.Conv2d(hidden_dim, hidden_dim, 3, stride, group=hidden_dim),
+                nn.BatchNorm2d(hidden_dim),
+                nn.ReLU6(),
+                nn.Conv2d(hidden_dim, oup, 1, 1),
+                nn.BatchNorm2d(oup)
+            ])
+        else:
+            self.conv = nn.SequentialCell([
+                nn.Conv2d(inp, hidden_dim, 1, 1),
+                nn.BatchNorm2d(hidden_dim),
+                nn.ReLU6(),
+
+                nn.Conv2d(hidden_dim, hidden_dim, 3, stride, group=hidden_dim),
+                nn.BatchNorm2d(hidden_dim),
+                nn.ReLU6(),
+
+                nn.Conv2d(hidden_dim, oup, 1, 1),
+                nn.BatchNorm2d(oup)
+            ])
+
+    def construct(self, input_x):
+        out = self.conv(input_x)
+        if self.use_res_connect:
+            out = input_x + out
+        return out
+
+
+class MobileNetV2(nn.Cell):
+    def __init__(self, num_class=1000, input_size=224, width_mul=1.):
+        super(MobileNetV2, self).__init__()
+        block = InvertedResidual
+        input_channel = 32
+        last_channel = 1280
+        inverted_residual_setting = [
+            [1, 16, 1, 1],
+            [6, 24, 2, 2],
+            [6, 32, 3, 2],
+            [6, 64, 4, 2],
+            [6, 96, 3, 1],
+            [6, 160, 3, 2],
+            [6, 230, 1, 1],
+        ]
+        if width_mul > 1.0:
+            last_channel = make_divisible(last_channel * width_mul)
+        self.last_channel = last_channel
+        features = [_conv_bn(3, input_channel, 3, 2)]
+
+        for t, c, n, s in inverted_residual_setting:
+            out_channel = make_divisible(c * width_mul) if t > 1 else c
+            for i in range(n):
+                if i == 0:
+                    features.append(block(input_channel, out_channel, s, t))
+                else:
+                    features.append(block(input_channel, out_channel, 1, t))
+                input_channel = out_channel
+
+        features.append(_conv_bn(input_channel, self.last_channel, 1))
+
+        self.features = nn.SequentialCell(features)
+        self.mean = P.ReduceMean(keep_dims=False)
+        self.classifier = nn.Dense(self.last_channel, num_class)
+
+    def construct(self, input_x):
+        out = input_x
+        out = self.features(out)
+        out = self.mean(out, (2, 3))
+        out = self.classifier(out)
+        return out

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

+"""mobile net v2"""
+from mindspore import nn
+from mindspore.ops import operations as P
+from mindspore.nn.layer import combined
+
+
+def make_divisible(input_x, div_by=8):
+    return int((input_x + div_by) // div_by)
+
+
+def _conv_bn(in_channel,
+             out_channel,
+             ksize,
+             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)])
+
+
+class InvertedResidual(nn.Cell):
+    def __init__(self, inp, oup, stride, expend_ratio):
+        super(InvertedResidual, self).__init__()
+        self.stride = stride
+        assert stride in [1, 2]
+
+        hidden_dim = int(inp * expend_ratio)
+        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)
+            ])
+        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)
+            ])
+        self.add = P.TensorAdd()
+
+    def construct(self, input_x):
+        out = self.conv(input_x)
+        if self.use_res_connect:
+            out = self.add(input_x, out)
+        return out
+
+
+class MobileNetV2(nn.Cell):
+    def __init__(self, num_class=1000, input_size=224, width_mul=1.):
+        super(MobileNetV2, self).__init__()
+        block = InvertedResidual
+        input_channel = 32
+        last_channel = 1280
+        inverted_residual_setting = [
+            [1, 16, 1, 1],
+            [6, 24, 2, 2],
+            [6, 32, 3, 2],
+            [6, 64, 4, 2],
+            [6, 96, 3, 1],
+            [6, 160, 3, 2],
+            [6, 230, 1, 1],
+        ]
+        if width_mul > 1.0:
+            last_channel = make_divisible(last_channel * width_mul)
+        self.last_channel = last_channel
+        features = [_conv_bn(3, input_channel, 3, 2)]
+
+        for t, c, n, s in inverted_residual_setting:
+            out_channel = make_divisible(c * width_mul) if t > 1 else c
+            for i in range(n):
+                if i == 0:
+                    features.append(block(input_channel, out_channel, s, t))
+                else:
+                    features.append(block(input_channel, out_channel, 1, t))
+                input_channel = out_channel
+
+        features.append(_conv_bn(input_channel, self.last_channel, 1))
+
+        self.features = nn.SequentialCell(features)
+        self.mean = P.ReduceMean(keep_dims=False)
+        self.classifier = combined.Dense(self.last_channel, num_class)
+
+    def construct(self, input_x):
+        out = input_x
+        out = self.features(out)
+        out = self.mean(out, (2, 3))
+        out = self.classifier(out)
+        return out

tests/ut/python/train/quant/test_quant.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.
+# ============================================================================
+""" tests for quant """
+import numpy as np
+from mindspore import Tensor
+from mindspore.train.quant import quant as qat
+from mindspore import nn
+import mindspore.ops.operations as P
+from mindspore.nn.layer import combined
+import mindspore.context as context
+from mobilenetv2_combined import MobileNetV2
+
+context.set_context(mode=context.GRAPH_MODE)
+
+
+class LeNet5(nn.Cell):
+    """
+    Lenet network
+
+    Args:
+        num_class (int): Num classes. Default: 10.
+
+    Returns:
+        Tensor, output tensor
+    Examples:
+        >>> LeNet(num_class=10)
+
+    """
+
+    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.max_pool2d = nn.MaxPool2d(kernel_size=2, stride=2)
+        self.flattern = nn.Flatten()
+
+    def construct(self, x):
+        x = self.conv1(x)
+        x = self.bn(x)
+        x = self.relu(x)
+        x = self.max_pool2d(x)
+        x = self.conv2(x)
+        x = self.max_pool2d(x)
+        x = self.flattern(x)
+        x = self.fc1(x)
+        x = self.fc2(x)
+        x = self.fc3(x)
+        return x
+
+
+def test_qat_lenet():
+    net = LeNet5()
+    net = qat.convert_quant_network(
+        net, quant_delay=0, bn_fold=False, freeze_bn=10000, weight_bits=8, act_bits=8)
+
+
+def test_qat_mobile():
+    net = MobileNetV2()
+    img = Tensor(np.ones((1, 3, 224, 224)).astype(np.float32))
+    net = qat.convert_quant_network(
+        net, quant_delay=0, bn_fold=False, freeze_bn=10000, weight_bits=8, act_bits=8)
+    net(img)
+
+
+def test_qat_mobile_train():
+    net = MobileNetV2(num_class=10)
+    img = Tensor(np.ones((1, 3, 224, 224)).astype(np.float32))
+    label = Tensor(np.ones((1, 10)).astype(np.float32))
+    net = qat.convert_quant_network(
+        net, quant_delay=0, bn_fold=False, freeze_bn=10000, weight_bits=8, act_bits=8)
+
+    loss = nn.SoftmaxCrossEntropyWithLogits(reduction='mean')
+    optimizer = nn.Momentum(net.trainable_params(),
+                            learning_rate=0.1, momentum=0.9)
+    net = nn.WithLossCell(net, loss)
+    net = nn.TrainOneStepCell(net, optimizer)
+    net(img, label)