forked from mindspore-Ecosystem/mindspore
!1084 add new interface quant combined
Merge pull request !1084 from SanjayChan/04quant
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4bb5c7b39a
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@ -97,7 +97,7 @@ class Cell:
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After invoked, can get all the cell's children's name prefix by '_param_prefix'.
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"""
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cells = self.cells_and_names
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cells = self.cells_and_names()
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for cell_name, cell in cells:
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cell._param_prefix = cell_name
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@ -0,0 +1,182 @@
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# Copyright 2020 Huawei Technologies Co., Ltd
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# ============================================================================
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"""Use combination of Conv, Dense, Relu, Batchnorm."""
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from .normalization import BatchNorm2d
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from .activation import get_activation
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from ..cell import Cell
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from . import conv, basic
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from ..._checkparam import ParamValidator as validator
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__all__ = ['Conv2d', 'Dense']
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class Conv2d(Cell):
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r"""
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A combination of convolution, Batchnorm, activation layer.
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For a more Detailed overview of Conv2d op.
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Args:
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in_channels (int): The number of input channel :math:`C_{in}`.
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out_channels (int): The number of output channel :math:`C_{out}`.
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kernel_size (Union[int, tuple]): The data type is int or tuple with 2 integers. Specifies the height
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and width of the 2D convolution window. Single int means the value if for both height and width of
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the kernel. A tuple of 2 ints means the first value is for the height and the other is for the
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width of the kernel.
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stride (int): Specifies stride for all spatial dimensions with the same value. Value of stride should be
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greater or equal to 1 but bounded by the height and width of the input. Default: 1.
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pad_mode (str): Specifies padding mode. The optional values are "same", "valid", "pad". Default: "same".
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padding (int): Implicit paddings on both sides of the input. Default: 0.
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dilation (int): Specifying the dilation rate to use for dilated convolution. If set to be :math:`k > 1`,
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there will be :math:`k - 1` pixels skipped for each sampling location. Its value should be greater
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or equal to 1 and bounded by the height and width of the input. Default: 1.
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group (int): Split filter into groups, `in_ channels` and `out_channels` should be
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divisible by the number of groups. Default: 1.
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has_bias (bool): Specifies whether the layer uses a bias vector. Default: False.
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weight_init (Union[Tensor, str, Initializer, numbers.Number]): Initializer for the convolution kernel.
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It can be a Tensor, a string, an Initializer or a numbers.Number. When a string is specified,
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values from 'TruncatedNormal', 'Normal', 'Uniform', 'HeUniform' and 'XavierUniform' distributions as well
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as constant 'One' and 'Zero' distributions are possible. Alias 'xavier_uniform', 'he_uniform', 'ones'
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and 'zeros' are acceptable. Uppercase and lowercase are both acceptable. Refer to the values of
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Initializer for more details. Default: 'normal'.
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bias_init (Union[Tensor, str, Initializer, numbers.Number]): Initializer for the bias vector. Possible
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Initializer and string are the same as 'weight_init'. Refer to the values of
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Initializer for more details. Default: 'zeros'.
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batchnorm (bool): Specifies to used batchnorm or not. Default: None.
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activation (string): Specifies activation type. The optional values are as following:
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'softmax', 'logsoftmax', 'relu', 'relu6', 'tanh', 'gelu', 'sigmoid',
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'prelu', 'leakyrelu', 'hswish', 'hsigmoid'. Default: None.
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Inputs:
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- **input** (Tensor) - Tensor of shape :math:`(N, C_{in}, H_{in}, W_{in})`.
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Outputs:
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Tensor of shape :math:`(N, C_{out}, H_{out}, W_{out})`.
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Examples:
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>>> net = combined.Conv2d(120, 240, 4, batchnorm=True, activation='ReLU')
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>>> input = Tensor(np.ones([1, 120, 1024, 640]), mindspore.float32)
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>>> net(input).shape()
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(1, 240, 1024, 640)
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"""
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def __init__(self,
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in_channels,
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out_channels,
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kernel_size,
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stride=1,
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pad_mode='same',
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padding=0,
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dilation=1,
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group=1,
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has_bias=False,
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weight_init='normal',
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bias_init='zeros',
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batchnorm=None,
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activation=None):
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super(Conv2d, self).__init__()
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self.conv = conv.Conv2d(
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in_channels,
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out_channels,
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kernel_size,
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stride,
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pad_mode,
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padding,
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dilation,
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group,
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has_bias,
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weight_init,
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bias_init)
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self.has_bn = batchnorm is not None
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self.has_act = activation is not None
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self.batchnorm = batchnorm
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if batchnorm is True:
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self.batchnorm = BatchNorm2d(out_channels)
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elif batchnorm is not None:
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validator.check_isinstance('batchnorm', batchnorm, (BatchNorm2d,))
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self.activation = get_activation(activation)
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def construct(self, x):
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x = self.conv(x)
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if self.has_bn:
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x = self.batchnorm(x)
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if self.has_act:
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x = self.activation(x)
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return x
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class Dense(Cell):
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r"""
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A combination of Dense, Batchnorm, activation layer.
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For a more Detailed overview of Dense op.
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Args:
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in_channels (int): The number of channels in the input space.
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out_channels (int): The number of channels in the output space.
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weight_init (Union[Tensor, str, Initializer, numbers.Number]): The trainable weight_init parameter. The dtype
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is same as input x. The values of str refer to the function `initializer`. Default: 'normal'.
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bias_init (Union[Tensor, str, Initializer, numbers.Number]): The trainable bias_init parameter. The dtype is
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same as input x. The values of str refer to the function `initializer`. Default: 'zeros'.
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has_bias (bool): Specifies whether the layer uses a bias vector. Default: True.
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activation (str): Regularizer function applied to the output of the layer, eg. 'relu'. Default: None.
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batchnorm (bool): Specifies to used batchnorm or not. Default: None.
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activation (string): Specifies activation type. The optional values are as following:
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'softmax', 'logsoftmax', 'relu', 'relu6', 'tanh', 'gelu', 'sigmoid',
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'prelu', 'leakyrelu', 'hswish', 'hsigmoid'. Default: None.
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Inputs:
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- **input** (Tensor) - Tensor of shape :math:`(N, in\_channels)`.
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Outputs:
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Tensor of shape :math:`(N, out\_channels)`.
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Examples:
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>>> net = nn.Dense(3, 4)
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>>> input = Tensor(np.random.randint(0, 255, [2, 3]), mindspore.float32)
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>>> net(input)
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"""
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def __init__(self,
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in_channels,
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out_channels,
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weight_init='normal',
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bias_init='zeros',
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has_bias=True,
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batchnorm=None,
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activation=None):
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super(Dense, self).__init__()
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self.dense = basic.Dense(
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in_channels,
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out_channels,
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weight_init,
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bias_init,
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has_bias)
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self.has_bn = batchnorm is not None
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self.has_act = activation is not None
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if batchnorm is True:
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self.batchnorm = BatchNorm2d(out_channels)
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elif batchnorm is not None:
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validator.check_isinstance('batchnorm', batchnorm, (BatchNorm2d,))
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self.activation = get_activation(activation)
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def construct(self, x):
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x = self.dense(x)
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if self.has_bn:
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x = self.batchnorm(x)
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if self.has_act:
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x = self.activation(x)
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return x
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@ -191,6 +191,8 @@ class Conv2dBatchNormQuant(Cell):
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stride,
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pad_mode,
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padding=0,
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dilation=1,
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group=1,
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eps=1e-5,
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momentum=0.9,
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weight_init=None,
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@ -198,7 +200,6 @@ class Conv2dBatchNormQuant(Cell):
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gamma_init=None,
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mean_init=None,
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var_init=None,
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group=1,
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quant_delay=0,
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freeze_bn=100000,
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fake=True,
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@ -0,0 +1,26 @@
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# Copyright 2020 Huawei Technologies Co., Ltd
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# ============================================================================
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"""
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quantization.
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User can use aware quantization to train a model. Mindspore supports quantization aware training,
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which models quantization errors in both the forward and backward passes using fake-quantization
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ops. Note that the entire computation is carried out in floating point. At the end of quantization
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aware training, Mindspore provides conversion functions to convert the trained model into lower precision.
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"""
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from .quant import convert_quant_network
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__all__ = ["convert_quant_network"]
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@ -0,0 +1,262 @@
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# Copyright 2020 Huawei Technologies Co., Ltd
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# ============================================================================
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"""aware quantization."""
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import re
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from ... import nn
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from ... import ops
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from ..._checkparam import ParamValidator as validator
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from ..._checkparam import Rel
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from ...nn.layer import combined
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from ...nn.layer import quant
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_ACTIVATION_MAP = {nn.ReLU: quant.ReLUQuant,
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nn.ReLU6: quant.ReLU6Quant,
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nn.HSigmoid: quant.HSigmoidQuant,
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nn.HSwish: quant.HSwishQuant}
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class _AddFakeQuantInputOutput(nn.Cell):
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"""
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Add FakeQuant at input and output of the Network. Only support one input and one output case.
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"""
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def __init__(self, network, quant_delay=0):
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super(_AddFakeQuantInputOutput, self).__init__(auto_prefix=False)
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self.network = network
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self.fake_quant_input = quant.FakeQuantWithMinMax(
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min_init=-6, max_init=6, quant_delay=quant_delay, ema=True)
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self.fake_quant_input.update_parameters_name('fake_quant_input')
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self.fake_quant_output = quant.FakeQuantWithMinMax(
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min_init=-6, max_init=6, quant_delay=quant_delay, ema=True)
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self.fake_quant_output.update_parameters_name('fake_quant_output')
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def construct(self, data):
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data = self.fake_quant_input(data)
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output = self.network(data)
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output = self.fake_quant_output(output)
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return output
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class _AddFakeQuantAfterSubCell(nn.Cell):
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"""
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Add FakeQuant after of the sub Cell.
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"""
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def __init__(self, subcell, quant_delay=0, num_bits=8):
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super(_AddFakeQuantAfterSubCell, self).__init__(auto_prefix=False)
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self.subcell = subcell
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self.fake_quant_act = quant.FakeQuantWithMinMax(min_init=-6,
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max_init=6,
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num_bits=num_bits,
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quant_delay=quant_delay,
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ema=True)
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def construct(self, *data):
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output = self.subcell(*data)
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output = self.fake_quant_act(output)
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return output
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class ConvertToQuantNetwork:
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"""
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Convert network to quantization aware network
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"""
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__quant_op_name__ = ["TensorAdd", "Sub", "Mul", "RealDiv"]
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def __init__(self,
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network,
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quant_delay=0,
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bn_fold=False,
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freeze_bn=0,
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weight_bits=8,
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act_bits=8,
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per_channel=False,
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symmetric=False,
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narrow_range=False):
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self.network = validator.check_isinstance(
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'network', network, (nn.Cell,))
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self.quant_delay = validator.check_integer(
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"quant delay", quant_delay, 0, Rel.GE)
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self.freeze_bn = validator.check_integer(
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"freeze bn", freeze_bn, 0, Rel.GE)
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self.weight_bits = validator.check_integer(
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"weights bit", weight_bits, 0, Rel.GE)
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self.act_bits = validator.check_integer(
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"activations bit", act_bits, 0, Rel.GE)
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self.bn_fold = validator.check_bool("bn fold", bn_fold)
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self.per_channel = validator.check_bool("per channel", per_channel)
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self.symmetric = validator.check_bool("symmetric", symmetric)
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self.narrow_range = validator.check_bool("narrow range", narrow_range)
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def _convert_op_name(self, name):
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pattern = re.compile(r'([A-Z]{1})')
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name_new = re.sub(pattern, r'_\1', name).lower()
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if name_new[0] == '_':
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name_new = name_new[1:]
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return name_new
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def run(self):
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self.network.update_cell_prefix()
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network = self._convert_subcells2quant(self.network)
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return network
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def _convert_subcells2quant(self, network):
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"""
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convet sub cell to quant cell
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"""
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cells = network.name_cells()
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change = False
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for name in cells:
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subcell = cells[name]
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if subcell == network:
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continue
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elif isinstance(subcell, combined.Conv2d):
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prefix = subcell.param_prefix
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new_subcell = self._convert_conv(subcell)
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new_subcell.update_parameters_name(prefix + '.')
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network.insert_child_to_cell(name, new_subcell)
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change = True
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elif isinstance(subcell, combined.Dense):
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prefix = subcell.param_prefix
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new_subcell = self._convert_dense(subcell)
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new_subcell.update_parameters_name(prefix + '.')
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network.insert_child_to_cell(name, new_subcell)
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change = True
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else:
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self._convert_subcells2quant(subcell)
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if isinstance(network, nn.SequentialCell) and change:
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network.cell_list = list(network.cells())
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# tensoradd to tensoradd quant
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add_list = []
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for name in network.__dict__:
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if name[0] == '_':
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continue
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attr = network.__dict__[name]
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if isinstance(attr, ops.Primitive) and attr.name in ConvertToQuantNetwork.__quant_op_name__:
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add_list.append((name, attr))
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for name, prim_op in add_list:
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prefix = name
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add_quant = _AddFakeQuantAfterSubCell(prim_op) # quant.TensorAddQuant()
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prefix = '.'.join([network.param_prefix, self._convert_op_name(prim_op.name)])
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add_quant.update_parameters_name(prefix + '.')
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del network.__dict__[name]
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network.insert_child_to_cell(name, add_quant)
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return network
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def _convert_conv(self, subcell):
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"""
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convet conv cell to combine cell
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"""
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conv_inner = subcell.conv
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bn_inner = subcell.batchnorm
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if subcell.batchnorm is not None and self.bn_fold:
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conv_inner = quant.Conv2dBatchNormQuant(conv_inner.in_channels,
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conv_inner.out_channels,
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kernel_size=conv_inner.kernel_size,
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stride=conv_inner.stride,
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pad_mode=conv_inner.pad_mode,
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padding=conv_inner.padding,
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dilation=conv_inner.dilation,
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group=conv_inner.group,
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eps=bn_inner.eps,
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momentum=bn_inner.momentum,
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quant_delay=self.quant_delay,
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freeze_bn=self.freeze_bn,
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per_channel=self.per_channel,
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num_bits=self.weight_bits,
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fake=True,
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symmetric=self.symmetric,
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narrow_range=self.narrow_range)
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del subcell.batchnorm
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subcell.batchnorm = None
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subcell.has_bn = False
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else:
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conv_inner = quant.Conv2dQuant(conv_inner.in_channels,
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conv_inner.out_channels,
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kernel_size=conv_inner.kernel_size,
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stride=conv_inner.stride,
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pad_mode=conv_inner.pad_mode,
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padding=conv_inner.padding,
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dilation=conv_inner.dilation,
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group=conv_inner.group,
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has_bias=conv_inner.has_bias,
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quant_delay=self.quant_delay,
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per_channel=self.per_channel,
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num_bits=self.weight_bits,
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symmetric=self.symmetric,
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narrow_range=self.narrow_range)
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subcell.conv = conv_inner
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if subcell.activation is not None:
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subcell.activation = self._convert_activation(subcell.activation)
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||||
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()
|
||||
|
|
@ -0,0 +1,100 @@
|
|||
"""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
|
||||
|
|
@ -0,0 +1,108 @@
|
|||
"""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
|
||||
|
|
@ -0,0 +1,94 @@
|
|||
# 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)
|
||||
Loading…
Reference in New Issue