!8187 move Conv2dBnAct,DenseBnAct to combined.py
Merge pull request !8187 from yuchaojie/quantpull/8187/MERGE
mindspore-ci-bot
5 years ago
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commit
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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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"""Combined cells."""
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from mindspore import nn
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from mindspore.ops.primitive import Primitive
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from mindspore._checkparam import Validator
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from .normalization import BatchNorm2d, BatchNorm1d
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from .activation import get_activation, LeakyReLU
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from ..cell import Cell
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__all__ = [
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'Conv2dBnAct',
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'DenseBnAct'
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]
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class Conv2dBnAct(Cell):
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r"""
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A combination of convolution, Batchnorm, activation layer.
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This part is 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 a tuple of 2 integers. Specifies the height
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and width of the 2D convolution window. Single int means the value is 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. The value of stride must be
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greater than or equal to 1 and lower than any one of 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): Specifies 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 must be greater than
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or equal to 1 and lower than any one of the height and width of the input. Default: 1.
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group (int): Splits filter into groups, `in_ channels` and `out_channels` must 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 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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has_bn (bool): Specifies to used batchnorm or not. Default: False.
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momentum (float): Momentum for moving average for batchnorm, must be [0, 1]. Default:0.9
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eps (float): Term added to the denominator to improve numerical stability for batchnorm, should be greater
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than 0. Default: 1e-5.
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activation (Union[str, Cell, Primitive]): 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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alpha (float): Slope of the activation function at x < 0 for LeakyReLU. Default: 0.2.
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after_fake(bool): Determine whether there must be a fake quantization operation after Cond2dBnAct.
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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 = nn.Conv2dBnAct(120, 240, 4, has_bn=True, activation='ReLU')
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>>> input = Tensor(np.ones([1, 120, 1024, 640]), mindspore.float32)
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>>> result = net(input)
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>>> result.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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has_bn=False,
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momentum=0.9,
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eps=1e-5,
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activation=None,
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alpha=0.2,
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after_fake=True):
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super(Conv2dBnAct, self).__init__()
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self.conv = nn.Conv2d(in_channels,
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out_channels,
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kernel_size=kernel_size,
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stride=stride,
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pad_mode=pad_mode,
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padding=padding,
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dilation=dilation,
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group=group,
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has_bias=has_bias,
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weight_init=weight_init,
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bias_init=bias_init)
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self.has_bn = Validator.check_bool(has_bn, "has_bn")
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self.has_act = activation is not None
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self.after_fake = Validator.check_bool(after_fake, "after_fake")
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if has_bn:
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self.batchnorm = BatchNorm2d(out_channels, eps, momentum)
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if activation == "leakyrelu":
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self.activation = LeakyReLU(alpha)
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else:
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self.activation = get_activation(activation) if isinstance(activation, str) else activation
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if activation is not None and not isinstance(self.activation, (Cell, Primitive)):
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raise TypeError("The activation must be str or Cell or Primitive,"" but got {}.".format(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 DenseBnAct(Cell):
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r"""
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A combination of Dense, Batchnorm, and the activation layer.
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This part is 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. 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. 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 (Cell): The regularization function applied to the output of the layer, eg. 'ReLU'. Default: None.
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has_bn (bool): Specifies to use batchnorm or not. Default: False.
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momentum (float): Momentum for moving average for batchnorm, must be [0, 1]. Default:0.9
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eps (float): Term added to the denominator to improve numerical stability for batchnorm, should be greater
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than 0. Default: 1e-5.
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activation (Union[str, Cell, Primitive]): 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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alpha (float): Slope of the activation function at x < 0 for LeakyReLU. Default: 0.2.
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after_fake(bool): Determine whether there must be a fake quantization operation after DenseBnAct.
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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.DenseBnAct(3, 4)
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>>> input = Tensor(np.random.randint(0, 255, [2, 3]), mindspore.float32)
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>>> result = net(input)
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>>> result.shape
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(2, 4)
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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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has_bn=False,
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momentum=0.9,
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eps=1e-5,
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activation=None,
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alpha=0.2,
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after_fake=True):
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super(DenseBnAct, self).__init__()
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self.dense = nn.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 = Validator.check_bool(has_bn, "has_bn")
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self.has_act = activation is not None
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self.after_fake = Validator.check_bool(after_fake, "after_fake")
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if has_bn:
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self.batchnorm = BatchNorm1d(out_channels, eps, momentum)
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if activation == "leakyrelu":
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self.activation = LeakyReLU(alpha)
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else:
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self.activation = get_activation(activation) if isinstance(activation, str) else activation
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if activation is not None and not isinstance(self.activation, (Cell, Primitive)):
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raise TypeError("The activation must be str or Cell or Primitive,"" but got {}.".format(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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