forked from huawei/mindspore2022
1382 lines
62 KiB
Python
1382 lines
62 KiB
Python
# 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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"""Quantization aware training."""
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from functools import partial
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import numpy as np
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from mindspore import nn
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import mindspore.common.dtype as mstype
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from mindspore.ops import operations as P
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from mindspore.ops import functional as F
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from mindspore.common.parameter import Parameter
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from mindspore.common.initializer import initializer
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from mindspore.common.tensor import Tensor
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from mindspore._checkparam import check_int_positive, check_bool, twice
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from mindspore._checkparam import Rel
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import mindspore.context as context
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from .normalization import BatchNorm2d, BatchNorm1d
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from .activation import get_activation, ReLU, LeakyReLU
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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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from ...ops.operations import _quant_ops as Q
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__all__ = [
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'Conv2dBnAct',
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'DenseBnAct',
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'FakeQuantWithMinMax',
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'Conv2dBnFoldQuant',
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'Conv2dBnWithoutFoldQuant',
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'Conv2dQuant',
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'DenseQuant',
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'ActQuant',
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'LeakyReLUQuant',
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'HSwishQuant',
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'HSigmoidQuant',
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'TensorAddQuant',
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'MulQuant',
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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 should 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): 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 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): 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 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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activation (Cell): 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 = 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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>>> 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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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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if context.get_context('device_target') == "Ascend" and group > 1:
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self.conv = conv.DepthwiseConv2d(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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else:
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self.conv = conv.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 = 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)
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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 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 (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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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', 'h-Swish', and 'h-Sigmoid'. 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.DenseBnAct(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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has_bn=False,
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activation=None,
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after_fake=True):
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super(DenseBnAct, 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 = 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 = after_fake
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if has_bn:
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self.batchnorm = BatchNorm1d(out_channels)
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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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class BatchNormFoldCell(Cell):
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"""
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Batch normalization folded.
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Args:
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momentum (float): Momentum value should be [0, 1]. Default: 0.9.
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epsilon (float): A small float number to avoid dividing by 0. 1e-5 if dtype in
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float32 else 1e-3. Default: 1e-5.
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freeze_bn (int): Delay in steps at which computation switches from regular batch
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norm to frozen mean and std. Default: 0.
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Inputs:
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- **x** (Tensor) - Tensor of shape :math:`(N, C, H, W)`.
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- **mean** (Tensor) - Tensor of shape :math:`(C,)`.
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- **variance** (Tensor) - Tensor of shape :math:`(C,)`.
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- **global_step** (Tensor) - Tensor to record current global step.
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Outputs:
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Tuple of 4 Tensor, the normalized input and the updated parameters.
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- **batch_mean** (Tensor) - Tensor of shape :math:`(C,)`.
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- **batch_std** (Tensor) - Tensor of shape :math:`(C,)`.
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- **running_mean** (Tensor) - Tensor of shape :math:`(C,)`.
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- **running_std** (Tensor) - Tensor of shape :math:`(C,)`.
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"""
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def __init__(self, momentum=0.9, epsilon=1e-5, freeze_bn=0):
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"""init batch norm fold layer"""
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super(BatchNormFoldCell, self).__init__()
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self.epsilon = epsilon
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self.is_gpu = context.get_context('device_target') == "GPU"
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if self.is_gpu:
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self.bn_train = Q.BatchNormFold(momentum, epsilon, is_training=True, freeze_bn=freeze_bn)
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self.bn_infer = Q.BatchNormFold(momentum, epsilon, is_training=False, freeze_bn=freeze_bn)
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else:
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self.bn_reduce = P.BNTrainingReduce()
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self.bn_update = Q.BatchNormFoldD(momentum, epsilon, is_training=True, freeze_bn=freeze_bn)
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def construct(self, x, mean, variance, global_step):
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if self.is_gpu:
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if self.training:
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batch_mean, batch_std, running_mean, running_std = self.bn_train(x, mean, variance, global_step)
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else:
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batch_mean, batch_std, running_mean, running_std = self.bn_infer(x, mean, variance, global_step)
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else:
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if self.training:
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x_sum, x_square_sum = self.bn_reduce(x)
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_, batch_mean, batch_std, running_mean, running_std, mean_updated, variance_updated = \
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self.bn_update(x, x_sum, x_square_sum, mean, variance)
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P.Assign()(mean, mean_updated)
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P.Assign()(variance, variance_updated)
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else:
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batch_mean = P.ZerosLike()(variance)
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batch_std = P.OnesLike()(variance)
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running_mean = P.TensorAdd()(mean, 0.)
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running_std = P.Sqrt()(P.TensorAdd()(variance, self.epsilon))
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return batch_mean, batch_std, running_mean, running_std
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class FakeQuantWithMinMax(Cell):
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r"""
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Quantization aware op. This OP provides the fake quantization observer function on data with min and max.
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Args:
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min_init (int, float): The dimension of channel or 1(layer). Default: -6.
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max_init (int, float): The dimension of channel or 1(layer). Default: 6.
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ema (bool): The exponential Moving Average algorithm updates min and max. Default: False.
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ema_decay (float): Exponential Moving Average algorithm parameter. Default: 0.999.
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per_channel (bool): Quantization granularity based on layer or on channel. Default: False.
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channel_axis (int): Quantization by channel axis. Default: 1.
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num_channels (int): declarate the min and max channel size, Default: 1.
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num_bits (int): The bit number of quantization, supporting 4 and 8bits. Default: 8.
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symmetric (bool): Whether the quantization algorithm is symmetric or not. Default: False.
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narrow_range (bool): Whether the quantization algorithm uses narrow range or not. Default: False.
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quant_delay (int): Quantization delay parameters according to the global step. Default: 0.
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Inputs:
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- **x** (Tensor) - The input of FakeQuantWithMinMax.
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Outputs:
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Tensor, with the same type and shape as the `x`.
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Examples:
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>>> fake_quant = FakeQuantWithMinMax()
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>>> input_x = Tensor(np.array([[1, 2, 1], [-2, 0, -1]]), mindspore.float32)
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>>> result = fake_quant(input_x)
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"""
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def __init__(self,
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min_init=-6,
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max_init=6,
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ema=False,
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ema_decay=0.999,
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per_channel=False,
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channel_axis=1,
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num_channels=1,
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num_bits=8,
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symmetric=False,
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narrow_range=False,
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quant_delay=0):
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"""init FakeQuantWithMinMax layer"""
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super(FakeQuantWithMinMax, self).__init__()
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validator.check_type("min_init", min_init, [int, float])
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validator.check_type("max_init", max_init, [int, float])
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validator.check("min_init", min_init, "max_init", max_init, rel=Rel.LT)
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validator.check_integer('quant_delay', quant_delay, 0, Rel.GE)
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self.min_init = min_init
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self.max_init = max_init
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self.num_bits = num_bits
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self.ema = ema
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self.ema_decay = ema_decay
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self.per_channel = per_channel
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self.num_channels = num_channels
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self.channel_axis = channel_axis
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self.quant_delay = quant_delay
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self.symmetric = symmetric
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self.narrow_range = narrow_range
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self.is_ascend = context.get_context('device_target') == "Ascend"
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# init tensor min and max for fake quant op
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if self.per_channel:
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min_array = np.array([self.min_init] * self.num_channels).astype(np.float32)
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max_array = np.array([self.max_init] * self.num_channels).astype(np.float32)
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else:
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min_array = np.array([self.min_init]).astype(np.float32)
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max_array = np.array([self.max_init]).astype(np.float32)
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self.minq = Parameter(Tensor(min_array), name='quant_min', requires_grad=False)
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self.maxq = Parameter(Tensor(max_array), name='quant_max', requires_grad=False)
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# init fake quant relative op
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if self.per_channel:
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quant_fun = partial(Q.FakeQuantPerChannel, channel_axis=self.channel_axis)
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ema_fun = partial(Q.MinMaxUpdatePerChannel, channel_axis=self.channel_axis)
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else:
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quant_fun = Q.FakeQuantPerLayer
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ema_fun = Q.MinMaxUpdatePerLayer
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self.ema_update = ema_fun(ema=self.ema, ema_decay=self.ema_decay)
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if self.is_ascend:
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self.fake_quant_train = quant_fun(num_bits=self.num_bits,
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symmetric=self.symmetric,
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narrow_range=self.narrow_range)
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self.fake_quant_infer = self.fake_quant_train
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else:
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quant_fun = partial(quant_fun,
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ema=self.ema,
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ema_decay=ema_decay,
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num_bits=self.num_bits,
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symmetric=self.symmetric,
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narrow_range=self.narrow_range,
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quant_delay=self.quant_delay)
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self.fake_quant_train = quant_fun(training=True)
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self.fake_quant_infer = quant_fun(training=False)
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def extend_repr(self):
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s = 'num_bits={}, symmetric={}, narrow_range={}, ema={}({}), per_channel={}({}, {}), ' \
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'quant_delay={}, min_init={}, max_init={}'.format(self.num_bits, self.symmetric, self.narrow_range,
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self.ema, self.ema_decay, self.per_channel,
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self.channel_axis, self.num_channels, self.quant_delay,
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self.min_init, self.max_init)
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return s
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def construct(self, x):
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if self.training:
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min_up, max_up = self.ema_update(x, self.minq, self.maxq)
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P.Assign()(self.minq, min_up)
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P.Assign()(self.maxq, max_up)
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out = self.fake_quant_train(x, self.minq, self.maxq)
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else:
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out = self.fake_quant_infer(x, self.minq, self.maxq)
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return out
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class Conv2dBnFoldQuant(Cell):
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r"""
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2D convolution with BatchNormal op folded construct.
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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]): Specifies the height and width of the 2D convolution window.
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stride (int): Specifies stride for all spatial dimensions with the same value.
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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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eps (float): Parameters for BatchNormal. Default: 1e-5.
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momentum (float): Parameters for BatchNormal op. Default: 0.997.
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weight_init (Union[Tensor, str, Initializer, numbers.Number]): Initializer for the
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convolution kernel. Default: 'normal'.
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beta_init (Union[Tensor, str, Initializer, numbers.Number]): Initializer for the
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beta vector. Default: 'zeros'.
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gamma_init (Union[Tensor, str, Initializer, numbers.Number]): Initializer for the
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gamma vector. Default: 'ones'.
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mean_init (Union[Tensor, str, Initializer, numbers.Number]): Initializer for the
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mean vector. Default: 'zeros'.
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var_init (Union[Tensor, str, Initializer, numbers.Number]): Initializer for the
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variance vector. Default: 'ones'.
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fake (bool): Whether Conv2dBnFoldQuant Cell adds FakeQuantWithMinMax op. Default: True.
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per_channel (bool): FakeQuantWithMinMax Parameters. Default: False.
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num_bits (int): The bit number of quantization, supporting 4 and 8bits. Default: 8.
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symmetric (bool): The quantization algorithm is symmetric or not. Default: False.
|
|
narrow_range (bool): The quantization algorithm uses narrow range or not. Default: False.
|
|
quant_delay (int): The Quantization delay parameters according to the global step. Default: 0.
|
|
freeze_bn (int): The quantization freeze BatchNormal op is according to the global step. Default: 100000.
|
|
|
|
Inputs:
|
|
- **x** (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:
|
|
>>> conv2d_bn = nn.Conv2dBnFoldQuant(1, 6, kernel_size=(2, 2), stride=(1, 1), pad_mode="valid")
|
|
>>> x = Tensor(np.random.randint(-2, 2, (2, 1, 1, 3)), mindspore.float32)
|
|
>>> y = conv2d_bn(x)
|
|
"""
|
|
|
|
def __init__(self,
|
|
in_channels,
|
|
out_channels,
|
|
kernel_size,
|
|
stride=1,
|
|
pad_mode='same',
|
|
padding=0,
|
|
dilation=1,
|
|
group=1,
|
|
eps=1e-5,
|
|
momentum=0.997,
|
|
weight_init='normal',
|
|
beta_init='zeros',
|
|
gamma_init='ones',
|
|
mean_init='zeros',
|
|
var_init='ones',
|
|
fake=True,
|
|
per_channel=False,
|
|
num_bits=8,
|
|
symmetric=False,
|
|
narrow_range=False,
|
|
quant_delay=0,
|
|
freeze_bn=100000):
|
|
"""init Conv2dBnFoldQuant layer"""
|
|
super(Conv2dBnFoldQuant, self).__init__()
|
|
self.in_channels = in_channels
|
|
self.out_channels = out_channels
|
|
self.kernel_size = twice(kernel_size)
|
|
self.stride = twice(stride)
|
|
self.pad_mode = pad_mode
|
|
self.padding = padding
|
|
self.dilation = twice(dilation)
|
|
self.group = group
|
|
self.eps = eps
|
|
self.momentum = momentum
|
|
self.quant_delay = quant_delay
|
|
self.freeze_bn = freeze_bn
|
|
self.fake = fake
|
|
self.num_bits = num_bits
|
|
self.per_channel = per_channel
|
|
self.symmetric = symmetric
|
|
self.narrow_range = narrow_range
|
|
self.is_gpu = context.get_context('device_target') == "GPU"
|
|
|
|
# initialize convolution op and Parameter
|
|
if context.get_context('device_target') == "Ascend" and group > 1:
|
|
validator.check_integer('group', group, in_channels, Rel.EQ)
|
|
validator.check_integer('group', group, out_channels, Rel.EQ)
|
|
self.conv = P.DepthwiseConv2dNative(channel_multiplier=1,
|
|
kernel_size=self.kernel_size,
|
|
pad_mode=pad_mode,
|
|
pad=padding,
|
|
stride=self.stride,
|
|
dilation=self.dilation)
|
|
weight_shape = [1, in_channels, *self.kernel_size]
|
|
channel_axis = 1
|
|
else:
|
|
self.conv = P.Conv2D(out_channel=out_channels,
|
|
kernel_size=self.kernel_size,
|
|
pad_mode=pad_mode,
|
|
pad=padding,
|
|
stride=self.stride,
|
|
dilation=self.dilation,
|
|
group=group)
|
|
weight_shape = [out_channels, in_channels // group, *self.kernel_size]
|
|
channel_axis = 0
|
|
self.weight = Parameter(initializer(weight_init, weight_shape), name='weight')
|
|
|
|
# initialize BatchNorm Parameter
|
|
self.gamma = Parameter(initializer(gamma_init, [out_channels]), name='gamma')
|
|
self.beta = Parameter(initializer(beta_init, [out_channels]), name='beta')
|
|
self.moving_mean = Parameter(initializer(mean_init, [out_channels]), name='moving_mean', requires_grad=False)
|
|
self.moving_variance = Parameter(initializer(var_init, [out_channels]), name='moving_variance',
|
|
requires_grad=False)
|
|
|
|
# initialize fake ops
|
|
self.fake_quant_weight = FakeQuantWithMinMax(min_init=-6,
|
|
max_init=6,
|
|
ema=False,
|
|
per_channel=per_channel,
|
|
channel_axis=channel_axis,
|
|
num_channels=out_channels,
|
|
num_bits=num_bits,
|
|
symmetric=symmetric,
|
|
narrow_range=narrow_range,
|
|
quant_delay=quant_delay)
|
|
self.batchnorm_fold = BatchNormFoldCell(epsilon=eps, momentum=momentum, freeze_bn=freeze_bn)
|
|
self.correct_mul = Q.CorrectionMul(channel_axis)
|
|
if context.get_context('device_target') == "Ascend":
|
|
self.batchnorm_fold2_train = Q.BatchNormFold2_D(freeze_bn=freeze_bn)
|
|
self.batchnorm_fold2_infer = Q.BatchNormFold2_D(freeze_bn=0)
|
|
elif context.get_context('device_target') == "GPU":
|
|
self.batchnorm_fold2_train = Q.BatchNormFold2(freeze_bn=freeze_bn)
|
|
self.batchnorm_fold2_infer = Q.BatchNormFold2(freeze_bn=0)
|
|
else:
|
|
raise ValueError("Unsupported platform: {}".format(context.get_context('device_target')))
|
|
self.step = Parameter(initializer('normal', [1], dtype=mstype.int32), name='step', requires_grad=False)
|
|
self.one = Tensor(1, mstype.int32)
|
|
self.assignadd = P.AssignAdd()
|
|
|
|
def extend_repr(self):
|
|
s = 'in_channels={}, out_channels={}, kernel_size={}, stride={}, ' \
|
|
'pad_mode={}, padding={}, dilation={}, group={}, ' \
|
|
'fake={}, freeze_bn={}, momentum={}, quant_delay={}'.format(self.in_channels, self.out_channels,
|
|
self.kernel_size, self.stride,
|
|
self.pad_mode, self.padding, self.dilation,
|
|
self.group,
|
|
self.fake, self.freeze_bn, self.momentum,
|
|
self.quant_delay)
|
|
return s
|
|
|
|
def construct(self, x):
|
|
out_conv = self.conv(x, self.weight)
|
|
# BN fold1
|
|
batch_mean, batch_std, running_mean, running_std = self.batchnorm_fold(out_conv,
|
|
self.moving_mean,
|
|
self.moving_variance,
|
|
self.step)
|
|
# fake weight
|
|
weight = self.correct_mul(self.weight, self.gamma, running_std)
|
|
if self.fake:
|
|
weight = self.fake_quant_weight(weight)
|
|
out = self.conv(x, weight)
|
|
# BN fold2
|
|
if self.is_gpu:
|
|
if self.training:
|
|
out = self.batchnorm_fold2_train(out, self.beta, self.gamma,
|
|
batch_std, batch_mean, running_std, running_mean, self.step)
|
|
F.control_depend(out, self.assignadd(self.step, self.one))
|
|
else:
|
|
out = self.batchnorm_fold2_infer(out, self.beta, self.gamma,
|
|
batch_std, batch_mean, running_std, running_mean, self.step)
|
|
else:
|
|
if self.training:
|
|
out = self.batchnorm_fold2_train(out, self.beta, self.gamma, batch_std, batch_mean, running_std)
|
|
F.control_depend(out, self.assignadd(self.step, self.one))
|
|
else:
|
|
out = self.batchnorm_fold2_infer(out, self.beta, self.gamma, running_std, running_mean, running_std)
|
|
return out
|
|
|
|
|
|
class Conv2dBnWithoutFoldQuant(Cell):
|
|
r"""
|
|
2D convolution + batchnorm without fold with fake quant construct.
|
|
|
|
This part is 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]): Specifies the height and width of the 2D convolution window.
|
|
stride (int): Specifies stride for all spatial dimensions with the same value. 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. 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.
|
|
eps (float): Parameters for BatchNormal. Default: 1e-5.
|
|
momentum (float): Parameters for BatchNormal op. Default: 0.997.
|
|
weight_init (Union[Tensor, str, Initializer, numbers.Number]): Initializer for the convolution kernel.
|
|
Default: 'normal'.
|
|
bias_init (Union[Tensor, str, Initializer, numbers.Number]): Initializer for the bias vector. Default: 'zeros'.
|
|
per_channel (bool): FakeQuantWithMinMax Parameters. Default: False.
|
|
num_bits (int): The bit number of quantization, supporting 4 and 8bits. Default: 8.
|
|
symmetric (bool): The quantization algorithm is symmetric or not. Default: False.
|
|
narrow_range (bool): The quantization algorithm uses narrow range or not. Default: False.
|
|
quant_delay (int): Quantization delay parameters according to the global step. Default: 0.
|
|
|
|
Inputs:
|
|
- **x** (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:
|
|
>>> conv2d_quant = nn.Conv2dBnWithoutFoldQuant(1, 6, kernel_size=(2, 2), stride=(1, 1), pad_mode="valid")
|
|
>>> x = Tensor(np.random.randint(-2, 2, (2, 1, 1, 3)), mstype.float32)
|
|
>>> y = conv2d_quant(x)
|
|
"""
|
|
|
|
def __init__(self,
|
|
in_channels,
|
|
out_channels,
|
|
kernel_size,
|
|
stride=1,
|
|
pad_mode='same',
|
|
padding=0,
|
|
dilation=1,
|
|
group=1,
|
|
has_bias=False,
|
|
eps=1e-5,
|
|
momentum=0.997,
|
|
weight_init='normal',
|
|
bias_init='zeros',
|
|
per_channel=False,
|
|
num_bits=8,
|
|
symmetric=False,
|
|
narrow_range=False,
|
|
quant_delay=0):
|
|
super(Conv2dBnWithoutFoldQuant, self).__init__()
|
|
if isinstance(kernel_size, int):
|
|
self.kernel_size = (kernel_size, kernel_size)
|
|
else:
|
|
self.kernel_size = kernel_size
|
|
self.in_channels = check_int_positive(in_channels)
|
|
self.out_channels = check_int_positive(out_channels)
|
|
self.has_bias = has_bias
|
|
self.stride = twice(stride)
|
|
self.dilation = twice(dilation)
|
|
self.pad_mode = pad_mode
|
|
self.padding = padding
|
|
self.group = group
|
|
self.quant_delay = quant_delay
|
|
|
|
weight_shape = [out_channels, in_channels // group, *self.kernel_size]
|
|
self.weight = Parameter(initializer(weight_init, weight_shape), name='weight')
|
|
|
|
self.bias_add = P.BiasAdd()
|
|
if check_bool(has_bias):
|
|
self.bias = Parameter(initializer(bias_init, [out_channels]), name='bias')
|
|
else:
|
|
self.bias = None
|
|
|
|
self.conv = P.Conv2D(out_channel=self.out_channels,
|
|
kernel_size=self.kernel_size,
|
|
mode=1,
|
|
pad_mode=self.pad_mode,
|
|
pad=self.padding,
|
|
stride=self.stride,
|
|
dilation=self.dilation,
|
|
group=self.group)
|
|
self.fake_quant_weight = FakeQuantWithMinMax(min_init=-6,
|
|
max_init=6,
|
|
ema=False,
|
|
per_channel=per_channel,
|
|
channel_axis=0,
|
|
num_channels=out_channels,
|
|
num_bits=num_bits,
|
|
symmetric=symmetric,
|
|
narrow_range=narrow_range,
|
|
quant_delay=quant_delay)
|
|
self.batchnorm = BatchNorm2d(out_channels, eps=eps, momentum=momentum)
|
|
|
|
def construct(self, x):
|
|
weight = self.fake_quant_weight(self.weight)
|
|
out = self.conv(x, weight)
|
|
if self.has_bias:
|
|
out = self.bias_add(out, self.bias)
|
|
out = self.batchnorm(out)
|
|
return out
|
|
|
|
def extend_repr(self):
|
|
s = 'in_channels={}, out_channels={}, kernel_size={}, stride={}, ' \
|
|
'pad_mode={}, padding={}, dilation={}, group={}, ' \
|
|
'has_bias={}, quant_delay={}'.format(self.in_channels, self.out_channels, self.kernel_size, self.stride,
|
|
self.pad_mode, self.padding, self.dilation, self.group,
|
|
self.has_bias, self.quant_delay)
|
|
return s
|
|
|
|
|
|
class Conv2dQuant(Cell):
|
|
r"""
|
|
2D convolution with fake quant op layer.
|
|
|
|
This part is 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]): Specifies the height and width of the 2D convolution window.
|
|
stride (int): Specifies stride for all spatial dimensions with the same value. 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. 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.
|
|
Default: 'normal'.
|
|
bias_init (Union[Tensor, str, Initializer, numbers.Number]): Initializer for the bias vector. Default: 'zeros'.
|
|
per_channel (bool): FakeQuantWithMinMax Parameters. Default: False.
|
|
num_bits (int): The bit number of quantization, supporting 4 and 8bits. Default: 8.
|
|
symmetric (bool): The quantization algorithm is symmetric or not. Default: False.
|
|
narrow_range (bool): The quantization algorithm uses narrow range or not. Default: False.
|
|
quant_delay (int): Quantization delay parameters according to the global step. Default: 0.
|
|
|
|
Inputs:
|
|
- **x** (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:
|
|
>>> conv2d_quant = nn.Conv2dQuant(1, 6, kernel_size= (2, 2), stride=(1, 1), pad_mode="valid")
|
|
>>> x = Tensor(np.random.randint(-2, 2, (2, 1, 1, 3)), mindspore.float32)
|
|
>>> y = conv2d_quant(x)
|
|
"""
|
|
|
|
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',
|
|
per_channel=False,
|
|
num_bits=8,
|
|
symmetric=False,
|
|
narrow_range=False,
|
|
quant_delay=0):
|
|
super(Conv2dQuant, self).__init__()
|
|
if isinstance(kernel_size, int):
|
|
self.kernel_size = (kernel_size, kernel_size)
|
|
else:
|
|
self.kernel_size = kernel_size
|
|
self.in_channels = check_int_positive(in_channels)
|
|
self.out_channels = check_int_positive(out_channels)
|
|
self.has_bias = has_bias
|
|
self.stride = twice(stride)
|
|
self.dilation = twice(dilation)
|
|
self.pad_mode = pad_mode
|
|
self.padding = padding
|
|
self.group = group
|
|
self.quant_delay = quant_delay
|
|
|
|
weight_shape = [out_channels, in_channels // group, *self.kernel_size]
|
|
self.weight = Parameter(initializer(weight_init, weight_shape), name='weight')
|
|
|
|
self.bias_add = P.BiasAdd()
|
|
if check_bool(has_bias):
|
|
self.bias = Parameter(initializer(bias_init, [out_channels]), name='bias')
|
|
else:
|
|
self.bias = None
|
|
|
|
self.conv = P.Conv2D(out_channel=self.out_channels,
|
|
kernel_size=self.kernel_size,
|
|
mode=1,
|
|
pad_mode=self.pad_mode,
|
|
pad=self.padding,
|
|
stride=self.stride,
|
|
dilation=self.dilation,
|
|
group=self.group)
|
|
self.fake_quant_weight = FakeQuantWithMinMax(min_init=-6,
|
|
max_init=6,
|
|
ema=False,
|
|
per_channel=per_channel,
|
|
channel_axis=0,
|
|
num_channels=out_channels,
|
|
num_bits=num_bits,
|
|
symmetric=symmetric,
|
|
narrow_range=narrow_range,
|
|
quant_delay=quant_delay)
|
|
|
|
def construct(self, x):
|
|
weight = self.fake_quant_weight(self.weight)
|
|
out = self.conv(x, weight)
|
|
if self.has_bias:
|
|
return self.bias_add(out, self.bias)
|
|
return out
|
|
|
|
def extend_repr(self):
|
|
s = 'in_channels={}, out_channels={}, kernel_size={}, stride={}, ' \
|
|
'pad_mode={}, padding={}, dilation={}, group={}, ' \
|
|
'has_bias={}, quant_delay={}'.format(self.in_channels, self.out_channels, self.kernel_size, self.stride,
|
|
self.pad_mode, self.padding, self.dilation, self.group,
|
|
self.has_bias, self.quant_delay)
|
|
return s
|
|
|
|
|
|
class DenseQuant(Cell):
|
|
r"""
|
|
The fully connected layer with fake quant op.
|
|
|
|
This part is a more detailed overview of Dense op.
|
|
|
|
Args:
|
|
in_channels (int): The dimension of the input space.
|
|
out_channels (int): The dimension of 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): The regularization function applied to the output of the layer, eg. 'relu'. Default: None.
|
|
per_channel (bool): FakeQuantWithMinMax Parameters. Default: False.
|
|
num_bits (int): The bit number of quantization, supporting 4 and 8bits. Default: 8.
|
|
symmetric (bool): The quantization algorithm is symmetric or not. Default: False.
|
|
narrow_range (bool): The quantization algorithm uses narrow range or not. Default: False.
|
|
quant_delay (int): Quantization delay parameters according to the global step. Default: 0.
|
|
|
|
Inputs:
|
|
- **x** (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:
|
|
>>> dense_quant = nn.DenseQuant(3, 6)
|
|
>>> input_x = Tensor(np.random.randint(-2, 2, (2, 3)), mindspore.float32)
|
|
>>> result = dense_quant(input_x)
|
|
"""
|
|
|
|
def __init__(
|
|
self,
|
|
in_channels,
|
|
out_channels,
|
|
weight_init='normal',
|
|
bias_init='zeros',
|
|
has_bias=True,
|
|
activation=None,
|
|
per_channel=False,
|
|
num_bits=8,
|
|
symmetric=False,
|
|
narrow_range=False,
|
|
quant_delay=0):
|
|
super(DenseQuant, self).__init__()
|
|
self.in_channels = check_int_positive(in_channels)
|
|
self.out_channels = check_int_positive(out_channels)
|
|
self.has_bias = check_bool(has_bias)
|
|
|
|
if isinstance(weight_init, Tensor):
|
|
if weight_init.dim() != 2 or weight_init.shape[0] != out_channels or \
|
|
weight_init.shape[1] != in_channels:
|
|
raise ValueError("weight_init shape error")
|
|
|
|
self.weight = Parameter(initializer(
|
|
weight_init, [out_channels, in_channels]), name="weight")
|
|
|
|
if self.has_bias:
|
|
if isinstance(bias_init, Tensor):
|
|
if bias_init.dim() != 1 or bias_init.shape[0] != out_channels:
|
|
raise ValueError("bias_init shape error")
|
|
|
|
self.bias = Parameter(initializer(
|
|
bias_init, [out_channels]), name="bias")
|
|
|
|
self.matmul = P.MatMul(transpose_b=True)
|
|
self.bias_add = P.BiasAdd()
|
|
|
|
self.activation = get_activation(activation)
|
|
self.activation_flag = self.activation is not None
|
|
self.fake_quant_weight = FakeQuantWithMinMax(min_init=-6,
|
|
max_init=6,
|
|
ema=False,
|
|
per_channel=per_channel,
|
|
channel_axis=0,
|
|
num_channels=out_channels,
|
|
num_bits=num_bits,
|
|
symmetric=symmetric,
|
|
narrow_range=narrow_range,
|
|
quant_delay=quant_delay)
|
|
|
|
def construct(self, x):
|
|
"""Use operators to construct the Dense layer."""
|
|
output = self.fake_quant_weight(self.weight)
|
|
output = self.matmul(x, output)
|
|
if self.has_bias:
|
|
output = self.bias_add(output, self.bias)
|
|
if self.activation_flag:
|
|
return self.activation(output)
|
|
return output
|
|
|
|
def extend_repr(self):
|
|
"""A pretty print for Dense layer."""
|
|
str_info = 'in_channels={}, out_channels={}, weight={}, has_bias={}'.format(
|
|
self.in_channels, self.out_channels, self.weight, self.has_bias)
|
|
if self.has_bias:
|
|
str_info = str_info + ', bias={}'.format(self.bias)
|
|
if self.activation_flag:
|
|
str_info = str_info + ', activation={}'.format(self.activation)
|
|
|
|
return str_info
|
|
|
|
|
|
class _QuantActivation(Cell):
|
|
r"""
|
|
Base class for quantization aware training activation function. Add Fake Quant OP after activation OP.
|
|
"""
|
|
|
|
def get_origin(self):
|
|
raise NotImplementedError
|
|
|
|
|
|
class ActQuant(_QuantActivation):
|
|
r"""
|
|
Quantization aware training activation function.
|
|
|
|
Add the fake quant op to the end of activation op, by which the output of activation op will be truncated.
|
|
Please check `FakeQuantWithMinMax` for more details.
|
|
|
|
Args:
|
|
activation (Cell): Activation cell class.
|
|
ema_decay (float): Exponential Moving Average algorithm parameter. Default: 0.999.
|
|
per_channel (bool): Quantization granularity based on layer or on channel. Default: False.
|
|
num_bits (int): The bit number of quantization, supporting 4 and 8bits. Default: 8.
|
|
symmetric (bool): The quantization algorithm is symmetric or not. Default: False.
|
|
narrow_range (bool): The quantization algorithm uses narrow range or not. Default: False.
|
|
quant_delay (int): Quantization delay parameters according to the global steps. Default: 0.
|
|
|
|
Inputs:
|
|
- **x** (Tensor) - The input of ReLU6Quant.
|
|
|
|
Outputs:
|
|
Tensor, with the same type and shape as the `x`.
|
|
|
|
Examples:
|
|
>>> act_quant = nn.ActQuant(nn.ReLU())
|
|
>>> input_x = Tensor(np.array([[1, 2, -1], [-2, 0, -1]]), mindspore.float32)
|
|
>>> result = act_quant(input_x)
|
|
"""
|
|
|
|
def __init__(self,
|
|
activation,
|
|
ema_decay=0.999,
|
|
per_channel=False,
|
|
num_bits=8,
|
|
symmetric=False,
|
|
narrow_range=False,
|
|
quant_delay=0):
|
|
super(ActQuant, self).__init__()
|
|
self.fake_quant_act = FakeQuantWithMinMax(min_init=0,
|
|
max_init=6,
|
|
ema=True,
|
|
ema_decay=ema_decay,
|
|
per_channel=per_channel,
|
|
num_bits=num_bits,
|
|
symmetric=symmetric,
|
|
narrow_range=narrow_range,
|
|
quant_delay=quant_delay)
|
|
self.act = activation
|
|
|
|
def construct(self, x):
|
|
x = self.act(x)
|
|
x = self.fake_quant_act(x)
|
|
return x
|
|
|
|
def get_origin(self):
|
|
return self.act
|
|
|
|
class LeakyReLUQuant(_QuantActivation):
|
|
r"""
|
|
LeakyReLUQuant activation function. Add Fake Quant OP after HSwish OP.
|
|
|
|
This part is a more detailed overview of HSwish op.
|
|
|
|
Args:
|
|
activation (Cell): Activation cell class.
|
|
ema_decay (float): Exponential Moving Average algorithm parameter. Default: 0.999.
|
|
per_channel (bool): Quantization granularity based on layer or on channel. Default: False.
|
|
num_bits (int): The bit number of quantization, supporting 4 and 8bits. Default: 8.
|
|
symmetric (bool): The quantization algorithm is symmetric or not. Default: False.
|
|
narrow_range (bool): The quantization algorithm uses narrow range or not. Default: False.
|
|
quant_delay (int): Quantization delay parameters according to the global step. Default: 0.
|
|
|
|
Inputs:
|
|
- **x** (Tensor) - The input of LeakyReLUQuant.
|
|
|
|
Outputs:
|
|
Tensor, with the same type and shape as the `x`.
|
|
|
|
Examples:
|
|
>>> activation = nn.LeakyReLUQuant(nn.LeakyReLU())
|
|
>>> input = Tensor(np.array([[1, 2, 1], [-2, 0, -1]]), mindspore.float32)
|
|
>>> result = activation(input)
|
|
"""
|
|
|
|
def __init__(self,
|
|
activation,
|
|
ema_decay=0.999,
|
|
per_channel=False,
|
|
num_bits=8,
|
|
symmetric=False,
|
|
narrow_range=False,
|
|
quant_delay=0):
|
|
super(LeakyReLUQuant, self).__init__()
|
|
self.fake_quant_act_before = FakeQuantWithMinMax(min_init=-6,
|
|
max_init=6,
|
|
ema=True,
|
|
ema_decay=ema_decay,
|
|
per_channel=per_channel,
|
|
num_bits=num_bits,
|
|
symmetric=symmetric,
|
|
narrow_range=narrow_range,
|
|
quant_delay=quant_delay)
|
|
self.fake_quant_act_after = FakeQuantWithMinMax(min_init=-6,
|
|
max_init=6,
|
|
ema=True,
|
|
ema_decay=ema_decay,
|
|
per_channel=per_channel,
|
|
num_bits=num_bits,
|
|
symmetric=symmetric,
|
|
narrow_range=narrow_range,
|
|
quant_delay=quant_delay)
|
|
if issubclass(activation.__class__, nn.LeakyReLU):
|
|
self.act = activation
|
|
else:
|
|
raise ValueError("Activation should be `nn.LeakyReLU`")
|
|
|
|
def construct(self, x):
|
|
x = self.fake_quant_act_before(x)
|
|
x = self.act(x)
|
|
x = self.fake_quant_act_after(x)
|
|
return x
|
|
|
|
def get_origin(self):
|
|
return self.act
|
|
|
|
|
|
|
|
class HSwishQuant(_QuantActivation):
|
|
r"""
|
|
HSwishQuant activation function. Add Fake Quant OP after HSwish OP.
|
|
|
|
This part is a more detailed overview of HSwish op.
|
|
|
|
Args:
|
|
activation (Cell): Activation cell class.
|
|
ema_decay (float): Exponential Moving Average algorithm parameter. Default: 0.999.
|
|
per_channel (bool): Quantization granularity based on layer or on channel. Default: False.
|
|
num_bits (int): The bit number of quantization, supporting 4 and 8bits. Default: 8.
|
|
symmetric (bool): Whether the quantization algorithm is symmetric or not. Default: False.
|
|
narrow_range (bool): Whether the quantization algorithm uses narrow range or not. Default: False.
|
|
quant_delay (int): Quantization delay parameters according to the global step. Default: 0.
|
|
|
|
Inputs:
|
|
- **x** (Tensor) - The input of HSwishQuant.
|
|
|
|
Outputs:
|
|
Tensor, with the same type and shape as the `x`.
|
|
|
|
Examples:
|
|
>>> activation = nn.HSwishQuant(nn.HSwish())
|
|
>>> input = Tensor(np.array([[1, 2, 1], [-2, 0, -1]]), mindspore.float32)
|
|
>>> result = activation(input)
|
|
"""
|
|
|
|
def __init__(self,
|
|
activation,
|
|
ema_decay=0.999,
|
|
per_channel=False,
|
|
num_bits=8,
|
|
symmetric=False,
|
|
narrow_range=False,
|
|
quant_delay=0):
|
|
super(HSwishQuant, self).__init__()
|
|
self.fake_quant_act_before = FakeQuantWithMinMax(min_init=-6,
|
|
max_init=6,
|
|
ema=True,
|
|
ema_decay=ema_decay,
|
|
per_channel=per_channel,
|
|
num_bits=num_bits,
|
|
symmetric=symmetric,
|
|
narrow_range=narrow_range,
|
|
quant_delay=quant_delay)
|
|
self.fake_quant_act_after = FakeQuantWithMinMax(min_init=-6,
|
|
max_init=6,
|
|
ema=True,
|
|
ema_decay=ema_decay,
|
|
per_channel=per_channel,
|
|
num_bits=num_bits,
|
|
symmetric=symmetric,
|
|
narrow_range=narrow_range,
|
|
quant_delay=quant_delay)
|
|
if issubclass(activation.__class__, nn.HSwish):
|
|
self.act = activation
|
|
else:
|
|
raise ValueError("Activation should be `nn.HSwish`")
|
|
|
|
def construct(self, x):
|
|
x = self.fake_quant_act_before(x)
|
|
x = self.act(x)
|
|
x = self.fake_quant_act_after(x)
|
|
return x
|
|
|
|
def get_origin(self):
|
|
return self.act
|
|
|
|
|
|
class HSigmoidQuant(_QuantActivation):
|
|
r"""
|
|
HSigmoidQuant activation function. Add Fake Quant OP before and after HSigmoid OP.
|
|
|
|
This part is a more detailed overview of HSigmoid op.
|
|
|
|
Args:
|
|
activation (Cell): Activation cell class.
|
|
ema_decay (float): Exponential Moving Average algorithm parameter. Default: 0.999.
|
|
per_channel (bool): Quantization granularity based on layer or on channel. Default: False.
|
|
num_bits (int): The bit number of quantization, supporting 4 and 8bits. Default: 8.
|
|
symmetric (bool): Whether the quantization algorithm is symmetric or not. Default: False.
|
|
narrow_range (bool): Whether the quantization algorithm uses narrow range or not. Default: False.
|
|
quant_delay (int): Quantization delay parameters according to the global step. Default: 0.
|
|
|
|
Inputs:
|
|
- **x** (Tensor) - The input of HSigmoidQuant.
|
|
|
|
Outputs:
|
|
Tensor, with the same type and shape as the `x`.
|
|
|
|
Examples:
|
|
>>> activation = nn.HSigmoidQuant(nn.HSigmoid())
|
|
>>> input = Tensor(np.array([[1, 2, 1], [-2, 0, -1]]), mindspore.float32)
|
|
>>> result = activation(input)
|
|
"""
|
|
|
|
def __init__(self,
|
|
activation,
|
|
ema_decay=0.999,
|
|
per_channel=False,
|
|
num_bits=8,
|
|
symmetric=False,
|
|
narrow_range=False,
|
|
quant_delay=0):
|
|
super(HSigmoidQuant, self).__init__()
|
|
self.fake_quant_act_before = FakeQuantWithMinMax(min_init=-6,
|
|
max_init=6,
|
|
ema=True,
|
|
ema_decay=ema_decay,
|
|
per_channel=per_channel,
|
|
num_bits=num_bits,
|
|
symmetric=symmetric,
|
|
narrow_range=narrow_range,
|
|
quant_delay=quant_delay)
|
|
self.fake_quant_act_after = FakeQuantWithMinMax(min_init=-6,
|
|
max_init=6,
|
|
ema=True,
|
|
ema_decay=ema_decay,
|
|
per_channel=per_channel,
|
|
num_bits=num_bits,
|
|
symmetric=symmetric,
|
|
narrow_range=narrow_range,
|
|
quant_delay=quant_delay)
|
|
if issubclass(activation.__class__, nn.HSigmoid):
|
|
self.act = activation
|
|
else:
|
|
raise ValueError("Activation should be `nn.HSigmoid`")
|
|
|
|
def construct(self, x):
|
|
x = self.fake_quant_act_before(x)
|
|
x = self.act(x)
|
|
x = self.fake_quant_act_after(x)
|
|
return x
|
|
|
|
def get_origin(self):
|
|
return self.act
|
|
|
|
|
|
class TensorAddQuant(Cell):
|
|
r"""
|
|
Add Fake Quant OP after TensorAdd OP.
|
|
|
|
This part is a more detailed overview of TensorAdd op.
|
|
|
|
Args:
|
|
ema_decay (float): Exponential Moving Average algorithm parameter. Default: 0.999.
|
|
per_channel (bool): Quantization granularity based on layer or on channel. Default: False.
|
|
num_bits (int): The bit number of quantization, supporting 4 and 8bits. Default: 8.
|
|
symmetric (bool): The quantization algorithm is symmetric or not. Default: False.
|
|
narrow_range (bool): The quantization algorithm uses narrow range or not. Default: False.
|
|
quant_delay (int): Quantization delay parameters according to the global step. Default: 0.
|
|
|
|
Inputs:
|
|
- **x** (Tensor) - The input of TensorAddQuant.
|
|
|
|
Outputs:
|
|
Tensor, with the same type and shape as the `x`.
|
|
|
|
Examples:
|
|
>>> add_quant = nn.TensorAddQuant()
|
|
>>> input_x = Tensor(np.array([[1, 2, 1], [-2, 0, -1]]), mindspore.float32)
|
|
>>> input_y = Tensor(np.random.randint(-2, 2, (2, 3)), mindspore.float32)
|
|
>>> result = add_quant(input_x, input_y)
|
|
"""
|
|
|
|
def __init__(self,
|
|
ema_decay=0.999,
|
|
per_channel=False,
|
|
num_bits=8,
|
|
symmetric=False,
|
|
narrow_range=False,
|
|
quant_delay=0):
|
|
super(TensorAddQuant, self).__init__()
|
|
self.fake_quant_act = FakeQuantWithMinMax(min_init=-6,
|
|
max_init=6,
|
|
ema=True,
|
|
ema_decay=ema_decay,
|
|
per_channel=per_channel,
|
|
num_bits=num_bits,
|
|
symmetric=symmetric,
|
|
narrow_range=narrow_range,
|
|
quant_delay=quant_delay)
|
|
self.add = P.TensorAdd()
|
|
|
|
def construct(self, x1, x2):
|
|
x = self.add(x1, x2)
|
|
x = self.fake_quant_act(x)
|
|
return x
|
|
|
|
|
|
class MulQuant(Cell):
|
|
r"""
|
|
Add Fake Quant OP after Mul OP.
|
|
|
|
This part is a more detailed overview of Mul op.
|
|
|
|
Args:
|
|
ema_decay (float): Exponential Moving Average algorithm parameter. Default: 0.999.
|
|
per_channel (bool): Quantization granularity based on layer or on channel. Default: False.
|
|
num_bits (int): The bit number of quantization, supporting 4 and 8bits. Default: 8.
|
|
symmetric (bool): The quantization algorithm is symmetric or not. Default: False.
|
|
narrow_range (bool): The quantization algorithm uses narrow range or not. Default: False.
|
|
quant_delay (int): Quantization delay parameters according to the global step. Default: 0.
|
|
|
|
Inputs:
|
|
- **x** (Tensor) - The input of MulQuant.
|
|
|
|
Outputs:
|
|
Tensor, with the same type and shape as the `x`.
|
|
|
|
"""
|
|
|
|
def __init__(self,
|
|
ema_decay=0.999,
|
|
per_channel=False,
|
|
num_bits=8,
|
|
symmetric=False,
|
|
narrow_range=False,
|
|
quant_delay=0):
|
|
super(MulQuant, self).__init__()
|
|
self.fake_quant_act = FakeQuantWithMinMax(min_init=-6,
|
|
max_init=6,
|
|
ema=True,
|
|
ema_decay=ema_decay,
|
|
per_channel=per_channel,
|
|
num_bits=num_bits,
|
|
symmetric=symmetric,
|
|
narrow_range=narrow_range,
|
|
quant_delay=quant_delay)
|
|
self.mul = P.Mul()
|
|
|
|
def construct(self, x1, x2):
|
|
x = self.mul(x1, x2)
|
|
x = self.fake_quant_act(x)
|
|
return x
|
|
|
|
|
|
class QuantBlock(Cell):
|
|
r"""
|
|
A quant block of Conv/Dense, activation layer for Ascend deploy.
|
|
|
|
Calculate Conv or Dense in Int8, with Quant and DeQuant.
|
|
|
|
Notes:
|
|
This block is only for deploy, and not trainable.
|
|
|
|
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): The regularization 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,
|
|
core_op,
|
|
weight,
|
|
quant_op,
|
|
dequant_op,
|
|
dequant_scale,
|
|
bias=None,
|
|
activation=None):
|
|
super(QuantBlock, self).__init__()
|
|
self.core_op = core_op
|
|
self.weight = weight
|
|
self.quant = quant_op
|
|
self.dequant = dequant_op
|
|
self.dequant_scale = dequant_scale
|
|
self.bias = bias
|
|
self.has_bias = bias is not None
|
|
self.activation = activation
|
|
self.has_act = activation is not None
|
|
if isinstance(activation, ReLU):
|
|
self.activation = None
|
|
self.has_act = False
|
|
self.dequant.add_prim_attr("relu_flag", True)
|
|
self.bias_add = P.BiasAdd()
|
|
|
|
def construct(self, x):
|
|
x = self.quant(x)
|
|
if self.has_bias:
|
|
x = self.core_op(x, self.weight, self.bias)
|
|
else:
|
|
x = self.core_op(x, self.weight)
|
|
if self.has_act:
|
|
x = self.activation(x)
|
|
x = self.dequant(x, self.dequant_scale)
|
|
return x
|
|
|
|
def extend_repr(self):
|
|
str_info = f'quant={self.quant}, core_op={type(self.core_op)}, weight=shape[{self.weight.shape}]'
|
|
if self.has_bias:
|
|
str_info = str_info + f', bias=shape[{self.bias.shape}]'
|
|
if self.has_act:
|
|
str_info = str_info + f', activation={self.activation}'
|
|
str_info = str_info + f', dequant={self.dequant}'
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return str_info
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