mindspore2022/mindspore/nn/layer/quant.py

# Copyright 2020 Huawei Technologies Co., Ltd
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
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# See the License for the specific language governing permissions and
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# ============================================================================
"""Quantization aware training."""

from functools import partial
from collections import namedtuple
import numpy as np
from mindspore import nn
import mindspore.common.dtype as mstype
from mindspore.ops.primitive import Primitive
from mindspore.ops import operations as P
from mindspore.ops import functional as F
from mindspore.common.parameter import Parameter
from mindspore.common.initializer import initializer
from mindspore.common.tensor import Tensor
from mindspore._checkparam import Validator, Rel, twice
from mindspore.compression.common import QuantDtype
import mindspore.context as context
from .normalization import BatchNorm2d, BatchNorm1d
from .activation import get_activation, ReLU, LeakyReLU
from ..cell import Cell
from ...ops.operations import _quant_ops as Q

__all__ = [
    'Conv2dBnAct',
    'DenseBnAct',
    'FakeQuantWithMinMaxObserver',
    'Conv2dBnFoldQuant',
    'Conv2dBnWithoutFoldQuant',
    'Conv2dQuant',
    'DenseQuant',
    'ActQuant',
    'LeakyReLUQuant',
    'HSwishQuant',
    'HSigmoidQuant',
    'TensorAddQuant',
    'MulQuant',
]


class Conv2dBnAct(Cell):
    r"""
    A combination of convolution, Batchnorm, activation 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]): The data type is int or a tuple of 2 integers. Specifies the height
            and width of the 2D convolution window. Single int means the value is for both height and width of
            the kernel. A tuple of 2 ints means the first value is for the height and the other is for the
            width of the kernel.
        stride (int): Specifies stride for all spatial dimensions with the same value. The value of stride must be
            greater than or equal to 1 and lower than any one of the height and width of the input. Default: 1.
        pad_mode (str): Specifies padding mode. The optional values are "same", "valid", "pad". Default: "same".
        padding (int): Implicit paddings on both sides of the input. Default: 0.
        dilation (int): Specifies the dilation rate to use for dilated convolution. If set to be :math:`k > 1`,
            there will be :math:`k - 1` pixels skipped for each sampling location. Its value must be greater than
            or equal to 1 and lower than any one of the height and width of the input. Default: 1.
        group (int): Splits filter into groups, `in_ channels` and `out_channels` must be
            divisible by the number of groups. Default: 1.
        has_bias (bool): Specifies whether the layer uses a bias vector. Default: False.
        weight_init (Union[Tensor, str, Initializer, numbers.Number]): Initializer for the convolution kernel.
            It can be a Tensor, a string, an Initializer or a number. When a string is specified,
            values from 'TruncatedNormal', 'Normal', 'Uniform', 'HeUniform' and 'XavierUniform' distributions as well
            as constant 'One' and 'Zero' distributions are possible. Alias 'xavier_uniform', 'he_uniform', 'ones'
            and 'zeros' are acceptable. Uppercase and lowercase are both acceptable. Refer to the values of
            Initializer for more details. Default: 'normal'.
        bias_init (Union[Tensor, str, Initializer, numbers.Number]): Initializer for the bias vector. Possible
            Initializer and string are the same as 'weight_init'. Refer to the values of
            Initializer for more details. Default: 'zeros'.
        has_bn (bool): Specifies to used batchnorm or not. Default: False.
        momentum (float): Momentum for moving average.Momentum value must be [0, 1].Default:0.9
        eps (float): Term added to the denominator to improve numerical stability. Should be greater than 0. Default:
                     1e-5.
        activation (Union[str, Cell, Primitive]): Specifies activation type. The optional values are as following:
            'softmax', 'logsoftmax', 'relu', 'relu6', 'tanh', 'gelu', 'sigmoid',
            'prelu', 'leakyrelu', 'hswish', 'hsigmoid'. Default: None.
        alpha (float): Slope of the activation function at x < 0. Default: 0.2.
        after_fake(bool): Determin whether there must be a fake quantization operation after Cond2dBnAct.

    Inputs:
        - **input** (Tensor) - Tensor of shape :math:`(N, C_{in}, H_{in}, W_{in})`.

    Outputs:
        Tensor of shape :math:`(N, C_{out}, H_{out}, W_{out})`.

    Examples:
        >>> net = Conv2dBnAct(120, 240, 4, has_bn=True, activation='ReLU')
        >>> input = Tensor(np.ones([1, 120, 1024, 640]), mindspore.float32)
        >>> net(input).shape
        (1, 240, 1024, 640)
    """

    def __init__(self,
                 in_channels,
                 out_channels,
                 kernel_size,
                 stride=1,
                 pad_mode='same',
                 padding=0,
                 dilation=1,
                 group=1,
                 has_bias=False,
                 weight_init='normal',
                 bias_init='zeros',
                 has_bn=False,
                 momentum=0.9,
                 eps=1e-5,
                 activation=None,
                 alpha=0.2,
                 after_fake=True):
        super(Conv2dBnAct, self).__init__()

        self.conv = nn.Conv2d(in_channels,
                              out_channels,
                              kernel_size=kernel_size,
                              stride=stride,
                              pad_mode=pad_mode,
                              padding=padding,
                              dilation=dilation,
                              group=group,
                              has_bias=has_bias,
                              weight_init=weight_init,
                              bias_init=bias_init)
        self.has_bn = Validator.check_bool(has_bn, "has_bn")
        self.has_act = activation is not None
        self.after_fake = after_fake
        if has_bn:
            self.batchnorm = BatchNorm2d(out_channels, eps, momentum)
        if activation == "leakyrelu":
            self.activation = LeakyReLU(alpha)
        else:
            self.activation = get_activation(activation) if isinstance(activation, str) else activation
            if activation is not None and not isinstance(self.activation, (Cell, Primitive)):
                raise TypeError("The activation must be str or Cell or Primitive,"" but got {}.".format(activation))

    def construct(self, x):
        x = self.conv(x)
        if self.has_bn:
            x = self.batchnorm(x)
        if self.has_act:
            x = self.activation(x)
        return x


class DenseBnAct(Cell):
    r"""
    A combination of Dense, Batchnorm, and the activation layer.

    This part is a more detailed overview of Dense op.

    Args:
        in_channels (int): The number of channels in the input space.
        out_channels (int): The number of channels in the output space.
        weight_init (Union[Tensor, str, Initializer, numbers.Number]): The trainable weight_init parameter. The dtype
            is same as input x. The values of str refer to the function `initializer`. Default: 'normal'.
        bias_init (Union[Tensor, str, Initializer, numbers.Number]): The trainable bias_init parameter. The dtype is
            same as input x. The values of str refer to the function `initializer`. Default: 'zeros'.
        has_bias (bool): Specifies whether the layer uses a bias vector. Default: True.
        activation (Cell): The regularization function applied to the output of the layer, eg. 'ReLU'. Default: None.
        has_bn (bool): Specifies to use batchnorm or not. Default: False.
        activation (Union[str, Cell, Primitive]): Specifies activation type. The optional values are as following:
            'Softmax', 'LogSoftmax', 'ReLU', 'ReLU6', 'Tanh', 'GELU', 'Sigmoid',
            'PReLU', 'LeakyReLU', 'h-Swish', and 'h-Sigmoid'. Default: None.
        after_fake(bool): Determin whether there must be a fake quantization operation after DenseBnAct.

    Inputs:
        - **input** (Tensor) - Tensor of shape :math:`(N, in\_channels)`.

    Outputs:
        Tensor of shape :math:`(N, out\_channels)`.

    Examples:
        >>> net = nn.DenseBnAct(3, 4)
        >>> input = Tensor(np.random.randint(0, 255, [2, 3]), mindspore.float32)
        >>> net(input)
    """

    def __init__(self,
                 in_channels,
                 out_channels,
                 weight_init='normal',
                 bias_init='zeros',
                 has_bias=True,
                 has_bn=False,
                 activation=None,
                 after_fake=True):
        super(DenseBnAct, self).__init__()
        self.dense = nn.Dense(
            in_channels,
            out_channels,
            weight_init,
            bias_init,
            has_bias)
        self.has_bn = Validator.check_bool(has_bn, "has_bn")
        self.has_act = activation is not None
        self.after_fake = after_fake
        if has_bn:
            self.batchnorm = BatchNorm1d(out_channels)
        self.activation = get_activation(activation) if isinstance(activation, str) else activation
        if activation is not None and not isinstance(self.activation, (Cell, Primitive)):
            raise TypeError("The activation must be str or Cell or Primitive,"" but got {}.".format(activation))

    def construct(self, x):
        x = self.dense(x)
        if self.has_bn:
            x = self.batchnorm(x)
        if self.has_act:
            x = self.activation(x)
        return x


class BatchNormFoldCell(Cell):
    """
    Batch normalization folded.

    Args:
        momentum (float): Momentum value must be [0, 1]. Default: 0.9.
        epsilon (float): A small float number to avoid dividing by 0. 1e-5 if dtype in
            float32 else 1e-3. Default: 1e-5.
        freeze_bn (int): Delay in steps at which computation switches from regular batch
            norm to frozen mean and std. Default: 0.

    Inputs:
        - **x** (Tensor) - Tensor of shape :math:`(N, C, H, W)`.
        - **mean** (Tensor) - Tensor of shape :math:`(C,)`.
        - **variance** (Tensor) - Tensor of shape :math:`(C,)`.
        - **global_step** (Tensor) - Tensor to record current global step.

    Outputs:
        Tuple of 4 Tensor, the normalized input and the updated parameters.

        - **batch_mean** (Tensor) - Tensor of shape :math:`(C,)`.
        - **batch_std** (Tensor) - Tensor of shape :math:`(C,)`.
        - **running_mean** (Tensor) - Tensor of shape :math:`(C,)`.
        - **running_std** (Tensor) - Tensor of shape :math:`(C,)`.

    """

    def __init__(self, momentum=0.9, epsilon=1e-5, freeze_bn=0):
        """Initialize batch norm fold layer"""
        super(BatchNormFoldCell, self).__init__()
        self.epsilon = epsilon
        self.is_gpu = context.get_context('device_target') == "GPU"
        if self.is_gpu:
            self.bn_train = Q.BatchNormFold(momentum, epsilon, is_training=True, freeze_bn=freeze_bn)
            self.bn_infer = Q.BatchNormFold(momentum, epsilon, is_training=False, freeze_bn=freeze_bn)
        else:
            self.bn_reduce = P.BNTrainingReduce()
            self.bn_update = Q.BatchNormFoldD(momentum, epsilon, is_training=True, freeze_bn=freeze_bn)

    def construct(self, x, mean, variance, global_step):
        if self.is_gpu:
            if self.training:
                batch_mean, batch_std, running_mean, running_std = self.bn_train(x, mean, variance, global_step)
            else:
                batch_mean, batch_std, running_mean, running_std = self.bn_infer(x, mean, variance, global_step)
        else:
            if self.training:
                x_sum, x_square_sum = self.bn_reduce(x)
                _, batch_mean, batch_std, running_mean, running_std, mean_updated, variance_updated = \
                    self.bn_update(x, x_sum, x_square_sum, mean, variance)
                P.Assign()(mean, mean_updated)
                P.Assign()(variance, variance_updated)
            else:
                batch_mean = P.ZerosLike()(variance)
                batch_std = P.OnesLike()(variance)
                running_mean = P.TensorAdd()(mean, 0.)
                running_std = P.Sqrt()(P.TensorAdd()(variance, self.epsilon))
        return batch_mean, batch_std, running_mean, running_std


def _partial_init(cls_or_self, **kwargs):
    """
    Wrapper that allows creation of class factories.

    This can be useful when there is a need to create classes with the same
    constructor arguments, but different instances.

    Example::
        >>> Foo.partial_init = classmethod(_partial_init)
        >>> foo_builder = Foo.partial_init(a=3, b=4).partial_init(answer=42)
        >>> foo_instance1 = foo_builder()
        >>> foo_instance2 = foo_builder()
        >>> id(foo_instance1) == id(foo_instance2)
        False
    """

    class _PartialWrapper:
        r"""
        class of wrapper that allows creation of class factories.
        """

        def __init__(self, p):
            self.p = p

        def __call__(self, *args, **keywords):
            return self.p(*args, **keywords)

        def __repr__(self):
            return self.p.__repr__()

        partial_init = _partial_init

    r = _PartialWrapper(partial(cls_or_self, **kwargs))
    return r


class Observer(Cell):
    """
    Base class of Observer. Observer is used to calculate the statistics of specific layer.

    Notes:
        This class is an abstract class.

    Args:
        quant_dtype (QuantDtype): The type of FakeQuant data.
    """

    def __init__(self, quant_dtype):
        super(Observer, self).__init__()
        self.quant_dtype = quant_dtype

    def extend_repr(self):
        s = f"dtype={self.dtype}"
        return s

    def construct(self):
        pass

    partial_init = classmethod(_partial_init)


class UniformQuantObserver(Observer):
    """
    The base class of Uniform Quantization Observer.

    Args:
        quant_dtype (QuantDtype): The type of FakeQuant data. Default: QuantDtype.INT8.
        per_channel (bool):  Quantization granularity based on layer or on channel. Default: False.
        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.
        num_channels (int): declarate the min and max channel size, Default: 1.

    Returns:
        Tensor.
    """

    min_max_map = {
        QuantDtype.INT2: (-2, 1),
        QuantDtype.INT3: (-4, 3),
        QuantDtype.INT4: (-8, 7),
        QuantDtype.INT5: (-16, 15),
        QuantDtype.INT6: (-32, 31),
        QuantDtype.INT7: (-64, 63),
        QuantDtype.INT8: (-128, 127),

        QuantDtype.UINT2: (0, 3),
        QuantDtype.UINT3: (0, 7),
        QuantDtype.UINT4: (0, 15),
        QuantDtype.UINT5: (0, 31),
        QuantDtype.UINT6: (0, 63),
        QuantDtype.UINT7: (0, 127),
        QuantDtype.UINT8: (0, 255)
    }

    def __init__(self, quant_dtype=QuantDtype.INT8, per_channel=False, symmetric=False, narrow_range=False,
                 num_channels=1):
        super(UniformQuantObserver, self).__init__(quant_dtype)
        self.per_channel = per_channel
        self.symmetric = symmetric
        self.narrow_range = narrow_range
        self.num_channels = num_channels


class FakeQuantWithMinMaxObserver(UniformQuantObserver):
    r"""
    Quantization aware op. This OP provides the fake quantization observer function on data with min and max.

    Args:
        min_init (int, float): The initialized min value. Default: -6.
        max_init (int, float): The initialized max value. Default: 6.
        ema (bool): The exponential Moving Average algorithm updates min and max. Default: False.
        ema_decay (float): Exponential Moving Average algorithm parameter. Default: 0.999.
        per_channel (bool):  Quantization granularity based on layer or on channel. Default: False.
        channel_axis (int): Quantization by channel axis. Default: 1.
        num_channels (int): declarate the min and max channel size, Default: 1.
        quant_dtype (QuantDtype): The datatype of quantization, supporting 4 and 8bits. Default: QuantDtype.INT8.
        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 FakeQuantWithMinMaxObserver.

    Outputs:
        Tensor, with the same type and shape as the `x`.

    Examples:
        >>> fake_quant = FakeQuantWithMinMaxObserver()
        >>> input_x = Tensor(np.array([[1, 2, 1], [-2, 0, -1]]), mindspore.float32)
        >>> result = fake_quant(input_x)
    """

    def __init__(self,
                 min_init=-6,
                 max_init=6,
                 ema=False,
                 ema_decay=0.999,
                 per_channel=False,
                 channel_axis=1,
                 num_channels=1,
                 quant_dtype=QuantDtype.INT8,
                 symmetric=False,
                 narrow_range=False,
                 quant_delay=0):
        """Initialize FakeQuantWithMinMaxObserver"""
        super(FakeQuantWithMinMaxObserver, self).__init__(quant_dtype=quant_dtype, per_channel=per_channel,
                                                          symmetric=symmetric, narrow_range=narrow_range,
                                                          num_channels=num_channels)
        Validator.check_type("min_init", min_init, [int, float])
        Validator.check_type("max_init", max_init, [int, float])
        Validator.check("min_init", min_init, "max_init", max_init, rel=Rel.LT)
        Validator.check_non_negative_int(quant_delay, 'quant_delay')
        self.min_init = min_init
        self.max_init = max_init
        self.quant_dtype = quant_dtype
        self.ema = ema
        self.ema_decay = ema_decay
        self.per_channel = per_channel
        self.num_channels = num_channels
        self.channel_axis = channel_axis
        self.quant_delay = quant_delay
        self.symmetric = symmetric
        self.narrow_range = narrow_range
        self.is_ascend = context.get_context('device_target') == "Ascend"

        # init tensor min and max for fake quant op
        if self.per_channel:
            min_array = np.array([self.min_init] * self.num_channels).astype(np.float32)
            max_array = np.array([self.max_init] * self.num_channels).astype(np.float32)
        else:
            min_array = np.array([self.min_init]).astype(np.float32)
            max_array = np.array([self.max_init]).astype(np.float32)
        self.minq = Parameter(Tensor(min_array), name='quant_min', requires_grad=False)
        self.maxq = Parameter(Tensor(max_array), name='quant_max', requires_grad=False)

        # init fake quant relative op
        if self.per_channel:
            quant_fun = partial(Q.FakeQuantPerChannel, channel_axis=self.channel_axis)
            ema_fun = partial(Q.MinMaxUpdatePerChannel, channel_axis=self.channel_axis)
        else:
            quant_fun = Q.FakeQuantPerLayer
            ema_fun = Q.MinMaxUpdatePerLayer

        self.ema_update = ema_fun(ema=self.ema, ema_decay=self.ema_decay)
        if self.is_ascend:
            self.fake_quant_train = quant_fun(num_bits=self.quant_dtype.num_bits,
                                              symmetric=self.symmetric,
                                              narrow_range=self.narrow_range,
                                              quant_delay=self.quant_delay)
            self.fake_quant_infer = self.fake_quant_train
        else:
            quant_fun = partial(quant_fun,
                                ema=self.ema,
                                ema_decay=ema_decay,
                                num_bits=self.quant_dtype.num_bits,
                                symmetric=self.symmetric,
                                narrow_range=self.narrow_range,
                                quant_delay=self.quant_delay)
            self.fake_quant_train = quant_fun(training=True)
            self.fake_quant_infer = quant_fun(training=False)

    def extend_repr(self):
        s = 'quant_dtype={}, symmetric={}, narrow_range={}, ema={}({}), per_channel={}({}, {}), ' \
            'quant_delay={}, min_init={}, max_init={}'.format(self.quant_dtype, self.symmetric, self.narrow_range,
                                                              self.ema, self.ema_decay, self.per_channel,
                                                              self.channel_axis, self.num_channels, self.quant_delay,
                                                              self.min_init, self.max_init)
        return s

    def construct(self, x):
        if self.training:
            min_up, max_up = self.ema_update(x, self.minq, self.maxq)
            P.Assign()(self.minq, min_up)
            P.Assign()(self.maxq, max_up)
            out = self.fake_quant_train(x, self.minq, self.maxq)
        else:
            out = self.fake_quant_infer(x, self.minq, self.maxq)
        return out


QuantConfig = namedtuple("QuantConfig", ['weight', 'activation'])

quant_config_default = QuantConfig(weight=FakeQuantWithMinMaxObserver, activation=FakeQuantWithMinMaxObserver)


class Conv2dBnFoldQuant(Cell):
    r"""
    2D convolution with BatchNormal op folded 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.
        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.
        eps (float): Parameters for BatchNormal. Default: 1e-5.
        momentum (float): Parameters for BatchNormal op. Default: 0.997.
        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'.
        beta_init (Union[Tensor, str, Initializer, numbers.Number]): Initializer for the
            beta vector. Default: 'zeros'.
        gamma_init (Union[Tensor, str, Initializer, numbers.Number]): Initializer for the
            gamma vector. Default: 'ones'.
        mean_init (Union[Tensor, str, Initializer, numbers.Number]): Initializer for the
            mean vector. Default: 'zeros'.
        var_init (Union[Tensor, str, Initializer, numbers.Number]): Initializer for the
            variance vector. Default: 'ones'.
        fake (bool): Whether Conv2dBnFoldQuant Cell adds FakeQuantWithMinMaxObserver. Default: True.
        quant_config (QuantConfig): Configs the oberser type of weight and activation. Default: quant_config_default.
        quant_dtype (QuantDtype): Specifies the FakeQuant datatype. Default: QuantDtype.INT8.
        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,
                 has_bias=False,
                 weight_init='normal',
                 bias_init='zeros',
                 beta_init='zeros',
                 gamma_init='ones',
                 mean_init='zeros',
                 var_init='ones',
                 fake=True,
                 quant_config=quant_config_default,
                 quant_dtype=QuantDtype.INT8,
                 freeze_bn=100000):
        """Initialize 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.has_bias = has_bias
        self.freeze_bn = freeze_bn
        self.fake = fake
        self.quant_config = quant_config
        self.quant_dtype = quant_dtype
        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_equal_int(group, in_channels, 'group')
            Validator.check_equal_int(group, out_channels, 'group')
            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')
        self.bias_add = P.BiasAdd()
        if Validator.check_bool(has_bias):
            self.bias = Parameter(initializer(bias_init, [out_channels]), name='bias')
        else:
            self.bias = None

        # 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 = quant_config.weight(min_init=-6,
                                                     max_init=6,
                                                     ema=False,
                                                     channel_axis=channel_axis,
                                                     num_channels=out_channels,
                                                     quant_dtype=quant_dtype)
        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.fake_quant_weight.quant_delay)
        return s

    def construct(self, x):
        out_conv = self.conv(x, self.weight)
        if self.has_bias:
            out_conv = self.bias_add(out_conv, self.bias)
        # 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)
        if self.has_bias:
            out = self.bias_add(out, self.bias)
        # 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): Specifies the dilation rate to use for dilated convolution. Default: 1.
        group (int): Splits filter into groups, `in_ channels` and `out_channels` must 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'.
        quant_config (QuantConfig): Configs the oberser type of weight and activation. Default: quant_config_default.
        quant_dtype (QuantDtype): Specifies the FakeQuant datatype. Default: QuantDtype.INT8.

    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',
                 quant_config=quant_config_default,
                 quant_dtype=QuantDtype.INT8):
        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 = Validator.check_positive_int(in_channels)
        self.out_channels = Validator.check_positive_int(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.bias_add = P.BiasAdd()
        if Validator.check_bool(has_bias):
            self.bias = Parameter(initializer(bias_init, [out_channels]), name='bias')
        else:
            self.bias = None
        # initialize convolution op and Parameter
        if context.get_context('device_target') == "Ascend" and group > 1:
            Validator.check_equal_int(group, in_channels, 'group')
            Validator.check_equal_int(group, out_channels, 'group')
            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=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)
            weight_shape = [out_channels, in_channels // group, *self.kernel_size]
            channel_axis = 0
        self.weight = Parameter(initializer(weight_init, weight_shape), name='weight')
        self.fake_quant_weight = quant_config.weight(min_init=-6,
                                                     max_init=6,
                                                     ema=False,
                                                     channel_axis=channel_axis,
                                                     num_channels=out_channels,
                                                     quant_dtype=quant_dtype)
        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.fake_quant_weight.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): Specifies the dilation rate to use for dilated convolution. Default: 1.
        group (int): Splits filter into groups, `in_ channels` and `out_channels` must 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'.
        quant_config (QuantConfig): Configs the oberser type of weight and activation. Default: quant_config_default.
        quant_dtype (QuantDtype): Specifies the FakeQuant datatype. Default: QuantDtype.INT8.

    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',
                 quant_config=quant_config_default,
                 quant_dtype=QuantDtype.INT8):
        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 = Validator.check_positive_int(in_channels)
        self.out_channels = Validator.check_positive_int(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

        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 Validator.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 = quant_config.weight(min_init=-6,
                                                     max_init=6,
                                                     ema=False,
                                                     channel_axis=0,
                                                     num_channels=out_channels,
                                                     quant_dtype=quant_dtype)

    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.fake_quant_weight.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 (Union[str, Cell, Primitive]): The regularization function applied to the output of the layer,
            eg. 'relu'. Default: None.
        quant_config (QuantConfig): Configs the oberser type of weight and activation. Default: quant_config_default.
        quant_dtype (QuantDtype): Specifies the FakeQuant datatype. Default: QuantDtype.INT8.

    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,
                 quant_config=quant_config_default,
                 quant_dtype=QuantDtype.INT8):
        super(DenseQuant, self).__init__()
        self.in_channels = Validator.check_positive_int(in_channels)
        self.out_channels = Validator.check_positive_int(out_channels)
        self.has_bias = Validator.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) if isinstance(activation, str) else activation
        if activation is not None and not isinstance(self.activation, (Cell, Primitive)):
            raise TypeError("The activation must be str or Cell or Primitive,"" but got {}.".format(activation))
        self.activation_flag = self.activation is not None
        self.fake_quant_weight = quant_config.weight(min_init=-6,
                                                     max_init=6,
                                                     ema=False,
                                                     channel_axis=0,
                                                     num_channels=out_channels,
                                                     quant_dtype=quant_dtype)

    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 `FakeQuantWithMinMaxObserver` or other observer for more details.

    Args:
        activation (Cell): Activation cell class.
        ema_decay (float): Exponential Moving Average algorithm parameter. Default: 0.999.
        quant_config (QuantConfig): Configs the oberser type of weight and activation. Default: quant_config_default.
        quant_dtype (QuantDtype): Specifies the FakeQuant datatype. Default: QuantDtype.INT8.

    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,
                 quant_config=quant_config_default,
                 quant_dtype=QuantDtype.INT8):
        super(ActQuant, self).__init__()
        self.fake_quant_act = quant_config.activation(min_init=-6,
                                                      max_init=6,
                                                      ema=False,
                                                      ema_decay=ema_decay,
                                                      quant_dtype=quant_dtype)
        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.
        quant_config (QuantConfig): Configs the oberser type of weight and activation. Default: quant_config_default.
        quant_dtype (QuantDtype): Specifies the FakeQuant datatype. Default: QuantDtype.INT8.

    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,
                 quant_config=quant_config_default,
                 quant_dtype=QuantDtype.INT8):
        super(LeakyReLUQuant, self).__init__()
        self.fake_quant_act_before = quant_config.activation(min_init=-6,
                                                             max_init=6,
                                                             ema=True,
                                                             ema_decay=ema_decay,
                                                             quant_dtype=quant_dtype)
        self.fake_quant_act_after = quant_config.activation(min_init=-6,
                                                            max_init=6,
                                                            ema=True,
                                                            ema_decay=ema_decay,
                                                            quant_dtype=quant_dtype)
        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.
        quant_config (QuantConfig): Configs the oberser type of weight and activation. Default: quant_config_default.
        quant_dtype (QuantDtype): Specifies the FakeQuant datatype. Default: QuantDtype.INT8.

    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,
                 quant_config=quant_config_default,
                 quant_dtype=QuantDtype.INT8):
        super(HSwishQuant, self).__init__()
        self.fake_quant_act_before = quant_config.activation(min_init=-6,
                                                             max_init=6,
                                                             ema=True,
                                                             ema_decay=ema_decay,
                                                             quant_dtype=quant_dtype)
        self.fake_quant_act_after = quant_config.activation(min_init=-6,
                                                            max_init=6,
                                                            ema=True,
                                                            ema_decay=ema_decay,
                                                            quant_dtype=quant_dtype)
        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.
        quant_config (QuantConfig): Configs the oberser type of weight and activation. Default: quant_config_default.
        quant_dtype (QuantDtype): Specifies the FakeQuant datatype. Default: QuantDtype.INT8.

    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,
                 quant_config=quant_config_default,
                 quant_dtype=QuantDtype.INT8):
        super(HSigmoidQuant, self).__init__()
        self.fake_quant_act_before = quant_config.activation(min_init=-6,
                                                             max_init=6,
                                                             ema=True,
                                                             ema_decay=ema_decay,
                                                             quant_dtype=quant_dtype)
        self.fake_quant_act_after = quant_config.activation(min_init=-6,
                                                            max_init=6,
                                                            ema=True,
                                                            ema_decay=ema_decay,
                                                            quant_dtype=quant_dtype)
        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.
        quant_config (QuantConfig): Configs the oberser type of weight and activation. Default: quant_config_default.
        quant_dtype (QuantDtype): Specifies the FakeQuant datatype. Default: QuantDtype.INT8.

    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,
                 quant_config=quant_config_default,
                 quant_dtype=QuantDtype.INT8):
        super(TensorAddQuant, self).__init__()
        self.fake_quant_act = quant_config.activation(min_init=-6,
                                                      max_init=6,
                                                      ema=True,
                                                      ema_decay=ema_decay,
                                                      quant_dtype=quant_dtype)
        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.
        quant_config (QuantConfig): Configs the oberser type of weight and activation. Default: quant_config_default.
        quant_dtype (QuantDtype): Specifies the FakeQuant datatype. Default: QuantDtype.INT8.

    Inputs:
        - **x** (Tensor) - The input of MulQuant.

    Outputs:
        Tensor, with the same type and shape as the `x`.

    Examples:
        >>> mul_quant = nn.MulQuant()
        >>> 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 = mul_quant(input_x, input_y)
    """

    def __init__(self,
                 ema_decay=0.999,
                 quant_config=quant_config_default,
                 quant_dtype=QuantDtype.INT8):
        super(MulQuant, self).__init__()
        self.fake_quant_act = quant_config.activation(min_init=-6,
                                                      max_init=6,
                                                      ema=True,
                                                      ema_decay=ema_decay,
                                                      quant_dtype=quant_dtype)
        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)`.
    """

    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
        self.bias_add = P.BiasAdd()

    def construct(self, x):
        x = self.quant(x)
        if self.has_bias:
            x = self.core_op(x, self.weight)
            x = self.bias_add(x, self.bias)
        else:
            x = self.core_op(x, self.weight)
        x = self.dequant(x, self.dequant_scale)
        x = F.cast(x, mstype.float32)
        if self.has_act:
            x = self.activation(x)
        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}'
        return str_info


class QuantMindirBlock(Cell):
    """A quant binary block of Conv/Dense, activation layer for export MINDIR model.

       Args:
        core_op (Cell): The operation cell.
        weight (Tensor): The weigth of the cell.
        bias (Tensor): The bias of the cell. Default: None.
        activation (str): The regularization function applied to the output of the layer, eg. 'relu'. Default: None.
        param_dict (dict): The information of the cell.
    """

    def __init__(self,
                 core_op,
                 weight,
                 bias=None,
                 activation=None,
                 param_dict=None):

        super(QuantMindirBlock, self).__init__()
        self.core_op = core_op
        if activation is not None:
            self.core_op.add_prim_attr("activation_name", activation.__class__.__name__)
        self.core_op.add_prim_attr("filter_maxq", Tensor(param_dict["filter_maxq"]))
        self.core_op.add_prim_attr("filter_minq", Tensor(param_dict["filter_minq"]))
        if param_dict["output_maxq"] is not None:
            self.core_op.add_prim_attr("output_maxq", Tensor(param_dict["output_maxq"]))
            self.core_op.add_prim_attr("output_minq", Tensor(param_dict["output_minq"]))
        self.core_op.add_prim_attr("symmetric", Tensor(param_dict["symmetric"]))
        if hasattr(core_op, 'pad_mode'):
            self.core_op.add_prim_attr("pad_mode", core_op.pad_mode)
        self.core_op.add_prim_attr("num_bits", Tensor(8))
        self.core_op.add_prim_attr("narrow_range", Tensor(False))
        if param_dict["input_maxq"] == 'None':
            self.core_op.add_prim_attr("mean", Tensor(param_dict["mean"]))
            self.core_op.add_prim_attr("std_dev", Tensor(param_dict["std_dev"]))
        elif param_dict["input_maxq"] is not None:
            self.core_op.add_prim_attr("input_maxq", Tensor(param_dict["input_maxq"]))
            self.core_op.add_prim_attr("input_minq", Tensor(param_dict["input_minq"]))

        self.weight = weight
        self.bias = bias
        self.has_bias = bias is not None
        self.activation = activation
        self.has_act = activation is not None
        self.bias_add = P.BiasAdd()
        if isinstance(activation, ReLU):
            self.activation = None
            self.has_act = False

    def construct(self, x):
        if self.has_bias:
            x = self.core_op(x, self.weight)
            x = self.bias_add(x, self.bias)
        else:
            x = self.core_op(x, self.weight)
        return x

    def extend_repr(self):
        str_info = f'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}'
        return str_info