BN融合的量化感知训练及其uint8推理的模拟

上一篇博客介绍了如何进行量化感知训练，并进行uint8前向推理，但是没有将BN层进行融合，这使得模型推理时的计算复杂度仍然有改进的空间，本篇博客讲述了如何进行BN融合的量化感知训练，并在训练完成后进行了uint8推理的模拟，以方便今后在FPGA上的部署。 代码：

from __future__ import absolute_import
from __future__ import division
from __future__ import print_function

import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.nn import init
from torch.nn.parameter import Parameter
from torch.autograd import Function


# ********************* range_trackers(范围统计器，统计量化前范围) *********************
class RangeTracker(nn.Module):
    def __init__(self, q_level):
        super().__init__()
        self.q_level = q_level

    def update_range(self, min_val, max_val):
        raise NotImplementedError

    @torch.no_grad()
    def forward(self, input):
        if self.q_level == 'L':  # A,min_max_shape=(1, 1, 1, 1),layer级
            min_val = torch.min(input)
            max_val = torch.max(input)
        elif self.q_level == 'C':  # W,min_max_shape=(N, 1, 1, 1),channel级
            min_val = torch.min(torch.min(torch.min(input, 3, keepdim=True)[0], 2, keepdim=True)[0], 1, keepdim=True)[0]
            max_val = torch.max(torch.max(torch.max(input, 3, keepdim=True)[0], 2, keepdim=True)[0], 1, keepdim=True)[0]

        self.update_range(min_val, max_val)


class GlobalRangeTracker(RangeTracker):  # W,min_max_shape=(N, 1, 1, 1),channel级,取本次和之前相比的min_max —— (N, C, W, H)
    def __init__(self, q_level, out_channels):
        super().__init__(q_level)
        self.register_buffer('min_val', torch.zeros(out_channels, 1, 1, 1))
        self.register_buffer('max_val', torch.zeros(out_channels, 1, 1, 1))
        self.register_buffer('first_w', torch.zeros(1))

    def update_range(self, min_val, max_val):
        temp_minval = self.min_val
        temp_maxval = self.max_val
        if self.first_w == 0:
            self.first_w.add_(1)
            self.min_val.add_(min_val)
            self.max_val.add_(max_val)
        else:
            self.min_val.add_(-temp_minval).add_(torch.min(temp_minval, min_val))
            self.max_val.add_(-temp_maxval).add_(torch.max(temp_maxval, max_val))


class AveragedRangeTracker(RangeTracker):  # A,min_max_shape=(1, 1, 1, 1),layer级,取running_min_max —— (N, C, W, H)
    def __init__(self, q_level, momentum=0.1):
        super().__init__(q_level)
        self.momentum = momentum
        self.register_buffer('min_val', torch.zeros(1))
        self.register_buffer('max_val', torch.zeros(1))
        self.register_buffer('first_a', torch.zeros(1))

    def update_range(self, min_val, max_val):
        if self.first_a == 0:
            self.first_a.add_(1)
            self.min_val.add_(min_val)
            self.max_val.add_(max_val)
        else:
            self.min_val.mul_(1 - self.momentum).add_(min_val * self.momentum)
            self.max_val.mul_(1 - self.momentum).add_(max_val * self.momentum)


# ********************* quantizers（量化器，量化） *********************
class Round(Function):

    @staticmethod
    def forward(self, input):
        output = torch.round(input)
        return output

    @staticmethod
    def backward(self, grad_output):
        grad_input = grad_output.clone()
        return grad_input


class Quantizer(nn.Module):
    def __init__(self, bits, range_tracker):
        super().__init__()
        self.bits = bits
        self.range_tracker = range_tracker
        self.register_buffer('scale', None)  # 量化比例因子
        self.register_buffer('zero_point', None)  # 量化零点

    def update_params(self):
        raise NotImplementedError

    # 量化
    def quantize(self, input):
        output = input * self.scale - self.zero_point
        return output

    def round(self, input):
        output = Round.apply(input)
        return output

    # 截断
    def clamp(self, input):
        output = torch.clamp(input, self.min_val, self.max_val)
        return output

    # 反量化
    def dequantize(self, input):
        output = (input + self.zero_point) / self.scale
        return output

    def forward(self, input):
        if self.bits == 32:
            output = input
        elif self.bits == 1:
            print('！Binary quantization is not supported ！')
            assert self.bits != 1
        else:
            self.range_tracker(input)
            self.update_params()
            output = self.quantize(input)  # 量化
            output = self.round(output)
            output = self.clamp(output)  # 截断
            output = self.dequantize(output)  # 反量化
        return output


class SignedQuantizer(Quantizer):
    def __init__(self, *args, **kwargs):
        super().__init__(*args, **kwargs)
        self.register_buffer('min_val', torch.tensor(-(1