AI+无线通信——Top7 （Baseline）总结

快来打我* 2023-01-13 10:17 136阅读 0赞

比赛已经告一段落，现在我们队兑现承诺，将比赛方案开源给大家，互勉互助，共同进步。

# 队伍介绍 #

我们的队伍名是Baseline，我们因分享Baseline结缘，所以就把队伍名叫Baseline。

队长： 方曦来自上海交通大学，研三。

队员 ：吕晓欣来自网易，AI工程师

队员：王浩来自北京星河亮点，软件研发

队员：杨新达来自广州一家企业，AI工程师

# 方案 #

## **摘要** ##

对于当前通信系统来说，物理层是通信服务得到保障的基础；而对于物理层来说，MIMO则是基本的支撑技术；对于MIMO来说，准确地确定信道质量并做有效反馈及利用又是必不可少的关键问题。

在国际标准化组织3GPP的讨论内，目前这部分工作是通过CSI 参考信号设计及CSI反馈机制完成。在当前的CSI反馈设计中，主要是依赖矢量量化、码本设计的方式来实现信道特征的提取与反馈，例如基于TYPE1、TYPE2的CSI反馈设计等。在目前的实践来看，这类反馈方式是有效的，但是由于其核心思想是基于信息抽取、码本反馈的方式，其所反馈的目标信息实际上是有损信道信息。

在本次大赛中，我们从计算机视觉角度建模，设计出一种基于CNN的自编码器结构。我们采用了带有SE结构的BCSP模块作为网络的基础组件，在计算效率和网络精度上都有较好的效果；采用带有误差恢复能力的量化模块，一方面能降低量化误差，同时也能提高编码器的训练效果；通过分析大赛数据，我们利用Fast-AutoAugment思路找到了4种数据增强方法，完美的解决了在384 附近bit数的网络过拟合问题；我们利用剪枝和降低量化精度的方式，大幅度加速了我们的训练过程。最终我们获得了第7名的好成绩。

**关键词**

无线通信， 信道反馈， 卷积神经网络， 注意力机制， 数据增强

## **1 注意力机制的使用** ##

在我们采用的注意力机制是SE-Net: Squeeze-and-Excitation Networks，简称SE-Net\[1\]，它赢得了最后一届ImageNet2017竞赛分类任务的冠军，其基本原理是对于每个输出channel，预测一个常数权重，对每个channel加权一下。结构如下图：

![20210418072554684.png][]

**图1：SE 注意力机制**

第一步每个通道H\*W个数全局平均池化得到一个标量，称之为Squeeze，然后两个FC得到01之间的一个权重值，对原始的每个HxW的每个元素乘以对应通道的权重，得到新的feature map，称之为Excitation。任意的原始网络结构，都可以通过这个Squeeze-Excitation的方式进行feature recalibration，如下图。

![watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L2hoaGhoaGhoaGh3d3d3d3d3d3d3_size_16_color_FFFFFF_t_70][]

**图2：SENet基础结构**

具体实现上就是一个Global Average Pooling-FC-ReLU-FC-Sigmoid，第一层的FC会把通道降下来，然后第二层FC再把通道升上去，得到和通道数相同的C个权重，每个权重用于给对应的一个通道进行加权。上图中的r就是缩减系数，实验确定选取16，可以得到较好的性能并且计算量相对较小。SENet的核心思想在于通过网络根据loss去学习特征权重，使得有效的feature map权重大，无效或效果小的feature map权重小的方式训练模型达到更好的结果。

**我们将SENet一个子结构，嵌入到C3和BottleneckCSP模块的最后一层。如图3所示。**

在该赛题中SE结构或者说注意力机制能够大幅度提升模型的拟合能力，让我们的模型能够成功的完成432bit 达标，但随之而来的模型过拟合现象困扰了我们很长时间。

## **2 量化误差恢复模块** ##

在量化编码过程中，经过量化-反量化操作，将会让原始编码丢失一部分信息，即量化误差，量化误差的存在不仅使得模型最终NMSE会比无量化操作的更高，还会减慢decoder的训练速度和效果。故我们提出量化误差恢复模块，即对反量化后的编码进行refine，使之更加接近无量化损失。

![watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L2hoaGhoaGhoaGh3d3d3d3d3d3d3_size_16_color_FFFFFF_t_70 1][]

**图3：基础模块图**

具体操作是，我们对量化-反量化后的编码，通过两层全连接（带有bn和非线性层），并经过标准化处理，得到值域与量化误差值域相同的输出（通过sigmoid以及scale等操作调整值域为\[-12*B*\+1,12*B*\+1\]![20210418072554660.png][]）以残差的方式加到原始反量化编码之后，以起到恢复量化误差的效果。同时，为了使得这个模块能更好地按设想工作，我们对此模块的输出增加了一路损失函数，使得恢复后的编码与量化前的编码更加接近。

设量化前编码为X，量化后编码为X’，我们的误差恢复模块为R，则额外监督表示为如下：L(X+R(X'), X)![20210418072554675.png][]。

![watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L2hoaGhoaGhoaGh3d3d3d3d3d3d3_size_16_color_FFFFFF_t_70 2][]

![watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L2hoaGhoaGhoaGh3d3d3d3d3d3d3_size_16_color_FFFFFF_t_70 3][]

**图4：误差恢复模块与Simsiam结构对比图**

上图同对比了误差恢复模块和自监督算法中的SimSiam\[2\]结构对比图。如果我们把量化误差看座位一种数据增强，那么量化误差修复这一块刚好可以看作一种自监督学习网络，同时存在一条支路在反向传播过程中能够计算出准确的梯度，让我们能够获得更好的encoder层。

**3 数据增强**

赛方提供的数据200\*3000是按序摆放的，通过分析，我们发现3000这个维度中各个数据似乎存在一些相似关系，在我们全部的数据增强过程中都不会去破坏这种模式。数据增强固然可以一定程度上缓解模型过拟合，但是如果设计的不得当，网络会学到很多没用的信息从而不能训到很低的nmse，为此我们借鉴了Fast-AutoAugment中的思想，对于每一种数据增强，我们利用原始数据训练的模型在验证集数据+该数据增强统计nmse，如果nmse过高，那么这种数据增强大概率改变了原始数据分布，不应该背采纳。通过这种方式我们选取了4中数据增强方法：

1.  1-X![20210418072554676.png][]
2.  实部虚部shuffle
3.  MixUp
4.  CutMix

传统的MixUp和CutMix会破坏数据原有的模式，所以我们对其进行一些改造。在样本采样过程中，我们只会选择属于同一种patten的两个样本进行融合；我们不会去破坏16这个维度的数值关系，所以CutMix过程中随机选择24行中的一部分进行替换，这是因为24这个维度虽然有patten，但是似乎不存在特别明显的数值关系。通过这种方式我们能偶成功的训练出384bit的模型。

![watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L2hoaGhoaGhoaGh3d3d3d3d3d3d3_size_16_color_FFFFFF_t_70 4][]

![watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L2hoaGhoaGhoaGh3d3d3d3d3d3d3_size_16_color_FFFFFF_t_70 5][]

**图5：数据增强效果图**

**5 剪枝与量化**

量化层我们选择了简单的均匀量化操作。量化bit数目选取上，考虑到任务更加侧重更小传输bit而不是极致的精度（低NMSE），故可以选择使用更小的量化bit数目，而太小的量化bit数目会导致量化误差过大，使得decoder训练更加困难也更容易过拟合。权衡上述，我们选择了使用Bit=3的量化操作。

训练初始模型时，我们首先选择使用bit数为432的bitstream构建模型进行训练，训练完成之后对encoder最末层全连接和decoder最前层全连接进行裁剪，得到384bit的autoencoder模型，然后进行进一步finetune，得到384bit模型（3bit\*128）。在比赛的最后阶段，我们选择对128个code中的6个，量化bit从3bit压缩到2bit量化，进一步finetune，得到最终提交的378bit模型。即最终提交的378bit模型中，有122个code采用3bit量化编码，6个code采用2bit量化。

**致谢**

感谢主办方提供数据，感谢DataFountain平台提供支持和及时的问题反馈！

# Code #

##       modelDesign.py ##

# =======================================================================================================================
    # =======================================================================================================================
    import numpy as np
    import torch.nn as nn
    import torch
    import torch.nn.functional as F
    from torch.utils.data import Dataset
    from collections import OrderedDict
    
    NUM_FEEDBACK_BITS_STARTS = 768
    NUM_FEEDBACK_BITS = 384  # pytorch版本一定要有这个参数
    channel_last = 1
    CR_dim = 128
    REFINEMENT = 1
    
    
    class Mish(torch.nn.Module):
        def __init__(self):
            super().__init__()
    
        def forward(self, x):
            x = x * (torch.tanh(torch.nn.functional.softplus(x)))
            return x
    
    
    ACT = nn.SiLU()
    
    
    # =======================================================================================================================
    # =======================================================================================================================
    # Number to Bit Defining Function Defining
    def Num2Bit(Num, B):
        Num_ = Num.type(torch.uint8)
    
        def integer2bit(integer, num_bits=B * 2):
            dtype = integer.type()
            exponent_bits = -torch.arange(-(num_bits - 1), 1).type(dtype)
            exponent_bits = exponent_bits.repeat(integer.shape + (1,))
            out = integer.unsqueeze(-1) // 2 ** exponent_bits
            return (out - (out % 1)) % 2
    
        bit = integer2bit(Num_)
        bit = (bit[:, :, B:]).reshape(-1, Num_.shape[1] * B)
        return bit.type(torch.float32)
    
    
    def Bit2Num(Bit, B):
        Bit_ = Bit.type(torch.float32)
        Bit_ = torch.reshape(Bit_, [-1, int(Bit_.shape[1] / B), B])
        num = torch.zeros(Bit_[:, :, 1].shape).cuda()
        for i in range(B):
            num = num + Bit_[:, :, i] * 2 ** (B - 1 - i)
        return num
    
    
    # =======================================================================================================================
    # =======================================================================================================================
    # Quantization and Dequantization Layers Defining
    class Quantization(torch.autograd.Function):
        @staticmethod
        def forward(ctx, x, B):
            ctx.constant = B
            step = 2 ** B
            out = torch.round(x * step - 0.5)
            out = Num2Bit(out, B)
            return out
    
        @staticmethod
        def backward(ctx, grad_output):
            # return as many input gradients as there were arguments.
            # Gradients of constant arguments to forward must be None.
            # Gradient of a number is the sum of its B bits.
            b, _ = grad_output.shape
            grad_num = torch.sum(grad_output.reshape(b, -1, ctx.constant), dim=2) / ctx.constant
            return grad_num, None
    
    
    class Dequantization(torch.autograd.Function):
        @staticmethod
        def forward(ctx, x, B):
            ctx.constant = B
            step = 2 ** B
            out = Bit2Num(x, B)
            out = (out + 0.5) / step
            return out
    
        @staticmethod
        def backward(ctx, grad_output):
            # return as many input gradients as there were arguments.
            # Gradients of non-Tensor arguments to forward must be None.
            # repeat the gradient of a Num for B time.
            b, c = grad_output.shape
            grad_output = grad_output.unsqueeze(2) / ctx.constant
            grad_bit = grad_output.expand(b, c, ctx.constant)
            return torch.reshape(grad_bit, (-1, c * ctx.constant)), None
    
    
    class QuantizationLayer(nn.Module):
        def __init__(self, B):
            super(QuantizationLayer, self).__init__()
            self.B = B
    
        def forward(self, x):
            out = Quantization.apply(x, self.B)
            return out
    
    
    class DequantizationLayer(nn.Module):
        def __init__(self, B):
            super(DequantizationLayer, self).__init__()
            self.B = B
    
        def forward(self, x):
            out = Dequantization.apply(x, self.B)
            return out
    
    
    # =======================================================================================================================
    # =======================================================================================================================
    # Encoder and Decoder Class Defining
    def autopad(k, p=None):  # kernel, padding
        # Pad to 'same'
        if p is None:
            p = k // 2 if isinstance(k, int) else [x // 2 for x in k]  # auto-pad
        return p
    
    
    class SEBlock(nn.Module):
    
        def __init__(self, input_channels, internal_neurons):
            super(SEBlock, self).__init__()
            self.down = nn.Conv2d(in_channels=input_channels, out_channels=internal_neurons, kernel_size=1, stride=1,
                                  bias=True, padding_mode='circular')
            self.up = nn.Conv2d(in_channels=internal_neurons, out_channels=input_channels, kernel_size=1, stride=1,
                                bias=True, padding_mode='circular')
    
        def forward(self, inputs):
            x = F.avg_pool2d(inputs, kernel_size=inputs.size(3))
            x = self.down(x)
            x = F.leaky_relu(x)
            x = self.up(x)
            x = torch.sigmoid(x)
            x = x.repeat(1, 1, inputs.size(2), inputs.size(3))
            return inputs * x
    
    
    class Conv(nn.Module):
        # Standard convolution
        def __init__(self, c1, c2, k=1, s=1, p=None, g=1, act=True):  # ch_in, ch_out, kernel, stride, padding, groups
            super(Conv, self).__init__()
            self.conv = nn.Conv2d(c1, c2, k, s, autopad(k, p), groups=g, bias=False)
            self.bn = nn.BatchNorm2d(c2)
            self.act = ACT
    
        def forward(self, x):
            return self.act(self.bn(self.conv(x)))
    
    
    class Bottleneck(nn.Module):
        # Standard bottleneck
        def __init__(self, c1, c2, shortcut=True, g=1, e=0.5):  # ch_in, ch_out, shortcut, groups, expansion
            super(Bottleneck, self).__init__()
            c_ = int(c2 * e)  # hidden channels
            self.cv1 = Conv(c1, c_, 1, 1)
            self.cv2 = Conv(c_, c2, 3, 1, g=g)
            self.add = shortcut and c1 == c2
    
        def forward(self, x):
            return x + self.cv2(self.cv1(x)) if self.add else self.cv2(self.cv1(x))
    
    
    class BottleneckCSP(nn.Module):
        # CSP Bottleneck https://github.com/WongKinYiu/CrossStagePartialNetworks
        def __init__(self, c1, c2, n=1, shortcut=True, g=1, e=0.5):  # ch_in, ch_out, number, shortcut, groups, expansion
            super(BottleneckCSP, self).__init__()
            c_ = int(c2 * e)  # hidden channels
            self.cv1 = Conv(c1, c_, 1, 1)
            self.cv2 = nn.Conv2d(c1, c_, 1, 1, bias=False)
            self.cv3 = nn.Conv2d(c_, c_, 1, 1, bias=False)
            self.cv4 = Conv(2 * c_, c2, 1, 1)
            self.bn = nn.BatchNorm2d(2 * c_)  # applied to cat(cv2, cv3)
            self.act = nn.LeakyReLU(0.1, inplace=True)
            self.m = nn.Sequential(*[Bottleneck(c_, c_, shortcut, g, e=1.0) for _ in range(n)])
            self.att = SEBlock(c2, c2 // 2)
    
        def forward(self, x):
            y1 = self.cv3(self.m(self.cv1(x)))
            y2 = self.cv2(x)
            return self.att(self.cv4(self.act(self.bn(torch.cat((y1, y2), dim=1)))))
    
    
    class C3(nn.Module):
        # CSP Bottleneck with 3 convolutions
        def __init__(self, c1, c2, n=1, shortcut=True, g=1, e=0.5):  # ch_in, ch_out, number, shortcut, groups, expansion
            super(C3, self).__init__()
            c_ = int(c2 * e)  # hidden channels
            self.cv1 = Conv(c1, c_, 1, 1)
            self.cv2 = Conv(c1, c_, 1, 1)
            self.cv3 = Conv(2 * c_, c2, 1)  # act=FReLU(c2)
            self.m = nn.Sequential(*[Bottleneck(c_, c_, shortcut, g, e=1.0) for _ in range(n)])
            # self.m = nn.Sequential(*[CrossConv(c_, c_, 3, 1, g, 1.0, shortcut) for _ in range(n)])
            self.att = SEBlock(c2, c2 // 2)
    
        def forward(self, x):
            return self.att(self.cv3(torch.cat((self.m(self.cv1(x)), self.cv2(x)), dim=1)))
    
    
    class Focus(nn.Module):
        # Focus wh information into c-space
        def __init__(self, c1, c2, k=1, s=1, p=None, g=1, act=True):  # ch_in, ch_out, kernel, stride, padding, groups
            super(Focus, self).__init__()
            self.conv = Conv(c1 * 4, c2, k, s, p, g, act)
            # self.contract = Contract(gain=2)
    
        def forward(self, x):  # x(b,c,w,h) -> y(b,4c,w/2,h/2)
            return self.conv(torch.cat([x[..., ::2, ::2], x[..., 1::2, ::2], x[..., ::2, 1::2], x[..., 1::2, 1::2]], 1))
            # return self.conv(self.contract(x))
    
    
    class Contract(nn.Module):
        # Contract width-height into channels, i.e. x(1,64,80,80) to x(1,256,40,40)
        def __init__(self, gain=2):
            super().__init__()
            self.gain = gain
    
        def forward(self, x):
            N, C, H, W = x.size()  # assert (H / s == 0) and (W / s == 0), 'Indivisible gain'
            s = self.gain
            x = x.view(N, C, H // s, s, W // s, s)  # x(1,64,40,2,40,2)
            x = x.permute(0, 3, 5, 1, 2, 4).contiguous()  # x(1,2,2,64,40,40)
            return x.view(N, C * s * s, H // s, W // s)  # x(1,256,40,40)
    
    
    class Expand(nn.Module):
        # Expand channels into width-height, i.e. x(1,64,80,80) to x(1,16,160,160)
        def __init__(self, c1, c2, gain=2, k=1, s=1, p=None, g=1, act=True):
            super().__init__()
            self.gain = gain
            self.conv = Conv(c1 // 4, c2, k, s, p, g, act)
    
        def forward(self, x):
            N, C, H, W = x.size()  # assert C / s ** 2 == 0, 'Indivisible gain'
            s = self.gain
            x = x.view(N, s, s, C // s ** 2, H, W)  # x(1,2,2,16,80,80)
            x = x.permute(0, 3, 4, 1, 5, 2).contiguous()  # x(1,16,80,2,80,2)
            return self.conv(x.view(N, C // s ** 2, H * s, W * s))  # x(1,16,160,160)
    
    
    def conv3x3(in_planes, out_planes, stride=1):
        """3x3 convolution with padding"""
        return nn.Conv2d(in_planes, out_planes, kernel_size=3, stride=stride,
                         padding=1, bias=True)
    
    
    class WLBlock(nn.Module):
        def __init__(self, paths, in_c, k=16, n=[1, 1], e=[1.0, 1.0], quantization=True):
    
            super(WLBlock, self).__init__()
            self.paths = paths
            self.n = n
            self.e = e
            self.k = k
            self.in_c = in_c
            for i in range(self.paths):
                self.__setattr__(str(i), nn.Sequential(OrderedDict([
                    ("Conv0", Conv(self.in_c, self.k, 3)),
                    ("BCSP_1", BottleneckCSP(self.k, self.k, n=self.n[i], e=self.e[i])),
                    ("C3_1", C3(self.k, self.k, n=self.n[i], e=self.n[i])),
                    ("Conv1", Conv(self.k, self.k, 3)),
                ])))
            self.conv1 = conv3x3(self.k * self.paths, self.k)
    
        def forward(self, x):
            outs = []
            for i in range(self.paths):
                _ = self.__getattr__(str(i))(x)
                outs.append(_)
            out = torch.cat(tuple(outs), dim=1)
            out = self.conv1(out)
            out = out + x if self.in_c == self.k else out
            return out
    
    
    class Encoder(nn.Module):
        B = 3
    
        def __init__(self, feedback_bits, quantization=True):
            super(Encoder, self).__init__()
            self.feedback_bits = feedback_bits
            self.k = 256
            self.encoder1 = nn.Sequential(OrderedDict([
                ("Conv0", Conv(2, 16, 5)),
                ("BCSP_1", BottleneckCSP(16, 16, n=2, e=0.5)),
                ("C3_1", C3(16, 16, n=1, e=2.0)),
                ("Conv1", Conv(16, self.k, 3))
            ]))
            self.encoder2 = nn.Sequential(OrderedDict([
                ("Focus0", Focus(2, 16)),
                ("BCSP_1", BottleneckCSP(16, 16, n=1, e=1.0)),
                ("C3_1", C3(16, 16, n=2, e=2.0)),
                ("Expand0", Expand(16, 16)),
                ("Conv1", Conv(16, self.k, 3))
            ]))
            self.encoder3 = nn.Sequential(OrderedDict([
                ("Conv0", Conv(2, 32, 3)),
                ("WLBlock1", WLBlock(3, 32, 32, [1, 2, 3], [0.5, 1, 1.5])),
                ("WLBlock2", WLBlock(2, 32, 32, [2, 4], [1, 2])),
                ("Conv1", Conv(32, self.k, 3)),
            ]))
            self.encoder_conv = nn.Sequential(OrderedDict([
                ("conv1x1", Conv(self.k * 3, 2, 1)),
            ]))
            self.fc = nn.Linear(768, int(NUM_FEEDBACK_BITS_STARTS / self.B))
            self.dim_verify = nn.Linear(int(NUM_FEEDBACK_BITS_STARTS / self.B), int(self.feedback_bits / self.B))
    
            self.sig = nn.Sigmoid()
            self.quantize = QuantizationLayer(self.B)
            self.quantization = quantization
    
        def forward(self, x):
            if channel_last:
                x = x.permute(0, 3, 1, 2).contiguous()
            x0 = x.view(-1, 768)
            encoder1 = self.encoder1(x)
            encoder2 = self.encoder2(x)
            encoder3 = self.encoder3(x)
            out = torch.cat((encoder1, encoder2, encoder3), dim=1)
            out = self.encoder_conv(out)
            out = out.view(-1, 768) + x0
            out = self.fc(out)
            out = self.dim_verify(out)
            out = self.sig(out)
            enq_data = out
            if self.quantization:
                out = self.quantize(out)
            elif self.quantization == 'check':
                out = out
            else:
                out = self.fake_quantize(out)
            return out, enq_data
    
    
    class Decoder(nn.Module):
        B = 3
    
        def __init__(self, feedback_bits, quantization=True):
            super(Decoder, self).__init__()
            self.k = 64
            self.feedback_bits = feedback_bits
            self.dequantize = DequantizationLayer(self.B)
            self.dim_verify = nn.Linear(int(self.feedback_bits / self.B), int(NUM_FEEDBACK_BITS_STARTS / self.B))
            self.fc = nn.Linear(int(NUM_FEEDBACK_BITS_STARTS / self.B), 768)
            self.ende_refinement = nn.Sequential(
                nn.Linear(int(self.feedback_bits / self.B), int(self.feedback_bits / self.B)),
                nn.BatchNorm1d(int(self.feedback_bits / self.B)),
                nn.ReLU(True),
                nn.Linear(int(self.feedback_bits / self.B), int(self.feedback_bits / self.B), bias=False),
                nn.Sigmoid(),
            )
            self.decoder1 = nn.Sequential(OrderedDict([
                ("Conv0", Conv(2, 16, 3)),
                ("BCSP_1", BottleneckCSP(16, 16, n=1, e=1.0)),
                ("Conv1", Conv(16, self.k, 1)),
            ]))
            self.decoder2 = nn.Sequential(OrderedDict([
                ("Conv0", Conv(2, 32, 5)),
                ("BCSP_1", BottleneckCSP(32, 32, n=4, e=2.0)),
                ("Conv1", Conv(32, self.k, 1)),
            ]))
            self.decoder3 = nn.Sequential(OrderedDict([
                ("Conv0", Conv(2, 32, (1, 3))),
                ("BCSP_1", BottleneckCSP(32, 32, n=4, e=2.0)),
                ("Conv1", Conv(32, self.k, 1)),
            ]))
            self.decoder4 = nn.Sequential(OrderedDict([
                ("Conv0", Conv(2, 32, (3, 1))),
                ("BCSP_1", BottleneckCSP(32, 32, n=4, e=2.0)),
                ("Conv1", Conv(32, self.k, 1)),
            ]))
            self.decoder5 = nn.Sequential(OrderedDict([
                ("Focus0", Focus(2, self.k)),
                ("WLBlock1", WLBlock(3, self.k, self.k, [1, 2, 3], [0.5, 1, 1.5])),
                ("WLBlock2", WLBlock(2, self.k, self.k, [2, 4], [1, 2])),
                ("Expand0", Expand(self.k, self.k)),
                ("Conv1", Conv(self.k, self.k, 1)),
            ]))
            self.decoder6 = nn.Sequential(OrderedDict([
                ("Conv0", Conv(2, 32, (3, 5))),
                ("BCSP_1", BottleneckCSP(32, 32, n=4, e=2.0)),
                ("Conv1", Conv(32, self.k, 5)),
            ]))
            self.decoder7 = nn.Sequential(OrderedDict([
                ("Conv0", Conv(2, 32, (5, 3))),
                ("BCSP_1", BottleneckCSP(32, 32, n=4, e=2.0)),
                ("Conv1", Conv(32, self.k, 3)),
            ]))
            self.decoder8 = nn.Sequential(OrderedDict([
                ("Focus0", Focus(2, self.k, 5)),
                ("WLBlock1", WLBlock(2, self.k, self.k, [1, 2], [0.5, 1])),
                ("WLBlock2", WLBlock(2, self.k, self.k, [1, 2], [1, 0.5])),
                ("Expand0", Expand(self.k, self.k)),
                ("Conv1", Conv(self.k, self.k, 5)),
            ]))
            if REFINEMENT:
                self.refinemodel = nn.Sequential(OrderedDict([
                    ("Conv0", Conv(2, 64, 3)),
                    ("WLBlock1", WLBlock(3, 64, 64, [1, 2, 3], [0.5, 1, 1.5])),
                    ("WLBlock2", WLBlock(2, 64, 64, [2, 4], [1, 2])),
                    ("WLBlock3", WLBlock(2, 64, 64, [2, 4], [1, 2])),
                    ("WLBlock4", WLBlock(2, 64, 64, [1, 3], [1, 2])),
                    ("Conv1", Conv(64, 2, 3)),
                ]))
            self.decoder_conv = conv3x3(self.k * 8, 2)
            self.sig = nn.Sigmoid()
            self.quantization = quantization
    
        def forward(self, x):
            if self.quantization:
                out = self.dequantize(x)
            else:
                out = x
            out = out.view(-1, int(self.feedback_bits / self.B))
            out_error = self.ende_refinement(out)
            out = out + out_error - 0.5
            deq_data = out
            out = self.dim_verify(out)
    
            out = self.sig(self.fc(out))
            out = out.view(-1, 2, 24, 16)
            out0 = out
            out1 = self.decoder1(out)
            out2 = self.decoder2(out)
            out3 = self.decoder3(out)
            out4 = self.decoder4(out)
            out5 = self.decoder5(out)
            out6 = self.decoder6(out)
            out7 = self.decoder7(out)
            out8 = self.decoder8(out)
            out = torch.cat((out1, out2, out3, out4, out5, out6, out7, out8), dim=1)
            out = self.decoder_conv(out) + out0
            out = self.sig(out)
            if REFINEMENT:
                out = self.sig(self.refinemodel(out)) - 0.5 + out
            if channel_last:
                out = out.permute(0, 2, 3, 1)
            return out, deq_data
    
    
    class AutoEncoder(nn.Module):
        def __init__(self, feedback_bits):
            super(AutoEncoder, self).__init__()
            self.encoder = Encoder(feedback_bits)
            self.decoder = Decoder(feedback_bits)
    
        def forward(self, x):
            feature, enq_data = self.encoder(x)
            out, deq_data = self.decoder(feature)
            return out, feature, enq_data, deq_data
    
    
    # =======================================================================================================================
    # =======================================================================================================================
    # NMSE Function Defining
    def NMSE(x, x_hat):
        x_real = np.reshape(x[:, :, :, 0], (len(x), -1))
        x_imag = np.reshape(x[:, :, :, 1], (len(x), -1))
        x_hat_real = np.reshape(x_hat[:, :, :, 0], (len(x_hat), -1))
        x_hat_imag = np.reshape(x_hat[:, :, :, 1], (len(x_hat), -1))
        x_C = x_real - 0.5 + 1j * (x_imag - 0.5)
        x_hat_C = x_hat_real - 0.5 + 1j * (x_hat_imag - 0.5)
        power = np.sum(abs(x_C) ** 2, axis=1)
        mse = np.sum(abs(x_C - x_hat_C) ** 2, axis=1)
        nmse = np.mean(mse / power)
        return nmse
    
    
    def Score(NMSE):
        score = 1 - NMSE
        return score
    
    
    def NMSE_cuda(x, x_hat):
        x_real = x[:, 0, :, :].view(len(x), -1) - 0.5
        x_imag = x[:, 1, :, :].view(len(x), -1) - 0.5
        x_hat_real = x_hat[:, 0, :, :].view(len(x_hat), -1) - 0.5
        x_hat_imag = x_hat[:, 1, :, :].view(len(x_hat), -1) - 0.5
        power = torch.sum(x_real ** 2 + x_imag ** 2, axis=1)
        mse = torch.sum((x_real - x_hat_real) ** 2 + (x_imag - x_hat_imag) ** 2, axis=1)
        nmse = mse / power
        return nmse
    
    
    class NMSELoss(nn.Module):
        def __init__(self, reduction='sum'):
            super(NMSELoss, self).__init__()
            self.reduction = reduction
    
        def forward(self, x_hat, x):
            nmse = NMSE_cuda(x, x_hat)
            if self.reduction == 'mean':
                nmse = torch.mean(nmse)
            else:
                nmse = torch.sum(nmse)
            return nmse
    
    
    # =======================================================================================================================
    # =======================================================================================================================
    import random
    
    
    # Data Loader Class Defining
    class DatasetFolder(Dataset):
        def __init__(self, matData, phase='val'):
            self.matdata = matData
            self.phase = phase
    
        def __getitem__(self, index):
            y = self.matdata[index]
            if self.phase == 'train' and random.random() < -0.5:
                y = y[::-1, :, :].copy()
            if self.phase == 'train' and random.random() < 0.5:
                y = y[:, ::-1, :].copy()
            if self.phase == 'train' and random.random() < 0.5:
                y = 1 - self.matdata[index]  # 数据中存在类似正交的关系
            if self.phase == 'train' and random.random() < 0.5:
                _ = y
                _[:, :, 0] = y[:, :, 1]
                _[:, :, 1] = y[:, :, 0]
                y = _  # 不同时刻数据实虚存在部分相等的情况
            if self.phase == 'train' and random.random() < 0.5:
                index_ = random.randint(0, self.matdata.shape[0] // 3000 - 1) * 3000 + index % 3000
                p = random.random()
                rows = max(int(24 * p), 1)
                _rows = [i for i in range(24)]
                random.shuffle(_rows)
                _rows = _rows[:rows]
                if random.random() < 0.7:
                    y[_rows] = self.matdata[index_][_rows]  # 不同采样点按行合并，保持采样点独有特性，减轻模型对24那个维度的依赖
                else:
                    y = (1 - p * 0.2) * y + (p * 0.2) * self.matdata[index_]  # 增加数值扰动，保持采样点独有特性
            return y
    
        def __len__(self):
            return self.matdata.shape[0]

##  modelTrain.py ##

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[20210418072554660.png]: /images/20221022/d43ae9d7bf954a93adc9eb49160f6db9.png
[20210418072554675.png]: /images/20221022/97f7f54fdbb84a1ebc98a6ecceacc93a.png
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[20210418072554676.png]: /images/20221022/f1cb06dc43564d4bb8492b9dec45cb7b.png
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