AI+无线通信总结——初赛算法实现(Top37)

先上代码。剩下的有时间在补，最终提交的初赛排名是37，但是没有跑完，跑完应该能进前20，但是由于算力不足，只跑了50个epoch。我先抛个砖，希望能引来玉。

完整代码：

Model_define_pytorch.py

#!/usr/bin/env python3

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

channelNum = 64


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


# This part implement the quantization and dequantization operations.
# The output of the encoder must be the bitstream.
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


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 four bits.
        b, _ = grad_output.shape
        grad_num = torch.sum(grad_output.reshape(b, -1, ctx.constant), dim=2)
        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 ResBlock(nn.Module):
    """
    Sequential residual blocks each of which consists of \
    two convolution layers.
    Args:
        ch (int): number of input and output channels.
        nblocks (int): number of residual blocks.
        shortcut (bool): if True, residual tensor addition is enabled.
    """

    def __init__(self, ch, nblocks=1, shortcut=True):
        super().__init__()
        self.shortcut = shortcut
        self.module_list = nn.ModuleList()
        for i in range(nblocks):
            resblock_one = nn.ModuleList()
            resblock_one.append(ConvBN(ch, ch, 1))
            resblock_one.append(Mish())
            resblock_one.append(ConvBN(ch, ch, 3))
            resblock_one.append(Mish())
            self.module_list.append(resblock_one)

    def forward(self, x):
        for module in self.module_list:
            h = x
            for res in module:
                h = res(h)
            x = x + h if self.shortcut else h
        return x


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


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 ConvBN(nn.Sequential):
    def __init__(self, in_planes, out_planes, kernel_size, stride=1, groups=1):
        if not isinstance(kernel_size, int):
            padding = [(i - 1) // 2 for i in kernel_size]
        else:
            padding = (kernel_size - 1) // 2
        super(ConvBN, self).__init__(OrderedDict([
            ('conv', nn.Conv2d(in_planes, out_planes, kernel_size, stride,
                               padding=padding, groups=groups, bias=False)),
            ('bn', nn.BatchNorm2d(out_planes)),
            ('Mish', Mish())
        ]))



class CRBlock64(nn.Module):
    def __init__(self):
        super(CRBlock64, self).__init__()
        self.convbncrb = ConvBN(channelNum, channelNum * 2, 3)
        self.path1 = Encoder_conv(channelNum * 2, 4)
        self.path2 = nn.Sequential(OrderedDict([
            ('conv1x5', ConvBN(channelNum * 2, channelNum * 2, [1, 5])),
            ('conv5x1', ConvBN(channelNum * 2, channelNum * 2, [5, 1])),
            ('conv5x1', ConvBN(channelNum * 2, channelNum * 2, 1)),
            ('conv5x1', ConvBN(channelNum * 2, channelNum * 2, 3)),
        ]))
        self.encoder_conv = Encoder_conv(channelNum * 4, 4)
        self.encoder_conv1 = ConvBN(channelNum * 4, channelNum, 1)
        self.identity = nn.Identity()
        self.relu = Mish()

    def forward(self, x):
        identity = self.identity(x)
        x = self.convbncrb(x)
        out1 = self.path1(x)
        out2 = self.path2(x)
        out = torch.cat((out1, out2), dim=1)
        out = self.relu(out)
        out = self.encoder_conv(out)
        out = self.encoder_conv1(out)
        out = self.relu(out + identity)
        return out


class CRBlock(nn.Module):
    def __init__(self):
        super(CRBlock, self).__init__()
        self.convban = nn.Sequential(OrderedDict([
            ("conv3x3_bn", ConvBN(channelNum, channelNum, 3)),
        ]))
        self.path1 = Encoder_conv(channelNum, 4)
        self.path2 = nn.Sequential(OrderedDict([
            ('conv1x5', ConvBN(channelNum, channelNum, [1, 5])),
            ('conv5x1', ConvBN(channelNum, channelNum, [5, 1])),
            ("conv9x1_bn", ConvBN(channelNum, channelNum, 1)),
        ]))
        self.encoder_conv = Encoder_conv(channelNum * 2)
        self.encoder_conv1 = ConvBN(channelNum * 2, channelNum, 1)
        self.identity = nn.Identity()
        self.relu = Mish()

    def forward(self, x):
        identity = self.identity(x)
        x = self.convban(x)
        out1 = self.path1(x)
        out2 = self.path2(x)
        out = torch.cat((out1, out2), dim=1)
        out = self.relu(out)
        out = self.encoder_conv(out)
        out = self.encoder_conv1(out)
        out = self.relu(out + identity)
        return out


class Encoder_conv(nn.Module):
    def __init__(self, in_planes=128, blocks=2):
        super().__init__()
        self.conv2 = ConvBN(in_planes, in_planes, [1, 9])
        self.conv3 = ConvBN(in_planes, in_planes, [9, 1])
        self.conv4 = ConvBN(in_planes, in_planes, 1)
        self.resBlock = ResBlock(ch=in_planes, nblocks=blocks)
        self.conv5 = ConvBN(in_planes, in_planes, [1, 7])
        self.conv6 = ConvBN(in_planes, in_planes, [7, 1])
        self.conv7 = ConvBN(in_planes, in_planes, 1)
        self.relu = Mish()

    def forward(self, input):
        x2 = self.conv2(input)
        x3 = self.conv3(x2)
        x4 = self.conv4(x3)
        r1 = self.resBlock(x4)
        x5 = self.conv5(r1)
        x6 = self.conv6(x5)
        x7 = self.conv7(x6)
        x7 = self.relu(x7 + x4)
        return x7


class Encoder(nn.Module):
    B = 4

    def __init__(self, feedback_bits, quantization=True):
        super(Encoder, self).__init__()
        self.convban = nn.Sequential(OrderedDict([
            ("conv3x3_bn", ConvBN(2, channelNum, 3)),
        ]))
        self.encoder1 = Encoder_conv(channelNum)
        self.encoder2 = nn.Sequential(OrderedDict([
            ('conv1x5', ConvBN(channelNum, channelNum, [1, 5])),
            ('conv5x1', ConvBN(channelNum, channelNum, [5, 1])),
            ("conv9x1_bn", ConvBN(channelNum, channelNum, 3)),
        ]))
        self.encoder_conv = Encoder_conv(channelNum * 2)
        self.encoder_conv1 = nn.Sequential(OrderedDict([
            ("conv1x1_bn", ConvBN(channelNum * 2, 2, 1)),
        ]))
        self.fc = nn.Linear(1024, int(feedback_bits / self.B))
        self.sig = nn.Sigmoid()
        self.quantize = QuantizationLayer(self.B)
        self.quantization = quantization

    def forward(self, x):
        x = self.convban(x)
        encode1 = self.encoder1(x)
        encode2 = self.encoder2(x)
        out = torch.cat((encode1, encode2), dim=1)
        out = self.encoder_conv(out)
        out = self.encoder_conv1(out)
        out = out.view(-1, 1024)
        out = self.fc(out)
        out = self.sig(out)
        if self.quantization:
            out = self.quantize(out)
        else:
            out = out
        return out


class Decoder(nn.Module):
    B = 4

    def __init__(self, feedback_bits, quantization=True):
        super(Decoder, self).__init__()
        self.feedback_bits = feedback_bits
        self.dequantize = DequantizationLayer(self.B)
        self.fc = nn.Linear(int(feedback_bits / self.B), 1024)
        decoder = OrderedDict([
            ("conv3x3_bn", ConvBN(2, channelNum, 3)),
            ("CRBlock1", CRBlock64()),
            ("CRBlock2", CRBlock()),
        ])
        self.decoder_feature = nn.Sequential(decoder)
        self.out_cov = conv3x3(channelNum, 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 = self.fc(out)
        out = out.view(-1, 2, 16, 32)
        out = self.decoder_feature(out)
        out = self.out_cov(out)
        out = self.sig(out)
        return out


# Note: Do not modify following class and keep it in your submission.
# feedback_bits is 128 by default.
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 = self.encoder(x)
        out = self.decoder(feature)
        return out


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 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


def Score(NMSE):
    score = 1 - NMSE
    return score


# dataLoader
class DatasetFolder(Dataset):

    def __init__(self, matData):
        self.matdata = matData

    def __len__(self):
        return self.matdata.shape[0]

    def __getitem__(self, index):
        return self.matdata[index]  # , self.matdata[index]

Model_train.py

'''
seefun . Aug 2020.
github.com/seefun | kaggle.com/seefun
'''

import numpy as np
import h5py
import torch
import os
import torch.nn as nn
import random

from Model_define_pytorch import AutoEncoder, DatasetFolder, NMSE_cuda, NMSELoss

# Parameters for training
gpu_list = '0'
os.environ["CUDA_VISIBLE_DEVICES"] = gpu_list


def seed_everything(seed=42):
    random.seed(seed)
    os.environ['PYHTONHASHSEED'] = str(seed)
    np.random.seed(seed)
    torch.manual_seed(seed)
    torch.cuda.manual_seed(seed)
    torch.backends.cudnn.deterministic = True


SEED = 42
seed_everything(SEED)

batch_size = 32
epochs = 150
learning_rate = 1e-3  # bigger to train faster
num_workers = 0
print_freq = 100
train_test_ratio = 0.8
# parameters for data
feedback_bits = 128
img_height = 16
img_width = 32
img_channels = 2

# Model construction
model = AutoEncoder(feedback_bits)

model.encoder.quantization = False
model.decoder.quantization = False

if len(gpu_list.split(',')) > 1:
    model = torch.nn.DataParallel(model).cuda()  # model.module
else:
    model = model.cuda()

criterion = NMSELoss(reduction='mean')  # nn.MSELoss()
criterion_test = NMSELoss(reduction='sum')

optimizer = torch.optim.AdamW(model.parameters(), lr=learning_rate)

# Data loading
data_load_address = './data'
mat = h5py.File(data_load_address + '/Hdata.mat', 'r')
data = np.transpose(mat['H_train'])  # shape=(320000, 1024)
data = data.astype('float32')
data = np.reshape(data, [len(data), img_channels, img_height, img_width])
# split data for training(80%) and validation(20%)
np.random.shuffle(data)
start = int(data.shape[0] * train_test_ratio)
x_train, x_test = data[:start], data[start:]

# dataLoader for training
train_dataset = DatasetFolder(x_train)
train_loader = torch.utils.data.DataLoader(
    train_dataset, batch_size=batch_size, shuffle=True, num_workers=num_workers, pin_memory=True, drop_last=True)

# dataLoader for training
test_dataset = DatasetFolder(x_test)
test_loader = torch.utils.data.DataLoader(
    test_dataset, batch_size=batch_size, shuffle=False, num_workers=num_workers, pin_memory=True)

best_loss = 100
for epoch in range(epochs):
    print('========================')
    print('lr:%.4e' % optimizer.param_groups[0]['lr'])
    # model training
    model.train()
    if epoch < epochs // 10:
        try:
            model.encoder.quantization = False
            model.decoder.quantization = False
        except:
            model.module.encoder.quantization = False
            model.module.decoder.quantization = False
    else:
        try:
            model.encoder.quantization = True
            model.decoder.quantization = True
        except:
            model.module.encoder.quantization = True
            model.module.decoder.quantization = True

    if epoch % 50 == 0 and epoch > 0:
        optimizer.param_groups[0]['lr'] = optimizer.param_groups[0]['lr'] * 0.1
    for i, input in enumerate(train_loader):
        input = input.cuda()
        output = model(input)

        loss = criterion(output, input)
        loss.backward()
        optimizer.step()
        optimizer.zero_grad()
        if i % print_freq == 0:
            print('Epoch: [{0}][{1}/{2}]\t'
                  'Loss {loss:.4f}\t'.format(
                epoch, i, len(train_loader), loss=loss.item()))
    model.eval()
    try:
        model.encoder.quantization = True
        model.decoder.quantization = True
    except:
        model.module.encoder.quantization = True
        model.module.decoder.quantization = True
    total_loss = 0
    with torch.no_grad():
        for i, input in enumerate(test_loader):
            # convert numpy to Tensor
            input = input.cuda()
            output = model(input)
            total_loss += criterion_test(output, input).item()
        average_loss = total_loss / len(test_dataset)
        print('NMSE %.4f' % average_loss)
        if average_loss < best_loss:
            # model save
            # save encoder
            modelSave1 = './Modelsave/encoder.pth.tar'
            try:
                torch.save({'state_dict': model.encoder.state_dict(), }, modelSave1)
            except:
                torch.save({'state_dict': model.module.encoder.state_dict(), }, modelSave1)
            # save decoder
            modelSave2 = './Modelsave/decoder.pth.tar'
            try:
                torch.save({'state_dict': model.decoder.state_dict(), }, modelSave2)
            except:
                torch.save({'state_dict': model.module.decoder.state_dict(), }, modelSave2)
            print('Model saved!')
            best_loss = average_loss

文章看完了，打赏可以有吗？