No Encoder Symbol Check

import math
import torch
import torchvision
from fractions import Fraction
import numpy as np
import torch.nn as nn
import torch.optim as optim
import torch.nn.functional as f
import matplotlib.pyplot as plt
import torchvision.transforms as tr
from torchvision import datasets
from torch.utils.data import DataLoader, Dataset
import time
import os
from skimage.metrics import structural_similarity as ssim

from params import *

device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
print(device)


def vector_power(vector):
    return vector.pow(2).sum()


def count_parameters(model):
    return sum(p.numel() for p in model.parameters() if p.requires_grad)


def calculate_ssim(batch1, batch2, win_size=3):
    ssim_values = []
    for img1, img2 in zip(batch1, batch2):
        img1 = img1.detach().permute(1, 2, 0).cpu().numpy()
        img2 = img2.detach().permute(1, 2, 0).cpu().numpy()

        ssim_value = ssim(img1, img2, multichannel=True, data_range=img2.max() - img2.min(), win_size=win_size)
        ssim_values.append(ssim_value)
    return np.mean(ssim_values)


transf = tr.Compose([tr.ToTensor()])

trainset = torchvision.datasets.CIFAR10(root='./data', train=True, download=True, transform=transf)
testset = torchvision.datasets.CIFAR10(root='./data', train=False, download=True, transform=transf)

trainloader = DataLoader(trainset, batch_size=params['BS'], shuffle=True)
testloader = DataLoader(testset, batch_size=params['BS'], shuffle=True)



class Decoder(nn.Module):
    def __init__(self, latent_dim):
        super(Decoder, self).__init__()
        self.latent_dim = latent_dim
        self.decoder = nn.Sequential(
            nn.Linear(self.latent_dim, 4 * 4 * 128),
            nn.ReLU(),
            nn.Unflatten(1, (128, 4, 4)),
            nn.ConvTranspose2d(128, 64, kernel_size=4, stride=2, padding=1),  # Output: [batch, 64, 8, 8]
            nn.ReLU(),
            nn.Conv2d(64, 64, kernel_size=3, padding=1),
            nn.ReLU(),
            nn.ConvTranspose2d(64, 32, kernel_size=4, stride=2, padding=1),  # Output: [batch, 32, 16, 16]
            nn.ReLU(),
            nn.Conv2d(32, 32, kernel_size=3, padding=1),
            nn.ReLU(),
            nn.ConvTranspose2d(32, 3, kernel_size=4, stride=2, padding=1),  # Output: [batch, 3, 32, 32]
            nn.Sigmoid()
        )

    def forward(self, x):
        return self.decoder(x)


class Autoencoder(nn.Module):
    def __init__(self, latent_dim):
        super(Autoencoder, self).__init__()
        self.latent_dim = latent_dim
        self.decoder = Decoder(latent_dim)

    def Power_norm(self, z, P=1 / np.sqrt(2)):

        batch_size, z_dim = z.shape
        z_power = torch.sqrt(torch.sum(z ** 2, 1))
        z_M = z_power.repeat(z_dim, 1)

        return np.sqrt(P * z_dim) * z / z_M.t()
    def Power_norm_complex(self, z, P=1 / np.sqrt(2)):

        batch_size, z_dim = z.shape
        z_com = torch.complex(z[:, 0:z_dim:2], z[:, 1:z_dim:2])
        z_com_conj = torch.complex(z[:, 0:z_dim:2], -z[:, 1:z_dim:2])
        z_power = torch.sum(z_com * z_com_conj, 1).real
        z_M = z_power.repeat(z_dim // 2, 1)
        z_nlz = np.sqrt(P * z_dim) * z_com / torch.sqrt(z_M.t())
        z_out = torch.zeros(batch_size, z_dim).to(device)
        z_out[:, 0:z_dim:2] = z_nlz.real
        z_out[:, 1:z_dim:2] = z_nlz.imag

        return z_out

    def AWGN_channel(self, x, snr, P=1):
        batch_size, length = x.shape
        gamma = 10 ** (snr / 10.0)
        noise = np.sqrt(P / gamma) * torch.randn(batch_size, length).cuda()
        y = x + noise
        return y

    def Fading_channel(self, x, snr, P=1):

        gamma = 10 ** (snr / 10.0)
        [batch_size, feature_length] = x.shape
        K = feature_length // 2

        h_I = torch.randn(batch_size, K).to(device)
        h_R = torch.randn(batch_size, K).to(device)
        h_com = torch.complex(h_I, h_R)
        x_com = torch.complex(x[:, 0:feature_length:2], x[:, 1:feature_length:2])
        y_com = h_com * x_com

        n_I = np.sqrt(P / gamma) * torch.randn(batch_size, K).to(device)
        n_R = np.sqrt(P / gamma) * torch.randn(batch_size, K).to(device)
        noise = torch.complex(n_I, n_R)

        y_add = y_com + noise
        y = y_add / h_com

        y_out = torch.zeros(batch_size, feature_length).to(device)
        y_out[:, 0:feature_length:2] = y.real
        y_out[:, 1:feature_length:2] = y.imag

        return y_out



    def forward(self, x, SNRdB, channel):

        #print(1)
        #print(x.shape)

        batch_size = x.shape[0]
        # 원본 이미지를 1차원 벡터로 변환
        x_flattened = x.view(batch_size, -1)

        #print(2)
        #print(x_flattened.shape)

        if channel == 'AWGN':
            normalized_x = self.Power_norm(x_flattened)
            channel_output = self.AWGN_channel(normalized_x, SNRdB)
        elif channel == 'Rayleigh':
            normalized_complex_x = self.Power_norm_complex(x_flattened)
            channel_output = self.Fading_channel(normalized_complex_x, SNRdB)

        decoded = self.decoder(channel_output)

        return decoded


device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
print(device)

# 데이터 로드
transf = tr.Compose([tr.ToTensor()])

testset = torchvision.datasets.CIFAR10(root='./data', train=False, download=True, transform=transf)
testloader = DataLoader(testset, batch_size=params['BS'], shuffle=False)

# 학습된 모델 로드
model_path = 'DeepJSCC(DIM=3072_SNR=40_PSNR=20.626_SSIM=0.81159).pt'  # 여기에 실제 모델 경로를 입력하세요
model = torch.load(model_path)
model.eval()

# 테스트 데이터 로더
testset = torchvision.datasets.CIFAR10(root='./data', train=False, download=True, transform=transf)
testloader = DataLoader(testset, batch_size=1, shuffle=False)

# SNR 및 채널 설정
SNRdB = 40  # 실험하고자 하는 SNR 값 설정
channel = 'Rayleigh'

# 복소수 평면에 매핑할 y 값 누적 리스트
y_complex_list = []

with torch.no_grad():
    for data in testloader:
        inputs = data[0].to(device)
        #encoded = model.encoder(inputs)

        batch_size = inputs.shape[0]
        # 원본 이미지를 1차원 벡터로 변환
        x_flattened = inputs.view(batch_size, -1)

        normalized_complex_x = model.Power_norm_complex(x_flattened)
        y = model.Fading_channel(normalized_complex_x, SNRdB)

        # 복소수 형태로 변환
        batch_size, feature_length = y.shape
        y_com = torch.complex(y[:, 0:feature_length:2], y[:, 1:feature_length:2])

        # 복소수 평면에 매핑
        y_complex_list.extend(y_com.cpu().numpy().flatten())

# y_complex_list를 2차원 복소수 평면에 시각화
y_complex_list = np.array(y_complex_list)

plt.figure(figsize=(10, 10))
plt.scatter(y_complex_list.real, y_complex_list.imag, s=1, alpha=0.5)
plt.xlim([-10, 10])
plt.ylim([-10, 10])
plt.title('Complex Plane Mapping of y in Fading Channel')
plt.xlabel('Real Part')
plt.ylabel('Imaginary Part')
plt.grid(True)
plt.show()