목록Wireless Comm. (94)

UOMOP

Cifar10 Rayleigh [1dB 10dB 20dB]

import math import torch import random import torchvision import torch.nn as nn from tqdm import tqdm import torch.optim as optim import torch.nn.functional as f import matplotlib.pyplot as plt import torchvision.transforms as tr from torch.utils.data import DataLoader import numpy as np import torchvision.transforms as transforms device = torch.device("cuda:0" if torch.cuda.is_available() else ..
Wireless Comm./CISL 2023. 7. 11. 20:27
Low Pass Filtering using cv2

def spectrum(img) : f = np.fft.fft2(img) fshift = np.fft.fftshift(f) magnitude_fshift = np.log(np.abs(fshift) + 1) return magnitude_fshift def average_filter(img, kernel_size) : return cv2.blur(img, (kernel_size, kernel_size)) def gaussian_filter(img, kernel_size) : # cv2.GaussianBlur의 parameter에는 sigma가 존재하는데, 이것은 가우시안 커널의 X, Y 방향의 표준편차이다. return cv2.GaussianBlur(img, (kernel_size, kernel_size)..
Wireless Comm./CISL 2023. 7. 11. 13:34
Cifar10 Rayleigh with SSIM

import math import torch import random import torchvision import torch.nn as nn from tqdm import tqdm import torch.optim as optim import torch.nn.functional as f import matplotlib.pyplot as plt import torchvision.transforms as tr from torch.utils.data import DataLoader import numpy as np import torchvision.transforms as transforms device = torch.device("cuda:0" if torch.cuda.is_available() else ..
Wireless Comm./CISL 2023. 7. 10. 20:28
Cifar10 Autoencoder 20230710 Rayleigh

import math import torch import random import torchvision import torch.nn as nn from tqdm import tqdm import torch.optim as optim import torch.nn.functional as f import matplotlib.pyplot as plt import torchvision.transforms as tr from torch.utils.data import DataLoader import numpy as np device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu") print(device) args = { 'BATCH_SIZE' :..
Wireless Comm./CISL 2023. 7. 10. 16:55
Basic AutoEncoder using Cifar10

import math import torch import random import torchvision import torch.nn as nn from tqdm import tqdm import torch.optim as optim import torch.nn.functional as f import matplotlib.pyplot as plt import torchvision.transforms as tr from torch.utils.data import DataLoader import numpy as np device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu") print(device) args = { 'BATCH_SIZE' :..
Wireless Comm./CISL 2023. 6. 10. 22:50
***AutoEncoder cifar10(color) 1dB, 10dB, 20dB

import math import torch import random import torchvision import torch.nn as nn from tqdm import tqdm import torch.optim as optim import torch.nn.functional as f import matplotlib.pyplot as plt import torchvision.transforms as tr from torch.utils.data import DataLoader import numpy as np args = { 'BATCH_SIZE' : 64, 'LEARNING_RATE' : 0.001, 'NUM_EPOCH' : 20, 'SNR_dB' : [1, 10, 20], 'latent_dim' :..
Wireless Comm./Python 2023. 6. 8. 20:25
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