UOMOP

import torchimport torch.nn as nnimport mathimport torch.nn.functional as fimport torch.optim as optimfrom params import *import timeimport osfrom torch.utils.data import DataLoaderimport matplotlib.pyplot as pltimport torchvision.transforms as transformsimport torch.nn.functional as Fimport mathimport torchimport torchvisionimport torch.nn as nnimport torch.optim as optimimport torch.nn.functio..

import matplotlib.pyplot as plt import torchvision.transforms as transforms import torch.nn.functional as F import math import torch import torchvision import torch.nn as nn import torch.optim as optim import torch.nn.functional as f from torch.utils.data import DataLoader, Dataset import time from params import * import os from tqdm import tqdm import numpy as np import cv2 device = torch.devic..

import matplotlib.pyplot as plt X = [0.3, 0.75, 0.9] Y1 = [83.4, 84.9, 83] Y2 = [84.9, 88.9, 87.3] # 각각의 그래프에 다른 색상을 지정 plt.plot(X, Y2, color='blue', marker='o', alpha=0.5, linewidth=2, label='My result (Cifar10)') plt.plot(X, Y1, color='red', marker='o', alpha=0.5, linewidth=2, label='Paper result (ImageNet)') plt.title("Paper result vs My result") plt.ylabel("Accuracy") plt.xlabel("Masking rat..

import matplotlib.pyplot as plt import numpy as np import pandas as pd a = [64.3, 69.12, 71] b = [55.2, 67.3, 73.2] year = ['0dB', '5dB', '10dB'] df = pd.DataFrame({'DeepJSCC' : a, 'MAE+JSCC' : b}, index = year) # 그림 사이즈, 바 굵기 조정 fig, ax = plt.subplots(figsize=(8,6)) bar_width = 0.35 # 연도가 4개이므로 0, 1, 2, 3 위치를 기준으로 삼음 index = np.arange(3) # 각 연도별로 3개 샵의 bar를 순서대로 나타내는 과정, 각 그래프는 0.25의 간격을 두고 그려짐..