elif opt.dataset_mode == 'CelebA': opt.dataroot = './data/celeba/CelebA_test' opt.load_size = 80 opt.crop_size = 64 opt.size = 64 dataset = create_dataset( opt) # create a dataset given opt.dataset_mode and other options dataset_size = len(dataset) print('#training images = %d' % dataset_size) else: raise Exception('Not implemented yet') ################################################################################################## # Set up the training procedure opt.C_channel = 12 opt.SNR = 20 opt.is_feedback = False opt.feedforward = 'EXPLICIT-RES' opt.N_pilot = 2 # Number of pilots for chanenl estimation opt.CE = 'MMSE' # Channel Estimation Method opt.EQ = 'MMSE' # Equalization Method opt.pilot = 'ZadoffChu' # QPSK or ZadoffChu opt.is_clip = False opt.CR = 0 if not opt.is_clip else 1 opt.is_regu_PAPR = False opt.is_regu_sigma = False
import torchvision import torchvision.transforms as transforms import scipy.io as sio import models.channel as chan import shutil from pytorch_msssim import ssim, ms_ssim, SSIM, MS_SSIM import math # Extract the options opt = TestOptions().parse() # For testing the neural networks, manually edit/add options below opt.gan_mode = 'none' # 'wgangp', 'lsgan', 'vanilla', 'none' opt.C_channel = 16 # The output channel number of encoder (Important: it controls the rate) opt.n_downsample= 2 # Downsample times opt.n_blocks = 2 # Numebr of residual blocks opt.first_kernel = 5 # The filter size of the first convolutional layer in encoder # Set the input dataset opt.dataset_mode = 'CIFAR10' # Current dataset: CIFAR10, CelebA # Set up the training procedure opt.batchSize = 1 # batch size opt.activation = 'tanh' # The output activation function at the last layer in the decoder opt.norm_EG = 'batch' if opt.dataset_mode == 'CIFAR10': opt.dataroot='./data'