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'
