opt.is_regu_sigma = False ############################################################################################################## if not opt.is_clip: opt.CR = 0 ######################################## OFDM setting ########################################### size_after_compress = (opt.size // (opt.n_downsample**2))**2 * (opt.C_channel // 2) opt.N = opt.batchSize # Batch size opt.P = 1 # Number of symbols opt.M = 64 # Number of subcarriers per symbol opt.K = 16 # Length of CP opt.L = 8 # Number of paths opt.decay = 4 opt.S = size_after_compress // opt.M # Number of packets opt.is_cfo = False opt.is_trick = True opt.is_cfo_random = False opt.max_ang = 1.7 opt.ang = 1.7 if opt.CE not in ['LS', 'MMSE', 'TRUE']: raise Exception("Channel estimation method not implemented") if opt.EQ not in ['ZF', 'MMSE']: raise Exception("Equalization method not implemented")
dataset_size = len(dataset) print('#training images = %d' % dataset_size) 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') opt.K = 256 # Display setting opt.checkpoints_dir = './Checkpoints/'+ opt.dataset_mode + '_VQVAE' opt.name = opt.gan_mode + '_K' + str(opt.K) output_path = './Images/' + opt.dataset_mode + '_VQVAE/' + opt.name # Choose the neural network model opt.model = 'VQVAE' opt.num_test = 10000 opt.how_many_channel = 5 opt.N = opt.how_many_channel