opt.n_layers_D = 3 opt.label_smooth = 1 # Label smoothing factor (for lsgan and vanilla gan only) opt.n_downsample = 3 # Downsample times opt.n_blocks = 2 # Numebr of residual blocks opt.first_kernel = 5 # The filter size of the first convolutional layer in encoder opt.batchSize = 64 opt.n_epochs = 30 # # of epochs without lr decay opt.n_epochs_decay = 30 # # of epochs with lr decay opt.lr_policy = 'linear' # decay policy. Availability: see options/train_options.py opt.beta1 = 0.5 # parameter for ADAM opt.lr = 5e-4 ############################ Things recommanded to be changed ########################################## # 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 = True opt.lam_PAPR = 0.5 opt.is_regu_sigma = False opt.lam_sigma = 100
opt.crop_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') if opt.channel == 'bsc': opt.ber = 0.1 # Set the bit flip rate for bsc channel opt.enc_type = 'hard' opt.sample_type = 'gumbel_softmax_hard' opt.temp = 0.5 channel_name = '_BER'+str(opt.ber)+'_'+opt.enc_type+'_'+opt.sample_type elif opt.channel == 'awgn': opt.SNR = 20 # Set the SNR for awgn channel channel_name = '_SNR'+str(opt.SNR) elif opt.channel == 'ofdm': pass else: raise Exception('Not implemented yet') # Display setting opt.checkpoints_dir = './Checkpoints/'+ opt.dataset_mode + '_' + opt.channel opt.name = opt.gan_mode + '_C' + str(opt.C_channel) + channel_name opt.display_env = opt.dataset_mode + '_' + opt.channel + '_' + opt.name # Choose the neural network model opt.model = 'StoGAN'
opt.label_smooth = 1 # Label smoothing factor (for lsgan and vanilla gan only) opt.n_downsample = 3 # Downsample times opt.n_blocks = 4 # Numebr of residual blocks opt.first_kernel = 5 # The filter size of the first convolutional layer in encoder opt.batch_size = 16 opt.n_epochs = 20 # # of epochs without lr decay opt.n_epochs_decay = 20 # # of epochs with lr decay opt.lr_policy = 'linear' # decay policy. Availability: see options/train_options.py opt.beta1 = 0.5 # parameter for ADAM opt.lr = 5e-4 ############################ Things recommanded to be changed ########################################## # Set up the training procedure opt.C_channel = 24 opt.SNR = 5 opt.is_feedback = False opt.feedforward = 'EXPLICIT-RES' opt.N_pilot = 1 # 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 1.2 opt.is_regu_PAPR = False opt.lam_PAPR = 0.1 opt.lam_h = 0.5
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 = 64 # batch size opt.n_epochs = 200 # # of epochs without lr decay opt.n_epochs_decay = 200 # # of epochs with lr decay opt.lr = 1e-3 # Initial learning rate opt.lr_policy = 'linear' # decay policy. Availability: see options/train_options.py opt.beta1 = 0.5 # parameter for ADAM opt.L = 8 opt.decay = 4 opt.SNR = 0 # Set up the loss function opt.lambda_L2 = 128 # The weight for L2 loss opt.is_Feat = False # Whether to use feature matching loss or not opt.lambda_feat = 1 ############################################################################################################## if opt.gan_mode == 'wgangp': opt.norm_D = 'instance' # Use instance normalization when using WGAN. Available: 'instance', 'batch', 'none' else: opt.norm_D = 'batch' # Used batch normalization otherwise opt.activation = 'sigmoid' # The output activation function at the last layer in the decoder opt.norm_EG = 'batch'