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
##############################################################################################################
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.iter_temp = 5000
##################################################################################################
# Set up the training procedure
opt.C_channel = 12
opt.SNR = 0
opt.is_feedback = False
opt.feedforward = 'EXPLICIT-CE-EQ'
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.lam_PAPR = 0.3
##############################################################################################################
print('#training images = %d' % dataset_size)
else:
raise Exception('Not implemented yet')
# Choose the channel
opt.channel = 'bsc' # Available channels: 'awgn', 'ofdm', 'bsc'
if opt.channel == 'bsc':
opt.ber = 0.05 # Set the bit flip rate for bsc channel
opt.enc_type = 'prob'
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 = 5 # 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
output_path = './Images/' + opt.dataset_mode + '_' + opt.channel + '/' + opt.name
# Choose the neural network model
opt.load_size = 80
opt.crop_size = 64
opt.size = 64
opt.serial_batches = True
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.iter_temp = 5000
##################################################################################################
# Set up the training procedure
opt.C_channel = 12
opt.SNR = 15
opt.is_feedback = False
opt.feedforward = forward_list[se]
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.lam_h = 0.5
opt.is_hloss = True
opt.is_clip = False
opt.CR = 0 if not opt.is_clip else 1.2
opt.is_regu_PAPR = False
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.iter_temp = 5000
##################################################################################################
# Set up the training procedure
opt.C_channel = 12
opt.SNR = 5
opt.is_feedback = False
opt.feedforward = forward_list[se]
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.lam_h = 0.5
opt.is_hloss = True
opt.is_clip = False
opt.CR = 0 if not opt.is_clip else 1.2
opt.is_regu_PAPR = False
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.L = 8
opt.decay = 4
opt.SNR = 10
# Display setting
opt.checkpoints_dir = './Checkpoints/' + opt.dataset_mode + '_PLAIN'
opt.name = opt.gan_mode + '_C' + str(opt.C_channel) + '_SNR_' + str(opt.SNR)
output_path = './Images/' + opt.dataset_mode + '/' + opt.name
# Choose the neural network model
opt.model = 'PLAIN'
opt.num_test = 10000
opt.how_many_channel = 5
opt.N = opt.how_many_channel
model = create_model(opt) # create a model given opt.model and other options
model.setup(opt) # regular setup: load and print networks; create schedulers
