def train(epoch, model, optimizer, args, use_cuda = False, verbose = True, mode = 'encoder'):
device = torch.device("cuda" if use_cuda else "cpu")
model.train()
start_time = time.time()
train_loss = 0.0
k_same_code_counter = 0
for batch_idx in range(int(args.num_block/args.batch_size)):
optimizer.zero_grad()
if args.is_k_same_code and mode == 'encoder':
if batch_idx == 0:
k_same_code_counter += 1
X_train = torch.randint(0, 2, (args.batch_size, args.block_len, args.code_rate_k), dtype=torch.float)
elif k_same_code_counter == args.k_same_code:
k_same_code_counter = 1
X_train = torch.randint(0, 2, (args.batch_size, args.block_len, args.code_rate_k), dtype=torch.float)
else:
k_same_code_counter += 1
else:
X_train = torch.randint(0, 2, (args.batch_size, args.block_len, args.code_rate_k), dtype=torch.float)
# train encoder/decoder with different SNR... seems to be a good practice.
if mode == 'encoder':
fwd_noise = generate_noise(X_train.shape, args, snr_low=args.train_enc_channel_low, snr_high=args.train_enc_channel_high, mode = 'encoder')
else:
fwd_noise = generate_noise(X_train.shape, args, snr_low=args.train_dec_channel_low, snr_high=args.train_dec_channel_high, mode = 'decoder')
X_train, fwd_noise = X_train.to(device), fwd_noise.to(device)
output, code = model(X_train, fwd_noise)
output = torch.clamp(output, 0.0, 1.0)
if mode == 'encoder':
loss = customized_loss(output, X_train, args, noise=fwd_noise, code = code)
else:
loss = customized_loss(output, X_train, args, noise=fwd_noise, code = code)
#loss = F.binary_cross_entropy(output, X_train)
loss.backward()
train_loss += loss.item()
optimizer.step()
end_time = time.time()
train_loss = train_loss /(args.num_block/args.batch_size)
if verbose:
print('====> Epoch: {} Average loss: {:.8f}'.format(epoch, train_loss), \
' running time', str(end_time - start_time))
return train_loss
def validate(model, optimizer, args, use_cuda=False, verbose=True):
device = torch.device("cuda" if use_cuda else "cpu")
model.eval()
test_bce_loss, test_custom_loss, test_ber = 0.0, 0.0, 0.0
with torch.no_grad():
num_test_batch = int(args.num_block / args.batch_size *
args.test_ratio)
for batch_idx in range(num_test_batch):
X_test = torch.randint(
0,
2, (args.batch_size, args.block_len, args.code_rate_k),
dtype=torch.float)
noise_shape = (args.batch_size, args.block_len, args.code_rate_n)
fwd_noise = generate_noise(noise_shape,
args,
snr_low=args.train_enc_channel_low,
snr_high=args.train_enc_channel_low)
X_test, fwd_noise = X_test.to(device), fwd_noise.to(device)
optimizer.zero_grad()
output, codes = model(X_test, fwd_noise)
output = torch.clamp(output, 0.0, 1.0)
output = output.detach()
X_test = X_test.detach()
test_bce_loss += F.binary_cross_entropy(output, X_test)
test_custom_loss += customized_loss(output,
X_test,
noise=fwd_noise,
args=args,
code=codes)
test_ber += errors_ber(output, X_test)
test_bce_loss /= num_test_batch
test_custom_loss /= num_test_batch
test_ber /= num_test_batch
if verbose:
print(
'====> Test set BCE loss',
float(test_bce_loss),
'Custom Loss',
float(test_custom_loss),
'with ber ',
float(test_ber),
)
report_loss = float(test_bce_loss)
report_ber = float(test_ber)
return report_loss, report_ber
def system(args, optimizer, enc, dec, use_cuda=False, verbose=True):
device = torch.device("cuda" if use_cuda else "cpu")
train_loss = 0.0
for batch_idx in range(int(args.num_block / args.batch_size)):
if args.is_variable_block_len:
block_len = np.random.randint(args.block_len_low,
args.block_len_high)
else:
block_len = args.block_len
optimizer.zero_grad()
# generate bit and noise
X_train = torch.randint(0,
2,
(args.batch_size, block_len, args.code_rate_k),
dtype=torch.float)
noise_shape = (args.batch_size, args.block_len, args.code_rate_n)
fwd_noise = generate_noise(noise_shape,
args,
snr_low=args.train_dec_channel_low,
snr_high=args.train_dec_channel_high,
mode='decoder')
X_train, fwd_noise = X_train.to(device), fwd_noise.to(device)
# pass system
if args.is_interleave == 0:
pass
elif args.is_same_interleaver == 0:
interleaver = RandInterlv.RandInterlv(args.block_len,
np.random.randint(0, 1000))
p_array = interleaver.p_array
enc.set_interleaver(p_array)
dec.set_interleaver(p_array)
else: # self.args.is_same_interleaver == 1
interleaver = RandInterlv.RandInterlv(args.block_len,
0) # not random anymore!
p_array = interleaver.p_array
enc.set_interleaver(p_array)
dec.set_interleaver(p_array)
codes = enc.encode(X_train)
if self.args.channel in [
'awgn', 't-dist', 'radar', 'ge_awgn', 'bikappa'
]:
# print("noise_type:",self.args.channel)
received_codes = codes + fwd_noise
elif self.args.channel == 'bec':
received_codes = codes * fwd_noise
elif self.args.channel in ['bsc', 'ge']:
received_codes = codes * (2.0 * fwd_noise - 1.0)
received_codes = received_codes.type(torch.FloatTensor)
else:
print('default AWGN channel')
received_codes = codes + fwd_noise
if args.rec_quantize:
myquantize = MyQuantize.apply
received_codes = myquantize(received_codes,
args.rec_quantize_level,
args.rec_quantize_level)
x_dec = dec(received_codes)
loss = customized_loss(output,
X_train,
args,
noise=fwd_noise,
code=code)
loss.backward()
train_loss += loss.item()
optimizer.step()
train_loss = train_loss / (args.num_block / args.batch_size)
if verbose:
print('====> Epoch: {} Average loss: {:.8f}'.format(epoch, train_loss), \
' running time', str(end_time - start_time))
def ftae_train(epoch,
model,
optimizer,
args,
use_cuda=False,
verbose=True,
mode='encoder'):
device = torch.device("cuda" if use_cuda else "cpu")
model.train()
start_time = time.time()
train_loss = 0.0
for batch_idx in range(int(args.num_block / args.batch_size)):
optimizer.zero_grad()
X_train = torch.randint(
0,
2, (args.batch_size, args.block_len, args.code_rate_k),
dtype=torch.float)
if mode == 'encoder':
fwd_noise = generate_noise(X_train.shape,
args,
snr_low=args.train_enc_channel_low,
snr_high=args.train_enc_channel_high,
mode='encoder')
else:
fwd_noise = generate_noise(X_train.shape,
args,
snr_low=args.train_dec_channel_low,
snr_high=args.train_dec_channel_high,
mode='decoder')
fb_noise = generate_noise(X_train.shape,
args,
snr_low=args.fb_channel_low,
snr_high=args.fb_channel_high,
mode='decoder')
X_train, fwd_noise, fb_noise = X_train.to(device), fwd_noise.to(
device), fb_noise.to(device)
output, code = model(X_train, fwd_noise, fb_noise)
output = torch.clamp(output, 0.0, 1.0)
if mode == 'encoder':
loss = customized_loss(output,
X_train,
args,
noise=fwd_noise,
code=code)
else:
loss = customized_loss(output,
X_train,
args,
noise=fwd_noise,
code=code)
loss.backward()
train_loss += loss.item()
optimizer.step()
end_time = time.time()
train_loss = train_loss / (args.num_block / args.batch_size)
if verbose:
print('====> Epoch: {} Average loss: {:.8f}'.format(epoch, train_loss), \
' running time', str(end_time - start_time))
return train_loss
