def __init__(self, args, p_array):
super(DEC_LargeRNN_rate2, self).__init__()
self.args = args
use_cuda = not args.no_cuda and torch.cuda.is_available()
self.this_device = torch.device("cuda" if use_cuda else "cpu")
self.interleaver = Interleaver(args, p_array)
self.deinterleaver = DeInterleaver(args, p_array)
self.dec1_rnns = torch.nn.ModuleList()
self.dec2_rnns = torch.nn.ModuleList()
self.dec1_outputs = torch.nn.ModuleList()
self.dec2_outputs = torch.nn.ModuleList()
for idx in range(args.num_iteration):
self.dec1_rnns.append(torch.nn.GRU(1 + args.num_iter_ft, args.dec_num_unit,
num_layers=2, bias=True, batch_first=True,
dropout=args.dropout, bidirectional=True))
self.dec2_rnns.append(torch.nn.GRU(1+ args.num_iter_ft, args.dec_num_unit,
num_layers=2, bias=True, batch_first=True,
dropout=args.dropout, bidirectional=True))
self.dec1_outputs.append(torch.nn.Linear(2*args.dec_num_unit, args.num_iter_ft))
if idx == args.num_iteration -1:
self.dec2_outputs.append(torch.nn.Linear(2*args.dec_num_unit, 1))
else:
self.dec2_outputs.append(torch.nn.Linear(2*args.dec_num_unit, args.num_iter_ft))
def __init__(self, args, p_array):
super(DEC_LargeCNN, self).__init__()
self.args = args
use_cuda = not args.no_cuda and torch.cuda.is_available()
self.this_device = torch.device("cuda" if use_cuda else "cpu")
self.interleaver = Interleaver(args, p_array)
self.deinterleaver = DeInterleaver(args, p_array)
self.dec1_cnns = torch.nn.ModuleList()
self.dec2_cnns = torch.nn.ModuleList()
self.dec1_outputs = torch.nn.ModuleList()
self.dec2_outputs = torch.nn.ModuleList()
for idx in range(args.num_iteration):
self.dec1_cnns.append(
SameShapeConv1d(num_layer=args.dec_num_layer,
in_channels=2 + args.num_iter_ft,
out_channels=args.dec_num_unit,
kernel_size=args.dec_kernel_size))
self.dec2_cnns.append(
SameShapeConv1d(num_layer=args.dec_num_layer,
in_channels=2 + args.num_iter_ft,
out_channels=args.dec_num_unit,
kernel_size=args.dec_kernel_size))
self.dec1_outputs.append(
torch.nn.Linear(args.dec_num_unit, args.num_iter_ft))
if idx == args.num_iteration - 1:
self.dec2_outputs.append(torch.nn.Linear(args.dec_num_unit, 1))
else:
self.dec2_outputs.append(
torch.nn.Linear(args.dec_num_unit, args.num_iter_ft))
def __init__(self, args, p_array):
super(TurboAE_decoder1D, self).__init__()
self.args = args
cuda = True if torch.cuda.is_available() else False
self.this_device = torch.device("cuda" if cuda else "cpu")
self.interleaver = Interleaver(args, p_array)
self.deinterleaver = DeInterleaver(args, p_array)
self.dec1_cnns = torch.nn.ModuleList()
self.dec2_cnns = torch.nn.ModuleList()
self.dec1_outputs = torch.nn.ModuleList()
self.dec2_outputs = torch.nn.ModuleList()
for idx in range(args.num_iteration):
self.dec1_cnns.append(SameShapeConv1d(num_layer=args.dec_num_layer, in_channels=2 + args.num_iter_ft,
out_channels= args.dec_num_unit, kernel_size = args.dec_kernel_size)
)
self.dec2_cnns.append(SameShapeConv1d(num_layer=args.dec_num_layer, in_channels=2 + args.num_iter_ft,
out_channels= args.dec_num_unit, kernel_size = args.dec_kernel_size)
)
self.dec1_outputs.append(torch.nn.Linear(args.dec_num_unit, args.num_iter_ft))
if idx == args.num_iteration -1:
self.dec2_outputs.append(torch.nn.Linear(args.dec_num_unit, args.code_rate_k))
else:
self.dec2_outputs.append(torch.nn.Linear(args.dec_num_unit, args.num_iter_ft))
# also need some CNN for f
self.ftstart = SameShapeConv2d(num_layer=args.dec_num_layer, in_channels=args.img_channels,
out_channels= args.dec_num_unit, kernel_size = args.dec_kernel_size)
self.ftend = SameShapeConv2d(num_layer=1, in_channels=args.dec_num_unit,
out_channels= args.img_channels, kernel_size = args.dec_kernel_size)
def __init__(self, args, p_array):
super(FTAE_Shareddecoder, self).__init__()
use_cuda = not args.no_cuda and torch.cuda.is_available()
self.this_device = torch.device("cuda" if use_cuda else "cpu")
self.args = args
# interleaver
self.p_array = p_array
self.interleaver = Interleaver(args, p_array)
self.deinterleaver = DeInterleaver(args, p_array)
if args.cnn_type == 'dense':
CNNModel = DenseSameShapeConv1d
else:
CNNModel = SameShapeConv1d
self.dec1_cnns = CNNModel(num_layer=args.dec_num_layer,
in_channels=2 + args.num_iter_ft,
out_channels=args.dec_num_unit,
kernel_size=args.dec_kernel_size)
self.dec1_outputs = torch.nn.Linear(args.dec_num_unit,
args.num_iter_ft)
self.dec2_cnns = CNNModel(num_layer=args.dec_num_layer,
in_channels=2 + args.num_iter_ft,
out_channels=args.dec_num_unit,
kernel_size=args.dec_kernel_size)
self.dec2_outputs = torch.nn.Linear(args.dec_num_unit,
args.num_iter_ft)
self.final_outputs = torch.nn.Linear(args.num_iter_ft, 1)
def __init__(self, args, p_array):
super(NeuralTurbofyDec, self).__init__()
self.args = args
self.interleaver = Interleaver(args, p_array)
self.deinterleaver = DeInterleaver(args, p_array)
self.dec_rnn = torch.nn.GRU(args.code_rate_n + args.num_iter_ft - 1 , args.dec_num_unit, num_layers=2, bias=True, batch_first=True,
dropout=args.dropout, bidirectional=True)
self.dec_out = torch.nn.Linear(2*args.dec_num_unit, args.num_iter_ft)
self.dec_final = torch.nn.Linear(args.num_iter_ft, 1)
use_cuda = not args.no_cuda and torch.cuda.is_available()
self.device = torch.device("cuda" if use_cuda else "cpu")
def __init__(self, args, p_array):
super(DEC_LargeCNN2Int, self).__init__()
self.args = args
use_cuda = not args.no_cuda and torch.cuda.is_available()
self.this_device = torch.device("cuda" if use_cuda else "cpu")
self.interleaver1 = Interleaver(args, p_array)
self.deinterleaver1 = DeInterleaver(args, p_array)
seed2 = 1000
rand_gen2 = mtrand.RandomState(seed2)
p_array2 = rand_gen2.permutation(arange(args.block_len))
print('p_array1 dec', p_array)
print('p_array2 dec', p_array2)
self.interleaver2 = Interleaver(args, p_array2)
self.deinterleaver2 = DeInterleaver(args, p_array2)
self.dec1_cnns = torch.nn.ModuleList()
self.dec2_cnns = torch.nn.ModuleList()
self.dec1_outputs = torch.nn.ModuleList()
self.dec2_outputs = torch.nn.ModuleList()
for idx in range(args.num_iteration):
self.dec1_cnns.append(
SameShapeConv1d(num_layer=args.dec_num_layer,
in_channels=2 + args.num_iter_ft,
out_channels=args.dec_num_unit,
kernel_size=args.dec_kernel_size))
self.dec2_cnns.append(
SameShapeConv1d(num_layer=args.dec_num_layer,
in_channels=2 + args.num_iter_ft,
out_channels=args.dec_num_unit,
kernel_size=args.dec_kernel_size))
self.dec1_outputs.append(
torch.nn.Linear(args.dec_num_unit, args.num_iter_ft))
if idx == args.num_iteration - 1:
self.dec2_outputs.append(torch.nn.Linear(args.dec_num_unit, 1))
else:
self.dec2_outputs.append(
torch.nn.Linear(args.dec_num_unit, args.num_iter_ft))
def __init__(self, args, p_array):
super(FTAE_decoder, self).__init__()
use_cuda = not args.no_cuda and torch.cuda.is_available()
self.this_device = torch.device("cuda" if use_cuda else "cpu")
self.args = args
# interleaver
self.p_array = p_array
self.interleaver = Interleaver(args, p_array)
self.deinterleaver = DeInterleaver(args, p_array)
# Decoder
self.dec1_cnns = torch.nn.ModuleList()
self.dec2_cnns = torch.nn.ModuleList()
self.dec1_outputs = torch.nn.ModuleList()
self.dec2_outputs = torch.nn.ModuleList()
if args.cnn_type =='dense':
CNNModel = DenseSameShapeConv1d
else:
CNNModel = SameShapeConv1d
for idx in range(args.num_iteration):
if self.args.codec == 'turboae_blockdelay_cnn':
self.dec1_cnns.append(CNNModel(num_layer=args.dec_num_layer, in_channels=2 + args.num_iter_ft,
out_channels= args.dec_num_unit, kernel_size = args.dec_kernel_size)
)
self.dec2_cnns.append(CNNModel(num_layer=args.dec_num_layer, in_channels=2 + args.num_iter_ft,
out_channels= args.dec_num_unit, kernel_size = args.dec_kernel_size)
)
self.dec1_outputs.append(torch.nn.Linear(args.dec_num_unit, args.num_iter_ft))
if idx == args.num_iteration -1:
self.dec2_outputs.append(torch.nn.Linear(args.dec_num_unit, args.code_rate_k))
else:
self.dec2_outputs.append(torch.nn.Linear(args.dec_num_unit, args.num_iter_ft))
else: # RNN based
self.dec1_cnns.append(torch.nn.GRU(2 + args.num_iter_ft, args.dec_num_unit,
num_layers=args.dec_num_layer, bias=True, batch_first=True,
dropout=0, bidirectional=True)
)
self.dec2_cnns.append(torch.nn.GRU(2 + args.num_iter_ft, args.dec_num_unit,
num_layers=args.dec_num_layer, bias=True, batch_first=True,
dropout=0, bidirectional=True)
)
self.dec1_outputs.append(torch.nn.Linear(2*args.dec_num_unit, args.num_iter_ft))
if idx == args.num_iteration -1:
self.dec2_outputs.append(torch.nn.Linear(2*args.dec_num_unit, args.code_rate_k))
else:
self.dec2_outputs.append(torch.nn.Linear(2*args.dec_num_unit, args.num_iter_ft))
class DEC_LargeCNN(torch.nn.Module):
def __init__(self, args, p_array):
super(DEC_LargeCNN, self).__init__()
self.args = args
use_cuda = not args.no_cuda and torch.cuda.is_available()
self.this_device = torch.device("cuda" if use_cuda else "cpu")
self.interleaver = Interleaver(args, p_array)
self.deinterleaver = DeInterleaver(args, p_array)
self.dec1_cnns = torch.nn.ModuleList()
self.dec2_cnns = torch.nn.ModuleList()
self.dec1_outputs = torch.nn.ModuleList()
self.dec2_outputs = torch.nn.ModuleList()
for idx in range(args.num_iteration):
self.dec1_cnns.append(
SameShapeConv1d(num_layer=args.dec_num_layer,
in_channels=2 + args.num_iter_ft,
out_channels=args.dec_num_unit,
kernel_size=args.dec_kernel_size))
self.dec2_cnns.append(
SameShapeConv1d(num_layer=args.dec_num_layer,
in_channels=2 + args.num_iter_ft,
out_channels=args.dec_num_unit,
kernel_size=args.dec_kernel_size))
self.dec1_outputs.append(
torch.nn.Linear(args.dec_num_unit, args.num_iter_ft))
if idx == args.num_iteration - 1:
self.dec2_outputs.append(torch.nn.Linear(args.dec_num_unit, 1))
else:
self.dec2_outputs.append(
torch.nn.Linear(args.dec_num_unit, args.num_iter_ft))
def set_parallel(self):
for idx in range(self.args.num_iteration):
self.dec1_cnns[idx] = torch.nn.DataParallel(self.dec1_cnns[idx])
self.dec2_cnns[idx] = torch.nn.DataParallel(self.dec2_cnns[idx])
self.dec1_outputs[idx] = torch.nn.DataParallel(
self.dec1_outputs[idx])
self.dec2_outputs[idx] = torch.nn.DataParallel(
self.dec2_outputs[idx])
def set_interleaver(self, p_array):
self.interleaver.set_parray(p_array)
self.deinterleaver.set_parray(p_array)
def forward(self, received):
received = received.type(torch.FloatTensor).to(self.this_device)
# Turbo Decoder
r_sys = received[:, :, 0].view(
(self.args.batch_size, self.args.block_len, 1))
r_sys_int = self.interleaver(r_sys)
r_par1 = received[:, :, 1].view(
(self.args.batch_size, self.args.block_len, 1))
r_par2 = received[:, :, 2].view(
(self.args.batch_size, self.args.block_len, 1))
#num_iteration,
prior = torch.zeros((self.args.batch_size, self.args.block_len,
self.args.num_iter_ft)).to(self.this_device)
for idx in range(self.args.num_iteration - 1):
x_this_dec = torch.cat([r_sys, r_par1, prior], dim=2)
x_dec = self.dec1_cnns[idx](x_this_dec)
x_plr = self.dec1_outputs[idx](x_dec)
if self.args.extrinsic:
x_plr = x_plr - prior
x_plr_int = self.interleaver(x_plr)
x_this_dec = torch.cat([r_sys_int, r_par2, x_plr_int], dim=2)
x_dec = self.dec2_cnns[idx](x_this_dec)
x_plr = self.dec2_outputs[idx](x_dec)
if self.args.extrinsic:
x_plr = x_plr - x_plr_int
prior = self.deinterleaver(x_plr)
# last round
x_this_dec = torch.cat([r_sys, r_par1, prior], dim=2)
x_dec = self.dec1_cnns[self.args.num_iteration - 1](x_this_dec)
x_plr = self.dec1_outputs[self.args.num_iteration - 1](x_dec)
if self.args.extrinsic:
x_plr = x_plr - prior
x_plr_int = self.interleaver(x_plr)
x_this_dec = torch.cat([r_sys_int, r_par2, x_plr_int], dim=2)
x_dec = self.dec2_cnns[self.args.num_iteration - 1](x_this_dec)
x_plr = self.dec2_outputs[self.args.num_iteration - 1](x_dec)
final = torch.sigmoid(self.deinterleaver(x_plr))
return final
class DEC_LargeRNN(torch.nn.Module):
def __init__(self, args, p_array):
super(DEC_LargeRNN, self).__init__()
self.args = args
use_cuda = not args.no_cuda and torch.cuda.is_available()
self.this_device = torch.device("cuda" if use_cuda else "cpu")
self.interleaver = Interleaver(args, p_array)
self.deinterleaver = DeInterleaver(args, p_array)
if args.dec_rnn == 'gru':
RNN_MODEL = torch.nn.GRU
elif args.dec_rnn == 'lstm':
RNN_MODEL = torch.nn.LSTM
else:
RNN_MODEL = torch.nn.RNN
self.dropout = torch.nn.Dropout(args.dropout)
self.dec1_rnns = torch.nn.ModuleList()
self.dec2_rnns = torch.nn.ModuleList()
self.dec1_outputs = torch.nn.ModuleList()
self.dec2_outputs = torch.nn.ModuleList()
for idx in range(args.num_iteration):
self.dec1_rnns.append(
RNN_MODEL(2 + args.num_iter_ft,
args.dec_num_unit,
num_layers=2,
bias=True,
batch_first=True,
dropout=args.dropout,
bidirectional=True))
self.dec2_rnns.append(
RNN_MODEL(2 + args.num_iter_ft,
args.dec_num_unit,
num_layers=2,
bias=True,
batch_first=True,
dropout=args.dropout,
bidirectional=True))
self.dec1_outputs.append(
torch.nn.Linear(2 * args.dec_num_unit, args.num_iter_ft))
if idx == args.num_iteration - 1:
self.dec2_outputs.append(
torch.nn.Linear(2 * args.dec_num_unit, 1))
else:
self.dec2_outputs.append(
torch.nn.Linear(2 * args.dec_num_unit, args.num_iter_ft))
def dec_act(self, inputs):
if self.args.dec_act == 'tanh':
return F.tanh(inputs)
elif self.args.dec_act == 'elu':
return F.elu(inputs)
elif self.args.dec_act == 'relu':
return F.relu(inputs)
elif self.args.dec_act == 'selu':
return F.selu(inputs)
elif self.args.dec_act == 'sigmoid':
return F.sigmoid(inputs)
elif self.args.dec_act == 'linear':
return inputs
else:
return inputs
def set_parallel(self):
for idx in range(self.args.num_iteration):
self.dec1_rnns[idx] = torch.nn.DataParallel(self.dec1_rnns[idx])
self.dec2_rnns[idx] = torch.nn.DataParallel(self.dec2_rnns[idx])
self.dec1_outputs[idx] = torch.nn.DataParallel(
self.dec1_outputs[idx])
self.dec2_outputs[idx] = torch.nn.DataParallel(
self.dec2_outputs[idx])
def set_interleaver(self, p_array):
self.interleaver.set_parray(p_array)
self.deinterleaver.set_parray(p_array)
def forward(self, received):
received = received.type(torch.FloatTensor).to(self.this_device)
# Turbo Decoder
r_sys = received[:, :, 0].view(
(self.args.batch_size, self.args.block_len, 1))
r_sys_int = self.interleaver(r_sys)
r_par1 = received[:, :, 1].view(
(self.args.batch_size, self.args.block_len, 1))
r_par2 = received[:, :, 2].view(
(self.args.batch_size, self.args.block_len, 1))
#num_iteration,
prior = torch.zeros((self.args.batch_size, self.args.block_len,
self.args.num_iter_ft)).to(self.this_device)
for idx in range(self.args.num_iteration - 1):
x_this_dec = torch.cat([r_sys, r_par1, prior], dim=2)
if self.args.is_parallel:
self.dec1_rnns[idx].module.flatten_parameters()
x_dec, _ = self.dec1_rnns[idx](x_this_dec)
x_plr = self.dec_act(self.dropout(self.dec1_outputs[idx](x_dec)))
if self.args.extrinsic:
x_plr = x_plr - prior
x_plr_int = self.interleaver(x_plr)
x_this_dec = torch.cat([r_sys_int, r_par2, x_plr_int], dim=2)
if self.args.is_parallel:
self.dec2_rnns[idx].module.flatten_parameters()
x_dec, _ = self.dec2_rnns[idx](x_this_dec)
x_plr = self.dec_act(self.dropout(self.dec2_outputs[idx](x_dec)))
if self.args.extrinsic:
x_plr = x_plr - x_plr_int
prior = self.deinterleaver(x_plr)
# last round
x_this_dec = torch.cat([r_sys, r_par1, prior], dim=2)
if self.args.is_parallel:
self.dec1_rnns[self.args.num_iteration -
1].module.flatten_parameters()
x_dec, _ = self.dec1_rnns[self.args.num_iteration - 1](x_this_dec)
x_plr = self.dec_act(
self.dropout(self.dec1_outputs[self.args.num_iteration -
1](x_dec)))
if self.args.extrinsic:
x_plr = x_plr - prior
x_plr_int = self.interleaver(x_plr)
x_this_dec = torch.cat([r_sys_int, r_par2, x_plr_int], dim=2)
if self.args.is_parallel:
self.dec2_rnns[self.args.num_iteration -
1].module.flatten_parameters()
x_dec, _ = self.dec2_rnns[self.args.num_iteration - 1](x_this_dec)
x_plr = self.dec_act(
self.dropout(self.dec2_outputs[self.args.num_iteration -
1](x_dec)))
logit = self.deinterleaver(x_plr)
final = torch.sigmoid(logit)
return final
class NeuralTurbofyDec(torch.nn.Module):
def __init__(self, args, p_array):
super(NeuralTurbofyDec, self).__init__()
self.args = args
self.interleaver = Interleaver(args, p_array)
self.deinterleaver = DeInterleaver(args, p_array)
self.dec_rnn = torch.nn.GRU(args.code_rate_n + args.num_iter_ft - 1 , args.dec_num_unit, num_layers=2, bias=True, batch_first=True,
dropout=args.dropout, bidirectional=True)
self.dec_out = torch.nn.Linear(2*args.dec_num_unit, args.num_iter_ft)
self.dec_final = torch.nn.Linear(args.num_iter_ft, 1)
use_cuda = not args.no_cuda and torch.cuda.is_available()
self.device = torch.device("cuda" if use_cuda else "cpu")
def enc_act(self, inputs):
if self.enc_act == 'tanh':
return F.tanh(inputs)
elif self.enc_act == 'elu':
return F.elu(inputs)
elif self.enc_act == 'relu':
return F.relu(inputs)
elif self.enc_act == 'selu':
return F.selu(inputs)
elif self.enc_act == 'sigmoid':
return F.sigmoid(inputs)
else:
return inputs
def set_interleaver(self, p_array):
self.interleaver.set_parray(p_array)
self.deinterleaver.set_parray(p_array)
def forward(self, inputs):
inputs = inputs.type(torch.FloatTensor).to(self.device)
##############################################################
#
# Neural Turbo Decoder
#
##############################################################
input_shape = inputs.shape
# Turbo Decoder
r_sys = inputs[:,:,0].view((input_shape[0], self.args.block_len, 1))
r_sys_int = self.interleaver(r_sys)
r_par1 = inputs[:,:,1].view((input_shape[0], self.args.block_len, 1))
r_par2 = inputs[:,:,2].view((input_shape[0], self.args.block_len, 1))
#num_iteration,
prior = torch.zeros((input_shape[0], self.args.block_len, self.args.num_iter_ft)).to(self.device)
for idx in range(self.args.num_iteration - 1):
x_this_dec = torch.cat([r_sys, r_par1, prior], dim = 2)
x_dec, _ = self.dec_rnn(x_this_dec)
x_plr = self.dec_out(x_dec)
if not self.args.extrinsic:
x_plr = x_plr - prior
x_plr_int = self.interleaver(x_plr)
x_this_dec = torch.cat([r_sys_int,r_par2, x_plr_int ], dim = 2)
x_dec, _ = self.dec_rnn(x_this_dec)
x_plr = self.dec_out(x_dec)
if not self.args.extrinsic:
x_plr = x_plr - x_plr_int
prior = self.deinterleaver(x_plr)
# last round
x_this_dec = torch.cat([r_sys, r_par1, prior], dim = 2)
x_dec, _ = self.dec_rnn(x_this_dec)
x_plr = self.dec_out(x_dec)
if not self.args.extrinsic:
x_plr = x_plr - prior
x_plr_int = self.interleaver(x_plr)
x_this_dec = torch.cat([r_sys_int,r_par2, x_plr_int ], dim = 2)
x_dec, _ = self.dec_rnn(x_this_dec)
x_dec = self.dec_out(x_dec)
x_final = self.dec_final(x_dec)
x_plr = torch.sigmoid(x_final)
final = self.deinterleaver(x_plr)
return final
class DEC_LargeRNN_rate2(torch.nn.Module):
def __init__(self, args, p_array):
super(DEC_LargeRNN_rate2, self).__init__()
self.args = args
use_cuda = not args.no_cuda and torch.cuda.is_available()
self.this_device = torch.device("cuda" if use_cuda else "cpu")
self.interleaver = Interleaver(args, p_array)
self.deinterleaver = DeInterleaver(args, p_array)
self.dec1_rnns = torch.nn.ModuleList()
self.dec2_rnns = torch.nn.ModuleList()
self.dec1_outputs = torch.nn.ModuleList()
self.dec2_outputs = torch.nn.ModuleList()
for idx in range(args.num_iteration):
self.dec1_rnns.append(torch.nn.GRU(1 + args.num_iter_ft, args.dec_num_unit,
num_layers=2, bias=True, batch_first=True,
dropout=args.dropout, bidirectional=True))
self.dec2_rnns.append(torch.nn.GRU(1+ args.num_iter_ft, args.dec_num_unit,
num_layers=2, bias=True, batch_first=True,
dropout=args.dropout, bidirectional=True))
self.dec1_outputs.append(torch.nn.Linear(2*args.dec_num_unit, args.num_iter_ft))
if idx == args.num_iteration -1:
self.dec2_outputs.append(torch.nn.Linear(2*args.dec_num_unit, 1))
else:
self.dec2_outputs.append(torch.nn.Linear(2*args.dec_num_unit, args.num_iter_ft))
def set_interleaver(self, p_array):
self.interleaver.set_parray(p_array)
self.deinterleaver.set_parray(p_array)
def set_parallel(self):
for idx in range(self.args.num_iteration):
self.dec1_rnns[idx] = torch.nn.DataParallel(self.dec1_rnns[idx])
self.dec2_rnns[idx] = torch.nn.DataParallel(self.dec2_rnns[idx])
self.dec1_outputs[idx] = torch.nn.DataParallel(self.dec1_outputs[idx])
self.dec2_outputs[idx] = torch.nn.DataParallel(self.dec2_outputs[idx])
def forward(self, received):
# Turbo Decoder
r_sys = received[:,:,0].view((self.args.batch_size, self.args.block_len, 1))
r_int = received[:,:,1].view((self.args.batch_size, self.args.block_len, 1))
prior = torch.zeros((self.args.batch_size, self.args.block_len, self.args.num_iter_ft)).to(self.this_device)
for idx in range(self.args.num_iteration - 1):
x_this_dec = torch.cat([r_sys, prior], dim = 2)
x_dec, _ = self.dec1_rnns[idx](x_this_dec)
x_plr = self.dec1_outputs[idx](x_dec)
if self.args.extrinsic:
x_plr = x_plr - prior
x_plr_int = self.interleaver(x_plr)
x_this_dec = torch.cat([r_int, x_plr_int ], dim = 2)
x_dec, _ = self.dec2_rnns[idx](x_this_dec)
x_plr = self.dec2_outputs[idx](x_dec)
if self.args.extrinsic:
x_plr = x_plr - x_plr_int
prior = self.deinterleaver(x_plr)
# last round
x_this_dec = torch.cat([r_sys, prior], dim = 2)
x_dec, _ = self.dec1_rnns[self.args.num_iteration - 1](x_this_dec)
x_plr = self.dec1_outputs[self.args.num_iteration - 1](x_dec)
if self.args.extrinsic:
x_plr = x_plr - prior
x_plr_int = self.interleaver(x_plr)
x_this_dec = torch.cat([r_int, x_plr_int ], dim = 2)
x_dec, _ = self.dec2_rnns[self.args.num_iteration - 1](x_this_dec)
x_plr = self.dec2_outputs[self.args.num_iteration - 1](x_dec)
final = torch.sigmoid(self.deinterleaver(x_plr))
return final