def __init__(self, args, p_array):
# turbofy only for code rate 1/3
super(CNN_encoder_rate3, self).__init__(args)
self.args = args
# Encoder
self.enc_cnn_1 = SameShapeConv1d(num_layer=args.enc_num_layer,
in_channels=args.code_rate_k,
out_channels=args.enc_num_unit,
kernel_size=args.dec_kernel_size)
self.enc_linear_1 = torch.nn.Linear(args.enc_num_unit, 1)
self.enc_cnn_2 = SameShapeConv1d(num_layer=args.enc_num_layer,
in_channels=args.code_rate_k,
out_channels=args.enc_num_unit,
kernel_size=args.dec_kernel_size)
self.enc_linear_2 = torch.nn.Linear(args.enc_num_unit, 1)
self.enc_cnn_3 = SameShapeConv1d(num_layer=args.enc_num_layer,
in_channels=args.code_rate_k,
out_channels=args.enc_num_unit,
kernel_size=args.dec_kernel_size)
self.enc_linear_3 = torch.nn.Linear(args.enc_num_unit, 1)
self.interleaver = Interleaver(args, p_array)
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(CCE_Turbo_Encoder1D, self).__init__()
self.args = args
cuda = True if torch.cuda.is_available() else False
self.this_device = torch.device("cuda" if cuda else "cpu")
# Define 1D Network for TurboAE
self.enc_cnn_1 = SameShapeConv1d(num_layer=args.cce_num_layer,
in_channels=args.code_rate_k,
out_channels=args.cce_num_unit,
kernel_size=args.dec_kernel_size)
self.enc_linear_1 = torch.nn.Linear(args.cce_num_unit, 1)
self.enc_cnn_2 = SameShapeConv1d(num_layer=args.cce_num_layer,
in_channels=args.code_rate_k,
out_channels=args.cce_num_unit,
kernel_size=args.dec_kernel_size)
self.enc_linear_2 = torch.nn.Linear(args.cce_num_unit, 1)
self.enc_cnn_3 = SameShapeConv1d(num_layer=args.cce_num_layer,
in_channels=args.code_rate_k,
out_channels=args.cce_num_unit,
kernel_size=args.dec_kernel_size)
self.enc_linear_3 = torch.nn.Linear(args.cce_num_unit, 1)
self.interleaver = Interleaver(args, p_array)
self.norm = torch.nn.BatchNorm1d(self.args.code_rate_n, affine=True)
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):
# turbofy only for code rate 1/3
super(ENC_interCNN2Int, self).__init__(args)
self.args = args
# Encoder
self.enc_cnn_1 = SameShapeConv1d(num_layer=args.enc_num_layer, in_channels=args.code_rate_k,
out_channels= args.enc_num_unit, kernel_size = args.dec_kernel_size)
self.enc_linear_1 = torch.nn.Linear(args.enc_num_unit, 1)
self.enc_cnn_2 = SameShapeConv1d(num_layer=args.enc_num_layer, in_channels=args.code_rate_k,
out_channels= args.enc_num_unit, kernel_size = args.dec_kernel_size)
self.enc_linear_2 = torch.nn.Linear(args.enc_num_unit, 1)
self.enc_cnn_3 = SameShapeConv1d(num_layer=args.enc_num_layer, in_channels=args.code_rate_k,
out_channels= args.enc_num_unit, kernel_size = args.dec_kernel_size)
self.enc_linear_3 = torch.nn.Linear(args.enc_num_unit, 1)
self.interleaver1 = Interleaver(args, p_array)
seed2 = 1000
rand_gen2 = mtrand.RandomState(seed2)
p_array2 = rand_gen2.permutation(arange(args.block_len))
print('p_array1', p_array)
print('p_array2', p_array2)
self.interleaver2 = Interleaver(args, p_array2)
def __init__(self, args):
super(DeModulation, 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
self.demod_layer = SameShapeConv1d(num_layer=args.demod_num_layer, in_channels=2,
out_channels= self.args.demod_num_unit, kernel_size = 1)
self.demod_final = SameShapeConv1d(num_layer=1, in_channels=args.demod_num_unit,
out_channels= args.mod_rate, kernel_size = 1, no_act = True)
def __init__(self,
args,
input_size=1,
is_systematic_bit=False,
is_interleave=False,
p_array=[]):
super(CNN_encoder, 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
self.is_systematic_bit = is_systematic_bit
self.is_interleave = is_interleave
if self.is_interleave:
self.interleaver = Interleaver(args, p_array)
self.p_array = p_array
# Encoder
self.enc_cnn = SameShapeConv1d(num_layer=args.enc_num_layer,
in_channels=input_size,
out_channels=args.enc_num_unit,
kernel_size=args.enc_kernel_size)
self.enc_linear = torch.nn.Linear(args.enc_num_unit, 1)
def __init__(self, args, p_array):
# turbofy only for code rate 1/3
super(ENC_interCNN, self).__init__(args)
self.args = args
# Encoder
if self.args.encoder == 'TurboAE_rate3_cnn':
self.enc_cnn_1 = SameShapeConv1d(num_layer=args.enc_num_layer,
in_channels=args.code_rate_k,
out_channels=args.enc_num_unit,
kernel_size=args.enc_kernel_size)
self.enc_cnn_2 = SameShapeConv1d(num_layer=args.enc_num_layer,
in_channels=args.code_rate_k,
out_channels=args.enc_num_unit,
kernel_size=args.enc_kernel_size)
self.enc_cnn_3 = SameShapeConv1d(num_layer=args.enc_num_layer,
in_channels=args.code_rate_k,
out_channels=args.enc_num_unit,
kernel_size=args.enc_kernel_size)
else: # Dense
self.enc_cnn_1 = DenseSameShapeConv1d(
num_layer=args.enc_num_layer,
in_channels=args.code_rate_k,
out_channels=args.enc_num_unit,
kernel_size=args.enc_kernel_size)
self.enc_cnn_2 = DenseSameShapeConv1d(
num_layer=args.enc_num_layer,
in_channels=args.code_rate_k,
out_channels=args.enc_num_unit,
kernel_size=args.enc_kernel_size)
self.enc_cnn_3 = DenseSameShapeConv1d(
num_layer=args.enc_num_layer,
in_channels=args.code_rate_k,
out_channels=args.enc_num_unit,
kernel_size=args.enc_kernel_size)
self.enc_linear_1 = torch.nn.Linear(args.enc_num_unit, 1)
self.enc_linear_2 = torch.nn.Linear(args.enc_num_unit, 1)
self.enc_linear_3 = torch.nn.Linear(args.enc_num_unit, 1)
self.interleaver = Interleaver(args, p_array)
def __init__(self, args):
super(CNN_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
self.dec_cnn = SameShapeConv1d(num_layer=args.dec_num_layer,
in_channels=args.code_rate_n,
out_channels=args.dec_num_unit,
kernel_size=args.dec_kernel_size)
self.dec_output = torch.nn.Linear(args.dec_num_unit, args.code_rate_k)
