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interleavers.py
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interleavers.py
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__author__ = 'yihanjiang'
import torch
import torch.nn.functional as F
class Interleaver(torch.nn.Module):
def __init__(self, args, p_array):
super(Interleaver, self).__init__()
self.args = args
self.p_array = torch.LongTensor(p_array).view(len(p_array))
def set_parray(self, p_array):
self.p_array = torch.LongTensor(p_array).view(len(p_array))
def forward(self, inputs):
inputs = inputs.permute(1,0,2)
res = inputs[self.p_array]
res = res.permute(1, 0, 2)
return res
class DeInterleaver(torch.nn.Module):
def __init__(self, args, p_array):
super(DeInterleaver, self).__init__()
self.args = args
self.reverse_p_array = [0 for _ in range(len(p_array))]
for idx in range(len(p_array)):
self.reverse_p_array[p_array[idx]] = idx
self.reverse_p_array = torch.LongTensor(self.reverse_p_array).view(len(p_array))
def set_parray(self, p_array):
self.reverse_p_array = [0 for _ in range(len(p_array))]
for idx in range(len(p_array)):
self.reverse_p_array[p_array[idx]] = idx
self.reverse_p_array = torch.LongTensor(self.reverse_p_array).view(len(p_array))
def forward(self, inputs):
inputs = inputs.permute(1,0,2)
res = inputs[self.reverse_p_array]
res = res.permute(1, 0, 2)
return res
# TBD: change 2D interleavers
# 2D interleavers seems not working well... Don't know why...
class Interleaver2Dold(torch.nn.Module):
def __init__(self, args, p_array):
super(Interleaver2D, self).__init__()
self.args = args
self.p_array = torch.LongTensor(p_array).view(len(p_array))#.view(args.img_size, args.img_size)
def set_parray(self, p_array):
self.p_array = torch.LongTensor(p_array).view(len(p_array))#.view(self.args.img_size, args.img_size)
def forward(self, inputs):
input_shape = inputs.shape
inputs = inputs.view(input_shape[0], input_shape[1], input_shape[2]*input_shape[3])
inputs = inputs.permute(2, 0, 1)
res = inputs[self.p_array]
res = res.permute(1, 2, 0)
res = res.view(input_shape)
return res
class DeInterleaver2Dold(torch.nn.Module):
def __init__(self, args, p_array):
super(DeInterleaver2D, self).__init__()
self.args = args
self.reverse_p_array = [0 for _ in range(len(p_array))]
for idx in range(len(p_array)):
self.reverse_p_array[p_array[idx]] = idx
self.reverse_p_array = torch.LongTensor(self.reverse_p_array).view(self.args.img_size**2)
def set_parray(self, p_array):
self.reverse_p_array = [0 for _ in range(len(p_array))]
for idx in range(len(p_array)):
self.reverse_p_array[p_array[idx]] = idx
self.reverse_p_array = torch.LongTensor(self.reverse_p_array).view(self.args.img_size**2)
def forward(self, inputs):
input_shape = inputs.shape
inputs = inputs.view(input_shape[0], input_shape[1], input_shape[2]* input_shape[3])
inputs = inputs.permute(2,0,1)
res = inputs[self.reverse_p_array]
res = res.permute(1,2,0)
res = res.view(input_shape)
return res
# TBD: change 2D interleavers
# Play with real 2D interleavers: p_array with 2-step interleaving.
class Interleaver2D(torch.nn.Module):
def __init__(self, args, p_array):
super(Interleaver2D, self).__init__()
self.args = args
self.p_array = torch.LongTensor(p_array).view(len(p_array))#.view(args.img_size, args.img_size)
def set_parray(self, p_array):
self.p_array = torch.LongTensor(p_array).view(len(p_array))#.view(self.args.img_size, args.img_size)
def forward(self, inputs):
input_shape = inputs.shape
inputs = inputs.view(input_shape[0], input_shape[1], input_shape[2]*input_shape[3])
inputs = inputs.permute(2, 0, 1)
res = inputs[self.p_array]
res = res.permute(1, 2, 0)
res = res.view(input_shape)
return res
class DeInterleaver2D(torch.nn.Module):
def __init__(self, args, p_array):
super(DeInterleaver2D, self).__init__()
self.args = args
self.reverse_p_array = [0 for _ in range(len(p_array))]
for idx in range(len(p_array)):
self.reverse_p_array[p_array[idx]] = idx
self.reverse_p_array = torch.LongTensor(self.reverse_p_array).view(self.args.img_size**2)
def set_parray(self, p_array):
self.reverse_p_array = [0 for _ in range(len(p_array))]
for idx in range(len(p_array)):
self.reverse_p_array[p_array[idx]] = idx
self.reverse_p_array = torch.LongTensor(self.reverse_p_array).view(self.args.img_size**2)
def forward(self, inputs):
input_shape = inputs.shape
inputs = inputs.view(input_shape[0], input_shape[1], input_shape[2]* input_shape[3])
inputs = inputs.permute(2,0,1)
res = inputs[self.reverse_p_array]
res = res.permute(1,2,0)
res = res.view(input_shape)
return res