/
rnn_all.py
1949 lines (1528 loc) · 98.8 KB
/
rnn_all.py
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from __future__ import print_function
import argparse
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
import torch.utils.data
from torch.optim import Optimizer
from torch.optim.lr_scheduler import LambdaLR
import pickle
import os
import time
from datetime import datetime
import matplotlib
matplotlib.use('AGG')
import matplotlib.pyplot as plt
import csv
from utils import snr_db2sigma, errors_ber, errors_bler, errors_bitwise_ber, log_sum_exp, moving_average, get_epos, get_minD, get_pairwiseD
from pac_code import *
from polar import *
import math
import random
import numpy as np
from tqdm import tqdm
from collections import namedtuple, Counter
import sys
# set up matplotlib
is_ipython = 'inline' in matplotlib.get_backend()
if is_ipython:
from IPython import display
plt.ion()
def str2bool(v):
if isinstance(v, bool):
return v
if v.lower() in ('yes', 'true', 't', 'y', '1'):
return True
elif v.lower() in ('no', 'false', 'f', 'n', '0'):
return False
else:
raise argparse.ArgumentTypeError('Boolean value expected.')
def get_args():
parser = argparse.ArgumentParser(description='PAC codes')
parser.add_argument('--id', type=str, default=None, help='ID: optional, to run multiple runs of same hyperparameters') #Will make a folder like init_932 , etc.
parser.add_argument('--N', type=int, default=32)#, choices=[4, 8, 16, 32, 64, 128], help='Polar code parameter N')
parser.add_argument('--K', type=int, default=12)#, choices= [3, 4, 8, 16, 32, 64], help='Polar code parameter K')
parser.add_argument('--target_K', type=int, default=None)#, choices= [3, 4, 8, 16, 32, 64], help='Polar code parameter K')
parser.add_argument('--test', dest = 'test', default=False, action='store_true', help='Testing?')
parser.add_argument('--code', type=str, default='PAC', choices=['PAC', 'Polar'], help='PAC or Polar?')
parser.add_argument('--rate_profile', type=str, default='RM', choices=['RM', 'rev_RM', 'polar', 'sorted', 'sorted_last', 'rev_polar','custom', 'random'], help='PAC rate profiling')
parser.add_argument('--random_seed', type=int, default=42)
parser.add_argument('--info_ind', type=int, default=63)#, choices= [3, 4, 8, 16, 32, 64], help='Polar code parameter K')
parser.add_argument('--rnn_type', type=str, default='GRU', choices=['GRU', 'LSTM'], help='RNN method')
parser.add_argument("--bidirectional", type=str2bool, nargs='?', const=True, default=False, help="Bidirectional RNN?")
parser.add_argument('--decoding_type', type=str, default='y_h0', choices=['y_h0', 'y_input', 'y_h0_out'], help='RNN method')
parser.add_argument('--target', type=str, default='gt', choices=['gt', 'llr'], help='training target')
parser.add_argument("--onehot", type=str2bool, nargs='?', const=True, default=False, help="input one-hot?")
parser.add_argument('--mult', type=int, default=1)#, multiplying factor to increase effective batch size
parser.add_argument('--print_freq', type=int, default=100)#, multiplying factor to increase effective batch size
parser.add_argument('--rnn_feature_size', type=int, default=256)#, choices=[32, 64, 128, 256, 512, 1024], help='num_iters')
parser.add_argument('--rnn_pool_type', type=str, default='last', choices=['last', 'average'], help='How to pool hidden states??')
parser.add_argument('--rnn_depth', type=int, default=2)#, choices=[32, 64, 128, 256, 512, 1024], help='num_iters')
parser.add_argument('--y_depth', type=int, default=3)#, choices=[3,4,5,6], help='num_iters')
parser.add_argument('--y_hidden_size', type=int, default=128)#, choices=[3,4,5,6], help='num_iters')
parser.add_argument('--out_linear_depth', type=int, default=1)#, choices=[3,4,5,6], help='num_iters')
parser.add_argument('--dropout', type=float, default=0.)#, choices=[64, 128, 256, 1024], help='number of blocks')
parser.add_argument("--use_skip", type=str2bool, nargs='?', const=True, default=False, help="use skip connection?")
parser.add_argument("--use_layernorm", type=str2bool, nargs='?', const=True, default=False, help="use skip connection?")
parser.add_argument('--weight0', type=float, default=None, help='weigh loss at bit 0 ')
parser.add_argument("--test_codes", type=str2bool, nargs='?', const=True, default=False, help="test_codes?")
parser.add_argument("--test_bitwise", type=str2bool, nargs='?', const=True, default=False, help="test_bitwise?")
# num_episodes = 50000
parser.add_argument('--num_steps', type=int, default=200000)#, choices=[100, 20000, 40000], help='number of blocks')
parser.add_argument('--batch_size', type=int, default=4096)#, choices=[64, 128, 256, 1024], help='number of blocks')
parser.add_argument('--activation', type=str, default='selu', choices=['selu', 'relu', 'elu', 'tanh', 'sigmoid'], help='activation function')
# TRAINING parameters
parser.add_argument('--initialization', type=str, default='He', choices=['Dontknow', 'He', 'Xavier'], help='initialization')
parser.add_argument('--optimizer_type', type=str, default='AdamW', choices=['Adam', 'RMS', 'AdamW'], help='optimizer type')
parser.add_argument('--scheduler', type=str, default=None, choices=['cosine','step'], help='optimizer type')
parser.add_argument('--loss', type=str, default='MSE', choices=['Huber', 'MSE', 'BCE'], help='loss function')
parser.add_argument('--loss_on_all', dest = 'loss_on_all', default=False, action='store_true', help='loss on all bits or only info bits')
parser.add_argument('--loss_only', type=int, default=None, help='loss only on x bits')
parser.add_argument('--split_batch', dest = 'split_batch', default=False, action='store_true', help='split batch - for teacher forcing')
parser.add_argument('--lr', type=float, default=0.001, help='Learning rate')
parser.add_argument('--lr_decay', type=int, default=None, help='learning rate decay frequency (in episodes)')
parser.add_argument('--lr_decay_gamma', type=float, default=0.1, help='learning rate decay factor')
parser.add_argument('--clip', type=float, default=0.25, help='gradient clipping factor')
parser.add_argument('--no_detach', dest = 'no_detach', default=False, action='store_true', help='detach previous output during rnn training?')
# TEACHER forcing
# if only tfr_max is given assume no annealing
parser.add_argument('--tfr_min', type=float, default=None, help='teacher forcing ratio minimum')
parser.add_argument('--tfr_max', type=float, default=0., help='teacher forcing ratio maximum')
parser.add_argument('--tfr_decay', type=float, default=10000, help='teacher forcing ratio decay parameter')
parser.add_argument('--teacher_steps', type=int, default=-10000, help='initial number of steps to do teacher forcing only')
# TESTING parameters
parser.add_argument('--dec_train_snr', type=float, default=-1., help='SNR at which decoder is trained')
parser.add_argument('--validation_snr', type=float, default=None, help='SNR at which decoder is validated')
parser.add_argument('--testing_snr', type=float, default=None, help='SNR at which decoder is validated')
parser.add_argument("--do_range_training", type=str2bool, nargs='?', const=True, default=False, help="Train on range of SNRs")
parser.add_argument('--model_save_per', type=int, default=10000, help='num of episodes after which model is saved')
parser.add_argument('--test_snr_start', type=float, default=-2., help='testing snr start')
parser.add_argument('--test_snr_end', type=float, default=4., help='testing snr end')
parser.add_argument('--snr_points', type=int, default=7, help='testing snr num points')
parser.add_argument('--test_batch_size', type=int, default=10000, help='number of blocks')
parser.add_argument('--test_size', type=int, default=100000, help='size of the batches')
parser.add_argument('--noise_type', type=str, choices=['awgn', 'fading', 'radar', 't-dist'], default='awgn')
parser.add_argument('--vv',type=float, default=5, help ='only for t distribution channel : degrees of freedom')
parser.add_argument('--radar_prob',type=float, default=0.05, help ='only for radar distribution channel')
parser.add_argument('--radar_power',type=float, default=5.0, help ='only for radar distribution channel')
parser.add_argument('--model_iters', type=int, default=None, help='by default load final model, option to load a model of x episodes')
parser.add_argument('--test_load_path', type=str, default=None, help='load test model given path')
parser.add_argument('--list_size', type=int, default=None)#, choices=[100, 20000, 40000], help='number of blocks')
parser.add_argument('--run_fano', dest = 'run_fano', default=False, action='store_true', help='run fano decoding')
parser.add_argument('--random_test', dest = 'random_test', default=False, action='store_true', help='run test on random data (default action is to test on same samples as Fano did)')
parser.add_argument('--save_path', type=str, default=None, help='save name')
parser.add_argument('--progressive_path', type=str, default=None, help='save name')
parser.add_argument('--load_path', type=str, default=None, help='load name')
parser.add_argument("--run_dumer", type=str2bool, nargs='?', const=True, default=True, help="run dumer during test?")
parser.add_argument("--run_ML", type=str2bool, nargs='?', const=True, default=False, help="run ML during test?")
# parser.add_argument('-id', type=int, default=100000)
parser.add_argument('--hard_decision', dest = 'hard_decision', default=False, action='store_true', help='polar code sc decoding hard decision?')
parser.add_argument('--gpu', type=int, default=-2, help='gpus used for training - e.g 0,1,3')
parser.add_argument('--anomaly', dest = 'anomaly', default=False, action='store_true', help='enable anomaly detection')
parser.add_argument('--only_args', dest = 'only_args', default=False, action='store_true')
parser.add_argument('--use_ynn', dest = 'use_ynn', default=False, action='store_true')
parser.add_argument('--reverse_order', dest = 'reverse_order', default=False, action='store_true')
parser.add_argument('--print_cust', dest = 'print_cust', default=False, action='store_true')
parser.add_argument('--fresh', dest = 'fresh', default=False, action='store_true')
args = parser.parse_args()
if args.target_K is None:
args.target_K = args.N // 2 if args.K <= args.N // 2 else args.K
if args.N == 4:
args.g = 7 # Convolutional coefficients are [1,1, 0, 1]
# args.M = 2 # log N
elif args.N == 8:
args.g = 13 # Convolutional coefficients are [1, 0, 1, 1]
# args.M = 3 # log N
elif args.N == 16:
args.g = 21 # [1, 0, 1, 0, 1]
elif args.N == 32:
args.g = 53 # [1, 1, 0, 1, 0, 1]
else:
args.g = 91
args.M = int(math.log(args.N, 2))
args.are_we_doing_ML = True if args.K <=0 else False
if args.run_ML:
args.are_we_doing_ML = True
# if args.N == args.K:
# args.are_we_doing_ML = True
# args.hard_decision = True # use hard-SC
if args.tfr_min is None:
args.tfr_min = args.tfr_max
if args.decoding_type == 'y_input' and not args.use_ynn:
args.y_depth = 0
if not args.out_linear_depth > 1:
args.y_hidden_size = 0
return args
def get_onehot(actions):
inds = (0.5 + 0.5*actions).long()
return torch.eye(2, device = inds.device)[inds].reshape(actions.shape[0], -1)
def get_cosine_with_hard_restarts_schedule_with_warmup(
optimizer: Optimizer, num_warmup_steps: int, num_training_steps: int, num_cycles: int = 1, last_epoch: int = -1
):
"""
Create a schedule with a learning rate that decreases following the values of the cosine function between the
initial lr set in the optimizer to 0, with several hard restarts, after a warmup period during which it increases
linearly between 0 and the initial lr set in the optimizer.
Args:
optimizer ([`~torch.optim.Optimizer`]):
The optimizer for which to schedule the learning rate.
num_warmup_steps (`int`):
The number of steps for the warmup phase.
num_training_steps (`int`):
The total number of training steps.
num_cycles (`int`, *optional*, defaults to 1):
The number of hard restarts to use.
last_epoch (`int`, *optional*, defaults to -1):
The index of the last epoch when resuming training.
Return:
`torch.optim.lr_scheduler.LambdaLR` with the appropriate schedule.
"""
def lr_lambda(current_step):
if current_step < num_warmup_steps:
return float(current_step) / float(max(1, num_warmup_steps))
progress = float(current_step - num_warmup_steps) / float(max(1, num_training_steps - num_warmup_steps))
if progress >= 1.0:
return 0.0
return max(0.0, 0.5 * (1.0 + math.cos(math.pi * ((float(num_cycles) * progress) % 1.0))))
return LambdaLR(optimizer, lr_lambda, last_epoch)
class RNN_Model(nn.Module):
def __init__(self, rnn_type, input_size, feature_size, output_size, num_rnn_layers, y_size, y_hidden_size, y_depth, activation = 'relu', dropout = 0., skip=False, out_linear_depth=1, y_output_size = None, bidirectional = False, use_layernorm = False):
super(RNN_Model, self).__init__()
assert rnn_type in ['GRU', 'LSTM']
self.input_size = input_size
self.activation = activation
self.feature_size = feature_size
self.output_size = output_size
self.skip = skip
self.num_rnn_layers = num_rnn_layers
self.bidirectional = bidirectional
self.rnn = getattr(nn, rnn_type)(self.input_size, self.feature_size, self.num_rnn_layers, bidirectional = self.bidirectional, batch_first = True)
self.rnn_type = rnn_type
self.dropout = dropout
self.drop = nn.Dropout(dropout)
self.y_depth = y_depth
self.y_size = y_size
self.y_hidden_size = y_hidden_size
self.out_linear_depth = out_linear_depth
self.y_output_size = (int(self.bidirectional) + 1)*self.num_rnn_layers*self.feature_size if y_output_size is None else y_output_size
if use_layernorm:
self.layernorm = nn.LayerNorm(self.feature_size)
else:
self.layernorm = nn.Identity()
#try:
if self.y_hidden_size > 0 and self.y_depth > 0:
self.y_linears = nn.ModuleList([nn.Linear(self.y_size, self.y_hidden_size, bias=True)])
self.y_linears.extend([nn.Linear(self.y_hidden_size, self.y_hidden_size, bias=True) for ii in range(1, self.y_depth-1)])
if (not hasattr(self, 'skip')) or (not self.skip):
self.y_linears.append(nn.Linear(self.y_hidden_size, self.y_output_size, bias=True))
else:
self.y_linears.append(nn.Linear(self.y_hidden_size, self.y_output_size - self.y_size, bias=True))
#except:
# pass
if self.out_linear_depth == 1:
self.linear = nn.Linear((int(self.bidirectional) + 1)*self.feature_size, self.output_size)
else:
layers = []
layers.append(nn.Linear((int(self.bidirectional) + 1)*self.feature_size, self.y_hidden_size))
for ii in range(1, self.out_linear_depth-1):
layers.append(nn.SELU())
layers.append(nn.Linear(self.y_hidden_size, self.y_hidden_size))
layers.append(nn.SELU())
layers.append(nn.Linear(self.y_hidden_size, self.output_size))
self.linear = nn.Sequential(*layers)
def act(self, inputs):
if self.activation == 'tanh':
return F.tanh(inputs)
elif self.activation == 'elu':
return F.elu(inputs)
elif self.activation == 'relu':
return F.relu(inputs)
elif self.activation == 'selu':
return F.selu(inputs)
elif self.activation == 'sigmoid':
return F.sigmoid(inputs)
elif self.activation == 'linear':
return inputs
else:
return inputs
def get_h0(self, y):
x = y.clone()
for ii, layer in enumerate(self.y_linears):
if ii != self.y_depth:
x = self.act(layer(x))
else:
x = layer(x)
if self.skip:
x = torch.cat([y, x], 1)
x = x.reshape(-1, self.feature_size, (int(self.bidirectional) + 1)*self.num_rnn_layers).permute(2, 0, 1).contiguous()
if self.rnn_type == 'GRU':
return x
else:
return (x,x)
def get_Fy(self, y):
Fy = y.clone()
for ii, layer in enumerate(self.y_linears):
if ii != self.y_depth:
Fy = self.act(layer(Fy))
else:
Fy = layer(Fy)
return Fy
def forward(self, input, hidden, Fy=None):
out, hidden = self.rnn(input, hidden)
out = self.drop(out)
out = self.layernorm(out)
if Fy is None:
decoded = self.linear(out)
else:
decoded = self.linear(torch.cat([Fy, out], -1))
decoded = decoded.view(-1, self.output_size)
return decoded, hidden
class RNN_decoder:
def __init__(self, decoding_type, N, info_inds, onehot = False, reverse_order = False):
self.decoding_type = decoding_type
self.N = N
self.info_inds = info_inds
self.onehot = onehot#
self.reverse_order = reverse_order
def decode(self, net, train, y, gt = None, teacher_forcing_ratio = 0., loss_inds = None):
if not self.onehot:
onehot_fn = lambda x:x
else:
onehot_fn = get_onehot
if self.reverse_order:
iter_range = list(range(self.N-1, -1, -1))
if gt is not None:
gt = gt.flip(1)
else:
iter_range = list(range(0, self.N))
if train: #training
net.train()
decoded = torch.ones(y.shape[0], self.N, device = y.device)
if random.random() < teacher_forcing_ratio: # do teacher forcing
assert gt is not None
assert gt.shape[1] == self.N
if self.decoding_type == 'y_h0':
hidden = net.get_h0(y)
for ii, jj in enumerate(iter_range): # don't assume first bit is always frozen
if ii == 0:
out, hidden = net(onehot_fn(torch.ones(y.shape[0], device = y.device)).view(-1, 1, net.input_size), hidden)
else:
out, hidden = net(onehot_fn(gt[:, ii-1]).view(-1, 1, net.input_size), hidden)
decoded[:, ii] = out.squeeze()
elif self.decoding_type == 'y_input':
if net.y_depth == 0:
Fy = y
else:
Fy = net.get_Fy(y)
hidden = torch.zeros((int(net.bidirectional) + 1)*net.num_rnn_layers, y.shape[0], net.feature_size, device = y.device)
if net.rnn_type == 'LSTM':
hidden = (hidden, hidden)
for ii, jj in enumerate(iter_range): # don't assume first bit is always frozen
if ii == 0:
out, hidden = net(torch.cat([Fy.unsqueeze(1), onehot_fn(torch.ones(y.shape[0], device = y.device)).view(-1, 1, net.input_size - self.N)], 2), hidden)
else:
out, hidden = net(torch.cat([Fy.unsqueeze(1), onehot_fn(gt[:, ii-1]).view(-1, 1, net.input_size - self.N)], 2), hidden)
decoded[:, ii] = out.squeeze()
elif self.decoding_type == 'y_h0_out':
hidden = net.get_h0(y)
Fy = hidden.clone().permute(1, 0, 2).contiguous().reshape(-1, 1, net.num_rnn_layers*net.feature_size)
for ii, jj in enumerate(iter_range): # don't assume first bit is always frozen
if ii == 0:
out, hidden = net(onehot_fn(torch.ones(y.shape[0], device = y.device)).view(-1, 1, net.input_size), hidden, Fy)
else:
out, hidden = net(onehot_fn(gt[:, ii-1]).view(-1, 1, net.input_size), hidden, Fy)
decoded[:, ii] = out.squeeze()
else: # student forcing
if self.decoding_type == 'y_h0':
hidden = net.get_h0(y)
for ii, jj in enumerate(iter_range): # don't assume first bit is always frozen
if ii == 0:
out, hidden = net(onehot_fn(torch.ones(y.shape[0], device = y.device)).view(-1, 1, net.input_size), hidden)
else:
if not args.no_detach:
out, hidden = net(onehot_fn(decoded[:, ii-1].sign()).view(-1, 1, net.input_size).detach().clone(), hidden)
else:
out, hidden = net(onehot_fn(decoded[:, ii-1].sign()).view(-1, 1, net.input_size).clone(), hidden)
if ii in self.info_inds:
decoded[:, ii] = out.squeeze()
elif self.decoding_type == 'y_input':
if net.y_depth == 0:
Fy = y
else:
Fy = net.get_Fy(y)
hidden = torch.zeros((int(net.bidirectional) + 1)*net.num_rnn_layers, y.shape[0], net.feature_size, device = y.device)
if net.rnn_type == 'LSTM':
hidden = (hidden, hidden)
for ii, jj in enumerate(iter_range): # don't assume first bit is always frozen
if ii == 0:
out, hidden = net(torch.cat([Fy.unsqueeze(1), onehot_fn(torch.ones(y.shape[0], device = y.device)).view(-1, 1, net.input_size - self.N)], 2), hidden)
else:
if not args.no_detach:
out, hidden = net(torch.cat([Fy.unsqueeze(1), onehot_fn(decoded[:, ii-1].sign()).view(-1, 1, net.input_size - self.N).detach().clone()], 2), hidden)
else:
out, hidden = net(torch.cat([Fy.unsqueeze(1), onehot_fn(decoded[:, ii-1].sign()).view(-1, 1, net.input_size - self.N).clone()], 2), hidden)
if ii in self.info_inds:
decoded[:, ii] = out.squeeze()
elif self.decoding_type == 'y_h0_out':
hidden = net.get_h0(y)
Fy = hidden.clone().permute(1, 0, 2).contiguous().reshape(-1, 1, net.num_rnn_layers*net.feature_size)
for ii, jj in enumerate(iter_range): # don't assume first bit is always frozen
if ii == 0:
out, hidden = net(onehot_fn(torch.ones(y.shape[0], device = y.device)).view(-1, 1, net.input_size), hidden, Fy)
else:
if not args.no_detach:
out, hidden = net(onehot_fn(decoded[:, ii-1].sign()).view(-1, 1, net.input_size).detach().clone().sign(), hidden, Fy)
else:
out, hidden = net(onehot_fn(decoded[:, ii-1].sign()).view(-1, 1, net.input_size).clone().sign(), hidden, Fy)
# out, hidden = net(decoded[:, ii-1].view(-1, 1, 1).clone(), hidden)
if ii in self.info_inds:
decoded[:, ii] = out.squeeze()
if not self.reverse_order:
return decoded
else:
return decoded.flip(1)
else: #test
net.eval()
if loss_inds is None:
loss_inds = self.info_inds
with torch.no_grad():
if gt is None:
decoded = torch.ones(y.shape[0], self.N, device = y.device)
else:
decoded = gt.clone()
if self.decoding_type == 'y_h0':
hidden = net.get_h0(y)
for ii, jj in enumerate(iter_range): # don't assume first bit is always frozen
if ii == 0:
out, hidden = net(onehot_fn(torch.ones(y.shape[0], device = y.device)).view(-1, 1, net.input_size), hidden)
else:
out, hidden = net(onehot_fn(decoded[:, ii-1].sign()).view(-1, 1, net.input_size), hidden)
if jj in loss_inds:
decoded[:, ii] = out.squeeze().sign()
elif self.decoding_type == 'y_input':
if net.y_depth == 0:
Fy = y
else:
Fy = net.get_Fy(y)
hidden = torch.zeros((int(net.bidirectional) + 1)*net.num_rnn_layers, y.shape[0], net.feature_size, device = y.device)
if net.rnn_type == 'LSTM':
hidden = (hidden, hidden)
for ii, jj in enumerate(iter_range): # don't assume first bit is always frozen
if ii == 0:
out, hidden = net(torch.cat([Fy.unsqueeze(1), onehot_fn(torch.ones(y.shape[0], device = y.device)).view(-1, 1, net.input_size - self.N)], 2), hidden)
else:
out, hidden = net(torch.cat([Fy.unsqueeze(1), onehot_fn(decoded[:, ii-1].sign()).view(-1, 1, net.input_size - self.N).detach().clone()], 2), hidden)
if jj in loss_inds:
decoded[:, ii] = out.squeeze().sign()
elif self.decoding_type == 'y_h0_out':
hidden = net.get_h0(y)
Fy = hidden.clone().permute(1, 0, 2).contiguous().reshape(-1, 1, net.num_rnn_layers*net.feature_size)
for ii, jj in enumerate(iter_range): # don't assume first bit is always frozen
if ii == 0:
out, hidden = net(onehot_fn(torch.ones(y.shape[0], device = y.device)).view(-1, 1, net.input_size), hidden, Fy)
else:
out, hidden = net(onehot_fn(decoded[:, ii-1].sign()).view(-1, 1, net.input_size), hidden, Fy)
if jj in loss_inds:
decoded[:, ii] = out.squeeze().sign()
if not self.reverse_order:
return decoded
else:
return decoded.flip(1)
def pruneLists(self, hidden_list, decoded_list, metric_list, L):
_, inds = torch.topk(-1*metric_list, L, 0) # select L gratest indices in every row
sorted_inds, _ = torch.sort(inds, 0)
batch_size = decoded_list.shape[1]
# llr_array_list = torch.gather(llr_array_list, 0, sorted_inds.unsqueeze(-1).unsqueeze(-1).repeat(1, 1, llr_array_list.shape[2], llr_array_list.shape[3]))
# partial_llrs_list = torch.gather(partial_llrs_list, 0, sorted_inds.unsqueeze(-1).unsqueeze(-1).repeat(1, 1, partial_llrs_list.shape[2], partial_llrs_list.shape[3]))
# metric_list = torch.gather(metric_list, 0, sorted_inds)
# u_hat_list = torch.gather(u_hat_list, 0, sorted_inds.unsqueeze(-1).repeat(1, 1, u_hat_list.shape[2]))
hshape = hidden_list.shape
h_list = hidden_list.permute(0, 2, 1, 3)
hidden_list = h_list[sorted_inds, torch.arange(batch_size)].permute(0, 2, 1, 3)
metric_list = metric_list[sorted_inds, torch.arange(batch_size)]
decoded_list = decoded_list[sorted_inds, torch.arange(batch_size)]
return hidden_list.contiguous(), decoded_list, metric_list
def list_decode(self, net, y, code, L = 1):
if not self.onehot:
onehot_fn = lambda x:x
else:
onehot_fn = get_onehot
loss_inds = self.info_inds
batch_size = y.shape[0]
net.eval()
with torch.no_grad():
decoded = torch.ones(y.shape[0], self.N, device = y.device)
if self.decoding_type == 'y_h0':
hidden = net.get_h0(y)
for ii in range(0, self.N): # don't assume first bit is always frozen
if ii in loss_inds:
decoded[:, ii] = out.squeeze().sign()
elif self.decoding_type == 'y_input':
if net.y_depth == 0:
Fy = y
else:
Fy = net.get_Fy(y)
elif self.decoding_type == 'y_h0_out':
hidden = net.get_h0(y)
Fy = hidden.clone().permute(1, 0, 2).contiguous().reshape(-1, 1, net.num_rnn_layers*net.feature_size)
hidden = torch.zeros(net.num_rnn_layers, y.shape[0], net.feature_size, device = y.device)
store_device = y.device #torch.device('cpu')
hidden_list = hidden.unsqueeze(0).cpu()
decoded_list = decoded.unsqueeze(0).cpu()
metric_list = torch.zeros(1, y.shape[0]).cpu()
for ii in range(self.N): # don't assume first bit is always frozen
list_size = hidden_list.shape[0]
if ii in self.info_inds:
metric_list = torch.vstack([metric_list, metric_list])
decoded_list = torch.vstack([decoded_list, decoded_list])
hidden_list = torch.vstack([hidden_list, hidden_list])
for list_index in range(list_size):
if self.decoding_type == 'y_h0':
if ii == 0:
out, hidden = net(onehot_fn(torch.ones(y.shape[0], device = y.device)).view(-1, 1, net.input_size), hidden)
else:
out, hidden = net(onehot_fn(decoded[:, ii-1].sign()).view(-1, 1, net.input_size), hidden)
elif self.decoding_type == 'y_input':
if ii == 0:
out, hidden = net(torch.cat([Fy.unsqueeze(1), onehot_fn(torch.ones(y.shape[0], device = y.device)).view(-1, 1, net.input_size - self.N)], 2), hidden_list[list_index].to(y.device))
else:
out, hidden = net(torch.cat([Fy.unsqueeze(1), onehot_fn(decoded_list[list_index, :, ii-1].to(y.device).sign()).view(-1, 1, net.input_size - self.N).detach().clone()], 2), hidden_list[list_index].to(y.device))
elif self.decoding_type == 'y_h0_out':
if ii == 0:
out, hidden = net(onehot_fn(torch.ones(y.shape[0], device = y.device)).view(-1, 1, net.input_size), hidden, Fy)
else:
out, hidden = net(onehot_fn(decoded[:, ii-1].sign()).view(-1, 1, net.input_size), hidden, Fy)
if ii in self.info_inds: # not frozen
decoded_list[list_index, :, ii] = out.squeeze().sign()
decoded_list[list_size+list_index, :, ii] = -1*out.squeeze().sign()
metric = torch.abs(out).cpu()
metric_list[list_size+list_index, :] += metric.squeeze()
hidden_list[list_index] = hidden
hidden_list[list_size+list_index] = hidden
else: # frozen
# decoded_list[list_index, :, ii] is already ones
# metric = torch.abs(out.cpu())*(out.sign().cpu() != 1*torch.ones_like(out).cpu()).float().cpu()
# metric_list[list_index, :] += metric.squeeze()
hidden_list[list_index] = hidden
if ii in self.info_inds:
if hidden_list.shape[0] > L:
hidden_list, decoded_list, metric_list = self.pruneLists(hidden_list, decoded_list, metric_list, L)
list_size = hidden_list.shape[0]
decoded = decoded_list[:, :, self.info_inds].detach().cpu()
codeword_list = code.encode_plotkin(decoded.reshape(-1, code.K)).reshape(list_size, batch_size, self.N)
inds = ((codeword_list - y.cpu().unsqueeze(0))**2).sum(2).argmin(0)
# get ML decision for each sample.
decoded = decoded[inds, torch.arange(batch_size)]
return decoded
def PAC_MAP_decode(noisy_codes, b_codebook):
b_noisy = noisy_codes.unsqueeze(1).repeat(1, 2**args.K, 1)
diff = (b_noisy - b_codebook).pow(2).sum(dim=2)
idx = diff.argmin(dim=1)
MAP_decoded_bits = all_message_bits[idx, :]
return MAP_decoded_bits
def test_RNN_and_Dumer_batch(net, msg_bits, corrupted_codewords, snr, run_dumer=True):
state = corrupted_codewords
start =time.time()
decoded_vhat = decoder.decode(net, False, corrupted_codewords)
decoded_msg_bits = decoded_vhat[:, code.info_inds].sign()
print('RNN : {}'.format(time.time() - start))
ber_RNN = errors_ber(msg_bits, decoded_msg_bits).item()
bler_RNN = errors_bler(msg_bits, decoded_msg_bits).item()
if run_dumer:
start = time.time()
_, decoded_Dumer_msg_bits, _ = code.pac_sc_decode(corrupted_codewords, snr)
print('SC : {}'.format(time.time() - start))
ber_Dumer = errors_ber(msg_bits, decoded_Dumer_msg_bits.sign()).item()
bler_Dumer = errors_bler(msg_bits, decoded_Dumer_msg_bits.sign()).item()
else:
ber_Dumer = 0.
bler_Dumer = 0.
if args.are_we_doing_ML:
MAP_decoded_bits = PAC_MAP_decode(corrupted_codewords.detach().cpu(), b_codebook)
ber_ML = errors_ber(msg_bits.cpu(), MAP_decoded_bits).item()
bler_ML = errors_bler(msg_bits.cpu(), MAP_decoded_bits).item()
return ber_RNN, bler_RNN, ber_Dumer, bler_Dumer, ber_ML, bler_ML
else:
return ber_RNN, bler_RNN, ber_Dumer, bler_Dumer
def test_fano(msg_bits, noisy_code, snr):
msg_bits = msg_bits.to('cpu') # run fano on cpu. required?
sigma = snr_db2sigma(snr)
noisy_code = noisy_code.to('cpu')
llrs = (2/sigma**2)*noisy_code
decoded_bits = torch.empty_like(msg_bits)
for ii, vv in enumerate(llrs):
v_hat, pm = pac.fano_decode(vv.unsqueeze(0), delta = 2, verbose = 0, maxDiversions = 1000, bias_type = 'p_e')
decoded_bits[ii] = pac.extract(v_hat)
ber_fano = errors_ber(msg_bits, decoded_bits).item()
bler_fano = errors_bler(msg_bits, decoded_bits).item()
return ber_fano, bler_fano
def test_full_data(net, code, snr_range, Test_Data_Generator, run_fano = False, run_dumer = True):
num_test_batches = len(Test_Data_Generator)
bers_RNN_test = [0. for ii in snr_range]
blers_RNN_test = [0. for ii in snr_range]
bers_Dumer_test = [0. for ii in snr_range]
blers_Dumer_test = [0. for ii in snr_range]
bers_ML_test = [0. for ii in snr_range]
blers_ML_test = [0. for ii in snr_range]
bers_fano_test = [0. for ii in snr_range]
blers_fano_test = [0. for ii in snr_range]
for (k, msg_bits) in enumerate(Test_Data_Generator):
msg_bits = msg_bits.to(device)
pac_code = code.encode(msg_bits)
for snr_ind, snr in enumerate(snr_range):
noisy_code = code.channel(pac_code, snr)#, args.noise_type, args.vv, args.radar_power, args.radar_prob)
if args.are_we_doing_ML:
ber_RNN, bler_RNN, ber_Dumer, bler_Dumer, ber_ML, bler_ML = test_RNN_and_Dumer_batch(net, msg_bits, noisy_code, snr)
else:
ber_RNN, bler_RNN, ber_Dumer, bler_Dumer = test_RNN_and_Dumer_batch(net, msg_bits, noisy_code, snr, run_dumer)
bers_RNN_test[snr_ind] += ber_RNN/num_test_batches
bers_Dumer_test[snr_ind] += ber_Dumer/num_test_batches
blers_RNN_test[snr_ind] += bler_RNN/num_test_batches
blers_Dumer_test[snr_ind] += bler_Dumer/num_test_batches
if args.are_we_doing_ML:
bers_ML_test[snr_ind] += ber_ML/num_test_batches
blers_ML_test[snr_ind] += bler_ML/num_test_batches
if run_fano:
ber_fano, bler_fano = test_fano(msg_bits, noisy_code, snr)
bers_fano_test[snr_ind] += ber_fano/num_test_batches
blers_fano_test[snr_ind] += bler_fano/num_test_batches
return bers_RNN_test, blers_RNN_test, bers_Dumer_test, blers_Dumer_test, bers_ML_test, blers_ML_test, bers_fano_test, blers_fano_test
def test_standard(net, msg_bits, received, run_fano = False, run_dumer = True):
snr_range = list(received.keys())
bers_RNN_test = [0. for ii in snr_range]
blers_RNN_test = [0. for ii in snr_range]
bers_Dumer_test = [0. for ii in snr_range]
blers_Dumer_test = [0. for ii in snr_range]
bers_ML_test = [0. for ii in snr_range]
blers_ML_test = [0. for ii in snr_range]
bers_fano_test = [0. for ii in snr_range]
blers_fano_test = [0. for ii in snr_range]
msg_bits = msg_bits.to(device)
for snr_ind, (snr, noisy_code) in enumerate(received.items()):
noisy_code = noisy_code.to(device)
if args.are_we_doing_ML:
ber_RNN, bler_RNN, ber_Dumer, bler_Dumer, ber_ML, bler_ML = test_RNN_and_Dumer_batch(net, msg_bits, noisy_code, snr)
else:
ber_RNN, bler_RNN, ber_Dumer, bler_Dumer = test_RNN_and_Dumer_batch(net, msg_bits, noisy_code, snr, run_dumer)
bers_RNN_test[snr_ind] += ber_RNN
bers_Dumer_test[snr_ind] += ber_Dumer
blers_RNN_test[snr_ind] += bler_RNN
blers_Dumer_test[snr_ind] += bler_Dumer
if args.are_we_doing_ML:
bers_ML_test[snr_ind] += ber_ML
blers_ML_test[snr_ind] += bler_ML
if run_fano:
ber_fano, bler_fano = test_fano(msg_bits, noisy_code, snr)
bers_fano_test[snr_ind] += ber_fano
blers_fano_test[snr_ind] += bler_fano
return bers_RNN_test, blers_RNN_test, bers_Dumer_test, blers_Dumer_test, bers_ML_test, blers_ML_test, bers_fano_test, blers_fano_test
def polar_RNN_full_test(net, polar, snr_range, Test_Data_Generator, run_ML=False, run_SCL = False, run_RNNL = False):
num_test_batches = len(Test_Data_Generator)
bers_RNN_test = [0. for ii in snr_range]
blers_RNN_test = [0. for ii in snr_range]
bers_RNNL_test = [0. for ii in snr_range]
blers_RNNL_test = [0. for ii in snr_range]
bers_SC_test = [0. for ii in snr_range]
blers_SC_test = [0. for ii in snr_range]
bers_SCL_test = [0. for ii in snr_range]
blers_SCL_test = [0. for ii in snr_range]
bers_ML_test = [0. for ii in snr_range]
blers_ML_test = [0. for ii in snr_range]
for (k, msg_bits) in enumerate(Test_Data_Generator):
msg_bits = msg_bits.to(device)
polar_code = polar.encode_plotkin(msg_bits)
gt = torch.ones(msg_bits.shape[0], args.N, device = msg_bits.device)
gt[:, code.info_inds] = msg_bits
for snr_ind, snr in enumerate(snr_range):
noisy_code = polar.channel(polar_code, snr, args.noise_type, args.vv, args.radar_power, args.radar_prob)
noise = noisy_code - polar_code
if args.loss_only is None:
# start = time.time()
SC_llrs, decoded_SC_msg_bits = polar.sc_decode_new(noisy_code, snr)
# print('SC : {}'.format(time.time() - start))
ber_SC = errors_ber(msg_bits, decoded_SC_msg_bits.sign()).item()
bler_SC = errors_bler(msg_bits, decoded_SC_msg_bits.sign()).item()
if run_SCL:
SCL_llrs, decoded_SCL_msg_bits = polar.scl_decode(noisy_code.cpu(), snr, args.list_size, False)
SCL_llrs, decoded_SCL_msg_bits = SCL_llrs.to(msg_bits.device), decoded_SCL_msg_bits.to(msg_bits.device)
ber_SCL = errors_ber(msg_bits, decoded_SCL_msg_bits.sign()).item()
bler_SCL = errors_bler(msg_bits, decoded_SCL_msg_bits.sign()).item()
else:
SC_llrs, decoded_SC_msg_bits = polar.sc_decode_new(noisy_code, snr, gt)
ber_SC = errors_ber(msg_bits[:, polar.msg_indices], decoded_SC_msg_bits[:, polar.msg_indices].sign()).item()
bler_SC = errors_bler(msg_bits[:, polar.msg_indices], decoded_SC_msg_bits[:, polar.msg_indices].sign()).item()
if run_SCL:
SCL_llrs, decoded_SCL_msg_bits = polar.scl_decode(noisy_code, snr, args.list_size, False)
ber_SCL = errors_ber(msg_bits[:, polar.msg_indices], decoded_SCL_msg_bits[:, polar.msg_indices].sign()).item()
bler_SCL = errors_bler(msg_bits[:, polar.msg_indices], decoded_SCL_msg_bits[:, polar.msg_indices].sign()).item()
if args.loss_only is None:
# start = time.time()
decoded_bits = decoder.decode(net, False, noisy_code)
decoded_RNN_msg_bits = decoded_bits[:, polar.info_positions].sign()
# print('RNN : {}'.format(time.time() - start))
ber_RNN = errors_ber(msg_bits, decoded_RNN_msg_bits.sign()).item()
bler_RNN = errors_bler(msg_bits, decoded_RNN_msg_bits.sign()).item()
if run_RNNL:
decoded_RNNL_msg_bits = decoder.list_decode(net, noisy_code, polar, args.list_size)
ber_RNNL = errors_ber(msg_bits.cpu(), decoded_RNNL_msg_bits.sign()).item()
bler_RNNL = errors_bler(msg_bits.cpu(), decoded_RNNL_msg_bits.sign()).item()
else:
decoded_bits = decoder.decode(net, False, noisy_code, gt, loss_inds=code.loss_inds)
decoded_RNN_msg_bits = decoded_bits[:, polar.info_positions].sign()
ber_RNN = errors_ber(msg_bits[:, polar.msg_indices], decoded_RNN_msg_bits[:, polar.msg_indices].sign()).item()
bler_RNN = errors_bler(msg_bits[:, polar.msg_indices], decoded_RNN_msg_bits[:, polar.msg_indices].sign()).item()
if run_ML:
if args.loss_only is not None:
all_msg_bits = []
for i in range(2**args.loss_only):
d = dec2bitarray(i, args.loss_only)
all_msg_bits.append(d)
all_message_bits = torch.from_numpy(np.array(all_msg_bits))
all_message_bits = 1 - 2*all_message_bits.float()
decoded = torch.zeros(noisy_code.shape[0], args.K)
for jj in range(noisy_code.shape[0]):
#if jj%100 == 0:
# print(jj)
test_msg_bits = msg_bits[jj:jj+1].repeat(all_message_bits.shape[0], 1).cpu()
test_msg_bits[:, code.msg_indices] = all_message_bits
codebook = 0.5*code.encode(test_msg_bits)+0.5
b_codebook = codebook.unsqueeze(0)
b_noisy = noisy_code[jj].cpu()
diff = (b_noisy - b_codebook).pow(2).sum(dim=2)
idx1 = diff.argmin(dim=1)
decoded[jj] = test_msg_bits[idx1, :]
ber_ML = errors_ber(msg_bits[:, polar.msg_indices].to(decoded.device), decoded[:, polar.msg_indices].sign()).item()
bler_ML = errors_bler(msg_bits[:, polar.msg_indices].to(decoded.device), decoded[:, polar.msg_indices].sign()).item()
else:
all_msg_bits = []
for i in range(2**args.K):
d = dec2bitarray(i, args.K)
all_msg_bits.append(d)
all_message_bits = torch.from_numpy(np.array(all_msg_bits))
all_message_bits = 1 - 2*all_message_bits.float()
codebook = code.encode(all_message_bits)
b_codebook = codebook.unsqueeze(0)
if args.N != args.K:
if args.K > 10:
idx = np.zeros(noisy_code.shape[0])
for jj in range(noisy_code.shape[0]):
b_noisy = noisy_code[jj].cpu()
diff = (b_noisy - b_codebook).pow(2).sum(dim=2)
idx1 = diff.argmin(dim=1)
idx[jj] = idx1
decoded = all_message_bits[idx, :]
else:
b_noisy = noisy_code.cpu().unsqueeze(1).repeat(1, 2**args.K, 1)
diff = (b_noisy - b_codebook).pow(2).sum(dim=2)
idx = diff.argmin(dim=1)
decoded = all_message_bits[idx, :]
elif args.N == args.K:
decoded_codeword = noisy_code.sign()
decoded = polar.encode_plotkin(decoded_codeword)
ber_ML = errors_ber(msg_bits[:, polar.msg_indices].to(decoded.device), decoded[:, polar.msg_indices].sign()).item()
bler_ML = errors_bler(msg_bits[:, polar.msg_indices].to(decoded.device), decoded[:, polar.msg_indices].sign()).item()
bers_ML_test[snr_ind] += ber_ML/num_test_batches
blers_ML_test[snr_ind] += bler_ML/num_test_batches
bers_RNN_test[snr_ind] += ber_RNN/num_test_batches
bers_SC_test[snr_ind] += ber_SC/num_test_batches
blers_RNN_test[snr_ind] += bler_RNN/num_test_batches
blers_SC_test[snr_ind] += bler_SC/num_test_batches
if run_SCL:
bers_SCL_test[snr_ind] += ber_SCL/num_test_batches
blers_SCL_test[snr_ind] += bler_SCL/num_test_batches
if run_RNNL:
bers_RNNL_test[snr_ind] += ber_RNNL/num_test_batches
blers_RNNL_test[snr_ind] += bler_RNNL/num_test_batches
# print(ber_SC, ber_ML)
return bers_RNN_test, blers_RNN_test, bers_SC_test, blers_SC_test, bers_SCL_test, blers_SCL_test, bers_RNNL_test, blers_RNNL_test, bers_ML_test, blers_ML_test
def test_model(net, code, snr, bitwise = False, tf = False, data = None):
if data is not None:
msg_bits, noisy_code = data
msg_bits, noisy_code = msg_bits.to(device), noisy_code.to(device)
encoded = code.encode(msg_bits)
else:
msg_bits = 1 - 2 * (torch.rand(args.test_batch_size, code.K, device=device) < 0.5).float()
encoded = code.encode(msg_bits)
noisy_code = code.channel(encoded, snr)#, args.noise_type, args.vv, args.radar_power, args.radar_prob)
gt = torch.ones(msg_bits.shape[0], code.N, device = msg_bits.device)
gt[:, code.info_inds] = msg_bits
if tf:
with torch.no_grad():
decoded_bits = decoder.decode(net, True, noisy_code, gt, 1)
else:
decoded_bits = decoder.decode(net, False, noisy_code)
decoded_RNN_msg_bits = decoded_bits[:, code.info_inds].sign()
ber_RNN = errors_ber(msg_bits, decoded_RNN_msg_bits.sign()).item()
bler_RNN = errors_bler(msg_bits, decoded_RNN_msg_bits.sign()).item()
if not bitwise:
return ber_RNN, bler_RNN
else:
ber_bitwise = errors_bitwise_ber(msg_bits, decoded_RNN_msg_bits.sign())
return ber_RNN, bler_RNN, ber_bitwise
def test_SC(code, snr, bitwise = False):
msg_bits = 1 - 2 * (torch.rand(args.test_batch_size, code.K, device=device) < 0.5).float()
encoded = code.encode(msg_bits)