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utils.py
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utils.py
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import argparse
import torch
import numpy as np
import pandas as pd
import random
from torch.autograd import Variable
import h5py
from scipy import signal
import math
import seaborn as sns
from sklearn.metrics import confusion_matrix
import matplotlib.pyplot as plt
from pyphysim.channels.fading import COST259_TUx, COST259_RAx, TdlChannel, TdlChannelProfile
from pyphysim.channels.fading_generators import JakesSampleGenerator, RayleighSampleGenerator
class LoadDataset():
def __init__(self,):
self.dataset_name = 'data'
self.labelset_name = 'label'
def _convert_to_complex(self, data):
'''Convert the loaded data to complex IQ samples.'''
num_row = data.shape[0]
num_col = data.shape[1]
data_complex = np.zeros([num_row,round(num_col/2)],dtype=complex)
data_complex = data[:,:round(num_col/2)] + 1j*data[:,round(num_col/2):]
return data_complex
def load_iq_samples(self, file_path, downsampling=True):
'''
Load IQ samples from a dataset.
INPUT:
FILE_PATH is the dataset path.
DEV_RANGE specifies the loaded device range.
PKT_RANGE specifies the loaded packets range.
RETURN:
DATA is the laoded complex IQ samples.
LABLE is the true label of each received packet.
'''
data = []
label = []
for file_name in file_path:
f = h5py.File(file_name,'r')
label_temp = f[self.labelset_name][:]
label_temp = np.transpose(label_temp)
data_temp = f[self.dataset_name]
data_temp = self._convert_to_complex(data_temp)
if downsampling:
sig_len = data_temp.shape[1]
data_temp = data_temp[:, 0:sig_len:2]
f.close()
data.extend(data_temp)
label.extend(label_temp)
data = np.array(data)
label = np.array(label)
return data, label
def load_iq_samples_range(self, file_path, pkt_range, downsampling=True):
'''
Load IQ samples from a dataset.
INPUT:
FILE_PATH is the dataset path.
DEV_RANGE specifies the loaded device range.
PKT_RANGE specifies the loaded packets range.
RETURN:
DATA is the laoded complex IQ samples.
LABLE is the true label of each received packet.
'''
f = h5py.File(file_path,'r')
label = f[self.labelset_name][:]
label = np.transpose(label)
sample_index_list = []
for dev_idx in np.unique(label):
sample_index_dev = np.where(label==dev_idx)[0][pkt_range].tolist()
sample_index_list.extend(sample_index_dev)
data = f[self.dataset_name][:]
data = data[sample_index_list]
data = self._convert_to_complex(data)
if downsampling:
sig_len = data.shape[1]
data = data[:, 0:sig_len:2]
label = label[sample_index_list]
f.close()
return data, label
def load_multiple_files(self, file_list, tx_range, pkt_range):
# file_list = os.listdir(folder_name)
# file_list = natsorted(file_list, key=lambda y: y.lower())
# file_list = [file_list[i] for i in rx_range]
num_rx = len(file_list)
num_tx = len(tx_range)
num_pkt = len(pkt_range)
data = []
tx_label = []
rx_label = []
for file_idx in range(num_rx):
print('Start loading dataset ' + str(file_idx + 1))
filename = file_list[file_idx]
# filename = folder_name + filename
[data_temp, tx_label_temp, _ ] = self.load_iq_samples(filename, tx_range, pkt_range)
rx_label_temp = np.ones(num_pkt*num_tx)*file_idx
data.extend(data_temp)
tx_label.extend(tx_label_temp)
rx_label.extend(rx_label_temp)
data = np.array(data)
tx_label = np.array(tx_label)
rx_label = np.array(rx_label)
return data, tx_label, rx_label
class LRScheduler:
def __init__(self, optimizer, patience=10, min_lr=1e-6, factor=0.1):
self.optimizer = optimizer
self.patience = patience
self.min_lr = min_lr
self.factor = factor
self.lr_scheduler = torch.optim.lr_scheduler.ReduceLROnPlateau(
self.optimizer,
mode='min',
patience=self.patience,
factor=self.factor,
min_lr=self.min_lr,
verbose=True
)
def __call__(self, val_loss):
self.lr_scheduler.step(val_loss)
class EarlyStopping:
def __init__(self, patience=20, min_delta=0):
self.min_delta = min_delta
self.patience = patience
self.counter = 0
self.best_loss = None
self.early_stop = False
def __call__(self, val_loss):
if self.best_loss is None:
self.best_loss = val_loss
elif self.best_loss - val_loss > self.min_delta:
self.best_loss = val_loss
self.counter = 0
elif self.best_loss - val_loss < self.min_delta:
self.counter += 1
# print(f"INFO: Early stopping counter {self.counter} of {self.patience}")
if self.counter >= self.patience:
print('INFO: Early stopping')
self.early_stop = True
def plt_cm(true_label, pred_label, num_predictable_classes):
conf_mat = confusion_matrix(true_label, pred_label)
classes = range(1, num_predictable_classes+1)
plt.figure(figsize=(4, 3))
sns.heatmap(conf_mat, annot=True,
fmt='d', cmap='Blues',
cbar=False,
xticklabels=classes,
yticklabels=classes)
plt.xlabel('Predicted Label', fontsize=14)
plt.ylabel('True Label', fontsize=14)
plt.tight_layout()
plt.savefig('confusion_matrix.pdf', bbox_inches='tight')
plt.show(block=True)
def to_onehot(label_in):
u, label_int = np.unique(label_in, return_inverse=True)
num_classes = len(u)
label_one_hot = np.eye(num_classes, dtype='uint8')[label_int]
return label_one_hot, num_classes