shuffle = False
# Creating data indices for training and validation splits:
dataset_size = len(data)
indices = list(range(dataset_size))
validation_split = 0.2
split = int(np.floor(validation_split * dataset_size))
print(split)
if shuffle:
np.random.seed(random_seed)
np.random.shuffle(indices)
train_indices, val_indices = indices[split:], indices[:split]
# Creating PT data samplers and loaders:
train_sampler = SubsetRandomSampler(train_indices)
valid_sampler = SubsetRandomSampler(val_indices)
trainloader = dataset.CSILoader(data, opt,sampler=train_sampler)
testloader = dataset.CSILoader(data, opt,sampler=valid_sampler)
print('==> Building model..')
# net = VGG('VGG19')
net = vgg.VGG('VGG11')
# net = ResNet18()
# net = GoogLeNet()
# net = DenseNet121()
# net = ResNeXt29_2x64d()
# net = MobileNet()
# net = DPN92()
# net = ShuffleNetG2()
# net = SENet18()
shuffle = True
# Creating data indices for training and validation splits:
data_train = dataset.CSISet(data_x_train, data_y_train)
data_test = dataset.CSISet(data_x_test, data_y_test)
# dataset_size = len(data)
# indices = list(range(dataset_size))
# split = 0.8
# split = int(np.floor(split * dataset_size))
# print(split)
# if shuffle:
# np.random.seed(opt.seed)
# np.random.shuffle(indices)
#train_indices, val_indices = indices[split:], indices[:split]
# train_indices, val_indices = indices[:split], indices[split:]
trainloader = dataset.CSILoader(data_train, opt, shuffle=True)
testloader = dataset.CSILoader(data_test, opt, shuffle=True)
print('==> Building model..')
net = vgg.VGG('VGG11', linear_in=2048)
# net = ResNet18()
#net = LeNet.LeNet(in_channel=3, linear_in=9216)
# net = DenseNet121()
# net = ResNeXt29_2x64d()
# net = MobileNet()
# net = DPN92()
# net = ShuffleNetG2()
# net = SENet18()
# result_folder = './results/'
# if not os.path.exists(result_folder):