def test(testloader, net, criterion, epoch, use_cuda, _delta, pairs, numeval,
flag, logger, viz_method):
net.eval()
original = []
features = []
labels = []
for batch_idx, (inputs, targets) in enumerate(testloader):
if use_cuda:
inputs = inputs.cuda()
with torch.no_grad():
inputs_Var = Variable(inputs)
enc, dec = net(inputs_Var)
features += list(enc.data.cpu().numpy())
labels += list(targets)
original += list(inputs.cpu().numpy())
original, features, labels = np.asarray(original).astype(np.float32), np.asarray(features).astype(np.float32), \
np.asarray(labels)
U = criterion.U.data.cpu().numpy()
change_in_assign = 0
assignment = -np.ones(len(labels))
if logger and epoch % 3 == 0:
logger.log_images('representatives',
plot_to_image(U, 'representatives', viz_method),
epoch)
# logs clustering measures only if sigma2 has reached the minimum (delta2)
if flag:
index, ari, ami, nmi, acc, n_components, assignment = computeObj(
U, pairs, _delta, labels, numeval)
# log to TensorBoard
change_in_assign = np.abs(oldassignment - index).sum()
if logger:
logger.log_value('ARI', ari, epoch)
logger.log_value('AMI', ami, epoch)
logger.log_value('NMI', nmi, epoch)
logger.log_value('ACC', acc, epoch)
logger.log_value('Numcomponents', n_components, epoch)
logger.log_value('labeldiff', change_in_assign, epoch)
oldassignment[...] = index
return features, U, change_in_assign, assignment
def test(testloader, net, criterion, epoch, use_cuda, _delta, pairs, numeval,
flag):
net.eval()
original = []
features = []
labels = []
for batch_idx, (inputs, targets) in enumerate(testloader):
if use_cuda:
inputs = inputs.cuda()
inputs_Var = Variable(inputs, volatile=True)
enc, dec = net(inputs_Var)
features += list(enc.data.cpu().numpy())
labels += list(targets)
original += list(inputs.cpu().numpy())
original, features, labels = np.asarray(original).astype(np.float32), np.asarray(features).astype(np.float32), \
np.asarray(labels)
U = criterion.U.data.cpu().numpy()
change_in_assign = 0
# logs clustering measures only if sigma2 has reached the minimum (delta2)
if flag:
index, ari, ami, nmi, acc, n_components = computeObj(
U, pairs, _delta, labels, numeval)
# log to TensorBoard
change_in_assign = np.abs(oldassignment - index).sum()
if args.tensorboard:
log_value('ARI', ari, epoch)
log_value('AMI', ami, epoch)
log_value('NMI', nmi, epoch)
log_value('ACC', acc, epoch)
log_value('Numcomponents', n_components, epoch)
log_value('labeldiff', change_in_assign, epoch)
oldassignment[...] = index
return features, U, change_in_assign