def hungarian_evaluate(subhead_index,
all_predictions,
class_names=None,
compute_purity=True,
compute_confusion_matrix=True,
confusion_matrix_file=None):
# Evaluate model based on hungarian matching between predicted cluster assignment and gt classes.
# This is computed only for the passed subhead index.
# Hungarian matching
head = all_predictions[subhead_index]
targets = head['targets'].cuda()
predictions = head['predictions'].cuda()
probs = head['probabilities'].cuda()
num_classes = torch.unique(targets).numel()
num_elems = targets.size(0)
match = _hungarian_match(predictions,
targets,
preds_k=num_classes,
targets_k=num_classes)
reordered_preds = torch.zeros(num_elems, dtype=predictions.dtype).cuda()
for pred_i, target_i in match:
reordered_preds[predictions == int(pred_i)] = int(target_i)
# Gather performance metrics
acc = int((reordered_preds == targets).sum()) / float(num_elems)
nmi = metrics.normalized_mutual_info_score(targets.cpu().numpy(),
predictions.cpu().numpy())
ari = metrics.adjusted_rand_score(targets.cpu().numpy(),
predictions.cpu().numpy())
report = metrics.classification_report(targets.cpu().numpy(),
reordered_preds.cpu().numpy(),
target_names=class_names)
print(report)
_, preds_top5 = probs.topk(5, 1, largest=True)
reordered_preds_top5 = torch.zeros_like(preds_top5)
for pred_i, target_i in match:
reordered_preds_top5[preds_top5 == int(pred_i)] = int(target_i)
correct_top5_binary = reordered_preds_top5.eq(
targets.view(-1, 1).expand_as(reordered_preds_top5))
top5 = float(correct_top5_binary.sum()) / float(num_elems)
# Compute confusion matrix
if compute_confusion_matrix:
confusion_matrix(reordered_preds.cpu().numpy(),
targets.cpu().numpy(), class_names,
confusion_matrix_file)
return {
'ACC': acc,
'ARI': ari,
'NMI': nmi,
'ACC Top-5': top5,
'hungarian_match': match
}
def validate(model, cfg):
torch.backends.cudnn.benchmark = True
val_dataset = Seg_dataset(cfg)
val_loader = data.DataLoader(val_dataset,
batch_size=1,
shuffle=False,
num_workers=4,
pin_memory=True)
total_batch = int(len(val_dataset)) + 1
hist = np.zeros((cfg.class_num, cfg.class_num))
with torch.no_grad():
for i, (img, label) in enumerate(val_loader):
image = img.cuda().detach()
output = model(image)
pred = torch.max(output, 1)[1].cpu().numpy().astype('int32')
label = label.numpy().astype('int32')
hist += confusion_matrix(pred.flatten(), label.flatten(),
cfg.class_num)
ious = per_class_iou(hist) * 100
miou = np.nanmean(ious)
print(f'\rBatch: {i + 1}/{total_batch}, mIOU: {miou:.2f}', end='')
print('\nPer class iou:')
for i, iou in enumerate(ious):
print(f'{i}: {iou:.2f}')
return miou
def train_(train_iter, net, opt, loss_function, loss_type, ind_ignore,
n_classes):
net.train()
train_loss = 0
total = 0
# Create the confusion matrix
cm = np.zeros((n_classes, n_classes))
nTrain = train_iter.nbatches
for batch_idx in range(nTrain):
all_data = train_iter.next()
data = all_data[0]
target = all_data[1]
data, target = data.transpose((0, 3, 1, 2)), target.transpose(
(0, 3, 1, 2))
data, target = torch.from_numpy(data), torch.from_numpy(target)
data, target = data.cuda(), target.cuda()
data, target = Variable(data), Variable(target)
opt.zero_grad()
output = net(data)
target = target.type(torch.FloatTensor).cuda()
_, target_indices = torch.max(target, 1)
_, output_indices = torch.max(output, 1)
flattened_output = output_indices.view(-1)
flattened_target = target_indices.view(-1)
if loss_type == 'cce_soft':
loss = cce_soft(output, target, ignore_label=ind_ignore)
else:
loss = loss_function(output, target_indices)
cm = confusion_matrix(cm,
flattened_output.data.cpu().numpy(),
flattened_target.data.cpu().numpy(), n_classes)
loss.backward()
nn.utils.clip_grad_norm(net.parameters(), max_norm=4)
opt.step()
train_loss += loss.data[0]
_, predicted = torch.max(output.data, 1)
total += target.size(0)
progress_bar(batch_idx, nTrain,
'Loss: %.3f' % (train_loss / (batch_idx + 1)))
del (output)
del (loss)
del (flattened_output)
del (output_indices)
jaccard_per_class, jaccard, accuracy = compute_metrics(cm)
metrics_string = print_metrics(train_loss, nTrain, n_classes,
jaccard_per_class, jaccard, accuracy)
print(metrics_string)
return jaccard, jaccard_per_class, accuracy, train_loss / (nTrain)
def hungarian_evaluate2(subhead_index,
all_predictions,
class_names=None,
compute_purity=True,
compute_confusion_matrix=True,
confusion_matrix_file=None):
# Evaluate model based on hungarian matching between predicted cluster assignment and gt classes.
# This is computed only for the passed subhead index.
# Hungarian matching
head = all_predictions[subhead_index]
targets = head['targets'].cuda()
predictions = head['predictions'].cuda()
num_classes = torch.unique(targets).numel()
num_elems = targets.size(0)
match = _hungarian_match(predictions,
targets,
preds_k=num_classes,
targets_k=num_classes)
reordered_preds = torch.zeros(num_elems, dtype=predictions.dtype).cuda()
for pred_i, target_i in match:
reordered_preds[predictions == int(pred_i)] = int(target_i)
# Gather performance metrics
acc = int((reordered_preds == targets).sum()) / float(num_elems)
nmi = metrics.normalized_mutual_info_score(targets.cpu().numpy(),
predictions.cpu().numpy())
ari = metrics.adjusted_rand_score(targets.cpu().numpy(),
predictions.cpu().numpy())
# Compute confusion matrix
if compute_confusion_matrix:
confusion_matrix(reordered_preds.cpu().numpy(),
targets.cpu().numpy(), class_names,
confusion_matrix_file)
return {'ACC': acc, 'ARI': ari, 'NMI': nmi, 'hungarian_match': match}
def hungarian_evaluate(subhead_index,
all_predictions,
class_names=None,
compute_purity=True,
compute_confusion_matrix=True,
confusion_matrix_file=None,
tf_writer=None,
epoch=0):
# Evaluate model based on hungarian matching between predicted cluster assignment and gt classes.
# This is computed only for the passed subhead index.
# Hungarian matching
head = all_predictions[subhead_index]
targets = head['targets'].cuda()
predictions = head['predictions'].cuda()
probs = head['probabilities'].cuda()
num_classes = torch.unique(targets).numel()
num_elems = targets.size(0)
match = _hungarian_match(predictions,
targets,
preds_k=num_classes,
targets_k=num_classes)
reordered_preds = torch.zeros(num_elems, dtype=predictions.dtype).cuda()
for pred_i, target_i in match:
reordered_preds[predictions == int(pred_i)] = int(target_i)
# Gather performance metrics
acc = int((reordered_preds == targets).sum()) / float(num_elems)
nmi = metrics.normalized_mutual_info_score(targets.cpu().numpy(),
predictions.cpu().numpy())
ari = metrics.adjusted_rand_score(targets.cpu().numpy(),
predictions.cpu().numpy())
_, preds_top5 = probs.topk(min(5, num_classes), 1, largest=True)
reordered_preds_top5 = torch.zeros_like(preds_top5)
for pred_i, target_i in match:
reordered_preds_top5[preds_top5 == int(pred_i)] = int(target_i)
correct_top5_binary = reordered_preds_top5.eq(
targets.view(-1, 1).expand_as(reordered_preds_top5))
top5 = float(correct_top5_binary.sum()) / float(num_elems)
reordered_preds = reordered_preds.cpu().numpy()
targets = targets.cpu().numpy()
if tf_writer is not None:
from sklearn.metrics import precision_score, recall_score, f1_score
precision = precision_score(targets,
reordered_preds,
average=None,
zero_division=0)
recall = recall_score(targets,
reordered_preds,
average=None,
zero_division=0)
f1 = f1_score(targets, reordered_preds, average=None, zero_division=0)
tf_writer.add_scalar('Evaluate/ACC', acc, epoch)
tf_writer.add_scalar('Evaluate/NMI', nmi, epoch)
tf_writer.add_scalar('Evaluate/ARI', ari, epoch)
for i in range(len(f1)):
tf_writer.add_scalar(f'Evaluate/f1_{i}', f1[i], epoch)
tf_writer.add_scalar(f'Evaluate/precision_{i}', precision[i],
epoch)
tf_writer.add_scalar(f'Evaluate/recall_{i}', recall[i], epoch)
# if epoch % cfg.embedding_freq == 0:
# tf_writer.add_embedding(intermediates, labels, images, epoch, cfg.session)
# Visualize confusion matrix with matplotlib
if compute_confusion_matrix:
confusion_matrix(reordered_preds, targets, class_names,
confusion_matrix_file)
return {
'ACC': acc,
'ARI': ari,
'NMI': nmi,
'ACC Top-5': top5,
'hungarian_match': match
}
def test_(test_iter, net, experiment_dir_final, loss_function, loss_type,
void_labels, save_test_images, n_classes):
ckt_names = ['best_jaccard.t7']
for ckt_name in ckt_names:
print('Testing checkpoint ' + ckt_name)
checkpoint = torch.load(
os.path.join(experiment_dir_final, 'checkpoint', ckt_name))
print('Checkpoint loaded for testing...')
net.load_state_dict(checkpoint['net'])
net.eval()
test_loss = 0
total = 0
# Create the confusion matrix
cm = np.zeros((n_classes, n_classes))
nTest = test_iter.nbatches
for batch_idx in range(nTest):
all_data = test_iter.next()
data_ = all_data[0]
target_ = all_data[1]
data, target = data_.transpose((0, 3, 1, 2)), target_.transpose(
(0, 3, 1, 2))
data, target = torch.from_numpy(data), torch.from_numpy(target)
data, target = data.cuda(), target.cuda()
data, target = Variable(data), Variable(target)
output = net(data)
target = target.type(torch.LongTensor).cuda()
_, target_indices = torch.max(target, 1)
_, output_indices = torch.max(output, 1)
flattened_output = output_indices.view(-1)
flattened_target = target_indices.view(-1)
loss = loss_function(output, target_indices)
cm = confusion_matrix(cm,
flattened_output.data.cpu().numpy(),
flattened_target.data.cpu().numpy(),
n_classes)
test_loss += loss.data[0]
_, predicted = torch.max(output.data, 1)
total += target.size(0)
progress_bar(batch_idx, test_iter.nbatches,
'Test loss: %.3f' % (test_loss / (batch_idx + 1)))
if save_test_images:
save_images(data_, target_, output, experiment_dir_final,
batch_idx, void_labels)
del (output)
del (loss)
del (flattened_output)
del (output_indices)
jaccard_per_class, jaccard, accuracy = compute_metrics(cm)
metrics_string = print_metrics(test_loss, nTest, n_classes,
jaccard_per_class, jaccard, accuracy)
print(metrics_string)
def val_(val_iter, net, opt, loss_function, loss_type, epoch, es_step,
ind_ignore, experiment_dir, max_patience, best_jacc, n_classes):
code = 0
net.eval()
test_loss = 0
total = 0
# Create the confusion matrix
cm = np.zeros((n_classes, n_classes))
nVal = val_iter.nbatches
for batch_idx in range(nVal):
all_data = val_iter.next()
data = all_data[0]
target = all_data[1]
data, target = data.transpose((0, 3, 1, 2)), target.transpose(
(0, 3, 1, 2))
data, target = torch.from_numpy(data), torch.from_numpy(target)
data, target = data.cuda(), target.cuda()
data, target = Variable(data), Variable(target)
output = net(data)
target = target.type(torch.FloatTensor).cuda()
_, target_indices = torch.max(target, 1)
_, output_indices = torch.max(output, 1)
flattened_output = output_indices.view(-1)
flattened_target = target_indices.view(-1)
if loss_type == 'cce_soft':
loss = cce_soft(output, target, ignore_label=ind_ignore)
else:
loss = loss_function(output, target_indices)
cm = confusion_matrix(cm,
flattened_output.data.cpu().numpy(),
flattened_target.data.cpu().numpy(), n_classes)
test_loss += loss.data[0]
_, predicted = torch.max(output.data, 1)
total += target.size(0)
progress_bar(batch_idx, val_iter.nbatches,
'Val loss: %.3f' % (test_loss / (batch_idx + 1)))
del (output)
del (loss)
del (flattened_output)
del (output_indices)
jaccard_per_class, jaccard, accuracy = compute_metrics(cm)
metrics_string = print_metrics(test_loss, nVal, n_classes,
jaccard_per_class, jaccard, accuracy)
print(metrics_string)
es_step, best_jacc = save_checkpoints(jaccard, net, epoch, opt,
experiment_dir, best_jacc, es_step)
# Early stopping
if es_step >= max_patience:
print('Early stopping! Max mean jaccard: ' + str(best_jacc))
code = 1
return es_step, best_jacc, code, jaccard, jaccard_per_class, accuracy, \
test_loss / (nVal)
