def test(self, kwargs):
self.reset_meter()
self.model.eval()
test_data = kwargs['test_data']
for data in tqdm(test_data):
imgs, labels = data
if opt.use_gpu:
imgs = imgs.cuda()
labels = labels.cuda()
imgs = Variable(imgs, volatile=True)
labels = Variable(labels, volatile=True)
# Forward.
scores = self.model(imgs)
loss = self.criterion(scores, labels)
# Update to metrics.
pred_labels = scores.max(dim=1)[1].data.cpu().numpy()
pred_labels = [i for i in pred_labels]
true_labels = labels.data.cpu().numpy()
true_labels = [i for i in true_labels]
eval_metrics = eval_semantic_segmentation(pred_labels, true_labels)
self.metric_meter['loss'].add(loss.data[0])
self.metric_meter['acc'].add(eval_metrics['mean_class_accuracy'])
self.metric_meter['iou'].add(eval_metrics['miou'])
# Plot metrics curve in tensorboard.
self.writer.add_scalars('loss',
{'test': self.metric_meter['loss'].value()[0]},
self.n_plot)
self.writer.add_scalars('acc',
{'test': self.metric_meter['acc'].value()[0]},
self.n_plot)
self.writer.add_scalars('iou',
{'test': self.metric_meter['iou'].value()[0]},
self.n_plot)
origin_img = inverse_normalization(imgs[0].cpu().data)
pred_seg = cm[pred_labels[0]]
gt_seg = cm[true_labels[0]]
self.writer.add_image('test ori_img', origin_img, self.n_plot)
self.writer.add_image('test gt', gt_seg, self.n_plot)
self.writer.add_image('test pred', pred_seg, self.n_plot)
self.n_plot += 1
self.metric_log['Test Loss'] = self.metric_meter['loss'].value()[0]
self.metric_log['Test Mean Class Accuracy'] = self.metric_meter[
'acc'].value()[0]
self.metric_log['Test Mean IoU'] = self.metric_meter['iou'].value()[0]
def test(self, kwargs):
self.reset_meter()
self.model.eval()
test_data = kwargs['test_data']
for data in tqdm(test_data):
imgs, labels = data
if opt.use_gpu:
imgs = imgs.cuda()
labels = labels.cuda()
imgs = Variable(imgs, volatile=True)
labels = Variable(labels, volatile=True)
# Forward.
scores = self.model(imgs)
loss = self.criterion(scores, labels)
# Update to metrics.
pred_labels = scores.max(dim=1)[1].data.cpu().numpy()
pred_labels = [i for i in pred_labels]
true_labels = labels.data.cpu().numpy()
true_labels = [i for i in true_labels]
eval_metrics = eval_semantic_segmentation(pred_labels, true_labels)
self.metric_meter['loss'].add(loss.data[0])
self.metric_meter['acc'].add(eval_metrics['mean_class_accuracy'])
self.metric_meter['iou'].add(eval_metrics['miou'])
# Plot metrics curve in tensorboard.
self.writer.add_scalars('loss',
{'test': self.metric_meter['loss'].value()[0]},
self.n_plot)
self.writer.add_scalars(
'acc', {'test': self.metric_meter['acc'].value()[0]}, self.n_plot)
self.writer.add_scalars(
'iou', {'test': self.metric_meter['iou'].value()[0]}, self.n_plot)
origin_img = inverse_normalization(imgs[0].cpu().data)
pred_seg = cm[pred_labels[0]]
gt_seg = cm[true_labels[0]]
self.writer.add_image('test ori_img', origin_img, self.n_plot)
self.writer.add_image('test gt', gt_seg, self.n_plot)
self.writer.add_image('test pred', pred_seg, self.n_plot)
self.n_plot += 1
self.metric_log['Test Loss'] = self.metric_meter['loss'].value()[0]
self.metric_log['Test Mean Class Accuracy'] = self.metric_meter[
'acc'].value()[0]
self.metric_log['Test Mean IoU'] = self.metric_meter['iou'].value()[0]
def train(self, kwargs):
self.reset_meter()
self.model.train()
train_data = kwargs['train_data']
for data in tqdm(train_data):
imgs, labels = data
if opt.use_gpu:
imgs = imgs.cuda()
labels = labels.cuda()
imgs = Variable(imgs)
labels = Variable(labels)
# Forward.
scores = self.model(imgs)
loss = self.criterion(scores, labels)
# Backward.
self.optimizer.zero_grad()
loss.backward()
self.optimizer.step()
# Update to metrics.
pred_labels = scores.max(dim=1)[1].data.cpu().numpy()
pred_labels = [i for i in pred_labels]
true_labels = labels.data.cpu().numpy()
true_labels = [i for i in true_labels]
eval_metrics = eval_semantic_segmentation(pred_labels, true_labels)
self.metric_meter['loss'].add(loss.data[0])
self.metric_meter['acc'].add(eval_metrics['mean_class_accuracy'])
self.metric_meter['iou'].add(eval_metrics['miou'])
if (self.n_iter + 1) % opt.plot_freq == 0:
# Plot metrics curve in tensorboard.
self.writer.add_scalars(
'loss', {'train': self.metric_meter['loss'].value()[0]},
self.n_plot)
self.writer.add_scalars(
'acc', {'train': self.metric_meter['acc'].value()[0]},
self.n_plot)
self.writer.add_scalars(
'iou', {'train': self.metric_meter['iou'].value()[0]},
self.n_plot)
# Show segmentation images.
# Get prediction segmentation and ground truth segmentation.
origin_image = inverse_normalization(imgs[0].cpu().data)
pred_seg = cm[pred_labels[0]]
gt_seg = cm[true_labels[0]]
self.writer.add_image('train ori_img', origin_image,
self.n_plot)
self.writer.add_image('train gt', gt_seg, self.n_plot)
self.writer.add_image('train pred', pred_seg, self.n_plot)
self.n_plot += 1
self.n_iter += 1
self.metric_log['Train Loss'] = self.metric_meter['loss'].value()[0]
self.metric_log['Train Mean Class Accuracy'] = self.metric_meter[
'acc'].value()[0]
self.metric_log['Train Mean IoU'] = self.metric_meter['iou'].value()[0]
def train(self, kwargs):
self.reset_meter()
self.model.train()
train_data = kwargs['train_data']
for data in tqdm(train_data):
imgs, labels = data
if opt.use_gpu:
imgs = imgs.cuda()
labels = labels.cuda()
imgs = Variable(imgs)
labels = Variable(labels)
# Forward.
scores = self.model(imgs)
loss = self.criterion(scores, labels)
# Backward.
self.optimizer.zero_grad()
loss.backward()
self.optimizer.step()
# Update to metrics.
pred_labels = scores.max(dim=1)[1].data.cpu().numpy()
pred_labels = [i for i in pred_labels]
true_labels = labels.data.cpu().numpy()
true_labels = [i for i in true_labels]
eval_metrics = eval_semantic_segmentation(pred_labels, true_labels)
self.metric_meter['loss'].add(loss.data[0])
self.metric_meter['acc'].add(eval_metrics['mean_class_accuracy'])
self.metric_meter['iou'].add(eval_metrics['miou'])
if (self.n_iter + 1) % opt.plot_freq == 0:
# Plot metrics curve in tensorboard.
self.writer.add_scalars(
'loss', {'train': self.metric_meter['loss'].value()[0]},
self.n_plot)
self.writer.add_scalars(
'acc', {'train': self.metric_meter['acc'].value()[0]},
self.n_plot)
self.writer.add_scalars(
'iou', {'train': self.metric_meter['iou'].value()[0]},
self.n_plot)
# Show segmentation images.
# Get prediction segmentation and ground truth segmentation.
origin_image = inverse_normalization(imgs[0].cpu().data)
pred_seg = cm[pred_labels[0]]
gt_seg = cm[true_labels[0]]
self.writer.add_image('train ori_img', origin_image,
self.n_plot)
self.writer.add_image('train gt', gt_seg, self.n_plot)
self.writer.add_image('train pred', pred_seg, self.n_plot)
self.n_plot += 1
self.n_iter += 1
self.metric_log['Train Loss'] = self.metric_meter['loss'].value()[0]
self.metric_log['Train Mean Class Accuracy'] = self.metric_meter[
'acc'].value()[0]
self.metric_log['Train Mean IoU'] = self.metric_meter['iou'].value()[0]