cam1031000-20140318120853-tarid82-frame3165-line1-pos744-12-822-266-box723-6-108-255.png'
)
gt_im = Image.open(
'/home/kidd/kidd1/Occ5000/Occ4000/annotationsLast/CAM31-2014-03-18-20140318120853-20140318121449-bak\
_floor4-cam1031000-20140318120853-tarid82-frame3165-line1-pos744-12-822-266-box723-6-108-255.png'
)
gt = np.array(gt_im)
gt_rgb = decode_segmap(gt, dataset="occ5000")
# Inference and set the visual color map
inputs = transform(image).to(device)
output = model(inputs.unsqueeze(0)).squeeze().cpu().numpy()
pred = np.argmax(output, axis=0)
pred_rgb = decode_segmap(pred, dataset="occ5000")
plt.subplot(1, 3, 1)
plt.imshow(image)
plt.subplot(1, 3, 2)
plt.imshow(gt_rgb)
plt.subplot(1, 3, 3)
plt.imshow(pred_rgb)
plt.show()
eval = Evaluator(13)
eval.reset()
eval.add_batch(gt, pred)
miou = eval.Mean_Intersection_over_Union()
print(miou)
class_miou = eval.Class_Intersection_over_Union()
print(class_miou)
class Trainer(object):
def __init__(self, args):
self.args = args
# Define Saver
self.saver = Saver(args)
self.saver.save_experiment_config()
# Define Tensorboard Summary
self.summary = TensorboardSummary(self.saver.experiment_dir)
self.writer = self.summary.create_summary()
# Define Dataloader
kwargs = {'num_workers': args.workers, 'pin_memory': True}
self.train_loader, self.val_loader, self.test_loader, self.nclass = make_data_loader(args, **kwargs)
# Define network
model = DFPENet(num_classes=self.nclass,
backbone=args.backbone,
output_stride=args.out_stride,
sync_bn=args.sync_bn,
freeze_bn=args.freeze_bn)
train_params = [{'params': model.get_1x_lr_params(), 'lr': args.lr},
{'params': model.get_10x_lr_params(), 'lr': args.lr * 10}]
# Define Optimizer
optimizer = torch.optim.SGD(train_params, momentum=args.momentum,
weight_decay=args.weight_decay, nesterov=args.nesterov)
# Define Criterion
# whether to use class balanced weights
if args.use_balanced_weights:
classes_weights_path = os.path.join(Path.db_root_dir(args.dataset), args.dataset+'_classes_weights.npy')
if os.path.isfile(classes_weights_path):
weight = np.load(classes_weights_path)
else:
weight = calculate_weigths_labels(args.dataset, self.train_loader, self.nclass)
weight = torch.from_numpy(weight.astype(np.float32))
else:
weight = None
self.criterion = SegmentationLosses(weight=weight, cuda=args.cuda).build_loss(mode=args.loss_type)
self.model, self.optimizer = model, optimizer
# Define Evaluator
self.evaluator = Evaluator(self.nclass)
# Define lr scheduler
self.scheduler = LR_Scheduler(args.lr_scheduler, args.lr,
args.epochs, len(self.train_loader))
# Using cuda
if args.cuda:
self.model = self.model.cuda()
self.model = torch.nn.DataParallel(self.model, device_ids=self.args.gpu_ids)
patch_replication_callback(self.model)
# Resuming checkpoint
self.best_pred = 0.0
if args.resume is not None:
if not os.path.isfile(args.resume):
raise RuntimeError("=> no checkpoint found at '{}'" .format(args.resume))
checkpoint = torch.load(args.resume)
args.start_epoch = checkpoint['epoch']
if args.cuda:
self.model.load_state_dict(checkpoint['state_dict'])
else:
self.model.load_state_dict(checkpoint['state_dict'])
if not args.ft:
self.optimizer.load_state_dict(checkpoint['optimizer'])
#self.best_pred = checkpoint['best_pred']
print("=> loaded checkpoint '{}' (epoch {})"
.format(args.resume, checkpoint['epoch']))
# Clear start epoch if fine-tuning
if args.ft:
args.start_epoch = 0
def training(self, epoch):
train_loss = 0.0
self.model.train()
tbar = tqdm(self.train_loader)
num_img_tr = len(self.train_loader)
for i, sample in enumerate(tbar):
image, target = sample['image'], sample['label']
if self.args.cuda:
image, target = image.cuda(), target.cuda()
self.scheduler(self.optimizer, i, epoch, self.best_pred)
self.optimizer.zero_grad()
output = self.model(image)
loss = self.criterion(output, target)
loss.backward()
self.optimizer.step()
train_loss += loss.item()
tbar.set_description('Train loss: %.3f' % (train_loss / (i + 1)))
self.writer.add_scalar('train/total_loss_iter', loss.item(), i + num_img_tr * epoch)
# Show 10 * 3 inference results each epoch
if i % (num_img_tr // 1) == 0:
global_step = i + num_img_tr * epoch
self.summary.visualize_image(self.writer, self.args.dataset, image, target, output, global_step)
self.writer.add_scalar('train/total_loss_epoch', train_loss, epoch)
print('[Epoch: %d, numImages: %5d]' % (epoch, i * self.args.batch_size + image.data.shape[0]))
print('Loss: %.3f' % train_loss)
if self.args.no_val:
# save checkpoint every epoch
is_best = False
self.saver.save_checkpoint({
'epoch': epoch + 1,
'state_dict': self.model.state_dict(),
'optimizer': self.optimizer.state_dict(),
'best_pred': self.best_pred,
}, is_best)
def validation(self, epoch):
self.model.eval()
self.evaluator.reset()
tbar = tqdm(self.val_loader, desc='\r')
test_loss = 0.0
for i, sample in enumerate(tbar):
image, target = sample['image'], sample['label']
if self.args.cuda:
image, target = image.cuda(), target.cuda()
with torch.no_grad():
output = self.model(image)
loss = self.criterion(output, target)
test_loss += loss.item()
tbar.set_description('Test loss: %.3f' % (test_loss / (i + 1)))
pred = output.data.cpu().numpy()
target = target.cpu().numpy()
pred = np.argmax(pred, axis=1)
# Add batch sample into evaluator
self.evaluator.add_batch(target, pred)
if epoch==200:
self.evaluator.plot_confusion_matrix(epoch)
# Fast test during the training
Rec = self.evaluator.Pixel_Accuracy_ALLClass()
Pre = self.evaluator.Pixel_Precision_ALLClass()
F1 = self.evaluator.F1_ALLClass()
F1_mean = self.evaluator.F1_MEANClass()
IoU = self.evaluator.Class_Intersection_over_Union()
Acc = self.evaluator.Pixel_Accuracy()
Acc_class = self.evaluator.Pixel_Accuracy_Class()
mIoU = self.evaluator.Mean_Intersection_over_Union()
FWIoU = self.evaluator.Frequency_Weighted_Intersection_over_Union()
self.writer.add_scalar('val/total_loss_epoch', test_loss, epoch)
self.writer.add_scalar('val/Rec[1]', Rec[1], epoch)
self.writer.add_scalar('val/Pre[1]', Pre[1], epoch)
self.writer.add_scalar('val/F1[0]', F1[0], epoch)
self.writer.add_scalar('val/F1[1]', F1[1], epoch)
self.writer.add_scalar('val/F1_mean', F1_mean, epoch)
self.writer.add_scalar('val/IoU[0]', IoU[0], epoch)
self.writer.add_scalar('val/IoU[1]', IoU[1], epoch)
self.writer.add_scalar('val/mIoU', mIoU, epoch)
self.writer.add_scalar('val/Acc', Acc, epoch)
self.writer.add_scalar('val/Acc_class', Acc_class, epoch)
self.writer.add_scalar('val/fwIoU', FWIoU, epoch)
print('Validation:')
print('[Epoch: %d, numImages: %5d]' % (epoch, i * self.args.batch_size + image.data.shape[0]))
print("F1[0]:{}, F1[1]:{}, F1_mean: {}".format(F1[0], F1[1], F1_mean, ))
print("IoU[0]:{}, IoU[1]:{}, mIoU: {}".format(IoU[0], IoU[1], mIoU))
print("Acc:{}, Acc_class:{}, mIoU:{}, fwIoU: {}".format(Acc, Acc_class, mIoU, FWIoU))
print("Rec[1]:{}, Pre[1]:{}".format(Rec[1], Pre[1]))
print('Loss: %.3f' % test_loss)
filename = "./rec.txt"
with open(filename,'a', encoding='utf-8') as f:
f.writelines(str(Rec[1])+'\n')
filename1 = "./pre.txt"
with open(filename1,'a', encoding='utf-8') as f1:
f1.writelines(str(Pre[1])+'\n')
filename2 = "./miou.txt"
with open(filename2,'a', encoding='utf-8') as f2:
f2.writelines(str(IoU[1])+'\n')
new_pred = IoU[1]
if new_pred > self.best_pred:
is_best = True
self.best_pred = new_pred
self.saver.save_checkpoint({
'epoch': epoch + 1,
'state_dict': self.model.state_dict(),
'optimizer': self.optimizer.state_dict(),
'best_pred': self.best_pred,
}, is_best)
