def eval_on_validation():
if not os.path.exists(config.run_dir):
os.mkdir(config.run_dir)
model = getattr(models, config.model)()
model = torch.nn.DataParallel(model) # multi-gpu
model.cuda()
print 'test on validation set.', config.model
print model
if config.load_model_path:
model.load_state_dict(torch.load(config.load_model_path))
# data
test_data = KITTIRoadFusion(config.root, split='val', num_features=19)
test_dataloader = DataLoader(test_data, batch_size=1, shuffle=False, num_workers=4)
# test
model.eval()
eval_acc = 0
eval_acc_cls = 0
eval_mean_iu = 0
eval_fwavacc = 0
li_pred = []
li_gt = []
total_time = 0
for i, (name, im, cloud, theta, shift, lb) in enumerate(test_dataloader):
im, cloud, theta, shift, lb = Variable(im), Variable(cloud), Variable(theta), Variable(shift), Variable(lb)
im, cloud, theta, shift, lb = im.float().cuda(), cloud.float().cuda(), theta.float().cuda(), shift.float().cuda(), lb.long().cuda()
start = time.clock()
_, pred = model(im, cloud, theta, shift) # inference
end = time.clock()
total_time += (end-start)
# pred = F.upsample_bilinear(pred, scale_factor=4)
# save image
label_pred = pred.data.cpu().numpy().squeeze()
label_pred = np.array(label_pred*255, dtype=np.uint8)
filename = os.path.join(config.run_dir, name[0])
print filename
# cv2.imwrite(filename, label_pred)
# Mean IoU
label_true = lb.data.cpu().numpy().astype(np.int8)
label_pred = pred.data.cpu().numpy().squeeze(0)
label_pred = (label_pred > 0.5).astype(np.int8)
for (label, prob) in zip(label_true, label_pred):
acc, acc_cls, mean_iu, fwavacc = label_accuracy_score(label, prob, n_class=2)
eval_acc += acc
eval_acc_cls += acc_cls
eval_mean_iu += mean_iu
eval_fwavacc += fwavacc
# MaxF
label_pred = pred.data.cpu().numpy().squeeze()
label_true = lb.data.cpu().numpy().squeeze()
li_pred.append(label_pred)
li_gt.append(label_true)
print 'Runtime ############# time(s) : %f ##########' % (total_time / test_dataloader.__len__())
print 'Validation ======ACC: %lf,Mean IoU: %lf======' % (eval_acc / test_dataloader.__len__(),
eval_mean_iu / test_dataloader.__len__())
eval_road(li_pred, li_gt)
def eval_on_validation_bev():
if not os.path.exists(config.run_dir):
os.mkdir(config.run_dir)
model = getattr(models, config.model)()
model = torch.nn.DataParallel(model) # multi-gpu
model.cuda()
print 'test on validation set.', config.model
print model
if config.load_model_path:
model.load_state_dict(torch.load(config.load_model_path))
# data
bev = BirdsEyeView()
test_data = KITTIRoadFusion(config.root, split='val', num_features=19, return_bev=True)
test_dataloader = DataLoader(test_data, batch_size=1, shuffle=False, num_workers=4)
# test
model.eval()
eval_acc = 0
eval_acc_cls = 0
eval_mean_iu = 0
eval_fwavacc = 0
li_pred = []
li_gt = []
for i, (name, im, cloud, theta, shift, _, lb) in enumerate(test_dataloader):
im, cloud, theta, shift, lb = Variable(im), Variable(cloud), Variable(theta), Variable(shift), Variable(lb)
im, cloud, theta, shift, lb = im.float().cuda(), cloud.float().cuda(), theta.float().cuda(), shift.float().cuda(), lb.long().cuda()
_, pred = model(im, cloud, theta, shift) # inference
pred = pred.data.cpu().numpy().squeeze()
theta = theta.data.cpu().numpy().squeeze()
shift = shift.data.cpu().numpy().squeeze()
label_pred = bev.transformLable2BEV((pred*255).astype(np.uint8), theta, shift)
label_true = lb.data.cpu().numpy().squeeze()
# save image
filename = os.path.join(config.run_dir, name[0])
print filename
cv2.imwrite(filename, label_pred)
label_pred = label_pred/255.
# Mean IoU
label_true_hard = np.expand_dims(label_true.astype(np.int8), axis=0)
label_pred_hard = np.expand_dims((label_pred > 0.5).astype(np.int8), axis=0)
for (label, prob) in zip(label_true_hard, label_pred_hard):
acc, acc_cls, mean_iu, fwavacc = label_accuracy_score(label, prob, n_class=2)
eval_acc += acc
eval_acc_cls += acc_cls
eval_mean_iu += mean_iu
eval_fwavacc += fwavacc
# MaxF
li_pred.append(label_pred)
li_gt.append(label_true)
print 'Validation ======ACC: %lf,Mean IoU: %lf======' % (eval_acc / test_dataloader.__len__(),
eval_mean_iu / test_dataloader.__len__())
eval_road(li_pred, li_gt)
def val(model, dataloader):
model.eval()
eval_acc = 0
eval_acc_cls = 0
eval_mean_iu = 0
eval_fwavacc = 0
li_pred = []
li_gt = []
for i, (_, im, cloud, theta, shift, lb) in enumerate(dataloader):
im, cloud, theta, shift, lb = Variable(im), Variable(cloud), Variable(theta), Variable(shift), Variable(lb)
im, cloud, theta, shift, lb = im.float().cuda(), cloud.float().cuda(), theta.float().cuda(), shift.float().cuda(), lb.long().cuda()
_, pred = model(im, cloud, theta, shift)
# Mean IoU
label_true = lb.data.cpu().numpy().astype(np.int8)
label_pred = pred.data.cpu().numpy().squeeze(0)
label_pred = (label_pred > 0.5).astype(np.int8)
for (label, prob) in zip(label_true, label_pred):
acc, acc_cls, mean_iu, fwavacc = label_accuracy_score(label, prob, n_class=2)
eval_acc += acc
eval_acc_cls += acc_cls
eval_mean_iu += mean_iu
eval_fwavacc += fwavacc
# MaxF
label_pred = pred.data.cpu().numpy().squeeze()
label_true = lb.data.cpu().numpy().squeeze()
li_pred.append(label_pred)
li_gt.append(label_true)
print 'Validation ======ACC: %lf,Mean IoU: %lf======' % (eval_acc/dataloader.__len__(),
eval_mean_iu/dataloader.__len__())
max_f = eval_road(li_pred, li_gt)
model.train()
return max_f
