def processImage(infile, args):
n_classes = 12
model = LinkNet(n_classes)
model.load_state_dict(torch.load(args.model_path))
if torch.cuda.is_available():
model = model.cuda(0)
model.eval()
gif = cv2.VideoCapture(infile)
cv2.namedWindow('camvid')
while (gif.isOpened()):
ret, frame = gif.read()
frame = cv2.resize(frame, (768, 576))
images = get_tensor(frame)
if torch.cuda.is_available():
images = Variable(images.cuda(0))
else:
images = Variable(images)
outputs = model(images)
pred = outputs.data.max(1)[1].cpu().numpy().reshape(576, 768)
pred = decode_segmap(pred)
vis = np.zeros((576, 1536, 3), np.uint8)
vis[:576, :768, :3] = frame
vis[:576, 768:1536, :3] = pred
cv2.imshow('camvid', vis)
cv2.waitKey(10)
def build_model(model_name, num_classes):
if model_name == 'SQNet':
return SQNet(classes=num_classes)
elif model_name == 'LinkNet':
return LinkNet(classes=num_classes)
elif model_name == 'SegNet':
return SegNet(classes=num_classes)
elif model_name == 'UNet':
return UNet(classes=num_classes)
elif model_name == 'ENet':
return ENet(classes=num_classes)
elif model_name == 'ERFNet':
return ERFNet(classes=num_classes)
elif model_name == 'CGNet':
return CGNet(classes=num_classes)
elif model_name == 'EDANet':
return EDANet(classes=num_classes)
elif model_name == 'ESNet':
return ESNet(classes=num_classes)
elif model_name == 'ESPNet':
return ESPNet(classes=num_classes)
elif model_name == 'LEDNet':
return LEDNet(classes=num_classes)
elif model_name == 'ESPNet_v2':
return EESPNet_Seg(classes=num_classes)
elif model_name == 'ContextNet':
return ContextNet(classes=num_classes)
elif model_name == 'FastSCNN':
return FastSCNN(classes=num_classes)
elif model_name == 'DABNet':
return DABNet(classes=num_classes)
elif model_name == 'FSSNet':
return FSSNet(classes=num_classes)
elif model_name == 'FPENet':
return FPENet(classes=num_classes)
def build_model(model_name, num_classes):
# for deeplabv3
model_map = {
'deeplabv3_resnet50': network.deeplabv3_resnet50,
'deeplabv3plus_resnet50': network.deeplabv3plus_resnet50,
'deeplabv3_resnet101': network.deeplabv3_resnet101,
'deeplabv3plus_resnet101': network.deeplabv3plus_resnet101,
'deeplabv3_mobilenet': network.deeplabv3_mobilenet,
'deeplabv3plus_mobilenet': network.deeplabv3plus_mobilenet
}
if model_name == 'SQNet':
return SQNet(classes=num_classes)
elif model_name == 'LinkNet':
return LinkNet(classes=num_classes)
elif model_name == 'SegNet':
return SegNet(classes=num_classes)
elif model_name == 'UNet':
return UNet(classes=num_classes)
elif model_name == 'ENet':
return ENet(classes=num_classes)
elif model_name == 'ERFNet':
return ERFNet(classes=num_classes)
elif model_name == 'CGNet':
return CGNet(classes=num_classes)
elif model_name == 'EDANet':
return EDANet(classes=num_classes)
elif model_name == 'ESNet':
return ESNet(classes=num_classes)
elif model_name == 'ESPNet':
return ESPNet(classes=num_classes)
elif model_name == 'LEDNet':
return LEDNet(classes=num_classes)
elif model_name == 'ESPNet_v2':
return EESPNet_Seg(classes=num_classes)
elif model_name == 'ContextNet':
return ContextNet(classes=num_classes)
elif model_name == 'FastSCNN':
return FastSCNN(classes=num_classes)
elif model_name == 'DABNet':
return DABNet(classes=num_classes)
elif model_name == 'FSSNet':
return FSSNet(classes=num_classes)
elif model_name == 'FPENet':
return FPENet(classes=num_classes)
elif model_name == 'FCN':
return FCN32VGG(classes=num_classes)
elif model_name in model_map.keys():
return model_map[model_name](num_classes, output_stride=8)
def validate(args):
# Setup Dataloader
data_loader = get_loader(args.dataset)
data_path = get_data_path(args.dataset)
loader = data_loader(data_path, split=args.split, is_transform=True)
n_classes = loader.n_classes
valloader = data.DataLoader(loader, batch_size=args.batch_size)
# Setup Model
model = LinkNet(n_classes)
model.load_state_dict(torch.load(args.model_path))
model.eval()
if torch.cuda.is_available():
model.cuda(0)
gts, preds = [], []
for i, (images, labels) in enumerate(valloader):
if torch.cuda.is_available():
images = Variable(images.cuda(0))
labels = Variable(labels.cuda(0))
else:
images = Variable(images)
labels = Variable(labels)
t1 = time.time()
outputs = model(images)
t2 = time.time()
print(t2 - t1)
pred = outputs.data.max(1)[1].cpu().numpy()
gt = labels.data.cpu().numpy()
for gt_, pred_ in zip(gt, pred):
gts.append(gt_)
preds.append(pred_)
score, class_iou = scores(gts, preds, n_class=n_classes)
for k, v in score.items():
print k, v
for i in range(n_classes):
print i, class_iou[i]