def preload_lanemarking(weights_path):
net = SCNN(pretrained=False)
mean = (0.3598, 0.3653, 0.3662)
std = (0.2573, 0.2663, 0.2756)
transform = Compose(Resize((800, 288)), ToTensor(),
Normalize(mean=mean, std=std))
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
save_dict = torch.load(weights_path, map_location='cpu')
net.load_state_dict(save_dict['net'])
net.eval()
net.to(device)
return {'net': net, 'transform': transform, 'device': device}
transform = Compose(Resize(resize_shape), ToTensor(),
Normalize(mean=mean, std=std))
dataset_name = exp_cfg['dataset'].pop('dataset_name')
Dataset_Type = getattr(dataset, dataset_name)
test_dataset = Dataset_Type(Dataset_Path['Tusimple'], "test", transform)
test_loader = DataLoader(test_dataset,
batch_size=32,
collate_fn=test_dataset.collate,
num_workers=4)
net = SCNN(input_size=resize_shape, pretrained=False)
save_name = os.path.join(exp_dir, exp_dir.split('/')[-1] + '_best.pth')
save_dict = torch.load(save_name, map_location='cpu')
print("\nloading", save_name, "...... From Epoch: ", save_dict['epoch'])
net.load_state_dict(save_dict['net'])
net = torch.nn.DataParallel(net.to(device))
net.eval()
# ------------ test ------------
out_path = os.path.join(exp_dir, "coord_output")
evaluation_path = os.path.join(exp_dir, "evaluate")
if not os.path.exists(out_path):
os.mkdir(out_path)
if not os.path.exists(evaluation_path):
os.mkdir(evaluation_path)
dump_to_json = []
progressbar = tqdm(range(len(test_loader)))
with torch.no_grad():
for batch_idx, sample in enumerate(test_loader):
img = sample['img'].to(device)
val_loader = DataLoader(val_dataset,
batch_size=8,
collate_fn=val_dataset.collate,
num_workers=4)
# ------------ preparation ------------
if exp_cfg['model'] == "scnn":
net = SCNN(resize_shape, pretrained=True)
elif exp_cfg['model'] == "enet_sad":
net = ENet_SAD(resize_shape, sad=True, dataset=dataset_name)
else:
raise Exception(
"Model not match. 'model' in 'cfg.json' should be 'scnn' or 'enet_sad'."
)
net = net.to(device)
net = torch.nn.DataParallel(net)
optimizer = optim.SGD(net.parameters(), **exp_cfg['optim'])
lr_scheduler = PolyLR(optimizer, 0.9, **exp_cfg['lr_scheduler'])
best_val_loss = 1e6
def train(epoch):
print("Train Epoch: {}".format(epoch))
net.train()
train_loss = 0
train_loss_seg = 0
train_loss_exist = 0
progressbar = tqdm(range(len(train_loader)))
Normalize(mean=mean, std=std))
# val_dataset = CULane(CULane_path, "val", transform)
# val_loader = DataLoader(val_dataset, batch_size=8, collate_fn=val_dataset.collate, num_workers=4)
test_dataset = CULane(CULane_path, "test", transform)
test_loader = DataLoader(test_dataset,
batch_size=8,
collate_fn=test_dataset.collate,
num_workers=4)
net = SCNN(pretrained=False)
save_name = os.path.join(exp_dir, exp_dir.split('/')[-1] + '_best.pth')
save_name = "/home/lion/hanyibo/SCNN/experiments/vgg_SCNN_DULR_w9/vgg_SCNN_DULR_w9.pth"
save_dict = torch.load(save_name, map_location='cpu')
print("loading", save_name, "......")
net.load_state_dict(save_dict['net'])
net.to(device)
net.eval()
# ------------ test ------------
out_path = os.path.join(exp_dir, "coord_output")
evaluation_path = os.path.join(exp_dir, "evaluate")
if not os.path.exists(out_path):
os.mkdir(out_path)
if not os.path.exists(evaluation_path):
os.mkdir(evaluation_path)
progressbar = tqdm(range(len(test_loader)))
with torch.no_grad():
for batch_idx, sample in enumerate(test_loader):
img = sample['img'].to(device)
img_name = sample['img_name']
transform_val_img = Resize(resize_shape)
transform_val_x = Compose(ToTensor(), Normalize(mean=mean, std=std))
transform_val = Compose(transform_val_img, transform_val_x)
val_dataset = Dataset_Type(Dataset_Path[dataset_name], "val", transform_val)
val_loader = DataLoader(val_dataset, batch_size=8, collate_fn=val_dataset.collate, num_workers=4)
# ------------ preparation ------------
if exp_cfg['model'] == "scnn":
net = SCNN(resize_shape, pretrained=True)
elif exp_cfg['model'] == "enet_sad":
net = ENet_SAD(resize_shape, sad=True)
else:
raise Exception("Model not match. 'model' in 'cfg.json' should be 'scnn' or 'enet_sad'.")
#net = net.to(device)
net = net.to("cpu")
net = torch.nn.DataParallel(net)
optimizer = optim.SGD(net.parameters(), **exp_cfg['optim'])
lr_scheduler = PolyLR(optimizer, 0.9, **exp_cfg['lr_scheduler'])
best_val_loss = 1e6
"""
def batch_processor(arg):
b_queue, data_loader = arg
while True:
if b_queue.empty():
sample = next(data_loader)
b_queue.put(sample)
b_queue.join()
"""
