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test.py
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from typing import Tuple
import cv2
import numpy as np
import scipy
import torch, os
import torchvision.transforms as transforms
from data.dataloader import SeqDistributedSampler
from data.dataset import LaneTestDataset
from model.model import parsingNet
from utils.common import merge_config
from utils.dist_utils import dist_print, dist_tqdm, is_main_process, synchronize
from data.constant import culane_row_anchor
from utils.img_transform import tensor2cvmat
def main():
torch.backends.cudnn.benchmark = True
args, cfg = merge_config()
cfg.batch_size = 1
distributed = False
if 'WORLD_SIZE' in os.environ:
distributed = int(os.environ['WORLD_SIZE']) > 1
if distributed:
torch.cuda.set_device(args.local_rank)
torch.distributed.init_process_group(backend='nccl', init_method='env://')
dist_print('start testing...')
assert cfg.backbone in ['18', '34', '50', '101', '152', '50next', '101next', '50wide', '101wide']
if cfg.dataset == 'CULane':
cls_num_per_lane = 18
elif cfg.dataset == 'Tusimple':
cls_num_per_lane = 56
else:
raise NotImplementedError
net = parsingNet(
pretrained=False,
backbone=cfg.backbone,
cls_dim=(cfg.griding_num + 1, cls_num_per_lane, cfg.num_lanes),
use_aux=False
).cuda() # we dont need auxiliary segmentation in testing
state_dict = torch.load(cfg.test_model, map_location='cpu')['model']
compatible_state_dict = {}
for k, v in state_dict.items():
if 'module.' in k:
compatible_state_dict[k[7 :]] = v
else:
compatible_state_dict[k] = v
net.load_state_dict(compatible_state_dict, strict=False)
if distributed:
net = torch.nn.parallel.DistributedDataParallel(net, device_ids=[args.local_rank])
if not os.path.exists(cfg.test_work_dir):
os.mkdir(cfg.test_work_dir)
output_path = os.path.join(cfg.test_work_dir, cfg.dataset)
if not os.path.exists(output_path) and is_main_process():
os.mkdir(output_path)
synchronize()
loader = get_test_loader(cfg.batch_size, cfg.data_root, cfg.dataset, distributed)
for i, data in enumerate(dist_tqdm(loader)):
imgs, names = data
imgs = imgs.cuda()
with torch.no_grad():
out = net(imgs) # [B, 201, 18, 4]
generate_lines(
out,
imgs,
names,
output_path,
cfg.griding_num,
localization_type='rel',
flip_updown=True,
)
# eval_lane(net, cfg.dataset, cfg.data_root, cfg.test_work_dir, cfg.griding_num, False, distributed)
def get_test_loader(batch_size, data_root, dataset, distributed):
img_transforms = transforms.Compose(
[
transforms.Resize((288, 800)),
transforms.ToTensor(),
transforms.Normalize((0.485, 0.456, 0.406), (0.229, 0.224, 0.225)),
]
)
if dataset == 'CULane':
test_dataset = LaneTestDataset(
data_root,
os.path.join(
data_root,
'list/train_gt.txt', # 'list/test.txt',
),
img_transform=img_transforms
)
cls_num_per_lane = 18
elif dataset == 'Tusimple':
test_dataset = LaneTestDataset(data_root, os.path.join(data_root, 'test.txt'), img_transform=img_transforms)
cls_num_per_lane = 56
if distributed:
sampler = SeqDistributedSampler(test_dataset, shuffle=False)
else:
sampler = torch.utils.data.SequentialSampler(test_dataset)
loader = torch.utils.data.DataLoader(test_dataset, batch_size=batch_size, sampler=sampler, num_workers=4)
return loader
COLOR_LIST = [
(b, g, r) for (color_name, (r, g, b)) in {
"green": (0, 255, 0),
"blue": (0, 0, 255),
"yellow": (255, 255, 0),
"cyan": (0, 255, 255),
}.items()
]
def generate_lines(
out: torch.Tensor,
imgs: torch.Tensor,
names: Tuple[str], # 文件名，相对于 CULaneROOT 的路径
output_path: str,
griding_num,
localization_type='abs',
flip_updown=False,
):
shape = imgs[0, 0].shape
img_h, img_w = imgs.shape[2 :]
col_sample = np.linspace(0, shape[1] - 1, griding_num)
col_sample_w = col_sample[1] - col_sample[0]
# j batch
for j in range(out.shape[0]):
out_j = out[j].data.cpu().numpy()
if flip_updown:
out_j = out_j[:, ::-1, :]
if localization_type == 'abs':
out_j = np.argmax(out_j, axis=0)
out_j[out_j == griding_num] = -1
out_j = out_j + 1
elif localization_type == 'rel':
prob = scipy.special.softmax(out_j[:-1, :, :], axis=0)
idx = np.arange(griding_num) + 1
idx = idx.reshape(-1, 1, 1)
loc = np.sum(prob * idx, axis=0)
out_j = np.argmax(out_j, axis=0)
loc[out_j == griding_num] = 0
out_j = loc
else:
raise NotImplementedError
frame = tensor2cvmat(imgs[j])
four_lanes = []
cls_num_per_lane = 18
row_anchor = culane_row_anchor
for i in range(out_j.shape[1]):
color = COLOR_LIST[i]
lane_i = []
if np.sum(out_j[:, i] != 0) > 2:
for k in range(out_j.shape[0]):
if out_j[k, i] > 0:
ppp = (
int(out_j[k, i] * col_sample_w * img_w / 800) - 1,
int(img_h * (row_anchor[cls_num_per_lane - 1 - k] / 288)) - 1
)
cv2.circle(frame, ppp, 5, color, -1)
lane_i.append(ppp)
four_lanes.append(lane_i)
# for i, lane_i in enumerate(four_lanes):
# color = COLOR_LIST[i]
# lane_x, lane_y = [], []
# min_y = img_h
# points = np.array([lane_i], dtype=np.int32) # (1, 10, 2)
# if points.shape[1] > 2:
# cv2.fillPoly(frame, points, color)
name = names[j]
cv2.imwrite(os.path.join(output_path, name), frame)
line_save_path = os.path.join(output_path, name[:-3] + 'lines.txt')
save_dir, _ = os.path.split(line_save_path)
if not os.path.exists(save_dir):
os.makedirs(save_dir)
with open(line_save_path, 'w') as fp:
for i in range(out_j.shape[1]):
if np.sum(out_j[:, i] != 0) > 2:
for k in range(out_j.shape[0]):
if out_j[k, i] > 0:
fp.write(
'%d %d ' % (int(out_j[k, i] * col_sample_w * 1640 / 800) - 1, int(590 - k * 20) - 1)
)
fp.write('\n')
if __name__ == '__main__':
main()