Culane和TUSimple在generate_lines()时逻辑不一致？

[Goooyi]

Culane的generate_line()会像论文中说的，按照k-th location来做expectation of predictions,详见line 24, line 26和line 44，
其中line 44有-1是因为画图的时候图像坐标从0开始对吗？

Ultra-Fast-Lane-Detection/evaluation/eval_wrapper.py

Lines 9 to 45 in 353df10

	def generate_lines(out, shape, names, output_path, griding_num, localization_type='abs', flip_updown=False):
	col_sample = np.linspace(0, shape[1] - 1, griding_num)
	col_sample_w = col_sample[1] - col_sample[0]
	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
	name = names[j]
	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')

但是在TUSimple的generate_lines()函数中，见line 72, 74, 81:

Ultra-Fast-Lane-Detection/evaluation/eval_wrapper.py

Lines 64 to 83 in 353df10

	def generate_tusimple_lines(out,shape,griding_num,localization_type='rel'):
	out = out.data.cpu().numpy()
	out_loc = np.argmax(out,axis=0)
	if localization_type == 'rel':
	prob = scipy.special.softmax(out[:-1, :, :], axis=0)
	idx = np.arange(griding_num)
	idx = idx.reshape(-1, 1, 1)
	loc = np.sum(prob * idx, axis=0)
	loc[out_loc == griding_num] = griding_num
	out_loc = loc
	lanes = []
	for i in range(out_loc.shape[1]):
	out_i = out_loc[:,i]
	lane = [int(round((loc + 0.5) * 1280.0 / (griding_num - 1))) if loc != griding_num else -2 for loc in out_i]
	lanes.append(lane)
	return lanes

首先72行没像culane一样+1计算出k-th location， 相当于用了每个grid格子的左端点？
然后81行+0.5是因为由于tusimple的griding_num=100所以+0.5相当于从格子左端点变成了格子的中间点？那为什么要/(griding_num-1)来除以区间个数呢？

[hwang12345]

@Goooyi 有同样的疑问，为什么两个数据集处理方式不同