def run_test(net,
data_root,
exp_name,
work_dir,
griding_num,
use_aux,
distributed,
batch_size=8):
# torch.backends.cudnn.benchmark = True
output_path = os.path.join(work_dir, exp_name)
if not os.path.exists(output_path) and is_main_process():
os.mkdir(output_path)
synchronize()
loader = get_test_loader(batch_size, data_root, 'CULane', distributed)
# import pdb;pdb.set_trace()
for i, data in enumerate(dist_tqdm(loader)):
imgs, names = data
imgs = imgs.cuda()
with torch.no_grad():
out = net(imgs)
if len(out) == 2 and use_aux:
out, seg_out = out
generate_lines(out,
imgs[0, 0].shape,
names,
output_path,
griding_num,
localization_type='rel',
flip_updown=True)
def eval_lane(net, dataset, data_root, work_dir, griding_num, use_aux, distributed):
net.eval()
if dataset == 'CULane':
run_test(net,data_root, 'culane_eval_tmp', work_dir, griding_num, use_aux, distributed)
synchronize() # wait for all results
if is_main_process():
res = call_culane_eval(data_root, 'culane_eval_tmp', work_dir)
TP,FP,FN = 0,0,0
for k, v in res.items():
val = float(v['Fmeasure']) if 'nan' not in v['Fmeasure'] else 0
val_tp,val_fp,val_fn = int(v['tp']),int(v['fp']),int(v['fn'])
TP += val_tp
FP += val_fp
FN += val_fn
dist_print(k,val)
P = TP * 1.0/(TP + FP)
R = TP * 1.0/(TP + FN)
F = 2*P*R/(P + R)
dist_print(F)
synchronize()
elif dataset == 'Tusimple':
exp_name = 'tusimple_eval_tmp'
run_test_tusimple(net, data_root, work_dir, exp_name, griding_num, use_aux, distributed)
synchronize() # wait for all results
if is_main_process():
combine_tusimple_test(work_dir,exp_name)
res = LaneEval.bench_one_submit(os.path.join(work_dir,exp_name + '.txt'),os.path.join(data_root,'test_label.json'))
res = json.loads(res)
for r in res:
dist_print(r['name'], r['value'])
synchronize()
def eval_lane(net, dataset, data_root, work_dir, distributed, cfg):
net.eval()
run_test(net, data_root, 'culane_eval_tmp', work_dir, distributed, cfg)
synchronize() # wait for all results
if is_main_process():
res = call_culane_eval(data_root, 'culane_eval_tmp', work_dir)
TP, FP, FN = 0, 0, 0
for k, v in res.items():
val = float(v['Fmeasure']) if 'nan' not in v['Fmeasure'] else 0
val_tp, val_fp, val_fn = int(v['tp']), int(v['fp']), int(v['fn'])
TP += val_tp
FP += val_fp
FN += val_fn
dist_print(k, val)
P = TP * 1.0 / (TP + FP)
R = TP * 1.0 / (TP + FN)
F = 2 * P * R / (P + R)
dist_print(F)
synchronize()
def run_test(net,
data_root,
exp_name,
work_dir,
distributed,
cfg,
batch_size=1):
# torch.backends.cudnn.benchmark = True
output_path = os.path.join(work_dir, exp_name)
if not os.path.exists(output_path) and is_main_process():
os.mkdir(output_path)
synchronize()
row_anchor = np.linspace(90, 255, 128).tolist()
col_sample = np.linspace(0, 1640 - 1, 256)
col_sample_w = col_sample[1] - col_sample[0]
loader = get_test_loader(batch_size, data_root, 'CULane', distributed)
filter_f = lambda x: int(np.round(x))
# import pdb;pdb.set_trace()
for i, data in enumerate(dist_tqdm(loader)):
imgs, names = data
imgs = imgs.cuda()
with torch.no_grad():
out = net(imgs)
for j in range(len(names)):
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 writer:
lane_exit_out = out["lane_exit_out"].sigmoid()
lane_exit_out = lane_exit_out > cfg.thresh_lc
for lane_index in range(lane_exit_out.size(1)):
if lane_exit_out[0][lane_index] == True:
x_list = []
y_list = []
vertex_wise_confidence_out = out[
"vertex_wise_confidence_out_" +
str(lane_index + 1)].sigmoid()
vertex_wise_confidence_out = vertex_wise_confidence_out > cfg.thresh_vc
row_wise_vertex_location_out = F.log_softmax(
out["row_wise_vertex_location_out_" +
str(lane_index + 1)],
dim=0)
row_wise_vertex_location_out = torch.argmax(
row_wise_vertex_location_out, dim=0)
row_wise_vertex_location_out[
~vertex_wise_confidence_out] = 256
row_wise_vertex_location_out = row_wise_vertex_location_out.detach(
).cpu().numpy()
estimator = RANSACRegressor(random_state=42,
min_samples=2,
residual_threshold=10.0)
##model = make_pipeline(PolynomialFeatures(2), estimator)
for k in range(row_wise_vertex_location_out.shape[0]):
if row_wise_vertex_location_out[k] != 256:
x = row_wise_vertex_location_out[
k] * col_sample_w
y = row_anchor[k] / 256 * 590
x_list.append(x)
y_list.append(y)
#writer.write('%d %d ' % (filter_f(row_wise_vertex_location_out[k] * col_sample_w), filter_f(row_anchor[k] / 256 * 590)))
#writer.write('\n')
if len(x_list) <= 1:
continue
X = np.array(x_list)
y = np.array(y_list)
y = y[:, np.newaxis]
y_plot = np.linspace(y.min(), y.max())
estimator.fit(y, X)
x_plot = estimator.predict(y_plot[:, np.newaxis])
for x, y in zip(x_plot, y_plot):
writer.write('%d %d ' % (filter_f(x), filter_f(y)))
writer.write('\n')