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infer_tusimple.py
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infer_tusimple.py
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import os
import json
from datetime import datetime
from statistics import mean
import argparse
import numpy as np
import cv2
from sklearn.metrics import accuracy_score, f1_score
import torch
from torch.utils.data import DataLoader
from datasets.tusimple import TuSimple, get_lanes_tusimple
from models.dla.pose_dla_dcn import get_pose_net
from models.erfnet.erfnet import ERFNet
from models.enet.ENet import ENet
from utils.affinity_fields import decodeAFs
from utils.metrics import match_multi_class, LaneEval
from utils.visualize import tensor2image, create_viz
parser = argparse.ArgumentParser('Options for inference with LaneAF models in PyTorch...')
parser.add_argument('--dataset-dir', type=str, default=None, help='path to dataset')
parser.add_argument('--output-dir', type=str, default=None, help='output directory for model and logs')
parser.add_argument('--snapshot', type=str, default=None, help='path to pre-trained model snapshot')
parser.add_argument('--split', type=str, default='test', help='dataset split to evaluate on (train/val/test)')
parser.add_argument('--seed', type=int, default=1, help='set seed to some constant value to reproduce experiments')
parser.add_argument('--no-cuda', action='store_true', default=False, help='do not use cuda for training')
parser.add_argument('--save-viz', action='store_true', default=False, help='save visualization depicting intermediate and final results')
args = parser.parse_args()
# check args
if args.dataset_dir is None:
assert False, 'Path to dataset not provided!'
if args.snapshot is None:
assert False, 'Model snapshot not provided!'
if args.split is ['train', 'val', 'test']:
assert False, 'Incorrect dataset split provided!'
# set batch size to 1 for visualization purposes
args.batch_size = 1
# setup args
args.cuda = not args.no_cuda and torch.cuda.is_available()
if args.output_dir is None:
args.output_dir = datetime.now().strftime("%Y-%m-%d-%H:%M-infer")
args.output_dir = os.path.join('.', 'experiments', 'tusimple', args.output_dir)
if not os.path.exists(args.output_dir):
os.makedirs(args.output_dir)
else:
assert False, 'Output directory already exists!'
# load args used from training snapshot (if available)
if os.path.exists(os.path.join(os.path.dirname(args.snapshot), 'config.json')):
with open(os.path.join(os.path.dirname(args.snapshot), 'config.json')) as f:
json_args = json.load(f)
# augment infer args with training args for model consistency
if 'backbone' in json_args.keys():
args.backbone = json_args['backbone']
else:
args.backbone = 'dla34'
# store config in output directory
with open(os.path.join(args.output_dir, 'config.json'), 'w') as f:
json.dump(vars(args), f)
# set random seed
torch.manual_seed(args.seed)
if args.cuda:
torch.cuda.manual_seed(args.seed)
kwargs = {'batch_size': args.batch_size, 'shuffle': False, 'num_workers': 1}
test_loader = DataLoader(TuSimple(args.dataset_dir, args.split, False), **kwargs)
# test function
def test(net):
net.eval()
out_vid = None
json_pred = [json.loads(line) for line in open(os.path.join(args.dataset_dir, 'seg_label', args.split+'.json')).readlines()]
for b_idx, sample in enumerate(test_loader):
input_img, input_seg, input_mask, input_af = sample
if args.cuda:
input_img = input_img.cuda()
input_seg = input_seg.cuda()
input_mask = input_mask.cuda()
input_af = input_af.cuda()
st_time = datetime.now()
# do the forward pass
outputs = net(input_img)[-1]
# convert to arrays
img = tensor2image(input_img.detach(), np.array(test_loader.dataset.mean),
np.array(test_loader.dataset.std))
mask_out = tensor2image(torch.sigmoid(outputs['hm']).repeat(1, 3, 1, 1).detach(),
np.array([0.0 for _ in range(3)], dtype='float32'), np.array([1.0 for _ in range(3)], dtype='float32'))
vaf_out = np.transpose(outputs['vaf'][0, :, :, :].detach().cpu().float().numpy(), (1, 2, 0))
haf_out = np.transpose(outputs['haf'][0, :, :, :].detach().cpu().float().numpy(), (1, 2, 0))
# decode AFs to get lane instances
seg_out = decodeAFs(mask_out[:, :, 0], vaf_out, haf_out, fg_thresh=128, err_thresh=5)
ed_time = datetime.now()
if torch.any(torch.isnan(input_seg)):
# if labels are not available, skip this step
pass
else:
# if test set labels are available
# re-assign lane IDs to match with ground truth
seg_out = match_multi_class(seg_out.astype(np.int64), input_seg[0, 0, :, :].detach().cpu().numpy().astype(np.int64))
# fill results in output structure
json_pred[b_idx]['run_time'] = (ed_time - st_time).total_seconds()*1000.
if json_pred[b_idx]['run_time'] > 200:
json_pred[b_idx]['run_time'] = 200
json_pred[b_idx]['lanes'] = get_lanes_tusimple(seg_out, json_pred[b_idx]['h_samples'], test_loader.dataset.samp_factor)
# write results to file
with open(os.path.join(args.output_dir, 'outputs.json'), 'a') as f:
json.dump(json_pred[b_idx], f)
f.write('\n')
# create video visualization
if args.save_viz:
img_out = create_viz(img, seg_out.astype(np.uint8), mask_out, vaf_out, haf_out)
if out_vid is None:
out_vid = cv2.VideoWriter(os.path.join(args.output_dir, 'out.mkv'),
cv2.VideoWriter_fourcc(*'H264'), 5, (img_out.shape[1], img_out.shape[0]))
out_vid.write(img_out)
print('Done with image {} out of {}...'.format(min(args.batch_size*(b_idx+1), len(test_loader.dataset)), len(test_loader.dataset)))
# benchmark on TuSimple
results = LaneEval.bench_one_submit(os.path.join(args.output_dir, 'outputs.json'), os.path.join(args.dataset_dir, 'seg_label', args.split+'.json'))
with open(os.path.join(args.output_dir, 'results.json'), 'w') as f:
json.dump(results, f)
if args.save_viz:
out_vid.release()
return
if __name__ == "__main__":
heads = {'hm': 1, 'vaf': 2, 'haf': 1}
if args.backbone == 'dla34':
model = get_pose_net(num_layers=34, heads=heads, head_conv=256, down_ratio=4)
elif args.backbone == 'erfnet':
model = ERFNet(heads=heads)
elif args.backbone == 'enet':
model = ENet(heads=heads)
model.load_state_dict(torch.load(args.snapshot), strict=True)
if args.cuda:
model.cuda()
print(model)
test(model)