search

xiaoyufenfei / Efficient-Segmentation-Networks

Lightweight models for real-time semantic segmentationon PyTorch (include SQNet, LinkNet, SegNet, UNet, ENet, ERFNet, EDANet, ESPNet, ESPNetv2, LEDNet, ESNet, FSSNet, CGNet, DABNet, Fast-SCNN, ContextNet, FPENet, etc.)
MIT License
914 stars 164 forks source link

run predict error #6

Closed mathpopo closed 4 years ago

mathpopo commented 4 years ago

Namespace(batch_size=1, checkpoint='', classes=11, cuda=True, dataset='camvid', gpus='0', model='ENet', num_workers=2, save_seg_dir='./server/camvid/predict/ENet') =====> use gpu id: '0' find file: ./dataset/inform/camvid_inform.pkl length of Validation set: 233 =====> beginning testing test set length: 233 Traceback (most recent call last): File "/home/Downloads/Efficient-Segmentation-Networks/predict.py", line 119, in test_model(args) File "/home/Downloads/Efficient-Segmentation-Networks/predict.py", line 102, in test_model predict(args, testLoader, model) File "/home/Downloads/Efficient-Segmentation-Networks/predict.py", line 44, in predict for i, (input, size, name) in enumerate(test_loader): ValueError: too many values to unpack (expected 3)

xiaoyufenfei commented 4 years ago

If you want to predict the Camvid dataset, you should use the test.py, predict.py is just used to get the Cityscapes dataset predictions for upload to the official url evaluation.