i have some problem do a test

I would like to test network on UCF101 after finetune in UCF101 using pretrained kinetics you provided.

i use resnet18. I want to get the accuracy of a video unit, not clip.

i use instruction below

python main.py --root_path UCF101 --video_path jpg --annotation_path ucf101_01.json --result_path test --dataset ucf101 --model resnet --model_depth 18 --n_classes 101 --batch_size 64 --n_threads 4 --pretrain_path 18result1s/save_200.pth --no_train --no_val --test --test_subset val --n_finetune_classes 101

jpg is the same as your data directory. 18results1s/save_200.pth is pretrained on Kinetics, finetune in UCF101’s network.

it make a error.

run dataset loading [0/3783] dataset loading [1000/3783] dataset loading [2000/3783] dataset loading [3000/3783] test test.py:42: UserWarning: volatile was removed and now has no effect. Use with torch.no_grad(): instead. inputs = Variable(inputs, volatile=True) test.py:45: UserWarning: Implicit dimension choice for softmax has been deprecated. Change the call to include dim=X as an argument. outputs = F.softmax(outputs) Traceback (most recent call last): File “main.py”, line 162, in <module> test.test(test_loader, model, opt, test_data.class_names) File “test.py”, line 50, in test test_results, class_names) File “test.py”, line 20, in calculate_video_results ‘label’: class_names[locs[i]], KeyError: tensor(12)

KeyError : tensor(12) , i change my instruction, the number in parentheses is change, maybe.

can you help me?

[hareeshdevarakonda]

@lee2h Hope this should help you similar kind of error i got please try in this way that is zero tensor error

video_results = [] for i in range(sorted_scores.size(0)): video_results.append({ ‘label’: class_names[torch.Tensor.item(locs[i])], ‘score’: float(sorted_scores[i]) })

[lee2h]

in command, i change pretrain_path to resume_path, it works well. i get UCF101 split 1, 82.2% video accuracy.

i will train and evaluate for another split and pytorch 0.5. if there is no problem, i will close this issue.

thank you for your awesome work.