TypeError: 'int' object is not iterable

[River-mao]

Hello, thanks for work! I have some problem when I try to train the model with pascal set. I follow the step show in the github page, and run: sh tool/train.sh pascal asgnet split0_resnet50 but I get the error as follow:

[2022-02-22 16:10:06,166 INFO train.py line 329 19996] >>>>>>>>>>>>>>>> Start Evaluation >>>>>>>>>>>>>>>> Traceback (most recent call last): File "/opt/conda/envs/dual_learning/lib/python3.8/runpy.py", line 194, in _run_module_as_main return _run_code(code, main_globals, None, File "/opt/conda/envs/dual_learning/lib/python3.8/runpy.py", line 87, in _run_code exec(code, run_globals) File "/home/tool/train.py", line 450, in <module> main() File "/home/tool/train.py", line 91, in main main_worker(args.train_gpu, args.ngpus_per_node, args) File "/home/tool/train.py", line 207, in main_worker loss_val, mIoU_val, mAcc_val, allAcc_val, class_miou = validate(val_loader, model, criterion) File "/home/tool/train.py", line 421, in validate allAcc = sum(intersection_meter.sum) / (sum(target_meter.sum) + 1e-10) TypeError: 'int' object is not iterable

I try to solve this, and I found the "intersection_meter.sum" belongs to "int" type, which leads to the error. As normal, the 'intersection_meter.sum' should be a list or array? Where ma'y I go wrong? Thanks a lot1

[Reagan1311]

You can check this function, which computes the area of intersection, and then the value will be updated in the intersection_meter.

Normally, the intersection_meter.sum should be a numpy array, and its shape should equal the number of classes.

[River-mao]

You can check this function, which computes the area of intersection, and then the value will be updated in the intersection_meter.

Normally, the intersection_meter.sum should be a numpy array, and its shape should equal the number of classes.

Thanks a lot, I found I made the mistake in the train.txt, so there were some data mismatched, which caused the error. And I have solved this problem.