loss_dict = {'loss_fcos_cls': tensor(nan, device='cuda:0', grad_fn=<DivBackward0>), 'loss_fcos_loc': tensor(0.5552, device='cuda:0', grad_fn=<DivBackward0>), 'loss_fcos_ctr': tensor(0.7676, device='cuda:0', grad_fn=<DivBackward0>), 'loss_mask': tensor(0.8649, device='...
报错FloatingPointError: Loss became infinite or NaN at iteration=88!,程序员大本营,技术文章内容聚合第一站。
Search before asking I have searched the YOLOv5 issues and discussions and found no similar questions. Question When I use VisDrone dataset to train YOLOv5, after a few epoch, the loss becomes nan, and there is no prediction. Someone sai...
However, I notice that if I try to calculate the gradient of BCELoss when the loss is infinite, I get NaN, which makes some sense--I'm guessing that's why BCEWithLogitsLoss clamps the loss to a finite value. My question is: should I mimick BCEWithLogitsLoss? In my opinion, it sho...
for t in range(1, 10000): # Don't infinite loop while learning action = select_action(state) state, reward, done, _ = env.step(action) if args.render: env.render() policy.rewards.append(reward) ep_reward += reward if done:
V663. Infinite loop is possible. The 'cin.eof()' condition is insufficient to break from the loop. Consider adding the 'cin.fail()' function call to the conditional expression. V664. Pointer is dereferenced on the initialization list before its check for null inside the body of a constructo...
Assay Kit (Promega) was used according to manufacturer’s recommendations and luciferase activity was measured using the Infinite M200 plate reader (Tecan). In the analysis step, the firefly luciferase activity was normalized against Renilla luciferase. The normalized firefly luciferase activity was furth...
V663. Infinite loop is possible. The 'cin.eof()' condition is insufficient to break from the loop. Consider adding the 'cin.fail()' function call to the conditional expression. V664. Pointer is dereferenced on the initialization list before its check for null inside the body of a constructo...
epoch is commonly smaller than the others... I still changed my generator to only output batches the right size. And voila, since then I dont get NaN and inf anymore. Not sure if this helps everybody but I still want to post what helped me. ...
I use the ciou loss in cascadercnn like this: model = dict( roi_head=dict( bbox_head=dict( reg_decoded_bbox=True, loss_bbox=dict(type='CIoULoss', loss_weight=10.0))) I trained the model with my own data, using 4 gpus, samples_per_gpu=2,...