How to train my own dataset with 10 classes?

[zhaowei0315]

I'm facing the following issue after changing DATASETS: DETECT_CLASSES with 10 classes.

/pytorch/aten/src/ATen/native/cuda/IndexKernel.cu:60: lambda ->auto::operator()(int)->auto: block: [1,0,0], thread: [111,0,0] Assertion index >= -sizes[i] && index < sizes[i] && "index out of bounds" failed. /pytorch/aten/src/ATen/native/cuda/IndexKernel.cu:60: lambda ->auto::operator()(int)->auto: block: [0,0,0], thread: [57,0,0] Assertion index >= -sizes[i] && index < sizes[i] && "index out of bounds" failed. /pytorch/aten/src/ATen/native/cuda/IndexKernel.cu:60: lambda ->auto::operator()(int)->auto: block: [0,0,0], thread: [58,0,0] Assertion index >= -sizes[i] && index < sizes[i] && "index out of bounds" failed. /pytorch/aten/src/ATen/native/cuda/IndexKernel.cu:60: lambda ->auto::operator()(int)->auto: block: [0,0,0], thread: [59,0,0] Assertion index >= -sizes[i] && index < sizes[i] && "index out of bounds" failed. /pytorch/aten/src/ATen/native/cuda/IndexKernel.cu:60: lambda ->auto::operator()(int)->auto: block: [0,0,0], thread: [60,0,0] Assertion index >= -sizes[i] && index < sizes[i] && "index out of bounds" failed. /pytorch/aten/src/ATen/native/cuda/IndexKernel.cu:60: lambda ->auto::operator()(int)->auto: block: [0,0,0], thread: [61,0,0] Assertion index >= -sizes[i] && index < sizes[i] && "index out of bounds" failed. /pytorch/aten/src/ATen/native/cuda/IndexKernel.cu:60: lambda ->auto::operator()(int)->auto: block: [0,0,0], thread: [62,0,0] Assertion index >= -sizes[i] && index < sizes[i] && "index out of bounds" failed. Traceback (most recent call last): File "tools/plain_train_net.py", line 104, in args=(args,), File "/fs/scratch/.xcserver_ai-initiative_backup2021/zfe5szh/SMOKE/smoke/engine/launch.py", line 56, in launch main_func(args) File "tools/plain_train_net.py", line 92, in main train(cfg, model, device, distributed) File "tools/plain_train_net.py", line 55, in train arguments File "/fs/scratch/.xcserver_ai-initiative_backup2021/zfe5szh/SMOKE/smoke/engine/trainer.py", line 69, in do_train loss_dict = model(images, targets) File "/home/zfe5szh/.conda/envs/SMOKE/lib/python3.7/site-packages/torch/nn/modules/module.py", line 541, in call result = self.forward(input, *kwargs) File "/fs/scratch/.xcserver_ai-initiative_backup2021/zfe5szh/SMOKE/smoke/modeling/detector/keypoint_detector.py", line 38, in forward result, detector_losses = self.heads(features, targets)############################################# File "/home/zfe5szh/.conda/envs/SMOKE/lib/python3.7/site-packages/torch/nn/modules/module.py", line 541, in call result = self.forward(input, kwargs) File "/fs/scratch/.xcserver_ai-initiative_backup2021/zfe5szh/SMOKE/smoke/modeling/heads/smoke_head/smoke_head.py", line 22, in forward loss_heatmap, loss_regression = self.loss_evaluator(x, targets) File "/fs/scratch/.xcserver_ai-initiative_backup2021/zfe5szh/SMOKE/smoke/modeling/heads/smoke_head/loss.py", line 117, in call predict_boxes3d = self.prepare_predictions(targets_variables, pred_regression) File "/fs/scratch/.xcserver_ai-initiative_backup2021/zfe5szh/SMOKE/smoke/modeling/heads/smoke_head/loss.py", line 79, in prepare_predictions targets_variables["flip_mask"] File "/fs/scratch/.xcserver_ai-initiative_backup2021/zfe5szh/SMOKE/smoke/modeling/smoke_coder.py", line 218, in decode_orientation cos_pos_idx = (vector_ori[:, 1] > 0).nonzero() RuntimeError: copy_if failed to synchronize: device-side assert triggered**