Torch-Pruning
Torch-Pruning copied to clipboard
VainF
Yolov8 upgrade ultralytics version
Hi all and thank you for your amazing work!
I managed easily to use your yolov8 example in order to train and prune it and it works like a charm, managed to load it for inference after and I gain 2 time speed-up.
Nevertheless, I need to upgrade to latest ultralytics version. For that I modify some import : from ultralytics.engine.trainer import BaseTrainer from ultralytics.utils import yaml_load, LOGGER, RANK, DEFAULT_CFG_DICT, DEFAULT_CFG_KEYS from ultralytics.utils.checks import check_yaml from ultralytics.utils.torch_utils import initialize_weights, de_parallel
instead of : from ultralytics.yolo.engine.model import TASK_MAP from ultralytics.yolo.engine.trainer import BaseTrainer from ultralytics.yolo.utils import yaml_load, LOGGER, RANK, DEFAULT_CFG_DICT, DEFAULT_CFG_KEYS from ultralytics.yolo.utils.checks import check_yaml from ultralytics.yolo.utils.torch_utils import initialize_weights, de_parallel
and I modify TASK_MAP[self.task][1] to self.task_map[self.task]['trainer'] .
Seems to work at the beginning but at step 1 finetune I got the following error:
Traceback (most recent call last):
File "yolov8_pruning_ultralytics_update_light.py", line 406, in
Is anyone managed to use latest YOLOV8 (ultralytics) version or have any clue on how can I fix it ?
In advance, thanks.
Hi all and thank you for your amazing work!
I managed easily to use your yolov8 example in order to train and prune it and it works like a charm, managed to load it for inference after and I gain 2 time speed-up.
Nevertheless, I need to upgrade to latest ultralytics version. For that I modify some import : from ultralytics.engine.trainer import BaseTrainer from ultralytics.utils import yaml_load, LOGGER, RANK, DEFAULT_CFG_DICT, DEFAULT_CFG_KEYS from ultralytics.utils.checks import check_yaml from ultralytics.utils.torch_utils import initialize_weights, de_parallel
instead of : from ultralytics.yolo.engine.model import TASK_MAP from ultralytics.yolo.engine.trainer import BaseTrainer from ultralytics.yolo.utils import yaml_load, LOGGER, RANK, DEFAULT_CFG_DICT, DEFAULT_CFG_KEYS from ultralytics.yolo.utils.checks import check_yaml from ultralytics.yolo.utils.torch_utils import initialize_weights, de_parallel
and I modify TASK_MAP[self.task][1] to self.task_map[self.task]['trainer'] .
Seems to work at the beginning but at step 1 finetune I got the following error:
Traceback (most recent call last): File "yolov8_pruning_ultralytics_update_light.py", line 406, in prune(args) File "yolov8_pruning_ultralytics_update_light.py", line 367, in prune model.train_v2(pruning=True, **pruning_cfg) File "yolov8_pruning_ultralytics_update_light.py", line 272, in train_v2 self.trainer.train() File "/home/ngolo/anaconda3/envs/torch-pruning/lib/python3.8/site-packages/ultralytics/engine/trainer.py", line 198, in train self._do_train(world_size) File "/home/ngolo/anaconda3/envs/torch-pruning/lib/python3.8/site-packages/ultralytics/engine/trainer.py", line 370, in _do_train self.loss, self.loss_items = self.model(batch) File "/home/ngolo/anaconda3/envs/torch-pruning/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1511, in _wrapped_call_impl return self._call_impl(*args, **kwargs) File "/home/ngolo/anaconda3/envs/torch-pruning/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1520, in _call_impl return forward_call(*args, **kwargs) File "/home/ngolo/anaconda3/envs/torch-pruning/lib/python3.8/site-packages/ultralytics/nn/tasks.py", line 88, in forward return self.loss(x, args, **kwargs) File "/home/ngolo/anaconda3/envs/torch-pruning/lib/python3.8/site-packages/ultralytics/nn/tasks.py", line 267, in loss return self.criterion(preds, batch) File "/home/ngolo/anaconda3/envs/torch-pruning/lib/python3.8/site-packages/ultralytics/utils/loss.py", line 219, in call pred_bboxes = self.bbox_decode(anchor_points, pred_distri) # xyxy, (b, h_w, 4) File "/home/ngolo/anaconda3/envs/torch-pruning/lib/python3.8/site-packages/ultralytics/utils/loss.py", line 191, in bbox_decode pred_dist = pred_dist.view(b, a, 4, c // 4).softmax(3).matmul(self.proj.type(pred_dist.dtype)) RuntimeError: Expected all tensors to be on the same device, but found at least two devices, cpu and cuda:0! (when checking argument for argument mat in method wrapper_CUDA_addmv)
Is anyone managed to use latest YOLOV8 (ultralytics) version or have any clue on how can I fix it ?
In advance, thanks.
Where you able to fix it? I am also trying but get a different error instead
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
Cell In[18], line 1
----> 1 prune(args)
Cell In[6], line 334, in prune(args)
332 pruning_cfg['name'] = f\"step_{i}_finetune\"
333 pruning_cfg['batch'] = batch_size # restore batch size
--> 334 model.train_v2(pruning=True, **pruning_cfg)
336 # post fine-tuning validation
337 pruning_cfg['name'] = f\"step_{i}_post_val\"
Cell In[6], line 243, in train_v2(self, pruning, **kwargs)
240 self.trainer.final_eval = final_eval_v2.__get__(self.trainer)
242 self.trainer.hub_session = self.session # attach optional HUB session
--> 243 self.trainer.train()
244 # Update model and cfg after training
245 if RANK in (-1, 0):
TypeError: BaseTrainer.train() missing 1 required positional argument: 'self'"
I then tried with self.train() instead and it seems ok but now I get to an AttrbuteError on DetectionTrainer
