Comments (13)
youngwanLEE
commented on June 8, 2024
1
@wirstrom
That's problem is because the pre-trained weight includes an optimizer that was used in the training step so your program loaded previous information.
I modified the centermask2-lite-V-39-eSE-FPN-ms-4x.pth (pretrained weight).
You have to re-download the weight file and try it again.
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wirstrom
commented on June 8, 2024
Update:
If I set cls_agnostic_mask = False as suggested in #1, I get the following error instead.
RuntimeError Traceback (most recent call last)
<ipython-input-2-771faee0e82e> in <module>()
59 torch.save(pth, "centermask2-lite-V-39-eSE-FPN-ms-4x.pth")
60
---> 61 train()
5 frames
<ipython-input-2-771faee0e82e> in train()
52 )
53
---> 54 trainer.train()
55
56 # Make training start from iteration 0
/content/centermask2/train_net.py in train(self)
95 OrderedDict of results, if evaluation is enabled. Otherwise None.
96 """
---> 97 self.train_loop(self.start_iter, self.max_iter)
98 if hasattr(self, "_last_eval_results") and comm.is_main_process():
99 verify_results(self.cfg, self._last_eval_results)
/content/centermask2/train_net.py in train_loop(self, start_iter, max_iter)
84 for self.iter in range(start_iter, max_iter):
85 self.before_step()
---> 86 self.run_step()
87 self.after_step()
88 self.after_train()
/usr/local/lib/python3.6/dist-packages/detectron2/engine/train_loop.py in run_step(self)
232 wrap the optimizer with your custom `step()` method.
233 """
--> 234 self.optimizer.step()
235
236 def _detect_anomaly(self, losses, loss_dict):
/usr/local/lib/python3.6/dist-packages/torch/optim/lr_scheduler.py in wrapper(*args, **kwargs)
64 instance._step_count += 1
65 wrapped = func.__get__(instance, cls)
---> 66 return wrapped(*args, **kwargs)
67
68 # Note that the returned function here is no longer a bound method,
/usr/local/lib/python3.6/dist-packages/torch/optim/sgd.py in step(self, closure)
104 d_p = buf
105
--> 106 p.data.add_(-group['lr'], d_p)
107
108 return loss
RuntimeError: output with shape [1, 128, 1, 1] doesn't match the broadcast shape [80, 128, 1, 1]I tried to change cfg.MODEL.FCOS.NUM_CLASSES cfg.MODEL.RETINANET.NUM_CLASSES from 80 to 1, but I still get the same error.
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youngwanLEE
commented on June 8, 2024
I modified the code.
Now you can pull the updated code and try it again.
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Nihar1989
commented on June 8, 2024
hi,
its not working,i tried it today
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youngwanLEE
commented on June 8, 2024
@Nihar1989 What's your problem?
Did you pull the updated code?
I already checked the code by using person-detection.
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wirstrom
commented on June 8, 2024
Thank you.
Now the first issue (RuntimeError: There were no tensor arguments to this ...) seems to be solved.
I am, however, getting the following error:
RuntimeError Traceback (most recent call last)
<ipython-input-9-7fb5229ee163> in <module>()
75 # pth = torch.load(pth_file)
76
---> 77 train()
<ipython-input-9-7fb5229ee163> in train()
64 # )
65
---> 66 trainer.train()
67
68 # Make training start from iteration 0
/content/centermask2/train_net.py in train(self)
95 OrderedDict of results, if evaluation is enabled. Otherwise None.
96 """
---> 97 self.train_loop(self.start_iter, self.max_iter)
98 if hasattr(self, "_last_eval_results") and comm.is_main_process():
99 verify_results(self.cfg, self._last_eval_results)
/content/centermask2/train_net.py in train_loop(self, start_iter, max_iter)
84 for self.iter in range(start_iter, max_iter):
85 self.before_step()
---> 86 self.run_step()
87 self.after_step()
88 self.after_train()
/usr/local/lib/python3.6/dist-packages/detectron2/engine/train_loop.py in run_step(self)
232 wrap the optimizer with your custom `step()` method.
233 """
--> 234 self.optimizer.step()
235
236 def _detect_anomaly(self, losses, loss_dict):
/usr/local/lib/python3.6/dist-packages/torch/optim/lr_scheduler.py in wrapper(*args, **kwargs)
64 instance._step_count += 1
65 wrapped = func.__get__(instance, cls)
---> 66 return wrapped(*args, **kwargs)
67
68 # Note that the returned function here is no longer a bound method,
/usr/local/lib/python3.6/dist-packages/torch/optim/sgd.py in step(self, closure)
104 d_p = buf
105
--> 106 p.data.add_(-group['lr'], d_p)
107
108 return loss
RuntimeError: output with shape [1, 128, 3, 3] doesn't match the broadcast shape [80, 128, 3, 3]from centermask2.
youngwanLEE
commented on June 8, 2024
Did you set cfg.MODEL.FCOS.NUM_CLASSES = 1?
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wirstrom
commented on June 8, 2024
Yes.
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wirstrom
commented on June 8, 2024
It works now :)
Thank you very much!
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youngwanLEE
commented on June 8, 2024
Have fun :)
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rohanshingade
commented on June 8, 2024
@youngwanLEE can you update the centermask2-V-99-eSE-FPN-ms-3x.pth weight as well? Facing issues while finetuning the model with person class only. Changes i made in the config file are as follows:
cfg.MODEL.FCOS.NUM_CLASSES = 1
cfg.MODEL.RETINANET.NUM_CLASSES = 1
cfg.MODEL.ROI_HEADS.NUM_CLASSES = 1
To register the custom dataset, used
register_coco_instances("human_dataset_val", {}, "/path/to/person.json", "/path/to/personimages/")
MetadataCatalog.get("human_dataset_val").set(thing_classes = ["person"])
I'm getting the following error:
[11/23 12:45:25 d2.evaluation.evaluator]: Inference done 159/176. 0.2748 s / img. ETA=0:00:04
[11/23 12:45:30 d2.evaluation.evaluator]: Total inference time: 0:00:47.439329 (0.277423 s / img per device, on 1 devices)
[11/23 12:45:30 d2.evaluation.evaluator]: Total inference pure compute time: 0:00:47 (0.275423 s / img per device, on 1 devices)
No predictions from the model!
No predictions from the model!
[11/23 12:45:30 d2.engine.defaults]: Evaluation results for human_dataset_val in csv format:
[11/23 12:45:30 d2.evaluation.testing]: copypaste: Task: bbox
[11/23 12:45:30 d2.evaluation.testing]: copypaste: AP,AP50,AP75,APs,APm,APl
[11/23 12:45:30 d2.evaluation.testing]: copypaste: nan,nan,nan,nan,nan,nan
[11/23 12:45:30 d2.evaluation.testing]: copypaste: Task: segm
[11/23 12:45:30 d2.evaluation.testing]: copypaste: AP,AP50,AP75,APs,APm,APl
[11/23 12:45:30 d2.evaluation.testing]: copypaste: nan,nan,nan,nan,nan,nan
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Paragjain10
commented on June 8, 2024
@youngwanLEE can you update the centermask2-V-99-eSE-FPN-ms-3x.pth weight as well? Facing issues while finetuning the model with person class only. Changes i made in the config file are as follows:
cfg.MODEL.FCOS.NUM_CLASSES = 1 cfg.MODEL.RETINANET.NUM_CLASSES = 1 cfg.MODEL.ROI_HEADS.NUM_CLASSES = 1To register the custom dataset, used
register_coco_instances("human_dataset_val", {}, "/path/to/person.json", "/path/to/personimages/") MetadataCatalog.get("human_dataset_val").set(thing_classes = ["person"])I'm getting the following error:
[11/23 12:45:25 d2.evaluation.evaluator]: Inference done 159/176. 0.2748 s / img. ETA=0:00:04 [11/23 12:45:30 d2.evaluation.evaluator]: Total inference time: 0:00:47.439329 (0.277423 s / img per device, on 1 devices) [11/23 12:45:30 d2.evaluation.evaluator]: Total inference pure compute time: 0:00:47 (0.275423 s / img per device, on 1 devices) No predictions from the model! No predictions from the model! [11/23 12:45:30 d2.engine.defaults]: Evaluation results for human_dataset_val in csv format: [11/23 12:45:30 d2.evaluation.testing]: copypaste: Task: bbox [11/23 12:45:30 d2.evaluation.testing]: copypaste: AP,AP50,AP75,APs,APm,APl [11/23 12:45:30 d2.evaluation.testing]: copypaste: nan,nan,nan,nan,nan,nan [11/23 12:45:30 d2.evaluation.testing]: copypaste: Task: segm [11/23 12:45:30 d2.evaluation.testing]: copypaste: AP,AP50,AP75,APs,APm,APl [11/23 12:45:30 d2.evaluation.testing]: copypaste: nan,nan,nan,nan,nan,nan
hello @rohanshingade,
Could you figure out what was causing nan values to be generated?
I am facing a similar problem. Would be helpful of you could let me know what was the solution to this.
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rohanshingade
commented on June 8, 2024
Does your dataset contain only labelled images?
If yes then,
def _eval_predictions(self, tasks, predictions):
"""
Evaluate predictions on the given tasks.
Fill self._results with the metrics of the tasks.
"""
self._logger.info("Preparing results for COCO format ...")
coco_results = list(itertools.chain(*[x["instances"] for x in predictions]))
human_detections = list()
for k in range(0,len(coco_results)):
temp3 = coco_results[k]['category_id']
if temp3 == 0:
#coco_results[k]['category_id'] = 1
human_detections.append(coco_results[k])
coco_results = human_detections.copy()
make the following changes in centermask2/centermask/evaluation/coco_evaluation.py
it detects only person and discards the rest of the predicted output.
I did this long back. so can't recall what exactly the issue was.
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