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Encountering an issue while PTQ Static on Pytorch Model. The process involves utilizing pytorch metrics for benchmarking such as ['Accuracy','F1']. The workflow is executed within the VS code Jupyter extension.
The issue arises as follows :
During the first execution of the entire notebook, everything runs smoothly without any issues.
However, upon executing the notebook for a second time, I encounter an error.Upon restarting the kernel in VS code it is running fine. But this should not be the case everytime why a restart of kerenel is required for metric.
The traceback for the error is as follows:
AssertionError Traceback (most recent call last)
Cell In[91], line 10
7 metrics = METRICS('pytorch')
8 top2 = metrics'Accuracy'
---> 10 q_model = quantization.fit(
11 model=model,
12 conf=conf,
13 calib_dataloader=dataloader,
14 eval_dataloader=dataloader,
15 eval_metric=top2,
16 )
in fit(model, conf, calib_dataloader, calib_func, eval_func, eval_dataloader, eval_metric, **kwargs)
151 wrapped_model = Model(model, conf=conf)
153 if eval_metric is not None:
--> 154 metric = register_customer_metric(eval_metric, conf.framework)
155 else:
156 metric = None
in register_customer_metric(user_metric, framework)
1681 metric_cfg = {name: id(user_metric)}
1682 metrics = METRICS(framework)
-> 1683 metrics.register(name, metric_cls)
1684 return metric_cfg
...
221 """
--> 222 assert name not in self.metrics.keys(), "registered metric name already exists."
223 self.metrics.update({name: metric_cls})
The text was updated successfully, but these errors were encountered:
DGP1607
changed the title
Error with Pytorch metrics during PTQ Static
Error with Pytorch metrics during PTQ Static
Dec 13, 2024
Registered metric name : <neural_compressor.metric.metric.Accuracy object at 0x7fb8a291d340>
Looks like in INC library, the registry is managed internally it may not be straightforward to determine exactly where a particular metric was registered. Is there any way to check were it is registered.
Describe the issue
Encountering an issue while PTQ Static on Pytorch Model. The process involves utilizing pytorch metrics for benchmarking such as ['Accuracy','F1']. The workflow is executed within the VS code Jupyter extension.
The issue arises as follows :
During the first execution of the entire notebook, everything runs smoothly without any issues.
However, upon executing the notebook for a second time, I encounter an error.Upon restarting the kernel in VS code it is running fine. But this should not be the case everytime why a restart of kerenel is required for metric.
The traceback for the error is as follows:
AssertionError Traceback (most recent call last)
Cell In[91], line 10
7 metrics = METRICS('pytorch')
8 top2 = metrics'Accuracy'
---> 10 q_model = quantization.fit(
11 model=model,
12 conf=conf,
13 calib_dataloader=dataloader,
14 eval_dataloader=dataloader,
15 eval_metric=top2,
16 )
in fit(model, conf, calib_dataloader, calib_func, eval_func, eval_dataloader, eval_metric, **kwargs)
151 wrapped_model = Model(model, conf=conf)
153 if eval_metric is not None:
--> 154 metric = register_customer_metric(eval_metric, conf.framework)
155 else:
156 metric = None
in register_customer_metric(user_metric, framework)
1681 metric_cfg = {name: id(user_metric)}
1682 metrics = METRICS(framework)
-> 1683 metrics.register(name, metric_cls)
1684 return metric_cfg
...
221 """
--> 222 assert name not in self.metrics.keys(), "registered metric name already exists."
223 self.metrics.update({name: metric_cls})
The text was updated successfully, but these errors were encountered: