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Hello,
I followed the example and come up with this:
model = resnet18(pretrained=True).eval() example_inputs = torch.randn(1, 3, 224, 224) DG = tp.DependencyGraph().build_dependency(model, example_inputs=example_inputs) group = DG.get_pruning_group( model.conv1, tp.prune_conv_out_channels, idxs=[2, 6, 9] ) scorer = tp.importance.MagnitudeImportance() imp_score = scorer(group) min_score = imp_score.min()
But I always got this error saying:
--------------------------------------------------------------------------- RuntimeError Traceback (most recent call last) Cell In[36], [line 4](vscode-notebook-cell:?execution_count=36&line=4) [2](vscode-notebook-cell:?execution_count=36&line=2) group = DG.get_pruning_group( model.conv1, tp.prune_conv_out_channels, idxs=[2, 6, 9] ) [3](vscode-notebook-cell:?execution_count=36&line=3) scorer = tp.importance.MagnitudeImportance() ----> [4](vscode-notebook-cell:?execution_count=36&line=4) imp_score = scorer(group) [5](vscode-notebook-cell:?execution_count=36&line=5) #imp_score is a 1-D tensor with length 3 for channels [2, 6, 9] [6](vscode-notebook-cell:?execution_count=36&line=6) min_score = imp_score.min() File [c:\Users\ANDIKA](file:///C:/Users/ANDIKA) WAHYU\AppData\Local\Programs\Python\Python310\lib\site-packages\torch\utils\_contextlib.py:115, in context_decorator.<locals>.decorate_context(*args, **kwargs) [112](file:///C:/Users/ANDIKA%20WAHYU/AppData/Local/Programs/Python/Python310/lib/site-packages/torch/utils/_contextlib.py:112) @functools.wraps(func) [113](file:///C:/Users/ANDIKA%20WAHYU/AppData/Local/Programs/Python/Python310/lib/site-packages/torch/utils/_contextlib.py:113) def decorate_context(*args, **kwargs): [114](file:///C:/Users/ANDIKA%20WAHYU/AppData/Local/Programs/Python/Python310/lib/site-packages/torch/utils/_contextlib.py:114) with ctx_factory(): --> [115](file:///C:/Users/ANDIKA%20WAHYU/AppData/Local/Programs/Python/Python310/lib/site-packages/torch/utils/_contextlib.py:115) return func(*args, **kwargs) File [c:\Users\ANDIKA](file:///C:/Users/ANDIKA) WAHYU\AppData\Local\Programs\Python\Python310\lib\site-packages\torch_pruning\pruner\importance.py:293, in GroupNormImportance.__call__(self, group) [290](file:///C:/Users/ANDIKA%20WAHYU/AppData/Local/Programs/Python/Python310/lib/site-packages/torch_pruning/pruner/importance.py:290) if len(group_imp) == 0: # skip groups without parameterized layers [291](file:///C:/Users/ANDIKA%20WAHYU/AppData/Local/Programs/Python/Python310/lib/site-packages/torch_pruning/pruner/importance.py:291) return None --> [293](file:///C:/Users/ANDIKA%20WAHYU/AppData/Local/Programs/Python/Python310/lib/site-packages/torch_pruning/pruner/importance.py:293) group_imp = self._reduce(group_imp, group_idxs) [294](file:///C:/Users/ANDIKA%20WAHYU/AppData/Local/Programs/Python/Python310/lib/site-packages/torch_pruning/pruner/importance.py:294) group_imp = self._normalize(group_imp, self.normalizer) [295](file:///C:/Users/ANDIKA%20WAHYU/AppData/Local/Programs/Python/Python310/lib/site-packages/torch_pruning/pruner/importance.py:295) return group_imp File [c:\Users\ANDIKA](file:///C:/Users/ANDIKA) WAHYU\AppData\Local\Programs\Python\Python310\lib\site-packages\torch_pruning\pruner\importance.py:180, in GroupNormImportance._reduce(self, group_imp, group_idxs) [178](file:///C:/Users/ANDIKA%20WAHYU/AppData/Local/Programs/Python/Python310/lib/site-packages/torch_pruning/pruner/importance.py:178) if self.group_reduction == "sum" or self.group_reduction == "mean": [179](file:///C:/Users/ANDIKA%20WAHYU/AppData/Local/Programs/Python/Python310/lib/site-packages/torch_pruning/pruner/importance.py:179) debug_file.write("Adding importance using scatter_add_\n") --> [180](file:///C:/Users/ANDIKA%20WAHYU/AppData/Local/Programs/Python/Python310/lib/site-packages/torch_pruning/pruner/importance.py:180) reduced_imp.scatter_add_(0, torch.tensor(root_idxs, device=imp.device), imp) # accumulated importance [181](file:///C:/Users/ANDIKA%20WAHYU/AppData/Local/Programs/Python/Python310/lib/site-packages/torch_pruning/pruner/importance.py:181) elif self.group_reduction == "max": [182](file:///C:/Users/ANDIKA%20WAHYU/AppData/Local/Programs/Python/Python310/lib/site-packages/torch_pruning/pruner/importance.py:182) # keep the max importance [183](file:///C:/Users/ANDIKA%20WAHYU/AppData/Local/Programs/Python/Python310/lib/site-packages/torch_pruning/pruner/importance.py:183) selected_imp = torch.index_select(reduced_imp, 0, torch.tensor(root_idxs, device=imp.device)) RuntimeError: index 6 is out of bounds for dimension 0 with size 3
what is wrong? i already doing some tracing into torch-pruning code, but to no avail. please help me
The text was updated successfully, but these errors were encountered:
Any kind of help is appreciated @VainF
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Hello,
I followed the example and come up with this:
But I always got this error saying:
what is wrong? i already doing some tracing into torch-pruning code, but to no avail. please help me
The text was updated successfully, but these errors were encountered: