Tweak bin and sample_n defaults to produce smoother histograms

spfrommer · Dec 30, 2023 · 95f270f · 95f270f
1 parent 9b4b603
commit 95f270f
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Showing 2 changed files with 3 additions and 3 deletions.
diff --git a/torchexplorer/api/api.py b/torchexplorer/api/api.py
@@ -49,8 +49,8 @@ def watch(
  log_freq: int = 500,
  ignore_io_grad_classes: list[type] = [],
  disable_inplace: bool = False,
- bins: int = 20,
- sample_n: Optional[int] = 100,
+ bins: int = 30,
+ sample_n: Optional[int] = 1000,
  reject_outlier_proportion: float = 0.1,
  time_log: tuple[str, Callable] = ('step', lambda module, step: step),
  delay_log_multi_backward: bool = False,

diff --git a/torchexplorer/vega/mock_vega_data.py b/torchexplorer/vega/mock_vega_data.py
@@ -48,7 +48,7 @@ def main():
  model = torchvision.models.resnet18()
  inplace_classes = [torchvision.models.resnet.BasicBlock]
  structure_wrapper = api.watch(
- model, log_freq=1, backend='none', bins=50, sample_n=500,
+ model, log_freq=1, backend='none', bins=20, sample_n=500,
  ignore_io_grad_classes=inplace_classes, disable_inplace=True
  )
  X, y = torch.randn(5, 3, 32, 32), torch.randn(5, 1000)