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Change mutable default arguments to None #6376

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merged 2 commits into from
Jan 9, 2023
Merged

Change mutable default arguments to None #6376

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ed700f5
Change mutable default argument to None
bwroblew Jan 9, 2023
9de1c8b
Update torch_geometric/nn/data_parallel.py
rusty1s Jan 9, 2023
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1 change: 1 addition & 0 deletions CHANGELOG.md
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Expand Up @@ -45,6 +45,7 @@ The format is based on [Keep a Changelog](http://keepachangelog.com/en/1.0.0/).
- Added support for dropping nodes in `utils.to_dense_batch` in case `max_num_nodes` is smaller than the number of nodes ([#6124](https://github.com/pyg-team/pytorch_geometric/pull/6124))
- Added the RandLA-Net architecture as an example ([#5117](https://github.com/pyg-team/pytorch_geometric/pull/5117))
### Changed
- Fix the default arguments of `DataParallel` class ([#6376](https://github.com/pyg-team/pytorch_geometric/pull/6376))
- Fix `ImbalancedSampler` on sliced `InMemoryDataset` ([#6374](https://github.com/pyg-team/pytorch_geometric/pull/6374))
- Breaking Change: Changed the interface and implementation of `GraphMultisetTransformer` ([#6343](https://github.com/pyg-team/pytorch_geometric/pull/6343))
- Fixed the approximate PPR variant in `transforms.GDC` to not crash on graphs with isolated nodes ([#6242](https://github.com/pyg-team/pytorch_geometric/pull/6242))
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10 changes: 5 additions & 5 deletions torch_geometric/nn/data_parallel.py
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Expand Up @@ -34,16 +34,16 @@ class DataParallel(torch.nn.DataParallel):
output_device (int or torch.device): Device location of output.
(default: :obj:`device_ids[0]`)
follow_batch (list or tuple, optional): Creates assignment batch
vectors for each key in the list. (default: :obj:`[]`)
vectors for each key in the list. (default: :obj:`None`)
exclude_keys (list or tuple, optional): Will exclude each key in the
list. (default: :obj:`[]`)
list. (default: :obj:`None`)
"""
def __init__(self, module, device_ids=None, output_device=None,
follow_batch=[], exclude_keys=[]):
follow_batch=None, exclude_keys=None):
super().__init__(module, device_ids, output_device)
self.src_device = torch.device(f'cuda:{self.device_ids[0]}')
self.follow_batch = follow_batch
self.exclude_keys = exclude_keys
self.follow_batch = follow_batch if follow_batch is not None else []
self.exclude_keys = exclude_keys if exclude_keys is not None else []

def forward(self, data_list):
""""""
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