Allows dicts batches in dataloader. #1354
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apaszke
reviewed
Apr 26, 2017
| # of tensors; in that case we collate each element in the tuple | ||
| elif isinstance(batch[0], collections.Mapping): | ||
| return {key: default_collate([d[key] for d in batch]) for key in batch[0]} | ||
| elif isinstance(batch[0], collections.Sequence): |
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apaszke
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Apr 28, 2017
torch/utils/data/dataloader.py
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| return {key: default_collate([d[key] for d in batch]) for key in batch[0]} | ||
| elif isinstance(batch[0], collections.Sequence): | ||
| # if each batch element is not a tensor, then it should be a sequence | ||
| # of tensors; in that case we collate each element in the sequence |
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soumith
approved these changes
Apr 28, 2017
Jiaming-Liu
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May 18, 2017
* Allow dicts in Dataloader * use collections.Sequence instead of collections.Iterable in dataloader
eqy
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Jan 20, 2022
* Refactor War Sync Insertion Pass (pytorch#1339) * Remove kir::Expr::scope_ (pytorch#1341) * Fusion IR Refactor (pytorch#1343) * Refactor KIR Step 1 - Remove kir::Node (pytorch#1347) * Refactor KIR Step 2 - TMP IrUtils change (pytorch#1348) * Refactor KIR Step 3 - Remove kir::Expr and kir::Val. (pytorch#1349) * Refactor KIR Step 4 - Remove kir::Bool,Double,Int,NamedScalar. (pytorch#1350) * Refactor KIR Step 5 - Remove kir::IterDomain/TensorDomain/TensorView (pytorch#1351) * Refactor KIR Step 6 - Remove kir::UnaryOp/BinaryOp/TernaryOp/ReductionOp/WelfordOp/BroadcastOp. (pytorch#1352) * Refactor KIR Step 7 - Remove kir dispatch (pytorch#1353) * Refactor KIR Step 8 - Clean up lower_utils (pytorch#1355) * Refactor KIR Step 9 - lower_utils ir_utils::applyReplacements. (pytorch#1354) * Refactor KIR Step 10 - Remove kir_printer in favor of io_stream (pytorch#1356)
jithunnair-amd
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Mar 18, 2024
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This is duplicate of #1131 (and #1350). In the previous PR, there is a test which failed. But I built pytorch from source and ran the tests. I couldn't reproduce the failing test; all tests pass fine.
Also, I have made small change: use
collection.Sequenceinstead ofcollections.Iterable. This is because set is a iterable but not sequence. Batching a set is ambiguous and therefore shouldn't be batched like a list.