Tensorneko

Latest version: v0.3.11

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0.3.10

Features:
- `Registry` for easier decorator-style class/func registration
- `read` and `write` are also in the top level of `tensorneko_util`

0.3.9

Features:
- `JsonReader` now can load jsonl files
- `WeightReader` and `WeightWriter` that can read and write the `pt`, `ckpt`, `safetensors` files
- `read` and `write` now is in the top level of the `tensorneko` package
- `import_tqdm` and `import_tqdm_auto` now are not private

0.3.8

Features:
- Group the metrics, losses, and system stats in logger
- Add `input_shape` arg for `BinaryClassifier`

Fix:
- `NekoModel` cannot train when the logger is `None`

0.3.7

Fix:
- Training error when using `lightning` 2.1
- Docs are not clear for `VideoReader`, `RoundRobinDataset`, `NestedDataset`, `ListDataset`.

0.3.6

Features:
- Better type hint for eventbus and dispatch
- Dynamic import for `mita_tqdm`
- `on_step` and `on_epoch` arguments for `GpuStatsLogger` and `SystemStatsLogger`
- `Timer` now doesn't require parentheses as decorator

Fix:
- `crop_with_padding` using numpy array
- `sample_indexes` not imported
- `FID` not working with `progress_bar=True`
- import error with pytorch lightning 2.1.3

0.3.5

Features:
- Add support for pytorch 2.1 and lightning 2.1
- Auto infer for `notebook.display`, now supports audio, video, image, and code.
- `ListDataset`, like `TensorDataset` in `torch` but directly uses list
- `SequentialIterSampler`, can split dataset to multiple epochs. Normally can be used for training in large-scale dataset but want to utilize the learning rate scheduler based on epochs.
- `BinaryClassifier`. A predefined model for binary classification.
- `sample_indexes`. Sample a continious indexes giving a source sequence length.

Fix:
- Import problem raise by `RoundRobinDataset`
- `FID` calculation problem when set `num_workers > 0`.
- `iou_2d` calculation error

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