# pytorch-reader
Read PyTorch checkpoint files (`.pt`, `.pth`) without PyTorch or Burn.
[](https://crates.io/crates/pytorch-reader)
[](https://docs.rs/pytorch-reader)
[](https://github.com/tracel-ai/burn/blob/main/LICENSE-MIT)
The crate parses the pickle inside a checkpoint and hands back each tensor's name, element
type and shape, with the bytes produced only when asked for. It reads every container
`torch.save` has written: the ZIP archive of PyTorch 1.6 and later, the legacy pickle stream
of 0.1.10 through 1.5, the TAR archive before that, and the plain pickle some tools write
for configuration on its own.
```rust
use pytorch_reader::PytorchReader;
let reader = PytorchReader::new("model.pt")?;
for tensor in reader.tensors().values() {
println!("{}: {:?} {:?}", tensor.name, tensor.dtype(), tensor.shape());
}
// Bytes are read from the file here, not at open.
let weight = reader.get("fc.weight").unwrap();
let bytes: Vec<u8> = weight.read()?;
# Ok::<(), Box<dyn std::error::Error>>(())
```
A checkpoint that nests its weights under a key (`"state_dict"`, `"model"`, ...) is opened
with `PytorchReader::with_top_level_key("checkpoint.pt", "state_dict")`. A full-model save
(`torch.save(model)` rather than `torch.save(model.state_dict())`) is read as the module's
parameters and buffers under the names `state_dict()` gives them; extra state
(`get_extra_state()`) and state dict hooks are not run. Non-tensor values (configuration dictionaries, say) can be deserialized into
any `serde` type with `PytorchReader::load_config`.
This is the reader behind [`burn-store`](https://crates.io/crates/burn-store), which wraps
its tensors for loading into [Burn](https://github.com/tracel-ai/burn) modules.
---
Part of the [Burn](https://github.com/tracel-ai/burn) deep learning framework. See the
[Burn Book](https://burn.dev/books/burn/) and the [API documentation](https://docs.rs/burn).