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"""
Utility functions for managing computation device
"""
import numpy as np
import torch
def to_device(batch, device, callback=None, non_blocking=False):
"""
Transfer data to another device (i.e. GPU, CPU:torch, CPU:numpy).
This function recursively processes nested data structures (lists, tuples, dicts)
and transfers each tensor to the specified device.
Args:
batch: Data to transfer (list, tuple, dict of tensors or other objects)
device: Target device - pytorch device (e.g., 'cuda', 'cpu') or 'numpy'
callback: Optional function that would be called on every element before processing
non_blocking: If True, allows asynchronous copy to GPU (may be faster)
Returns:
Data with the same structure as input but with tensors transferred to target device
"""
if callback:
batch = callback(batch)
if isinstance(batch, dict):
return {
k: to_device(v, device, non_blocking=non_blocking) for k, v in batch.items()
}
if isinstance(batch, (tuple, list)):
return type(batch)(
to_device(x, device, non_blocking=non_blocking) for x in batch
)
x = batch
if device == "numpy":
if isinstance(x, torch.Tensor):
x = x.detach().cpu().numpy()
elif x is not None:
if isinstance(x, np.ndarray):
x = torch.from_numpy(x)
if torch.is_tensor(x):
x = x.to(device, non_blocking=non_blocking)
return x
def to_numpy(x):
"""Convert data to numpy arrays.
Args:
x: Input data (can be tensor, array, or nested structure)
Returns:
Data with the same structure but with tensors converted to numpy arrays
"""
return to_device(x, "numpy")
def to_cpu(x):
"""Transfer data to CPU.
Args:
x: Input data (can be tensor, array, or nested structure)
Returns:
Data with the same structure but with tensors moved to CPU
"""
return to_device(x, "cpu")
def to_cuda(x):
"""Transfer data to CUDA device (GPU).
Args:
x: Input data (can be tensor, array, or nested structure)
Returns:
Data with the same structure but with tensors moved to GPU
"""
return to_device(x, "cuda")
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