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25a49bc | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 | # Copyright 2023 Google LLC
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
#
# Author: spopov@google.com (Stefan Popov)
"""Miscellaneous utilities."""
import dataclasses
import re
from typing import Any, Callable, Iterable, Optional, TypeVar, Union
import numpy as np
import torch as t
InputTensor = Union[t.Tensor, np.ndarray, int, float, Iterable]
TorchDevice = Union[t.device, str, None]
T = TypeVar("T")
class TensorContainerMixin:
"""Allows unified operation on all tensors contained in a dataclass."""
def _apply(self, fn: Callable[[t.Tensor], t.Tensor]):
result = []
for field in dataclasses.fields(self):
field = getattr(self, field.name)
if t.is_tensor(field):
field = fn(field)
elif isinstance(field, list) or isinstance(field, tuple):
field = [fn(e) if t.is_tensor(e) else e for e in field]
elif isinstance(field, TensorContainerMixin):
field = field._apply(fn)
result.append(field)
return type(self)(*result)
def cuda(self: T) -> T:
return self._apply(lambda v: v.cuda())
def cpu(self: T) -> T:
return self._apply(lambda v: v.cpu())
def detach(self: T) -> T:
return self._apply(lambda v: v.detach())
def numpy(self: T) -> T:
return self._apply(lambda v: v.numpy())
def to(self: T, device: TorchDevice) -> T:
return self._apply(lambda v: v.to(device))
def __getitem__(self: T, index: Any) -> T:
return self._apply(lambda v: v[index])
def get_structure(self) -> dict[str, str]:
"""Debugging routine, returns type and shape of the each field."""
result = {}
for field in dataclasses.fields(self):
v = getattr(self, field.name)
if t.is_tensor(v):
v: t.Tensor = v.detach()
dtype = re.sub(r"^torch\.", "", str(v.dtype))
structure = f"t.{dtype}{list(v.shape)}({v.device})"
elif isinstance(v, np.ndarray):
structure = f"np.{v.dtype.name}{list(v.shape)}"
elif isinstance(v, list):
structure = f"list[{len(v)}]"
elif isinstance(v, tuple):
structure = f"tuple[{len(v)}]"
else:
structure = f"{type(v).__name__}"
result[field.name] = structure
return result
def to_tensor(v: InputTensor, dtype: t.dtype,
device: Optional[Union[t.device, str]] = None) -> t.Tensor:
"""Converts a value to tensor, checking the type.
Args:
v: The value to convert. If it is already a tensor or an array, this
function checks that the type is equal to dtype. Otherwise, uses
torch.as_tensor to convert it to tensor.
dtype: The required type.
device: The target tensor device (optional(.
Returns:
The resulting tensor
"""
if not t.is_tensor(v):
if hasattr(v, "__array_interface__"):
# Preserve the types of arrays. The must match the given type.
v = t.as_tensor(v)
else:
v = t.as_tensor(v, dtype=dtype)
if v.dtype != dtype:
raise ValueError(f"Expecting type '{dtype}', found '{v.dtype}'")
if device is not None:
v = v.to(device)
return v
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