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# All rights reserved.
#
# This source code is licensed under the BSD-style license found in the
# LICENSE file in the root directory of this source tree.
# pyre-unsafe
import collections
import dataclasses
import time
from contextlib import contextmanager
from typing import Any, Callable, Dict, Iterable, Iterator
import torch
@contextmanager
def evaluating(net: torch.nn.Module):
"""Temporarily switch to evaluation mode."""
istrain = net.training
try:
net.eval()
yield net
finally:
if istrain:
net.train()
def try_to_cuda(t: Any) -> Any:
"""
Try to move the input variable `t` to a cuda device.
Args:
t: Input.
Returns:
t_cuda: `t` moved to a cuda device, if supported.
"""
try:
t = t.cuda()
except AttributeError:
pass
return t
def try_to_cpu(t: Any) -> Any:
"""
Try to move the input variable `t` to a cpu device.
Args:
t: Input.
Returns:
t_cpu: `t` moved to a cpu device, if supported.
"""
try:
t = t.cpu()
except AttributeError:
pass
return t
def dict_to_cuda(batch: Dict[Any, Any]) -> Dict[Any, Any]:
"""
Move all values in a dictionary to cuda if supported.
Args:
batch: Input dict.
Returns:
batch_cuda: `batch` moved to a cuda device, if supported.
"""
return {k: try_to_cuda(v) for k, v in batch.items()}
def dict_to_cpu(batch):
"""
Move all values in a dictionary to cpu if supported.
Args:
batch: Input dict.
Returns:
batch_cpu: `batch` moved to a cpu device, if supported.
"""
return {k: try_to_cpu(v) for k, v in batch.items()}
def dataclass_to_cuda_(obj):
"""
Move all contents of a dataclass to cuda inplace if supported.
Args:
batch: Input dataclass.
Returns:
batch_cuda: `batch` moved to a cuda device, if supported.
"""
for f in dataclasses.fields(obj):
setattr(obj, f.name, try_to_cuda(getattr(obj, f.name)))
return obj
def dataclass_to_cpu_(obj):
"""
Move all contents of a dataclass to cpu inplace if supported.
Args:
batch: Input dataclass.
Returns:
batch_cuda: `batch` moved to a cpu device, if supported.
"""
for f in dataclasses.fields(obj):
setattr(obj, f.name, try_to_cpu(getattr(obj, f.name)))
return obj
# TODO: test it
def cat_dataclass(batch, tensor_collator: Callable):
"""
Concatenate all fields of a list of dataclasses `batch` to a single
dataclass object using `tensor_collator`.
Args:
batch: Input list of dataclasses.
Returns:
concatenated_batch: All elements of `batch` concatenated to a single
dataclass object.
tensor_collator: The function used to concatenate tensor fields.
"""
elem = batch[0]
collated = {}
for f in dataclasses.fields(elem):
elem_f = getattr(elem, f.name)
if elem_f is None:
collated[f.name] = None
elif torch.is_tensor(elem_f):
collated[f.name] = tensor_collator([getattr(e, f.name) for e in batch])
elif dataclasses.is_dataclass(elem_f):
collated[f.name] = cat_dataclass(
[getattr(e, f.name) for e in batch], tensor_collator
)
elif isinstance(elem_f, collections.abc.Mapping):
collated[f.name] = {
k: (
tensor_collator([getattr(e, f.name)[k] for e in batch])
if elem_f[k] is not None
else None
)
for k in elem_f
}
else:
raise ValueError("Unsupported field type for concatenation")
return type(elem)(**collated)
def recursive_visitor(it: Iterable[Any]) -> Iterator[Any]:
for x in it:
if isinstance(x, Iterable) and not isinstance(x, (str, bytes)):
yield from recursive_visitor(x)
else:
yield x
def get_inlier_indicators(
tensor: torch.Tensor, dim: int, outlier_rate: float
) -> torch.Tensor:
remove_elements = int(min(outlier_rate, 1.0) * tensor.shape[dim] / 2)
hi = torch.topk(tensor, remove_elements, dim=dim).indices.tolist()
lo = torch.topk(-tensor, remove_elements, dim=dim).indices.tolist()
remove_indices = set(recursive_visitor([hi, lo]))
keep_indices = tensor.new_ones(tensor.shape[dim : dim + 1], dtype=torch.bool)
keep_indices[list(remove_indices)] = False
return keep_indices
class Timer:
"""
A simple class for timing execution.
Example::
with Timer():
print("This print statement is timed.")
"""
def __init__(self, name="timer", quiet=False):
self.name = name
self.quiet = quiet
def __enter__(self):
self.start = time.time()
return self
def __exit__(self, *args):
self.end = time.time()
self.interval = self.end - self.start
if not self.quiet:
print("%20s: %1.6f sec" % (self.name, self.interval))
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