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# Copyright 2024 EPFL and Apple Inc.
#
# 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
#
# http://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.
import hashlib
import collections.abc
from itertools import repeat
import torchvision.transforms.functional as TF
from fourm.utils.data_constants import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD
def denormalize(img, mean=IMAGENET_DEFAULT_MEAN, std=IMAGENET_DEFAULT_STD):
"""
Denormalizes an image.
Args:
img (torch.Tensor): Image to denormalize.
mean (tuple): Mean to use for denormalization.
std (tuple): Standard deviation to use for denormalization.
"""
return TF.normalize(
img.clone(),
mean= [-m/s for m, s in zip(mean, std)],
std= [1/s for s in std]
)
def generate_uint15_hash(seed_str):
"""Generates a hash of the seed string as an unsigned int15 integer"""
return int(hashlib.sha256(seed_str.encode('utf-8')).hexdigest(), 16) % (2**15)
# From PyTorch internals
def _ntuple(n):
def parse(x):
if isinstance(x, collections.abc.Iterable):
return x
return tuple(repeat(x, n))
return parse
to_1tuple = _ntuple(1)
to_2tuple = _ntuple(2)
to_3tuple = _ntuple(3)
to_4tuple = _ntuple(4)
to_ntuple = _ntuple
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