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# coding=utf-8 | |
# Copyright 2022 The IDEA Authors. All rights reserved. | |
# | |
# 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. | |
# ------------------------------------------------------------------------------------------------ | |
# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved | |
# ------------------------------------------------------------------------------------------------ | |
# Misc functions | |
# Modified from: | |
# https://github.com/facebookresearch/detr/blob/main/util/misc.py | |
# ------------------------------------------------------------------------------------------------ | |
from typing import List, Optional | |
import torch | |
import torchvision | |
from torch import Tensor | |
def interpolate(input, size=None, scale_factor=None, mode="nearest", align_corners=None): | |
# type: (Tensor, Optional[List[int]], Optional[float], str, Optional[bool]) -> Tensor | |
""" | |
Equivalent to ``torch.nn.functional.interpolate``. | |
""" | |
return torchvision.ops.misc.interpolate(input, size, scale_factor, mode, align_corners) | |
def inverse_sigmoid(x, eps=1e-3): | |
""" | |
The inverse function for sigmoid activation function. | |
Note: It might face numberical issues with fp16 small eps. | |
""" | |
x = x.clamp(min=0, max=1) | |
x1 = x.clamp(min=eps) | |
x2 = (1 - x).clamp(min=eps) | |
return torch.log(x1 / x2) | |