source_code/SegMamba/monai/networks/layers/weight_init.py

# Copyright (c) MONAI Consortium
# 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.

from __future__ import annotations

import math

import torch


def _no_grad_trunc_normal_(tensor, mean, std, a, b):
    """Tensor initialization with truncated normal distribution.
    Based on:
    https://people.sc.fsu.edu/~jburkardt/presentations/truncated_normal.pdf
    https://github.com/rwightman/pytorch-image-models

    Args:
       tensor: an n-dimensional `torch.Tensor`.
       mean: the mean of the normal distribution.
       std: the standard deviation of the normal distribution.
       a: the minimum cutoff value.
       b: the maximum cutoff value.
    """

    def norm_cdf(x):
        return (1.0 + math.erf(x / math.sqrt(2.0))) / 2.0

    with torch.no_grad():
        l = norm_cdf((a - mean) / std)
        u = norm_cdf((b - mean) / std)
        tensor.uniform_(2 * l - 1, 2 * u - 1)
        tensor.erfinv_()
        tensor.mul_(std * math.sqrt(2.0))
        tensor.add_(mean)
        tensor.clamp_(min=a, max=b)
        return tensor


def trunc_normal_(tensor, mean=0.0, std=1.0, a=-2.0, b=2.0):
    """Tensor initialization with truncated normal distribution.
    Based on:
    https://github.com/rwightman/pytorch-image-models

    Args:
       tensor: an n-dimensional `torch.Tensor`
       mean: the mean of the normal distribution
       std: the standard deviation of the normal distribution
       a: the minimum cutoff value
       b: the maximum cutoff value
    """

    if std <= 0:
        raise ValueError("the standard deviation should be greater than zero.")

    if a >= b:
        raise ValueError("minimum cutoff value (a) should be smaller than maximum cutoff value (b).")

    return _no_grad_trunc_normal_(tensor, mean, std, a, b)