Delete utils

utils/misc.py

@@ -1,72 +0,0 @@
-# Copyright 2023 solo-learn development team.
-
-# Permission is hereby granted, free of charge, to any person obtaining a copy of
-# this software and associated documentation files (the "Software"), to deal in
-# the Software without restriction, including without limitation the rights to use,
-# copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the
-# Software, and to permit persons to whom the Software is furnished to do so,
-# subject to the following conditions:
-
-# The above copyright notice and this permission notice shall be included in all copies
-# or substantial portions of the Software.
-
-# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED,
-# INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR
-# PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE
-# FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR
-# OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER
-# DEALINGS IN THE SOFTWARE.
-
-import logging
-import math
-
-import torch
-
-
-def _no_grad_trunc_normal_(tensor, mean, std, a, b):
-    """Copy & paste from PyTorch official master until it's in a few official releases - RW
-    Method based on https://people.sc.fsu.edu/~jburkardt/presentations/truncated_normal.pdf
-    """
-
-    def norm_cdf(x):
-        """Computes standard normal cumulative distribution function"""
-
-        return (1.0 + math.erf(x / math.sqrt(2.0))) / 2.0
-
-    if (mean < a - 2 * std) or (mean > b + 2 * std):
-        logging.warn(
-            "mean is more than 2 std from [a, b] in nn.init.trunc_normal_. "
-            "The distribution of values may be incorrect.",
-            stacklevel=2,
-        )
-
-    with torch.no_grad():
-        # Values are generated by using a truncated uniform distribution and
-        # then using the inverse CDF for the normal distribution.
-        # Get upper and lower cdf values
-        l = norm_cdf((a - mean) / std)
-        u = norm_cdf((b - mean) / std)
-
-        # Uniformly fill tensor with values from [l, u], then translate to
-        # [2l-1, 2u-1].
-        tensor.uniform_(2 * l - 1, 2 * u - 1)
-
-        # Use inverse cdf transform for normal distribution to get truncated
-        # standard normal
-        tensor.erfinv_()
-
-        # Transform to proper mean, std
-        tensor.mul_(std * math.sqrt(2.0))
-        tensor.add_(mean)
-
-        # Clamp to ensure it's in the proper range
-        tensor.clamp_(min=a, max=b)
-        return tensor
-
-
-def trunc_normal_(tensor, mean=0.0, std=1.0, a=-2.0, b=2.0):
-    """Copy & paste from PyTorch official master until it's in a few official releases - RW
-    Method based on https://people.sc.fsu.edu/~jburkardt/presentations/truncated_normal.pdf
-    """
-
-    return _no_grad_trunc_normal_(tensor, mean, std, a, b)