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# Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
#
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
#
import math
import torch
from logging import getLogger
logger = getLogger()
def _no_grad_trunc_normal_(tensor, mean, std, a, b):
# Cut & 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. + math.erf(x / math.sqrt(2.))) / 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.))
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., std=1., a=-2., b=2.):
# type: (Tensor, float, float, float, float) -> Tensor
return _no_grad_trunc_normal_(tensor, mean, std, a, b)
def apply_masks(x, masks):
"""
:param x: tensor of shape [B (batch-size), N (num-patches), D (feature-dim)]
:param masks: list of tensors containing indices of patches in [N] to keep
"""
all_x = []
for m in masks:
mask_keep = m.unsqueeze(-1).repeat(1, 1, x.size(-1))
all_x += [torch.gather(x, dim=1, index=mask_keep)]
return torch.cat(all_x, dim=0)
def repeat_interleave_batch(x, B, repeat):
N = len(x) // B
x = torch.cat([
torch.cat([x[i*B:(i+1)*B] for _ in range(repeat)], dim=0)
for i in range(N)
], dim=0)
return x
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