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import math
import warnings
import torch
from dist import cached_grid
from utils import log as log_utils
LOGGER = log_utils.getLogger(__name__)
def mask_selector(masks_softmaxed, top=2, size_norm=False):
"""Select <top> centre most masks and sumthem """
b, k, *other_dims, h, w = masks_softmaxed.shape
masks_softmaxed = masks_softmaxed.view(b, k, 1, h, w)
g = cached_grid(h, w, device=masks_softmaxed.device, dtype=masks_softmaxed.dtype)
x = g[0, 0] / (w - 1) - .5
y = g[0, 1] / (h - 1) - .5
v = (x ** 2 + y ** 2) * 2
assert len(v.shape) == 2
v = v.view(*[1] * (len(masks_softmaxed) - 2), h, w)
scores = (masks_softmaxed * (1 - v)).sum([-1, -2]).view(b, k)
scores = scores / (masks_softmaxed.flatten(-3).sum(-1) + 1e-6)
LOGGER.debug_once(f"Selector -- masks in {masks_softmaxed.shape}; scores {scores.shape}")
best_idxs = scores.topk(top, dim=-1).indices[..., None, None, None].expand(-1, -1, -1, h, w)
wrst_idxs = (-scores).topk(k - top, dim=-1).indices[..., None, None, None].expand(-1, -1, -1, h, w)
LOGGER.debug_once(f"Selector -- inds {best_idxs.shape} {wrst_idxs.shape}")
masks_out = torch.empty(b, 2, 1, h, w, device=masks_softmaxed.device, dtype=masks_softmaxed.dtype)
centre_most_masks = torch.gather(masks_softmaxed, 1, best_idxs).sum(1, keepdim=True)
other_masks = torch.gather(masks_softmaxed, 1, wrst_idxs).sum(1, keepdim=True)
LOGGER.debug_once(f"Selector -- best {centre_most_masks.shape} others {other_masks.shape}")
masks_out[:, 1:] = centre_most_masks
masks_out[:, :1] = other_masks
return masks_out.view(b, 2, *other_dims, h, w)
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.
if (mean < a - 2 * std) or (mean > b + 2 * std):
warnings.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.))
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)
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