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import torch
from torch import Tensor
import math
import warnings
from typing import Tuple, Union
from einops import rearrange
import os
from definition import PRETRAINED_BACKBONE
from ..configs.base_config import base_cfg
def pair(t: Union[int, Tuple[int, int]]) -> Tuple[int, int]:
return t if isinstance(t, tuple) else (t, t)
def build_2d_sincos_posemb(h: int, w: int, embed_dim=1024, temperature=10000.0):
"""Sine-cosine positional embeddings from MoCo-v3
Source: https://github.com/facebookresearch/moco-v3/blob/main/vits.py
"""
grid_w = torch.arange(w, dtype=torch.float32)
grid_h = torch.arange(h, dtype=torch.float32)
grid_w, grid_h = torch.meshgrid(grid_w, grid_h, indexing="xy")
assert (
embed_dim % 4 == 0
), "Embed dimension must be divisible by 4 for 2D sin-cos position embedding"
pos_dim = embed_dim // 4
omega = torch.arange(pos_dim, dtype=torch.float32) / pos_dim
omega = 1.0 / (temperature**omega)
out_w = torch.einsum("m,d->md", [grid_w.flatten(), omega])
out_h = torch.einsum("m,d->md", [grid_h.flatten(), omega])
pos_emb = torch.cat(
[torch.sin(out_w), torch.cos(out_w), torch.sin(out_h), torch.cos(out_h)], dim=1
)[None, :, :]
pos_emb = rearrange(pos_emb, "b (h w) d -> b d h w", h=h, w=w, d=embed_dim)
return pos_emb
def _no_grad_trunc_normal_(tensor: Tensor, mean: float, std: float, a: float, b: float):
# 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 (1.0 + math.erf(x / math.sqrt(2.0))) / 2.0
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(2.0))
tensor.add_(mean)
# Clamp to ensure it's in the proper range
tensor.clamp_(min=a, max=b)
return tensor
def trunc_normal_(tensor: Tensor, mean=0.0, std=1.0, a=-2.0, b=2.0):
# type: (Tensor, float, float, float, float) -> Tensor
r"""Fills the input Tensor with values drawn from a truncated
normal distribution. The values are effectively drawn from the
normal distribution :math:`\mathcal{N}(\text{mean}, \text{std}^2)`
with values outside :math:`[a, b]` redrawn until they are within
the bounds. The method used for generating the random values works
best when :math:`a \leq \text{mean} \leq b`.
Args:
tensor: an n-dimensional `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
Examples:
>>> w = torch.empty(3, 5)
>>> nn.init.trunc_normal_(w)
"""
return _no_grad_trunc_normal_(tensor, mean, std, a, b)
def drop_path(x: Tensor, drop_prob: float = 0.0, training: bool = False):
"""Drop paths (Stochastic Depth) per sample (when applied in main path of residual blocks).
This is the same as the DropConnect impl I created for EfficientNet, etc networks, however,
the original name is misleading as 'Drop Connect' is a different form of dropout in a separate paper...
See discussion: https://github.com/tensorflow/tpu/issues/494#issuecomment-532968956 ... I've opted for
changing the layer and argument names to 'drop path' rather than mix DropConnect as a layer name and use
'survival rate' as the argument.
"""
if drop_prob == 0.0 or not training:
return x
keep_prob = 1 - drop_prob
shape = (x.shape[0],) + (1,) * (
x.ndim - 1
) # work with diff dim tensors, not just 2D ConvNets
random_tensor = keep_prob + torch.rand(shape, dtype=x.dtype, device=x.device)
random_tensor.floor_() # binarize
output = x.div(keep_prob) * random_tensor
return output
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