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| import math | |
| import warnings | |
| from itertools import repeat | |
| import torch | |
| from torch import nn | |
| from torch._six import container_abcs | |
| def drop_path(x, 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 | |
| class DropPath(nn.Module): | |
| """ | |
| Drop paths (Stochastic Depth) per sample | |
| (when applied in main path of residual blocks). | |
| """ | |
| def __init__(self, drop_prob: float = None): | |
| super(DropPath, self).__init__() | |
| self.drop_prob = drop_prob | |
| def forward(self, x): | |
| return drop_path(x, self.drop_prob, self.training) | |
| # From PyTorch internals | |
| def _ntuple(n: int): | |
| def parse(x): | |
| if isinstance(x, container_abcs.Iterable): | |
| return x | |
| return tuple(repeat(x, n)) | |
| return parse | |
| to_1tuple = _ntuple(1) | |
| to_2tuple = _ntuple(2) | |
| to_3tuple = _ntuple(3) | |
| to_4tuple = _ntuple(4) | |
| def _no_grad_trunc_normal_( | |
| tensor: torch.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 | |
| lower = norm_cdf((a - mean) / std) | |
| upper = norm_cdf((b - mean) / std) | |
| # Uniformly fill tensor with values from [l, u], then translate to | |
| # [2l-1, 2u-1]. | |
| tensor.uniform_(2 * lower - 1, 2 * upper - 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: torch.tensor, | |
| mean: float = 0.0, | |
| std: float = 1.0, | |
| a: float = -2.0, | |
| b: float = 2.0, | |
| ): | |
| 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 `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 | |
| Examples: | |
| >>> w = torch.empty(3, 5) | |
| >>> nn.init.trunc_normal_(w) | |
| """ | |
| return _no_grad_trunc_normal_(tensor, mean, std, a, b) | |