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| import torch | |
| from torch import nn | |
| def drop_path(x, drop_prob: float = 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. 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=None): | |
| super(DropPath, self).__init__() | |
| self.drop_prob = drop_prob | |
| def forward(self, x): | |
| return drop_path(x, self.drop_prob, self.training) | |
| from itertools import repeat | |
| from torch._six import container_abcs | |
| # From PyTorch internals | |
| def _ntuple(n): | |
| 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) | |
| import torch | |
| import math | |
| import warnings | |
| 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 | |
| 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) |