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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 | |