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- """ Position Embedding Utilities
- Hacked together by / Copyright 2022 Ross Wightman
- """
- import logging
- import math
- from typing import List, Tuple, Optional, Union
- import torch
- import torch.nn.functional as F
- from ._fx import register_notrace_function
- _logger = logging.getLogger(__name__)
- @torch.fx.wrap
- @register_notrace_function
- def resample_abs_pos_embed(
- posemb: torch.Tensor,
- new_size: List[int],
- old_size: Optional[List[int]] = None,
- num_prefix_tokens: int = 1,
- interpolation: str = 'bicubic',
- antialias: bool = True,
- verbose: bool = False,
- ):
- # sort out sizes, assume square if old size not provided
- num_pos_tokens = posemb.shape[1]
- num_new_tokens = new_size[0] * new_size[1] + num_prefix_tokens
- if num_new_tokens == num_pos_tokens and new_size[0] == new_size[1]:
- return posemb
- if old_size is None:
- hw = int(math.sqrt(num_pos_tokens - num_prefix_tokens))
- old_size = hw, hw
- if num_prefix_tokens:
- posemb_prefix, posemb = posemb[:, :num_prefix_tokens], posemb[:, num_prefix_tokens:]
- else:
- posemb_prefix, posemb = None, posemb
- # do the interpolation
- embed_dim = posemb.shape[-1]
- orig_dtype = posemb.dtype
- posemb = posemb.float() # interpolate needs float32
- posemb = posemb.reshape(1, old_size[0], old_size[1], -1).permute(0, 3, 1, 2)
- posemb = F.interpolate(posemb, size=new_size, mode=interpolation, antialias=antialias)
- posemb = posemb.permute(0, 2, 3, 1).reshape(1, -1, embed_dim)
- posemb = posemb.to(orig_dtype)
- # add back extra (class, etc) prefix tokens
- if posemb_prefix is not None:
- posemb = torch.cat([posemb_prefix, posemb], dim=1)
- if not torch.jit.is_scripting() and verbose:
- _logger.info(f'Resized position embedding: {old_size} to {new_size}.')
- return posemb
- @torch.fx.wrap
- @register_notrace_function
- def resample_abs_pos_embed_nhwc(
- posemb: torch.Tensor,
- new_size: List[int],
- interpolation: str = 'bicubic',
- antialias: bool = True,
- verbose: bool = False,
- ):
- if new_size[0] == posemb.shape[-3] and new_size[1] == posemb.shape[-2]:
- return posemb
- orig_dtype = posemb.dtype
- posemb = posemb.float()
- posemb = posemb.reshape(1, posemb.shape[-3], posemb.shape[-2], posemb.shape[-1]).permute(0, 3, 1, 2)
- posemb = F.interpolate(posemb, size=new_size, mode=interpolation, antialias=antialias)
- posemb = posemb.permute(0, 2, 3, 1).to(orig_dtype)
- if not torch.jit.is_scripting() and verbose:
- _logger.info(f'Resized position embedding: {posemb.shape[-3:-1]} to {new_size}.')
- return posemb
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