mmpretrain/tests/test_models/test_backbones/utils.py

32 lines
1.2 KiB
Python

# Copyright (c) OpenMMLab. All rights reserved.
import math
import torch
import torch.nn.functional as F
def timm_resize_pos_embed(posemb, posemb_new, num_tokens=1, gs_new=()):
"""Timm version pos embed resize function.
copied from https://github.com/rwightman/pytorch-image-models/blob/master/timm/models/vision_transformer.py
""" # noqa:E501
ntok_new = posemb_new.shape[1]
if num_tokens:
posemb_tok, posemb_grid = posemb[:, :num_tokens], posemb[0,
num_tokens:]
ntok_new -= num_tokens
else:
posemb_tok, posemb_grid = posemb[:, :0], posemb[0]
gs_old = int(math.sqrt(len(posemb_grid)))
if not len(gs_new): # backwards compatibility
gs_new = [int(math.sqrt(ntok_new))] * 2
assert len(gs_new) >= 2
posemb_grid = posemb_grid.reshape(1, gs_old, gs_old,
-1).permute(0, 3, 1, 2)
posemb_grid = F.interpolate(
posemb_grid, size=gs_new, mode='bicubic', align_corners=False)
posemb_grid = posemb_grid.permute(0, 2, 3,
1).reshape(1, gs_new[0] * gs_new[1], -1)
posemb = torch.cat([posemb_tok, posemb_grid], dim=1)
return posemb