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# Copyright (c) EPFL VILAB.
# All rights reserved.
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
# --------------------------------------------------------
# Based on BEiT, timm, DINO DeiT and MAE-priv code bases
# https://github.com/microsoft/unilm/tree/master/beit
# https://github.com/rwightman/pytorch-image-models/tree/master/timm
# https://github.com/facebookresearch/deit
# https://github.com/facebookresearch/dino
# https://github.com/BUPT-PRIV/MAE-priv
# --------------------------------------------------------
import re
import torch
def interpolate_pos_embed_vit(model, checkpoint_model):
if 'pos_embed' in checkpoint_model:
pos_embed_checkpoint = checkpoint_model['pos_embed']
embedding_size = pos_embed_checkpoint.shape[-1]
num_patches = model.patch_embed.num_patches
num_extra_tokens = model.pos_embed.shape[-2] - num_patches
# height (== width) for the checkpoint position embedding
orig_size = int((pos_embed_checkpoint.shape[-2] - num_extra_tokens) ** 0.5)
# height (== width) for the new position embedding
new_size = int(num_patches ** 0.5)
# class_token and dist_token are kept unchanged
if orig_size != new_size:
print("Position interpolate from %dx%d to %dx%d" % (orig_size, orig_size, new_size, new_size))
extra_tokens = pos_embed_checkpoint[:, :num_extra_tokens]
# only the position tokens are interpolated
pos_tokens = pos_embed_checkpoint[:, num_extra_tokens:]
pos_tokens = pos_tokens.reshape(-1, orig_size, orig_size, embedding_size).permute(0, 3, 1, 2)
pos_tokens = torch.nn.functional.interpolate(
pos_tokens, size=(new_size, new_size), mode='bicubic', align_corners=False)
pos_tokens = pos_tokens.permute(0, 2, 3, 1).flatten(1, 2)
new_pos_embed = torch.cat((extra_tokens, pos_tokens), dim=1)
checkpoint_model['pos_embed'] = new_pos_embed
def interpolate_pos_embed_multimae(model, checkpoint_model):
pattern = "input_adapters\.(.*)\.pos_emb"
matched_keys = [k for k in checkpoint_model if bool(re.match(pattern, k))]
for key in matched_keys:
domain = re.match(pattern, key).group(1) # group(0) is entire matched regex
if getattr(model.input_adapters, domain, None) is not None:
pos_embed_checkpoint = checkpoint_model[key]
_, _, orig_H, orig_W = pos_embed_checkpoint.shape
_, _, new_H, new_W = getattr(model.input_adapters, domain).pos_emb.shape
if (orig_H != new_H) or (orig_W != new_W):
print(f"Key {key}: Position interpolate from {orig_H}x{orig_W} to {new_H}x{new_W}")
pos_embed_checkpoint = torch.nn.functional.interpolate(
pos_embed_checkpoint, size=(new_H, new_W), mode='bicubic', align_corners=False)
checkpoint_model[key] = pos_embed_checkpoint
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