modelling_vitv2.py

from typing import Union

import torch

from transformers import PreTrainedModel

from configuration_vitv2 import ViTv2Config
from hf_src.model.image.vitv2.transformer import ViTv2


class ViTv2PretrainedModel(PreTrainedModel):
    config_class = ViTv2Config

    def __init__(self, config: ViTv2Config):

        super().__init__(config)

        self.backbone = ViTv2(
            img_size=config.img_size,
            patch_size=config.patch_size,
            embed_dim=config.embed_dim,
            depth=config.depth,
            num_heads=config.num_heads,
            mlp_ratio=config.mlp_ratio,
            init_values=config.init_values,
            num_register_tokens=config.num_register_tokens,
        )

        self.post_init()

    def forward(self, *args, **kwargs) -> dict[str, Union[torch.Tensor, None]]:
        return self.backbone(*args, **kwargs)