a
    h|}                  
   @   s  U d dl Z d dlmZ d dlmZ d dlmZmZmZm	Z	 d dl
Z
d dlmZ ddlmZmZ ddlmZmZ ddlmZ d	d
lmZmZmZ d	dlmZ d	dlmZmZ g dZG dd deZG dd deZ G dd dej!Z"G dd dej!Z#G dd dej!Z$e%e%e%e%e%e	e e&ee$d	ddZ'deiZ(e)e*ef e+d< i e(dddZ,G d d! d!eZ-G d"d# d#eZ.G d$d% d%eZ/G d&d' d'eZ0G d(d) d)eZ1e ed*e-j2fd+dd,d-e	e- e&ee$d.d/d0Z3e ed*e.j2fd+dd,d-e	e. e&ee$d.d1d2Z4e ed*e/j2fd+dd,d-e	e/ e&ee$d.d3d4Z5e ed*e0j2fd+dd,d-e	e0 e&ee$d.d5d6Z6e ed7d+dd,d-e	e1 e&ee$d.d8d9Z7d@e%e%d<e*e&d<d=d>d?Z8dS )A    N)OrderedDict)partial)AnyCallable
NamedTupleOptional   )Conv2dNormActivationMLP)ImageClassificationInterpolationMode)_log_api_usage_once   )register_modelWeightsWeightsEnum)_IMAGENET_CATEGORIES)_ovewrite_named_paramhandle_legacy_interface)VisionTransformerViT_B_16_WeightsViT_B_32_WeightsViT_L_16_WeightsViT_L_32_WeightsViT_H_14_Weightsvit_b_16vit_b_32vit_l_16vit_l_32vit_h_14c                   @   sV   e Zd ZU eed< eed< eed< ejZedej	f ed< ej
Zedej	f ed< dS )ConvStemConfigout_channelskernel_sizestride.
norm_layeractivation_layerN)__name__
__module____qualname__int__annotations__nnZBatchNorm2dr$   r   ModuleZReLUr%    r-   r-   S/var/www/auris/lib/python3.9/site-packages/torchvision/models/vision_transformer.pyr        s
   
r    c                       s:   e Zd ZdZdZeeed fddZ fddZ  Z	S )MLPBlockzTransformer MLP block.r   )in_dimmlp_dimdropoutc                    sd   t  j|||gtjd |d |  D ]:}t|tjr$tj|j	 |j
d ur$tjj|j
dd q$d S )N)r%   Zinplacer2   ư>std)super__init__r+   ZGELUmodules
isinstanceLinearinitZxavier_uniform_weightbiasnormal_)selfr0   r1   r2   m	__class__r-   r.   r7   -   s    
zMLPBlock.__init__c              	      s   | dd }|d u s|dk rxtdD ]R}	dD ]H}
| d|	d  d|
 }| d|	  d|
 }||v r,||||< q,q$t ||||||| d S )Nversionr   )r<   r=   Zlinear_r   .   )getrangepopr6   _load_from_state_dict)r?   Z
state_dictprefixZlocal_metadatastrictZmissing_keysZunexpected_keysZ
error_msgsrC   itypeZold_keyZnew_keyrA   r-   r.   rI   6   s"    
zMLPBlock._load_from_state_dict)
r&   r'   r(   __doc___versionr)   floatr7   rI   __classcell__r-   r-   rA   r.   r/   (   s   	r/   c                	       sZ   e Zd ZdZeejddfeeeeee	de
jjf d fddZe
jdd	d
Z  ZS )EncoderBlockzTransformer encoder block.r3   Zeps.)	num_heads
hidden_dimr1   r2   attention_dropoutr$   c                    sV   t    || _||| _tj|||dd| _t|| _||| _	t
|||| _d S )NT)r2   Zbatch_first)r6   r7   rT   ln_1r+   ZMultiheadAttentionself_attentionDropoutr2   ln_2r/   mlp)r?   rT   rU   r1   r2   rV   r$   rA   r-   r.   r7   Y   s    	


zEncoderBlock.__init__inputc                 C   sj   t | dkd|j  | |}| j|||dd\}}| |}|| }| |}| |}|| S )NrE   2Expected (batch_size, seq_length, hidden_dim) got F)Zneed_weights)	torch_assertdimshaperW   rX   r2   rZ   r[   )r?   r]   x_yr-   r-   r.   forwardn   s    



zEncoderBlock.forwardr&   r'   r(   rN   r   r+   	LayerNormr)   rP   r   r_   r,   r7   Tensorrf   rQ   r-   r-   rA   r.   rR   V   s   	rR   c                       s^   e Zd ZdZeejddfeeeeeeee	de
jjf d fddZe
jdd	d
Z  ZS )Encoderz?Transformer Model Encoder for sequence to sequence translation.r3   rS   .)
seq_length
num_layersrT   rU   r1   r2   rV   r$   c	                    s~   t    ttd||jdd| _t|| _	t
 }	t|D ] }
t|||||||	d|
 < qBt|	| _||| _d S )Nr   g{Gz?r4   Zencoder_layer_)r6   r7   r+   	Parameterr_   emptyr>   pos_embeddingrY   r2   r   rG   rR   
Sequentiallayersln)r?   rk   rl   rT   rU   r1   r2   rV   r$   rq   rL   rA   r-   r.   r7   }   s    
zEncoder.__init__r\   c                 C   s<   t | dkd|j  || j }| | | |S )NrE   r^   )r_   r`   ra   rb   ro   rr   rq   r2   )r?   r]   r-   r-   r.   rf      s    
zEncoder.forwardrg   r-   r-   rA   r.   rj   z   s   rj   c                       s   e Zd ZdZddddeejdddfeeeeeeeeee	e e
dejjf e	ee  d fd	d
ZejejdddZejdddZ  ZS )r   z;Vision Transformer as per https://arxiv.org/abs/2010.11929.        i  Nr3   rS   .)
image_size
patch_sizerl   rT   rU   r1   r2   rV   num_classesrepresentation_sizer$   conv_stem_configsc                    s  t    t|  t|| dkd || _|| _|| _|| _|| _	|| _
|	| _|
| _|| _|d urt }d}t|D ]:\}}|d| t||j|j|j|j|jd |j}qx|dtj||dd || _ntjd|||d	| _|| d
 }ttdd|| _|d7 }t||||||||| _|| _t }|
d u rRt ||	|d< n,t ||
|d< t! |d< t |
|	|d< t|| _"t#| jtjr| jj$| jjd  | jjd  }tj%j&| jj't()d| d | jj*d urbtj%+| jj* nj| jj,d urbt#| jj,tjrbtj%j-| jj,j'dt()d| jj,j d | jj,j*d urbtj%+| jj,j* t.| j"drt#| j"j/tj r| j"j/j0}tj%j&| j"j/j't()d| d tj%+| j"j/j* t#| j"j1tj rtj%+| j"j1j' tj%+| j"j1j* d S )Nr   z&Input shape indivisible by patch size!rE   Zconv_bn_relu_)in_channelsr!   r"   r#   r$   r%   	conv_lastr   )ry   r!   r"   )ry   r!   r"   r#   r   head
pre_logitsZactr4   rs   g       @)meanr5   )2r6   r7   r   r_   r`   rt   ru   rU   r1   rV   r2   rv   rw   r$   r+   rp   	enumerateZ
add_moduler	   r!   r"   r#   r%   ZConv2d	conv_projrm   zerosclass_tokenrj   encoderrk   r   r:   ZTanhheadsr9   ry   r;   Ztrunc_normal_r<   mathsqrtr=   Zzeros_rz   r>   hasattrr|   Zin_featuresr{   )r?   rt   ru   rl   rT   rU   r1   r2   rV   rv   rw   r$   rx   Zseq_projZprev_channelsrL   Zconv_stem_layer_configrk   Zheads_layersZfan_inrA   r-   r.   r7      s    


   
 zVisionTransformer.__init__)rc   returnc           	      C   s   |j \}}}}| j}t|| jkd| j d| d t|| jkd| j d| d || }|| }| |}||| j|| }|ddd}|S )NzWrong image height! Expected z	 but got !zWrong image width! Expected r   r   r   )	rb   ru   r_   r`   rt   r   reshaperU   permute)	r?   rc   nchwpZn_hZn_wr-   r-   r.   _process_input  s    ""
z VisionTransformer._process_input)rc   c                 C   s^   |  |}|jd }| j|dd}tj||gdd}| |}|d d df }| |}|S )Nr   r   ra   )r   rb   r   expandr_   catr   r   )r?   rc   r   Zbatch_class_tokenr-   r-   r.   rf   !  s    



zVisionTransformer.forward)r&   r'   r(   rN   r   r+   rh   r)   rP   r   r   r_   r,   listr    r7   ri   r   rf   rQ   r-   r-   rA   r.   r      s.   

ir   )	ru   rl   rT   rU   r1   weightsprogresskwargsr   c           
   	   K   s   |d urTt |dt|jd  |jd d |jd d ks>J t |d|jd d  |dd}tf || ||||d|}	|r|	|j|d	d
 |	S )Nrv   
categoriesmin_sizer   r   rt      )rt   ru   rl   rT   rU   r1   T)r   Z
check_hash)r   lenmetarH   r   Zload_state_dictZget_state_dict)
ru   rl   rT   rU   r1   r   r   r   rt   modelr-   r-   r.   _vision_transformer4  s$    
 
r   r   _COMMON_METAz(https://github.com/facebookresearch/SWAGz:https://github.com/facebookresearch/SWAG/blob/main/LICENSE)recipelicensec                   @   s   e Zd Zedeeddi edddddd	d
idddddZedeeddej	di e
dddddd
idddddZedeeddej	di e
ddddddd
idddd dZeZd!S )"r   z9https://download.pytorch.org/models/vit_b_16-c867db91.pthr   	crop_sizei(r   r   zNhttps://github.com/pytorch/vision/tree/main/references/classification#vit_b_16ImageNet-1KgS㥛DT@g1ZW@zacc@1zacc@5gMb1@g(\t@
                These weights were trained from scratch by using a modified version of `DeIT
                <https://arxiv.org/abs/2012.12877>`_'s training recipe.
            
num_paramsr   r   _metrics_ops
_file_size_docsurlZ
transformsr   z>https://download.pytorch.org/models/vit_b_16_swag-9ac1b537.pth  r   resize_sizeinterpolationi^-)r   r   g~jtSU@giX@gˡEK@g|?5^t@
                These weights are learnt via transfer learning by end-to-end fine-tuning the original
                `SWAG <https://arxiv.org/abs/2201.08371>`_ weights on ImageNet-1K data.
            r   r   r   r   r   r   zAhttps://download.pytorch.org/models/vit_b_16_lc_swag-4e70ced5.pth+https://github.com/pytorch/vision/pull/5793gbX9xT@gQX@
                These weights are composed of the original frozen `SWAG <https://arxiv.org/abs/2201.08371>`_ trunk
                weights and a linear classifier learnt on top of them trained on ImageNet-1K data.
            r   r   r   r   r   r   r   Nr&   r'   r(   r   r   r   r   IMAGENET1K_V1r   BICUBIC_COMMON_SWAG_METAIMAGENET1K_SWAG_E2E_V1IMAGENET1K_SWAG_LINEAR_V1DEFAULTr-   r-   r-   r.   r   _  s   
r   c                   @   sH   e Zd Zedeeddi edddddd	d
idddddZeZdS )r   z9https://download.pytorch.org/models/vit_b_32-d86f8d99.pthr   r   i1Br   zNhttps://github.com/pytorch/vision/tree/main/references/classification#vit_b_32r   g|?5^R@gW@r   gA`Т@gl	u@r   r   r   N	r&   r'   r(   r   r   r   r   r   r   r-   r-   r-   r.   r     s(   
r   c                   @   s   e Zd Zedeedddi eddddd	d
didddddZedeeddej	di e
ddddddidddddZedeeddej	di e
dddddddiddd d!dZeZd"S )#r   z9https://download.pytorch.org/models/vit_l_16-852ce7e3.pthr      )r   r   i#r   zNhttps://github.com/pytorch/vision/tree/main/references/classification#vit_l_16r   g|?5^S@gFԨW@r   gףp=
N@g;O$@a  
                These weights were trained from scratch by using a modified version of TorchVision's
                `new training recipe
                <https://pytorch.org/blog/how-to-train-state-of-the-art-models-using-torchvision-latest-primitives/>`_.
            r   r   z>https://download.pytorch.org/models/vit_l_16_swag-4f3808c9.pth   r   i0)r   r   gjtV@gT㥛ĠX@gƟv@gy&11@r   r   zAhttps://download.pytorch.org/models/vit_l_16_lc_swag-4d563306.pthr   gMbXIU@g^I[X@r   r   Nr   r-   r-   r-   r.   r     s   r   c                   @   sH   e Zd Zedeeddi edddddd	d
idddddZeZdS )r   z9https://download.pytorch.org/models/vit_l_32-c7638314.pthr   r   i[Er   zNhttps://github.com/pytorch/vision/tree/main/references/classification#vit_l_32r   g|?5>S@gGzDW@r   gK7.@gE@r   r   r   Nr   r-   r-   r-   r.   r     s(   
r   c                   @   s   e Zd Zedeeddejdi edddddd	id
ddddZ	edeeddejdi eddddddd	idddddZ
e	ZdS )r   z>https://download.pytorch.org/models/vit_h_14_swag-80465313.pth  r   i%)r   r   r   gS#V@g#~jX@r   g~jŏ@gK7I@r   r   r   zAhttps://download.pytorch.org/models/vit_h_14_lc_swag-c1eb923e.pthr   r   i@%r   gZd;OmU@gQnX@g=
ףpd@gIk֢@r   r   N)r&   r'   r(   r   r   r   r   r   r   r   r   r   r-   r-   r-   r.   r   2  s`   r   
pretrained)r   T)r   r   )r   r   r   r   c              
   K   s(   t | } tf ddddd| |d|S )a  
    Constructs a vit_b_16 architecture from
    `An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale <https://arxiv.org/abs/2010.11929>`_.

    Args:
        weights (:class:`~torchvision.models.ViT_B_16_Weights`, optional): The pretrained
            weights to use. See :class:`~torchvision.models.ViT_B_16_Weights`
            below for more details and possible values. By default, no pre-trained weights are used.
        progress (bool, optional): If True, displays a progress bar of the download to stderr. Default is True.
        **kwargs: parameters passed to the ``torchvision.models.vision_transformer.VisionTransformer``
            base class. Please refer to the `source code
            <https://github.com/pytorch/vision/blob/main/torchvision/models/vision_transformer.py>`_
            for more details about this class.

    .. autoclass:: torchvision.models.ViT_B_16_Weights
        :members:
                ru   rl   rT   rU   r1   r   r   )r   verifyr   r   r   r   r-   r-   r.   r   k  s    
r   c              
   K   s(   t | } tf ddddd| |d|S )a  
    Constructs a vit_b_32 architecture from
    `An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale <https://arxiv.org/abs/2010.11929>`_.

    Args:
        weights (:class:`~torchvision.models.ViT_B_32_Weights`, optional): The pretrained
            weights to use. See :class:`~torchvision.models.ViT_B_32_Weights`
            below for more details and possible values. By default, no pre-trained weights are used.
        progress (bool, optional): If True, displays a progress bar of the download to stderr. Default is True.
        **kwargs: parameters passed to the ``torchvision.models.vision_transformer.VisionTransformer``
            base class. Please refer to the `source code
            <https://github.com/pytorch/vision/blob/main/torchvision/models/vision_transformer.py>`_
            for more details about this class.

    .. autoclass:: torchvision.models.ViT_B_32_Weights
        :members:
        r   r   r   r   )r   r   r   r   r-   r-   r.   r     s    
r   c              
   K   s(   t | } tf ddddd| |d|S )a  
    Constructs a vit_l_16 architecture from
    `An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale <https://arxiv.org/abs/2010.11929>`_.

    Args:
        weights (:class:`~torchvision.models.ViT_L_16_Weights`, optional): The pretrained
            weights to use. See :class:`~torchvision.models.ViT_L_16_Weights`
            below for more details and possible values. By default, no pre-trained weights are used.
        progress (bool, optional): If True, displays a progress bar of the download to stderr. Default is True.
        **kwargs: parameters passed to the ``torchvision.models.vision_transformer.VisionTransformer``
            base class. Please refer to the `source code
            <https://github.com/pytorch/vision/blob/main/torchvision/models/vision_transformer.py>`_
            for more details about this class.

    .. autoclass:: torchvision.models.ViT_L_16_Weights
        :members:
    r            r   )r   r   r   r   r-   r-   r.   r     s    
r   c              
   K   s(   t | } tf ddddd| |d|S )a  
    Constructs a vit_l_32 architecture from
    `An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale <https://arxiv.org/abs/2010.11929>`_.

    Args:
        weights (:class:`~torchvision.models.ViT_L_32_Weights`, optional): The pretrained
            weights to use. See :class:`~torchvision.models.ViT_L_32_Weights`
            below for more details and possible values. By default, no pre-trained weights are used.
        progress (bool, optional): If True, displays a progress bar of the download to stderr. Default is True.
        **kwargs: parameters passed to the ``torchvision.models.vision_transformer.VisionTransformer``
            base class. Please refer to the `source code
            <https://github.com/pytorch/vision/blob/main/torchvision/models/vision_transformer.py>`_
            for more details about this class.

    .. autoclass:: torchvision.models.ViT_L_32_Weights
        :members:
    r   r   r   r   r   r   )r   r   r   r   r-   r-   r.   r     s    
r   )r   Nc              
   K   s(   t | } tf ddddd| |d|S )a  
    Constructs a vit_h_14 architecture from
    `An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale <https://arxiv.org/abs/2010.11929>`_.

    Args:
        weights (:class:`~torchvision.models.ViT_H_14_Weights`, optional): The pretrained
            weights to use. See :class:`~torchvision.models.ViT_H_14_Weights`
            below for more details and possible values. By default, no pre-trained weights are used.
        progress (bool, optional): If True, displays a progress bar of the download to stderr. Default is True.
        **kwargs: parameters passed to the ``torchvision.models.vision_transformer.VisionTransformer``
            base class. Please refer to the `source code
            <https://github.com/pytorch/vision/blob/main/torchvision/models/vision_transformer.py>`_
            for more details about this class.

    .. autoclass:: torchvision.models.ViT_H_14_Weights
        :members:
       r   r   i   i   r   )r   r   r   r   r-   r-   r.   r     s    
r   bicubicFzOrderedDict[str, torch.Tensor])rt   ru   model_stateinterpolation_modereset_headsr   c                 C   sh  |d }|j \}}}|dkr,td|j  | | d d }	|	|krd|d8 }|	d8 }	|ddddddf }
|ddddddf }|ddd}tt|}|| |krtd||  d| |d|||}| | }tjj	|||d	d
}|d||	}|ddd}t
j|
|gdd}||d< |rdt }| D ]\}}|ds@|||< q@|}|S )a  This function helps interpolate positional embeddings during checkpoint loading,
    especially when you want to apply a pre-trained model on images with different resolution.

    Args:
        image_size (int): Image size of the new model.
        patch_size (int): Patch size of the new model.
        model_state (OrderedDict[str, torch.Tensor]): State dict of the pre-trained model.
        interpolation_mode (str): The algorithm used for upsampling. Default: bicubic.
        reset_heads (bool): If true, not copying the state of heads. Default: False.

    Returns:
        OrderedDict[str, torch.Tensor]: A state dict which can be loaded into the new model.
    zencoder.pos_embeddingr   z%Unexpected position embedding shape: r   Nr   zPseq_length is not a perfect square! Instead got seq_length_1d * seq_length_1d = z and seq_length = T)sizemodeZalign_cornersr   r   )rb   
ValueErrorr   r)   r   r   r   r+   Z
functionalZinterpolater_   r   r   items
startswith)rt   ru   r   r   r   ro   r   rk   rU   Znew_seq_lengthZpos_embedding_tokenZpos_embedding_imgZseq_length_1dZnew_seq_length_1dZnew_pos_embedding_imgZnew_pos_embeddingZmodel_state_copykvr-   r-   r.   interpolate_embeddings  sF    
r   )r   F)9r   collectionsr   	functoolsr   typingr   r   r   r   r_   Ztorch.nnr+   Zops.miscr	   r
   Ztransforms._presetsr   r   utilsr   Z_apir   r   r   Z_metar   _utilsr   r   __all__r    r/   r,   rR   rj   r   r)   boolr   r   dictstrr*   r   r   r   r   r   r   r   r   r   r   r   r   r   r-   r-   r-   r.   <module>   s   
.$& !OP9$ $ $ $ $$  