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    ¯Ê¹jO  ã                   @   s‚   d Z ddlZddlmZ ddlZddlZddlmZ ddlm	Z
 ddlmZ ddlmZ dd	lmZ d
d„ ZG dd„ dejƒZdS )zå PyTorch Conditionally Parameterized Convolution (CondConv)

Paper: CondConv: Conditionally Parameterized Convolutions for Efficient Inference
(https://arxiv.org/abs/1904.04971)

Hacked together by / Copyright 2020 Ross Wightman
é    N)Úpartial)Únn)Ú
functionalé   )Ú	to_2tuple)Úconv2d_same)Úget_padding_valuec                    s   ‡ ‡‡fdd„}|S )Nc                    s`   t  ˆ ¡}t| jƒdks4| jd ˆks4| jd |kr<tdƒ‚tˆƒD ]}ˆ| |  ˆ ¡ƒ qDdS )zCondConv initializer function.é   r   r   z<CondConv variables must have shape [num_experts, num_params]N)ÚnpÚprodÚlenÚshapeÚ
ValueErrorÚrangeÚview)ÚweightZ
num_paramsÚi©Úexpert_shapeÚinitializerÚnum_experts© úV/var/www/html/bwave/python/venv/lib/python3.8/site-packages/timm/layers/cond_conv2d.pyÚcondconv_initializer   s    
ÿÿz6get_condconv_initializer.<locals>.condconv_initializerr   )r   r   r   r   r   r   r   Úget_condconv_initializer   s    	r   c                       s<   e Zd ZdZdddgZd‡ fd
d„	Zdd„ Zdd„ Z‡  ZS )Ú
CondConv2daO   Conditionally Parameterized Convolution
    Inspired by: https://github.com/tensorflow/tpu/blob/master/models/official/efficientnet/condconv/condconv_layers.py

    Grouped convolution hackery for parallel execution of the per-sample kernel filters inspired by this discussion:
    https://github.com/pytorch/pytorch/issues/17983
    Úin_channelsÚout_channelsÚdynamic_paddingé   r   Ú Fé   c
                    sô   t t| ƒ ¡  || _|| _t|ƒ| _t|ƒ| _t||||d�\}
}|| _	t|
ƒ| _
t|ƒ| _|| _|	| _| j| j| j f| j | _d}| jD ]}||9 }qŒtj t | j|¡¡| _|rÜ| jf| _tj t | j| j¡¡| _n|  dd ¡ |  ¡  d S )N)ÚstrideÚdilationr   Úbias)Úsuperr   Ú__init__r   r   r   Úkernel_sizer"   r   r   Úpaddingr#   Úgroupsr   Úweight_shapeÚtorchr   Ú	ParameterZTensorr   Ú
bias_shaper$   Zregister_parameterÚreset_parameters)Úselfr   r   r'   r"   r(   r#   r)   r$   r   Zpadding_valZis_padding_dynamicZweight_num_paramÚwd©Ú	__class__r   r   r&   +   s4    

   ÿ





zCondConv2d.__init__c                 C   s†   t ttjjt d¡d�| j| jƒ}|| j	ƒ | j
d k	r‚t | jdd … ¡}dt |¡ }t ttjj| |d�| j| jƒ}|| j
ƒ d S )Né   )Úar   )r4   Úb)r   r   r   ÚinitZkaiming_uniform_ÚmathÚsqrtr   r*   r   r$   r
   r   Zuniform_r-   )r/   Zinit_weightZfan_inÚboundZ	init_biasr   r   r   r.   I   s      ÿ

  ÿzCondConv2d.reset_parametersc              	   C   s   |j \}}}}t || j¡}|| j | j| j f| j }| |¡}d }	| j	d k	rnt || j	¡}	|	 || j ¡}	| 
d|| ||¡}| jr¬t|||	| j| j| j| j| d�}
n$tj|||	| j| j| j| j| d�}
|
 ddddg¡ || j|
j d |
j d ¡}
|
S )Nr   )r"   r(   r#   r)   r   r	   r   éþÿÿÿéÿÿÿÿ)r   r+   Úmatmulr   r   r   r)   r'   r   r$   Zreshaper   r   r"   r(   r#   ÚFZconv2dZpermute)r/   ÚxZrouting_weightsÚBÚCÚHÚWr   Znew_weight_shaper$   Úoutr   r   r   ÚforwardT   s<    

     þ     þ,zCondConv2d.forward)r   r   r    r   r   Fr!   )	Ú__name__Ú
__module__Ú__qualname__Ú__doc__Z__constants__r&   r.   rD   Ú__classcell__r   r   r1   r   r   "   s   
            ÿr   )rH   r7   Ú	functoolsr   Únumpyr
   r+   r   Ztorch.nnr   r=   Zhelpersr   r   r(   r   r   ÚModuler   r   r   r   r   Ú<module>   s   