U
    (dD                  	   @   s  d dl mZ d dlmZmZmZmZmZ 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mZmZmZ ddlmZ dd	lmZmZ dd
lmZ ddlmZmZ ddlmZm Z m!Z! dddddddddg	Z"G dd deZ#G dd deZ$G dd deZ%eee#e$f  ee& ee e'e'ee%dddZ(ded 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Z-ed(d)d* fd+dd,d-d.eee*ef  e'e'ee%d/d0dZ.ed(d1d* fd+dd,d-d.eee+ef  e'e'ee%d/d2dZ/ed(d3d* fd+dd,d-d.eee,ef  e'e'ee%d/d4dZ0dd,d-d.eee-ef  e'e'ee%d/d5dZ1dd6lm2Z2 dd7l3m4Z4 e2e*j5j6e+j5j6e,j5j6d8Z7dS )9    )partial)AnyTypeUnionListOptionalN)Tensor)
Bottleneck
BasicBlockResNetResNet18_WeightsResNet50_WeightsResNeXt101_32X8D_WeightsResNeXt101_64X4D_Weights   )ImageClassification   )WeightsEnumWeights)_IMAGENET_CATEGORIES)handle_legacy_interface_ovewrite_named_param   )_fuse_modules_replace_reluquantize_modelQuantizableResNetResNet18_QuantizedWeightsResNet50_QuantizedWeights!ResNeXt101_32X8D_QuantizedWeights!ResNeXt101_64X4D_QuantizedWeightsresnet18resnet50resnext101_32x8dresnext101_64x4dc                       sL   e Zd Zeedd fddZeedddZdee ddd	d
Z	  Z
S )QuantizableBasicBlockNargskwargsreturnc                    s    t  j|| tjj | _d S N)super__init__torchnn	quantizedFloatFunctionaladd_reluselfr'   r(   	__class__ J/tmp/pip-unpacked-wheel-vx7f76es/torchvision/models/quantization/resnet.pyr,   &   s    zQuantizableBasicBlock.__init__xr)   c                 C   s\   |}|  |}| |}| |}| |}| |}| jd k	rJ| |}| j||}|S r*   )conv1bn1reluconv2bn2
downsampler1   r3   r9   identityoutr6   r6   r7   forward*   s    






zQuantizableBasicBlock.forwardis_qatr)   c                 C   s>   t | dddgddgg|dd | jr:t | jdd	g|dd d S )
Nr:   r;   r<   r=   r>   TZinplace01r   r?   r3   rE   r6   r6   r7   
fuse_model;   s    z QuantizableBasicBlock.fuse_model)N__name__
__module____qualname__r   r,   r   rC   r   boolrK   __classcell__r6   r6   r4   r7   r%   %   s   r%   c                       sL   e Zd Zeedd fddZeedddZdee ddd	d
Z	  Z
S )QuantizableBottleneckNr&   c                    s:   t  j|| tj | _tjdd| _tjdd| _d S )NFrF   )	r+   r,   r.   r/   r0   skip_add_reluZReLUrelu1relu2r2   r4   r6   r7   r,   B   s    zQuantizableBottleneck.__init__r8   c                 C   sz   |}|  |}| |}| |}| |}| |}| |}| |}| |}| jd k	rh| |}| j	
||}|S r*   )r:   r;   rT   r=   r>   rU   conv3bn3r?   rS   r1   r@   r6   r6   r7   rC   H   s    









zQuantizableBottleneck.forwardrD   c                 C   sF   t | dddgdddgddgg|d	d
 | jrBt | jddg|d	d
 d S )Nr:   r;   rT   r=   r>   rU   rV   rW   TrF   rG   rH   rI   rJ   r6   r6   r7   rK   Z   s       z QuantizableBottleneck.fuse_model)NrL   r6   r6   r4   r7   rR   A   s   rR   c                       sL   e Zd Zeedd fddZeedddZdee ddd	d
Z	  Z
S )r   Nr&   c                    s.   t  j|| tjj | _tjj | _d S r*   )	r+   r,   r-   ZaoZquantizationZ	QuantStubquantZDeQuantStubdequantr2   r4   r6   r7   r,   c   s    zQuantizableResNet.__init__r8   c                 C   s"   |  |}| |}| |}|S r*   )rX   Z_forward_implrY   )r3   r9   r6   r6   r7   rC   i   s    


zQuantizableResNet.forwardrD   c                 C   sJ   t | dddg|dd |  D ]&}t|tks:t|tkr|| qdS )a  Fuse conv/bn/relu modules in resnet models

        Fuse conv+bn+relu/ Conv+relu/conv+Bn modules to prepare for quantization.
        Model is modified in place.  Note that this operation does not change numerics
        and the model after modification is in floating point
        r:   r;   r<   TrF   N)r   modulestyperR   r%   rK   )r3   rE   mr6   r6   r7   rK   r   s    zQuantizableResNet.fuse_model)NrL   r6   r6   r4   r7   r   b   s   	)blocklayersweightsprogressquantizer(   r)   c                 K   s   |d k	r:t |dt|jd  d|jkr:t |d|jd  |dd}t| |f|}t| |rjt|| |d k	r||j|d |S )NZnum_classes
categoriesbackendfbgemm)r`   )	r   lenmetapopr   r   r   Zload_state_dictZget_state_dict)r]   r^   r_   r`   ra   r(   rc   modelr6   r6   r7   _resnet   s    

ri   )r   r   rd   zdhttps://github.com/pytorch/vision/tree/main/references/classification#post-training-quantized-modelsz
        These weights were produced by doing Post Training Quantization (eager mode) on top of the unquantized
        weights listed below.
    )Zmin_sizerb   rc   recipeZ_docsc                
   @   s>   e Zd Zedeeddedejddddid	d
Z	e	Z
dS )r   zJhttps://download.pytorch.org/models/quantized/resnet18_fbgemm_16fa66dd.pth   	crop_sizei(^ ImageNet-1KgV-_Q@g r8V@zacc@1zacc@5
num_paramsunquantized_metricsurlZ
transformsrf   N)rM   rN   rO   r   r   r   _COMMON_METAr   IMAGENET1K_V1IMAGENET1K_FBGEMM_V1DEFAULTr6   r6   r6   r7   r      s   
c                
   @   sn   e Zd Zedeeddedejddddid	d
Z	edeedddedej
ddddid	d
ZeZdS )r   zJhttps://download.pytorch.org/models/quantized/resnet50_fbgemm_bf931d71.pthrk   rl   i(rn   g{GR@gjt4W@ro   rp   rt   zJhttps://download.pytorch.org/models/quantized/resnet50_fbgemm-23753f79.pth   rm   Zresize_sizeg5^IT@gX9vW@N)rM   rN   rO   r   r   r   rv   r   rw   rx   IMAGENET1K_V2IMAGENET1K_FBGEMM_V2ry   r6   r6   r6   r7   r      s6   
c                
   @   sn   e Zd Zedeeddedejddddid	d
Z	edeedddedej
ddddid	d
ZeZdS )r   zQhttps://download.pytorch.org/models/quantized/resnext101_32x8_fbgemm_09835ccf.pthrk   rl   i(Jrn   gvS@gQW@ro   rp   rt   zQhttps://download.pytorch.org/models/quantized/resnext101_32x8_fbgemm-ee16d00c.pthrz   r{   g~jT@g rX@N)rM   rN   rO   r   r   r   rv   r   rw   rx   r|   r}   ry   r6   r6   r6   r7   r      s6   
c                   @   sB   e Zd Zedeedddeddejddd	d
iddZ	e	Z
dS )r    zRhttps://download.pytorch.org/models/quantized/resnext101_64x4d_fbgemm-605a1cb3.pthrk   rz   r{   i(mz+https://github.com/pytorch/vision/pull/5935rn   gxT@g/X@ro   )rq   rj   rr   rs   rt   N)rM   rN   rO   r   r   r   rv   r   rw   rx   ry   r6   r6   r6   r7   r       s   Z
pretrainedc                 C   s   |  ddrtjS tjS Nra   F)getr   rx   r   rw   r(   r6   r6   r7   <lambda>  s    
r   )r_   TF)r_   r`   ra   )r_   r`   ra   r(   r)   c                 K   s.   |rt nt| } ttddddg| ||f|S )a  ResNet-18 model from
    `Deep Residual Learning for Image Recognition <https://arxiv.org/abs/1512.03385>`_

    .. note::
        Note that ``quantize = True`` returns a quantized model with 8 bit
        weights. Quantized models only support inference and run on CPUs.
        GPU inference is not yet supported.

    Args:
        weights (:class:`~torchvision.models.quantization.ResNet18_QuantizedWeights` or :class:`~torchvision.models.ResNet18_Weights`, optional): The
            pretrained weights for the model. See
            :class:`~torchvision.models.quantization.ResNet18_QuantizedWeights` 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.
        quantize (bool, optional): If True, return a quantized version of the model. Default is False.
        **kwargs: parameters passed to the ``torchvision.models.quantization.QuantizableResNet``
            base class. Please refer to the `source code
            <https://github.com/pytorch/vision/blob/main/torchvision/models/quantization/resnet.py>`_
            for more details about this class.

    .. autoclass:: torchvision.models.quantization.ResNet18_QuantizedWeights
        :members:

    .. autoclass:: torchvision.models.ResNet18_Weights
        :members:
        :noindex:
    r   )r   r   verifyri   r%   r_   r`   ra   r(   r6   r6   r7   r!     s    ,c                 C   s   |  ddrtjS tjS r~   )r   r   rx   r   rw   r   r6   r6   r7   r   C  s    
c                 K   s.   |rt nt| } ttddddg| ||f|S )a  ResNet-50 model from
    `Deep Residual Learning for Image Recognition <https://arxiv.org/abs/1512.03385>`_

    .. note::
        Note that ``quantize = True`` returns a quantized model with 8 bit
        weights. Quantized models only support inference and run on CPUs.
        GPU inference is not yet supported.

    Args:
        weights (:class:`~torchvision.models.quantization.ResNet50_QuantizedWeights` or :class:`~torchvision.models.ResNet50_Weights`, optional): The
            pretrained weights for the model. See
            :class:`~torchvision.models.quantization.ResNet50_QuantizedWeights` 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.
        quantize (bool, optional): If True, return a quantized version of the model. Default is False.
        **kwargs: parameters passed to the ``torchvision.models.quantization.QuantizableResNet``
            base class. Please refer to the `source code
            <https://github.com/pytorch/vision/blob/main/torchvision/models/quantization/resnet.py>`_
            for more details about this class.

    .. autoclass:: torchvision.models.quantization.ResNet50_QuantizedWeights
        :members:

    .. autoclass:: torchvision.models.ResNet50_Weights
        :members:
        :noindex:
    r         )r   r   r   ri   rR   r   r6   r6   r7   r"   @  s    ,c                 C   s   |  ddrtjS tjS r~   )r   r   rx   r   rw   r   r6   r6   r7   r   t  s    
c                 K   sF   |rt nt| } t|dd t|dd ttddddg| ||f|S )a  ResNeXt-101 32x8d model from
    `Aggregated Residual Transformation for Deep Neural Networks <https://arxiv.org/abs/1611.05431>`_

    .. note::
        Note that ``quantize = True`` returns a quantized model with 8 bit
        weights. Quantized models only support inference and run on CPUs.
        GPU inference is not yet supported.

    Args:
        weights (:class:`~torchvision.models.quantization.ResNeXt101_32X8D_QuantizedWeights` or :class:`~torchvision.models.ResNeXt101_32X8D_Weights`, optional): The
            pretrained weights for the model. See
            :class:`~torchvision.models.quantization.ResNet101_32X8D_QuantizedWeights` 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.
        quantize (bool, optional): If True, return a quantized version of the model. Default is False.
        **kwargs: parameters passed to the ``torchvision.models.quantization.QuantizableResNet``
            base class. Please refer to the `source code
            <https://github.com/pytorch/vision/blob/main/torchvision/models/quantization/resnet.py>`_
            for more details about this class.

    .. autoclass:: torchvision.models.quantization.ResNeXt101_32X8D_QuantizedWeights
        :members:

    .. autoclass:: torchvision.models.ResNeXt101_32X8D_Weights
        :members:
        :noindex:
    groups    width_per_group   r   r      )r   r   r   r   ri   rR   r   r6   r6   r7   r#   q  s    ,c                 K   sF   |rt nt| } t|dd t|dd ttddddg| ||f|S )a  ResNeXt-101 64x4d model from
    `Aggregated Residual Transformation for Deep Neural Networks <https://arxiv.org/abs/1611.05431>`_

    .. note::
        Note that ``quantize = True`` returns a quantized model with 8 bit
        weights. Quantized models only support inference and run on CPUs.
        GPU inference is not yet supported.

    Args:
        weights (:class:`~torchvision.models.quantization.ResNeXt101_64X4D_QuantizedWeights` or :class:`~torchvision.models.ResNeXt101_64X4D_Weights`, optional): The
            pretrained weights for the model. See
            :class:`~torchvision.models.quantization.ResNet101_64X4D_QuantizedWeights` 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.
        quantize (bool, optional): If True, return a quantized version of the model. Default is False.
        **kwargs: parameters passed to the ``torchvision.models.quantization.QuantizableResNet``
            base class. Please refer to the `source code
            <https://github.com/pytorch/vision/blob/main/torchvision/models/quantization/resnet.py>`_
            for more details about this class.

    .. autoclass:: torchvision.models.quantization.ResNeXt101_64X4D_QuantizedWeights
        :members:

    .. autoclass:: torchvision.models.ResNeXt101_64X4D_Weights
        :members:
        :noindex:
    r   @   r   r   r   r   )r    r   r   r   ri   rR   r   r6   r6   r7   r$     s    $)
_ModelURLs)
model_urls)Zresnet18_fbgemmZresnet50_fbgemmZresnext101_32x8d_fbgemm)8	functoolsr   typingr   r   r   r   r   r-   Ztorch.nnr.   r   Ztorchvision.models.resnetr	   r
   r   r   r   r   r   Ztransforms._presetsr   Z_apir   r   Z_metar   _utilsr   r   utilsr   r   r   __all__r%   rR   r   intrP   ri   rv   r   r   r   r    r!   r"   r#   r$   r   Zresnetr   rx   ru   Zquant_model_urlsr6   r6   r6   r7   <module>   s   $
!""
)
)
-,