U
|
¨_l ã @ s d Z ddlZddlm mZ ddlmZ e ¡ dd Z dddZ
|
d d
|
Z d d Z d d Z dd Zdd ZG dd deZG dd deZdS )z9
|
@author: liaoxingyu
|
@contact: sherlockliao01@gmail.com
|
é N)Úcommc s@ fddt tj ¡ D }tjj| dd tj|dd}|S )z
|
Performs all_gather operation on the provided tensors.
|
*** Warning ***: torch.distributed.all_gather has no gradient.
|
c s g | ]}t ¡qS © )ÚtorchÚ ones_like)Ú.0Ú_©Útensorr ú ./modeling/losses/metric_loss.pyÚ
|
<listcomp> s ÿz%concat_all_gather.<locals>.<listcomp>F)Úasync_opr ©Údim)Úranger Ú distributedÚget_world_sizeÚ
|
all_gatherÚcat)r Ztensors_gatherÚoutputr r r
|
Úconcat_all_gather s
|
ÿr éÿÿÿÿc C s( d| t j| d|dd | ¡d } | S )zNormalizing to unit length along the specified dimension.
|
Args:
|
x: pytorch Variable
|
Returns:
|
x: pytorch Variable, same shape as input
|
g ð?é T©Úkeepdimçê-q=)r ÚnormÚ expand_as)ÚxÚaxisr r r
|
Ú normalize s $r c C s | d¡| d¡ }}t | d¡jddd ||¡}t |d¡jddd ||¡ ¡ }|| }| dd| | ¡ ¡ |jdd ¡ }|S ) Nr r é Tr éþÿÿÿr )Úmin) Úsizer ÚpowÚsumÚexpandÚtÚaddmm_ÚclampÚsqrt)r ÚyÚmÚnÚxxÚyyÚdistr r r
|
Úeuclidean_dist' s "r1 c C s | d¡| d¡ }}t | | dd¡¡}t t t | d¡d¡¡ |d¡ d|¡t t t |d¡d¡¡ d|¡ |d¡ }|| }d| S )Nr r r ) r# r ÚmatmulÚ transposer* r% r$ ÚviewÚrepeat)r r+ Zbs1Zbs2Zfrac_upZ frac_downZcosiner r r
|
Ú cosine_dist1 s ((ÿr6 c C sT t j| | dddd }| | }t jt |¡| dddd }t |¡| | }|S )Nr T)r r r gíµ ÷Æ°>)r Úmaxr% Úexp)r0 ÚmaskZmax_vÚdiffÚZÚWr r r
|
Úsoftmax_weights: s
|
r= c C s t | ¡ dkst| d¡}tj| | ¡ |d¡ddd\}}tj| | ¡ |d¡ddd\}}| d¡}| d¡}||fS )a For each anchor, find the hardest positive and negative sample.
|
Args:
|
dist_mat: pair wise distance between samples, shape [N, M]
|
is_pos: positive index with shape [N, M]
|
is_neg: negative index with shape [N, M]
|
Returns:
|
dist_ap: pytorch Variable, distance(anchor, positive); shape [N]
|
dist_an: pytorch Variable, distance(anchor, negative); shape [N]
|
p_inds: pytorch LongTensor, with shape [N];
|
indices of selected hard positive samples; 0 <= p_inds[i] <= N - 1
|
n_inds: pytorch LongTensor, with shape [N];
|
indices of selected hard negative samples; 0 <= n_inds[i] <= N - 1
|
NOTE: Only consider the case in which all labels have same num of samples,
|
thus we can cope with all anchors in parallel.
|
r r r r Tr ) Úlenr# ÚAssertionErrorr r7 Ú
|
contiguousr4 r" Úsqueeze)Údist_matÚis_posÚis_negÚNÚdist_apZrelative_p_indsÚdist_anZrelative_n_indsr r r
|
Úhard_example_miningB s
|
ÿ
|
ÿ
|
|
|
rH c C sv t | ¡ dkst| ¡ }| ¡ }| | }| | }t||}t| |}tj|| dd}tj|| dd}||fS )aY For each anchor, find the weighted positive and negative sample.
|
Args:
|
dist_mat: pytorch Variable, pair wise distance between samples, shape [N, N]
|
is_pos:
|
is_neg:
|
Returns:
|
dist_ap: pytorch Variable, distance(anchor, positive); shape [N]
|
dist_an: pytorch Variable, distance(anchor, negative); shape [N]
|
r r r )r> r# r? Úfloatr= r r% )rB rC rD rF rG Z
|
weights_apZ
|
weights_anr r r
|
Úweighted_example_miningl s
|
|
rJ c @ s e Zd ZdZdd Zdd ZdS )Ú TripletLosszÁModified from Tong Xiao's open-reid (https://github.com/Cysu/open-reid).
|
Related Triplet Loss theory can be found in paper 'In Defense of the Triplet
|
Loss for Person Re-Identification'.c C s< |j jjj| _|j jjj| _|j jjj| _|j jjj | _
|
d S ©N) ÚMODELÚLOSSESÚTRIÚMARGINÚ_marginÚ NORM_FEATÚ_normalize_featureÚSCALEÚ_scaleÚ HARD_MININGÚ _hard_mining©ÚselfÚcfgr r r
|
Ú__init__ s zTripletLoss.__init__c C sB | j rt|dd}t ¡ dkr0t|}t|}n|}|}t||}| ¡ \}}| |d¡ ||¡ | |d¡ ||¡
|
¡ ¡}| |d¡ ||¡ | |d¡ ||¡
|
¡ ¡} | j rÂt ||| \}
|
} nt||| \}
|
} | ¡ | ¡ d¡} | jdkrtj| |
|
| | jd} n0t | |
|
| ¡} | tdkr8tj| |
|
| dd} | | j S )Nr )r r r )ÚmarginÚInfg333333Ó?)rS r r r r r1 r# r4 r& Úeqr' ÚnerW rH rJ ÚnewÚ
|
resize_as_Úfill_rQ ÚFÚmargin_ranking_lossÚsoft_margin_lossrI rU )rY Ú embeddingÚtargetsÚ all_embeddingÚ all_targetsrB rE ÚMrC rD rF rG r+ Úlossr r r
|
Ú__call__ s*
|
|
.. zTripletLoss.__call__N)Ú__name__Ú
|
__module__Ú __qualname__Ú__doc__r[ rl r r r r
|
rK s rK c @ s e Zd Zdd Zdd ZdS )Ú
|
CircleLossc C s. |j jjj| _|j jjj| _|j jjj| _d S rL ) rM rN ÚCIRCLErT rU rP r, ÚALPHAÚsrX r r r
|
r[ ´ s zCircleLoss.__init__c C sh t j|dd}t ¡ dkr,t|}t|}n|}|}t || ¡ ¡}| ¡ \}}| |d¡
|
||¡ | |d¡
|
||¡ ¡ ¡}| |d¡
|
||¡ | |d¡
|
||¡ ¡ ¡} || ¡ |d¡}
|
|| ¡ |d¡} t |
|
¡ d | j ¡} t | ¡ | j ¡} d| j }| j}| j | |
|
| }| j| | | }t tj|ddtj|dd ¡ ¡ }|| j S )Nr r r )rc r r r r r r2 r' r# r4 r& r^ r_ r@ ÚreluÚdetachr, rt ÚsoftplusÚ logsumexpÚmeanrU )rY rf rg rh ri rB rE rj rC rD Ús_pÚs_nÚalpha_pÚalpha_nÚdelta_pÚdelta_nZlogit_pZlogit_nrk r r r
|
rl º s(
|
..
|
&zCircleLoss.__call__N)rm rn ro r[ rl r r r r
|
rq ³ s rq )r )rp r Útorch.nn.functionalÚnnÚ
|
functionalrc Úutilsr Úno_gradr r r1 r6 r= rH rJ ÚobjectrK rq r r r r
|
Ú<module> s
|
|
|
*-
|
