a
d @ s d dl mZmZmZ d dlZd dlmZ d dlmZ d dl m
Z
m Z m Z ddl
mZ ddlmZ G dd d eZG d
d d eZG d d
d
eZdS ) )CallableDictOptionalN)
DataLoader)accuracymean_average_precisionmean_iou )Trainer)
Configurationc @ s* e Zd ZdZejeeef dddZ dS )ImageClassifierTrainera Module to be used for image classification purposes.
The module subclasses :py:class:`~kornia.x.Trainer` and overrides the
:py:func:`~kornia.x.Trainer.evaluate` function implementing a standard
:py:func:`~kornia.metrics.accuracy` topk@[1, 5].
.. seealso::
Learn how to use this class in the following
`example `__.
argsreturnc G s> t |dkrt|\}}t||dd\}}t| | dS )N )r )Ztopk)Ztop1Ztop5)lenAssertionErrorr dictitem)selfr outtargetZacc1Zacc5 r Z/var/www/html/stable-diffusion-webui/venv/lib/python3.9/site-packages/kornia/x/trainers.pycompute_metrics s
z&ImageClassifierTrainer.compute_metricsN
__name__
__module__ __qualname____doc__torchTensorr strfloatr r r r r r
s r c @ s* e Zd ZdZejeeef dddZ dS )SemanticSegmentationTrainera Module to be used for semantic segmentation purposes.
The module subclasses :py:class:`~kornia.x.Trainer` and overrides the
:py:func:`~kornia.x.Trainer.evaluate` function implementing IoU :py:func:`~kornia.metrics.mean_iou`.
.. seealso::
Learn how to use this class in the following
`example `__.
r
c G sB t |dkrt|\}}t|d||jd }t| dS )Nr r )iou)r r r Zargmaxshapemeanr r )r r r r r& r r r r , s
z+SemanticSegmentationTrainer.compute_metricsNr r r r r r% ! s
r% c
s e Zd ZdZdejeeeej ej j
ej j j e
eeeef ee dd fdd
ZejedddZejejd d
d Zejeeef d fd d
Z ZS )ObjectDetectionTrainera Module to be used for object detection purposes.
The module subclasses :py:class:`~kornia.x.Trainer` and overrides the
:py:func:`~kornia.x.Trainer.evaluate` function implementing IoU :py:func:`~kornia.metrics.mean_iou`.
.. seealso::
Learn how to use this class in the following
`example `__.
N) modeltrain_dataloadervalid_dataloader criterion optimizer schedulerconfig num_classes callbacksloss_computed_by_modelr c
s6 | d u r i } t |||||||| |
| _|| _d S )N)super__init__r3 r1 ) r r* r+ r, r- r. r/ r0 r1 r2 r3 __class__r r r5 ? s
z ObjectDetectionTrainer.__init__)r* samplec C s* | j r |jr ||d |d S ||d S )Ninputr )r3 Ztraining)r r* r8 r r r on_modelS s z ObjectDetectionTrainer.on_modelr
c G sf | j rJt|d tr4tdd |d D S tt|d S | j d u r\t
d| j | S )Nr c S s g | ] \}}|qS r r ).0_vr r r
\ z7ObjectDetectionTrainer.compute_loss..zD`criterion` should not be None if `loss_computed_by_model` is False.) r3
isinstancer r! stackitemsr( listsumr- RuntimeError)r r r r r compute_lossX s
z#ObjectDetectionTrainer.compute_lossc s t |d trd|d v rd|d v rd|d v rt |d trd|d v rd|d v rtdd |d D dd |d D d d |d D d
d |d D d d |d D | jd d
\}}d| iS t j| S )Nr boxeslabelsscoresr c S s g | ] }|d qS rG r r; ar r r r> m r? z:ObjectDetectionTrainer.compute_metrics..c S s g | ] }|d qS rH r rK r r r r> n r? c S s g | ] }|d qS )rI r rK r r r r> o r? c S s g | ] }|d qS rJ r rK r r r r> p r? c S s g | ] }|d qS rM r rK r r r r> q r? gư>)Z n_classes thresholdmAP)r@ r r r1 r r4 r )r r rO r< r6 r r r b s2
z&ObjectDetectionTrainer.compute_metrics)NN)r r r r nnModuler r r! ZoptimZ OptimizerZ lr_schedulerZCosineAnnealingLRr intr r# r boolr5 r r: r" rF r$ r
__classcell__r r r6 r r) 4 s&
r) )typingr r r r! Ztorch.nnrP Ztorch.utils.datar Zkornia.metricsr r r Ztrainerr
utilsr r r% r) r r r r s