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# Ultralytics YOLO 🚀, AGPL-3.0 license
from multiprocessing. pool import ThreadPool
from pathlib import Path
from ultralytics. models . yolo . segment import SegmentationValidator
from ultralytics. utils . metrics import SegmentMetrics
import numpy as np
import torch
import torch . nn . functional as F
from ultralytics . models . yolo . detect import DetectionValidator
from ultralytics . utils import LOGGER , NUM_THREADS , ops
from ultralytics . utils . checks import check_requirements
from ultralytics . utils . metrics import SegmentMetrics , box_iou , mask_iou
from ultralytics . utils . plotting import output_to_target , plot_images
class FastSAMValidator ( DetectionValidator ) :
class FastSAMValidator ( SegmentationValidator ) :
def __init__ ( self , dataloader = None , save_dir = None , pbar = None , args = None , _callbacks = None ) :
""" Initialize SegmentationValidator and set task to ' segment ' , metrics to SegmentMetrics. """
super ( ) . __init__ ( dataloader , save_dir , pbar , args , _callbacks )
self . args . task = ' segment '
self . args . plots = False # disable ConfusionMatrix and other plots to avoid errors
self . metrics = SegmentMetrics ( save_dir = self . save_dir , on_plot = self . on_plot )
def preprocess ( self , batch ) :
""" Preprocesses batch by converting masks to float and sending to device. """
batch = super ( ) . preprocess ( batch )
batch [ ' masks ' ] = batch [ ' masks ' ] . to ( self . device ) . float ( )
return batch
def init_metrics ( self , model ) :
""" Initialize metrics and select mask processing function based on save_json flag. """
super ( ) . init_metrics ( model )
self . plot_masks = [ ]
if self . args . save_json :
check_requirements ( ' pycocotools>=2.0.6 ' )
self . process = ops . process_mask_upsample # more accurate
else :
self . process = ops . process_mask # faster
def get_desc ( self ) :
""" Return a formatted description of evaluation metrics. """
return ( ' %22s ' + ' %11s ' * 10 ) % ( ' Class ' , ' Images ' , ' Instances ' , ' Box(P ' , ' R ' , ' mAP50 ' , ' mAP50-95) ' , ' Mask(P ' ,
' R ' , ' mAP50 ' , ' mAP50-95) ' )
def postprocess ( self , preds ) :
""" Post-processes YOLO predictions and returns output detections with proto. """
p = ops . non_max_suppression ( preds [ 0 ] ,
self . args . conf ,
self . args . iou ,
labels = self . lb ,
multi_label = True ,
agnostic = self . args . single_cls ,
max_det = self . args . max_det ,
nc = self . nc )
proto = preds [ 1 ] [ - 1 ] if len ( preds [ 1 ] ) == 3 else preds [ 1 ] # second output is len 3 if pt, but only 1 if exported
return p , proto
def update_metrics ( self , preds , batch ) :
""" Metrics. """
for si , ( pred , proto ) in enumerate ( zip ( preds [ 0 ] , preds [ 1 ] ) ) :
idx = batch [ ' batch_idx ' ] == si
cls = batch [ ' cls ' ] [ idx ]
bbox = batch [ ' bboxes ' ] [ idx ]
nl , npr = cls . shape [ 0 ] , pred . shape [ 0 ] # number of labels, predictions
shape = batch [ ' ori_shape ' ] [ si ]
correct_masks = torch . zeros ( npr , self . niou , dtype = torch . bool , device = self . device ) # init
correct_bboxes = torch . zeros ( npr , self . niou , dtype = torch . bool , device = self . device ) # init
self . seen + = 1
if npr == 0 :
if nl :
self . stats . append ( ( correct_bboxes , correct_masks , * torch . zeros (
( 2 , 0 ) , device = self . device ) , cls . squeeze ( - 1 ) ) )
if self . args . plots :
self . confusion_matrix . process_batch ( detections = None , labels = cls . squeeze ( - 1 ) )
continue
# Masks
midx = [ si ] if self . args . overlap_mask else idx
gt_masks = batch [ ' masks ' ] [ midx ]
pred_masks = self . process ( proto , pred [ : , 6 : ] , pred [ : , : 4 ] , shape = batch [ ' img ' ] [ si ] . shape [ 1 : ] )
# Predictions
if self . args . single_cls :
pred [ : , 5 ] = 0
predn = pred . clone ( )
ops . scale_boxes ( batch [ ' img ' ] [ si ] . shape [ 1 : ] , predn [ : , : 4 ] , shape ,
ratio_pad = batch [ ' ratio_pad ' ] [ si ] ) # native-space pred
# Evaluate
if nl :
height , width = batch [ ' img ' ] . shape [ 2 : ]
tbox = ops . xywh2xyxy ( bbox ) * torch . tensor (
( width , height , width , height ) , device = self . device ) # target boxes
ops . scale_boxes ( batch [ ' img ' ] [ si ] . shape [ 1 : ] , tbox , shape ,
ratio_pad = batch [ ' ratio_pad ' ] [ si ] ) # native-space labels
labelsn = torch . cat ( ( cls , tbox ) , 1 ) # native-space labels
correct_bboxes = self . _process_batch ( predn , labelsn )
# TODO: maybe remove these `self.` arguments as they already are member variable
correct_masks = self . _process_batch ( predn ,
labelsn ,
pred_masks ,
gt_masks ,
overlap = self . args . overlap_mask ,
masks = True )
if self . args . plots :
self . confusion_matrix . process_batch ( predn , labelsn )
# Append correct_masks, correct_boxes, pconf, pcls, tcls
self . stats . append ( ( correct_bboxes , correct_masks , pred [ : , 4 ] , pred [ : , 5 ] , cls . squeeze ( - 1 ) ) )
pred_masks = torch . as_tensor ( pred_masks , dtype = torch . uint8 )
if self . args . plots and self . batch_i < 3 :
self . plot_masks . append ( pred_masks [ : 15 ] . cpu ( ) ) # filter top 15 to plot
# Save
if self . args . save_json :
pred_masks = ops . scale_image ( pred_masks . permute ( 1 , 2 , 0 ) . contiguous ( ) . cpu ( ) . numpy ( ) ,
shape ,
ratio_pad = batch [ ' ratio_pad ' ] [ si ] )
self . pred_to_json ( predn , batch [ ' im_file ' ] [ si ] , pred_masks )
# if self.args.save_txt:
# save_one_txt(predn, save_conf, shape, file=save_dir / 'labels' / f'{path.stem}.txt')
def finalize_metrics ( self , * args , * * kwargs ) :
""" Sets speed and confusion matrix for evaluation metrics. """
self . metrics . speed = self . speed
self . metrics . confusion_matrix = self . confusion_matrix
def _process_batch ( self , detections , labels , pred_masks = None , gt_masks = None , overlap = False , masks = False ) :
"""
Return correct prediction matrix
Arguments :
detections ( array [ N , 6 ] ) , x1 , y1 , x2 , y2 , conf , class
labels ( array [ M , 5 ] ) , class , x1 , y1 , x2 , y2
Returns :
correct ( array [ N , 10 ] ) , for 10 IoU levels
"""
if masks :
if overlap :
nl = len ( labels )
index = torch . arange ( nl , device = gt_masks . device ) . view ( nl , 1 , 1 ) + 1
gt_masks = gt_masks . repeat ( nl , 1 , 1 ) # shape(1,640,640) -> (n,640,640)
gt_masks = torch . where ( gt_masks == index , 1.0 , 0.0 )
if gt_masks . shape [ 1 : ] != pred_masks . shape [ 1 : ] :
gt_masks = F . interpolate ( gt_masks [ None ] , pred_masks . shape [ 1 : ] , mode = ' bilinear ' , align_corners = False ) [ 0 ]
gt_masks = gt_masks . gt_ ( 0.5 )
iou = mask_iou ( gt_masks . view ( gt_masks . shape [ 0 ] , - 1 ) , pred_masks . view ( pred_masks . shape [ 0 ] , - 1 ) )
else : # boxes
iou = box_iou ( labels [ : , 1 : ] , detections [ : , : 4 ] )
return self . match_predictions ( detections [ : , 5 ] , labels [ : , 0 ] , iou )
def plot_val_samples ( self , batch , ni ) :
""" Plots validation samples with bounding box labels. """
plot_images ( batch [ ' img ' ] ,
batch [ ' batch_idx ' ] ,
batch [ ' cls ' ] . squeeze ( - 1 ) ,
batch [ ' bboxes ' ] ,
batch [ ' masks ' ] ,
paths = batch [ ' im_file ' ] ,
fname = self . save_dir / f ' val_batch { ni } _labels.jpg ' ,
names = self . names ,
on_plot = self . on_plot )
def plot_predictions ( self , batch , preds , ni ) :
""" Plots batch predictions with masks and bounding boxes. """
plot_images (
batch [ ' img ' ] ,
* output_to_target ( preds [ 0 ] , max_det = 15 ) , # not set to self.args.max_det due to slow plotting speed
torch . cat ( self . plot_masks , dim = 0 ) if len ( self . plot_masks ) else self . plot_masks ,
paths = batch [ ' im_file ' ] ,
fname = self . save_dir / f ' val_batch { ni } _pred.jpg ' ,
names = self . names ,
on_plot = self . on_plot ) # pred
self . plot_masks . clear ( )
def pred_to_json ( self , predn , filename , pred_masks ) :
""" Save one JSON result. """
# Example result = {"image_id": 42, "category_id": 18, "bbox": [258.15, 41.29, 348.26, 243.78], "score": 0.236}
from pycocotools . mask import encode # noqa
def single_encode ( x ) :
""" Encode predicted masks as RLE and append results to jdict. """
rle = encode ( np . asarray ( x [ : , : , None ] , order = ' F ' , dtype = ' uint8 ' ) ) [ 0 ]
rle [ ' counts ' ] = rle [ ' counts ' ] . decode ( ' utf-8 ' )
return rle
stem = Path ( filename ) . stem
image_id = int ( stem ) if stem . isnumeric ( ) else stem
box = ops . xyxy2xywh ( predn [ : , : 4 ] ) # xywh
box [ : , : 2 ] - = box [ : , 2 : ] / 2 # xy center to top-left corner
pred_masks = np . transpose ( pred_masks , ( 2 , 0 , 1 ) )
with ThreadPool ( NUM_THREADS ) as pool :
rles = pool . map ( single_encode , pred_masks )
for i , ( p , b ) in enumerate ( zip ( predn . tolist ( ) , box . tolist ( ) ) ) :
self . jdict . append ( {
' image_id ' : image_id ,
' category_id ' : self . class_map [ int ( p [ 5 ] ) ] ,
' bbox ' : [ round ( x , 3 ) for x in b ] ,
' score ' : round ( p [ 4 ] , 5 ) ,
' segmentation ' : rles [ i ] } )
def eval_json ( self , stats ) :
""" Return COCO-style object detection evaluation metrics. """
if self . args . save_json and self . is_coco and len ( self . jdict ) :
anno_json = self . data [ ' path ' ] / ' annotations/instances_val2017.json ' # annotations
pred_json = self . save_dir / ' predictions.json ' # predictions
LOGGER . info ( f ' \n Evaluating pycocotools mAP using { pred_json } and { anno_json } ... ' )
try : # https://github.com/cocodataset/cocoapi/blob/master/PythonAPI/pycocoEvalDemo.ipynb
check_requirements ( ' pycocotools>=2.0.6 ' )
from pycocotools . coco import COCO # noqa
from pycocotools . cocoeval import COCOeval # noqa
for x in anno_json , pred_json :
assert x . is_file ( ) , f ' { x } file not found '
anno = COCO ( str ( anno_json ) ) # init annotations api
pred = anno . loadRes ( str ( pred_json ) ) # init predictions api (must pass string, not Path)
for i , eval in enumerate ( [ COCOeval ( anno , pred , ' bbox ' ) , COCOeval ( anno , pred , ' segm ' ) ] ) :
if self . is_coco :
eval . params . imgIds = [ int ( Path ( x ) . stem ) for x in self . dataloader . dataset . im_files ] # im to eval
eval . evaluate ( )
eval . accumulate ( )
eval . summarize ( )
idx = i * 4 + 2
stats [ self . metrics . keys [ idx + 1 ] ] , stats [
self . metrics . keys [ idx ] ] = eval . stats [ : 2 ] # update mAP50-95 and mAP50
except Exception as e :
LOGGER . warning ( f ' pycocotools unable to run: { e } ' )
return stats
Glenn Jocher
1 year ago
committed by
GitHub