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Revert augment_hyps (#70)

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Laughing
2022-12-09 04:19:53 -06:00
committed by GitHub
parent d63ee112d4
commit e629335f6d
4 changed files with 29 additions and 76 deletions
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30
ultralytics/yolo/v8/detect/val.py
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@ -9,7 +9,7 @@ from ultralytics.yolo.data import build_dataloader
from ultralytics.yolo.engine.trainer import DEFAULT_CONFIG
from ultralytics.yolo.engine.validator import BaseValidator
from ultralytics.yolo.utils import ops
from ultralytics.yolo.utils.checks import check_file, check_requirements
from ultralytics.yolo.utils.checks import check_file
from ultralytics.yolo.utils.files import yaml_load
from ultralytics.yolo.utils.metrics import ConfusionMatrix, DetMetrics, box_iou
from ultralytics.yolo.utils.plotting import output_to_target, plot_images
@ -20,15 +20,16 @@ class DetectionValidator(BaseValidator):
def __init__(self, dataloader=None, save_dir=None, pbar=None, logger=None, args=None):
super().__init__(dataloader, save_dir, pbar, logger, args)
if self.args.save_json:
check_requirements(['pycocotools'])
self.process = ops.process_mask_upsample # more accurate
else:
self.process = ops.process_mask # faster
self.data_dict = yaml_load(check_file(self.args.data)) if self.args.data else None
self.is_coco = False
self.class_map = None
self.targets = None
self.metrics = DetMetrics(save_dir=self.save_dir, plot=self.args.plots)
self.iouv = torch.linspace(0.5, 0.95, 10, device=self.device) # iou vector for mAP@0.5:0.95
self.niou = self.iouv.numel()
self.seen = 0
self.jdict = []
self.stats = []
def preprocess(self, batch):
batch["img"] = batch["img"].to(self.device, non_blocking=True)
@ -44,11 +45,7 @@ class DetectionValidator(BaseValidator):
return batch
def init_metrics(self, model):
if self.training:
head = de_parallel(model).model[-1]
else:
head = de_parallel(model).model.model[-1]
head = model.model[-1] if self.training else model.model.model[-1]
if self.data:
self.is_coco = isinstance(self.data.get('val'),
str) and self.data['val'].endswith(f'coco{os.sep}val2017.txt')
@ -57,15 +54,8 @@ class DetectionValidator(BaseValidator):
self.names = model.names
if isinstance(self.names, (list, tuple)): # old format
self.names = dict(enumerate(self.names))
self.iouv = torch.linspace(0.5, 0.95, 10, device=self.device) # iou vector for mAP@0.5:0.95
self.niou = self.iouv.numel()
self.seen = 0
self.metrics.names = self.names
self.confusion_matrix = ConfusionMatrix(nc=self.nc)
self.metrics = DetMetrics(save_dir=self.save_dir, plot=self.args.plots, names=self.names)
self.loss = torch.zeros(3, device=self.device)
self.jdict = []
self.stats = []
def get_desc(self):
return ('%22s' + '%11s' * 6) % ('Class', 'Images', 'Instances', 'Box(P', "R", "mAP50", "mAP50-95)")
@ -135,7 +125,7 @@ class DetectionValidator(BaseValidator):
return metrics
def print_results(self):
pf = '%22s' + '%11i' * 2 + '%11.3g' * 4 # print format
pf = '%22s' + '%11i' * 2 + '%11.3g' * len(self.metric_keys) # print format
self.logger.info(pf % ("all", self.seen, self.nt_per_class.sum(), *self.metrics.mean_results()))
if self.nt_per_class.sum() == 0:
self.logger.warning(