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Revert augment_hyps (#70)

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Laughing 2 years ago committed by GitHub
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commit e629335f6d
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4 changed files with 29 additions and 76 deletions
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  1. 2
      ultralytics/yolo/data/build.py
  2. 27
      ultralytics/yolo/utils/configs/default.yaml
  3. 30
      ultralytics/yolo/v8/detect/val.py
  4. 46
      ultralytics/yolo/v8/segment/val.py

2
ultralytics/yolo/data/build.py
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@ -65,7 +65,7 @@ def build_dataloader(cfg, batch_size, img_path, stride=32, label_path=None, rank
img_size=cfg.img_size,
batch_size=batch_size,
augment=True if mode == "train" else False, # augmentation
hyp=cfg.get("augment_hyp", None),
hyp=cfg, # TODO: probably add a get_hyps_from_cfg function
rect=cfg.rect if mode == "train" else True, # rectangular batches
cache=None if cfg.noval else cfg.get("cache", None),
single_cls=cfg.get("single_cls", False),

27
ultralytics/yolo/utils/configs/default.yaml
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@ -83,20 +83,19 @@ fl_gamma: 0.0 # focal loss gamma (efficientDet default gamma=1.5)
label_smoothing: 0.0
nbs: 64 # nominal batch size
# anchors: 3
augment_hyp:
hsv_h: 0.015 # image HSV-Hue augmentation (fraction)
hsv_s: 0.7 # image HSV-Saturation augmentation (fraction)
hsv_v: 0.4 # image HSV-Value augmentation (fraction)
degrees: 0.0 # image rotation (+/- deg)
translate: 0.1 # image translation (+/- fraction)
scale: 0.5 # image scale (+/- gain)
shear: 0.0 # image shear (+/- deg)
perspective: 0.0 # image perspective (+/- fraction), range 0-0.001
flipud: 0.0 # image flip up-down (probability)
fliplr: 0.5 # image flip left-right (probability)
mosaic: 1.0 # image mosaic (probability)
mixup: 0.0 # image mixup (probability)
copy_paste: 0.0 # segment copy-paste (probability)
hsv_h: 0.015 # image HSV-Hue augmentation (fraction)
hsv_s: 0.7 # image HSV-Saturation augmentation (fraction)
hsv_v: 0.4 # image HSV-Value augmentation (fraction)
degrees: 0.0 # image rotation (+/- deg)
translate: 0.1 # image translation (+/- fraction)
scale: 0.5 # image scale (+/- gain)
shear: 0.0 # image shear (+/- deg)
perspective: 0.0 # image perspective (+/- fraction), range 0-0.001
flipud: 0.0 # image flip up-down (probability)
fliplr: 0.5 # image flip left-right (probability)
mosaic: 1.0 # image mosaic (probability)
mixup: 0.0 # image mixup (probability)
copy_paste: 0.0 # segment copy-paste (probability)
# Hydra configs --------------------------------------------------------------------------------------------------------
hydra:

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(

46
ultralytics/yolo/v8/segment/val.py
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@ -8,8 +8,7 @@ import torch.nn.functional as F
from ultralytics.yolo.data import build_dataloader
from ultralytics.yolo.engine.trainer import DEFAULT_CONFIG
from ultralytics.yolo.utils import ops
from ultralytics.yolo.utils.checks import check_file, check_requirements
from ultralytics.yolo.utils.files import yaml_load
from ultralytics.yolo.utils.checks import check_requirements
from ultralytics.yolo.utils.metrics import ConfusionMatrix, SegmentMetrics, box_iou, mask_iou
from ultralytics.yolo.utils.plotting import output_to_target, plot_images
from ultralytics.yolo.utils.torch_utils import de_parallel
@ -26,10 +25,7 @@ class SegmentationValidator(DetectionValidator):
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 = SegmentMetrics(save_dir=self.save_dir, plot=self.args.plots)
def preprocess(self, batch):
batch["img"] = batch["img"].to(self.device, non_blocking=True)
@ -46,29 +42,18 @@ class SegmentationValidator(DetectionValidator):
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')
self.class_map = ops.coco80_to_coco91_class() if self.is_coco else list(range(1000))
self.nm = head.nm if hasattr(head, "nm") else 32
self.nc = head.nc
self.nm = head.nm if hasattr(head, "nm") else 32
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 = SegmentMetrics(save_dir=self.save_dir, plot=self.args.plots, names=self.names)
self.loss = torch.zeros(4, device=self.device)
self.jdict = []
self.stats = []
self.plot_masks = []
def get_desc(self):
@ -150,21 +135,6 @@ class SegmentationValidator(DetectionValidator):
# callbacks.run('on_val_image_end', pred, predn, path, names, im[si])
'''
def print_results(self):
pf = '%22s' + '%11i' * 2 + '%11.3g' * 8 # 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(
f'WARNING ⚠️ no labels found in {self.args.task} set, can not compute metrics without labels')
# Print results per class
if (self.args.verbose or (self.nc < 50 and not self.training)) and self.nc > 1 and len(self.stats):
for i, c in enumerate(self.metrics.ap_class_index):
self.logger.info(pf % (self.names[c], self.seen, self.nt_per_class[c], *self.metrics.class_result(i)))
if self.args.plots:
self.confusion_matrix.plot(save_dir=self.save_dir, names=list(self.names.values()))
def _process_batch(self, detections, labels, iouv, pred_masks=None, gt_masks=None, overlap=False, masks=False):
"""
Return correct prediction matrix
@ -202,12 +172,6 @@ class SegmentationValidator(DetectionValidator):
correct[matches[:, 1].astype(int), i] = True
return torch.tensor(correct, dtype=torch.bool, device=iouv.device)
def get_dataloader(self, dataset_path, batch_size):
# TODO: manage splits differently
# calculate stride - check if model is initialized
gs = max(int(de_parallel(self.model).stride if self.model else 0), 32)
return build_dataloader(self.args, batch_size, img_path=dataset_path, stride=gs, mode="val")[0]
# TODO: probably add this to class Metrics
@property
def metric_keys(self):

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