# Ultralytics YOLO 🚀, GPL-3.0 license
import os
from pathlib import Path
import hydra
import numpy as np
import torch
from ultralytics.yolo.data import build_dataloader
from ultralytics.yolo.data.dataloaders.v5loader import create_dataloader
from ultralytics.yolo.engine.validator import BaseValidator
from ultralytics.yolo.utils import DEFAULT_CONFIG, colorstr, ops, yaml_load
from ultralytics.yolo.utils.checks import check_file, check_requirements
from ultralytics.yolo.utils.metrics import ConfusionMatrix, DetMetrics, box_iou
from ultralytics.yolo.utils.plotting import output_to_target, plot_images
from ultralytics.yolo.utils.torch_utils import de_parallel
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)
self.data_dict = yaml_load(check_file(self.args.data), append_filename=True) if self.args.data else None
self.is_coco = False
self.class_map = None
self.metrics = DetMetrics(save_dir=self.save_dir)
self.iouv = torch.linspace(0.5, 0.95, 10) # iou vector for mAP@0.5:0.95
self.niou = self.iouv.numel()
def preprocess(self, batch):
batch["img"] = batch["img"].to(self.device, non_blocking=True)
batch["img"] = (batch["img"].half() if self.args.half else batch["img"].float()) / 255
for k in ["batch_idx", "cls", "bboxes"]:
batch[k] = batch[k].to(self.device)
nb = len(batch["img"])
self.lb = [torch.cat([batch["cls"], batch["bboxes"]], dim=-1)[batch["batch_idx"] == i]
for i in range(nb)] if self.args.save_hybrid else [] # for autolabelling
return batch
def init_metrics(self, model):
head = model.model[-1] if self.training else model.model.model[-1]
val = self.data.get('val', '') # validation path
self.is_coco = isinstance(val, str) and val.endswith(f'coco{os.sep}val2017.txt') # is COCO dataset
self.class_map = ops.coco80_to_coco91_class() if self.is_coco else list(range(1000))
self.args.save_json |= self.is_coco and not self.training # run on final val if training COCO
self.nc = head.nc
self.names = model.names
self.metrics.names = self.names
self.metrics.plot = self.args.plots
self.confusion_matrix = ConfusionMatrix(nc=self.nc)
self.seen = 0
self.jdict = []
self.stats = []
def get_desc(self):
return ('%22s' + '%11s' * 6) % ('Class', 'Images', 'Instances', 'Box(P', "R", "mAP50", "mAP50-95)")
def postprocess(self, preds):
preds = ops.non_max_suppression(preds,
self.args.conf,
self.args.iou,
labels=self.lb,
multi_label=True,
agnostic=self.args.single_cls,
max_det=self.args.max_det)
return preds
def update_metrics(self, preds, batch):
# Metrics
for si, pred in enumerate(preds):
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_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, *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
# 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
if self.args.plots:
self.confusion_matrix.process_batch(predn, labelsn)
self.stats.append((correct_bboxes, pred[:, 4], pred[:, 5], cls.squeeze(-1))) # (conf, pcls, tcls)
# Save
if self.args.save_json:
self.pred_to_json(predn, batch["im_file"][si])
# if self.args.save_txt:
# save_one_txt(predn, save_conf, shape, file=save_dir / 'labels' / f'{path.stem}.txt')
def get_stats(self):
stats = [torch.cat(x, 0).cpu().numpy() for x in zip(*self.stats)] # to numpy
if len(stats) and stats[0].any():
self.metrics.process(*stats)
self.nt_per_class = np.bincount(stats[-1].astype(int), minlength=self.nc) # number of targets per class
return self.metrics.results_dict
def print_results(self):
pf = '%22s' + '%11i' * 2 + '%11.3g' * len(self.metrics.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(
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 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):
"""
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
"""
iou = box_iou(labels[:, 1:], detections[:, :4])
correct = np.zeros((detections.shape[0], self.iouv.shape[0])).astype(bool)
correct_class = labels[:, 0:1] == detections[:, 5]
for i in range(len(self.iouv)):
x = torch.where((iou >= self.iouv[i]) & correct_class) # IoU > threshold and classes match
if x[0].shape[0]:
matches = torch.cat((torch.stack(x, 1), iou[x[0], x[1]][:, None]),
1).cpu().numpy() # [label, detect, iou]
if x[0].shape[0] > 1:
matches = matches[matches[:, 2].argsort()[::-1]]
matches = matches[np.unique(matches[:, 1], return_index=True)[1]]
# matches = matches[matches[:, 2].argsort()[::-1]]
matches = matches[np.unique(matches[:, 0], return_index=True)[1]]
correct[matches[:, 1].astype(int), i] = True
return torch.tensor(correct, dtype=torch.bool, device=detections.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 create_dataloader(path=dataset_path,
imgsz=self.args.imgsz,
batch_size=batch_size,
stride=gs,
hyp=dict(self.args),
cache=False,
pad=0.5,
rect=True,
workers=self.args.workers,
prefix=colorstr(f'{self.args.mode}: '),
shuffle=False,
seed=self.args.seed)[0] if self.args.v5loader else \
build_dataloader(self.args, batch_size, img_path=dataset_path, stride=gs, mode="val")[0]
def plot_val_samples(self, batch, ni):
plot_images(batch["img"],
batch["batch_idx"],
batch["cls"].squeeze(-1),
batch["bboxes"],
paths=batch["im_file"],
fname=self.save_dir / f"val_batch{ni}_labels.jpg",
names=self.names)
def plot_predictions(self, batch, preds, ni):
plot_images(batch["img"],
*output_to_target(preds, max_det=15),
paths=batch["im_file"],
fname=self.save_dir / f'val_batch{ni}_pred.jpg',
names=self.names) # pred
def pred_to_json(self, predn, filename):
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
for p, b in 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)})
def eval_json(self, stats):
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
self.logger.info(f'\nEvaluating 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)
eval = COCOeval(anno, pred, 'bbox')
if self.is_coco:
eval.params.imgIds = [int(Path(x).stem) for x in self.dataloader.dataset.im_files] # images to eval
eval.evaluate()
eval.accumulate()
eval.summarize()
stats[self.metrics.keys[-1]], stats[self.metrics.keys[-2]] = eval.stats[:2] # update mAP50-95 and mAP50
except Exception as e:
self.logger.warning(f'pycocotools unable to run: {e}')
return stats
@hydra.main(version_base=None, config_path=str(DEFAULT_CONFIG.parent), config_name=DEFAULT_CONFIG.name)
def val(cfg):
cfg.data = cfg.data or "coco128.yaml"
validator = DetectionValidator(args=cfg)
validator(model=cfg.model)
if __name__ == "__main__":
val()