|
|
# Ultralytics YOLO 🚀, GPL-3.0 license |
|
|
|
|
|
import os |
|
|
from pathlib import Path |
|
|
|
|
|
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_CFG, LOGGER, colorstr, ops |
|
|
from ultralytics.yolo.utils.checks import 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, args=None): |
|
|
super().__init__(dataloader, save_dir, pbar, args) |
|
|
self.args.task = 'detect' |
|
|
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): |
|
|
val = self.data.get(self.args.split, '') # 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.names = model.names |
|
|
self.nc = len(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 finalize_metrics(self, *args, **kwargs): |
|
|
self.metrics.speed = self.speed |
|
|
self.metrics.confusion_matrix = self.confusion_matrix |
|
|
|
|
|
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 |
|
|
LOGGER.info(pf % ('all', self.seen, self.nt_per_class.sum(), *self.metrics.mean_results())) |
|
|
if self.nt_per_class.sum() == 0: |
|
|
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 and not self.training and self.nc > 1 and len(self.stats): |
|
|
for i, c in enumerate(self.metrics.ap_class_index): |
|
|
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=vars(self.args), |
|
|
cache=False, |
|
|
pad=0.5, |
|
|
rect=self.args.rect, |
|
|
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, names=self.data['names'], |
|
|
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 |
|
|
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: |
|
|
LOGGER.warning(f'pycocotools unable to run: {e}') |
|
|
return stats |
|
|
|
|
|
|
|
|
def val(cfg=DEFAULT_CFG, use_python=False): |
|
|
model = cfg.model or 'yolov8n.pt' |
|
|
data = cfg.data or 'coco128.yaml' |
|
|
|
|
|
args = dict(model=model, data=data) |
|
|
if use_python: |
|
|
from ultralytics import YOLO |
|
|
YOLO(model).val(**args) |
|
|
else: |
|
|
validator = DetectionValidator(args=args) |
|
|
validator(model=args['model']) |
|
|
|
|
|
|
|
|
if __name__ == '__main__': |
|
|
val()
|
|
|
|
