import os import hydra import numpy as np import torch import torch.nn.functional as F 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.files import yaml_load from ultralytics.yolo.utils.metrics import ConfusionMatrix, Metric, ap_per_class, box_iou, fitness_detection 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) 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 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 self.nb, _, self.height, self.width = batch["img"].shape # batch size, channels, height, width self.targets = torch.cat((batch["batch_idx"].view(-1, 1), batch["cls"].view(-1, 1), batch["bboxes"]), 1) self.targets = self.targets.to(self.device) height, width = batch["img"].shape[2:] self.targets[:, 2:] *= torch.tensor((width, height, width, height), device=self.device) # to pixels self.lb = [self.targets[self.targets[:, 0] == i, 1:] for i in range(self.nb)] if self.args.save_hybrid else [] # for autolabelling return batch def init_metrics(self, model): if self.training: head = de_parallel(model).model[-1] else: head = de_parallel(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.nc = head.nc 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.confusion_matrix = ConfusionMatrix(nc=self.nc) self.metrics = Metric() self.loss = torch.zeros(4, device=self.device) 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_thres, self.args.iou_thres, 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): labels = self.targets[self.targets[:, 0] == si, 1:] nl, npr = labels.shape[0], pred.shape[0] # number of labels, predictions shape = batch["ori_shape"][si] # path = batch["shape"][si][0] 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), labels[:, 0])) if self.args.plots: self.confusion_matrix.process_batch(detections=None, labels=labels[:, 0]) continue # Predictions if self.args.single_cls: pred[:, 5] = 0 predn = pred.clone() ops.scale_boxes(batch["img"][si].shape[1:], predn[:, :4], shape) # native-space pred # Evaluate if nl: tbox = ops.xywh2xyxy(labels[:, 1:5]) # target boxes ops.scale_boxes(batch["img"][si].shape[1:], tbox, shape) # native-space labels labelsn = torch.cat((labels[:, 0:1], tbox), 1) # native-space labels correct_bboxes = self._process_batch(predn, labelsn, self.iouv) # 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], labels[:, 0])) # (conf, pcls, tcls) # TODO: Save/log ''' if self.args.save_txt: save_one_txt(predn, save_conf, shape, file=save_dir / 'labels' / f'{path.stem}.txt') if self.args.save_json: pred_masks = scale_image(im[si].shape[1:], pred_masks.permute(1, 2, 0).contiguous().cpu().numpy(), shape, shapes[si][1]) save_one_json(predn, jdict, path, class_map, pred_masks) # append to COCO-JSON dictionary # callbacks.run('on_val_image_end', pred, predn, path, names, im[si]) ''' 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(): results = ap_per_class(*stats, plot=self.args.plots, save_dir=self.save_dir, names=self.names) self.metrics.update(results[2:]) self.nt_per_class = np.bincount(stats[3].astype(int), minlength=self.nc) # number of targets per class metrics = {"fitness": fitness_detection(np.array(self.metrics.mean_results()).reshape(1, -1))} metrics |= zip(self.metric_keys, self.metrics.mean_results()) return metrics def print_results(self): pf = '%22s' + '%11i' * 2 + '%11.3g' * 4 # 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): """ 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], iouv.shape[0])).astype(bool) correct_class = labels[:, 0:1] == detections[:, 5] for i in range(len(iouv)): x = torch.where((iou >= 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=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: align with train loss metrics @property def metric_keys(self): return ["metrics/precision(B)", "metrics/recall(B)", "metrics/mAP_0.5(B)", "metrics/mAP_0.5:0.95(B)"] def plot_val_samples(self, batch, ni): images = batch["img"] cls = batch["cls"].squeeze(-1) bboxes = batch["bboxes"] paths = batch["im_file"] batch_idx = batch["batch_idx"] plot_images(images, batch_idx, cls, bboxes, paths=paths, fname=self.save_dir / f"val_batch{ni}_labels.jpg", names=self.names) def plot_predictions(self, batch, preds, ni): images = batch["img"] paths = batch["im_file"] plot_images(images, *output_to_target(preds, max_det=15), paths, self.save_dir / f'val_batch{ni}_pred.jpg', self.names) # pred @hydra.main(version_base=None, config_path=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()