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# Ultralytics YOLO 🚀, GPL-3.0 license |
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import os |
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from pathlib import Path |
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import hydra |
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import numpy as np |
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import torch |
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from ultralytics.yolo.data import build_dataloader |
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from ultralytics.yolo.data.dataloaders.v5loader import create_dataloader |
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from ultralytics.yolo.engine.validator import BaseValidator |
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from ultralytics.yolo.utils import DEFAULT_CONFIG, colorstr, ops, yaml_load |
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from ultralytics.yolo.utils.checks import check_file, check_requirements |
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from ultralytics.yolo.utils.metrics import ConfusionMatrix, DetMetrics, box_iou |
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from ultralytics.yolo.utils.plotting import output_to_target, plot_images |
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from ultralytics.yolo.utils.torch_utils import de_parallel |
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class DetectionValidator(BaseValidator): |
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def __init__(self, dataloader=None, save_dir=None, pbar=None, logger=None, args=None): |
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super().__init__(dataloader, save_dir, pbar, logger, args) |
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self.data_dict = yaml_load(check_file(self.args.data), append_filename=True) if self.args.data else None |
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self.is_coco = False |
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self.class_map = None |
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self.metrics = DetMetrics(save_dir=self.save_dir) |
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self.iouv = torch.linspace(0.5, 0.95, 10) # iou vector for mAP@0.5:0.95 |
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self.niou = self.iouv.numel() |
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def preprocess(self, batch): |
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batch["img"] = batch["img"].to(self.device, non_blocking=True) |
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batch["img"] = (batch["img"].half() if self.args.half else batch["img"].float()) / 255 |
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for k in ["batch_idx", "cls", "bboxes"]: |
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batch[k] = batch[k].to(self.device) |
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nb = len(batch["img"]) |
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self.lb = [torch.cat([batch["cls"], batch["bboxes"]], dim=-1)[batch["batch_idx"] == i] |
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for i in range(nb)] if self.args.save_hybrid else [] # for autolabelling |
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return batch |
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def init_metrics(self, model): |
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head = model.model[-1] if self.training else model.model.model[-1] |
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val = self.data.get('val', '') # validation path |
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self.is_coco = isinstance(val, str) and val.endswith(f'coco{os.sep}val2017.txt') # is COCO dataset |
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self.class_map = ops.coco80_to_coco91_class() if self.is_coco else list(range(1000)) |
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self.args.save_json |= self.is_coco and not self.training # run on final val if training COCO |
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self.nc = head.nc |
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self.names = model.names |
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self.metrics.names = self.names |
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self.metrics.plot = self.args.plots |
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self.confusion_matrix = ConfusionMatrix(nc=self.nc) |
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self.seen = 0 |
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self.jdict = [] |
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self.stats = [] |
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def get_desc(self): |
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return ('%22s' + '%11s' * 6) % ('Class', 'Images', 'Instances', 'Box(P', "R", "mAP50", "mAP50-95)") |
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def postprocess(self, preds): |
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preds = ops.non_max_suppression(preds, |
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self.args.conf, |
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self.args.iou, |
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labels=self.lb, |
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multi_label=True, |
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agnostic=self.args.single_cls, |
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max_det=self.args.max_det) |
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return preds |
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def update_metrics(self, preds, batch): |
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# Metrics |
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for si, pred in enumerate(preds): |
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idx = batch["batch_idx"] == si |
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cls = batch["cls"][idx] |
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bbox = batch["bboxes"][idx] |
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nl, npr = cls.shape[0], pred.shape[0] # number of labels, predictions |
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shape = batch["ori_shape"][si] |
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correct_bboxes = torch.zeros(npr, self.niou, dtype=torch.bool, device=self.device) # init |
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self.seen += 1 |
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if npr == 0: |
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if nl: |
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self.stats.append((correct_bboxes, *torch.zeros((2, 0), device=self.device), cls.squeeze(-1))) |
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if self.args.plots: |
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self.confusion_matrix.process_batch(detections=None, labels=cls.squeeze(-1)) |
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continue |
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# Predictions |
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if self.args.single_cls: |
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pred[:, 5] = 0 |
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predn = pred.clone() |
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ops.scale_boxes(batch["img"][si].shape[1:], predn[:, :4], shape, |
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ratio_pad=batch["ratio_pad"][si]) # native-space pred |
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# Evaluate |
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if nl: |
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height, width = batch["img"].shape[2:] |
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tbox = ops.xywh2xyxy(bbox) * torch.tensor( |
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(width, height, width, height), device=self.device) # target boxes |
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ops.scale_boxes(batch["img"][si].shape[1:], tbox, shape, |
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ratio_pad=batch["ratio_pad"][si]) # native-space labels |
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labelsn = torch.cat((cls, tbox), 1) # native-space labels |
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correct_bboxes = self._process_batch(predn, labelsn) |
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# TODO: maybe remove these `self.` arguments as they already are member variable |
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if self.args.plots: |
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self.confusion_matrix.process_batch(predn, labelsn) |
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self.stats.append((correct_bboxes, pred[:, 4], pred[:, 5], cls.squeeze(-1))) # (conf, pcls, tcls) |
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# Save |
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if self.args.save_json: |
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self.pred_to_json(predn, batch["im_file"][si]) |
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# if self.args.save_txt: |
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# save_one_txt(predn, save_conf, shape, file=save_dir / 'labels' / f'{path.stem}.txt') |
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def get_stats(self): |
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stats = [torch.cat(x, 0).cpu().numpy() for x in zip(*self.stats)] # to numpy |
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if len(stats) and stats[0].any(): |
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self.metrics.process(*stats) |
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self.nt_per_class = np.bincount(stats[-1].astype(int), minlength=self.nc) # number of targets per class |
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return self.metrics.results_dict |
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def print_results(self): |
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pf = '%22s' + '%11i' * 2 + '%11.3g' * len(self.metrics.keys) # print format |
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self.logger.info(pf % ("all", self.seen, self.nt_per_class.sum(), *self.metrics.mean_results())) |
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if self.nt_per_class.sum() == 0: |
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self.logger.warning( |
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f'WARNING ⚠️ no labels found in {self.args.task} set, can not compute metrics without labels') |
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# Print results per class |
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if (self.args.verbose or not self.training) and self.nc > 1 and len(self.stats): |
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for i, c in enumerate(self.metrics.ap_class_index): |
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self.logger.info(pf % (self.names[c], self.seen, self.nt_per_class[c], *self.metrics.class_result(i))) |
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if self.args.plots: |
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self.confusion_matrix.plot(save_dir=self.save_dir, names=list(self.names.values())) |
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def _process_batch(self, detections, labels): |
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""" |
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Return correct prediction matrix |
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Arguments: |
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detections (array[N, 6]), x1, y1, x2, y2, conf, class |
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labels (array[M, 5]), class, x1, y1, x2, y2 |
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Returns: |
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correct (array[N, 10]), for 10 IoU levels |
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""" |
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iou = box_iou(labels[:, 1:], detections[:, :4]) |
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correct = np.zeros((detections.shape[0], self.iouv.shape[0])).astype(bool) |
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correct_class = labels[:, 0:1] == detections[:, 5] |
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for i in range(len(self.iouv)): |
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x = torch.where((iou >= self.iouv[i]) & correct_class) # IoU > threshold and classes match |
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if x[0].shape[0]: |
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matches = torch.cat((torch.stack(x, 1), iou[x[0], x[1]][:, None]), |
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1).cpu().numpy() # [label, detect, iou] |
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if x[0].shape[0] > 1: |
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matches = matches[matches[:, 2].argsort()[::-1]] |
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matches = matches[np.unique(matches[:, 1], return_index=True)[1]] |
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# matches = matches[matches[:, 2].argsort()[::-1]] |
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matches = matches[np.unique(matches[:, 0], return_index=True)[1]] |
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correct[matches[:, 1].astype(int), i] = True |
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return torch.tensor(correct, dtype=torch.bool, device=detections.device) |
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def get_dataloader(self, dataset_path, batch_size): |
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# TODO: manage splits differently |
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# calculate stride - check if model is initialized |
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gs = max(int(de_parallel(self.model).stride if self.model else 0), 32) |
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return create_dataloader(path=dataset_path, |
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imgsz=self.args.imgsz, |
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batch_size=batch_size, |
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stride=gs, |
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hyp=dict(self.args), |
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cache=False, |
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pad=0.5, |
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rect=True, |
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workers=self.args.workers, |
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prefix=colorstr(f'{self.args.mode}: '), |
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shuffle=False, |
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seed=self.args.seed)[0] if self.args.v5loader else \ |
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build_dataloader(self.args, batch_size, img_path=dataset_path, stride=gs, mode="val")[0] |
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def plot_val_samples(self, batch, ni): |
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plot_images(batch["img"], |
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batch["batch_idx"], |
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batch["cls"].squeeze(-1), |
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batch["bboxes"], |
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paths=batch["im_file"], |
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fname=self.save_dir / f"val_batch{ni}_labels.jpg", |
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names=self.names) |
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def plot_predictions(self, batch, preds, ni): |
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plot_images(batch["img"], |
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*output_to_target(preds, max_det=15), |
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paths=batch["im_file"], |
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fname=self.save_dir / f'val_batch{ni}_pred.jpg', |
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names=self.names) # pred |
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def pred_to_json(self, predn, filename): |
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stem = Path(filename).stem |
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image_id = int(stem) if stem.isnumeric() else stem |
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box = ops.xyxy2xywh(predn[:, :4]) # xywh |
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box[:, :2] -= box[:, 2:] / 2 # xy center to top-left corner |
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for p, b in zip(predn.tolist(), box.tolist()): |
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self.jdict.append({ |
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'image_id': image_id, |
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'category_id': self.class_map[int(p[5])], |
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'bbox': [round(x, 3) for x in b], |
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'score': round(p[4], 5)}) |
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def eval_json(self, stats): |
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if self.args.save_json and self.is_coco and len(self.jdict): |
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anno_json = self.data['path'] / "annotations/instances_val2017.json" # annotations |
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pred_json = self.save_dir / "predictions.json" # predictions |
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self.logger.info(f'\nEvaluating pycocotools mAP using {pred_json} and {anno_json}...') |
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try: # https://github.com/cocodataset/cocoapi/blob/master/PythonAPI/pycocoEvalDemo.ipynb |
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check_requirements('pycocotools>=2.0.6') |
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from pycocotools.coco import COCO # noqa |
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from pycocotools.cocoeval import COCOeval # noqa |
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for x in anno_json, pred_json: |
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assert x.is_file(), f"{x} file not found" |
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anno = COCO(str(anno_json)) # init annotations api |
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pred = anno.loadRes(str(pred_json)) # init predictions api (must pass string, not Path) |
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eval = COCOeval(anno, pred, 'bbox') |
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if self.is_coco: |
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eval.params.imgIds = [int(Path(x).stem) for x in self.dataloader.dataset.im_files] # images to eval |
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eval.evaluate() |
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eval.accumulate() |
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eval.summarize() |
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stats[self.metrics.keys[-1]], stats[self.metrics.keys[-2]] = eval.stats[:2] # update mAP50-95 and mAP50 |
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except Exception as e: |
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self.logger.warning(f'pycocotools unable to run: {e}') |
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return stats |
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@hydra.main(version_base=None, config_path=str(DEFAULT_CONFIG.parent), config_name=DEFAULT_CONFIG.name) |
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def val(cfg): |
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cfg.model = cfg.model or "yolov8n.pt" |
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cfg.data = cfg.data or "coco128.yaml" |
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validator = DetectionValidator(args=cfg) |
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validator(model=cfg.model) |
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if __name__ == "__main__": |
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val()
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