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262 lines
12 KiB
262 lines
12 KiB
# 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 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_CFG, LOGGER, colorstr, ops
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from ultralytics.yolo.utils.checks import 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, args=None, _callbacks=None):
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super().__init__(dataloader, save_dir, pbar, args, _callbacks)
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self.args.task = 'detect'
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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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val = self.data.get(self.args.split, '') # validation path
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self.is_coco = isinstance(val, str) and 'coco' in val and val.endswith(f'{os.sep}val2017.txt') # is COCO
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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.names = model.names
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self.nc = len(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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file = self.save_dir / 'labels' / f'{Path(batch["im_file"][si]).stem}.txt'
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self.save_one_txt(predn, self.args.save_conf, shape, file)
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def finalize_metrics(self, *args, **kwargs):
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self.metrics.speed = self.speed
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self.metrics.confusion_matrix = self.confusion_matrix
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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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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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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 and 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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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=vars(self.args),
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cache=False,
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pad=0.5,
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rect=self.args.rect,
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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, data_info=self.data,
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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 save_one_txt(self, predn, save_conf, shape, file):
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gn = torch.tensor(shape)[[1, 0, 1, 0]] # normalization gain whwh
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for *xyxy, conf, cls in predn.tolist():
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xywh = (ops.xyxy2xywh(torch.tensor(xyxy).view(1, 4)) / gn).view(-1).tolist() # normalized xywh
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line = (cls, *xywh, conf) if save_conf else (cls, *xywh) # label format
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with open(file, 'a') as f:
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f.write(('%g ' * len(line)).rstrip() % line + '\n')
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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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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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LOGGER.warning(f'pycocotools unable to run: {e}')
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return stats
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def val(cfg=DEFAULT_CFG, use_python=False):
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model = cfg.model or 'yolov8n.pt'
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data = cfg.data or 'coco128.yaml'
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args = dict(model=model, data=data)
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if use_python:
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from ultralytics import YOLO
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YOLO(model).val(**args)
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else:
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validator = DetectionValidator(args=args)
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validator(model=args['model'])
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if __name__ == '__main__':
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val()
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