diff --git a/ultralytics/yolo/data/augment.py b/ultralytics/yolo/data/augment.py index 9636b1e..bd8d7e9 100644 --- a/ultralytics/yolo/data/augment.py +++ b/ultralytics/yolo/data/augment.py @@ -463,6 +463,8 @@ class LetterBox: dw /= 2 # divide padding into 2 sides dh /= 2 + if labels.get("ratio_pad"): + labels["ratio_pad"] = (labels["ratio_pad"], (dw, dh)) # for evaluation if shape[::-1] != new_unpad: # resize img = cv2.resize(img, new_unpad, interpolation=cv2.INTER_LINEAR) diff --git a/ultralytics/yolo/data/base.py b/ultralytics/yolo/data/base.py index e835441..cff9cfa 100644 --- a/ultralytics/yolo/data/base.py +++ b/ultralytics/yolo/data/base.py @@ -179,6 +179,10 @@ class BaseDataset(Dataset): def get_label_info(self, index): label = self.labels[index].copy() label["img"], label["ori_shape"], label["resized_shape"] = self.load_image(index) + label["ratio_pad"] = ( + label["resized_shape"][0] / label["ori_shape"][0], + label["resized_shape"][1] / label["ori_shape"][1], + ) # for evaluation if self.rect: label["rect_shape"] = self.batch_shapes[self.batch[index]] label = self.update_labels_info(label) diff --git a/ultralytics/yolo/data/dataloaders/v5loader.py b/ultralytics/yolo/data/dataloaders/v5loader.py index 9dee14b..1015eaf 100644 --- a/ultralytics/yolo/data/dataloaders/v5loader.py +++ b/ultralytics/yolo/data/dataloaders/v5loader.py @@ -895,7 +895,7 @@ class LoadImagesAndLabels(Dataset): batch_idx, cls, bboxes = torch.cat(label, 0).split((1, 1, 4), dim=1) return { 'ori_shape': tuple((x[0] if x else None) for x in shapes), - 'resized_shape': tuple(tuple(x.shape[1:]) for x in im), + 'ratio_pad': tuple((x[1] if x else None) for x in shapes), 'im_file': path, 'img': torch.stack(im, 0), 'cls': cls, diff --git a/ultralytics/yolo/data/dataset.py b/ultralytics/yolo/data/dataset.py index d94f7c5..72ed3b4 100644 --- a/ultralytics/yolo/data/dataset.py +++ b/ultralytics/yolo/data/dataset.py @@ -127,7 +127,7 @@ class YOLODataset(BaseDataset): mosaic = self.augment and not self.rect transforms = mosaic_transforms(self, self.imgsz, hyp) if mosaic else affine_transforms(self.imgsz, hyp) else: - transforms = Compose([LetterBox(new_shape=(self.imgsz, self.imgsz))]) + transforms = Compose([LetterBox(new_shape=(self.imgsz, self.imgsz), scaleup=False)]) transforms.append( Format(bbox_format="xywh", normalize=True, diff --git a/ultralytics/yolo/engine/trainer.py b/ultralytics/yolo/engine/trainer.py index 30a15e0..7c60454 100644 --- a/ultralytics/yolo/engine/trainer.py +++ b/ultralytics/yolo/engine/trainer.py @@ -224,7 +224,7 @@ class BaseTrainer: if rank in {0, -1}: self.test_loader = self.get_dataloader(self.testset, batch_size=batch_size * 2, rank=-1, mode="val") self.validator = self.get_validator() - metric_keys = self.validator.metric_keys + self.label_loss_items(prefix="val") + metric_keys = self.validator.metrics.keys + self.label_loss_items(prefix="val") self.metrics = dict(zip(metric_keys, [0] * len(metric_keys))) # TODO: init metrics for plot_results()? self.ema = ModelEMA(self.model) self.resume_training(ckpt) diff --git a/ultralytics/yolo/utils/metrics.py b/ultralytics/yolo/utils/metrics.py index 0d450e1..b80e8b5 100644 --- a/ultralytics/yolo/utils/metrics.py +++ b/ultralytics/yolo/utils/metrics.py @@ -469,7 +469,7 @@ class Metric: def mean_results(self): """Mean of results, return mp, mr, map50, map""" - return self.mp, self.mr, self.map50, self.map + return [self.mp, self.mr, self.map50, self.map] def class_result(self, i): """class-aware result, return p[i], r[i], ap50[i], ap[i]""" @@ -520,6 +520,7 @@ class DetMetrics: def get_maps(self, nc): return self.metric.get_maps(nc) + @property def fitness(self): return self.metric.fitness() @@ -527,6 +528,10 @@ class DetMetrics: def ap_class_index(self): return self.metric.ap_class_index + @property + def results_dict(self): + return dict(zip(self.keys + ["fitness"], self.mean_results() + [self.fitness])) + class SegmentMetrics: @@ -578,6 +583,7 @@ class SegmentMetrics: def get_maps(self, nc): return self.metric_box.get_maps(nc) + self.metric_mask.get_maps(nc) + @property def fitness(self): return self.metric_mask.fitness() + self.metric_box.fitness() @@ -585,3 +591,30 @@ class SegmentMetrics: def ap_class_index(self): # boxes and masks have the same ap_class_index return self.metric_box.ap_class_index + + @property + def results_dict(self): + return dict(zip(self.keys + ["fitness"], self.mean_results() + [self.fitness])) + + +class ClassifyMetrics: + + def __init__(self) -> None: + self.top1 = 0 + self.top5 = 0 + + def process(self, correct): + acc = torch.stack((correct[:, 0], correct.max(1).values), dim=1) # (top1, top5) accuracy + self.top1, self.top5 = acc.mean(0).tolist() + + @property + def fitness(self): + return self.top5 + + @property + def results_dict(self): + return dict(zip(self.keys + ["fitness"], [self.top1, self.top5, self.fitness])) + + @property + def keys(self): + return ["top1", "top5"] diff --git a/ultralytics/yolo/v8/classify/val.py b/ultralytics/yolo/v8/classify/val.py index d10de32..161db77 100644 --- a/ultralytics/yolo/v8/classify/val.py +++ b/ultralytics/yolo/v8/classify/val.py @@ -4,10 +4,15 @@ import torch from ultralytics.yolo.data import build_classification_dataloader from ultralytics.yolo.engine.validator import BaseValidator from ultralytics.yolo.utils import DEFAULT_CONFIG +from ultralytics.yolo.utils.metrics import ClassifyMetrics class ClassificationValidator(BaseValidator): + def __init__(self, dataloader=None, save_dir=None, pbar=None, logger=None, args=None): + super().__init__(dataloader, save_dir, pbar, logger, args) + self.metrics = ClassifyMetrics() + def init_metrics(self, model): self.correct = torch.tensor([], device=next(model.parameters()).device) @@ -23,17 +28,12 @@ class ClassificationValidator(BaseValidator): self.correct = torch.cat((self.correct, correct_in_batch)) def get_stats(self): - acc = torch.stack((self.correct[:, 0], self.correct.max(1).values), dim=1) # (top1, top5) accuracy - top1, top5 = acc.mean(0).tolist() - return {"top1": top1, "top5": top5, "fitness": top5} + self.metrics.process(self.correct) + return self.metrics.results_dict def get_dataloader(self, dataset_path, batch_size): return build_classification_dataloader(path=dataset_path, imgsz=self.args.imgsz, batch_size=batch_size) - @property - def metric_keys(self): - return ["top1", "top5"] - @hydra.main(version_base=None, config_path=str(DEFAULT_CONFIG.parent), config_name=DEFAULT_CONFIG.name) def val(cfg): diff --git a/ultralytics/yolo/v8/detect/val.py b/ultralytics/yolo/v8/detect/val.py index 27ae5a1..ff65318 100644 --- a/ultralytics/yolo/v8/detect/val.py +++ b/ultralytics/yolo/v8/detect/val.py @@ -22,7 +22,6 @@ class DetectionValidator(BaseValidator): 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.targets = None self.metrics = DetMetrics(save_dir=self.save_dir, plot=self.args.plots) self.iouv = torch.linspace(0.5, 0.95, 10) # iou vector for mAP@0.5:0.95 self.niou = self.iouv.numel() @@ -30,13 +29,13 @@ class DetectionValidator(BaseValidator): 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 + for k in ["batch_idx", "cls", "bboxes"]: + batch[k] = batch[k].to(self.device) + + nb, _, height, width = batch["img"].shape + batch["bboxes"] *= torch.tensor((width, height, width, height), device=self.device) # to pixels + 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 @@ -69,36 +68,39 @@ class DetectionValidator(BaseValidator): 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 + 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] - # 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])) + 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=labels[:, 0]) + 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) # native-space pred + ops.scale_boxes(batch["img"][si].shape[1:], predn[:, :4], shape, + ratio_pad=batch["ratio_pad"][si]) # 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 + tbox = ops.xywh2xyxy(bbox) # 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], labels[:, 0])) # (conf, pcls, tcls) + self.stats.append((correct_bboxes, pred[:, 4], pred[:, 5], cls.squeeze(-1))) # (conf, pcls, tcls) # Save if self.args.save_json: @@ -111,12 +113,10 @@ class DetectionValidator(BaseValidator): 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 - fitness = {"fitness": self.metrics.fitness()} - metrics = dict(zip(self.metric_keys, self.metrics.mean_results())) - return {**metrics, **fitness} + return self.metrics.results_dict def print_results(self): - pf = '%22s' + '%11i' * 2 + '%11.3g' * len(self.metric_keys) # print format + 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( @@ -166,18 +166,13 @@ class DetectionValidator(BaseValidator): hyp=dict(self.args), cache=False, pad=0.5, - rect=self.args.rect, + 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] - # TODO: align with train loss metrics - @property - def metric_keys(self): - return ["metrics/precision(B)", "metrics/recall(B)", "metrics/mAP50(B)", "metrics/mAP50-95(B)"] - def plot_val_samples(self, batch, ni): plot_images(batch["img"], batch["batch_idx"], @@ -226,7 +221,7 @@ class DetectionValidator(BaseValidator): eval.evaluate() eval.accumulate() eval.summarize() - stats[self.metric_keys[-1]], stats[self.metric_keys[-2]] = eval.stats[:2] # update mAP50-95 and mAP50 + 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 diff --git a/ultralytics/yolo/v8/segment/val.py b/ultralytics/yolo/v8/segment/val.py index 6dc0bc1..801e006 100644 --- a/ultralytics/yolo/v8/segment/val.py +++ b/ultralytics/yolo/v8/segment/val.py @@ -22,17 +22,8 @@ class SegmentationValidator(DetectionValidator): self.metrics = SegmentMetrics(save_dir=self.save_dir, plot=self.args.plots) 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 + batch = super().preprocess(batch) batch["masks"] = batch["masks"].to(self.device).float() - 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): @@ -72,10 +63,11 @@ class SegmentationValidator(DetectionValidator): def update_metrics(self, preds, batch): # Metrics for si, (pred, proto) in enumerate(zip(preds[0], preds[1])): - labels = self.targets[self.targets[:, 0] == si, 1:] - nl, npr = labels.shape[0], pred.shape[0] # number of labels, predictions + 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] - # path = batch["shape"][si][0] correct_masks = torch.zeros(npr, self.niou, dtype=torch.bool, device=self.device) # init correct_bboxes = torch.zeros(npr, self.niou, dtype=torch.bool, device=self.device) # init self.seen += 1 @@ -83,13 +75,13 @@ class SegmentationValidator(DetectionValidator): if npr == 0: if nl: self.stats.append((correct_masks, correct_bboxes, *torch.zeros( - (2, 0), device=self.device), labels[:, 0])) + (2, 0), device=self.device), cls.squeeze(-1))) if self.args.plots: - self.confusion_matrix.process_batch(detections=None, labels=labels[:, 0]) + self.confusion_matrix.process_batch(detections=None, labels=cls.squeeze(-1)) continue # Masks - midx = [si] if self.args.overlap_mask else self.targets[:, 0] == si + midx = [si] if self.args.overlap_mask else idx gt_masks = batch["masks"][midx] pred_masks = self.process(proto, pred[:, 6:], pred[:, :4], shape=batch["img"][si].shape[1:]) @@ -101,9 +93,9 @@ class SegmentationValidator(DetectionValidator): # Evaluate if nl: - tbox = ops.xywh2xyxy(labels[:, 1:5]) # target boxes + tbox = ops.xywh2xyxy(bbox) # 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 + 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 correct_masks = self._process_batch(predn, @@ -114,7 +106,8 @@ class SegmentationValidator(DetectionValidator): masks=True) if self.args.plots: self.confusion_matrix.process_batch(predn, labelsn) - self.stats.append((correct_masks, correct_bboxes, pred[:, 4], pred[:, 5], labels[:, 0])) # conf, pcls, tcls + self.stats.append((correct_masks, correct_bboxes, pred[:, 4], pred[:, + 5], cls.squeeze(-1))) # conf, pcls, tcls pred_masks = torch.as_tensor(pred_masks, dtype=torch.uint8) if self.args.plots and self.batch_i < 3: @@ -165,19 +158,6 @@ class SegmentationValidator(DetectionValidator): correct[matches[:, 1].astype(int), i] = True return torch.tensor(correct, dtype=torch.bool, device=detections.device) - # TODO: probably add this to class Metrics - @property - def metric_keys(self): - return [ - "metrics/precision(B)", - "metrics/recall(B)", - "metrics/mAP50(B)", - "metrics/mAP50-95(B)", # metrics - "metrics/precision(M)", - "metrics/recall(M)", - "metrics/mAP50(M)", - "metrics/mAP50-95(M)",] - def plot_val_samples(self, batch, ni): plot_images(batch["img"], batch["batch_idx"], @@ -243,8 +223,8 @@ class SegmentationValidator(DetectionValidator): eval.accumulate() eval.summarize() idx = i * 4 + 2 - stats[self.metric_keys[idx + 1]], stats[ - self.metric_keys[idx]] = eval.stats[:2] # update mAP50-95 and mAP50 + stats[self.metrics.keys[idx + 1]], stats[ + self.metrics.keys[idx]] = eval.stats[:2] # update mAP50-95 and mAP50 except Exception as e: self.logger.warning(f'pycocotools unable to run: {e}') return stats