463 lines
18 KiB
Python
463 lines
18 KiB
Python
# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/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.data import build_dataloader, build_yolo_dataset, converter
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from ultralytics.engine.validator import BaseValidator
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from ultralytics.utils import LOGGER, ops
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from ultralytics.utils.checks import check_requirements
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from ultralytics.utils.metrics import ConfusionMatrix, DetMetrics, box_iou
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from ultralytics.utils.plotting import output_to_target, plot_images
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class DetectionValidator(BaseValidator):
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"""
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A class extending the BaseValidator class for validation based on a detection model.
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This class implements validation functionality specific to object detection tasks, including metrics calculation,
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prediction processing, and visualization of results.
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Attributes:
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nt_per_class (np.ndarray): Number of targets per class.
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nt_per_image (np.ndarray): Number of targets per image.
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is_coco (bool): Whether the dataset is COCO.
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is_lvis (bool): Whether the dataset is LVIS.
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class_map (List): Mapping from model class indices to dataset class indices.
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metrics (DetMetrics): Object detection metrics calculator.
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iouv (torch.Tensor): IoU thresholds for mAP calculation.
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niou (int): Number of IoU thresholds.
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lb (List): List for storing ground truth labels for hybrid saving.
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jdict (List): List for storing JSON detection results.
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stats (Dict): Dictionary for storing statistics during validation.
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Examples:
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>>> from ultralytics.models.yolo.detect import DetectionValidator
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>>> args = dict(model="yolo11n.pt", data="coco8.yaml")
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>>> validator = DetectionValidator(args=args)
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>>> validator()
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"""
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def __init__(self, dataloader=None, save_dir=None, pbar=None, args=None, _callbacks=None):
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"""
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Initialize detection validator with necessary variables and settings.
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Args:
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dataloader (torch.utils.data.DataLoader, optional): Dataloader to use for validation.
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save_dir (Path, optional): Directory to save results.
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pbar (Any, optional): Progress bar for displaying progress.
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args (Dict, optional): Arguments for the validator.
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_callbacks (List, optional): List of callback functions.
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"""
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super().__init__(dataloader, save_dir, pbar, args, _callbacks)
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self.nt_per_class = None
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self.nt_per_image = None
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self.is_coco = False
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self.is_lvis = False
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self.class_map = None
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self.args.task = "detect"
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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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self.lb = [] # for autolabelling
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if self.args.save_hybrid and self.args.task == "detect":
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LOGGER.warning(
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"WARNING ⚠️ 'save_hybrid=True' will append ground truth to predictions for autolabelling.\n"
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"WARNING ⚠️ 'save_hybrid=True' will cause incorrect mAP.\n"
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)
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def preprocess(self, batch):
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"""
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Preprocess batch of images for YOLO validation.
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Args:
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batch (Dict): Batch containing images and annotations.
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Returns:
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(Dict): Preprocessed batch.
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"""
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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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if self.args.save_hybrid and self.args.task == "detect":
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height, width = batch["img"].shape[2:]
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nb = len(batch["img"])
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bboxes = batch["bboxes"] * torch.tensor((width, height, width, height), device=self.device)
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self.lb = [
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torch.cat([batch["cls"][batch["batch_idx"] == i], bboxes[batch["batch_idx"] == i]], dim=-1)
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for i in range(nb)
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]
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return batch
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def init_metrics(self, model):
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"""
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Initialize evaluation metrics for YOLO detection validation.
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Args:
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model (torch.nn.Module): Model to validate.
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"""
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val = self.data.get(self.args.split, "") # validation path
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self.is_coco = (
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isinstance(val, str)
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and "coco" in val
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and (val.endswith(f"{os.sep}val2017.txt") or val.endswith(f"{os.sep}test-dev2017.txt"))
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) # is COCO
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self.is_lvis = isinstance(val, str) and "lvis" in val and not self.is_coco # is LVIS
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self.class_map = converter.coco80_to_coco91_class() if self.is_coco else list(range(1, len(model.names) + 1))
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self.args.save_json |= self.args.val and (self.is_coco or self.is_lvis) and not self.training # run final val
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self.names = model.names
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self.nc = len(model.names)
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self.end2end = getattr(model, "end2end", False)
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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, conf=self.args.conf)
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self.seen = 0
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self.jdict = []
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self.stats = dict(tp=[], conf=[], pred_cls=[], target_cls=[], target_img=[])
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def get_desc(self):
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"""Return a formatted string summarizing class metrics of YOLO model."""
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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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"""
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Apply Non-maximum suppression to prediction outputs.
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Args:
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preds (torch.Tensor): Raw predictions from the model.
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Returns:
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(List[torch.Tensor]): Processed predictions after NMS.
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"""
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return ops.non_max_suppression(
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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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nc=self.nc,
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multi_label=True,
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agnostic=self.args.single_cls or self.args.agnostic_nms,
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max_det=self.args.max_det,
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end2end=self.end2end,
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rotated=self.args.task == "obb",
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)
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def _prepare_batch(self, si, batch):
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"""
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Prepare a batch of images and annotations for validation.
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Args:
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si (int): Batch index.
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batch (Dict): Batch data containing images and annotations.
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Returns:
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(Dict): Prepared batch with processed annotations.
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"""
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idx = batch["batch_idx"] == si
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cls = batch["cls"][idx].squeeze(-1)
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bbox = batch["bboxes"][idx]
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ori_shape = batch["ori_shape"][si]
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imgsz = batch["img"].shape[2:]
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ratio_pad = batch["ratio_pad"][si]
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if len(cls):
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bbox = ops.xywh2xyxy(bbox) * torch.tensor(imgsz, device=self.device)[[1, 0, 1, 0]] # target boxes
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ops.scale_boxes(imgsz, bbox, ori_shape, ratio_pad=ratio_pad) # native-space labels
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return {"cls": cls, "bbox": bbox, "ori_shape": ori_shape, "imgsz": imgsz, "ratio_pad": ratio_pad}
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def _prepare_pred(self, pred, pbatch):
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"""
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Prepare predictions for evaluation against ground truth.
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Args:
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pred (torch.Tensor): Model predictions.
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pbatch (Dict): Prepared batch information.
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Returns:
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(torch.Tensor): Prepared predictions in native space.
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"""
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predn = pred.clone()
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ops.scale_boxes(
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pbatch["imgsz"], predn[:, :4], pbatch["ori_shape"], ratio_pad=pbatch["ratio_pad"]
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) # native-space pred
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return predn
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def update_metrics(self, preds, batch):
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"""
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Update metrics with new predictions and ground truth.
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Args:
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preds (List[torch.Tensor]): List of predictions from the model.
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batch (Dict): Batch data containing ground truth.
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"""
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for si, pred in enumerate(preds):
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self.seen += 1
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npr = len(pred)
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stat = dict(
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conf=torch.zeros(0, device=self.device),
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pred_cls=torch.zeros(0, device=self.device),
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tp=torch.zeros(npr, self.niou, dtype=torch.bool, device=self.device),
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)
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pbatch = self._prepare_batch(si, batch)
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cls, bbox = pbatch.pop("cls"), pbatch.pop("bbox")
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nl = len(cls)
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stat["target_cls"] = cls
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stat["target_img"] = cls.unique()
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if npr == 0:
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if nl:
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for k in self.stats.keys():
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self.stats[k].append(stat[k])
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if self.args.plots:
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self.confusion_matrix.process_batch(detections=None, gt_bboxes=bbox, gt_cls=cls)
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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 = self._prepare_pred(pred, pbatch)
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stat["conf"] = predn[:, 4]
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stat["pred_cls"] = predn[:, 5]
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# Evaluate
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if nl:
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stat["tp"] = self._process_batch(predn, bbox, cls)
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if self.args.plots:
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self.confusion_matrix.process_batch(predn, bbox, cls)
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for k in self.stats.keys():
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self.stats[k].append(stat[k])
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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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self.save_one_txt(
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predn,
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self.args.save_conf,
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pbatch["ori_shape"],
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self.save_dir / "labels" / f"{Path(batch['im_file'][si]).stem}.txt",
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)
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def finalize_metrics(self, *args, **kwargs):
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"""
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Set final values for metrics speed and confusion matrix.
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Args:
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*args (Any): Variable length argument list.
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**kwargs (Any): Arbitrary keyword arguments.
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"""
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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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"""
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Calculate and return metrics statistics.
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Returns:
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(Dict): Dictionary containing metrics results.
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"""
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stats = {k: torch.cat(v, 0).cpu().numpy() for k, v in self.stats.items()} # to numpy
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self.nt_per_class = np.bincount(stats["target_cls"].astype(int), minlength=self.nc)
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self.nt_per_image = np.bincount(stats["target_img"].astype(int), minlength=self.nc)
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stats.pop("target_img", None)
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if len(stats):
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self.metrics.process(**stats, on_plot=self.on_plot)
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return self.metrics.results_dict
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def print_results(self):
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"""Print training/validation set metrics per class."""
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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(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(
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pf % (self.names[c], self.nt_per_image[c], self.nt_per_class[c], *self.metrics.class_result(i))
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)
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if self.args.plots:
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for normalize in True, False:
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self.confusion_matrix.plot(
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save_dir=self.save_dir, names=self.names.values(), normalize=normalize, on_plot=self.on_plot
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)
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def _process_batch(self, detections, gt_bboxes, gt_cls):
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"""
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Return correct prediction matrix.
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Args:
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detections (torch.Tensor): Tensor of shape (N, 6) representing detections where each detection is
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(x1, y1, x2, y2, conf, class).
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gt_bboxes (torch.Tensor): Tensor of shape (M, 4) representing ground-truth bounding box coordinates. Each
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bounding box is of the format: (x1, y1, x2, y2).
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gt_cls (torch.Tensor): Tensor of shape (M,) representing target class indices.
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Returns:
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(torch.Tensor): Correct prediction matrix of shape (N, 10) for 10 IoU levels.
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"""
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iou = box_iou(gt_bboxes, detections[:, :4])
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return self.match_predictions(detections[:, 5], gt_cls, iou)
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def build_dataset(self, img_path, mode="val", batch=None):
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"""
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Build YOLO Dataset.
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Args:
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img_path (str): Path to the folder containing images.
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mode (str): `train` mode or `val` mode, users are able to customize different augmentations for each mode.
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batch (int, optional): Size of batches, this is for `rect`.
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Returns:
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(Dataset): YOLO dataset.
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"""
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return build_yolo_dataset(self.args, img_path, batch, self.data, mode=mode, stride=self.stride)
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def get_dataloader(self, dataset_path, batch_size):
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"""
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Construct and return dataloader.
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Args:
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dataset_path (str): Path to the dataset.
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batch_size (int): Size of each batch.
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Returns:
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(torch.utils.data.DataLoader): Dataloader for validation.
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"""
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dataset = self.build_dataset(dataset_path, batch=batch_size, mode="val")
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return build_dataloader(dataset, batch_size, self.args.workers, shuffle=False, rank=-1) # return dataloader
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def plot_val_samples(self, batch, ni):
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"""
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Plot validation image samples.
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Args:
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batch (Dict): Batch containing images and annotations.
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ni (int): Batch index.
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"""
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plot_images(
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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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on_plot=self.on_plot,
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)
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def plot_predictions(self, batch, preds, ni):
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"""
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Plot predicted bounding boxes on input images and save the result.
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Args:
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batch (Dict): Batch containing images and annotations.
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preds (List[torch.Tensor]): List of predictions from the model.
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ni (int): Batch index.
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"""
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plot_images(
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batch["img"],
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*output_to_target(preds, max_det=self.args.max_det),
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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,
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on_plot=self.on_plot,
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) # pred
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def save_one_txt(self, predn, save_conf, shape, file):
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"""
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Save YOLO detections to a txt file in normalized coordinates in a specific format.
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Args:
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predn (torch.Tensor): Predictions in the format (x1, y1, x2, y2, conf, class).
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save_conf (bool): Whether to save confidence scores.
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shape (tuple): Shape of the original image.
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file (Path): File path to save the detections.
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"""
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from ultralytics.engine.results import Results
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Results(
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np.zeros((shape[0], shape[1]), dtype=np.uint8),
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path=None,
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names=self.names,
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boxes=predn[:, :6],
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).save_txt(file, save_conf=save_conf)
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def pred_to_json(self, predn, filename):
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"""
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Serialize YOLO predictions to COCO json format.
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Args:
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predn (torch.Tensor): Predictions in the format (x1, y1, x2, y2, conf, class).
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filename (str): Image filename.
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"""
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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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{
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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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}
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)
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def eval_json(self, stats):
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"""
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Evaluate YOLO output in JSON format and return performance statistics.
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Args:
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stats (Dict): Current statistics dictionary.
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Returns:
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(Dict): Updated statistics dictionary with COCO/LVIS evaluation results.
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"""
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if self.args.save_json and (self.is_coco or self.is_lvis) and len(self.jdict):
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pred_json = self.save_dir / "predictions.json" # predictions
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anno_json = (
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self.data["path"]
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/ "annotations"
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/ ("instances_val2017.json" if self.is_coco else f"lvis_v1_{self.args.split}.json")
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) # annotations
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pkg = "pycocotools" if self.is_coco else "lvis"
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LOGGER.info(f"\nEvaluating {pkg} 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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for x in pred_json, anno_json:
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assert x.is_file(), f"{x} file not found"
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check_requirements("pycocotools>=2.0.6" if self.is_coco else "lvis>=0.5.3")
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if self.is_coco:
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from pycocotools.coco import COCO # noqa
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from pycocotools.cocoeval import COCOeval # noqa
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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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val = COCOeval(anno, pred, "bbox")
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else:
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from lvis import LVIS, LVISEval
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anno = LVIS(str(anno_json)) # init annotations api
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pred = anno._load_json(str(pred_json)) # init predictions api (must pass string, not Path)
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val = LVISEval(anno, pred, "bbox")
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val.params.imgIds = [int(Path(x).stem) for x in self.dataloader.dataset.im_files] # images to eval
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val.evaluate()
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val.accumulate()
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val.summarize()
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if self.is_lvis:
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val.print_results() # explicitly call print_results
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# update mAP50-95 and mAP50
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stats[self.metrics.keys[-1]], stats[self.metrics.keys[-2]] = (
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val.stats[:2] if self.is_coco else [val.results["AP50"], val.results["AP"]]
|
|
)
|
|
except Exception as e:
|
|
LOGGER.warning(f"{pkg} unable to run: {e}")
|
|
return stats
|