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263 lines
13 KiB
263 lines
13 KiB
import os
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from multiprocessing.pool import ThreadPool
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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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import torch.nn.functional as F
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from ultralytics.yolo.utils import DEFAULT_CONFIG, NUM_THREADS, ops
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from ultralytics.yolo.utils.checks import check_requirements
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from ultralytics.yolo.utils.metrics import ConfusionMatrix, SegmentMetrics, box_iou, mask_iou
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from ultralytics.yolo.utils.plotting import output_to_target, plot_images
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from ..detect import DetectionValidator
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class SegmentationValidator(DetectionValidator):
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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.metrics = SegmentMetrics(save_dir=self.save_dir, plot=self.args.plots)
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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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batch["masks"] = batch["masks"].to(self.device).float()
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self.nb, _, self.height, self.width = batch["img"].shape # batch size, channels, height, width
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self.targets = torch.cat((batch["batch_idx"].view(-1, 1), batch["cls"].view(-1, 1), batch["bboxes"]), 1)
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self.targets = self.targets.to(self.device)
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height, width = batch["img"].shape[2:]
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self.targets[:, 2:] *= torch.tensor((width, height, width, height), device=self.device) # to pixels
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self.lb = [self.targets[self.targets[:, 0] == i, 1:]
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for i in range(self.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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if self.data:
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self.is_coco = self.data.get('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.nm = head.nm if hasattr(head, "nm") else 32
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self.names = model.names
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self.metrics.names = self.names
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self.confusion_matrix = ConfusionMatrix(nc=self.nc)
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self.plot_masks = []
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self.seen = 0
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self.jdict = []
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self.stats = []
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if self.args.save_json:
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self.process = ops.process_mask_upsample # more accurate
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else:
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self.process = ops.process_mask # faster
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def get_desc(self):
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return ('%22s' + '%11s' * 10) % ('Class', 'Images', 'Instances', 'Box(P', "R", "mAP50", "mAP50-95)", "Mask(P",
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"R", "mAP50", "mAP50-95)")
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def postprocess(self, preds):
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p = ops.non_max_suppression(preds[0],
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self.args.conf_thres,
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self.args.iou_thres,
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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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nm=self.nm)
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return p, preds[1][-1]
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def update_metrics(self, preds, batch):
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# Metrics
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for si, (pred, proto) in enumerate(zip(preds[0], preds[1])):
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labels = self.targets[self.targets[:, 0] == si, 1:]
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nl, npr = labels.shape[0], pred.shape[0] # number of labels, predictions
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shape = batch["ori_shape"][si]
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# path = batch["shape"][si][0]
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correct_masks = torch.zeros(npr, self.niou, dtype=torch.bool, device=self.device) # init
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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_masks, correct_bboxes, *torch.zeros(
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(2, 0), device=self.device), labels[:, 0]))
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if self.args.plots:
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self.confusion_matrix.process_batch(detections=None, labels=labels[:, 0])
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continue
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# Masks
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midx = [si] if self.args.overlap_mask else self.targets[:, 0] == si
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gt_masks = batch["masks"][midx]
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pred_masks = self.process(proto, pred[:, 6:], pred[:, :4], shape=batch["img"][si].shape[1:])
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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) # native-space pred
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# Evaluate
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if nl:
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tbox = ops.xywh2xyxy(labels[:, 1:5]) # target boxes
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ops.scale_boxes(batch["img"][si].shape[1:], tbox, shape) # native-space labels
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labelsn = torch.cat((labels[:, 0:1], 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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correct_masks = self._process_batch(predn,
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labelsn,
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pred_masks,
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gt_masks,
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overlap=self.args.overlap_mask,
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masks=True)
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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_masks, correct_bboxes, pred[:, 4], pred[:, 5], labels[:, 0])) # conf, pcls, tcls
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pred_masks = torch.as_tensor(pred_masks, dtype=torch.uint8)
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if self.args.plots and self.batch_i < 3:
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self.plot_masks.append(pred_masks[:15].cpu()) # filter top 15 to plot
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# Save
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if self.args.save_json:
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pred_masks = ops.scale_image(batch["img"][si].shape[1:],
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pred_masks.permute(1, 2, 0).contiguous().cpu().numpy(), shape)
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self.pred_to_json(predn, batch["im_file"][si], pred_masks)
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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 _process_batch(self, detections, labels, pred_masks=None, gt_masks=None, overlap=False, masks=False):
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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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if masks:
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if overlap:
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nl = len(labels)
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index = torch.arange(nl, device=gt_masks.device).view(nl, 1, 1) + 1
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gt_masks = gt_masks.repeat(nl, 1, 1) # shape(1,640,640) -> (n,640,640)
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gt_masks = torch.where(gt_masks == index, 1.0, 0.0)
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if gt_masks.shape[1:] != pred_masks.shape[1:]:
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gt_masks = F.interpolate(gt_masks[None], pred_masks.shape[1:], mode="bilinear", align_corners=False)[0]
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gt_masks = gt_masks.gt_(0.5)
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iou = mask_iou(gt_masks.view(gt_masks.shape[0], -1), pred_masks.view(pred_masks.shape[0], -1))
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else: # boxes
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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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# TODO: probably add this to class Metrics
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@property
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def metric_keys(self):
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return [
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"metrics/precision(B)",
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"metrics/recall(B)",
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"metrics/mAP50(B)",
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"metrics/mAP50-95(B)", # metrics
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"metrics/precision(M)",
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"metrics/recall(M)",
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"metrics/mAP50(M)",
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"metrics/mAP50-95(M)",]
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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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batch["masks"],
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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[0], max_det=15),
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torch.cat(self.plot_masks, dim=0) if len(self.plot_masks) else self.plot_masks,
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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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self.plot_masks.clear()
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def pred_to_json(self, predn, filename, pred_masks):
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# Save one JSON result {"image_id": 42, "category_id": 18, "bbox": [258.15, 41.29, 348.26, 243.78], "score": 0.236}
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from pycocotools.mask import encode
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def single_encode(x):
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rle = encode(np.asarray(x[:, :, None], order="F", dtype="uint8"))[0]
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rle["counts"] = rle["counts"].decode("utf-8")
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return rle
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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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pred_masks = np.transpose(pred_masks, (2, 0, 1))
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with ThreadPool(NUM_THREADS) as pool:
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rles = pool.map(single_encode, pred_masks)
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for i, (p, b) in enumerate(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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'segmentation': rles[i]})
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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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for i, eval in enumerate([COCOeval(anno, pred, 'bbox'), COCOeval(anno, pred, 'segm')]):
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if self.is_coco:
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eval.params.imgIds = [int(Path(x).stem)
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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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idx = i * 4 + 2
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stats[self.metric_keys[idx + 1]], stats[
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self.metric_keys[idx]] = 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.data = cfg.data or "coco128-seg.yaml"
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validator = SegmentationValidator(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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