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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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import torch.nn.functional as F |
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from ultralytics.yolo.engine.validator import BaseValidator |
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from ultralytics.yolo.utils import ops |
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from ultralytics.yolo.utils.checks import check_requirements |
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from ultralytics.yolo.utils.files import yaml_load |
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from ultralytics.yolo.utils.metrics import (ConfusionMatrix, Metrics, ap_per_class_box_and_mask, box_iou, |
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fitness_segmentation, mask_iou) |
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from ultralytics.yolo.utils.torch_utils import de_parallel |
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class SegmentationValidator(BaseValidator): |
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def __init__(self, dataloader, pbar=None, logger=None, args=None): |
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super().__init__(dataloader, pbar, logger, args) |
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if self.args.save_json: |
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check_requirements(['pycocotools']) |
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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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self.data_dict = yaml_load(self.args.data) if self.args.data else None |
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self.is_coco = False |
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self.class_map = None |
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self.targets = None |
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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()) / 225 |
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batch["bboxes"] = batch["bboxes"].to(self.device) |
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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.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 = de_parallel(model).model[-1] |
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if self.data_dict: |
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self.is_coco = isinstance(self.data_dict.get('val'), |
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str) and self.data_dict['val'].endswith(f'coco{os.sep}val2017.txt') |
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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.nc = head.nc |
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self.nm = head.nm |
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self.names = model.names |
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if isinstance(self.names, (list, tuple)): # old format |
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self.names = dict(enumerate(self.names)) |
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self.iouv = torch.linspace(0.5, 0.95, 10, device=self.device) # iou vector for mAP@0.5:0.95 |
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self.niou = self.iouv.numel() |
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self.seen = 0 |
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self.confusion_matrix = ConfusionMatrix(nc=self.nc) |
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self.metrics = Metrics() |
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self.loss = torch.zeros(4, device=self.device) |
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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' * 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[0], preds[2]) |
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def update_metrics(self, preds, batch): |
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# Metrics |
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plot_masks = [] # masks for plotting |
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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 = Path(batch["im_file"][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, batch["shape"][si][1]) # 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, batch["shapes"][si][1]) # 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, self.iouv) |
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correct_masks = self._process_batch(predn, labelsn, self.iouv, pred_masks, gt_masks, 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[:, |
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0])) # (conf, pcls, tcls) |
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pred_masks = torch.as_tensor(pred_masks, dtype=torch.uint8) |
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if self.plots and self.batch_i < 3: |
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plot_masks.append(pred_masks[:15].cpu()) # filter top 15 to plot |
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# TODO: Save/log |
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''' |
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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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if self.args.save_json: |
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pred_masks = scale_image(im[si].shape[1:], |
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pred_masks.permute(1, 2, 0).contiguous().cpu().numpy(), shape, shapes[si][1]) |
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save_one_json(predn, jdict, path, class_map, pred_masks) # append to COCO-JSON dictionary |
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# callbacks.run('on_val_image_end', pred, predn, path, names, im[si]) |
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''' |
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# TODO Plot images |
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''' |
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if self.args.plots and self.batch_i < 3: |
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if len(plot_masks): |
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plot_masks = torch.cat(plot_masks, dim=0) |
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plot_images_and_masks(im, targets, masks, paths, save_dir / f'val_batch{batch_i}_labels.jpg', names) |
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plot_images_and_masks(im, output_to_target(preds, max_det=15), plot_masks, paths, |
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save_dir / f'val_batch{batch_i}_pred.jpg', names) # pred |
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''' |
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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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# TODO: save_dir |
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results = ap_per_class_box_and_mask(*stats, plot=self.args.plots, save_dir='', names=self.names) |
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self.metrics.update(results) |
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self.nt_per_class = np.bincount(stats[4].astype(int), minlength=self.nc) # number of targets per class |
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keys = ["mp_bbox", "mr_bbox", "map50_bbox", "map_bbox", "mp_mask", "mr_mask", "map50_mask", "map_mask"] |
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metrics = {"fitness": fitness_segmentation(np.array(self.metrics.mean_results()).reshape(1, -1))} |
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metrics |= zip(keys, self.metrics.mean_results()) |
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return metrics |
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def print_results(self): |
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pf = '%22s' + '%11i' * 2 + '%11.3g' * 8 # print format |
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self.logger.info(pf % ("all", self.seen, self.nt_per_class.sum(), *self.metrics.mean_results())) |
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if self.nt_per_class.sum() == 0: |
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self.logger.warning( |
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f'WARNING ⚠️ no labels found in {self.args.task} set, can not compute metrics without labels') |
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# Print results per class |
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if (self.args.verbose or (self.nc < 50 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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self.logger.info(pf % (self.names[c], self.seen, self.nt_per_class[c], *self.metrics.class_result(i))) |
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# plot TODO: save_dir |
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if self.args.plots: |
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self.confusion_matrix.plot(save_dir='', names=list(self.names.values())) |
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def _process_batch(self, detections, labels, iouv, 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], 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(iouv)): |
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x = torch.where((iou >= 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=iouv.device)
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