import os
import hydra
import numpy as np
import torch
import torch.nn.functional as F
from ultralytics.yolo.engine.trainer import DEFAULT_CONFIG
from ultralytics.yolo.utils import ops
from ultralytics.yolo.utils.metrics import ConfusionMatrix, SegmentMetrics, box_iou, mask_iou
from ultralytics.yolo.utils.plotting import output_to_target, plot_images
from ..detect import DetectionValidator
class SegmentationValidator(DetectionValidator):
def __init__(self, dataloader=None, save_dir=None, pbar=None, logger=None, args=None):
super().__init__(dataloader, save_dir, pbar, logger, args)
if self.args.save_json:
self.process = ops.process_mask_upsample # more accurate
else:
self.process = ops.process_mask # faster
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["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):
head = model.model[-1] if self.training else model.model.model[-1]
if self.data:
self.is_coco = self.data.get('val', '').endswith(f'coco{os.sep}val2017.txt') # is COCO dataset
self.class_map = ops.coco80_to_coco91_class() if self.is_coco else list(range(1000))
self.args.save_json |= self.is_coco and not self.training # run on final val if training COCO
self.nc = head.nc
self.nm = head.nm if hasattr(head, "nm") else 32
self.names = model.names
self.metrics.names = self.names
self.confusion_matrix = ConfusionMatrix(nc=self.nc)
self.plot_masks = []
self.seen = 0
self.jdict = []
self.stats = []
def get_desc(self):
return ('%22s' + '%11s' * 10) % ('Class', 'Images', 'Instances', 'Box(P', "R", "mAP50", "mAP50-95)", "Mask(P",
"R", "mAP50", "mAP50-95)")
def postprocess(self, preds):
p = ops.non_max_suppression(preds[0],
self.args.conf_thres,
self.args.iou_thres,
labels=self.lb,
multi_label=True,
agnostic=self.args.single_cls,
max_det=self.args.max_det,
nm=self.nm)
return p, preds[1], preds[2]
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
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
if npr == 0:
if nl:
self.stats.append((correct_masks, correct_bboxes, *torch.zeros(
(2, 0), device=self.device), labels[:, 0]))
if self.args.plots:
self.confusion_matrix.process_batch(detections=None, labels=labels[:, 0])
continue
# Masks
midx = [si] if self.args.overlap_mask else self.targets[:, 0] == si
gt_masks = batch["masks"][midx]
pred_masks = self.process(proto, pred[:, 6:], pred[:, :4], shape=batch["img"][si].shape[1:])
# 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
# 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
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,
labelsn,
pred_masks,
gt_masks,
overlap=self.args.overlap_mask,
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
pred_masks = torch.as_tensor(pred_masks, dtype=torch.uint8)
if self.args.plots and self.batch_i < 3:
self.plot_masks.append(pred_masks[:15].cpu()) # filter top 15 to plot
# TODO: Save/log
'''
if self.args.save_txt:
save_one_txt(predn, save_conf, shape, file=save_dir / 'labels' / f'{path.stem}.txt')
if self.args.save_json:
pred_masks = scale_image(im[si].shape[1:],
pred_masks.permute(1, 2, 0).contiguous().cpu().numpy(), shape, shapes[si][1])
save_one_json(predn, jdict, path, class_map, pred_masks) # append to COCO-JSON dictionary
# callbacks.run('on_val_image_end', pred, predn, path, names, im[si])
'''
def _process_batch(self, detections, labels, pred_masks=None, gt_masks=None, overlap=False, masks=False):
"""
Return correct prediction matrix
Arguments:
detections (array[N, 6]), x1, y1, x2, y2, conf, class
labels (array[M, 5]), class, x1, y1, x2, y2
Returns:
correct (array[N, 10]), for 10 IoU levels
"""
if masks:
if overlap:
nl = len(labels)
index = torch.arange(nl, device=gt_masks.device).view(nl, 1, 1) + 1
gt_masks = gt_masks.repeat(nl, 1, 1) # shape(1,640,640) -> (n,640,640)
gt_masks = torch.where(gt_masks == index, 1.0, 0.0)
if gt_masks.shape[1:] != pred_masks.shape[1:]:
gt_masks = F.interpolate(gt_masks[None], pred_masks.shape[1:], mode="bilinear", align_corners=False)[0]
gt_masks = gt_masks.gt_(0.5)
iou = mask_iou(gt_masks.view(gt_masks.shape[0], -1), pred_masks.view(pred_masks.shape[0], -1))
else: # boxes
iou = box_iou(labels[:, 1:], detections[:, :4])
correct = np.zeros((detections.shape[0], self.iouv.shape[0])).astype(bool)
correct_class = labels[:, 0:1] == detections[:, 5]
for i in range(len(self.iouv)):
x = torch.where((iou >= self.iouv[i]) & correct_class) # IoU > threshold and classes match
if x[0].shape[0]:
matches = torch.cat((torch.stack(x, 1), iou[x[0], x[1]][:, None]),
1).cpu().numpy() # [label, detect, iou]
if x[0].shape[0] > 1:
matches = matches[matches[:, 2].argsort()[::-1]]
matches = matches[np.unique(matches[:, 1], return_index=True)[1]]
# matches = matches[matches[:, 2].argsort()[::-1]]
matches = matches[np.unique(matches[:, 0], return_index=True)[1]]
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"],
batch["cls"].squeeze(-1),
batch["bboxes"],
batch["masks"],
paths=batch["im_file"],
fname=self.save_dir / f"val_batch{ni}_labels.jpg",
names=self.names)
def plot_predictions(self, batch, preds, ni):
plot_images(batch["img"],
*output_to_target(preds[0], max_det=15),
torch.cat(self.plot_masks, dim=0) if len(self.plot_masks) else self.plot_masks,
paths=batch["im_file"],
fname=self.save_dir / f'val_batch{ni}_pred.jpg',
names=self.names) # pred
self.plot_masks.clear()
@hydra.main(version_base=None, config_path=str(DEFAULT_CONFIG.parent), config_name=DEFAULT_CONFIG.name)
def val(cfg):
cfg.data = cfg.data or "coco128-seg.yaml"
validator = SegmentationValidator(args=cfg)
validator(model=cfg.model)
if __name__ == "__main__":
val()