ultralytics 8.0.239 Ultralytics Actions and hub-sdk adoption (#7431)
Signed-off-by: Glenn Jocher <glenn.jocher@ultralytics.com> Co-authored-by: UltralyticsAssistant <web@ultralytics.com> Co-authored-by: Burhan <62214284+Burhan-Q@users.noreply.github.com> Co-authored-by: Kayzwer <68285002+Kayzwer@users.noreply.github.com>
This commit is contained in:
Glenn Jocher
GitHub
parent
e795277391
commit
fe27db2f6e
139 changed files with 6870 additions and 5125 deletions
|
|
@ -34,7 +34,7 @@ class DetectionValidator(BaseValidator):
|
|||
self.nt_per_class = None
|
||||
self.is_coco = False
|
||||
self.class_map = None
|
||||
self.args.task = 'detect'
|
||||
self.args.task = "detect"
|
||||
self.metrics = DetMetrics(save_dir=self.save_dir, on_plot=self.on_plot)
|
||||
self.iouv = torch.linspace(0.5, 0.95, 10) # iou vector for mAP@0.5:0.95
|
||||
self.niou = self.iouv.numel()
|
||||
|
|
@ -42,25 +42,30 @@ class DetectionValidator(BaseValidator):
|
|||
|
||||
def preprocess(self, batch):
|
||||
"""Preprocesses batch of images for YOLO training."""
|
||||
batch['img'] = batch['img'].to(self.device, non_blocking=True)
|
||||
batch['img'] = (batch['img'].half() if self.args.half else batch['img'].float()) / 255
|
||||
for k in ['batch_idx', 'cls', 'bboxes']:
|
||||
batch["img"] = batch["img"].to(self.device, non_blocking=True)
|
||||
batch["img"] = (batch["img"].half() if self.args.half else batch["img"].float()) / 255
|
||||
for k in ["batch_idx", "cls", "bboxes"]:
|
||||
batch[k] = batch[k].to(self.device)
|
||||
|
||||
if self.args.save_hybrid:
|
||||
height, width = batch['img'].shape[2:]
|
||||
nb = len(batch['img'])
|
||||
bboxes = batch['bboxes'] * torch.tensor((width, height, width, height), device=self.device)
|
||||
self.lb = [
|
||||
torch.cat([batch['cls'][batch['batch_idx'] == i], bboxes[batch['batch_idx'] == i]], dim=-1)
|
||||
for i in range(nb)] if self.args.save_hybrid else [] # for autolabelling
|
||||
height, width = batch["img"].shape[2:]
|
||||
nb = len(batch["img"])
|
||||
bboxes = batch["bboxes"] * torch.tensor((width, height, width, height), device=self.device)
|
||||
self.lb = (
|
||||
[
|
||||
torch.cat([batch["cls"][batch["batch_idx"] == i], bboxes[batch["batch_idx"] == i]], dim=-1)
|
||||
for i in range(nb)
|
||||
]
|
||||
if self.args.save_hybrid
|
||||
else []
|
||||
) # for autolabelling
|
||||
|
||||
return batch
|
||||
|
||||
def init_metrics(self, model):
|
||||
"""Initialize evaluation metrics for YOLO."""
|
||||
val = self.data.get(self.args.split, '') # validation path
|
||||
self.is_coco = isinstance(val, str) and 'coco' in val and val.endswith(f'{os.sep}val2017.txt') # is COCO
|
||||
val = self.data.get(self.args.split, "") # validation path
|
||||
self.is_coco = isinstance(val, str) and "coco" in val and val.endswith(f"{os.sep}val2017.txt") # is COCO
|
||||
self.class_map = converter.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.names = model.names
|
||||
|
|
@ -74,26 +79,28 @@ class DetectionValidator(BaseValidator):
|
|||
|
||||
def get_desc(self):
|
||||
"""Return a formatted string summarizing class metrics of YOLO model."""
|
||||
return ('%22s' + '%11s' * 6) % ('Class', 'Images', 'Instances', 'Box(P', 'R', 'mAP50', 'mAP50-95)')
|
||||
return ("%22s" + "%11s" * 6) % ("Class", "Images", "Instances", "Box(P", "R", "mAP50", "mAP50-95)")
|
||||
|
||||
def postprocess(self, preds):
|
||||
"""Apply Non-maximum suppression to prediction outputs."""
|
||||
return ops.non_max_suppression(preds,
|
||||
self.args.conf,
|
||||
self.args.iou,
|
||||
labels=self.lb,
|
||||
multi_label=True,
|
||||
agnostic=self.args.single_cls,
|
||||
max_det=self.args.max_det)
|
||||
return ops.non_max_suppression(
|
||||
preds,
|
||||
self.args.conf,
|
||||
self.args.iou,
|
||||
labels=self.lb,
|
||||
multi_label=True,
|
||||
agnostic=self.args.single_cls,
|
||||
max_det=self.args.max_det,
|
||||
)
|
||||
|
||||
def _prepare_batch(self, si, batch):
|
||||
"""Prepares a batch of images and annotations for validation."""
|
||||
idx = batch['batch_idx'] == si
|
||||
cls = batch['cls'][idx].squeeze(-1)
|
||||
bbox = batch['bboxes'][idx]
|
||||
ori_shape = batch['ori_shape'][si]
|
||||
imgsz = batch['img'].shape[2:]
|
||||
ratio_pad = batch['ratio_pad'][si]
|
||||
idx = batch["batch_idx"] == si
|
||||
cls = batch["cls"][idx].squeeze(-1)
|
||||
bbox = batch["bboxes"][idx]
|
||||
ori_shape = batch["ori_shape"][si]
|
||||
imgsz = batch["img"].shape[2:]
|
||||
ratio_pad = batch["ratio_pad"][si]
|
||||
if len(cls):
|
||||
bbox = ops.xywh2xyxy(bbox) * torch.tensor(imgsz, device=self.device)[[1, 0, 1, 0]] # target boxes
|
||||
ops.scale_boxes(imgsz, bbox, ori_shape, ratio_pad=ratio_pad) # native-space labels
|
||||
|
|
@ -103,8 +110,9 @@ class DetectionValidator(BaseValidator):
|
|||
def _prepare_pred(self, pred, pbatch):
|
||||
"""Prepares a batch of images and annotations for validation."""
|
||||
predn = pred.clone()
|
||||
ops.scale_boxes(pbatch['imgsz'], predn[:, :4], pbatch['ori_shape'],
|
||||
ratio_pad=pbatch['ratio_pad']) # native-space pred
|
||||
ops.scale_boxes(
|
||||
pbatch["imgsz"], predn[:, :4], pbatch["ori_shape"], ratio_pad=pbatch["ratio_pad"]
|
||||
) # native-space pred
|
||||
return predn
|
||||
|
||||
def update_metrics(self, preds, batch):
|
||||
|
|
@ -112,19 +120,21 @@ class DetectionValidator(BaseValidator):
|
|||
for si, pred in enumerate(preds):
|
||||
self.seen += 1
|
||||
npr = len(pred)
|
||||
stat = dict(conf=torch.zeros(0, device=self.device),
|
||||
pred_cls=torch.zeros(0, device=self.device),
|
||||
tp=torch.zeros(npr, self.niou, dtype=torch.bool, device=self.device))
|
||||
stat = dict(
|
||||
conf=torch.zeros(0, device=self.device),
|
||||
pred_cls=torch.zeros(0, device=self.device),
|
||||
tp=torch.zeros(npr, self.niou, dtype=torch.bool, device=self.device),
|
||||
)
|
||||
pbatch = self._prepare_batch(si, batch)
|
||||
cls, bbox = pbatch.pop('cls'), pbatch.pop('bbox')
|
||||
cls, bbox = pbatch.pop("cls"), pbatch.pop("bbox")
|
||||
nl = len(cls)
|
||||
stat['target_cls'] = cls
|
||||
stat["target_cls"] = cls
|
||||
if npr == 0:
|
||||
if nl:
|
||||
for k in self.stats.keys():
|
||||
self.stats[k].append(stat[k])
|
||||
# TODO: obb has not supported confusion_matrix yet.
|
||||
if self.args.plots and self.args.task != 'obb':
|
||||
if self.args.plots and self.args.task != "obb":
|
||||
self.confusion_matrix.process_batch(detections=None, gt_bboxes=bbox, gt_cls=cls)
|
||||
continue
|
||||
|
||||
|
|
@ -132,24 +142,24 @@ class DetectionValidator(BaseValidator):
|
|||
if self.args.single_cls:
|
||||
pred[:, 5] = 0
|
||||
predn = self._prepare_pred(pred, pbatch)
|
||||
stat['conf'] = predn[:, 4]
|
||||
stat['pred_cls'] = predn[:, 5]
|
||||
stat["conf"] = predn[:, 4]
|
||||
stat["pred_cls"] = predn[:, 5]
|
||||
|
||||
# Evaluate
|
||||
if nl:
|
||||
stat['tp'] = self._process_batch(predn, bbox, cls)
|
||||
stat["tp"] = self._process_batch(predn, bbox, cls)
|
||||
# TODO: obb has not supported confusion_matrix yet.
|
||||
if self.args.plots and self.args.task != 'obb':
|
||||
if self.args.plots and self.args.task != "obb":
|
||||
self.confusion_matrix.process_batch(predn, bbox, cls)
|
||||
for k in self.stats.keys():
|
||||
self.stats[k].append(stat[k])
|
||||
|
||||
# Save
|
||||
if self.args.save_json:
|
||||
self.pred_to_json(predn, batch['im_file'][si])
|
||||
self.pred_to_json(predn, batch["im_file"][si])
|
||||
if self.args.save_txt:
|
||||
file = self.save_dir / 'labels' / f'{Path(batch["im_file"][si]).stem}.txt'
|
||||
self.save_one_txt(predn, self.args.save_conf, pbatch['ori_shape'], file)
|
||||
file = self.save_dir / "labels" / f'{Path(batch["im_file"][si]).stem}.txt'
|
||||
self.save_one_txt(predn, self.args.save_conf, pbatch["ori_shape"], file)
|
||||
|
||||
def finalize_metrics(self, *args, **kwargs):
|
||||
"""Set final values for metrics speed and confusion matrix."""
|
||||
|
|
@ -159,19 +169,19 @@ class DetectionValidator(BaseValidator):
|
|||
def get_stats(self):
|
||||
"""Returns metrics statistics and results dictionary."""
|
||||
stats = {k: torch.cat(v, 0).cpu().numpy() for k, v in self.stats.items()} # to numpy
|
||||
if len(stats) and stats['tp'].any():
|
||||
if len(stats) and stats["tp"].any():
|
||||
self.metrics.process(**stats)
|
||||
self.nt_per_class = np.bincount(stats['target_cls'].astype(int),
|
||||
minlength=self.nc) # number of targets per class
|
||||
self.nt_per_class = np.bincount(
|
||||
stats["target_cls"].astype(int), minlength=self.nc
|
||||
) # number of targets per class
|
||||
return self.metrics.results_dict
|
||||
|
||||
def print_results(self):
|
||||
"""Prints training/validation set metrics per class."""
|
||||
pf = '%22s' + '%11i' * 2 + '%11.3g' * len(self.metrics.keys) # print format
|
||||
LOGGER.info(pf % ('all', self.seen, self.nt_per_class.sum(), *self.metrics.mean_results()))
|
||||
pf = "%22s" + "%11i" * 2 + "%11.3g" * len(self.metrics.keys) # print format
|
||||
LOGGER.info(pf % ("all", self.seen, self.nt_per_class.sum(), *self.metrics.mean_results()))
|
||||
if self.nt_per_class.sum() == 0:
|
||||
LOGGER.warning(
|
||||
f'WARNING ⚠️ no labels found in {self.args.task} set, can not compute metrics without labels')
|
||||
LOGGER.warning(f"WARNING ⚠️ no labels found in {self.args.task} set, can not compute metrics without labels")
|
||||
|
||||
# Print results per class
|
||||
if self.args.verbose and not self.training and self.nc > 1 and len(self.stats):
|
||||
|
|
@ -180,10 +190,9 @@ class DetectionValidator(BaseValidator):
|
|||
|
||||
if self.args.plots:
|
||||
for normalize in True, False:
|
||||
self.confusion_matrix.plot(save_dir=self.save_dir,
|
||||
names=self.names.values(),
|
||||
normalize=normalize,
|
||||
on_plot=self.on_plot)
|
||||
self.confusion_matrix.plot(
|
||||
save_dir=self.save_dir, names=self.names.values(), normalize=normalize, on_plot=self.on_plot
|
||||
)
|
||||
|
||||
def _process_batch(self, detections, gt_bboxes, gt_cls):
|
||||
"""
|
||||
|
|
@ -201,7 +210,7 @@ class DetectionValidator(BaseValidator):
|
|||
iou = box_iou(gt_bboxes, detections[:, :4])
|
||||
return self.match_predictions(detections[:, 5], gt_cls, iou)
|
||||
|
||||
def build_dataset(self, img_path, mode='val', batch=None):
|
||||
def build_dataset(self, img_path, mode="val", batch=None):
|
||||
"""
|
||||
Build YOLO Dataset.
|
||||
|
||||
|
|
@ -214,28 +223,32 @@ class DetectionValidator(BaseValidator):
|
|||
|
||||
def get_dataloader(self, dataset_path, batch_size):
|
||||
"""Construct and return dataloader."""
|
||||
dataset = self.build_dataset(dataset_path, batch=batch_size, mode='val')
|
||||
dataset = self.build_dataset(dataset_path, batch=batch_size, mode="val")
|
||||
return build_dataloader(dataset, batch_size, self.args.workers, shuffle=False, rank=-1) # return dataloader
|
||||
|
||||
def plot_val_samples(self, batch, ni):
|
||||
"""Plot validation image samples."""
|
||||
plot_images(batch['img'],
|
||||
batch['batch_idx'],
|
||||
batch['cls'].squeeze(-1),
|
||||
batch['bboxes'],
|
||||
paths=batch['im_file'],
|
||||
fname=self.save_dir / f'val_batch{ni}_labels.jpg',
|
||||
names=self.names,
|
||||
on_plot=self.on_plot)
|
||||
plot_images(
|
||||
batch["img"],
|
||||
batch["batch_idx"],
|
||||
batch["cls"].squeeze(-1),
|
||||
batch["bboxes"],
|
||||
paths=batch["im_file"],
|
||||
fname=self.save_dir / f"val_batch{ni}_labels.jpg",
|
||||
names=self.names,
|
||||
on_plot=self.on_plot,
|
||||
)
|
||||
|
||||
def plot_predictions(self, batch, preds, ni):
|
||||
"""Plots predicted bounding boxes on input images and saves the result."""
|
||||
plot_images(batch['img'],
|
||||
*output_to_target(preds, max_det=self.args.max_det),
|
||||
paths=batch['im_file'],
|
||||
fname=self.save_dir / f'val_batch{ni}_pred.jpg',
|
||||
names=self.names,
|
||||
on_plot=self.on_plot) # pred
|
||||
plot_images(
|
||||
batch["img"],
|
||||
*output_to_target(preds, max_det=self.args.max_det),
|
||||
paths=batch["im_file"],
|
||||
fname=self.save_dir / f"val_batch{ni}_pred.jpg",
|
||||
names=self.names,
|
||||
on_plot=self.on_plot,
|
||||
) # pred
|
||||
|
||||
def save_one_txt(self, predn, save_conf, shape, file):
|
||||
"""Save YOLO detections to a txt file in normalized coordinates in a specific format."""
|
||||
|
|
@ -243,8 +256,8 @@ class DetectionValidator(BaseValidator):
|
|||
for *xyxy, conf, cls in predn.tolist():
|
||||
xywh = (ops.xyxy2xywh(torch.tensor(xyxy).view(1, 4)) / gn).view(-1).tolist() # normalized xywh
|
||||
line = (cls, *xywh, conf) if save_conf else (cls, *xywh) # label format
|
||||
with open(file, 'a') as f:
|
||||
f.write(('%g ' * len(line)).rstrip() % line + '\n')
|
||||
with open(file, "a") as f:
|
||||
f.write(("%g " * len(line)).rstrip() % line + "\n")
|
||||
|
||||
def pred_to_json(self, predn, filename):
|
||||
"""Serialize YOLO predictions to COCO json format."""
|
||||
|
|
@ -253,28 +266,31 @@ class DetectionValidator(BaseValidator):
|
|||
box = ops.xyxy2xywh(predn[:, :4]) # xywh
|
||||
box[:, :2] -= box[:, 2:] / 2 # xy center to top-left corner
|
||||
for p, b in zip(predn.tolist(), box.tolist()):
|
||||
self.jdict.append({
|
||||
'image_id': image_id,
|
||||
'category_id': self.class_map[int(p[5])],
|
||||
'bbox': [round(x, 3) for x in b],
|
||||
'score': round(p[4], 5)})
|
||||
self.jdict.append(
|
||||
{
|
||||
"image_id": image_id,
|
||||
"category_id": self.class_map[int(p[5])],
|
||||
"bbox": [round(x, 3) for x in b],
|
||||
"score": round(p[4], 5),
|
||||
}
|
||||
)
|
||||
|
||||
def eval_json(self, stats):
|
||||
"""Evaluates YOLO output in JSON format and returns performance statistics."""
|
||||
if self.args.save_json and self.is_coco and len(self.jdict):
|
||||
anno_json = self.data['path'] / 'annotations/instances_val2017.json' # annotations
|
||||
pred_json = self.save_dir / 'predictions.json' # predictions
|
||||
LOGGER.info(f'\nEvaluating pycocotools mAP using {pred_json} and {anno_json}...')
|
||||
anno_json = self.data["path"] / "annotations/instances_val2017.json" # annotations
|
||||
pred_json = self.save_dir / "predictions.json" # predictions
|
||||
LOGGER.info(f"\nEvaluating pycocotools mAP using {pred_json} and {anno_json}...")
|
||||
try: # https://github.com/cocodataset/cocoapi/blob/master/PythonAPI/pycocoEvalDemo.ipynb
|
||||
check_requirements('pycocotools>=2.0.6')
|
||||
check_requirements("pycocotools>=2.0.6")
|
||||
from pycocotools.coco import COCO # noqa
|
||||
from pycocotools.cocoeval import COCOeval # noqa
|
||||
|
||||
for x in anno_json, pred_json:
|
||||
assert x.is_file(), f'{x} file not found'
|
||||
assert x.is_file(), f"{x} file not found"
|
||||
anno = COCO(str(anno_json)) # init annotations api
|
||||
pred = anno.loadRes(str(pred_json)) # init predictions api (must pass string, not Path)
|
||||
eval = COCOeval(anno, pred, 'bbox')
|
||||
eval = COCOeval(anno, pred, "bbox")
|
||||
if self.is_coco:
|
||||
eval.params.imgIds = [int(Path(x).stem) for x in self.dataloader.dataset.im_files] # images to eval
|
||||
eval.evaluate()
|
||||
|
|
@ -282,5 +298,5 @@ class DetectionValidator(BaseValidator):
|
|||
eval.summarize()
|
||||
stats[self.metrics.keys[-1]], stats[self.metrics.keys[-2]] = eval.stats[:2] # update mAP50-95 and mAP50
|
||||
except Exception as e:
|
||||
LOGGER.warning(f'pycocotools unable to run: {e}')
|
||||
LOGGER.warning(f"pycocotools unable to run: {e}")
|
||||
return stats
|
||||
|
|
|
|||
Loading…
Add table
Add a link
Reference in a new issue