Update Actions with Lychee and GitHub Token (#12592)
* Add Ultralytics Actions * Auto-format by Ultralytics actions * Update format.yml Signed-off-by: Glenn Jocher <glenn.jocher@ultralytics.com> --------- Signed-off-by: Glenn Jocher <glenn.jocher@ultralytics.com> Co-authored-by: UltralyticsAssistant <web@ultralytics.com> Co-authored-by: Glenn Jocher <glenn.jocher@ultralytics.com>
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@@ -1,6 +1,6 @@
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# YOLOv5 🚀 by Ultralytics, AGPL-3.0 license
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"""
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Validate a trained YOLOv5 detection model on a detection dataset
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Validate a trained YOLOv5 detection model on a detection dataset.
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Usage:
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$ python val.py --weights yolov5s.pt --data coco128.yaml --img 640
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@@ -39,9 +39,23 @@ ROOT = Path(os.path.relpath(ROOT, Path.cwd())) # relative
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from models.common import DetectMultiBackend
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from utils.callbacks import Callbacks
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from utils.dataloaders import create_dataloader
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from utils.general import (LOGGER, TQDM_BAR_FORMAT, Profile, check_dataset, check_img_size, check_requirements,
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check_yaml, coco80_to_coco91_class, colorstr, increment_path, non_max_suppression,
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print_args, scale_boxes, xywh2xyxy, xyxy2xywh)
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from utils.general import (
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LOGGER,
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TQDM_BAR_FORMAT,
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Profile,
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check_dataset,
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check_img_size,
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check_requirements,
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check_yaml,
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coco80_to_coco91_class,
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colorstr,
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increment_path,
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non_max_suppression,
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print_args,
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scale_boxes,
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xywh2xyxy,
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xyxy2xywh,
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)
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from utils.metrics import ConfusionMatrix, ap_per_class, box_iou
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from utils.plots import output_to_target, plot_images, plot_val_study
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from utils.torch_utils import select_device, smart_inference_mode
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@@ -53,8 +67,8 @@ def save_one_txt(predn, save_conf, shape, file):
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for *xyxy, conf, cls in predn.tolist():
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xywh = (xyxy2xywh(torch.tensor(xyxy).view(1, 4)) / gn).view(-1).tolist() # normalized xywh
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line = (cls, *xywh, conf) if save_conf else (cls, *xywh) # label format
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with open(file, 'a') as f:
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f.write(('%g ' * len(line)).rstrip() % line + '\n')
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with open(file, "a") as f:
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f.write(("%g " * len(line)).rstrip() % line + "\n")
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def save_one_json(predn, jdict, path, class_map):
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@@ -63,11 +77,14 @@ def save_one_json(predn, jdict, path, class_map):
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box = 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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jdict.append({
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'image_id': image_id,
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'category_id': 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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jdict.append(
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{
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"image_id": image_id,
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"category_id": 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 process_batch(detections, labels, iouv):
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@@ -98,47 +115,47 @@ def process_batch(detections, labels, iouv):
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@smart_inference_mode()
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def run(
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data,
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weights=None, # model.pt path(s)
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batch_size=32, # batch size
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imgsz=640, # inference size (pixels)
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conf_thres=0.001, # confidence threshold
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iou_thres=0.6, # NMS IoU threshold
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max_det=300, # maximum detections per image
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task='val', # train, val, test, speed or study
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device='', # cuda device, i.e. 0 or 0,1,2,3 or cpu
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workers=8, # max dataloader workers (per RANK in DDP mode)
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single_cls=False, # treat as single-class dataset
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augment=False, # augmented inference
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verbose=False, # verbose output
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save_txt=False, # save results to *.txt
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save_hybrid=False, # save label+prediction hybrid results to *.txt
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save_conf=False, # save confidences in --save-txt labels
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save_json=False, # save a COCO-JSON results file
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project=ROOT / 'runs/val', # save to project/name
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name='exp', # save to project/name
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exist_ok=False, # existing project/name ok, do not increment
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half=True, # use FP16 half-precision inference
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dnn=False, # use OpenCV DNN for ONNX inference
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model=None,
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dataloader=None,
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save_dir=Path(''),
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plots=True,
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callbacks=Callbacks(),
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compute_loss=None,
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data,
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weights=None, # model.pt path(s)
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batch_size=32, # batch size
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imgsz=640, # inference size (pixels)
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conf_thres=0.001, # confidence threshold
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iou_thres=0.6, # NMS IoU threshold
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max_det=300, # maximum detections per image
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task="val", # train, val, test, speed or study
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device="", # cuda device, i.e. 0 or 0,1,2,3 or cpu
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workers=8, # max dataloader workers (per RANK in DDP mode)
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single_cls=False, # treat as single-class dataset
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augment=False, # augmented inference
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verbose=False, # verbose output
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save_txt=False, # save results to *.txt
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save_hybrid=False, # save label+prediction hybrid results to *.txt
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save_conf=False, # save confidences in --save-txt labels
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save_json=False, # save a COCO-JSON results file
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project=ROOT / "runs/val", # save to project/name
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name="exp", # save to project/name
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exist_ok=False, # existing project/name ok, do not increment
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half=True, # use FP16 half-precision inference
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dnn=False, # use OpenCV DNN for ONNX inference
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model=None,
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dataloader=None,
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save_dir=Path(""),
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plots=True,
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callbacks=Callbacks(),
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compute_loss=None,
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):
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# Initialize/load model and set device
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training = model is not None
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if training: # called by train.py
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device, pt, jit, engine = next(model.parameters()).device, True, False, False # get model device, PyTorch model
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half &= device.type != 'cpu' # half precision only supported on CUDA
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half &= device.type != "cpu" # half precision only supported on CUDA
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model.half() if half else model.float()
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else: # called directly
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device = select_device(device, batch_size=batch_size)
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# Directories
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save_dir = increment_path(Path(project) / name, exist_ok=exist_ok) # increment run
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(save_dir / 'labels' if save_txt else save_dir).mkdir(parents=True, exist_ok=True) # make dir
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(save_dir / "labels" if save_txt else save_dir).mkdir(parents=True, exist_ok=True) # make dir
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# Load model
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model = DetectMultiBackend(weights, device=device, dnn=dnn, data=data, fp16=half)
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@@ -151,16 +168,16 @@ def run(
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device = model.device
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if not (pt or jit):
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batch_size = 1 # export.py models default to batch-size 1
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LOGGER.info(f'Forcing --batch-size 1 square inference (1,3,{imgsz},{imgsz}) for non-PyTorch models')
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LOGGER.info(f"Forcing --batch-size 1 square inference (1,3,{imgsz},{imgsz}) for non-PyTorch models")
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# Data
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data = check_dataset(data) # check
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# Configure
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model.eval()
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cuda = device.type != 'cpu'
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is_coco = isinstance(data.get('val'), str) and data['val'].endswith(f'coco{os.sep}val2017.txt') # COCO dataset
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nc = 1 if single_cls else int(data['nc']) # number of classes
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cuda = device.type != "cpu"
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is_coco = isinstance(data.get("val"), str) and data["val"].endswith(f"coco{os.sep}val2017.txt") # COCO dataset
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nc = 1 if single_cls else int(data["nc"]) # number of classes
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iouv = torch.linspace(0.5, 0.95, 10, device=device) # iou vector for mAP@0.5:0.95
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niou = iouv.numel()
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@@ -168,36 +185,40 @@ def run(
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if not training:
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if pt and not single_cls: # check --weights are trained on --data
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ncm = model.model.nc
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assert ncm == nc, f'{weights} ({ncm} classes) trained on different --data than what you passed ({nc} ' \
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f'classes). Pass correct combination of --weights and --data that are trained together.'
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assert ncm == nc, (
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f"{weights} ({ncm} classes) trained on different --data than what you passed ({nc} "
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f"classes). Pass correct combination of --weights and --data that are trained together."
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)
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model.warmup(imgsz=(1 if pt else batch_size, 3, imgsz, imgsz)) # warmup
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pad, rect = (0.0, False) if task == 'speed' else (0.5, pt) # square inference for benchmarks
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task = task if task in ('train', 'val', 'test') else 'val' # path to train/val/test images
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dataloader = create_dataloader(data[task],
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imgsz,
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batch_size,
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stride,
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single_cls,
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pad=pad,
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rect=rect,
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workers=workers,
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prefix=colorstr(f'{task}: '))[0]
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pad, rect = (0.0, False) if task == "speed" else (0.5, pt) # square inference for benchmarks
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task = task if task in ("train", "val", "test") else "val" # path to train/val/test images
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dataloader = create_dataloader(
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data[task],
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imgsz,
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batch_size,
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stride,
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single_cls,
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pad=pad,
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rect=rect,
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workers=workers,
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prefix=colorstr(f"{task}: "),
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)[0]
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seen = 0
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confusion_matrix = ConfusionMatrix(nc=nc)
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names = model.names if hasattr(model, 'names') else model.module.names # get class names
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names = model.names if hasattr(model, "names") else model.module.names # get class names
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if isinstance(names, (list, tuple)): # old format
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names = dict(enumerate(names))
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class_map = coco80_to_coco91_class() if is_coco else list(range(1000))
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s = ('%22s' + '%11s' * 6) % ('Class', 'Images', 'Instances', 'P', 'R', 'mAP50', 'mAP50-95')
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s = ("%22s" + "%11s" * 6) % ("Class", "Images", "Instances", "P", "R", "mAP50", "mAP50-95")
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tp, fp, p, r, f1, mp, mr, map50, ap50, map = 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0
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dt = Profile(device=device), Profile(device=device), Profile(device=device) # profiling times
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loss = torch.zeros(3, device=device)
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jdict, stats, ap, ap_class = [], [], [], []
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callbacks.run('on_val_start')
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callbacks.run("on_val_start")
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pbar = tqdm(dataloader, desc=s, bar_format=TQDM_BAR_FORMAT) # progress bar
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for batch_i, (im, targets, paths, shapes) in enumerate(pbar):
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callbacks.run('on_val_batch_start')
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callbacks.run("on_val_batch_start")
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with dt[0]:
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if cuda:
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im = im.to(device, non_blocking=True)
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@@ -218,13 +239,9 @@ def run(
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targets[:, 2:] *= torch.tensor((width, height, width, height), device=device) # to pixels
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lb = [targets[targets[:, 0] == i, 1:] for i in range(nb)] if save_hybrid else [] # for autolabelling
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with dt[2]:
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preds = non_max_suppression(preds,
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conf_thres,
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iou_thres,
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labels=lb,
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multi_label=True,
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agnostic=single_cls,
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max_det=max_det)
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preds = non_max_suppression(
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preds, conf_thres, iou_thres, labels=lb, multi_label=True, agnostic=single_cls, max_det=max_det
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)
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# Metrics
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for si, pred in enumerate(preds):
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@@ -259,18 +276,18 @@ def run(
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# Save/log
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if save_txt:
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(save_dir / 'labels').mkdir(parents=True, exist_ok=True)
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save_one_txt(predn, save_conf, shape, file=save_dir / 'labels' / f'{path.stem}.txt')
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(save_dir / "labels").mkdir(parents=True, exist_ok=True)
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save_one_txt(predn, save_conf, shape, file=save_dir / "labels" / f"{path.stem}.txt")
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if save_json:
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save_one_json(predn, jdict, path, class_map) # 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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callbacks.run("on_val_image_end", pred, predn, path, names, im[si])
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# Plot images
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if plots and batch_i < 3:
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plot_images(im, targets, paths, save_dir / f'val_batch{batch_i}_labels.jpg', names) # labels
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plot_images(im, output_to_target(preds), paths, save_dir / f'val_batch{batch_i}_pred.jpg', names) # pred
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plot_images(im, targets, paths, save_dir / f"val_batch{batch_i}_labels.jpg", names) # labels
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plot_images(im, output_to_target(preds), paths, save_dir / f"val_batch{batch_i}_pred.jpg", names) # pred
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callbacks.run('on_val_batch_end', batch_i, im, targets, paths, shapes, preds)
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callbacks.run("on_val_batch_end", batch_i, im, targets, paths, shapes, preds)
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# Compute metrics
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stats = [torch.cat(x, 0).cpu().numpy() for x in zip(*stats)] # to numpy
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@@ -281,10 +298,10 @@ def run(
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nt = np.bincount(stats[3].astype(int), minlength=nc) # number of targets per class
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# Print results
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pf = '%22s' + '%11i' * 2 + '%11.3g' * 4 # print format
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LOGGER.info(pf % ('all', seen, nt.sum(), mp, mr, map50, map))
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pf = "%22s" + "%11i" * 2 + "%11.3g" * 4 # print format
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LOGGER.info(pf % ("all", seen, nt.sum(), mp, mr, map50, map))
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if nt.sum() == 0:
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LOGGER.warning(f'WARNING ⚠️ no labels found in {task} set, can not compute metrics without labels')
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LOGGER.warning(f"WARNING ⚠️ no labels found in {task} set, can not compute metrics without labels")
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# Print results per class
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if (verbose or (nc < 50 and not training)) and nc > 1 and len(stats):
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@@ -292,35 +309,35 @@ def run(
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LOGGER.info(pf % (names[c], seen, nt[c], p[i], r[i], ap50[i], ap[i]))
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# Print speeds
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t = tuple(x.t / seen * 1E3 for x in dt) # speeds per image
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t = tuple(x.t / seen * 1e3 for x in dt) # speeds per image
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if not training:
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shape = (batch_size, 3, imgsz, imgsz)
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LOGGER.info(f'Speed: %.1fms pre-process, %.1fms inference, %.1fms NMS per image at shape {shape}' % t)
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LOGGER.info(f"Speed: %.1fms pre-process, %.1fms inference, %.1fms NMS per image at shape {shape}" % t)
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# Plots
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if plots:
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confusion_matrix.plot(save_dir=save_dir, names=list(names.values()))
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callbacks.run('on_val_end', nt, tp, fp, p, r, f1, ap, ap50, ap_class, confusion_matrix)
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callbacks.run("on_val_end", nt, tp, fp, p, r, f1, ap, ap50, ap_class, confusion_matrix)
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# Save JSON
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if save_json and len(jdict):
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w = Path(weights[0] if isinstance(weights, list) else weights).stem if weights is not None else '' # weights
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anno_json = str(Path('../datasets/coco/annotations/instances_val2017.json')) # annotations
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w = Path(weights[0] if isinstance(weights, list) else weights).stem if weights is not None else "" # weights
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anno_json = str(Path("../datasets/coco/annotations/instances_val2017.json")) # annotations
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if not os.path.exists(anno_json):
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anno_json = os.path.join(data['path'], 'annotations', 'instances_val2017.json')
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pred_json = str(save_dir / f'{w}_predictions.json') # predictions
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LOGGER.info(f'\nEvaluating pycocotools mAP... saving {pred_json}...')
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with open(pred_json, 'w') as f:
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anno_json = os.path.join(data["path"], "annotations", "instances_val2017.json")
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pred_json = str(save_dir / f"{w}_predictions.json") # predictions
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LOGGER.info(f"\nEvaluating pycocotools mAP... saving {pred_json}...")
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with open(pred_json, "w") as f:
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json.dump(jdict, f)
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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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check_requirements("pycocotools>=2.0.6")
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from pycocotools.coco import COCO
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from pycocotools.cocoeval import COCOeval
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anno = COCO(anno_json) # init annotations api
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pred = anno.loadRes(pred_json) # init predictions api
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eval = COCOeval(anno, pred, 'bbox')
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eval = COCOeval(anno, pred, "bbox")
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if is_coco:
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eval.params.imgIds = [int(Path(x).stem) for x in dataloader.dataset.im_files] # image IDs to evaluate
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eval.evaluate()
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@@ -328,12 +345,12 @@ def run(
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eval.summarize()
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map, map50 = eval.stats[:2] # update results (mAP@0.5:0.95, mAP@0.5)
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except Exception as e:
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LOGGER.info(f'pycocotools unable to run: {e}')
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LOGGER.info(f"pycocotools unable to run: {e}")
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# Return results
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model.float() # for training
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if not training:
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s = f"\n{len(list(save_dir.glob('labels/*.txt')))} labels saved to {save_dir / 'labels'}" if save_txt else ''
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s = f"\n{len(list(save_dir.glob('labels/*.txt')))} labels saved to {save_dir / 'labels'}" if save_txt else ""
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LOGGER.info(f"Results saved to {colorstr('bold', save_dir)}{s}")
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maps = np.zeros(nc) + map
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for i, c in enumerate(ap_class):
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@@ -343,71 +360,71 @@ def run(
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def parse_opt():
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parser = argparse.ArgumentParser()
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parser.add_argument('--data', type=str, default=ROOT / 'data/coco128.yaml', help='dataset.yaml path')
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parser.add_argument('--weights', nargs='+', type=str, default=ROOT / 'yolov5s.pt', help='model path(s)')
|
||||
parser.add_argument('--batch-size', type=int, default=32, help='batch size')
|
||||
parser.add_argument('--imgsz', '--img', '--img-size', type=int, default=640, help='inference size (pixels)')
|
||||
parser.add_argument('--conf-thres', type=float, default=0.001, help='confidence threshold')
|
||||
parser.add_argument('--iou-thres', type=float, default=0.6, help='NMS IoU threshold')
|
||||
parser.add_argument('--max-det', type=int, default=300, help='maximum detections per image')
|
||||
parser.add_argument('--task', default='val', help='train, val, test, speed or study')
|
||||
parser.add_argument('--device', default='', help='cuda device, i.e. 0 or 0,1,2,3 or cpu')
|
||||
parser.add_argument('--workers', type=int, default=8, help='max dataloader workers (per RANK in DDP mode)')
|
||||
parser.add_argument('--single-cls', action='store_true', help='treat as single-class dataset')
|
||||
parser.add_argument('--augment', action='store_true', help='augmented inference')
|
||||
parser.add_argument('--verbose', action='store_true', help='report mAP by class')
|
||||
parser.add_argument('--save-txt', action='store_true', help='save results to *.txt')
|
||||
parser.add_argument('--save-hybrid', action='store_true', help='save label+prediction hybrid results to *.txt')
|
||||
parser.add_argument('--save-conf', action='store_true', help='save confidences in --save-txt labels')
|
||||
parser.add_argument('--save-json', action='store_true', help='save a COCO-JSON results file')
|
||||
parser.add_argument('--project', default=ROOT / 'runs/val', help='save to project/name')
|
||||
parser.add_argument('--name', default='exp', help='save to project/name')
|
||||
parser.add_argument('--exist-ok', action='store_true', help='existing project/name ok, do not increment')
|
||||
parser.add_argument('--half', action='store_true', help='use FP16 half-precision inference')
|
||||
parser.add_argument('--dnn', action='store_true', help='use OpenCV DNN for ONNX inference')
|
||||
parser.add_argument("--data", type=str, default=ROOT / "data/coco128.yaml", help="dataset.yaml path")
|
||||
parser.add_argument("--weights", nargs="+", type=str, default=ROOT / "yolov5s.pt", help="model path(s)")
|
||||
parser.add_argument("--batch-size", type=int, default=32, help="batch size")
|
||||
parser.add_argument("--imgsz", "--img", "--img-size", type=int, default=640, help="inference size (pixels)")
|
||||
parser.add_argument("--conf-thres", type=float, default=0.001, help="confidence threshold")
|
||||
parser.add_argument("--iou-thres", type=float, default=0.6, help="NMS IoU threshold")
|
||||
parser.add_argument("--max-det", type=int, default=300, help="maximum detections per image")
|
||||
parser.add_argument("--task", default="val", help="train, val, test, speed or study")
|
||||
parser.add_argument("--device", default="", help="cuda device, i.e. 0 or 0,1,2,3 or cpu")
|
||||
parser.add_argument("--workers", type=int, default=8, help="max dataloader workers (per RANK in DDP mode)")
|
||||
parser.add_argument("--single-cls", action="store_true", help="treat as single-class dataset")
|
||||
parser.add_argument("--augment", action="store_true", help="augmented inference")
|
||||
parser.add_argument("--verbose", action="store_true", help="report mAP by class")
|
||||
parser.add_argument("--save-txt", action="store_true", help="save results to *.txt")
|
||||
parser.add_argument("--save-hybrid", action="store_true", help="save label+prediction hybrid results to *.txt")
|
||||
parser.add_argument("--save-conf", action="store_true", help="save confidences in --save-txt labels")
|
||||
parser.add_argument("--save-json", action="store_true", help="save a COCO-JSON results file")
|
||||
parser.add_argument("--project", default=ROOT / "runs/val", help="save to project/name")
|
||||
parser.add_argument("--name", default="exp", help="save to project/name")
|
||||
parser.add_argument("--exist-ok", action="store_true", help="existing project/name ok, do not increment")
|
||||
parser.add_argument("--half", action="store_true", help="use FP16 half-precision inference")
|
||||
parser.add_argument("--dnn", action="store_true", help="use OpenCV DNN for ONNX inference")
|
||||
opt = parser.parse_args()
|
||||
opt.data = check_yaml(opt.data) # check YAML
|
||||
opt.save_json |= opt.data.endswith('coco.yaml')
|
||||
opt.save_json |= opt.data.endswith("coco.yaml")
|
||||
opt.save_txt |= opt.save_hybrid
|
||||
print_args(vars(opt))
|
||||
return opt
|
||||
|
||||
|
||||
def main(opt):
|
||||
check_requirements(ROOT / 'requirements.txt', exclude=('tensorboard', 'thop'))
|
||||
check_requirements(ROOT / "requirements.txt", exclude=("tensorboard", "thop"))
|
||||
|
||||
if opt.task in ('train', 'val', 'test'): # run normally
|
||||
if opt.task in ("train", "val", "test"): # run normally
|
||||
if opt.conf_thres > 0.001: # https://github.com/ultralytics/yolov5/issues/1466
|
||||
LOGGER.info(f'WARNING ⚠️ confidence threshold {opt.conf_thres} > 0.001 produces invalid results')
|
||||
LOGGER.info(f"WARNING ⚠️ confidence threshold {opt.conf_thres} > 0.001 produces invalid results")
|
||||
if opt.save_hybrid:
|
||||
LOGGER.info('WARNING ⚠️ --save-hybrid will return high mAP from hybrid labels, not from predictions alone')
|
||||
LOGGER.info("WARNING ⚠️ --save-hybrid will return high mAP from hybrid labels, not from predictions alone")
|
||||
run(**vars(opt))
|
||||
|
||||
else:
|
||||
weights = opt.weights if isinstance(opt.weights, list) else [opt.weights]
|
||||
opt.half = torch.cuda.is_available() and opt.device != 'cpu' # FP16 for fastest results
|
||||
if opt.task == 'speed': # speed benchmarks
|
||||
opt.half = torch.cuda.is_available() and opt.device != "cpu" # FP16 for fastest results
|
||||
if opt.task == "speed": # speed benchmarks
|
||||
# python val.py --task speed --data coco.yaml --batch 1 --weights yolov5n.pt yolov5s.pt...
|
||||
opt.conf_thres, opt.iou_thres, opt.save_json = 0.25, 0.45, False
|
||||
for opt.weights in weights:
|
||||
run(**vars(opt), plots=False)
|
||||
|
||||
elif opt.task == 'study': # speed vs mAP benchmarks
|
||||
elif opt.task == "study": # speed vs mAP benchmarks
|
||||
# python val.py --task study --data coco.yaml --iou 0.7 --weights yolov5n.pt yolov5s.pt...
|
||||
for opt.weights in weights:
|
||||
f = f'study_{Path(opt.data).stem}_{Path(opt.weights).stem}.txt' # filename to save to
|
||||
f = f"study_{Path(opt.data).stem}_{Path(opt.weights).stem}.txt" # filename to save to
|
||||
x, y = list(range(256, 1536 + 128, 128)), [] # x axis (image sizes), y axis
|
||||
for opt.imgsz in x: # img-size
|
||||
LOGGER.info(f'\nRunning {f} --imgsz {opt.imgsz}...')
|
||||
LOGGER.info(f"\nRunning {f} --imgsz {opt.imgsz}...")
|
||||
r, _, t = run(**vars(opt), plots=False)
|
||||
y.append(r + t) # results and times
|
||||
np.savetxt(f, y, fmt='%10.4g') # save
|
||||
subprocess.run(['zip', '-r', 'study.zip', 'study_*.txt'])
|
||||
np.savetxt(f, y, fmt="%10.4g") # save
|
||||
subprocess.run(["zip", "-r", "study.zip", "study_*.txt"])
|
||||
plot_val_study(x=x) # plot
|
||||
else:
|
||||
raise NotImplementedError(f'--task {opt.task} not in ("train", "val", "test", "speed", "study")')
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
if __name__ == "__main__":
|
||||
opt = parse_opt()
|
||||
main(opt)
|
||||
Reference in new issue
Block a user