Bai360/Cotton2

1# YOLOv5 🚀 by Ultralytics, GPL-3.0 license2"""3Validate a trained YOLOv5 classification model on a classification dataset4 5Usage:6    $ bash data/scripts/get_imagenet.sh --val  # download ImageNet val split (6.3G, 50000 images)7    $ python classify/val.py --weights yolov5m-cls.pt --data ../datasets/imagenet --img 224  # validate ImageNet8 9Usage - formats:10    $ python classify/val.py --weights yolov5s-cls.pt                 # PyTorch11                                       yolov5s-cls.torchscript        # TorchScript12                                       yolov5s-cls.onnx               # ONNX Runtime or OpenCV DNN with --dnn13                                       yolov5s-cls_openvino_model     # OpenVINO14                                       yolov5s-cls.engine             # TensorRT15                                       yolov5s-cls.mlmodel            # CoreML (macOS-only)16                                       yolov5s-cls_saved_model        # TensorFlow SavedModel17                                       yolov5s-cls.pb                 # TensorFlow GraphDef18                                       yolov5s-cls.tflite             # TensorFlow Lite19                                       yolov5s-cls_edgetpu.tflite     # TensorFlow Edge TPU20                                       yolov5s-cls_paddle_model       # PaddlePaddle21"""22 23import argparse24import os25import sys26from pathlib import Path27 28import torch29from tqdm import tqdm30 31FILE = Path(__file__).resolve()32ROOT = FILE.parents[1]  # YOLOv5 root directory33if str(ROOT) not in sys.path:34    sys.path.append(str(ROOT))  # add ROOT to PATH35ROOT = Path(os.path.relpath(ROOT, Path.cwd()))  # relative36 37from models.common import DetectMultiBackend38from utils.dataloaders import create_classification_dataloader39from utils.general import (LOGGER, TQDM_BAR_FORMAT, Profile, check_img_size, check_requirements, colorstr,40                           increment_path, print_args)41from utils.torch_utils import select_device, smart_inference_mode42 43 44@smart_inference_mode()45def run(46    data=ROOT / '../datasets/mnist',  # dataset dir47    weights=ROOT / 'yolov5s-cls.pt',  # model.pt path(s)48    batch_size=128,  # batch size49    imgsz=224,  # inference size (pixels)50    device='',  # cuda device, i.e. 0 or 0,1,2,3 or cpu51    workers=8,  # max dataloader workers (per RANK in DDP mode)52    verbose=False,  # verbose output53    project=ROOT / 'runs/val-cls',  # save to project/name54    name='exp',  # save to project/name55    exist_ok=False,  # existing project/name ok, do not increment56    half=False,  # use FP16 half-precision inference57    dnn=False,  # use OpenCV DNN for ONNX inference58    model=None,59    dataloader=None,60    criterion=None,61    pbar=None,62):63    # Initialize/load model and set device64    training = model is not None65    if training:  # called by train.py66        device, pt, jit, engine = next(model.parameters()).device, True, False, False  # get model device, PyTorch model67        half &= device.type != 'cpu'  # half precision only supported on CUDA68        model.half() if half else model.float()69    else:  # called directly70        device = select_device(device, batch_size=batch_size)71 72        # Directories73        save_dir = increment_path(Path(project) / name, exist_ok=exist_ok)  # increment run74        save_dir.mkdir(parents=True, exist_ok=True)  # make dir75 76        # Load model77        model = DetectMultiBackend(weights, device=device, dnn=dnn, fp16=half)78        stride, pt, jit, engine = model.stride, model.pt, model.jit, model.engine79        imgsz = check_img_size(imgsz, s=stride)  # check image size80        half = model.fp16  # FP16 supported on limited backends with CUDA81        if engine:82            batch_size = model.batch_size83        else:84            device = model.device85            if not (pt or jit):86                batch_size = 1  # export.py models default to batch-size 187                LOGGER.info(f'Forcing --batch-size 1 square inference (1,3,{imgsz},{imgsz}) for non-PyTorch models')88 89        # Dataloader90        data = Path(data)91        test_dir = data / 'test' if (data / 'test').exists() else data / 'val'  # data/test or data/val92        dataloader = create_classification_dataloader(path=test_dir,93                                                      imgsz=imgsz,94                                                      batch_size=batch_size,95                                                      augment=False,96                                                      rank=-1,97                                                      workers=workers)98 99    model.eval()100    pred, targets, loss, dt = [], [], 0, (Profile(), Profile(), Profile())101    n = len(dataloader)  # number of batches102    action = 'validating' if dataloader.dataset.root.stem == 'val' else 'testing'103    desc = f"{pbar.desc[:-36]}{action:>36}" if pbar else f"{action}"104    bar = tqdm(dataloader, desc, n, not training, bar_format=TQDM_BAR_FORMAT, position=0)105    with torch.cuda.amp.autocast(enabled=device.type != 'cpu'):106        for images, labels in bar:107            with dt[0]:108                images, labels = images.to(device, non_blocking=True), labels.to(device)109 110            with dt[1]:111                y = model(images)112 113            with dt[2]:114                pred.append(y.argsort(1, descending=True)[:, :5])115                targets.append(labels)116                if criterion:117                    loss += criterion(y, labels)118 119    loss /= n120    pred, targets = torch.cat(pred), torch.cat(targets)121    correct = (targets[:, None] == pred).float()122    acc = torch.stack((correct[:, 0], correct.max(1).values), dim=1)  # (top1, top5) accuracy123    top1, top5 = acc.mean(0).tolist()124 125    if pbar:126        pbar.desc = f"{pbar.desc[:-36]}{loss:>12.3g}{top1:>12.3g}{top5:>12.3g}"127    if verbose:  # all classes128        LOGGER.info(f"{'Class':>24}{'Images':>12}{'top1_acc':>12}{'top5_acc':>12}")129        LOGGER.info(f"{'all':>24}{targets.shape[0]:>12}{top1:>12.3g}{top5:>12.3g}")130        for i, c in model.names.items():131            aci = acc[targets == i]132            top1i, top5i = aci.mean(0).tolist()133            LOGGER.info(f"{c:>24}{aci.shape[0]:>12}{top1i:>12.3g}{top5i:>12.3g}")134 135        # Print results136        t = tuple(x.t / len(dataloader.dataset.samples) * 1E3 for x in dt)  # speeds per image137        shape = (1, 3, imgsz, imgsz)138        LOGGER.info(f'Speed: %.1fms pre-process, %.1fms inference, %.1fms post-process per image at shape {shape}' % t)139        LOGGER.info(f"Results saved to {colorstr('bold', save_dir)}")140 141    return top1, top5, loss142 143 144def parse_opt():145    parser = argparse.ArgumentParser()146    parser.add_argument('--data', type=str, default=ROOT / '../datasets/mnist', help='dataset path')147    parser.add_argument('--weights', nargs='+', type=str, default=ROOT / 'yolov5s-cls.pt', help='model.pt path(s)')148    parser.add_argument('--batch-size', type=int, default=128, help='batch size')149    parser.add_argument('--imgsz', '--img', '--img-size', type=int, default=224, help='inference size (pixels)')150    parser.add_argument('--device', default='', help='cuda device, i.e. 0 or 0,1,2,3 or cpu')151    parser.add_argument('--workers', type=int, default=8, help='max dataloader workers (per RANK in DDP mode)')152    parser.add_argument('--verbose', nargs='?', const=True, default=True, help='verbose output')153    parser.add_argument('--project', default=ROOT / 'runs/val-cls', help='save to project/name')154    parser.add_argument('--name', default='exp', help='save to project/name')155    parser.add_argument('--exist-ok', action='store_true', help='existing project/name ok, do not increment')156    parser.add_argument('--half', action='store_true', help='use FP16 half-precision inference')157    parser.add_argument('--dnn', action='store_true', help='use OpenCV DNN for ONNX inference')158    opt = parser.parse_args()159    print_args(vars(opt))160    return opt161 162 163def main(opt):164    check_requirements(exclude=('tensorboard', 'thop'))165    run(**vars(opt))166 167 168if __name__ == "__main__":169    opt = parse_opt()170    main(opt)171