# YOLOv5 🚀 by Ultralytics, AGPL-3.0 license """ Validate a trained YOLOv5 classification model on a classification dataset Usage: $ bash data/scripts/get_imagenet.sh --val # download ImageNet val split (6.3G, 50000 images) $ python classify/val.py --weights yolov5m-cls.pt --data ../datasets/imagenet --img 224 # validate ImageNet Usage - formats: $ python classify/val.py --weights yolov5s-cls.pt # PyTorch yolov5s-cls.torchscript # TorchScript yolov5s-cls.onnx # ONNX Runtime or OpenCV DNN with --dnn yolov5s-cls_openvino_model # OpenVINO yolov5s-cls.engine # TensorRT yolov5s-cls.mlmodel # CoreML (macOS-only) yolov5s-cls_saved_model # TensorFlow SavedModel yolov5s-cls.pb # TensorFlow GraphDef yolov5s-cls.tflite # TensorFlow Lite yolov5s-cls_edgetpu.tflite # TensorFlow Edge TPU yolov5s-cls_paddle_model # PaddlePaddle """ import argparse import os import sys from pathlib import Path import torch from tqdm import tqdm FILE = Path(__file__).resolve() ROOT = FILE.parents[1] # YOLOv5 root directory if str(ROOT) not in sys.path: sys.path.append(str(ROOT)) # add ROOT to PATH ROOT = Path(os.path.relpath(ROOT, Path.cwd())) # relative from models.common import DetectMultiBackend from utils.dataloaders import create_classification_dataloader from utils.general import (LOGGER, TQDM_BAR_FORMAT, Profile, check_img_size, check_requirements, colorstr, increment_path, print_args) from utils.torch_utils import select_device, smart_inference_mode @smart_inference_mode() def run( data=ROOT / '../datasets/mnist', # dataset dir weights=ROOT / 'yolov5s-cls.pt', # model.pt path(s) batch_size=128, # batch size imgsz=224, # inference size (pixels) device='', # cuda device, i.e. 0 or 0,1,2,3 or cpu workers=8, # max dataloader workers (per RANK in DDP mode) verbose=False, # verbose output project=ROOT / 'runs/val-cls', # save to project/name name='exp', # save to project/name exist_ok=False, # existing project/name ok, do not increment half=False, # use FP16 half-precision inference dnn=False, # use OpenCV DNN for ONNX inference model=None, dataloader=None, criterion=None, pbar=None, ): # Initialize/load model and set device training = model is not None if training: # called by train.py device, pt, jit, engine = next(model.parameters()).device, True, False, False # get model device, PyTorch model half &= device.type != 'cpu' # half precision only supported on CUDA model.half() if half else model.float() else: # called directly device = select_device(device, batch_size=batch_size) # Directories save_dir = increment_path(Path(project) / name, exist_ok=exist_ok) # increment run save_dir.mkdir(parents=True, exist_ok=True) # make dir # Load model model = DetectMultiBackend(weights, device=device, dnn=dnn, fp16=half) stride, pt, jit, engine = model.stride, model.pt, model.jit, model.engine imgsz = check_img_size(imgsz, s=stride) # check image size half = model.fp16 # FP16 supported on limited backends with CUDA if engine: batch_size = model.batch_size else: device = model.device if not (pt or jit): batch_size = 1 # export.py models default to batch-size 1 LOGGER.info(f'Forcing --batch-size 1 square inference (1,3,{imgsz},{imgsz}) for non-PyTorch models') # Dataloader data = Path(data) test_dir = data / 'test' if (data / 'test').exists() else data / 'val' # data/test or data/val dataloader = create_classification_dataloader(path=test_dir, imgsz=imgsz, batch_size=batch_size, augment=False, rank=-1, workers=workers) model.eval() pred, targets, loss, dt = [], [], 0, (Profile(), Profile(), Profile()) n = len(dataloader) # number of batches action = 'validating' if dataloader.dataset.root.stem == 'val' else 'testing' desc = f'{pbar.desc[:-36]}{action:>36}' if pbar else f'{action}' bar = tqdm(dataloader, desc, n, not training, bar_format=TQDM_BAR_FORMAT, position=0) with torch.cuda.amp.autocast(enabled=device.type != 'cpu'): for images, labels in bar: with dt[0]: images, labels = images.to(device, non_blocking=True), labels.to(device) with dt[1]: y = model(images) with dt[2]: pred.append(y.argsort(1, descending=True)[:, :5]) targets.append(labels) if criterion: loss += criterion(y, labels) loss /= n pred, targets = torch.cat(pred), torch.cat(targets) correct = (targets[:, None] == pred).float() acc = torch.stack((correct[:, 0], correct.max(1).values), dim=1) # (top1, top5) accuracy top1, top5 = acc.mean(0).tolist() if pbar: pbar.desc = f'{pbar.desc[:-36]}{loss:>12.3g}{top1:>12.3g}{top5:>12.3g}' if verbose: # all classes LOGGER.info(f"{'Class':>24}{'Images':>12}{'top1_acc':>12}{'top5_acc':>12}") LOGGER.info(f"{'all':>24}{targets.shape[0]:>12}{top1:>12.3g}{top5:>12.3g}") for i, c in model.names.items(): acc_i = acc[targets == i] top1i, top5i = acc_i.mean(0).tolist() LOGGER.info(f'{c:>24}{acc_i.shape[0]:>12}{top1i:>12.3g}{top5i:>12.3g}') # Print results t = tuple(x.t / len(dataloader.dataset.samples) * 1E3 for x in dt) # speeds per image shape = (1, 3, imgsz, imgsz) LOGGER.info(f'Speed: %.1fms pre-process, %.1fms inference, %.1fms post-process per image at shape {shape}' % t) LOGGER.info(f"Results saved to {colorstr('bold', save_dir)}") return top1, top5, loss def parse_opt(): parser = argparse.ArgumentParser() parser.add_argument('--data', type=str, default=ROOT / '../datasets/mnist', help='dataset path') parser.add_argument('--weights', nargs='+', type=str, default=ROOT / 'yolov5s-cls.pt', help='model.pt path(s)') parser.add_argument('--batch-size', type=int, default=128, help='batch size') parser.add_argument('--imgsz', '--img', '--img-size', type=int, default=224, help='inference size (pixels)') 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('--verbose', nargs='?', const=True, default=True, help='verbose output') parser.add_argument('--project', default=ROOT / 'runs/val-cls', 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() print_args(vars(opt)) return opt def main(opt): check_requirements(exclude=('tensorboard', 'thop')) run(**vars(opt)) if __name__ == '__main__': opt = parse_opt() main(opt)