yolov5/classify/predict.py

# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
"""
Run YOLOv5 classification inference on images, videos, directories, and globs.

Usage - sources:
    $ python classify/predict.py --weights yolov5s.pt --source img.jpg        # image
                                                               vid.mp4        # video
                                                               path/          # directory
                                                               'path/*.jpg'   # glob

Usage - formats:
    $ python classify/predict.py --weights yolov5s-cls.pt                 # PyTorch
                                           yolov5s-cls.torchscript        # TorchScript
                                           yolov5s-cls.onnx               # ONNX Runtime or OpenCV DNN with --dnn
                                           yolov5s-cls.xml                # 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
"""

import argparse
import os
import sys
from pathlib import Path

import torch.nn.functional as F

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.augmentations import classify_transforms
from utils.dataloaders import IMG_FORMATS, VID_FORMATS, LoadImages
from utils.general import LOGGER, Profile, check_file, check_requirements, colorstr, increment_path, print_args
from utils.torch_utils import select_device, smart_inference_mode


@smart_inference_mode()
def run(
        weights=ROOT / 'yolov5s-cls.pt',  # model.pt path(s)
        source=ROOT / 'data/images',  # file/dir/URL/glob
        imgsz=224,  # inference size
        device='',  # cuda device, i.e. 0 or 0,1,2,3 or cpu
        half=False,  # use FP16 half-precision inference
        dnn=False,  # use OpenCV DNN for ONNX inference
        project=ROOT / 'runs/predict-cls',  # save to project/name
        name='exp',  # save to project/name
        exist_ok=False,  # existing project/name ok, do not increment
):
    source = str(source)
    is_file = Path(source).suffix[1:] in (IMG_FORMATS + VID_FORMATS)
    is_url = source.lower().startswith(('rtsp://', 'rtmp://', 'http://', 'https://'))
    if is_url and is_file:
        source = check_file(source)  # download

    dt = Profile(), Profile(), Profile()
    device = select_device(device)

    # 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)
    model.warmup(imgsz=(1, 3, imgsz, imgsz))  # warmup
    dataset = LoadImages(source, img_size=imgsz, transforms=classify_transforms(imgsz))
    for seen, (path, im, im0s, vid_cap, s) in enumerate(dataset):
        # Image
        with dt[0]:
            im = im.unsqueeze(0).to(device)
            im = im.half() if model.fp16 else im.float()

        # Inference
        with dt[1]:
            results = model(im)

        # Post-process
        with dt[2]:
            p = F.softmax(results, dim=1)  # probabilities
            i = p.argsort(1, descending=True)[:, :5].squeeze().tolist()  # top 5 indices
            # if save:
            #    imshow_cls(im, f=save_dir / Path(path).name, verbose=True)
            LOGGER.info(
                f"{s}{imgsz}x{imgsz} {', '.join(f'{model.names[j]} {p[0, j]:.2f}' for j in i)}, {dt[1].dt * 1E3:.1f}ms")

    # Print results
    t = tuple(x.t / (seen + 1) * 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 p


def parse_opt():
    parser = argparse.ArgumentParser()
    parser.add_argument('--weights', nargs='+', type=str, default=ROOT / 'yolov5s-cls.pt', help='model path(s)')
    parser.add_argument('--source', type=str, default=ROOT / 'data/images', help='file/dir/URL/glob')
    parser.add_argument('--imgsz', '--img', '--img-size', type=int, default=224, help='train, val image size (pixels)')
    parser.add_argument('--device', default='', help='cuda device, i.e. 0 or 0,1,2,3 or cpu')
    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('--project', default=ROOT / 'runs/predict-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')
    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)