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app/yolov5/classify/predict.py
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214
app/yolov5/classify/predict.py
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# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
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"""
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Run YOLOv5 classification inference on images, videos, directories, globs, YouTube, webcam, streams, etc.
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Usage - sources:
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$ python classify/predict.py --weights yolov5s-cls.pt --source 0 # webcam
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img.jpg # image
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vid.mp4 # video
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path/ # directory
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'path/*.jpg' # glob
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'https://youtu.be/Zgi9g1ksQHc' # YouTube
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'rtsp://example.com/media.mp4' # RTSP, RTMP, HTTP stream
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Usage - formats:
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$ python classify/predict.py --weights yolov5s-cls.pt # PyTorch
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yolov5s-cls.torchscript # TorchScript
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yolov5s-cls.onnx # ONNX Runtime or OpenCV DNN with --dnn
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yolov5s-cls.xml # OpenVINO
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yolov5s-cls.engine # TensorRT
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yolov5s-cls.mlmodel # CoreML (macOS-only)
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yolov5s-cls_saved_model # TensorFlow SavedModel
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yolov5s-cls.pb # TensorFlow GraphDef
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yolov5s-cls.tflite # TensorFlow Lite
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yolov5s-cls_edgetpu.tflite # TensorFlow Edge TPU
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"""
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import argparse
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import os
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import platform
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import sys
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from pathlib import Path
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import torch
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import torch.nn.functional as F
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FILE = Path(__file__).resolve()
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ROOT = FILE.parents[1] # YOLOv5 root directory
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if str(ROOT) not in sys.path:
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sys.path.append(str(ROOT)) # add ROOT to PATH
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ROOT = Path(os.path.relpath(ROOT, Path.cwd())) # relative
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from models.common import DetectMultiBackend
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from utils.augmentations import classify_transforms
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from utils.dataloaders import IMG_FORMATS, VID_FORMATS, LoadImages, LoadStreams
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from utils.general import (LOGGER, Profile, check_file, check_img_size, check_imshow, check_requirements, colorstr, cv2,
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increment_path, print_args, strip_optimizer)
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from utils.plots import Annotator
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from utils.torch_utils import select_device, smart_inference_mode
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@smart_inference_mode()
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def run(
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weights=ROOT / 'yolov5s-cls.pt', # model.pt path(s)
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source=ROOT / 'data/images', # file/dir/URL/glob, 0 for webcam
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data=ROOT / 'data/coco128.yaml', # dataset.yaml path
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imgsz=(224, 224), # inference size (height, width)
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device='', # cuda device, i.e. 0 or 0,1,2,3 or cpu
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view_img=False, # show results
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save_txt=False, # save results to *.txt
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nosave=False, # do not save images/videos
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augment=False, # augmented inference
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visualize=False, # visualize features
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update=False, # update all models
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project=ROOT / 'runs/predict-cls', # save results to project/name
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name='exp', # save results to project/name
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exist_ok=False, # existing project/name ok, do not increment
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half=False, # use FP16 half-precision inference
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dnn=False, # use OpenCV DNN for ONNX inference
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vid_stride=1, # video frame-rate stride
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):
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source = str(source)
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save_img = not nosave and not source.endswith('.txt') # save inference images
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is_file = Path(source).suffix[1:] in (IMG_FORMATS + VID_FORMATS)
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is_url = source.lower().startswith(('rtsp://', 'rtmp://', 'http://', 'https://'))
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webcam = source.isnumeric() or source.endswith('.txt') or (is_url and not is_file)
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if is_url and is_file:
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source = check_file(source) # download
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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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# Load model
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device = select_device(device)
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model = DetectMultiBackend(weights, device=device, dnn=dnn, data=data, fp16=half)
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stride, names, pt = model.stride, model.names, model.pt
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imgsz = check_img_size(imgsz, s=stride) # check image size
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# Dataloader
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if webcam:
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view_img = check_imshow()
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dataset = LoadStreams(source, img_size=imgsz, transforms=classify_transforms(imgsz[0]), vid_stride=vid_stride)
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bs = len(dataset) # batch_size
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else:
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dataset = LoadImages(source, img_size=imgsz, transforms=classify_transforms(imgsz[0]), vid_stride=vid_stride)
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bs = 1 # batch_size
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vid_path, vid_writer = [None] * bs, [None] * bs
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# Run inference
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model.warmup(imgsz=(1 if pt else bs, 3, *imgsz)) # warmup
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seen, windows, dt = 0, [], (Profile(), Profile(), Profile())
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for path, im, im0s, vid_cap, s in dataset:
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with dt[0]:
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im = torch.Tensor(im).to(device)
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im = im.half() if model.fp16 else im.float() # uint8 to fp16/32
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if len(im.shape) == 3:
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im = im[None] # expand for batch dim
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# Inference
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with dt[1]:
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results = model(im)
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# Post-process
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with dt[2]:
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pred = F.softmax(results, dim=1) # probabilities
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# Process predictions
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for i, prob in enumerate(pred): # per image
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seen += 1
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if webcam: # batch_size >= 1
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p, im0 = path[i], im0s[i].copy()
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s += f'{i}: '
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else:
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p, im0 = path, im0s.copy()
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p = Path(p) # to Path
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save_path = str(save_dir / p.name) # im.jpg
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s += '%gx%g ' % im.shape[2:] # print string
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annotator = Annotator(im0, example=str(names), pil=True)
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# Print results
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top5i = prob.argsort(0, descending=True)[:5].tolist() # top 5 indices
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s += f"{', '.join(f'{names[j]} {prob[j]:.2f}' for j in top5i)}, "
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# Write results
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if save_img or view_img: # Add bbox to image
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text = '\n'.join(f'{prob[j]:.2f} {names[j]}' for j in top5i)
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annotator.text((32, 32), text, txt_color=(255, 255, 255))
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# Stream results
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im0 = annotator.result()
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if view_img:
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if platform.system() == 'Linux' and p not in windows:
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windows.append(p)
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cv2.namedWindow(str(p), cv2.WINDOW_NORMAL | cv2.WINDOW_KEEPRATIO) # allow window resize (Linux)
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cv2.resizeWindow(str(p), im0.shape[1], im0.shape[0])
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cv2.imshow(str(p), im0)
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cv2.waitKey(1) # 1 millisecond
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# Save results (image with detections)
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if save_img:
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if dataset.mode == 'image':
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cv2.imwrite(save_path, im0)
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else: # 'video' or 'stream'
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if vid_path[i] != save_path: # new video
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vid_path[i] = save_path
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if isinstance(vid_writer[i], cv2.VideoWriter):
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vid_writer[i].release() # release previous video writer
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if vid_cap: # video
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fps = vid_cap.get(cv2.CAP_PROP_FPS)
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w = int(vid_cap.get(cv2.CAP_PROP_FRAME_WIDTH))
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h = int(vid_cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
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else: # stream
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fps, w, h = 30, im0.shape[1], im0.shape[0]
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save_path = str(Path(save_path).with_suffix('.mp4')) # force *.mp4 suffix on results videos
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vid_writer[i] = cv2.VideoWriter(save_path, cv2.VideoWriter_fourcc(*'mp4v'), fps, (w, h))
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vid_writer[i].write(im0)
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# Print time (inference-only)
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LOGGER.info(f"{s}{dt[1].dt * 1E3:.1f}ms")
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# Print results
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t = tuple(x.t / seen * 1E3 for x in dt) # speeds per image
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LOGGER.info(f'Speed: %.1fms pre-process, %.1fms inference, %.1fms NMS per image at shape {(1, 3, *imgsz)}' % t)
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if save_txt or save_img:
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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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if update:
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strip_optimizer(weights[0]) # update model (to fix SourceChangeWarning)
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def parse_opt():
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parser = argparse.ArgumentParser()
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parser.add_argument('--weights', nargs='+', type=str, default=ROOT / 'yolov5s-cls.pt', help='model path(s)')
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parser.add_argument('--source', type=str, default=ROOT / 'data/images', help='file/dir/URL/glob, 0 for webcam')
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parser.add_argument('--data', type=str, default=ROOT / 'data/coco128.yaml', help='(optional) dataset.yaml path')
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parser.add_argument('--imgsz', '--img', '--img-size', nargs='+', type=int, default=[224], help='inference size h,w')
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parser.add_argument('--device', default='', help='cuda device, i.e. 0 or 0,1,2,3 or cpu')
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parser.add_argument('--view-img', action='store_true', help='show results')
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parser.add_argument('--save-txt', action='store_true', help='save results to *.txt')
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parser.add_argument('--nosave', action='store_true', help='do not save images/videos')
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parser.add_argument('--augment', action='store_true', help='augmented inference')
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parser.add_argument('--visualize', action='store_true', help='visualize features')
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parser.add_argument('--update', action='store_true', help='update all models')
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parser.add_argument('--project', default=ROOT / 'runs/predict-cls', help='save results to project/name')
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parser.add_argument('--name', default='exp', help='save results to project/name')
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parser.add_argument('--exist-ok', action='store_true', help='existing project/name ok, do not increment')
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parser.add_argument('--half', action='store_true', help='use FP16 half-precision inference')
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parser.add_argument('--dnn', action='store_true', help='use OpenCV DNN for ONNX inference')
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parser.add_argument('--vid-stride', type=int, default=1, help='video frame-rate stride')
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opt = parser.parse_args()
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opt.imgsz *= 2 if len(opt.imgsz) == 1 else 1 # expand
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print_args(vars(opt))
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return opt
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def main(opt):
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check_requirements(exclude=('tensorboard', 'thop'))
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run(**vars(opt))
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if __name__ == "__main__":
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opt = parse_opt()
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main(opt)
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331
app/yolov5/classify/train.py
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app/yolov5/classify/train.py
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# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
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"""
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Train a YOLOv5 classifier model on a classification dataset
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Usage - Single-GPU training:
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$ python classify/train.py --model yolov5s-cls.pt --data imagenette160 --epochs 5 --img 128
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Usage - Multi-GPU DDP training:
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$ python -m torch.distributed.run --nproc_per_node 4 --master_port 1 classify/train.py --model yolov5s-cls.pt --data imagenet --epochs 5 --img 224 --device 0,1,2,3
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Datasets: --data mnist, fashion-mnist, cifar10, cifar100, imagenette, imagewoof, imagenet, or 'path/to/data'
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YOLOv5-cls models: --model yolov5n-cls.pt, yolov5s-cls.pt, yolov5m-cls.pt, yolov5l-cls.pt, yolov5x-cls.pt
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Torchvision models: --model resnet50, efficientnet_b0, etc. See https://pytorch.org/vision/stable/models.html
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"""
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import argparse
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import os
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import subprocess
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import sys
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import time
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from copy import deepcopy
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from datetime import datetime
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from pathlib import Path
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import torch
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import torch.distributed as dist
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import torch.hub as hub
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import torch.optim.lr_scheduler as lr_scheduler
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import torchvision
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from torch.cuda import amp
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from tqdm import tqdm
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FILE = Path(__file__).resolve()
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ROOT = FILE.parents[1] # YOLOv5 root directory
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if str(ROOT) not in sys.path:
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sys.path.append(str(ROOT)) # add ROOT to PATH
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ROOT = Path(os.path.relpath(ROOT, Path.cwd())) # relative
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from classify import val as validate
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from models.experimental import attempt_load
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from models.yolo import ClassificationModel, DetectionModel
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from utils.dataloaders import create_classification_dataloader
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from utils.general import (DATASETS_DIR, LOGGER, WorkingDirectory, check_git_status, check_requirements, colorstr,
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download, increment_path, init_seeds, print_args, yaml_save)
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from utils.loggers import GenericLogger
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from utils.plots import imshow_cls
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from utils.torch_utils import (ModelEMA, model_info, reshape_classifier_output, select_device, smart_DDP,
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smart_optimizer, smartCrossEntropyLoss, torch_distributed_zero_first)
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LOCAL_RANK = int(os.getenv('LOCAL_RANK', -1)) # https://pytorch.org/docs/stable/elastic/run.html
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RANK = int(os.getenv('RANK', -1))
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WORLD_SIZE = int(os.getenv('WORLD_SIZE', 1))
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def train(opt, device):
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init_seeds(opt.seed + 1 + RANK, deterministic=True)
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save_dir, data, bs, epochs, nw, imgsz, pretrained = \
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opt.save_dir, Path(opt.data), opt.batch_size, opt.epochs, min(os.cpu_count() - 1, opt.workers), \
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opt.imgsz, str(opt.pretrained).lower() == 'true'
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cuda = device.type != 'cpu'
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# Directories
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wdir = save_dir / 'weights'
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wdir.mkdir(parents=True, exist_ok=True) # make dir
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last, best = wdir / 'last.pt', wdir / 'best.pt'
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# Save run settings
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yaml_save(save_dir / 'opt.yaml', vars(opt))
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# Logger
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logger = GenericLogger(opt=opt, console_logger=LOGGER) if RANK in {-1, 0} else None
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# Download Dataset
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with torch_distributed_zero_first(LOCAL_RANK), WorkingDirectory(ROOT):
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data_dir = data if data.is_dir() else (DATASETS_DIR / data)
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if not data_dir.is_dir():
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LOGGER.info(f'\nDataset not found ⚠️, missing path {data_dir}, attempting download...')
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t = time.time()
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if str(data) == 'imagenet':
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subprocess.run(f"bash {ROOT / 'data/scripts/get_imagenet.sh'}", shell=True, check=True)
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else:
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url = f'https://github.com/ultralytics/yolov5/releases/download/v1.0/{data}.zip'
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download(url, dir=data_dir.parent)
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s = f"Dataset download success ✅ ({time.time() - t:.1f}s), saved to {colorstr('bold', data_dir)}\n"
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LOGGER.info(s)
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# Dataloaders
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nc = len([x for x in (data_dir / 'train').glob('*') if x.is_dir()]) # number of classes
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trainloader = create_classification_dataloader(path=data_dir / 'train',
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imgsz=imgsz,
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batch_size=bs // WORLD_SIZE,
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augment=True,
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cache=opt.cache,
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rank=LOCAL_RANK,
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workers=nw)
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test_dir = data_dir / 'test' if (data_dir / 'test').exists() else data_dir / 'val' # data/test or data/val
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if RANK in {-1, 0}:
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testloader = create_classification_dataloader(path=test_dir,
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imgsz=imgsz,
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batch_size=bs // WORLD_SIZE * 2,
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augment=False,
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cache=opt.cache,
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rank=-1,
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workers=nw)
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# Model
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with torch_distributed_zero_first(LOCAL_RANK), WorkingDirectory(ROOT):
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if Path(opt.model).is_file() or opt.model.endswith('.pt'):
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model = attempt_load(opt.model, device='cpu', fuse=False)
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elif opt.model in torchvision.models.__dict__: # TorchVision models i.e. resnet50, efficientnet_b0
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model = torchvision.models.__dict__[opt.model](weights='IMAGENET1K_V1' if pretrained else None)
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else:
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m = hub.list('ultralytics/yolov5') # + hub.list('pytorch/vision') # models
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raise ModuleNotFoundError(f'--model {opt.model} not found. Available models are: \n' + '\n'.join(m))
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if isinstance(model, DetectionModel):
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LOGGER.warning("WARNING: pass YOLOv5 classifier model with '-cls' suffix, i.e. '--model yolov5s-cls.pt'")
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model = ClassificationModel(model=model, nc=nc, cutoff=opt.cutoff or 10) # convert to classification model
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reshape_classifier_output(model, nc) # update class count
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for m in model.modules():
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if not pretrained and hasattr(m, 'reset_parameters'):
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m.reset_parameters()
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if isinstance(m, torch.nn.Dropout) and opt.dropout is not None:
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m.p = opt.dropout # set dropout
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for p in model.parameters():
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p.requires_grad = True # for training
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model = model.to(device)
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# Info
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if RANK in {-1, 0}:
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model.names = trainloader.dataset.classes # attach class names
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model.transforms = testloader.dataset.torch_transforms # attach inference transforms
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model_info(model)
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if opt.verbose:
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LOGGER.info(model)
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images, labels = next(iter(trainloader))
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file = imshow_cls(images[:25], labels[:25], names=model.names, f=save_dir / 'train_images.jpg')
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logger.log_images(file, name='Train Examples')
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logger.log_graph(model, imgsz) # log model
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# Optimizer
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optimizer = smart_optimizer(model, opt.optimizer, opt.lr0, momentum=0.9, decay=opt.decay)
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# Scheduler
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lrf = 0.01 # final lr (fraction of lr0)
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# lf = lambda x: ((1 + math.cos(x * math.pi / epochs)) / 2) * (1 - lrf) + lrf # cosine
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lf = lambda x: (1 - x / epochs) * (1 - lrf) + lrf # linear
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scheduler = lr_scheduler.LambdaLR(optimizer, lr_lambda=lf)
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# scheduler = lr_scheduler.OneCycleLR(optimizer, max_lr=lr0, total_steps=epochs, pct_start=0.1,
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# final_div_factor=1 / 25 / lrf)
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# EMA
|
||||
ema = ModelEMA(model) if RANK in {-1, 0} else None
|
||||
|
||||
# DDP mode
|
||||
if cuda and RANK != -1:
|
||||
model = smart_DDP(model)
|
||||
|
||||
# Train
|
||||
t0 = time.time()
|
||||
criterion = smartCrossEntropyLoss(label_smoothing=opt.label_smoothing) # loss function
|
||||
best_fitness = 0.0
|
||||
scaler = amp.GradScaler(enabled=cuda)
|
||||
val = test_dir.stem # 'val' or 'test'
|
||||
LOGGER.info(f'Image sizes {imgsz} train, {imgsz} test\n'
|
||||
f'Using {nw * WORLD_SIZE} dataloader workers\n'
|
||||
f"Logging results to {colorstr('bold', save_dir)}\n"
|
||||
f'Starting {opt.model} training on {data} dataset with {nc} classes for {epochs} epochs...\n\n'
|
||||
f"{'Epoch':>10}{'GPU_mem':>10}{'train_loss':>12}{f'{val}_loss':>12}{'top1_acc':>12}{'top5_acc':>12}")
|
||||
for epoch in range(epochs): # loop over the dataset multiple times
|
||||
tloss, vloss, fitness = 0.0, 0.0, 0.0 # train loss, val loss, fitness
|
||||
model.train()
|
||||
if RANK != -1:
|
||||
trainloader.sampler.set_epoch(epoch)
|
||||
pbar = enumerate(trainloader)
|
||||
if RANK in {-1, 0}:
|
||||
pbar = tqdm(enumerate(trainloader), total=len(trainloader), bar_format='{l_bar}{bar:10}{r_bar}{bar:-10b}')
|
||||
for i, (images, labels) in pbar: # progress bar
|
||||
images, labels = images.to(device, non_blocking=True), labels.to(device)
|
||||
|
||||
# Forward
|
||||
with amp.autocast(enabled=cuda): # stability issues when enabled
|
||||
loss = criterion(model(images), labels)
|
||||
|
||||
# Backward
|
||||
scaler.scale(loss).backward()
|
||||
|
||||
# Optimize
|
||||
scaler.unscale_(optimizer) # unscale gradients
|
||||
torch.nn.utils.clip_grad_norm_(model.parameters(), max_norm=10.0) # clip gradients
|
||||
scaler.step(optimizer)
|
||||
scaler.update()
|
||||
optimizer.zero_grad()
|
||||
if ema:
|
||||
ema.update(model)
|
||||
|
||||
if RANK in {-1, 0}:
|
||||
# Print
|
||||
tloss = (tloss * i + loss.item()) / (i + 1) # update mean losses
|
||||
mem = '%.3gG' % (torch.cuda.memory_reserved() / 1E9 if torch.cuda.is_available() else 0) # (GB)
|
||||
pbar.desc = f"{f'{epoch + 1}/{epochs}':>10}{mem:>10}{tloss:>12.3g}" + ' ' * 36
|
||||
|
||||
# Test
|
||||
if i == len(pbar) - 1: # last batch
|
||||
top1, top5, vloss = validate.run(model=ema.ema,
|
||||
dataloader=testloader,
|
||||
criterion=criterion,
|
||||
pbar=pbar) # test accuracy, loss
|
||||
fitness = top1 # define fitness as top1 accuracy
|
||||
|
||||
# Scheduler
|
||||
scheduler.step()
|
||||
|
||||
# Log metrics
|
||||
if RANK in {-1, 0}:
|
||||
# Best fitness
|
||||
if fitness > best_fitness:
|
||||
best_fitness = fitness
|
||||
|
||||
# Log
|
||||
metrics = {
|
||||
"train/loss": tloss,
|
||||
f"{val}/loss": vloss,
|
||||
"metrics/accuracy_top1": top1,
|
||||
"metrics/accuracy_top5": top5,
|
||||
"lr/0": optimizer.param_groups[0]['lr']} # learning rate
|
||||
logger.log_metrics(metrics, epoch)
|
||||
|
||||
# Save model
|
||||
final_epoch = epoch + 1 == epochs
|
||||
if (not opt.nosave) or final_epoch:
|
||||
ckpt = {
|
||||
'epoch': epoch,
|
||||
'best_fitness': best_fitness,
|
||||
'model': deepcopy(ema.ema).half(), # deepcopy(de_parallel(model)).half(),
|
||||
'ema': None, # deepcopy(ema.ema).half(),
|
||||
'updates': ema.updates,
|
||||
'optimizer': None, # optimizer.state_dict(),
|
||||
'opt': vars(opt),
|
||||
'date': datetime.now().isoformat()}
|
||||
|
||||
# Save last, best and delete
|
||||
torch.save(ckpt, last)
|
||||
if best_fitness == fitness:
|
||||
torch.save(ckpt, best)
|
||||
del ckpt
|
||||
|
||||
# Train complete
|
||||
if RANK in {-1, 0} and final_epoch:
|
||||
LOGGER.info(f'\nTraining complete ({(time.time() - t0) / 3600:.3f} hours)'
|
||||
f"\nResults saved to {colorstr('bold', save_dir)}"
|
||||
f"\nPredict: python classify/predict.py --weights {best} --source im.jpg"
|
||||
f"\nValidate: python classify/val.py --weights {best} --data {data_dir}"
|
||||
f"\nExport: python export.py --weights {best} --include onnx"
|
||||
f"\nPyTorch Hub: model = torch.hub.load('ultralytics/yolov5', 'custom', '{best}')"
|
||||
f"\nVisualize: https://netron.app\n")
|
||||
|
||||
# Plot examples
|
||||
images, labels = (x[:25] for x in next(iter(testloader))) # first 25 images and labels
|
||||
pred = torch.max(ema.ema(images.to(device)), 1)[1]
|
||||
file = imshow_cls(images, labels, pred, model.names, verbose=False, f=save_dir / 'test_images.jpg')
|
||||
|
||||
# Log results
|
||||
meta = {"epochs": epochs, "top1_acc": best_fitness, "date": datetime.now().isoformat()}
|
||||
logger.log_images(file, name='Test Examples (true-predicted)', epoch=epoch)
|
||||
logger.log_model(best, epochs, metadata=meta)
|
||||
|
||||
|
||||
def parse_opt(known=False):
|
||||
parser = argparse.ArgumentParser()
|
||||
parser.add_argument('--model', type=str, default='yolov5s-cls.pt', help='initial weights path')
|
||||
parser.add_argument('--data', type=str, default='imagenette160', help='cifar10, cifar100, mnist, imagenet, ...')
|
||||
parser.add_argument('--epochs', type=int, default=10, help='total training epochs')
|
||||
parser.add_argument('--batch-size', type=int, default=64, help='total batch size for all GPUs')
|
||||
parser.add_argument('--imgsz', '--img', '--img-size', type=int, default=128, help='train, val image size (pixels)')
|
||||
parser.add_argument('--nosave', action='store_true', help='only save final checkpoint')
|
||||
parser.add_argument('--cache', type=str, nargs='?', const='ram', help='--cache images in "ram" (default) or "disk"')
|
||||
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('--project', default=ROOT / 'runs/train-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('--pretrained', nargs='?', const=True, default=True, help='start from i.e. --pretrained False')
|
||||
parser.add_argument('--optimizer', choices=['SGD', 'Adam', 'AdamW', 'RMSProp'], default='Adam', help='optimizer')
|
||||
parser.add_argument('--lr0', type=float, default=0.001, help='initial learning rate')
|
||||
parser.add_argument('--decay', type=float, default=5e-5, help='weight decay')
|
||||
parser.add_argument('--label-smoothing', type=float, default=0.1, help='Label smoothing epsilon')
|
||||
parser.add_argument('--cutoff', type=int, default=None, help='Model layer cutoff index for Classify() head')
|
||||
parser.add_argument('--dropout', type=float, default=None, help='Dropout (fraction)')
|
||||
parser.add_argument('--verbose', action='store_true', help='Verbose mode')
|
||||
parser.add_argument('--seed', type=int, default=0, help='Global training seed')
|
||||
parser.add_argument('--local_rank', type=int, default=-1, help='Automatic DDP Multi-GPU argument, do not modify')
|
||||
return parser.parse_known_args()[0] if known else parser.parse_args()
|
||||
|
||||
|
||||
def main(opt):
|
||||
# Checks
|
||||
if RANK in {-1, 0}:
|
||||
print_args(vars(opt))
|
||||
check_git_status()
|
||||
check_requirements()
|
||||
|
||||
# DDP mode
|
||||
device = select_device(opt.device, batch_size=opt.batch_size)
|
||||
if LOCAL_RANK != -1:
|
||||
assert opt.batch_size != -1, 'AutoBatch is coming soon for classification, please pass a valid --batch-size'
|
||||
assert opt.batch_size % WORLD_SIZE == 0, f'--batch-size {opt.batch_size} must be multiple of WORLD_SIZE'
|
||||
assert torch.cuda.device_count() > LOCAL_RANK, 'insufficient CUDA devices for DDP command'
|
||||
torch.cuda.set_device(LOCAL_RANK)
|
||||
device = torch.device('cuda', LOCAL_RANK)
|
||||
dist.init_process_group(backend="nccl" if dist.is_nccl_available() else "gloo")
|
||||
|
||||
# Parameters
|
||||
opt.save_dir = increment_path(Path(opt.project) / opt.name, exist_ok=opt.exist_ok) # increment run
|
||||
|
||||
# Train
|
||||
train(opt, device)
|
||||
|
||||
|
||||
def run(**kwargs):
|
||||
# Usage: from yolov5 import classify; classify.train.run(data=mnist, imgsz=320, model='yolov5m')
|
||||
opt = parse_opt(True)
|
||||
for k, v in kwargs.items():
|
||||
setattr(opt, k, v)
|
||||
main(opt)
|
||||
return opt
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
opt = parse_opt()
|
||||
main(opt)
|
||||
168
app/yolov5/classify/val.py
Normal file
168
app/yolov5/classify/val.py
Normal file
@@ -0,0 +1,168 @@
|
||||
# YOLOv5 🚀 by Ultralytics, GPL-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.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
|
||||
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, 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='{l_bar}{bar:10}{r_bar}{bar:-10b}', 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():
|
||||
aci = acc[targets == i]
|
||||
top1i, top5i = aci.mean(0).tolist()
|
||||
LOGGER.info(f"{c:>24}{aci.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)
|
||||
Reference in New Issue
Block a user