使用detect.py 和 hub 推理。

hshs0123 commented 2 years ago

E:\anaconda3\envs\pytorch\python.exe C:/yolov5-master/detect.py detect: weights=C:/Users/10980/PycharmProjects/best.pt, source=runs/detect/exp25/zidane.jpg, imgsz=[640, 640], conf_thres=0.25, iou_thres=0.45, max_det=1000, device=0, view_img=True, save_txt=False, save_conf=False, save_crop=False, nosave=False, classes=None, agnostic_nms=False, augment=False, visualize=False, update=False, project=runs/detect, name=exp, exist_ok=False, line_thickness=3, hide_labels=False, hide_conf=False, half=False YOLOv5 2021-9-21 torch 1.9.1 CUDA:0 (NVIDIA GeForce RTX 2080 Ti, 11264.0MB)

Fusing layers... Model Summary: 224 layers, 7056607 parameters, 0 gradients, 16.3 GFLOPs image 1/1 C:\yolov5-master\runs\detect\exp25\zidane.jpg: 384x640 2 person_heads, 2 person_vboxs, Done. (0.016s) Speed: 0.0ms pre-process, 15.6ms inference, 0.0ms NMS per image at shape (1, 3, 640, 640) Results saved to runs\detect\exp26 #############################################

E:\anaconda3\envs\pytorch\python.exe C:/Users/10980/PycharmProjects/msstestcapture.py Using cache found in C:\Users\10980/.cache\torch\hub\ultralytics_yolov5_master YOLOv5 2021-9-24 torch 1.9.1 CUDA:0 (NVIDIA GeForce RTX 2080 Ti, 11264.0MB)

Fusing layers... Model Summary: 224 layers, 7266973 parameters, 0 gradients Adding AutoShape... image 1/1: 720x1280 2 persons, 2 ties Speed: 15.6ms pre-process, 15.6ms inference, 0.0ms NMS per image at shape (1, 3, 384, 640)

Process finished with exit code 0

Run detect.py 为什么速度更快？同一张图片。看时间是跳过了pre-process. Speed: 0.0ms pre-process, 15.6ms inference, 0.0ms NMS per image at shape (1, 3, 640, 640) 但是为什么hub 不是这样呢？或者怎么提升这个速度呢？谢谢！

# Model
# model = torch.hub.load('ultralytics/yolov5', 'yolov5s')  # or yolov5m, yolov5l, yolov5x, custom

# Images
img = 'C:/yolov5-master/GettyImages-688402807_header-1024x575.jpg'  # or file, Path, PIL, OpenCV, numpy, list

# Inference
results = model(img)
# results.save()
# Results
results.print()  # or .show(), .save(), .crop(), .pandas(), etc.

E:\anaconda3\envs\pytorch\python.exe C:/Users/10980/PycharmProjects/msstestcapture.py Using cache found in C:\Users\10980/.cache\torch\hub\ultralytics_yolov5_master YOLOv5 2021-9-24 torch 1.9.1 CUDA:0 (NVIDIA GeForce RTX 2080 Ti, 11264.0MB)

Fusing layers... Model Summary: 224 layers, 7266973 parameters, 0 gradients Adding AutoShape... image 1/1: 720x1280 2 persons, 2 ties Speed: 15.6ms pre-process, 15.6ms inference, 0.0ms NMS per image at shape (1, 3, 384, 640)

github-actions[bot] commented 2 years ago

👋 你好 @hshs0123, 如有任何问题，请首先检查你的运行指令有没有问题，如果指令没有问题，请尝试更新作者仓库的最新代码：

  # 如果没下载官方代码
  $ git clone https://github.com/ultralytics/yolov5.git
  $ cd yolov5
  $ pip install -r requirements.txt

  # 如果已下载官方代码
  $ cd yolov5
  $ git reset --hard
  $ git pull
  $ pip install -r requirements.txt

更多请参考⭐️英文官方教程

依赖

Python版本3.6或更高，python依赖库都在requirements.txt 里面，直接pip install -r requirements.txt即可。如果你使用Windows的话，尽量使用CUDA10.2和对应版本的pytorch，CUDA11+会有些许问题。

环境

下面是已经配置好环境的免费GPU训练环境：

Google Colab and Kaggle :
Google Cloud : GCP 快速上手教程
Amazon Deep Learning AMI. See AWS 快速上手教程
Docker Image. Docker 快速上手教程

wudashuo commented 2 years ago

python上我还没见过0ms的前后处理，只在C++上见过，你应该改过detect.py吧？看看你统计时间的代码是不是动过？而且还改错了，git pull一下最新的代码吧。还有你15.6ms的pre-process有点离谱，而且跟检测时间一模一样，是不是这块代码弄错了

hshs0123 commented 2 years ago

python上我还没见过0ms的前后处理，只在C++上见过，你应该改过detect.py吧？看看你统计时间的代码是不是动过？而且还改错了，git pull一下最新的代码吧。还有你15.6ms的pre-process有点离谱，而且跟检测时间一模一样，是不是这块代码弄错了

重新下载了一下。但结果似乎还是一样。肯定是最新的了。

E:\anaconda3\envs\pytorch\python.exe C:/Users/10980/yolov5-master/loadformhub.py
Using cache found in C:\Users\10980/.cache\torch\hub\ultralytics_yolov5_master
YOLOv5  2021-9-30 torch 1.9.1 CUDA:0 (NVIDIA GeForce RTX 2080 Ti, 11264.0MB)

Fusing layers... 
Model Summary: 224 layers, 7266973 parameters, 0 gradients
Adding AutoShape... 
100%|██████████| 165k/165k [00:00<00:00, 3.30MB/s]
100%|██████████| 476k/476k [00:00<00:00, 5.12MB/s]
image 1/2: 720x1280 2 persons, 2 ties
image 2/2: 1080x810 4 persons, 1 bus
Speed: 9.0ms pre-process, 9.0ms inference, 2.5ms NMS per image at shape (2, 3, 640, 640)
Saved 2 images to runs\detect\exp12

Process finished with exit code 0

上面全是直接照搬sample的。

##############################

E:\anaconda3\envs\pytorch\python.exe C:/Users/10980/yolov5-master/detect.py
detect: weights=yolov5s.pt, source=data\images, imgsz=[640, 640], conf_thres=0.25, iou_thres=0.45, max_det=1000, device=, view_img=False, save_txt=False, save_conf=False, save_crop=False, nosave=False, classes=None, agnostic_nms=False, augment=False, visualize=False, update=False, project=runs\detect, name=exp, exist_ok=False, line_thickness=3, hide_labels=False, hide_conf=False, half=False
YOLOv5  2021-9-30 torch 1.9.1 CUDA:0 (NVIDIA GeForce RTX 2080 Ti, 11264.0MB)

Fusing layers... 
Model Summary: 224 layers, 7266973 parameters, 0 gradients
image 1/2 C:\Users\10980\yolov5-master\data\images\bus.jpg: 640x480 4 persons, 1 bus, 1 fire hydrant, Done. (0.016s)
image 2/2 C:\Users\10980\yolov5-master\data\images\zidane.jpg: 384x640 2 persons, 2 ties, Done. (0.016s)
Speed: 0.0ms pre-process, 16.0ms inference, 2.0ms NMS per image at shape (1, 3, 640, 640)
Results saved to runs\detect\exp13

Process finished with exit code 0

hshs0123 commented 2 years ago

python上我还没见过0ms的前后处理，只在C++上见过，你应该改过detect.py吧？看看你统计时间的代码是不是动过？而且还改错了，git pull一下最新的代码吧。还有你15.6ms的pre-process有点离谱，而且跟检测时间一模一样，是不是这块代码弄错了

重新下载了一下。但结果似乎还是一样。肯定是最新的了。

E:\anaconda3\envs\pytorch\python.exe C:/Users/10980/yolov5-master/loadformhub.py
Using cache found in C:\Users\10980/.cache\torch\hub\ultralytics_yolov5_master
YOLOv5  2021-9-30 torch 1.9.1 CUDA:0 (NVIDIA GeForce RTX 2080 Ti, 11264.0MB)

Fusing layers... 
Model Summary: 224 layers, 7266973 parameters, 0 gradients
Adding AutoShape... 
100%|██████████| 165k/165k [00:00<00:00, 3.30MB/s]
100%|██████████| 476k/476k [00:00<00:00, 5.12MB/s]
image 1/2: 720x1280 2 persons, 2 ties
image 2/2: 1080x810 4 persons, 1 bus
Speed: 9.0ms pre-process, 9.0ms inference, 2.5ms NMS per image at shape (2, 3, 640, 640)
Saved 2 images to runs\detect\exp12

Process finished with exit code 0

上面全是直接照搬sample的。

##############################

E:\anaconda3\envs\pytorch\python.exe C:/Users/10980/yolov5-master/detect.py
detect: weights=yolov5s.pt, source=data\images, imgsz=[640, 640], conf_thres=0.25, iou_thres=0.45, max_det=1000, device=, view_img=False, save_txt=False, save_conf=False, save_crop=False, nosave=False, classes=None, agnostic_nms=False, augment=False, visualize=False, update=False, project=runs\detect, name=exp, exist_ok=False, line_thickness=3, hide_labels=False, hide_conf=False, half=False
YOLOv5  2021-9-30 torch 1.9.1 CUDA:0 (NVIDIA GeForce RTX 2080 Ti, 11264.0MB)

Fusing layers... 
Model Summary: 224 layers, 7266973 parameters, 0 gradients
image 1/2 C:\Users\10980\yolov5-master\data\images\bus.jpg: 640x480 4 persons, 1 bus, 1 fire hydrant, Done. (0.016s)
image 2/2 C:\Users\10980\yolov5-master\data\images\zidane.jpg: 384x640 2 persons, 2 ties, Done. (0.016s)
Speed: 0.0ms pre-process, 16.0ms inference, 2.0ms NMS per image at shape (1, 3, 640, 640)
Results saved to runs\detect\exp13

Process finished with exit code 0

################### 这就分析了一张图片


E:\anaconda3\envs\pytorch\python.exe C:/Users/10980/yolov5-master/loadformhub.py
Using cache found in C:\Users\10980/.cache\torch\hub\ultralytics_yolov5_master
YOLOv5  2021-9-30 torch 1.9.1 CUDA:0 (NVIDIA GeForce RTX 2080 Ti, 11264.0MB)

Fusing layers... Model Summary: 224 layers, 7266973 parameters, 0 gradients Adding AutoShape... image 1/1: 1080x810 4 persons, 1 bus, 1 fire hydrant Speed: 19.0ms pre-process, 17.0ms inference, 5.0ms NMS per image at shape (1, 3, 640, 480) Saved 1 image to runs\detect\exp16

Process finished with exit code 0

hshs0123 commented 2 years ago

python上我还没见过0ms的前后处理，只在C++上见过，你应该改过detect.py吧？看看你统计时间的代码是不是动过？而且还改错了，git pull一下最新的代码吧。还有你15.6ms的pre-process有点离谱，而且跟检测时间一模一样，是不是这块代码弄错了

重新下载了一下。但结果似乎还是一样。肯定是最新的了。

E:\anaconda3\envs\pytorch\python.exe C:/Users/10980/yolov5-master/loadformhub.py
Using cache found in C:\Users\10980/.cache\torch\hub\ultralytics_yolov5_master
YOLOv5  2021-9-30 torch 1.9.1 CUDA:0 (NVIDIA GeForce RTX 2080 Ti, 11264.0MB)

Fusing layers... 
Model Summary: 224 layers, 7266973 parameters, 0 gradients
Adding AutoShape... 
100%|██████████| 165k/165k [00:00<00:00, 3.30MB/s]
100%|██████████| 476k/476k [00:00<00:00, 5.12MB/s]
image 1/2: 720x1280 2 persons, 2 ties
image 2/2: 1080x810 4 persons, 1 bus
Speed: 9.0ms pre-process, 9.0ms inference, 2.5ms NMS per image at shape (2, 3, 640, 640)
Saved 2 images to runs\detect\exp12

Process finished with exit code 0

上面全是直接照搬sample的。 ##############################

E:\anaconda3\envs\pytorch\python.exe C:/Users/10980/yolov5-master/detect.py
detect: weights=yolov5s.pt, source=data\images, imgsz=[640, 640], conf_thres=0.25, iou_thres=0.45, max_det=1000, device=, view_img=False, save_txt=False, save_conf=False, save_crop=False, nosave=False, classes=None, agnostic_nms=False, augment=False, visualize=False, update=False, project=runs\detect, name=exp, exist_ok=False, line_thickness=3, hide_labels=False, hide_conf=False, half=False
YOLOv5  2021-9-30 torch 1.9.1 CUDA:0 (NVIDIA GeForce RTX 2080 Ti, 11264.0MB)

Fusing layers... 
Model Summary: 224 layers, 7266973 parameters, 0 gradients
image 1/2 C:\Users\10980\yolov5-master\data\images\bus.jpg: 640x480 4 persons, 1 bus, 1 fire hydrant, Done. (0.016s)
image 2/2 C:\Users\10980\yolov5-master\data\images\zidane.jpg: 384x640 2 persons, 2 ties, Done. (0.016s)
Speed: 0.0ms pre-process, 16.0ms inference, 2.0ms NMS per image at shape (1, 3, 640, 640)
Results saved to runs\detect\exp13

Process finished with exit code 0

################### 这就分析了一张图片

E:\anaconda3\envs\pytorch\python.exe C:/Users/10980/yolov5-master/loadformhub.py
Using cache found in C:\Users\10980/.cache\torch\hub\ultralytics_yolov5_master
YOLOv5  2021-9-30 torch 1.9.1 CUDA:0 (NVIDIA GeForce RTX 2080 Ti, 11264.0MB)

Fusing layers... 
Model Summary: 224 layers, 7266973 parameters, 0 gradients
Adding AutoShape... 
image 1/1: 1080x810 4 persons, 1 bus, 1 fire hydrant
Speed: 19.0ms pre-process, 17.0ms inference, 5.0ms NMS per image at shape (1, 3, 640, 480)
Saved 1 image to runs\detect\exp16

Process finished with exit code 0


# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
"""
Run inference on images, videos, directories, streams, etc.

Usage: $ python path/to/detect.py --source path/to/img.jpg --weights yolov5s.pt --img 640 """

import argparse import sys from pathlib import Path

import cv2 import numpy as np import torch import torch.backends.cudnn as cudnn

FILE = Path(file).resolve() ROOT = FILE.parents[0] # YOLOv5 root directory if str(ROOT) not in sys.path: sys.path.append(str(ROOT)) # add ROOT to PATH ROOT = ROOT.relative_to(Path.cwd()) # relative

from models.experimental import attempt_load from utils.datasets import LoadImages, LoadStreams from utils.general import apply_classifier, check_img_size, check_imshow, check_requirements, check_suffix, colorstr, \ increment_path, non_max_suppression, print_args, save_one_box, scale_coords, set_logging, \ strip_optimizer, xyxy2xywh from utils.plots import Annotator, colors from utils.torch_utils import load_classifier, select_device, time_sync

@torch.no_grad() def run(weights=ROOT / 'yolov5s.pt', # model.pt path(s) source=ROOT / 'data/images', # file/dir/URL/glob, 0 for webcam imgsz=640, # inference size (pixels) conf_thres=0.25, # confidence threshold iou_thres=0.45, # NMS IOU threshold max_det=1000, # maximum detections per image device='', # cuda device, i.e. 0 or 0,1,2,3 or cpu view_img=False, # show results save_txt=False, # save results to *.txt save_conf=False, # save confidences in --save-txt labels save_crop=False, # save cropped prediction boxes nosave=False, # do not save images/videos classes=None, # filter by class: --class 0, or --class 0 2 3 agnostic_nms=False, # class-agnostic NMS augment=False, # augmented inference visualize=False, # visualize features update=False, # update all models project=ROOT / 'runs/detect', # save results to project/name name='exp', # save results to project/name exist_ok=False, # existing project/name ok, do not increment line_thickness=3, # bounding box thickness (pixels) hide_labels=False, # hide labels hide_conf=False, # hide confidences half=False, # use FP16 half-precision inference ): source = str(source) save_img = not nosave and not source.endswith('.txt') # save inference images webcam = source.isnumeric() or source.endswith('.txt') or source.lower().startswith( ('rtsp://', 'rtmp://', 'http://', 'https://'))

# Directories
save_dir = increment_path(Path(project) / name, exist_ok=exist_ok)  # increment run
(save_dir / 'labels' if save_txt else save_dir).mkdir(parents=True, exist_ok=True)  # make dir

# Initialize
set_logging()
device = select_device(device)
half &= device.type != 'cpu'  # half precision only supported on CUDA

# Load model
w = weights[0] if isinstance(weights, list) else weights
classify, suffix, suffixes = False, Path(w).suffix.lower(), ['.pt', '.onnx', '.tflite', '.pb', '']
check_suffix(w, suffixes)  # check weights have acceptable suffix
pt, onnx, tflite, pb, saved_model = (suffix == x for x in suffixes)  # backend booleans
stride, names = 64, [f'class{i}' for i in range(1000)]  # assign defaults
if pt:
    model = attempt_load(weights, map_location=device)  # load FP32 model
    stride = int(model.stride.max())  # model stride
    names = model.module.names if hasattr(model, 'module') else model.names  # get class names
    if half:
        model.half()  # to FP16
    if classify:  # second-stage classifier
        modelc = load_classifier(name='resnet50', n=2)  # initialize
        modelc.load_state_dict(torch.load('resnet50.pt', map_location=device)['model']).to(device).eval()
elif onnx:
    check_requirements(('onnx', 'onnxruntime'))
    import onnxruntime
    session = onnxruntime.InferenceSession(w, None)
else:  # TensorFlow models
    check_requirements(('tensorflow>=2.4.1',))
    import tensorflow as tf
    if pb:  # https://www.tensorflow.org/guide/migrate#a_graphpb_or_graphpbtxt
        def wrap_frozen_graph(gd, inputs, outputs):
            x = tf.compat.v1.wrap_function(lambda: tf.compat.v1.import_graph_def(gd, name=""), [])  # wrapped import
            return x.prune(tf.nest.map_structure(x.graph.as_graph_element, inputs),
                           tf.nest.map_structure(x.graph.as_graph_element, outputs))

        graph_def = tf.Graph().as_graph_def()
        graph_def.ParseFromString(open(w, 'rb').read())
        frozen_func = wrap_frozen_graph(gd=graph_def, inputs="x:0", outputs="Identity:0")
    elif saved_model:
        model = tf.keras.models.load_model(w)
    elif tflite:
        interpreter = tf.lite.Interpreter(model_path=w)  # load TFLite model
        interpreter.allocate_tensors()  # allocate
        input_details = interpreter.get_input_details()  # inputs
        output_details = interpreter.get_output_details()  # outputs
        int8 = input_details[0]['dtype'] == np.uint8  # is TFLite quantized uint8 model
imgsz = check_img_size(imgsz, s=stride)  # check image size

# Dataloader
if webcam:
    view_img = check_imshow()
    cudnn.benchmark = True  # set True to speed up constant image size inference
    dataset = LoadStreams(source, img_size=imgsz, stride=stride, auto=pt)
    bs = len(dataset)  # batch_size
else:
    dataset = LoadImages(source, img_size=imgsz, stride=stride, auto=pt)
    bs = 1  # batch_size
vid_path, vid_writer = [None] * bs, [None] * bs

# Run inference
if pt and device.type != 'cpu':
    model(torch.zeros(1, 3, *imgsz).to(device).type_as(next(model.parameters())))  # run once
dt, seen = [0.0, 0.0, 0.0], 0
for path, img, im0s, vid_cap in dataset:
    t1 = time_sync()
    if onnx:
        img = img.astype('float32')
    else:
        img = torch.from_numpy(img).to(device)
        img = img.half() if half else img.float()  # uint8 to fp16/32
    img = img / 255.0  # 0 - 255 to 0.0 - 1.0
    if len(img.shape) == 3:
        img = img[None]  # expand for batch dim
    t2 = time_sync()
    dt[0] += t2 - t1

    # Inference
    if pt:
        visualize = increment_path(save_dir / Path(path).stem, mkdir=True) if visualize else False
        pred = model(img, augment=augment, visualize=visualize)[0]
    elif onnx:
        pred = torch.tensor(session.run([session.get_outputs()[0].name], {session.get_inputs()[0].name: img}))
    else:  # tensorflow model (tflite, pb, saved_model)
        imn = img.permute(0, 2, 3, 1).cpu().numpy()  # image in numpy
        if pb:
            pred = frozen_func(x=tf.constant(imn)).numpy()
        elif saved_model:
            pred = model(imn, training=False).numpy()
        elif tflite:
            if int8:
                scale, zero_point = input_details[0]['quantization']
                imn = (imn / scale + zero_point).astype(np.uint8)  # de-scale
            interpreter.set_tensor(input_details[0]['index'], imn)
            interpreter.invoke()
            pred = interpreter.get_tensor(output_details[0]['index'])
            if int8:
                scale, zero_point = output_details[0]['quantization']
                pred = (pred.astype(np.float32) - zero_point) * scale  # re-scale
        pred[..., 0] *= imgsz[1]  # x
        pred[..., 1] *= imgsz[0]  # y
        pred[..., 2] *= imgsz[1]  # w
        pred[..., 3] *= imgsz[0]  # h
        pred = torch.tensor(pred)
    t3 = time_sync()
    dt[1] += t3 - t2

    # NMS
    pred = non_max_suppression(pred, conf_thres, iou_thres, classes, agnostic_nms, max_det=max_det)
    dt[2] += time_sync() - t3

    # Second-stage classifier (optional)
    if classify:
        pred = apply_classifier(pred, modelc, img, im0s)

    # Process predictions
    for i, det in enumerate(pred):  # per image
        seen += 1
        if webcam:  # batch_size >= 1
            p, s, im0, frame = path[i], f'{i}: ', im0s[i].copy(), dataset.count
        else:
            p, s, im0, frame = path, '', im0s.copy(), getattr(dataset, 'frame', 0)

        p = Path(p)  # to Path
        save_path = str(save_dir / p.name)  # img.jpg
        txt_path = str(save_dir / 'labels' / p.stem) + ('' if dataset.mode == 'image' else f'_{frame}')  # img.txt
        s += '%gx%g ' % img.shape[2:]  # print string
        gn = torch.tensor(im0.shape)[[1, 0, 1, 0]]  # normalization gain whwh
        imc = im0.copy() if save_crop else im0  # for save_crop
        annotator = Annotator(im0, line_width=line_thickness, example=str(names))
        if len(det):
            # Rescale boxes from img_size to im0 size
            det[:, :4] = scale_coords(img.shape[2:], det[:, :4], im0.shape).round()

            # Print results
            for c in det[:, -1].unique():
                n = (det[:, -1] == c).sum()  # detections per class
                s += f"{n} {names[int(c)]}{'s' * (n > 1)}, "  # add to string

            # Write results
            for *xyxy, conf, cls in reversed(det):
                if save_txt:  # Write to file
                    xywh = (xyxy2xywh(torch.tensor(xyxy).view(1, 4)) / gn).view(-1).tolist()  # normalized xywh
                    line = (cls, *xywh, conf) if save_conf else (cls, *xywh)  # label format
                    with open(txt_path + '.txt', 'a') as f:
                        f.write(('%g ' * len(line)).rstrip() % line + '\n')

                if save_img or save_crop or view_img:  # Add bbox to image
                    c = int(cls)  # integer class
                    label = None if hide_labels else (names[c] if hide_conf else f'{names[c]} {conf:.2f}')
                    annotator.box_label(xyxy, label, color=colors(c, True))
                    if save_crop:
                        save_one_box(xyxy, imc, file=save_dir / 'crops' / names[c] / f'{p.stem}.jpg', BGR=True)

        # Print time (inference-only)
        print(f'{s}Done. ({t3 - t2:.3f}s)')

        # Stream results
        im0 = annotator.result()
        if view_img:
            cv2.imshow(str(p), im0)
            cv2.waitKey(1)  # 1 millisecond

        # Save results (image with detections)
        if save_img:
            if dataset.mode == 'image':
                cv2.imwrite(save_path, im0)
            else:  # 'video' or 'stream'
                if vid_path[i] != save_path:  # new video
                    vid_path[i] = save_path
                    if isinstance(vid_writer[i], cv2.VideoWriter):
                        vid_writer[i].release()  # release previous video writer
                    if vid_cap:  # video
                        fps = vid_cap.get(cv2.CAP_PROP_FPS)
                        w = int(vid_cap.get(cv2.CAP_PROP_FRAME_WIDTH))
                        h = int(vid_cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
                    else:  # stream
                        fps, w, h = 30, im0.shape[1], im0.shape[0]
                        save_path += '.mp4'
                    vid_writer[i] = cv2.VideoWriter(save_path, cv2.VideoWriter_fourcc(*'mp4v'), fps, (w, h))
                vid_writer[i].write(im0)

# Print results
t = tuple(x / seen * 1E3 for x in dt)  # speeds per image
print(f'Speed: %.1fms pre-process, %.1fms inference, %.1fms NMS per image at shape {(1, 3, *imgsz)}' % t)
if save_txt or save_img:
    s = f"\n{len(list(save_dir.glob('labels/*.txt')))} labels saved to {save_dir / 'labels'}" if save_txt else ''
    print(f"Results saved to {colorstr('bold', save_dir)}{s}")
if update:
    strip_optimizer(weights)  # update model (to fix SourceChangeWarning)

def parse_opt(): parser = argparse.ArgumentParser() parser.add_argument('--weights', nargs='+', type=str, default=ROOT / 'yolov5s.pt', help='model path(s)') parser.add_argument('--source', type=str, default=ROOT / 'data/images', help='file/dir/URL/glob, 0 for webcam') parser.add_argument('--imgsz', '--img', '--img-size', nargs='+', type=int, default=[640], help='inference size h,w') parser.add_argument('--conf-thres', type=float, default=0.25, help='confidence threshold') parser.add_argument('--iou-thres', type=float, default=0.45, help='NMS IoU threshold') parser.add_argument('--max-det', type=int, default=1000, help='maximum detections per image') parser.add_argument('--device', default='', help='cuda device, i.e. 0 or 0,1,2,3 or cpu') parser.add_argument('--view-img', action='store_true', help='show results') parser.add_argument('--save-txt', action='store_true', help='save results to .txt') parser.add_argument('--save-conf', action='store_true', help='save confidences in --save-txt labels') parser.add_argument('--save-crop', action='store_true', help='save cropped prediction boxes') parser.add_argument('--nosave', action='store_true', help='do not save images/videos') parser.add_argument('--classes', nargs='+', type=int, help='filter by class: --classes 0, or --classes 0 2 3') parser.add_argument('--agnostic-nms', action='store_true', help='class-agnostic NMS') parser.add_argument('--augment', action='store_true', help='augmented inference') parser.add_argument('--visualize', action='store_true', help='visualize features') parser.add_argument('--update', action='store_true', help='update all models') parser.add_argument('--project', default=ROOT / 'runs/detect', help='save results to project/name') parser.add_argument('--name', default='exp', help='save results to project/name') parser.add_argument('--exist-ok', action='store_true', help='existing project/name ok, do not increment') parser.add_argument('--line-thickness', default=3, type=int, help='bounding box thickness (pixels)') parser.add_argument('--hide-labels', default=False, action='store_true', help='hide labels') parser.add_argument('--hide-conf', default=False, action='store_true', help='hide confidences') parser.add_argument('--half', action='store_true', help='use FP16 half-precision inference') opt = parser.parse_args() opt.imgsz = 2 if len(opt.imgsz) == 1 else 1 # expand print_args(FILE.stem, opt) return opt

def main(opt): check_requirements(exclude=('tensorboard', 'thop')) run(**vars(opt))

if name == "main": opt = parse_opt() main(opt)

wudashuo commented 2 years ago

既然是官方代码，那应该没问题，0ms的预处理可能就是你硬件好，速度太快了，我这边一般都是零点几毫秒的预处理时间。
从hub上加载确实是要慢的，因为你的代码要调用./cache目录下的yolov5工程。如果你的图片还是官方样例的img = 'https://ultralytics.com/images/zidane.jpg', 那可能从网络加载图片的时间也会被算进去，时间就更长了。还有一些建议：
不要使用Windows，会遇见各种各样奇怪的问题
善用git，别再下压缩包了

wudashuo / yolov5

使用detect.py 和 hub 推理。 #15

依赖

环境