运行时错误：对象没有属性 nms：

Question

所以我按照本教程构建了一个对象检测，但我得到了一个我的朋友都没有遇到的错误（记住代码是在 MacOS 中运行）。我附上了错误消息的屏幕截图，我不断收到该消息，以防有任何帮助。

import torchvision
import cv2
import matplotlib.pyplot as plt
from PIL import Image
from torchvision import transforms as T

model = torchvision.models.detection.fasterrcnn_resnet50_fpn(pretrained=True)
model.eval()

COCO_INSTANCE_CATEGORY_NAMES = [
    '__background__', 'person', 'bicycle', 'car', 'motorcycle', 'airplane', 'bus',
    'train', 'truck', 'boat', 'traffic light', 'fire hydrant', 'N/A', 'stop sign',
    'parking meter', 'bench', 'bird', 'cat', 'dog', 'horse', 'sheep', 'cow',
    'elephant', 'bear', 'zebra', 'giraffe', 'N/A', 'backpack', 'umbrella', 'N/A', 'N/A',
    'handbag', 'tie', 'suitcase', 'frisbee', 'skis', 'snowboard', 'sports ball',
    'kite', 'baseball bat', 'baseball glove', 'skateboard', 'surfboard', 'tennis racket',
    'bottle', 'N/A', 'wine glass', 'cup', 'fork', 'knife', 'spoon', 'bowl',
    'banana', 'apple', 'sandwich', 'orange', 'broccoli', 'carrot', 'hot dog', 'pizza',
    'donut', 'cake', 'chair', 'couch', 'potted plant', 'bed', 'N/A', 'dining table',
    'N/A', 'N/A', 'toilet', 'N/A', 'tv', 'laptop', 'mouse', 'remote', 'keyboard', 'cell phone',
    'microwave', 'oven', 'toaster', 'sink', 'refrigerator', 'N/A', 'book',
    'clock', 'vase', 'scissors', 'teddy bear', 'hair drier', 'toothbrush'
]


def get_prediction(img_path, threshold):
    img = Image.open(img_path)  # Load the image
    transform = T.Compose([T.ToTensor()])  # Defing PyTorch Transform
    img = transform(img)  # Apply the transform to the image
    pred = model([img])  # Pass the image to the model
    pred_class = [COCO_INSTANCE_CATEGORY_NAMES[i] for i in list(pred[0]['labels'].numpy())]  # Get the Prediction Score
    pred_boxes = [[(i[0], i[1]), (i[2], i[3])] for i in list(pred[0]['boxes'].detach().numpy())]  # Bounding boxes
    pred_score = list(pred[0]['scores'].detach().numpy())
    pred_t = [pred_score.index(x) for x in pred_score if x > threshold][
        -1]  # Get list of index with score greater than threshold.
    pred_boxes = pred_boxes[:pred_t + 1]
    pred_class = pred_class[:pred_t + 1]
    return pred_boxes, pred_class


def object_detection_api(img_path, threshold=0.5, rect_th=3, text_size=3, text_th=3):
    boxes, pred_cls = get_prediction(img_path, threshold)  # Get predictions
    img = cv2.imread(img_path)  # Read image with cv2
    img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)  # Convert to RGB
    for i in range(len(boxes)):
        cv2.rectangle(img, boxes[i][0], boxes[i][1], color=(0, 255, 0),
                      thickness=rect_th)  # Draw Rectangle with the coordinates
        cv2.putText(img, pred_cls[i], boxes[i][0], cv2.FONT_HERSHEY_SIMPLEX, text_size, (0, 255, 0),
                    thickness=text_th)  # Write the prediction class
    plt.figure(figsize=(20, 30))  # display the output image
    plt.imshow(img)
    plt.xticks([])
    plt.yticks([])
    plt.show()


object_detection_api('./people.jpg', threshold=0.8)

Answer 1

我不知道为什么它不起作用，但更新我的 python 版本后它最终起作用了（3.7.6 --> 3.8.5）。

Answer 2

在 ML 工作区中使用相同的 python 版本（3.8.5）时出现此错误。