我已經(jīng)訓(xùn)練了一個(gè)掩碼RCNN網(wǎng)絡(luò)，例如蘋果的分割。我能夠加載權(quán)重并為我的測(cè)試圖像生成預(yù)測(cè)。正在生成的掩碼似乎位于正確的位置，但掩模本身沒有真正的形式。它看起來(lái)就像一堆像素訓(xùn)練是根據(jù)本文中的數(shù)據(jù)集完成的，以下是用于訓(xùn)練和生成權(quán)重的代碼的github鏈接預(yù)測(cè)代碼如下。（我省略了我創(chuàng)建路徑變量并分配路徑的部分）import osimport globimport numpy as npimport pandas as pdimport cv2 as cvimport fileinputimport torchimport torch.utils.dataimport torchvisionfrom data.apple_dataset import AppleDatasetfrom torchvision.models.detection.faster_rcnn import FastRCNNPredictorfrom torchvision.models.detection.mask_rcnn import MaskRCNNPredictorimport utility.utils as utilsimport utility.transforms as Tfrom PIL import Imagefrom matplotlib import pyplot as plt%matplotlib inlinedef get_transform(train):    transforms = []    transforms.append(T.ToTensor())    if train:        transforms.append(T.RandomHorizontalFlip(0.5))    return T.Compose(transforms)def get_maskrcnn_model_instance(num_classes):    # load an instance segmentation model pre-trained pre-trained on COCO    model = torchvision.models.detection.maskrcnn_resnet50_fpn(pretrained=False)    # get number of input features for the classifier    in_features = model.roi_heads.box_predictor.cls_score.in_features    # replace the pre-trained head with a new one    model.roi_heads.box_predictor = FastRCNNPredictor(in_features, num_classes)    # now get the number of input features for the mask classifier    in_features_mask = model.roi_heads.mask_predictor.conv5_mask.in_channels    hidden_layer = 256    # and replace the mask predictor with a new one    model.roi_heads.mask_predictor = MaskRCNNPredictor(in_features_mask, hidden_layer, num_classes)    return modelnum_classes = 2device = torch.device('cpu')model = get_maskrcnn_model_instance(num_classes)checkpoint = torch.load('model_49.pth', map_location=device)model.load_state_dict(checkpoint['model'], strict=False)dataset_test = AppleDataset(test_image_files_path, get_transform(train=False))img, _ = dataset_test[1]model.eval()with torch.no_grad():    prediction = model([img.to(device)])