def create_model(num_classes):
backbone = resnet50_fpn_backbone()
model = FasterRCNN(backbone=backbone, num_classes=91)
# 载入预训练模型权重
weights_dict = torch.load("./backbone/fasterrcnn_resnet50_fpn_coco.pth")
missing_keys, unexpected_keys = model.load_state_dict(weights_dict, strict=False)
if len(missing_keys) != 0 or len(unexpected_keys) != 0:
print("missing_keys: ", missing_keys)
print("unexpected_keys: ", unexpected_keys)
# 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)
return model
def create_model(num_classes, device):
backbone = resnet50_fpn_backbone()
# 训练自己数据集时不要修改这里的91，修改的是传入的num_classes参数
model = FasterRCNN(backbone=backbone, num_classes=91)
# 载入预训练模型权重
# https://download.pytorch.org/models/fasterrcnn_resnet50_fpn_coco-258fb6c6.pth
weights_dict = torch.load("./backbone/fasterrcnn_resnet50_fpn_coco.pth", map_location=device)
missing_keys, unexpected_keys = model.load_state_dict(weights_dict, strict=False)
if len(missing_keys) != 0 or len(unexpected_keys) != 0:
print("missing_keys: ", missing_keys)
print("unexpected_keys: ", unexpected_keys)
# 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)
return model
def create_model(num_classes):
backbone = resnet50_fpn_backbone()
# 训练自己数据集时不要修改这里的91，修改的是传入的num_classes参数
model = FasterRCNN(backbone=backbone, num_classes=91)
# 载入预训练模型权重
# https://download.pytorch.org/models/fasterrcnn_resnet50_fpn_coco-258fb6c6.pth
#weights_dict = torch.load("./backbone/fasterrcnn_resnet50_fpn_coco.pth")
weights_dict = torch.load("/Users/mengfanhui/Documents/GitR/deep-learning-for-image-processing/pytorch_object_detection/faster_rcnn/backbone/fasterrcnn_resnet50_fpn_coco-258fb6c6.pth")
missing_keys, unexpected_keys = model.load_state_dict(weights_dict, strict=False)
if len(missing_keys) != 0 or len(unexpected_keys) != 0:
print("missing_keys: ", missing_keys)
print("unexpected_keys: ", unexpected_keys)
# 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)
return model
