train predict与fcn源码章节相同，唯一不同的是create_model创建的是lraspp结构（在src文件夹中的lraspp_model）
def lraspp_mobilenetv3_large(num_classes=21, pretrain_backbone=False):#定义函数实例化lraspp模型
# 'mobilenetv3_large_imagenet': 'https://download.pytorch.org/models/mobilenet_v3_large-8738ca79.pth'
# 'lraspp_mobilenet_v3_large_coco': 'https://download.pytorch.org/models/lraspp_mobilenet_v3_large-d234d4ea.pth'
backbone = mobilenet_v3_large(dilated=True)#需要使用膨胀卷积
if pretrain_backbone:
# 载入mobilenetv3 large backbone预训练权重
backbone.load_state_dict(torch.load("mobilenet_v3_large.pth", map_location='cpu'))
backbone = backbone.features
# Gather the indices of blocks which are strided. These are the locations of C1, ..., Cn-1 blocks.
# The first and last blocks are always included because they are the C0 (conv1) and Cn.
stage_indices = [0] + [i for i, b in enumerate(backbone) if getattr(b, "is_strided", False)] + [len(backbone) - 1]
low_pos = stage_indices[-4] # use C2 here which has output_stride = 8
high_pos = stage_indices[-1] # use C5 which has output_stride = 16
low_channels = backbone[low_pos].out_channels
high_channels = backbone[high_pos].out_channels
return_layers = {str(low_pos): "low", str(high_pos): "high"}
backbone = IntermediateLayerGetter(backbone, return_layers=return_layers)
model = LRASPP(backbone, low_channels, high_channels, num_classes)
return model
