Python E_senet示例

示例#1

def define_model(is_resnet, is_densenet, is_senet):
    if is_resnet:
        original_model = resnet.resnet50(pretrained = True)
        Encoder = modules.E_resnet(original_model) 
        model = net.model(Encoder, num_features=2048, block_channel = [256, 512, 1024, 2048])
    if is_densenet:
        original_model = densenet.densenet161(pretrained=True)
        Encoder = modules.E_densenet(original_model)
        model = net.model(Encoder, num_features=2208, block_channel = [192, 384, 1056, 2208])
    if is_senet:
        original_model = senet.senet154(pretrained='imagenet')
        Encoder = modules.E_senet(original_model)
        model = net.model(Encoder, num_features=2048, block_channel = [256, 512, 1024, 2048])

    return model

示例#2

文件： demo.py 项目： klrc/volume-estimation

def define_model(target):
    model = None
    if target is 'resnet':
        original_model = resnet.resnet50(pretrained=True)
        encoder = modules.E_resnet(original_model)
        model = net.model(encoder,
                          num_features=2048,
                          block_channel=[256, 512, 1024, 2048])
    elif target is 'densenet':
        original_model = densenet.densenet161(pretrained=True)
        encoder = modules.E_densenet(original_model)
        model = net.model(encoder,
                          num_features=2208,
                          block_channel=[192, 384, 1056, 2208])
    elif target is 'senet':
        original_model = senet.senet154(pretrained='imagenet')
        encoder = modules.E_senet(original_model)
        model = net.model(encoder,
                          num_features=2048,
                          block_channel=[256, 512, 1024, 2048])
    return model