models.py 文件源码

def build_fcn_segmenter(input_var, shape, version=2):
    ret = {}

    if version == 2:
        ret['input'] = la = InputLayer(shape, input_var)
        ret['conv%d'%len(ret)] = la = bn(Conv2DLayer(la, num_filters=8, filter_size=7))
        ret['conv%d'%len(ret)] = la = bn(Conv2DLayer(la, num_filters=16, filter_size=3))
        ret['pool%d'%len(ret)] = la = MaxPool2DLayer(la, pool_size=2)
        ret['conv%d'%len(ret)] = la = bn(Conv2DLayer(la, num_filters=32, filter_size=3))
        ret['pool%d'%len(ret)] = la = MaxPool2DLayer(la, pool_size=2)
        ret['conv%d'%len(ret)] = la = bn(Conv2DLayer(la, num_filters=64, filter_size=3))
        ret['pool%d'%len(ret)] = la = MaxPool2DLayer(la, pool_size=2)
        ret['conv%d'%len(ret)] = la = bn(Conv2DLayer(la, num_filters=64, filter_size=3))
        ret['dec%d'%len(ret)] = la = bn(Conv2DLayer(la, num_filters=64, filter_size=3,
            pad='full'))
        ret['ups%d'%len(ret)] = la = Upscale2DLayer(la, scale_factor=2)
        ret['dec%d'%len(ret)] = la = bn(Conv2DLayer(la, num_filters=64, filter_size=3,
            pad='full'))
        ret['ups%d'%len(ret)] = la = Upscale2DLayer(la, scale_factor=2)
        ret['dec%d'%len(ret)] = la = bn(Conv2DLayer(la, num_filters=32, filter_size=7,
            pad='full'))
        ret['ups%d'%len(ret)] = la = Upscale2DLayer(la, scale_factor=2)
        ret['dec%d'%len(ret)] = la = bn(Conv2DLayer(la, num_filters=16, filter_size=3,
            pad='full'))
        ret['conv%d'%len(ret)] = la = bn(Conv2DLayer(la, num_filters=8, filter_size=7))
        ret['output'] = la = Conv2DLayer(la, num_filters=1, filter_size=7,
                pad='full', nonlinearity=nn.nonlinearities.sigmoid)

    return ret, nn.layers.get_output(ret['output']), \
            nn.layers.get_output(ret['output'], deterministic=True)