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我想使用以下 python 代码（用于 pycaffe）生成一个类似于 U-Net 的深度学习架构：

from caffe import layers as L
from caffe import params as P
import caffe
from caffe.coord_map import crop
from PythonDataLayer import PythonDataLayer


# Some macro functions
def max_pool(bottom):
    return L.Pooling(bottom, kernel_size=2, stride=2, pool=P.Pooling.MAX)

def macro_deconv(bottom, _num_output , _kernel_size, _stride):
    deconv = L.Deconvolution(bottom,
                         convolution_param=dict(num_output=_num_output,kernel_size=_kernel_size, stride=_stride),
                         param=[dict(lr_mult=1,decay_mult=1)]
                         )
    return deconv, L.ReLU(deconv, in_place=True)

def conv(bottom, _num_output, _kernel_size):
    c = L.Convolution(bottom,
                          num_output=_num_output,
                          kernel_size=_kernel_size,
                          pad=0, weight_filler=dict(type='xavier'),
                          param=[{'lr_mult':1},{'lr_mult':0.1}],
                          bias_filler=dict(type='constant', value=0))
    return c, L.ReLU(c, in_place=True)

def crop_merge(bottom_down, bottom_up):
    c = crop(bottom_down, bottom_up)
    m = L.Concat(bottom_up, c)
    return c,m





def unet():
    net = caffe.NetSpec()

    pydata_params = dict()
    pydata_params['image_file_list'] = '/home/xx/workspace/TRAIN_DATA_RAW.txt'
    pydata_params['label_file_list'] = '/home/xx/workspace/TRAIN_LABEL_RAW.txt'
    net.data, net.label = L.Python(module='PythonDataLayer',     layer='PythonDataLayer', ntop=2, param_str=str(pydata_params))

    # Level 1 down
    net.down_conv1a, net.down_relu1a = conv(net.data, 24, 3)
    net.pool1 = max_pool(net.down_relu1a)

    # Level 2 down
    net.down_conv2a, net.down_relu2a = conv(net.pool1, 32, 3)
    net.down_conv2b, net.down_relu2b = conv(net.down_relu2a, 32, 3)
    net.pool2 = max_pool(net.down_relu2b)

    # Level 3 down
    net.down_conv3a, net.down_relu3a = conv(net.pool2, 32, 3)
    net.down_conv3b, net.down_relu3b = conv(net.down_relu3a, 32, 3)
    net.pool3 = max_pool(net.down_relu3b)

    #Bottom level
    net.conv4a, net.relu4a = conv(net.pool3, 32, 3)
    net.conv4b, net.relu4b = conv(net.relu4a, 32, 3)

    # Level 3 up
    net.up_deconv4, net.up_relu3a = macro_deconv(net.relu4b, 32, 2, 2)
    net.up_crop3, net.up_merge3 = crop_merge(net.down_relu3b, net.up_relu3a)
    net.up_conv3a, net.up_relu3b = conv(net.up_merge3, 32, 3)
    net.up_conv3b, net.up_relu3c = conv(net.up_relu3b, 32, 3)

    # Level 2 up
    net.up_deconv3, net.up_relu2a = macro_deconv(net.up_relu3c, 32, 2, 2)
    net.up_crop2, net.up_merge2 = crop_merge(net.down_relu2b, net.up_relu2a)
    net.up_conv2a, net.up_relu2b = conv(net.up_merge2, 32, 3)
    net.up_conv2b, net.up_relu2c = conv(net.up_relu2b, 32, 3)

    # Level 1 up
    net.up_deconv2, net.up_relu1a = macro_deconv(net.up_relu2c, 32, 2, 2)
    net.up_crop1, net.up_merge1 = crop_merge(net.down_relu1a, net.up_relu1a)
    net.up_conv1a, net.up_relu1b = conv(net.up_merge1, 32, 3)
    net.up_conv1b, net.up_relu1c = conv(net.up_relu1b, 32, 3)

    # Final layer
    net.last = L.Convolution(net.up_relu1c,
                          num_output=2,
                          kernel_size=1,
                          pad=0,
                          weight_filler=dict(type='xavier'),
                          param=[{'lr_mult':1},{'lr_mult':0.1}],
                          bias_filler=dict(type='constant', value=0))

    net.loss = L.EuclideanLoss(net.fullyconv, net.label)

    return net.to_proto()


print unet()

执行时返回以下错误消息：

xx@pc-01:~/workspace$ python UNET.py
Traceback (most recent call last):
  File "UNET.py", line 109, in <module>
    print unet()
  File "UNET.py", line 79, in unet
    net.up_crop2, net.up_merge2 = crop_merge(net.down_relu2b, net.up_relu2a)
  File "UNET.py", line 34, in crop_merge
    c = crop(bottom_down, bottom_up)
  File "/usr/local/caffe/python/caffe/coord_map.py", line 178, in crop
    ax, a, b = coord_map_from_to(top_from, top_to)
  File "/usr/local/caffe/python/caffe/coord_map.py", line 168, in coord_map_from                           _to
    raise RuntimeError('Could not compute map between tops; are they '
RuntimeError: Could not compute map between tops; are they connected by spatial layers?

我不知道问题是什么。如果我减少层使得只有一个反卷积+裁剪+合并步骤，则生成网络描述字符串没有问题。

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