layers.py 文件源码

def InceptionLayer(incoming,param_dict,block_name):
    branch = [0]*len(param_dict)
    # Loop across branches
    for i,dict in enumerate(param_dict):
        for j,style in enumerate(dict['style']): # Loop up branch
            branch[i] = C2D(
                incoming = branch[i] if j else incoming,
                num_filters = dict['num_filters'][j],
                filter_size = dict['filter_size'][j],
                pad =  dict['pad'][j] if 'pad' in dict else None,
                stride = dict['stride'][j],
                W = initmethod('relu'),
                nonlinearity = dict['nonlinearity'][j],
                name = block_name+'_'+str(i)+'_'+str(j)) if style=='convolutional'\
            else NL(lasagne.layers.dnn.Pool2DDNNLayer(
                incoming=incoming if j == 0 else branch[i],
                pool_size = dict['filter_size'][j],
                mode = dict['mode'][j],
                stride = dict['stride'][j],
                pad = dict['pad'][j],
                name = block_name+'_'+str(i)+'_'+str(j)),
                nonlinearity = dict['nonlinearity'][j]) if style=='pool'\
            else lasagne.layers.DilatedConv2DLayer(
                incoming = lasagne.layers.PadLayer(incoming = incoming if j==0 else branch[i],width = dict['pad'][j]) if 'pad' in dict else incoming if j==0 else branch[i],
                num_filters = dict['num_filters'][j],
                filter_size = dict['filter_size'][j],
                dilation = dict['dilation'][j],
                # pad = dict['pad'][j] if 'pad' in dict else None,
                W = initmethod('relu'),
                nonlinearity = dict['nonlinearity'][j],
                name = block_name+'_'+str(i)+'_'+str(j))  if style== 'dilation'\
            else DL(
                    incoming = incoming if j==0 else branch[i],
                    num_units = dict['num_filters'][j],
                    W = initmethod('relu'),
                    b = None,
                    nonlinearity = dict['nonlinearity'][j],
                    name = block_name+'_'+str(i)+'_'+str(j))   
                # Apply Batchnorm    
            branch[i] = BN(branch[i],name = block_name+'_bnorm_'+str(i)+'_'+str(j)) if dict['bnorm'][j] else branch[i]
        # Concatenate Sublayers        

    return CL(incomings=branch,name=block_name)

# Convenience function to define an inception-style block with upscaling