AttentionNet.py 文件源码

def build_context_network(downsample):
    if not isinstance(downsample, lasagne.layers.Layer):
        l_in = lasagne.layers.InputLayer((None, 1, downsample_rows, downsample_cols), downsample)
    else:
        l_in = downsample
    first_conv = lasagne.layers.Conv2DLayer(l_in,
                                             context_number_of_convolving_filters,
                                             context_convolving_filter_size, 
                                             stride = 1,
                                             pad = 'same',
                                             nonlinearity = nl.rectify)
    first_pool = lasagne.layers.MaxPool2DLayer(first_conv, context_pool_rate)
    second_conv = lasagne.layers.Conv2DLayer(first_pool,
                                             context_number_of_convolving_filters,
                                             context_convolving_filter_size, 
                                             stride = 1,
                                             pad = 'same',
                                             nonlinearity = nl.rectify)
    second_pool = lasagne.layers.MaxPool2DLayer(second_conv, context_pool_rate)
    third_conv = lasagne.layers.Conv2DLayer(second_pool,
                                             context_number_of_convolving_filters,
                                             context_convolving_filter_size, 
                                             stride = 1,
                                             pad = 'same',
                                             nonlinearity = nl.rectify)
    third_pool = lasagne.layers.MaxPool2DLayer(third_conv, context_pool_rate)
    fc = lasagne.layers.DenseLayer(third_pool, 
                                       glimpse_output_size*recurrent_output_size, 
                                       nonlinearity = nl.rectify)
    output = lasagne.layers.ReshapeLayer(fc, (-1, glimpse_output_size, recurrent_output_size))
    return output