googlenet.py 文件源码

def inceptionE(self, input_layer, nfilt, pool_mode):
        # Corresponds to figure 7 in the paper
        l1 = self.bn_conv(input_layer, num_filters=nfilt[0][0], filter_size=1)

        l2 = self.bn_conv(input_layer, num_filters=nfilt[1][0], filter_size=1)
        l2a = self.bn_conv(l2, num_filters=nfilt[1][1], filter_size=(1, 3), pad=(0, 1))
        l2b = self.bn_conv(l2, num_filters=nfilt[1][2], filter_size=(3, 1), pad=(1, 0))

        l3 = self.bn_conv(input_layer, num_filters=nfilt[2][0], filter_size=1)
        l3 = self.bn_conv(l3, num_filters=nfilt[2][1], filter_size=3, pad=1)
        l3a = self.bn_conv(l3, num_filters=nfilt[2][2], filter_size=(1, 3), pad=(0, 1))
        l3b = self.bn_conv(l3, num_filters=nfilt[2][3], filter_size=(3, 1), pad=(1, 0))

        l4 = Pool2DLayer(
            input_layer, pool_size=3, stride=1, pad=1, mode=pool_mode)

        l4 = self.bn_conv(l4, num_filters=nfilt[3][0], filter_size=1)

        return ConcatLayer([l1, l2a, l2b, l3a, l3b, l4])