adgm.py 文件源码

def encoder(self, x):
        with tf.variable_scope('encoder'):
            net = resnet_utils.conv2d_same(x, 64, 7, stride=2, scope='conv1')
            net = tf.pad(net, [[0, 0], [1, 1], [1, 1], [0, 0]])
            x = slim.max_pool2d(net, [3, 3], stride=2, padding='VALID', scope='pool1')
            x_features_all, _ = resnet_v1.resnet_v1(x,
                                                self._blocks_encoder,
                                                global_pool=False,
                                                include_root_block=False,
                                                scope=self._resnet_scope)
            x_features_all = tf.reduce_mean(x_features_all, axis=[1, 2])
            x_features_labeled, x_features_unlabeled = tf.split(x_features_all, 2)

        x_features_tiled = tf.tile(x_features_unlabeled, [self._num_classes, 1])  # (100, 256) --> (2100, 256)
        x_features = tf.concat([x_features_labeled, x_features_tiled], 0) # (2100, 256) --> (2200, 256)
        return x_features