dcn.py 文件源码

def extract_features(inputs, k_idxs, map_h):
    """Extract top k fine features

       NOTE.
            do not use tf.image.extract_glimpse ops to get input patches
            (cf. https://github.com/tensorflow/tensorflow/issues/2134)
    """

    def _extract_feature(inputs, idxs):

        idxs = tf.expand_dims(idxs,1)

        idx_i = tf.floordiv(idxs, map_h)
        idx_j = tf.mod(idxs, map_h)

        # NOTE: the below origins are starting points, not center!
        origin_i = 2*(2*idx_i+1)+3 - 5 + 2
        origin_j = 2*(2*idx_j+1)+3 - 5 + 2

        origin_centers = tf.concat(1,[origin_i,origin_j])

        # NOTE: size also depends on the architecture
        #patches = tf.image.extract_glimpse(inputs, size=[14,14], offsets=origin_centers, 
        #                                   centered=False, normalized=False)
        patches = extract_patches(inputs, size=[14,14], offsets=origin_centers)

        #fine_features = fine_layers(patches)
        fine_features = []

        src_idxs = tf.concat(1,[idx_i,idx_j])

        return fine_features, src_idxs, patches

    k_features = []
    k_src_idxs = []
    k_patches = []
    for i in xrange(N_PATCHES):
        fine_feature, src_idx, patches = _extract_feature(inputs,k_idxs[:,i])
        k_features.append(fine_feature)
        k_src_idxs.append(src_idx)
        k_patches.append(patches)


    concat_patches = tf.concat(0,k_patches)
    concat_k_features = fine_layers(concat_patches)
    k_features = tf.split(0,N_PATCHES,concat_k_features)

    return k_features, k_src_idxs, k_patches