SYMBOL INDEX (144 symbols across 34 files)

FILE: global-loc/app/build-index.cc
  function main (line 18) | int main(int argc, char** argv) {

FILE: global-loc/app/time-query.cc
  function main (line 23) | int main(int argc, char** argv) {

FILE: global-loc/include/global-loc/kd-tree-index.h
  type Nabo (line 18) | typedef Nabo::NearestNeighbourSearch<float> NNSearch;
  function descriptor_size_ (line 28) | KDTreeIndex(const unsigned descriptor_size): descriptor_size_(descriptor...
  function AddDescriptors (line 32) | void AddDescriptors(const DescriptorMatrixType& descriptors) {

FILE: global-loc/include/global-loc/pca-reduction.h
  function class (line 6) | class PcaReduction {

FILE: global-loc/include/global-loc/place-retrieval.h
  function class (line 17) | class PlaceRetrieval {

FILE: global-loc/include/global-loc/tensorflow-net.h
  function class (line 24) | class TensorflowNet {
  function PerformInference (line 60) | void PerformInference(const cv::Mat& image, DescriptorType* descriptor) {
  function descriptor_size (line 98) | unsigned descriptor_size() {

FILE: global-loc/test/test_build_index.cc
  function main (line 11) | int main () {

FILE: global-loc/test/test_inference.cc
  function main (line 10) | int main() {

FILE: global-loc/test/test_opencv.cc
  function main (line 8) | int main () {

FILE: global-loc/test/test_query_index.cc
  function main (line 14) | int main () {

FILE: global-loc/test/test_tensorflow.cc
  function main (line 9) | int main()

FILE: notebooks/utils.py
  function plot_imgs (line 6) | def plot_imgs(imgs, titles=None, cmap='brg', ylabel='', normalize=True, ...
  function draw_datches (line 40) | def draw_datches(img1, kp1, img2, kp2, matches, color=None, kp_radius=5,

FILE: retrievalnet/downloading/download_google_landmarks.py
  function parse_data (line 13) | def parse_data(data_file, num=None):
  function download_image (line 23) | def download_image(key_url, output_dir):
  function download_func (line 72) | def download_func(key_url):

FILE: retrievalnet/retrievalnet/datasets/__init__.py
  function get_dataset (line 1) | def get_dataset(name):
  function _module_to_class (line 6) | def _module_to_class(name):

FILE: retrievalnet/retrievalnet/datasets/base_dataset.py
  class BaseDataset (line 7) | class BaseDataset(metaclass=ABCMeta):
    method _init_dataset (line 22) | def _init_dataset(self, **config):
    method _get_data (line 41) | def _get_data(self, dataset, split_name, **config):
    method get_tf_datasets (line 62) | def get_tf_datasets(self):
    method get_training_set (line 71) | def get_training_set(self):
    method get_validation_set (line 80) | def get_validation_set(self):
    method get_test_set (line 89) | def get_test_set(self):
    method __init__ (line 98) | def __init__(self, **config):
    method _get_set_generator (line 114) | def _get_set_generator(self, set_name):

FILE: retrievalnet/retrievalnet/datasets/descriptor_distillation.py
  class DescriptorDistillation (line 11) | class DescriptorDistillation(BaseDataset):
    method _init_dataset (line 25) | def _init_dataset(self, **config):
    method _get_data (line 52) | def _get_data(self, paths, split_name, **config):

FILE: retrievalnet/retrievalnet/datasets/nclt.py
  class Nclt (line 11) | class Nclt(BaseDataset):
    method _init_dataset (line 24) | def _init_dataset(self, **config):
    method get_pose_file (line 65) | def get_pose_file(sequence):
    class Undistort (line 72) | class Undistort(object):
      method __init__ (line 73) | def __init__(self, fin, scale=1.0, fmask=None):
      method undistort (line 99) | def undistort(self, img, crop=True):
    method _get_data (line 109) | def _get_data(self, paths, split_name, **config):

FILE: retrievalnet/retrievalnet/evaluation.py
  function normalize (line 6) | def normalize(l, axis=-1):
  function is_gt_match_3D (line 10) | def is_gt_match_3D(query_poses, ref_poses, distance_thresh, angle_thresh):
  function is_gt_match_2D (line 21) | def is_gt_match_2D(query_poses, ref_poses, distance_thresh, angle_thresh):
  function retrieval (line 31) | def retrieval(ref_descriptors, query_descriptors, max_num_nn, pca_dim=0):
  function compute_tp_fp (line 42) | def compute_tp_fp(ref_descriptors, query_descriptors,
  function compute_recall (line 53) | def compute_recall(*arg, **kwarg):

FILE: retrievalnet/retrievalnet/models/__init__.py
  function get_model (line 1) | def get_model(name):
  function _module_to_class (line 6) | def _module_to_class(name):

FILE: retrievalnet/retrievalnet/models/backbones/mobilenet_v2.py
  function mobilenet (line 85) | def mobilenet(input_tensor,
  function mobilenet_base (line 158) | def mobilenet_base(input_tensor, depth_multiplier=1.0, **kwargs):
  function training_scope (line 165) | def training_scope(**kwargs):

FILE: retrievalnet/retrievalnet/models/backbones/resnet_v1.py
  function bottleneck (line 68) | def bottleneck(inputs,
  function resnet_v1 (line 131) | def resnet_v1(inputs,
  function resnet_v1_block (line 244) | def resnet_v1_block(scope, base_depth, num_units, stride):
  function resnet_v1_50 (line 268) | def resnet_v1_50(inputs,
  function resnet_v1_101 (line 292) | def resnet_v1_101(inputs,
  function resnet_v1_152 (line 316) | def resnet_v1_152(inputs,
  function resnet_v1_200 (line 340) | def resnet_v1_200(inputs,

FILE: retrievalnet/retrievalnet/models/backbones/utils/conv_blocks.py
  function _fixed_padding (line 24) | def _fixed_padding(inputs, kernel_size, rate=1):
  function _make_divisible (line 50) | def _make_divisible(v, divisor, min_value=None):
  function _split_divisible (line 60) | def _split_divisible(num, num_ways, divisible_by=8):
  function _v1_compatible_scope_naming (line 79) | def _v1_compatible_scope_naming(scope):
  function split_separable_conv2d (line 92) | def split_separable_conv2d(input_tensor,
  function expand_input_by_factor (line 158) | def expand_input_by_factor(n, divisible_by=8):
  function expanded_conv (line 163) | def expanded_conv(input_tensor,
  function split_conv (line 315) | def split_conv(input_tensor,

FILE: retrievalnet/retrievalnet/models/backbones/utils/mobilenet.py
  function apply_activation (line 32) | def apply_activation(x, name=None, activation_fn=None):
  function _fixed_padding (line 36) | def _fixed_padding(inputs, kernel_size, rate=1):
  function _make_divisible (line 62) | def _make_divisible(v, divisor, min_value=None):
  function _set_arg_scope_defaults (line 73) | def _set_arg_scope_defaults(defaults):
  function depth_multiplier (line 97) | def depth_multiplier(output_params,
  function op (line 112) | def op(opfunc, **params):
  class NoOpScope (line 117) | class NoOpScope(object):
    method __enter__ (line 120) | def __enter__(self):
    method __exit__ (line 123) | def __exit__(self, exc_type, exc_value, traceback):
  function safe_arg_scope (line 127) | def safe_arg_scope(funcs, **kwargs):
  function mobilenet_base (line 149) | def mobilenet_base(  # pylint: disable=invalid-name
  function _scope_all (line 298) | def _scope_all(scope, default_scope=None):
  function mobilenet (line 305) | def mobilenet(inputs,
  function global_pool (line 389) | def global_pool(input_tensor, pool_op=tf.nn.avg_pool):
  function training_scope (line 415) | def training_scope(is_training=True,

FILE: retrievalnet/retrievalnet/models/backbones/utils/resnet_utils.py
  class Block (line 46) | class Block(collections.namedtuple('Block', ['scope', 'unit_fn', 'args'])):
  function subsample (line 59) | def subsample(inputs, factor, scope=None):
  function conv2d_same (line 77) | def conv2d_same(inputs, num_outputs, kernel_size, stride, rate=1, scope=...
  function stack_blocks_dense (line 126) | def stack_blocks_dense(net, blocks, output_stride=None,
  function resnet_arg_scope (line 222) | def resnet_arg_scope(weight_decay=0.0001,

FILE: retrievalnet/retrievalnet/models/base_model.py
  class Mode (line 8) | class Mode:
  class BaseModel (line 14) | class BaseModel(metaclass=ABCMeta):
    method _model (line 42) | def _model(self, inputs, mode, **config):
    method _loss (line 65) | def _loss(self, outputs, inputs, **config):
    method _metrics (line 81) | def _metrics(self, outputs, inputs, **config):
    method __init__ (line 97) | def __init__(self, data={}, n_gpus=1, data_shape=None, **config):
    method _gpu_tower (line 124) | def _gpu_tower(self, data, mode):
    method _train_graph (line 173) | def _train_graph(self, data):
    method _eval_graph (line 201) | def _eval_graph(self, data):
    method _pred_graph (line 207) | def _pred_graph(self, data):
    method _build_graph (line 213) | def _build_graph(self):
    method train (line 271) | def train(self, iterations, validation_interval=100, output_dir=None,
    method predict (line 305) | def predict(self, data, keys='*', batch=False):
    method evaluate (line 325) | def evaluate(self, dataset, max_iterations=None, mute=False):
    method _checkpoint_var_search (line 357) | def _checkpoint_var_search(self, checkpoint_path):
    method load (line 383) | def load(self, checkpoint_path, flexible_restore=True):
    method save (line 405) | def save(self, checkpoint_path):
    method close (line 411) | def close(self):
    method __enter__ (line 414) | def __enter__(self):
    method __exit__ (line 417) | def __exit__(self, *args):

FILE: retrievalnet/retrievalnet/models/delf.py
  class Delf (line 9) | class Delf(BaseModel):
    method tower (line 25) | def tower(image, mode, config):
    method _model (line 49) | def _model(self, inputs, mode, **config):
    method _loss (line 55) | def _loss(self, outputs, inputs, **config):
    method _metrics (line 58) | def _metrics(self, outputs, inputs, **config):

FILE: retrievalnet/retrievalnet/models/delf_triplets.py
  class DelfTriplets (line 8) | class DelfTriplets(BaseModel):
    method _model (line 29) | def _model(self, inputs, mode, **config):
    method _loss (line 40) | def _loss(self, outputs, inputs, **config):
    method _metrics (line 44) | def _metrics(self, outputs, inputs, **config):

FILE: retrievalnet/retrievalnet/models/layers.py
  function image_normalization (line 5) | def image_normalization(image, pixel_value_offset=128.0, pixel_value_sca...
  function delf_attention (line 9) | def delf_attention(feature_map, config, is_training, arg_scope=None):
  function vlad (line 31) | def vlad(feature_map, config, is_training):
  function dimensionality_reduction (line 68) | def dimensionality_reduction(descriptor, config):
  function triplet_loss (line 84) | def triplet_loss(outputs, inputs, **config):
  function vlad_legacy (line 98) | def vlad_legacy(inputs, num_clusters, assign_weight_initializer=None,
  function matconvnetNormalize (line 135) | def matconvnetNormalize(inputs, epsilon):

FILE: retrievalnet/retrievalnet/models/mobilenetvlad.py
  class Mobilenetvlad (line 9) | class Mobilenetvlad(BaseModel):
    method _model (line 27) | def _model(self, inputs, mode, **config):
    method _descriptor_l2_error (line 48) | def _descriptor_l2_error(self, inputs, outputs):
    method _loss (line 52) | def _loss(self, outputs, inputs, **config):
    method _metrics (line 55) | def _metrics(self, outputs, inputs, **config):

FILE: retrievalnet/retrievalnet/models/netvlad_original.py
  class NetvladOriginal (line 8) | class NetvladOriginal(BaseModel):
    method _model (line 20) | def _model(self, inputs, mode, **config):
    method _loss (line 82) | def _loss(self, outputs, inputs, **config):
    method _metrics (line 85) | def _metrics(self, outputs, inputs, **config):

FILE: retrievalnet/retrievalnet/models/netvlad_triplets.py
  class NetvladTriplets (line 9) | class NetvladTriplets(BaseModel):
    method tower (line 29) | def tower(image, mode, config):
    method _model (line 47) | def _model(self, inputs, mode, **config):
    method _loss (line 58) | def _loss(self, outputs, inputs, **config):
    method _metrics (line 62) | def _metrics(self, outputs, inputs, **config):

FILE: retrievalnet/retrievalnet/train.py
  function train (line 20) | def train(config, n_iter, output_dir, checkpoint_name='model.ckpt'):
  function set_seed (line 38) | def set_seed(seed):
  function _init_graph (line 44) | def _init_graph(config, with_dataset=False):
  function _cli_train (line 61) | def _cli_train(config, output_dir):

FILE: retrievalnet/retrievalnet/utils/stdout_capturing.py
  function flush (line 16) | def flush():
  function capture_outputs (line 30) | def capture_outputs(filename):

FILE: retrievalnet/retrievalnet/utils/tools.py
  function dict_update (line 4) | def dict_update(d, u):