SYMBOL INDEX (178 symbols across 35 files) FILE: CUB-Hierarchy/encode_hierarchy.py function read_hierarchy (line 7) | def read_hierarchy(filename): function encode_class_names (line 48) | def encode_class_names(hierarchy, initial_labels): function save_hierarchy (line 78) | def save_hierarchy(hierarchy, filename): function plot_hierarchy (line 86) | def plot_hierarchy(hierarchy, filename): FILE: Cifar-Hierarchy/encode_hierarchy.py function read_hierarchy (line 7) | def read_hierarchy(filename): function encode_class_names (line 44) | def encode_class_names(hierarchy, initial_labels): function save_hierarchy (line 74) | def save_hierarchy(hierarchy, filename): function plot_hierarchy (line 82) | def plot_hierarchy(hierarchy, filename): FILE: class_hierarchy.py class ClassHierarchy (line 7) | class ClassHierarchy(object): method __init__ (line 10) | def __init__(self, parents, children): method _compute_heights (line 32) | def _compute_heights(self): method is_tree (line 46) | def is_tree(self): method all_hypernym_depths (line 55) | def all_hypernym_depths(self, id, use_min_depth = False): method all_hypernym_distances (line 81) | def all_hypernym_distances(self, id): method root_paths (line 103) | def root_paths(self, id): method lcs (line 123) | def lcs(self, a, b, use_min_depth = False): method shortest_path_length (line 143) | def shortest_path_length(self, a, b): method depth (line 159) | def depth(self, id, use_min_depth = False): method wup_similarity (line 179) | def wup_similarity(self, a, b): method lcs_height (line 199) | def lcs_height(self, a, b): method hierarchical_precision (line 211) | def hierarchical_precision(self, retrieved, labels, ks = [1, 10, 50, 1... method save (line 319) | def save(self, filename, is_a_relations = False): method from_file (line 338) | def from_file(cls, rel_file, is_a_relations = False, id_type = str): FILE: clr_callback.py class CyclicLR (line 6) | class CyclicLR(Callback): method __init__ (line 65) | def __init__(self, base_lr=0.001, max_lr=0.006, step_size=2000., mode=... method _reset (line 93) | def _reset(self, new_base_lr=None, new_max_lr=None, method clr (line 106) | def clr(self): method on_train_begin (line 114) | def on_train_begin(self, logs={}): method on_batch_end (line 122) | def on_batch_end(self, epoch, logs=None): FILE: compute_class_embedding.py function unitsphere_embedding (line 14) | def unitsphere_embedding(class_sim): function sim_approx (line 44) | def sim_approx(class_sim, num_dim = None): function euclidean_embedding (line 75) | def euclidean_embedding(class_dist, solver = 'general'): function mds (line 144) | def mds(class_dist, num_dim = None): FILE: datasets/__init__.py function get_data_generator (line 21) | def get_data_generator(dataset, data_root, classes = None): FILE: datasets/cars.py class CarsGenerator (line 8) | class CarsGenerator(FileDatasetGenerator): method __init__ (line 10) | def __init__(self, root_dir, classes = None, annotation_file = 'cars_a... FILE: datasets/cifar.py class CifarGenerator (line 9) | class CifarGenerator(TinyDatasetGenerator): method __init__ (line 12) | def __init__(self, root_dir, classes = None, reenumerate = False, cifa... FILE: datasets/common.py function tqdm (line 20) | def tqdm(it, **kwargs): class DataSequence (line 26) | class DataSequence(Sequence): method __init__ (line 29) | def __init__(self, data_generator, ids, labels, batch_size = 32, shuff... method __len__ (line 87) | def __len__(self): method __getitem__ (line 93) | def __getitem__(self, idx): method on_epoch_end (line 107) | def on_epoch_end(self): class FileDatasetGenerator (line 126) | class FileDatasetGenerator(object): method __init__ (line 129) | def __init__(self, root_dir, cropsize = (224, 224), default_target_siz... method _compute_stats (line 186) | def _compute_stats(self, mean = None, std = None): method flow_train (line 210) | def flow_train(self, batch_size = 32, include_labels = True, shuffle =... method flow_test (line 239) | def flow_test(self, batch_size = 32, include_labels = True, shuffle = ... method train_sequence (line 268) | def train_sequence(self, batch_size = 32, shuffle = True, target_size ... method test_sequence (line 301) | def test_sequence(self, batch_size = 32, shuffle = False, target_size ... method _flow (line 334) | def _flow(self, filenames, labels = None, batch_size = 32, shuffle = F... method compose_batch (line 380) | def compose_batch(self, filenames, cropsize = None, randcrop = False, ... method _load_image (line 435) | def _load_image(self, filename, target_size = None, randzoom = False): method _transform (line 475) | def _transform(self, img, normalize = True, method _load_and_transform (line 545) | def _load_and_transform(self, filename, target_size = None, normalize ... method labels_train (line 585) | def labels_train(self): method labels_test (line 596) | def labels_test(self): method num_classes (line 607) | def num_classes(self): method num_train (line 614) | def num_train(self): method num_test (line 621) | def num_test(self): method num_channels (line 628) | def num_channels(self): class TinyDatasetGenerator (line 635) | class TinyDatasetGenerator(object): method __init__ (line 638) | def __init__(self, X_train, X_test, y_train, y_test, method flow_train (line 673) | def flow_train(self, batch_size = 32, include_labels = True, shuffle =... method flow_test (line 696) | def flow_test(self, batch_size = 32, include_labels = True, shuffle = ... method train_sequence (line 719) | def train_sequence(self, batch_size = 32, shuffle = True, augment = Tr... method test_sequence (line 745) | def test_sequence(self, batch_size = 32, shuffle = False, augment = Fa... method compose_batch (line 771) | def compose_batch(self, indices, train, augment = False): method labels_train (line 800) | def labels_train(self): method labels_test (line 810) | def labels_test(self): method num_classes (line 820) | def num_classes(self): method num_train (line 827) | def num_train(self): method num_test (line 834) | def num_test(self): method num_channels (line 841) | def num_channels(self): function distort_color (line 848) | def distort_color(img, fast_mode=True, function random_brightness (line 896) | def random_brightness(img, max_delta=32./255.): function random_brightness_hsv (line 905) | def random_brightness_hsv(img, max_delta=32./255.): function random_hue (line 915) | def random_hue(img, max_delta=0.2): function random_saturation (line 926) | def random_saturation(img, low=0.5, high=1.5): function random_contrast (line 936) | def random_contrast(img, low=0.5, high=1.5): FILE: datasets/flowers.py class FlowersGenerator (line 8) | class FlowersGenerator(FileDatasetGenerator): method __init__ (line 10) | def __init__(self, root_dir, classes = None, img_dir = 'jpg', label_fi... FILE: datasets/ilsvrc.py class ILSVRCGenerator (line 14) | class ILSVRCGenerator(FileDatasetGenerator): method __init__ (line 16) | def __init__(self, root_dir, classes = None, mean = IMAGENET_MEAN, std... FILE: datasets/inat.py class INatGenerator (line 27) | class INatGenerator(FileDatasetGenerator): method __init__ (line 29) | def __init__(self, root_dir, train_file='train2018.json', val_file='va... method get_tuples_for_supercategory (line 96) | def get_tuples_for_supercategory(self, fname, image_folder, supercateg... FILE: datasets/nab.py class NABGenerator (line 7) | class NABGenerator(FileDatasetGenerator): method __init__ (line 9) | def __init__(self, root_dir, classes = None, img_dir = 'images', img_l... method train_sequence (line 96) | def train_sequence(self, batch_size = 32, shuffle = True, target_size ... FILE: datasets/subdirectory.py class SubDirectoryGenerator (line 8) | class SubDirectoryGenerator(FileDatasetGenerator): method __init__ (line 10) | def __init__(self, root_dir, classes = None, img_dir = '.', train_list... FILE: evaluate_classification_accuracy.py function train_and_predict (line 20) | def train_and_predict(data, model, layer = None, normalize = False, augm... function nn_classification (line 51) | def nn_classification(data, centroids, model, layer = None, custom_objec... function extract_predictions (line 74) | def extract_predictions(data, model, layer = None, custom_objects = {}, ... function evaluate (line 88) | def evaluate(y_pred, data_generator, hierarchy): function print_performance (line 110) | def print_performance(perf, metrics = METRICS): function str2bool (line 126) | def str2bool(v): FILE: evaluate_retrieval.py function tqdm (line 13) | def tqdm(it, **kwargs): function pairwise_retrieval (line 22) | def pairwise_retrieval(features, normalize = False, return_generator = T... function print_performance (line 76) | def print_performance(perf, metrics = METRICS): function write_performance (line 92) | def write_performance(perf, csv_file, prec_type = 'LCS_HEIGHT'): function plot_performance (line 105) | def plot_performance(perf, kmax = 100, prec_type = 'LCS_HEIGHT', clip_ah... function str2bool (line 144) | def str2bool(v): FILE: iNaturalist-Hierarchy/iNaturalist_hierarchies.py function generate_parent_child_pairs (line 4) | def generate_parent_child_pairs(path, supercategory=None): FILE: learn_center_loss.py function center_loss_model (line 17) | def center_loss_model(base_model, centroids): function transform_inputs (line 44) | def transform_inputs(X, y, num_classes): FILE: learn_classifier.py function transform_inputs (line 17) | def transform_inputs(X, y, num_classes, label_smoothing = 0): FILE: learn_devise.py function transform_inputs (line 16) | def transform_inputs(X, y, embedding): FILE: learn_image_embeddings.py function cls_model (line 16) | def cls_model(embed_model, num_classes, cls_base = None): function transform_inputs (line 48) | def transform_inputs(X, y, embedding, num_classes = None): FILE: learn_labelembedding.py function cross_entropy (line 17) | def cross_entropy(logit, prob): function labelembed_loss (line 21) | def labelembed_loss(out1, out2, tar, targets, tau = 2., alpha = 0.9, bet... function labelembed_model (line 40) | def labelembed_model(base_model, num_classes, **kwargs): function transform_inputs (line 59) | def transform_inputs(X, y, num_classes): FILE: models/DenseNet/densenet.py function preprocess_input (line 39) | def preprocess_input(x, data_format=None): function DenseNet (line 79) | def DenseNet(input_shape=None, depth=40, nb_dense_block=3, growth_rate=1... function DenseNetFCN (line 245) | def DenseNetFCN(input_shape, nb_dense_block=5, growth_rate=16, nb_layers... function DenseNetImageNet121 (line 366) | def DenseNetImageNet121(input_shape=None, function DenseNetImageNet169 (line 383) | def DenseNetImageNet169(input_shape=None, function DenseNetImageNet201 (line 400) | def DenseNetImageNet201(input_shape=None, function DenseNetImageNet264 (line 417) | def DenseNetImageNet264(input_shape=None, function DenseNetImageNet161 (line 434) | def DenseNetImageNet161(input_shape=None, function __conv_block (line 451) | def __conv_block(ip, nb_filter, bottleneck=False, dropout_rate=None, wei... function __dense_block (line 481) | def __dense_block(x, nb_layers, nb_filter, growth_rate, bottleneck=False... function __transition_block (line 515) | def __transition_block(ip, nb_filter, compression=1.0, weight_decay=1e-4): function __transition_up_block (line 537) | def __transition_up_block(ip, nb_filters, type='deconv', weight_decay=1E... function __create_dense_net (line 562) | def __create_dense_net(nb_classes, img_input, include_top, depth=40, nb_... function __create_fcn_dense_net (line 664) | def __create_fcn_dense_net(nb_classes, img_input, include_top, nb_dense_... FILE: models/DenseNet/densenet_fast.py function conv_block (line 15) | def conv_block(ip, nb_filter, dropout_rate=None, weight_decay=1E-4): function transition_block (line 37) | def transition_block(ip, nb_filter, dropout_rate=None, weight_decay=1E-4): function dense_block (line 64) | def dense_block(x, nb_layers, nb_filter, growth_rate, dropout_rate=None,... function create_dense_net (line 92) | def create_dense_net(nb_classes, img_dim, depth=40, nb_dense_block=3, gr... FILE: models/DenseNet/subpixel.py class SubPixelUpscaling (line 16) | class SubPixelUpscaling(Layer): method __init__ (line 55) | def __init__(self, scale_factor=2, data_format=None, **kwargs): method build (line 61) | def build(self, input_shape): method call (line 64) | def call(self, x, mask=None): method compute_output_shape (line 68) | def compute_output_shape(self, input_shape): method get_config (line 76) | def get_config(self): FILE: models/DenseNet/tensorflow_backend.py function depth_to_space (line 8) | def depth_to_space(input, scale, data_format=None): FILE: models/DenseNet/theano_backend.py function depth_to_space (line 11) | def depth_to_space(input, scale, data_format=None): FILE: models/cifar_pyramidnet.py function PyramidNet (line 31) | def PyramidNet(depth, alpha, bottleneck = True, FILE: models/cifar_resnet.py class ChannelPadding (line 28) | class ChannelPadding(Layer): method __init__ (line 40) | def __init__(self, padding=1, data_format=None, **kwargs): method compute_output_shape (line 46) | def compute_output_shape(self, input_shape): method call (line 57) | def call(self, inputs): method get_config (line 63) | def get_config(self): function simple_block (line 69) | def simple_block(input_tensor, filters, prefix, kernel_size = 3, stride ... function unit (line 128) | def unit(input_tensor, filters, n, prefix, kernel_size = 3, stride = 1, ... function SmallResNet (line 149) | def SmallResNet(n = 9, filters = [16, 32, 64], FILE: models/plainnet.py function PlainNet (line 5) | def PlainNet(output_dim, FILE: models/wide_residual_network.py function initial_conv (line 8) | def initial_conv(input): function expand_conv (line 19) | def expand_conv(init, base, k, strides=(1, 1)): function conv_block (line 39) | def conv_block(input, base, k=1, dropout=0.0): function create_wide_residual_network (line 60) | def create_wide_residual_network(input_dim, nb_classes=100, N=2, k=1, dr... FILE: plot_hierarchy.py function plot_hierarchy (line 9) | def plot_hierarchy(hierarchy, filename, class_names = None): FILE: plot_recall_precision.py function tqdm (line 14) | def tqdm(it, **kwargs): FILE: sgdr_callback.py class SGDR (line 6) | class SGDR(Callback): method __init__ (line 34) | def __init__(self, min_lr=0.0, max_lr=0.05, base_epochs=10, mul_epochs... method _reset (line 48) | def _reset(self, new_min_lr=None, new_max_lr=None, method sgdr (line 63) | def sgdr(self): method on_train_begin (line 68) | def on_train_begin(self, logs=None): method on_epoch_end (line 75) | def on_epoch_end(self, epoch, logs=None): FILE: utils.py function squared_distance (line 34) | def squared_distance(y_true, y_pred): function mean_distance (line 39) | def mean_distance(y_true, y_pred): function inv_correlation (line 44) | def inv_correlation(y_true, y_pred): function top_k_acc (line 49) | def top_k_acc(k): function nn_accuracy (line 57) | def nn_accuracy(embedding, dot_prod_sim = False, k = 1): function devise_ranking_loss (line 103) | def devise_ranking_loss(embedding, margin = 0.1): function l2norm (line 125) | def l2norm(x): function build_network (line 130) | def build_network(num_outputs, architecture, classification = False, no_... function get_custom_objects (line 279) | def get_custom_objects(architecture): function get_lr_schedule (line 288) | def get_lr_schedule(schedule, num_samples, batch_size, schedule_args = {}): function add_lr_schedule_arguments (line 402) | def add_lr_schedule_arguments(parser): class TemplateModelCheckpoint (line 422) | class TemplateModelCheckpoint(keras.callbacks.ModelCheckpoint): method __init__ (line 425) | def __init__(self, tpl_model, filepath, *args, **kwargs): method on_epoch_end (line 431) | def on_epoch_end(self, epoch, logs=None):