SYMBOL INDEX (58 symbols across 12 files)

FILE: configure.py
  function configure (line 7) | def configure():

FILE: evaluation.py
  function one_hot (line 26) | def one_hot(label):
  function MHD_3D (line 34) | def MHD_3D(pred, label):
  function Evaluate (line 69) | def Evaluate(label_dir, pred_dir, pred_id, patch_size, checkpoint_num,

FILE: generate_tfrecord.py
  function _float_feature (line 15) | def _float_feature(value):
  function _bytes_feature (line 19) | def _bytes_feature(value):
  function _int64_feature (line 23) | def _int64_feature(value):
  function cut_edge (line 27) | def cut_edge(data):
  function convert_labels (line 72) | def convert_labels(labels):
  function load_subject (line 89) | def load_subject(raw_data_dir, subject_id):
  function prepare_validation (line 120) | def prepare_validation(cutted_image, patch_size, overlap_stepsize):
  function write_training_examples (line 149) | def write_training_examples(T1, T2, label, original_shape, cut_size, out...
  function write_validation_examples (line 178) | def write_validation_examples(T1, T2, label, patch_size, cut_size, overl...
  function write_prediction_examples (line 221) | def write_prediction_examples(T1, T2, patch_size, cut_size, overlap_step...
  function generate_files (line 260) | def generate_files(raw_data_dir, output_path, valid_id, pred_id, patch_s...

FILE: input_fn.py
  function get_filenames (line 12) | def get_filenames(data_dir, mode, valid_id, pred_id, overlap_step, patch...
  function decode_train (line 39) | def decode_train(serialized_example):
  function decode_valid (line 71) | def decode_valid(serialized_example):
  function decode_pred (line 99) | def decode_pred(serialized_example):
  function crop_image (line 125) | def crop_image(image_T1, image_T2, label, cut_size):
  function normalize_image (line 140) | def normalize_image(image_T1, image_T2, label):
  function input_function (line 154) | def input_function(data_dir, mode, patch_size, batch_size, buffer_size, ...

FILE: main.py
  function main (line 12) | def main(_):

FILE: model.py
  class Model (line 15) | class Model(object):
    method __init__ (line 17) | def __init__(self, conf):
    method _model_fn (line 21) | def _model_fn(self, features, labels, mode):
    method train (line 103) | def train(self):
    method predict (line 164) | def predict(self):

FILE: network.py
  class Network (line 10) | class Network(object):
    method __init__ (line 12) | def __init__(self, conf):
    method __call__ (line 20) | def __call__(self, inputs, training):
    method _build_network (line 38) | def _build_network(self, inputs, training):
    method _output_block_layer (line 97) | def _output_block_layer(self, inputs, training):
    method _encoding_block_layer (line 113) | def _encoding_block_layer(self, inputs, filters, block_fn,
    method _att_decoding_block_layer (line 148) | def _att_decoding_block_layer(self, inputs, skip_inputs, filters,
    method _decoding_block_layer (line 186) | def _decoding_block_layer(self, inputs, skip_inputs, filters,
    method _residual_block (line 223) | def _residual_block(self, inputs, filters, training,
    method _attention_block (line 266) | def _attention_block(self, inputs, filters, training,

FILE: utils/DiceRatio.py
  function dice_ratio (line 3) | def dice_ratio(pred, label):

FILE: utils/HausdorffDistance.py
  function HausdorffDist (line 14) | def HausdorffDist(A,B):
  function ModHausdorffDist (line 37) | def ModHausdorffDist(A,B):

FILE: utils/attention.py
  function multihead_attention_3d (line 9) | def multihead_attention_3d(inputs, total_key_filters, total_value_filters,
  function compute_qkv_3d (line 69) | def compute_qkv_3d(inputs, total_key_filters, total_value_filters, layer...
  function split_heads_3d (line 101) | def split_heads_3d(x, num_heads):
  function split_last_dimension (line 115) | def split_last_dimension(x, n):
  function global_attention_3d (line 137) | def global_attention_3d(q, k, v, training, name=None):
  function reshape_range (line 169) | def reshape_range(tensor, i, j, shape):
  function flatten_3d (line 179) | def flatten_3d(x):
  function scatter_3d (line 189) | def scatter_3d(x, shape):
  function dot_product_attention (line 197) | def dot_product_attention(q, k, v, bias, training, dropout_rate=0.0, nam...
  function combine_heads_3d (line 231) | def combine_heads_3d(x):
  function combine_last_two_dimensions (line 244) | def combine_last_two_dimensions(x):

FILE: utils/basic_ops.py
  function Pool3d (line 12) | def Pool3d(inputs, kernel_size, strides):
  function Deconv3D (line 22) | def Deconv3D(inputs, filters, kernel_size, strides, use_bias=False):
  function Conv3D (line 35) | def Conv3D(inputs, filters, kernel_size, strides, use_bias=False):
  function Dilated_Conv3D (line 48) | def Dilated_Conv3D(inputs, filters, kernel_size, dilation_rate, use_bias...
  function BN_ReLU (line 62) | def BN_ReLU(inputs, training):

FILE: visualize.py
  function Visualize (line 28) | def Visualize(label_dir, pred_dir, pred_id, patch_size, checkpoint_num,