def get_data(self, tfrecord_dir, batch_size): # Read video data having labels A to T. self.dataset_trainAT = get_split_mfcc_lips('trainAT', tfrecord_dir) _, self.videos_train, self.labels_trainAT = load_batch_mfcc_lips( self.dataset_trainAT, batch_size=batch_size) # Read audio data having labels U to Z. self.dataset_trainUZ = get_split_mfcc_lips('trainUZ', tfrecord_dir) self.mfccs_train, _, self.labels_trainUZ = load_batch_mfcc_lips( self.dataset_trainUZ, batch_size=batch_size, is_training=False) # Read data for test. self.dataset_test = get_split_mfcc_lips('validation', tfrecord_dir) _, self.videos_test, self.labels_test = load_batch_mfcc_lips( self.dataset_test, batch_size=batch_size, is_training=False) # Read all the data with labels A to T once before the beginning # of training in order to do KNN later. self.all_mfccs, self.all_videos, all_labels = load_batch_mfcc_lips( self.dataset_trainAT, shuffle=False, batch_size=self.dataset_trainAT.num_samples) # Some methods in `TrainClassify` use `self.dataset_train`. # This is a small problem and should be changed. self.dataset_train = self.dataset_trainAT return self.dataset_trainAT
def get_data(self, tfrecord_dir, batch_size): self.dataset_train = get_split_mfcc_lips('trainAT', tfrecord_dir) _, self.videos_train, self.labels_train = load_batch_mfcc_lips( self.dataset_train, batch_size=batch_size) self.dataset_test = get_split_mfcc_lips('validation', tfrecord_dir) _, self.videos_test, self.labels_test = load_batch_mfcc_lips( self.dataset_test, batch_size=batch_size, is_training=False) return self.dataset_train
def get_data(self, split_name, tfrecord_dir, batch_size, shuffle): self.dataset = get_split_mfcc_lips(split_name, tfrecord_dir) if batch_size is None: batch_size = self.dataset.num_samples _, self.videos, self.labels = load_batch_mfcc_lips( self.dataset, batch_size=batch_size, shuffle=shuffle, is_training=False) return self.dataset