def train(run_name, speaker, start_epoch, stop_epoch, img_c, img_w, img_h, frames_n, absolute_max_string_len, minibatch_size):
DATASET_DIR = os.path.join(CURRENT_PATH, speaker, 'datasets')
OUTPUT_DIR = os.path.join(CURRENT_PATH, speaker, 'results')
LOG_DIR = os.path.join(CURRENT_PATH, speaker, 'logs')
curriculum = Curriculum(curriculum_rules)
lip_gen = BasicGenerator(dataset_path=DATASET_DIR,
minibatch_size=minibatch_size,
img_c=img_c, img_w=img_w, img_h=img_h, frames_n=frames_n,
absolute_max_string_len=absolute_max_string_len,
curriculum=curriculum, start_epoch=start_epoch).build()
lipnet = LipNet(img_c=img_c, img_w=img_w, img_h=img_h, frames_n=frames_n,
absolute_max_string_len=absolute_max_string_len, output_size=lip_gen.get_output_size())
lipnet.summary()
adam = Adam(lr=0.0001, beta_1=0.9, beta_2=0.999, epsilon=1e-08)
# the loss calc occurs elsewhere, so use a dummy lambda func for the loss
lipnet.model.compile(loss={'ctc': lambda y_true, y_pred: y_pred}, optimizer=adam)
# load weight if necessary
if start_epoch > 0:
weight_file = os.path.join(OUTPUT_DIR, os.path.join(run_name, 'weights%02d.h5' % (start_epoch - 1)))
lipnet.model.load_weights(weight_file)
if start_epoch < 1:
weight_file = os.path.join(OUTPUT_DIR, os.path.join(CURRENT_PATH,speaker,'results', 'weightsa.h5'))
lipnet.model.load_weights(weight_file)
spell = Spell(path=PREDICT_DICTIONARY)
decoder = Decoder(greedy=PREDICT_GREEDY, beam_width=PREDICT_BEAM_WIDTH,
postprocessors=[labels_to_text, spell.sentence])
# define callbacks
statistics = Statistics(lipnet, lip_gen.next_val(), decoder, 256, output_dir=os.path.join(OUTPUT_DIR, run_name))
visualize = Visualize(os.path.join(OUTPUT_DIR, run_name), lipnet, lip_gen.next_val(), decoder, num_display_sentences=minibatch_size)
tensorboard = TensorBoard(log_dir=os.path.join(LOG_DIR, run_name))
csv_logger = CSVLogger(os.path.join(LOG_DIR, "{}-{}.csv".format('training',run_name)), separator=',', append=True)
checkpoint = ModelCheckpoint(os.path.join(OUTPUT_DIR, run_name, "weights{epoch:02d}.h5"), monitor='val_loss', save_weights_only=True, mode='auto', period=1)
lipnet.model.fit_generator(generator=lip_gen.next_train(),
steps_per_epoch=lip_gen.default_training_steps, epochs=stop_epoch,
validation_data=lip_gen.next_val(), validation_steps=lip_gen.default_validation_steps,
callbacks=[checkpoint, statistics, visualize, lip_gen, tensorboard, csv_logger],
initial_epoch=start_epoch,
verbose=1,
max_q_size=5,
workers=2,
pickle_safe=True)
def stats(weight_path, dataset_path, img_c, img_w, img_h, frames_n,
absolute_max_string_len, minibatch_size):
lip_gen = BasicGenerator(
dataset_path=dataset_path,
minibatch_size=minibatch_size,
img_c=img_c,
img_w=img_w,
img_h=img_h,
frames_n=frames_n,
absolute_max_string_len=absolute_max_string_len).build()
lipnet = LipNet(img_c=img_c,
img_w=img_w,
img_h=img_h,
frames_n=frames_n,
absolute_max_string_len=absolute_max_string_len,
output_size=lip_gen.get_output_size())
adam = Adam(lr=0.0001, beta_1=0.9, beta_2=0.999, epsilon=1e-08)
lipnet.model.compile(loss={
'ctc': lambda y_true, y_pred: y_pred
},
optimizer=adam)
lipnet.model.load_weights(weight_path)
spell = Spell(path=PREDICT_DICTIONARY)
decoder = Decoder(greedy=PREDICT_GREEDY,
beam_width=PREDICT_BEAM_WIDTH,
postprocessors=[labels_to_text, spell.sentence])
statistics = Statistics(lipnet,
lip_gen.next_val(),
decoder,
256,
output_dir=None)
lip_gen.on_train_begin()
statistics.on_epoch_end(0)