def train(gpu_num=None, with_generator=False, show_info=True):
print('network creating ... ', end='', flush=True)
network = UNetPP(INPUT_IMAGE_SHAPE, start_filter=START_FILTER, depth=DEPTH, class_num=CLASS_NUM)
print('... created')
if show_info:
network.plot_model_summary('../model_plot.png')
network.show_model_summary()
if isinstance(gpu_num, int):
model = network.get_parallel_model(gpu_num, with_comple=True)
else:
model = network.get_model(with_comple=True)
model_filename = os.path.join(DIR_MODEL, File_MODEL)
callbacks = [ KC.TensorBoard()
, HistoryCheckpoint(filepath='LearningCurve_{history}.png'
, verbose=1
, period=10
)
, KC.ModelCheckpoint(filepath=model_filename
, verbose=1
, save_weights_only=True
#, save_best_only=True
, period=10
)
]
print('data generator creating ... ', end='', flush=True)
train_generator = DataGenerator(DIR_INPUTS, DIR_TEACHERS, INPUT_IMAGE_SHAPE)
print('... created')
if with_generator:
train_data_num = train_generator.data_size()
#valid_data_num = train_generator.valid_data_size()
his = model.fit_generator(train_generator.generator(batch_size=BATCH_SIZE)
, steps_per_epoch=math.ceil(train_data_num / BATCH_SIZE)
, epochs=EPOCHS
, verbose=1
, use_multiprocessing=True
, callbacks=callbacks
#, validation_data=valid_generator
#, validation_steps=math.ceil(valid_data_num / BATCH_SIZE)
)
else:
print('data generateing ... ') #, end='', flush=True)
inputs, teachers = train_generator.generate_data()
print('... generated')
history = model.fit(inputs, teachers, batch_size=BATCH_SIZE, epochs=EPOCHS
, shuffle=True, verbose=1, callbacks=callbacks)
print('model saveing ... ', end='', flush=True)
model.save_weights(model_filename)
print('... saved')
print('learning_curve saveing ... ', end='', flush=True)
save_learning_curve(history)
print('... saved')
def train(gpu_num=None,
with_generator=False,
load_model=False,
show_info=True):
print('network creating ... ', end='', flush=True)
network = M2Det(INPUT_IMAGE_SHAPE, BATCH_SIZE, class_num=CLASS_NUM)
print('... created')
if show_info:
network.plot_model_summary('../model_plot.png')
network.show_model_summary()
if isinstance(gpu_num, int):
model = network.get_parallel_model(gpu_num, with_compile=True)
else:
model = network.get_model(with_compile=True)
model_filename = os.path.join(DIR_MODEL, FILE_MODEL)
callbacks = [
KC.TensorBoard(),
HistoryCheckpoint(filepath='LearningCurve_{history}.png',
verbose=1,
period=10),
KC.ModelCheckpoint(filepath=model_filename,
verbose=1,
save_weights_only=True,
save_best_only=True,
period=10)
]
if load_model:
print('loading weghts ... ', end='', flush=True)
model.load_weights(model_filename)
print('... loaded')
print('data generating ...', end='', flush=True)
priors = network.get_prior_boxes()
bbox_util = BBoxUtility(CLASS_NUM, priors)
train_generator = DataGenerator(DIR_TRAIN_INPUTS,
DIR_TRAIN_TEACHERS,
bbox_util,
INPUT_IMAGE_SHAPE,
with_norm=WITH_NORM)
valid_generator = DataGenerator(DIR_VALID_INPUTS,
DIR_VALID_TEACHERS,
bbox_util,
INPUT_IMAGE_SHAPE,
with_norm=WITH_NORM)
print('... created')
if with_generator:
train_data_num = train_generator.data_size()
valid_data_num = valid_generator.data_size()
history = model.fit_generator(
train_generator.generator(batch_size=BATCH_SIZE),
steps_per_epoch=math.ceil((train_data_num / BATCH_SIZE) * 2),
epochs=EPOCHS,
verbose=1,
use_multiprocessing=True,
callbacks=callbacks,
validation_data=valid_generator.generator(batch_size=BATCH_SIZE),
validation_steps=math.ceil(valid_data_num / BATCH_SIZE))
else:
print('data generateing ... ') #, end='', flush=True)
train_inputs, train_teachers = train_generator.generate_data(
batch_size=BATCH_SIZE)
valid_data = valid_generator.generate_data(batch_size=BATCH_SIZE)
print('... generated')
history = model.fit(train_inputs,
train_teachers,
batch_size=BATCH_SIZE,
epochs=EPOCHS,
validation_data=valid_data,
shuffle=True,
verbose=1,
callbacks=callbacks)
print('model saveing ... ', end='', flush=True)
model.save_weights(model_filename)
print('... saved')
print('learning_curve saveing ... ', end='', flush=True)
save_learning_curve(history)
print('... saved')
def train(gpu_num=None,
with_generator=False,
load_model=False,
show_info=True):
print('network creating ... ') #, end='', flush=True)
network = UNet(INPUT_IMAGE_SHAPE, CLASS_NUM)
print('... created')
model = network.model()
if show_info:
model.summary()
if isinstance(gpu_num, int):
model = multi_gpu_model(model, gpus=gpu_num)
model.compile(optimizer='adam',
loss=DiceLossByClass(INPUT_IMAGE_SHAPE,
CLASS_NUM).dice_coef_loss)
model_filename = os.path.join(DIR_MODEL, File_MODEL)
callbacks = [
KC.TensorBoard(),
HistoryCheckpoint(filepath='LearningCurve_{history}.png',
verbose=1,
period=10),
KC.ModelCheckpoint(filepath=model_filename,
verbose=1,
save_weights_only=True,
save_best_only=True,
period=10)
]
if load_model:
print('loading weghts ... ', end='', flush=True)
model.load_weights(model_filename)
print('... loaded')
print('data generating ...', end='', flush=True)
train_generator = DataGenerator(DIR_TRAIN_INPUTS,
DIR_TRAIN_TEACHERS,
INPUT_IMAGE_SHAPE,
include_padding=(PADDING, PADDING))
valid_generator = DataGenerator(DIR_VALID_INPUTS,
DIR_VALID_TEACHERS,
INPUT_IMAGE_SHAPE,
include_padding=(PADDING, PADDING))
print('... created')
if with_generator:
train_data_num = train_generator.data_size()
history = model.fit_generator(
train_generator.generator(batch_size=BATCH_SIZE),
steps_per_epoch=math.ceil(train_data_num / BATCH_SIZE),
epochs=EPOCHS,
verbose=1,
use_multiprocessing=True,
callbacks=callbacks,
validation_data=valid_generator.generator(batch_size=BATCH_SIZE),
validation_steps=math.ceil(valid_data_num / BATCH_SIZE))
else:
print('data generateing ... ') #, end='', flush=True)
inputs, teachers = train_generator.generate_data()
valid_data = valid_generator.generate_data()
print('... generated')
history = model.fit(inputs,
teachers,
batch_size=BATCH_SIZE,
epochs=EPOCHS,
validation_data=valid_data,
shuffle=True,
verbose=1,
callbacks=callbacks)
print('model saveing ... ', end='', flush=True)
model.save_weights(model_filename)
print('... saved')
print('learning_curve saveing ... ', end='', flush=True)
save_learning_curve(history)
print('... saved')
