def _main():
epoch_end_first = 20 #30
epoch_end_final = 2 #60
model_name = 'xx'
log_dir = 'logs/000/'
model_path = 'model_data/new_small_mobilenets2_trained_weights_final.h5'
rain_path = '2007_train.txt'
val_path = '2007_val.txt'
# test_path = '2007_test.txt'
classes_path = 'class/voc_classes.txt'
anchors_path = 'anchors/yolo_anchors.txt'
class_names = get_classes(classes_path)
num_classes = len(class_names)
anchors = get_anchors(anchors_path)
input_shape = (416, 416) # multiple of 32, hw
num_anchors = len(anchors)
image_input = Input(shape=(None, None, 3))
with tf.device('/cpu:0'):
template_model = create_model(
input_shape,
anchors,
num_classes,
load_pretrained=False,
freeze_body=1,
weights_path="") # make sure you know what you freeze
logging = TensorBoard(log_dir=log_dir)
with open(train_path) as f:
train_lines = f.readlines()
train_lines = train_lines[:1]
with open(val_path) as f:
val_lines = f.readlines()
val_lines = val_lines[:1]
# with open(test_path) as f:
# test_lines = f.readlines()
num_train = int(len(train_lines))
num_val = int(len(val_lines))
model = multi_gpu_model(template_model, gpus=GPUs)
print('use the multi_gpu_model for model training ')
modelsave_checkpoint = ModelSaveCheckpoint(model=template_model,
folder_path=log_dir)
reduce_lr = ReduceLROnPlateau(monitor='val_loss',
factor=0.3,
patience=3,
verbose=1)
early_stopping = EarlyStopping(monitor='val_loss',
min_delta=0,
patience=10,
verbose=1)
# Train with frozen layers first, to get a stable loss.
# Adjust num epochs to your dataset. This step is enough to obtain a not bad model.
if True:
batch_size = 32 * GPUs
print('Train on {} samples, val on {} samples, with batch size {}.'.
format(num_train, num_val, batch_size))
model.compile(optimizer=Adam(lr=1e-3),
loss={
'yolo_loss': lambda y_true, y_pred: y_pred
})
model.fit_generator(data_generator_wrapper(train_lines, batch_size,
input_shape, anchors,
num_classes),
steps_per_epoch=max(1, num_train // batch_size),
validation_data=data_generator_wrapper(
val_lines, batch_size, input_shape, anchors,
num_classes),
validation_steps=max(1, num_val // batch_size),
epochs=epoch_end_first,
initial_epoch=0,
callbacks=[logging, modelsave_checkpoint])
'''save the model config and weigths on cpu field share the weights'''
with tf.device('/cpu:0'):
template_model.save(log_dir + 'frozen_weight.h5')
if True:
batch_size = 32 * GPUs
print('Train on {} samples, val on {} samples, with batch size {}.'.
format(num_train, num_val, batch_size))
for i in range(len(model.layers)):
model.layers[i].trainable = True
model.compile(optimizer=Adam(lr=1e-4),
loss={
'yolo_loss': lambda y_true, y_pred: y_pred
})
print('Unfreeze all of the layers.')
model.fit_generator(
data_generator_wrapper(train_lines, batch_size, input_shape,
anchors, num_classes),
steps_per_epoch=max(1, num_train // batch_size),
validation_data=data_generator_wrapper(val_lines, batch_size,
input_shape, anchors,
num_classes),
validation_steps=max(1, num_val // batch_size),
epochs=epoch_end_final,
initial_epoch=epoch_end_first,
callbacks=[logging, reduce_lr,
modelsave_checkpoint]) #early_stopping,
'''save the model config and weigths on cpu field share the weights'''
with tf.device('/cpu:0'):
template_model.save(log_dir + 'final_weight.h5')
def _main():
epoch_end_first = 1 #30
epoch_end_final = 2 #60
model_name = 'distillation_small_mobilenets2'
log_dir = 'logs/000/'
model_path = 'model_data/fake_trained_weights_final_mobilenet.h5'
#teacher_path ="logs/new_yolo_000/last_loss16.9831-val_loss16.9831.h5"
teacher_path = "model_data/trained_weights_final.h5"
train_path = '2007_train.txt'
val_path = '2007_val.txt'
# test_path = '2007_test.txt'
classes_path = 'class/voc_classes.txt'
anchors_path = 'anchors/yolo_anchors.txt'
class_names = get_classes(classes_path)
num_classes = len(class_names)
anchors = get_anchors(anchors_path)
input_shape = (416, 416) # multiple of 32, hw
num_anchors = len(anchors)
image_input = Input(shape=(None, None, 3))
is_tiny_version = len(anchors) == 6 # default setting
if is_tiny_version:
model = create_tiny_model(
input_shape,
anchors,
num_classes,
freeze_body=2,
weights_path='model_data/tiny_yolo_weights.h5')
else:
model, student, teacher = create_model(
input_shape,
anchors,
num_classes,
load_pretrained=False,
freeze_body=2,
weights_path=model_path,
teacher_weights_path=teacher_path
) # make sure you know what you freeze
#student.summary()
#student.save_weights("s.h5")
logging = TensorBoard(log_dir=log_dir)
checkpointStudent = DistillCheckpointCallback(student, model_name, log_dir)
checkpointTeacher = DistillCheckpointCallback(teacher, "yolo", log_dir)
#checkpoint = ModelCheckpoint(log_dir + 'ep{epoch:03d}-loss{loss:.3f}-val_loss{val_loss:.3f}.h5', monitor='val_loss', save_weights_only=True, save_best_only=True, period=3)
reduce_lr = ReduceLROnPlateau(monitor='val_loss',
factor=0.1,
patience=3,
verbose=1)
early_stopping = EarlyStopping(monitor='val_loss',
min_delta=0,
patience=10,
verbose=1)
with open(train_path) as f:
train_lines = f.readlines()
train_lines = train_lines[:1]
with open(val_path) as f:
val_lines = f.readlines()
val_lines = val_lines[:1]
# with open(test_path) as f:
# test_lines = f.readlines()
num_train = int(len(train_lines))
num_val = int(len(val_lines))
meanAP = AveragePrecision(
data_generator_wrapper(val_lines, 1, input_shape, anchors,
num_classes), num_val, input_shape,
len(anchors) // 3, anchors, num_classes, log_dir)
# Train with frozen layers first, to get a stable loss.
# Adjust num epochs to your dataset. This step is enough to obtain a not bad model.
if True:
model.compile(
optimizer=Adam(lr=1e-3),
loss={
# use custom yolo_loss Lambda layer.
'yolo_custom_loss': lambda y_true, y_pred: y_pred
})
batch_size = 1 #4#24#32
print('Train on {} samples, val on {} samples, with batch size {}.'.
format(num_train, num_val, batch_size))
history = model.fit_generator(
data_generator_wrapper(train_lines, batch_size, input_shape,
anchors, num_classes),
steps_per_epoch=max(1, num_train // batch_size),
validation_data=data_generator_wrapper(val_lines, batch_size,
input_shape, anchors,
num_classes),
validation_steps=max(1, num_val // batch_size),
epochs=epoch_end_first,
initial_epoch=0,
callbacks=[logging, checkpointStudent, checkpointTeacher])
last_loss = history.history['loss'][-1]
last_val_loss = history.history['val_loss'][-1]
hist = "loss{0:.4f}-val_loss{1:.4f}".format(last_loss, last_val_loss)
model.save(hist + "model_checkpoint.h5")
student.save_weights(log_dir + "last_" + hist + ".h5")
student.save_weights(log_dir + model_name +
'_trained_weights_stage_1.h5')
teacher.save_weights(log_dir + "teacher" + model_name +
'_trained_weights_stage_1.h5')
# Unfreeze and continue training, to fine-tune.
# Train longer if the result is not good.
if False:
for i in range(len(student.layers)):
student.layers[i].trainable = True
model.compile(optimizer=Adam(lr=1e-4),
loss={
'yolo_custom_loss': lambda y_true, y_pred: y_pred
}) # recompile to apply the change
print('Unfreeze all of the layers.')
batch_size = 4 #32 note that more GPU memory is required after unfreezing the body
print('Train on {} samples, val on {} samples, with batch size {}.'.
format(num_train, num_val, batch_size))
history = model.fit_generator(
data_generator_wrapper(train_lines, batch_size, input_shape,
anchors, num_classes),
steps_per_epoch=max(1, num_train // batch_size),
validation_data=data_generator_wrapper(val_lines, batch_size,
input_shape, anchors,
num_classes),
validation_steps=max(1, num_val // batch_size),
epochs=epoch_end_final,
initial_epoch=epoch_end_first,
callbacks=[
logging, reduce_lr, checkpointStudent, checkpointTeacher
]) #, early_stopping
model.save_weights(log_dir + model_name + '_trained_weights_final.h5')
last_loss = history.history['loss'][-1]
last_val_loss = history.history['val_loss'][-1]
hist = "loss{0:.4f}-val_loss{0:.4f}".format(last_loss, last_val_loss)
student.save_weights(log_dir + "last_" + hist + ".h5")
student.save_weights(log_dir + model_name +
'_trained_weights_final.h5')
teacher.save_weights(log_dir + "teacher" + model_name +
'_trained_weights_final.h5')
def _main():
epoch_end_first = 50
epoch_end_final = 100
model_name = 'eld_small_mobilenets2'
log_dir = 'logs/000/'
model_path = 'model_data/eld_small_mobilenets2_trained_weights_final.h5'
annotation_path = 'elderly.txt'
classes_path = 'class/elderly_classes.txt'
anchors_path = 'anchors/elderly_anchors.txt'
class_names = get_classes(classes_path)
num_classes = len(class_names)
anchors = get_anchors(anchors_path)
input_shape = (416,416) # multiple of 32, hw
num_anchors = len(anchors)
image_input = Input(shape=(None, None, 3))
is_tiny_version = len(anchors)==6 # default setting
if is_tiny_version:
model = create_tiny_model(input_shape, anchors, num_classes,
freeze_body=2, weights_path='model_data/tiny_yolo_weights.h5')
else:
model = create_model(input_shape, anchors, num_classes,load_pretrained=False,
freeze_body=2, weights_path=model_path) # make sure you know what you freeze
logging = TensorBoard(log_dir=log_dir)
checkpoint = ModelCheckpoint(log_dir + 'ep{epoch:03d}-loss{loss:.3f}-val_loss{val_loss:.3f}.h5',
monitor='val_loss', save_weights_only=True, save_best_only=True, period=3)
reduce_lr = ReduceLROnPlateau(monitor='val_loss', factor=0.1, patience=3, verbose=1)
early_stopping = EarlyStopping(monitor='val_loss', min_delta=0, patience=10, verbose=1)
val_split = 0.1
with open(annotation_path) as f:
lines = f.readlines()
np.random.seed(10101)
np.random.shuffle(lines)
np.random.seed(None)
num_val = int(len(lines)*val_split)
num_train = len(lines) - num_val
train_lines = lines[:num_train]
val_lines = lines[num_train:]
num_train = int(len(train_lines))
num_val = int(len(val_lines))
#print('Train on {} samples, val on {} samples.'.format(num_train, num_val))
#print('Train on {} samples, val on {} samples.'.format( len(train_lines), len(val_lines)))
#print(train_lines)
#print(val_lines)
#meanAP = AveragePrecision(data_generator_wrapper(val_lines , 1 , input_shape, anchors, num_classes) , num_val , input_shape , len(anchors)//3 , anchors ,num_classes,log_dir)
# Train with frozen layers first, to get a stable loss.
# Adjust num epochs to your dataset. This step is enough to obtain a not bad model.
if True:
model.compile(optimizer=Adam(lr=1e-3), loss={
# use custom yolo_loss Lambda layer.
'yolo_loss' : lambda y_true, y_pred: y_pred})
batch_size = 1#32
print('Train on {} samples, val on {} samples, with batch size {}.'.format(num_train, num_val, batch_size))
history = model.fit_generator(data_generator_wrapper(train_lines, batch_size, input_shape, anchors, num_classes),
steps_per_epoch=max(1, num_train//batch_size),
validation_data=data_generator_wrapper(train_lines, batch_size, input_shape, anchors, num_classes),
validation_steps=max(1, num_val//batch_size),
epochs=epoch_end_first,
initial_epoch=0,
callbacks=[logging, checkpoint])#, meanAP
last_loss = history.history['loss'][-1]
last_val_loss = history.history['val_loss'][-1]
hist = "loss{0:.4f}-val_loss{1:.4f}".format(last_loss,last_val_loss)
model.save_weights(log_dir + "last_"+ hist + ".h5")
model.save_weights(log_dir + model_name+'_trained_weights_stage_1.h5')
# Unfreeze and continue training, to fine-tune.
# Train longer if the result is not good.
if False:
for i in range(len(model.layers)):
model.layers[i].trainable = True
model.compile(optimizer=Adam(lr=1e-4), loss={
'yolo_loss' : lambda y_true, y_pred: y_pred}) # recompile to apply the change
print('Unfreeze all of the layers.')
batch_size = 20#32 note that more GPU memory is required after unfreezing the body
print('Train on {} samples, val on {} samples, with batch size {}.'.format(num_train, num_val, batch_size))
history = model.fit_generator(data_generator_wrapper(train_lines, batch_size, input_shape, anchors, num_classes),
steps_per_epoch=max(1, num_train//batch_size),
validation_data=data_generator_wrapper(val_lines, batch_size, input_shape, anchors, num_classes),
validation_steps=max(1, num_val//batch_size),
epochs=epoch_end_final,
initial_epoch=epoch_end_first,
callbacks=[logging, checkpoint, reduce_lr ])#, meanAP, early_stopping
last_loss = history.history['loss'][-1]
last_val_loss = history.history['val_loss'][-1]
hist = "loss{0:.4f}-val_loss{0:.4f}".format(last_loss,last_val_loss)
model.save_weights(log_dir + "last_"+ hist + ".h5")
model.save_weights(log_dir + model_name + '_trained_weights_final.h5')