def train(self,
img_folder,
ann_folder,
nb_epoch,
project_folder,
batch_size=8,
jitter=True,
learning_rate=1e-4,
train_times=1,
valid_times=1,
valid_img_folder="",
valid_ann_folder="",
first_trainable_layer=None,
metrics="mAP",
validation_freq=1):
# 1. get annotations
train_annotations, valid_annotations = get_train_annotations(
self._labels,
img_folder,
ann_folder,
valid_img_folder,
valid_ann_folder,
is_only_detect=False)
# 1. get batch generator
valid_batch_size = len(valid_annotations) * valid_times
if valid_batch_size < batch_size:
raise ValueError(
"Not enough validation images: batch size {} is larger than {} validation images. Add more validation images or decrease batch size!"
.format(batch_size, valid_batch_size))
train_batch_generator = self._get_batch_generator(train_annotations,
batch_size,
train_times,
jitter=jitter)
valid_batch_generator = self._get_batch_generator(valid_annotations,
batch_size,
valid_times,
jitter=False)
# 2. To train model get keras model instance & loss fucntion
model = self._yolo_network.get_model(first_trainable_layer)
loss = self._get_loss_func(batch_size)
# 3. Run training loop
return train(model,
loss,
train_batch_generator,
valid_batch_generator,
learning_rate=learning_rate,
nb_epoch=nb_epoch,
project_folder=project_folder,
first_trainable_layer=first_trainable_layer,
network=self,
metrics="mAP",
validation_freq=validation_freq)
def train(self,
img_folder,
ann_folder,
nb_epoch,
project_folder,
batch_size=8,
jitter=True,
learning_rate=1e-4,
train_times=1,
valid_times=1,
valid_img_folder="",
valid_ann_folder="",
first_trainable_layer=None,
metrics="mAP"):
# 1. get annotations
train_annotations, valid_annotations = get_train_annotations(
self._labels,
img_folder,
ann_folder,
valid_img_folder,
valid_ann_folder,
is_only_detect=False)
# 1. get batch generator
train_batch_generator = self._get_batch_generator(train_annotations,
batch_size,
train_times,
jitter=jitter)
valid_batch_generator = self._get_batch_generator(valid_annotations,
batch_size,
valid_times,
jitter=False)
# 2. To train model get keras model instance & loss fucntion
model = self._yolo_network.get_model(first_trainable_layer)
loss = self._get_loss_func(batch_size)
# 3. Run training loop
return train(model,
loss,
train_batch_generator,
valid_batch_generator,
learning_rate=learning_rate,
nb_epoch=nb_epoch,
project_folder=project_folder,
first_trainable_layer=first_trainable_layer,
network=self,
metrics="mAP")