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DNA_Recognizer_Trainer.py
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# Modified from training program in keras-retinanet, only using customized (CSV) part.
import os
import keras
import tensorflow as tf
# import from keras-retinanet
from keras_retinanet import models
from keras_retinanet import losses
from keras_retinanet.callbacks import RedirectModel
from keras_retinanet.utils.keras_version import check_keras_version
from keras_retinanet.models.retinanet import retinanet_bbox
from keras_retinanet.utils.anchors import AnchorParameters, make_shapes_callback
from keras_retinanet.preprocessing.csv_generator import CSVGenerator
from keras_retinanet.utils.transform import random_transform_generator
# Support utility functions
def get_session():
config = tf.ConfigProto()
config.gpu_options.allow_growth = True
return tf.Session(config=config)
def makedirs(path):
try:
os.makedirs(path)
except OSError:
if not os.path.isdir(path):
raise
class IdentifierTrainer():
def __init__(self, backbone="resnet50", random_transform=False, batch_size=2, step=100,
epoch=40, anchor_settings=None):
""" Initializer
Args:
backbone
string indicate which backbone network to use (only for ones defined in
keras-retinanet)
random_transform
boolean indicate whether image is randomly transformed when training
batch_size
number of images sent for each step during training
step
number of steps(batches) to run for each epoch during training
epoch
number of epochs for this training
anchor_settings
a list of lists indicate anchor box parameters. please reference
keras-retinanet's document for how to set up anchor box parameters
"""
check_keras_version()
self._backbone_name = backbone
self._backbone = models.backbone(backbone)
self._img_preprocessor = self._backbone.preprocess_image
self._batch_size = batch_size
self._step = step
self._epoch = epoch
if random_transform:
self._transform_generator = random_transform_generator(
min_rotation=-0.1,
max_rotation=0.1,
min_translation=(-0.1, -0.1),
max_translation=(0.1, 0.1),
min_shear=-0.1,
max_shear=0.1,
min_scaling=(0.9, 0.9),
max_scaling=(1.1, 1.1),
flip_x_chance=0.5,
flip_y_chance=0.5,
)
else:
self._transform_generator = random_transform_generator(flip_x_chance=0.5)
self._common_args = {
'batch_size' : batch_size,
'preprocess_image' : self._img_preprocessor
}
if anchor_settings:
self._anchor_params = AnchorParameters(*anchor_settings)
else:
self._anchor_params = AnchorParameters(
[16, 32, 64, 128, 256],
[8, 16, 32, 64, 128],
[7.5, 10, 12.5, 15, 17.5, 20, 25, 30, 40, 50],
[0.75, 1, 1.2, 1.4, 1.6]
)
def __create_generator(self, annotation_path, class_path):
train_generator = CSVGenerator(
annotation_path,
class_path,
transform_generator=self._transform_generator,
**(self._common_args)
)
return train_generator
def __create_models(self, backbone_retinanet, num_classes):
anchor_params = self._anchor_params
num_anchors = anchor_params.num_anchors()
model = backbone_retinanet(num_classes, num_anchors=num_anchors, modifier=None)
training_model = model
prediction_model = retinanet_bbox(model=model, anchor_params=anchor_params)
training_model.compile(
loss={
'regression' : losses.smooth_l1(),
'classification': losses.focal()
},
optimizer=keras.optimizers.adam(lr=1e-5, clipnorm=0.001)
)
return model, training_model, prediction_model
def __create_callbacks(self, model, training_model, prediction_model, tensorboard_dir,
snapshot_dir, snapshot_tag, snapshot_freq):
callbacks = []
if tensorboard_dir:
tensorboard_callback = keras.callbacks.TensorBoard(
log_dir=tensorboard_dir, histogram_freq=0, batch_size=self._batch_size,
write_graph=True, write_grads=False, write_images=False,
embeddings_freq=0, embeddings_layer_names=None,
embeddings_metadata=None
)
callbacks.append(tensorboard_callback)
if snapshot_dir:
makedirs(snapshot_dir)
s_tag = snapshot_tag + "_" if snapshot_tag else ""
checkpoint = keras.callbacks.ModelCheckpoint(
os.path.join(
snapshot_dir,
'{tag}{backbone}_{{epoch:02d}}.h5'.format(tag=s_tag, backbone=self._backbone_name)
),
verbose=1,
period=snapshot_freq
)
checkpoint = RedirectModel(checkpoint, model)
callbacks.append(checkpoint)
callbacks.append(keras.callbacks.ReduceLROnPlateau(
monitor='loss', factor=0.1, patience=2, verbose=1,
mode='auto', min_delta=0.0001, cooldown=0, min_lr=0
))
return callbacks
def train(self, annotation_path, class_path, snapshot_src=None, tensorboard_dir=None,
snapshot_dir=None, snapshot_tag=None, snapshot_freq=10):
""" Train routine.
Params:
annotation_path: file path for target annotations
class_path: file path for class-id mapping
snapshot_src: file path of snapshot to continue training
tensorboard_dir: directory for saving tensorboard
snapshot_dir: directory for saving snapshots
shapshot_tag: tag used as filename identifier for snapshots
shapshot_freq: epoch interval for saving snapshots
"""
if self._epoch % snapshot_freq != 0:
print("Error: Snapshot saving interval should be set to factor of total epochns ({}).".format(self._epoch))
return
# Load network backbone
generator = self.__create_generator(annotation_path, class_path)
if snapshot_src:
model = models.load_model(snapshot_src, backbone_name=self._backbone_name)
training_model = model
prediction_model = retinanet_bbox(model=model, anchor_params=self._anchor_params)
else:
model, training_model, prediction_model = self.__create_models(
self._backbone.retinanet, generator.num_classes()
)
print(model.summary())
if 'vgg' in self._backbone_name or 'densenet' in self._backbone_name:
generator.compute_shapes = make_shapes_callback(model)
callbacks = self.__create_callbacks(
model,
training_model,
prediction_model,
tensorboard_dir,
snapshot_dir,
snapshot_tag,
snapshot_freq
)
training_model.fit_generator(
generator=generator,
steps_per_epoch=self._step,
epochs=self._epoch,
verbose=1,
callbacks=callbacks
)
# Usage example
trainer = IdentifierTrainer(batch_size=1, step=100, epoch=30)
trainer.train("./annotation.csv", "./class_mapping.csv", snapshot_src=None, tensorboard_dir="./board",
snapshot_dir="./snapshots", snapshot_tag="test_train", snapshot_freq=10)