def gen_data(self):
# upload init_args parameters for dataset loader:
init_args = self.init_args_dict()
batch_size = self.batch_size
# generate datasets for train/validation
datagen = NeMOImageGenerator(image_shape=self.input_shape,
image_resample=True,
pixelwise_center=True,
pixel_mean=self.pixel_mean,
pixelwise_std_normalization=True,
pixel_std=self.pixel_std)
train_loader = ImageSetLoader(**init_args['image_set_loader']['train'])
val_loader = ImageSetLoader(**init_args['image_set_loader']['val'])
# generate model data
self.train_generator = datagen.flow_from_imageset(
class_mode='categorical',
classes=self.num_classes,
batch_size=batch_size,
shuffle=True,
image_set_loader=train_loader)
self.validation_generator = datagen.flow_from_imageset(
class_mode='categorical',
classes=self.num_classes,
batch_size=batch_size,
shuffle=True,
image_set_loader=val_loader)
return self.train_generator, self.validation_generator
def gen_data(self):
# upload init_args parameters for dataset loader:
init_args = init_args_dict()
# generate datasets for train/validation
self.datagen = NeMOImageGenerator(image_shape=self.input_shape,
image_resample=True,
pixelwise_center=True,
pixel_mean=self.pixel_mean,
pixelwise_std_normalization=True,
pixel_std=self.pixel_std)
self.train_loader = ImageSetLoader(
**init_args['image_set_loader']['train'])
self.val_loader = ImageSetLoader(
**init_args['image_set_loader']['val'])
return self.datagen, self.train_loader, self.val_loader
def NeMO_FCN_vgg16(self, image_shape = image_shape , weight_decay = weight_decay, lr = lr):
os.environ["CUDA_VISIBLE_DEVICES"] = "0"
global _SESSION
config = tf.ConfigProto(allow_soft_placement=True)
config.gpu_options.allow_growth = True
_SESSION = tf.Session(config=config)
K.set_session(_SESSION)
with open("init_args.yml", 'r') as stream:
try:
init_args = yaml.load(stream)
except yaml.YAMLError as exc:
print(exc)
checkpointer = ModelCheckpoint(filepath="./tmp/fcn_vgg16_weights.h5", verbose=1, save_best_only=True)
lr_reducer = ReduceLROnPlateau(monitor='val_loss',
factor=np.sqrt(0.1),
cooldown=0,
patience=10, min_lr=1e-12)
early_stopper = EarlyStopping(monitor='val_loss',
min_delta=0.001,
patience=30)
nan_terminator = TerminateOnNaN()
SaveWeights = WeightsSaver(filepath='./weights/', N=10)
#csv_logger = CSVLogger('output/tmp_fcn_vgg16.csv')
#'output/{}_fcn_vgg16.csv'.format(datetime.datetime.now().isoformat()))
#check_num = CheckNumericsOps(validation_data=[np.random.random((1, 224, 224, 3)), 1],
# histogram_freq=100)
# log history during model fit
csv_logger = CSVLogger('output/log.csv', append=True, separator=';')
datagen = NeMOImageGenerator(image_shape=[100, 100, 3],
image_resample=True,
pixelwise_center=True,
pixel_mean=[127.5, 127.5, 127.5],
pixelwise_std_normalization=True,
pixel_std=[127.5, 127.5, 127.5])
train_loader = ImageSetLoader(**init_args['image_set_loader']['train'])
val_loader = ImageSetLoader(**init_args['image_set_loader']['val'])
fcn_vgg16 = FCN(input_shape=(100, 100, 3), classes=4, weight_decay=3e-3,
weights='imagenet', trainable_encoder=True)
optimizer = keras.optimizers.Adam(1e-4)
fcn_vgg16.compile(optimizer=optimizer,
loss='categorical_crossentropy',
metrics=['accuracy'])
fcn_vgg16.fit_generator(
datagen.flow_from_imageset(
class_mode='categorical',
classes=4,
batch_size=10,
shuffle=True,
image_set_loader=train_loader),
steps_per_epoch=80,
epochs=2,
validation_data=datagen.flow_from_imageset(
class_mode='categorical',
classes=4,
batch_size=4,
shuffle=True,
image_set_loader=val_loader),
validation_steps=20,
verbose=1,
callbacks=[lr_reducer, early_stopper, nan_terminator,checkpointer, csv_logger, SaveWeights])
def gen_data(self):
# generate train set
init_args_type <class 'dict'>
{'image_set_loader': {'val': {'image_format': 'png', 'image_dir': '../Images/Valid_Patches/',
'label_format': 'png', 'image_set': '../Images/Valid_Patches/NeMO_valid.txt',
'target_size': [100, 100],
'label_dir': '../Images/ValidRef_Patches/', 'color_mode': 'rgb'},
'test': {'image_format': 'jpg', 'image_dir': '../data/VOC2011/JPEGImages/',
'label_format': 'png',
'image_set': '../data/VOC2011/ImageSets/Segmentation/test.txt',
'target_size': [224, 224], 'label_dir': '../data/VOC2011/SegmentationClass',
'color_mode': 'rgb'},
'train': {'image_format': 'png', 'image_dir': '../Images/Training_Patches/',
'label_format': 'png',
'image_set': '../Images/Training_Patches/NeMO_train.txt',
'target_size': [100, 100],
'label_dir': '../Images/TrainingRef_Patches/',
'color_mode': 'rgb'}}
#### Optimize on NeMO_FCN vgg16 based model
# Input:
# exporttrainpath: Directory for exported patch images
# exportlabelpath: Directory for exported segmented images
# txtfilename: Name of text file to record image names (remember to include '.txt')
# image_shape: (row x col x channel)
# image_size: Size of image patch to use in opt (for symmetric images)
# N: Number of images per class (NOTE: because these are segmented maps, the class is only affiliated with the center pixel)
# lastchannelremove: Remove last channel or not
# labelkey: Naming convention of class labels (NOTE: must be same # as the # of classes)
def NeMO_FCN_vgg16(self, image_shape = image_shape , weight_decay = weight_decay, lr = lr):
os.environ["CUDA_VISIBLE_DEVICES"] = "0"
global _SESSION
config = tf.ConfigProto(allow_soft_placement=True)
config.gpu_options.allow_growth = True
_SESSION = tf.Session(config=config)
K.set_session(_SESSION)
with open("init_args.yml", 'r') as stream:
try:
init_args = yaml.load(stream)
except yaml.YAMLError as exc:
print(exc)
checkpointer = ModelCheckpoint(filepath="./tmp/fcn_vgg16_weights.h5", verbose=1, save_best_only=True)
lr_reducer = ReduceLROnPlateau(monitor='val_loss',
factor=np.sqrt(0.1),
cooldown=0,
patience=10, min_lr=1e-12)
early_stopper = EarlyStopping(monitor='val_loss',
min_delta=0.001,
patience=30)
nan_terminator = TerminateOnNaN()
SaveWeights = WeightsSaver(filepath='./weights/', N=10)
#csv_logger = CSVLogger('output/tmp_fcn_vgg16.csv')
#'output/{}_fcn_vgg16.csv'.format(datetime.datetime.now().isoformat()))
#check_num = CheckNumericsOps(validation_data=[np.random.random((1, 224, 224, 3)), 1],
# histogram_freq=100)
# log history during model fit
csv_logger = CSVLogger('output/log.csv', append=True, separator=';')
datagen = NeMOImageGenerator(image_shape=[100, 100, 3],
image_resample=True,
pixelwise_center=True,
pixel_mean=[127.5, 127.5, 127.5],
pixelwise_std_normalization=True,
pixel_std=[127.5, 127.5, 127.5])
train_loader = ImageSetLoader(**init_args['image_set_loader']['train'])
val_loader = ImageSetLoader(**init_args['image_set_loader']['val'])
fcn_vgg16 = FCN(input_shape=(100, 100, 3), classes=4, weight_decay=3e-3,
weights='imagenet', trainable_encoder=True)
optimizer = keras.optimizers.Adam(1e-4)
fcn_vgg16.compile(optimizer=optimizer,
loss='categorical_crossentropy',
metrics=['accuracy'])
fcn_vgg16.fit_generator(
datagen.flow_from_imageset(
class_mode='categorical',
classes=4,
batch_size=10,
shuffle=True,
image_set_loader=train_loader),
steps_per_epoch=80,
epochs=2,
validation_data=datagen.flow_from_imageset(
class_mode='categorical',
classes=4,
batch_size=4,
shuffle=True,
image_set_loader=val_loader),
validation_steps=20,
verbose=1,
callbacks=[lr_reducer, early_stopper, nan_terminator,checkpointer, csv_logger, SaveWeights])
def val_loader():
print(init_args['image_set_loader']['val'])
return ImageSetLoader(**init_args['image_set_loader']['val'])
def NeMOtrain_loader():
return ImageSetLoader(**init_args['image_set_loader']['train'])
#'output/{}_fcn_vgg16.csv'.format(datetime.datetime.now().isoformat()))
#check_num = CheckNumericsOps(validation_data=[np.random.random((1, 224, 224, 3)), 1],
# histogram_freq=100)
# log history during model fit
csv_logger = CSVLogger('output/log.csv', append=True, separator=';')
datagen = NeMOImageGenerator(image_shape=[image_size, image_size, 3],
image_resample=True,
pixelwise_center=True,
pixel_mean=[127.5, 127.5, 127.5],
pixelwise_std_normalization=True,
pixel_std=[127.5, 127.5, 127.5])
train_loader = ImageSetLoader(**init_args['image_set_loader']['train'])
val_loader = ImageSetLoader(**init_args['image_set_loader']['val'])
fcn_vgg16 = FCN(input_shape=(image_size, image_size, 3), classes=4, weight_decay=3e-3,
weights='imagenet', trainable_encoder=True)
optimizer = keras.optimizers.Adam(1e-4)
fcn_vgg16.compile(optimizer=optimizer,
loss='categorical_crossentropy',
metrics=['accuracy'])
fcn_vgg16.fit_generator(
datagen.flow_from_imageset(
class_mode='categorical',
classes=4,
batch_size=20,