def __init__(self, model, fn_A, fn_B, batch_size, *args):
self.batch_size = batch_size
self.model = model
generator = TrainingDataGenerator(self.random_transform_args, 160)
self.images_A = generator.minibatchAB(fn_A, self.batch_size)
self.images_B = generator.minibatchAB(fn_B, self.batch_size)
def __init__(self, model, fn_A, fn_B, batch_size=64):
self.batch_size = batch_size
self.model = model
generator = TrainingDataGenerator(self.random_transform_args, 160)
self.images_A = generator.minibatchAB(fn_A, self.batch_size)
self.images_B = generator.minibatchAB(fn_B, self.batch_size)
def __init__(self, model, fn_A, fn_B, batch_size, *args):
self.batch_size = batch_size
self.model = model
from timeit import default_timer as clock
self._clock = clock
generator = TrainingDataGenerator(self.random_transform_args, 160, 5, zoom=self.model.IMAGE_SHAPE[0]//64)
self.images_A = generator.minibatchAB(fn_A, self.batch_size)
self.images_B = generator.minibatchAB(fn_B, self.batch_size)
self.generator = generator
def __init__(self, model, fn_A, fn_B, batch_size, *args):
self.batch_size = batch_size
self.model = model
from timeit import default_timer as clock
self._clock = clock
generator = TrainingDataGenerator(self.random_transform_args, 160, 5, zoom=2)
self.images_A = generator.minibatchAB(fn_A, self.batch_size)
self.images_B = generator.minibatchAB(fn_B, self.batch_size)
self.generator = generator
def __init__(self, model, fn_A, fn_B, batch_size, *args):
self.batch_size = batch_size
self.model = model
#generator = TrainingDataGenerator(self.random_transform_args, 160)
# make sre to keep zoom=2 or you won't get 128x128 vectors as input
#generator = TrainingDataGenerator(self.random_transform_args, 220, 5, zoom=2)
generator = TrainingDataGenerator(self.random_transform_args,
160,
6,
zoom=2)
#generator = TrainingDataGenerator(self.random_transform_args, 180, 7, zoom=2)
self.images_A = generator.minibatchAB(fn_A, self.batch_size)
self.images_B = generator.minibatchAB(fn_B, self.batch_size)
self.generator = generator
def get_image_data(self, input_images, batch_size):
""" Get training images """
random_transform_args = {
'rotation_range': 0,
'zoom_range': 0,
'shift_range': 0,
'random_flip': 0
}
zoom = 1
if hasattr(self.trainer.model, 'IMAGE_SHAPE'):
zoom = self.trainer.model.IMAGE_SHAPE[0] // 64
generator = TrainingDataGenerator(random_transform_args, 160, zoom)
batch = generator.minibatchAB(input_images, batch_size,
doShuffle=False)
return next(batch)[2]