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]