def create_random_gen(self, images, labels): gen = data.rescaled_patches_gen_augmented(images, labels, self.estimate_scale, patch_size=self.patch_size, chunk_size=self.chunk_size, num_chunks=self.num_chunks_train, augmentation_params=self.augmentation_params) def random_gen(): for chunk_x, chunk_y, chunk_shape in gen: yield [chunk_x[:, None, :, :]], chunk_y return buffering.buffered_gen_threaded(random_gen())
def estimate_zmuv_params(self): gen = data.rescaled_patches_gen_augmented( self.images_train, self.labels_train, self.estimate_scale, patch_size=self.patch_size, chunk_size=self.chunk_size, num_chunks=1, augmentation_params=self.augmentation_params) chunk_x, _, _ = next(gen) self.zmuv_mean = chunk_x.mean() self.zmuv_std = chunk_x.std()
def create_random_gen(self, images, labels): gen = data.rescaled_patches_gen_augmented( images, labels, self.estimate_scale, patch_size=self.patch_size, chunk_size=self.chunk_size, num_chunks=self.num_chunks_train, augmentation_params=self.augmentation_params) def random_gen(): for chunk_x, chunk_y, chunk_shape in gen: yield [chunk_x[:, None, :, :]], chunk_y return buffering.buffered_gen_threaded(random_gen())
def estimate_zmuv_params(self): gen = data.rescaled_patches_gen_augmented(self.images_train, self.labels_train, self.estimate_scale, patch_size=self.patch_size, chunk_size=self.chunk_size, num_chunks=1, augmentation_params=self.augmentation_params) chunk_x, _, _ = gen.next() self.zmuv_mean = chunk_x.mean() self.zmuv_std = chunk_x.std()