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()
