def create_random_gen(self, *args):
# we ignore the args
train_chunk_size = int(round(self.chunk_size * self.train_sample_weight))
pseudo_chunk_size = self.chunk_size - train_chunk_size
train_gen = data.multiscale_patches_gen_augmented(self.images_train, self.labels_train, self.scale_factors, patch_sizes=self.patch_sizes,
chunk_size=train_chunk_size, num_chunks=self.num_chunks_train, augmentation_params=self.augmentation_params)
pseudo_gen = data.multiscale_patches_gen_augmented(self.images_pseudo, self.labels_pseudo, self.scale_factors, patch_sizes=self.patch_sizes,
chunk_size=pseudo_chunk_size, num_chunks=self.num_chunks_train, augmentation_params=self.augmentation_params)
def random_gen():
indices = np.arange(self.chunk_size)
for a, b in itertools.izip(train_gen, pseudo_gen):
(chunk_x1, chunk_y1, chunk_shape), (chunk_x2, chunk_y2, _) = a, b
np.random.shuffle(indices)
chunk_y = np.concatenate((chunk_y1, chunk_y2), 0)[indices]
chunk_x = []
for k in xrange(len(chunk_x1)):
chunk_x += [np.concatenate((chunk_x1[k], chunk_x2[k]), 0)[indices]]
chunk_x[k] -= self.zmuv_means[k]
chunk_x[k] /= self.zmuv_stds[k]
chunk_x[k] = chunk_x[k][:, None, :, :]
yield chunk_x, chunk_y
return buffering.buffered_gen_threaded(random_gen())
def create_random_gen(self, *args):
# we ignore the args
train_chunk_size = int(
round(self.chunk_size * self.train_sample_weight))
pseudo_chunk_size = self.chunk_size - train_chunk_size
train_gen = data.multiscale_patches_gen_augmented(
self.images_train,
self.labels_train,
self.scale_factors,
patch_sizes=self.patch_sizes,
chunk_size=train_chunk_size,
num_chunks=self.num_chunks_train,
augmentation_params=self.augmentation_params)
pseudo_gen = data.multiscale_patches_gen_augmented(
self.images_pseudo,
self.labels_pseudo,
self.scale_factors,
patch_sizes=self.patch_sizes,
chunk_size=pseudo_chunk_size,
num_chunks=self.num_chunks_train,
augmentation_params=self.augmentation_params)
def random_gen():
indices = np.arange(self.chunk_size)
for a, b in zip(train_gen, pseudo_gen):
(chunk_x1, chunk_y1, chunk_shape), (chunk_x2, chunk_y2,
_) = a, b
np.random.shuffle(indices)
chunk_y = np.concatenate((chunk_y1, chunk_y2), 0)[indices]
chunk_x = []
for k in range(len(chunk_x1)):
chunk_x += [
np.concatenate((chunk_x1[k], chunk_x2[k]), 0)[indices]
]
chunk_x[k] -= self.zmuv_means[k]
chunk_x[k] /= self.zmuv_stds[k]
chunk_x[k] = chunk_x[k][:, None, :, :]
yield chunk_x, chunk_y
return buffering.buffered_gen_threaded(random_gen())
def estimate_zmuv_params(self):
gen = data.multiscale_patches_gen_augmented(
self.images_train,
self.labels_train,
self.scale_factors,
patch_sizes=self.patch_sizes,
chunk_size=self.chunk_size,
num_chunks=1,
augmentation_params=self.augmentation_params)
chunks_x, _, _ = next(gen)
self.zmuv_means = [chunk_x.mean() for chunk_x in chunks_x]
self.zmuv_stds = [chunk_x.std() for chunk_x in chunks_x]
def create_random_gen(self, images, labels):
gen = data.multiscale_patches_gen_augmented(images, labels, self.scale_factors, patch_sizes=self.patch_sizes,
chunk_size=self.chunk_size, num_chunks=self.num_chunks_train, augmentation_params=self.augmentation_params)
def random_gen():
for chunks_x, chunk_y, chunk_shape in gen:
for k in xrange(len(chunks_x)):
chunks_x[k] -= self.zmuv_means[k]
chunks_x[k] /= self.zmuv_stds[k]
chunks_x[k] = chunks_x[k][:, None, :, :]
yield chunks_x, chunk_y
return buffering.buffered_gen_threaded(random_gen())
def create_random_gen(self, images, labels):
gen = data.multiscale_patches_gen_augmented(
images,
labels,
self.scale_factors,
patch_sizes=self.patch_sizes,
chunk_size=self.chunk_size,
num_chunks=self.num_chunks_train,
augmentation_params=self.augmentation_params)
def random_gen():
for chunks_x, chunk_y, chunk_shape in gen:
for k in range(len(chunks_x)):
chunks_x[k] -= self.zmuv_means[k]
chunks_x[k] /= self.zmuv_stds[k]
chunks_x[k] = chunks_x[k][:, None, :, :]
yield chunks_x, chunk_y
return buffering.buffered_gen_threaded(random_gen())
def estimate_zmuv_params(self):
gen = data.multiscale_patches_gen_augmented(self.images_train, self.labels_train, self.scale_factors, patch_sizes=self.patch_sizes,
chunk_size=self.chunk_size, num_chunks=1, augmentation_params=self.augmentation_params)
chunks_x, _, _ = gen.next()
self.zmuv_means = [chunk_x.mean() for chunk_x in chunks_x]
self.zmuv_stds = [chunk_x.std() for chunk_x in chunks_x]