def test_block_iter0(self):
files = match_img_files(self.class_dir)
block_iter = _block_iter(
block_tensor=files,
label=self.label,
block_length=2,
block_bound=False,
sample_block_random=False,
seed=None,
)
labels = [self.label] * len(files)
files_list = list(zip(files, labels))
block_list = list(block_iter)
self.assertEqual(block_list, files_list)
def _interleave_fn_image_files(
input_dir,
label,
block_length,
block_bound=True,
sample_block_random=False,
seed=None,
):
img_files = match_img_files(input_dir)
block_iter = _block_iter(
img_files,
label,
block_length=block_length,
block_bound=block_bound,
sample_block_random=sample_block_random,
seed=seed,
)
return block_iter
def test_block_iter1(self):
""" test block bound"""
files = match_img_files(self.class_dir)
block_len = 2
block_iter = _block_iter(
block_tensor=files,
label=self.label,
block_length=block_len,
block_bound=True,
sample_block_random=False,
seed=None,
)
labels = [self.label] * len(files)
files_list = list(zip(files, labels))[:block_len]
block_list = list(block_iter)
self.assertEqual(block_list, files_list)
def _interleave_fn_image_triplet_files(
input_dir,
label,
block_length,
block_bound=True,
sample_block_random=False,
seed=None,
):
img_files = match_img_files(input_dir)
# if no images found in folder, assume it is a triplet folder
if tf.shape(img_files)[0] == 0:
triplets = match_img_files_triplet(input_dir)
block_iter = _block_iter_triplet(
triplets,
label,
block_length=block_length,
block_bound=block_bound,
sample_block_random=sample_block_random,
seed=seed,
)
# call .repeat(1) to make it a RepeatDataset, so both if/else branches are same dataset type.
block_iter = block_iter.repeat(1)
else:
block_iter = _block_iter(
img_files,
label,
block_length=block_length,
block_bound=block_bound,
sample_block_random=sample_block_random,
seed=seed,
)
# call .repeat(1) to make it a RepeatDataset, so both if/else branches are same dataset type.
block_iter = block_iter.repeat(1)
return block_iter
def flat_map_fn(input_dir, label):
files = match_img_files(input_dir)
n_files = tf.shape(files)[0]
y = tf.tile([label], [n_files])
return tf.data.Dataset.from_tensor_slices((files, y))
def test_read_img_files_shape(self):
files = match_img_files(img_folder)
self.assertAllEqual(tf.shape(files), (3, ))