def test_custom_data_provider(self):
if tf.executing_eagerly():
# dataset.make_initializable_iterator is not supported when eager
# execution is enabled.
return
file_pattern = os.path.join(self.testdata_dir, '*.jpg')
batch_size = 3
patch_size = 8
images_list = data_provider.provide_custom_data(
batch_size=batch_size,
image_file_patterns=[file_pattern, file_pattern],
patch_size=patch_size)
for images in images_list:
self.assertListEqual([batch_size, patch_size, patch_size, 3],
images.shape.as_list())
self.assertEqual(tf.float32, images.dtype)
with self.cached_session() as sess:
sess.run(tf.local_variables_initializer())
sess.run(tf.tables_initializer())
images_out_list = sess.run(images_list)
for images_out in images_out_list:
self.assertTupleEqual((batch_size, patch_size, patch_size, 3),
images_out.shape)
self.assertTrue(np.all(np.abs(images_out) <= 1.0))
def _get_data(image_set_x_file_pattern, image_set_y_file_pattern, batch_size,
patch_size):
"""Returns image Tensors from a custom provider or TFDS."""
if image_set_x_file_pattern and image_set_y_file_pattern:
image_file_patterns = [image_set_x_file_pattern, image_set_y_file_pattern]
else:
if image_set_x_file_pattern or image_set_y_file_pattern:
raise ValueError('Both image patterns or neither must be provided.')
image_file_patterns = None
images_x, images_y = data_provider.provide_custom_data(
batch_size=batch_size,
image_file_patterns=image_file_patterns,
patch_size=patch_size)
return images_x, images_y
def test_custom_data_provider(self):
file_pattern = os.path.join(self.testdata_dir, '*.jpg')
batch_size = 3
patch_size = 8
images_list = data_provider.provide_custom_data(
batch_size=batch_size,
image_file_patterns=[file_pattern, file_pattern],
patch_size=patch_size)
for images in images_list:
self.assertListEqual([batch_size, patch_size, patch_size, 3],
images.shape.as_list())
self.assertEqual(tf.float32, images.dtype)
with self.cached_session() as sess:
sess.run(tf.compat.v1.local_variables_initializer())
sess.run(tf.compat.v1.tables_initializer())
images_out_list = sess.run(images_list)
for images_out in images_out_list:
self.assertTupleEqual((batch_size, patch_size, patch_size, 3),
images_out.shape)
self.assertTrue(np.all(np.abs(images_out) <= 1.0))