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# Copyright 2022 Big Vision Authors.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Tests for pp_ops."""
import copy
from big_vision.pp.proj.uvim import pp_ops as pp
import numpy as np
import tensorflow as tf
def get_image_data(dtype=tf.uint8):
img = tf.random.uniform((640, 320, 3), 0, 255, tf.int32) # Can't ask uint8!?
return {"image": tf.cast(img, dtype)}
class PreprocessOpsTest(tf.test.TestCase):
def tfrun(self, ppfn, data={}): # pylint: disable=dangerous-default-value
# Run once as standalone, as could happen eg in colab.
yield {k: np.array(v) for k, v in ppfn(copy.deepcopy(data)).items()}
if not data: # tf.data doesn't like completely empty dict...k
data = {"dummy": 0.0}
# And then once again as part of tfdata pipeline.
# You'd be surprised how much these two differ!
tfdata = tf.data.Dataset.from_tensors(copy.deepcopy(data))
for npdata in tfdata.map(ppfn).as_numpy_iterator():
yield npdata
def test_randu(self):
for output in self.tfrun(pp.get_randu("flip")):
self.assertEqual(output["flip"].shape, ())
self.assertAllGreaterEqual(output["flip"], 0.0)
self.assertAllLessEqual(output["flip"], 1.0)
def test_det_flip_lr(self):
# Test both dtypes to make it can be applied correctly to both.
for dtype in [tf.uint8, tf.float32]:
image_data = get_image_data(dtype)
for out in self.tfrun(pp.get_det_fliplr(randkey="rand"),
{"rand": 0.1, **image_data}):
self.assertTrue(np.all(image_data["image"] == out["image"]))
self.assertEqual(out["image"].dtype, dtype)
for out in self.tfrun(pp.get_det_fliplr(randkey="rand"),
{"rand": 0.6, **image_data}):
self.assertTrue(np.all(image_data["image"][:, ::-1, :] == out["image"]))
self.assertEqual(out["image"].dtype, dtype)
def test_inception_box(self):
for out in self.tfrun(pp.get_inception_box(), get_image_data()):
self.assertEqual(out["box"][0].shape, (2,))
self.assertEqual(out["box"][1].shape, (2,))
def test_crop_box(self):
data = get_image_data()
data["box"] = (tf.constant([0.5, 0.4]), tf.constant([0.25, 0.3]))
for out in self.tfrun(pp.get_crop_box(), data):
self.assertEqual(out["image"].shape, (160, 96, 3))
self.assertAllEqual(
data["image"][320:320 + 160, 128:128 + 96],
out["image"])
def test_make_canonical(self):
orig = np.array([
[1, 0, 3, 3, -1],
[1, 0, 3, 3, -1],
[1, 0, 2, 2, 2],
[1, 0, 0, -1, -1]
], np.int32)[:, :, None]
expected = np.array([
[2, 0, 1, 1, -1],
[2, 0, 1, 1, -1],
[2, 0, 3, 3, 3],
[2, 0, 0, -1, -1]
], np.int32)[:, :, None]
for out in self.tfrun(pp.get_make_canonical(), {"labels": orig}):
self.assertTrue(np.all(out["labels"] == expected))
# Test it only affects last channel.
for out in self.tfrun(pp.get_make_canonical(),
{"labels": tf.tile(orig, (1, 1, 3))}):
self.assertAllEqual(out["labels"][..., 0], orig[..., 0])
self.assertAllEqual(out["labels"][..., 1], orig[..., 0])
self.assertAllEqual(out["labels"][..., 2], expected[..., 0])
def test_nyu_depth(self):
image = tf.zeros((5, 7, 3), dtype=tf.uint8)
depth = tf.zeros((5, 7), dtype=tf.float16)
data = {
"image": image,
"depth": depth
}
output = pp.get_nyu_depth()(data)
self.assertEqual(output["image"].shape, (5, 7, 3))
self.assertEqual(output["image"].dtype, tf.uint8)
self.assertEqual(output["labels"].shape, (5, 7, 1))
self.assertEqual(output["labels"].dtype, tf.float32)
def test_nyu_eval_crop(self):
image = tf.zeros((480, 640, 3), dtype=tf.uint8)
depth = tf.zeros((480, 640), dtype=tf.float16)
data = {
"image": image,
"depth": depth
}
data = pp.get_nyu_depth()(data)
output = pp.get_nyu_eval_crop()(data)
self.assertEqual(output["image"].shape, (426, 560, 3))
self.assertEqual(output["image"].dtype, tf.uint8)
self.assertEqual(output["labels"].shape, (426, 560, 1))
self.assertEqual(output["labels"].dtype, tf.float32)
if __name__ == "__main__":
tf.test.main()
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