def test_dataset_val(self):
config = LocalConfig()
dataset = Dataset().get_val()
for batch in dataset.take(1):
image = ((batch[0].numpy()[0, :, :, :] + 1) / 2 * 255).astype(
np.int32)
output = np.sum(image)
expected = np.array(8123856, dtype=np.int32)
self.assertAllCloseAccordingToType(expected, output)
self.assertEqual(config.ENCODER_INPUT_SHAPE, image.shape)
kp2d = ((batch[1].numpy()[0, :, :2] + 1) / 2 *
image.shape[:2]).astype(np.int32)
output = np.sum(kp2d)
expected = np.array(4040, dtype=np.int32)
self.assertAllCloseAccordingToType(expected, output)
self.assertEqual((config.NUM_KP2D, 2), kp2d.shape)
vis = batch[1].numpy()[0, :, 2].astype(np.int32)
output = np.sum(vis)
expected = np.array(17, dtype=np.int32)
self.assertAllCloseAccordingToType(expected, output)
self.assertEqual((config.NUM_KP2D, ), vis.shape)
kp3d = batch[2].numpy()[0, :, :]
output = np.sum(kp3d)
expected = np.array(56.9217948, dtype=np.float32)
self.assertAllCloseAccordingToType(expected, output)
self.assertEqual((config.NUM_KP3D, 3), kp3d.shape)
# check if has3d flag is included in correct shape
has3d = batch[3].numpy()[0]
self.assertEqual(tf.constant(1, tf.int64), has3d)
self.assertEqual((config.BATCH_SIZE, ), batch[3].shape)
def test_dataset_smpl(self):
config = LocalConfig()
dataset = Dataset().get_smpl()
for batch in dataset.take(1):
shape = (config.BATCH_SIZE * config.ITERATIONS, (config.NUM_POSE_PARAMS + config.NUM_SHAPE_PARAMS))
self.assertEqual(shape, batch.shape)
pose = batch[0].numpy()[:config.NUM_POSE_PARAMS:]
mean = tf.reduce_mean(pose)
expected = np.array(0.0411809, dtype=np.float32)
self.assertAllCloseAccordingToType(expected, mean)
shape = batch[0].numpy()[-config.NUM_SHAPE_PARAMS:]
mean = tf.reduce_mean(shape)
expected = np.array(0.12554605, dtype=np.float32)
self.assertAllCloseAccordingToType(expected, mean)
def test_dataset_test(self):
config = LocalConfig()
dataset = Dataset().get_test()
for batch in dataset.take(1):
image = ((batch[0].numpy()[0, :, :, :] + 1) / 2 * 255).astype(np.int32)
output = np.sum(image)
expected = np.array(10050903, dtype=np.int32)
self.assertAllCloseAccordingToType(expected, output)
self.assertEqual(config.ENCODER_INPUT_SHAPE, image.shape)
kp3d = batch[1].numpy()[0, :, :]
output = np.sum(kp3d)
expected = np.array(38780.2031, dtype=np.float32)
self.assertAllCloseAccordingToType(expected, output)
self.assertEqual((config.NUM_KP3D, 3), kp3d.shape)
# check if sequence flag is included in correct shape
sequence = batch[2].numpy()[0].decode("utf-8")
self.assertEqual('TS1', sequence)
def test_dataset_train(self):
config = LocalConfig()
dataset = Dataset().get_train()
for batch in dataset.take(1):
image = ((batch[0].numpy()[0, :, :, :] + 1) / 2 * 255).astype(
np.int32)
output = np.sum(image)
expected = np.array(6991299, dtype=np.int32)
self.assertAllCloseAccordingToType(
expected, output) # this can sometimes fail with output=66
self.assertEqual(config.BATCH_SIZE, batch[0].shape[0])
self.assertEqual(config.ENCODER_INPUT_SHAPE, batch[0].shape[1:])
kp2d = ((batch[1].numpy()[0, :, :2] + 1) / 2 *
image.shape[:2]).astype(np.int32)
output = np.sum(kp2d)
expected = np.array(3818, dtype=np.int32)
self.assertAllCloseAccordingToType(expected, output)
self.assertEqual((config.BATCH_SIZE, config.NUM_KP2D, 3),
batch[1].shape)
vis = batch[1].numpy()[0, :, 2].astype(np.int32)
output = np.sum(vis)
expected = np.array(17, dtype=np.int32)
self.assertAllCloseAccordingToType(expected, output)
kp3d = batch[2].numpy()[0, :, :]
output = np.sum(kp3d)
expected = np.array(4.11272e-06, dtype=np.float32)
self.assertAllCloseAccordingToType(expected, output)
self.assertEqual((config.BATCH_SIZE, config.NUM_KP3D, 3),
batch[2].shape)
# check if has3d flag is included in correct shape
has3d = batch[3].numpy()[0]
self.assertEqual(tf.constant(1, tf.int64), has3d)
self.assertEqual((config.BATCH_SIZE, ), batch[3].shape)
