def _val_step(self, images, kp2d, kp3d, has3d):
tf.keras.backend.set_learning_phase(0)
result = self.generator(images, training=False)
# only use last computed theta (from accumulated iterative feedback loop)
_, kp2d_pred, kp3d_pred, _, _, _ = result[-1]
vis = kp2d[:, :, 2]
kp2d_norm = tf.norm(
kp2d_pred[:, :self.config.NUM_KP2D, :] - kp2d[:, :, :2],
axis=2) * vis
kp2d_mpjpe = tf.reduce_sum(kp2d_norm) / tf.reduce_sum(vis)
self.kp2d_mpjpe_log(kp2d_mpjpe)
if self.config.USE_3D:
# check if at least one 3d sample available
if tf.reduce_sum(has3d) > 0:
kp3d_real = tf.boolean_mask(kp3d, has3d)
kp3d_predict = tf.boolean_mask(kp3d_pred, has3d)
kp3d_predict = kp3d_predict[:, :self.config.NUM_KP3D, :]
kp3d_real = batch_align_by_pelvis(kp3d_real)
kp3d_predict = batch_align_by_pelvis(kp3d_predict)
kp3d_mpjpe = tf.norm(kp3d_predict - kp3d_real, axis=2)
kp3d_mpjpe = tf.reduce_mean(kp3d_mpjpe)
aligned_kp3d = batch_compute_similarity_transform(
kp3d_real, kp3d_predict)
kp3d_mpjpe_aligned = tf.norm(aligned_kp3d - kp3d_real, axis=2)
kp3d_mpjpe_aligned = tf.reduce_mean(kp3d_mpjpe_aligned)
self.kp3d_mpjpe_log.update_state(kp3d_mpjpe)
self.kp3d_mpjpe_aligned_log.update_state(kp3d_mpjpe_aligned)
def _test_step(self, image, kp3d, return_kps=False):
tf.keras.backend.set_learning_phase(0)
if len(tf.shape(image)) is not 4:
image = tf.expand_dims(image, 0)
kp3d = tf.expand_dims(kp3d, 0)
result = self.generator(image, training=False)
# only use last computed theta (from accumulated iterative feedback loop)
_, _, kp3d_pred, _, _, _ = result[-1]
factor = tf.constant(1000, tf.float32)
kp3d, kp3d_predict = kp3d * factor, kp3d_pred * factor # convert back from m -> mm
kp3d_predict = kp3d_predict[:, :self.config.NUM_KP3D, :]
real_kp3d = batch_align_by_pelvis(kp3d)
predict_kp3d = batch_align_by_pelvis(kp3d_predict)
kp3d_mpjpe = tf.norm(real_kp3d - predict_kp3d, axis=2)
aligned_kp3d = batch_compute_similarity_transform(
real_kp3d, predict_kp3d)
kp3d_mpjpe_aligned = tf.norm(real_kp3d - aligned_kp3d, axis=2)
if return_kps:
return kp3d_mpjpe, kp3d_mpjpe_aligned, predict_kp3d, real_kp3d
return kp3d_mpjpe, kp3d_mpjpe_aligned, None, None
def test_batch_compute_similarity_transform(self):
pred_3d, real_3d = get_kp3d(self.config)
output = batch_compute_similarity_transform(real_3d, pred_3d)
mean = tf.reduce_mean(output)
expected = np.array(1.806683, dtype=np.float32)
self.assertAllCloseAccordingToType(expected, mean)
self.assertEqual((self.config.BATCH_SIZE, self.config.NUM_KP3D, 3),
output.shape)