def create_loss(self, coarse, fine, gt, alpha):
loss_coarse = chamfer(coarse, gt)
add_train_summary('train/coarse_loss', loss_coarse)
update_coarse = add_valid_summary('valid/coarse_loss', loss_coarse)
loss_fine = chamfer(fine, gt)
add_train_summary('train/fine_loss', loss_fine)
update_fine = add_valid_summary('valid/fine_loss', loss_fine)
loss = loss_coarse + alpha * loss_fine
add_train_summary('train/loss', loss)
update_loss = add_valid_summary('valid/loss', loss)
return loss, [update_coarse, update_fine, update_loss]
def create_loss(self, coarse, fine, gt, alpha):
gt_ds = gt[:, :coarse.shape[1], :]
loss_coarse = earth_mover(coarse, gt_ds)
add_train_summary('train/coarse_loss', loss_coarse)
update_coarse = add_valid_summary('valid/coarse_loss', loss_coarse)
loss_fine = chamfer(fine, gt)
add_train_summary('train/fine_loss', loss_fine)
update_fine = add_valid_summary('valid/fine_loss', loss_fine)
loss = loss_coarse + alpha * loss_fine
add_train_summary('train/loss', loss)
update_loss = add_valid_summary('valid/loss', loss)
return loss, [update_coarse, update_fine, update_loss]
def create_loss(self, coarse_highres, coarse, fine, gt, theta):
loss_coarse_highres = chamfer(coarse_highres, gt)
loss_coarse = chamfer(coarse, gt)
add_train_summary('train/coarse_loss', loss_coarse)
update_coarse = add_valid_summary('valid/coarse_loss', loss_coarse)
loss_fine = chamfer(fine, gt)
add_train_summary('train/fine_loss', loss_fine)
update_fine = add_valid_summary('valid/fine_loss', loss_fine)
repulsion_loss = get_repulsion_loss4(coarse)
loss = 0.5 * loss_coarse_highres + loss_coarse + theta * loss_fine + 0.2 * repulsion_loss
add_train_summary('train/loss', loss)
update_loss = add_valid_summary('valid/loss', loss)
return loss, loss_fine, [update_coarse, update_fine, update_loss]
def create_loss(self, coarse, fine, gt, alpha):
gt_ds = gt[:, :coarse.shape[1], :]
loss_coarse = 10 * earth_mover(coarse[:, :, 0:3], gt_ds[:, :, 0:3])
_, retb, _, retd = tf_nndistance.nn_distance(coarse[:, :, 0:3],
gt_ds[:, :, 0:3])
for i in range(np.shape(gt_ds)[0]):
index = tf.expand_dims(retb[i], -1)
sem_feat = tf.nn.softmax(coarse[i, :, 3:], -1)
sem_gt = tf.cast(
tf.one_hot(
tf.gather_nd(tf.cast(gt_ds[i, :, 3] * 80 * 12, tf.int32),
index), 12), tf.float32)
loss_sem_coarse = tf.reduce_mean(-tf.reduce_sum(
0.9 * sem_gt * tf.log(1e-6 + sem_feat) + (1 - 0.9) *
(1 - sem_gt) * tf.log(1e-6 + 1 - sem_feat), [1]))
loss_coarse += loss_sem_coarse
add_train_summary('train/coarse_loss', loss_coarse)
update_coarse = add_valid_summary('valid/coarse_loss', loss_coarse)
loss_fine = 10 * chamfer(fine[:, :, 0:3], gt[:, :, 0:3])
_, retb, _, retd = tf_nndistance.nn_distance(fine[:, :, 0:3], gt[:, :,
0:3])
for i in range(np.shape(gt)[0]):
index = tf.expand_dims(retb[i], -1)
sem_feat = tf.nn.softmax(fine[i, :, 3:], -1)
sem_gt = tf.cast(
tf.one_hot(
tf.gather_nd(tf.cast(gt[i, :, 3] * 80 * 12, tf.int32),
index), 12), tf.float32)
loss_sem_fine = tf.reduce_mean(-tf.reduce_sum(
0.9 * sem_gt * tf.log(1e-6 + sem_feat) + (1 - 0.9) *
(1 - sem_gt) * tf.log(1e-6 + 1 - sem_feat), [1]))
loss_fine += loss_sem_fine
add_train_summary('train/fine_loss', loss_fine)
update_fine = add_valid_summary('valid/fine_loss', loss_fine)
loss = loss_coarse + alpha * loss_fine
add_train_summary('train/loss', loss)
update_loss = add_valid_summary('valid/loss', loss)
return loss, [update_coarse, update_fine, update_loss]
def compute_loss(self, inputs, gt_pose, est_pose):
# see equation (1) from IT-net
# est_pose: world -> body coord
# gt_pose: body -> world coord (to to invert when applying to inputs)
est_inputs = transform_tf(inputs, est_pose)
gt_inputs = transform_tf(inputs, tf.linalg.inv(gt_pose))
sq_dist = tf.reduce_sum(tf.square(est_inputs - gt_inputs), axis=2)
loss = tf.reduce_mean(tf.reduce_mean(sq_dist, axis=1), axis=0)
add_train_summary('train/loss', loss)
update_loss = add_valid_summary('valid/loss', loss)
return loss, update_loss
def create_loss(self, outputs, gt):
loss = chamfer(outputs, gt)
add_train_summary('train/loss', loss)
update_loss = add_valid_summary('valid/loss', loss)
return loss, update_loss