else:
snapshot_folder = join(FLAGS.exp, 'snapshots')
# GPU configuration
config = tf.ConfigProto()
config.gpu_options.allow_growth = True
with tf.Session(config=config) as sess:
sess.run(tf.global_variables_initializer())
saver = tf.train.Saver()
load_previous_checkpoint(snapshot_folder,
saver,
sess,
is_training=False)
tflearn.is_training(False, session=sess)
if FLAGS.eval_set == 'valid':
_pred_scaled = sess.run(reconstr_img_scaled,
feed_dict={img_inp: batch_ip})
if FLAGS.visualize:
for i in xrange(BATCH_SIZE):
cv2.imshow('', batch_ip[0])
print 'Displaying Pr scaled icp 1k'
print _pred_scaled
show3d_balls.showpoints(_pred_scaled[i], ballradius=3)
else:
print 'Invalid dataset. Choose from [shapenet, pix3d]'
sys.exit(1)
def calculate_metrics(models,
batches,
pcl_gt_scaled,
pred_scaled,
indices=None):
if FLAGS.visualize:
iters = range(batches)
else:
iters = tqdm(range(batches))
epoch_chamfer = 0.
epoch_forward = 0.
epoch_backward = 0.
epoch_emd = 0.
ph_gt = tf.placeholder(tf.float32, (BATCH_SIZE, NUM_EVAL_POINTS, 3),
name='ph_gt')
ph_pr = tf.placeholder(tf.float32, (BATCH_SIZE, NUM_EVAL_POINTS, 3),
name='ph_pr')
dists_forward, dists_backward, chamfer_distance = get_chamfer_metrics(
ph_gt, ph_pr)
emd = get_emd_metrics(ph_gt, ph_pr, BATCH_SIZE, NUM_EVAL_POINTS)
for cnt in iters:
start = time.time()
if FLAGS.dataset == 'shapenet':
batch_ip, batch_gt = fetch_batch_shapenet(models, indices, cnt,
BATCH_SIZE)
elif FLAGS.dataset == 'pix3d':
batch_ip, batch_gt = fetch_batch_pix3d(models, cnt, BATCH_SIZE)
_gt_scaled, _pr_scaled = sess.run([pcl_gt_scaled, pred_scaled],
feed_dict={
pcl_gt: batch_gt,
img_inp: batch_ip
})
_pr_scaled_icp = []
for i in xrange(BATCH_SIZE):
rand_indices = np.random.permutation(NUM_POINTS)[:NUM_EVAL_POINTS]
T, _, _ = icp(_gt_scaled[i],
_pr_scaled[i][rand_indices],
tolerance=1e-10,
max_iterations=1000)
_pr_scaled_icp.append(
np.matmul(_pr_scaled[i][rand_indices], T[:3, :3]) - T[:3, 3])
_pr_scaled_icp = np.array(_pr_scaled_icp).astype('float32')
C, F, B, E = sess.run(
[chamfer_distance, dists_forward, dists_backward, emd],
feed_dict={
ph_gt: _gt_scaled,
ph_pr: _pr_scaled_icp
})
epoch_chamfer += C.mean() / batches
epoch_forward += F.mean() / batches
epoch_backward += B.mean() / batches
epoch_emd += E.mean() / batches
if FLAGS.visualize:
for i in xrange(BATCH_SIZE):
print '-' * 50
print C[i], F[i], B[i], E[i]
print '-' * 50
cv2.imshow('', batch_ip[i])
print 'Displaying Gt scaled 1k'
show3d_balls.showpoints(_gt_scaled[i], ballradius=3)
print 'Displaying Pr scaled icp 1k'
show3d_balls.showpoints(_pr_scaled_icp[i], ballradius=3)
if cnt % 10 == 0:
print '%d / %d' % (cnt, batches)
if not FLAGS.visualize:
log_values(csv_path, epoch_chamfer, epoch_forward, epoch_backward,
epoch_emd)
return
)
epoch_chamfer += C.mean() / batches
epoch_forward += F.mean() / batches
epoch_backward += B.mean() / batches
epoch_emd += E.mean() / batches
if FLAGS.visualize:
for i in xrange(BATCH_SIZE):
print '-'*50
print C[i], F[i], B[i], E[i]
print '-'*50
cv2.imshow('', batch_ip[i])
print 'Displaying Gt scaled 1k'
show3d_balls.showpoints(_gt_scaled[i], ballradius=3)
print 'Displaying Pr scaled icp 1k'
show3d_balls.showpoints(_pr_scaled_icp[i], ballradius=3)
if cnt%10 == 0:
print '%d / %d' % (cnt, batches)
if not FLAGS.visualize:
log_values(csv_path, epoch_chamfer, epoch_forward, epoch_backward, epoch_emd)
return
if __name__ == '__main__':
# Create Placeholders
img_inp = tf.placeholder(tf.float32, shape=(BATCH_SIZE, HEIGHT, WIDTH, 3), name='img_inp')