def _augment_batch_data(self, batch_data):
if self.normal_channel:
rotated_data = provider.rotate_point_cloud_with_normal(batch_data)
rotated_data = provider.rotate_perturbation_point_cloud_with_normal(rotated_data)
else:
rotated_data = provider.rotate_point_cloud(batch_data)
rotated_data = provider.rotate_perturbation_point_cloud(rotated_data)
jittered_data = provider.random_scale_point_cloud(rotated_data[:,:,0:3])
jittered_data = provider.shift_point_cloud(jittered_data)
jittered_data = provider.jitter_point_cloud(jittered_data)
rotated_data[:,:,0:3] = jittered_data
return provider.shuffle_points(rotated_data)
def _augment_batch_data(self, batch_data):
if self.normal_channel:
rotated_data = provider.rotate_point_cloud_with_normal(batch_data)
rotated_data = provider.rotate_perturbation_point_cloud_with_normal(rotated_data)
else:
rotated_data = provider.rotate_point_cloud(batch_data)
rotated_data = provider.rotate_perturbation_point_cloud(rotated_data)
jittered_data = provider.random_scale_point_cloud(rotated_data[:,:,0:3])
jittered_data = provider.shift_point_cloud(jittered_data)
jittered_data = provider.jitter_point_cloud(jittered_data)
rotated_data[:,:,0:3] = jittered_data
return provider.shuffle_points(rotated_data)
def augment_batch_data(batch_data):
if FLAGS.normal:
rotated_data = provider.rotate_point_cloud_with_normal(batch_data)
rotated_data = provider.rotate_perturbation_point_cloud_with_normal(
rotated_data)
else:
rotated_data = provider.rotate_point_cloud(batch_data)
rotated_data = provider.rotate_perturbation_point_cloud(rotated_data)
jittered_data = provider.random_scale_point_cloud(rotated_data[:, :, 0:3])
jittered_data = provider.shift_point_cloud(jittered_data)
jittered_data = provider.jitter_point_cloud(jittered_data)
rotated_data[:, :, 0:3] = jittered_data
return rotated_data
def _augment_batch_data(self, batch_data, augment, rotate=0):
if augment:
#augment points
jittered_data = provider.random_scale_point_cloud(batch_data[:, :,
0:3])
jittered_data = provider.shift_point_cloud(jittered_data)
jittered_data = provider.jitter_point_cloud(jittered_data)
batch_data[:, :, 0:3] = jittered_data
if rotate == 2:
#rotated points and normal
batch_data = provider.rotate_point_cloud_with_normal(batch_data)
elif rotate == 3:
batch_data = provider.rotate_perturbation_point_cloud_with_normal(
batch_data)
return provider.shuffle_points(batch_data)
def get_batch(dataset, idxs, start_idx, end_idx, rotate=0):
bsize = end_idx - start_idx
batch_data = np.zeros((bsize, NUM_POINT, 6))
batch_label = np.zeros((bsize, NUM_POINT), dtype=np.int32)
batch_cls_label = np.zeros((bsize, ), dtype=np.int32)
for i in range(bsize):
ps, normal, seg, cls = dataset[idxs[i + start_idx]]
batch_data[i, :, 0:3] = ps
batch_data[i, :, 3:6] = normal
batch_label[i, :] = seg
batch_cls_label[i] = cls
if rotate == 2:
# rotated points and normal
batch_data = provider.rotate_point_cloud_with_normal(batch_data)
elif rotate == 3:
batch_data = provider.rotate_perturbation_point_cloud_with_normal(
batch_data)
return batch_data, batch_label, batch_cls_label
def augment_batch_data_MODELNET(batch_data, is_include_normal):
'''
is_include_normal=False: xyz
is_include_normal=True: xyznxnynz
'''
if is_include_normal:
rotated_data = provider.rotate_point_cloud_with_normal(batch_data)
rotated_data = provider.rotate_perturbation_point_cloud_with_normal(
rotated_data)
else:
rotated_data = provider.rotate_point_cloud(batch_data)
rotated_data = provider.rotate_perturbation_point_cloud(rotated_data)
jittered_data = provider.random_scale_point_cloud(rotated_data[:, :, 0:3])
jittered_data = provider.shift_point_cloud(jittered_data)
jittered_data = provider.jitter_point_cloud(jittered_data)
rotated_data[:, :, 0:3] = jittered_data
return provider.shuffle_points(rotated_data)
def _augment_batch_data(self, batch_data):
if self.normal_channel:
rotated_data = provider.rotate_point_cloud_with_normal(batch_data)
rotated_data = provider.rotate_perturbation_point_cloud_with_normal(
rotated_data)
else:
rotated_data = provider.rotate_point_cloud(batch_data)
rotated_data = provider.rotate_perturbation_point_cloud(
rotated_data)
jittered_data = provider.random_scale_point_cloud(
rotated_data[:, :, 0:3],
scale_low=self.scale_low,
scale_high=self.scale_high)
jittered_data = provider.shift_point_cloud(
jittered_data, shift_range=self.shift_range)
jittered_data = provider.jitter_point_cloud(jittered_data,
sigma=self.jitter_sigma,
clip=0.1)
rotated_data[:, :, 0:3] = jittered_data
if self.shuffle_points:
return provider.shuffle_points(rotated_data)
else:
return rotated_data
def eval_one_epoch(sess, ops, test_writer):
""" ops: dict mapping from string to tf ops """
global EPOCH_CNT
global BEST_ACC
global BEST_CLS_ACC
is_training = False
# Make sure batch data is of same size
cur_batch_data = np.zeros((BATCH_SIZE,NUM_POINT,TEST_DATASET.num_channel()))
cur_batch_label = np.zeros((BATCH_SIZE), dtype=np.int32)
total_correct = 0
total_seen = 0
loss_sum = 0
batch_idx = 0
shape_ious = []
total_seen_class = [0 for _ in range(NUM_CLASSES)]
total_correct_class = [0 for _ in range(NUM_CLASSES)]
log_string(str(datetime.now()))
log_string('---- EPOCH %03d EVALUATION ----'%(EPOCH_CNT))
while TEST_DATASET.has_next_batch():
batch_data, batch_label = TEST_DATASET.next_batch(augment=False)
bsize = batch_data.shape[0]
# print('Batch: %03d, batch size: %d'%(batch_idx, bsize))
# for the last batch in the epoch, the bsize:end are from last batch
cur_batch_data[0:bsize,...] = batch_data
cur_batch_label[0:bsize] = batch_label
if ROTATE_FLAG:
batch_pred_sum = np.zeros((BATCH_SIZE, NUM_CLASSES)) # score for classes
for vote_idx in range(12):
# Shuffle point order to achieve different farthest samplings
shuffled_indices = np.arange(NUM_POINT)
np.random.shuffle(shuffled_indices)
if NORMAL_FLAG:
rotated_data = provider.rotate_point_cloud_by_angle_with_normal(cur_batch_data[:, shuffled_indices, :],
vote_idx/float(12) * np.pi * 2)
rotated_data = provider.rotate_perturbation_point_cloud_with_normal(rotated_data)
else:
rotated_data = provider.rotate_point_cloud_by_angle(cur_batch_data[:, shuffled_indices, :],
vote_idx/float(12) * np.pi * 2)
rotated_data = provider.rotate_perturbation_point_cloud(rotated_data)
jittered_data = provider.random_scale_point_cloud(rotated_data[:,:,0:3])
jittered_data = provider.jitter_point_cloud(jittered_data)
rotated_data[:,:,0:3] = jittered_data
# else:
# rotated_data = provider.rotate_point_cloud_by_angle(cur_batch_data[:, shuffled_indices, :],
# vote_idx/float(12) * np.pi * 2)
feed_dict = {ops['pointclouds_pl']: rotated_data,
ops['labels_pl']: cur_batch_label,
ops['is_training_pl']: is_training}
loss_val, pred_val = sess.run([ops['loss'], ops['pred']], feed_dict=feed_dict)
batch_pred_sum += pred_val
pred_val = np.argmax(batch_pred_sum, 1)
else:
feed_dict = {ops['pointclouds_pl']: cur_batch_data,
ops['labels_pl']: cur_batch_label,
ops['is_training_pl']: is_training}
summary, step, loss_val, pred_val = sess.run([ops['merged'], ops['step'],
ops['loss'], ops['pred']], feed_dict=feed_dict)
test_writer.add_summary(summary, step)
pred_val = np.argmax(pred_val, 1)
correct = np.sum(pred_val[0:bsize] == batch_label[0:bsize])
total_correct += correct
total_seen += bsize
loss_sum += loss_val
batch_idx += 1
for i in range(bsize):
l = batch_label[i]
total_seen_class[l] += 1
total_correct_class[l] += (pred_val[i] == l)
current_acc = total_correct / float(total_seen)
current_cls_acc = np.mean(np.array(total_correct_class)/np.array(total_seen_class,dtype=np.float))
log_string('eval mean loss: %f' % (loss_sum / float(batch_idx)))
log_string('eval accuracy: %f'% (current_acc))
log_string('eval avg class acc: %f' % (current_cls_acc))
best_acc_flag, best_cls_acc_flag = False, False
if current_acc > BEST_ACC:
BEST_ACC = current_acc
best_acc_flag = True
if current_cls_acc > BEST_CLS_ACC:
BEST_CLS_ACC = current_cls_acc
best_cls_acc_flag = True
log_string('eval best accuracy: %f'% (BEST_ACC))
log_string('eval best avg class acc: %f'% (BEST_CLS_ACC))
EPOCH_CNT += 1
TEST_DATASET.reset()
return (best_acc_flag, best_cls_acc_flag)
