def get_hard_samples(sess, ops):
is_training = True
train_idxs = np.arange(0, len(TRAIN_DATASET))
np.random.shuffle(train_idxs)
num_batches = len(TRAIN_DATASET) / BATCH_SIZE
hard_neg_idxs = []
# test on training set
for batch_idx in range(num_batches):
start_idx = batch_idx * BATCH_SIZE
end_idx = (batch_idx + 1) * BATCH_SIZE
batch_data, batch_cls_label, batch_label, batch_center, \
batch_hclass, batch_hres, \
batch_sclass, batch_sres, \
batch_rot_angle, batch_feature_vec = \
get_batch(TRAIN_DATASET, train_idxs, start_idx, end_idx,
NUM_POINT, NUM_CHANNEL)
feed_dict = {
ops['pointclouds_pl']: batch_data,
ops['features_pl']: batch_feature_vec,
ops['cls_label_pl']: batch_cls_label,
ops['is_training_pl']: is_training,
}
cls_logits_val = sess.run(ops['cls_logits'], feed_dict=feed_dict)
cls_preds_val = np.argmax(cls_logits_val, 1)
incorrect = cls_preds_val != batch_cls_label
false_positive = np.logical_and(incorrect, batch_cls_label == 3)
for i, sample_idx in enumerate(range(start_idx, end_idx)):
# if false_positive[i]:
if incorrect[i]:
hard_neg_idxs.append(sample_idx)
log_string("Find {0} hard negative samples".format(len(hard_neg_idxs)))
return hard_neg_idxs
def main_batch_from_rgb_detection(output_filename, result_dir=None):
ps_list = []
segp_list = []
center_list = []
heading_cls_list = []
heading_res_list = []
size_cls_list = []
size_res_list = []
rot_angle_list = []
score_list = []
test_idxs = np.arange(0, len(TEST_DATASET))
print(len(TEST_DATASET))
raw_input()
batch_size = 32
num_batches = int((len(TEST_DATASET)+batch_size-1)/batch_size)
batch_data_to_feed = np.zeros((batch_size, NUM_POINT, NUM_CHANNEL))
batch_one_hot_to_feed = np.zeros((batch_size, NUM_CLASS))
sess, ops = get_model(batch_size=batch_size, num_point=NUM_POINT)
for batch_idx in range(num_batches):
print(batch_idx)
start_idx = batch_idx * batch_size
end_idx = min(len(TEST_DATASET), (batch_idx+1) * batch_size)
cur_batch_size = end_idx - start_idx
batch_data, batch_rot_angle, batch_rgb_prob, batch_one_hot_vec = get_batch(TEST_DATASET, test_idxs, start_idx, end_idx, NUM_POINT, NUM_CHANNEL, from_rgb_detection=True)
batch_data_to_feed[0:cur_batch_size,...] = batch_data
batch_one_hot_to_feed[0:cur_batch_size,:] = batch_one_hot_vec
batch_output, batch_center_pred, batch_hclass_pred, batch_hres_pred, batch_sclass_pred, batch_sres_pred, batch_scores = inference(sess, ops, batch_data_to_feed, batch_one_hot_to_feed, batch_size=batch_size)
print(batch_hclass_pred.shape, batch_hres_pred.shape)
print(batch_sclass_pred.shape, batch_sres_pred.shape)
for i in range(cur_batch_size):
ps_list.append(batch_data[i,...])
segp_list.append(batch_output[i,...])
center_list.append(batch_center_pred[i,:])
heading_cls_list.append(batch_hclass_pred[i])
heading_res_list.append(batch_hres_pred[i])
size_cls_list.append(batch_sclass_pred[i])
size_res_list.append(batch_sres_pred[i,:])
rot_angle_list.append(batch_rot_angle[i])
#score_list.append(batch_scores[i] + np.log(batch_rgb_prob[i])) # Combine 3D BOX score and 2D RGB detection score
score_list.append(batch_rgb_prob[i]) # 2D RGB detection score
if FLAGS.dump_result:
save_zipped_pickle([ps_list, segp_list, center_list, heading_cls_list, heading_res_list, size_cls_list, size_res_list, rot_angle_list, score_list], output_filename)
# Write detection results for KITTI evaluation
print(len(ps_list))
raw_input()
write_detection_results(result_dir, TEST_DATASET.id_list, TEST_DATASET.type_list, TEST_DATASET.box2d_list, center_list, heading_cls_list, heading_res_list, size_cls_list, size_res_list, rot_angle_list, score_list)
# Make sure for each frame (no matter if we have measurment for that frame), there is a TXT file
output_dir = os.path.join(result_dir, 'data')
to_fill_filename_list = [line.rstrip()+'.txt' for line in open(FLAGS.idx_path)]
fill_files(output_dir, to_fill_filename_list)
def test(output_filename, result_dir=None):
''' Test frustum pointnets with GT 2D boxes.
Write test results to KITTI format label files.
todo (rqi): support variable number of points.
'''
total_time = 0.0
ps_list = []
seg_list = []
segp_list = []
center_list = []
heading_cls_list = []
heading_res_list = []
size_cls_list = []
size_res_list = []
rot_angle_list = []
score_list = []
test_idxs = np.arange(0, len(TEST_DATASET))
batch_size = BATCH_SIZE
num_batches = len(TEST_DATASET) / batch_size
sess, ops = get_session_and_ops(batch_size=batch_size, num_point=NUM_POINT)
correct_cnt = 0
profile_list = []
for batch_idx in range(RUN_SIZE):
print('batch idx: %d' % (batch_idx))
start_idx = batch_idx * batch_size
end_idx = (batch_idx + 1) * batch_size
batch_data, batch_label, batch_center, \
batch_hclass, batch_hres, batch_sclass, batch_sres, \
batch_rot_angle, batch_one_hot_vec = \
get_batch(TEST_DATASET, test_idxs, start_idx, end_idx,
NUM_POINT, NUM_CHANNEL)
print("batch_data: ", batch_data.shape)
print("batch_one_hot_vec: ", batch_one_hot_vec.shape)
#batch_output, batch_center_pred, \
#batch_hclass_pred, batch_hres_pred, \
#batch_sclass_pred, batch_sres_pred, batch_scores = \
t1 = inference(sess,
ops,
batch_data,
batch_one_hot_vec,
batch_size=batch_size)
if (batch_idx < 5): continue
# total_time += (end_time - start_time)
total_time += t1
print("Avg time: ", total_time / RUN_SIZE)
def eval_one_epoch(sess, ops, test_writer, tracks=False, lstm_params=None):
''' Simple evaluation for one epoch on the frustum dataset.
ops is dict mapping from string to tf ops """
'''
global EPOCH_CNT
is_training = False
log_string(str(datetime.now()))
log_string('---- EPOCH %03d EVALUATION ----' % (EPOCH_CNT))
test_idxs = np.arange(0, len(TEST_DATASET))
num_batches = len(TEST_DATASET) / BATCH_SIZE
# To collect statistics
total_correct = 0
total_seen = 0
loss_sum = 0
total_seen_class = [0 for _ in range(NUM_CLASSES)]
total_correct_class = [0 for _ in range(NUM_CLASSES)]
iou2ds_sum = 0
iou3ds_sum = 0
iou3d_correct_cnt = 0
iou3d_correct_cnt_05 = 0
# E: This is necessary to collect features of batches before the evaluation
if tracks:
for batch_idx in range(int(num_batches)):
start_idx = batch_idx * BATCH_SIZE
end_idx = (batch_idx + 1) * BATCH_SIZE
# E: Get also batch_indices which shows the (world_id,frame_id,track_id) of the objects in the batch
# E: Batch indices are valid (non-empty) only if the tracks flag is True
batch_data, batch_label, batch_center, \
batch_hclass, batch_hres, \
batch_sclass, batch_sres, \
batch_rot_angle, batch_one_hot_vec, batch_indices = \
get_batch(TEST_DATASET, test_idxs, start_idx, end_idx,
NUM_POINT, NUM_CHANNEL,tracks=tracks)
# Emec added the feature line
# E: Get the features at the prev time steps of the objects in the batch
batch_feat_lstm = get_batch_features(
TEST_DATASET.feature_dict,
batch_wft=batch_indices,
tau=lstm_params['tau'],
feat_len=lstm_params['feat_vec_len'],
rev_order=True)
# E: Get the number of tracks at the tau prev. time steps for each object in the batch: How many of the tau-1 frames before the current frames of the objects contain the same object with the same track id
batch_seq_len = batch_track_num(
feature_dict=TEST_DATASET.feature_dict, wfts=batch_indices)
feed_dict = {
ops['pointclouds_pl']: batch_data,
ops['one_hot_vec_pl']: batch_one_hot_vec,
ops['labels_pl']: batch_label,
ops['centers_pl']: batch_center,
ops['heading_class_label_pl']: batch_hclass,
ops['heading_residual_label_pl']: batch_hres,
ops['size_class_label_pl']: batch_sclass,
ops['size_residual_label_pl']: batch_sres,
ops['is_training_pl']: is_training,
ops['end_points']['lstm_layer']['feat_input']: batch_feat_lstm,
ops['end_points']['lstm_layer']['pf_seq_len']: batch_seq_len
}
'''
summary, step, loss_val, logits_val, iou2ds, iou3ds, box_est_feature_vec = \
sess.run([ops['merged'], ops['step'],
ops['loss'], ops['logits'],
ops['end_points']['iou2ds'], ops['end_points']['iou3ds'],
ops['end_points']['box_est_feature_vec']],
feed_dict=feed_dict)
'''
box_est_feature_vec = \
sess.run(ops['end_points']['box_est_feature_vec'],
feed_dict=feed_dict)
update_batch_features(feature_dict=TEST_DATASET.feature_dict,
batch_wft=batch_indices,
batch_feat_vecs=box_est_feature_vec)
# Simple evaluation with batches
for batch_idx in range(int(num_batches)):
start_idx = batch_idx * BATCH_SIZE
end_idx = (batch_idx + 1) * BATCH_SIZE
# E: Get also batch_indices which shows the (world_id,frame_id,track_id) of the objects in the batch
# E: Batch indices are valid (non-empty) only if the tracks flag is True
batch_data, batch_label, batch_center, \
batch_hclass, batch_hres, \
batch_sclass, batch_sres, \
batch_rot_angle, batch_one_hot_vec, batch_indices = \
get_batch(TEST_DATASET, test_idxs, start_idx, end_idx,
NUM_POINT, NUM_CHANNEL,tracks=tracks)
if tracks:
# Emec added the feature line
# E: Get the features at the prev time steps of the objects in the batch
batch_feat_lstm = get_batch_features(
TEST_DATASET.feature_dict,
batch_wft=batch_indices,
tau=lstm_params['tau'],
feat_len=lstm_params['feat_vec_len'],
rev_order=True)
# E: Get the number of tracks at the tau prev. time steps for each object in the batch: How many of the tau-1 frames before the current frames of the objects contain the same object with the same track id
batch_seq_len = batch_track_num(
feature_dict=TEST_DATASET.feature_dict, wfts=batch_indices)
feed_dict = {
ops['pointclouds_pl']: batch_data,
ops['one_hot_vec_pl']: batch_one_hot_vec,
ops['labels_pl']: batch_label,
ops['centers_pl']: batch_center,
ops['heading_class_label_pl']: batch_hclass,
ops['heading_residual_label_pl']: batch_hres,
ops['size_class_label_pl']: batch_sclass,
ops['size_residual_label_pl']: batch_sres,
ops['is_training_pl']: is_training,
ops['end_points']['lstm_layer']['feat_input']: batch_feat_lstm,
ops['end_points']['lstm_layer']['pf_seq_len']: batch_seq_len
}
summary, step, loss_val, logits_val, iou2ds, iou3ds, box_est_feature_vec = \
sess.run([ops['merged'], ops['step'],
ops['loss'], ops['logits'],
ops['end_points']['iou2ds'], ops['end_points']['iou3ds'],
ops['end_points']['box_est_feature_vec']],
feed_dict=feed_dict)
update_batch_features(feature_dict=TEST_DATASET.feature_dict,
batch_wft=batch_indices,
batch_feat_vecs=box_est_feature_vec)
else:
feed_dict = {
ops['pointclouds_pl']: batch_data,
ops['one_hot_vec_pl']: batch_one_hot_vec,
ops['labels_pl']: batch_label,
ops['centers_pl']: batch_center,
ops['heading_class_label_pl']: batch_hclass,
ops['heading_residual_label_pl']: batch_hres,
ops['size_class_label_pl']: batch_sclass,
ops['size_residual_label_pl']: batch_sres,
ops['is_training_pl']: is_training
}
summary, step, loss_val, logits_val, iou2ds, iou3ds = \
sess.run([ops['merged'], ops['step'],
ops['loss'], ops['logits'],
ops['end_points']['iou2ds'], ops['end_points']['iou3ds']],
feed_dict=feed_dict)
test_writer.add_summary(summary, step)
preds_val = np.argmax(logits_val, 2)
correct = np.sum(preds_val == batch_label)
total_correct += correct
total_seen += (BATCH_SIZE * NUM_POINT)
loss_sum += loss_val
for l in range(NUM_CLASSES):
total_seen_class[l] += np.sum(batch_label == l)
total_correct_class[l] += (np.sum((preds_val == l)
& (batch_label == l)))
iou2ds_sum += np.sum(iou2ds)
iou3ds_sum += np.nansum(iou3ds)
iou3d_correct_cnt += np.sum(iou3ds >= 0.7)
iou3d_correct_cnt_05 += np.sum(iou3ds >= 0.5)
for i in range(BATCH_SIZE):
segp = preds_val[i, :]
segl = batch_label[i, :]
part_ious = [0.0 for _ in range(NUM_CLASSES)]
for l in range(NUM_CLASSES):
if (np.sum(segl == l) == 0) and (np.sum(segp == l) == 0):
part_ious[l] = 1.0 # class not present
else:
part_ious[l] = np.sum((segl == l) & (segp == l)) / \
float(np.sum((segl == l) | (segp == l)))
log_string('eval mean loss: %f' % (loss_sum / float(num_batches)))
log_string('eval segmentation accuracy: %f' % \
(total_correct / float(total_seen)))
log_string('eval segmentation avg class acc: %f' % \
(np.mean(np.array(total_correct_class) / \
np.array(total_seen_class, dtype=np.float))))
log_string('eval box IoU (ground/3D): %f / %f' % \
(iou2ds_sum / float(num_batches * BATCH_SIZE), iou3ds_sum / \
float(num_batches * BATCH_SIZE)))
log_string('eval box estimation accuracy (IoU=0.7): %f' % \
(float(iou3d_correct_cnt) / float(num_batches * BATCH_SIZE)))
log_string('eval box estimation accuracy (IoU=0.5): %f' % \
(float(iou3d_correct_cnt_05) / float(num_batches * BATCH_SIZE)))
EPOCH_CNT += 1
eval_box_est_acc = float(iou3d_correct_cnt) / float(
num_batches * BATCH_SIZE)
return eval_box_est_acc
def test_from_rgb_detection(output_filename, result_dir=None):
''' Test frustum pointents with 2D boxes from a RGB detector.
Write test results to KITTI format label files.
todo (rqi): support variable number of points.
'''
ps_list = []
segp_list = []
center_list = []
heading_cls_list = []
heading_res_list = []
size_cls_list = []
size_res_list = []
rot_angle_list = []
score_list = []
onehot_list = []
test_idxs = np.arange(0, len(TEST_DATASET))
print(len(TEST_DATASET))
batch_size = BATCH_SIZE
num_batches = int((len(TEST_DATASET)+batch_size-1)/batch_size)
batch_data_to_feed = np.zeros((batch_size, NUM_POINT, NUM_CHANNEL))
batch_one_hot_to_feed = np.zeros((batch_size, 3))
sess, ops = get_session_and_ops(batch_size=batch_size, num_point=NUM_POINT)
for batch_idx in range(num_batches):
print('batch idx: %d' % (batch_idx))
start_idx = batch_idx * batch_size
end_idx = min(len(TEST_DATASET), (batch_idx+1) * batch_size)
cur_batch_size = end_idx - start_idx
batch_data, batch_rot_angle, batch_rgb_prob, batch_one_hot_vec = \
get_batch(TEST_DATASET, test_idxs, start_idx, end_idx,
NUM_POINT, NUM_CHANNEL, from_rgb_detection=True)
batch_data_to_feed[0:cur_batch_size,...] = batch_data
batch_one_hot_to_feed[0:cur_batch_size,:] = batch_one_hot_vec
# Run one batch inference
batch_output, batch_center_pred, batch_hclass_pred, batch_hres_pred, \
batch_sclass_pred, batch_sres_pred, batch_scores = inference(sess, ops, batch_data_to_feed, batch_one_hot_to_feed, batch_size=batch_size)
for i in range(cur_batch_size):
ps_list.append(batch_data[i,...])
segp_list.append(batch_output[i,...])
center_list.append(batch_center_pred[i,:])
heading_cls_list.append(batch_hclass_pred[i])
heading_res_list.append(batch_hres_pred[i])
size_cls_list.append(batch_sclass_pred[i])
size_res_list.append(batch_sres_pred[i,:])
rot_angle_list.append(batch_rot_angle[i])
#score_list.append(batch_scores[i])
score_list.append(batch_rgb_prob[i]) # 2D RGB detection score
onehot_list.append(batch_one_hot_vec[i])
if FLAGS.dump_result:
with open(output_filename, 'wp') as fp:
pickle.dump(ps_list, fp)
pickle.dump(segp_list, fp)
pickle.dump(center_list, fp)
pickle.dump(heading_cls_list, fp)
pickle.dump(heading_res_list, fp)
pickle.dump(size_cls_list, fp)
pickle.dump(size_res_list, fp)
pickle.dump(rot_angle_list, fp)
pickle.dump(score_list, fp)
pickle.dump(onehot_list, fp)
# Write detection results for KITTI evaluation
print('Number of point clouds: %d' % (len(ps_list)))
write_detection_results(result_dir, TEST_DATASET.id_list,
TEST_DATASET.type_list, TEST_DATASET.box2d_list,
center_list, heading_cls_list, heading_res_list,
size_cls_list, size_res_list, rot_angle_list, score_list)
# Make sure for each frame (no matter if we have measurment for that frame),
# there is a TXT file
output_dir = os.path.join(result_dir, 'data')
if FLAGS.idx_path is not None:
to_fill_filename_list = [line.rstrip()+'.txt' \
for line in open(FLAGS.idx_path)]
fill_files(output_dir, to_fill_filename_list)
def train_one_epoch(sess, ops, train_writer):
''' Training for one epoch on the frustum dataset.
ops is dict mapping from string to tf ops
'''
is_training = True
log_string(str(datetime.now()))
# Shuffle train samples
train_idxs = np.arange(0, len(TRAIN_DATASET))
np.random.shuffle(train_idxs)
num_batches = len(TRAIN_DATASET) / BATCH_SIZE
# To collect statistics
total_correct = 0
total_seen = 0
loss_sum = 0
iou2ds_sum = 0
iou3ds_sum = 0
iou3d_correct_cnt = 0
# Training with batches
for batch_idx in range(num_batches):
start_idx = batch_idx * BATCH_SIZE
end_idx = (batch_idx + 1) * BATCH_SIZE
batch_data, batch_label, batch_center, \
batch_hclass, batch_hres, \
batch_sclass, batch_sres, \
batch_rot_angle, batch_one_hot_vec = \
get_batch(TRAIN_DATASET, train_idxs, start_idx, end_idx,
NUM_POINT, NUM_CHANNEL)
feed_dict = {
ops['pointclouds_pl']: batch_data,
ops['one_hot_vec_pl']: batch_one_hot_vec,
ops['labels_pl']: batch_label,
ops['centers_pl']: batch_center,
ops['heading_class_label_pl']: batch_hclass,
ops['heading_residual_label_pl']: batch_hres,
ops['size_class_label_pl']: batch_sclass,
ops['size_residual_label_pl']: batch_sres,
ops['is_training_pl']: is_training,
}
summary, step, _, loss_val, logits_val, centers_pred_val, \
iou2ds, iou3ds = \
sess.run([ops['merged'], ops['step'], ops['train_op'], ops['loss'],
ops['logits'], ops['centers_pred'],
ops['end_points']['iou2ds'], ops['end_points']['iou3ds']],
feed_dict=feed_dict)
train_writer.add_summary(summary, step)
preds_val = np.argmax(logits_val, 2)
correct = np.sum(preds_val == batch_label)
total_correct += correct
total_seen += (BATCH_SIZE * NUM_POINT)
loss_sum += loss_val
iou2ds_sum += np.sum(iou2ds)
iou3ds_sum += np.sum(iou3ds)
print("iou2ds", iou2ds)
print("iou3ds", iou3ds)
print("iou2ds_sum", iou2ds_sum)
print("iou3ds_sum", iou3ds_sum)
print("centers_pred_val", centers_pred_val)
print("box center", batch_center)
print("logits_val", logits_val)
iou3d_correct_cnt += np.sum(iou3ds >= 0.5)
if (batch_idx + 1) % 10 == 0:
log_string(' -- %03d / %03d --' % (batch_idx + 1, num_batches))
log_string('mean loss: %f' % (loss_sum / 10))
log_string('segmentation accuracy: %f' % \
(total_correct / float(total_seen)))
log_string('box IoU (ground/3D): %f / %f' % \
(iou2ds_sum / float(BATCH_SIZE*10), iou3ds_sum / float(BATCH_SIZE*10)))
log_string('box estimation accuracy (IoU=0.5): %f' % \
(float(iou3d_correct_cnt)/float(BATCH_SIZE*10)))
total_correct = 0
total_seen = 0
loss_sum = 0
iou2ds_sum = 0
iou3ds_sum = 0
iou3d_correct_cnt = 0
def eval_one_epoch(sess, ops, test_writer):
""" ops: dict mapping from string to tf ops """
global EPOCH_CNT
is_training = False
test_idxs = np.arange(0, len(TEST_DATASET))
num_batches = len(TEST_DATASET) / BATCH_SIZE
total_correct = 0
total_seen = 0
loss_sum = 0
shape_ious = []
total_seen_class = [0 for _ in range(NUM_CLASSES)]
total_correct_class = [0 for _ in range(NUM_CLASSES)]
iou2ds_sum = 0
iou3ds_sum = 0
log_string(str(datetime.now()))
log_string('---- EPOCH %03d EVALUATION ----' % (EPOCH_CNT))
for batch_idx in range(num_batches):
start_idx = batch_idx * BATCH_SIZE
end_idx = (batch_idx + 1) * BATCH_SIZE
batch_data, batch_label, batch_center, batch_hclass, batch_hres, batch_sclass, batch_sres, batch_rot_angle, batch_one_hot_vec = get_batch(
TEST_DATASET, test_idxs, start_idx, end_idx, NUM_POINT,
NUM_CHANNEL)
feed_dict = {
ops['pointclouds_pl']: batch_data,
ops['one_hot_vec_pl']: batch_one_hot_vec,
ops['labels_pl']: batch_label,
ops['centers_pl']: batch_center,
ops['heading_class_label_pl']: batch_hclass,
ops['heading_residual_label_pl']: batch_hres,
ops['size_class_label_pl']: batch_sclass,
ops['size_residual_label_pl']: batch_sres,
ops['is_training_pl']: is_training
}
summary, step, loss_val, logits_val, iou2ds, iou3ds = sess.run(
[
ops['merged'], ops['step'], ops['loss'], ops['logits'],
ops['end_points']['iou2ds'], ops['end_points']['iou3ds']
],
feed_dict=feed_dict)
test_writer.add_summary(summary, step)
preds_val = np.argmax(logits_val, 2)
correct = np.sum(preds_val == batch_label)
total_correct += correct
total_seen += (BATCH_SIZE * NUM_POINT)
loss_sum += loss_val
for l in range(NUM_CLASSES):
total_seen_class[l] += np.sum(batch_label == l)
total_correct_class[l] += (np.sum((preds_val == l)
& (batch_label == l)))
iou2ds_sum += np.sum(iou2ds)
iou3ds_sum += np.sum(iou3ds)
for i in range(BATCH_SIZE):
segp = preds_val[i, :]
segl = batch_label[i, :]
part_ious = [0.0 for _ in range(NUM_CLASSES)]
for l in range(NUM_CLASSES):
if (np.sum(segl == l) == 0) and (
np.sum(segp == l)
== 0): # part is not present, no logitsiction as well
part_ious[l] = 1.0
else:
part_ious[l] = np.sum((segl == l) & (segp == l)) / float(
np.sum((segl == l) | (segp == l)))
shape_ious.append(np.mean(part_ious))
#if batch_idx == 100:
# break
print(len(shape_ious))
log_string('eval mean loss: %f' % (loss_sum / float(num_batches)))
log_string('eval accuracy: %f' % (total_correct / float(total_seen)))
log_string('eval avg class acc: %f' % (np.mean(
np.array(total_correct_class) /
np.array(total_seen_class, dtype=np.float))))
log_string('eval mIoU: %f' % (np.mean(shape_ious)))
log_string('eval box IoU (ground/3D): %f / %f' %
(iou2ds_sum / float(num_batches * BATCH_SIZE),
iou3ds_sum / float(num_batches * BATCH_SIZE)))
EPOCH_CNT += 1
return loss_sum / float(num_batches)
def eval_one_epoch(sess, ops, test_writer):
""" ops: dict mapping from string to tf ops """
global EPOCH_CNT
is_training = False
test_idxs = np.arange(0, len(TEST_DATASET))
num_batches = len(TEST_DATASET)/BATCH_SIZE
total_correct = 0
total_seen = 0
loss_sum = 0
shape_ious = []
total_seen_class = [0 for _ in range(NUM_CLASSES)]
total_correct_class = [0 for _ in range(NUM_CLASSES)]
iou2ds_sum = 0
iou3ds_sum = 0
log_string(str(datetime.now()))
log_string('---- EPOCH %03d EVALUATION ----'%(EPOCH_CNT))
for batch_idx in range(num_batches):
start_idx = batch_idx * BATCH_SIZE
end_idx = (batch_idx+1) * BATCH_SIZE
batch_data, batch_label, batch_center, batch_hclass, batch_hres, batch_sclass, batch_sres, batch_rot_angle, batch_one_hot_vec = get_batch(TEST_DATASET, test_idxs, start_idx, end_idx, NUM_POINT, NUM_CHANNEL)
feed_dict = {ops['pointclouds_pl']: batch_data,
ops['one_hot_vec_pl']: batch_one_hot_vec,
ops['labels_pl']: batch_label,
ops['centers_pl']: batch_center,
ops['heading_class_label_pl']: batch_hclass,
ops['heading_residual_label_pl']: batch_hres,
ops['size_class_label_pl']: batch_sclass,
ops['size_residual_label_pl']: batch_sres,
ops['is_training_pl']: is_training}
summary, step, loss_val, logits_val, iou2ds, iou3ds = sess.run([ops['merged'], ops['step'],
ops['loss'], ops['logits'],
ops['end_points']['iou2ds'], ops['end_points']['iou3ds']], feed_dict=feed_dict)
test_writer.add_summary(summary, step)
preds_val = np.argmax(logits_val, 2)
correct = np.sum(preds_val == batch_label)
total_correct += correct
total_seen += (BATCH_SIZE*NUM_POINT)
loss_sum += loss_val
for l in range(NUM_CLASSES):
total_seen_class[l] += np.sum(batch_label==l)
total_correct_class[l] += (np.sum((preds_val==l) & (batch_label==l)))
iou2ds_sum += np.sum(iou2ds)
iou3ds_sum += np.sum(iou3ds)
for i in range(BATCH_SIZE):
segp = preds_val[i,:]
segl = batch_label[i,:]
part_ious = [0.0 for _ in range(NUM_CLASSES)]
for l in range(NUM_CLASSES):
if (np.sum(segl==l) == 0) and (np.sum(segp==l) == 0): # part is not present, no logitsiction as well
part_ious[l] = 1.0
else:
part_ious[l] = np.sum((segl==l) & (segp==l)) / float(np.sum((segl==l) | (segp==l)))
shape_ious.append(np.mean(part_ious))
#if batch_idx == 100:
# break
print(len(shape_ious))
log_string('eval mean loss: %f' % (loss_sum / float(num_batches)))
log_string('eval accuracy: %f'% (total_correct / float(total_seen)))
log_string('eval avg class acc: %f' % (np.mean(np.array(total_correct_class)/np.array(total_seen_class,dtype=np.float))))
log_string('eval mIoU: %f' % (np.mean(shape_ious)))
log_string('eval box IoU (ground/3D): %f / %f' % (iou2ds_sum / float(num_batches*BATCH_SIZE), iou3ds_sum / float(num_batches*BATCH_SIZE)))
EPOCH_CNT += 1
return loss_sum/float(num_batches)
def eval_one_epoch(sess, ops, test_writer):
''' Simple evaluation for one epoch on the frustum dataset.
ops is dict mapping from string to tf ops """
'''
global EPOCH_CNT
is_training = False
log_string(str(datetime.now()))
log_string('---- EPOCH %03d EVALUATION ----' % (EPOCH_CNT))
test_idxs = np.arange(0, len(TEST_DATASET))
num_batches = len(TEST_DATASET) / BATCH_SIZE
# To collect statistics
total_cls_correct = 0
total_cls_seen = 0
total_seen_class = [0 for _ in range(NUM_OBJ_CLASSES)]
total_correct_class = [0 for _ in range(NUM_OBJ_CLASSES)]
total_correct = 0
total_seen = 0
loss_sum = 0
total_obj_sample = 0
iou2ds_sum = 0
iou3ds_sum = 0
iou3d_correct_cnt = 0
# Simple evaluation with batches
for batch_idx in range(num_batches):
start_idx = batch_idx * BATCH_SIZE
end_idx = (batch_idx + 1) * BATCH_SIZE
batch_data, batch_cls_label, batch_label, batch_center, \
batch_hclass, batch_hres, \
batch_sclass, batch_sres, \
batch_rot_angle, batch_feature_vec = \
get_batch(TEST_DATASET, test_idxs, start_idx, end_idx,
NUM_POINT, NUM_CHANNEL)
feed_dict = {
ops['pointclouds_pl']: batch_data,
ops['features_pl']: batch_feature_vec,
ops['cls_label_pl']: batch_cls_label,
ops['labels_pl']: batch_label,
ops['centers_pl']: batch_center,
ops['heading_class_label_pl']: batch_hclass,
ops['heading_residual_label_pl']: batch_hres,
ops['size_class_label_pl']: batch_sclass,
ops['size_residual_label_pl']: batch_sres,
ops['is_training_pl']: is_training
}
summary, step, loss_val, loss_endpoints, cls_logits_val, logits_val, iou2ds, iou3ds = \
sess.run([ops['merged'], ops['step'],
ops['loss'], ops['loss_endpoints'], ops['cls_logits'], ops['logits'],
ops['end_points']['iou2ds'], ops['end_points']['iou3ds']],
feed_dict=feed_dict)
test_writer.add_summary(summary, step)
if np.isnan(loss_val):
print('nan loss in batch: ', batch_idx)
print('loss_endpoints: ', loss_endpoints)
# classification acc
cls_preds_val = np.argmax(cls_logits_val, 1)
cls_correct = np.sum(cls_preds_val == batch_cls_label)
total_cls_correct += cls_correct
total_cls_seen += BATCH_SIZE
for l in range(NUM_OBJ_CLASSES):
total_seen_class[l] += np.sum(batch_cls_label == l)
total_correct_class[l] += (np.sum((cls_preds_val == l)
& (batch_cls_label == l)))
# only calculate seg acc and regression performance with object labels
obj_mask = batch_cls_label < g_type2onehotclass['NonObject']
obj_sample_num = np.sum(obj_mask)
total_obj_sample += obj_sample_num
# segmentation acc
preds_val = np.argmax(logits_val, 2)
correct = np.sum(preds_val[obj_mask] == batch_label[obj_mask])
total_correct += correct
total_seen += (obj_sample_num * NUM_POINT)
loss_sum += loss_val
iou2ds_sum += np.sum(iou2ds[obj_mask])
iou3ds_sum += np.sum(iou3ds[obj_mask])
iou3d_correct_cnt += np.sum(iou3ds[obj_mask] >= 0.7)
# for i in range(BATCH_SIZE):
# segp = preds_val[i,:]
# segl = batch_label[i,:]
# part_ious = [0.0 for _ in range(NUM_SEG_CLASSES)]
# for l in range(NUM_SEG_CLASSES):
# if (np.sum(segl==l) == 0) and (np.sum(segp==l) == 0):
# part_ious[l] = 1.0 # class not present
# else:
# part_ious[l] = np.sum((segl==l) & (segp==l)) / \
# float(np.sum((segl==l) | (segp==l)))
log_string('eval mean loss: %f' % (loss_sum / float(num_batches)))
log_string('classification accuracy: %f' % \
(total_cls_correct / float(total_cls_seen)))
log_string('eval segmentation accuracy: %f'% \
(total_correct / float(total_seen)))
avg_cls_acc = np.mean(np.array(total_correct_class) / \
np.array(total_seen_class,dtype=np.float))
log_string('eval classification avg class acc: %f' % avg_cls_acc)
log_string('eval box IoU (ground/3D): %f / %f' % \
(iou2ds_sum / float(total_obj_sample), iou3ds_sum / \
float(total_obj_sample)))
log_string('eval box estimation accuracy (IoU=0.7): %f' % \
(float(iou3d_correct_cnt)/float(total_obj_sample)))
box_estimation_acc = float(iou3d_correct_cnt) / float(total_obj_sample)
mean_loss = loss_sum / float(num_batches)
EPOCH_CNT += 1
return mean_loss, avg_cls_acc
def eval_one_epoch(fpointnet,device):
'''
@author chonepieceyb
:param fpointnet: 网络对象
:param device: 设备
:return:
'''
# get data
global EPOCH_CNT
log_string(str(datetime.now()))
log_string('---- EPOCH %03d EVALUATION ----' % (EPOCH_CNT))
test_idxs = np.arange(0, len(TEST_DATASET))
num_batches = len(TEST_DATASET) // BATCH_SIZE
# To collect statistics
total_correct = 0
total_seen = 0
loss_sum = 0
total_seen_class = [0 for _ in range(NUM_CLASSES)]
total_correct_class = [0 for _ in range(NUM_CLASSES)]
iou2ds_sum = 0
iou3ds_sum = 0
iou3d_correct_cnt = 0
fpointnet.eval() # 训练模式
for batch_idx in range(int(num_batches)):
start_idx = batch_idx * BATCH_SIZE
end_idx = (batch_idx+1)* BATCH_SIZE
batch_data, batch_label, batch_center, \
batch_hclass, batch_hres, \
batch_sclass, batch_sres, \
batch_rot_angle, batch_one_hot_vec = \
get_batch(TEST_DATASET, test_idxs, start_idx, end_idx,
NUM_POINT, NUM_CHANNEL)
# convert to torch tensor and change data format
batch_data_gpu = torch.from_numpy(batch_data).permute(0,2,1).to(device,dtype=torch.float32) #
batch_label_gpu= torch.from_numpy(batch_label).to(device,dtype=torch.int64)
batch_center_gpu = torch.from_numpy(batch_center).to(device,dtype=torch.float32)
batch_hclass_gpu = torch.from_numpy(batch_hclass).to(device,dtype=torch.int64)
batch_hres_gpu = torch.from_numpy(batch_hres).to(device,dtype=torch.float32)
batch_sclass_gpu = torch.from_numpy(batch_sclass).to(device,dtype=torch.int64)
batch_sres_gpu = torch.from_numpy(batch_sres).to(device,dtype=torch.float32)
batch_one_hot_vec_gpu = torch.from_numpy(batch_one_hot_vec).to(device ,dtype=torch.float32)
# eval
with torch.no_grad():
end_points = fpointnet.forward(batch_data_gpu,batch_one_hot_vec_gpu)
loss, losses = get_loss(batch_label_gpu,batch_center_gpu,batch_hclass_gpu,batch_hres_gpu,batch_sclass_gpu,batch_sres_gpu,end_points)
#get data and transform dataformat from torch style to tensorflow style
loss_val = loss.cpu().data.numpy()
logits_val = end_points['mask_logits'].data.cpu().numpy()
iou2ds,iou3ds,accuracy = compute_summary(end_points,batch_label_gpu,batch_center,batch_hclass,batch_hres,batch_sclass,batch_sres)
preds_val = np.argmax(logits_val, 1)
correct = np.sum(preds_val == batch_label)
total_correct += correct
total_seen += (BATCH_SIZE * NUM_POINT)
loss_sum += loss_val
for l in range(NUM_CLASSES):
total_seen_class[l] += np.sum(batch_label == l)
total_correct_class[l] += (np.sum((preds_val == l) & (batch_label == l)))
iou2ds_sum += np.sum(iou2ds)
iou3ds_sum += np.sum(iou3ds)
iou3d_correct_cnt += np.sum(iou3ds >= 0.7)
for i in range(BATCH_SIZE):
segp = preds_val[i,:]
segl = batch_label[i,:]
part_ious = [0.0 for _ in range(NUM_CLASSES)]
for l in range(NUM_CLASSES):
if (np.sum(segl==l) == 0) and (np.sum(segp==l) == 0):
part_ious[l] = 1.0 # class not present
else:
part_ious[l] = np.sum((segl==l) & (segp==l)) / \
float(np.sum((segl==l) | (segp==l)))
log_string('eval mean loss: %f' % (loss_sum / float(num_batches)))
log_string('eval segmentation accuracy: %f' % \
(total_correct / float(total_seen)))
log_string('eval segmentation avg class acc: %f' % \
(np.mean(np.array(total_correct_class) / \
np.array(total_seen_class, dtype=np.float))))
log_string('eval box IoU (ground/3D): %f / %f' % \
(iou2ds_sum / float(num_batches * BATCH_SIZE), iou3ds_sum / \
float(num_batches * BATCH_SIZE)))
log_string('eval box estimation accuracy (IoU=0.7): %f' % \
(float(iou3d_correct_cnt) / float(num_batches * BATCH_SIZE)))
EPOCH_CNT += 1
def train_one_epoch(fpointnet,device,optimizer):
'''
@author Qiao
:param fpointnet: 网络
:param device: 设备
:return:
'''
global EPOCH_CNT
log_string(str(datetime.now()))
log_string('---- EPOCH %03d TRAINING ----' % (EPOCH_CNT))
# 按照原始数据集大小进行取值
if FLAGS.train_batch_num == None:
train_idxs = np.arange(0, len(TRAIN_DATASET))
np.random.shuffle(train_idxs) #随机
num_batches = len(TRAIN_DATASET)//BATCH_SIZE
else:
num_batches = int(FLAGS.train_batch_num)
num_batches = min(num_batches,len(TRAIN_DATASET)//BATCH_SIZE)
train_idxs = np.arange(0, BATCH_SIZE*num_batches)
np.random.shuffle(train_idxs)
# To collect statistics
total_correct = 0
total_seen = 0
loss_sum = 0
iou2ds_sum = 0
iou3ds_sum = 0
iou3d_correct_cnt = 0
# Training with batches
for batch_idx in range(num_batches):
start_idx = batch_idx * BATCH_SIZE
end_idx = (batch_idx+1) * BATCH_SIZE
batch_data, batch_label, batch_center, \
batch_hclass, batch_hres, \
batch_sclass, batch_sres, \
batch_rot_angle, batch_one_hot_vec = \
get_batch(TRAIN_DATASET, train_idxs, start_idx, end_idx,
NUM_POINT, NUM_CHANNEL)
pointclouds_pl = torch.from_numpy(batch_data)
pointclouds_pl = pointclouds_pl.permute(0, 2, 1)
pointclouds_pl = pointclouds_pl.to(device,dtype=torch.float32)
one_hot_vec_pl = torch.from_numpy(batch_one_hot_vec)
one_hot_vec_pl = one_hot_vec_pl.to(device,dtype=torch.float32)
labels_pl = torch.from_numpy(batch_label).to(device,dtype=torch.int64)
centers_pl = torch.from_numpy(batch_center).to(device,dtype=torch.float32)
heading_class_label_pl = torch.from_numpy(batch_hclass).to(device,dtype=torch.int64)
heading_residual_label_pl = torch.from_numpy(batch_hres).to(device,dtype=torch.float32)
size_class_label_pl = torch.from_numpy(batch_sclass).to(device,dtype=torch.int64)
size_residual_label_pl = torch.from_numpy(batch_sres).to(device,dtype=torch.float32)
fpointnet.train()
end_points = fpointnet.forward(pointclouds_pl, one_hot_vec_pl)
loss, losses = get_loss(labels_pl, centers_pl,\
heading_class_label_pl, heading_residual_label_pl,\
size_class_label_pl, size_residual_label_pl, end_points)
optimizer.zero_grad()
loss.backward()
optimizer.step()
loss_val = loss.cpu().detach().numpy()
logits_val = end_points['mask_logits'].cpu().detach().numpy()
iou2ds,iou3ds,accuracy = compute_summary(end_points,labels_pl ,batch_center,\
batch_hclass,batch_hres,batch_sclass,batch_sres)
preds_val = np.argmax(logits_val, 1)
correct = np.sum(preds_val == batch_label)
total_correct += correct
total_seen += (BATCH_SIZE*NUM_POINT)
loss_sum += loss_val
iou2ds_sum += np.sum(iou2ds)
iou3ds_sum += np.sum(iou3ds)
iou3d_correct_cnt += np.sum(iou3ds>=0.7)
iou2d_t = np.sum(iou2ds)/float(BATCH_SIZE)
iou3d_t = np.sum(iou3ds)/float(BATCH_SIZE)
writer.add_scalar('iou2ds', iou2d_t, global_step=EPOCH_CNT*batch_idx)
writer.add_scalar('iou3ds', iou3d_t, global_step=EPOCH_CNT*batch_idx)
for key,value in losses.items():
writer.add_scalar(key, losses[key].cpu().data.numpy(), global_step=EPOCH_CNT*batch_idx)
# writer.add_scalar('total_loss', loss, global_step=EPOCH_CNT*batch_idx)
for param_group in optimizer.param_groups:
learning_rate = param_group['lr']
writer.add_scalar('learning_rate', learning_rate, global_step=EPOCH_CNT*batch_idx)
writer.add_scalar('segmentation accuracy', accuracy, global_step=EPOCH_CNT*batch_idx)
if (batch_idx+1)%10 == 0:
log_string(' -- %03d / %03d --' % (batch_idx+1, num_batches))
log_string('mean loss: %f' % (loss_sum / 10))
log_string('segmentation accuracy: %f' % \
(total_correct / float(total_seen)))
log_string('box IoU (ground/3D): %f / %f' % \
(iou2ds_sum / float(BATCH_SIZE*10), iou3ds_sum / float(BATCH_SIZE*10)))
log_string('box estimation accuracy (IoU=0.7): %f' % \
(float(iou3d_correct_cnt)/float(BATCH_SIZE*10)))
total_correct = 0
total_seen = 0
loss_sum = 0
iou2ds_sum = 0
iou3ds_sum = 0
iou3d_correct_cnt = 0
def test(output_filename, result_dir=None):
''' Test frustum pointnets with GT 2D boxes.
Write test results to KITTI format label files.
todo (rqi): support variable number of points.
'''
ps_list = []
seg_list = []
segp_list = []
center_list = []
heading_cls_list = []
heading_res_list = []
size_cls_list = []
size_res_list = []
rot_angle_list = []
score_list = []
test_idxs = np.arange(0, len(TEST_DATASET))
batch_size = BATCH_SIZE
num_batches = len(TEST_DATASET)/batch_size
sess, ops = get_session_and_ops(batch_size=batch_size, num_point=NUM_POINT)
correct_cnt = 0
for batch_idx in range(num_batches):
print('batch idx: %d' % (batch_idx))
start_idx = batch_idx * batch_size
end_idx = (batch_idx+1) * batch_size
batch_data, batch_label, batch_center, \
batch_hclass, batch_hres, batch_sclass, batch_sres, \
batch_rot_angle, batch_one_hot_vec = \
get_batch(TEST_DATASET, test_idxs, start_idx, end_idx,
NUM_POINT, NUM_CHANNEL)
batch_output, batch_center_pred, \
batch_hclass_pred, batch_hres_pred, \
batch_sclass_pred, batch_sres_pred, batch_scores = \
inference(sess, ops, batch_data,
batch_one_hot_vec, batch_size=batch_size)
correct_cnt += np.sum(batch_output==batch_label)
for i in range(batch_output.shape[0]):
ps_list.append(batch_data[i,...])
seg_list.append(batch_label[i,...])
segp_list.append(batch_output[i,...])
center_list.append(batch_center_pred[i,:])
heading_cls_list.append(batch_hclass_pred[i])
heading_res_list.append(batch_hres_pred[i])
size_cls_list.append(batch_sclass_pred[i])
size_res_list.append(batch_sres_pred[i,:])
rot_angle_list.append(batch_rot_angle[i])
score_list.append(batch_scores[i])
print("Segmentation accuracy: %f" % \
(correct_cnt / float(batch_size*num_batches*NUM_POINT)))
if FLAGS.dump_result:
with open(output_filename, 'wp') as fp:
pickle.dump(ps_list, fp)
pickle.dump(seg_list, fp)
pickle.dump(segp_list, fp)
pickle.dump(center_list, fp)
pickle.dump(heading_cls_list, fp)
pickle.dump(heading_res_list, fp)
pickle.dump(size_cls_list, fp)
pickle.dump(size_res_list, fp)
pickle.dump(rot_angle_list, fp)
pickle.dump(score_list, fp)
# Write detection results for KITTI evaluation
write_detection_results(result_dir, TEST_DATASET.id_list,
TEST_DATASET.type_list, TEST_DATASET.box2d_list, center_list,
heading_cls_list, heading_res_list,
size_cls_list, size_res_list, rot_angle_list, score_list)
def test_from_rgb_detection(output_filename, result_dir=None):
''' Test frustum pointents with 2D boxes from a RGB detector.
Write test results to KITTI format label files.
todo (rqi): support variable number of points.
'''
ps_list = []
segp_list = []
center_list = []
heading_cls_list = []
heading_res_list = []
size_cls_list = []
size_res_list = []
rot_angle_list = []
score_list = []
onehot_list = []
test_idxs = np.arange(0, len(TEST_DATASET))
print(len(TEST_DATASET))
batch_size = BATCH_SIZE
num_batches = int((len(TEST_DATASET)+batch_size-1)/batch_size)
batch_data_to_feed = np.zeros((batch_size, NUM_POINT, NUM_CHANNEL))
batch_one_hot_to_feed = np.zeros((batch_size, 3))
sess, ops = get_session_and_ops(batch_size=batch_size, num_point=NUM_POINT)
for batch_idx in range(num_batches):
print('batch idx: %d' % (batch_idx))
start_idx = batch_idx * batch_size
end_idx = min(len(TEST_DATASET), (batch_idx+1) * batch_size)
cur_batch_size = end_idx - start_idx
batch_data, batch_rot_angle, batch_rgb_prob, batch_one_hot_vec = \
get_batch(TEST_DATASET, test_idxs, start_idx, end_idx,
NUM_POINT, NUM_CHANNEL, from_rgb_detection=True)
batch_data_to_feed[0:cur_batch_size,...] = batch_data
batch_one_hot_to_feed[0:cur_batch_size,:] = batch_one_hot_vec
# Run one batch inference
batch_output, batch_center_pred, \
batch_hclass_pred, batch_hres_pred, \
batch_sclass_pred, batch_sres_pred, batch_scores = \
inference(sess, ops, batch_data_to_feed,
batch_one_hot_to_feed, batch_size=batch_size)
for i in range(cur_batch_size):
ps_list.append(batch_data[i,...])
segp_list.append(batch_output[i,...])
center_list.append(batch_center_pred[i,:])
heading_cls_list.append(batch_hclass_pred[i])
heading_res_list.append(batch_hres_pred[i])
size_cls_list.append(batch_sclass_pred[i])
size_res_list.append(batch_sres_pred[i,:])
rot_angle_list.append(batch_rot_angle[i])
#score_list.append(batch_scores[i])
score_list.append(batch_rgb_prob[i]) # 2D RGB detection score
onehot_list.append(batch_one_hot_vec[i])
if FLAGS.dump_result:
with open(output_filename, 'wp') as fp:
pickle.dump(ps_list, fp)
pickle.dump(segp_list, fp)
pickle.dump(center_list, fp)
pickle.dump(heading_cls_list, fp)
pickle.dump(heading_res_list, fp)
pickle.dump(size_cls_list, fp)
pickle.dump(size_res_list, fp)
pickle.dump(rot_angle_list, fp)
pickle.dump(score_list, fp)
pickle.dump(onehot_list, fp)
# Write detection results for KITTI evaluation
print('Number of point clouds: %d' % (len(ps_list)))
write_detection_results(result_dir, TEST_DATASET.id_list,
TEST_DATASET.type_list, TEST_DATASET.box2d_list,
center_list, heading_cls_list, heading_res_list,
size_cls_list, size_res_list, rot_angle_list, score_list)
# Make sure for each frame (no matter if we have measurment for that frame),
# there is a TXT file
output_dir = os.path.join(result_dir, 'data')
if FLAGS.idx_path is not None:
to_fill_filename_list = [line.rstrip()+'.txt' \
for line in open(FLAGS.idx_path)]
fill_files(output_dir, to_fill_filename_list)
def test_from_video_detection(dataset, output_filename, result_dir=None, sequence_dir=None):
''' Test frustum pointents with 2D boxes from a RGB detector.
Write test results to KITTI format label files.
todo (rqi): support variable number of points.
'''
ps_list = []
segp_list = []
center_list = []
heading_cls_list = []
heading_res_list = []
size_cls_list = []
size_res_list = []
rot_angle_list = []
score_list = []
onehot_list = []
hypotheses_list = []
is_hypotheses = dataset.is_hypotheses
test_idxs = np.arange(0, len(dataset))
print(len(dataset))
batch_size = BATCH_SIZE
num_batches = int((len(dataset) + batch_size - 1) / batch_size)
batch_data_to_feed = np.zeros((batch_size, NUM_POINT, NUM_CHANNEL))
batch_one_hot_to_feed = np.zeros((batch_size, 3))
sess, ops = get_session_and_ops(batch_size=batch_size, num_point=NUM_POINT)
for batch_idx in range(num_batches):
print('batch idx: %d' % (batch_idx))
start_idx = batch_idx * batch_size
end_idx = min(len(dataset), (batch_idx + 1) * batch_size)
cur_batch_size = end_idx - start_idx
cur_batch = get_batch(dataset, test_idxs, start_idx, end_idx,
NUM_POINT, NUM_CHANNEL, from_rgb_detection=True)
batch_data, batch_rot_angle, batch_rgb_prob = cur_batch[:3]
cur_batch = cur_batch[3:]
if dataset.one_hot:
batch_one_hot_vec = cur_batch.pop(0)
if dataset.is_hypotheses:
batch_hypotheses = cur_batch.pop(0)
batch_data_to_feed[0:cur_batch_size, ...] = batch_data
batch_one_hot_to_feed[0:cur_batch_size, :] = batch_one_hot_vec
# Run one batch inference
batch_output, batch_center_pred, \
batch_hclass_pred, batch_hres_pred, \
batch_sclass_pred, batch_sres_pred, batch_scores = \
inference(sess, ops, batch_data_to_feed,
batch_one_hot_to_feed, batch_size=batch_size)
for i in range(cur_batch_size):
ps_list.append(batch_data[i, ...])
segp_list.append(batch_output[i, ...])
center_list.append(batch_center_pred[i, :])
heading_cls_list.append(batch_hclass_pred[i])
heading_res_list.append(batch_hres_pred[i])
size_cls_list.append(batch_sclass_pred[i])
size_res_list.append(batch_sres_pred[i, :])
rot_angle_list.append(batch_rot_angle[i])
# score_list.append(batch_scores[i])
score_list.append(batch_rgb_prob[i]) # 2D RGB detection score
onehot_list.append(batch_one_hot_vec[i])
if is_hypotheses:
hypotheses_list.append(batch_hypotheses[i])
if FLAGS.dump_result:
with open(output_filename, 'wb') as fp:
pickle.dump(ps_list, fp)
pickle.dump(segp_list, fp)
pickle.dump(center_list, fp)
pickle.dump(heading_cls_list, fp)
pickle.dump(heading_res_list, fp)
pickle.dump(size_cls_list, fp)
pickle.dump(size_res_list, fp)
pickle.dump(rot_angle_list, fp)
pickle.dump(score_list, fp)
pickle.dump(onehot_list, fp)
if is_hypotheses:
pickle.dump(hypotheses_list, fp)
# Write detection results for KITTI evaluation
print('Number of point clouds: %d' % (len(ps_list)))
write_detection_results(result_dir, dataset.id_list,
dataset.type_list, dataset.box2d_list,
center_list, heading_cls_list, heading_res_list,
size_cls_list, size_res_list, rot_angle_list, score_list, hypotheses_list)
# Make sure for each frame (no matter if we have measurment for that frame),
# there is a TXT file
output_dir = os.path.join(result_dir, 'data')
if sequence_dir is not None:
image_dir = os.path.join(sequence_dir, "image_03/data")
to_fill_filename_list = [os.path.splitext(f)[0] + '.txt' \
for f in os.listdir(image_dir)]
fill_files(output_dir, to_fill_filename_list)
def eval_one_epoch(sess, ops, test_writer):
''' Simple evaluation for one epoch on the frustum dataset.
ops is dict mapping from string to tf ops """
'''
global EPOCH_CNT
is_training = False
log_string(str(datetime.now()))
log_string('---- EPOCH %03d EVALUATION ----'%(EPOCH_CNT))
test_idxs = np.arange(0, len(TEST_DATASET))
num_batches = len(TEST_DATASET)/BATCH_SIZE
# To collect statistics
total_correct = 0
total_seen = 0
loss_sum = 0
total_seen_class = [0 for _ in range(NUM_CLASSES)]
total_correct_class = [0 for _ in range(NUM_CLASSES)]
iou2ds_sum = 0
iou3ds_sum = 0
iou3d_correct_cnt = 0
# Simple evaluation with batches
for batch_idx in range(num_batches):
start_idx = batch_idx * BATCH_SIZE
end_idx = (batch_idx+1) * BATCH_SIZE
batch_data, batch_label, batch_center, \
batch_hclass, batch_hres, \
batch_sclass, batch_sres, \
batch_rot_angle, batch_one_hot_vec = \
get_batch(TEST_DATASET, test_idxs, start_idx, end_idx,
NUM_POINT, NUM_CHANNEL)
feed_dict = {ops['pointclouds_pl']: batch_data,
ops['one_hot_vec_pl']: batch_one_hot_vec,
ops['labels_pl']: batch_label,
ops['centers_pl']: batch_center,
ops['heading_class_label_pl']: batch_hclass,
ops['heading_residual_label_pl']: batch_hres,
ops['size_class_label_pl']: batch_sclass,
ops['size_residual_label_pl']: batch_sres,
ops['is_training_pl']: is_training}
summary, step, loss_val, logits_val, iou2ds, iou3ds = \
sess.run([ops['merged'], ops['step'],
ops['loss'], ops['logits'],
ops['end_points']['iou2ds'], ops['end_points']['iou3ds']],
feed_dict=feed_dict)
test_writer.add_summary(summary, step)
preds_val = np.argmax(logits_val, 2)
correct = np.sum(preds_val == batch_label)
total_correct += correct
total_seen += (BATCH_SIZE*NUM_POINT)
loss_sum += loss_val
for l in range(NUM_CLASSES):
total_seen_class[l] += np.sum(batch_label==l)
total_correct_class[l] += (np.sum((preds_val==l) & (batch_label==l)))
iou2ds_sum += np.sum(iou2ds)
iou3ds_sum += np.sum(iou3ds)
iou3d_correct_cnt += np.sum(iou3ds>=0.7)
for i in range(BATCH_SIZE):
segp = preds_val[i,:]
segl = batch_label[i,:]
part_ious = [0.0 for _ in range(NUM_CLASSES)]
for l in range(NUM_CLASSES):
if (np.sum(segl==l) == 0) and (np.sum(segp==l) == 0):
part_ious[l] = 1.0 # class not present
else:
part_ious[l] = np.sum((segl==l) & (segp==l)) / \
float(np.sum((segl==l) | (segp==l)))
log_string('eval mean loss: %f' % (loss_sum / float(num_batches)))
log_string('eval segmentation accuracy: %f'% \
(total_correct / float(total_seen)))
log_string('eval segmentation avg class acc: %f' % \
(np.mean(np.array(total_correct_class) / \
np.array(total_seen_class,dtype=np.float))))
log_string('eval box IoU (ground/3D): %f / %f' % \
(iou2ds_sum / float(num_batches*BATCH_SIZE), iou3ds_sum / \
float(num_batches*BATCH_SIZE)))
log_string('eval box estimation accuracy (IoU=0.7): %f' % \
(float(iou3d_correct_cnt)/float(num_batches*BATCH_SIZE)))
EPOCH_CNT += 1
def train_one_epoch(sess, ops, train_writer):
''' Training for one epoch on the frustum dataset.
ops is dict mapping from string to tf ops
'''
is_training = True
log_string(str(datetime.now()))
# Shuffle train samples
train_idxs = np.arange(0, len(TRAIN_DATASET))
np.random.shuffle(train_idxs)
num_batches = len(TRAIN_DATASET)/BATCH_SIZE
# To collect statistics
total_correct = 0
total_seen = 0
loss_sum = 0
iou2ds_sum = 0
iou3ds_sum = 0
iou3d_correct_cnt = 0
# Training with batches
for batch_idx in range(num_batches):
start_idx = batch_idx * BATCH_SIZE
end_idx = (batch_idx+1) * BATCH_SIZE
batch_data, batch_label, batch_center, \
batch_hclass, batch_hres, \
batch_sclass, batch_sres, \
batch_rot_angle, batch_one_hot_vec = \
get_batch(TRAIN_DATASET, train_idxs, start_idx, end_idx,
NUM_POINT, NUM_CHANNEL)
feed_dict = {ops['pointclouds_pl']: batch_data,
ops['one_hot_vec_pl']: batch_one_hot_vec,
ops['labels_pl']: batch_label,
ops['centers_pl']: batch_center,
ops['heading_class_label_pl']: batch_hclass,
ops['heading_residual_label_pl']: batch_hres,
ops['size_class_label_pl']: batch_sclass,
ops['size_residual_label_pl']: batch_sres,
ops['is_training_pl']: is_training,}
summary, step, _, loss_val, logits_val, centers_pred_val, \
iou2ds, iou3ds = \
sess.run([ops['merged'], ops['step'], ops['train_op'], ops['loss'],
ops['logits'], ops['centers_pred'],
ops['end_points']['iou2ds'], ops['end_points']['iou3ds']],
feed_dict=feed_dict)
train_writer.add_summary(summary, step)
preds_val = np.argmax(logits_val, 2)
correct = np.sum(preds_val == batch_label)
total_correct += correct
total_seen += (BATCH_SIZE*NUM_POINT)
loss_sum += loss_val
iou2ds_sum += np.sum(iou2ds)
iou3ds_sum += np.sum(iou3ds)
iou3d_correct_cnt += np.sum(iou3ds>=0.7)
if (batch_idx+1)%10 == 0:
log_string(' -- %03d / %03d --' % (batch_idx+1, num_batches))
log_string('mean loss: %f' % (loss_sum / 10))
log_string('segmentation accuracy: %f' % \
(total_correct / float(total_seen)))
log_string('box IoU (ground/3D): %f / %f' % \
(iou2ds_sum / float(BATCH_SIZE*10), iou3ds_sum / float(BATCH_SIZE*10)))
log_string('box estimation accuracy (IoU=0.7): %f' % \
(float(iou3d_correct_cnt)/float(BATCH_SIZE*10)))
total_correct = 0
total_seen = 0
loss_sum = 0
iou2ds_sum = 0
iou3ds_sum = 0
iou3d_correct_cnt = 0
def train_one_epoch(sess, ops, train_writer, idxs_to_use=None):
''' Training for one epoch on the frustum dataset.
ops is dict mapping from string to tf ops
'''
is_training = True
log_string(str(datetime.now()))
# Shuffle train samples
if not idxs_to_use:
train_idxs = np.arange(0, len(TRAIN_DATASET))
else:
log_string('Training with classification hard samples.')
train_idxs = idxs_to_use
np.random.shuffle(train_idxs)
num_batches = len(train_idxs) / BATCH_SIZE
# To collect statistics
total_cls_correct = 0
total_cls_seen = 0
total_correct = 0
total_seen = 0
loss_sum = 0
total_obj_sample = 0
iou2ds_sum = 0
iou3ds_sum = 0
iou3d_correct_cnt = 0
# Training with batches
for batch_idx in range(num_batches):
start_idx = batch_idx * BATCH_SIZE
end_idx = (batch_idx + 1) * BATCH_SIZE
batch_data, batch_cls_label, batch_label, batch_center, \
batch_hclass, batch_hres, \
batch_sclass, batch_sres, \
batch_rot_angle, batch_feature_vec = \
get_batch(TRAIN_DATASET, train_idxs, start_idx, end_idx,
NUM_POINT, NUM_CHANNEL)
feed_dict = {
ops['pointclouds_pl']: batch_data,
ops['features_pl']: batch_feature_vec,
ops['cls_label_pl']: batch_cls_label,
ops['labels_pl']: batch_label,
ops['centers_pl']: batch_center,
ops['heading_class_label_pl']: batch_hclass,
ops['heading_residual_label_pl']: batch_hres,
ops['size_class_label_pl']: batch_sclass,
ops['size_residual_label_pl']: batch_sres,
ops['is_training_pl']: is_training,
}
summary, step, _, loss_val, cls_logits_val, logits_val, centers_pred_val, \
iou2ds, iou3ds = \
sess.run([ops['merged'], ops['step'], ops['train_op'], ops['loss'],
ops['cls_logits'], ops['logits'], ops['centers_pred'],
ops['end_points']['iou2ds'], ops['end_points']['iou3ds']],
feed_dict=feed_dict)
train_writer.add_summary(summary, step)
# classification acc
cls_preds_val = np.argmax(cls_logits_val, 1)
cls_correct = np.sum(cls_preds_val == batch_cls_label)
total_cls_correct += cls_correct
total_cls_seen += BATCH_SIZE
# only calculate seg acc and regression performance with object labels
obj_mask = batch_cls_label < g_type2onehotclass['NonObject']
obj_sample_num = np.sum(obj_mask)
total_obj_sample += obj_sample_num
# segmentation acc
preds_val = np.argmax(logits_val, 2)
correct = np.sum(preds_val[obj_mask] == batch_label[obj_mask])
total_correct += correct
total_seen += (obj_sample_num * NUM_POINT)
loss_sum += loss_val
iou2ds_sum += np.sum(iou2ds[obj_mask])
iou3ds_sum += np.sum(iou3ds[obj_mask])
iou3d_correct_cnt += np.sum(iou3ds[obj_mask] >= 0.7)
if (batch_idx + 1) % 10 == 0:
log_string(' -- %03d / %03d --' % (batch_idx + 1, num_batches))
log_string('mean loss: %f' % (loss_sum / 10))
log_string('classification accuracy: %f' % \
(total_cls_correct / float(total_cls_seen)))
if total_seen > 0:
log_string('segmentation accuracy: %f' % \
(total_correct / float(total_seen)))
log_string('box IoU (ground/3D): %f / %f' % \
(iou2ds_sum / float(total_obj_sample), iou3ds_sum / float(total_obj_sample)))
log_string('box estimation accuracy (IoU=0.7): %f' % \
(float(iou3d_correct_cnt)/float(total_obj_sample)))
total_cls_correct = 0
total_correct = 0
total_cls_seen = 0
total_seen = 0
total_obj_sample = 0
loss_sum = 0
iou2ds_sum = 0
iou3ds_sum = 0
iou3d_correct_cnt = 0
def evaluate(sess, ops, test_writer):
'''
'''
#global frustum map
global frustum_map
is_training = False
test_idxs = np.arange(0, len(TEST_DATASET))
num_batches = len(TEST_DATASET)/BATCH_SIZE
# To collect statistics
total_correct = 0
total_seen = 0
loss_sum = 0
total_seen_class = [0 for _ in range(NUM_CLASSES)]
total_correct_class = [0 for _ in range(NUM_CLASSES)]
iou2ds_sum = 0
iou3ds_sum = 0
iou3d_correct_cnt = 0
#for batch_idx in range(num_batches):
for batch_idx in range(17334):
start_idx = batch_idx * BATCH_SIZE
end_idx = (batch_idx+1) * BATCH_SIZE
batch_data, batch_label, batch_center, \
batch_hclass, batch_hres, \
batch_sclass, batch_sres, \
batch_rot_angle, batch_one_hot_vec = \
get_batch(TEST_DATASET, test_idxs, start_idx, end_idx,
NUM_POINT, NUM_CHANNEL)
feed_dict = {ops['pointclouds_pl']: batch_data,
ops['one_hot_vec_pl']: batch_one_hot_vec,
ops['labels_pl']: batch_label,
ops['centers_pl']: batch_center,
ops['heading_class_label_pl']: batch_hclass,
ops['heading_residual_label_pl']: batch_hres,
ops['size_class_label_pl']: batch_sclass,
ops['size_residual_label_pl']: batch_sres,
ops['is_training_pl']: is_training}
summary, loss_val, logits_val, iou2ds, iou3ds = \
sess.run([ops['merged'],
ops['loss'], ops['logits'],
ops['end_points']['iou2ds'], ops['end_points']['iou3ds']],
feed_dict=feed_dict)
test_writer.add_summary(summary)
preds_val = np.argmax(logits_val, 2)
correct = np.sum(preds_val == batch_label)
total_correct += correct
total_seen += (BATCH_SIZE*NUM_POINT)
loss_sum += loss_val
for l in range(NUM_CLASSES):
total_seen_class[l] += np.sum(batch_label==l)
total_correct_class[l] += (np.sum((preds_val==l) & (batch_label==l)))
iou2ds_sum += np.sum(iou2ds)
iou3ds_sum += np.sum(iou3ds)
iou3d_correct_cnt += np.sum(iou3ds>=0.7)
for i in range(BATCH_SIZE):
segp = preds_val[i,:]
segl = batch_label[i,:]
part_ious = [0.0 for _ in range(NUM_CLASSES)]
for l in range(NUM_CLASSES):
if (np.sum(segl==l) == 0) and (np.sum(segp==l) == 0):
part_ious[l] = 1.0 # class not present
else:
part_ious[l] = np.sum((segl==l) & (segp==l)) / \
float(np.sum((segl==l) | (segp==l)))
if iou3ds < 0.7:
log_string('**** Frustum Number %03d from Image %06d and 2D box %06d****' % (batch_idx,frustum_map[batch_idx]['img_indx'],frustum_map[batch_idx]['obj_indx']))
log_string('loss: %f' % (loss_val))
log_string('Frustum segmentation accuracy: %f'% (correct / float(NUM_POINT)))
log_string('Frustum box IoU (ground/3D): %f / %f' % (iou2ds, iou3ds))
log_string("*********** Summary Results **********")
log_string('eval mean loss: %f' % (loss_sum / float(num_batches)))
log_string('eval segmentation accuracy: %f'% \
(total_correct / float(total_seen)))
log_string('eval segmentation avg class acc: %f' % \
(np.mean(np.array(total_correct_class) / \
np.array(total_seen_class,dtype=np.float))))
log_string('eval box IoU (ground/3D): %f / %f' % \
(iou2ds_sum / float(num_batches*BATCH_SIZE), iou3ds_sum / \
float(num_batches*BATCH_SIZE)))
log_string('eval box estimation accuracy (IoU=0.7): %f' % \
(float(iou3d_correct_cnt)/float(num_batches*BATCH_SIZE)))
def train_one_epoch(sess, ops, train_writer):
""" ops: dict mapping from string to tf ops """
is_training = True
# Shuffle train samples
train_idxs = np.arange(0, len(TRAIN_DATASET))
np.random.shuffle(train_idxs)
num_batches = len(TRAIN_DATASET)/BATCH_SIZE
log_string(str(datetime.now()))
total_correct = 0
total_seen = 0
loss_sum = 0
iou2ds_sum = 0
iou3ds_sum = 0
for batch_idx in range(num_batches):
start_idx = batch_idx * BATCH_SIZE
end_idx = (batch_idx+1) * BATCH_SIZE
batch_data, batch_label, batch_center, batch_hclass, batch_hres, batch_sclass, batch_sres, batch_rot_angle, batch_one_hot_vec = get_batch(TRAIN_DATASET, train_idxs, start_idx, end_idx, NUM_POINT, NUM_CHANNEL)
#print 'Ground truth centers: ', batch_center
# Augment batched point clouds by rotation and jittering
aug_data = batch_data
#aug_data = provider.random_scale_point_cloud(batch_data)
#aug_data = provider.jitter_point_cloud(aug_data)
feed_dict = {ops['pointclouds_pl']: aug_data,
ops['one_hot_vec_pl']: batch_one_hot_vec,
ops['labels_pl']: batch_label,
ops['centers_pl']: batch_center,
ops['heading_class_label_pl']: batch_hclass,
ops['heading_residual_label_pl']: batch_hres,
ops['size_class_label_pl']: batch_sclass,
ops['size_residual_label_pl']: batch_sres,
ops['is_training_pl']: is_training,}
summary, step, _, loss_val, logits_val, centers_pred_val, iou2ds, iou3ds = sess.run([ops['merged'], ops['step'],
ops['train_op'], ops['loss'], ops['logits'], ops['centers_pred'],
ops['end_points']['iou2ds'], ops['end_points']['iou3ds']], feed_dict=feed_dict)
#print 'Predicted centers: ', centers_pred_val
train_writer.add_summary(summary, step)
preds_val = np.argmax(logits_val, 2)
correct = np.sum(preds_val == batch_label)
total_correct += correct
total_seen += (BATCH_SIZE*NUM_POINT)
loss_sum += loss_val
iou2ds_sum += np.sum(iou2ds)
iou3ds_sum += np.sum(iou3ds)
if (batch_idx+1)%10 == 0:
log_string(' -- %03d / %03d --' % (batch_idx+1, num_batches))
log_string('mean loss: %f' % (loss_sum / 10))
log_string('accuracy: %f' % (total_correct / float(total_seen)))
log_string('Box IoU (ground/3D): %f / %f' % (iou2ds_sum / float(BATCH_SIZE*10), iou3ds_sum / float(BATCH_SIZE*10)))
total_correct = 0
total_seen = 0
loss_sum = 0
iou2ds_sum = 0
iou3ds_sum = 0
def main_batch(output_filename, result_dir=None):
ps_list = []
seg_list = []
segp_list = []
center_list = []
heading_cls_list = []
heading_res_list = []
size_cls_list = []
size_res_list = []
rot_angle_list = []
score_list = []
test_idxs = np.arange(0, len(TEST_DATASET))
batch_size = 32
num_batches = int((len(TEST_DATASET)+batch_size-1)/batch_size)
batch_data_to_feed = np.zeros((batch_size, NUM_POINT, NUM_CHANNEL))
batch_one_hot_to_feed = np.zeros((batch_size, NUM_CLASS))
sess, ops = get_model(batch_size=batch_size, num_point=NUM_POINT)
correct_cnt = 0
for batch_idx in range(num_batches):
#if batch_idx == 50: break #TODO: remove this line!!
print(batch_idx)
start_idx = batch_idx * batch_size
end_idx = min(len(TEST_DATASET), (batch_idx+1) * batch_size)
cur_batch_size = end_idx - start_idx
batch_data, batch_label, batch_center, batch_hclass, batch_hres, batch_sclass, batch_sres, batch_rot_angle, batch_one_hot_vec = get_batch(TEST_DATASET, test_idxs, start_idx, end_idx, NUM_POINT, NUM_CHANNEL)
batch_data_to_feed[0:cur_batch_size,...] = batch_data
batch_one_hot_to_feed[0:cur_batch_size,:] = batch_one_hot_vec
batch_output, batch_center_pred, batch_hclass_pred, batch_hres_pred, batch_sclass_pred, batch_sres_pred, batch_scores = inference(sess, ops, batch_data_to_feed, batch_one_hot_to_feed, batch_size=batch_size)
print(batch_hclass_pred.shape, batch_hres_pred.shape)
print(batch_sclass_pred.shape, batch_sres_pred.shape)
#raw_input()
correct_cnt += np.sum(batch_output[0:cur_batch_size,...]==batch_label[0:cur_batch_size,...])
for i in range(cur_batch_size):
ps_list.append(batch_data[i,...])
seg_list.append(batch_label[i,...])
segp_list.append(batch_output[i,...])
center_list.append(batch_center_pred[i,:])
heading_cls_list.append(batch_hclass_pred[i])
heading_res_list.append(batch_hres_pred[i])
size_cls_list.append(batch_sclass_pred[i])
size_res_list.append(batch_sres_pred[i,:])
rot_angle_list.append(batch_rot_angle[i])
score_list.append(batch_scores[i])
print("Accuracy: ", correct_cnt / float(len(TEST_DATASET)*NUM_POINT))
save_zipped_pickle([ps_list, segp_list, center_list, heading_cls_list, heading_res_list, size_cls_list, size_res_list, rot_angle_list, score_list], output_filename)
# Write detection results for KITTI evaluation
write_detection_results(result_dir, TEST_DATASET.id_list, TEST_DATASET.type_list, TEST_DATASET.box2d_list, center_list, heading_cls_list, heading_res_list, size_cls_list, size_res_list, rot_angle_list, score_list)
def test_from_rgb_detection(output_filename, result_dir=None):
''' Test frustum pointents with 2D boxes from a RGB detector.
Write test results to KITTI format label files.
todo (rqi): support variable number of points.
'''
ps_list = []
segp_list = []
center_list = []
heading_cls_list = []
heading_res_list = []
size_cls_list = []
size_res_list = []
rot_angle_list = []
score_list = []
onehot_list = []
test_idxs = np.arange(0, len(TEST_DATASET))
print(len(TEST_DATASET))
batch_size = BATCH_SIZE
num_batches = int((len(TEST_DATASET) + batch_size - 1) / batch_size)
total_time = 0.0
profile_list = []
time_list = []
batch_data_to_feed = np.zeros((batch_size, NUM_POINT, NUM_CHANNEL))
batch_one_hot_to_feed = np.zeros((batch_size, 3))
sess, ops = get_session_and_ops(batch_size=batch_size, num_point=NUM_POINT)
for batch_idx in range(RUN_SIZE):
print('batch idx: %d' % (batch_idx))
start_idx = batch_idx * batch_size
end_idx = min(len(TEST_DATASET), (batch_idx + 1) * batch_size)
cur_batch_size = end_idx - start_idx
batch_data, batch_rot_angle, batch_rgb_prob, batch_one_hot_vec = \
get_batch(TEST_DATASET, test_idxs, start_idx, end_idx,
NUM_POINT, NUM_CHANNEL, from_rgb_detection=True)
print("batch data: ", batch_data.shape)
print("batch_one_hot_vec: ", batch_one_hot_vec.shape)
batch_data_to_feed[0:cur_batch_size, ...] = batch_data
batch_one_hot_to_feed[0:cur_batch_size, :] = batch_one_hot_vec
# Run one batch inference
time_diff = inference(sess,
ops,
batch_data_to_feed,
batch_one_hot_to_feed,
batch_size=batch_size)
if (batch_idx < 3): continue
print(time_diff)
total_time += time_diff
time_list.append(time_diff)
'''
profile = parse_timeline("timeline.json")
tmp_list = dict2list(profile, pointnet_ops)
profile_list.append(tmp_list)
print(tmp_list)
'''
print('Avg. time:', statistics.mean(time_list))
print('Time stddev: ', statistics.stdev(time_list))
# Write detection results for KITTI evaluation
avg_list = []
dev_list = []
for i in range(len(profile_list[0])):
avg_list.append(statistics.mean([item[i] for item in profile_list]))
dev_list.append(statistics.stdev([item[i] for item in profile_list]))
print(avg_list)
print(dev_list)
def train_one_epoch(sess, ops, train_writer):
''' Training for one epoch on the frustum dataset.
ops is dict mapping from string to tf ops
'''
is_training = True
log_string(str(datetime.now()))
# Shuffle train samples
train_idxs = np.arange(0, len(TRAIN_DATASET))
np.random.shuffle(train_idxs)
num_batches = len(TRAIN_DATASET)//BATCH_SIZE
# To collect statistics
total_correct = 0
total_seen = 0
loss_sum = 0
iou2ds_sum = 0
iou3ds_sum = 0
iou3d_correct_cnt = 0
# Training with batches
for batch_idx in range(num_batches):
start_idx = batch_idx * BATCH_SIZE
end_idx = (batch_idx+1) * BATCH_SIZE
batch_data, batch_label, batch_center, \
batch_hclass, batch_hres, \
batch_sclass, batch_sres, \
batch_rot_angle, batch_one_hot_vec = \
get_batch(TRAIN_DATASET, train_idxs, start_idx, end_idx,
NUM_POINT, NUM_CHANNEL)
feed_dict = {ops['pointclouds_pl']: batch_data,
ops['one_hot_vec_pl']: batch_one_hot_vec,
ops['labels_pl']: batch_label,
ops['centers_pl']: batch_center,
ops['heading_class_label_pl']: batch_hclass,
ops['heading_residual_label_pl']: batch_hres,
ops['size_class_label_pl']: batch_sclass,
ops['size_residual_label_pl']: batch_sres,
ops['is_training_pl']: is_training,}
summary, step, _, loss_val, logits_val, centers_pred_val, \
iou2ds, iou3ds = \
sess.run([ops['merged'], ops['step'], ops['train_op'], ops['loss'],
ops['end_points']['mask_logits'], ops['end_points']['center'],
ops['end_points']['iou2ds'], ops['end_points']['iou3ds']],
feed_dict=feed_dict)
train_writer.add_summary(summary, step)
preds_val = np.argmax(logits_val, 2)
correct = np.sum(preds_val == batch_label)
total_correct += correct
total_seen += (BATCH_SIZE*NUM_POINT)
loss_sum += loss_val
iou2ds_sum += np.sum(iou2ds)
iou3ds_sum += np.sum(iou3ds)
iou3d_correct_cnt += np.sum(iou3ds>=0.7)
if (batch_idx+1)%LOG_FREQ == 0:
segacc = total_correct / float(total_seen)
iou2, iou3 = iou2ds_sum / float(BATCH_SIZE*LOG_FREQ), iou3ds_sum / float(BATCH_SIZE*LOG_FREQ)
boxacc = float(iou3d_correct_cnt)/float(BATCH_SIZE*LOG_FREQ)
content = ' %03d / %03d loss: %2.4f segacc: %.4f IoU(ground/3D): %.4f / %.4f boxAcc(0.7): %.4f' \
%(batch_idx+1, num_batches, loss_sum / LOG_FREQ, segacc, iou2, iou3, boxacc)
log_string(content)
total_correct = 0
total_seen = 0
loss_sum = 0
iou2ds_sum = 0
iou3ds_sum = 0
iou3d_correct_cnt = 0
def train_one_epoch(sess, ops, train_writer):
""" ops: dict mapping from string to tf ops """
is_training = True
# Shuffle train samples
train_idxs = np.arange(0, len(TRAIN_DATASET))
np.random.shuffle(train_idxs)
num_batches = len(TRAIN_DATASET) / BATCH_SIZE
log_string(str(datetime.now()))
total_correct = 0
total_seen = 0
loss_sum = 0
iou2ds_sum = 0
iou3ds_sum = 0
for batch_idx in range(num_batches):
start_idx = batch_idx * BATCH_SIZE
end_idx = (batch_idx + 1) * BATCH_SIZE
batch_data, batch_label, batch_center, batch_hclass, batch_hres, batch_sclass, batch_sres, batch_rot_angle, batch_one_hot_vec = get_batch(
TRAIN_DATASET, train_idxs, start_idx, end_idx, NUM_POINT,
NUM_CHANNEL)
#print 'Ground truth centers: ', batch_center
# Augment batched point clouds by rotation and jittering
aug_data = batch_data
#aug_data = provider.random_scale_point_cloud(batch_data)
#aug_data = provider.jitter_point_cloud(aug_data)
feed_dict = {
ops['pointclouds_pl']: aug_data,
ops['one_hot_vec_pl']: batch_one_hot_vec,
ops['labels_pl']: batch_label,
ops['centers_pl']: batch_center,
ops['heading_class_label_pl']: batch_hclass,
ops['heading_residual_label_pl']: batch_hres,
ops['size_class_label_pl']: batch_sclass,
ops['size_residual_label_pl']: batch_sres,
ops['is_training_pl']: is_training,
}
summary, step, _, loss_val, logits_val, centers_pred_val, iou2ds, iou3ds = sess.run(
[
ops['merged'], ops['step'], ops['train_op'], ops['loss'],
ops['logits'], ops['centers_pred'],
ops['end_points']['iou2ds'], ops['end_points']['iou3ds']
],
feed_dict=feed_dict)
#print 'Predicted centers: ', centers_pred_val
train_writer.add_summary(summary, step)
preds_val = np.argmax(logits_val, 2)
correct = np.sum(preds_val == batch_label)
total_correct += correct
total_seen += (BATCH_SIZE * NUM_POINT)
loss_sum += loss_val
iou2ds_sum += np.sum(iou2ds)
iou3ds_sum += np.sum(iou3ds)
if (batch_idx + 1) % 10 == 0:
log_string(' -- %03d / %03d --' % (batch_idx + 1, num_batches))
log_string('mean loss: %f' % (loss_sum / 10))
log_string('accuracy: %f' % (total_correct / float(total_seen)))
log_string('Box IoU (ground/3D): %f / %f' %
(iou2ds_sum / float(BATCH_SIZE * 10),
iou3ds_sum / float(BATCH_SIZE * 10)))
total_correct = 0
total_seen = 0
loss_sum = 0
iou2ds_sum = 0
iou3ds_sum = 0
def eval_one_epoch(sess, ops, test_writer, epoch, saver):
''' Simple evaluation for one epoch on the frustum dataset.
ops is dict mapping from string to tf ops """
'''
global EPOCH_CNT, best_boxacc, best_epoch
is_training = False
log_string(str(datetime.now()))
log_string('---- EPOCH %03d EVALUATION ----'%(EPOCH_CNT))
test_idxs = np.arange(0, len(TEST_DATASET))
num_batches = len(TEST_DATASET)//BATCH_SIZE
# To collect statistics
total_correct = 0
total_seen = 0
loss_sum = 0
total_seen_class = [0 for _ in range(NUM_CLASSES)]
total_correct_class = [0 for _ in range(NUM_CLASSES)]
iou2ds_sum = 0
iou3ds_sum = 0
iou3d_correct_cnt = 0
# Simple evaluation with batches
for batch_idx in range(num_batches):
start_idx = batch_idx * BATCH_SIZE
end_idx = (batch_idx+1) * BATCH_SIZE
batch_data, batch_label, batch_center, \
batch_hclass, batch_hres, \
batch_sclass, batch_sres, \
batch_rot_angle, batch_one_hot_vec = \
get_batch(TEST_DATASET, test_idxs, start_idx, end_idx,
NUM_POINT, NUM_CHANNEL)
feed_dict = {ops['pointclouds_pl']: batch_data,
ops['one_hot_vec_pl']: batch_one_hot_vec,
ops['labels_pl']: batch_label,
ops['centers_pl']: batch_center,
ops['heading_class_label_pl']: batch_hclass,
ops['heading_residual_label_pl']: batch_hres,
ops['size_class_label_pl']: batch_sclass,
ops['size_residual_label_pl']: batch_sres,
ops['is_training_pl']: is_training}
summary, step, loss_val, logits_val, iou2ds, iou3ds = \
sess.run([ops['merged'], ops['step'],
ops['loss'], ops['end_points']['mask_logits'],
ops['end_points']['iou2ds'], ops['end_points']['iou3ds']],
feed_dict=feed_dict)
test_writer.add_summary(summary, step)
preds_val = np.argmax(logits_val, 2)
correct = np.sum(preds_val == batch_label)
total_correct += correct
total_seen += (BATCH_SIZE*NUM_POINT)
loss_sum += loss_val
#print("preds_val:",preds_val.shape)
#print("batch_label:",batch_label.shape)
for l in range(NUM_CLASSES):
total_seen_class[l] += np.sum(batch_label==l)
total_correct_class[l] += (np.sum((preds_val==l) & (batch_label==l)))
iou2ds_sum += np.sum(iou2ds)
iou3ds_sum += np.sum(iou3ds)
iou3d_correct_cnt += np.sum(iou3ds>=0.7)
for i in range(BATCH_SIZE):
segp = preds_val[i,:]
segl = batch_label[i,:]
part_ious = [0.0 for _ in range(NUM_CLASSES)]
for l in range(NUM_CLASSES):
if (np.sum(segl==l) == 0) and (np.sum(segp==l) == 0):
part_ious[l] = 1.0 # class not present
else:
part_ious[l] = np.sum((segl==l) & (segp==l)) / \
float(np.sum((segl==l) | (segp==l)))
mean_loss = loss_sum / float(num_batches)
segacc = total_correct / float(total_seen)
segclassacc = np.mean(np.array(total_correct_class) / \
np.array(total_seen_class,dtype=np.float))
iou2 = iou2ds_sum / float(num_batches*BATCH_SIZE)
iou3 = iou3ds_sum / float(num_batches*BATCH_SIZE)
boxacc = float(iou3d_correct_cnt)/float(num_batches*BATCH_SIZE)
content = ' loss: %2.4f segacc: %.4f seg_C_acc: %.4f IoU(ground/3D): %.4f / %.4f boxAcc(0.7): %.4f' \
%(mean_loss, segacc, segclassacc, iou2, iou3, boxacc)
log_string(content)
model_name = "model_%s_%1.3f.ckpt" % (epoch, boxacc)
save_path = saver.save(sess, os.path.join(LOG_DIR, model_name))
log_string("Model saved in file: %s" % save_path)
if boxacc> best_boxacc:
best_boxacc = boxacc
best_epoch = epoch
best_model_path = os.path.join(LOG_DIR, "model_best.ckpt")
save_path = saver.save(sess, os.path.join(LOG_DIR, model_name))
log_string("Best Model( %s ) saved in file: %s" % (model_name, best_model_path))
EPOCH_CNT += 1
def eval_one_epoch(sess, ops, test_dataset, res, split):
''' Simple evaluation for one epoch on the frustum dataset.
ops is dict mapping from string to tf ops """
'''
global EPOCH_CNT
is_training = False
log_string(str(datetime.now()))
log_string(res + '---- EPOCH %03d EVALUATION ----' % (EPOCH_CNT))
test_idxs = np.arange(0, len(test_dataset))
num_batches = len(test_dataset) / BATCH_SIZE
# To collect statistics
total_correct = 0
total_seen = 0
loss_sum = 0
total_seen_class = [0 for _ in range(NUM_CLASSES)]
total_correct_class = [0 for _ in range(NUM_CLASSES)]
iou2ds_sum = 0
iou3ds_sum = 0
iou3d_correct_cnt = 0
box_pred_nbr_sum = 0
ps_list = []
seg_list = []
segp_list = []
center_list = []
heading_cls_list = []
heading_res_list = []
size_cls_list = []
size_res_list = []
rot_angle_list = []
score_list = []
center_GT = []
heading_class_GT = []
heading_res_GT = []
size_class_GT = []
size_residual_GT = []
# Simple evaluation with batches
for batch_idx in range(num_batches):
start_idx = batch_idx * BATCH_SIZE
end_idx = (batch_idx + 1) * BATCH_SIZE
batch_data, batch_label, batch_center, \
batch_hclass, batch_hres, \
batch_sclass, batch_sres, \
batch_rot_angle, batch_one_hot_vec = \
get_batch(test_dataset, test_idxs, start_idx, end_idx,
NUM_POINT, NUM_CHANNEL)
feed_dict = {
ops['pointclouds_pl']: batch_data,
ops['one_hot_vec_pl']: batch_one_hot_vec,
ops['labels_pl']: batch_label,
ops['centers_pl']: batch_center,
ops['heading_class_label_pl']: batch_hclass,
ops['heading_residual_label_pl']: batch_hres,
ops['size_class_label_pl']: batch_sclass,
ops['size_residual_label_pl']: batch_sres,
ops['is_training_pl']: is_training
}
summary, step, loss_val, logits_val, \
centers_pred_val,heading_scores, heading_residuals, size_scores, size_residuals,\
iou2ds, iou3ds, box_pred_nbr = \
sess.run([ops['merged'], ops['step'],
ops['loss'], ops['logits'],
ops['end_points']['center'],ops['end_points']['heading_scores'],
ops['end_points']['heading_residuals'],ops['end_points']['size_scores'],
ops['end_points']['size_residuals'],
ops['end_points']['iou2ds'], ops['end_points']['iou3ds'],ops['end_points']['box_pred_nbr']],
feed_dict=feed_dict)
#test_writer.add_summary(summary, step)
batch_seg_prob = softmax(logits_val)[:, :, 1] # BxN
batch_seg_mask = np.argmax(logits_val, 2) # BxN
mask_mean_prob = np.sum(batch_seg_prob * batch_seg_mask, 1) # B,
mask_mean_prob = mask_mean_prob / np.sum(batch_seg_mask, 1)
heading_prob = np.max(softmax(heading_scores), 1) # B
size_prob = np.max(softmax(size_scores), 1) # B,
batch_scores = np.log(mask_mean_prob) + np.log(heading_prob) + np.log(
size_prob)
heading_cls = np.argmax(heading_scores, 1) # B
size_cls = np.argmax(size_scores, 1) # B
heading_res = np.array([heading_residuals[i, heading_cls[i]] \
for i in range(batch_data.shape[0])])
size_res = np.vstack([size_residuals[i, size_cls[i], :] \
for i in range(batch_data.shape[0])])
preds_val = np.argmax(logits_val, 2)
correct = np.sum(preds_val == batch_label)
total_correct += correct
total_seen += (BATCH_SIZE * NUM_POINT)
loss_sum += loss_val
for i in range(batch_data.shape[0]):
ps_list.append(batch_data[i, ...])
seg_list.append(batch_label[i, ...])
segp_list.append(preds_val[i, ...])
center_list.append(centers_pred_val[i, :])
heading_cls_list.append(heading_cls[i])
heading_res_list.append(heading_res[i])
size_cls_list.append(size_cls[i])
size_res_list.append(size_res[i, :])
rot_angle_list.append(batch_rot_angle[i])
score_list.append(batch_scores[i])
center_GT.append(batch_center[i])
heading_class_GT.append(batch_hclass[i])
heading_res_GT.append(batch_hres[i])
size_class_GT.append(batch_sclass[i])
size_residual_GT.append(batch_sres[i])
correct = np.sum(preds_val == batch_label)
for l in range(NUM_CLASSES):
total_seen_class[l] += np.sum(batch_label == l)
total_correct_class[l] += (np.sum((preds_val == l)
& (batch_label == l)))
iou2ds_sum += np.sum(iou2ds)
iou3ds_sum += np.sum(iou3ds)
iou3d_correct_cnt += np.sum(iou3ds >= 0.5)
box_pred_nbr_sum += np.sum(box_pred_nbr)
for i in range(BATCH_SIZE):
segp = preds_val[i, :]
segl = batch_label[i, :]
part_ious = [0.0 for _ in range(NUM_CLASSES)]
for l in range(NUM_CLASSES):
if (np.sum(segl == l) == 0) and (np.sum(segp == l) == 0):
part_ious[l] = 1.0 # class not present
else:
part_ious[l] = np.sum((segl==l) & (segp==l)) / \
float(np.sum((segl==l) | (segp==l)))
print("num_batches", num_batches)
log_string(res + 'eval mean loss: %f' % (loss_sum / float(num_batches)))
log_string(res+'eval segmentation accuracy: %f'% \
(total_correct / float(total_seen)))
log_string(res+'eval segmentation avg class acc: %f' % \
(np.mean(np.array(total_correct_class) / \
np.array(total_seen_class,dtype=np.float))))
log_string(res+'eval box IoU (ground/3D): %f / %f' % \
(iou2ds_sum / max(float(box_pred_nbr_sum),1.0), iou3ds_sum / \
max(float(box_pred_nbr_sum),1.0)))
log_string(res+'eval box estimation accuracy (IoU=0.5): %f' % \
(float(iou3d_correct_cnt)/max(float(box_pred_nbr_sum),1.0)))
IOU3d, GT_box_list, pred_box_list = compare_box_iou(
res, split, test_dataset.id_list, test_dataset.indice_box,
size_residual_GT, size_class_GT, heading_res_GT, heading_class_GT,
center_GT, score_list, size_res_list, size_cls_list, heading_res_list,
heading_cls_list, center_list, segp_list, seg_list)
eval_per_frame(test_dataset.id_list, test_dataset.indice_box, ps_list,
seg_list, segp_list, GT_box_list, pred_box_list, IOU3d,
score_list)
write_detection_results_test(res, split, test_dataset.id_list, center_list,
heading_cls_list, heading_res_list,
size_cls_list, size_res_list, rot_angle_list,
segp_list, score_list)
def test_from_rgb_detection(output_filename, result_dir=None):
global lstm_parameters
''' Test frustum pointents with 2D boxes from a RGB detector.
Write test results to KITTI format label files.
todo (rqi): support variable number of points.
'''
ps_list = []
segp_list = []
center_list = []
heading_cls_list = []
heading_res_list = []
size_cls_list = []
size_res_list = []
rot_angle_list = []
score_list = []
onehot_list = []
test_idxs = np.arange(0, len(TEST_DATASET))
print(len(TEST_DATASET))
batch_size = BATCH_SIZE
num_batches = len(TEST_DATASET)/batch_size#int((len(TEST_DATASET)+batch_size-1)/batch_size)
batch_data_to_feed = np.zeros((batch_size, NUM_POINT, NUM_CHANNEL))
batch_one_hot_to_feed = np.zeros((batch_size, 3))
sess, ops = get_session_and_ops(batch_size=batch_size, num_point=NUM_POINT, num_channel=NUM_CHANNEL,
lstm_params=lstm_parameters)
# To get features of all frames
if FLAGS.tracks:
for batch_idx in range(int(num_batches)):
start_idx = batch_idx * BATCH_SIZE
end_idx = (batch_idx + 1) * BATCH_SIZE
# E: Get also batch_indices which shows the (world_id,frame_id,track_id) of the objects in the batch
# E: Batch indices are valid (non-empty) only if the tracks flag is True
'''
batch_data, batch_label, batch_center, \
batch_hclass, batch_hres, \
batch_sclass, batch_sres, \
batch_rot_angle, batch_one_hot_vec, batch_indices = \
'''
batch_data, batch_rot_angle, batch_rgb_prob, batch_one_hot_vec, batch_indices = \
get_batch(TEST_DATASET, test_idxs, start_idx, end_idx,
NUM_POINT, NUM_CHANNEL,tracks=FLAGS.tracks, from_rgb_detection=True)
# Emec added the feature line
# E: Get the features at the prev time steps of the objects in the batch
batch_feat_lstm = get_batch_features(TEST_DATASET.feature_dict,
batch_wft=batch_indices,tau=FLAGS.tau,
feat_len=feat_len,rev_order=True)
# E: Get the number of tracks at the tau prev. time steps for each object in the batch: How many of the tau-1 frames before the current frames of the objects contain the same object with the same track id
batch_seq_len = batch_track_num(feature_dict=TEST_DATASET.feature_dict,wfts=batch_indices)
feed_dict = {ops['pointclouds_pl']: batch_data,
ops['one_hot_vec_pl']: batch_one_hot_vec,
ops['is_training_pl']: False,
ops['end_points']['lstm_layer']['feat_input']:batch_feat_lstm,
ops['end_points']['lstm_layer']['pf_seq_len']:batch_seq_len}
box_est_feature_vec = \
sess.run(ops['end_points']['box_est_feature_vec'],
feed_dict=feed_dict)
update_batch_features(feature_dict=TEST_DATASET.feature_dict,batch_wft=batch_indices,
batch_feat_vecs=box_est_feature_vec)
for batch_idx in range(int(num_batches)):
# print('batch idx: %d' % (batch_idx))
start_idx = batch_idx * batch_size
end_idx = min(len(TEST_DATASET), (batch_idx+1) * batch_size)
cur_batch_size = end_idx - start_idx
batch_data, batch_rot_angle, batch_rgb_prob, batch_one_hot_vec, batch_indices = \
get_batch(TEST_DATASET, test_idxs, start_idx, end_idx,
NUM_POINT, NUM_CHANNEL, from_rgb_detection=True)
batch_data_to_feed[0:cur_batch_size,...] = batch_data
batch_one_hot_to_feed[0:cur_batch_size,:] = batch_one_hot_vec
# Run one batch inference
if FLAGS.tracks:
batch_output, batch_center_pred, \
batch_hclass_pred, batch_hres_pred, \
batch_sclass_pred, batch_sres_pred, batch_scores = \
inference(sess, ops, batch_data_to_feed,
batch_one_hot_to_feed, batch_size=batch_size,
track_data=[batch_feat_lstm,batch_seq_len])
else:
batch_output, batch_center_pred, \
batch_hclass_pred, batch_hres_pred, \
batch_sclass_pred, batch_sres_pred, batch_scores = \
inference(sess, ops, batch_data_to_feed,
batch_one_hot_to_feed, batch_size=batch_size)
for i in range(cur_batch_size):
ps_list.append(batch_data[i,...])
segp_list.append(batch_output[i,...])
center_list.append(batch_center_pred[i,:])
heading_cls_list.append(batch_hclass_pred[i])
heading_res_list.append(batch_hres_pred[i])
size_cls_list.append(batch_sclass_pred[i])
size_res_list.append(batch_sres_pred[i,:])
rot_angle_list.append(batch_rot_angle[i])
#score_list.append(batch_scores[i])
score_list.append(batch_rgb_prob[i]) # 2D RGB detection score
onehot_list.append(batch_one_hot_vec[i])
if FLAGS.dump_result:
with open(output_filename, 'wb') as fp:
pickle.dump(ps_list, fp)
pickle.dump(segp_list, fp)
pickle.dump(center_list, fp)
pickle.dump(heading_cls_list, fp)
pickle.dump(heading_res_list, fp)
pickle.dump(size_cls_list, fp)
pickle.dump(size_res_list, fp)
pickle.dump(rot_angle_list, fp)
pickle.dump(score_list, fp)
pickle.dump(onehot_list, fp)
# Write detection results for KITTI evaluation
# print('Number of point clouds: %d' % (len(ps_list)))
# Write detection results for KITTI evaluation
if TRACKING:
write_track_detection_results(result_dir, TEST_DATASET.id_list,
TEST_DATASET.type_list, TEST_DATASET.box2d_list, center_list,
heading_cls_list, heading_res_list,
size_cls_list, size_res_list, rot_angle_list, score_list,dataset=TEST_DATASET)
#write_detection_results(result_dir, TEST_DATASET.id_list,
# TEST_DATASET.type_list, TEST_DATASET.box2d_list,
# center_list, heading_cls_list, heading_res_list,
# size_cls_list, size_res_list, rot_angle_list, score_list)
# Make sure for each frame (no matter if we have measurment for that frame),
# there is a TXT file
'''
def eval_one_epoch(sess, ops, test_writer):
''' Simple evaluation for one epoch on the frustum dataset.
ops is dict mapping from string to tf ops """
'''
global EPOCH_CNT
is_training = False
log_string(str(datetime.now()))
log_string('---- EPOCH %03d EVALUATION ----' % (EPOCH_CNT))
test_idxs = np.arange(0, len(TEST_DATASET))
num_batches = len(TEST_DATASET) / BATCH_SIZE
# To collect statistics
total_correct = 0
total_seen = 0
loss_sum = 0
total_seen_class = [0 for _ in range(NUM_CLASSES)]
total_correct_class = [0 for _ in range(NUM_CLASSES)]
iou2ds_sum = 0
iou3ds_sum = 0
iou3d_correct_cnt = 0
# Simple evaluation with batches
for batch_idx in range(num_batches):
start_idx = batch_idx * BATCH_SIZE
end_idx = (batch_idx + 1) * BATCH_SIZE
batch_data, batch_label, batch_center, \
batch_hclass, batch_hres, \
batch_sclass, batch_sres, \
batch_rot_angle, batch_one_hot_vec = \
get_batch(TEST_DATASET, test_idxs, start_idx, end_idx,
NUM_POINT, NUM_CHANNEL)
feed_dict = {
ops['pointclouds_pl']: batch_data,
ops['one_hot_vec_pl']: batch_one_hot_vec,
ops['labels_pl']: batch_label,
ops['centers_pl']: batch_center,
ops['heading_class_label_pl']: batch_hclass,
ops['heading_residual_label_pl']: batch_hres,
ops['size_class_label_pl']: batch_sclass,
ops['size_residual_label_pl']: batch_sres,
ops['is_training_pl']: is_training
}
summary, step, loss_val, logits_val, iou2ds, iou3ds = \
sess.run([ops['merged'], ops['step'],
ops['loss'], ops['logits'],
ops['end_points']['iou2ds'], ops['end_points']['iou3ds']],
feed_dict=feed_dict)
test_writer.add_summary(summary, step)
preds_val = np.argmax(logits_val, 2)
correct = np.sum(preds_val == batch_label)
total_correct += correct
total_seen += (BATCH_SIZE * NUM_POINT)
loss_sum += loss_val
for l in range(NUM_CLASSES):
total_seen_class[l] += np.sum(batch_label == l)
total_correct_class[l] += (np.sum((preds_val == l)
& (batch_label == l)))
iou2ds_sum += np.sum(iou2ds)
iou3ds_sum += np.sum(iou3ds)
iou3d_correct_cnt += np.sum(iou3ds >= 0.5)
for i in range(BATCH_SIZE):
segp = preds_val[i, :]
segl = batch_label[i, :]
part_ious = [0.0 for _ in range(NUM_CLASSES)]
for l in range(NUM_CLASSES):
if (np.sum(segl == l) == 0) and (np.sum(segp == l) == 0):
part_ious[l] = 1.0 # class not present
else:
part_ious[l] = np.sum((segl==l) & (segp==l)) / \
float(np.sum((segl==l) | (segp==l)))
log_string('eval mean loss: %f' % (loss_sum / float(num_batches)))
log_string('eval segmentation accuracy: %f'% \
(total_correct / float(total_seen)))
log_string('eval segmentation avg class acc: %f' % \
(np.mean(np.array(total_correct_class) / \
np.array(total_seen_class,dtype=np.float))))
log_string('eval box IoU (ground/3D): %f / %f' % \
(iou2ds_sum / float(num_batches*BATCH_SIZE), iou3ds_sum / \
float(num_batches*BATCH_SIZE)))
log_string('eval box estimation accuracy (IoU=0.7): %f' % \
(float(iou3d_correct_cnt)/float(num_batches*BATCH_SIZE)))
EPOCH_CNT += 1
def test(output_filename, result_dir=None):
''' Test frustum pointnets with GT 2D boxes.
Write test results to KITTI format label files.
todo (rqi): support variable number of points.
'''
global lstm_parameters
ps_list = []
seg_list = []
segp_list = []
center_list = []
heading_cls_list = []
heading_res_list = []
size_cls_list = []
size_res_list = []
rot_angle_list = []
score_list = []
test_idxs = np.arange(0, len(TEST_DATASET))
batch_size = BATCH_SIZE
num_batches = len(TEST_DATASET)/batch_size
sess, ops = get_session_and_ops(batch_size=batch_size, num_point=NUM_POINT, num_channel=NUM_CHANNEL,
lstm_params=lstm_parameters)
correct_cnt = 0
# To get features of all frames
if FLAGS.tracks:
for batch_idx in range(int(num_batches)):
start_idx = batch_idx * BATCH_SIZE
end_idx = (batch_idx + 1) * BATCH_SIZE
# E: Get also batch_indices which shows the (world_id,frame_id,track_id) of the objects in the batch
# E: Batch indices are valid (non-empty) only if the tracks flag is True
batch_data, batch_label, batch_center, \
batch_hclass, batch_hres, \
batch_sclass, batch_sres, \
batch_rot_angle, batch_one_hot_vec, batch_indices = \
get_batch(TEST_DATASET, test_idxs, start_idx, end_idx,
NUM_POINT, NUM_CHANNEL,tracks=FLAGS.tracks)
# Emec added the feature line
# E: Get the features at the prev time steps of the objects in the batch
batch_feat_lstm = get_batch_features(TEST_DATASET.feature_dict,
batch_wft=batch_indices,tau=FLAGS.tau,
feat_len=feat_len,rev_order=True)
# E: Get the number of tracks at the tau prev. time steps for each object in the batch: How many of the tau-1 frames before the current frames of the objects contain the same object with the same track id
batch_seq_len = batch_track_num(feature_dict=TEST_DATASET.feature_dict,wfts=batch_indices)
feed_dict = {ops['pointclouds_pl']: batch_data,
ops['one_hot_vec_pl']: batch_one_hot_vec,
ops['labels_pl']: batch_label,
ops['centers_pl']: batch_center,
ops['heading_class_label_pl']: batch_hclass,
ops['heading_residual_label_pl']: batch_hres,
ops['size_class_label_pl']: batch_sclass,
ops['size_residual_label_pl']: batch_sres,
ops['is_training_pl']: False,
ops['end_points']['lstm_layer']['feat_input']:batch_feat_lstm,
ops['end_points']['lstm_layer']['pf_seq_len']:batch_seq_len}
box_est_feature_vec = \
sess.run(ops['end_points']['box_est_feature_vec'],
feed_dict=feed_dict)
update_batch_features(feature_dict=TEST_DATASET.feature_dict,batch_wft=batch_indices,
batch_feat_vecs=box_est_feature_vec)
print('Inference started!')
for batch_idx in range(int(num_batches)):
# print('batch idx: %d' % (batch_idx))
start_idx = batch_idx * batch_size
end_idx = (batch_idx+1) * batch_size
if FLAGS.tracks:
batch_data, batch_label, batch_center, \
batch_hclass, batch_hres, batch_sclass, batch_sres, \
batch_rot_angle, batch_one_hot_vec,batch_indices = \
get_batch(TEST_DATASET, test_idxs, start_idx, end_idx,
NUM_POINT, NUM_CHANNEL,tracks=FLAGS.tracks)
batch_feat_lstm = get_batch_features(TEST_DATASET.feature_dict,
batch_wft=batch_indices,tau=FLAGS.tau,
feat_len=feat_len,rev_order=True)
batch_seq_len = batch_track_num(feature_dict=TEST_DATASET.feature_dict,wfts=batch_indices)
batch_output, batch_center_pred, \
batch_hclass_pred, batch_hres_pred, \
batch_sclass_pred, batch_sres_pred, batch_scores = \
inference(sess, ops, batch_data,
batch_one_hot_vec, batch_size=batch_size,
track_data=[batch_feat_lstm,batch_seq_len])
else:
batch_data, batch_label, batch_center, \
batch_hclass, batch_hres, batch_sclass, batch_sres, \
batch_rot_angle, batch_one_hot_vec,batch_indices = \
get_batch(TEST_DATASET, test_idxs, start_idx, end_idx,
NUM_POINT, NUM_CHANNEL,tracks=FLAGS.tracking)
batch_output, batch_center_pred, \
batch_hclass_pred, batch_hres_pred, \
batch_sclass_pred, batch_sres_pred, batch_scores = \
inference(sess, ops, batch_data,
batch_one_hot_vec, batch_size=batch_size)
correct_cnt += np.sum(batch_output==batch_label)
for i in range(batch_output.shape[0]):
ps_list.append(batch_data[i,...])
seg_list.append(batch_label[i,...])
segp_list.append(batch_output[i,...])
center_list.append(batch_center_pred[i,:])
heading_cls_list.append(batch_hclass_pred[i])
heading_res_list.append(batch_hres_pred[i])
size_cls_list.append(batch_sclass_pred[i])
size_res_list.append(batch_sres_pred[i,:])
rot_angle_list.append(batch_rot_angle[i])
score_list.append(batch_scores[i])
print("Segmentation accuracy: %f" % \
(correct_cnt / float(batch_size*num_batches*NUM_POINT)))
if FLAGS.dump_result:
with open(output_filename, 'wb') as fp:
pickle.dump(ps_list, fp)
pickle.dump(seg_list, fp)
pickle.dump(segp_list, fp)
pickle.dump(center_list, fp)
pickle.dump(heading_cls_list, fp)
pickle.dump(heading_res_list, fp)
pickle.dump(size_cls_list, fp)
pickle.dump(size_res_list, fp)
pickle.dump(rot_angle_list, fp)
pickle.dump(score_list, fp)
# Write detection results for KITTI evaluation
if TRACKING:
write_track_detection_results(result_dir, TEST_DATASET.id_list,
TEST_DATASET.type_list, TEST_DATASET.box2d_list, center_list,
heading_cls_list, heading_res_list,
size_cls_list, size_res_list, rot_angle_list, score_list,dataset=TEST_DATASET)
else:
write_detection_results(result_dir, TEST_DATASET.id_list,
TEST_DATASET.type_list, TEST_DATASET.box2d_list, center_list,
heading_cls_list, heading_res_list,
size_cls_list, size_res_list, rot_angle_list, score_list)
def test(output_filename, result_dir=None):
''' Test frustum pointnets with GT 2D boxes.
Write test results to KITTI format label files.
todo (rqi): support variable number of points.
'''
ps_list = []
seg_list = []
segp_list = []
center_list = []
heading_cls_list = []
heading_res_list = []
size_cls_list = []
size_res_list = []
rot_angle_list = []
score_list = []
test_idxs = np.arange(0, len(TEST_DATASET))
batch_size = BATCH_SIZE
num_batches = int(len(TEST_DATASET)/batch_size)
sess, ops = get_session_and_ops(batch_size=batch_size, num_point=NUM_POINT)
correct_cnt = 0
for batch_idx in range(num_batches):
print('batch idx: %d' % (batch_idx))
start_idx = batch_idx * batch_size
end_idx = (batch_idx+1) * batch_size
batch_data, batch_label, batch_center, \
batch_hclass, batch_hres, batch_sclass, batch_sres, \
batch_rot_angle, batch_one_hot_vec = \
get_batch(TEST_DATASET, test_idxs, start_idx, end_idx,
NUM_POINT, NUM_CHANNEL)
batch_output, batch_center_pred, batch_hclass_pred, batch_hres_pred, \
batch_sclass_pred, batch_sres_pred,
batch_scores = inference(sess, ops, batch_data, batch_one_hot_vec, batch_size=batch_size)
correct_cnt += np.sum(batch_output==batch_label)
for i in range(batch_output.shape[0]):
ps_list.append(batch_data[i,...])
seg_list.append(batch_label[i,...])
segp_list.append(batch_output[i,...])
center_list.append(batch_center_pred[i,:])
heading_cls_list.append(batch_hclass_pred[i])
heading_res_list.append(batch_hres_pred[i])
size_cls_list.append(batch_sclass_pred[i])
size_res_list.append(batch_sres_pred[i,:])
rot_angle_list.append(batch_rot_angle[i])
score_list.append(batch_scores[i])
print("Segmentation accuracy: %f" % \
(correct_cnt / float(batch_size*num_batches*NUM_POINT)))
if FLAGS.dump_result:
with open(output_filename, 'wp') as fp:
pickle.dump(ps_list, fp)
pickle.dump(seg_list, fp)
pickle.dump(segp_list, fp)
pickle.dump(center_list, fp)
pickle.dump(heading_cls_list, fp)
pickle.dump(heading_res_list, fp)
pickle.dump(size_cls_list, fp)
pickle.dump(size_res_list, fp)
pickle.dump(rot_angle_list, fp)
pickle.dump(score_list, fp)
# Write detection results for KITTI evaluation
write_detection_results(result_dir, TEST_DATASET.id_list,
TEST_DATASET.type_list, TEST_DATASET.box2d_list, center_list,
heading_cls_list, heading_res_list,
size_cls_list, size_res_list, rot_angle_list, score_list)
def train_one_epoch(sess, ops, train_writer, tracks=False, lstm_params=None):
''' Training for one epoch on the frustum dataset.
ops is dict mapping from string to tf ops
tracks: To utilize the track ids from the dataset and also generate feature dictionaries
'''
is_training = True
log_string(str(datetime.now()))
# Shuffle train samples
train_idxs = np.arange(0, len(TRAIN_DATASET))
np.random.shuffle(train_idxs)
num_batches = len(TRAIN_DATASET) / BATCH_SIZE
# To collect statistics
total_correct = 0
total_seen = 0
loss_sum = 0
iou2ds_sum = 0
iou3ds_sum = 0
iou3d_correct_cnt = 0
all_return = []
# Training with batches
for batch_idx in range(int(num_batches)):
start_idx = batch_idx * BATCH_SIZE
end_idx = (batch_idx + 1) * BATCH_SIZE
# Emec added batch indices
# E: Get also batch_indices which shows the (world_id,frame_id,track_id) of the objects in the batch
# E: Batch indices are valid (non-empty) only if the tracks flag is True
batch_data, batch_label, batch_center, \
batch_hclass, batch_hres, \
batch_sclass, batch_sres, \
batch_rot_angle, batch_one_hot_vec,batch_indices = \
get_batch(TRAIN_DATASET, train_idxs, start_idx, end_idx,
NUM_POINT, NUM_CHANNEL,tracks=tracks)
# E: If the lstm layers will be used
if tracks:
# Emec added the feature line
# E: Get the features at the prev time steps of the objects in the batch
batch_feat_lstm = get_batch_features(
TRAIN_DATASET.feature_dict,
batch_wft=batch_indices,
tau=lstm_params['tau'],
feat_len=lstm_params['feat_vec_len'],
rev_order=True)
# E: Get the number of tracks at the tau prev. time steps for each object in the batch: How many of the tau-1 frames before the current frames of the objects contain the same object with the same track id
batch_seq_len = batch_track_num(
feature_dict=TRAIN_DATASET.feature_dict, wfts=batch_indices)
# E: Append the feed dictionary with the lstm parameters
feed_dict = {
ops['pointclouds_pl']: batch_data,
ops['one_hot_vec_pl']: batch_one_hot_vec,
ops['labels_pl']: batch_label,
ops['centers_pl']: batch_center,
ops['heading_class_label_pl']: batch_hclass,
ops['heading_residual_label_pl']: batch_hres,
ops['size_class_label_pl']: batch_sclass,
ops['size_residual_label_pl']: batch_sres,
ops['is_training_pl']: is_training,
ops['end_points']['lstm_layer']['feat_input']: batch_feat_lstm,
ops['end_points']['lstm_layer']['pf_seq_len']: batch_seq_len
}
# Emec added box_est_feature_vec
summary, step, _, loss_val, logits_val, centers_pred_val, iou2ds, iou3ds, box_est_feature_vec = \
sess.run(
[ops['merged'], ops['step'], ops['train_op'], ops['loss'], ops['logits'], ops['centers_pred'],
ops['end_points']['iou2ds'], ops['end_points']['iou3ds'],ops['end_points']['box_est_feature_vec'] ],
feed_dict=feed_dict)
else:
feed_dict = {
ops['pointclouds_pl']: batch_data,
ops['one_hot_vec_pl']: batch_one_hot_vec,
ops['labels_pl']: batch_label,
ops['centers_pl']: batch_center,
ops['heading_class_label_pl']: batch_hclass,
ops['heading_residual_label_pl']: batch_hres,
ops['size_class_label_pl']: batch_sclass,
ops['size_residual_label_pl']: batch_sres,
ops['is_training_pl']: is_training
}
# Emec added box_est_feature_vec
summary, step, _, loss_val, logits_val, centers_pred_val, iou2ds, iou3ds, box_est_feature_vec = \
sess.run(
[ops['merged'], ops['step'], ops['train_op'], ops['loss'], ops['logits'], ops['centers_pred'],
ops['end_points']['iou2ds'], ops['end_points']['iou3ds'],ops['end_points']['box_est_feature_vec'] ],
feed_dict=feed_dict)
train_writer.add_summary(summary, step)
preds_val = np.argmax(logits_val, 2)
correct = np.sum(preds_val == batch_label)
total_correct += correct
total_seen += (BATCH_SIZE * NUM_POINT)
loss_sum += loss_val
iou2ds_sum += np.sum(iou2ds)
iou3ds_sum += np.nansum(iou3ds)
iou3d_correct_cnt += np.sum(iou3ds >= 0.7)
# Emec added below
if tracks:
all_return.append(
[batch_indices, box_est_feature_vec, batch_feat_lstm])
update_batch_features(feature_dict=TRAIN_DATASET.feature_dict,
batch_wft=batch_indices,
batch_feat_vecs=box_est_feature_vec)
log_string('number of batches: %d' % num_batches)
log_string('training mean loss: %f' % (loss_sum / float(num_batches)))
log_string('training segmentation accuracy: %f' %
(total_correct / float(total_seen)))
log_string('training box IoU (ground/3D): %f / %f' %
(iou2ds_sum / float(num_batches * BATCH_SIZE),
iou3ds_sum / float(num_batches * BATCH_SIZE)))
log_string('training box estimation accuracy (IoU=0.7): %f' %
(float(iou3d_correct_cnt) / float(num_batches * BATCH_SIZE)))
if tracks:
return all_return
