def train_one_epoch(sess, ops, train_writer):
""" ops: dict mapping from string to tf ops """
is_training = True
# Shuffle train files
train_file_idxs = np.arange(0, len(TRAIN_FILES)) # 每一次的5个train文件的顺序都是不一样的
np.random.shuffle(train_file_idxs)
for fn in range(len(TRAIN_FILES)): # 对每一个train文件
log_string('----train file' + str(fn) + '-----')
current_data, current_label = provider.loadDataFile(
TRAIN_FILES[train_file_idxs[fn]])
current_data = current_data[:, 0:
NUM_POINT, :] # 采样1024个点,current代表这个文件中所有的点云
current_data, current_label, _ = provider.shuffle_data(
current_data, np.squeeze(current_label)) # 每个点云之间的顺序被打乱
current_label = np.squeeze(current_label) # 移除数组中单一维度的数据
file_size = current_data.shape[0] # 点云的总数
num_batches = file_size // BATCH_SIZE # 需要几个batch
total_correct = 0
total_seen = 0
loss_sum = 0
for batch_idx in range(num_batches):
start_idx = batch_idx * BATCH_SIZE
end_idx = (batch_idx + 1) * BATCH_SIZE
# Augment batched point clouds by rotation and jittering
rotated_data = provider.rotate_point_cloud(
current_data[start_idx:end_idx, :, :])
jittered_data = provider.jitter_point_cloud(rotated_data)
jittered_data = provider.random_scale_point_cloud(jittered_data)
jittered_data = provider.rotate_perturbation_point_cloud(
jittered_data)
jittered_data = provider.shift_point_cloud(jittered_data)
feed_dict = {
ops['pointclouds_pl']: jittered_data,
ops['labels_pl']: current_label[start_idx:end_idx],
ops['is_training_pl']: is_training,
} # feed_dict的key一定是place_holder
summary, step, _, loss_val, pred_val = sess.run(
[
ops['merged'], ops['step'], ops['train_op'], ops['loss'],
ops['pred']
],
feed_dict=feed_dict)
print("--train file:{}, batch_idx:{},step:{}".format(
str(fn), str(batch_idx), str(step)))
train_writer.add_summary(summary, step) # 只有train才保存训练过程的曲线
pred_val = np.argmax(pred_val, 1)
correct = np.sum(pred_val == current_label[start_idx:end_idx])
total_correct += correct
total_seen += BATCH_SIZE
loss_sum += loss_val
log_string('mean loss: %f' % (loss_sum / float(num_batches)))
log_string('accuracy: %f' % (total_correct / float(total_seen)))
def train_one_epoch(sess, ops, train_writer, dataset, verbose=True):
"""
Train model for one epoch
"""
global EPOCH_CNT
is_training = True
# Shuffle train samples
train_idxs = np.arange(0, len(dataset))
np.random.shuffle(train_idxs)
num_batches = len(dataset) / FLAGS['BATCH_SIZE'] # discards samples if dataset not divisible by batch size
log_string('[' + str(datetime.now()) + ' | EPOCH ' + str(EPOCH_CNT) + '] Starting training.', printout=False)
loss_sum, batch_print_steps = 0, 10
for batch_idx in range(num_batches):
start_idx, end_idx = batch_idx * FLAGS['BATCH_SIZE'], (batch_idx + 1) * FLAGS['BATCH_SIZE']
batch_data, batch_label = get_batch(dataset, train_idxs, start_idx, end_idx)
# Perturb point clouds:
batch_data[:,:,:3] = provider.jitter_point_cloud(batch_data[:,:,:3])
batch_data[:,:,:3] = provider.rotate_perturbation_point_cloud(batch_data[:,:,:3])
batch_data[:,:,:3] = provider.shift_point_cloud(batch_data[:,:,:3])
batch_data[:,:,:3] = provider.random_point_dropout(batch_data[:,:,:3],
max_dropout_ratio=FLAGS['MAX_POINT_DROPOUT_RATIO'])
feed_dict = {ops['pointclouds_pl']: batch_data,
ops['labels_pl']: batch_label,
ops['is_training_pl']: is_training}
summary, step, _, loss_val, pred_val = sess.run([ops['merged'], ops['step'], ops['train_op'],
ops['loss'], ops['pred']], feed_dict=feed_dict)
train_writer.add_summary(summary, step)
loss_sum += loss_val
if batch_idx % batch_print_steps == 0:
log_string('[Batch %03d] Mean Loss: %f' % ((batch_idx + 1), (loss_sum / batch_print_steps)), printout=verbose)
loss_sum = 0
def get_batch_wdp(dataset, batch_idx):
bsize = BATCH_SIZE
batch_data = np.zeros((bsize, NUM_POINT, 3))
batch_feats = np.zeros((bsize, NUM_POINT, 2))
batch_label = np.zeros((bsize, NUM_POINT), dtype=np.int32)
batch_smpw = np.zeros((bsize, NUM_POINT), dtype=np.float32)
batch_ctx = np.zeros((bsize, NUM_CLASSES), dtype=np.float32)
for i in range(bsize):
ps,seg,smpw,feat = get_batch(dataset,batch_idx)
if np.random.random() >= 0.65:
ps = provider.rotate_perturbation_point_cloud(ps.reshape(1, *ps.shape), angle_sigma=0.01, angle_clip=0.01)
ps = ps.squeeze()
batch_data[i,...] = ps
batch_label[i,:] = seg
batch_smpw[i,:] = smpw
batch_feats[i,:] = feat
# dropout_ratio = np.random.random()*0.875 # 0-0.875
# drop_idx = np.where(np.random.random((ps.shape[0]))<=dropout_ratio)[0]
# batch_data[i,drop_idx,:] = batch_data[i,0,:]
# batch_label[i,drop_idx] = batch_label[i,0]
# batch_smpw[i,drop_idx] *= 0
# mask = np.ones(len(ps), dtype=np.int32)
# mask[drop_idx] = 0
inds, _ = np.histogram(seg, range(NUM_CLASSES+1))
batch_ctx[i] = np.array(inds > 0, dtype=np.int32)
return batch_data, batch_label, batch_smpw, batch_feats, batch_ctx
def augment_batch_data(batch_data):
rotated_data = provider.rotate_point_cloud_with_normal(batch_data)
jittered_data = provider.random_scale_point_cloud(rotated_data[:, :, 0:3])
jittered_data = provider.rotate_perturbation_point_cloud(jittered_data)
jittered_data = provider.shift_point_cloud(jittered_data)
jittered_data = provider.jitter_point_cloud(jittered_data)
rotated_data[:, :, 0:3] = jittered_data
return rotated_data
def _augment_batch_data(self, batch_data):
rotated_data = provider.rotate_point_cloud(batch_data)
rotated_data = provider.rotate_perturbation_point_cloud(rotated_data)
jittered_data = provider.random_scale_point_cloud(rotated_data[:,:,0:3])
jittered_data = provider.shift_point_cloud(jittered_data)
jittered_data = provider.jitter_point_cloud(jittered_data)
rotated_data[:,:,0:3] = jittered_data
return provider.shuffle_points(rotated_data)
def train_one_epoch(sess, ops, train_writer):
""" ops: dict mapping from string to tf ops """
is_training = True
# Shuffle train files
train_file_idxs = np.arange(0, len(TRAIN_FILES))
np.random.shuffle(train_file_idxs)
for fn in range(len(TRAIN_FILES)):
log_string('----' + str(fn) + '-----')
current_data, current_label = provider.loadDataFile(
TRAIN_FILES[train_file_idxs[fn]])
current_data = current_data[:, 0:NUM_POINT, :]
current_data, current_label, _ = provider.shuffle_data(
current_data, np.squeeze(current_label))
current_label = np.squeeze(current_label)
file_size = current_data.shape[0]
num_batches = file_size // BATCH_SIZE
total_correct = 0
total_seen = 0
loss_sum = 0
for batch_idx in range(num_batches):
start_idx = batch_idx * BATCH_SIZE
end_idx = (batch_idx + 1) * BATCH_SIZE
# Augment batched point clouds by rotation and jittering
rotated_data = provider.rotate_point_cloud(
current_data[start_idx:end_idx, :, :])
jittered_data = provider.jitter_point_cloud(rotated_data)
jittered_data = provider.random_scale_point_cloud(jittered_data)
jittered_data = provider.rotate_perturbation_point_cloud(
jittered_data)
jittered_data = provider.shift_point_cloud(jittered_data)
feed_dict = {
ops['pointclouds_pl']: jittered_data,
ops['labels_pl']: current_label[start_idx:end_idx],
ops['is_training_pl']: is_training,
}
summary, step, _, loss_val, pred_val = sess.run(
[
ops['merged'], ops['step'], ops['train_op'], ops['loss'],
ops['pred']
],
feed_dict=feed_dict)
train_writer.add_summary(summary, step)
pred_val = np.argmax(pred_val, 1)
correct = np.sum(pred_val == current_label[start_idx:end_idx])
total_correct += correct
total_seen += BATCH_SIZE
loss_sum += loss_val
log_string('mean loss: %f' % (loss_sum / float(num_batches)))
log_string('accuracy: %f' % (total_correct / float(total_seen)))
def _augment_batch_data(self, batch_data):
if self.normal_channel:
rotated_data = provider.rotate_point_cloud_with_normal(batch_data)
rotated_data = provider.rotate_perturbation_point_cloud_with_normal(rotated_data)
else:
rotated_data = provider.rotate_point_cloud(batch_data)
rotated_data = provider.rotate_perturbation_point_cloud(rotated_data)
jittered_data = provider.random_scale_point_cloud(rotated_data[:,:,0:3])
jittered_data = provider.shift_point_cloud(jittered_data)
jittered_data = provider.jitter_point_cloud(jittered_data)
rotated_data[:,:,0:3] = jittered_data
return provider.shuffle_points(rotated_data)
def _augment_batch_data(self, batch_data):
if self.normal_channel:
rotated_data = provider.rotate_point_cloud_with_normal(batch_data)
rotated_data = provider.rotate_perturbation_point_cloud_with_normal(rotated_data)
else:
rotated_data = provider.rotate_point_cloud(batch_data)
rotated_data = provider.rotate_perturbation_point_cloud(rotated_data)
jittered_data = provider.random_scale_point_cloud(rotated_data[:,:,0:3])
jittered_data = provider.shift_point_cloud(jittered_data)
jittered_data = provider.jitter_point_cloud(jittered_data)
rotated_data[:,:,0:3] = jittered_data
return provider.shuffle_points(rotated_data)
def augment_batch_data_MODELNET(batch_data, is_include_normal):
'''
is_include_normal=False: xyz
is_include_normal=True: xyznxnynz
'''
if is_include_normal:
rotated_data = provider.rotate_point_cloud_with_normal(batch_data)
rotated_data = provider.rotate_perturbation_point_cloud_with_normal(
rotated_data)
else:
rotated_data = provider.rotate_point_cloud(batch_data)
rotated_data = provider.rotate_perturbation_point_cloud(rotated_data)
jittered_data = provider.random_scale_point_cloud(rotated_data[:, :, 0:3])
jittered_data = provider.shift_point_cloud(jittered_data)
jittered_data = provider.jitter_point_cloud(jittered_data)
rotated_data[:, :, 0:3] = jittered_data
return provider.shuffle_points(rotated_data)
def get_example(self, i):
"""Return i-th data"""
if self.augment:
rotated_data = provider.rotate_point_cloud(self.data[i:i +
1, :, :])
jittered_data = provider.jitter_point_cloud(rotated_data)
jittered_data = provider.random_scale_point_cloud(jittered_data)
jittered_data = provider.rotate_perturbation_point_cloud(
jittered_data)
jittered_data = provider.shift_point_cloud(jittered_data)
point_data = jittered_data[0]
else:
point_data = self.data[i]
# pint_data (2048, 3): (num_point, k) --> convert to (k, num_point, 1)
point_data = np.transpose(point_data.astype(np.float32), (1, 0))[:, :,
None]
assert point_data.dtype == np.float32
assert self.label[i].dtype == np.int32
return point_data, self.label[i]
def __data_generation(self, batch_idx):
x = np.zeros((self.batch_size, self.npoints, 3))
y = np.zeros((self.batch_size, ))
for i, idx in enumerate(batch_idx, 0):
x[i] = self.datas[
idx, 0:self.
npoints, :] # take the first n points. TODO: random choice
y[i] = self.labels[idx]
if self.augment and np.random.rand() > 0.5:
# implement data augmentation to the whole BATCH
rotated_x = provider.rotate_point_cloud(x) # rotate around x-axis
rotated_x = provider.rotate_perturbation_point_cloud(
rotated_x) # slightly rotate around every aixs
jittered_x = provider.random_scale_point_cloud(
rotated_x) # random scale a little bit
jittered_x = provider.shift_point_cloud(
jittered_x) # shift a little
jittered_x = provider.jitter_point_cloud(
jittered_x) # add random noise (jitter)
jittered_x = provider.shuffle_points(
jittered_x) # shuffle the point. for FPS
x = jittered_x
return x, keras.utils.to_categorical(y, num_classes=len(self.cat))
def _augment_batch_data(self, batch_data):
if self.normal_channel:
rotated_data = provider.rotate_point_cloud_with_normal(batch_data)
rotated_data = provider.rotate_perturbation_point_cloud_with_normal(
rotated_data)
else:
rotated_data = provider.rotate_point_cloud(batch_data)
rotated_data = provider.rotate_perturbation_point_cloud(
rotated_data)
jittered_data = provider.random_scale_point_cloud(
rotated_data[:, :, 0:3],
scale_low=self.scale_low,
scale_high=self.scale_high)
jittered_data = provider.shift_point_cloud(
jittered_data, shift_range=self.shift_range)
jittered_data = provider.jitter_point_cloud(jittered_data,
sigma=self.jitter_sigma,
clip=0.1)
rotated_data[:, :, 0:3] = jittered_data
if self.shuffle_points:
return provider.shuffle_points(rotated_data)
else:
return rotated_data
def train_one_epoch(sess, ops, train_writer):
""" ops: dict mapping from string to tf ops """
is_training = True
# Shuffle train files
#train_file_idxs = np.arange(0, len(TRAIN_FILES))
# np.random.shuffle(train_file_idxs)
train_files = TRAIN_FILES
# print(len(train_files))
random.shuffle(train_files)
total_correct = 0
total_seen = 0
loss_sum = 0
total_labels = np.array([0, 0, 0], dtype=np.int32)
gen = provider.data_generator(train_files, BATCH_SIZE)
# for fn in range(len(TRAIN_FILES)):
for fn in range(125000//BATCH_SIZE): # total 125000 shards
# log_string('----' + str(fn) + '-----')
data_q, label_q, seg_q = next(gen)
if data_q == None:
print("an epoch is done in steps: ", fn)
#print("total_labels:", total_labels)
break
#print(data_q[0].shape, label_q[0], seg_q[0].shape)
# current_data, current_label, current_seg = provider.loadsegDataFile(train_files[fn])
# current_data = current_data[:, 0:NUM_POINT, :]
# current_data, current_label, current_seg_ = provider.shuffle_data(current_data, np.squeeze(current_label))
# current_label = np.squeeze(current_label)
# current_seg = np.squeeze(current_seg)
# file_size = int(TRAIN_FILES[train_file_idxs[fn]].split('_')[1])
file_size = BATCH_SIZE
num_batches = 1
current_data = np.array(data_q)
current_label = np.array(label_q)
current_seg = np.array(seg_q)
rs_label = np.eye(5)[current_label]
#print(current_label.shape, rs_label.shape, current_label, rs_label)
# sys.exit()
rs_seg = current_seg
#train_data=np.concatenate((rs_current_data, rs_pottery),axis=0)
train_data = current_data
# Augment batched point clouds by rotation and jittering
jittered_data = provider.rotate_perturbation_point_cloud(
train_data, angle_sigma=np.pi/2, angle_clip=np.pi)
jittered_data = provider.jitter_point_cloud(jittered_data)
jittered_data = provider.random_scale_point_cloud(jittered_data)
jittered_data = provider.shift_point_cloud(jittered_data)
# print(jittered_data.shape)
# print(rs_seg.shape)
feed_dict = {ops['pointclouds_pl']: jittered_data,
ops['labels_pl']: rs_seg,
ops['labels_pl_c']: rs_label,
ops['is_training_pl']: is_training
}
summary, step, _, loss_val, pred_val, pred_c_val = sess.run([
ops['merged'],
ops['step'],
ops['train_op'],
ops['loss'],
# ops['cls_loss'],
# ops['reg_loss'],
ops['pred'],
ops['pred_c'],
], feed_dict=feed_dict)
train_writer.add_summary(summary, step)
#correct = np.sum(np.argmax(pred_val) == rs_seg0)
#print('pred, label, loss:', np.argmax(pred_val), rs_seg0, loss_val)
#correct = np.prod(np.argmax(pred_val, axis=1) == np.argmax(rs_seg, axis=1))
print('loss:', loss_val)
#total_correct += correct
total_seen += 1
loss_sum += loss_val
#total_labels[rs_seg0] += 1
if fn % 100 == 100 - 1:
# COMBINE FEATURES
log_string('mean loss: %f' % (loss_sum / float(total_seen)))
print('label:', rs_seg)
print('pred:', pred_val)
print('label_c:', current_label)
print('pred_c:', np.argmax(pred_c_val, axis=1))
print('loss_val:', loss_val)
correct = np.sum(np.argmax(pred_c_val, axis=1)
== np.argmax(rs_label, axis=1))
print('cls_acc_c:', correct)
def train_one_epoch(sess, train_writer):
""" ops: dict mapping from string to tf ops """
is_training = True
# Shuffle train files
train_file_idxs = np.arange(0, len(TRAIN_FILES))
np.random.shuffle(train_file_idxs)
mean_acc = 0
for fn in range(len(TRAIN_FILES)):
print('----' + str(fn) + '-----')
current_data, current_label = provider.loadDataFile(
TRAIN_FILES[train_file_idxs[fn]])
current_data = current_data[:, 0:self.num_points, :]
current_data, current_label, _ = provider.shuffle_data(
current_data, np.squeeze(current_label))
current_label = np.squeeze(current_label)
file_size = current_data.shape[0]
num_batches = file_size // self.batch_size
total_correct = 0
total_seen = 0
loss_sum = 0
for batch_idx in range(num_batches):
start_idx = batch_idx * self.batch_size
end_idx = (batch_idx + 1) * self.batch_size
# Augment batched point clouds by rotation and jittering
rotated_data = provider.rotate_point_cloud(
current_data[start_idx:end_idx, :, :])
jittered_data = provider.jitter_point_cloud(rotated_data)
jittered_data = provider.random_scale_point_cloud(
jittered_data)
jittered_data = provider.rotate_perturbation_point_cloud(
jittered_data)
jittered_data = provider.shift_point_cloud(jittered_data)
soft_targets = current_label[start_idx:end_idx]
n_values = np.zeros((soft_targets.size, self.num_classes))
n_values[np.arange(soft_targets.size), soft_targets] = 1
soft_targets = n_values
if teacher_flag:
soft_targets = teacher_model.predict(
current_data[start_idx:end_idx, :, :])
# print(soft_targets, current_label[start_idx:end_idx])
if not self.quantize_delay:
feed_dict = {
# ops['pointclouds_pl']: jittered_data,
self.pointclouds_pl:
current_data[start_idx:end_idx, :, :],
self.labels_pl:
current_label[start_idx:end_idx],
self.is_training:
is_training,
self.soft_Y:
soft_targets,
self.flag:
teacher_flag,
self.softmax_temperature:
self.temperature
}
else:
feed_dict = {
# ops['pointclouds_pl']: jittered_data,
self.pointclouds_pl:
current_data[start_idx:end_idx, :, :],
self.labels_pl:
current_label[start_idx:end_idx],
self.soft_Y:
soft_targets,
self.flag:
teacher_flag,
self.softmax_temperature:
self.temperature
# ops['is_training_pl']: is_training,
}
# print(feed_dict)
summary, step, _, loss_val, pred_val = sess.run(
[
self.merged_summary_op, self.batch, self.train_op,
self.total_loss, self.prediction
],
feed_dict=feed_dict)
# sess.run(ops['mask_update_op'])
train_writer.add_summary(summary, step)
pred_val = np.argmax(pred_val, 1)
correct = np.sum(
pred_val == current_label[start_idx:end_idx])
total_correct += correct
total_seen += self.batch_size
loss_sum += loss_val
print('mean loss: %f' % (loss_sum / float(num_batches)))
print('accuracy: %f' % (total_correct / float(total_seen)))
mean_acc += total_correct / float(total_seen)
return mean_acc / len(TRAIN_FILES)
def eval_one_epoch(sess, ops, num_votes=1, topk=1):
error_cnt = 0
is_training = False
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)]
fout = open(os.path.join(DUMP_DIR, 'pred_label.txt'), 'w')
np.random.seed(101)
for fn in range(len(TEST_FILES)):
log_string('----' + str(fn) + '----')
current_data, current_label = provider.loadDataFile(TEST_FILES[fn])
if FLAGS.random_pc_order:
current_data = change_point_cloud_order(current_data)
current_data = current_data[:, 0:NUM_POINT, :]
current_label = np.squeeze(current_label)
print(current_data.shape)
file_size = current_data.shape[0]
num_batches = file_size // BATCH_SIZE
print(file_size)
for batch_idx in range(num_batches):
start_idx = batch_idx * BATCH_SIZE
end_idx = (batch_idx + 1) * BATCH_SIZE
cur_batch_size = end_idx - start_idx
# Aggregating BEG
batch_loss_sum = 0 # sum of losses for the batch
batch_pred_sum = np.zeros(
(cur_batch_size, NUM_CLASSES)) # score for classes
batch_pred_max = np.ones(
(cur_batch_size, NUM_CLASSES)) * (-999999) # score for classes
batch_pred_classes = np.zeros(
(cur_batch_size, NUM_CLASSES)) # 0/1 for classes
for vote_idx in range(num_votes):
# Shuffle point order to achieve different farthest samplings
shuffled_indices = np.arange(NUM_POINT)
np.random.shuffle(shuffled_indices)
rotated_data = provider.rotate_point_cloud_by_angle(
current_data[start_idx:end_idx, shuffled_indices, :],
vote_idx / float(num_votes) * np.pi * 2)
jittered_data = provider.random_scale_point_cloud(rotated_data)
jittered_data = provider.rotate_perturbation_point_cloud(
jittered_data)
jittered_data = provider.jitter_point_cloud(jittered_data)
feed_dict = {
ops['pointclouds_pl']: rotated_data,
ops['labels_pl']: current_label[start_idx:end_idx],
ops['is_training_pl']: is_training
}
loss_val, pred_val = sess.run([ops['loss'], ops['pred']],
feed_dict=feed_dict)
batch_pred_sum += pred_val
batch_pred_val = np.argmax(pred_val, 1)
for el_idx in range(cur_batch_size):
batch_pred_classes[el_idx, batch_pred_val[el_idx]] += 1
batch_loss_sum += (loss_val * cur_batch_size /
float(num_votes))
# pred_val_topk = np.argsort(batch_pred_sum, axis=-1)[:,-1*np.array(range(topk))-1]
# pred_val = np.argmax(batch_pred_classes, 1)
pred_val = np.argmax(batch_pred_sum, 1)
# Aggregating END
correct = np.sum(pred_val == current_label[start_idx:end_idx])
# correct = np.sum(pred_val_topk[:,0:topk] == label_val)
total_correct += correct
total_seen += cur_batch_size
loss_sum += batch_loss_sum
for i in range(start_idx, end_idx):
l = current_label[i]
total_seen_class[l] += 1
total_correct_class[l] += (pred_val[i - start_idx] == l)
fout.write('%d, %d\n' % (pred_val[i - start_idx], l))
if pred_val[
i -
start_idx] != l and FLAGS.visu: # ERROR CASE, DUMP!
img_filename = '%d_label_%s_pred_%s.jpg' % (
error_cnt, SHAPE_NAMES[l],
SHAPE_NAMES[pred_val[i - start_idx]])
img_filename = os.path.join(DUMP_DIR, img_filename)
output_img = pc_util.point_cloud_three_views(
np.squeeze(current_data[i, :, :]))
scipy.misc.imsave(img_filename, output_img)
error_cnt += 1
log_string('eval mean loss: %f' % (loss_sum / float(total_seen)))
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))))
class_accuracies = np.array(total_correct_class) / np.array(
total_seen_class, dtype=np.float)
for i, name in enumerate(SHAPE_NAMES):
log_string('%10s:\t%0.3f' % (name, class_accuracies[i]))
def train_one_epoch(sess, ops, train_writer):
""" ops: dict mapping from string to tf ops """
is_training = True
# Shuffle train files
train_file_idxs = np.arange(0, len(LABELS))
np.random.shuffle(train_file_idxs)
current_data = np.empty([len(LABELS), NUM_POINT, 3], dtype=float)
current_label = np.empty([len(LABELS), 1], dtype=int)
for fn in range(len(LABELS)):
cut1, cut2, _ = provider.loadDataFile_cut_2(
TRAIN_FILES[train_file_idxs[fn]], False)
idx = np.random.randint(cut1.shape[0], size=NUM_POINT / 2)
cut1 = cut1[idx, :]
idx = np.random.randint(cut2.shape[0], size=NUM_POINT / 2)
cut2 = cut2[idx, :]
current_data[fn] = np.concatenate((cut1, cut2), axis=0)
current_label[fn] = LABELS[train_file_idxs[fn]]
current_label = np.squeeze(current_label)
file_size = current_data.shape[0]
num_batches = file_size // BATCH_SIZE
total_correct = 0
total_seen = 0
loss_sum = 0
for batch_idx in range(num_batches):
start_idx = batch_idx * BATCH_SIZE
end_idx = (batch_idx + 1) * BATCH_SIZE
# Augment batched point clouds by rotation and jittering
rotated_data = provider.rotate_point_cloud(
current_data[start_idx:end_idx, :, :])
jittered_data = provider.jitter_point_cloud(rotated_data)
jittered_data = provider.random_scale_point_cloud(jittered_data)
jittered_data = provider.rotate_perturbation_point_cloud(jittered_data)
jittered_data = provider.shift_point_cloud(jittered_data)
feed_dict = {
ops['pointclouds_pl']: jittered_data,
ops['labels_pl']: current_label[start_idx:end_idx],
ops['is_training_pl']: is_training,
}
summary, step, _, loss_val, pred_val, feature = sess.run(
[
ops['merged'], ops['step'], ops['train_op'], ops['loss'],
ops['pred'], ops['feat']
],
feed_dict=feed_dict)
train_writer.add_summary(summary, step)
pred_val = np.argmax(pred_val, 1)
correct = np.sum(pred_val == current_label[start_idx:end_idx])
total_correct += correct
total_seen += BATCH_SIZE
loss_sum += loss_val
if batch_idx % 100 == 0:
log_string('mean loss: {0:f} accuracy: {1:f}'.format(
loss_sum / float(batch_idx + 1),
total_correct / float(total_seen)))
log_string('mean loss: {0:f} accuracy: {1:f}'.format(
loss_sum / float(batch_idx + 1), total_correct / float(total_seen)))
def train_one_epoch(sess, ops, writer, epoch):
""" ops: dict mapping from string to tf ops """
is_training = True
log_string(str(datetime.now()))
# shuffle training files order
random.shuffle(train_h5_fn_list)
for item in train_h5_fn_list:
cur_h5_fn = os.path.join(data_in_dir, item)
print('Reading data from ', cur_h5_fn)
pts, gt_label, gt_mask, gt_valid, gt_other_mask, _ = load_data(cur_h5_fn)
# shuffle data order
n_shape = pts.shape[0]
idx = np.arange(n_shape)
np.random.shuffle(idx)
pts = pts[idx, ...]
gt_label = gt_label[idx, ...]
gt_mask = gt_mask[idx, ...]
gt_valid = gt_valid[idx, ...]
gt_other_mask = gt_other_mask[idx, ...]
# data augmentation to pts
pts = provider.jitter_point_cloud(pts)
pts = provider.shift_point_cloud(pts)
pts = provider.random_scale_point_cloud(pts)
pts = provider.rotate_perturbation_point_cloud(pts)
num_batch = n_shape // BATCH_SIZE
for i in range(num_batch):
start_idx = i * BATCH_SIZE
end_idx = (i + 1) * BATCH_SIZE
cur_pts = pts[start_idx: end_idx, ...]
cur_gt_label = gt_label[start_idx: end_idx, ...]
cur_gt_mask = gt_mask[start_idx: end_idx, ...]
cur_gt_valid = gt_valid[start_idx: end_idx, ...]
cur_gt_other_mask = gt_other_mask[start_idx: end_idx, ...]
feed_dict = {ops['pc_pl']: cur_pts,
ops['label_pl']: cur_gt_label,
ops['gt_mask_pl']: cur_gt_mask,
ops['gt_valid_pl']: cur_gt_valid,
ops['gt_other_mask_pl']: cur_gt_other_mask,
ops['is_training_pl']: is_training}
summary, step, _, lr_val, bn_decay_val, seg_loss_val, ins_loss_val, other_ins_loss_val, l21_norm_loss_val, conf_loss_val, loss_val, seg_pred_val, \
mask_pred_val, other_mask_pred_val = sess.run([ops['merged'], ops['step'], ops['train_op'], ops['learning_rate'], ops['bn_decay'], \
ops['seg_loss'], ops['ins_loss'], ops['other_ins_loss'], ops['l21_norm_loss'], ops['conf_loss'], ops['loss'], \
ops['seg_pred'], ops['mask_pred'], ops['other_mask_pred']], feed_dict=feed_dict)
writer.add_summary(summary, step)
seg_pred_id = np.argmax(seg_pred_val, axis=-1)
seg_acc = np.mean(seg_pred_id == cur_gt_label)
log_string('[Train Epoch %03d, Batch %03d, LR: %f, BN_DECAY: %f] Loss: %f = %f x %f (seg_loss, Seg Acc: %f) + %f x %f (ins_loss) + %f x %f (other_ins_loss) + %f x %f (l21_norm_loss) + %f x %f (conf_loss)' \
% (epoch, i, lr_val, bn_decay_val, loss_val, FLAGS.seg_loss_weight, seg_loss_val, seg_acc, \
FLAGS.ins_loss_weight, ins_loss_val, FLAGS.other_ins_loss_weight, other_ins_loss_val, \
FLAGS.l21_norm_loss_weight, l21_norm_loss_val, FLAGS.conf_loss_weight, conf_loss_val))
def eval_one_epoch(sess, ops, test_writer):
""" ops: dict mapping from string to tf ops """
global EPOCH_CNT
global BEST_ACC
global BEST_CLS_ACC
is_training = False
# Make sure batch data is of same size
cur_batch_data = np.zeros((BATCH_SIZE,NUM_POINT,TEST_DATASET.num_channel()))
cur_batch_label = np.zeros((BATCH_SIZE), dtype=np.int32)
total_correct = 0
total_seen = 0
loss_sum = 0
batch_idx = 0
shape_ious = []
total_seen_class = [0 for _ in range(NUM_CLASSES)]
total_correct_class = [0 for _ in range(NUM_CLASSES)]
log_string(str(datetime.now()))
log_string('---- EPOCH %03d EVALUATION ----'%(EPOCH_CNT))
while TEST_DATASET.has_next_batch():
batch_data, batch_label = TEST_DATASET.next_batch(augment=False)
bsize = batch_data.shape[0]
# print('Batch: %03d, batch size: %d'%(batch_idx, bsize))
# for the last batch in the epoch, the bsize:end are from last batch
cur_batch_data[0:bsize,...] = batch_data
cur_batch_label[0:bsize] = batch_label
if ROTATE_FLAG:
batch_pred_sum = np.zeros((BATCH_SIZE, NUM_CLASSES)) # score for classes
for vote_idx in range(12):
# Shuffle point order to achieve different farthest samplings
shuffled_indices = np.arange(NUM_POINT)
np.random.shuffle(shuffled_indices)
if NORMAL_FLAG:
rotated_data = provider.rotate_point_cloud_by_angle_with_normal(cur_batch_data[:, shuffled_indices, :],
vote_idx/float(12) * np.pi * 2)
rotated_data = provider.rotate_perturbation_point_cloud_with_normal(rotated_data)
else:
rotated_data = provider.rotate_point_cloud_by_angle(cur_batch_data[:, shuffled_indices, :],
vote_idx/float(12) * np.pi * 2)
rotated_data = provider.rotate_perturbation_point_cloud(rotated_data)
jittered_data = provider.random_scale_point_cloud(rotated_data[:,:,0:3])
jittered_data = provider.jitter_point_cloud(jittered_data)
rotated_data[:,:,0:3] = jittered_data
# else:
# rotated_data = provider.rotate_point_cloud_by_angle(cur_batch_data[:, shuffled_indices, :],
# vote_idx/float(12) * np.pi * 2)
feed_dict = {ops['pointclouds_pl']: rotated_data,
ops['labels_pl']: cur_batch_label,
ops['is_training_pl']: is_training}
loss_val, pred_val = sess.run([ops['loss'], ops['pred']], feed_dict=feed_dict)
batch_pred_sum += pred_val
pred_val = np.argmax(batch_pred_sum, 1)
else:
feed_dict = {ops['pointclouds_pl']: cur_batch_data,
ops['labels_pl']: cur_batch_label,
ops['is_training_pl']: is_training}
summary, step, loss_val, pred_val = sess.run([ops['merged'], ops['step'],
ops['loss'], ops['pred']], feed_dict=feed_dict)
test_writer.add_summary(summary, step)
pred_val = np.argmax(pred_val, 1)
correct = np.sum(pred_val[0:bsize] == batch_label[0:bsize])
total_correct += correct
total_seen += bsize
loss_sum += loss_val
batch_idx += 1
for i in range(bsize):
l = batch_label[i]
total_seen_class[l] += 1
total_correct_class[l] += (pred_val[i] == l)
current_acc = total_correct / float(total_seen)
current_cls_acc = np.mean(np.array(total_correct_class)/np.array(total_seen_class,dtype=np.float))
log_string('eval mean loss: %f' % (loss_sum / float(batch_idx)))
log_string('eval accuracy: %f'% (current_acc))
log_string('eval avg class acc: %f' % (current_cls_acc))
best_acc_flag, best_cls_acc_flag = False, False
if current_acc > BEST_ACC:
BEST_ACC = current_acc
best_acc_flag = True
if current_cls_acc > BEST_CLS_ACC:
BEST_CLS_ACC = current_cls_acc
best_cls_acc_flag = True
log_string('eval best accuracy: %f'% (BEST_ACC))
log_string('eval best avg class acc: %f'% (BEST_CLS_ACC))
EPOCH_CNT += 1
TEST_DATASET.reset()
return (best_acc_flag, best_cls_acc_flag)
def train_one_epoch(sess, ops, train_writer):
""" ops: dict mapping from string to tf ops """
is_training = True
# Shuffle train files
train_file_idxs = np.arange(0, len(TRAIN_FILES))
np.random.shuffle(train_file_idxs)
current_data_1 = np.empty([3 * len(TRAIN_FILES), NUM_POINT, 3],
dtype=float)
current_data_2 = np.empty([3 * len(TRAIN_FILES), NUM_POINT, 3],
dtype=float)
current_label = np.empty([3 * len(TRAIN_FILES), 1], dtype=int)
fn = 0
count = 0
while fn < len(TRAIN_FILES) - 1:
# log_string('----' + str(fn) + '-----')
total_current = []
a1, a2, _ = provider.loadDataFile_cut_2(
TRAIN_FILES[train_file_idxs[fn]])
idx = np.random.randint(a1.shape[0], size=NUM_POINT)
a1 = a1[idx, :]
idx = np.random.randint(a2.shape[0], size=NUM_POINT)
a2 = a2[idx, :]
total_current.append(a1)
total_current.append(a2)
fn = fn + 1
b1, b2, _ = provider.loadDataFile_cut_2(
TRAIN_FILES[train_file_idxs[fn]])
idx = np.random.randint(b1.shape[0], size=NUM_POINT)
b1 = b1[idx, :]
idx = np.random.randint(b2.shape[0], size=NUM_POINT)
b2 = b2[idx, :]
total_current.append(b1)
total_current.append(b2)
fn = fn + 1
pair_num = 0
for index in range(len(total_current)):
for index2 in range(index + 1, len(total_current)):
current_data_1[6 * count +
pair_num, :, :] = total_current[index]
current_data_2[6 * count +
pair_num, :, :] = total_current[index2]
if (index < 2) and (index2 >= 2):
current_label[6 * count + pair_num, :] = 0
else:
current_label[6 * count + pair_num, :] = 1
pair_num = pair_num + 1
count = count + 1
current_label = np.squeeze(current_label)
file_size = current_data_1.shape[0]
num_batches = file_size // BATCH_SIZE
total_correct = 0
total_seen = 0
loss_sum = 0
for batch_idx in range(num_batches):
start_idx = batch_idx * BATCH_SIZE
end_idx = (batch_idx + 1) * BATCH_SIZE
# shuffle each batch
data_1 = current_data_1[start_idx:end_idx, :, :]
data_2 = current_data_2[start_idx:end_idx, :, :]
label = current_label[start_idx:end_idx]
combine_data = np.concatenate((data_1, data_2), axis=2)
combine_data, label, _ = provider.shuffle_data(combine_data,
np.squeeze(label))
data_1 = combine_data[:, :, 0:3]
data_2 = combine_data[:, :, 3:6]
label = np.squeeze(label)
# Augment batched point clouds by rotation and jittering
rotated_data_1 = provider.rotate_point_cloud(data_1)
jittered_data_1 = provider.jitter_point_cloud(rotated_data_1)
jittered_data_1 = provider.random_scale_point_cloud(jittered_data_1)
jittered_data_1 = provider.rotate_perturbation_point_cloud(
jittered_data_1)
jittered_data_1 = provider.shift_point_cloud(jittered_data_1)
rotated_data_2 = provider.rotate_point_cloud(data_2)
jittered_data_2 = provider.jitter_point_cloud(rotated_data_2)
jittered_data_2 = provider.random_scale_point_cloud(jittered_data_2)
jittered_data_2 = provider.rotate_perturbation_point_cloud(
jittered_data_2)
jittered_data_2 = provider.shift_point_cloud(jittered_data_2)
feed_dict = {
ops['pointclouds_pl_1']: jittered_data_1,
ops['pointclouds_pl_2']: jittered_data_2,
ops['labels_pl']: label,
ops['is_training_pl']: is_training
}
summary, step, _, loss_val, pred_val = sess.run([
ops['merged'], ops['step'], ops['train_op'], ops['loss'],
ops['pred']
],
feed_dict=feed_dict)
train_writer.add_summary(summary, step)
pred_val = np.argmax(pred_val, 1)
correct = np.sum(pred_val == label)
total_correct += correct
total_seen += BATCH_SIZE
loss_sum += loss_val
if batch_idx % 50 == 0:
log_string('mean loss: {0:f} accuracy: {1:f}'.format(
loss_sum / float(batch_idx + 1),
total_correct / float(total_seen)))
log_string('mean loss: {0:f} accuracy: {1:f}'.format(
loss_sum / float(batch_idx + 1), total_correct / float(total_seen)))
def eval_one_epoch(sess, ops, test_writer):
""" ops: dict mapping from string to tf ops """
is_training = False
total_correct = 0
total_seen = 0
loss_sum = 0
loss_sum_c = 0
total_seen_class = [0 for _ in range(NUM_CLASSES)]
total_correct_class = [0 for _ in range(NUM_CLASSES)]
total_labels = np.array([0, 0, 0], dtype=np.int32)
gen = provider.data_generator(TEST_FILES, BATCH_SIZE)
# for fn in range(len(TEST_FILES)):
for fn in range(125000//BATCH_SIZE): # total 125000 shards
data_q, label_q, seg_q = next(gen)
if data_q == None:
print("an epoch is done in steps: ", fn)
#print("total_labels:", total_labels)
break
#file_size = int(TEST_FILES[fn].split('_')[1])
file_size = BATCH_SIZE
num_batches = 1
current_data = np.array(data_q)
current_label = np.array(label_q)
current_seg = np.array(seg_q)
"""
if current_label_i == 0 or current_label_i == 4: # pot #1 or #4
if current_seg_i[0] == 1:
rs_seg0 = 0
elif current_seg_i[3] == 1:
rs_seg0 = 2
else:
rs_seg0 = 1
else:
if current_seg_i[0] == 1:
rs_seg0 = 0
elif current_seg_i[2] == 1:
rs_seg0 = 2
else:
rs_seg0 = 1
rs_seg = np.eye(3)[rs_seg0]
#print(rs_seg)
#rs_seg = np.eye(16)[rs_seg0] # rs_seg.shape=(4,2)
#rs_seg = np.eye(8)[rs_seg0] # rs_seg.shape=(4,2)
#print(rs_seg.shape, rs_seg)
"""
rs_label = np.eye(5)[current_label]
rs_seg = current_seg
#test_data=np.concatenate((rs_current_data, rs_pottery),axis=0)
test_data = current_data
jittered_data = provider.rotate_perturbation_point_cloud(
test_data, angle_sigma=np.pi/2, angle_clip=np.pi)
jittered_data = provider.jitter_point_cloud(jittered_data)
jittered_data = provider.random_scale_point_cloud(jittered_data)
jittered_data = provider.shift_point_cloud(jittered_data)
feed_dict = {ops['pointclouds_pl']: jittered_data,
ops['labels_pl']: rs_seg,
ops['labels_pl_c']: rs_label,
ops['is_training_pl']: is_training
}
# summary,
step, cls_loss, cls_loss_c, pred_val, pred_c_val = sess.run([
# ops['merged'],
ops['step'],
ops['cls_loss'],
ops['cls_loss_c'],
ops['pred'],
ops['pred_c'],
], feed_dict=feed_dict)
#correct = np.sum(np.argmax(pred_val) == rs_seg0)
#print(np.argmax(pred_c_val, axis=1), np.argmax(rs_label, axis=1))
correct = np.sum(np.argmax(pred_c_val, axis=1)
== np.argmax(rs_label, axis=1))
print('cls_loss, cls_loss_c, cls_acc_c:',
cls_loss, cls_loss_c, correct)
#print('pred, label, loss:', np.argmax(pred_val, axis=1), np.argmax(rs_seg, axis=1), loss_val)
total_correct += correct / float(BATCH_SIZE)
total_seen += 1
# loss_sum += (loss_val*BATCH_SIZE)
loss_sum += cls_loss
loss_sum_c += cls_loss_c
#total_labels[rs_seg0] += 1
loss_avg = loss_sum / float(total_seen)
loss_avg_c = loss_sum_c / float(total_seen)
acc_avg_c = total_correct / float(total_seen)
log_string('eval mean cls_loss: %f' % loss_avg)
log_string('eval mean cls_loss_c: %f' % loss_avg_c)
log_string('eval mean acc_loss_c: %f' % acc_avg_c)
val_loss_summary.value[0].simple_value = (loss_avg)
val_loss_c_summary.value[0].simple_value = (loss_avg_c)
val_acc_c_summary.value[0].simple_value = (acc_avg_c)
test_writer.add_summary(val_loss_summary, step)
test_writer.add_summary(val_loss_c_summary, step)
test_writer.add_summary(val_acc_c_summary, step)
test_writer.flush()
#print("total_labels:", total_labels)
print('label:', rs_seg)
print('pred:', pred_val)
print('cls_loss:', cls_loss)
print('label_c:', current_label)
print('pred_c:', np.argmax(pred_c_val, axis=1))
print('cls_loss_c:', cls_loss_c)
print('cls_acc_c:', correct)
return loss_avg, loss_avg_c
def train_one_epoch(epoch_num):
### NOTE: is_training = False: We do not update bn parameters during training due to the small batch size. This requires pre-training PointNet with large batchsize (say 32).
is_training = False
allow_full_loss = 0.0
if epoch_num > 5:
allow_full_loss = 1.0
printout(flog, str(datetime.now()))
random.shuffle(train_h5_fn_list)
for item in train_h5_fn_list:
cur_h5_fn = os.path.join(data_in_dir, item)
printout(flog, 'Reading data from: %s' % cur_h5_fn)
pts, semseg_one_hot, semseg_mask, insseg_one_hot, insseg_mask = load_data(
cur_h5_fn)
# shuffle data order
n_shape = pts.shape[0]
idx = np.arange(n_shape)
np.random.shuffle(idx)
pts = pts[idx, ...]
semseg_one_hot = semseg_one_hot[idx, ...]
semseg_mask = semseg_mask[idx, ...]
insseg_one_hot = insseg_one_hot[idx, ...]
insseg_mask = insseg_mask[idx, ...]
# data augmentation to pts
pts = provider.jitter_point_cloud(pts)
pts = provider.shift_point_cloud(pts)
pts = provider.random_scale_point_cloud(pts)
pts = provider.rotate_perturbation_point_cloud(pts)
total_loss = 0.0
total_grouperr = 0.0
total_same = 0.0
total_diff = 0.0
total_same = 0.0
total_pos = 0.0
total_acc = 0.0
num_batch = n_shape // BATCH_SIZE
for i in range(num_batch):
start_idx = i * BATCH_SIZE
end_idx = (i + 1) * BATCH_SIZE
feed_dict = {
pointclouds_ph: pts[start_idx:end_idx, ...],
ptsseglabel_ph: semseg_one_hot[start_idx:end_idx, ...],
ptsgroup_label_ph: insseg_one_hot[start_idx:end_idx,
...],
pts_seglabel_mask_ph: semseg_mask[start_idx:end_idx,
...],
pts_group_mask_ph: insseg_mask[start_idx:end_idx, ...],
is_training_ph: is_training,
alpha_ph: min(10., (float(epoch_num) / 5.) * 2. + 2.),
allow_full_loss_pl: allow_full_loss
}
_, loss_val, lr_val, simmat_val, semseg_logits_val, \
grouperr_val, same_val, same_cnt_val, diff_val, diff_cnt_val, pos_val, pos_cnt_val = sess.run([\
train_op, loss, learning_rate, \
net_output['simmat'], net_output['semseg_logits'], \
grouperr, same, same_cnt, diff, diff_cnt, pos, pos_cnt], feed_dict=feed_dict)
seg_pred = np.argmax(semseg_logits_val, axis=-1)
gt_mask_val = (np.sum(
semseg_one_hot[start_idx:end_idx, ...], axis=-1) > 0)
seg_gt = np.argmax(semseg_one_hot[start_idx:end_idx, ...],
axis=-1)
correct = ((seg_pred == seg_gt) + (~gt_mask_val) > 0)
acc_val = np.mean(correct)
total_acc += acc_val
total_loss += loss_val
total_grouperr += grouperr_val
total_diff += diff_val / max(1e-6, diff_cnt_val)
total_same += same_val / max(1e-6, same_cnt_val)
total_pos += pos_val / max(1e-6, pos_cnt_val)
if i % 10 == 9:
printout(flog, 'Batch: %d, LR: %f, loss: %f, FullLoss: %f, SegAcc: %f, grouperr: %f, same: %f, diff: %f, pos: %f' % \
(i, lr_val, total_loss/10, allow_full_loss, total_acc/10, total_grouperr/10, total_same/10, total_diff/10, total_pos/10))
lr_sum, batch_sum, train_loss_sum, group_err_sum = sess.run( \
[lr_op, batch, total_train_loss_sum_op, group_err_op], \
feed_dict={total_training_loss_ph: total_loss / 10.,
group_err_loss_ph: total_grouperr / 10., })
train_writer.add_summary(train_loss_sum, batch_sum)
train_writer.add_summary(lr_sum, batch_sum)
train_writer.add_summary(group_err_sum, batch_sum)
total_grouperr = 0.0
total_loss = 0.0
total_diff = 0.0
total_same = 0.0
total_pos = 0.0
same_cnt0 = 0
total_acc = 0.0