def write_mesh_scannet_to_tfrecords(dir_path,
label_path,
output_path,
record_name,
mesh_list,
start_level=0,
level_num=3):
reader = DataReader()
writer = tf.python_io.TFRecordWriter(output_path + record_name +
'.tfrecords')
count = 0
one_iter_time = 0
random.shuffle(mesh_list)
total_num = len(mesh_list)
for mesh_name in mesh_list:
print("Start mesh " + mesh_name)
time_start = time.time()
shape_list = []
maxpool_indices_list = []
maxpool_offset_list = []
maxpool_arg_list = []
conv_indices_list = []
conv_weight_list = []
conv_offset_list = []
conv_shape_list = []
unpool_indices_list = []
for j in range(start_level, start_level + level_num):
para_name = dir_path + mesh_name + '_' + str(j) + '_pad.para'
para_shape, indices, axis_indices, weights = reader.read_para(
para_name)
shape_list = shape_list + para_shape
indices = decode_intlist_from_bytes(indices)
axis_indices = decode_intlist_from_bytes(axis_indices)
weights = decode_doublelist_from_bytes(weights)
if (j == start_level):
mesh_path = dir_path + mesh_name + '_' + str(j) + '.obj'
mesh = openmesh.read_trimesh(mesh_path)
mesh.update_vertex_normals()
normal_list = mesh.vertex_normals()
points = mesh.points()
z_list = points[:, 2]
rgb_list, label = reader.read_rgb_label(label_path +
mesh_name + '.rgbl')
#label = [0] * shape_list[0]
label = flat_list(label)
if (len(label) != shape_list[0]):
print("[Error]Unequal v_num")
return
#print("decode finished")
conv_indices = Conv_Matrix_arg(para_shape, indices, axis_indices,
weights)
conv_offset_list.append(len(conv_indices))
conv_indices = flat_list(conv_indices)
conv_indices_list = conv_indices_list + conv_indices
conv_weight_list = conv_weight_list + weights
if (j > start_level):
hrch_name = dir_path + mesh_name + '_' + str(j) + '.hrch'
cvt_nums, cover_vts = reader.read_hrch(hrch_name)
hrch_axis_name = dir_path + mesh_name + '_' + str(j) + '.pool'
cover_vts_axis = reader.read_pool(hrch_axis_name)
maxpool_arg, maxpool_indices = MaxPooling_Matrix_arg(
shape_list[(j - start_level - 1) * 4],
shape_list[(j - start_level) * 4], para_shape[1], cvt_nums,
cover_vts, cover_vts_axis)
maxpool_arg_list.append(maxpool_arg)
#flat
maxpool_offset_list.append(len(maxpool_indices))
maxpool_indices = flat_list(maxpool_indices)
maxpool_indices_list = maxpool_indices_list + maxpool_indices
unpooling_indices = AveUnPooling_Matrix_arg(
shape_list[(j - start_level - 1) * 4],
shape_list[(j - start_level) * 4], para_shape[1], cvt_nums,
cover_vts, cover_vts_axis)
unpooling_indices = flat_list(unpooling_indices)
unpool_indices_list = unpool_indices_list + unpooling_indices
new_feature = {
'mesh_name':
_bytes_feature(mesh_name.encode()),
'shape':
_bytes_feature(IntList_to_Bytes(shape_list)),
'label':
_bytes_feature(IntList_to_Bytes(label)),
'z':
_bytes_feature(Float32List_to_Bytes(z_list)),
'normal':
_bytes_feature(Float32List_to_Bytes(normal_list)),
'rgb':
_bytes_feature(Float32List_to_Bytes(rgb_list)),
'maxpool/offset':
_bytes_feature(IntList_to_Bytes(maxpool_offset_list)),
'maxpool/arg':
_bytes_feature(IntList_to_Bytes(maxpool_arg_list)),
'maxpool/indices':
_bytes_feature(IntList_to_Bytes(maxpool_indices_list)),
'unpooling/indices':
_bytes_feature(IntList_to_Bytes(unpool_indices_list)),
'conv/offset':
_bytes_feature(IntList_to_Bytes(conv_offset_list)),
'conv/indices':
_bytes_feature(IntList_to_Bytes(conv_indices_list)),
'conv/weights':
_bytes_feature(Float32List_to_Bytes(conv_weight_list))
}
#height scaled
input_feature = np.array([], dtype=np.float32)
for j in range(start_level, start_level + level_num):
feature_name = dir_path + mesh_name + '_' + str(
j) + '_reweighted.input'
feature_channel, grid_feature = reader.read_grid_feature(
feature_name)
grid_feature = np.reshape(grid_feature, (-1, 4))
grid_feature = grid_feature[:, 3]
input_feature = np.concatenate((input_feature, grid_feature),
axis=0)
new_feature['input_feature'] = _bytes_feature(
Float32List_to_Bytes(input_feature))
new_feature['feature_channel'] = _int64_feature(feature_channel)
example = tf.train.Example(features=tf.train.Features(
feature=new_feature))
writer.write(example.SerializeToString())
time_end = time.time()
one_iter_time = (
(time_end - time_start) + one_iter_time * count) / (count + 1)
time_left = one_iter_time * (total_num - count)
print("Finish mesh " + mesh_name)
print(str(100.0 * count / total_num) + "%" + "finished!")
print("%.2f min left!" % (time_left / 60))
count = count + 1
writer.close()
def write_class_data_to_tfrecords(dir_path,
label_path,
output_path,
record_name,
mesh_list,
start_level=0,
level_num=3):
reader = DataReader()
labels = reader.read_labels(label_path)
writer = tf.python_io.TFRecordWriter(output_path + record_name +
'.tfrecords')
count = 0
one_iter_time = 0
random.shuffle(mesh_list)
total_num = len(mesh_list)
for i in mesh_list:
time_start = time.time()
label = labels[i]
shape_list = []
maxpool_indices_list = []
maxpool_offset_list = []
maxpool_arg_list = []
conv_indices_list = []
conv_weight_list = []
conv_offset_list = []
for j in range(start_level, start_level + level_num):
para_name = dir_path + 'T' + str(i) + '_' + str(j) + '_pad.para'
para_shape, indices, axis_indices, weights = reader.read_para(
para_name)
#print(shape)
shape_list = shape_list + para_shape
#print("shape_list:")
#print(shape_list)
indices = decode_intlist_from_bytes(indices)
axis_indices = decode_intlist_from_bytes(axis_indices)
weights = decode_doublelist_from_bytes(weights)
#print("decode finished")
conv_indices = Conv_Matrix_arg(para_shape, indices, axis_indices,
weights)
#print("finish build matrix")
#flat
conv_offset_list.append(len(conv_indices))
conv_indices = flat_list(conv_indices)
conv_indices_list = conv_indices_list + conv_indices
conv_weight_list = conv_weight_list + weights
#print(conv_offset_list)
#print(len(weights))
if (j > 0):
hrch_name = dir_path + 'T' + str(i) + '_' + str(j) + '.hrch'
cvt_nums, cover_vts = reader.read_hrch(hrch_name)
hrch_axis_name = dir_path + 'T' + str(i) + '_' + str(
j) + '.pool'
cover_vts_axis = reader.read_pool(hrch_axis_name)
maxpool_arg, maxpool_indices = MaxPooling_Matrix_arg(
shape_list[(j - 1) * 4], shape_list[j * 4], para_shape[1],
cvt_nums, cover_vts, cover_vts_axis)
maxpool_arg_list.append(maxpool_arg)
#flat
maxpool_offset_list.append(len(maxpool_indices))
maxpool_indices = flat_list(maxpool_indices)
maxpool_indices_list = maxpool_indices_list + maxpool_indices
new_feature = {
'label':
_int64_feature(label),
'mesh_id':
_int64_feature(i),
'shape':
_bytes_feature(IntList_to_Bytes(shape_list)),
'maxpool/offset':
_bytes_feature(IntList_to_Bytes(maxpool_offset_list)),
'maxpool/arg':
_bytes_feature(IntList_to_Bytes(maxpool_arg_list)),
'maxpool/indices':
_bytes_feature(IntList_to_Bytes(maxpool_indices_list)),
'conv/offset':
_bytes_feature(IntList_to_Bytes(conv_offset_list)),
'conv/indices':
_bytes_feature(IntList_to_Bytes(conv_indices_list)),
'conv/weights':
_bytes_feature(Float32List_to_Bytes(conv_weight_list))
}
feature_name = dir_path + 'T' + str(i) + '_0_reweighted.input'
feature_channel, grid_feature = reader.read_grid_feature(feature_name)
new_feature['input_feature'] = _bytes_feature(
Float32List_to_Bytes(grid_feature))
new_feature['feature_channel'] = _int64_feature(feature_channel)
example = tf.train.Example(features=tf.train.Features(
feature=new_feature))
writer.write(example.SerializeToString())
time_end = time.time()
one_iter_time = (
(time_end - time_start) + one_iter_time * count) / (count + 1)
time_left = one_iter_time * (total_num - count)
print("Finish mesh " + str(i))
print(str(100.0 * count / total_num) + "%" + "finished!")
print("%.2f min left!" % (time_left / 60))
count = count + 1
writer.close()