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main_DALES.py
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from os.path import join
from tester_DALES import ModelTester
from helper_ply import read_ply
from tool import ConfigDALES as cfg
from tool import DataProcessing as DP
# from helper_tool import Plot
import tensorflow as tf
import numpy as np
import time, pickle, argparse, glob, os, importlib
from DR-Net import Network
class DALES:
def __init__(self, labeled_point, retrain):
self.name = 'DALES'
self.path = '/home/ubuntu/data/dales'
self.label_to_names = {0: 'unknown',
1: 'Ground',
2: 'Vegetation',
3: 'Cars',
4: 'Trucks',
5: 'Power lines',
6: 'Fences',
7: 'Poles',
8: 'Buildings'}
self.num_classes = len(self.label_to_names)
self.label_values = np.sort([k for k, v in self.label_to_names.items()]) # class number
self.label_to_idx = {l: i for i, l in enumerate(self.label_values)} # label:idx
self.ignored_labels = np.array([0])
self.val_split = 1
self.all_train_files = glob.glob(join(self.path, 'original_ply', 'train', '*.ply'))
self.all_test_files = glob.glob(join(self.path, 'original_ply', 'test', '*.ply'))
self.all_files = self.all_train_files + self.all_test_files
#initialize
if '%' in labeled_point:
r = float(labeled_point[:-1]) / 100
self.num_with_anno_per_batch = max(int(cfg.num_points * r), 1)
else:
self.num_with_anno_per_batch = cfg.num_classes
# Initiate containers
self.num_per_class = np.zeros(self.num_classes)
self.val_proj = []
self.val_labels = []
self.possibility = {}
self.min_possibility = {}
self.input_trees = {'training': [], 'validation': []}
self.input_labels = {'training': [], 'validation': []}
self.input_names = {'training': [], 'validation': []}
self.load_sub_sampled_clouds(cfg.sub_grid_size, labeled_point, retrain)
for ignore_label in self.ignored_labels:
self.num_per_class = np.delete(self.num_per_class, ignore_label)
def load_sub_sampled_clouds(self, sub_grid_size, labeled_point, retrain):
train_tree_path = join(self.path, 'input_{:.3f}'.format(sub_grid_size), 'train')
test_tree_path = join(self.path, 'input_{:.3f}'.format(sub_grid_size), 'test')
for i, file_path in enumerate(self.all_files):
t0 = time.time()
cloud_idx = file_path.split('/')[-1][:-4] #
split_name = file_path.split('/')[-2]
if split_name == 'train':
cloud_split = 'training'
tree_path = train_tree_path
else:
cloud_split = 'validation'
tree_path = test_tree_path
# Name of the input files
kd_tree_file = join(tree_path, '{:s}_KDTree.pkl'.format(cloud_idx)) #
sub_ply_file = join(tree_path, '{:s}.ply'.format(cloud_idx)) # ӵ
data = read_ply(sub_ply_file)
sub_labels = data['class']
# compute num_per_class in training set
if cloud_split == 'training':
self.num_per_class += DP.get_num_class_from_label(sub_labels, self.num_classes)
# ======================================== #
# Random Sparse Annotation #
# ======================================== #
if cloud_split == 'training':
if '%' in labeled_point:
num_pts = len(sub_labels)
r = float(labeled_point[:-1]) / 100
num_with_anno = max(int(num_pts * r), 1)
num_without_anno = num_pts - num_with_anno
idx_without_anno = np.random.choice(num_pts, num_without_anno, replace=False)
sub_labels[idx_without_anno] = 0
else:
for i in range(self.num_classes):
ind_per_class = np.where(sub_labels == i)[0] # index of points belongs to a specific class
num_per_class = len(ind_per_class)
if num_per_class > 0:
num_with_anno = int(labeled_point)
num_without_anno = num_per_class - num_with_anno
idx_without_anno = np.random.choice(ind_per_class, num_without_anno, replace=False)
sub_labels[idx_without_anno] = 0
# =================================================================== #
# retrain the model with predicted pseudo labels #
# =================================================================== #
if retrain:
pseudo_label_path = './test'
temp = read_ply(join(pseudo_label_path, cloud_name + '.ply'))
pseudo_label = temp['pred']
pseudo_label_ratio = 0.01
pseudo_label[sub_labels != 0] = sub_labels[sub_labels != 0]
sub_labels = pseudo_label
self.num_with_anno_per_batch = int(cfg.num_points * pseudo_label_ratio)
# Read pkl with search tree
with open(kd_tree_file, 'rb') as f:
search_tree = pickle.load(f)
self.input_trees[cloud_split] += [search_tree]
self.input_labels[cloud_split] += [sub_labels]
self.input_names[cloud_split] += [cloud_idx]
size = sub_labels.shape[0] * 4 * 7
# print('{:s}/{:s} {:.1f} MB loaded in {:.1f}s'.format(kd_tree_file.split('/')[-2],
# kd_tree_file.split('/')[-1],
# size * 1e-6, time.time() - t0))
print('\nPreparing reprojected indices for testing')
# Get validation and test reprojected indices
for i, file_path in enumerate(self.all_test_files):
t0 = time.time()
cloud_idx = file_path.split('/')[-1][:-4]
# Validation projection and labels
proj_file = join(test_tree_path, '{:s}_proj.pkl'.format(cloud_idx))
with open(proj_file, 'rb') as f:
proj_idx, labels = pickle.load(f)
self.val_proj += [proj_idx]
self.val_labels += [labels]
# print('{:s} done in {:.1f}s'.format(cloud_idx, time.time() - t0))
# Generate the input data flow
def get_batch_gen(self, split):
if split == 'training':
num_per_epoch = cfg.train_steps * cfg.batch_size # ÿ epoch ж ٵ
elif split == 'validation':
num_per_epoch = cfg.val_steps * cfg.val_batch_size
self.possibility[split] = [] # ĸ
self.min_possibility[split] = [] # Ƶĸ
# Random initialize
for i, tree in enumerate(self.input_labels[split]): # һ ʼ
# print(tree.data.shape[0])
self.possibility[split] += [np.random.rand(tree.data.shape[0]) * 1e-3]
self.min_possibility[split] += [float(np.min(self.possibility[split][-1]))]
def spatially_regular_gen():
# Generator loop
for i in range(num_per_epoch): # ÿ epoch ĵ Ŀ
# Choose the cloud with the lowest probability
cloud_idx = int(np.argmin(self.min_possibility[split])) # С
# choose the point with the minimum of possibility in the cloud as query point
point_ind = np.argmin(self.possibility[split][cloud_idx]) # С
# Get all points within the cloud from tree structure
points = np.array(self.input_trees[split][cloud_idx].data, copy=False) # ṹ ҵ ӵ
# Center point of input region
center_point = points[point_ind, :].reshape(1, -1) # С Ϊ ĵ
# Add noise to the center point
noise = np.random.normal(scale=cfg.noise_init / 10, size=center_point.shape)
pick_point = center_point + noise.astype(center_point.dtype) # ĵ ϼӸ
# Check if the number of points in the selected cloud is less than the predefined num_points
if len(points) < cfg.num_points:
# Query all points within the cloud
queried_idx = self.input_trees[split][cloud_idx].query(pick_point, k=len(points))[1][0] # ĵ Χ Ϊ
else:
# Query the predefined number of points
queried_idx = self.input_trees[split][cloud_idx].query(pick_point, k=cfg.num_points)[1][0]
# Shuffle index
queried_idx = DP.shuffle_idx(queried_idx) #
# Get corresponding points and colors based on the index
queried_pc_xyz = points[queried_idx]
queried_pc_xyz = queried_pc_xyz - pick_point # Ļ
queried_pc_labels = self.input_labels[split][cloud_idx][queried_idx]
# Update the possibility of the selected points
dists = np.sum(np.square((points[queried_idx] - pick_point).astype(np.float32)), axis=1)
delta = np.square(1 - dists / np.max(dists))
self.possibility[split][cloud_idx][queried_idx] += delta # ԽԶ ʼӵ Խ
self.min_possibility[split][cloud_idx] = float(np.min(self.possibility[split][cloud_idx]))
# up_sampled with replacement
if len(points) < cfg.num_points:
queried_pc_xyz, queried_idx, queried_pc_labels = \
DP.data_aug_no_color(queried_pc_xyz, queried_pc_labels, queried_idx, cfg.num_points)
if split == 'training':
unique_label_value = np.unique(queried_pc_labels)
if len(unique_label_value) <= 1:
continue
else:
# ================================================================== #
# Keep the same number of labeled points per batch #
# ================================================================== #
idx_with_anno = np.where(queried_pc_labels != self.ignored_labels[0])[0]
num_with_anno = len(idx_with_anno)
if num_with_anno > self.num_with_anno_per_batch:
idx_with_anno = np.random.choice(idx_with_anno, self.num_with_anno_per_batch, replace=False)
elif num_with_anno < self.num_with_anno_per_batch:
dup_idx = np.random.choice(idx_with_anno, self.num_with_anno_per_batch - len(idx_with_anno))
idx_with_anno = np.concatenate([idx_with_anno, dup_idx], axis=0)
xyz_with_anno = queried_pc_xyz[idx_with_anno]
labels_with_anno = queried_pc_labels[idx_with_anno]
else:
xyz_with_anno = queried_pc_xyz
labels_with_anno = queried_pc_labels
if True:
yield (queried_pc_xyz.astype(np.float32),
queried_pc_labels,
queried_idx.astype(np.int32),
np.array([cloud_idx], dtype=np.int32),
xyz_with_anno.astype(np.float32),
labels_with_anno.astype(np.int32))
gen_func = spatially_regular_gen
gen_types = (tf.float32, tf.int32, tf.int32, tf.int32, tf.float32, tf.int32)
gen_shapes = ([None, 3], [None], [None], [None], [None, 3], [None])
return gen_func, gen_types, gen_shapes
@staticmethod
def get_tf_mapping2():
def tf_map(batch_xyz, batch_labels, batch_pc_idx, batch_cloud_idx, batch_xyz_anno, batch_label_anno):
batch_features = batch_xyz
input_points = []
input_neighbors = []
input_neighbors_1 = []
input_neighbors_2 = []
input_pools = []
input_up_samples = []
for i in range(cfg.num_layers):
neighbour_idx = tf.py_func(DP.knn_search, [batch_xyz, batch_xyz, cfg.k_n], tf.int32)
neighbour_idx_1 = tf.py_func(DP.knn_search, [batch_xyz, batch_xyz, cfg.k_nn1], tf.int32)
neighbour_idx_2 = tf.py_func(DP.knn_search, [batch_xyz, batch_xyz, cfg.k_nn2], tf.int32)
sub_points = batch_xyz[:, :tf.shape(batch_xyz)[1] // cfg.sub_sampling_ratio[i], :]
pool_i = neighbour_idx[:, :tf.shape(batch_xyz)[1] // cfg.sub_sampling_ratio[i], :]
up_i = tf.py_func(DP.knn_search, [sub_points, batch_xyz, 1], tf.int32)
input_points.append(batch_xyz)
input_neighbors.append(neighbour_idx)
input_neighbors_1.append(neighbour_idx_1)
input_neighbors_2.append(neighbour_idx_2)
input_pools.append(pool_i)
input_up_samples.append(up_i)
batch_xyz = sub_points
input_list = input_points + input_neighbors + input_neighbors_1 + input_neighbors_2 + input_pools + input_up_samples
input_list += [batch_features, batch_labels, batch_pc_idx, batch_cloud_idx, batch_xyz_anno,
batch_label_anno]
return input_list
return tf_map
def init_input_pipeline(self):
print('Initiating input pipelines')
cfg.ignored_label_inds = [self.label_to_idx[ign_label] for ign_label in self.ignored_labels]
gen_function, gen_types, gen_shapes = self.get_batch_gen('training')
gen_function_val, _, _ = self.get_batch_gen('validation')
self.train_data = tf.data.Dataset.from_generator(gen_function, gen_types, gen_shapes)
self.val_data = tf.data.Dataset.from_generator(gen_function_val, gen_types, gen_shapes)
self.batch_train_data = self.train_data.batch(cfg.batch_size)
self.batch_val_data = self.val_data.batch(cfg.val_batch_size)
map_func = self.get_tf_mapping2()
self.batch_train_data = self.batch_train_data.map(map_func=map_func)
self.batch_val_data = self.batch_val_data.map(map_func=map_func)
self.batch_train_data = self.batch_train_data.prefetch(cfg.batch_size)
self.batch_val_data = self.batch_val_data.prefetch(cfg.val_batch_size)
iter = tf.data.Iterator.from_structure(self.batch_train_data.output_types, self.batch_train_data.output_shapes)
self.flat_inputs = iter.get_next()
self.train_init_op = iter.make_initializer(self.batch_train_data)
self.val_init_op = iter.make_initializer(self.batch_val_data)
if __name__ == '__main__':
parser = argparse.ArgumentParser()
parser.add_argument('--gpu', type=int, default=0, help='the number of GPUs to use [default: 0]')
parser.add_argument('--labeled_point', type=str, default='0.1%', help='0.1%/1%/10%/100%')
parser.add_argument('--mode', type=str, default='train', help='options: train, test')
# parser.add_argument('--gen_pseudo', default=False, action='store_true', help='generate pseudo labels or not')
parser.add_argument('--retrain', default=False, action='store_true', help='Re-training with pseudo labels or not')
FLAGS = parser.parse_args()
# MODEL = importlib.import_module(FLAGS.model) # import network module
# Network = MODEL.Network
os.environ["CUDA_DEVICE_ORDER"] = "PCI_BUS_ID"
os.environ['CUDA_VISIBLE_DEVICES'] = str(FLAGS.gpu)
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'
Mode = FLAGS.mode
print('Settings:')
print('Mode:', FLAGS.mode)
print('Labeled_point', FLAGS.labeled_point)
# print('gen_pseudo', FLAGS.gen_pseudo)
print('retrain', FLAGS.retrain)
dataset = DALES(FLAGS.labeled_point, FLAGS.retrain)
dataset.init_input_pipeline()
if Mode == 'train':
model = Network(dataset, cfg, FLAGS.retrain) #
model.train(dataset) # ѵ
elif Mode == 'test':
cfg.saving = False
model = Network(dataset, cfg)
chosen_snapshot = -1
logs = np.sort([os.path.join('results', f) for f in os.listdir(join('results')) if f.startswith('Log')])
# print(logs)
chosen_folder = logs[-1]
snap_path = join(chosen_folder, 'snapshots')
snap_steps = [int(f[:-5].split('-')[-1]) for f in os.listdir(snap_path) if f[-5:] == '.meta']
chosen_step = np.sort(snap_steps)[-1]
chosen_snap = os.path.join(snap_path, 'snap-{:d}'.format(chosen_step))
print(chosen_snap)
tester = ModelTester(model, dataset, restore_snap=chosen_snap)
tester.test(model, dataset)