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test_shapenet_seg.py
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test_shapenet_seg.py
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#!/usr/bin/python3
"""Testing On ShapeNet Parts Segmentation Task."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import os
import sys
import math
import argparse
import importlib
import data_utils
import numpy as np
import tensorflow as tf
from datetime import datetime
def main():
parser = argparse.ArgumentParser()
parser.add_argument('--filelist', '-f', help='Path to input .h5 filelist (.txt)', required=True)
parser.add_argument('--category', '-c', help='Path to category list file (.txt)', required=True)
parser.add_argument('--data_folder', '-d', help='Path to *.pts directory', required=True)
parser.add_argument('--load_ckpt', '-l', help='Path to a check point file for load', required=True)
parser.add_argument('--repeat_num', '-r', help='Repeat number', type=int, default=1)
parser.add_argument('--sample_num', help='Point sample num', type=int, default=2048)
parser.add_argument('--model', '-m', help='Model to use', required=True)
parser.add_argument('--setting', '-x', help='Setting to use', required=True)
parser.add_argument('--save_ply', '-s', help='Save results as ply', action='store_true')
args = parser.parse_args()
print(args)
model = importlib.import_module(args.model)
setting_path = os.path.join(os.path.dirname(__file__), args.model)
sys.path.append(setting_path)
setting = importlib.import_module(args.setting)
sample_num = setting.sample_num
output_folder = args.data_folder + '_pred_nips_' + str(args.repeat_num)
category_list = [(category, int(label_num)) for (category, label_num) in
[line.split() for line in open(args.category, 'r')]]
offset = 0
category_range = dict()
for category, category_label_seg_max in category_list:
category_range[category] = (offset, offset + category_label_seg_max)
offset = offset + category_label_seg_max
folder = os.path.join(output_folder, category)
if not os.path.exists(folder):
os.makedirs(folder)
input_filelist = []
output_filelist = []
output_ply_filelist = []
for category in sorted(os.listdir(args.data_folder)):
data_category_folder = os.path.join(args.data_folder, category)
for filename in sorted(os.listdir(data_category_folder)):
input_filelist.append(os.path.join(args.data_folder, category, filename))
output_filelist.append(os.path.join(output_folder, category, filename[0:-3] + 'seg'))
output_ply_filelist.append(os.path.join(output_folder + '_ply', category, filename[0:-3] + 'ply'))
# Prepare inputs
print('{}-Preparing datasets...'.format(datetime.now()))
data, label, data_num, _, _ = data_utils.load_seg(args.filelist)
batch_num = data.shape[0]
max_point_num = data.shape[1]
batch_size = args.repeat_num * math.ceil(data.shape[1] / sample_num)
print('{}-{:d} testing batches.'.format(datetime.now(), batch_num))
######################################################################
# Placeholders
indices = tf.placeholder(tf.int32, shape=(batch_size, None, 2), name="indices")
is_training = tf.placeholder(tf.bool, name='is_training')
pts_fts = tf.placeholder(tf.float32, shape=(None, max_point_num, setting.data_dim), name='pts_fts')
######################################################################
######################################################################
pts_fts_sampled = tf.gather_nd(pts_fts, indices=indices, name='pts_fts_sampled')
if setting.data_dim > 3:
points_sampled, features_sampled = tf.split(pts_fts_sampled,
[3, setting.data_dim - 3],
axis=-1,
name='split_points_features')
if not setting.use_extra_features:
features_sampled = None
else:
points_sampled = pts_fts_sampled
features_sampled = None
net = model.Net(points_sampled, features_sampled, is_training, setting)
logits = net.logits
probs_op = tf.nn.softmax(logits, name='probs')
saver = tf.train.Saver()
parameter_num = np.sum([np.prod(v.shape.as_list()) for v in tf.trainable_variables()])
print('{}-Parameter number: {:d}.'.format(datetime.now(), parameter_num))
with tf.Session() as sess:
# Load the model
saver.restore(sess, args.load_ckpt)
print('{}-Checkpoint loaded from {}!'.format(datetime.now(), args.load_ckpt))
indices_batch_indices = np.tile(np.reshape(np.arange(batch_size), (batch_size, 1, 1)), (1, sample_num, 1))
for batch_idx in range(batch_num):
points_batch = data[[batch_idx] * batch_size, ...]
object_label = label[batch_idx]
point_num = data_num[batch_idx]
category = category_list[object_label][0]
label_start, label_end = category_range[category]
tile_num = math.ceil((sample_num * batch_size) / point_num)
indices_shuffle = np.tile(np.arange(point_num), tile_num)[0:sample_num * batch_size]
np.random.shuffle(indices_shuffle)
indices_batch_shuffle = np.reshape(indices_shuffle, (batch_size, sample_num, 1))
indices_batch = np.concatenate((indices_batch_indices, indices_batch_shuffle), axis=2)
probs = sess.run([probs_op],
feed_dict={
pts_fts: points_batch,
indices: indices_batch,
is_training: False,
})
probs_2d = np.reshape(probs, (sample_num * batch_size, -1))
predictions = [(-1, 0.0)] * point_num
for idx in range(sample_num * batch_size):
point_idx = indices_shuffle[idx]
probs = probs_2d[idx, label_start:label_end]
confidence = np.amax(probs)
seg_idx = np.argmax(probs)
if confidence > predictions[point_idx][1]:
predictions[point_idx] = (seg_idx, confidence)
labels = []
with open(output_filelist[batch_idx], 'w') as file_seg:
for seg_idx, _ in predictions:
file_seg.write('%d\n' % (seg_idx))
labels.append(seg_idx)
# read the coordinates from the txt file for verification
coordinates = [[float(value) for value in xyz.split(' ')]
for xyz in open(input_filelist[batch_idx], 'r') if len(xyz.split(' ')) == 3]
assert (point_num == len(coordinates))
if args.save_ply:
data_utils.save_ply_property(np.array(coordinates), np.array(labels), 6, output_ply_filelist[batch_idx])
print('{}-[Testing]-Iter: {:06d} saved to {}'.format(datetime.now(), batch_idx, output_filelist[batch_idx]))
sys.stdout.flush()
######################################################################
print('{}-Done!'.format(datetime.now()))
if __name__ == '__main__':
main()