test_shapenet.py

'''
MIT License

Copyright (c) 2018 Wentao Yuan

Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.
'''

import argparse
import csv
import importlib
import models
import numpy as np
import os
import tensorflow as tf
import time
from io_util import read_pcd, save_pcd
from tf_util import chamfer, earth_mover
from visu_util import plot_pcd_three_views


def test(args):
    inputs = tf.placeholder(tf.float32, (1, None, 3))
    npts = tf.placeholder(tf.int32, (1,))
    gt = tf.placeholder(tf.float32, (1, args.num_gt_points, 3))
    model_module = importlib.import_module('.%s' % args.model_type, 'models')
    model = model_module.Model(inputs, npts, gt, tf.constant(1.0))

    output = tf.placeholder(tf.float32, (1, args.num_gt_points, 3))
    cd_op = chamfer(output, gt)
    emd_op = earth_mover(output, gt)

    config = tf.ConfigProto()
    config.gpu_options.allow_growth = True
    config.allow_soft_placement = True
    sess = tf.Session(config=config)

    saver = tf.train.Saver()
    saver.restore(sess, args.checkpoint)

    os.makedirs(args.results_dir, exist_ok=True)
    csv_file = open(os.path.join(args.results_dir, 'results.csv'), 'w')
    writer = csv.writer(csv_file)
    writer.writerow(['id', 'cd', 'emd'])

    with open(args.list_path) as file:
        model_list = file.read().splitlines()
    total_time = 0
    total_cd = 0
    total_emd = 0
    cd_per_cat = {}
    emd_per_cat = {}
    for i, model_id in enumerate(model_list):
        partial = read_pcd(os.path.join(args.data_dir, 'partial', '%s.pcd' % model_id))
        complete = read_pcd(os.path.join(args.data_dir, 'complete', '%s.pcd' % model_id))
        start = time.time()
        completion = sess.run(model.outputs, feed_dict={inputs: [partial], npts: [partial.shape[0]]})
        total_time += time.time() - start
        cd, emd = sess.run([cd_op, emd_op], feed_dict={output: completion, gt: [complete]})
        total_cd += cd
        total_emd += emd
        writer.writerow([model_id, cd, emd])

        synset_id, model_id = model_id.split('/')
        if not cd_per_cat.get(synset_id):
            cd_per_cat[synset_id] = []
        if not emd_per_cat.get(synset_id):
            emd_per_cat[synset_id] = []
        cd_per_cat[synset_id].append(cd)
        emd_per_cat[synset_id].append(emd)

        if i % args.plot_freq == 0:
            os.makedirs(os.path.join(args.results_dir, 'plots', synset_id), exist_ok=True)
            plot_path = os.path.join(args.results_dir, 'plots', synset_id, '%s.png' % model_id)
            plot_pcd_three_views(plot_path, [partial, completion[0], complete],
                                 ['input', 'output', 'ground truth'],
                                 'CD %.4f  EMD %.4f' % (cd, emd),
                                 [5, 0.5, 0.5])
        if args.save_pcd:
            os.makedirs(os.path.join(args.results_dir, 'pcds', synset_id), exist_ok=True)
            save_pcd(os.path.join(args.results_dir, 'pcds', '%s.pcd' % model_id), completion[0])
    csv_file.close()
    sess.close()

    print('Average time: %f' % (total_time / len(model_list)))
    print('Average Chamfer distance: %f' % (total_cd / len(model_list)))
    print('Average Earth mover distance: %f' % (total_emd / len(model_list)))
    print('Chamfer distance per category')
    for synset_id in cd_per_cat.keys():
        print(synset_id, '%f' % np.mean(cd_per_cat[synset_id]))
    print('Earth mover distance per category')
    for synset_id in emd_per_cat.keys():
        print(synset_id, '%f' % np.mean(emd_per_cat[synset_id]))


if __name__ == '__main__':
    parser = argparse.ArgumentParser()
    parser.add_argument('--list_path', default='data/shapenet/test.list')
    parser.add_argument('--data_dir', default='data/shapenet/test')
    parser.add_argument('--model_type', default='pcn_emd')
    parser.add_argument('--checkpoint', default='data/trained_models/pcn_emd')
    parser.add_argument('--results_dir', default='results/shapenet_pcn_emd')
    parser.add_argument('--num_gt_points', type=int, default=16384)
    parser.add_argument('--plot_freq', type=int, default=100)
    parser.add_argument('--save_pcd', action='store_true')
    args = parser.parse_args()

    test(args)