test.py

# -*-coding:utf-8-*-
# @Time    : 2021/1/21 下午3:15
# @Author  : LiuFeng
# @File    : test.py

import os
import socket
import argparse
import numpy as np
import tensorflow as tf
from models import pointnet2 as model
import utils.provider as provider

parser = argparse.ArgumentParser()
parser.add_argument('--gpu', type=int, default=0, help='GPU to use [default: GPU 0]')
parser.add_argument('--test_file', default='test.py', help='Model name [default: model]')
parser.add_argument('--log_dir', default='cls_log/pointnet2_log', help='Log dir [default: log]')
parser.add_argument('--batch_size', type=int, default=4, help='Batch Size during training [default: 1]')
parser.add_argument('--model', default='pointnet2', help='Model name: pointnet_cls or pointnet_cls_basic')
parser.add_argument('--visu', action='store_true', help='Whether to dump image for error case [default: False]')
parser.add_argument('--num_point', type=int, default=1024, help='Point Number [256/512/1024/2048] [default: 1024]')
parser.add_argument('--model_path', default='logs/cls_log/pointnet2_log/model.ckpt', help='model checkpoint file path')

FLAGS = parser.parse_args()

GPU_INDEX = FLAGS.gpu
TEST_FILE = FLAGS.test_file
NUM_POINT = FLAGS.num_point
BATCH_SIZE = FLAGS.batch_size
MODEL_PATH = FLAGS.model_path

pwd = os.getcwd()
BASE_DIR = pwd

MODEL_PATH = os.path.join(BASE_DIR, MODEL_PATH)

LOG_DIR = FLAGS.log_dir
LOG_DIR = os.path.join(BASE_DIR, os.path.join("logs", LOG_DIR))

TRAIN_FILE = os.path.join(BASE_DIR, TEST_FILE + ".py")
os.system('cp {} {}'.format(TEST_FILE, LOG_DIR))
LOG_FOUT = open(os.path.join(LOG_DIR, 'log_test.txt'), 'w')

NUM_CLASSES = 40
SHAPE_NAMES = [line.rstrip() for line in
               open(os.path.join(BASE_DIR, 'datasets/modelnet40_ply_hdf5_2048/shape_names.txt'))]

HOSTNAME = socket.gethostname()

# ModelNet40 official train/test split
TEST_FILES = provider.getDataFiles(os.path.join(BASE_DIR, 'datasets/modelnet40_ply_hdf5_2048/test_files.txt'))


def log_string(out_str):
    LOG_FOUT.write(out_str + '\n')
    LOG_FOUT.flush()
    print(out_str)


def evaluate(num_votes):
    is_training = False

    with tf.device('/gpu:' + str(GPU_INDEX)):
        pointclouds_pl, labels_pl = model.get_placeholder(BATCH_SIZE, NUM_POINT)
        is_training_pl = tf.placeholder(tf.bool, shape=())

        # simple model
        pred = model.get_model(pointclouds_pl, is_training_pl)
        loss = model.get_loss(pred, labels_pl)

        # Add ops to save and restore all the variables.
        saver = tf.train.Saver()

    # Create a session
    config = tf.ConfigProto()
    config.gpu_options.allow_growth = True
    config.allow_soft_placement = True
    config.log_device_placement = True
    sess = tf.Session(config=config)

    # Restore variables from disk.
    saver.restore(sess, MODEL_PATH)
    log_string("Model restored.")

    ops = {'pointclouds_pl': pointclouds_pl,
           'labels_pl': labels_pl,
           'is_training_pl': is_training_pl,
           'pred': pred,
           'loss': loss}

    eval_one_epoch(sess, ops, num_votes)


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(LOG_DIR, 'pred_label.txt'), 'w')
    for fn in range(len(TEST_FILES)):
        log_string('----' + str(fn) + '----')
        current_data, current_label = provider.loadDataFile(TEST_FILES[fn])
        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_classes = np.zeros((cur_batch_size, NUM_CLASSES))  # 0/1 for classes
            for vote_idx in range(num_votes):
                rotated_data = provider.rotate_point_cloud_by_angle(current_data[start_idx:end_idx, :, :],
                                                                    vote_idx / float(num_votes) * np.pi * 2)
                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))

    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]))


if __name__ == '__main__':
    with tf.Graph().as_default():
        evaluate(num_votes=1)
    LOG_FOUT.close()