train.py

""" Training (stage 2) function for 3DIoUMatch

Written by: Yezhen Cong, 2020
Based on: VoteNet and SESS
"""
import argparse
import os
import sys
from datetime import datetime

import numpy as np
import torch
import torch.optim as optim
from torch import nn
from torch.utils.data import DataLoader

from models.votenet_iou_branch import VoteNet

BASE_DIR = os.path.dirname(os.path.abspath(__file__))
ROOT_DIR = BASE_DIR
sys.path.append(os.path.join(ROOT_DIR, 'utils'))
sys.path.append(os.path.join(ROOT_DIR, 'pointnet2'))
sys.path.append(os.path.join(ROOT_DIR, 'models'))
from pointnet2.pytorch_utils import BNMomentumScheduler
from utils.tf_visualizer import Visualizer as TfVisualizer
from models.ap_helper import APCalculator, parse_predictions, parse_groundtruths
from models.loss_helper_labeled import get_labeled_loss
from models.loss_helper_unlabeled import get_unlabeled_loss
from models.loss_helper import get_loss

# new
from models.augmentor import Augmentor
import copy
from scannet.model_util_scannet import rotate_aligned_boxes
import pointnet2_utils
from scipy.spatial.distance import cdist
import random
import time
from utils.train_utils import select_bboxes_train, adjust_nums_of_points, augment_objs, displace_points, update_votes, get_pseudo_bboxess

parser = argparse.ArgumentParser()
parser.add_argument('--model', default='votenet', help='Model file name.')
parser.add_argument('--dataset', default='scannet', help='Dataset name.')
parser.add_argument('--labeled_sample_list', default='scannetv2_train.txt',
                    help='Labeled sample list from a certain percentage of training [static]')
parser.add_argument('--detector_checkpoint', default=None)
# new
parser.add_argument('--augmentor_checkpoint', default=None)

parser.add_argument('--log_dir', default='./temp', help='Dump dir to save model checkpoint')
parser.add_argument('--num_point', type=int, default=40000, help='Point Number')
parser.add_argument('--no_height', action='store_true', help='Do NOT use height signal in Votenet input.')
parser.add_argument('--use_color', action='store_true', help='Use RGB color in Votenet input.')
parser.add_argument('--num_target', type=int, default=128, help='Proposal number')
parser.add_argument('--vote_factor', type=int, default=1, help='Vote factor')
parser.add_argument('--cluster_sampling', default='seed_fps',
                    help='Sampling strategy for vote clusters: vote_fps, seed_fps, random')
parser.add_argument('--ap_iou_thresh', type=float, default=0.25, help='AP IoU threshold')
parser.add_argument('--max_epoch', type=int, default=1001, help='Epoch to run')
parser.add_argument('--batch_size', default='4,8', help='Batch Size during training, labeled + unlabeled')
parser.add_argument('--learning_rate', type=float, default=0.002, help='Initial learning rate')
parser.add_argument('--weight_decay', type=float, default=0, help='Optimization L2 weight decay')
parser.add_argument('--bn_decay_step', type=int, default=20, help='Period of BN decay (in epochs)')
parser.add_argument('--bn_decay_rate', type=float, default=0.5, help='Decay rate for BN decay')
parser.add_argument('--lr_decay_steps', default='400, 600, 800, 900',
                    help='When to decay the learning rate (in epochs)')
parser.add_argument('--lr_decay_rates', default='0.3, 0.3, 0.1, 0.1',
                    help='Decay rates for lr decay')
parser.add_argument('--ema_decay',  type=float,  default=0.999, metavar='ALPHA',
                    help='ema variable decay rate')
parser.add_argument('--unlabeled_loss_weight', type=float, default=2.0, metavar='WEIGHT',
                    help='use unlabeled loss with given weight')
parser.add_argument('--use_iou_for_nms', action='store_true', help='whether use iou to guide test-time nms')
parser.add_argument('--print_interval', type=int, default=25, help='batch interval to print loss')
parser.add_argument('--eval_interval', type=int, default=25, help='epoch interval to evaluate model')
parser.add_argument('--save_interval', type=int, default=200, help='epoch interval to save model')
parser.add_argument('--resume', action='store_true', help='resume training instead of just loading a pre-train model')
parser.add_argument('--eval', action='store_true')
parser.add_argument('--conf_thresh', type=float, default=0.05)
parser.add_argument('--view_stats', action='store_true')
parser.add_argument('--opt_rate', type=float, default=5e-4, help='Optimization rate for eval with opt')
parser.add_argument('--opt_step', type=int, default=0, help='Optimization step for eval with opt')
parser.add_argument('--box_num', type=int, default=3, help='num of sample boxes')
parser.add_argument('--box_num_unlabeled', type=int, default=3, help='num of sample boxes')
parser.add_argument('--obj_points_num', type=int, default=1024, help='num of points to augment in the object')

FLAGS = parser.parse_args()
print(FLAGS)

# ------------------------------------------------------------------------- GLOBAL CONFIG BEG
print('\n************************** GLOBAL CONFIG BEG **************************')
# new
OBJECT_POINTS_NUM = FLAGS.obj_points_num

batch_size_list = [int(x) for x in FLAGS.batch_size.split(',')]
BATCH_SIZE = batch_size_list[0] + batch_size_list[1]
NUM_POINT = FLAGS.num_point
MAX_EPOCH = FLAGS.max_epoch
BASE_LEARNING_RATE = FLAGS.learning_rate
BN_DECAY_STEP = FLAGS.bn_decay_step
BN_DECAY_RATE = FLAGS.bn_decay_rate
LR_DECAY_STEPS = [int(x) for x in FLAGS.lr_decay_steps.split(',')]
LR_DECAY_RATES = [float(x) for x in FLAGS.lr_decay_rates.split(',')]
assert(len(LR_DECAY_STEPS) == len(LR_DECAY_RATES))

LOG_DIR = FLAGS.log_dir
if not os.path.exists(LOG_DIR):
    os.mkdir(LOG_DIR)

DEFAULT_CHECKPOINT_PATH = os.path.join(LOG_DIR, 'checkpoint.tar')
detector_checkpoint = FLAGS.detector_checkpoint if FLAGS.detector_checkpoint is not None \
    else DEFAULT_CHECKPOINT_PATH

LOG_FOUT = open(os.path.join(LOG_DIR, 'log_train.txt'), 'a')
LOG_FOUT.write(str(FLAGS)+'\n')
if not FLAGS.eval:
    PERFORMANCE_FOUT = open(os.path.join(LOG_DIR, 'best.txt'), 'w')


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


def my_worker_init_fn(worker_id):
    np.random.seed(np.random.get_state()[1][0] + worker_id)


# Init datasets and dataloaders
if FLAGS.dataset == 'scannet':
    sys.path.append(os.path.join(ROOT_DIR, 'scannet'))
    from scannet.scannet_detection_dataset import ScannetDetectionDataset
    from scannet.scannet_ssl_dataset import ScannetSSLLabeledDataset, ScannetSSLUnlabeledDataset
    from scannet.model_util_scannet import ScannetDatasetConfig
    DATASET_CONFIG = ScannetDatasetConfig()
    LABELED_DATASET = ScannetSSLLabeledDataset(labeled_sample_list=FLAGS.labeled_sample_list,
                                                        num_points=NUM_POINT,
                                                        augment=True,
                                                        use_color=FLAGS.use_color,
                                                        use_height=(not FLAGS.no_height))
    UNLABELED_DATASET = ScannetSSLUnlabeledDataset(labeled_sample_list=FLAGS.labeled_sample_list,
                                                   num_points=NUM_POINT,
                                                   use_color=FLAGS.use_color,
                                                   use_height=(not FLAGS.no_height),
                                                   load_labels=FLAGS.view_stats,
                                                   augment=True)
    TEST_DATASET = ScannetDetectionDataset('val',
                                            num_points=NUM_POINT, augment=False,
                                            use_color=FLAGS.use_color, use_height=(not FLAGS.no_height))
else:
    print('Unknown dataset %s. Exiting...'%(FLAGS.dataset))
    exit(-1)
log_string('Dataset sizes: labeled-{0}; unlabeled-{1}; VALID-{2}'.format(len(LABELED_DATASET),
                                                            len(UNLABELED_DATASET), len(TEST_DATASET)))

LABELED_DATALOADER = DataLoader(LABELED_DATASET, batch_size=batch_size_list[0],
                              shuffle=True, num_workers=batch_size_list[0], worker_init_fn=my_worker_init_fn)
UNLABELED_DATALOADER = DataLoader(UNLABELED_DATASET, batch_size=batch_size_list[1],
                              shuffle=True, num_workers=batch_size_list[1]//2, worker_init_fn=my_worker_init_fn)
TEST_DATALOADER = DataLoader(TEST_DATASET, batch_size=8,
                             shuffle=False, num_workers=4, worker_init_fn=my_worker_init_fn)


def create_model(ema=False):
    model = VoteNet(num_class=DATASET_CONFIG.num_class,
                          num_heading_bin=DATASET_CONFIG.num_heading_bin,
                          num_size_cluster=DATASET_CONFIG.num_size_cluster,
                          mean_size_arr=DATASET_CONFIG.mean_size_arr,
                          dataset_config=DATASET_CONFIG,
                          num_proposal=FLAGS.num_target,
                          input_feature_dim=num_input_channel,
                          vote_factor=FLAGS.vote_factor,
                          sampling=FLAGS.cluster_sampling)
    if ema:
        for param in model.parameters():
            param.detach_()
    return model


num_input_channel = int(FLAGS.use_color)*3 + int(not FLAGS.no_height)*1
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")

detector = create_model()
ema_detector = create_model(ema=True)

# new
augmentor = Augmentor()

if torch.cuda.device_count() > 1:
    log_string("Let's use %d GPUs!" % (torch.cuda.device_count()))
    # dim = 0 [30, xxx] -> [10, ...], [10, ...], [10, ...] on 3 GPUs
    detector = nn.DataParallel(detector)
    ema_detector = nn.DataParallel(ema_detector)
    # new
    augmentor = nn.DataParallel(augmentor)

detector.to(device)
ema_detector.to(device)
# new
augmentor.to(device)

train_labeled_criterion = get_labeled_loss
train_unlabeled_criterion = get_unlabeled_loss
test_detector_criterion = get_loss

# Load the Adam optimizer
optimizer = optim.Adam(detector.parameters(), lr=BASE_LEARNING_RATE, weight_decay=FLAGS.weight_decay)

# Load checkpoint if there is any
if detector_checkpoint is not None and os.path.isfile(detector_checkpoint):
    checkpoint = torch.load(detector_checkpoint)
    pretrained_dict = checkpoint['model_state_dict']

    ########
    model_dict = detector.state_dict()
    # 1. filter out unnecessary keys
    pretrained_dict = {k: v for k, v in pretrained_dict.items() if k in model_dict}
    # 2. overwrite entries in the existing state dict
    model_dict.update(pretrained_dict)
    # 3. load the new state dict
    detector.load_state_dict(model_dict)
    model_dict = ema_detector.state_dict()
    model_dict.update(pretrained_dict)
    ema_detector.load_state_dict(model_dict)
    ########

    if FLAGS.resume:
        optimizer.load_state_dict(checkpoint['optimizer_state_dict'])
        start_epoch = checkpoint['epoch']

    # detector.load_state_dict(pretrained_dict)
    # ema_detector.load_state_dict(pretrained_dict)
    epoch_ckpt = checkpoint['epoch']
    print("Loaded votenet checkpoint %s (epoch: %d)" % (detector_checkpoint, epoch_ckpt))

if FLAGS.augmentor_checkpoint is not None and os.path.isfile(FLAGS.augmentor_checkpoint):
    checkpoint = torch.load(FLAGS.augmentor_checkpoint)
    pretrained_dict = checkpoint['model_state_dict']

    ########
    model_dict = augmentor.state_dict()
    # 1. filter out unnecessary keys
    pretrained_dict = {k: v for k, v in pretrained_dict.items() if k in model_dict}
    # 2. overwrite entries in the existing state dict
    model_dict.update(pretrained_dict)
    # 3. load the new state dict
    augmentor.load_state_dict(model_dict)
    ########

    # augmentor.load_state_dict(pretrained_dict)
    epoch_ckpt = checkpoint['epoch']
    print("Loaded augmentor checkpoint %s (epoch: %d)" % (FLAGS.augmentor_checkpoint, epoch_ckpt))

# Decay Batchnorm momentum from 0.5 to 0.999
# note: pytorch's BN momentum (default 0.1)= 1 - tensorflow's BN momentum
# inherited this from VoteNet and SESS
BN_MOMENTUM_INIT = 0.5
BN_MOMENTUM_MAX = 0.001
bn_lbmd = lambda it: max(BN_MOMENTUM_INIT * BN_DECAY_RATE ** (int(it / BN_DECAY_STEP)), BN_MOMENTUM_MAX)
bnm_scheduler = BNMomentumScheduler(detector, bn_lambda=bn_lbmd, last_epoch=-1)
if FLAGS.resume:
    bnm_scheduler.step(start_epoch)


def get_current_lr(epoch):
    # stairstep update
    lr = BASE_LEARNING_RATE
    for i, lr_decay_epoch in enumerate(LR_DECAY_STEPS):
        if epoch >= lr_decay_epoch:
            lr *= LR_DECAY_RATES[i]
    return lr


def adjust_learning_rate(optimizer, epoch):
    lr = get_current_lr(epoch)
    for param_group in optimizer.param_groups:
        param_group['lr'] = lr


# TFBoard Visualizers
TRAIN_VISUALIZER = TfVisualizer(LOG_DIR, 'train')
TEST_VISUALIZER = TfVisualizer(LOG_DIR, 'test')


# Used for Pseudo box generation and AP calculation
CONFIG_DICT = {'dataset_config': DATASET_CONFIG, 'unlabeled_batch_size': batch_size_list[1], 'dataset': FLAGS.dataset,

               'remove_empty_box': False, 'use_3d_nms': True, 'nms_iou': 0.25,
               'use_old_type_nms': False, 'cls_nms': True, 'use_iou_for_nms': FLAGS.use_iou_for_nms,

               'per_class_proposal': True, 'conf_thresh': FLAGS.conf_thresh,

               'obj_threshold': 0.9, 'cls_threshold': 0.9,
               'use_lhs': True, 'iou_threshold': 0.25,

               'use_unlabeled_obj_loss': False, 'use_unlabeled_vote_loss': False, 'vote_loss_size_factor': 1.0,

               'samecls_match': False, 'view_stats': FLAGS.view_stats }

for key in CONFIG_DICT.keys():
    if key != 'dataset_config':
        log_string(key + ': ' + str(CONFIG_DICT[key]))

print('************************** GLOBAL CONFIG END **************************')
# ------------------------------------------------------------------------- GLOBAL CONFIG END


def update_ema_variables(model, ema_model, alpha, global_step):
    # Use the true average until the exponential average is more correct
    alpha = min(1 - 1 / (global_step + 1), alpha)
    for ema_param, param in zip(ema_model.parameters(), model.parameters()):
        ema_param.data.mul_(alpha).add_(1 - alpha, param.data)


def tb_name(key):
    if 'loss' in key:
        return 'loss/' + key
    elif 'acc' in key:
        return 'acc/' + key
    elif 'ratio' in key:
        return 'ratio/' + key
    elif 'value' in key:
        return 'value/' + key
    else:
        return 'other/' + key

# new
def in_hull(p, hull):
    from scipy.spatial import Delaunay
    if not isinstance(hull,Delaunay):
        hull = Delaunay(hull)
    return hull.find_simplex(p)>=0

# new
def extract_pc_in_box3d(pc, box3d):
    ''' pc: (N,3), box3d: (8,3) '''
    box3d_roi_inds = in_hull(pc[:,0:3], box3d)
    return box3d_roi_inds

# new
def my_compute_box_3d(center, size):
    l,w,h = size
    x_corners = [-l,l,l,-l,-l,l,l,-l]
    y_corners = [w,w,-w,-w,w,w,-w,-w]
    z_corners = [h,h,h,h,-h,-h,-h,-h]
    corners_3d = np.vstack([x_corners, y_corners, z_corners])
    corners_3d[0,:] += center[0]
    corners_3d[1,:] += center[1]
    corners_3d[2,:] += center[2]
    return np.transpose(corners_3d)

def train_one_epoch(global_step):
    stat_dict = {}  # collect statistics
    adjust_learning_rate(optimizer, EPOCH_CNT)
    bnm_scheduler.step()  # decay BN momentum
    detector.train()  # set model to training mode
    ema_detector.train()
    augmentor.eval()


    unlabeled_dataloader_iterator = iter(UNLABELED_DATALOADER)

    for batch_idx, batch_data_label in enumerate(LABELED_DATALOADER):
        try:
            batch_data_unlabeled = next(unlabeled_dataloader_iterator)
        except StopIteration:
            unlabeled_dataloader_iterator = iter(UNLABELED_DATALOADER)
            batch_data_unlabeled = next(unlabeled_dataloader_iterator)

        for key in batch_data_unlabeled:
            if type(batch_data_unlabeled[key]) == list:
                batch_data_label[key] = torch.cat([batch_data_label[key]] + batch_data_unlabeled[key], dim=0)#.to(device)
            else:
                batch_data_label[key] = torch.cat((batch_data_label[key], batch_data_unlabeled[key]), dim=0)#.to(device)

        for key in batch_data_label:
            batch_data_label[key] = batch_data_label[key].to(device)


        aug_batch_data_label = {}
        for key in batch_data_label:
            aug_batch_data_label[key] = batch_data_label[key].detach().clone()
            
        # get pseudo labels to select bboxs in unlabeled scenes
        ema_inputs = {'point_clouds': batch_data_label['ema_point_clouds']}
        with torch.no_grad():
            ema_end_points = ema_detector.forward_with_pred_jitter(ema_inputs)
        pred_bboxes = get_pseudo_bboxess(batch_data_label,
                                         ema_end_points,
                                         CONFIG_DICT,
                                         DATASET_CONFIG,
                                         FLAGS.box_num_unlabeled)

        # start to prepare bboxes to augment
        labeled_num = torch.nonzero(batch_data_label['supervised_mask']).squeeze(1).shape[0]
        # select random bboxes to augment
        batch_obj_index, selected_bbox, sel_bbox_nums = select_bboxes_train(batch_data_label,
                                                                            FLAGS.dataset,
                                                                            FLAGS.box_num,
                                                                            FLAGS.box_num_unlabeled,
                                                                            pred_bboxes,
                                                                            labeled_num)

        # points: [box_size, OBJECT_POINTS_NUM, 3]
        if sel_bbox_nums > 1:
            # Adjust the numbers of points in the bbox to the assign number
            points, near_points_list = adjust_nums_of_points(batch_data_label, batch_obj_index, OBJECT_POINTS_NUM)

            # Augment selected objects
            displacement = augment_objs(augmentor, points, OBJECT_POINTS_NUM)

            # add displacement to points
            displace_points(batch_data_label,
                            aug_batch_data_label, 
                            batch_obj_index, 
                            selected_bbox,
                            near_points_list,
                            displacement)
            # update point vote
            update_votes(FLAGS.dataset, aug_batch_data_label, DATASET_CONFIG, FLAGS.num_point, labeled_num)
        else:
            print('batch bbox num < 2')


        inputs = {'point_clouds': batch_data_label['point_clouds']}
        aug_inputs = {'point_clouds': aug_batch_data_label['point_clouds']}

        optimizer.zero_grad()
        end_points = detector.forward_with_pred_jitter(inputs)
        aug_end_points = detector.forward_with_pred_jitter(aug_inputs)

        # Compute loss and gradients, update parameters.
        for key in batch_data_label:
            assert(key not in end_points)
            end_points[key] = batch_data_label[key]
        for key in aug_batch_data_label:
            assert (key not in aug_end_points)
            aug_end_points[key] = aug_batch_data_label[key]

        detection_loss, end_points = train_labeled_criterion(end_points, DATASET_CONFIG, CONFIG_DICT)
        unlabeled_loss, end_points = train_unlabeled_criterion(end_points, ema_end_points, DATASET_CONFIG, CONFIG_DICT)

        aug_detection_loss, aug_end_points = train_labeled_criterion(aug_end_points, DATASET_CONFIG, CONFIG_DICT)
        aug_unlabeled_loss, aug_end_points = train_unlabeled_criterion(aug_end_points, ema_end_points, DATASET_CONFIG, CONFIG_DICT)

        # weighted unlabeled loss
        WUL = (unlabeled_loss + aug_unlabeled_loss) * FLAGS.unlabeled_loss_weight
        loss = detection_loss + WUL + aug_detection_loss

        end_points['loss'] = loss
        loss.backward()

        optimizer.step()
        global_step += 1
        update_ema_variables(detector, ema_detector, FLAGS.ema_decay, global_step)

        # Accumulate statistics and print out
        for key in end_points:
            if 'loss' in key or 'acc' in key or 'ratio' in key or 'value' in key:
                if key not in stat_dict: stat_dict[key] = 0
                stat_dict[key] += end_points[key].item()

        batch_interval = FLAGS.print_interval
        if (batch_idx + 1) % batch_interval == 0:
            log_string(' ---- batch: %03d ----' % (batch_idx + 1))
            TRAIN_VISUALIZER.log_scalars({tb_name(key): stat_dict[key] / batch_interval for key in stat_dict},
                                         (EPOCH_CNT * len(LABELED_DATALOADER) + batch_idx) * BATCH_SIZE)
            for key in sorted(stat_dict.keys()):
                log_string('mean %s: %f' % (key, stat_dict[key] / batch_interval))
                stat_dict[key] = 0

    return global_step


AP_IOU_THRESHOLDS = [0.25, 0.5]
BEST_MAP = [0.0, 0.0]


def evaluate_one_epoch():
    stat_dict = {}  # collect statistics
    ap_calculator_list = [APCalculator(iou_thresh, DATASET_CONFIG.class2type)
                          for iou_thresh in AP_IOU_THRESHOLDS]
    detector.eval()  # set model to eval mode (for bn and dp)
    for batch_idx, batch_data_label in enumerate(TEST_DATALOADER):
        for key in batch_data_label:
            batch_data_label[key] = batch_data_label[key].to(device)

        # Forward pass
        inputs = {'point_clouds': batch_data_label['point_clouds']}
        with torch.no_grad():
            end_points = detector(inputs)

        # Compute loss
        for key in batch_data_label:
            assert (key not in end_points)
            end_points[key] = batch_data_label[key]
        loss, end_points = test_detector_criterion(end_points, DATASET_CONFIG)

        batch_gt_map_cls = parse_groundtruths(end_points, CONFIG_DICT)
        batch_pred_map_cls = parse_predictions(end_points, CONFIG_DICT)

        # Accumulate statistics and print out
        for key in end_points:
            if 'loss' in key or 'acc' in key or 'ratio' in key or 'value' in key:
                if key not in stat_dict: stat_dict[key] = 0
                stat_dict[key] += end_points[key].item()

        for ap_calculator in ap_calculator_list:
            ap_calculator.step(batch_pred_map_cls, batch_gt_map_cls)

            # Log statistics
    TEST_VISUALIZER.log_scalars({tb_name(key): stat_dict[key] / float(batch_idx + 1) for key in stat_dict},
                                (EPOCH_CNT + 1) * len(LABELED_DATALOADER) * BATCH_SIZE)
    for key in sorted(stat_dict.keys()):
        log_string('eval mean %s: %f' % (key, stat_dict[key] / (float(batch_idx + 1))))

    # Evaluate average precision
    map = []
    for i, ap_calculator in enumerate(ap_calculator_list):
        print('-' * 10, 'iou_thresh: %f' % (AP_IOU_THRESHOLDS[i]), '-' * 10)
        metrics_dict = ap_calculator.compute_metrics()
        for key in metrics_dict:
            log_string('eval %s: %f' % (key, metrics_dict[key]))
        TEST_VISUALIZER.log_scalars({'metrics_' + str(AP_IOU_THRESHOLDS[i]) + '/' + key: metrics_dict[key] for key in metrics_dict if key in ['mAP', 'AR']},
                                    (EPOCH_CNT + 1) * len(LABELED_DATALOADER) * BATCH_SIZE)
        map.append(metrics_dict['mAP'])

    mean_loss = stat_dict['detection_loss'] / float(batch_idx + 1)
    return mean_loss, map

def evaluate_with_opt():
    stat_dict = {}  # collect statistics
    ap_calculator_list = [APCalculator(iou_thresh, DATASET_CONFIG.class2type) \
                          for iou_thresh in AP_IOU_THRESHOLDS]

    detector.eval()  # set model to eval mode (for bn and dp)
    for batch_idx, batch_data_label in enumerate(TEST_DATALOADER):
        for key in batch_data_label:
            batch_data_label[key] = batch_data_label[key].to(device)

        # Forward pass
        inputs = {'point_clouds': batch_data_label['point_clouds']}

        optimizer.zero_grad()
        end_points = detector(inputs, iou_opt=True)
        center = end_points['center']
        size_class = torch.argmax(end_points['size_scores'], dim=-1)
        sem_cls = end_points['sem_cls_scores'].argmax(-1)
        size = end_points['size']
        heading = end_points['heading']
        iou = end_points['iou_scores']
        origin_iou = iou.clone()
        iou_gathered = torch.gather(iou, dim=2, index=sem_cls.unsqueeze(-1).detach()).squeeze(-1).contiguous().view(-1)
        iou_gathered.backward(torch.ones(iou_gathered.shape).cuda())
        # max_iou = iou_gathered.view(center.shape[:2])
        center_grad = center.grad
        size_grad = size.grad
        mask = torch.ones(center.shape).cuda()
        count = 0

        for k in end_points.keys():
            end_points[k] = end_points[k].detach()
        while True:
            center_ = center.detach() + FLAGS.opt_rate * center_grad * mask
            size_ = size.detach() + FLAGS.opt_rate * size_grad * mask
            heading_ = heading.detach()
            optimizer.zero_grad()
            center_.requires_grad = True
            size_.requires_grad = True
            end_points_ = detector.forward_onlyiou_faster(end_points, center_, size_, heading_)
            iou = end_points_['iou_scores']
            iou_gathered = torch.gather(iou, dim=2, index=sem_cls.unsqueeze(-1).detach()).squeeze(-1).contiguous().view(-1)
            iou_gathered.backward(torch.ones(iou_gathered.shape).cuda())
            center_grad = center_.grad
            size_grad = size_.grad
            # cur_iou = iou_gathered.view(center.shape[:2])
            # mask[cur_iou < max_iou - 0.1] = 0
            # mask[torch.abs(cur_iou - max_iou) < 0.001] = 0
            # print(mask.sum().float() /mask.view(-1).shape[-1])
            count += 1
            if count > FLAGS.opt_step:
                break
            center = center_
            size = size_

        end_points['center'] = center_
        B, K = size_class.shape[:2]
        mean_size_arr = DATASET_CONFIG.mean_size_arr
        mean_size_arr = torch.from_numpy(mean_size_arr.astype(np.float32)).cuda()  # (num_size_cluster,3)
        size_base = torch.index_select(mean_size_arr, 0, size_class.view(-1))
        size_base = size_base.view(B, K, 3)
        end_points['size_residuals'] = (size_ * 2 - size_base).unsqueeze(2).expand(-1, -1, DATASET_CONFIG.num_size_cluster, -1)
        optimizer.zero_grad()

        # if FLAGS.first_nms:
        #     end_points['iou_scores'] = origin_iou

        # Compute loss
        for key in batch_data_label:
            assert (key not in end_points)
            end_points[key] = batch_data_label[key]
        loss, end_points = test_detector_criterion(end_points, DATASET_CONFIG)

        batch_pred_map_cls = parse_predictions(end_points, CONFIG_DICT)
        batch_gt_map_cls = parse_groundtruths(end_points, CONFIG_DICT)

        # Accumulate statistics and print out
        for key in end_points:
            if 'loss' in key or 'acc' in key or 'ratio' in key or 'value' in key:
                if key not in stat_dict: stat_dict[key] = 0
                stat_dict[key] += end_points[key].item()

        # ap_calculator.step(batch_pred_map_cls, batch_gt_map_cls)
        for ap_calculator in ap_calculator_list:
            ap_calculator.step(batch_pred_map_cls, batch_gt_map_cls)

    # Log statistics
    TEST_VISUALIZER.log_scalars({tb_name(key): stat_dict[key] / float(batch_idx + 1) for key in stat_dict},
                                (EPOCH_CNT + 1) * len(LABELED_DATALOADER) * BATCH_SIZE)
    for key in sorted(stat_dict.keys()):
        log_string('eval mean %s: %f' % (key, stat_dict[key] / (float(batch_idx + 1))))

    # Evaluate average precision
    map = []
    for i, ap_calculator in enumerate(ap_calculator_list):
        print('-' * 10, 'iou_thresh: %f' % (AP_IOU_THRESHOLDS[i]), '-' * 10)
        metrics_dict = ap_calculator.compute_metrics()
        for key in metrics_dict:
            log_string('eval %s: %f' % (key, metrics_dict[key]))
        TEST_VISUALIZER.log_scalars({'metrics_' + str(AP_IOU_THRESHOLDS[i]) + '/' + key: metrics_dict[key] for key in metrics_dict if key in ['mAP', 'AR']},
                                    (EPOCH_CNT + 1) * len(LABELED_DATALOADER) * BATCH_SIZE)
        map.append(metrics_dict['mAP'])

    mean_loss = stat_dict['detection_loss'] / float(batch_idx + 1)
    return mean_loss, map

def train():
    global EPOCH_CNT
    global start_epoch
    global_step = 0
    loss = 0
    EPOCH_CNT = 0
    global BEST_MAP
    if FLAGS.eval:
        np.random.seed()
        if FLAGS.opt_step > 0:
            evaluate_with_opt()
        else:
            evaluate_one_epoch()
        sys.exit(0)
    start_from = 0
    if FLAGS.resume:
        start_from = start_epoch

    for epoch in range(start_from, MAX_EPOCH):
        EPOCH_CNT = epoch
        log_string('\n**** EPOCH %03d, STEP %d ****' % (epoch, global_step))
        log_string("Current epoch: %d, obj threshold = %.3f & cls threshold = %.3f" % (epoch, CONFIG_DICT['obj_threshold'], CONFIG_DICT['cls_threshold']))
        log_string('Current learning rate: %f' % (get_current_lr(epoch)))
        log_string('Current BN decay momentum: %f' % (bnm_scheduler.lmbd(bnm_scheduler.last_epoch)))
        log_string(str(datetime.now()))

        # in numpy 1.18.5 this actually sets `np.random.get_state()[1][0]` to default value
        # so the test data is consistent as the initial seed is the same
        np.random.seed()
        global_step = train_one_epoch(global_step)
        map = 0.0
        if EPOCH_CNT > 0 and EPOCH_CNT % FLAGS.eval_interval == 0:
            loss, map = evaluate_one_epoch()
        # save checkpoint
        save_dict = {'epoch': epoch + 1,  # after training one epoch, the start_epoch should be epoch+1
                     'optimizer_state_dict': optimizer.state_dict(),
                     'loss': loss
                     }
        try:  # with nn.DataParallel() the net is added as a submodule of DataParallel
            save_dict['model_state_dict'] = detector.module.state_dict()
            save_dict['ema_model_state_dict'] = ema_detector.module.state_dict()
        except:
            save_dict['model_state_dict'] = detector.state_dict()
            save_dict['ema_model_state_dict'] = ema_detector.state_dict()
        torch.save(save_dict, os.path.join(LOG_DIR, 'checkpoint.tar'))

        if EPOCH_CNT % FLAGS.save_interval == 0:
            save_dict = {'epoch': epoch + 1,  # after training one epoch, the start_epoch should be epoch+1
                         'optimizer_state_dict': optimizer.state_dict(),
                         'loss': loss
                         }
            try:  # with nn.DataParallel() the net is added as a submodule of DataParallel
                save_dict['model_state_dict'] = detector.module.state_dict()
                save_dict['ema_model_state_dict'] = ema_detector.module.state_dict()
            except:
                save_dict['model_state_dict'] = detector.state_dict()
                save_dict['ema_model_state_dict'] = ema_detector.state_dict()
            torch.save(save_dict, os.path.join(LOG_DIR, 'checkpoint_%d.tar' % EPOCH_CNT))

        if EPOCH_CNT > 0 and EPOCH_CNT % FLAGS.eval_interval == 0:
            if map[0] + map[1] > BEST_MAP[0] + BEST_MAP[1]:
                BEST_MAP = map
                save_dict = {'epoch': epoch + 1,  # after training one epoch, the start_epoch should be epoch+1
                             'optimizer_state_dict': optimizer.state_dict(),
                             'loss': loss
                             }
                try:  # with nn.DataParallel() the net is added as a submodule of DataParallel
                    save_dict['model_state_dict'] = detector.module.state_dict()
                    save_dict['ema_model_state_dict'] = ema_detector.module.state_dict()
                except:
                    save_dict['model_state_dict'] = detector.state_dict()
                    save_dict['ema_model_state_dict'] = ema_detector.state_dict()
                torch.save(save_dict, os.path.join(LOG_DIR, 'best_checkpoint_sum_{}.tar'.format(EPOCH_CNT)))
            PERFORMANCE_FOUT.write('epoch: ' + str(EPOCH_CNT) + '\n' + \
                                   'best: ' + str(BEST_MAP[0].item()) + ', ' + str(BEST_MAP[1].item()) + '\n')
            PERFORMANCE_FOUT.flush()


if __name__ == '__main__':
    train()