voxelnet train.py

import glob
import argparse
import os
import time
import sys
import tensorflow as tf
from itertools import count

from config import cfg
from model import RPN3D
from utils import *
from utils.kitti_loader import iterate_data, sample_test_data
from train_hook import check_if_should_pause

parser = argparse.ArgumentParser(description=‘training’)
parser.add_argument(’-i’, ‘–max-epoch’, type=int, nargs=’?’, default=160,
help=‘max epoch’)
parser.add_argument(’-n’, ‘–tag’, type=str, nargs=’?’, default=‘default’,
help=‘set log tag’)
parser.add_argument(’-b’, ‘–single-batch-size’, type=int, nargs=’?’, default=2,
help=‘set batch size’)
parser.add_argument(’-l’, ‘–lr’, type=float, nargs=’?’, default=0.001,
help=‘set learning rate’)
parser.add_argument(’-al’, ‘–alpha’, type=float, nargs=’?’, default=1.0,
help=‘set alpha in los function’)
parser.add_argument(’-be’, ‘–beta’, type=float, nargs=’?’, default=10.0,
help=‘set beta in los function’)
parser.add_argument(’–output-path’, type=str, nargs=’?’,
default=’./predictions’, help=‘results output dir’)
parser.add_argument(’-v’, ‘–vis’, type=bool, nargs=’?’, default=False,
help=‘set the flag to True if dumping visualizations’)
args = parser.parse_args()

dataset_dir = cfg.DATA_DIR
train_dir = os.path.join(cfg.DATA_DIR, ‘training’)
val_dir = os.path.join(cfg.DATA_DIR, ‘validation’)
log_dir = os.path.join(’./log’, args.tag)
save_model_dir = os.path.join(’./save_model’, args.tag)
os.makedirs(log_dir, exist_ok=True)
os.makedirs(save_model_dir, exist_ok=True)

def main(_):
# TODO: split file support
with tf.Graph().as_default():
global save_model_dir
start_epoch = 0
global_counter = 0

    gpu_options = tf.GPUOptions(per_process_gpu_memory_fraction=cfg.GPU_MEMORY_FRACTION,
                                visible_device_list=cfg.GPU_AVAILABLE,
                                allow_growth=True)
    config = tf.ConfigProto(
        gpu_options=gpu_options,
        device_count={
            "GPU": cfg.GPU_USE_COUNT,
        },
        allow_soft_placement=True,
    )
    with tf.Session(config=config) as sess:
        model = RPN3D(
            cls=cfg.DETECT_OBJ,
            single_batch_size=args.single_batch_size,
            learning_rate=args.lr,
            max_gradient_norm=5.0,
            alpha=args.alpha,
            beta=args.beta,
            avail_gpus=cfg.GPU_AVAILABLE.split(',')
        )
        # param init/restore
        if tf.train.get_checkpoint_state(save_model_dir):
            print("Reading model parameters from %s" % save_model_dir)
            model.saver.restore(
                sess, tf.train.latest_checkpoint(save_model_dir))
            start_epoch = model.epoch.eval() + 1
            global_counter = model.global_step.eval() + 1
        else:
            print("Created model with fresh parameters.")
            tf.global_variables_initializer().run()

        # train and validate
        is_summary, is_summary_image, is_validate = False, False, False

        summary_interval = 5
        summary_val_interval = 10
        summary_writer = tf.summary.FileWriter(log_dir, sess.graph)


        # training
        for epoch in range(start_epoch, args.max_epoch):
            counter = 0
            batch_time = time.time()
            for batch in iterate_data(train_dir, shuffle=True, aug=True, is_testset=False, batch_size=args.single_batch_size * cfg.GPU_USE_COUNT, multi_gpu_sum=cfg.GPU_USE_COUNT):
                
                counter += 1
                global_counter += 1
                
                if counter % summary_interval == 0:
                    is_summary = True
                else:
                    is_summary = False
                
                start_time = time.time()
                ret = model.train_step( sess, batch, train=True, summary = is_summary )
                forward_time = time.time() - start_time
                batch_time = time.time() - batch_time

                
                print('train: {} @ epoch:{}/{} loss: {:.4f} reg_loss: {:.4f} cls_loss: {:.4f} cls_pos_loss: {:.4f} cls_neg_loss: {:.4f} forward time: {:.4f} batch time: {:.4f}'.format(counter,epoch, args.max_epoch, ret[0], ret[1], ret[2], ret[3], ret[4], forward_time, batch_time))
                with open('log/train.txt', 'a') as f:
                    f.write( 'train: {} @ epoch:{}/{} loss: {:.4f} reg_loss: {:.4f} cls_loss: {:.4f} cls_pos_loss: {:.4f} cls_neg_loss: {:.4f} forward time: {:.4f} batch time: {:.4f} \n'.format(counter, epoch, args.max_epoch, ret[0], ret[1], ret[2], ret[3], ret[4], forward_time, batch_time) )
                
                #print(counter, summary_interval, counter % summary_interval)
                if counter % summary_interval == 0:
                    print("summary_interval now")
                    summary_writer.add_summary(ret[-1], global_counter)
                        
                #print(counter, summary_val_interval, counter % summary_val_interval)
                if counter % summary_val_interval == 0:
                    print("summary_val_interval now")
                    batch = sample_test_data(val_dir, args.single_batch_size * cfg.GPU_USE_COUNT, multi_gpu_sum=cfg.GPU_USE_COUNT)
                    
                    ret = model.validate_step(sess, batch, summary=True)
                    summary_writer.add_summary(ret[-1], global_counter)
                    
                    try:
                        ret = model.predict_step(sess, batch, summary=True)
                        summary_writer.add_summary(ret[-1], global_counter)
                    except:
                        print("prediction skipped due to error")
                
                if check_if_should_pause(args.tag):
                    model.saver.save(sess, os.path.join(save_model_dir, 'checkpoint'), global_step=model.global_step)
                    print('pause and save model @ {} steps:{}'.format(save_model_dir, model.global_step.eval()))
                    sys.exit(0)
                        
                batch_time = time.time()
            
            sess.run(model.epoch_add_op)
            
            model.saver.save(sess, os.path.join(save_model_dir, 'checkpoint'), global_step=model.global_step)
    
            # dump test data every 10 epochs
            if ( epoch + 1 ) % 10 == 0:
                # create output folder
                os.makedirs(os.path.join(args.output_path, str(epoch)), exist_ok=True)
                os.makedirs(os.path.join(args.output_path, str(epoch), 'data'), exist_ok=True)
                if args.vis:
                    os.makedirs(os.path.join(args.output_path, str(epoch), 'vis'), exist_ok=True)
                
                for batch in iterate_data(val_dir, shuffle=False, aug=False, is_testset=False, batch_size=args.single_batch_size * cfg.GPU_USE_COUNT, multi_gpu_sum=cfg.GPU_USE_COUNT):
                    
                    if args.vis:
                        tags, results, front_images, bird_views, heatmaps = model.predict_step(sess, batch, summary=False, vis=True)
                    else:
                        tags, results = model.predict_step(sess, batch, summary=False, vis=False)
                            
                    for tag, result in zip(tags, results):
                        of_path = os.path.join(args.output_path, str(epoch), 'data', tag + '.txt')
                        with open(of_path, 'w+') as f:
                            labels = box3d_to_label([result[:, 1:8]], [result[:, 0]], [result[:, -1]], coordinate='lidar')[0]
                            for line in labels:
                                f.write(line)
                            print('write out {} objects to {}'.format(len(labels), tag))
                    # dump visualizations
                    if args.vis:
                        for tag, front_image, bird_view, heatmap in zip(tags, front_images, bird_views, heatmaps):
                            front_img_path = os.path.join( args.output_path, str(epoch),'vis', tag + '_front.jpg'  )
                            bird_view_path = os.path.join( args.output_path, str(epoch), 'vis', tag + '_bv.jpg'  )
                            heatmap_path = os.path.join( args.output_path, str(epoch), 'vis', tag + '_heatmap.jpg'  )
                            cv2.imwrite( front_img_path, front_image )
                            cv2.imwrite( bird_view_path, bird_view )
                            cv2.imwrite( heatmap_path, heatmap )
    
                # execute evaluation code
                cmd_1 = "./kitti_eval/launch_test.sh"
                cmd_2 = os.path.join( args.output_path, str(epoch) )
                cmd_3 = os.path.join( args.output_path, str(epoch), 'log' )
                os.system( " ".join( [cmd_1, cmd_2, cmd_3] ) )
                    
                    

        print('train done. total epoch:{} iter:{}'.format(
            epoch, model.global_step.eval()))
            
        # finallly save model
        model.saver.save(sess, os.path.join(
            save_model_dir, 'checkpoint'), global_step=model.global_step)

if name == ‘main’:
tf.app.run(main)