text: # --------------------------------------------------------
  # Pytorch multi-GPU Faster R-CNN
  # Licensed under The MIT License [see LICENSE for details]
  # Written by Jiasen Lu, Jianwei Yang, based on code from Ross Girshick
  # --------------------------------------------------------
  from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  
  import _init_paths
  import os
  import sys
  import numpy as np
  import argparse
  import pprint
  import pdb
  import time
  
  import torch
  from torch.autograd import Variable
  import torch.nn as nn
  import torch.optim as optim
  
  import torchvision.transforms as transforms
  from torch.utils.data.sampler import Sampler
  
  from roi_data_layer.roidb import combined_roidb
  from roi_data_layer.roibatchLoader import roibatchLoader
  from model.utils.config import cfg, cfg_from_file, cfg_from_list, get_output_dir
  from model.utils.net_utils import weights_normal_init, save_net, load_net, \
        adjust_learning_rate, save_checkpoint, clip_gradient
  
  from model.faster_rcnn.vgg16 import vgg16
  from model.faster_rcnn.resnet import resnet
  
  def parse_args():
    """
    Parse input arguments
    """
    parser = argparse.ArgumentParser(description='Train a Fast R-CNN network')
    parser.add_argument('--dataset', dest='dataset',
                        help='training dataset',
                        default='cityperson', type=str)
    parser.add_argument('--net', dest='net',
                      help='vgg16, res101',
                      default='vgg16', type=str)
    parser.add_argument('--start_epoch', dest='start_epoch',
                        help='starting epoch',
                        default=1, type=int)
    parser.add_argument('--epochs', dest='max_epochs',
                        help='number of epochs to train',
                        default=20, type=int)
    parser.add_argument('--disp_interval', dest='disp_interval',
                        help='number of iterations to display',
                        default=100, type=int)
    parser.add_argument('--checkpoint_interval', dest='checkpoint_interval',
                        help='number of iterations to display',
                        default=10000, type=int)
  
    parser.add_argument('--save_dir', dest='save_dir',
                        help='directory to save models', default="~/project/ped-detection/data/save_dir",
                        nargs=argparse.REMAINDER)       ##------------------------------------save_dir
    parser.add_argument('--nw', dest='num_workers',
                        help='number of worker to load data',
                        default=4, type=int)  #------------------------------------------
    parser.add_argument('--cuda', dest='cuda',
                        help='whether use CUDA',
                        action='store_true')
    parser.add_argument('--ls', dest='large_scale',
                        help='whether use large imag scale',
                        action='store_true')                      
    parser.add_argument('--mGPUs', dest='mGPUs',
                        help='whether use multiple GPUs',
                        action='store_true')
    parser.add_argument('--bs', dest='batch_size',
                        help='batch_size',
                        default=1, type=int)
    parser.add_argument('--cag', dest='class_agnostic',
                        help='whether perform class_agnostic bbox regression',
                        action='store_true')
  
  # config optimization
    parser.add_argument('--o', dest='optimizer',
                        help='training optimizer',
                        default="sgd", type=str)
    parser.add_argument('--lr', dest='lr',
                        help='starting learning rate',
                        default=0.001, type=float)
    parser.add_argument('--lr_decay_step', dest='lr_decay_step',
                        help='step to do learning rate decay, unit is epoch',
                        default=5, type=int)
    parser.add_argument('--lr_decay_gamma', dest='lr_decay_gamma',
                        help='learning rate decay ratio',
                        default=0.1, type=float)
  
  # set training session
    parser.add_argument('--s', dest='session',
                        help='training session',
                        default=1, type=int)
  
  # resume trained model
    parser.add_argument('--r', dest='resume',
                        help='resume checkpoint or not',
                        default=False, type=bool)
    parser.add_argument('--checksession', dest='checksession',
                        help='checksession to load model',
                        default=1, type=int)
    parser.add_argument('--checkepoch', dest='checkepoch',
                        help='checkepoch to load model',
                        default=1, type=int)
    parser.add_argument('--checkpoint', dest='checkpoint',
                        help='checkpoint to load model',
                        default=0, type=int)
  # log and diaplay
    parser.add_argument('--use_tfboard', dest='use_tfboard',
                        help='whether use tensorflow tensorboard',
                        default=False, type=bool)
  
    args = parser.parse_args()
    return args
  
  
  class sampler(Sampler):
    def __init__(self, train_size, batch_size):
      self.num_data = train_size
      self.num_per_batch = int(train_size / batch_size)
      self.batch_size = batch_size
      self.range = torch.arange(0,batch_size).view(1, batch_size).long()
      self.leftover_flag = False
      if train_size % batch_size:
        self.leftover = torch.arange(self.num_per_batch*batch_size, train_size).long()
        self.leftover_flag = True
  
    def __iter__(self):
      rand_num = torch.randperm(self.num_per_batch).view(-1,1) * self.batch_size
      self.rand_num = rand_num.expand(self.num_per_batch, self.batch_size) + self.range
  
      self.rand_num_view = self.rand_num.view(-1)
  
      if self.leftover_flag:
        self.rand_num_view = torch.cat((self.rand_num_view, self.leftover),0)
  
      return iter(self.rand_num_view)
  
    def __len__(self):
      return self.num_data
  
  if __name__ == '__main__':
  
    args = parse_args()
    args.cuda = True;
    print('Called with args:')
    print(args)
  
    if args.use_tfboard:
      from model.utils.logger import Logger
      # Set the logger
      logger = Logger('./logs')
  
    if args.dataset == "pascal_voc":
        args.imdb_name = "voc_2007_trainval"
        args.imdbval_name = "voc_2007_test"
        args.set_cfgs = ['ANCHOR_SCALES', '[8, 16, 32]', 'ANCHOR_RATIOS', '[0.5,1,2]', 'MAX_NUM_GT_BOXES', '20']
    elif args.dataset == "pascal_voc_0712":
        args.imdb_name = "voc_2007_trainval+voc_2012_trainval"
        args.imdbval_name = "voc_2007_test"
        args.set_cfgs = ['ANCHOR_SCALES', '[8, 16, 32]', 'ANCHOR_RATIOS', '[0.5,1,2]', 'MAX_NUM_GT_BOXES', '20']
    elif args.dataset == "coco":
        args.imdb_name = "coco_2014_train+coco_2014_valminusminival"
        args.imdbval_name = "coco_2014_minival"
        args.set_cfgs = ['ANCHOR_SCALES', '[4, 8, 16, 32]', 'ANCHOR_RATIOS', '[0.5,1,2]', 'MAX_NUM_GT_BOXES', '50']
    elif args.dataset == "imagenet":
        args.imdb_name = "imagenet_train"
        args.imdbval_name = "imagenet_val"
        args.set_cfgs = ['ANCHOR_SCALES', '[4, 8, 16, 32]', 'ANCHOR_RATIOS', '[0.5,1,2]', 'MAX_NUM_GT_BOXES', '30']
    elif args.dataset == "vg":
        # train sizes: train, smalltrain, minitrain
        # train scale: ['150-50-20', '150-50-50', '500-150-80', '750-250-150', '1750-700-450', '1600-400-20']
        args.imdb_name = "vg_150-50-50_minitrain"
        args.imdbval_name = "vg_150-50-50_minival"
        args.set_cfgs = ['ANCHOR_SCALES', '[4, 8, 16, 32]', 'ANCHOR_RATIOS', '[0.5,1,2]', 'MAX_NUM_GT_BOXES', '50']
    elif args.dataset == "cityperson":
        args.imdb_name = "cityperson_train"
        args.imdbval_name = "cityperson_test"
        args.set_cfgs = None
        #args.set_cfgs = ['ANCHOR_SCALES', '[4, 8, 16, 32]', 'ANCHOR_RATIOS', '[0.5,1,2]', 'MAX_NUM_GT_BOXES', '50']
  
    args.cfg_file = "cfgs/{}_ls.yml".format(args.net) if args.large_scale else "cfgs/{}.yml".format(args.net)
  
    if args.cfg_file is not None:
      cfg_from_file(args.cfg_file)
    if args.set_cfgs is not None:
      cfg_from_list(args.set_cfgs)
  
    print('Using config:')
    pprint.pprint(cfg)
    np.random.seed(cfg.RNG_SEED)
  
    #torch.backends.cudnn.benchmark = True
    if torch.cuda.is_available() and not args.cuda:
      print("WARNING: You have a CUDA device, so you should probably run with --cuda")
  
    # train set
    # -- Note: Use validation set and disable the flipped to enable faster loading.
    cfg.TRAIN.USE_FLIPPED = True
    cfg.USE_GPU_NMS = args.cuda
    imdb, roidb, ratio_list, ratio_index = combined_roidb(args.imdb_name)
    train_size = len(roidb)  #
  
    print('{:d} roidb entries'.format(len(roidb)))
  
    output_dir = args.save_dir + "/" + args.net + "/" + args.dataset
    if not os.path.exists(output_dir):
      os.makedirs(output_dir)
  
    sampler_batch = sampler(train_size, args.batch_size)
  
    dataset = roibatchLoader(roidb, ratio_list, ratio_index, args.batch_size, \
                             imdb.num_classes, training=True)
  
    dataloader = torch.utils.data.DataLoader(dataset, batch_size=args.batch_size,
                              sampler=sampler_batch, num_workers=args.num_workers)
  
    # initilize the tensor holder here.
    im_data = torch.FloatTensor(1)
    im_info = torch.FloatTensor(1)
    num_boxes = torch.LongTensor(1)
    gt_boxes = torch.FloatTensor(1)
    ignore_region = torch.FloatTensor(1)
    num_ignore = torch.LongTensor(1)
  
  
    # ship to cuda
    if args.cuda:
      im_data = im_data.cuda()
      im_info = im_info.cuda()
      num_boxes = num_boxes.cuda()
      gt_boxes = gt_boxes.cuda()
      ignore_region = ignore_region.cuda()
      num_ignore = num_ignore.cuda()
  
    # make variable
    im_data = Variable(im_data)
    im_info = Variable(im_info)
    num_boxes = Variable(num_boxes)
    gt_boxes = Variable(gt_boxes)
    ignore_region = Variable(ignore_region)
    num_ignore = Variable(num_ignore)
  
    if args.cuda:
      cfg.CUDA = True
  
    # args.net = 'res101'
    # initilize the network here.
    if args.net == 'vgg16':
      fasterRCNN = vgg16(imdb.classes, pretrained=True, class_agnostic=args.class_agnostic)
    elif args.net == 'res101':
      fasterRCNN = resnet(imdb.classes, 101, pretrained=True, class_agnostic=args.class_agnostic)
    elif args.net == 'res50':
      fasterRCNN = resnet(imdb.classes, 50, pretrained=True, class_agnostic=args.class_agnostic)
    elif args.net == 'res152':
      fasterRCNN = resnet(imdb.classes, 152, pretrained=True, class_agnostic=args.class_agnostic)
    else:
      print("network is not defined")
      pdb.set_trace()
  
    fasterRCNN.create_architecture()
  
    lr = cfg.TRAIN.LEARNING_RATE
    lr = args.lr
    #tr_momentum = cfg.TRAIN.MOMENTUM
    #tr_momentum = args.momentum
  
    params = []
    for key, value in dict(fasterRCNN.named_parameters()).items():
      print(key)
      if value.requires_grad:
        if 'bias' in key:
          params += [{'params':[value],'lr':lr*(cfg.TRAIN.DOUBLE_BIAS + 1), \
                  'weight_decay': cfg.TRAIN.BIAS_DECAY and cfg.TRAIN.WEIGHT_DECAY or 0}]  ########################
        else:
          params += [{'params':[value],'lr':lr, 'weight_decay': cfg.TRAIN.WEIGHT_DECAY}]
  
    if args.optimizer == "adam":
      lr = lr * 0.1
      optimizer = torch.optim.Adam(params)
  
    elif args.optimizer == "sgd":
      optimizer = torch.optim.SGD(params, momentum=cfg.TRAIN.MOMENTUM)
  
    if args.resume:
      load_name = os.path.join(output_dir,
        'faster_rcnn_{}_{}_{}.pth'.format(args.checksession, args.checkepoch, args.checkpoint))
      print("loading checkpoint %s" % (load_name))
      checkpoint = torch.load(load_name)
      args.session = checkpoint['session']
      args.start_epoch = checkpoint['epoch']
      fasterRCNN.load_state_dict(checkpoint['model'])
      optimizer.load_state_dict(checkpoint['optimizer'])
      lr = optimizer.param_groups[0]['lr']
      if 'pooling_mode' in checkpoint.keys():
        cfg.POOLING_MODE = checkpoint['pooling_mode']
      print("loaded checkpoint %s" % (load_name))
  
    if args.mGPUs:
      fasterRCNN = nn.DataParallel(fasterRCNN)
  
    # device = torch.device("cuda:2" if torch.cuda.is_available() else "cpu")
    # fasterRCNN.to(device)
  
    # if torch.cuda.device_count() > 1:
    #   fasterRCNN = nn.DataParallel(fasterRCNN)
    #   print("Let's use", torch.cuda.device_count(), "GPUs!")
  
    if args.cuda:
      fasterRCNN.cuda()
  
    iters_per_epoch = int(train_size / args.batch_size)
  
    for epoch in range(args.start_epoch, args.max_epochs + 1):
      # setting to train mode
      fasterRCNN.train()
      loss_temp = 0
      start = time.time()
  
      if epoch % (args.lr_decay_step + 1) == 0:
          adjust_learning_rate(optimizer, args.lr_decay_gamma)
          lr *= args.lr_decay_gamma
  
      data_iter = iter(dataloader)
      for step in range(iters_per_epoch):
        data = next(data_iter)
        im_data.data.resize_(data[0].size()).copy_(data[0])
        im_info.data.resize_(data[1].size()).copy_(data[1])
        gt_boxes.data.resize_(data[2].size()).copy_(data[2])
        num_boxes.data.resize_(data[3].size()).copy_(data[3])
        ignore_region.data.resize_(data[4].size()).copy_(data[4])
        num_ignore.data.resize_(data[5].size()).copy_(data[5])
  
        fasterRCNN.zero_grad()
        rois, cls_prob, bbox_pred, \
        rpn_loss_cls, rpn_loss_box, \
        RCNN_loss_cls, RCNN_loss_bbox, \
        rois_label = fasterRCNN(im_data, im_info, gt_boxes, num_boxes, ignore_region, num_ignore)
  
        loss = rpn_loss_cls.mean() + rpn_loss_box.mean() \
             + RCNN_loss_cls.mean() + RCNN_loss_bbox.mean()
        loss_temp += loss.data[0]
  
        # backward
        optimizer.zero_grad()
        loss.backward()
        if args.net == "vgg16":
            clip_gradient(fasterRCNN, 10.)
        optimizer.step()
  
        if step % args.disp_interval == 0:
          end = time.time()
          if step > 0:
            loss_temp /= args.disp_interval
  
          if args.mGPUs:
            loss_rpn_cls = rpn_loss_cls.mean().data[0]
            loss_rpn_box = rpn_loss_box.mean().data[0]
            loss_rcnn_cls = RCNN_loss_cls.mean().data[0]
            loss_rcnn_box = RCNN_loss_bbox.mean().data[0]
            fg_cnt = torch.sum(rois_label.data.ne(0))
            bg_cnt = rois_label.data.numel() - fg_cnt
          else:
            loss_rpn_cls = rpn_loss_cls.data[0]
            loss_rpn_box = rpn_loss_box.data[0]
            loss_rcnn_cls = RCNN_loss_cls.data[0]
            loss_rcnn_box = RCNN_loss_bbox.data[0]
            fg_cnt = torch.sum(rois_label.data.ne(0))
            bg_cnt = rois_label.data.numel() - fg_cnt
  
          print("[session %d][epoch %2d][iter %4d/%4d] loss: %.4f, lr: %.2e" \
                                  % (args.session, epoch, step, iters_per_epoch, loss_temp, lr))
          print("\t\t\tfg/bg=(%d/%d), time cost: %f" % (fg_cnt, bg_cnt, end-start))
          print("\t\t\trpn_cls: %.4f, rpn_box: %.4f, rcnn_cls: %.4f, rcnn_box %.4f" \
                        % (loss_rpn_cls, loss_rpn_box, loss_rcnn_cls, loss_rcnn_box))
          if args.use_tfboard:
            info = {
              'loss': loss_temp,
              'loss_rpn_cls': loss_rpn_cls,
              'loss_rpn_box': loss_rpn_box,
              'loss_rcnn_cls': loss_rcnn_cls,
              'loss_rcnn_box': loss_rcnn_box
            }
            for tag, value in info.items():
              logger.scalar_summary(tag, value, step)
  
          loss_temp = 0
          start = time.time()
  
      if args.mGPUs:
        save_name = os.path.join(output_dir, 'faster_rcnn_{}_{}_{}.pth'.format(args.session, epoch, step))
        save_checkpoint({
          'session': args.session,
          'epoch': epoch + 1,
          'model': fasterRCNN.module.state_dict(),
          'optimizer': optimizer.state_dict(),
          'pooling_mode': cfg.POOLING_MODE,
          'class_agnostic': args.class_agnostic,
        }, save_name)
      else:
        save_name = os.path.join(output_dir, 'faster_rcnn_{}_{}_{}.pth'.format(args.session, epoch, step))
        save_checkpoint({
          'session': args.session,
          'epoch': epoch + 1,
          'model': fasterRCNN.state_dict(),
          'optimizer': optimizer.state_dict(),
          'pooling_mode': cfg.POOLING_MODE,
          'class_agnostic': args.class_agnostic,
        }, save_name)
      print('save model: {}'.format(save_name))
  
      end = time.time()
      print(end - start)
  