code: # --------------------------------------------------------
  # Tensorflow 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 cv2
  
  import torch
  from torch.autograd import Variable
  import torch.nn as nn
  import torch.optim as optim
  import pickle
  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.rpn.bbox_transform import clip_boxes
  from model.nms.nms_wrapper import nms
  from model.rpn.bbox_transform import bbox_transform_inv
  from model.utils.net_utils import save_net, load_net, vis_detections
  
  from model.faster_rcnn.vgg16 import vgg16
  from model.faster_rcnn.resnet import resnet
  
  try:
      xrange          # Python 2
  except NameError:
      xrange = range  # Python 3
  
  
  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='pascal_voc', type=str)
    parser.add_argument('--cfg', dest='cfg_file',
                        help='optional config file',
                        default='cfgs/vgg16.yml', type=str)
    parser.add_argument('--net', dest='net',
                        help='vgg16, res50, res101, res152',
                        default='res101', type=str)
    parser.add_argument('--set', dest='set_cfgs',
                        help='set config keys', default=None,
                        nargs=argparse.REMAINDER)
    parser.add_argument('--load_dir', dest='load_dir',
                        help='directory to load models', default="models",
                        type=str)
    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('--cag', dest='class_agnostic',
                        help='whether perform class_agnostic bbox regression',
                        action='store_true')
    parser.add_argument('--parallel_type', dest='parallel_type',
                        help='which part of model to parallel, 0: all, 1: model before roi pooling',
                        default=0, type=int)
    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 network',
                        default=1, type=int)
    parser.add_argument('--checkpoint', dest='checkpoint',
                        help='checkpoint to load network',
                        default=10021, type=int)
    parser.add_argument('--vis', dest='vis',
                        help='visualization mode',
                        action='store_true')
    parser.add_argument('--input_dir', dest='input_dir',
                        help='directory to save models',
                        type=str)
    args = parser.parse_args()
    return args
  
  lr = cfg.TRAIN.LEARNING_RATE
  momentum = cfg.TRAIN.MOMENTUM
  weight_decay = cfg.TRAIN.WEIGHT_DECAY
  
  if __name__ == '__main__':
  
    args = parse_args()
  
    print('Called with args:')
    print(args)
  
    if torch.cuda.is_available() and not args.cuda:
      print("WARNING: You have a CUDA device, so you should probably run with --cuda")
  
    np.random.seed(cfg.RNG_SEED)
    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]']
    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]']
    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]']
    elif args.dataset == "imagenet":
        args.imdb_name = "imagenet_train"
        args.imdbval_name = "imagenet_val"
        args.set_cfgs = ['ANCHOR_SCALES', '[8, 16, 32]', 'ANCHOR_RATIOS', '[0.5,1,2]']
    elif args.dataset == "vg":
        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]']
  
    args.cfg_file = "cfgs/{}/{}_ls.yml".format(args.dataset, args.net) if args.large_scale else "cfgs/{}/{}.yml".format(
        args.dataset, 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)
  
    cfg.TRAIN.USE_FLIPPED = False
    imdb, roidb, ratio_list, ratio_index = combined_roidb(args.imdbval_name, False)
    imdb.competition_mode(on=True)
  
    print('{:d} roidb entries'.format(len(roidb)))
  
    input_dir = args.input_dir
    if not os.path.exists(input_dir):
      raise Exception('There is no input directory for loading network from ' + input_dir)
    load_name = os.path.join(input_dir,
      'faster_rcnn_{}_{}_{}.pth'.format(args.checksession, args.checkepoch, args.checkpoint))
  
    # initilize the network here.
    if args.net == 'vgg16':
      fasterRCNN = vgg16(imdb.classes, pretrained=False, class_agnostic=args.class_agnostic)
    elif args.net == 'res101':
      fasterRCNN = resnet(imdb.classes, 101, pretrained=False, class_agnostic=args.class_agnostic)
    elif args.net == 'res50':
      fasterRCNN = resnet(imdb.classes, 50, pretrained=False, class_agnostic=args.class_agnostic)
    elif args.net == 'res152':
      fasterRCNN = resnet(imdb.classes, 152, pretrained=False, class_agnostic=args.class_agnostic)
    else:
      print("network is not defined")
      pdb.set_trace()
  
    fasterRCNN.create_architecture()
  
    print("load checkpoint %s" % (load_name))
    checkpoint = torch.load(load_name)
    fasterRCNN.load_state_dict(checkpoint['model'])
    if 'pooling_mode' in checkpoint.keys():
      cfg.POOLING_MODE = checkpoint['pooling_mode']
  
  
    print('load model successfully!')
    # 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)
  
    # 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()
  
    # make variable
    im_data = Variable(im_data)
    im_info = Variable(im_info)
    num_boxes = Variable(num_boxes)
    gt_boxes = Variable(gt_boxes)
  
    if args.cuda:
      cfg.CUDA = True
  
    if args.cuda:
      fasterRCNN.cuda()
  
    start = time.time()
    max_per_image = 100
  
    vis = args.vis
  
    if vis:
      thresh = 0.05
    else:
      thresh = 0.0
  
    save_name = 'faster_rcnn_10'
    num_images = len(imdb.image_index)
    all_boxes = [[[] for _ in xrange(num_images)]
                 for _ in xrange(imdb.num_classes)]
  
    output_dir = get_output_dir(imdb, save_name)
    dataset = roibatchLoader(roidb, ratio_list, ratio_index, 1, \
                          imdb.num_classes, training=False, normalize = False)
    dataloader = torch.utils.data.DataLoader(dataset, batch_size=1,
                              shuffle=False, num_workers=0,
                              pin_memory=True)
  
    data_iter = iter(dataloader)
  
    _t = {'im_detect': time.time(), 'misc': time.time()}
    det_file = os.path.join(output_dir, 'detections.pkl')
  
    fasterRCNN.eval()
    empty_array = np.transpose(np.array([[],[],[],[],[]]), (1,0))
    for i in range(num_images):
  
        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])
  
        det_tic = time.time()
        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)
  
        scores = cls_prob.data
        boxes = rois.data[:, :, 1:5]
  
        if cfg.TEST.BBOX_REG:
            # Apply bounding-box regression deltas
            box_deltas = bbox_pred.data
            if cfg.TRAIN.BBOX_NORMALIZE_TARGETS_PRECOMPUTED:
            # Optionally normalize targets by a precomputed mean and stdev
              if args.class_agnostic:
                  box_deltas = box_deltas.view(-1, 4) * torch.FloatTensor(cfg.TRAIN.BBOX_NORMALIZE_STDS).cuda() \
                             + torch.FloatTensor(cfg.TRAIN.BBOX_NORMALIZE_MEANS).cuda()
                  box_deltas = box_deltas.view(1, -1, 4)
              else:
                  box_deltas = box_deltas.view(-1, 4) * torch.FloatTensor(cfg.TRAIN.BBOX_NORMALIZE_STDS).cuda() \
                             + torch.FloatTensor(cfg.TRAIN.BBOX_NORMALIZE_MEANS).cuda()
                  box_deltas = box_deltas.view(1, -1, 4 * len(imdb.classes))
  
            pred_boxes = bbox_transform_inv(boxes, box_deltas, 1)
            pred_boxes = clip_boxes(pred_boxes, im_info.data, 1)
        else:
            # Simply repeat the boxes, once for each class
            _ = torch.from_numpy(np.tile(boxes, (1, scores.shape[1])))
            pred_boxes = _.cuda() if args.cuda > 0 else _
  
        pred_boxes /= data[1][0][2].item()
  
        scores = scores.squeeze()
        pred_boxes = pred_boxes.squeeze()
        det_toc = time.time()
        detect_time = det_toc - det_tic
        misc_tic = time.time()
        if vis:
            im = cv2.imread(imdb.image_path_at(i))
            im2show = np.copy(im)
        for j in xrange(1, imdb.num_classes):
            inds = torch.nonzero(scores[:,j]>thresh).view(-1)
            # if there is det
            if inds.numel() > 0:
              cls_scores = scores[:,j][inds]
              _, order = torch.sort(cls_scores, 0, True)
              if args.class_agnostic:
                cls_boxes = pred_boxes[inds, :]
              else:
                cls_boxes = pred_boxes[inds][:, j * 4:(j + 1) * 4]
  
              cls_dets = torch.cat((cls_boxes, cls_scores.unsqueeze(1)), 1)
              # cls_dets = torch.cat((cls_boxes, cls_scores), 1)
              cls_dets = cls_dets[order]
              keep = nms(cls_dets, cfg.TEST.NMS)
              cls_dets = cls_dets[keep.view(-1).long()]
              if vis:
                im2show = vis_detections(im2show, imdb.classes[j], cls_dets.cpu().numpy(), 0.3)
              all_boxes[j][i] = cls_dets.cpu().numpy()
            else:
              all_boxes[j][i] = empty_array
  
        # Limit to max_per_image detections *over all classes*
        if max_per_image > 0:
            image_scores = np.hstack([all_boxes[j][i][:, -1]
                                      for j in xrange(1, imdb.num_classes)])
            if len(image_scores) > max_per_image:
                image_thresh = np.sort(image_scores)[-max_per_image]
                for j in xrange(1, imdb.num_classes):
                    keep = np.where(all_boxes[j][i][:, -1] >= image_thresh)[0]
                    all_boxes[j][i] = all_boxes[j][i][keep, :]
  
        misc_toc = time.time()
        nms_time = misc_toc - misc_tic
  
        sys.stdout.write('im_detect: {:d}/{:d} {:.3f}s {:.3f}s   \r' \
            .format(i + 1, num_images, detect_time, nms_time))
        sys.stdout.flush()
  
        if vis:
            cv2.imwrite('result.png', im2show)
            pdb.set_trace()
            #cv2.imshow('test', im2show)
            #cv2.waitKey(0)
  
    with open(det_file, 'wb') as f:
        pickle.dump(all_boxes, f, pickle.HIGHEST_PROTOCOL)
  
    print('Evaluating detections')
    imdb.evaluate_detections(all_boxes, output_dir)
  
    end = time.time()
    print("test time: %0.4fs" % (end - start))
  
apis: torch.utils.data.DataLoader torch.FloatTensor numpy.tile torch.nonzero torch.load numpy.sort torch.autograd.Variable numpy.random.seed numpy.copy torch.cuda.is_available numpy.array numpy.where torch.LongTensor torch.sort