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 torchvision.transforms as transforms
  import torchvision.datasets as dset
  # from scipy.misc import imread
  from imageio import imread
  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.utils.blob import im_list_to_blob
  from model.faster_rcnn.vgg16 import vgg16
  from model.faster_rcnn.resnet import resnet
  import pdb
  
  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="/srv/share/jyang375/models")
      parser.add_argument('--image_dir', dest='image_dir',
                          help='directory to load images for demo',
                          default="images")
      parser.add_argument('--cuda', dest='cuda',
                          help='whether use CUDA',
                          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('--bs', dest='batch_size',
                          help='batch_size',
                          default=1, type=int)
      parser.add_argument('--vis', dest='vis',
                          help='visualization mode',
                          action='store_true')
      parser.add_argument('--webcam_num', dest='webcam_num',
                          help='webcam ID number',
                          default=-1, type=int)
  
      args = parser.parse_args()
      return args
  
  
  lr = cfg.TRAIN.LEARNING_RATE
  momentum = cfg.TRAIN.MOMENTUM
  weight_decay = cfg.TRAIN.WEIGHT_DECAY
  
  
  def _get_image_blob(im):
      """Converts an image into a network input.
      Arguments:
        im (ndarray): a color image in BGR order
      Returns:
        blob (ndarray): a data blob holding an image pyramid
        im_scale_factors (list): list of image scales (relative to im) used
          in the image pyramid
      """
      im_orig = im.astype(np.float32, copy=True)
      im_orig -= cfg.PIXEL_MEANS
  
      im_shape = im_orig.shape
      im_size_min = np.min(im_shape[0:2])
      im_size_max = np.max(im_shape[0:2])
  
      processed_ims = []
      im_scale_factors = []
  
      for target_size in cfg.TEST.SCALES:
          im_scale = float(target_size) / float(im_size_min)
          # Prevent the biggest axis from being more than MAX_SIZE
          if np.round(im_scale * im_size_max) > cfg.TEST.MAX_SIZE:
              im_scale = float(cfg.TEST.MAX_SIZE) / float(im_size_max)
          im = cv2.resize(im_orig, None, None, fx=im_scale, fy=im_scale,
                          interpolation=cv2.INTER_LINEAR)
          im_scale_factors.append(im_scale)
          processed_ims.append(im)
  
      # Create a blob to hold the input images
      blob = im_list_to_blob(processed_ims)
  
      return blob, np.array(im_scale_factors)
  
  
  if __name__ == '__main__':
  
      args = parse_args()
  
      print('Called with args:')
      print(args)
  
      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)
  
      cfg.USE_GPU_NMS = args.cuda
  
      print('Using config:')
      pprint.pprint(cfg)
      np.random.seed(cfg.RNG_SEED)
  
      # train set
      # -- Note: Use validation set and disable the flipped to enable faster loading.
  
      # input_dir = args.load_dir + "/" + args.net + "/" + args.dataset
      # 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))
      load_name = args.load_dir
      pascal_classes = np.asarray(['__background__',
                                   'aeroplane', 'bicycle', 'bird', 'boat',
                                   'bottle', 'bus', 'car', 'cat', 'chair',
                                   'cow', 'diningtable', 'dog', 'horse',
                                   'motorbike', 'person', 'pottedplant',
                                   'sheep', 'sofa', 'train', 'tvmonitor'])
  
      # initilize the network here.
      if args.net == 'vgg16':
          fasterRCNN = vgg16(pascal_classes, pretrained=False, class_agnostic=args.class_agnostic)
      elif args.net == 'res101':
          fasterRCNN = resnet(pascal_classes, 101, pretrained=False, class_agnostic=args.class_agnostic)
      elif args.net == 'res50':
          fasterRCNN = resnet(pascal_classes, 50, pretrained=False, class_agnostic=args.class_agnostic)
      elif args.net == 'res152':
          fasterRCNN = resnet(pascal_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))
      if args.cuda > 0:
          checkpoint = torch.load(load_name)
      else:
          checkpoint = torch.load(load_name, map_location=(lambda storage, loc: storage))
      fasterRCNN.load_state_dict(checkpoint['model'])
      if 'pooling_mode' in checkpoint.keys():
          cfg.POOLING_MODE = checkpoint['pooling_mode']
  
      print('load model successfully!')
  
      # pdb.set_trace()
  
      print("load checkpoint %s" % (load_name))
  
      # 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 > 0:
          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, volatile=True)
      im_info = Variable(im_info, volatile=True)
      num_boxes = Variable(num_boxes, volatile=True)
      gt_boxes = Variable(gt_boxes, volatile=True)
  
      if args.cuda > 0:
          cfg.CUDA = True
  
      if args.cuda > 0:
          fasterRCNN.cuda()
  
      fasterRCNN.eval()
  
      start = time.time()
      max_per_image = 100
      thresh = 0.05
      vis = True
  
      webcam_num = args.webcam_num
      # Set up webcam or get image directories
      if webcam_num >= 0:
          cap = cv2.VideoCapture(webcam_num)
          num_images = 0
      else:
          imglist = os.listdir(args.image_dir)
          num_images = len(imglist)
  
      s = time.time()
      print('Loaded Photo: {} images.'.format(num_images))
  
      while (num_images >= 0):
          total_tic = time.time()
          if webcam_num == -1:
              num_images -= 1
  
  
          im_file = os.path.join(args.image_dir, imglist[num_images])
          # im = cv2.imread(im_file)
          im_in = np.array(imread(im_file))
          if len(im_in.shape) == 2:
              im_in = im_in[:, :, np.newaxis]
              im_in = np.concatenate((im_in, im_in, im_in), axis=2)   # one channel to three same channel
          # rgb -> bgr
          im = im_in[:, :, ::-1]
  
          blobs, im_scales = _get_image_blob(im)
          assert len(im_scales) == 1, "Only single-image batch implemented"
          im_blob = blobs
          im_info_np = np.array([[im_blob.shape[1], im_blob.shape[2], im_scales[0]]], dtype=np.float32)
  
          im_data_pt = torch.from_numpy(im_blob)
          im_data_pt = im_data_pt.permute(0, 3, 1, 2)
          im_info_pt = torch.from_numpy(im_info_np)
  
          im_data.data.resize_(im_data_pt.size()).copy_(im_data_pt)
          im_info.data.resize_(im_info_pt.size()).copy_(im_info_pt)
          gt_boxes.data.resize_(1, 1, 5).zero_()
          num_boxes.data.resize_(1).zero_()
  
          # pdb.set_trace()
          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:
                      if args.cuda > 0:
                          box_deltas = box_deltas.view(-1, 4) * torch.FloatTensor(cfg.TRAIN.BBOX_NORMALIZE_STDS).cuda() \
                                       + torch.FloatTensor(cfg.TRAIN.BBOX_NORMALIZE_MEANS).cuda()
                      else:
                          box_deltas = box_deltas.view(-1, 4) * torch.FloatTensor(cfg.TRAIN.BBOX_NORMALIZE_STDS) \
                                       + torch.FloatTensor(cfg.TRAIN.BBOX_NORMALIZE_MEANS)
  
                      box_deltas = box_deltas.view(1, -1, 4)
                  else:
                      if args.cuda > 0:
                          box_deltas = box_deltas.view(-1, 4) * torch.FloatTensor(cfg.TRAIN.BBOX_NORMALIZE_STDS).cuda() \
                                       + torch.FloatTensor(cfg.TRAIN.BBOX_NORMALIZE_MEANS).cuda()
                      else:
                          box_deltas = box_deltas.view(-1, 4) * torch.FloatTensor(cfg.TRAIN.BBOX_NORMALIZE_STDS) \
                                       + torch.FloatTensor(cfg.TRAIN.BBOX_NORMALIZE_MEANS)
                      box_deltas = box_deltas.view(1, -1, 4 * len(pascal_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
              pred_boxes = np.tile(boxes, (1, scores.shape[1]))
  
          pred_boxes /= im_scales[0]
  
          scores = scores.squeeze()
          pred_boxes = pred_boxes.squeeze()
          det_toc = time.time()
          detect_time = det_toc - det_tic
          misc_tic = time.time()
          if vis:
              im2show = np.copy(im)
          for j in xrange(1, len(pascal_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, force_cpu=not cfg.USE_GPU_NMS)
                  cls_dets = cls_dets[keep.view(-1).long()]
                  if vis:
                      im2show = vis_detections(im2show, pascal_classes[j], cls_dets.cpu().numpy(), 0.5)
  
          misc_toc = time.time()
          nms_time = misc_toc - misc_tic
  
          if webcam_num == -1:
              sys.stdout.write('im_detect: {:d}/{:d} {:.3f}s {:.3f}s   \r' \
                               .format(num_images + 1, len(imglist), detect_time, nms_time))
              sys.stdout.flush()
  
          if vis and webcam_num == -1:
              # cv2.imshow('test', im2show)
              # cv2.waitKey(0)
              result_path = os.path.join(args.image_dir, imglist[num_images][:-4] + "_det.jpg")
              cv2.imwrite(result_path, im2show)
          else:
              im2showRGB = cv2.cvtColor(im2show, cv2.COLOR_BGR2RGB)
              cv2.imshow("frame", im2showRGB)
              total_toc = time.time()
              total_time = total_toc - total_tic
              frame_rate = 1 / total_time
              print('Frame rate:', frame_rate)
              if cv2.waitKey(1) & 0xFF == ord('q'):
                  break
  
      e = time.time()
      print(e - s, 's')
  
      if webcam_num >= 0:
          cap.release()
          cv2.destroyAllWindows()
  
apis: numpy.tile torch.FloatTensor torch.nonzero torch.load torch.autograd.Variable numpy.random.seed numpy.asarray numpy.copy numpy.max torch.from_numpy numpy.min numpy.array numpy.concatenate torch.LongTensor numpy.round torch.sort