code: # --------------------------------------------------------
  # 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 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.roi_layers 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')
      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.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)
  
      # 和训练的时候不一样,训练的时候是true
      cfg.TRAIN.USE_FLIPPED = False
      # 进行数据的准备，combined_roidb(args.imdb_name)是数据准备的核心部分。
      # 返回的imdb是类pascal_voc的一个实例，后面只用到了其的一些路径，作用不大。roidb则包含了训练网络所需要的所有信息。下面看一下它的产生过程
      imdb, roidb, ratio_list, ratio_index = combined_roidb(args.imdbval_name, False)
      # 注意这个
      imdb.competition_mode(on=True)
  
      print('{:d} roidb entries'.format(len(roidb)))
  
      # 读取模型的数据
      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))
  
      # 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:
          # 将所有的模型参数(parameters)和buffers赋值GPU
          fasterRCNN.cuda()
  
      start = time.time()
      max_per_image = 100
  
      # vis的意思是 visualization mode
      vis = args.vis
  
      if vis:
          thresh = 0.05
      else:
          thresh = 0.0
  
      save_name = 'faster_rcnn_10'
      # num_images的值是4952
      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)
          with torch.no_grad():
              im_data.resize_(data[0].size()).copy_(data[0])
              im_info.resize_(data[1].size()).copy_(data[1])
              gt_boxes.resize_(data[2].size()).copy_(data[2])
              num_boxes.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))
              # 从这里看出,faster rcnn的最后bbox预测模块,是在rois的基础上,预测的偏差值tx,ty,tw,th
              pred_boxes = bbox_transform_inv(boxes, box_deltas, 1)
              # 这里应该是对改装过后的box进行剪裁
              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 /= 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_boxes[order, :], cls_scores[order], 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 torch.no_grad numpy.copy torch.cuda.is_available numpy.array numpy.where torch.LongTensor torch.sort