demo.py

"""visualize results of the pre-trained model """

# Example
# python demo.py --net res101 --dataset vg --load_dir models --cuda
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.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.utils.blob import im_list_to_blob
from model.faster_rcnn.vgg16 import vgg16
from model.faster_rcnn.resnet import resnet
import pdb

import matplotlib.pyplot as plt

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='vg', type=str)
  parser.add_argument('--cfg', dest='cfg_file',
                      help='optional config file',
                      default='cfgs/res101.yml', type=str)
  parser.add_argument('--net', dest='net',
                      help='vgg16, res50, res101, res152',
                      default='res101', type=str)
  parser.add_argument('--load_dir', dest='load_dir',
                      help='directory to load models',
                      default="models")
  parser.add_argument('--image_dir', dest='image_dir',
                      help='directory to load images for demo',
                      default="images")
  parser.add_argument('--image_file', dest='image_file',
                      help='the file name of load images for demo',
                      default="img1.jpg")
  parser.add_argument('--classes_dir', dest='classes_dir',
                      help='directory to load object classes for classification',
                      default="data/genome/1600-400-20")
  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('--set', dest='set_cfgs',
                      help='set config keys', default=None,
                      nargs=argparse.REMAINDER)
  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('--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


conf_thresh = 0.4
MIN_BOXES = 10
MAX_BOXES = 36

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)

  # set cfg according to the dataset used to train the pre-trained model
  if args.dataset == "pascal_voc":
    args.set_cfgs = ['ANCHOR_SCALES', '[8, 16, 32]', 'ANCHOR_RATIOS', '[0.5,1,2]']
  elif args.dataset == "pascal_voc_0712":
      args.set_cfgs = ['ANCHOR_SCALES', '[8, 16, 32]', 'ANCHOR_RATIOS', '[0.5,1,2]']
  elif args.dataset == "coco":
      args.set_cfgs = ['ANCHOR_SCALES', '[4, 8, 16, 32]', 'ANCHOR_RATIOS', '[0.5,1,2]']
  elif args.dataset == "imagenet":
      args.set_cfgs = ['ANCHOR_SCALES', '[8, 16, 32]', 'ANCHOR_RATIOS', '[0.5,1,2]']
  elif args.dataset == "vg":
      args.set_cfgs = ['ANCHOR_SCALES', '[4, 8, 16, 32]', 'ANCHOR_RATIOS', '[0.5,1,2]']

  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)

  # Load faster rcnn model
  if not os.path.exists(args.load_dir):
      raise Exception('There is no input directory for loading network from ' + args.load_dir)
  load_name = os.path.join(args.load_dir, 'faster_rcnn_{}_{}.pth'.format(args.net, args.dataset))

  # Load classes
  classes = ['__background__']
  with open(os.path.join(args.classes_dir, 'objects_vocab.txt')) as f:
      for object in f.readlines():
          classes.append(object.split(',')[0].lower().strip())

  # initilize the network here.
  if args.net == 'res101':
      fasterRCNN = resnet(classes, 101, pretrained=False, class_agnostic=args.class_agnostic)
  else:
      print("network is not defined")
      pdb.set_trace()

  # Build faster rcnn
  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!')

  # 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
  with torch.no_grad():
      im_data = Variable(im_data)
      im_info = Variable(im_info)
      num_boxes = Variable(num_boxes)
      gt_boxes = Variable(gt_boxes)

  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

  im_file = os.path.join(args.image_dir, args.image_file)
  # 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)
  # 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)

  with torch.no_grad():
          im_data.resize_(im_data_pt.size()).copy_(im_data_pt)
          im_info.resize_(im_info_pt.size()).copy_(im_info_pt)
          gt_boxes.resize_(1, 1, 5).zero_()
          num_boxes.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(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()

  max_conf = torch.zeros((pred_boxes.shape[0]))
  if args.cuda > 0:
      max_conf = max_conf.cuda()

  if vis:
      im2show = np.copy(im)
  for j in xrange(1, len(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)
        keep = nms(cls_boxes[order, :], cls_scores[order], cfg.TEST.NMS)
        cls_dets = cls_dets[keep.view(-1).long()]
        index = inds[order[keep]]
        max_conf[index] = torch.where(scores[index, j] > max_conf[index], scores[index, j], max_conf[index])
        if vis:
          im2show = vis_detections(im2show, classes[j], cls_dets.cpu().numpy(), 0.5)


  if args.cuda > 0:
      keep_boxes = torch.where(max_conf >= conf_thresh, max_conf, torch.tensor(0.0).cuda())
  else:
      keep_boxes = torch.where(max_conf >= conf_thresh, max_conf, torch.tensor(0.0))
  keep_boxes = torch.squeeze(torch.nonzero(keep_boxes))
  if len(keep_boxes) < MIN_BOXES:
      keep_boxes = torch.argsort(max_conf, descending = True)[:MIN_BOXES]
  elif len(keep_boxes) > MAX_BOXES:
      keep_boxes = torch.argsort(max_conf, descending = True)[:MAX_BOXES]

  im = cv2.cvtColor(im, cv2.COLOR_BGR2RGB)
  plt.imshow(im)

  boxes = pred_boxes[keep_boxes]
  objects = torch.argmax(scores[keep_boxes][:,1:], dim=1)

  for i in range(len(keep_boxes)):
      kind = objects[i]+1
      bbox = boxes[i, kind * 4: (kind + 1) * 4]
      # bbox = boxes[i]
      if bbox[0] == 0:
          bbox[0] = 1
      if bbox[1] == 0:
          bbox[1] = 1
      cls = classes[objects[i]+1]
      plt.gca().add_patch(
          plt.Rectangle((bbox[0], bbox[1]),
                        bbox[2] - bbox[0],
                        bbox[3] - bbox[1], fill=False,
                        edgecolor='red', linewidth=2, alpha=0.5)
              )
      plt.gca().text(bbox[0], bbox[1] - 2,
                  '%s' % (cls),
                  bbox=dict(facecolor='blue', alpha=0.5),
                  fontsize=10, color='white')
  plt.show()
  print ('boxes=%d' % (len(keep_boxes)))