test_net_cgan_update.py

# --------------------------------------------------------
# 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

import sys
sys.path.insert(0, './lib/model/cgan')
from model.cgan.data import CreateDataLoader
from model.cgan.options.test_options import TestOptions
from model.cgan.models import create_model
from model.cgan.util.visualizer import save_images
from model.cgan.util import util
from copy import deepcopy

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_dual 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('--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('--load_dir', dest='load_dir',
                      help='directory to load models', default="models",
                      type=str)

  parser.add_argument('--save_dir', dest='save_dir',
                      help='directory to save models', default="models",
                      type=str)
  parser.add_argument('--nw', dest='num_workers',
                      help='number of worker to load data',
                      default=0, 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_tfb', dest='use_tfboard',
                      help='whether use tensorboard',
                      action='store_true')
  #cgan base options
  parser.add_argument('--dataroot', type=str, default='/raid/intern_chaitanya/data/VOCdevkit/VOC2007/JPEGImages',help='path to images (should have subfolders trainA, trainB, valA, valB, etc)')
  parser.add_argument('--batch_size', type=int, default=1, help='input batch size')
  parser.add_argument('--loadSize', type=int, default=286, help='scale images to this size')
  parser.add_argument('--fineSize', type=int, default=256, help='then crop to this size')
  parser.add_argument('--display_winsize', type=int, default=256, help='display window size for both visdom and HTML')
  parser.add_argument('--input_nc', type=int, default=3, help='# of input image channels')
  parser.add_argument('--output_nc', type=int, default=3, help='# of output image channels')
  parser.add_argument('--ngf', type=int, default=64, help='# of gen filters in first conv layer')
  parser.add_argument('--ndf', type=int, default=64, help='# of discrim filters in first conv layer')
  parser.add_argument('--netD', type=str, default='basic', help='selects model to use for netD')
  parser.add_argument('--netG', type=str, default='resnet_9blocks', help='selects model to use for netG')
  parser.add_argument('--n_layers_D', type=int, default=3, help='only used if netD==n_layers')
  parser.add_argument('--gpu_ids', type=str, default='0', help='gpu ids: e.g. 0  0,1,2, 0,2. use -1 for CPU')
  parser.add_argument('--name', type=str, default='experiment_name', help='name of the experiment. It decides where to store samples and models')
  parser.add_argument('--dataset_mode', type=str, default='single', help='chooses how datasets are loaded. [unaligned | aligned | single]')
  parser.add_argument('--model', type=str, default='test',
                      help='chooses which model to use. cycle_gan, pix2pix, test')
  parser.add_argument('--direction', type=str, default='AtoB', help='AtoB or BtoA')
  parser.add_argument('--epoch', type=str, default='latest', help='which epoch to load? set to latest to use latest cached model')
  parser.add_argument('--num_threads', default=4, type=int, help='# threads for loading data')
  parser.add_argument('--checkpoints_dir', type=str, default='./checkpoints', help='models are saved here')
  parser.add_argument('--norm', type=str, default='instance', help='instance normalization or batch normalization')
  parser.add_argument('--serial_batches', action='store_true', help='if true, takes images in order to make batches, otherwise takes them randomly')
  parser.add_argument('--no_dropout', action='store_true', help='no dropout for the generator')
  parser.add_argument('--max_dataset_size', type=int, default=float("inf"), help='Maximum number of samples allowed per dataset. If the dataset directory contains more than max_dataset_size, only a subset is loaded.')
  parser.add_argument('--resize_or_crop', type=str, default='resize_and_crop', help='scaling and cropping of images at load time [resize_and_crop|crop|scale_width|scale_width_and_crop|none]')
  parser.add_argument('--no_flip', action='store_true', help='if specified, do not flip the images for data augmentation')
  parser.add_argument('--init_type', type=str, default='normal', help='network initialization [normal|xavier|kaiming|orthogonal]')
  parser.add_argument('--init_gain', type=float, default=0.02, help='scaling factor for normal, xavier and orthogonal.')
  parser.add_argument('--verbose', action='store_true', help='if specified, print more debugging information')
  parser.add_argument('--suffix', default='', type=str, help='customized suffix: opt.name = opt.name + suffix: e.g., {model}_{netG}_size{loadSize}')
  # cgan test options
  parser.add_argument('--ntest', type=int, default=float("inf"), help='# of test examples.')
  parser.add_argument('--results_dir', type=str, default='./results/', help='saves results here.')
  parser.add_argument('--aspect_ratio', type=float, default=1.0, help='aspect ratio of result images')
  parser.add_argument('--phase', type=str, default='test', help='train, val, test, etc')
  #  Dropout and Batchnorm has different behavioir during training and test.
  parser.add_argument('--eval', action='store_true', help='use eval mode during test time.')
  parser.add_argument('--num_test', type=int, default=1, help='how many test images to run')
  parser.add_argument('--vis', dest='vis',
                      help='visualization mode',
                      action='store_true')
  parser.set_defaults(model='test')
  args = parser.parse_args()
  return args

lr = cfg.TRAIN.LEARNING_RATE
momentum = cfg.TRAIN.MOMENTUM
weight_decay = cfg.TRAIN.WEIGHT_DECAY

def get_cgan_model():

  opt = TestOptions().parse()
  opt.num_threads = 1
  opt.batch_size =1
  opt.serial_batches = True
  opt.no_flip = True
  opt.display_id = -1
  opt.suffix = 'B'
  model = create_model(opt)
  model.setup(opt)
#   device = torch.device('cuda:{}'.format(0))
  net1, net2 = deepcopy(model.netG_A), deepcopy(model.netG_B)
  net2 = net2.module

  return net2

class Resize_GPU(nn.Module):
    def __init__(self, h, w):
        super(Resize_GPU, self).__init__()
        self.op =  nn.AdaptiveAvgPool2d((h,w))
    def forward(self, x):
        x = self.op(x)
        return x

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)

  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.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_cgan_b_to_a = os.path.join(input_dir,
    'cgan_b_to_a_{}_{}_{}.pth'.format(args.checksession, args.checkepoch, args.checkpoint))
  # initilize the network here.

  if args.net in ['res101_cgan_update', 'res101_cgan_update_coco'] :
    fasterRCNN = resnet(imdb.classes, 101, pretrained=False, class_agnostic=args.class_agnostic)
  else:
    print("network is not defined")
    pdb.set_trace()

  fasterRCNN.create_architecture()

  cgan_b_to_a = get_cgan_model()
  device_0 = torch.device('cuda:{}'.format(0))
  cgan_b_to_a.load_state_dict(torch.load(load_name_cgan_b_to_a, map_location=str(device_0))['model'])
  
  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')

  if args.use_tfboard:
    from tensorboardX import SummaryWriter
    logger = SummaryWriter(f'logs/{cfg.EXP_DIR}_test/')

  fasterRCNN.eval()
  cgan_b_to_a.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])

      im_shape = im_data.size()
      nw_resize = Resize_GPU(im_shape[2], im_shape[3])

      im_data_1ch = im_data.narrow(1, 0, 1)

      im_data_1 = cgan_b_to_a(im_data_1ch)
      im_data_1 = nw_resize(im_data_1)

      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_1, 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
          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
  
      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,
        #     # 'loss_feat': loss_feat
        #   }
        #   logger.add_scalars("logs_s_{}/losses".format(args.checksession), info, 1 * num_images + i)

          import torchvision.utils as vutils
          x1 = vutils.make_grid(im_data, normalize=True, scale_each=True)
          logger.add_image("images_s_{}/input_3ch_image".format(args.checksession), x1, num_images + i)

          x2 = vutils.make_grid(im_data_1, normalize=True, scale_each=True)
          logger.add_image("images_s_{}/generated_first_domain".format(args.checksession), x2, num_images + i)

          x3 = vutils.make_grid(im_data_1ch, normalize=True, scale_each=True)
          logger.add_image("images_s_{}/1ch_from_3ch".format(args.checksession), x3, num_images + i)
         
          # x2 = vutils.make_grid(im_data_1, normalize=True, scale_each=True)
          # from PIL import Image
          # logger.add_figure("images_s_{}/bbox_pred".format(args.checksession), Image.fromarray(im2show), num_images + i)
  
      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(f'images_output_2/result_{i}.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))