write_rpn_base_feat.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
from collections import defaultdict
import json
import scipy.io
from PIL import Image

import torchvision.transforms as transforms
import torchvision.datasets as dset
from scipy.misc 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('--target_dir', dest='target_dir',
                      help='directory to load images for demo',
                      default="images_det")
  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)
  parser.add_argument('--ls', dest='large_scale',
                      help='whether use large imag scale',
                      action='store_true')
  parser.add_argument('--output_feat_root', default='/z/dat/yc2/segment_thumbnail_all_split_1fps/base_feat', type=str)
  parser.add_argument('--split', default='testing', type=str)
  parser.add_argument('--rpn_proposal_root', default='/z/dat/yc2/segment_thumbnail_box_annotation_1fps', type=str)

  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)


def pil_loader(path):
    # open path as file to avoid ResourceWarning (https://github.com/python-pillow/Pillow/issues/835)
    with open(path, 'rb') as f:
        with Image.open(f) as img:
            return img.convert('RGB')


if __name__ == '__main__':

  args = parse_args()

  print('Called with args:')
  print(args)

  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]', 'MAX_NUM_GT_BOXES', '20']
  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]', 'MAX_NUM_GT_BOXES', '20']
  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]', 'MAX_NUM_GT_BOXES', '50']
  elif args.dataset == "imagenet":
      args.imdb_name = "imagenet_train"
      args.imdbval_name = "imagenet_val"
      args.set_cfgs = ['ANCHOR_SCALES', '[4, 8, 16, 32]', 'ANCHOR_RATIOS', '[0.5,1,2]', 'MAX_NUM_GT_BOXES', '30']
  elif args.dataset == "vg":
      # train sizes: train, smalltrain, minitrain
      # train scale: ['150-50-20', '150-50-50', '500-150-80', '750-250-150', '1750-700-450', '1600-400-20']
      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]', 'MAX_NUM_GT_BOXES', '50']

  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)

  # args inspection
  assert(args.large_scale == True)

  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))

  if args.dataset == 'pascal_voc':
      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'])
  elif args.dataset == 'coco':
      pascal_classes = np.asarray(['__background__',
                           'person', 'bicycle', 'car', 'motorcycle', 'airplane', 'bus', 'train', 'truck', 'boat', 'traffic light',
                           'fire hydrant', 'stop sign', 'parking meter', 'bench', 'bird', 'cat', 'dog', 'horse', 'sheep', 'cow',
                           'elephant', 'bear', 'zebra', 'giraffe', 'backpack', 'umbrella', 'handbag', 'tie', 'suitcase', 'frisbee',
                           'skis', 'snowboard', 'sports ball', 'kite', 'baseball bat', 'baseball glove', 'skateboard', 'surfboard',
                           'tennis racket', 'bottle', 'wine glass', 'cup', 'fork', 'knife', 'spoon', 'bowl', 'banana', 'apple',
                           'sandwich', 'orange', 'broccoli', 'carrot', 'hot dog', 'pizza', 'donut', 'cake', 'chair', 'couch',
                           'potted plant', 'bed', 'dining table', 'toilet', 'tv', 'laptop', 'mouse', 'remote', 'keyboard',
                           'cell phone', 'microwave', 'oven', 'toaster', 'sink', 'refrigerator', 'book', 'clock', 'vase',
                           'scissors', 'teddy bear', 'hair drier', 'toothbrush'])
  else:
      raise Exception('not implemented!')

  # 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))
  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!')

  # 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()
  thresh = 0.05
  vis = args.vis

  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)-1

  print('Loaded Photo: {} images.'.format(num_images+1))

  # load the 200 proposals
  split = args.split
  rpn_proposal_root = args.rpn_proposal_root
  total_num_proposals = 200 # always load all the proposals we have

  rpn_chunk = []

  rpn_lst_file = os.path.join(rpn_proposal_root, split+'-box-'+str(total_num_proposals)+'-edgebox.txt')
  rpn_chunk_file = os.path.join(rpn_proposal_root, split+'-box-'+str(total_num_proposals)+'-edgebox.mat')

  with open(rpn_lst_file) as f:
      rpn_lst = []
      rpn_dict = {}
      for j, r in enumerate(f):
          ind = r.strip()
          rpn_lst.append(ind[:15]+ind[15:-4].zfill(4)+'.jpg')
          rpn_dict[ind] = j

      print(len(imglist), len(rpn_lst))
      print(set([x for x in rpn_lst if rpn_lst.count(x) > 1]))
      print(len(set(imglist)), len(set(rpn_lst)))
      print(set(rpn_lst).difference(set(imglist)))
      print(set(imglist).difference(set(rpn_lst)))
      assert(len(imglist) == len(rpn_lst))
      imglist = rpn_lst[::-1]

  rpn_chunk.append(torch.from_numpy(scipy.io.loadmat(rpn_chunk_file)['edge_box_data'].astype(np.float32)))

  rpn_chunk = torch.cat(rpn_chunk).cpu()
  rpn_chunk = rpn_chunk.permute(0,2,1)

  # results = []
  while (num_images >= 0):
      print(num_images)
      total_tic = time.time()

      # Get image from the webcam
      if webcam_num >= 0:
        if not cap.isOpened():
          raise RuntimeError("Webcam could not open. Please check connection.")
        ret, frame = cap.read()
        im_in = np.array(frame)
      # Load the demo image
      else:
        im_file = os.path.join(args.image_dir, imglist[num_images])
        # im = cv2.imread(im_file)
        im_in = np.array(imread(im_file)) # height x width x 3
      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)

      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, base_feat = fasterRCNN(im_data, im_info, gt_boxes, num_boxes)

      base_feat = fasterRCNN.RCNN_base(im_data)
      base_feat = base_feat.data

      img_name = imglist[num_images]
      prefix_name = img_name[:15]+str(int(img_name[15:19])).zfill(2)
      img_name = prefix_name+'.jpg'
      feat_name = prefix_name+'.pth'

      original_w, original_h = pil_loader(os.path.join('/z/dat/yc2/segment_thumbnail_all_split_1fps', split, imglist[num_images])).size

      _, C, H, W = base_feat.size()
      box_w = 720
      box_h = int(box_w*original_h/original_w)

      frm_rpn = rpn_chunk[rpn_dict[img_name]] # num_proposals x 4 (x_tl, y_tl, x_br, y_br)
      num_proposals = frm_rpn.size(0)
      frm_rpn = frm_rpn-0.5 # coordinates are 1-indexed from edgebox

      frm_rpn[:,0] = torch.floor(frm_rpn[:,0]/box_w*W-0.5)
      frm_rpn[:,2] = torch.ceil(frm_rpn[:,2]/box_w*W-0.5)
      frm_rpn[:,1] = torch.floor(frm_rpn[:,1]/box_h*H-0.5)
      frm_rpn[:,3] = torch.ceil(frm_rpn[:,3]/box_h*H-0.5)
      frm_rpn = frm_rpn.int()

      rpn_feat = []
      for i in range(num_proposals):
          coord = frm_rpn[i]
          try:
              rpn_feat.append(torch.mean(base_feat[0, :, max(coord[1],0):max(coord[3],1),
                                     max(coord[0],0):max(coord[2],1)].contiguous().view(C, -1), dim=1))
          except:
              rpn_feat.append(torch.mean(base_feat[0].view(C, -1), dim=1))
              print('size error!')
              print(coord, H, W)
              print(img_name)

      # results.append(torch.stack(rpn_feat, dim=1).cpu())
      torch.save(torch.stack(rpn_feat, dim=1).cpu(), os.path.join(args.output_feat_root, args.split, feat_name))

      if webcam_num == -1:
        num_images -= 1

  # torch.save(torch.stack(results), args.output_chunk)