generate_annotations.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 scipy.misc import imread
import pickle
from model.utils.blob import im_list_to_blob
from roi_data_layer.roidb import combined_roidb_from_imdb
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.faster_rcnn.vgg16 import vgg16
from model.faster_rcnn.resnet import resnet
from datasets.deepequations import deepequations
import re

from deepequations_eval import compute_pr_curve, compute_tokenwise_eval, save_detections_to_deep_equations_json

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('--imdb_name', dest='imdb_name',
                        help='imdb dataset name.', 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="/mnt/raid00/lucaw/faster-rcnn.pytorch/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')
    parser.add_argument('--dpi', dest='dpi',
                        help='image dpi',
                        default=100, type=int)
    parser.add_argument('--iou_threshold', dest='iou_threshold',
                        help='iou threshold',
                        default=0.5, type=float)

    parser.add_argument('--dataset_dir', dest='dataset_dir',
                        help='dev kit path to construct the imdb, where images annotations.json and split.json should be',
                        type=str)
    parser.add_argument('--output_dir', dest='output_dir',
                        help='output dir for annotations',
                        default='.', type=str)
    parser.add_argument('--output_filename', dest='output_filename',
                        help='output filename for annotations', 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)

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")

    args.set_cfgs = ['ANCHOR_SCALES', '[1, 2, 4, 8, 16]', 'ANCHOR_RATIOS', '[0.1, 0.2, 0.4, 1]',
                     'MAX_NUM_GT_BOXES', '40']

    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)

    if not os.path.exists(args.output_dir):
        os.makedirs(args.output_dir)

    print('Using config:')
    pprint.pprint(cfg)

    cfg.TRAIN.USE_FLIPPED = False
    imdb = deepequations(args.imdb_name, devkit_path=args.dataset_dir)
    #imdb, roidb, ratio_list, ratio_index = combined_roidb_from_imdb(imdb, False)
    #imdb.competition_mode(on=True)

    #print('{:d} roidb entries'.format(len(roidb)))

    input_dir = args.load_dir + "/" + args.net + "/" + 'deepequations'
    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, 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:
        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)


    _t = {'im_detect': time.time(), 'misc': time.time()}
    det_file = os.path.join(args.output_dir, 'detections.pkl')

    run_feedforward = True

    fasterRCNN.eval()

    img_names = [imdb.image_id_at(i) for i in range(imdb.num_images)]
    imglist = sizes = [imdb.image_path_at(i) for i in range(imdb.num_images)]
    num_images = len(imglist)
    empty_array = np.transpose(np.array([[], [], [], [], []]), (1, 0))

    classes = imdb.classes

    while (num_images >= 0):
        total_tic = time.time()
        print('num images{} '.format(num_images))

        i = num_images-1

        num_images -= 1

        im_file = imglist[i]
        print('processing image: {}'.format(im_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)

        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(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, 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_dets, 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(os.path.join(args.output_dir, '{}_det.png'.format(imdb.image_id_at(i))), 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)

    img_names = [imdb.image_id_at(i) for i in range(imdb.num_images)]
    img_filenames = sizes = [imdb.image_path_at(i) for i in range(imdb.num_images)]
    result_cache_dir = det_file = os.path.join(args.output_dir, 'detections.pkl')
    # aggregate all results for quantitative evaluations
    all_results = {}


    print("All boxes: {} ".format(all_boxes))
    for i in range(len(imglist)):
        image_name = imdb.image_id_at(i)
        image_filename = imdb.image_path_at(i)
        bb_by_class = {}
        for j in xrange(1, imdb.num_classes):
            bb_by_class[imdb.classes[j]] = [box for box in all_boxes[j][i] if box[-1] > 0.8]
        all_results[image_name] = bb_by_class

    data_path = imdb._data_path
    testset_fn_only = [x
                       for x in
                       img_names]  # various dpi
    foreground_classes = imdb.classes[1:]
    print("number of images", len(img_names))
    save_detections_to_deep_equations_json(os.path.join(args.output_dir, args.output_filename),
                                           all_results, imdb.classes)
    end = time.time()
    print("test time: %0.4fs" % (end - start))