test.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 os
import sys
import numpy as np
import pprint
import time
import _init_paths
import cv2

import torch

from torch.autograd import Variable
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.rpn.bbox_transform import bbox_transform_inv
from model.utils.net_utils import vis_detections
from model.utils.parser_func import parse_args, set_dataset_args
from datasets.config_dataset import cfg_d

import pdb

try:
    xrange          # Python 2
except NameError:
    xrange = range  # Python 3



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)
    args = set_dataset_args(args,test=True)
    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.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_d.prefix = args.d_prefix

    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)))

    # initilize the network here.
    from model.faster_rcnn.vgg16 import vgg16

    fasterRCNN = vgg16(imdb.classes, pretrained=False, class_agnostic=args.class_agnostic)
    fasterRCNN.create_architecture()

    print("load checkpoint %s" % (args.load_name))
    checkpoint = torch.load(args.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
    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:
        cfg.CUDA = True
        fasterRCNN.cuda()

    start = time.time()
    max_per_image = 100
    thresh = 0.0

    save_name = args.load_name.split('/')[-1]
    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, path_return=True)
    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))

    vis = True


    for i in range(num_images):
        data = next(data_iter)
        import pdb
        #if i == 4025:
            #pdb.set_trace()
            #continue
        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])

        det_tic = time.time()
        rois, cls_prob, bbox_pred, _ = fasterRCNN(im_data, im_info, gt_boxes, num_boxes)

        scores = cls_prob.data
        boxes = rois.data[:, :, 1:5]
        # d_pred = d_pred.data
        path = data[4]

        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
            _ = torch.from_numpy(np.tile(boxes, (1, scores.shape[1])))
            pred_boxes = _.cuda() if args.cuda > 0 else _

        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_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(), 0.7)
                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()

    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))