def test(test_loader, target_net):
box_num = np.array([0, 0])
correct_cnt, total_cnt = np.array([0, 0]), np.array([0, 0])
print '========== Testing ======='
results_obj = []
results_region = []
batch_time = network.AverageMeter()
end = time.time()
im_counter = 0
for i, sample in enumerate(test_loader):
correct_cnt_t, total_cnt_t = np.array([0, 0]), np.array([0, 0])
# Forward pass
# measure the data loading time
im_data = Variable(sample['visual'].cuda(), volatile=True)
im_counter += im_data.size(0)
im_info = sample['image_info']
gt_objects = sample['objects']
gt_regions = sample['regions']
object_rois, region_rois = target_net(im_data, im_info)[1:]
results_obj.append(object_rois.cpu().data.numpy())
results_region.append(region_rois.cpu().data.numpy())
box_num[0] += object_rois.size(0)
box_num[1] += region_rois.size(0)
correct_cnt_t[0], total_cnt_t[0] = check_recall(
object_rois, gt_objects, 50)
correct_cnt_t[1], total_cnt_t[1] = check_recall(
region_rois, gt_regions, 50)
correct_cnt += correct_cnt_t
total_cnt += total_cnt_t
batch_time.update(time.time() - end)
end = time.time()
if (i + 1) % 100 == 0 and i > 0:
print(
'[{0}/{10}] Time: {1:2.3f}s/img).'
'\t[object] Avg: {2:2.2f} Boxes/im, Top-50 recall: {3:2.3f} ({4:d}/{5:d})'
'\t[region] Avg: {6:2.2f} Boxes/im, Top-50 recall: {7:2.3f} ({8:d}/{9:d})'
.format(i + 1, batch_time.avg, box_num[0] / float(im_counter),
correct_cnt[0] / float(total_cnt[0]) * 100,
correct_cnt[0], total_cnt[0],
box_num[1] / float(im_counter),
correct_cnt[1] / float(total_cnt[1]) * 100,
correct_cnt[1], total_cnt[1], len(test_loader)))
recall = correct_cnt / total_cnt.astype(np.float)
print '====== Done Testing ===='
return recall, results_obj, results_region
def test(test_loader, target_net):
box_num = 0
correct_cnt, total_cnt = 0., 0.
print '========== Testing ======='
results = []
batch_time = network.AverageMeter()
end = time.time()
im_counter = 0
for i, sample in enumerate(test_loader):
correct_cnt_t, total_cnt_t = 0., 0.
# Forward pass
im_data = Variable(sample['visual'].cuda(), volatile=True)
im_counter += im_data.size(0)
im_info = sample['image_info']
gt_objects = sample['objects']
object_rois = target_net(im_data, im_info)[1]
results.append(object_rois.cpu().data.numpy())
box_num += object_rois.size(0)
correct_cnt_t, total_cnt_t = check_recall(object_rois, gt_objects, 200)
correct_cnt += correct_cnt_t
total_cnt += total_cnt_t
batch_time.update(time.time() - end)
end = time.time()
if (i + 1) % 100 == 0 and i > 0:
print('[{0}/{6}] Time: {1:2.3f}s/img).'
'\t[object] Avg: {2:2.2f} Boxes/im, Top-200 recall: {3:2.3f} ({4:.0f}/{5:.0f})'.format(
i + 1, batch_time.avg,
box_num / float(im_counter), correct_cnt / float(total_cnt)* 100, correct_cnt, total_cnt,
len(test_loader)))
recall = correct_cnt / float(total_cnt)
print '====== Done Testing ===='
return recall, results