def eval_loc(cls_logits,
cls_map,
img_path,
label,
gt_boxes,
topk=(1, 5),
threshold=None,
mode='union',
iou_th=0.5):
top_boxes, top_maps, gt_known_box, gt_known_map = get_topk_boxes_hier(
cls_logits[0],
cls_map,
img_path,
label,
topk=topk,
threshold=threshold,
mode=mode)
top1_box, top5_boxes = top_boxes
# update result record
(locerr_1, locerr_5), top1_wrong_detail = evaluate.locerr(
(top1_box, top5_boxes),
label.data.long().numpy(),
gt_boxes,
topk=(1, 5),
iou_th=iou_th)
locerr_gt_known, _ = evaluate.locerr((gt_known_box, ),
label.data.long().numpy(),
gt_boxes,
topk=(1, ),
iou_th=iou_th)
return locerr_1, locerr_5, locerr_gt_known[
0], top_maps, top5_boxes, gt_known_map, top1_wrong_detail
def eval_loc_scg(cls_logits,
top_cams,
gt_known_cams,
aff_maps,
img_path,
label,
gt_boxes,
topk=(1, 5),
threshold=None,
mode='union',
fg_th=0.1,
bg_th=0.01,
iou_th=0.5,
sc_maps_fo=None):
top_boxes, top_maps = get_topk_boxes_hier_scg(cls_logits[0],
top_cams,
aff_maps,
img_path,
topk=topk,
threshold=threshold,
mode=mode,
fg_th=fg_th,
bg_th=bg_th,
sc_maps_fo=sc_maps_fo)
top1_box, top5_boxes = top_boxes
# update result record
(locerr_1, locerr_5), top1_wrong_detail = evaluate.locerr(
(top1_box, top5_boxes),
label.data.long().numpy(),
gt_boxes,
topk=(1, 5),
iou_th=iou_th)
gt_known_boxes, gt_known_maps = get_topk_boxes_hier_scg(
cls_logits[0],
gt_known_cams,
aff_maps,
img_path,
topk=(1, ),
threshold=threshold,
mode=mode,
gt_labels=label,
fg_th=fg_th,
bg_th=bg_th,
sc_maps_fo=sc_maps_fo)
# update result record
locerr_gt_known, _ = evaluate.locerr(gt_known_boxes,
label.data.long().numpy(),
gt_boxes,
topk=(1, ),
iou_th=iou_th)
return locerr_1, locerr_5, locerr_gt_known[
0], top_maps, top5_boxes, top1_wrong_detail
def val(args):
with open(args.test_box, 'r') as f:
gt_boxes = [
map(float,
x.strip().split(' ')[2:]) for x in f.readlines()
]
gt_boxes = [(box[0], box[1], box[0] + box[2] - 1, box[1] + box[3] - 1)
for box in gt_boxes]
# meters
top1_clsacc = AverageMeter()
top1_locerr = AverageMeter()
top5_clsacc = AverageMeter()
top5_locerr = AverageMeter()
top1_clsacc.reset()
top1_locerr.reset()
top5_clsacc.reset()
top5_locerr.reset()
# get model
model = get_model(args)
model.eval()
# get data
_, valcls_loader, valloc_loader = data_loader(args, test_path=True)
assert len(valcls_loader) == len(valloc_loader), \
'Error! Different size for two dataset: loc({}), cls({})'.format(len(valloc_loader), len(valcls_loader))
# testing
DEBUG = True
if DEBUG:
# show_idxs = np.arange(20)
np.random.seed(2333)
show_idxs = np.arange(len(valcls_loader))
np.random.shuffle(show_idxs)
show_idxs = show_idxs[:20]
# evaluation classification task
pred_prob1 = []
pred_prob2 = []
pred_prob3 = []
for dat in tqdm(valcls_loader):
# parse data
img_path, img, label_in = dat
if args.tencrop == 'True':
bs, ncrops, c, h, w = img.size()
img = img.view(-1, c, h, w)
label_input = label_in.repeat(10, 1)
label = label_input.view(-1)
else:
label = label_in
# forward pass
img, label = img.cuda(), label.cuda()
img_var, label_var = Variable(img), Variable(label)
logits = model(img_var)
# get classification prob
logits0 = logits[-1]
logits0 = F.softmax(logits0, dim=1)
if args.tencrop == 'True':
logits0 = logits0.view(1, ncrops, -1).mean(1)
pred_prob3.append(logits0.cpu().data.numpy())
logits1 = logits[-2]
logits1 = F.softmax(logits1, dim=1)
if args.tencrop == 'True':
logits1 = logits1.view(1, ncrops, -1).mean(1)
pred_prob2.append(logits1.cpu().data.numpy())
logits2 = logits[-3]
logits2 = F.softmax(logits2, dim=1)
if args.tencrop == 'True':
logits2 = logits2.view(1, ncrops, -1).mean(1)
pred_prob1.append(logits2.cpu().data.numpy())
# update result record
prec1_1, prec5_1 = evaluate.accuracy(logits0.cpu().data,
label_in.long(),
topk=(1, 5))
top1_clsacc.update(prec1_1[0].numpy(), img.size()[0])
top5_clsacc.update(prec5_1[0].numpy(), img.size()[0])
pred_prob1 = np.concatenate(pred_prob1, axis=0)
pred_prob2 = np.concatenate(pred_prob2, axis=0)
pred_prob3 = np.concatenate(pred_prob3, axis=0)
print('== cls err')
print('Top1: {:.2f} Top5: {:.2f}\n'.format(100.0 - top1_clsacc.avg,
100.0 - top5_clsacc.avg))
thresholds = map(float, args.threshold.split(','))
thresholds = list(thresholds)
for th in thresholds:
top1_locerr.reset()
top5_locerr.reset()
for idx, dat in tqdm(enumerate(valloc_loader)):
# parse data
img_path, img, label = dat
# forward pass
img, label = img.cuda(), label.cuda()
img_var, label_var = Variable(img), Variable(label)
logits = model(img_var)
child_map = F.upsample(model.module.get_child_maps(),
size=(28, 28),
mode='bilinear',
align_corners=True)
child_map = child_map.cpu().data.numpy()
parent_maps = F.upsample(model.module.get_parent_maps(),
size=(28, 28),
mode='bilinear',
align_corners=True)
parent_maps = parent_maps.cpu().data.numpy()
root_maps = model.module.get_root_maps()
root_maps = root_maps.cpu().data.numpy()
top_boxes, top_maps = get_topk_boxes_hier(pred_prob3[idx, :],
pred_prob2[idx, :],
pred_prob1[idx, :],
child_map,
parent_maps,
root_maps,
img_path[0],
args.input_size,
args.crop_size,
topk=(1, 5),
threshold=th,
mode='union')
top1_box, top5_boxes = top_boxes
# update result record
locerr_1, locerr_5 = evaluate.locerr(
(top1_box, top5_boxes),
label.cpu().data.long().numpy(),
gt_boxes[idx],
topk=(1, 5))
top1_locerr.update(locerr_1, img.size()[0])
top5_locerr.update(locerr_5, img.size()[0])
if DEBUG:
if idx in show_idxs:
save_im_heatmap_box(
img_path[0],
top_maps,
top5_boxes,
'../figs/',
gt_label=label.cpu().data.long().numpy(),
gt_box=gt_boxes[idx])
print('=========== threshold: {} ==========='.format(th))
print('== loc err')
print('Top1: {:.2f} Top5: {:.2f}\n'.format(top1_locerr.avg,
top5_locerr.avg))
def val(args):
with open(args.test_box, 'r') as f:
gt_boxes = [
map(float,
x.strip().split(' ')[2:]) for x in f.readlines()
]
gt_boxes = [(box[0], box[1], box[0] + box[2] - 1, box[1] + box[3] - 1)
for box in gt_boxes]
# meters
top1_clsacc = AverageMeter()
top1_locerr = AverageMeter()
top5_clsacc = AverageMeter()
top5_locerr = AverageMeter()
top1_clsacc.reset()
top1_locerr.reset()
top5_clsacc.reset()
top5_locerr.reset()
# get model
model = get_model(args)
model.eval()
# get data
_, valcls_loader, valloc_loader = data_loader(args, test_path=True)
assert len(valcls_loader) == len(valloc_loader), \
'Error! Different size for two dataset: loc({}), cls({})'.format(len(valloc_loader), len(valcls_loader))
# testing
VISLOC = True
if VISLOC:
# show_idxs = np.arange(20)
np.random.seed(2333)
show_idxs = np.arange(len(valcls_loader))
np.random.shuffle(show_idxs)
show_idxs = show_idxs[:20]
# evaluation classification task
pred_prob = []
for dat in tqdm(valcls_loader):
# parse data
img_path, img, label_in = dat
if args.tencrop == 'True':
bs, ncrops, c, h, w = img.size()
img = img.view(-1, c, h, w)
label_input = label_in.repeat(10, 1)
label = label_input.view(-1)
else:
label = label_in
# forward pass
img, label = img.cuda(), label.cuda()
img_var, label_var = Variable(img), Variable(label)
logits = model(img_var)
# get classification prob
logits0 = logits
logits0 = F.softmax(logits0, dim=1)
if args.tencrop == 'True':
logits0 = logits0.view(1, ncrops, -1).mean(1)
pred_prob.append(logits0.cpu().data.numpy())
# update result record
prec1_1, prec5_1 = evaluate.accuracy(logits0.cpu().data,
label_in.long(),
topk=(1, 5))
top1_clsacc.update(prec1_1[0].numpy(), img.size()[0])
top5_clsacc.update(prec5_1[0].numpy(), img.size()[0])
pred_prob = np.concatenate(pred_prob, axis=0)
# with open('pred_prob.pkl', 'w') as f:
# cPickle.dump(pred_prob, f)
print('== cls err')
print('Top1: {:.2f} Top5: {:.2f}\n'.format(100.0 - top1_clsacc.avg,
100.0 - top5_clsacc.avg))
# with open('pred_prob.pkl', 'r') as f:
# pred_prob = cPickle.load(f)
# evaluation localization task
thresholds = map(float, args.threshold.split(','))
thresholds = list(thresholds)
for th in thresholds:
top1_locerr.reset()
top5_locerr.reset()
for idx, dat in tqdm(enumerate(valloc_loader)):
# parse data
img_path, img, label = dat
# forward pass
img, label = img.cuda(), label.cuda()
img_var, label_var = Variable(img), Variable(label)
logits = model(img_var)
# get localization boxes
cam_map = model.module.get_cam_maps() # not normalized
cam_map = cam_map.cpu().data.numpy()
top_boxes, top_maps = get_topk_boxes(pred_prob[idx, :],
cam_map,
img_path[0],
args.input_size,
args.crop_size,
topk=(1, 5),
threshold=th,
mode='union')
top1_box, top5_boxes = top_boxes
# update result record
locerr_1, locerr_5 = evaluate.locerr(
(top1_box, top5_boxes),
label.cpu().data.long().numpy(),
gt_boxes[idx],
topk=(1, 5))
top1_locerr.update(locerr_1, img.size()[0])
top5_locerr.update(locerr_5, img.size()[0])
if VISLOC:
if idx in show_idxs:
save_im_heatmap_box(
img_path[0],
top_maps,
top5_boxes,
'../figs/',
gt_label=label.cpu().data.long().numpy(),
gt_box=gt_boxes[idx])
print('=========== threshold: {} ==========='.format(th))
print('== loc err')
print('Top1: {:.2f} Top5: {:.2f}\n'.format(top1_locerr.avg,
top5_locerr.avg))