def model_eval(self, preds, gts):
assert(self.opt.EVAL_METRIC in ['mean_accu', 'accuracy']), \
"Currently don't support the evaluation metric you specified."
if self.opt.EVAL_METRIC == "mean_accu":
res = mean_accuracy(preds, gts)
elif self.opt.EVAL_METRIC == "accuracy":
res = accuracy(preds, gts)
return res
def test(args):
# prepare data
dataloader = prepare_data()
# initialize model
model_state_dict = None
fx_pretrained = True
bn_domain_map = {}
if cfg.WEIGHTS != '':
weights_dict = torch.load(cfg.WEIGHTS)
model_state_dict = weights_dict['weights']
bn_domain_map = weights_dict['bn_domain_map']
fx_pretrained = False
if args.adapted_model:
num_domains_bn = 2
else:
num_domains_bn = 1
net = model.danet(num_classes=cfg.DATASET.NUM_CLASSES,
state_dict=model_state_dict,
feature_extractor=cfg.MODEL.FEATURE_EXTRACTOR,
fx_pretrained=fx_pretrained,
dropout_ratio=cfg.TRAIN.DROPOUT_RATIO,
fc_hidden_dims=cfg.MODEL.FC_HIDDEN_DIMS,
num_domains_bn=num_domains_bn)
net = torch.nn.DataParallel(net)
if torch.cuda.is_available():
net.cuda()
# test
res = {}
res['path'], res['preds'], res['gt'], res['probs'] = [], [], [], []
net.eval()
if cfg.TEST.DOMAIN in bn_domain_map:
domain_id = bn_domain_map[cfg.TEST.DOMAIN]
else:
domain_id = 0
with torch.no_grad():
net.module.set_bn_domain(domain_id)
for sample in iter(dataloader):
res['path'] += sample['Path']
if cfg.DATA_TRANSFORM.WITH_FIVE_CROP:
n, ncrop, c, h, w = sample['Img'].size()
sample['Img'] = sample['Img'].view(-1, c, h, w)
img = to_cuda(sample['Img'])
probs = net(img)['probs']
probs = probs.view(n, ncrop, -1).mean(dim=1)
else:
img = to_cuda(sample['Img'])
probs = net(img)['probs']
preds = torch.max(probs, dim=1)[1]
res['preds'] += [preds]
res['probs'] += [probs]
if 'Label' in sample:
label = to_cuda(sample['Label'])
res['gt'] += [label]
print('Processed %d samples.' % len(res['path']))
preds = torch.cat(res['preds'], dim=0)
save_preds(res['path'], preds, cfg.SAVE_DIR)
if 'gt' in res and len(res['gt']) > 0:
gts = torch.cat(res['gt'], dim=0)
probs = torch.cat(res['probs'], dim=0)
assert (cfg.EVAL_METRIC == 'mean_accu'
or cfg.EVAL_METRIC == 'accuracy')
if cfg.EVAL_METRIC == "mean_accu":
eval_res = mean_accuracy(probs, gts)
print('Test mean_accu: %.4f' % (eval_res))
elif cfg.EVAL_METRIC == "accuracy":
eval_res = accuracy(probs, gts)
print('Test accuracy: %.4f' % (eval_res))
print('Finished!')