def metric_eval(self, im, boxes, anchor_feature):
box_regions = me_extract_regions(np.array(im), boxes)
box_regions = process_regions(box_regions)
box_regions = torch.Tensor(box_regions)
box_regions = (Variable(box_regions)).type(torch.FloatTensor).cuda()
box_features, class_result = self.metric_model(box_regions)
class_result = torch.softmax(class_result, dim=1)
ap_dist = torch.norm(anchor_feature - box_features, 2, dim=1).view(-1)
return ap_dist
def metric_init(self, im, init_box):
self.metric_model = ft_net(class_num=1120)
path = os.path.join(base_path, 'utils/metric_net/metric_model/metric_model.pt')
self.metric_model.eval()
self.metric_model = self.metric_model.cuda()
self.metric_model.load_state_dict(torch.load(path))
tmp = np.random.rand(1, 3, 107, 107)
tmp = (Variable(torch.Tensor(tmp))).type(torch.FloatTensor).cuda()
# get target feature
self.metric_model(tmp)
init_box = init_box.reshape((1, 4))
anchor_region = me_extract_regions(im, init_box)
anchor_region = process_regions(anchor_region)
anchor_region = torch.Tensor(anchor_region)
anchor_region = (Variable(anchor_region)).type(torch.FloatTensor).cuda()
self.anchor_feature, _ = self.metric_model(anchor_region)
