crowd_count.py 文件源码

def forward(self,  im_data, gt_data=None, gt_cls_label=None, ce_weights=None):        
        im_data = network.np_to_variable(im_data, is_cuda=True, is_training=self.training)                        
        density_map, density_cls_score = self.CCN(im_data)
        density_cls_prob = F.softmax(density_cls_score)

        if self.training:                        
            gt_data = network.np_to_variable(gt_data, is_cuda=True, is_training=self.training)            
            gt_cls_label = network.np_to_variable(gt_cls_label, is_cuda=True, is_training=self.training,dtype=torch.FloatTensor)                        
            self.loss_mse, self.cross_entropy = self.build_loss(density_map, density_cls_prob, gt_data, gt_cls_label, ce_weights)


        return density_map