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
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