def forward(self, image, im_info, gt_boxes=None): image = image - torch.tensor(config.image_mean[None, :, None, None], dtype=image.dtype, device=image.device) image = get_padded_tensor(image, 64) if self.training: return self._forward_train(image, im_info, gt_boxes) else: return self._forward_test(image, im_info)
def forward(self, image, im_info, gt_boxes=None): image = (image - torch.tensor(config.image_mean[None, :, None, None]).type_as(image)) / ( torch.tensor(config.image_std[None, :, None, None]).type_as(image)) image = get_padded_tensor(image, 64) if self.training: return self._forward_train(image, im_info, gt_boxes) else: return self._forward_test(image, im_info)
def forward(self, image, im_info, gt_boxes=None): # pre-processing the data image = (image - torch.tensor(config.image_mean[ None, :, None, None]).type_as(image)) / (torch.tensor( config.image_std[None, :, None, None]).type_as(image)) image = get_padded_tensor(image, 64) # do inference # stride: 128,64,32,16,8, p7->p3 fpn_fms = self.FPN(image) anchors_list = self.R_Anchor(fpn_fms) pred_cls_list, pred_reg_list = self.R_Head(fpn_fms) # release the useless data if self.training: loss_dict = self.R_Criteria(pred_cls_list, pred_reg_list, anchors_list, gt_boxes, im_info) return loss_dict else: #pred_bbox = union_inference( # anchors_list, pred_cls_list, pred_reg_list, im_info) return anchors_list, pred_cls_list, pred_reg_list, im_info