Source File: voc.py From ssds.pytorch with MIT License 5 votes def pull_tensor(self, index): '''Returns the original image at an index in tensor form Note: not using self.__getitem__(), as any transformations passed in could mess up this functionality. Argument: index (int): index...
defdeform_conv2d(input:Tensor,weight:Tensor,offset:Optional[Tensor]=None,mask:Optional[Tensor]=None,bias:Optional[Tensor]=None,stride:Union[int,Tuple[int,int]]=1,padding:Union[int,Tuple[int,int]]=0,dilation:Union[int,Tuple[int,int]]=1,groups:int=1)->Tensor: ...defdeform_conv_transpose...
input_x=blob.view(1,c,h,w)defto_numpy(tensor):returntensor.detach().cpu().numpy()iftensor.requires_gradelsetensor.cpu().numpy()# computeONNXRuntime output prediction ort_inputs={ort_session.get_inputs()[0].name:to_numpy(input_x)}ort_outs=ort_session.run(None,ort_inputs)#(N,4)...
本教程仅适用于Torchvision版本>=0.16或每晚。如果您使用的是<=0.15的torchvision，请按照本教程进行操...
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Downloading: "https://download.pytorch.org/models/maskrcnn_resnet50_fpn_coco-bf2d0c1e.pth" to /home/matti/.cache/torch/hub/checkpoints/maskrcnn_resnet50_fpn_coco-bf2d0c1e.pth [{'boxes': tensor([[219.7444, 168.1722, 400.7378, 384.0263], ...
推断后,会返回每个图像的预测结果 List[Dict[Tensor]]。Dict 包含字段有: boxes (FloatTensor[N, 4]): 预测框 [x1, y1, x2, y2], x 范围[0,W], y 范围[0,H] labels (Int64Tensor[N]): 预测类别 scores (Tensor[N]): 预测评分 代码语言:javascript 复制 predictions = model(images) pred = ...
(1).在训练期间,模型需要输入tensors和targets(字典列表),包含boxes和labels。 boxes类型为FloatTensor[N,4],其中N为图像数;4为[x1,y1,x2,y2],即ground-truth box的左上和右下角坐标,它们的值要合理范围内。 labels类型为Int64Tensor[N],每个ground-truth box的class label。
tv_tensors/__init__.py /usr/lib/python3/dist-packages/torchvision/tv_tensors/_bounding_boxes.py /usr/lib/python3/dist-packages/torchvision/tv_tensors/_dataset_wrapper.py /usr/lib/python3/dist-packages/torchvision/tv_tensors/_image.py /usr/lib/python3/dist-packages/torchvision/tv_tensors...
defget_yolo_feature_vec(self,coords):feature_map=self.get_feature_map()ratio=self.img_size/feature_map.size()[2]#coords = (10,10,100,100)coords=torch.cat((torch.Tensor([0]),torch.Tensor(coords))).view(1,5).cuda()#coords = torch.Tensor(coords).view(1,4).cuda()#print(feature_...