推理时，如何使用 EfficientDet-Pytorch 设置目标参数？

如何解决推理时，如何使用 EfficientDet-Pytorch 设置目标参数？

我第一次在研究使用高效det-pytorch的客观检测代码。

我使用预训练的权重模型作为参考：

https://www.kaggle.com/shonenkov/inference-efficientdet

https://github.com/toandaominh1997/EfficientDet.Pytorch

https://github.com/toandaominh1997/EfficientDet.Pytorch

我试图检查efficientdet 模型的输出...

from effdet import get_efficientdet_config,EfficientDet,DetBenchTrain
from effdet.efficientdet import HeadNet

#load sample efficientdet code

config = get_efficientdet_config('tf_efficientdet_d0')
config.image_size = [512,512]
config.norm_kwargs=dict(eps=.001,momentum=.01)

net = EfficientDet(config,pretrained_backbone=False)
checkpoint = torch.load('efficientdet_d0-d92fd44f.pth')

net.load_state_dict(checkpoint)
#net.reset_head(num_classes=1)
#net.class_net = HeadNet(config,num_outputs=config.num_classes)

net=DetBenchTrain(net,config)
print("Loaded pretrained weights")
#>>>Loaded pretrained weights"

#img.shape:[3,512,512]

net.eval()
with torch.no_grad():
    detected=net(img.unsqueeze(0))

#error　of target is below.
TypeError: forward() missing 1 required positional argument: 'target'

并参考 https://www.kaggle.com/shonenkov/inference-efficientdet 我试过下面的代码。

def make_predictions(images,score_threshold=0.22):
    predictions = []
    with torch.no_grad():
        det = net(images,torch.tensor([1]*images.shape[0]).float())
        print(det.shape)
        for i in range(images.shape[0]):
            boxes = det[i].detach().cpu().numpy()[:,:4]    
            scores = det[i].detach().cpu().numpy()[:,4]
            indexes = np.where(scores > score_threshold)[0]
            boxes = boxes[indexes]
            boxes[:,2] = boxes[:,2] + boxes[:,0]
            boxes[:,3] = boxes[:,3] + boxes[:,1]
            predictions.append({
                'boxes': boxes[indexes],'scores': scores[indexes],})
    return [predictions]

output=make_predictions(img.unsqueeze(0))

#error is below...
---------------------------------------------------------------------------
IndexError                                Traceback (most recent call last)
<ipython-input-412-e81ace805280> in <module>
----> 1 output=make_predictions(img.unsqueeze(0))

<ipython-input-407-a6aab7dca874> in make_predictions(images,score_threshold)
      3     predictions = []
      4     with torch.no_grad():
----> 5         det = net(images,torch.tensor([1]*images.shape[0]).float())
      6         print(det.shape)
      7         for i in range(images.shape[0]):

/opt/anaconda3/envs/yohenv/lib/python3.7/site-packages/torch/nn/modules/module.py in _call_impl(self,*input,**kwargs)
    720             result = self._slow_forward(*input,**kwargs)
    721         else:
--> 722             result = self.forward(*input,**kwargs)
    723         for hook in itertools.chain(
    724                 _global_forward_hooks.values(),/opt/anaconda3/envs/yohenv/lib/python3.7/site-packages/effdet/bench.py in forward(self,x,target)
    117         else:
    118             cls_targets,box_targets,num_positives = self.anchor_labeler.batch_label_anchors(
--> 119                 target['bbox'],target['cls'])
    120 
    121         loss,class_loss,box_loss = self.loss_fn(class_out,box_out,cls_targets,num_positives)

IndexError: too many indices for tensor of dimension 1

使用 TestData 进行推理时，如何设置目标参数？

很抱歉给您带来不便，您能给我建议吗？