IndexError: tuple index out of range环境信息Hardware Environment(Ascend/GPU/CPU) / 硬件环境: Please delete the backend not involved / 请删除不涉及的后端: /device AscendSoftware Environment / 软件环境 (Mandatory / 必填): -- MindSpore version (e.g., 2.0.0) :2.1.0 -- Python version (e.g...
🐛 Bug Got list index out of range ERROR when using results.print() To Reproduce (REQUIRED) Input: import torch from PIL import Image import numpy as np image_path = 'zidane.jpg' # or file, Path, PIL, OpenCV, numpy, list image = Image.ope...
:param boxes: numpy array of shape (r, 2), where r is the number of rows :param clusters: numpy array of shape (k, 2) where k is the number of clusters :return: average IoU as a single float """ return np.mean([np.max(iou(boxes[i], clusters)) for i in range(boxes.shape[...
:param boxes: numpy array of shape (r, 4) :return: numpy array of shape (r, 2) """ new_boxes = boxes.copy() for row in range(new_boxes.shape[0]): new_boxes[row][2] = np.abs(new_boxes[row][2] - new_boxes[row][0]) new_boxes[row][3] = np.abs(new_boxes[row][3]...
IndexError: tuple index out of range The shape of tensor called pred is [1,100] using tflite model, but is [1, 17640, 85] using pt model with the same options for both. I tried to set nc = 10 (class number of VisDrone) and it run correctly but fail detection. If i export my...
(out_channels*e)self.cv1=Conv(in_channels,c_,1,1)self.cv2=Conv(in_channels,c_,1,1)self.cv3=Conv(2*c_,out_channels,1)self.m=nn.Sequential(*[Bottleneck(c_,c_,shortcut,g,e=1.0)for_inrange(n)])defforward(self,x:torch.Tensor)->torch.Tensor:returnself.cv3(torch.cat((self.m(...
nn.functionalasFfromfunctoolsimportpartialfromtimm.models.layersimportDropPath,to_2tuple,trunc_normal_...
names = [str(i) for i in range(self.yaml['nc'])] # default names # inplace指的是原地操作 如x+=1 有利于节约内存 # self.inplace=True 默认True 不使用加速推理 self.inplace = self.yaml.get('inplace', True) 这里是通过parse_model来进行解析和建立模型的。 到这一步为止,我们的yolo模型...
(as_tuple=False).T x = torch.cat((box[i], x[i,5+ j,None], j[:,None].float(), mask[i]),1) """ (Pdb) pp x.shape torch.Size([17039, 6]) """ else:# best class only conf, j = x[:,5:mi].max(1, keepdim=...
[range(len(lb)), lb[:, 0].long() + 5] = 1.0 # cls # x = torch.cat((x, v), 0) x = np.concatenate((x, v), 0) # If none remain process next image if not x.shape[0]: continue # Compute conf x[:, 5:] *= x[:, 4:5] # conf = obj_conf * cls_conf # Box ...