[]foriinrange(self.nl):y.append(self.one2one_cv2[i](x[i]))cls=torch.sigmoid(self.one2one_cv3[i](x[i]))cls_sum=torch.clamp(cls.sum(1,keepdim=True),0,1)y.append(cls)y.append(cls_sum)returnyone2one=self.forward_feat([xi.detach()forxiinx],self.one2one_cv2,self.one2one...
Cv2.Rectangle(image, new OpenCvSharp.Point(position_boxes[index].TopLeft.X, position_boxes[index].TopLeft.Y + 30), new OpenCvSharp.Point(position_boxes[index].BottomRight.X, position_boxes[index].TopLeft.Y), new Scalar(0, 255, 255), -1); Cv2.PutText(image, class_ids[index] + "-"...
def non_max_suppression(self, prediction, num_classes, input_shape, image_shape, letterbox_image, conf_thres=0.5, nms_thres=0.4): # prediction [batch_size, num_anchors, 85] output = [None for _ in range(len(prediction))] for i, image_pred in enumerate(prediction): # class_conf [num...
具体实现如下:(4 mosaic 方法,输入的index是图片的索引) defload_mosaic(self,index):# YOLOv5 4-mosaic loader. Loads 1 image + 3 random images into a 4-image mosaiclabels4,segments4=[],[]s=self.img_size#random.uniform()指定最小值和最大值范围内生成浮点数yc,xc=(int(random.uniform(-x,2...
inrange(cxcy.size()[0]):# 对应于数据集中的每个框 这里cxcy.size()[0] == num_samples# 2,计算第 i 个目标中心点落在哪个 `grid` 上,`target` 相应位置的两个框的置信度值设为 `1`,同时对应类别值也置为 `1`;cxcy_sample=cxcy[i]ij=(cxcy_sample/cell_size).ceil()-1# ij 是一个list,...
list_index = range(num) tv = int(num * trainval_percent) tr = int(tv * train_percent) trainval = random.sample(list_index, tv) train = random.sample(trainval, tr) file_trainval = open(txtsavepath + '/trainval.txt', 'w') ...
辅助训练头:训练p6模型时需使用train_aux.py,否则将导致IndexError: list index out of range错误。train_aux.py引用loss.py中build_targets2函数时可能出现RuntimeError: indices should be either on cpu or on the same device as the indexed tensor (cpu)错误,官方修复了build_targets函数而没有修改build_...
task_attn_module(mid_feat) return out # 定义融合模块 class Fusion(nn.Module): def __init__(self, inc_list): super().__init__() self.fusion_conv = nn.ModuleList([nn.Conv2d(inc, inc, 1) for inc in inc_list]) def forward(self, x): for i in range(len(x)): x[i] = ...
Mat mask_roi = new Mat(reshape_mask, new OpenCvSharp.Range(my1, my2), new OpenCvSharp.Range(mx1, mx2)); // Convert the segmented area to the actual size of the image Mat actual_maskm = new Mat(); Cv2.Resize(mask_roi, actual_maskm, new Size(box_x2 - box_x1, box_y2 - box...
BatchNorm2d(dim), ) points = list(itertools.product(range(self.resolution), range(self.resolution))) N = len(points) attention_offsets = {} idxs = [] for p1 in points: for p2 in points: offset = (abs(p1[0] - p2[0]), abs(p1[1] - p2[1])) if offset not in attention_...