这是一个yolox-pytorch的源码，可以用于训练自己的模型。

在使用YOLOX模型 对视频进行预测时，出现了如下错误： cv2.error: OpenCV(4.5.4-dev) D:\a\opencv-python\opencv-python\opencv\modules\imgproc\src\color.cpp:182: error: (-215:Assertion failed) !_src.empty() in function 'cv::cvtColor'

请问如何解决呀？

hi，博主 关于训练自己的数据集， 已经将对应格式的文件放到相同路径下的文件夹内，新添了自己的cls.txt。训练完成后，也有训练框。 但是，测试map时，仍然有原voc的测试类别，想问一下这是什么情况呢？ （ classes_path = 'model_data/fire.txt'已经就改）

复现代码是 output = torch.cat([reg_output, obj_output, cls_output], 1) 源代码是 output = torch.cat( [reg_output, obj_output.sigmoid(), cls_output.sigmoid()], 1 ) 复现代码没加激活函数啊？

Traceback (most recent call last): File "O:\graduate\yolov7-bubbliiiing\predict.py", line 15, in yolo = YOLO() File "O:\graduate\yolov7-bubbliiiing\yolo.py", line 95, in init self.generate() File "O:\graduate\yolov7-bubbliiiing\yolo.py", line 108, in generate self.net.load_state_dict(torch.load(self.model_path, map_location=device)) File "D:\Anaconda\envs\pytorch-gpu\lib\site-packages\torch\serialization.py", line 592, in load return _load(opened_zipfile, map_location, pickle_module, **pickle_load_args) File "D:\Anaconda\envs\pytorch-gpu\lib\site-packages\torch\serialization.py", line 851, in _load result = unpickler.load() ModuleNotFoundError: No module named 'models'

这是跑大佬您的yolov7时运行predict.py的问题，发错地方了😂

up，最近看了一些论文显示involution卷积效果不错，想来替换试试，但是involution官方代码，参数和yolox的不太匹配，调整了好久都一直报错，能麻烦up指点一下 参数该如何修改呢qwq？ import torch.nn as nn from mmcv.cnn import ConvModule

class involution(nn.Module):

def __init__(self,
             channels,
             kernel_size,
             stride):
    super(involution, self).__init__()
    self.kernel_size = kernel_size
    self.stride = stride
    self.channels = channels
    reduction_ratio = 4
    self.group_channels = 16
    self.groups = self.channels // self.group_channels
    self.conv1 = ConvModule(
        in_channels=channels,
        out_channels=channels // reduction_ratio,
        kernel_size=1,
        conv_cfg=None,
        norm_cfg=dict(type='BN'),
        act_cfg=dict(type='ReLU'))
    self.conv2 = ConvModule(
        in_channels=channels // reduction_ratio,
        out_channels=kernel_size**2 * self.groups,
        kernel_size=1,
        stride=1,
        conv_cfg=None,
        norm_cfg=None,
        act_cfg=None)
    if stride > 1:
        self.avgpool = nn.AvgPool2d(stride, stride)
    self.unfold = nn.Unfold(kernel_size, 1, (kernel_size-1)//2, stride)

def forward(self, x):
    weight = self.conv2(self.conv1(x if self.stride == 1 else self.avgpool(x)))
    b, c, h, w = weight.shape
    weight = weight.view(b, self.groups, self.kernel_size**2, h, w).unsqueeze(2)
    out = self.unfold(x).view(b, self.groups, self.group_channels, self.kernel_size**2, h, w)
    out = (weight * out).sum(dim=3).view(b, self.channels, h, w)
    return out