报错如下：
(torch) E:\python\Awesome-Backbones>python tools/pth2pt.py models\repvgg\repvgg_A0.py
Loading Train_Epoch042-Loss0.012.pth

Traceback (most recent call last):
File "tools/pth2pt.py", line 125, in
main()
File "tools/pth2pt.py", line 121, in main
traced_script_module = torch.jit.trace(model, example)
File "D:\anaconda3\envs\torch\lib\site-packages\torch\jit_trace.py", line 742, in trace
_module_class,
File "D:\anaconda3\envs\torch\lib\site-packages\torch\jit_trace.py", line 940, in trace_module
_force_outplace,
File "D:\anaconda3\envs\torch\lib\site-packages\torch\nn\modules\module.py", line 725, in _call_impl
result = self._slow_forward(*input, **kwargs)
File "D:\anaconda3\envs\torch\lib\site-packages\torch\nn\modules\module.py", line 709, in _slow_forward
result = self.forward(*input, **kwargs)
File "E:\python\Awesome-Backbones\models\build.py", line 125, in forward
return self.forward_train(x,**kwargs)
TypeError: forward_train() missing 1 required positional argument: 'targets'

请问博主有做这方面的工作不，期待您的回复

Traceback (most recent call last):
File "tools/train1.py", line 178, in
main()
File "tools/train1.py", line 172, in main
train(model,runner, lr_update_func, device, epoch, data_cfg.get('train').get('epoches'), meta)
File "/hy-tmp/Awe/utils/train_utils.py", line 215, in train
losses = model(images,targets=targets,return_loss=True)
File "/usr/local/lib/python3.8/dist-packages/torch/nn/modules/module.py", line 727, in _call_impl
result = self.forward(*input, **kwargs)
File "/hy-tmp/Awe/models/build.py", line 125, in forward
return self.forward_train(x,**kwargs)
File "/hy-tmp/Awe/models/build.py", line 130, in forward_train
x = self.extract_feat(x)
File "/hy-tmp/Awe/models/build.py", line 113, in extract_feat
x = self.backbone(img)
File "/usr/local/lib/python3.8/dist-packages/torch/nn/modules/module.py", line 727, in _call_impl
result = self.forward(*input, **kwargs)
File "/hy-tmp/Awe/configs/backbones/swin_transformer.py", line 409, in forward
x, hw_shape = self.patch_embed(x)
File "/usr/local/lib/python3.8/dist-packages/torch/nn/modules/module.py", line 727, in _call_impl
result = self.forward(*input, **kwargs)
File "/hy-tmp/Awe/configs/common/transformer.py", line 221, in forward
x = self.projection(x)
File "/usr/local/lib/python3.8/dist-packages/torch/nn/modules/module.py", line 727, in _call_impl
result = self.forward(*input, **kwargs)
File "/usr/local/lib/python3.8/dist-packages/torch/nn/modules/conv.py", line 423, in forward
return self._conv_forward(input, self.weight)
File "/usr/local/lib/python3.8/dist-packages/torch/nn/modules/conv.py", line 419, in _conv_forward
return F.conv2d(input, weight, self.bias, self.stride,
RuntimeError: Input type (torch.cuda.FloatTensor) and weight type (torch.FloatTensor) should be the same

在

加model.to(device),后报另一个错误

在用mobilevit网络进行评估时，在配置文件中已经把路径给修改成训练过程中保存的val集acc最高的那个模型权重文件了，窗口也显示加载的是这个模型，但是评估的结果和保存的结果不一致，并且多次执行python tools/evaluation.py models/mobilevit/mobilevit_s.py时每次结果都不一样，按理说模型确定，test集确定，每次的预测结果应该都一样的,可以看到从模型名称看到结果应该是84%才对，找不到是哪个步骤出了问题

请问一下，有什么方法可以查看模型的计算量flops吗

想把输出导出到文件里，但是不知道从哪儿下手

请问可以得到模型的推理速度，如对多张模型的推理耗时ms和fps吗？希望可以添加

我导，这个训练利用率有问题吗，上一回跑swin transformer v2的时候没有出现偶尔是0的现象

请问这个实时图像的创建在哪一个文件中 (想知道横纵坐标，颜色等详细信息)

👀

可以增加注意力机制功能，让用户选择某一种网络是否添加注意力机制

Loading resnet18-f37072fd.pth
The model and loaded state dict do not match exactly

我在進行預訓練模型出錯，請問如何解決，謝謝

models 和 config 这俩文件夹，是不是反了？

您好，我想问一下存储库中提供的预训练权重是在imagenet上预训练得到的吗？

安装这两个安装不上

opencv_contrib_python
opencv-python-headless

我在训练shufflenet_v2时，总是异常中断，请问是什么问题呢

你好up，我是用自己的数据集制作的，请问这个报错如何解决

实际生产中会有很多数据不平衡的情况，可否集成mixup,balance-mixup等数据增强的方法

+Model info---------+----------------------+---------------+-----------------+
| Backbone | Neck | Head | Loss |
+-------------------+----------------------+---------------+-----------------+
| SwinTransformerV2 | GlobalAveragePooling | LinearClsHead | LabelSmoothLoss |
+-------------------+----------------------+---------------+-----------------+
Initialize the weights.
Loading swinv2_base_patch4_window8_256.pth
The model and loaded state dict do not match exactly

unexpected key in source state_dict: model

missing keys in source state_dict: backbone.patch_embed.projection.weight, backbone.patch_embed.projection.bias, backbone.patch_embed.norm.weight, backbone.patch_embed.norm.bias, backbone.stages.0.blocks.0.attn.w_msa.logit_scale, backbone.stages.0.blocks.0.attn.w_msa.q_bias, backbone.stages.0.blocks.0.attn.w_msa.v_bias, backbone.stages.0.blocks.0.attn.w_msa.relative_coords_table, backbone.stages.0.blocks.0.attn.w_msa.relative_position_index, backbone.stages.0.blocks.0.attn.w_msa.cpb_mlp.0.weight, backbone.stages.0.blocks.0.attn.w_msa.cpb_mlp.0.bias, backbone.stages.0.blocks.0.attn.w_msa.cpb_mlp.2.weight, backbone.stages.0.blocks.0.attn.w_msa.qkv.weight, backbone.stages.0.blocks.0.attn.w_msa.proj.weight, backbone.stages.0.blocks.0.attn.w_msa.proj.bias, backbone.stages.0.blocks.0.norm1.weight, backbone.stages.0.blocks.0.norm1.bias, backbone.stages.0.blocks.0.ffn.layers.0.0.weight, backbone.stages.0.blocks.0.ffn.layers.0.0.bias, backbone.stages.0.blocks.0.ffn.layers.1.weight, 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/home/user/miniconda3/envs/wmh/lib/python3.8/site-packages/torch/functional.py:445: UserWarning: torch.meshgrid: in an upcoming release, it will be required to pass the indexing argument. (Triggered internally at ../aten/src/ATen/native/TensorShape.cpp:2157.)
return _VF.meshgrid(tensors, **kwargs) # type: ignore[attr-defined]

用的resnet34，除了文件夹位置没有修改任何代码，好奇怪的结果，其他epoch没问题。

1）可以考虑把每个epoch训练的loss，精度，验证的精度和loss等相关信息存放在一个CSV文件中，这样可以方便之后画图
2）可以考虑把每次训练的控制台输出同时添加到某个日志文件中
只是友善的建议，博主可以视情况采纳

最近在训练自己的数据集时，发现几个Transformer网络有时会出现loss=nan的情况，并且一旦出现这个情况，训练结果就不会发生任何的update.

想知道loss=nan会在怎样的情况下发生？谢谢

作者您好，最近在使用您的这个项目在测试实验，请问如何在哪里可以修改输入图片大小，我的图片是自制的比如39、189。模型的默认输入是224224。直接调用也没问题，但是好像是resize图片把图片变成了224224，我想使用原本图片的大小作为输入

为了修改这个小bug，我修改了utils中的inference中部分语句以及单张图片测试的部分代码，目前已解决该bug

修改处为：
inference代码中新增label_names
def inference_model(model, image, val_pipeline, classes_names,label_names):

以及result['pred_class'] = classes_names[label_names.index(result['pred_label'])]

单张图片测试代码中修改了
classes_names, label_names = get_info(args.classes_map)

result = inference_model(model, args.img, val_pipeline, classes_names,label_names)

经测试修改后的代码已不会出现annoation顺序影响测试结果显示的小bug

up, 关于多标签分类 ,我将如何处理自己的数据集

(pytorch-gpu) C:\ai-project\Awesome-Backbones-main>python tools/evaluation.py models/shufflenet/shufflenet_v2.py
tools/evaluation.py:1: DeprecationWarning: the imp module is deprecated in favour of importlib; see the module's documentation for alternative uses
import imp
Loading Val_Epoch001-Acc100.000.pth

Traceback (most recent call last):
File "tools/evaluation.py", line 169, in
main()
File "tools/evaluation.py", line 146, in main
test_dataset = Mydataset(test_datas, val_pipeline)
File "C:\ai-project\Awesome-Backbones-main\utils\dataloader.py", line 16, in init
self.pipeline = Compose(self.cfg)
File "C:\ai-project\Awesome-Backbones-main\core\datasets\compose.py", line 23, in init
transform = build_from_cfg(transform, PIPELINES)
File "C:\ai-project\Awesome-Backbones-main\core\datasets\build.py", line 61, in build_from_cfg
f'{obj_type} is not in the {registry.name} registry')
KeyError: 'RandomHorizontalFlip is not in the pipeline registry'

maybe it update the title? i cant find and install that module. My mistake "from utils.train_utils import get_info ModuleNotFoundError: No module named 'utils.train_utils'"

这个图片左上角的测试数据怎么不重叠

模型评估的结果是测试集的吗

可以在你模型里修改吗，具体是怎么修改呢？在head上把topk=(1, 5)变为topk=(1,2)吗？这样也不对

Hello!

First of all, I would like to thank you for great job! But it seems that EfficientNetV2 don't work. I tried this:
python tools/train.py models/efficientnetv2/efficientnetv2_xl_512.py
And received an error:
"AttributeError: 'EnhancedConvModule' object has no attribute 'in_channels'"
Could you fix it, please?

感谢您提供的代码库，我目前的任务是关于含量多少的分类问题，如何通过修改head输出，使其能够成为解决回归问题的head，比如说我现在建立了三个梯度1,10,100分类模型，那我怎样预测5或者50的梯度，需要怎样在您的基础上修改

我想尝试改用lion优化器试下效果，我应该修改哪些地方？

Loading Train_Epoch077-Loss0.423.pth <All keys matched successfully> Traceback (most recent call last): File "tools/single_test.py", line 42, in <module> main() File "tools/single_test.py", line 36, in main result = inference_model(model, args.img, val_pipeline, classes_names) File "/home/Awesome-Backbones/utils/inference.py", line 48, in inference_model if val_pipeline[0]['type'] != 'LoadImageFromFile': TypeError: 'NoneType' object is not subscriptable

运行 python tools/evaluation.py，能正常显示，测试 python tools/single_test.py 就报错了... 打印 val_pipeline，是None，想问有什么原因导致发生的呢？谢谢

你好 想请教一下这种已经分好类的数据集我想合并类别需要怎么修改 比如说把car bus truck都合并成car

您好，我想问一下怎么使用single_test.py推理视频

我下载的pth文件可以进行load，但是在训练之后为什么就不可以了，是因为模型结构发生了变化吗，那我应该怎么加载呢，比如当我想要进行部署的时候

可以增加输出backbone提取的feature的功能，并保存npy文件到指定文件夹。这样可以方便做分类降维可视化。还有就是可以提供打印网络结构的功能，最后再次谢谢大佬提供的框架！

这个库非常棒，我在前几天使用你的库完成了一个小任务，我在resnet的基础上又增加了一个head，在这部分做了些修改（包括一些中间件），这块我看原本是只支持更换head，但是没有做对增加的支持，不知道是不是我没有仔细看，这块是否需要完善一下。还有single test部分，我看只对单一的图片做预测，这块是否需要实现对一个batch的支持，并且是否需要将val_pipeline这部分拆分出来，这样可以提前对数据进行处理。以上是我的使用感受，欢迎探讨，最终感谢你的框架。

另外Conformer、PoolFormer、TNT的训练配置文件是还没有更新嘛

up主好棒！！继续冲冲(｡ì _ í｡)

请问类别激活图可视化中的EigenGradCAM方法是在哪篇论文中提出的呢，想了解一下原理

报错，应该怎么修改呢

Originally posted by @joanna63 in #1 (comment)

请问，各个模型的输入图像是256256，处理后变成224224。如果我想让输入图像大一些，以提高准确率，该怎样设置？比如我的原始图像是1280*960的，请问我在哪里修改？谢谢！

您好，我制作了自己的数据集，有12个分类，但是为什么会报这个错误，迁移学习那里已经设置了true了

目前的源码是只支持单卡训练，但是我想加快训练生成模型，如何进行多卡训练呢