syencil / mobile-yolov5-pruning-distillation Goto Github PK

i can not run it ,it give me the message:

Traceback (most recent call last):
File "train.py", line 762, in
opt.cfg = check_file(opt.cfg) # check file
File "/home/demon/CLionProjects/mobile-yolov5-pruning-distillation/utils/utils.py", line 101, in check_file
if os.path.isfile(file):
File "/home/demon/anaconda3/envs/pytorch_gpu/lib/python3.8/genericpath.py", line 30, in isfile
st = os.stat(path)

 def compute_pruning_loss(p, prunable_modules, model, loss):
    ft = torch.cuda.FloatTensor if p[0].is_cuda else torch.Tensor
    ll1 = ft([0])
    h = model.hyp  # hyperparameters
    red = 'mean'  # Loss reduction (sum or mean)
    if prunable_modules is not None:
        for m in prunable_modules:
            ll1 += m.weight.norm(1)  # BN 层 gamma 值 的 L1 范数
        ll1 /= len(prunable_modules)
    ll1 *= h['sl']
    bs = p[0].shape[0]  # batch size
    loss += ll1 * bs
    return loss

请教下函数中 平均范数为啥要乘上 batch_size?

大佬好，剪枝代码看不太懂，有写相应的解释性的博客吗？

如上面所说

Hi
Could you share your pretrained weight?
(mobilenet_v2-b0353104.pth)

When I download the weights, it turns that this folder is in your google drive trash can.
https://drive.google.com/drive/folders/1Drs_Aiu7xx6S-ix95f9kNsA6ueKRpN2J

Thank you.

我对剪枝后的模型蒸馏后发现，精度上去的同时参数量也恢复到剪枝前的参数量了。

Why did mAP all become 0 after the second epoch when I used mobilev2-yolo5s to train my data?

有相应的前端demo提供吗，看你提供的链接是404.

I would like to ask what is the voc data format of your training? Whether it needs to be processed into xywh format after downloading voc

请问能不能提供一关于你这里所采用的的剪枝蒸馏策略的参考资料？万分感谢。另外，想请教一下，数据集的改变是否会影响剪枝蒸馏的策略与效果

Hello, excuse me again. I ran your code: pruning and distilling mobilenet. One can reduce the parameters and the other can improve the accuracy. Why not use the pruning model as a student model of distillation?

大佬您好，我运行detect.py报错 x[i] = x[i].view(bs, self.na, self.no, ny, nx).permute(0, 1, 3, 4, 2).contiguous()
RuntimeError: shape '[1, 3, 85, 80, 64]' is invalid for input of size 655360
运行train.py拿来训练也报错，是不是这不能拿来训练或者测试呢？

你好，我尝试使用yolov5s作为模型进行训练，命令行为
python train.py --type vocs
但是在训练完1个epoch后测试验证集时，报错

Traceback (most recent call last):
File "train.py", line 802, in
train(hyp)
File "train.py", line 453, in train
results, maps, times = test.test(opt.data,
File "/home/cwy/mobile-yolov5-pruning-distillation/test.py", line 112, in test
inf_out, train_out = model(img, augment=augment)
File "/home/cwy/.conda/envs/yolo/lib/python3.8/site-packages/torch/nn/modules/module.py", line 727, in _call_impl
result = self.forward(*input, **kwargs)
File "/home/cwy/.conda/envs/yolo/lib/python3.8/site-packages/torch/nn/parallel/distributed.py", line 621, in forward
outputs = self.parallel_apply(self._module_copies[:len(inputs)], inputs, kwargs)
File "/home/cwy/.conda/envs/yolo/lib/python3.8/site-packages/torch/nn/parallel/distributed.py", line 646, in parallel_apply
return parallel_apply(replicas, inputs, kwargs, self.device_ids[:len(replicas)])
File "/home/cwy/.conda/envs/yolo/lib/python3.8/site-packages/torch/nn/parallel/parallel_apply.py", line 86, in parallel_apply
output.reraise()
File "/home/cwy/.conda/envs/yolo/lib/python3.8/site-packages/torch/_utils.py", line 428, in reraise
raise self.exc_type(msg)
RuntimeError: Caught RuntimeError in replica 1 on device 1.
Original Traceback (most recent call last):
File "/home/cwy/.conda/envs/yolo/lib/python3.8/site-packages/torch/nn/parallel/parallel_apply.py", line 61, in _worker
output = module(*input, **kwargs)
File "/home/cwy/.conda/envs/yolo/lib/python3.8/site-packages/torch/nn/modules/module.py", line 727, in _call_impl
result = self.forward(*input, **kwargs)
File "/home/cwy/mobile-yolov5-pruning-distillation/models/yolo.py", line 94, in forward
return self.forward_once(x, profile) # single-scale inference, train
File "/home/cwy/mobile-yolov5-pruning-distillation/models/yolo.py", line 111, in forward_once
x = m(x) # run
File "/home/cwy/.conda/envs/yolo/lib/python3.8/site-packages/torch/nn/modules/module.py", line 727, in _call_impl
result = self.forward(*input, **kwargs)
File "/home/cwy/mobile-yolov5-pruning-distillation/models/common.py", line 86, in forward
return self.conv(torch.cat([x[..., ::2, ::2], x[..., 1::2, ::2], x[..., ::2, 1::2], x[..., 1::2, 1::2]], 1))
File "/home/cwy/.conda/envs/yolo/lib/python3.8/site-packages/torch/nn/modules/module.py", line 727, in _call_impl
result = self.forward(*input, **kwargs)
File "/home/cwy/mobile-yolov5-pruning-distillation/models/common.py", line 21, in forward
return self.act(self.bn(self.conv(x)))
File "/home/cwy/.conda/envs/yolo/lib/python3.8/site-packages/torch/nn/modules/module.py", line 727, in _call_impl
result = self.forward(*input, **kwargs)
File "/home/cwy/.conda/envs/yolo/lib/python3.8/site-packages/torch/nn/modules/conv.py", line 423, in forward
return self._conv_forward(input, self.weight)
File "/home/cwy/.conda/envs/yolo/lib/python3.8/site-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.HalfTensor) and weight type (torch.cuda.FloatTensor) should be the same

尝试了很多方法，但还是报同样的错误，想请教一下该如何解决？

我在yolov5最新的工程文件上采用mobilenet作为骨干网络,此外采用mobilenet的权重文件收敛速度变得很慢,但是我直接跑作者的代码却收敛的很快,我能问问大概是什么原因吗?

训练 mvocs模型的时候再完成一个epoch后就报上面的错，谁知道怎么解决吗？

按照你的步骤一步一步，是要比之前更轻量吗，内存暂用更小吗，一般pruning剪枝率给到多少呢，蒸馏一般选择策略几呢，，我大概总共3，4万多张图片。

蒸馏时用v5的v2权重AttributeError: 'Detect' object has no attribute 'm'错误，剪枝后map只有0.4几咋回事呢，我是按照快速开始来的。

我诚恳的想请教您一些问题，方便联系吗？[email protected]

我先是通过 python train.py --type mvocs 进行训练，再通过python3 train.py --type smvocs将上一步的best_mvocs.pt作为本次训练的输入模型，再通过python3 pruning.py -t 0.1将上一步的best_smvocs.pt作为本次的输入模型，运行python3 pruning.py -t 0.1就报段错误

your work as following:
sparse learning-->prune-->finetune-->distill ?

我发现您并未给出yolov5s在VOC上训练的权重，因此将无法复现README中的实验，请问能否上传一下呢

when I train it with 'train.py', the output model is non-pruned, as large as original model.
Do I need a *.yaml for pruned model, how to get it?
Thanks.

prunable_modules = []
prunable_module_type = (nn.BatchNorm2d, )
for i, m in enumerate(model.modules()):
if i in ignore_idx:
continue
if isinstance(m, prunable_module_type):
prunable_modules.append(m)

as mentioned above, model structure was not change!
in addtion, BN of conv module was not appended prunable_modules.
where pruning?

it existed error to your pruned code.

您好，感谢您开源您的工作，请问您在剪枝部分ignore_idx=[230, 260, 290],这个[230, 260, 290]分别指的是哪些层，或者您是否有一个权重可以共享

1. 问：为什么有时候训练mAP会变成0？
   答：这要分情况讨论，我看大多数就是loss->0，val的map->0，这是很明显的overfit了。可以参考一下原来写的csdn文章

2. 问：torch.nn.modules.module.ModuleAttributeError: 'Model' object has no attribute 'module'
   答：这是原git挖的坑。你以为的模型加载->直接读取权重，恢复模型，构建梯度关系。实际上的模型加载->从yaml中构建一个随机权重的模型->得到梯度关系->加载每一层保存的权重。前者用在纯前向推理，后者用在需要梯度关系的时候，比如剪枝。还有就是保证原始工程结构，注意export PATH=$PWD

3. 问：yolo5s的预训练权重没了怎么办？
   答：我现在手上也没了。不过这个git主要是用更轻量的mobilev2-yolo5s，所以这里大家也可以来尝试一下。如果需要用yolov5，还是用他们最新的，对着我这个改改代码还是很轻松的Readme中有一个网盘链接，可以试一下

4. 问：recall低或者precision低
   答：和阈值有关，所以我这里只关注map作为中间指标。如果再剪枝或者蒸馏中出现map降低，也是很正常的，和你的数据集以及策略都有关，毕竟每个论文都是吹自己是sota。还需要自己鉴别。

5. 问：剪枝之后的模型能保存yaml文件吗?可不可以蒸馏或者进行ncnn转换？
   答：可以，但是没必要用yaml文件。这个时候你直接加载模型就行了，参考ft(finetune)模式。如果你很介意，那你就写个逆向的脚本就行了，不难，锻炼一下自己。

6. 问：模型训练的细节，剪枝蒸馏参考的文献等
   答：基本都在readme中有写，细节直接看代码就好了，不难，就当作学习。补充一下，剪枝是韩松的基于bn层剪枝，蒸馏则是用了针对一阶段、二阶段、分类网络等等的方式，具体细节肯定和原文有出入，但是差异不大注意鉴别。还有就是不要问我为什么不用什么什么方法，我不可能帮你实验完所有的，你可以自己动手。我所选的都是具有代表性的，很经典的方法，但是不一定最好。

7. 问：yolov5在你这里剪枝蒸馏有一些bug
   答：一针对yolov5我没有具体测试过，别人作者都还在更新呢，肯定是有bug的。二我这个项目主要是想做个更轻量的模型出来，但是我能保证只要按照我的步骤一步一步肯定能复现出来，我连随机数种子都给你们了>_<。

8. 问：mobilev2-yolo5s收敛慢，但是yolo5s就很快？
   答：别人在coco上预训练过的，参数量和计算量是我的两倍，capacity就很大。我这个就backbone用的imagenet权重，head全部随机初始化，收敛慢很正常，capacity小导致精度低也很正常。本身轻量化的思路就是从整体结构设计、剪枝、蒸馏、量化这4个点来做的。你要拿着yolov5x训练完了再做轻量化我也没得说，这也是个思路。

新手想请教一个比较基础的问题，剪枝后的模型finetune训练次数设置的足够高（比如跟正常训练一样100个epochs）可以取得很不错的mAP，这样做有问题吗？finetune的训练次数有没有什么限制比如只允许在10，20 次这样

感谢作者的repo，真的学到了很多，在看蒸馏实验室遇到了一些疑惑想和作者请教一下

1. Strategy1 Output Based Distillation，repo中关于Teacher部分的蒸馏损失全都是用MSELoss计算的，而论文中仍然采用和原始YOLO相同的Loss，会不会这一块限制Student网络的调优
2. Strategy2 Feature Based + Output Based Distillation，repo中似乎是通过参数控制一起训练的，而论文中是先冻结Feature Map之后的参数进行Feature Distillation，然后再解冻进行全局Distillation，会不会这部分限制了Strategy2的效果导致最后mAP偏低；且论文里Convert应该只有卷积，没有加上BN和Relu两个操作

problem as above.
your code about yolo.py was inconsistent with ultralytics / yolov5
it was occur error that Detect has no 'export'.

RuntimeError: The size of tensor a (3) must match the size of tensor b (5) at non-singleton dimension 0

first very thank you for your job
i just use pruning.py ,at last save just save a whole new model ,how can i get the new yaml.could teach me?

稀疏化训练后，运行剪枝报错FileNotFoundError: [Errno 2] No such file or directory: 'render_img/before_pruning.jpg'，这个文件是哪里生成的呢？谢谢

思考再三，还是做出如下声明：
本git仅供学习！！！ 拒绝伸手党
本git目的是帮助你大体了解一些做工程的一些基本流程，换backbone、剪枝、蒸馏、量化都是常规操作，包括利用现有框架往C++和android上转。
如果你要用来商用，或者做课设，做毕设，完全没有问题，遵从原git的相关开源协议即可。时间仓促，代码细节上难免会存在纰漏，但是只要按照readme复现整个流程是不会有问题的。很多可能的问题、细节在readme和代码注释中都有详细记录。包括一些关于实现的疑问在一些issue中我都解释过。
欢迎提供好的idea或者碰到了一些思路上的问题也欢迎共同来探讨，但是拒绝伸手党。
每个人的代码环境，操作方式都不一样，不可能简单的通过ctrl c+v 错误信息来帮你直接debug。
如果有商业合作需求，欢迎私下联系我。

RuntimeError: Given groups=1, weight of size [1, 255, 1, 1], expected input[1, 512, 8, 8] to have 255 channels, but got 512 channels instead
我运行那个mob-yolov5s.yaml，报错这个！

Thank you for sharing the code. When I run the pruning operation, there is a missing image. Can you provide me with this picture？

运行train.py想自己像YOLOv5y一样训练模型训练报错 'Got {}, but numpy array, torch tensor, or caffe2 blob name are expected.'.format(type(x)))
NotImplementedError: Got <class 'NoneType'>, but numpy array, torch tensor, or caffe2 blob name are expected.

env :

ubuntu1804
torch: 1.6.0
torchvision: 0.7.0
Training: python3 train.py --type vocs python3 train.py --type dmvocs
export: export PYTHONPATH="$PWD" && python models/onnx_export.py --weights outputs/dmvocs/weights/best_dmvocs.pt --img 640 --batch 1

Error:

Namespace(batch_size=1, img_size=[640], weights='outputs/dmvocs/weights/best_dmvocs.pt')
Fusing layers...
Model Summary: 148 layers, 3.59629e+06 parameters, 3.31818e+06 gradients
Traceback (most recent call last):
  File "models/onnx_export.py", line 35, in <module>
    _ = model(img)  # dry run
  File "/opt/conda/lib/python3.6/site-packages/torch/nn/modules/module.py", line 722, in _call_impl
    result = self.forward(*input, **kwargs)
  File "/data/byronnar/mobile-yolov5-pruning-distillation/models/yolo.py", line 94, in forward
    return self.forward_once(x, profile)  # single-scale inference, train
  File "/data/byronnar/mobile-yolov5-pruning-distillation/models/yolo.py", line 111, in forward_once
    x = m(x)  # run
  File "/opt/conda/lib/python3.6/site-packages/torch/nn/modules/module.py", line 722, in _call_impl
    result = self.forward(*input, **kwargs)
  File "/data/byronnar/mobile-yolov5-pruning-distillation/models/common.py", line 24, in fuseforward
    return self.act(self.conv(x))
  File "/opt/conda/lib/python3.6/site-packages/torch/nn/modules/module.py", line 722, in _call_impl
    result = self.forward(*input, **kwargs)
  File "/opt/conda/lib/python3.6/site-packages/torch/nn/modules/conv.py", line 419, in forward
    return self._conv_forward(input, self.weight)
  File "/opt/conda/lib/python3.6/site-packages/torch/nn/modules/conv.py", line 416, in _conv_forward
    self.padding, self.dilation, self.groups)
RuntimeError: Expected 4-dimensional input for 4-dimensional weight [32, 3, 3, 3], but got 3-dimensional input of size [1, 3, 640] instead

How can I solve this problem? Looking forward to your reply! Thank you!

当我执行前两个训练命令时，出现了以下错误，剩余的训练命令也类似，请问怎么解决呢qaq

voc_train.txt
voc_test.txt
where can get them?

test.py文件使用剪枝后的模型会报错，报错信息如下：
Traceback (most recent call last):
File "/home/demon/PycharmProjects/mobile-yolov5-pruning-distillation/test.py", line 357, in
test(opt.data,
File "/home/demon/PycharmProjects/mobile-yolov5-pruning-distillation/test.py", line 89, in test
names = {k: v for k, v in enumerate(model.names if hasattr(model, 'names') else model.module.names)}
File "/home/demon/anaconda3/envs/pytorch_gpu/lib/python3.8/site-packages/torch/nn/modules/module.py", line 771, in getattr
raise ModuleAttributeError("'{}' object has no attribute '{}'".format(
torch.nn.modules.module.ModuleAttributeError: 'Model' object has no attribute 'module'

training dataset: coco2017
model weights: yolov5s.pt
classes = 80
why was lower to value of p or r when pruning?

as following:
Epoch gpu_mem GIoU obj cls total targets img_size
3/49 2.66G 0.06193 0.0939 0.04484 0.2007 141 640: 100%|██████████████████| 7393/7393 [2:41:07<00:00, 1.31s/
Class Images Targets P R [email protected] [email protected]:.95: 100%|██████████| 313/313 [02:54<00:00, 1.80it/s]
all 5e+03 3.63e+04 0.000964 0.000638 5.18e-05 1.53e-05

I set test params（conf_trhesh=0.3, iou_thresh=0.3, batch=24,) when training the VOC2012 and 2017 data sets,but
the recall rate after 120 epochs iteration is only 0.3xx, is this normal?

感谢大佬的工程。
我在蒸馏时，发现Precision下降挺多的，看到您的几个蒸馏方案中，也是Precision比没有蒸馏前低一些，这个问题可以通过啥方法改善么？
单分类的，倒是没什么大的下降~