剪枝分割网络报错-bisenetv2

[GeneralJing]

File "examples/torchpruner/prune_by_class_bisenetv2.py", line 39, in <module>
    model, context = torchpruner.set_cut(model, result)
  File "site-packages/torchpruner-0.0.1-py3.8.egg/torchpruner/model_pruner.py", line 71, in set_cut
  File "site-packages/torchpruner-0.0.1-py3.8.egg/torchpruner/module_pruner/pruners.py", line 188, in set_cut
  File "site-packages/torchpruner-0.0.1-py3.8.egg/torchpruner/module_pruner/pruners.py", line 60, in set_cut
  File "site-packages/torchpruner-0.0.1-py3.8.egg/torchpruner/module_pruner/prune_function.py", line 42, in set_cut_tensor
IndexError: index 78 is out of bounds for dimension 0 with size 78

[GeneralJing]

有解决的消息了吗？是跟分组卷积有关吗？

[gdh1995]

呃这两天有别的事情，还没看多久，晚上我再自己测测

[GeneralJing]

嗯 好的 辛苦大佬，有消息及时回复下，自己手动剪，感觉比较步骤比较繁琐

[gdh1995]

能贴你的 prune_by_class_bisenetv2.py 吗？我创建 lib/models/bisenetv2.py#BiSeNetV2(19)后 graph.build_graph 没跑过去。bisnet 是刚从 https://github.com/CoinCheung/BiSeNet 找的。

[GeneralJing]

import sys

sys.path.append("..")
import torch
import torchpruner
import torchvision
import numpy as np
from bisenetv2 import BiSeNetV2

#以下代码示例了对每一个BN层去除其weight系数绝对值前20%小的层

#加载模型
model = torchvision.models.vgg11_bn()
print('model-origin:', model)
#jzy
#model = BiSeNetV2(n_classes=9, aux_mode='pred')
model = BiSeNetV2(n_classes=9)
model.load_state_dict(torch.load('/home/zxz/torch-model-compression/examples/torchpruner/model_final.pth', map_location='cuda'), strict=False)
print('model-origin:', model)

# 创建ONNXGraph对象，绑定需要被剪枝的模型
graph = torchpruner.ONNXGraph(model)
##build ONNX静态图结构，需要指定输入的张量
graph.build_graph(inputs=(torch.zeros(8, 3, 320, 640),))

# 遍历所有的Module
for key in graph.modules:
    module = graph.modules[key]
    # 如果该module对应了BN层
    if isinstance(module.nn_object, torch.nn.BatchNorm2d):
        # 获取该对象
        nn_object = module.nn_object
        # 排序，取前20%小的权重值对应的index
        weight = nn_object.weight.detach().cpu().numpy()
        index = np.argsort(np.abs(weight))[: int(weight.shape[0] * 0.2)]
        print('index:', index)
        result = module.cut_analysis("weight", index=index, dim=0)
        model, context = torchpruner.set_cut(model, result)

# 新的model即为剪枝后的模型
print('model-pruned:', model)

这个代码。

[GeneralJing]

comment框是markdown格式的，我的注释在这很奇怪

[gdh1995]

markdown的多行代码块语法是 三个 ` 连着表示开头和结尾，比如

# // 开头
# ``` [ + 空格 + 语言名]
# 具体内容
# // 结尾
# ``` 

[GeneralJing]

嗯嗯 有空看看mk语法，大佬看问题 嘿嘿

[gdh1995]

你用的 bisenetv2.py 是哪个文件？BiSeNet-master/lib/models/bisenetv2.py 还是 BiSeNet-master/old/bisenetv2/bisenetv2.py ？

另外我用官方仓库的模型（old/README.md的百度网盘文件 model_final.pth）好像加载不了，大概是参数不一致……厚颜来要你的原始模型了，能发的话，网盘或者发到 [email protected] 都行；不方便的话，我就再看看。我现在因为懒得自己在coco上训 练，缺少实际可用的参数张量，卡在前边某一个分组卷积的 cut_analysis 步骤了Orz

[GeneralJing]

用的是BiSeNet-master/lib/models/bisenetv2.py这个，模型的话在公司，可以给你发一个训练几个epoch的版本，因为是其他人训练的，也不方便给最新的。得明天给你了。

[gdh1995]

嗯谢谢！

[jzy-hxf]

Traceback (most recent call last): File "G:/py/torch-model-compression-main/examples/torchpruner/prune_and_recovery.py", line 16, in graph.build_graph(inputs=(torch.zeros(1, 3, 224, 224),)) File "G:\py\torch-model-compression-main\torchpruner\graph.py", line 458, in build_graph graph, params_dict, torch_out = torch.onnx.utils._model_to_graph( TypeError: _model_to_graph() got an unexpected keyword argument '_retain_param_name'

好几个都是 加载模型 时出现上述错误。我是刚入门的小白，请问这个怎么处理，程序跑不通，不知道怎么改这个。 model = torchvision.models.resnet50()

[gdh1995]

@jzy-hxf 抱歉之前没注意消息。具体到你这个问题，是torch版本比较新（1.11还是多少以上）造成的。你把这个项目里出现的 _retain_param_name 都去掉就行了，不影响结果。

[gdh1995]

@GeneralJing 抱歉我好久没注意这个。我确认了几遍代码，应该是 examples/torchpruner/prune_by_class.py 写的有问题，每次执行 torchpruner.set_cut 后 graph 的部分信息会过时，所以需要重新创建 graph。graph.modules 是稳定的，可以预先算好 keys

for key in list(graph.modules):
    # ...
    model, context = torchpruner.set_cut(model, result)
    graph = torchpruner.ONNXGraph(model)  # 本行可以省略
    graph.build_graph(inputs=(torch.zeros(1, 3, 224, 224),))

我一会去改一下示例代码。

[GeneralJing]

“下个周末看一下”，重新定义了下个周末哈哈哈哈，好的，多谢。

[Durobert]

参考你的例子，对BiSeNetv2模型剪枝，报如下错误，这个怎么解决，能看一下吗

index: [34 53 31 46 12 49 36 60 18 68 39 57 67 13 14]
key: self.segment.S3.0.dwconv1.1
Traceback (most recent call last):
  File "model_pruning.py", line 33, in <module>
    result = module.cut_analysis("weight", index=index, dim=0)
  File "/usr/local/lib/python3.6/dist-packages/torchpruner-0.1.0-py3.6.egg/torchpruner/graph.py", line 333, in cut_analysis
  File "/usr/local/lib/python3.6/dist-packages/torchpruner-0.1.0-py3.6.egg/torchpruner/graph.py", line 246, in cut_analysis
  File "/usr/local/lib/python3.6/dist-packages/torchpruner-0.1.0-py3.6.egg/torchpruner/graph.py", line 269, in cut_analysis_with_mask
  File "/usr/local/lib/python3.6/dist-packages/torchpruner-0.1.0-py3.6.egg/torchpruner/operator/onnx_operator.py", line 489, in analysis
  File "/usr/local/lib/python3.6/dist-packages/torchpruner-0.1.0-py3.6.egg/torchpruner/mask_utils.py", line 276, in indexs
RuntimeError: All the data is masked

[Durobert]

我的代码如下

import imp
import os
import sys
BASE_DIR = os.path.dirname(os.path.abspath(__file__))
sys.path.append(os.path.join(BASE_DIR, '..'))
import torch
import torchpruner
import numpy as np
from models.bisenetv2 import BiSeNetV2


device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')

# 加载模型
model = BiSeNetV2(n_classes=4)
checkpoint = torch.load('../results/05-05_01-59/checkpoint_best.pkl', map_location='cpu')
model.load_state_dict(checkpoint['model_state_dict'])
model.to(device)
model.eval()

graph = torchpruner.ONNXGraph(model)
graph.build_graph(inputs=(torch.zeros(1, 3, 640, 512),))

for key in list(graph.modules):
    module = graph.modules[key]
    if isinstance(module.nn_object, torch.nn.BatchNorm2d):
        nn_object = module.nn_object
        weight = nn_object.weight.detach().cpu().numpy()
        index = np.argsort(np.abs(weight))[: int(weight.shape[0] * 0.2)]
        print('index:', index)
        print('key:', key)
        result = module.cut_analysis("weight", index=index, dim=0)
        model, context = torchpruner.set_cut(model, result)
        graph = torchpruner.ONNXGraph(model)
        graph.build_graph(inputs=(torch.zeros(1, 3, 640, 512),))

print('model-pruned:', model)
torch.save(model, '../results/05-05_01-59/checkpoint_best_pruning.pkl')