一个计算mxnet模型的操作和参数的工具。
mxop的Python项目详细描述
mxop:mxnet操作摘要
它只对gluon有效。
安装
- 派皮
pip install --index-url https://pypi.org/simple/ mxop
- Github（最新）
pip install --upgrade git+https://github.com/hey-yahei/OpSummary.MXNet.git
使用量
胶子- 计算操作数
frommxop.gluonimportcount_opsop_counter=count_ops(net)# net is the gluon model you want to count OPs
- 计数参数
frommxop.gluonimportcount_paramsparams_counter=count_params(net,input_size)# net is the gluon model you want to count parameters# input_size is the shape of your input
- 打印摘要
frommxop.gluonimportop_summaryop_summary(net,input_size)# net is the gluon model you want to count# input_size is the shape of your input
测试
运行tests/test_gluon_utils.py来计算MXNet模型zoo中所有模型的操作和参数
结果：
| Model | Params(M) | Multiplication(G) | *Params(M) | *Multiplication(G) | Top1 Acc | Top5 Acc |
|---|---|---|---|---|---|---|
| AlexNet | 61.10 | 0.71 | 2.47 | 0.66 | 0.5492 | 0.7803 |
| VGG11 | 132.86 | 7.61 | 9.22 | 7.49 | 0.6662 | 0.8734 |
| VGG13 | 133.04 | 11.30 | 9.40 | 11.18 | 0.6774 | 0.8811 |
| VGG16 | 138.63 | 15.47 | 14.71 | 15.35 | 0.7323 | 0.9132 |
| VGG19 | 143.67 | 19.63 | 20.02 | 19.51 | 0.7411 | 0.9135 |
| VGG11_bn | 132.87 | 7.62 | 9.23 | 7.49 | 0.6859 | 0.8872 |
| VGG13_bn | 133.06 | 11.32 | 9.42 | 11.20 | 0.6884 | 0.8882 |
| VGG16_bn | 138.37 | 15.48 | 14.73 | 15.36 | 0.7310 | 0.9176 |
| VGG19_bn | 143.69 | 19.65 | 20.05 | 19.52 | 0.7433 | 0.9185 |
| Inception_v3 | 23.87 | 5.72 | 21.82 | 5.72 | 0.7755 | 0.9364 |
| ResNet18_v1 | 11.70 | 1.82 | 11.19 | 1.82 | 0.7093 | 0.8992 |
| ResNet34_v1 | 21.81 | 3.67 | 21.3 | 3.67 | 0.7437 | 0.9187 |
| ResNet50_v1 | 25.63 | 3.87 | 23.58 | 3.87 | 0.7647 | 0.9313 |
| ResNet101_v1 | 44.70 | 7.59 | 42.65 | 7.58 | 0.7834 | 0.9401 |
| ResNet152_v1 | 60.40 | 11.30 | 58.36 | 11.30 | 0.7900 | 0.9438 |
| ResNet18_v2 | 11.70 | 1.82 | 11.18 | 1.82 | 0.7100 | 0.8992 |
| ResNet34_v2 | 21.81 | 3.67 | 21.30 | 3.67 | 0.7440 | 0.9208 |
| ResNet50_v2 | 25.60 | 4.10 | 23.55 | 4.10 | 0.7711 | 0.9343 |
| ResNet101_v2 | 44.64 | 7.82 | 42.59 | 7.81 | 0.7853 | 0.9417 |
| ResNet152_v2 | 60.33 | 11.54 | 58.28 | 11.53 | 0.7921 | 0.9431 |
| DenseNet121 | 8.06 | 2.85 | 7.04 | 2.85 | 0.7497 | 0.9225 |
| DenseNet161 | 28.90 | 7.76 | 26.69 | 7.76 | 0.7770 | 0.9380 |
| DenseNet169 | 14.31 | 3.38 | 12.64 | 3.38 | 0.7617 | 0.9317 |
| DenseNet201 | 20.24 | 4.32 | 18.32 | 4.31 | 0.7732 | 0.9362 |
| MobileNet_v1_1.00 | 4.25 | 0.57 | 3.23 | 0.57 | 0.7105 | 0.9006 |
| MobileNet_v1_0.75 | 2.60 | 0.33 | 1.83 | 0.33 | 0.6738 | 0.8782 |
| MobileNet_v1_0.50 | 1.34 | 0.15 | 0.83 | 0.15 | 0.6307 | 0.8475 |
| MobileNet_v1_0.25 | 0.48 | 0.04 | 0.22 | 0.04 | 0.5185 | 0.7608 |
| MobileNet_v2_1.00 | 3.54 | 0.32 | 2.26 | 0.32 | 0.7192 | 0.9056 |
| MobileNet_v2_0.75 | 2.65 | 0.19 | 1.37 | 0.19 | 0.6961 | 0.8895 |
| MobileNet_v2_0.50 | 1.98 | 0.10 | 0.70 | 0.09 | 0.6449 | 0.8547 |
| MobileNet_v2_0.25 | 1.53 | 0.03 | 0.25 | 0.03 | 0.5074 | 0.7456 |
| SqueezeNet1_0 | 1.25 | 0.82 | 0.74 | 0.73 | 0.5611 | 0.7909 |
| SqueezeNet1_1 | 1.24 | 0.35 | 0.72 | 0.26 | 0.5496 | 0.7817 |
用于比较用作主干的分类模型--
*params col显示没有最后几层的模型的参数数目。
*乘法列显示没有最后几层的模型的乘法数。
待办事项
- []计算每个层的操作和参数。
- []支持MXNET的符号模型。
- []支持量化模型。
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