mxop 0.2.0

A tool to count OPs and paramters of MXNet model.

MXOP: MXNet-OpSummary

It only works for gluon yet.

Reference: THOP: PyTorch-OpCounter

Installation

• PyPi

  pip install --index-url https://pypi.org/simple/ mxop
  

• Github (latest)

  pip install --upgrade git+https://github.com/hey-yahei/OpSummary.MXNet.git
  

Usage

Gluon

• Count OPs

  from mxop.gluon import count_ops
  op_counter = count_ops(net)   # net is the gluon model you want to count OPs 
  

• Count parameters

  from mxop.gluon import count_params
  params_counter = count_params(net, input_size)   # net is the gluon model you want to count parameters
                                                   # input_size is the shape of your input 
  

• Print summary

  from mxop.gluon import op_summary
  op_summary(net, input_size)   # net is the gluon model you want to count
                                # input_size is the shape of your input 
  

Test

Run tests/test_gluon_utils.py to count OPs and parameters for all models in model zoo of MXNet.

Result:

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

To compare for classification models used as backbone--
*Params col shows the number of parameters for models without last several layers.
*Multiplication col shows the number of Multiplication for models without last several layers.

TODO

• Count OPs and parameters for each layer.
• Support Symbol model for MXNet.
• Support quantized models.