leaderj1001/MobileNetV3-Pytorch

Implementing Searching for MobileNetV3 paper using Pytorch


repo name	leaderj1001/MobileNetV3-Pytorch
repo link	https://github.com/leaderj1001/MobileNetV3-Pytorch
homepage
language	Python
size (curr.)	70443 kB
stars (curr.)	183
created	2019-05-08
license	MIT License

Implementing Searching for MobileNetV3 paper using Pytorch

The current model is a very early model. I will modify it as a general model as soon as possible.

Paper

Searching for MobileNetV3 paper
Author: Andrew Howard(Google Research), Mark Sandler(Google Research, Grace Chu(Google Research), Liang-Chieh Chen(Google Research), Bo Chen(Google Research), Mingxing Tan(Google Brain), Weijun Wang(Google Research), Yukun Zhu(Google Research), Ruoming Pang(Google Brain), Vijay Vasudevan(Google Brain), Quoc V. Le(Google Brain), Hartwig Adam(Google Research)

Todo

Experimental need for ImageNet dataset.
Code refactoring

MobileNetV3 Block

Experiments

For CIFAR-100 data, I experimented with resize (224, 224).

Datasets	Model	acc1	acc5	Epoch	Parameters
CIFAR-100	MobileNetV3(LARGE)	70.44%	91.34%	80	3.99M
CIFAR-100	MobileNetV3(SMALL)	67.04%	89.41%	55	1.7M
IMAGENET	MobileNetV3(LARGE) WORK IN PROCESS				5.15M
IMAGENET	MobileNetV3(SMALL) WORK IN PROCESS				2.94M

Usage

Train

python main.py

If you want to change hyper-parameters, you can check “python main.py –help”

Options:

--dataset-mode (str) - which dataset you use, (example: CIFAR10, CIFAR100), (default: CIFAR100).
--epochs (int) - number of epochs, (default: 100).
--batch-size (int) - batch size, (default: 128).
--learning-rate (float) - learning rate, (default: 1e-1).
--dropout (float) - dropout rate, (default: 0.3).
--model-mode (str) - which network you use, (example: LARGE, SMALL), (default: LARGE).
--load-pretrained (bool) - (default: False).
--evaluate (bool) - Used when testing. (default: False).
--multiplier (float) - (default: 1.0).

Test

python main.py --evaluate True

Put the saved model file in the checkpoint folder and saved graph file in the saved_graph folder and type “python main.py –evaluate True”.
If you want to change hyper-parameters, you can check “python test.py –help”

Options:

--dataset-mode (str) - which dataset you use, (example: CIFAR10, CIFAR100), (default: CIFAR100).
--epochs (int) - number of epochs, (default: 100).
--batch-size (int) - batch size, (default: 128).
--learning-rate (float) - learning rate, (default: 1e-1).
--dropout (float) - dropout rate, (default: 0.3).
--model-mode (str) - which network you use, (example: LARGE, SMALL), (default: LARGE).
--load-pretrained (bool) - (default: False).
--evaluate (bool) - Used when testing. (default: False).
--multiplier (float) - (default: 1.0).

Number of Parameters

import torch

from model import MobileNetV3

def get_model_parameters(model):
    total_parameters = 0
    for layer in list(model.parameters()):
        layer_parameter = 1
        for l in list(layer.size()):
            layer_parameter *= l
        total_parameters += layer_parameter
    return total_parameters

tmp = torch.randn((128, 3, 224, 224))
model = MobileNetV3(model_mode="LARGE", multiplier=1.0)
print("Number of model parameters: ", get_model_parameters(model))