# MobilenetV3

**Repository Path**: yangdashi/MobilenetV3

## Basic Information

- **Project Name**: MobilenetV3
- **Description**: 1、其特点：将最后一层的平均池化层前移，并且后面用1*1的卷积核来进行代替，并且是有h_swish激活函数，并且添加了Dropout夹在最后一层1*1的卷积层前。
2、V3是融合了三种模型的思想：mobilenetv1的深度可分离卷积（depthwise separable convolutions）mobilenetv2的具有线性瓶颈的逆残差结构(the inverted residual with linear bottleneck)和MnasNet的Squeeze and excitation结构的轻量级注意模型。
- **Primary Language**: Python
- **License**: MIT
- **Default Branch**: master
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 0
- **Created**: 2019-10-29
- **Last Updated**: 2020-12-19

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

1、其特点：将最后一层的平均池化层前移，并且后面用1*1的卷积核来进行代替，并且是有h_swish激活函数，并且添加了Dropout夹在最后一层1*1的卷积层前。
 2、V3是融合了三种模型的思想：mobilenetv1的深度可分离卷积（depthwise separable convolutions）mobilenetv2的具有线性瓶颈的逆残差结构(the inverted residual with linear bottleneck)和MnasNet的Squeeze and excitation结构的轻量级注意模型。
参考博客：[轻量级网络——MobileNetV3终于来了](https://blog.csdn.net/DL_wly/article/details/90168883)
# 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](https://arxiv.org/abs/1905.02244)
- 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
![캡처](https://images.gitee.com/uploads/images/2019/1029/120206_d3399724_1184321.png)

## Experiments
- For CIFAR-100 data, I experimented with resize (224, 224).<br>

| 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
```python
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))
```

## Requirements
- torch==1.0.1