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MobileNetv4.py 0 → 100644
View file @ 68bc58a9
#!/usr/bin/python
# -*- coding: utf-8 -*-
# @Time : 2024/3/24 10:27
# @Author : 'IReverser'
# @FileName: vmamba.py
# Reference: https://github.com/jaiwei98/MobileNetV4-pytorch
from typing import Any, Callable, Dict, List, Mapping, Optional, Tuple, Union
import torch
import torch.nn as nn
import torch.nn.functional as F
from model_config import MODEL_SPECS
def make_divisible(
value: float,
divisor: int,
min_value: Optional[float] = None,
round_down_protect: bool = True,
) -> int:
"""
This function is copied from here
"https://github.com/tensorflow/models/blob/master/official/vision/modeling/layers/nn_layers.py"
This is to ensure that all layers have channels that are divisible by 8.
Args:
value: A `float` of original value.
divisor: An `int` of the divisor that need to be checked upon.
min_value: A `float` of minimum value threshold.
round_down_protect: A `bool` indicating whether round down more than 10%
will be allowed.
Returns:
The adjusted value in `int` that is divisible against divisor.
"""
if min_value is None:
min_value = divisor
new_value = max(min_value, int(value + divisor / 2) // divisor * divisor)
# Make sure that round down does not go down by more than 10%.
if round_down_protect and new_value < 0.9 * value:
new_value += divisor
return int(new_value)
def conv2d(in_channels, out_channels, kernel_size=3, stride=1, groups=1, bias=False, norm=True, act=True):
conv = nn.Sequential()
padding = (kernel_size - 1) // 2
conv.append(nn.Conv2d(in_channels, out_channels, kernel_size, stride, padding, bias=bias, groups=groups))
if norm:
conv.append(nn.BatchNorm2d(out_channels))
if act:
conv.append(nn.ReLU6())
return conv
class InvertedResidual(nn.Module):
def __init__(self, in_channels, out_channels, stride, expand_ratio, act=False, squeeze_exactation=False):
super(InvertedResidual, self).__init__()
self.stride = stride
assert stride in [1, 2]
hidden_dim = int(round(in_channels * expand_ratio))
self.block = nn.Sequential()
if expand_ratio != 1:
self.block.add_module("exp_1x1", conv2d(in_channels, hidden_dim, kernel_size=3, stride=stride))
if squeeze_exactation:
self.block.add_module("conv_3x3", conv2d(hidden_dim, hidden_dim, kernel_size=3, stride=stride, groups=hidden_dim))
self.block.add_module("res_1x1", conv2d(hidden_dim, out_channels, kernel_size=1, stride=1, act=act))
self.use_res_connect = self.stride == 1 and in_channels == out_channels
def forward(self, x):
if self.use_res_connect:
return x + self.block(x)
else:
return self.block(x)
class UniversalInvertedBottleneckBlock(nn.Module):
def __init__(self, in_channels, out_channels, start_dw_kernel_size, middle_dw_kernel_size, middle_dw_downsample,
stride, expand_ratio):
"""An inverted bottleneck block with optional depthwises.
Referenced from here https://github.com/tensorflow/models/blob/master/official/vision/modeling/layers/nn_blocks.py
"""
super(UniversalInvertedBottleneckBlock, self).__init__()
# starting depthwise conv
self.start_dw_kernel_size = start_dw_kernel_size
if self.start_dw_kernel_size:
stride_ = stride if not middle_dw_downsample else 1
self._start_dw_ = conv2d(in_channels, in_channels, kernel_size=start_dw_kernel_size, stride=stride_, groups=in_channels, act=False)
# expansion with 1x1 convs
expand_filters = make_divisible(in_channels * expand_ratio, 8)
self._expand_conv = conv2d(in_channels, expand_filters, kernel_size=1)
# middle depthwise conv
self.middle_dw_kernel_size = middle_dw_kernel_size
if self.middle_dw_kernel_size:
stride_ = stride if middle_dw_downsample else 1
self._middle_dw = conv2d(expand_filters, expand_filters, kernel_size=middle_dw_kernel_size, stride=stride_, groups=expand_filters)
# projection with 1x1 convs
self._proj_conv = conv2d(expand_filters, out_channels, kernel_size=1, stride=1, act=False)
# expand depthwise conv (not used)
# _end_dw_kernel_size = 0
# self._end_dw = conv2d(out_channels, out_channels, kernel_size=_end_dw_kernel_size, stride=stride, groups=in_channels, act=False)
def forward(self, x):
if self.start_dw_kernel_size:
x = self._start_dw_(x)
# print("_start_dw_", x.shape)
x = self._expand_conv(x)
# print("_expand_conv", x.shape)
if self.middle_dw_kernel_size:
x = self._middle_dw(x)
# print("_middle_dw", x.shape)
x = self._proj_conv(x)
# print("_proj_conv", x.shape)
return x
class MultiQueryAttentionLayerWithDownSampling(nn.Module):
def __init__(self, in_channels, num_heads, key_dim, value_dim, query_h_strides, query_w_strides, kv_strides, dw_kernel_size=3, dropout=0.0):
"""Multi Query Attention with spatial downsampling.
Referenced from here https://github.com/tensorflow/models/blob/master/official/vision/modeling/layers/nn_blocks.py
3 parameters are introduced for the spatial downsampling:
1. kv_strides: downsampling factor on Key and Values only.
2. query_h_strides: vertical strides on Query only.
3. query_w_strides: horizontal strides on Query only.
This is an optimized version.
1. Projections in Attention is explict written out as 1x1 Conv2D.
2. Additional reshapes are introduced to bring a up to 3x speed up.
"""
super(MultiQueryAttentionLayerWithDownSampling, self).__init__()
self.num_heads = num_heads
self.key_dim = key_dim
self.value_dim = value_dim
self.query_h_strides = query_h_strides
self.query_w_strides = query_w_strides
self.kv_strides = kv_strides
self.dw_kernel_size = dw_kernel_size
self.dropout = dropout
self.head_dim = self.key_dim // num_heads
if self.query_h_strides > 1 or self.query_w_strides > 1:
self._query_downsampling_norm = nn.BatchNorm2d(in_channels)
self._query_proj = conv2d(in_channels, self.num_heads * self.key_dim, 1, 1, norm=False, act=False)
if self.kv_strides > 1:
self._key_dw_conv = conv2d(in_channels, in_channels, dw_kernel_size, kv_strides, groups=in_channels,
norm=True, act=False)
self._value_dw_conv = conv2d(in_channels, in_channels, dw_kernel_size, kv_strides, groups=in_channels,
norm=True, act=False)
self._key_proj = conv2d(in_channels, key_dim, 1, 1, norm=False, act=False)
self._value_proj = conv2d(in_channels, key_dim, 1, 1, norm=False, act=False)
self._output_proj = conv2d(num_heads * key_dim, in_channels, 1, 1, norm=False, act=False)
self.dropout = nn.Dropout(p=dropout)
def forward(self, x):
bs, seq_len, _, _ = x.size()
# print(x.size())
if self.query_h_strides > 1 or self.query_w_strides > 1:
q = F.avg_pool2d(self.query_h_strides, self.query_w_strides)
q = self._query_downsampling_norm(q)
q = self._query_proj(q)
else:
q = self._query_proj(x)
px = q.size(2)
q = q.view(bs, self.num_heads, -1, self.key_dim) # [batch_size, num_heads, seq_len, key_dim]
if self.kv_strides > 1:
k = self._key_dw_conv(x)
k = self._key_proj(k)
v = self._value_dw_conv(x)
v = self._value_proj(v)
else:
k = self._key_proj(x)
v = self._value_proj(x)
k = k.view(bs, 1, self.key_dim, -1) # [batch_size, 1, key_dim, seq_length]
v = v.view(bs, 1, -1, self.key_dim) # [batch_size, 1, seq_length, key_dim]
# calculate attention score
# print(q.shape, k.shape, v.shape)
attn_score = torch.matmul(q, k) / (self.head_dim ** 0.5)
attn_score = self.dropout(attn_score)
attn_score = F.softmax(attn_score, dim=-1)
# context = torch.einsum('bnhm,bmv->bnhv', attn_score, v)
# print(attn_score.shape, v.shape)
context = torch.matmul(attn_score, v)
context = context.view(bs, self.num_heads * self.key_dim, px, px)
output = self._output_proj(context)
# print(output.shape)
return output
class MNV4layerScale(nn.Module):
def __init__(self, init_value):
"""LayerScale as introduced in CaiT: https://arxiv.org/abs/2103.17239
Referenced from here https://github.com/tensorflow/models/blob/master/official/vision/modeling/layers/nn_blocks.py
As used in MobileNetV4.
Attributes:
init_value (float): value to initialize the diagonal matrix of LayerScale.
"""
super(MNV4layerScale, self).__init__()
self.init_value = init_value
def forward(self, x):
gamma = self.init_value * torch.ones(x.size(-1), dtype=x.dtype, device=x.device)
return x * gamma
class MultiHeadSelfAttentionBlock(nn.Module):
def __init__(self, in_channels, num_heads, key_dim, value_dim, query_h_strides, query_w_strides,
kv_strides, use_layer_scale, use_multi_query, use_residual=True):
super(MultiHeadSelfAttentionBlock, self).__init__()
self.query_h_strides = query_h_strides
self.query_w_strides = query_w_strides
self.kv_strides = kv_strides
self.use_layer_scale = use_layer_scale
self.use_multi_query = use_multi_query
self.use_residual = use_residual
self._input_norm = nn.BatchNorm2d(in_channels)
if self.use_multi_query:
self.multi_query_attention = MultiQueryAttentionLayerWithDownSampling(
in_channels, num_heads, key_dim, value_dim, query_h_strides, query_w_strides, kv_strides
)
else:
self.multi_head_attention = nn.MultiheadAttention(in_channels, num_heads, kdim=key_dim)
if use_layer_scale:
self.layer_scale_init_value = 1e-5
self.layer_scale = MNV4layerScale(self.layer_scale_init_value)
def forward(self, x):
# Not using CPE, skipped
# input norm
shortcut = x
x = self._input_norm(x)
# multi query
if self.use_multi_query:
# print(x.size())
x = self.multi_query_attention(x)
# print(x.size())
else:
x = self.multi_head_attention(x, x)
# layer scale
if self.use_layer_scale:
x = self.layer_scale(x)
# use residual
if self.use_residual:
x = x + shortcut
return x
def build_blocks(layer_spec):
global msha
if not layer_spec.get("block_name"):
return nn.Sequential()
block_names = layer_spec["block_name"]
layers = nn.Sequential()
if block_names == "convbn":
schema_ = ["in_channels", "out_channels", "kernel_size", "stride"]
for i in range(layer_spec["num_blocks"]):
args = dict(zip(schema_, layer_spec["block_specs"][i]))
layers.add_module(f"convbn_{i}", conv2d(**args))
elif block_names == "uib":
schema_ = ["in_channels", "out_channels", "start_dw_kernel_size", "middle_dw_kernel_size", "middle_dw_downsample",
"stride", "expand_ratio", "msha"]
for i in range(layer_spec["num_blocks"]):
args = dict(zip(schema_, layer_spec["block_specs"][i]))
msha = args.pop("msha") if "msha" in args else 0
layers.add_module(f"uib_{i}", UniversalInvertedBottleneckBlock(**args))
if msha:
msha_schema_ = [
"in_channels", "num_heads", "key_dim", "value_dim", "query_h_strides", "query_w_strides", "kv_strides",
"use_layer_scale", "use_multi_query", "use_residual"
]
args = dict(zip(msha_schema_, [args["out_channels"]] + (msha)))
layers.add_module(
f"msha_{i}", MultiHeadSelfAttentionBlock(**args)
)
elif block_names == "fused_ib":
schema_ = ["in_channels", "out_channels", "stride", "expand_ratio", "act"]
for i in range(layer_spec["num_blocks"]):
args = dict(zip(schema_, layer_spec["block_specs"][i]))
layers.add_module(f"fused_ib_{i}", InvertedResidual(**args))
else:
raise NotImplementedError
return layers
class MobileNetV4(nn.Module):
def __init__(self, model, num_classes=1000):
# MobileNetV4ConvSmall MobileNetV4ConvMedium MobileNetV4ConvLarge
# MobileNetV4HybridMedium MobileNetV4HybridLarge
"""Params to initiate MobilenNetV4
Args:
model : support 5 types of models as indicated in
"https://github.com/tensorflow/models/blob/master/official/vision/modeling/backbones/mobilenet.py"
"""
super(MobileNetV4, self).__init__()
# print(MODEL_SPECS.keys(), model not in MODEL_SPECS.keys())
assert model in MODEL_SPECS.keys()
self.model = model
self.num_classes = num_classes
self.spec = MODEL_SPECS[self.model]
# conv0
self.conv0 = build_blocks(self.spec["conv0"])
# layer1
self.layer1 = build_blocks(self.spec["layer1"])
# layer2
self.layer2 = build_blocks(self.spec["layer2"])
# layer3
self.layer3 = build_blocks(self.spec["layer3"])
# layer4
self.layer4 = build_blocks(self.spec["layer4"])
# layer5
self.layer5 = build_blocks(self.spec["layer5"])
# classify [optional]
self.fc = nn.Linear(1280, num_classes)
def forward(self, x, is_feat=False):
x0 = self.conv0(x)
x1 = self.layer1(x0)
x2 = self.layer2(x1)
x3 = self.layer3(x2)
x4 = self.layer4(x3)
x5 = self.layer5(x4)
x5 = F.adaptive_avg_pool2d(x5, 1)
out = self.fc(x5.flatten(1))
if is_feat:
return [x1, x2, x3, x4, x5], out
else:
return out
def create_mobilenetv4(model_name: str, num_classes: int = 1000):
model = MobileNetV4(model_name, num_classes)
return model
# MNV4ConvSmall, MNV4ConvMedium, MNV4ConvLarge, MNV4HybridMedium, MNV4HybridLarge
if __name__ == '__main__':
x = torch.rand((2, 3, 224, 224))
model = create_mobilenetv4(model_name="MNV4HybridLarge")
feats, out = model(x)
print("logit: ", out.shape)
for index, feat in enumerate(feats):
print(f"{index}: ", feat.shape)
from torchsummary import summary
# summary(create_mobilenetv4(model_name="MNV4HybridLarge"), (3, 224, 224))
print(sum([i.numel() for i in model.parameters()]) / 1024 / 1024, "MB")
README.md 0 → 100644
View file @ 68bc58a9
# MobileNetv4
轻量化之王MobileNetV4，手机推理速度3.8ms，在移动CPU、DSP、GPU以及苹果M处理器和谷歌Pixel Edge TPU全都高性能。
## 论文
`MobileNetV4 - Universal Models for the Mobile Ecosystem`
- https://arxiv.org/pdf/2404.10518
## 模型结构
通用UIB块在倒瓶颈块中引入两个可选的DW，一个在扩展层之前，另一个在扩展层和投影层之间，很好地统一了几个重要现有块，包括原始的IB块、ConvNext块以及ViT中的FFN块。此外，UIB还引入了一种新的变体：额外的深度卷积IB（ExtraDW）块；MobileMQA一个专为加速器优化的新型注意力块，它能提供超过39%的推理速度提升。
<div align=center>
<img src="./doc/structure.png"/>
</div>
## 算法原理
利用标准组件引入新的通用反转瓶颈UIB和移动MQA层，并结合改进的神经架构搜索（NAS）方法改进mobilenet，然后将这些与一种新颖的、最先进的蒸馏方法相结合。
<div align=center>
<img src="./doc/algorithm.png"/>
</div>
## 环境配置
```
mv mobilenetv4_pytorch MobileNetv4 # 去框架名后缀
```
### Docker（方法一）
```
docker pull image.sourcefind.cn:5000/dcu/admin/base/pytorch:2.1.0-centos7.6-dtk24.04-py310
# <your IMAGE ID>为以上拉取的docker的镜像ID替换，本镜像为：c85ed27005f2
docker run -it --shm-size=32G -v $PWD/MobileNetv4:/home/MobileNetv4 -v /opt/hyhal:/opt/hyhal:ro --privileged=true --device=/dev/kfd --device=/dev/dri/ --group-add video --name mobilenetv4 <your IMAGE ID> bash
cd /home/MobileNetv4
pip install -r requirements.txt # requirements.txt
```
### Dockerfile（方法二）
```
cd MobileNetv4/docker
docker build --no-cache -t mobilenetv4:latest .
docker run --shm-size=32G --name mobilenetv4 -v /opt/hyhal:/opt/hyhal:ro --privileged=true --device=/dev/kfd --device=/dev/dri/ --group-add video -v $PWD/../../MobileNetv4:/home/MobileNetv4 -it mobilenetv4 bash
# 若遇到Dockerfile启动的方式安装环境需要长时间等待，可注释掉里面的pip安装，启动容器后再安装python库：pip install -r requirements.txt。
```
### Anaconda（方法三）
1、关于本项目DCU显卡所需的特殊深度学习库可从光合开发者社区下载安装：
- https://developer.hpccube.com/tool/
```
DTK驱动:dtk24.04
python:python3.10
torch:2.1.0
torchvision:0.16.0
```
`Tips：以上dtk驱动、python、torch等DCU相关工具版本需要严格一一对应。`
2、其它非特殊库参照requirements.txt安装
```
pip install -r requirements.txt # requirements.txt
```
## 数据集
源论文采用`ImageNet`训练，本步骤说明采用数据集`flowers`，项目中已提供[`flowers`](./datasets/flowers/)迷你数据集进行试用，解压即可，完整数据集请从以下官网下载：
- https://www.kaggle.com/datasets/alxmamaev/flowers-recognition?resource=download
数据目录结构如下：
```
datasets/flowers
├── train
├── ├── daisy
├── ├── ├── xxx.jpg
├── ├── └── xxx.jpg
├── ├── dandelion
├── ├── ├── xxx.jpg
├── ├── └── xxx.jpg
├── ├── rose
├── ├── ├── xxx.jpg
├── ├── └── xxx.jpg
├── ├── sunflower
├── ├── ├── xxx.jpg
├── ├── └── xxx.jpg
├── └── tulip
├── ├── ├── xxx.jpg
├── ├── └── xxx.jpg
└── val
├── ├── daisy
├── ├── ├── xxx.jpg
├── ├── └── xxx.jpg
├── ├── dandelion
├── ├── ├── xxx.jpg
├── ├── └── xxx.jpg
├── ├── rose
├── ├── ├── xxx.jpg
├── ├── └── xxx.jpg
├── ├── sunflower
├── ├── ├── xxx.jpg
├── ├── └── xxx.jpg
├── └── tulip
├── ├── ├── xxx.jpg
├── ├── └── xxx.jpg
```
## 训练
### 单机单卡
```
python train.py --data_path "./datasets/flowers" --num_classes 5 --input_size 256 --gpu 0 # params: 2.383641M
```
更多资料可参考源项目的[`readme_origin`](./readme_origin.md)
## 推理
```
python predict.py
# MODEL_PATH = './checkpoints/model_MNV4ConvSmall_seed901_best.pt' # 使用MNV4ConvSmall训练权重，训练结果位于checkpoints下。
```
## result
`输入：`
```
"results/6089825811_80f253fbe1.jpg"
```
<div align=center>
<img src="./doc/6089825811_80f253fbe1.png"/>
</div>
`输出：`
```
Vertification picture: 6089825811_80f253fbe1.jpg
Recognition result: daisy
Recognition confidence: 0.9952988
```
### 精度
max epoch为300，推理框架：pytorch。
| device | Train_Loss | Train_Acc@1 |
|:---------:|:------:|:------:|
| DCU Z100L | 0.25923 | 94.460 |
| GPU V100S | 0.29128 | 90.720 |
## 应用场景
### 算法类别
`图像识别`
### 热点应用行业
`制造,电商,医疗,能源,教育`
## 源码仓库及问题反馈
- http://developer.hpccube.com/codes/modelzoo/mobilenetv4_pytorch.git
## 参考资料
- https://github.com/Reversev/Deeplearning_pytorch/blob/master/CV_net/MobileNetv4/predict.py
- https://github.com/tensorflow/models/blob/master/official/vision/modeling/backbones/mobilenet.py
- https://github.com/jiaowoguanren0615/MobileNetV4/tree/main
- https://www.jianshu.com/p/992f0ebf656a
classes_indices.json 0 → 100644
View file @ 68bc58a9
{
"0": "daisy",
"1": "dandelion",
"2": "rose",
"3": "sunflower",
"4": "tulip"
}
\ No newline at end of file
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