MobileNetv2模型原理介绍

前言

MobileNet是2017年由Google团队提出的轻量级CNN网络，专注于移动端、嵌入式或IoT设备。它使用深度可分离卷积的思想来减小模型参数与运算量，同时引入宽度系数和分辨率系数以满足不同应用场景的需求。MobileNetV2则采用倒残差结构和Linear Bottlenecks来优化模型，提高准确率并缩小模型尺寸。

操作步骤

数据加载

import math
import numpy as np
import os
import randomfrom matplotlib import pyplot as plt
from easydict import EasyDict
from PIL import Image
import numpy as np
import mindspore.nn as nn
from mindspore import ops as P
from mindspore.ops import add
from mindspore import Tensor
import mindspore.common.dtype as mstype
import mindspore.dataset as de
import mindspore.dataset.vision as C
import mindspore.dataset.transforms as C2
import mindspore as ms
from mindspore import set_context, nn, Tensor, load_checkpoint, save_checkpoint, export
from mindspore.train import Model
from mindspore.train import Callback, LossMonitor, ModelCheckpoint, CheckpointConfigos.environ['GLOG_v'] = '3' # Log level includes 3(ERROR), 2(WARNING), 1(INFO), 0(DEBUG).
os.environ['GLOG_logtostderr'] = '0' # 0：输出到文件，1：输出到屏幕
os.environ['GLOG_log_dir'] = '../../log' # 日志目录
os.environ['GLOG_stderrthreshold'] = '2' # 输出到目录也输出到屏幕：3(ERROR), 2(WARNING), 1(INFO), 0(DEBUG).
set_context(mode=ms.GRAPH_MODE, device_target="CPU", device_id=0) # 设置采用图模式执行，设备为Ascend#

# 垃圾分类数据集标签，以及用于标签映射的字典。
garbage_classes = {'干垃圾': ['贝壳', '打火机', '旧镜子', '扫把', '陶瓷碗', '牙刷', '一次性筷子', '脏污衣服'],'可回收物': ['报纸', '玻璃制品', '篮球', '塑料瓶', '硬纸板', '玻璃瓶', '金属制品', '帽子', '易拉罐', '纸张'],'湿垃圾': ['菜叶', '橙皮', '蛋壳', '香蕉皮'],'有害垃圾': ['电池', '药片胶囊', '荧光灯', '油漆桶']
}class_cn = ['贝壳', '打火机', '旧镜子', '扫把', '陶瓷碗', '牙刷', '一次性筷子', '脏污衣服','报纸', '玻璃制品', '篮球', '塑料瓶', '硬纸板', '玻璃瓶', '金属制品', '帽子', '易拉罐', '纸张','菜叶', '橙皮', '蛋壳', '香蕉皮','电池', '药片胶囊', '荧光灯', '油漆桶']
class_en = ['Seashell', 'Lighter','Old Mirror', 'Broom','Ceramic Bowl', 'Toothbrush','Disposable Chopsticks','Dirty Cloth','Newspaper', 'Glassware', 'Basketball', 'Plastic Bottle', 'Cardboard','Glass Bottle', 'Metalware', 'Hats', 'Cans', 'Paper','Vegetable Leaf','Orange Peel', 'Eggshell','Banana Peel','Battery', 'Tablet capsules','Fluorescent lamp', 'Paint bucket']index_en = {'Seashell': 0, 'Lighter': 1, 'Old Mirror': 2, 'Broom': 3, 'Ceramic Bowl': 4, 'Toothbrush': 5, 'Disposable Chopsticks': 6, 'Dirty Cloth': 7,'Newspaper': 8, 'Glassware': 9, 'Basketball': 10, 'Plastic Bottle': 11, 'Cardboard': 12, 'Glass Bottle': 13, 'Metalware': 14, 'Hats': 15, 'Cans': 16, 'Paper': 17,'Vegetable Leaf': 18, 'Orange Peel': 19, 'Eggshell': 20, 'Banana Peel': 21,'Battery': 22, 'Tablet capsules': 23, 'Fluorescent lamp': 24, 'Paint bucket': 25}# 训练超参
config = EasyDict({"num_classes": 26,"image_height": 224,"image_width": 224,#"data_split": [0.9, 0.1],"backbone_out_channels":1280,"batch_size": 16,"eval_batch_size": 8,"epochs": 10,"lr_max": 0.05,"momentum": 0.9,"weight_decay": 1e-4,"save_ckpt_epochs": 1,"dataset_path": "./data_en","class_index": index_en,"pretrained_ckpt": "./mobilenetV2-200_1067.ckpt" # mobilenetV2-200_1067.ckpt 
})

对垃圾分类数据集进行数据预处理，包括读取数据集、归一化、修改图像频道等操作。对训练集进行RandomCropDecodeResize、RandomHorizontalFlip、RandomColorAdjust、shuffle等操作，对测试集进行Decode、Resize、CenterCrop等操作。

MobileNetV2模型的训练与测试

训练策略

一般情况下，模型训练时采用静态学习率，如0.01。随着训练步数的增加，模型逐渐趋于收敛，对权重参数的更新幅度应该逐渐降低，以减小模型训练后期的抖动。所以，模型训练时可以采用动态下降的学习率，常见的学习率下降策略有：

__all__ = ['MobileNetV2', 'MobileNetV2Backbone', 'MobileNetV2Head', 'mobilenet_v2']def _make_divisible(v, divisor, min_value=None):if min_value is None:min_value = divisornew_v = max(min_value, int(v + divisor / 2) // divisor * divisor)if new_v < 0.9 * v:new_v += divisorreturn new_vclass GlobalAvgPooling(nn.Cell):"""Global avg pooling definition.Args:Returns:Tensor, output tensor.Examples:>>> GlobalAvgPooling()"""def __init__(self):super(GlobalAvgPooling, self).__init__()def construct(self, x):x = P.mean(x, (2, 3))return xclass ConvBNReLU(nn.Cell):"""Convolution/Depthwise fused with Batchnorm and ReLU block definition.Args:in_planes (int): Input channel.out_planes (int): Output channel.kernel_size (int): Input kernel size.stride (int): Stride size for the first convolutional layer. Default: 1.groups (int): channel group. Convolution is 1 while Depthiwse is input channel. Default: 1.Returns:Tensor, output tensor.Examples:>>> ConvBNReLU(16, 256, kernel_size=1, stride=1, groups=1)"""def __init__(self, in_planes, out_planes, kernel_size=3, stride=1, groups=1):super(ConvBNReLU, self).__init__()padding = (kernel_size - 1) // 2in_channels = in_planesout_channels = out_planesif groups == 1:conv = nn.Conv2d(in_channels, out_channels, kernel_size, stride, pad_mode='pad', padding=padding)else:out_channels = in_planesconv = nn.Conv2d(in_channels, out_channels, kernel_size, stride, pad_mode='pad',padding=padding, group=in_channels)layers = [conv, nn.BatchNorm2d(out_planes), nn.ReLU6()]self.features = nn.SequentialCell(layers)def construct(self, x):output = self.features(x)return outputclass InvertedResidual(nn.Cell):"""Mobilenetv2 residual block definition.Args:inp (int): Input channel.oup (int): Output channel.stride (int): Stride size for the first convolutional layer. Default: 1.expand_ratio (int): expand ration of input channelReturns:Tensor, output tensor.Examples:>>> ResidualBlock(3, 256, 1, 1)"""def __init__(self, inp, oup, stride, expand_ratio):super(InvertedResidual, self).__init__()assert stride in [1, 2]hidden_dim = int(round(inp * expand_ratio))self.use_res_connect = stride == 1 and inp == ouplayers = []if expand_ratio != 1:layers.append(ConvBNReLU(inp, hidden_dim, kernel_size=1))layers.extend([ConvBNReLU(hidden_dim, hidden_dim,stride=stride, groups=hidden_dim),nn.Conv2d(hidden_dim, oup, kernel_size=1,stride=1, has_bias=False),nn.BatchNorm2d(oup),])self.conv = nn.SequentialCell(layers)self.cast = P.Cast()def construct(self, x):identity = xx = self.conv(x)if self.use_res_connect:return P.add(identity, x)return xclass MobileNetV2Backbone(nn.Cell):"""MobileNetV2 architecture.Args:class_num (int): number of classes.width_mult (int): Channels multiplier for round to 8/16 and others. Default is 1.has_dropout (bool): Is dropout used. Default is falseinverted_residual_setting (list): Inverted residual settings. Default is Noneround_nearest (list): Channel round to . Default is 8Returns:Tensor, output tensor.Examples:>>> MobileNetV2(num_classes=1000)"""def __init__(self, width_mult=1., inverted_residual_setting=None, round_nearest=8,input_channel=32, last_channel=1280):super(MobileNetV2Backbone, self).__init__()block = InvertedResidual# setting of inverted residual blocksself.cfgs = inverted_residual_settingif inverted_residual_setting is None:self.cfgs = [# t, c, n, s[1, 16, 1, 1],[6, 24, 2, 2],[6, 32, 3, 2],[6, 64, 4, 2],[6, 96, 3, 1],[6, 160, 3, 2],[6, 320, 1, 1],]# building first layerinput_channel = _make_divisible(input_channel * width_mult, round_nearest)self.out_channels = _make_divisible(last_channel * max(1.0, width_mult), round_nearest)features = [ConvBNReLU(3, input_channel, stride=2)]# building inverted residual blocksfor t, c, n, s in self.cfgs:output_channel = _make_divisible(c * width_mult, round_nearest)for i in range(n):stride = s if i == 0 else 1features.append(block(input_channel, output_channel, stride, expand_ratio=t))input_channel = output_channelfeatures.append(ConvBNReLU(input_channel, self.out_channels, kernel_size=1))self.features = nn.SequentialCell(features)self._initialize_weights()def construct(self, x):x = self.features(x)return xdef _initialize_weights(self):"""Initialize weights.Args:Returns:None.Examples:>>> _initialize_weights()"""self.init_parameters_data()for _, m in self.cells_and_names():if isinstance(m, nn.Conv2d):n = m.kernel_size[0] * m.kernel_size[1] * m.out_channelsm.weight.set_data(Tensor(np.random.normal(0, np.sqrt(2. / n),m.weight.data.shape).astype("float32")))if m.bias is not None:m.bias.set_data(Tensor(np.zeros(m.bias.data.shape, dtype="float32")))elif isinstance(m, nn.BatchNorm2d):m.gamma.set_data(Tensor(np.ones(m.gamma.data.shape, dtype="float32")))m.beta.set_data(Tensor(np.zeros(m.beta.data.shape, dtype="float32")))@propertydef get_features(self):return self.featuresclass MobileNetV2Head(nn.Cell):"""MobileNetV2 architecture.Args:class_num (int): Number of classes. Default is 1000.has_dropout (bool): Is dropout used. Default is falseReturns:Tensor, output tensor.Examples:>>> MobileNetV2(num_classes=1000)"""def __init__(self, input_channel=1280, num_classes=1000, has_dropout=False, activation="None"):super(MobileNetV2Head, self).__init__()# mobilenet headhead = ([GlobalAvgPooling(), nn.Dense(input_channel, num_classes, has_bias=True)] if not has_dropout else[GlobalAvgPooling(), nn.Dropout(0.2), nn.Dense(input_channel, num_classes, has_bias=True)])self.head = nn.SequentialCell(head)self.need_activation = Trueif activation == "Sigmoid":self.activation = nn.Sigmoid()elif activation == "Softmax":self.activation = nn.Softmax()else:self.need_activation = Falseself._initialize_weights()def construct(self, x):x = self.head(x)if self.need_activation:x = self.activation(x)return xdef _initialize_weights(self):"""Initialize weights.Args:Returns:None.Examples:>>> _initialize_weights()"""self.init_parameters_data()for _, m in self.cells_and_names():if isinstance(m, nn.Dense):m.weight.set_data(Tensor(np.random.normal(0, 0.01, m.weight.data.shape).astype("float32")))if m.bias is not None:m.bias.set_data(Tensor(np.zeros(m.bias.data.shape, dtype="float32")))@propertydef get_head(self):return self.headclass MobileNetV2(nn.Cell):"""MobileNetV2 architecture.Args:class_num (int): number of classes.width_mult (int): Channels multiplier for round to 8/16 and others. Default is 1.has_dropout (bool): Is dropout used. Default is falseinverted_residual_setting (list): Inverted residual settings. Default is Noneround_nearest (list): Channel round to . Default is 8Returns:Tensor, output tensor.Examples:>>> MobileNetV2(backbone, head)"""def __init__(self, num_classes=1000, width_mult=1., has_dropout=False, inverted_residual_setting=None, \round_nearest=8, input_channel=32, last_channel=1280):super(MobileNetV2, self).__init__()self.backbone = MobileNetV2Backbone(width_mult=width_mult, \inverted_residual_setting=inverted_residual_setting, \round_nearest=round_nearest, input_channel=input_channel, last_channel=last_channel).get_featuresself.head = MobileNetV2Head(input_channel=self.backbone.out_channel, num_classes=num_classes, \has_dropout=has_dropout).get_headdef construct(self, x):x = self.backbone(x)x = self.head(x)return xclass MobileNetV2Combine(nn.Cell):"""MobileNetV2Combine architecture.Args:backbone (Cell): the features extract layers.head (Cell):  the fully connected layers.Returns:Tensor, output tensor.Examples:>>> MobileNetV2(num_classes=1000)"""def __init__(self, backbone, head):super(MobileNetV2Combine, self).__init__(auto_prefix=False)self.backbone = backboneself.head = headdef construct(self, x):x = self.backbone(x)x = self.head(x)return xdef mobilenet_v2(backbone, head):return MobileNetV2Combine(backbone, head)

在进行深度学习模型训练前的准备工作，包括定义训练函数、读取数据、实例化模型、定义优化器和损失函数。其中详细介绍了损失函数和优化器的概念，以及训练过程中损失函数的作用和优化器的使用。同时还说明了在训练MobileNetV2模型时对参数的固定和损失函数的选择，以及训练过程中损失值和精度的变化情况。