• MobileNet系列(5):使用pytorch搭建MobileNetV3并基于迁移学习训练

    本博文实现的MobileNetV3的代码，参考pytorch官方实现的mobilenet源码

    在这里插入图片描述

    MobileNetV3 网络结构表

    详细的MobileNetV3 网络讲解，参考博文：MobileNet系列(4):MobileNetv3网络详解

    代码详解

    打开model_v3.py文件

    • 将channel调整到离它最近的8的整数倍
    def _make_divisible(ch,divisor=8,min_ch=None):
	if min_ch is None:
		min_ch=divisor
	new_ch=max(min_ch,int(ch+divisor/2)//divisor*divisor)
	if new_ch <0.9*ch:
		new_ch += divisor
	return new_ch

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    • V3网络的卷积结构
      在V3网络中使用的卷积，基本上都是：卷积Conv +BN+激活函数,这里定义一个卷积类ConvBNActivation
    class ConvBNActivation(nn.Sequential):
	def __init__(self,
				in_planes:int,
				out_planes:int,
				kernel_size:int =3,
				stride:int =1,
				groups:int=1,
				norm_layer:Optional[Callable[...,nn.Module]]=None,
				activation_layer:Optional[Callable[...,nn.Module]]=None
				):
		padding=(kernel_size-1)//2
		if norm_layer is None:
			norm_layer =nn.BatchNorm2d
		if activation_layer is None:
			activation_layer = nn.ReLU6
		super(ConvBNActivation,self).__init__(nn.Conv2d(in_channels=in_planes,
														out_channels=out_planes,
														kernel_size=kernel_size,
														stride=stride,
														padding=padding,
														groups=groups,
														bias=False,
														norm_layer(out_planes),
														activation_layer))		

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    • SE模块
      在这里插入图片描述
    • 在之前博客中，我们有讲到过SE模块，其实是两个全连接层。对于第一个全连接层，它的节点个数是输入特征矩阵channel的1/4,而第二个全链接层它的节点个数与我们输入特征矩阵的channel是保持一致的。
      注意：第一个卷积层它的激活函是ReLU，第二个卷积层它的激活函数使h-sigmoid激活函数。

    SE模块的代码如下：

    class SqueezeExcitaion(nn.Module):
	def __init__(self,input_c:int,squeeze_factor:int=4):
		super(SqueezeExcitaion,self).__init__()
		squeeze_c=_make_divisible(input_c//squeeze_factor,8)
		self.fc1 = nn.Conv2d(input_c,squeeze_c,1)
		self.fc2 = nn.Conv2d(squeeze_c,input_c,1)
	
	def forward(self,x:Tensor)  -> Tensor:
		scale = F.adaptive_avg_pool2d(x,output_size=(1,1))
		scale =self.fc1(scale)
		scale=F.relu(scale,inplace=True)
		scale=self.fc2(scale)
		scale=F.hardsigmoid(scale,inplace=True)
		return scale * x	

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    网络配置InvertedResidualConfig
    针对MobileNetv3每一层的参数，参数如下表所示：
    在这里插入图片描述
    网络参数配置类：InvertedResidualConfig

    class InvertedResidualConfig:
	def __init__(self,
				input_c:int,
				kernel:int,
				expanded_c:int,
				out_c:int,
				use_se:bool,
				activation:str,
				stride:int,
				width_multi:float) #width_multi 卷积层使用channel的倍率因子
			self.input_c=self.adjust_channels(input_c,width_multi)
			self.kernel=kernel
			self.expanded_c=self.adjust_channels(expanded_c,width_multi)
			self.out_c=self.adjust_channels(out_c,width_multi)
			self.use_se=use_se
			self.use_hs=ativation=="HS" #whether using h-switch activation
			self.stride=stride
	@staticmethod
	def adjust_channels(channels:int,width:float):
		return _make_divisivle(channels*width_multi,8)

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    bneck模块
    mobilenectv3网络是由一系列的bneck堆叠形成的，bneck模块详解参考之前博客。
    在这里插入图片描述

    创建类InvertedResidual继承nn.Module

    class InvertedResidual(nn.Module):
	def __init__(self,
				cnf:InvertedResidualConfig,
				norm_layer:Callable[...,nn.Module]):
		super(InvertedResidual,self).__init__()
		
		if cnf.stride not in [1,2]:
			raise ValueError("illegal stride value.")
		self.use_res_connect=(cnf.stride ==1 and cnf.input_c=cnf.out_c)
		layers:List[nn.Module] = []
		# 使用nn.Hardswish pytorch版本需要1.7或以上
		activation_layer = nn.Hardswish if cnf.use_hs else nn.ReLU 
		
		#expand
		#网络结构中，第一个bneck的 input channel和exp size大小相等，即没有通过1x1卷积升维，因此第一个bneck没有1x1卷积
		if cnf.expanded_c ! = cnf.input_c: #相等没有1x1卷积升维，不等表示有1x1卷积升维
			layers.append(ConvBNActivation(cnf.input_c,
											cnf.expanded_c,
											kernel_size=1,
											norm_layer=norm_layer,
											activation_layer=activation_layer
										))
		# depthwise
		layers.append(ConvBNActivation(
									 cnf.expanded_c,
									 cnf.expanded_c,
									 kernel_size=cnf.kernel,
									 stride=cnf.stride,
									 groups=cnf.expanded_c, # depthwise卷积 groups=通道数
									 norm_layer=norm_layer,
									 activation_layer=activation_layer
									))
		if cnf.use_se:
			layers.append(SqueezeExcitaion(cnf.expaned_c))
		
		# 1x1降维的卷积层
		layers.append(ConvBNActivation( cnf.expanded_c,
										cnf.out_c,
										kernel_size=1,
										norm_layer=norm_layer,
										activation_layer=nn.Identity #线性激活
										))
		self.block=nn.Sequential(*layers)
		self.out_channel=cnf.out_c
	
	def forward(self,x:Tensor) -> Tensor
		result=self.block(x)
		if self.use_res_connect:
			result +=x
		return result

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    • 构建MobileNetV3
    class MobileNetV3(nn.Module):
	def __init__(self,
				inverted_residual_setting:List[InvertedResidualConfig],
				last_channel:int,
				num_classes:int =1000,
				block:Optional[Callable[...,nn.Module]]=None,
				norm_layer:Optional[Callable[...,nn.Module]]=None
				):
			super(MobileNetVe,self).__init__()
			
			if not inverted_residual_setting:
				raise ValueError("The inverted_residual_setting should not be empty")
			elif not (isinstance(inverted_residual_setting,List) and
			         all([isInstance(s,InvertedResidualConfig) for s in inverted_residual_setting]))
			     raise TypeError("The inverted_residual_setting should be List[InvertedResidualConfig]")
			
			if block is None:
				block = InvertedResidual
			
			if norm_layer is None:
				norm_layer=partial(nn.BatchNorm2d,eps=0.001,momentum=0.01)
			
			layers:List[nn.Module] = []
			
			# building first layer
			fisrtconv_output_c =inverted_residual_setting[0].input_c
			layers.append(ConvBNActivation(3,
											firstconv_output_c,
											kernel_size=3,
											stride=2,
											norm_layer=norm_layer,
											activation_layer=nn.Hardwish))
			# building inverted residual block
			for cnf in inverted_residual_setting:
				layers.append(block(cnf,norm_layer))
			
			# building last several layers
			lastconv_input_c=inverted_residual_setting[-1].out_c
			lastconv_output_c=6*lastconv_input_c
			layers.append(ConvBNActivation(lastconv_input_c,
										  lastconv_output_c,
										  kernel_size=1,
										  norm_layer =norm_layer,
										  activation_layer=nn.Hardswish
		    ))
		    self.features =nn.Sequential(*layers)
		    self.avgpool=nn.AdaptiveAvgPool2d(1)
		    self.classifier=nn.Sequential(nn.Linear(lastconv_output_c,last_channel),
		    							 nn.Hardswish(inplace=True),
		    							 nn.Dropout(p=0.2,inplace=True),
		    							 nn.Linear(last_channel,num_classes))
		    
		    # initial weights
		    for m in self.modules():
		    	if isinstance(m.nn.Conv2d):
		    		nn.init.kaiming_normal_(m.weight,mode="fan_out")
		    		if m.bias is not None:
		    			nn.init.zeros_(m.bias)
		    		elif isinstance(m,(nn.BatchNorm2d,nn.GroupNorm)):
		    			nn.init.ones_(m.weight)
		    			nn.init.zers_(m.bias)
		    		elif isinstance(m,nn.Linear):
		    			nn.init.normal_(m.weight,0,0.01)
		    			nn.init.zeros(m.bias)
	def _forward_impl(self,x:Tensor) ->Tensor:
		x=self.features(x)
		x=self.avgpool(x)
		x=torch.flatten(x,1)
		x=self.classifier(x)
		return x
	
	def forward(self,x:Tensor) ->Tensor:
		return self._forward_impl(x)

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    • 构建MobileNetV3 Large
    def mobilenet_v3_large(num_classes: int = 1000,
                       reduced_tail: bool = False) -> MobileNetV3:
    """
    Constructs a large MobileNetV3 architecture from
    "Searching for MobileNetV3" <https://arxiv.org/abs/1905.02244>.

    weights_link:
    https://download.pytorch.org/models/mobilenet_v3_large-8738ca79.pth

    Args:
        num_classes (int): number of classes
        reduced_tail (bool): If True, reduces the channel counts of all feature layers
            between C4 and C5 by 2. It is used to reduce the channel redundancy in the
            backbone for Detection and Segmentation.
    """
    width_multi = 1.0
    bneck_conf = partial(InvertedResidualConfig, width_multi=width_multi)
    adjust_channels = partial(InvertedResidualConfig.adjust_channels, width_multi=width_multi)

    reduce_divider = 2 if reduced_tail else 1

    inverted_residual_setting = [
        # input_c, kernel, expanded_c, out_c, use_se, activation, stride
        bneck_conf(16, 3, 16, 16, False, "RE", 1),
        bneck_conf(16, 3, 64, 24, False, "RE", 2),  # C1
        bneck_conf(24, 3, 72, 24, False, "RE", 1),
        bneck_conf(24, 5, 72, 40, True, "RE", 2),  # C2
        bneck_conf(40, 5, 120, 40, True, "RE", 1),
        bneck_conf(40, 5, 120, 40, True, "RE", 1),
        bneck_conf(40, 3, 240, 80, False, "HS", 2),  # C3
        bneck_conf(80, 3, 200, 80, False, "HS", 1),
        bneck_conf(80, 3, 184, 80, False, "HS", 1),
        bneck_conf(80, 3, 184, 80, False, "HS", 1),
        bneck_conf(80, 3, 480, 112, True, "HS", 1),
        bneck_conf(112, 3, 672, 112, True, "HS", 1),
        bneck_conf(112, 5, 672, 160 // reduce_divider, True, "HS", 2),  # C4
        bneck_conf(160 // reduce_divider, 5, 960 // reduce_divider, 160 // reduce_divider, True, "HS", 1),
        bneck_conf(160 // reduce_divider, 5, 960 // reduce_divider, 160 // reduce_divider, True, "HS", 1),
    ]
    last_channel = adjust_channels(1280 // reduce_divider)  # C5

    return MobileNetV3(inverted_residual_setting=inverted_residual_setting,
                       last_channel=last_channel,
                       num_classes=num_classes)

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    • 构建MobileNetV3 Small
    def mobilenet_v3_small(num_classes: int = 1000,
                       reduced_tail: bool = False) -> MobileNetV3:
    """
    Constructs a large MobileNetV3 architecture from
    "Searching for MobileNetV3" <https://arxiv.org/abs/1905.02244>.

    weights_link:
    https://download.pytorch.org/models/mobilenet_v3_small-047dcff4.pth

    Args:
        num_classes (int): number of classes
        reduced_tail (bool): If True, reduces the channel counts of all feature layers
            between C4 and C5 by 2. It is used to reduce the channel redundancy in the
            backbone for Detection and Segmentation.
    """
    width_multi = 1.0
    bneck_conf = partial(InvertedResidualConfig, width_multi=width_multi)
    adjust_channels = partial(InvertedResidualConfig.adjust_channels, width_multi=width_multi)

    reduce_divider = 2 if reduced_tail else 1

    inverted_residual_setting = [
        # input_c, kernel, expanded_c, out_c, use_se, activation, stride
        bneck_conf(16, 3, 16, 16, True, "RE", 2),  # C1
        bneck_conf(16, 3, 72, 24, False, "RE", 2),  # C2
        bneck_conf(24, 3, 88, 24, False, "RE", 1),
        bneck_conf(24, 5, 96, 40, True, "HS", 2),  # C3
        bneck_conf(40, 5, 240, 40, True, "HS", 1),
        bneck_conf(40, 5, 240, 40, True, "HS", 1),
        bneck_conf(40, 5, 120, 48, True, "HS", 1),
        bneck_conf(48, 5, 144, 48, True, "HS", 1),
        bneck_conf(48, 5, 288, 96 // reduce_divider, True, "HS", 2),  # C4
        bneck_conf(96 // reduce_divider, 5, 576 // reduce_divider, 96 // reduce_divider, True, "HS", 1),
        bneck_conf(96 // reduce_divider, 5, 576 // reduce_divider, 96 // reduce_divider, True, "HS", 1)
    ]
    last_channel = adjust_channels(1024 // reduce_divider)  # C5

    return MobileNetV3(inverted_residual_setting=inverted_residual_setting,
                       last_channel=last_channel,
                       num_classes=num_classes)

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    网络训练

    训练的图像数据集下载
    train.py

    import os
import json

import torch
import torch.nn as nn
import torch.optim as optim
from torchvision import transforms, datasets
from tqdm import tqdm

from model_v2 import MobileNetV2  # 可以替换为MobileNetV3 MobileNetV3-Large MobileNetV3-Smalll


def main():
    device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
    print("using {} device.".format(device))

    batch_size = 16 #根据显存大小调整
    epochs = 5

    data_transform = {
        "train": transforms.Compose([transforms.RandomResizedCrop(224),
                                     transforms.RandomHorizontalFlip(),
                                     transforms.ToTensor(),
                                     transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])]),
        "val": transforms.Compose([transforms.Resize(256),
                                   transforms.CenterCrop(224),
                                   transforms.ToTensor(),
                                   transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])])}

    data_root = os.path.abspath(os.path.join(os.getcwd(), "../.."))  # get data root path
    image_path = os.path.join(data_root, "data_set", "flower_data")  # flower data set path
    assert os.path.exists(image_path), "{} path does not exist.".format(image_path)
    train_dataset = datasets.ImageFolder(root=os.path.join(image_path, "train"),
                                         transform=data_transform["train"])
    train_num = len(train_dataset)

    # {'daisy':0, 'dandelion':1, 'roses':2, 'sunflower':3, 'tulips':4}
    flower_list = train_dataset.class_to_idx
    cla_dict = dict((val, key) for key, val in flower_list.items())
    # write dict into json file
    json_str = json.dumps(cla_dict, indent=4)
    with open('class_indices.json', 'w') as json_file:
        json_file.write(json_str)

    nw = min([os.cpu_count(), batch_size if batch_size > 1 else 0, 8])  # number of workers
    print('Using {} dataloader workers every process'.format(nw))

    train_loader = torch.utils.data.DataLoader(train_dataset,
                                               batch_size=batch_size, shuffle=True,
                                               num_workers=nw)

    validate_dataset = datasets.ImageFolder(root=os.path.join(image_path, "val"),
                                            transform=data_transform["val"])
    val_num = len(validate_dataset)
    validate_loader = torch.utils.data.DataLoader(validate_dataset,
                                                  batch_size=batch_size, shuffle=False,
                                                  num_workers=nw)

    print("using {} images for training, {} images for validation.".format(train_num,
                                                                           val_num))

    # create model
    net = MobileNetV2(num_classes=5)   # 可以替换为MobileNetV3 MobileNetV3-Large MobileNetV3-Smalll

    # load pretrain weights
    # download url: https://download.pytorch.org/models/mobilenet_v2-b0353104.pth
    model_weight_path = "./mobilenet_v2.pth"  # 可以替换MobileNetV3 MobileNetV3-Large MobileNetV3-Smalll 的预训练权重

    assert os.path.exists(model_weight_path), "file {} dose not exist.".format(model_weight_path)
    pre_weights = torch.load(model_weight_path, map_location=device)

    # delete classifier weights
    pre_dict = {k: v for k, v in pre_weights.items() if net.state_dict()[k].numel() == v.numel()}
    missing_keys, unexpected_keys = net.load_state_dict(pre_dict, strict=False)

    # freeze features weights
    for param in net.features.parameters():
        param.requires_grad = False

    net.to(device)

    # define loss function
    loss_function = nn.CrossEntropyLoss()

    # construct an optimizer
    params = [p for p in net.parameters() if p.requires_grad]
    optimizer = optim.Adam(params, lr=0.0001)

    best_acc = 0.0
    save_path = './MobileNetV2.pth'
    train_steps = len(train_loader)
    for epoch in range(epochs):
        # train
        net.train()
        running_loss = 0.0
        train_bar = tqdm(train_loader)
        for step, data in enumerate(train_bar):
            images, labels = data
            optimizer.zero_grad()
            logits = net(images.to(device))
            loss = loss_function(logits, labels.to(device))
            loss.backward()
            optimizer.step()

            # print statistics
            running_loss += loss.item()

            train_bar.desc = "train epoch[{}/{}] loss:{:.3f}".format(epoch + 1,
                                                                     epochs,
                                                                     loss)

        # validate
        net.eval()
        acc = 0.0  # accumulate accurate number / epoch
        with torch.no_grad():
            val_bar = tqdm(validate_loader)
            for val_data in val_bar:
                val_images, val_labels = val_data
                outputs = net(val_images.to(device))
                # loss = loss_function(outputs, test_labels)
                predict_y = torch.max(outputs, dim=1)[1]
                acc += torch.eq(predict_y, val_labels.to(device)).sum().item()

                val_bar.desc = "valid epoch[{}/{}]".format(epoch + 1,
                                                           epochs)
        val_accurate = acc / val_num
        print('[epoch %d] train_loss: %.3f  val_accuracy: %.3f' %
              (epoch + 1, running_loss / train_steps, val_accurate))

        if val_accurate > best_acc:
            best_acc = val_accurate
            torch.save(net.state_dict(), save_path)

    print('Finished Training')


if __name__ == '__main__':
    main()


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    模型预测

    predict.py

    import os
import json

import torch
from PIL import Image
from torchvision import transforms
import matplotlib.pyplot as plt

from model_v2 import MobileNetV2


def main():
    device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")

    data_transform = transforms.Compose(
        [transforms.Resize(256),
         transforms.CenterCrop(224),
         transforms.ToTensor(),
         transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])])

    # load image
    img_path = "../tulip.jpg"
    assert os.path.exists(img_path), "file: '{}' dose not exist.".format(img_path)
    img = Image.open(img_path)
    plt.imshow(img)
    # [N, C, H, W]
    img = data_transform(img)
    # expand batch dimension
    img = torch.unsqueeze(img, dim=0)

    # read class_indict
    json_path = './class_indices.json'
    assert os.path.exists(json_path), "file: '{}' dose not exist.".format(json_path)

    json_file = open(json_path, "r")
    class_indict = json.load(json_file)

    # create model
    model = MobileNetV2(num_classes=5).to(device)
    # load model weights
    model_weight_path = "./MobileNetV2.pth"
    model.load_state_dict(torch.load(model_weight_path, map_location=device))
    model.eval()
    with torch.no_grad():
        # predict class
        output = torch.squeeze(model(img.to(device))).cpu()
        predict = torch.softmax(output, dim=0)
        predict_cla = torch.argmax(predict).numpy()

    print_res = "class: {}   prob: {:.3}".format(class_indict[str(predict_cla)],
                                                 predict[predict_cla].numpy())
    plt.title(print_res)
    print(print_res)
    plt.show()


if __name__ == '__main__':
    main()

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    模型预测效果
    在这里插入图片描述

    源码下载

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  • 原文地址：https://blog.csdn.net/weixin_38346042/article/details/125520003