1 MobileNet v1网络

论文链接：MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications

1.1 Depthwise convolution（DW卷积）

DW卷积大大减少了运算量和参数数量
卷积核的深度channel=1
in_channels=卷积核个数=out_channels

1.1.1Depthwise separable convolution（深度可分的卷积操作）

深度可分的卷积操作包括DW（Depthwise）和PW（Pointwise）

传统卷积：

DW+PW卷积：

MobileNet Comparison to Popular Models

1.2 增加超参数α和β

2 MobileNet v2网络

相比MobileNet v1 网络准确率更高 参数更小
论文链接：MobileNetV2: Inverted Residuals and Linear Bottlenecks

2.1 Inverted Residuals（倒残差结构）

2.2 Linear Bottlenecks

针对倒残差结构最后一个1×1的卷积层，它使用了线性的激活函数而不是relu激活函数
只有stride=1且in_channels=out_channels时，才有捷径分支

relu6激活函数公式：
$$f(x)=min(max(x,0),6)$$

MobileNet v2网络模型结构参数

2.3 MobileNet v3

论文链接：Searching for MobileNetV3

3 课程代码

3.1 modle_v2.py

from torch import nn
import torchdef _make_divisible(ch, divisor=8, min_ch=None):"""This function is taken from the original tf repo.It ensures that all layers have a channel number that is divisible by 8It can be seen here:https://github.com/tensorflow/models/blob/master/research/slim/nets/mobilenet/mobilenet.py"""if min_ch is None:min_ch = divisornew_ch = max(min_ch, int(ch + divisor / 2) // divisor * divisor)# Make sure that round down does not go down by more than 10%.if new_ch < 0.9 * ch:new_ch += divisorreturn new_chclass ConvBNReLU(nn.Sequential):def __init__(self, in_channel, out_channel, kernel_size=3, stride=1, groups=1):padding = (kernel_size - 1) // 2super(ConvBNReLU, self).__init__(nn.Conv2d(in_channel, out_channel, kernel_size, stride, padding, groups=groups, bias=False),nn.BatchNorm2d(out_channel),nn.ReLU6(inplace=True))class InvertedResidual(nn.Module):def __init__(self, in_channel, out_channel, stride, expand_ratio):super(InvertedResidual, self).__init__()hidden_channel = in_channel * expand_ratioself.use_shortcut = stride == 1 and in_channel == out_channellayers = []if expand_ratio != 1:# 1x1 pointwise convlayers.append(ConvBNReLU(in_channel, hidden_channel, kernel_size=1))layers.extend([# 3x3 depthwise convConvBNReLU(hidden_channel, hidden_channel, stride=stride, groups=hidden_channel),# 1x1 pointwise conv(linear)nn.Conv2d(hidden_channel, out_channel, kernel_size=1, bias=False),nn.BatchNorm2d(out_channel),])self.conv = nn.Sequential(*layers)def forward(self, x):if self.use_shortcut:return x + self.conv(x)else:return self.conv(x)class MobileNetV2(nn.Module):def __init__(self, num_classes=1000, alpha=1.0, round_nearest=8):super(MobileNetV2, self).__init__()block = InvertedResidualinput_channel = _make_divisible(32 * alpha, round_nearest)last_channel = _make_divisible(1280 * alpha, round_nearest)inverted_residual_setting = [# 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],]features = []# conv1 layerfeatures.append(ConvBNReLU(3, input_channel, stride=2))# building inverted residual residual blockesfor t, c, n, s in inverted_residual_setting:output_channel = _make_divisible(c * alpha, 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_channel# building last several layersfeatures.append(ConvBNReLU(input_channel, last_channel, 1))# combine feature layersself.features = nn.Sequential(*features)# building classifierself.avgpool = nn.AdaptiveAvgPool2d((1, 1))self.classifier = nn.Sequential(nn.Dropout(0.2),nn.Linear(last_channel, num_classes))# weight initializationfor 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.init.ones_(m.weight)nn.init.zeros_(m.bias)elif isinstance(m, nn.Linear):nn.init.normal_(m.weight, 0, 0.01)nn.init.zeros_(m.bias)def forward(self, x):x = self.features(x)x = self.avgpool(x)x = torch.flatten(x, 1)x = self.classifier(x)return x

3.2 train.py

import os
import sys
import jsonimport torch
import torch.nn as nn
import torch.optim as optim
from torchvision import transforms, datasets
from tqdm import tqdmfrom model_v2 import MobileNetV2def main():device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")print("using {} device.".format(device))batch_size = 16epochs = 5data_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 pathimage_path = os.path.join(data_root, "data_set", "flower_data")  # flower data set pathassert 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_idxcla_dict = dict((val, key) for key, val in flower_list.items())# write dict into json filejson_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 workersprint('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 modelnet = MobileNetV2(num_classes=5)# load pretrain weights# download url: https://download.pytorch.org/models/mobilenet_v2-b0353104.pthmodel_weight_path = "mobilenet_v2.pth"assert os.path.exists(model_weight_path), "file {} dose not exist.".format(model_weight_path)pre_weights = torch.load(model_weight_path, map_location='cpu')# delete classifier weightspre_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 weightsfor param in net.features.parameters():param.requires_grad = Falsenet.to(device)# define loss functionloss_function = nn.CrossEntropyLoss()# construct an optimizerparams = [p for p in net.parameters() if p.requires_grad]optimizer = optim.Adam(params, lr=0.0001)best_acc = 0.0save_path = './MobileNetV2.pth'train_steps = len(train_loader)for epoch in range(epochs):# trainnet.train()running_loss = 0.0train_bar = tqdm(train_loader, file=sys.stdout)for step, data in enumerate(train_bar):images, labels = dataoptimizer.zero_grad()logits = net(images.to(device))loss = loss_function(logits, labels.to(device))loss.backward()optimizer.step()# print statisticsrunning_loss += loss.item()train_bar.desc = "train epoch[{}/{}] loss:{:.3f}".format(epoch + 1,epochs,loss)# validatenet.eval()acc = 0.0  # accumulate accurate number / epochwith torch.no_grad():val_bar = tqdm(validate_loader, file=sys.stdout)for val_data in val_bar:val_images, val_labels = val_dataoutputs = 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_numprint('[epoch %d] train_loss: %.3f  val_accuracy: %.3f' %(epoch + 1, running_loss / train_steps, val_accurate))if val_accurate > best_acc:best_acc = val_accuratetorch.save(net.state_dict(), save_path)print('Finished Training')if __name__ == '__main__':main()

3.3 predict.py

import os
import jsonimport torch
from PIL import Image
from torchvision import transforms
import matplotlib.pyplot as pltfrom model_v2 import MobileNetV2def 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 imageimg_path = "1.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 dimensionimg = torch.unsqueeze(img, dim=0)# read class_indictjson_path = './class_indices.json'assert os.path.exists(json_path), "file: '{}' dose not exist.".format(json_path)with open(json_path, "r") as f:class_indict = json.load(f)# create modelmodel = MobileNetV2(num_classes=5).to(device)# load model weightsmodel_weight_path = "./MobileNetV2.pth"model.load_state_dict(torch.load(model_weight_path, map_location=device))model.eval()with torch.no_grad():# predict classoutput = 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)for i in range(len(predict)):print("class: {:10}   prob: {:.3}".format(class_indict[str(i)],predict[i].numpy()))plt.show()if __name__ == '__main__':main()

1.jpg预测的是向日葵

2.jpg预测的是蒲公英