深度学习笔记35_Inception v1算法实战与解析

🍨 本文为🔗365天深度学习训练营中的学习记录博客
🍖 原作者：K同学啊 | 接辅导、项目定制

一、我的环境：

1.语言环境：Python 3.8

2.编译器：Pycharm

3.深度学习环境：

torch==1.12.1+cu113
torchvision==0.13.1+cu113

二、GPU设置：

若使用的是cpu则可忽略

import torchdevice = torch.device("cuda" if torch.cuda.is_available() else "cpu")
print(device)

三、导入数据：

import os,PIL,random,pathlibdata_dir = './data/'
data_dir = pathlib.Path(data_dir)data_paths = list(data_dir.glob('*'))
classeNames = [str(path).split("\\")[1] for path in data_paths]
print(classeNames)

运行结果：

['Monkeypox', 'Others']

total_datadir = './data/'# 关于transforms.Compose的更多介绍可以参考：https://blog.csdn.net/qq_38251616/article/details/124878863
train_transforms = transforms.Compose([transforms.Resize([224, 224]),  # 将输入图片resize成统一尺寸transforms.ToTensor(),          # 将PIL Image或numpy.ndarray转换为tensor，并归一化到[0,1]之间transforms.Normalize(           # 标准化处理-->转换为标准正太分布（高斯分布），使模型更容易收敛mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])  # 其中 mean=[0.485,0.456,0.406]与std=[0.229,0.224,0.225] 从数据集中随机抽样计算得到的。
])total_data = datasets.ImageFolder(total_datadir,transform=train_transforms)
print(total_data)

运行结果：

Dataset ImageFolderNumber of datapoints: 2142Root location: ./data/StandardTransform
Transform: Compose(Resize(size=[224, 224], interpolation=bilinear, max_size=None, antialias=None)ToTensor()Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]))

四、数据可视化：

# 指定图像文件夹路径
image_folder = './data/Others/'# 获取文件夹中的所有图像文件
image_files = [f for f in os.listdir(image_folder) if f.endswith((".jpg", ".png", ".jpeg"))]# 创建Matplotlib图像
fig, axes = plt.subplots(2, 4, figsize=(16, 6))# 使用列表推导式加载和显示图像
for ax, img_file in zip(axes.flat, image_files):img_path = os.path.join(image_folder, img_file)img = Image.open(img_path)ax.imshow(img)ax.axis('off')# 显示图像
plt.tight_layout()
plt.show()

运行结果：

五、划分数据集

train_size = int(0.8 * len(total_data))
test_size  = len(total_data) - train_size
train_dataset, test_dataset = torch.utils.data.random_split(total_data, [train_size, test_size])
print(train_size,test_size)

batch_size = 32train_dl = torch.utils.data.DataLoader(train_dataset,batch_size=batch_size,shuffle=True,num_workers=1)
test_dl = torch.utils.data.DataLoader(test_dataset,batch_size=batch_size,shuffle=True,num_workers=1)

for X, y in test_dl:print("Shape of X [N, C, H, W]: ", X.shape)print("Shape of y: ", y.shape, y.dtype)break

运行结果：

Shape of X [N, C, H, W]:  torch.Size([32, 3, 224, 224])
Shape of y:  torch.Size([32]) torch.int64

六、构建模型

class inception_block(nn.Module):def __init__(self, in_channels, ch1x1, ch3x3red, ch3x3, ch5x5red, ch5x5, pool_proj):super(inception_block, self).__init__()# 1x1 conv branchself.branch1 = nn.Sequential(nn.Conv2d(in_channels, ch1x1, kernel_size=1),nn.BatchNorm2d(ch1x1),nn.ReLU(inplace=True))# 1x1 conv -> 3x3 conv branchself.branch2 = nn.Sequential(nn.Conv2d(in_channels, ch3x3red, kernel_size=1),nn.BatchNorm2d(ch3x3red),nn.ReLU(inplace=True),nn.Conv2d(ch3x3red, ch3x3, kernel_size=3, padding=1),nn.BatchNorm2d(ch3x3),nn.ReLU(inplace=True))# 1x1 conv -> 5x5 conv branchself.branch3 = nn.Sequential(nn.Conv2d(in_channels, ch5x5red, kernel_size=1),nn.BatchNorm2d(ch5x5red),nn.ReLU(inplace=True),nn.Conv2d(ch5x5red, ch5x5, kernel_size=5, padding=2),nn.BatchNorm2d(ch5x5),nn.ReLU(inplace=True))# 3x3 max pooling -> 1x1 conv branchself.branch4 = nn.Sequential(nn.MaxPool2d(kernel_size=3, stride=1, padding=1),nn.Conv2d(in_channels, pool_proj, kernel_size=1),nn.BatchNorm2d(pool_proj),nn.ReLU(inplace=True))def forward(self, x):# compute forward pass through all branches# and concatenate the outout feature mapsbranch1_output = self.branch1(x)branch2_output = self.branch2(x)branch3_output = self.branch3(x)branch4_output = self.branch4(x)outputs = [branch1_output, branch2_output, branch3_output, branch4_output]return torch.cat(outputs, 1)class InceptionV1(nn.Module):def __init__(self, num_classes=4):super(InceptionV1, self).__init__()self.conv1 = nn.Conv2d(3, 64, kernel_size=7, stride=2, padding=3)self.maxpool1 = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)self.conv2 = nn.Conv2d(64, 64, kernel_size=1, stride=1, padding=0)self.conv3 = nn.Conv2d(64, 192, kernel_size=3, stride=1, padding=1)self.maxpool2 = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)self.inception3a = inception_block(192, 64, 96, 128, 16, 32, 32)self.inception3b = inception_block(256, 128, 128, 192, 32, 96, 64)self.maxpool3 = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)self.inception4a = inception_block(480, 192, 96, 208, 16, 48, 64)self.inception4b = inception_block(512, 160, 112, 224, 24, 64, 64)self.inception4c = inception_block(512, 128, 128, 256, 24, 64, 64)self.inception4d = inception_block(512, 112, 144, 288, 32, 64, 64)self.inception4e = inception_block(528, 256, 160, 320, 32, 128, 128)self.maxpool4 = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)self.inception5a = inception_block(832, 256, 160, 320, 32, 128, 128)self.inception5b = nn.Sequential(inception_block(832, 384, 192, 384, 48, 128, 128),nn.AvgPool2d(kernel_size=7, stride=1, padding=0),nn.Dropout(0.4))# 全连接网络层，用于分类self.classifier = nn.Sequential(nn.Linear(in_features=1024, out_features=1024),nn.ReLU(),nn.Linear(in_features=1024, out_features=num_classes),nn.Softmax(dim=1))def forward(self, x):x = self.conv1(x)x = F.relu(x)x = self.maxpool1(x)x = self.conv2(x)x = F.relu(x)x = self.conv3(x)x = F.relu(x)x = self.maxpool2(x)x = self.inception3a(x)x = self.inception3b(x)x = self.maxpool3(x)x = self.inception4a(x)x = self.inception4b(x)x = self.inception4c(x)x = self.inception4d(x)x = self.inception4e(x)x = self.maxpool4(x)x = self.inception5a(x)x = self.inception5b(x)x = torch.flatten(x, start_dim=1)x = self.classifier(x)return x# 调用并将模型转移到GPU中
model = InceptionV1().to(device)

加载并打印模型


torchsummary.summary(model, (3, 224, 224))
print(model)

运行结果：

----------------------------------------------------------------Layer (type)               Output Shape         Param #
================================================================Conv2d-1         [-1, 64, 112, 112]           9,472MaxPool2d-2           [-1, 64, 56, 56]               0Conv2d-3           [-1, 64, 56, 56]           4,160Conv2d-4          [-1, 192, 56, 56]         110,784MaxPool2d-5          [-1, 192, 28, 28]               0Conv2d-6           [-1, 64, 28, 28]          12,352BatchNorm2d-7           [-1, 64, 28, 28]             128ReLU-8           [-1, 64, 28, 28]               0Conv2d-9           [-1, 96, 28, 28]          18,528BatchNorm2d-10           [-1, 96, 28, 28]             192ReLU-11           [-1, 96, 28, 28]               0Conv2d-12          [-1, 128, 28, 28]         110,720BatchNorm2d-13          [-1, 128, 28, 28]             256ReLU-14          [-1, 128, 28, 28]               0Conv2d-15           [-1, 16, 28, 28]           3,088BatchNorm2d-16           [-1, 16, 28, 28]              32ReLU-17           [-1, 16, 28, 28]               0Conv2d-18           [-1, 32, 28, 28]          12,832BatchNorm2d-19           [-1, 32, 28, 28]              64ReLU-20           [-1, 32, 28, 28]               0MaxPool2d-21          [-1, 192, 28, 28]               0Conv2d-22           [-1, 32, 28, 28]           6,176BatchNorm2d-23           [-1, 32, 28, 28]              64ReLU-24           [-1, 32, 28, 28]               0inception_block-25          [-1, 256, 28, 28]               0Conv2d-26          [-1, 128, 28, 28]          32,896BatchNorm2d-27          [-1, 128, 28, 28]             256ReLU-28          [-1, 128, 28, 28]               0Conv2d-29          [-1, 128, 28, 28]          32,896BatchNorm2d-30          [-1, 128, 28, 28]             256ReLU-31          [-1, 128, 28, 28]               0Conv2d-32          [-1, 192, 28, 28]         221,376BatchNorm2d-33          [-1, 192, 28, 28]             384ReLU-34          [-1, 192, 28, 28]               0Conv2d-35           [-1, 32, 28, 28]           8,224BatchNorm2d-36           [-1, 32, 28, 28]              64ReLU-37           [-1, 32, 28, 28]               0Conv2d-38           [-1, 96, 28, 28]          76,896BatchNorm2d-39           [-1, 96, 28, 28]             192ReLU-40           [-1, 96, 28, 28]               0MaxPool2d-41          [-1, 256, 28, 28]               0Conv2d-42           [-1, 64, 28, 28]          16,448BatchNorm2d-43           [-1, 64, 28, 28]             128ReLU-44           [-1, 64, 28, 28]               0inception_block-45          [-1, 480, 28, 28]               0MaxPool2d-46          [-1, 480, 14, 14]               0Conv2d-47          [-1, 192, 14, 14]          92,352BatchNorm2d-48          [-1, 192, 14, 14]             384ReLU-49          [-1, 192, 14, 14]               0Conv2d-50           [-1, 96, 14, 14]          46,176BatchNorm2d-51           [-1, 96, 14, 14]             192ReLU-52           [-1, 96, 14, 14]               0Conv2d-53          [-1, 208, 14, 14]         179,920BatchNorm2d-54          [-1, 208, 14, 14]             416ReLU-55          [-1, 208, 14, 14]               0Conv2d-56           [-1, 16, 14, 14]           7,696BatchNorm2d-57           [-1, 16, 14, 14]              32ReLU-58           [-1, 16, 14, 14]               0Conv2d-59           [-1, 48, 14, 14]          19,248BatchNorm2d-60           [-1, 48, 14, 14]              96ReLU-61           [-1, 48, 14, 14]               0MaxPool2d-62          [-1, 480, 14, 14]               0Conv2d-63           [-1, 64, 14, 14]          30,784BatchNorm2d-64           [-1, 64, 14, 14]             128ReLU-65           [-1, 64, 14, 14]               0inception_block-66          [-1, 512, 14, 14]               0Conv2d-67          [-1, 160, 14, 14]          82,080BatchNorm2d-68          [-1, 160, 14, 14]             320ReLU-69          [-1, 160, 14, 14]               0Conv2d-70          [-1, 112, 14, 14]          57,456BatchNorm2d-71          [-1, 112, 14, 14]             224ReLU-72          [-1, 112, 14, 14]               0Conv2d-73          [-1, 224, 14, 14]         226,016BatchNorm2d-74          [-1, 224, 14, 14]             448ReLU-75          [-1, 224, 14, 14]               0Conv2d-76           [-1, 24, 14, 14]          12,312BatchNorm2d-77           [-1, 24, 14, 14]              48ReLU-78           [-1, 24, 14, 14]               0Conv2d-79           [-1, 64, 14, 14]          38,464BatchNorm2d-80           [-1, 64, 14, 14]             128ReLU-81           [-1, 64, 14, 14]               0MaxPool2d-82          [-1, 512, 14, 14]               0Conv2d-83           [-1, 64, 14, 14]          32,832BatchNorm2d-84           [-1, 64, 14, 14]             128ReLU-85           [-1, 64, 14, 14]               0inception_block-86          [-1, 512, 14, 14]               0Conv2d-87          [-1, 128, 14, 14]          65,664BatchNorm2d-88          [-1, 128, 14, 14]             256ReLU-89          [-1, 128, 14, 14]               0Conv2d-90          [-1, 128, 14, 14]          65,664BatchNorm2d-91          [-1, 128, 14, 14]             256ReLU-92          [-1, 128, 14, 14]               0Conv2d-93          [-1, 256, 14, 14]         295,168BatchNorm2d-94          [-1, 256, 14, 14]             512ReLU-95          [-1, 256, 14, 14]               0Conv2d-96           [-1, 24, 14, 14]          12,312BatchNorm2d-97           [-1, 24, 14, 14]              48ReLU-98           [-1, 24, 14, 14]               0Conv2d-99           [-1, 64, 14, 14]          38,464BatchNorm2d-100           [-1, 64, 14, 14]             128ReLU-101           [-1, 64, 14, 14]               0MaxPool2d-102          [-1, 512, 14, 14]               0Conv2d-103           [-1, 64, 14, 14]          32,832BatchNorm2d-104           [-1, 64, 14, 14]             128ReLU-105           [-1, 64, 14, 14]               0inception_block-106          [-1, 512, 14, 14]               0Conv2d-107          [-1, 112, 14, 14]          57,456BatchNorm2d-108          [-1, 112, 14, 14]             224ReLU-109          [-1, 112, 14, 14]               0Conv2d-110          [-1, 144, 14, 14]          73,872BatchNorm2d-111          [-1, 144, 14, 14]             288ReLU-112          [-1, 144, 14, 14]               0Conv2d-113          [-1, 288, 14, 14]         373,536BatchNorm2d-114          [-1, 288, 14, 14]             576ReLU-115          [-1, 288, 14, 14]               0Conv2d-116           [-1, 32, 14, 14]          16,416BatchNorm2d-117           [-1, 32, 14, 14]              64ReLU-118           [-1, 32, 14, 14]               0Conv2d-119           [-1, 64, 14, 14]          51,264BatchNorm2d-120           [-1, 64, 14, 14]             128ReLU-121           [-1, 64, 14, 14]               0MaxPool2d-122          [-1, 512, 14, 14]               0Conv2d-123           [-1, 64, 14, 14]          32,832BatchNorm2d-124           [-1, 64, 14, 14]             128ReLU-125           [-1, 64, 14, 14]               0inception_block-126          [-1, 528, 14, 14]               0Conv2d-127          [-1, 256, 14, 14]         135,424BatchNorm2d-128          [-1, 256, 14, 14]             512ReLU-129          [-1, 256, 14, 14]               0Conv2d-130          [-1, 160, 14, 14]          84,640BatchNorm2d-131          [-1, 160, 14, 14]             320ReLU-132          [-1, 160, 14, 14]               0Conv2d-133          [-1, 320, 14, 14]         461,120BatchNorm2d-134          [-1, 320, 14, 14]             640ReLU-135          [-1, 320, 14, 14]               0Conv2d-136           [-1, 32, 14, 14]          16,928BatchNorm2d-137           [-1, 32, 14, 14]              64ReLU-138           [-1, 32, 14, 14]               0Conv2d-139          [-1, 128, 14, 14]         102,528BatchNorm2d-140          [-1, 128, 14, 14]             256ReLU-141          [-1, 128, 14, 14]               0MaxPool2d-142          [-1, 528, 14, 14]               0Conv2d-143          [-1, 128, 14, 14]          67,712BatchNorm2d-144          [-1, 128, 14, 14]             256ReLU-145          [-1, 128, 14, 14]               0inception_block-146          [-1, 832, 14, 14]               0MaxPool2d-147            [-1, 832, 7, 7]               0Conv2d-148            [-1, 256, 7, 7]         213,248BatchNorm2d-149            [-1, 256, 7, 7]             512ReLU-150            [-1, 256, 7, 7]               0Conv2d-151            [-1, 160, 7, 7]         133,280BatchNorm2d-152            [-1, 160, 7, 7]             320ReLU-153            [-1, 160, 7, 7]               0Conv2d-154            [-1, 320, 7, 7]         461,120BatchNorm2d-155            [-1, 320, 7, 7]             640ReLU-156            [-1, 320, 7, 7]               0Conv2d-157             [-1, 32, 7, 7]          26,656BatchNorm2d-158             [-1, 32, 7, 7]              64ReLU-159             [-1, 32, 7, 7]               0Conv2d-160            [-1, 128, 7, 7]         102,528BatchNorm2d-161            [-1, 128, 7, 7]             256ReLU-162            [-1, 128, 7, 7]               0MaxPool2d-163            [-1, 832, 7, 7]               0Conv2d-164            [-1, 128, 7, 7]         106,624BatchNorm2d-165            [-1, 128, 7, 7]             256ReLU-166            [-1, 128, 7, 7]               0inception_block-167            [-1, 832, 7, 7]               0Conv2d-168            [-1, 384, 7, 7]         319,872BatchNorm2d-169            [-1, 384, 7, 7]             768ReLU-170            [-1, 384, 7, 7]               0Conv2d-171            [-1, 192, 7, 7]         159,936BatchNorm2d-172            [-1, 192, 7, 7]             384ReLU-173            [-1, 192, 7, 7]               0Conv2d-174            [-1, 384, 7, 7]         663,936BatchNorm2d-175            [-1, 384, 7, 7]             768ReLU-176            [-1, 384, 7, 7]               0Conv2d-177             [-1, 48, 7, 7]          39,984BatchNorm2d-178             [-1, 48, 7, 7]              96ReLU-179             [-1, 48, 7, 7]               0Conv2d-180            [-1, 128, 7, 7]         153,728BatchNorm2d-181            [-1, 128, 7, 7]             256ReLU-182            [-1, 128, 7, 7]               0MaxPool2d-183            [-1, 832, 7, 7]               0Conv2d-184            [-1, 128, 7, 7]         106,624BatchNorm2d-185            [-1, 128, 7, 7]             256ReLU-186            [-1, 128, 7, 7]               0inception_block-187           [-1, 1024, 7, 7]               0AvgPool2d-188           [-1, 1024, 1, 1]               0Dropout-189           [-1, 1024, 1, 1]               0Linear-190                 [-1, 1024]       1,049,600ReLU-191                 [-1, 1024]               0Linear-192                    [-1, 4]           4,100Softmax-193                    [-1, 4]               0
================================================================
Total params: 7,041,172
Trainable params: 7,041,172
Non-trainable params: 0
----------------------------------------------------------------
Input size (MB): 0.57
Forward/backward pass size (MB): 69.61
Params size (MB): 26.86
Estimated Total Size (MB): 97.05
----------------------------------------------------------------
InceptionV1((conv1): Conv2d(3, 64, kernel_size=(7, 7), stride=(2, 2), padding=(3, 3))(maxpool1): MaxPool2d(kernel_size=3, stride=2, padding=1, dilation=1, ceil_mode=False)(conv2): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1))(conv3): Conv2d(64, 192, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))(maxpool2): MaxPool2d(kernel_size=3, stride=2, padding=1, dilation=1, ceil_mode=False)(inception3a): inception_block((branch1): Sequential((0): Conv2d(192, 64, kernel_size=(1, 1), stride=(1, 1))(1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)(2): ReLU(inplace=True))(branch2): Sequential((0): Conv2d(192, 96, kernel_size=(1, 1), stride=(1, 1))(1): BatchNorm2d(96, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)(2): ReLU(inplace=True)(3): Conv2d(96, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))(4): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)(5): ReLU(inplace=True))(branch3): Sequential((0): Conv2d(192, 16, kernel_size=(1, 1), stride=(1, 1))(1): BatchNorm2d(16, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)(2): ReLU(inplace=True)(3): Conv2d(16, 32, kernel_size=(5, 5), stride=(1, 1), padding=(2, 2))(4): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)(5): ReLU(inplace=True))(branch4): Sequential((0): MaxPool2d(kernel_size=3, stride=1, padding=1, dilation=1, ceil_mode=False)(1): Conv2d(192, 32, kernel_size=(1, 1), stride=(1, 1))(2): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)(3): ReLU(inplace=True)))(inception3b): inception_block((branch1): Sequential((0): Conv2d(256, 128, kernel_size=(1, 1), stride=(1, 1))(1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)(2): ReLU(inplace=True))(branch2): Sequential((0): Conv2d(256, 128, kernel_size=(1, 1), stride=(1, 1))(1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)(2): ReLU(inplace=True)(3): Conv2d(128, 192, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))(4): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)(5): ReLU(inplace=True))(branch3): Sequential((0): Conv2d(256, 32, kernel_size=(1, 1), stride=(1, 1))(1): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)(2): ReLU(inplace=True)(3): Conv2d(32, 96, kernel_size=(5, 5), stride=(1, 1), padding=(2, 2))(4): BatchNorm2d(96, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)(5): ReLU(inplace=True))(branch4): Sequential((0): MaxPool2d(kernel_size=3, stride=1, padding=1, dilation=1, ceil_mode=False)(1): Conv2d(256, 64, kernel_size=(1, 1), stride=(1, 1))(2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)(3): ReLU(inplace=True)))(maxpool3): MaxPool2d(kernel_size=3, stride=2, padding=1, dilation=1, ceil_mode=False)(inception4a): inception_block((branch1): Sequential((0): Conv2d(480, 192, kernel_size=(1, 1), stride=(1, 1))(1): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)(2): ReLU(inplace=True))(branch2): Sequential((0): Conv2d(480, 96, kernel_size=(1, 1), stride=(1, 1))(1): BatchNorm2d(96, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)(2): ReLU(inplace=True)(3): Conv2d(96, 208, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))(4): BatchNorm2d(208, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)(5): ReLU(inplace=True))(branch3): Sequential((0): Conv2d(480, 16, kernel_size=(1, 1), stride=(1, 1))(1): BatchNorm2d(16, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)(2): ReLU(inplace=True)(3): Conv2d(16, 48, kernel_size=(5, 5), stride=(1, 1), padding=(2, 2))(4): BatchNorm2d(48, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)(5): ReLU(inplace=True))(branch4): Sequential((0): MaxPool2d(kernel_size=3, stride=1, padding=1, dilation=1, ceil_mode=False)(1): Conv2d(480, 64, kernel_size=(1, 1), stride=(1, 1))(2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)(3): ReLU(inplace=True)))(inception4b): inception_block((branch1): Sequential((0): Conv2d(512, 160, kernel_size=(1, 1), stride=(1, 1))(1): BatchNorm2d(160, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)(2): ReLU(inplace=True))(branch2): Sequential((0): Conv2d(512, 112, kernel_size=(1, 1), stride=(1, 1))(1): BatchNorm2d(112, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)(2): ReLU(inplace=True)(3): Conv2d(112, 224, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))(4): BatchNorm2d(224, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)(5): ReLU(inplace=True))(branch3): Sequential((0): Conv2d(512, 24, kernel_size=(1, 1), stride=(1, 1))(1): BatchNorm2d(24, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)(2): ReLU(inplace=True)(3): Conv2d(24, 64, kernel_size=(5, 5), stride=(1, 1), padding=(2, 2))(4): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)(5): ReLU(inplace=True))(branch4): Sequential((0): MaxPool2d(kernel_size=3, stride=1, padding=1, dilation=1, ceil_mode=False)(1): Conv2d(512, 64, kernel_size=(1, 1), stride=(1, 1))(2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)(3): ReLU(inplace=True)))(inception4c): inception_block((branch1): Sequential((0): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1))(1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)(2): ReLU(inplace=True))(branch2): Sequential((0): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1))(1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)(2): ReLU(inplace=True)(3): Conv2d(128, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))(4): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)(5): ReLU(inplace=True))(branch3): Sequential((0): Conv2d(512, 24, kernel_size=(1, 1), stride=(1, 1))(1): BatchNorm2d(24, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)(2): ReLU(inplace=True)(3): Conv2d(24, 64, kernel_size=(5, 5), stride=(1, 1), padding=(2, 2))(4): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)(5): ReLU(inplace=True))(branch4): Sequential((0): MaxPool2d(kernel_size=3, stride=1, padding=1, dilation=1, ceil_mode=False)(1): Conv2d(512, 64, kernel_size=(1, 1), stride=(1, 1))(2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)(3): ReLU(inplace=True)))(inception4d): inception_block((branch1): Sequential((0): Conv2d(512, 112, kernel_size=(1, 1), stride=(1, 1))(1): BatchNorm2d(112, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)(2): ReLU(inplace=True))(branch2): Sequential((0): Conv2d(512, 144, kernel_size=(1, 1), stride=(1, 1))(1): BatchNorm2d(144, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)(2): ReLU(inplace=True)(3): Conv2d(144, 288, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))(4): BatchNorm2d(288, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)(5): ReLU(inplace=True))(branch3): Sequential((0): Conv2d(512, 32, kernel_size=(1, 1), stride=(1, 1))(1): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)(2): ReLU(inplace=True)(3): Conv2d(32, 64, kernel_size=(5, 5), stride=(1, 1), padding=(2, 2))(4): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)(5): ReLU(inplace=True))(branch4): Sequential((0): MaxPool2d(kernel_size=3, stride=1, padding=1, dilation=1, ceil_mode=False)(1): Conv2d(512, 64, kernel_size=(1, 1), stride=(1, 1))(2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)(3): ReLU(inplace=True)))(inception4e): inception_block((branch1): Sequential((0): Conv2d(528, 256, kernel_size=(1, 1), stride=(1, 1))(1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)(2): ReLU(inplace=True))(branch2): Sequential((0): Conv2d(528, 160, kernel_size=(1, 1), stride=(1, 1))(1): BatchNorm2d(160, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)(2): ReLU(inplace=True)(3): Conv2d(160, 320, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))(4): BatchNorm2d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)(5): ReLU(inplace=True))(branch3): Sequential((0): Conv2d(528, 32, kernel_size=(1, 1), stride=(1, 1))(1): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)(2): ReLU(inplace=True)(3): Conv2d(32, 128, kernel_size=(5, 5), stride=(1, 1), padding=(2, 2))(4): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)(5): ReLU(inplace=True))(branch4): Sequential((0): MaxPool2d(kernel_size=3, stride=1, padding=1, dilation=1, ceil_mode=False)(1): Conv2d(528, 128, kernel_size=(1, 1), stride=(1, 1))(2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)(3): ReLU(inplace=True)))(maxpool4): MaxPool2d(kernel_size=3, stride=2, padding=1, dilation=1, ceil_mode=False)(inception5a): inception_block((branch1): Sequential((0): Conv2d(832, 256, kernel_size=(1, 1), stride=(1, 1))(1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)(2): ReLU(inplace=True))(branch2): Sequential((0): Conv2d(832, 160, kernel_size=(1, 1), stride=(1, 1))(1): BatchNorm2d(160, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)(2): ReLU(inplace=True)(3): Conv2d(160, 320, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))(4): BatchNorm2d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)(5): ReLU(inplace=True))(branch3): Sequential((0): Conv2d(832, 32, kernel_size=(1, 1), stride=(1, 1))(1): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)(2): ReLU(inplace=True)(3): Conv2d(32, 128, kernel_size=(5, 5), stride=(1, 1), padding=(2, 2))(4): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)(5): ReLU(inplace=True))(branch4): Sequential((0): MaxPool2d(kernel_size=3, stride=1, padding=1, dilation=1, ceil_mode=False)(1): Conv2d(832, 128, kernel_size=(1, 1), stride=(1, 1))(2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)(3): ReLU(inplace=True)))(inception5b): Sequential((0): inception_block((branch1): Sequential((0): Conv2d(832, 384, kernel_size=(1, 1), stride=(1, 1))(1): BatchNorm2d(384, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)(2): ReLU(inplace=True))(branch2): Sequential((0): Conv2d(832, 192, kernel_size=(1, 1), stride=(1, 1))(1): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)(2): ReLU(inplace=True)(3): Conv2d(192, 384, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))(4): BatchNorm2d(384, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)(5): ReLU(inplace=True))(branch3): Sequential((0): Conv2d(832, 48, kernel_size=(1, 1), stride=(1, 1))(1): BatchNorm2d(48, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)(2): ReLU(inplace=True)(3): Conv2d(48, 128, kernel_size=(5, 5), stride=(1, 1), padding=(2, 2))(4): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)(5): ReLU(inplace=True))(branch4): Sequential((0): MaxPool2d(kernel_size=3, stride=1, padding=1, dilation=1, ceil_mode=False)(1): Conv2d(832, 128, kernel_size=(1, 1), stride=(1, 1))(2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)(3): ReLU(inplace=True)))(1): AvgPool2d(kernel_size=7, stride=1, padding=0)(2): Dropout(p=0.4, inplace=False))(classifier): Sequential((0): Linear(in_features=1024, out_features=1024, bias=True)(1): ReLU()(2): Linear(in_features=1024, out_features=4, bias=True)(3): Softmax(dim=1))
)

七、训练函数

# 训练循环
def train(dataloader, model, loss_fn, optimizer):size = len(dataloader.dataset)  # 训练集的大小num_batches = len(dataloader)  # 批次数目, (size/batch_size，向上取整)train_loss, train_acc = 0, 0  # 初始化训练损失和正确率for X, y in dataloader:  # 获取图片及其标签X, y = X.to(device), y.to(device)# 计算预测误差pred = model(X)  # 网络输出loss = loss_fn(pred, y)  # 计算网络输出pred和真实值y之间的差距，y为真实值，计算二者差值即为损失# 反向传播optimizer.zero_grad()  # grad属性归零loss.backward()  # 反向传播optimizer.step()  # 每一步自动更新# 记录acc与losstrain_acc += (pred.argmax(1) == y).type(torch.float).sum().item()train_loss += loss.item()train_acc /= sizetrain_loss /= num_batchesreturn train_acc, train_loss

八、测试函数

def test(dataloader, model, loss_fn):size = len(dataloader.dataset)  # 训练集的大小num_batches = len(dataloader)  # 批次数目, (size/batch_size，向上取整)test_loss, test_acc = 0, 0  # 初始化测试损失和正确率# 当不进行训练时，停止梯度更新，节省计算内存消耗# with torch.no_grad():for imgs, target in dataloader:  # 获取图片及其标签with torch.no_grad():imgs, target = imgs.to(device), target.to(device)# 计算误差tartget_pred = model(imgs)  # 网络输出loss = loss_fn(tartget_pred, target)  # 计算网络输出和真实值之间的差距，targets为真实值，计算二者差值即为损失# 记录acc与losstest_loss += loss.item()test_acc += (tartget_pred.argmax(1) == target).type(torch.float).sum().item()test_acc /= sizetest_loss /= num_batchesreturn test_acc, test_loss

九、模型训练

if __name__ == "__main__":main()

def main():
optimizer = torch.optim.Adam(model.parameters(), lr=1e-4)loss_fn = nn.CrossEntropyLoss()  # 创建损失函数epochs = 40train_loss = []train_acc = []test_loss = []test_acc = []best_acc = 0  # 设置一个最佳准确率，作为最佳模型的判别指标if hasattr(torch.cuda, 'empty_cache'):torch.cuda.empty_cache()for epoch in range(epochs):model.train()epoch_train_acc, epoch_train_loss = train(train_dl, model, loss_fn, optimizer)# scheduler.step() #更新学习率（调用官方动态学习率接口时使用）model.eval()epoch_test_acc, epoch_test_loss = test(test_dl, model, loss_fn)# 保存最佳模型到best_modelif epoch_test_acc > best_acc:best_acc = epoch_test_accbest_model = copy.deepcopy(model)train_acc.append(epoch_train_acc)train_loss.append(epoch_train_loss)test_acc.append(epoch_test_acc)test_loss.append(epoch_test_loss)# 获取当前的学习率lr = optimizer.state_dict()['param_groups'][0]['lr']template = ('Epoch: {:2d}. Train_acc: {:.1f}%, Train_loss: {:.3f}, Test_acc:{:.1f}%, Test_loss:{:.3f}, Lr: {:.2E}')print(template.format(epoch + 1, epoch_train_acc * 100, epoch_train_loss, epoch_test_acc * 100, epoch_test_loss, lr))PATH = './best_model.pth'torch.save(model.state_dict(), PATH)print('Done')

运行结果：

Epoch:  1. Train_acc: 59.4%, Train_loss: 1.142, Test_acc:61.8%, Test_loss:1.118, Lr: 1.00E-04
Epoch:  2. Train_acc: 61.3%, Train_loss: 1.116, Test_acc:61.1%, Test_loss:1.131, Lr: 1.00E-04
Epoch:  3. Train_acc: 63.6%, Train_loss: 1.096, Test_acc:61.3%, Test_loss:1.115, Lr: 1.00E-04
Epoch:  4. Train_acc: 68.0%, Train_loss: 1.057, Test_acc:64.1%, Test_loss:1.099, Lr: 1.00E-04
Epoch:  5. Train_acc: 66.8%, Train_loss: 1.071, Test_acc:64.3%, Test_loss:1.095, Lr: 1.00E-04
Epoch:  6. Train_acc: 63.7%, Train_loss: 1.100, Test_acc:70.2%, Test_loss:1.042, Lr: 1.00E-04
Epoch:  7. Train_acc: 67.9%, Train_loss: 1.059, Test_acc:70.6%, Test_loss:1.037, Lr: 1.00E-04
Epoch:  8. Train_acc: 64.6%, Train_loss: 1.093, Test_acc:71.6%, Test_loss:1.027, Lr: 1.00E-04
Epoch:  9. Train_acc: 70.2%, Train_loss: 1.033, Test_acc:66.7%, Test_loss:1.070, Lr: 1.00E-04
Epoch: 10. Train_acc: 69.2%, Train_loss: 1.045, Test_acc:70.4%, Test_loss:1.032, Lr: 1.00E-04
Epoch: 11. Train_acc: 69.4%, Train_loss: 1.048, Test_acc:71.3%, Test_loss:1.036, Lr: 1.00E-04
Epoch: 12. Train_acc: 69.9%, Train_loss: 1.045, Test_acc:68.5%, Test_loss:1.054, Lr: 1.00E-04
Epoch: 13. Train_acc: 68.8%, Train_loss: 1.053, Test_acc:73.7%, Test_loss:1.012, Lr: 1.00E-04
Epoch: 14. Train_acc: 72.5%, Train_loss: 1.016, Test_acc:76.7%, Test_loss:0.978, Lr: 1.00E-04
Epoch: 15. Train_acc: 72.3%, Train_loss: 1.016, Test_acc:71.3%, Test_loss:1.035, Lr: 1.00E-04
Epoch: 16. Train_acc: 73.5%, Train_loss: 1.007, Test_acc:74.8%, Test_loss:1.003, Lr: 1.00E-04
Epoch: 17. Train_acc: 74.7%, Train_loss: 0.991, Test_acc:80.7%, Test_loss:0.941, Lr: 1.00E-04
Epoch: 18. Train_acc: 74.3%, Train_loss: 0.998, Test_acc:76.2%, Test_loss:0.974, Lr: 1.00E-04
Epoch: 19. Train_acc: 75.5%, Train_loss: 0.987, Test_acc:77.4%, Test_loss:0.972, Lr: 1.00E-04
Epoch: 20. Train_acc: 73.0%, Train_loss: 1.010, Test_acc:76.2%, Test_loss:0.988, Lr: 1.00E-04
Epoch: 21. Train_acc: 74.8%, Train_loss: 0.990, Test_acc:78.3%, Test_loss:0.957, Lr: 1.00E-04
Epoch: 22. Train_acc: 75.5%, Train_loss: 0.985, Test_acc:79.5%, Test_loss:0.950, Lr: 1.00E-04
Epoch: 23. Train_acc: 80.0%, Train_loss: 0.943, Test_acc:80.2%, Test_loss:0.935, Lr: 1.00E-04
Epoch: 24. Train_acc: 78.9%, Train_loss: 0.952, Test_acc:80.9%, Test_loss:0.936, Lr: 1.00E-04
Epoch: 25. Train_acc: 73.6%, Train_loss: 1.005, Test_acc:69.9%, Test_loss:1.045, Lr: 1.00E-04
Epoch: 26. Train_acc: 78.6%, Train_loss: 0.959, Test_acc:81.4%, Test_loss:0.931, Lr: 1.00E-04
Epoch: 27. Train_acc: 77.2%, Train_loss: 0.970, Test_acc:69.5%, Test_loss:1.047, Lr: 1.00E-04
Epoch: 28. Train_acc: 77.1%, Train_loss: 0.973, Test_acc:80.7%, Test_loss:0.939, Lr: 1.00E-04
Epoch: 29. Train_acc: 79.2%, Train_loss: 0.949, Test_acc:76.7%, Test_loss:0.972, Lr: 1.00E-04
Epoch: 30. Train_acc: 78.3%, Train_loss: 0.954, Test_acc:83.0%, Test_loss:0.915, Lr: 1.00E-04
Epoch: 31. Train_acc: 81.3%, Train_loss: 0.925, Test_acc:80.4%, Test_loss:0.938, Lr: 1.00E-04
Epoch: 32. Train_acc: 81.0%, Train_loss: 0.929, Test_acc:84.4%, Test_loss:0.898, Lr: 1.00E-04
Epoch: 33. Train_acc: 82.0%, Train_loss: 0.921, Test_acc:85.8%, Test_loss:0.884, Lr: 1.00E-04
Epoch: 34. Train_acc: 83.8%, Train_loss: 0.903, Test_acc:84.8%, Test_loss:0.898, Lr: 1.00E-04
Epoch: 35. Train_acc: 84.8%, Train_loss: 0.895, Test_acc:87.6%, Test_loss:0.866, Lr: 1.00E-04
Epoch: 36. Train_acc: 79.7%, Train_loss: 0.940, Test_acc:82.5%, Test_loss:0.921, Lr: 1.00E-04
Epoch: 37. Train_acc: 85.3%, Train_loss: 0.894, Test_acc:81.6%, Test_loss:0.925, Lr: 1.00E-04
Epoch: 38. Train_acc: 80.0%, Train_loss: 0.941, Test_acc:75.5%, Test_loss:0.985, Lr: 1.00E-04
Epoch: 39. Train_acc: 81.4%, Train_loss: 0.926, Test_acc:81.4%, Test_loss:0.935, Lr: 1.00E-04
Epoch: 40. Train_acc: 83.5%, Train_loss: 0.909, Test_acc:85.5%, Test_loss:0.886, Lr: 1.00E-04
Done

十、模型评估

    warnings.filterwarnings("ignore")  # 忽略警告信息plt.rcParams['font.sans-serif'] = ['SimHei']  # 用来正常显示中文标签plt.rcParams['axes.unicode_minus'] = False  # 用来正常显示负号plt.rcParams['figure.dpi'] = 100  # 分辨率epochs_range = range(epochs)plt.figure(figsize=(12, 3))plt.subplot(1, 2, 1)plt.plot(epochs_range, train_acc, label='Training Accuracy')plt.plot(epochs_range, test_acc, label='Test Accuracy')plt.legend(loc='lower right')plt.title('Training and Validation Accuracy')plt.subplot(1, 2, 2)plt.plot(epochs_range, train_loss, label='Training Loss')plt.plot(epochs_range, test_loss, label='Test Loss')plt.legend(loc='upper right')plt.title('Training and Validation Loss')plt.show()

运行结果：

十一、总结

本次Inception v1算法实战与解析项目总结如下：

Inception Module是Inception V1的核心组成单元，提出了卷积层的并行结构，实现了在同一层就可以提取不同的特征，如下图（a)所示。

按照这样的结构来增加网络的深度，虽然可以提升性能，但是还面临计算量大（参数多）的问题。为改善这种现象，Inception Module借鉴Network-in-Network的思想，使用1x1的卷积核实现降维操作（也间接增加了网络的深度），以此来减少网络的参数量与计算量，如上图b所示。

备注举例：假如前一层的输出为100x100x128，经过具有256个5x5卷积核的卷积层之后（stride=1, pad=2）, 输出数据为100x100x256.其中，卷积层的参数为5x5x128x256+256。例如上一层输出先经过具有32个1x1卷积核的卷积层（1x1卷积降低了通道数，且特征图尺寸不变），经过具有256个5x5卷积核的卷积层，最终的输出数据仍为100x100x256，但卷积参数量以及减少为(128x1x1x32+32)+(32x5x5x256+256)，参数数量减少为原来的约四分之一。其计算量由原先的8.191x10e9，降低至2.048x10e9。