72 lines
2.1 KiB
Python
72 lines
2.1 KiB
Python
import torch
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import torch.nn as nn
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import torch.nn.functional as F
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class FoodCNN(nn.Module):
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def __init__(self):
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super(FoodCNN, self).__init__()
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# 第一个卷积块
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self.conv1 = nn.Conv2d(3, 32, 3, padding=1)
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self.conv2 = nn.Conv2d(32, 32, 3, padding=1)
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self.pool1 = nn.MaxPool2d(2, 2)
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self.dropout1 = nn.Dropout2d(0.25)
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# 第二个卷积块
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self.conv3 = nn.Conv2d(32, 64, 3, padding=1)
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self.conv4 = nn.Conv2d(64, 64, 3, padding=1)
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self.pool2 = nn.MaxPool2d(2, 2)
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self.dropout2 = nn.Dropout2d(0.25)
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# 第三个卷积块
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self.conv5 = nn.Conv2d(64, 128, 3, padding=1)
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self.conv6 = nn.Conv2d(128, 128, 3, padding=1)
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self.pool3 = nn.MaxPool2d(2, 2)
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self.dropout3 = nn.Dropout2d(0.25)
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# 全连接层
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self.fc1 = nn.Linear(128 * 4 * 4, 512)
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self.dropout4 = nn.Dropout(0.5)
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self.fc2 = nn.Linear(512, 2) # 2分类
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def forward(self, x):
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# 第一个卷积块
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x = F.relu(self.conv1(x))
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x = F.relu(self.conv2(x))
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x = self.pool1(x)
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x = self.dropout1(x)
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# 第二个卷积块
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x = F.relu(self.conv3(x))
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x = F.relu(self.conv4(x))
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x = self.pool2(x)
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x = self.dropout2(x)
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# 第三个卷积块
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x = F.relu(self.conv5(x))
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x = F.relu(self.conv6(x))
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x = self.pool3(x)
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x = self.dropout3(x)
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# 展平
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x = x.view(-1, 128 * 4 * 4)
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# 全连接层
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x = F.relu(self.fc1(x))
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x = self.dropout4(x)
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x = self.fc2(x)
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return x
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# 1. 初始化模型
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model = FoodCNN()
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# 2. 加载训练好的权重
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model.load_state_dict(torch.load("../model/01/best_food_model.pth", map_location='cpu'))
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model.eval() # 设置为推理模式
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# 3. 创建示例输入 (假设输入是 3x224x224 的图片)
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example_input = torch.randn(1, 3, 224, 224)
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# 4. 转换为 TorchScript
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traced_script_module = torch.jit.trace(model, example_input)
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traced_script_module.save("../model/01/best_food_model_mobile.pt")
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