AgriTech/model.py at main · SMITAGADE/AgriTech · GitHub

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import torch.nn as nn

class PlantDiseaseNet(nn.Module):
    def __init__(self, num_classes=38):  # original model had 38 classes
        super(PlantDiseaseNet, self).__init__()

        self.conv1 = nn.Sequential(
            nn.Conv2d(3, 64, kernel_size=3, padding=1),
            nn.BatchNorm2d(64),
            nn.ReLU(inplace=True)
        )

        self.conv2 = nn.Sequential(
            nn.Conv2d(64, 128, kernel_size=3, padding=1),
            nn.BatchNorm2d(128),
            nn.ReLU(inplace=True)
        )

        self.res1 = nn.Sequential(
            nn.Sequential(
                nn.Conv2d(128, 128, kernel_size=3, padding=1),
                nn.BatchNorm2d(128),
                nn.ReLU(inplace=True)
            ),
            nn.Sequential(
                nn.Conv2d(128, 128, kernel_size=3, padding=1),
                nn.BatchNorm2d(128),
                nn.ReLU(inplace=True)
            )
        )

        self.conv3 = nn.Sequential(
            nn.Conv2d(128, 256, kernel_size=3, padding=1),
            nn.BatchNorm2d(256),
            nn.ReLU(inplace=True)
        )

        self.conv4 = nn.Sequential(
            nn.Conv2d(256, 512, kernel_size=3, padding=1),
            nn.BatchNorm2d(512),
            nn.ReLU(inplace=True)
        )

        self.res2 = nn.Sequential(
            nn.Sequential(
                nn.Conv2d(512, 512, kernel_size=3, padding=1),
                nn.BatchNorm2d(512),
                nn.ReLU(inplace=True)
            ),
            nn.Sequential(
                nn.Conv2d(512, 512, kernel_size=3, padding=1),
                nn.BatchNorm2d(512),
                nn.ReLU(inplace=True)
            )
        )

        self.pool = nn.AdaptiveAvgPool2d((1, 1))
        self.classifier = nn.Sequential(
            nn.Flatten(),
            nn.Dropout(0.5),
            nn.Linear(512, num_classes)
        )

    def forward(self, x):
        x = self.conv1(x)
        x = self.conv2(x)
        res = x
        for layer in self.res1:
            x = layer(x)
        x = x + res
        x = self.conv3(x)
        x = self.conv4(x)
        res = x
        for layer in self.res2:
            x = layer(x)
        x = x + res
        x = self.pool(x)
        x = self.classifier(x)
        return x