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import torch
import torch.nn as nn
import torch.nn.functional as F
from torchvision import transforms
from PIL import Image
class PlantDiseaseClassifier(nn.Module):
def __init__(self, num_classes, dropout_rate=0.3):
super(PlantDiseaseClassifier, self).__init__()
# Use EfficientNet as backbone
from torchvision import models
self.backbone = models.efficientnet_b2(pretrained=False)
# Get feature dimension
num_features = self.backbone.classifier[1].in_features
# Replace classifier with custom head
self.backbone.classifier = nn.Identity()
# Attention mechanism
self.attention = nn.Sequential(
nn.AdaptiveAvgPool2d(1),
nn.Flatten(),
nn.Linear(num_features, num_features // 4),
nn.ReLU(),
nn.Linear(num_features // 4, num_features),
nn.Sigmoid()
)
# Custom classifier head
self.classifier = nn.Sequential(
nn.Dropout(dropout_rate),
nn.Linear(num_features, 512),
nn.BatchNorm1d(512),
nn.ReLU(),
nn.Dropout(dropout_rate * 0.5),
nn.Linear(512, 256),
nn.BatchNorm1d(256),
nn.ReLU(),
nn.Dropout(dropout_rate * 0.3),
nn.Linear(256, num_classes)
)
def forward(self, x):
features = self.backbone.features(x)
pooled = F.adaptive_avg_pool2d(features, 1)
pooled = torch.flatten(pooled, 1)
attention_weights = self.attention(features)
attended_features = pooled * attention_weights
output = self.classifier(attended_features)
return output
def load_model(model_path):
checkpoint = torch.load(model_path, map_location='cpu')
num_classes = len(checkpoint['class_names'])
model = PlantDiseaseClassifier(num_classes=num_classes)
model.load_state_dict(checkpoint['model_state_dict'])
model.eval()
return model, checkpoint['class_names']
def predict_image(image_path, model, class_names):
transform = transforms.Compose([
transforms.Resize((224, 224)),
transforms.ToTensor(),
transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
])
image = Image.open(image_path).convert('RGB')
image_tensor = transform(image).unsqueeze(0)
with torch.no_grad():
outputs = model(image_tensor)
probabilities = F.softmax(outputs, dim=1)[0]
# Get top predictions
top_probs, top_indices = torch.topk(probabilities, 3)
results = []
for i in range(len(top_indices)):
results.append({
"label": class_names[top_indices[i].item()],
"score": top_probs[i].item()
})
return results
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