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import torch
import torch.nn as nn
from PIL import Image
from torch import Tensor
from torchvision import transforms
from model import VGG16WithCNN
def getModel(device: torch.device, model_path: str):
model = VGG16WithCNN(5)
# 加载训练好的权重
model.load_state_dict(
torch.load(
model_path,
weights_only=True,
)
)
model.to(device)
return model
def preprocess_image(image_path: str, image_size=(224, 224)):
"""
预处理图片，使其符合模型输入要求
"""
transform = transforms.Compose(
[
transforms.Resize(image_size),
transforms.ToTensor(),
transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),
]
)
# 打开图片并转换
image: Image.Image = Image.open(image_path).convert("RGB")
image_tensor: Tensor = transform(image)
# 添加batch维度
image_tensor = image_tensor.unsqueeze(0)
return image_tensor
def predict_single_image(
image_path: str, model: nn.Module, device: torch.device, class_names: list[str]
) -> str:
"""
预测单个图片的标签
Args:
image_path: 图片路径
model: 模型
device: 设备
Returns:
预测的标签名
"""
image_tensor = preprocess_image(image_path)
image_tensor = image_tensor.to(device)
# 预测
model.eval()
with torch.no_grad():
output = model(image_tensor)
_, pred = torch.max(output, 1)
predicted_label = class_names[int(pred.item())]
return predicted_label
if __name__ == "__main__":
# 测试单张图片预测
# 注意：需要替换为实际的测试图片路径
p = "./checkpoints/vgg_net_model_50.pth"
class_names = [
"Bacterialblight",
"Blast",
"Brownspot",
"Healthy",
"Tungro",
]
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
model = getModel(device=device, model_path=p)
test_image_path = "./images/BLAST1_011.jpg"
try:
predicted_label = predict_single_image(
test_image_path, model, device, class_names=class_names
)
print("\nSingle image prediction result:")
print(f"Image: {test_image_path}")
print(f"Predicted label: {predicted_label}")
except FileNotFoundError:
print("Please provide a valid image path to test single image prediction")
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