95 lines
No EOL
3.8 KiB
Python
95 lines
No EOL
3.8 KiB
Python
import gradio as gr
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import torch
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from torchvision import transforms
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from PIL import Image
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from Model import Net # 根据上传的 Model.py，模型类名为 Net
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# 1. 基础配置与类别映射
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# 根据 Merge_classes.py，1=厨余垃圾, 2=可回收物, 3=其他垃圾, 4=有害垃圾
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class_names = ['厨余垃圾', '可回收物', '其他垃圾', '有害垃圾']
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# 设备自动选择逻辑，保持与 Train.py 和 Evaluate.py 一致
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device = torch.device('cuda' if torch.cuda.is_available() else 'xpu' if torch.xpu.is_available() else 'mps' if torch.mps.is_available() else 'cpu')
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print(f"当前使用的推理设备: {device}")
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# 2. 初始化模型并加载最佳权重
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model = Net(num_classes=4)
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try:
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# 采用与 Evaluate.py 一致的健壮加载方式
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state_dict = torch.load('best_model.pth', map_location=device)
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if 'model_state_dict' in state_dict:
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state_dict = state_dict['model_state_dict']
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elif 'model' in state_dict:
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state_dict = state_dict['model']
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model.load_state_dict(state_dict)
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model = model.to(device).eval()
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print("✅ 成功加载 best_model.pth 权重")
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except Exception as e:
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print(f"⚠️ 模型加载失败，请确保目录下存在 best_model.pth。错误信息: {e}")
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# 3. 定义数据预处理流程 (必须与 Evaluate.py 中的 val_transform 保持完全一致)
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transform = transforms.Compose([
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transforms.Resize((256, 256)),
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transforms.ToTensor(),
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transforms.Normalize(
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mean=[0.485, 0.456, 0.406],
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std=[0.229, 0.224, 0.225]
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)
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])
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# 4. 核心推理函数
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def predict(image):
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if image is None:
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return None
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# Gradio 传入的 pil 图像，确保转为 RGB 格式
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image = image.convert('RGB')
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# 预处理并增加 batch 维度
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input_tensor = transform(image).unsqueeze(0).to(device)
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with torch.no_grad():
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outputs = model(input_tensor)
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# 使用 Softmax 将 logits 转换为 0~1 的概率分布
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probabilities = torch.softmax(outputs, dim=1)[0]
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# 组装为 Gradio Label 组件需要的字典格式 { "类别名": 概率值 }
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result_dict = {class_names[i]: float(probabilities[i]) for i in range(len(class_names))}
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return result_dict
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# 5. 构建与美化 Gradio 界面
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with gr.Blocks(theme=gr.themes.Soft(), title="Trash Division 垃圾分类识别") as demo:
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gr.Markdown(
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"""
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<div style="text-align: center; max-width: 800px; margin: 0 auto;">
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<h1>🗑️ Trash Division - 智能垃圾分类系统</h1>
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<p>基于 <b>ResNet-34</b> 架构，支持精准识别：<b>厨余垃圾、可回收物、其他垃圾、有害垃圾</b>。</p>
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<p><i>同济大学 Python 人工智能程序设计课程小组作业</i></p>
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</div>
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"""
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)
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with gr.Row():
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with gr.Column(scale=1):
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# type="pil" 让 Gradio 直接传 PIL Image 对象给预测函数，配合 torchvision 最方便
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image_input = gr.Image(type="pil", label="上传垃圾图片 (支持拍照)")
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with gr.Row():
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clear_btn = gr.Button("清空", variant="secondary")
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submit_btn = gr.Button("开始识别", variant="primary")
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with gr.Column(scale=1):
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label_output = gr.Label(num_top_classes=4, label="预测结果与置信度")
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# 绑定点击事件
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submit_btn.click(fn=predict, inputs=image_input, outputs=label_output)
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clear_btn.click(lambda: (None, None), inputs=None, outputs=[image_input, label_output])
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if __name__ == "__main__":
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# 启动 Web 界面
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demo.launch(
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server_name="127.0.0.1",
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server_port=7860,
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share=False, # 如果你想生成临时公网链接分享给同学测试，改为 True
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inbrowser=True # 运行后自动在默认浏览器中打开
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) |