79 lines
2.7 KiB
Python
79 lines
2.7 KiB
Python
"""
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现在基本上不用这个脚本，这个是早期的，现在用toAndroidEmbedding.py
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"""
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import torch
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from net import create_food_cnn, create_mobile_food_cnn
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import os
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def main():
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print("开始转换模型为移动端格式...")
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# 1. 加载训练好的基础模型权重
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base_model = create_food_cnn()
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model_path = "../model/10/best_food_model.pth"
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if not os.path.exists(model_path):
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print(f"错误：模型文件不存在 {model_path}")
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return
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base_model.load_state_dict(torch.load(model_path, map_location='cpu'))
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print("✓ 基础模型权重加载成功")
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# 2. 创建移动端模型并复制权重
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mobile_model = create_mobile_food_cnn()
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mobile_model.load_state_dict(base_model.state_dict())
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mobile_model.eval()
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print("✓ 移动端模型创建成功")
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# 3. 测试模型
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# 创建示例输入 (模拟Android端输入：1x3x224x224，值范围0-255)
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example_input = torch.randint(0, 256, (1, 3, 224, 224), dtype=torch.float32)
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with torch.no_grad():
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output = mobile_model(example_input)
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print(f"✓ 模型测试成功，输出形状: {output.shape}")
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print(f" 输出值范围: [{output.min():.3f}, {output.max():.3f}]")
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# 4. 转换为 TorchScript
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try:
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traced_model = torch.jit.trace(mobile_model, example_input)
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# 保存模型
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output_path = "../model/10/best_food_model_mobile.pt"
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traced_model.save(output_path)
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print(f"✓ TorchScript模型保存成功: {output_path}")
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# 验证保存的模型
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loaded_model = torch.jit.load(output_path)
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with torch.no_grad():
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loaded_output = loaded_model(example_input)
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print(f"✓ 保存的模型验证成功，输出形状: {loaded_output.shape}")
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# 检查输出是否一致
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if torch.allclose(output, loaded_output, atol=1e-6):
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print("✓ 模型输出一致性验证通过")
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else:
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print("⚠ 警告：模型输出存在差异")
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except Exception as e:
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print(f"✗ TorchScript转换失败: {e}")
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return
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print("\n" + "="*50)
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print("移动端模型转换完成！")
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print("="*50)
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print("使用说明：")
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print("1. Android端输入图片应为 [batch, 3, height, width] 格式")
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print("2. 像素值范围：0-255 (uint8 或 float32)")
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print("3. 模型会自动处理：")
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print(" - 归一化到 [0,1]")
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print(" - 缩放到 32x32")
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print(" - ImageNet标准化")
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print("4. 输出：3个类别的logits")
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print("="*50)
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if __name__ == "__main__":
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main() |