grcnn单张图片预测代码

### GRCNN 单张图片预测实现代码 为了展示如何使用GRCNN模型对单张图片进行预测，下面提供了一个Python代码示例。此代码假设已经有一个预训练好的GRCNN模型，并且该模型可以加载并用于推理模式下处理新输入的图像。 ```python import torch from torchvision import transforms from PIL import Image import numpy as np def load_image(image_path): """Load and preprocess an image.""" img = Image.open(image_path).convert('RGB') transform = transforms.Compose([ transforms.Resize((256, 256)), transforms.ToTensor(), transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]) ]) img_tensor = transform(img) return img_tensor.unsqueeze(0) def predict_single_image(model, device, image_path): """Predict segmentation mask for a single input image using the trained model.""" model.eval() with torch.no_grad(): tensor_img = load_image(image_path).to(device) output = model(tensor_img)['out'] pred_mask = torch.argmax(output.squeeze(), dim=0).detach().cpu().numpy() return pred_mask if __name__ == "__main__": # Load your pretrained GR-CNN model here. grcnn_model = ... # Placeholder for loading or defining the actual GR-CNN architecture # Set up GPU/CPU environment use_cuda = torch.cuda.is_available() device = torch.device("cuda" if use_cuda else "cpu") # Move model to appropriate device (GPU/CPU). grcnn_model.to(device) test_image_path = 'path_to_your_test_image.jpg' prediction_result = predict_single_image(grcnn_model, device, test_image_path) print(f"The shape of predicted mask is {prediction_result.shape}") ``` 上述代码实现了从读取一张测试图片到通过已有的GR-CNN模型得到其对应的分割掩码的过程[^1]。注意，在实际应用中需要替换`grcnn_model = ...`这行代码中的省略号部分为具体的模型定义或加载语句。