56 lines
1.6 KiB
Python
56 lines
1.6 KiB
Python
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import pandas as pd
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import torch
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from torch.utils.data import DataLoader, TensorDataset
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# 读取Excel文件
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df = pd.read_excel('loss-metal-compress.xlsx')
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# 假设你的模型需要的数据是前300行
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data = df.iloc[300:600, 1].values
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# 将数据转换为Tensor
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data_tensor = torch.tensor(data, dtype=torch.float32).unsqueeze(0) # 增加一个批次维度
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# 需要填充的0的数量
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padding_size = 371 - data_tensor.size(1)
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# 如果需要填充的0的数量大于0，则进行填充
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if padding_size > 0:
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# 创建一个形状为[1, padding_size]的0张量
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padding_tensor = torch.zeros(1, padding_size, dtype=torch.float32)
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# 将原始数据和0张量拼接起来
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data_tensor_padded = torch.cat((data_tensor, padding_tensor), dim=1)
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else:
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data_tensor_padded = data_tensor
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# 包装成TensorDataset和DataLoader
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dataset = TensorDataset(data_tensor_padded)
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dataloader = DataLoader(dataset, batch_size=1, shuffle=False)
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# 确定设备
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device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
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# device = 'cpu'
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print(f"Using device: {device}")
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# 加载你的模型
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model = torch.load('Sound.pth', map_location=device) # 确保模型加载到正确的设备
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model.to(device) # 再次确保模型在正确的设备上
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model.eval() # 设置为评估模式
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# 进行预测
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predictions = []
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with torch.no_grad():
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for batch in dataloader:
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inputs = batch[0].to(device) # 将输入数据移动到相同的设备
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outputs = model(inputs)
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_, predicted = torch.max(outputs, 1)
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predictions.extend(predicted.cpu().numpy()) # 将预测结果移动回CPU并转换为numpy数组
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# 打印预测结果
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print(predictions)
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