import pandas as pd
import torch
from torch.utils.data import DataLoader, TensorDataset

# 读取Excel文件
df = pd.read_excel('loss-metal-compress.xlsx')

# 假设你的模型需要的数据是前300行
data = df.iloc[300:600, 1].values


# 将数据转换为Tensor
data_tensor = torch.tensor(data, dtype=torch.float32).unsqueeze(0)  # 增加一个批次维度

# 需要填充的0的数量
padding_size = 371 - data_tensor.size(1)

# 如果需要填充的0的数量大于0，则进行填充
if padding_size > 0:
    # 创建一个形状为[1, padding_size]的0张量
    padding_tensor = torch.zeros(1, padding_size, dtype=torch.float32)
    # 将原始数据和0张量拼接起来
    data_tensor_padded = torch.cat((data_tensor, padding_tensor), dim=1)
else:
    data_tensor_padded = data_tensor

# 包装成TensorDataset和DataLoader
dataset = TensorDataset(data_tensor_padded)
dataloader = DataLoader(dataset, batch_size=1, shuffle=False)

# 确定设备
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
# device = 'cpu'
print(f"Using device: {device}")

# 加载你的模型
model = torch.load('Sound.pth', map_location=device)  # 确保模型加载到正确的设备
model.to(device)  # 再次确保模型在正确的设备上
model.eval()  # 设置为评估模式

# 进行预测
predictions = []
with torch.no_grad():
    for batch in dataloader:
        inputs = batch[0].to(device)  # 将输入数据移动到相同的设备
        outputs = model(inputs)
        _, predicted = torch.max(outputs, 1)
        predictions.extend(predicted.cpu().numpy())  # 将预测结果移动回CPU并转换为numpy数组

# 打印预测结果
print(predictions)