56 lines
1.6 KiB
Python
56 lines
1.6 KiB
Python
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)
|
||
|
||
|
||
|
||
|