test_BNN.py

import pickle

import torch
import torch.nn as nn
import numpy as np
import pandas as pd
from matplotlib import pyplot as plt

from torch.utils.data import Dataset, DataLoader

import net_BNN

# 使用已训练的模型进行推理

def test_plot(dataloader,model,device,criterion):
    total_loss = 0.0
    num_samples = 0
    num_sample = 10
    model.eval()
    with torch.no_grad():
        predictions = []
        true_labels = []
        cov = []
        # upp = []
        # low = []

        for test_features, test_labels in dataloader:
            # outputs = model(test_features.to(device))

            # MC sample
            sample_output = torch.zeros(num_sample, test_labels.shape[0]).to(device)
            for i in range(num_sample):
                sample_output[i] = model(test_features.to(device)).reshape(-1)

            outputs = torch.Tensor(sample_output.mean(dim=0).unsqueeze(1)).to(device)
            covs = torch.Tensor(sample_output.std(dim=0).unsqueeze(1)).to(device)

            loss = criterion(outputs, test_labels.to(device))
            total_loss += loss.item() * test_features.size(0)
            num_samples += test_features.size(0)

            predictions.append((outputs).tolist())
            cov.append(covs.tolist())
            true_labels.append((test_labels).tolist())
            # upp.append(np.percentile(sample_output.cpu().detach().numpy() * float(C_test),95).tolist())
            # low.append(np.percentile(sample_output.cpu().detach().numpy() * float(C_test),5).tolist())

        average_loss = total_loss / num_samples
        print('Validation Loss: {:.8f}'.format(average_loss))
    #

    x = range(len(sum(predictions, [])))

    pred_array = np.array(sum(predictions, [])).flatten()
    var_array = np.array(sum(cov, [])).flatten()

    plt.plot(sum(predictions,[]), label='Predictions')

    plt.plot(sum(true_labels,[]), label='True Labels')
    # plt.fill_between(x, upp, low, alpha=0.5, label='Confidence Interval')
    plt.fill_between(x, pred_array + var_array, pred_array - var_array, alpha=0.5, label='Confidence Interval')

    plt.legend()
    plt.xlabel('Sample Index')
    plt.ylabel('Cycle Capacity/Ah')
    plt.show()

#
base_path = r'E:\member\ShiJH\Battery Datasets\SNL_18650_LFP Datasets\modified_dataset'
attrib_feature = ['Current','Voltage','Environment_Temperature','Cell_Temperature','SOC']
attrib_label = ['SOH']

with open('./min_max_values.pkl', 'rb') as f:
    min_val, max_val = pickle.load(f)

max_len = 200
input_dim = len(attrib_feature)
output_dim = 1
hidden_dim = 64
num_layers = 2
num_heads = 4
lr = 1e-3
max_seq_len = 200
batch_size = 5
C_rated = 1.1




testdata_path = base_path + str('\modified_SNL_18650_LFP_25C_0-100_0.5-3C_b_timeseries.csv')
test_data = pd.read_csv(testdata_path)
test_data['SOH'] = test_data['cycle capacity'] / test_data['cycle capacity'].values[0]
# C_val = val_data[attrib_label].values[0]
# scaler_val = train_dataset.scaler
test_dataset = net_BNN.CycleDataset(
    data=test_data,
    attrib_x=attrib_feature,
    attrib_y=attrib_label,
    max_len=max_len,
    C_rated=C_rated,
    min_val=min_val,
    max_val=max_val,
    mode='test')
test_dataloader = DataLoader(test_dataset, batch_size=batch_size,shuffle=False,drop_last=True)
# 导入网络结构
model = net_BNN.ATBNN_Model(
input_dim = input_dim,
output_dim = output_dim,
hidden_dim = hidden_dim,
num_layers = num_layers,
num_heads = num_heads,
batch_size = batch_size,
max_seq_len= max_seq_len)

# model.load_state_dict(torch.load("./model_B/model_epoch634_Trainloss0.00013560_ValLoss0.00107357.pt"))  #LOSS=0
# model.load_state_dict(torch.load("./model_B/model_epoch2168_Trainloss0.00001729_ValLoss0.00107112.pt"))
model.load_state_dict(torch.load("./model_epoch8876_Trainloss0.00001546_ValLoss0.00022519.pt"))

# model.load_state_dict(torch.load("./model/model_epoch1.pt"))

device = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')
model.to(device)
criterion = nn.MSELoss(reduction='mean')


test_plot(test_dataloader,model,device,criterion)