torch模型重新载入准确率很低，基本上不能预测

pytorch训练模型
训练集和验证集准确率都快100，然后用torch.save保存模型，之后再读取模型重新预测，模型基本上准确率不超过50%，有时是0%或者1%。用训练集和验证集测试也是相同的结果，都很低。但如果把预测代码放到train文件的末尾，又能正常预测。
训练代码

import scipy.io as sio
from torch.utils.data import TensorDataset, DataLoader
import numpy as np
import torch
from torch.autograd import Variable
import matplotlib.pyplot as plt
SEED = 1
np.random.seed(SEED)
torch.manual_seed(SEED)
torch.cuda.manual_seed_all(SEED)
torch.backends.cudnn.deterministic = True
torch.backends.cudnn.benchmark = False
from tqdm import tqdm

from model.conv_ince_resnet import *
from dataloader import *

batch_size = 64
num_epochs = 20

train_data,train_label,test_data, test_label,c= datagenerator('dataset/train.h5','dataset/trainlabel.txt','dataset/test.h5', 'dataset/testlabel.txt')

num_train_instances = len(train_data)
train_label = np.array(train_label)
train_data = torch.from_numpy(train_data).type(torch.FloatTensor)
train_label = torch.from_numpy(train_label).type(torch.LongTensor)
train_data = train_data.view(num_train_instances, 1, -1)
train_label = train_label.view(num_train_instances, 1)

train_dataset = TensorDataset(train_data, train_label)
train_data_loader = DataLoader(dataset=train_dataset, batch_size=batch_size, shuffle=True)



num_test_instances = len(test_data)
test_label = np.array(test_label)
test_data = torch.from_numpy(test_data).type(torch.FloatTensor)
test_label = torch.from_numpy(test_label).type(torch.LongTensor)
test_data = test_data.view(num_test_instances, 1, -1)
test_label = test_label.view(num_test_instances, 1)

test_dataset = TensorDataset(test_data, test_label)
test_data_loader = DataLoader(dataset=test_dataset, batch_size=64, shuffle=True)


msresnet = MSResNet(input_channel=13545, layers=[1, 1, 1, 1], num_classes=c)
msresnet = msresnet.cuda()

trace_module = torch.torch.jit.script(msresnet)
print(trace_module.code)

criterion = nn.CrossEntropyLoss().cuda()


optimizer = torch.optim.Adam(msresnet.parameters(), lr=0.001)
scheduler = torch.optim.lr_scheduler.MultiStepLR(optimizer, milestones=[300, 500, 600, 700, 800, 900], gamma=0.1)
train_loss = np.zeros([num_epochs, 1])
test_loss = np.zeros([num_epochs, 1])
train_acc = np.zeros([num_epochs, 1])
test_acc = np.zeros([num_epochs, 1])

for epoch in range(num_epochs):
    print('Epoch:', epoch)
    msresnet.train()
    scheduler.step(epoch)
    # for i, (samples, labels) in enumerate(train_data_loader):
    loss_x = 0
    for (samples, labels) in tqdm(train_data_loader):
        samplesV = Variable(samples.cuda())
        labels = labels.squeeze()
        labelsV = Variable(labels.cuda())

        # Forward + Backward + Optimize

        predict_label = msresnet(samplesV)
        optimizer.zero_grad()
        loss = criterion(predict_label[0], labelsV)

        loss_x += loss.item()

        loss.backward()
        optimizer.step()



    train_loss[epoch] = loss_x / num_train_instances
    print('trainloss:',train_loss[epoch])

    msresnet.eval()
    correct_train = 0
    for i, (samples, labels) in enumerate(train_data_loader):
        with torch.no_grad():
            samplesV = Variable(samples.cuda())
            labels = labels.squeeze()
            labelsV = Variable(labels.cuda())


            predict_label = msresnet(samplesV)
            prediction = predict_label[0].data.max(1)[1]
            correct_train += prediction.eq(labelsV.data.long()).sum()

            loss = criterion(predict_label[0], labelsV)


    print("Training accuracy:", (100*float(correct_train)/num_train_instances))

    train_acc[epoch] = 100*float(correct_train)/num_train_instances

    trainacc = str(100*float(correct_train)/num_train_instances)[0:6]


    loss_x = 0
    correct_test = 0
    for i, (samples, labels) in enumerate(test_data_loader):
        with torch.no_grad():
            samplesV = Variable(samples.cuda())
            labels = labels.squeeze()
            labelsV = Variable(labels.cuda())
            # labelsV = labelsV.view(-1)

        predict_label = msresnet(samplesV)
        prediction = predict_label[0].data.max(1)[1]
        correct_test += prediction.eq(labelsV.data.long()).sum()

        loss = criterion(predict_label[0], labelsV)
        loss_x += loss.item()

    print("Test accuracy:", (100 * float(correct_test) / num_test_instances))

    test_loss[epoch] = loss_x / num_test_instances
    test_acc[epoch] = 100 * float(correct_test) / num_test_instances
    print('valloss:', test_loss[epoch])
    testacc = str(100 * float(correct_test) / num_test_instances)[0:6]

    if epoch == 0:
        temp_test = correct_test
        temp_train = correct_train
    elif correct_test>temp_test:
        torch.save(msresnet, 'weights/changingResnet/ChaningSpeed_Train' + trainacc + 'Test' + testacc + '.pkl')
        temp_test = correct_test
        temp_train = correct_train
    torch.save(msresnet, 'weights/changingResnet/ChaningSpeed.pkl')
sio.savemat('result/changingResnet/TrainLoss_' + 'ChangingSpeed_Train' + str(100*float(temp_train)/num_train_instances)[0:6] + 'Test' + str(100*float(temp_test)/num_test_instances)[0:6] + '.mat', {'train_loss': train_loss})
sio.savemat('result/changingResnet/TestLoss_' + 'ChangingSpeed_Train' + str(100*float(temp_train)/num_train_instances)[0:6] + 'Test' + str(100*float(temp_test)/num_test_instances)[0:6] + '.mat', {'test_loss': test_loss})
sio.savemat('result/changingResnet/TrainAccuracy_' + 'ChangingSpeed_Train' + str(100*float(temp_train)/num_train_instances)[0:6] + 'Test' + str(100*float(temp_test)/num_test_instances)[0:6] + '.mat', {'train_acc': train_acc})
sio.savemat('result/changingResnet/TestAccuracy_' + 'ChangingSpeed_Train' + str(100*float(temp_train)/num_train_instances)[0:6] + 'Test' + str(100*float(temp_test)/num_test_instances)[0:6] + '.mat', {'test_acc': test_acc})
print(str(100*float(temp_test)/num_test_instances)[0:6])
plt.figure('1')
plt.plot(train_loss)
plt.show()
plt.figure('2')
plt.plot(test_loss)
plt.show()
plt.figure('3')
plt.plot(train_acc)
plt.show()
plt.figure('4')
plt.plot(test_acc)
plt.show()

测试代码

import scipy.io as sio
from torch.utils.data import TensorDataset, DataLoader
import numpy as np
import torch
import torch.nn as nn
from torch.autograd import Variable
import torch.nn.functional as F
import matplotlib.pyplot as plt
import math
import time
import torch
from torch import nn
from torch.autograd import Variable
import numpy as np
import matplotlib.pyplot as plt
import scipy.io as sio
from torch.utils.data import TensorDataset, DataLoader
import numpy as np
import torch
import torch.nn as nn
from torch.autograd import Variable
import torch.nn.functional as F
import matplotlib.pyplot as plt
import math
import time
from tqdm import tqdm
from dataloader import *
train_data,train_label,test_data, test_label,c= datagenerator('dataset/train.h5','dataset/trainlabel.txt','dataset/test.h5', 'dataset/testlabel.txt')

batch_size = 100

num_test_instances = len(test_data)

test_data = torch.from_numpy(test_data).type(torch.FloatTensor)
test_label = np.array(test_label)
test_label = torch.from_numpy(test_label).type(torch.LongTensor)
test_data = test_data.view(num_test_instances, 1, -1)
test_label = test_label.view(num_test_instances, 1)

test_dataset = TensorDataset(test_data, test_label)
test_data_loader = DataLoader(dataset=test_dataset, batch_size=batch_size, shuffle=True)

msresnet = torch.load('weights/changingResnet/ChaningSpeed.pkl')
a = msresnet.state_dict()

msresnet = msresnet.cuda()
msresnet.eval()

correct_test = 0
for i, (samples, labels) in enumerate(test_data_loader):
    with torch.no_grad():
        samplesV = Variable(samples.cuda())
        labels = labels.squeeze()
        labelsV = Variable(labels.cuda())
        # labelsV = labelsV.view(-1)
    predict_label = msresnet(samplesV)
    prediction = predict_label[0].data.max(1)[1]
    correct_test += prediction.eq(labelsV.data.long()).sum()

print("Test accuracy:", (100 * float(correct_test) / num_test_instances))

放那么大段代码，是让人给你debug嘛？这是很简单的问题，直接对比训练集和测试集的模型参数即可。