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test_12_2.py
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test_12_2.py
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# -*- coding: utf-8 -*-
# @Time : 3/22/20 19:15
# @Author : huangting
# @FileName: train.py
# @Software: PyCharm
import argparse
import os
import dataLoader
from model import CNN_LSTM_2
import torch
import torch.nn as nn
import torch.optim as optim
import numpy as np
from keras.utils.np_utils import to_categorical
parser = argparse.ArgumentParser(description='面部表情识别')
parser.add_argument("--model", default="CNN-LSTM", type=str, help="需要训练的模型")
parser.add_argument("--bs", default=100, type=int, help="batchsize")
parser.add_argument("--lr", default=0.0001, type=float, help="学习率")
parser.add_argument("--ts", default=10, type=int, help="网络处理数据的时间步长")
parser.add_argument("--dl", default=0, type=int, help="延时预测的长度")
parser.add_argument("--lb", default=1, type=int, help="滑窗处理的步长")
parser.add_argument("--dr", default="./data", type=str, help="数据所在的根路径")
parser.add_argument("--ep", default=200, type=int, help="训练的总轮数")
parser.add_argument('--re', default=True, help='是否从checkpoint处开始训练')
parser.add_argument("--ckpt", default="./checkpoints/res_10/CNN-LSTM-15.t7", help="checkpoint的地址")
parser.add_argument("--gpu", default=False, help="是否使用GPU进行训T练")
parser.add_argument("--cuda", default="0，1，2，3", type=str, help="用于训练GPU的代号")
cfg, unknown = parser.parse_known_args()
os.environ["CUDA_VISIBLE_DEVICES"] = cfg.cuda # 配置环境中的GPUF
start_epoch = 0 # 开始训练的轮数
best_test_acc = 0 # 最好的测试准确率
best_test_acc_epoch = 0 # 最好的测试准确率时的轮数
print("--------------加载测试数据中------------")
testRoot = cfg.dr + "/orig1/test"
X_test, y_test, mask_test = dataLoader.window_process(testRoot, "./data/test1", cfg.dl, cfg.ts, cfg.lb)
X_test = np.array(X_test)
y_test = np.array(y_test)
mask_test = np.array(mask_test)
y_test = to_categorical(y_test, 22)
# 对数据进行随机打乱
permutation = np.random.permutation(X_test.shape[0])
X_test = X_test[permutation, :, :, :]
y_test = y_test[permutation, :]
mask_test = mask_test[permutation, :, :]
testData_sum = len(X_test) # 训练样本总数
print("--------------加载测试数据完成------------")
use_cuda = torch.cuda.is_available() # 环境中是否有GPU
net = CNN_LSTM_2.CNN_LSTM(cfg.ts)
if use_cuda and cfg.gpu:
net = net.cuda()
net = nn.DataParallel(net)
if cfg.re:
print('------------------------------')
print('==> 加载checkpoint ')
if not os.path.exists(cfg.ckpt):
raise AssertionError['找不到路径']
checkpoint = torch.load(cfg.ckpt)
net.load_state_dict(checkpoint['net'])
best_test_acc = checkpoint['best_test_acc']
print('best_test_acc is %.4f%%'%best_test_acc)
best_test_acc_epoch = checkpoint['best_test_acc_epoch']
print('best_test_acc_epoch is %d'%best_test_acc_epoch)
start_epoch = checkpoint['best_test_acc_epoch'] + 1
else:
print('------------------------------')
print('==> 构建新的模型')
optimizer = optim.Adam(net.parameters(), lr=cfg.lr) # 实例化梯度下降算法
MSELoss = nn.MSELoss() # 实例化loss
def test(epoch):
global test_acc
global best_test_acc
global best_test_acc_epoch
net.eval()
total = 0
correct = 0
confuse = torch.zeros(22, 22) # 定义混淆矩阵
for i in range(0, testData_sum, cfg.bs):
if testData_sum - i >= cfg.bs:
inputs = X_test[i:(i+cfg.bs), :, :, :]
target = y_test[i:(i+cfg.bs), :]
mask = mask_test[i:(i + cfg.bs), :, :]
else:
inputs = X_test[i:testData_sum, :, :, :]
target = y_test[i:testData_sum, :]
mask = mask_test[i:testData_sum, :, :]
inputs = torch.Tensor(inputs)
target = torch.Tensor(target)
mask = torch.Tensor(mask)
if use_cuda and cfg.gpu:
inputs = inputs.cuda()
target = target.cuda()
with torch.no_grad():
outputs = net(inputs, mask)
_, predicted = torch.max(outputs[8].data, 1)
_, trueValue = torch.max(target.data, 1)
for j in range(predicted.size()[0]):
confuse[predicted[j], trueValue[j]] += 1
total += target.size(0)
correct += predicted.eq(trueValue.data).sum()
for categorical in range(22):
confuse[categorical] = confuse[categorical] / confuse[categorical].sum()
print(confuse.data)
test_acc = 100.0 * int(correct.data) / total
print('在 %d 个样本中, %d 个被准确预测' % (total, correct))
print('测试准确率为 %.4f%%' % test_acc)
print("一轮测试已经完成")
return
if __name__ == "__main__":
test(0)