plt.savefig(name)
'''
调用函数，读取数据集
训练网络
'''
if __name__ == '__main__':
print(torch.cuda.is_available())
device = torch.device('cuda:1')
# 数据集路径
batch_size = 32
path = [r"VehicleData/train/", r"VehicleData/test/"]
data = LoadData(batch_size, path)
train = data.getTrainDataset()
test = data.getTestDataset()
net = Vgg16(4).cuda(1)
early_stopping = EarlyStopping(patience=10, verbose=True)
loss_func = nn.CrossEntropyLoss()
# 需要把参数传递进去进行优化
# 这个不要放到循环离去，否则会循环定义，显存会爆炸
optimizer = optim.SGD(net.parameters(), lr=0.001)
scheduler = ReduceLROnPlateau(optimizer,
'min',
factor=0.5,
patience=5,
verbose=True)
#!!!需要探索一些多GPU运行机理
# net = torch.nn.DataParallel(Model.cuda(), device_ids=[3])
