ZymHedy
commented
3 years ago Thank you for your work What environment did you run the program? Why do I use 24 GB of GPU memory and still have an out-of-memory exception when I run test。 RuntimeError: CUDA out of memory.
the same as you.I run test on teslap40 with 24GB,after cuda out of memory,I del imgs to release cuda memory,it works,code :
qf, q_pids, q_camids = [], [], []
for batch_idx, (images, pids, camids) in enumerate(queryloader):
print('{}th'.format(batch_idx))
if use_gpu:
imgs = images.cuda()
del images
# print('loader img:{}'.format(imgs.shape))
imgs = imgs.squeeze(dim=0)
with torch.no_grad():
# imgs = Variable(imgs, volatile=True) # 将imgs装进Variable中,
# volatile=True的节点不会求导，即使requires_grad=True，也不会进行反向传播，对于不需要反向传播的情景(inference，测试推断)，
# 该参数可以实现一定速度的提升，并节省一半的显存，因为其不需要保存梯度
# print('variable img:{}'.format(imgs.shape))
n, s, c, h, w = imgs.size() # b=1, n=batchs, s=图片的长度
# assert (b == 1) # 断言函数
# imgs = imgs.view(b * n, s, c, h, w)
imgs = imgs.reshape(imgs.size(0) * imgs.size(1), imgs.size(2), imgs.size(3), imgs.size(4))
# print('view img:{}'.format(imgs.shape))
features = model(imgs) # 喂给模型图片，获得特征
del imgs
features = features.view(n, -1) # view()函数作用是将一个多行的Tensor,拼接成一行。
features = torch.mean(features, 0) # 取平均值
features = features.data.cpu()
qf.append(features) # 向列表尾部追加一个新元素，序列特征
q_pids.extend(pids) # 向列表尾部追加一个列表,人的身份person id
q_camids.extend(camids) # 摄像机的id
print('unlock cuda')
torch.cuda.empty_cache()
print('-----------------------------------------')
qf = torch.stack(qf) # 堆叠
q_pids = np.asarray(q_pids) # 将列表转换为数组
q_camids = np.asarray(q_camids)`but I got a new error when computing dismat
RuntimeError: The expanded size of the tensor (9330) must match the existing size (1980) at non-singleton dimension 0. Target sizes: [9330, 1980]. Tensor sizes: [1980, 1]
hppy152
Thank you for your work What environment did you run the program? Why do I use 24 GB of GPU memory and still have an out-of-memory exception when I run test。 RuntimeError: CUDA out of memory.