关于detector的test

[Carl-Lei]

@lfz 你好，我在训练网络的时候，detector模型的训练部分可以实现，但是在执行test的时候出现错误，提示时out of memory, 但我训练的时候是可以通过的啊。我也尝试将test时的batch_size减小，但还是不行。 test函数里面 for i in range(len(splitlist)-1): input = Variable(data[splitlist[i]:splitlist[i+1]], volatile = True).cuda() inputcoord = Variable(coord[splitlist[i]:splitlist[i+1]], volatile = True).cuda() if isfeat: output,feature = net(input,inputcoord) featurelist.append(feature.data.cpu().numpy()) else: output = net(input,inputcoord) outputlist.append(output.data.cpu().numpy())

i=0时可以执行，当i=1时报错

错误信息： Traceback (most recent call last): File "main.py", line 353, in main() File "main.py", line 122, in main test(test_loader, net, get_pbb, save_dir,config) File "main.py", line 300, in test output = net(input,inputcoord) File "C:\ProgramData\Anaconda3\lib\site-packages\torch\nn\modules\module.py", line 491, in call result = self.forward(*input, kwargs) File "C:\ProgramData\Anaconda3\lib\site-packages\torch\nn\parallel\data_parallel.py", line 114, in forward outputs = self.parallel_apply(replicas, inputs, kwargs) File "C:\ProgramData\Anaconda3\lib\site-packages\torch\nn\parallel\data_parallel.py", line 124, in parallel_apply return parallel_apply(replicas, inputs, kwargs, self.device_ids[:len(replicas)]) File "C:\ProgramData\Anaconda3\lib\site-packages\torch\nn\parallel\parallel_apply.py", line 65, in parallel_apply raise output File "C:\ProgramData\Anaconda3\lib\site-packages\torch\nn\parallel\parallel_apply.py", line 41, in _worker output = module(*input, *kwargs) File "C:\ProgramData\Anaconda3\lib\site-packages\torch\nn\modules\module.py", line 491, in call result = self.forward(input, kwargs) File "D:\mydsb\dsb_test\training\detector\res18.py", line 94, in forward out = self.preBlock(x)#16 File "C:\ProgramData\Anaconda3\lib\site-packages\torch\nn\modules\module.py", line 491, in call result = self.forward(*input, *kwargs) File "C:\ProgramData\Anaconda3\lib\site-packages\torch\nn\modules\container.py", line 91, in forward input = module(input) File "C:\ProgramData\Anaconda3\lib\site-packages\torch\nn\modules\module.py", line 491, in call result = self.forward(input, **kwargs) File "C:\ProgramData\Anaconda3\lib\site-packages\torch\nn\modules\conv.py", line 421, in forward self.padding, self.dilation, self.groups) RuntimeError: cuda runtime error (2) : out of memory at c:\programdata\miniconda3\conda-bld\pytorch_1524543037166\work\aten\src\thc\generic/THCStorage.cu:58

我用的是两块GTX1080Ti，下面时bash文件 cd detector eps=100 CUDA_VISIBLE_DEVICES=0,1 python main.py --model res18 -b 4 --epochs $eps --save-dir res18 CUDA_VISIBLE_DEVICES=0,1 python main.py --model res18 -b 2 --resume results/res18/$eps.ckpt --test 1 cp results/res18/$eps.ckpt ../../model/detector2.ckpt

我看训练时图像尺寸为128128128，测试时的图像尺寸为208208208，是不是和这个有关系啊？

[zhan-zhang]

I d suggest you to temporarily remove dataparallel and set batch size to 1 so that you can manually debug it. The size 208 definitely fit for a 1080Ti card.

[Carl-Lei]

我在main.py的第96行后面添加了一句del output 问题解决了。不过在运行到第65个文件的时候，又出现了错误。 Traceback (most recent call last): File "main.py", line 349, in main() File "main.py", line 121, in main test(test_loader, net, get_pbb, save_dir,config) File "main.py", line 268, in test for i_name, (data, target, coord, nzhw) in enumerate(data_loader): File "C:\ProgramData\Anaconda3\lib\site-packages\torch\utils\data\dataloader.py", line 272, in next return self._process_next_batch(batch) File "C:\ProgramData\Anaconda3\lib\site-packages\torch\utils\data\dataloader.py", line 307, in _process_next_batch raise batch.exc_type(batch.exc_msg) MemoryError: Traceback (most recent call last): File "C:\ProgramData\Anaconda3\lib\site-packages\torch\utils\data\dataloader.py", line 57, in _worker_loop samples = collate_fn([dataset[i] for i in batch_indices]) File "C:\ProgramData\Anaconda3\lib\site-packages\torch\utils\data\dataloader.py", line 57, in samples = collate_fn([dataset[i] for i in batch_indices]) File "D:\mydsb\DSB2017-master\training\detector\data.py", line 118, in getitem imgs = (imgs.astype(np.float32)-128)/128 MemoryError

Exception ignored in: <bound method _DataLoaderIter.del of <torch.utils.data.dataloader._DataLoaderIter object at 0x00000196E28DC3C8>> Traceback (most recent call last): File "C:\ProgramData\Anaconda3\lib\site-packages\torch\utils\data\dataloader.py", line 349, in del self._shutdown_workers() File "C:\ProgramData\Anaconda3\lib\site-packages\torch\utils\data\dataloader.py", line 328, in _shutdown_workers self.worker_result_queue.get() File "C:\ProgramData\Anaconda3\lib\multiprocessing\queues.py", line 337, in get return _ForkingPickler.loads(res) File "C:\ProgramData\Anaconda3\lib\site-packages\torch\multiprocessing\reductions.py", line 86, in rebuild_storage_filename storage = cls._new_shared_filename(manager, handle, size) RuntimeError: Couldn't open shared event: , error code: <2> at ..\src\TH\THAllocator.c:218

[zp678]

我的也是这个问题，在执行test的时候出现错误，提示时out of memory, 但我训练的时候是可以通过的啊。我也尝试将test时的batch_size减小，但还是不行。不知道您解决了没有？希望可以指教一下，万分感谢@Carl-Lei

[melody-rain]

I met the same problem too.

[m1sta4es]

you can add 'with torch.no_grad():' before 'input = Variable(data[splitlist[i]:splitlist[i+1]], volatile = True).cuda() inputcoord = Variable(coord[splitlist[i]:splitlist[i+1]], volatile = True).cuda()'

[jwjuven]

Under Pytorch 1.x+, you should use the following codes:

def test(data_loader, net, get_pbb, save_dir, config): ... use_cuda = torch.cuda.is_available() device = torch.device("cuda" if use_cuda else "cpu") .. for i_name, (data, target, coord, nzhw) in enumerate(data_loader): ... input = data[splitlist[i] : splitlist[i + 1]].to(device, non_blocking=True) inputcoord = coord[splitlist[i] : splitlist[i + 1]].to(device, non_blocking=True) with torch.no_grad(): if isfeat: output,feature = net(input, inputcoord) featurelist.append(feature.data.cpu().numpy()) else: output = net(input, inputcoord) ....