Hello. I get the error:
CUDA out of memory. Tried to allocate 352.00 MiB (GPU 0; 4.00 GiB total capacity; 2.04 GiB already allocated; 302.80 MiB free; 593.57 MiB cached)
when I try to predict disparities. Is there any change I can make to your code, to make it possible to predict disparities?
Full error log:
C:\Users\sarim\AppData\Local\Programs\Python\Python37\lib\site-packages\torch\nn\functional.py:2457: UserWarning: nn.functional.upsample is deprecated. Use nn.functional.interpolate instead.
warnings.warn("nn.functional.upsample is deprecated. Use nn.functional.interpolate instead.")
C:\Users\sarim\AppData\Local\Programs\Python\Python37\lib\site-packages\torch\nn\functional.py:2539: UserWarning: Default upsampling behavior when mode=bilinear is changed to align_corners=False since 0.4.0. Please specify align_corners=True if the old behavior is desired. See the documentation of nn.Upsample for details.
"See the documentation of nn.Upsample for details.".format(mode))
C:\Users\sarim\AppData\Local\Programs\Python\Python37\lib\site-packages\torch\nn\functional.py:2539: UserWarning: Default upsampling behavior when mode=trilinear is changed to align_corners=False since 0.4.0. Please specify align_corners=True if the old behavior is desired. See the documentation of nn.Upsample for details.
"See the documentation of nn.Upsample for details.".format(mode))
Traceback (most recent call last):
File "C:/Users/sarim/PycharmProjects/thesis/DPC/psmnet/submission.py", line 125, in <module>
main()
File "C:/Users/sarim/PycharmProjects/thesis/DPC/psmnet/submission.py", line 112, in main
pred_disp = test(imgL,imgR)
File "C:/Users/sarim/PycharmProjects/thesis/DPC/psmnet/submission.py", line 83, in test
output = model(imgL,imgR)
File "C:\Users\sarim\AppData\Local\Programs\Python\Python37\lib\site-packages\torch\nn\modules\module.py", line 493, in __call__
result = self.forward(*input, **kwargs)
File "C:\Users\sarim\AppData\Local\Programs\Python\Python37\lib\site-packages\torch\nn\parallel\data_parallel.py", line 150, in forward
return self.module(*inputs[0], **kwargs[0])
File "C:\Users\sarim\AppData\Local\Programs\Python\Python37\lib\site-packages\torch\nn\modules\module.py", line 493, in __call__
result = self.forward(*input, **kwargs)
File "C:\Users\sarim\PycharmProjects\thesis\DPC\psmnet\models\stackhourglass.py", line 159, in forward
pred3 = disparityregression(self.maxdisp)(pred3)
File "C:\Users\sarim\AppData\Local\Programs\Python\Python37\lib\site-packages\torch\nn\modules\module.py", line 493, in __call__
result = self.forward(*input, **kwargs)
File "C:\Users\sarim\PycharmProjects\thesis\DPC\psmnet\models\submodule.py", line 62, in forward
disp = self.disp.repeat(x.size()[0],1,x.size()[2],x.size()[3])
RuntimeError: CUDA out of memory. Tried to allocate 352.00 MiB (GPU 0; 4.00 GiB total capacity; 2.04 GiB already allocated; 302.80 MiB free; 593.57 MiB cached)
Process finished with exit code 1
Edit: I got it to work by changing your code in stackhourglass.py (in class PSMNet):
def forward(self, left, right):
refimg_fea = self.feature_extraction(left)
targetimg_fea = self.feature_extraction(right)
# matching
cost = Variable(
torch.FloatTensor(refimg_fea.size()[0], refimg_fea.size()[1] * 2, self.maxdisp // 4, refimg_fea.size()[2],
refimg_fea.size()[3]).zero_()).cuda()
for i in range(self.maxdisp // 4):
if i > 0:
cost[:, :refimg_fea.size()[1], i, :, i:] = refimg_fea[:, :, :, i:]
cost[:, refimg_fea.size()[1]:, i, :, i:] = targetimg_fea[:, :, :, :-i]
else:
cost[:, :refimg_fea.size()[1], i, :, :] = refimg_fea
cost[:, refimg_fea.size()[1]:, i, :, :] = targetimg_fea
cost = cost.contiguous()
del refimg_fea
del targetimg_fea
cost0 = self.dres0(cost)
del cost
cost0 = self.dres1(cost0) + cost0
out1, pre1, post1 = self.dres2(cost0, None, None)
out1 = out1 + cost0
out2, pre2, post2 = self.dres3(out1, pre1, post1)
del post1
del pre2
out2 = out2 + cost0
out3, pre3, post3 = self.dres4(out2, pre1, post2)
del post3
del pre3
del post2
del pre1
out3 = out3 + cost0
del cost0
cost1 = self.classif1(out1)
del out1
cost2 = self.classif2(out2) + cost1
del out2
cost3 = self.classif3(out3) + cost2
del out3
if self.training:
cost1 = F.upsample(cost1, [self.maxdisp, left.size()[2], left.size()[3]], mode='trilinear')
cost2 = F.upsample(cost2, [self.maxdisp, left.size()[2], left.size()[3]], mode='trilinear')
cost1 = torch.squeeze(cost1, 1)
pred1 = F.softmax(cost1, dim=1)
pred1 = disparityregression(self.maxdisp)(pred1)
cost2 = torch.squeeze(cost2, 1)
pred2 = F.softmax(cost2, dim=1)
pred2 = disparityregression(self.maxdisp)(pred2)
del cost1
del cost2
cost3 = F.upsample(cost3, [self.maxdisp, left.size()[2], left.size()[3]], mode='trilinear')
cost3 = torch.squeeze(cost3, 1)
pred3 = F.softmax(cost3, dim=1)
del cost3
pred3 = disparityregression(self.maxdisp)(pred3)
if self.training:
return pred1, pred2, pred3
else:
return pred3
Hello. I get the error:
CUDA out of memory. Tried to allocate 352.00 MiB (GPU 0; 4.00 GiB total capacity; 2.04 GiB already allocated; 302.80 MiB free; 593.57 MiB cached)when I try to predict disparities. Is there any change I can make to your code, to make it possible to predict disparities? Full error log:Edit: I got it to work by changing your code in stackhourglass.py (in class PSMNet):