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View Code? Open in Web Editor NEWMemory-Efficient Implementation of DenseNets via PyTorch 1.0
License: BSD 3-Clause "New" or "Revised" License
Memory-Efficient Implementation of DenseNets via PyTorch 1.0
License: BSD 3-Clause "New" or "Revised" License
Before testing the efficient densenet implementation, out = F.dropout(out, p=0.5, training=self.training)
at Line 184 in densenet.py
should be commented.
Then if I set multigpus = True
in test_densenet.py
, running python test_densenet.py
will get the following error:
Traceback (most recent call last):
File "test_densenet.py", line 47, in <module>
out_effi.sum().backward()
File "/home/changmao/miniconda3/lib/python3.5/site-packages/torch/tensor.py", line 93, in backward
torch.autograd.backward(self, gradient, retain_graph, create_graph)
File "/home/changmao/miniconda3/lib/python3.5/site-packages/torch/autograd/__init__.py", line 89, in backward
allow_unreachable=True) # allow_unreachable flag
RuntimeError: one of the variables needed for gradient computation has been modified by an inplace operation
And I cannot locate the improper inplace operation. All I know is that the error seems occur after the error-backpropagation of few efficient bottleneck modules. All the code is run by Pytorch v0.4.0.
hi, I used the pytorch 0.3 version from https://github.com/gpleiss/efficient_densenet_pytorch. It seems the 0.3 version cost far less memory than your 0.4 version. Do you have tried to compare this?
Hi,
I have traied this model and train it based on this training code.
I tried the model using densenet40 with grouth_rate = 12 on that but only got 6.0% error rate(5.24% inthe report) when using the non-efficiency implementation and 6.1% with efficiency implementation
By the way, the memmory efficiency implementation works pretty good!
Could you please help with that?
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