From 72be2f470ce8f63e6bc0add251677335dce2becd Mon Sep 17 00:00:00 2001 From: Brandon Amos Date: Fri, 10 Feb 2017 11:19:39 -0500 Subject: [PATCH] Fill in the README. --- README.md | 167 ++++++++++++++++++++++++++++++++-- attic/debugging-discussion.md | 43 ++++++++- images/adam-loss-error.png | Bin 0 -> 66511 bytes images/densenet-err-table.png | Bin 0 -> 81725 bytes images/sgd-loss-error.png | Bin 0 -> 71671 bytes 5 files changed, 198 insertions(+), 12 deletions(-) create mode 100644 images/adam-loss-error.png create mode 100644 images/densenet-err-table.png create mode 100644 images/sgd-loss-error.png diff --git a/README.md b/README.md index 2af8e9e..1fac6dc 100644 --- a/README.md +++ b/README.md @@ -4,6 +4,8 @@ This is a [PyTorch](http://pytorch.org/) implementation of the DenseNet-BC architecture as described in the paper [Densely Connected Convolutional Networks](https://arxiv.org/abs/1608.06993) by G. Huang, Z. Liu, K. Weinberger, and L. van der Maaten. +This implementation gets a CIFAR-10+ error rate of +~4.81 with a 100-layer DenseNet-BC with a growth rate of 12. Their official implementation and links to many other third-party implementations are available in the [liuzhuang13/DenseNet](https://github.com/liuzhuang13/DenseNet) @@ -11,15 +13,166 @@ repo on GitHub. ![](images/header.png) ---- +# Why DenseNet? -This repo is not complete and I'll fill in more details -here soon. We just overcame a PyTorch bug that you -can learn more about [here](attic/debugging-discussion.md). -PyTorch master will soon contain a cudnn patch making the -code in this repo work. +As this table from the DenseNet paper shows, it provides +competitive state of the art results on CIFAR-10, +CIFAR-100, and SVHN. --Brandon / 2017-02-09 +![](images/densenet-err-table.png) + +# Why yet another DenseNet implementation? + +PyTorch is a great new framework and it's nice to have these +kinds of re-implementations around so that they can be integrated +with other PyTorch projects. + +# How do you know this implementation is correct? + +Interestingly while implementing this, I had a lot of +trouble getting it to converge and looked at every part +of the code closer than I usually would. +I compared all of the model's hidden states and gradients +with the official implementation to make sure my code was correct +and even trained a VGG-style network on CIFAR-10 with the +training code here. +It turns out that I uncovered a new critical PyTorch +bug (now fixed) that was causing this. + +I have left around my original message about how this +isn't working and the things that I have checked +[in this document](attic/debugging-discussion.md). +I think this should be interesting for other people to +see my development and debugging strategies when +having issues implementing a model that's known +to converge. +I also started +[this PyTorch forum thread](https://discuss.pytorch.org/t/help-debugging-densenet-model-on-cifar-10/412), +which has a few other discussion points. +You may also be interested in +[my script that +compares PyTorch gradients to Torch gradients](https://github.com/bamos/densenet.pytorch/blob/master/attic/compare-pytorch-and-torch-grads.py) +and +[my script that numerically checks PyTorch gradients](https://github.com/bamos/densenet.pytorch/blob/master/attic/numcheck-grads.py). + +My convergence issues were due to a critical PyTorch bug +related to using `torch.cat` with convolutions with cuDNN +enabled (which it is by default when CUDA is used). +This bug caused incorrect gradients and the fix to +this bug is to disable cuDNN. +The oversight in my debugging strategies that caused me to +not find this error is that I did not think to disable cuDNN. +Until now, I have assumed that the cuDNN option in frameworks +are bug-free, but have learned that this is not always the case. +I may have also found something if I would have numerically +debugged `torch.cat` layers with convolutions instead of +fully connected layers. + +Adam fixed the PyTorch bug that caused this in +[this PR](https://github.com/pytorch/pytorch/pull/708) +and has been merged into Torch's master branch. +**If you are interested in using the DenseNet code in +this repository, make sure your PyTorch version +contains this patch and was downloaded after 2017-02-10.** + +# What does the PyTorch compute graph of the model look like? + +You can see the compute graph [here](images/graph.png), +which I created with [make_graph.py](https://github.com/bamos/densenet.pytorch/blob/master/make_graph.py), +which I copied from +[Adam Paszke's gist](https://gist.github.com/apaszke/01aae7a0494c55af6242f06fad1f8b70). +Adam says PyTorch will soon have a better way to create +compute graphs. + +# How does this implementation perform? + +By default, this repo trains a 100-layer DenseNet-BC with +an growth rate of 12 on the CIFAR-10 dataset with +data augmentations. +Due to GPU memory sizes, this is the largest model I am able to run. +The paper reports a final test error of 4.51 with this +architecture and we obtain a final test error of ~4.81. + +![](images/sgd-loss-error.png) + +# Why don't people use ADAM instead of SGD for training ResNet-style models? + +I also tried training a net with ADAM and found that it didn't +converge as well with the default hyper-parameters compared +to SGD with a reasonable learning rate schedule. + +![](images/adam-loss-error.png) + +# What about the non-BC version? + +I haven't tested this as thoroughly, you should make sure +it's working as expected if you plan to use and modify it. +Let me know if you find anything wrong with it. + +# A paradigm for ML code + +I like to include a few features in my projects +that I don't see in some other re-implementations +that are present in this repo. +The training code in `train.py` uses `argparse` so the batch size +and some other hyper-params can easily be changed +and as the model is training, progress is written +out to csv files in a work directory also defined +by the arguments. +Then a separate script `plot.py` plots the +progress written out by the training script. +The training script calls `plot.py` after every epoch, +but it can importantly be run on its own so figures +can be tweaked without re-running the entire experiment. + +# Help wanted: Improving memory utilization and multi-GPU support + +I think there are ways to improve the memory utilization +in this code as in the +[the official space-efficient Torch implementation](https://github.com/gaohuang/DenseNet_lite). +I also would be interested in multi-GPU support. + +# Running the code and viewing convergence + +First install PyTorch (ideally in an anaconda3 distribution). +[./train.py](./train.py) will create a model, start training it, +and save progress to `args.save`, which is +`work/cifar10.base` by default. +The training script will call [plot.py](./plot.py) after +every epoch to create plots from the saved progress. + +# Citations + +The following is a [BibTeX](http://www.bibtex.org/) +entry for the DenseNet paper that you should cite +if you use this model. + +``` +@article{Huang2016Densely, + author = {Huang, Gao and Liu, Zhuang and Weinberger, Kilian Q.}, + title = {Densely Connected Convolutional Networks}, + journal = {arXiv preprint arXiv:1608.06993}, + year = {2016} +} +``` + +If you use this implementation, please also consider citing this implementation and +code repository with the following BibTeX or plaintext entry. +The BibTeX entry requires the `url` LaTeX package. + +``` +@misc{amos2017densenet, + title = {{A PyTorch Implementation of DenseNet}}, + author = {Amos, Brandon and Kolter, J. Zico}, + howpublished = {\url{http://github.com/bamos/densenet.pytorch}}, + note = {Accessed: [Insert date here]} +} + +Brandon Amos, J. Zico Kolter +A PyTorch Implementation of DenseNet +http://github.com/bamos/densenet.pytorch. +Accessed: [Insert date here] +``` # Licensing diff --git a/attic/debugging-discussion.md b/attic/debugging-discussion.md index 83e4fd6..1f9e436 100644 --- a/attic/debugging-discussion.md +++ b/attic/debugging-discussion.md @@ -1,3 +1,36 @@ +The following is the old message I included in this repository +when I was having trouble with convergence. +I also posted in +[this PyTorch forum thread](https://discuss.pytorch.org/t/help-debugging-densenet-model-on-cifar-10/412). +You may also be interested in +[my script that +compares PyTorch gradients to Torch gradients](https://github.com/bamos/densenet.pytorch/blob/master/attic/compare-pytorch-and-torch-grads.py) +and +[my script that numerically checks PyTorch gradients](https://github.com/bamos/densenet.pytorch/blob/master/attic/numcheck-grads.py). + +[Adam Paszke](https://github.com/apaszke) helped me find out +that my convergence issues were due to a critical PyTorch bug +related to using `torch.cat` with convolutions with cuDNN +enabled (which it is by default when CUDA is used). +This bug caused incorrect gradients and the fix to +this bug is to disable cuDNN. +The oversight in my debugging strategies that caused me to +not find this error is that I did not think to disable cuDNN. +Until now, I have assumed that the cuDNN option in frameworks +are bug-free, but have learned that this is not always the case. +I may have also found something if I would have numerically +debugged `torch.cat` layers with convolutions instead of +fully connected layers. + +Adam fixed the PyTorch bug that caused this in +[this PR](https://github.com/pytorch/pytorch/pull/708) +and has been merged into Torch's master branch. +**If you are interested in using the DenseNet code in +this repository, make sure your PyTorch version +contains this patch and was downloaded after 2017-02-10.** + +--- + # Help wanted: CIFAR-10 experiments not converging I am not sure why my implementation is not converging @@ -51,7 +84,7 @@ LuaTorch weight gradients: Since my model's hidden states and gradients look good, it seems like there might be an issue with the training code in -[train.py](train.py), which I started with +[train.py](../train.py), which I started with [the official PyTorch MNIST example](https://github.com/pytorch/examples/blob/master/mnist/main.py). My training code here will successfully train a VGG model on CIFAR-10 (not included in this repo) @@ -78,14 +111,14 @@ I have uploaded the source for this check in [numcheck-grads.py](numcheck-grads.py). + My random weight initialization is the same as the official implementation. -+ The [model graph](./images/graph.png) looks reasonable. ++ The [model graph](../images/graph.png) looks reasonable. # Running the code and viewing convergence -[./train.py](train.py) will create a model, start training it, +[./train.py](../train.py) will create a model, start training it, and save progress to `args.save`, which is `work/cifar10.base` by default. -The training script will call [plot.py](plot.py) after +The training script will call [plot.py](../plot.py) after every epoch to create plots from the saved data. Here's a typical convergence plot that the current @@ -94,7 +127,7 @@ In many cases like this, the network quickly gets stuck in a bad place. 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