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We implemented a simple wrapper of the implementation. CIFAR-10ĬIFAR-10 is downloaded automatically and is saved in the -data_dir folder (default is. Other splits can be regenerated by specifying the split as the first argument. The dataset generated using this command should be close to our training dataset. Python experiments/net_generator.py train 1000000. To generate a new DeepNets-1M dataset, the following command can be used:

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To train or evaluate on DeepNets-1M, first download the dataset file by running.

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Note 2: To evaluate/train on ImageNet, follow the data instructions below on how to prepare the ImageNet dataset. in our BN-Free split) have the same accuracies regardless of the batch size.

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So to evaluate such networks, our code computes batch statistics on the evaluation set with batch size = 64. Note 1: For the networks with batch norm, the running statistics of batch norm layers are not predicted (since these statistics are not trainable parameters). # That's it! The model can be now evaluated on CIFAR-10 to obtain top1=58.6%.įull example for ResNet-50 and other torchvision models can be found in examples/torch_models.py and examples/all_torch_models.py. Model = ghn(model) # predict parameters in < 1 second on GPU/CPU Model = adjust_net(model) # adjust to a 32x32 input Model = models.resnet50(num_classes=10) # ResNet-50 Ghn = GHN2('cifar10') # load our GHN-2 trained on CIFAR-10 # while GHNs on CIFAR-10 were trained on 32x32 inputs. # since torchvision models expect a 224x224 input, # the first layer(s) of the network for a 32x32 image size, # On CIFAR-10, we have an additional step of adjusting Minimal example: predict parameters for ResNet-50 Other checkpoints (ablated GHN models, some networks trained with SGD) are available here. ‡ Denotes the architecture index in the test split of DeepNets-1M.Įach GHN checkpoint takes just a few megabytes and is stored in the checkpoints folder of this repository. On ImageNet, even though the performance is low, the predicted parameters are very useful for fine-tuning. Our GHNs can still predict good parameters for such unseen architectures. trained GHNs have not observed such or even similar architectures during training). * ResNet-50 is an unseen architecture (i.e.

Below are top-1 accuracies on CIFAR-10 and top-5 accuracies on ImageNet using the parameters predicted by one of the GHNs.

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It's also possible to use conda to install this by running:Ĭonda create -name ppuda -file requirements.txt -channel default -channel anaconda -channel conda-forge -channel pytorch Available GHNs To make it work, graphviz may need to be installed as sudo apt-get install graphviz graphviz-dev.

Fine-tuning predicted parameters on other tasksįor graph visualizations we use pygraphviz.

Minimal example: predict parameters for ResNet-50.

Video walking through the colab notebook and showing parameter prediction in real time. We provide a Google Colab notebook to experiment with parameter prediction on the CIFAR-10 dataset. Parameter prediction by GHNs is performed in a single forward pass and on average takes < 1 second either on GPU or CPU! Our improved GHNs trained on our DeepNets-1M allow to predict parameters for diverse networks, even if they are very different from those used to train GHNs (e.g. This repository also contains the DeepNets-1M dataset of neural architectures proposed in our paper to train and evaluate GHNs. This repository contains the code to train and evaluate Graph HyperNetworks (GHNs). Parameter Prediction for Unseen Deep Architectures (NeurIPS 2021)Īuthors: Boris Knyazev, Michal Drozdzal, Graham Taylor, Adriana Romero-Soriano Overview