Training EMBs using OpenAI's resources

This repository contains an adapted code for OpenAI's Implicit Generation and Generalization in Energy Based Models.

Installing locally

Install the system's requirement

brew install gcc@6
brew install open-mpi
brew install pkg-config

There is a bug in open-mpi for the specific libraries in this problem (PMIX ERROR: ERROR) that can be fixed with:

export PMIX_MCA_gds=^ds12

Install requirements.txt

Install Python's requirements in a virtual environment:

virtualenv venv
source venv/bin/activate
pip install -r requirements.txt

Note that this is an adapted requirement file since the OpenAI's original is not complete/correct.

Install MuJoCo

Download and install MuJoCo.

You will also need to register for a license, which asks for a machine ID. The documentation on the website is incomplete, so just download the suggested script and run:

mv getid_osx getid_osx.dms
./getid_osx.dms

Download pre-trained models (exmples)

Download all pre-trained models and unzip into a local folder cachedir:

mkdir cachedir

Setting results directory

OpenAI's original code contains hardcoded constants that only work on Linux. We changed this to a constant (ROOT_DIR = "./results") in the top of data.py.

---

Running

Parallelization with mpiexec

All code supports horovod execution, so model training can be increased substantially by using multiple different workers by running each command.

mpiexec -n <worker_num>  <command>

Examples of Training on example datasets

CIFAR-10 Unconditional:

python train.py --exp=cifar10_uncond --dataset=cifar10 --num_steps=60 --batch_size=128 --step_lr=10.0 --proj_norm=0.01 --zero_kl --replay_batch --large_model

This should generate the following output:

Instructions for updating:
Use tf.gfile.GFile.
2020-05-10 22:12:32.471415: W tensorflow/core/framework/op_def_util.cc:355] Op BatchNormWithGlobalNormalization is deprecated. It will cease to work in GraphDef version 9. Use tf.nn.batch_normalization().
64 batch size
Local rank:  0 1
Loading data...
Files already downloaded and verified
Files already downloaded and verified
Files already downloaded and verified
Files already downloaded and verified
Done loading...
WARNING:tensorflow:From /Users/mia/dev/ebm_code_release/venv/lib/python3.7/site-packages/tensorflow/python/framework/op_def_library.py:263: colocate_with (from tensorflow.python.framework.ops) is deprecated and will be removed in a future version.
Instructions for updating:
Colocations handled automatically by placer.
Building graph...
WARNING:tensorflow:From /Users/mia/dev/ebm_code_release/venv/lib/python3.7/site-packages/tensorflow/python/ops/math_ops.py:3066: to_int32 (from tensorflow.python.ops.math_ops) is deprecated and will be removed in a future version.
Instructions for updating:
Use tf.cast instead.
Finished processing loop construction ...
Started gradient computation...
Applying gradients...
Finished applying gradients.
Model has a total of 7567880 parameters
Initializing variables...
Start broadcast
End broadcast
Obtained a total of e_pos: -0.0025530937127768993, e_pos_std: 0.09564747661352158, e_neg: -0.22276005148887634, e_diff: 0.22020696103572845, e_neg_std: 0.016306934878230095, temp: 1, loss_e: -0.22276005148887634, eps: 0.0, label_ent: 2.272536277770996, l
oss_ml: 0.22020693123340607, loss_total: 0.2792498469352722, x_grad: 0.0009156676824204624, x_grad_first: 0.0009156676824204624, x_off: 0.31731340289115906, iter: 0, gamma: [0.], context_0/c1_pre/cweight:0: 0.0731438547372818, context_0/res_optim_res_c1/
cweight:0: 4.732660444095593e-11, context_0/res_optim_res_c1/gb:0: 3.4007335836250263e-10, context_0/res_optim_res_c2/cweight:0: 0.9494612216949463, context_0/res_optim_res_c2/g:0: 1.8536269741353806e-10, context_0/res_optim_res_c2/gb:0: 6.27235652306268
3e-10, context_0/res_optim_res_c2/cb:0: 1.1606662297936055e-09, context_0/res_1_res_c1/cweight:0: 6.714453298917178e-11, context_0/res_1_res_c1/gb:0: 3.6198691266697836e-10, context_0/res_1_res_c2/cweight:0: 0.6582950353622437, context_0/res_1_res_c2/g:0
: 1.669797633496728e-10, context_0/res_1_res_c2/gb:0: 5.911696687732615e-10, context_0/res_1_res_c2/cb:0: 1.1932842491901852e-09, context_0/res_2_res_c1/cweight:0: 8.567072745657711e-11, context_0/res_2_res_c1/gb:0: 6.868505764145993e-10, context_0/res_2
_res_c2/cweight:0: 0.46929678320884705, context_0/res_2_res_c2/g:0: 1.655784120924153e-10, context_0/res_2_res_c2/gb:0: 8.058526068666083e-10, context_0/res_2_res_c2/cb:0: 1.0161046448686761e-09, context_0/res_2_res_adaptive/cweight:0: 0.0194275379180908
2, context_0/res_3_res_c1/cweight:0: 4.011655244107182e-11, context_0/res_3_res_c1/gb:0: 5.064903496609929e-10, context_0/res_3_res_c2/cweight:0: 0.32239994406700134, context_0/res_3_res_c2/g:0: 9.758494012857e-11, context_0/res_3_res_c2/gb:0: 7.75612463
1441708e-10, context_0/res_3_res_c2/cb:0: 6.362700366580043e-10, context_0/res_4_res_c1/cweight:0: 4.090133440270982e-11, context_0/res_4_res_c1/gb:0: 6.013010089844784e-10, context_0/res_4_res_c2/cweight:0: 0.34806951880455017, context_0/res_4_res_c2/g:
0: 8.414659247168998e-11, context_0/res_4_res_c2/gb:0: 6.443054978433338e-10, context_0/res_4_res_c2/cb:0: 5.496815780325903e-10, context_0/res_5_res_c1/cweight:0: 3.990113794927197e-11, context_0/res_5_res_c1/gb:0: 3.807749116013781e-10, context_0/res_5
_res_c2/cweight:0: 0.22841960191726685, context_0/res_5_res_c2/g:0: 4.942361797599659e-11, context_0/res_5_res_c2/gb:0: 7.697342763179904e-10, context_0/res_5_res_c2/cb:0: 3.1796060229183354e-10, context_0/fc5/wweight:0: 3.081033706665039, context_0/fc5/
b:0: 0.4506262540817261,

................................................................................................................................
Inception score of 1.2397289276123047 with std of 0.0

CIFAR-10 Conditional:

python train.py --exp=cifar10_cond --dataset=cifar10 --num_steps=60 --batch_size=128 --step_lr=10.0 --proj_norm=0.01 --zero_kl --replay_batch --cclass

ImageNet 32x32 Conditional:

python train.py --exp=imagenet_cond --num_steps=60  --wider_model --batch_size=32 step_lr=10.0 --proj_norm=0.01 --replay_batch --cclass --zero_kl --dataset=imagenet --imagenet_path=<imagenet32x32 path>

ImageNet 128x128 Conditional:

python train.py --exp=imagenet_cond --num_steps=50 --batch_size=16 step_lr=100.0 --replay_batch --swish_act --cclass --zero_kl --dataset=imagenetfull --imagenet_datadir=<full imagenet path>

Imagenet Demo

The imagenet_demo.py file contains code to experiments with EBMs on conditional ImageNet 128x128. To generate a gif on sampling, you can run the command:

python imagenet_demo.py --exp=imagenet128_cond --resume_iter=2238000 --swish_act

The ebm_sandbox.py file contains several different tasks that can be used to evaluate EBMs, which are defined by different settings of task flag in the file. For example, to visualize cross class mappings in CIFAR-10, you can run:

python ebm_sandbox.py --task=crossclass --num_steps=40 --exp=cifar10_cond --resume_iter=74700

Generalization

To test generalization to out of distribution classification for SVHN (with similar commands for other datasets)

python ebm_sandbox.py --task=mixenergy --num_steps=40 --exp=cifar10_large_model_uncond --resume_iter=121200 --large_model --svhnmix --cclass=False

To test classification on CIFAR-10 using a conditional model under either L2 or Li perturbations

python ebm_sandbox.py --task=label --exp=cifar10_wider_model_cond --resume_iter=21600 --lnorm=-1 --pgd=<number of pgd steps> --num_steps=10 --lival=<li bound value> --wider_model

Concept Combination

To train EBMs on conditional dSprites dataset, you can train each model seperately on each conditioned latent in cond_pos, cond_rot, cond_shape, cond_scale, with an example command given below.

python train.py --dataset=dsprites --exp=dsprites_cond_pos --zero_kl --num_steps=20 --step_lr=500.0 --swish_act  --cond_pos --replay_batch -cclass

Once models are trained, they can be sampled from jointly by running:

python ebm_combine.py --task=conceptcombine --exp_size=<exp_size> --exp_shape=<exp_shape> --exp_pos=<exp_pos> --exp_rot=<exp_rot> --resume_size=<resume_size> --resume_shape=<resume_shape> --resume_rot=<resume_rot> --resume_pos=<resume_pos>