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add README.md and custom requirements.txt
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README.md
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README.md
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# Z ML Energy-Based Models
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This repository contains adapted code for [OpenAI's Implicit Generation and Generalization in Energy Based Models](https://arxiv.org/pdf/1903.08689.pdf).
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## Installing locally
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### Install system's requirement
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```bash
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brew install gcc@6
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brew install open-mpi
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brew install pkg-config
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```
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There is a [bug](https://github.com/open-mpi/ompi/issues/7516) in open-mpi for the specific libraries in this problem (`PMIX ERROR: ERROR`) that can be fixed with:
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```
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export PMIX_MCA_gds=^ds12
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```
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### Install requirements.txt
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Install Python's requirements in a virtual environment:
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```bash
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virtualenv venv
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source venv/bin/activate
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pip install -r requirements.txt
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```
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Note that this is an adapted requirement file, since the [OpenAI's original](https://github.com/openai/ebm_code_release/blob/master/requirements.txt) is not complete/correct.
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### Install MuJoCo
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Download and install [MuJoCo](https://www.roboti.us/index.html).
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You will also need to register for a license, which asks for a machine ID. The documentation in the website is incomplete, so just download the suggested script and run:
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```bash
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mv getid_osx.dmg getid_osx.dms
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./getid_osx.dms
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```
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### Download pretrained models (exmples)
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Download all [pretrained models](https://sites.google.com/view/igebm/home) and unzip into a local folder `cachedir`:
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```bash
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mkdir cachedir
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```
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### Setting results directory
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OpenAI's original code contains [hardcoded constants that only work on Linux](https://github.com/openai/ebm_code_release/blob/master/data.py#L218). We changed this to a constant (`ROOT_DIR = "./results"`) in the top of `data.py`.
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----
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## Running
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### Parallelization with `mpiexec`
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All code supports horovod execution, so model training can be increased substantially by using multiple different workers by running each command.
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```
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mpiexec -n <worker_num> <command>
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```
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### Examples of Training on example datasets
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#### CIFAR-10 Unconditional:
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```
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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
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```
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This should generate the following output:
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```bash
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Instructions for updating:
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Use tf.gfile.GFile.
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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().
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64 batch size
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Local rank: 0 1
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Loading data...
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Files already downloaded and verified
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Files already downloaded and verified
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Files already downloaded and verified
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Files already downloaded and verified
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Done loading...
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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.
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Instructions for updating:
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Colocations handled automatically by placer.
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Building graph...
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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.
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Instructions for updating:
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Use tf.cast instead.
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Finished processing loop construction ...
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Started gradient computation...
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Applying gradients...
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Finished applying gradients.
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Model has a total of 7567880 parameters
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Initializing variables...
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Start broadcast
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End broadcast
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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
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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/
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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
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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
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: 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
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_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
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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
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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:
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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
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_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/
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b:0: 0.4506262540817261,
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................................................................................................................................
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Inception score of 1.2397289276123047 with std of 0.0
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```
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#### CIFAR-10 Conditional:
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```
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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
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```
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#### ImageNet 32x32 Conditional:
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```
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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>
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```
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#### ImageNet 128x128 Conditional:
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```
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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>
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```
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#### Imagenet Demo
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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:
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```
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python imagenet_demo.py --exp=imagenet128_cond --resume_iter=2238000 --swish_act
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```
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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:
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```
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python ebm_sandbox.py --task=crossclass --num_steps=40 --exp=cifar10_cond --resume_iter=74700
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```
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#### Generalization
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To test generalization to out of distribution classification for SVHN (with similar commands for other datasets)
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```
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python ebm_sandbox.py --task=mixenergy --num_steps=40 --exp=cifar10_large_model_uncond --resume_iter=121200 --large_model --svhnmix --cclass=False
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```
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To test classification on CIFAR-10 using a conditional model under either L2 or Li perturbations
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```
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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
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```
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#### Concept Combination
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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.
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```
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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
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```
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Once models are trained, they can be sampled from jointly by running:
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```
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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>
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```
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---
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## TODO
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* Run in docker/kubernetes/Orquestra.
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* Run and extract resuts from all examples.
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* Make tests/benchamer with `mpiexec`.
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* Create an `env` file for constants and parameters.
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* Upgrade all the libraries, upgrade to TF2.
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---
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## References
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* [Our internal docs in EBMs](https://docs.zapos.io/algos/classical_machine_learning/energy_based_models/).
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