# QuantumAI: Training EMBs using OpenAI's resources
This repository contains my adapted code from [OpenAI's Implicit Generation and Generalization in Energy Based Models](https://arxiv.org/pdf/1903.08689.pdf), plus some resources from external researchers.
---
## Installing locally
### Install the system's requirement
```bash
brew install gcc@6
brew install open-mpi
brew install pkg-config
```
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:
```
export PMIX_MCA_gds=^ds12
```
### Install requirements.txt
Install Python's requirements in a virtual environment:
```bash
virtualenv venv
source venv/bin/activate
pip install -r requirements.txt
```
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.
### Install MuJoCo
Download and install [MuJoCo](https://www.roboti.us/index.html).
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:
```bash
mv getid_osx getid_osx.dms
./getid_osx.dms
```
---
## Download pre-trained models (exmples)
Download all [pre-trained models](https://sites.google.com/view/igebm/home) and unzip into a local folder `cachedir`:
```bash
mkdir cachedir
```
### Setting results directory
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`.
----
## Running
### Parallelization with `mpiexec`
All code supports [`horovod` execution](https://github.com/horovod/horovod), so model training can be increased substantially by using multiple different workers by running each command.
```
mpiexec -n
```
### 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:
```bash
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=
```
#### 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=
```
#### 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= --num_steps=10 --lival= --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_shape= --exp_pos= --exp_rot= --resume_size= --resume_shape= --resume_rot= --resume_pos=
```