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chores: refactor for the new ai research, add linter, gh action, etc (#27)
This commit is contained in:
Marina von Steinkirch, PhD
von-steinkirch
parent
fb4ab80dc3
commit
d5467e559f
40 changed files with 5177 additions and 2476 deletions
106
EBMs/README.md
106
EBMs/README.md
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@ -1,12 +1,15 @@
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## quantum ai: training energy-based-models using openai
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## quantum ai: training energy-based-models using openAI
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<br>
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#### ⚛️ this repository contains my adapted code from [opeani's implicit generation and generalization in energy-based-models](https://arxiv.org/pdf/1903.08689.pdf)
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#### ⚛️ this repository contains my adapted code from [opeani's implicit generation and generalization in
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energy-based-models](https://arxiv.org/pdf/1903.08689.pdf)
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<br>
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---
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### installing
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<br>
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@ -19,7 +22,8 @@ brew install pkg-config
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<br>
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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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* there is a **[bug](https://github.com/open-mpi/ompi/issues/7516)** in open-mpi for the specific libraries in this
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problem (`PMIX ERROR: ERROR`) that can be fixed with:
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<br>
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@ -40,7 +44,8 @@ pip install -r requirements.txt
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```
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<br>
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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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* note that this is an adapted requirement file since the **[openai's
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original](https://github.com/openai/ebm_code_release/blob/master/requirements.txt)** is not complete/correct
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* finally, 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
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* the documentation on the website is incomplete, so just download the suggested script and run:
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@ -64,7 +69,8 @@ mv getid_osx getid_osx.dms
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<br>
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* download all **[pre-trained models](https://sites.google.com/view/igebm/home)** and unzip them into a local folder `cachedir`:
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* download all **[pre-trained models](https://sites.google.com/view/igebm/home)** and unzip them into a local folder
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`cachedir`:
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<br>
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@ -78,7 +84,8 @@ mkdir cachedir
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<br>
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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)**
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* openai's original code contains **[hardcoded constants that only work on
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Linux](https://github.com/openai/ebm_code_release/blob/master/data.py#L218)**
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* i changed this to a constant (`ROOT_DIR = "./results"`) in the top of `data.py`
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<br>
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@ -87,7 +94,8 @@ mkdir cachedir
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<br>
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* 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:
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* all code supports **[`horovod` execution](https://github.com/horovod/horovod)**, so model training can be increased
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substantially by using multiple different workers by running each command:
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<br>
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@ -102,7 +110,8 @@ mpiexec -n <worker_num> <command>
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<br>
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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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python train.py --exp=cifar10_uncond --dataset=cifar10 --num_steps=60 --batch_size=128 --step_lr=10.0 --proj_norm=0.01
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--zero_kl --replay_batch --large_model
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```
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* this should generate the following output:
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@ -112,7 +121,8 @@ python train.py --exp=cifar10_uncond --dataset=cifar10 --num_steps=60 --batch_si
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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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2020-05-10 22:12:32.471415: W tensorflow/core/framework/op_def_util.cc:355] Op BatchNormWithGlobalNormalization is
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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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@ -121,11 +131,15 @@ 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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WARNING:tensorflow:From
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/Users/mia/dev/ebm_code_release/venv/lib/python3.7/site-packages/tensorflow/python/framework/op_def_library.py:263:
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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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WARNING:tensorflow:From
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/Users/mia/dev/ebm_code_release/venv/lib/python3.7/site-packages/tensorflow/python/ops/math_ops.py:3066: to_int32 (from
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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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@ -136,16 +150,36 @@ 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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Obtained a total of e_pos: -0.0025530937127768993, e_pos_std: 0.09564747661352158, e_neg: -0.22276005148887634, e_diff:
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0.22020696103572845, e_neg_std: 0.016306934878230095, temp: 1, loss_e: -0.22276005148887634, eps: 0.0, label_ent:
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2.272536277770996, l
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oss_ml: 0.22020693123340607, loss_total: 0.2792498469352722, x_grad: 0.0009156676824204624, x_grad_first:
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0.0009156676824204624, x_off: 0.31731340289115906, iter: 0, gamma: [0.], context_0/c1_pre/cweight:0: 0.0731438547372818,
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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,
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context_0/res_optim_res_c2/cweight:0: 0.9494612216949463, context_0/res_optim_res_c2/g:0: 1.8536269741353806e-10,
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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,
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context_0/res_1_res_c1/gb:0: 3.6198691266697836e-10, context_0/res_1_res_c2/cweight:0: 0.6582950353622437,
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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:
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1.1932842491901852e-09, context_0/res_2_res_c1/cweight:0: 8.567072745657711e-11, context_0/res_2_res_c1/gb:0:
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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:
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8.058526068666083e-10, context_0/res_2_res_c2/cb:0: 1.0161046448686761e-09, context_0/res_2_res_adaptive/cweight:0:
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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,
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context_0/res_3_res_c2/cweight:0: 0.32239994406700134, context_0/res_3_res_c2/g:0: 9.758494012857e-11,
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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:
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4.090133440270982e-11, context_0/res_4_res_c1/gb:0: 6.013010089844784e-10, context_0/res_4_res_c2/cweight:0:
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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:
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5.496815780325903e-10, context_0/res_5_res_c1/cweight:0: 3.990113794927197e-11, context_0/res_5_res_c1/gb:0:
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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:
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7.697342763179904e-10, context_0/res_5_res_c2/cb:0: 3.1796060229183354e-10, context_0/fc5/wweight:0: 3.081033706665039,
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context_0/fc5/
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b:0: 0.4506262540817261,
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................................................................................................................................
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@ -159,7 +193,8 @@ Inception score of 1.2397289276123047 with std of 0.0
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<br>
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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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python train.py --exp=cifar10_cond --dataset=cifar10 --num_steps=60 --batch_size=128 --step_lr=10.0 --proj_norm=0.01
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--zero_kl --replay_batch --cclass
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```
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<br>
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@ -169,7 +204,8 @@ python train.py --exp=cifar10_cond --dataset=cifar10 --num_steps=60 --batch_size
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<br>
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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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python train.py --exp=imagenet_cond --num_steps=60 --wider_model --batch_size=32 step_lr=10.0 --proj_norm=0.01
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--replay_batch --cclass --zero_kl --dataset=imagenet --imagenet_path=<imagenet32x32 path>
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```
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<br>
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@ -179,7 +215,8 @@ python train.py --exp=imagenet_cond --num_steps=60 --wider_model --batch_size=3
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<br>
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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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python train.py --exp=imagenet_cond --num_steps=50 --batch_size=16 step_lr=100.0 --replay_batch --swish_act --cclass
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--zero_kl --dataset=imagenetfull --imagenet_datadir=<full imagenet path>
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```
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<br>
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@ -197,7 +234,8 @@ python train.py --exp=imagenet_cond --num_steps=50 --batch_size=16 step_lr=100.0
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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
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* the ebm_sandbox.py file contains several different tasks that can be used to evaluate ebms, which are defined by
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different settings of task flag in the file
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* for example, to visualize cross class mappings in cifar-10, you can run:
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<br>
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@ -217,7 +255,8 @@ python ebm_sandbox.py --task=crossclass --num_steps=40 --exp=cifar10_cond --resu
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<br>
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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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python ebm_sandbox.py --task=mixenergy --num_steps=40 --exp=cifar10_large_model_uncond --resume_iter=121200
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--large_model --svhnmix --cclass=False
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```
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<br>
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@ -227,7 +266,8 @@ python ebm_sandbox.py --task=mixenergy --num_steps=40 --exp=cifar10_large_model_
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<br>
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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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python ebm_sandbox.py --task=label --exp=cifar10_wider_model_cond --resume_iter=21600 --lnorm=-1 --pgd=<number of pgd
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steps> --num_steps=10 --lival=<li bound value> --wider_model
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```
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<br>
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<br>
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* to train ebms on conditional dsprites dataset, you can train each model separately on each conditioned latent in `cond_pos`, `cond_rot`, `cond_shape`, `cond_scale`, with an example command given below:
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* to train ebms on conditional dsprites dataset, you can train each model separately on each conditioned latent in
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`cond_pos`, `cond_rot`, `cond_shape`, `cond_scale`, with an example command given below:
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<br>
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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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python train.py --dataset=dsprites --exp=dsprites_cond_pos --zero_kl --num_steps=20 --step_lr=500.0 --swish_act
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--cond_pos --replay_batch -cclass
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```
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<br>
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@ -249,5 +291,7 @@ python train.py --dataset=dsprites --exp=dsprites_cond_pos --zero_kl --num_steps
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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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python ebm_combine.py --task=conceptcombine --exp_size=<exp_size> --exp_shape=<exp_shape> --exp_pos=<exp_pos>
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--exp_rot=<exp_rot> --resume_size=<resume_size> --resume_shape=<resume_shape> --resume_rot=<resume_rot>
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--resume_pos=<resume_pos>
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```
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Add table
Add a link
Reference in a new issue