diff --git a/.benchmark_pattern b/.benchmark_pattern old mode 100644 new mode 100755 diff --git a/.gitignore b/.gitignore old mode 100644 new mode 100755 index a41103d0b7..3387b64eee --- a/.gitignore +++ b/.gitignore @@ -1,3 +1,229 @@ + +# Created by https://www.gitignore.io/api/emacs,macos,python,jupyternotebook,jupyternotebooks +# Edit at https://www.gitignore.io/?templates=emacs,macos,python,jupyternotebook,jupyternotebooks + +### Emacs ### +# -*- mode: gitignore; -*- +*~ +\#*\# +/.emacs.desktop +/.emacs.desktop.lock +*.elc +auto-save-list +tramp +.\#* + +# Org-mode +.org-id-locations +*_archive + +# flymake-mode +*_flymake.* + +# eshell files +/eshell/history +/eshell/lastdir + +# elpa packages +/elpa/ + +# reftex files +*.rel + +# AUCTeX auto folder +/auto/ + +# cask packages +.cask/ +dist/ + +# Flycheck +flycheck_*.el + +# server auth directory +/server/ + +# projectiles files +.projectile + +# directory configuration +.dir-locals.el + +# network security +/network-security.data + + +### JupyterNotebook ### +.ipynb_checkpoints +*/.ipynb_checkpoints/* + +# Remove previous ipynb_checkpoints +# git rm -r .ipynb_checkpoints/ +# + +### JupyterNotebooks ### +# gitignore template for Jupyter Notebooks +# website: http://jupyter.org/ + + +# Remove previous ipynb_checkpoints +# git rm -r .ipynb_checkpoints/ +# + +### macOS ### +# General +.DS_Store +.AppleDouble +.LSOverride + +# Icon must end with two \r +Icon + +# Thumbnails +._* + +# Files that might appear in the root of a volume +.DocumentRevisions-V100 +.fseventsd +.Spotlight-V100 +.TemporaryItems +.Trashes +.VolumeIcon.icns +.com.apple.timemachine.donotpresent + +# Directories potentially created on remote AFP share +.AppleDB +.AppleDesktop +Network Trash Folder +Temporary Items +.apdisk + +### Python ### +# Byte-compiled / optimized / DLL files +__pycache__/ +*.py[cod] +*$py.class + +# C extensions +*.so + +# Distribution / packaging +.Python +build/ +develop-eggs/ +downloads/ +eggs/ +.eggs/ +lib/ +lib64/ +parts/ +sdist/ +var/ +wheels/ +share/python-wheels/ +*.egg-info/ +.installed.cfg +*.egg +MANIFEST + +# PyInstaller +# Usually these files are written by a python script from a template +# before PyInstaller builds the exe, so as to inject date/other infos into it. +*.manifest +*.spec + +# Installer logs +pip-log.txt +pip-delete-this-directory.txt + +# Unit test / coverage reports +htmlcov/ +.tox/ +.nox/ +.coverage +.coverage.* +.cache +nosetests.xml +coverage.xml +*.cover +.hypothesis/ +.pytest_cache/ + +# Translations +*.mo +*.pot + +# Django stuff: +*.log +local_settings.py +db.sqlite3 + +# Flask stuff: +instance/ +.webassets-cache + +# Scrapy stuff: +.scrapy + +# Sphinx documentation +docs/_build/ + +# PyBuilder +target/ + +# Jupyter Notebook + +# IPython +profile_default/ +ipython_config.py + +# pyenv +.python-version + +# celery beat schedule file +celerybeat-schedule + +# SageMath parsed files +*.sage.py + +# Environments +.env +.venv +env/ +venv/ +ENV/ +env.bak/ +venv.bak/ + +# Spyder project settings +.spyderproject +.spyproject + +# Rope project settings +.ropeproject + +# mkdocs documentation +/site + +# mypy +.mypy_cache/ +.dmypy.json +dmypy.json + +# Pyre type checker +.pyre/ + +### Python Patch ### +.venv/ + +# End of https://www.gitignore.io/api/emacs,macos,python,jupyternotebook,jupyternotebooks + + +# ------------------------------------------------------------------------------- +# ================== +# Open AI Settings +# ================== + *.swp *.pyc *.pkl @@ -34,3 +260,6 @@ src .cache MUJOCO_LOG.TXT +TRAIN.sh +MAKE_TRAINING_DATA.sh +projection/*.sh diff --git a/.gitmodules b/.gitmodules new file mode 100755 index 0000000000..7ad9e4aadf --- /dev/null +++ b/.gitmodules @@ -0,0 +1,3 @@ +[submodule "mujoco-py"] + path = mujoco-py + url = git@github.com:openai/mujoco-py.git diff --git a/.travis.yml b/.travis.yml old mode 100644 new mode 100755 diff --git a/Dockerfile b/Dockerfile old mode 100644 new mode 100755 diff --git a/LICENSE b/LICENSE old mode 100644 new mode 100755 diff --git a/README.md b/README.md old mode 100644 new mode 100755 index e4f8697d09..2d5ef49525 --- a/README.md +++ b/README.md @@ -1,26 +1,54 @@ - [![Build status](https://travis-ci.org/openai/baselines.svg?branch=master)](https://travis-ci.org/openai/baselines) +# HumanWareFundemental: Sysngy Team +This repository is cloned from [openai/baselines](https://github.com/openai/baselines) and modifided for our reseach. Don't make PR for ofiginal repogitory. -# Baselines -OpenAI Baselines is a set of high-quality implementations of reinforcement learning algorithms. +## Train model with DDPG +以下のコマンドで学習済みモデルを作成する. tensorflowのモデルを保存するディレクトリを`--lodir_tf` で指定する. + +例 +``` +python -m baselines.her.experiment.train \ + --env GraspBlock-v0 \ + --num_cpu 1 \ + --n_epochs 100 \ + --logdir_tf < Dierctory path to save tensorflow model> +``` + + +## Action and Q-value Generation +以下のコマンドで学習モデルをロードし, 指定したディレクトリにアクションなどを書き出す. `--logdir_tf`で学習済みのモデルを指定し, `--logdir_aq`でactionやQ-valueなどを出力するディレクトリを指定する. -These algorithms will make it easier for the research community to replicate, refine, and identify new ideas, and will create good baselines to build research on top of. Our DQN implementation and its variants are roughly on par with the scores in published papers. We expect they will be used as a base around which new ideas can be added, and as a tool for comparing a new approach against existing ones. -## Prerequisites -Baselines requires python3 (>=3.5) with the development headers. You'll also need system packages CMake, OpenMPI and zlib. Those can be installed as follows -### Ubuntu - -```bash -sudo apt-get update && sudo apt-get install cmake libopenmpi-dev python3-dev zlib1g-dev ``` - -### Mac OS X -Installation of system packages on Mac requires [Homebrew](https://brew.sh). With Homebrew installed, run the following: -```bash -brew install cmake openmpi +python -m baselines.her.experiment.test \ + --env GraspBlock-v0 \ + --num_cpu 1 --n_epochs 5 \ + --logdir_tf < path to saved model > \ + --logdir_aq < path to save actions etc... > ``` - -## Virtual environment + +### Log File +ログファイルには以下の項目が記述されている. + ++ `goal/desired`: ゴール (`g`) ++ `goal/achieved`: 到達点 (`ag`) ++ `observation`: 観測 (`o`) ++ `action`: action, shape=[EpisodeNo, Batch, Sequence, env.action_space] ++ `Qvalue`: Q-value, shape=[EpisodeNo, Batch, Sequence, env.action_space] ++ `fc`: Critic Networkの中間出力 (fc2), shape=[EpisodeNo, Batch, Sequence, n_unit(=256)] + + + + + +-------------------------------------- +## Memo +TBA + + +---------------------------------------- +## Initial Setup +### Virtual environment From the general python package sanity perspective, it is a good idea to use virtual environments (virtualenvs) to make sure packages from different projects do not interfere with each other. You can install virtualenv (which is itself a pip package) via ```bash pip install virtualenv @@ -37,7 +65,7 @@ To activate a virtualenv: More thorough tutorial on virtualenvs and options can be found [here](https://virtualenv.pypa.io/en/stable/) -## Installation +### Installation - Clone the repo and cd into it: ```bash git clone https://github.com/openai/baselines.git @@ -59,89 +87,16 @@ More thorough tutorial on virtualenvs and options can be found [here](https://vi pip install -e . ``` -### MuJoCo -Some of the baselines examples use [MuJoCo](http://www.mujoco.org) (multi-joint dynamics in contact) physics simulator, which is proprietary and requires binaries and a license (temporary 30-day license can be obtained from [www.mujoco.org](http://www.mujoco.org)). Instructions on setting up MuJoCo can be found [here](https://github.com/openai/mujoco-py) - -## Testing the installation -All unit tests in baselines can be run using pytest runner: -``` -pip install pytest -pytest -``` +- Install original environment -## Training models -Most of the algorithms in baselines repo are used as follows: ```bash -python -m baselines.run --alg= --env= [additional arguments] +cd gym-grasp +pip install -e . ``` -### Example 1. PPO with MuJoCo Humanoid -For instance, to train a fully-connected network controlling MuJoCo humanoid using PPO2 for 20M timesteps -```bash -python -m baselines.run --alg=ppo2 --env=Humanoid-v2 --network=mlp --num_timesteps=2e7 -``` -Note that for mujoco environments fully-connected network is default, so we can omit `--network=mlp` -The hyperparameters for both network and the learning algorithm can be controlled via the command line, for instance: -```bash -python -m baselines.run --alg=ppo2 --env=Humanoid-v2 --network=mlp --num_timesteps=2e7 --ent_coef=0.1 --num_hidden=32 --num_layers=3 --value_network=copy -``` -will set entropy coefficient to 0.1, and construct fully connected network with 3 layers with 32 hidden units in each, and create a separate network for value function estimation (so that its parameters are not shared with the policy network, but the structure is the same) - -See docstrings in [common/models.py](baselines/common/models.py) for description of network parameters for each type of model, and -docstring for [baselines/ppo2/ppo2.py/learn()](baselines/ppo2/ppo2.py#L152) for the description of the ppo2 hyperparamters. - -### Example 2. DQN on Atari -DQN with Atari is at this point a classics of benchmarks. To run the baselines implementation of DQN on Atari Pong: -``` -python -m baselines.run --alg=deepq --env=PongNoFrameskip-v4 --num_timesteps=1e6 -``` - -## Saving, loading and visualizing models -The algorithms serialization API is not properly unified yet; however, there is a simple method to save / restore trained models. -`--save_path` and `--load_path` command-line option loads the tensorflow state from a given path before training, and saves it after the training, respectively. -Let's imagine you'd like to train ppo2 on Atari Pong, save the model and then later visualize what has it learnt. -```bash -python -m baselines.run --alg=ppo2 --env=PongNoFrameskip-v4 --num_timesteps=2e7 --save_path=~/models/pong_20M_ppo2 -``` -This should get to the mean reward per episode about 20. To load and visualize the model, we'll do the following - load the model, train it for 0 steps, and then visualize: -```bash -python -m baselines.run --alg=ppo2 --env=PongNoFrameskip-v4 --num_timesteps=0 --load_path=~/models/pong_20M_ppo2 --play -``` - -*NOTE:* At the moment Mujoco training uses VecNormalize wrapper for the environment which is not being saved correctly; so loading the models trained on Mujoco will not work well if the environment is recreated. If necessary, you can work around that by replacing RunningMeanStd by TfRunningMeanStd in [baselines/common/vec_env/vec_normalize.py](baselines/common/vec_env/vec_normalize.py#L12). This way, mean and std of environment normalizing wrapper will be saved in tensorflow variables and included in the model file; however, training is slower that way - hence not including it by default - -## Loading and vizualizing learning curves and other training metrics -See [here](docs/viz/viz.ipynb) for instructions on how to load and display the training data. -## Subpackages -- [A2C](baselines/a2c) -- [ACER](baselines/acer) -- [ACKTR](baselines/acktr) -- [DDPG](baselines/ddpg) -- [DQN](baselines/deepq) -- [GAIL](baselines/gail) -- [HER](baselines/her) -- [PPO1](baselines/ppo1) (obsolete version, left here temporarily) -- [PPO2](baselines/ppo2) -- [TRPO](baselines/trpo_mpi) +### MuJoCo +Some of the baselines examples use [MuJoCo](http://www.mujoco.org) (multi-joint dynamics in contact) physics simulator, which is proprietary and requires binaries and a license (temporary 30-day license can be obtained from [www.mujoco.org](http://www.mujoco.org)). Instructions on setting up MuJoCo can be found [here](https://github.com/openai/mujoco-py) -## Benchmarks -Results of benchmarks on Mujoco (1M timesteps) and Atari (10M timesteps) are available -[here for Mujoco](https://htmlpreview.github.com/?https://github.com/openai/baselines/blob/master/benchmarks_mujoco1M.htm) -and -[here for Atari](https://htmlpreview.github.com/?https://github.com/openai/baselines/blob/master/benchmarks_atari10M.htm) -respectively. Note that these results may be not on the latest version of the code, particular commit hash with which results were obtained is specified on the benchmarks page. - -To cite this repository in publications: - - @misc{baselines, - author = {Dhariwal, Prafulla and Hesse, Christopher and Klimov, Oleg and Nichol, Alex and Plappert, Matthias and Radford, Alec and Schulman, John and Sidor, Szymon and Wu, Yuhuai and Zhokhov, Peter}, - title = {OpenAI Baselines}, - year = {2017}, - publisher = {GitHub}, - journal = {GitHub repository}, - howpublished = {\url{https://github.com/openai/baselines}}, - } - diff --git a/baselines/__init__.py b/baselines/__init__.py old mode 100644 new mode 100755 diff --git a/baselines/a2c/README.md b/baselines/a2c/README.md old mode 100644 new mode 100755 diff --git a/baselines/a2c/__init__.py b/baselines/a2c/__init__.py old mode 100644 new mode 100755 diff --git a/baselines/a2c/a2c.py b/baselines/a2c/a2c.py old mode 100644 new mode 100755 diff --git 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old mode 100644 new mode 100755 diff --git a/baselines/common/vec_env/vec_normalize.py b/baselines/common/vec_env/vec_normalize.py old mode 100644 new mode 100755 diff --git a/baselines/common/vec_env/vec_video_recorder.py b/baselines/common/vec_env/vec_video_recorder.py old mode 100644 new mode 100755 diff --git a/baselines/custom_logger.py b/baselines/custom_logger.py new file mode 100755 index 0000000000..ee17699142 --- /dev/null +++ b/baselines/custom_logger.py @@ -0,0 +1,15 @@ +import datetime as dt + +""" +For Corlor, check this site. ++ https://qiita.com/ironguy/items/8fb3ddadb3c4c986496d +""" + +class CustomLoggerObject(object): + def __init__(self): + self.LOG_FMT = "{color}| {asctime} | {levelname:<5s} | {message} \033[0m" + + def info(self, msg): + asctime = dt.datetime.now().strftime("%Y/%m/%d %H:%M:%S") + print(self.LOG_FMT.format(color="\033[37m", asctime=asctime, levelname="INFO", message=msg)) + diff --git a/baselines/ddpg/ddpg.py b/baselines/ddpg/ddpg.py index 37551d4931..35c8e17782 100755 --- a/baselines/ddpg/ddpg.py +++ b/baselines/ddpg/ddpg.py @@ -11,6 +11,11 @@ import baselines.common.tf_util as U from baselines import logger +# -------------------------------------------------------------------------------------- +from baselines.custom_logger import CustomLoggerObject +clogger = CustomLoggerObject() +clogger.info("MyLogger is working!!") +# -------------------------------------------------------------------------------------- import numpy as np try: @@ -118,6 +123,7 @@ def learn(network, env, start_time = time.time() + clogger.info("Start Training [nb_epochs={}]".format(nb_epochs)) epoch_episode_rewards = [] epoch_episode_steps = [] @@ -125,6 +131,7 @@ def learn(network, env, epoch_qs = [] epoch_episodes = 0 for epoch in range(nb_epochs): + clogger.info("Start Epoch={}".format(epoch)) for cycle in range(nb_epoch_cycles): # Perform rollouts. if nenvs > 1: @@ -134,7 +141,7 @@ def learn(network, env, for t_rollout in range(nb_rollout_steps): # Predict next action. action, q, _, _ = agent.step(obs, apply_noise=True, compute_Q=True) - + clogger.info("action.shape={}, q={}".format(action.shape, q)) # Execute next action. if rank == 0 and render: env.render() @@ -210,6 +217,7 @@ def learn(network, env, mpi_size = MPI.COMM_WORLD.Get_size() else: mpi_size = 1 + clogger.info("Finish Training {}".format(time.time())) # Log stats. # XXX shouldn't call np.mean on variable length lists diff --git a/baselines/deepq/README.md b/baselines/deepq/README.md old mode 100644 new mode 100755 diff --git a/baselines/deepq/__init__.py b/baselines/deepq/__init__.py old mode 100644 new mode 100755 diff --git a/baselines/deepq/build_graph.py b/baselines/deepq/build_graph.py old mode 100644 new mode 100755 diff --git a/baselines/deepq/deepq.py b/baselines/deepq/deepq.py old mode 100644 new mode 100755 diff --git a/baselines/deepq/defaults.py b/baselines/deepq/defaults.py old mode 100644 new mode 100755 diff --git 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b/baselines/gail/statistics.py old mode 100644 new mode 100755 diff --git a/baselines/gail/trpo_mpi.py b/baselines/gail/trpo_mpi.py old mode 100644 new mode 100755 diff --git a/baselines/her/README.md b/baselines/her/README.md old mode 100644 new mode 100755 diff --git a/baselines/her/__init__.py b/baselines/her/__init__.py old mode 100644 new mode 100755 diff --git a/baselines/her/actor_critic.py b/baselines/her/actor_critic.py old mode 100644 new mode 100755 index d5443fe0c3..596b234f93 --- a/baselines/her/actor_critic.py +++ b/baselines/her/actor_critic.py @@ -32,8 +32,33 @@ def __init__(self, inputs_tf, dimo, dimg, dimu, max_u, o_stats, g_stats, hidden, # Networks. with tf.variable_scope('pi'): - self.pi_tf = self.max_u * tf.tanh(nn( - input_pi, [self.hidden] * self.layers + [self.dimu])) + # self.pi_tf = self.max_u * tf.tanh(nn( + # input_pi, [self.hidden] * self.layers + [self.dimu])) + + # 3-Layers FC Network + ## FC1 + fc1 = tf.layers.dense(inputs=input_pi, + units=self.hidden, + kernel_initializer=tf.contrib.layers.xavier_initializer(), + reuse=None, + name='fc1') + fc1 = tf.nn.relu(fc1) + ## FC2 + fc2 = tf.layers.dense(inputs=fc1, + units=self.hidden, + kernel_initializer=tf.contrib.layers.xavier_initializer(), + reuse=None, + name='fc2') + fc2 = tf.nn.relu(fc2) + ## FC3 + fc3 = tf.layers.dense(inputs=fc2, + units=self.dimu, + kernel_initializer=tf.contrib.layers.xavier_initializer(), + reuse=None, + name='fc3') + self.pi_tf_fc2 = fc2 + self.pi_tf = fc3 + with tf.variable_scope('Q'): # for policy training input_Q = tf.concat(axis=1, values=[o, g, self.pi_tf / self.max_u]) diff --git a/baselines/her/ddpg.py b/baselines/her/ddpg.py old mode 100644 new mode 100755 index 96384da4c4..6a06d96f6d --- a/baselines/her/ddpg.py +++ b/baselines/her/ddpg.py @@ -120,13 +120,13 @@ def _preprocess_og(self, o, ag, g): return o, g def get_actions(self, o, ag, g, noise_eps=0., random_eps=0., use_target_net=False, - compute_Q=False): + compute_Q=False,): o, g = self._preprocess_og(o, ag, g) policy = self.target if use_target_net else self.main # values to compute vals = [policy.pi_tf] if compute_Q: - vals += [policy.Q_pi_tf] + vals += [policy.Q_pi_tf, policy.pi_tf_fc2] # feed feed = { policy.o_tf: o.reshape(-1, self.dimo), @@ -150,6 +150,7 @@ def get_actions(self, o, ag, g, noise_eps=0., random_eps=0., use_target_net=Fals return ret[0] else: return ret + def initDemoBuffer(self, demoDataFile, update_stats=True): #function that initializes the demo buffer diff --git a/baselines/her/experiment/__init__.py b/baselines/her/experiment/__init__.py old mode 100644 new mode 100755 diff --git a/baselines/her/experiment/config.py b/baselines/her/experiment/config.py old mode 100644 new mode 100755 index 8cc36e6ee1..5ac6dadb02 --- a/baselines/her/experiment/config.py +++ b/baselines/her/experiment/config.py @@ -1,10 +1,18 @@ import numpy as np import gym +import gym_grasp from baselines import logger from baselines.her.ddpg import DDPG from baselines.her.her import make_sample_her_transitions +# -------------------------------------------------------------------------------------- +from baselines.custom_logger import CustomLoggerObject +clogger = CustomLoggerObject() +clogger.info("MyLogger is working!!") +# -------------------------------------------------------------------------------------- + + DEFAULT_ENV_PARAMS = { 'FetchReach-v1': { @@ -170,12 +178,16 @@ def configure_dims(params): env = cached_make_env(params['make_env']) env.reset() obs, _, _, info = env.step(env.action_space.sample()) + dims = { 'o': obs['observation'].shape[0], 'u': env.action_space.shape[0], 'g': obs['desired_goal'].shape[0], } + clogger.info("input_dims = {}".format(dims)) + clogger.info("env.action_apace={}".format(env.action_space)) + clogger.info("env.observation_space={}".format(env.observation_space)) for key, value in info.items(): value = np.array(value) if value.ndim == 0: diff --git a/baselines/her/experiment/data_generation/fetch_data_generation.py b/baselines/her/experiment/data_generation/fetch_data_generation.py old mode 100644 new mode 100755 diff --git a/baselines/her/experiment/play.py b/baselines/her/experiment/play.py old mode 100644 new mode 100755 diff --git a/baselines/her/experiment/plot.py b/baselines/her/experiment/plot.py old mode 100644 new mode 100755 diff --git a/baselines/her/experiment/test.py b/baselines/her/experiment/test.py new file mode 100755 index 0000000000..6095643c09 --- /dev/null +++ b/baselines/her/experiment/test.py @@ -0,0 +1,241 @@ +import os +import sys + +import click +import numpy as np +import json +from mpi4py import MPI + +from baselines import logger +from baselines.common import set_global_seeds +from baselines.common.mpi_moments import mpi_moments +import baselines.her.experiment.config as config +from baselines.her.rollout import RolloutWorker +from baselines.her.util import mpi_fork + +from subprocess import CalledProcessError +import h5py + + +# -------------------------------------------------------------------------------------- +from baselines.custom_logger import CustomLoggerObject +clogger = CustomLoggerObject() +clogger.info("MyLogger is working!!") +# -------------------------------------------------------------------------------------- + + +def mpi_average(value): + if value == []: + value = [0.] + if not isinstance(value, list): + value = [value] + return mpi_moments(np.array(value))[0] + + +def test(policy, rollout_worker, evaluator, + n_epochs, n_test_rollouts, n_cycles, n_batches, policy_save_interval, + save_policies, demo_file, logdir_aq, **kwargs): + clogger.info("Logdir for actions & Q-values: {}".format(logdir_aq)) + rank = MPI.COMM_WORLD.Get_rank() + + latest_policy_path = os.path.join(logger.get_dir(), 'policy_latest.pkl') + best_policy_path = os.path.join(logger.get_dir(), 'policy_best.pkl') + periodic_policy_path = os.path.join(logger.get_dir(), 'policy_{}.pkl') + + logger.info("Training...") + best_success_rate = -1 + + if policy.bc_loss == 1: policy.initDemoBuffer(demo_file) #initialize demo buffer if training with demonstrations + for epoch in range(n_epochs): + clogger.info("Start: Epoch {}/{}".format(epoch, n_epochs)) + + # test + evaluator.clear_history() + episode_box = {"g":[],"ag":[],"o":[],"u":[],"q":[], "fc":[]} + for _ in range(n_test_rollouts): + episode = evaluator.generate_rollouts(is_train=False) + clogger.info("Episode = {}".format(episode.keys())) + for key in episode.keys(): + # clogger.info(" - {}: {}".format(key, episode[key].shape)) + if key in episode_box.keys(): + episode_box[key].append(episode[key][np.newaxis, :]) + + + # Dump episode info + for key in episode_box.keys(): + # episode_box[key].append(episode[key]) + l = len(episode[key]) + episode_box[key] = np.concatenate(episode_box[key], axis=0) + clogger.info(" - {:<4}: {:>4} => {}".format(key, l, episode_box[key].shape)) + + filename = os.path.join(logdir_aq, 'epoch{}.h5'.format(epoch)) + with h5py.File(filename, 'w') as f: + f.create_group('goal') + f['goal'].create_dataset('desired', data=episode_box["g"]) + f['goal'].create_dataset('achieved', data=episode_box["ag"]) + f.create_dataset('obeservation', data=episode_box["o"]) + f.create_dataset('action', data=episode_box["u"]) + f.create_dataset('Qvalue', data=episode_box["q"]) + f.create_dataset('fc', data=episode_box["fc"]) + + + + # record logs + logger.record_tabular('epoch', epoch) + for key, val in evaluator.logs('test'): + logger.record_tabular(key, mpi_average(val)) + # for key, val in rollout_worker.logs('train'): + # logger.record_tabular(key, mpi_average(val)) + for key, val in policy.logs(): + logger.record_tabular(key, mpi_average(val)) + + if rank == 0: + clogger.info("Show table") + logger.dump_tabular() + + # save the policy if it's better than the previous ones + success_rate = mpi_average(evaluator.current_success_rate()) + if rank == 0 and success_rate >= best_success_rate and save_policies: + best_success_rate = success_rate + logger.info('New best success rate: {}. Saving policy to {} ...'.format(best_success_rate, best_policy_path)) + evaluator.save_policy(best_policy_path) + evaluator.save_policy(latest_policy_path) + if rank == 0 and policy_save_interval > 0 and epoch % policy_save_interval == 0 and save_policies: + policy_path = periodic_policy_path.format(epoch) + logger.info('Saving periodic policy to {} ...'.format(policy_path)) + evaluator.save_policy(policy_path) + + # make sure that different threads have different seeds + local_uniform = np.random.uniform(size=(1,)) + root_uniform = local_uniform.copy() + MPI.COMM_WORLD.Bcast(root_uniform, root=0) + if rank != 0: + assert local_uniform[0] != root_uniform[0] + + +def launch( + env, logdir, n_epochs, num_cpu, seed, replay_strategy, policy_save_interval, clip_return, + demo_file, logdir_tf=None, logdir_aq=None, override_params={}, save_policies=True +): + assert logdir_tf, "Test mode need `logdir_tf`" + # Fork for multi-CPU MPI implementation. + if num_cpu > 1: + try: + whoami = mpi_fork(num_cpu, ['--bind-to', 'core']) + except CalledProcessError: + # fancy version of mpi call failed, try simple version + whoami = mpi_fork(num_cpu) + + if whoami == 'parent': + sys.exit(0) + import baselines.common.tf_util as U + U.single_threaded_session().__enter__() + rank = MPI.COMM_WORLD.Get_rank() + + # Configure logging + if rank == 0: + if logdir or logger.get_dir() is None: + logger.configure(dir=logdir) + else: + logger.configure() + logdir = logger.get_dir() + assert logdir is not None + os.makedirs(logdir, exist_ok=True) + + # Seed everything. + rank_seed = seed + 1000000 * rank + set_global_seeds(rank_seed) + + # Prepare params. + params = config.DEFAULT_PARAMS + params['env_name'] = env + params['replay_strategy'] = replay_strategy + if env in config.DEFAULT_ENV_PARAMS: + params.update(config.DEFAULT_ENV_PARAMS[env]) # merge env-specific parameters in + params.update(**override_params) # makes it possible to override any parameter + with open(os.path.join(logger.get_dir(), 'params.json'), 'w') as f: + json.dump(params, f) + params = config.prepare_params(params) + config.log_params(params, logger=logger) + + if num_cpu == 1: + logger.warn() + logger.warn('*** Warning ***') + logger.warn( + 'You are running HER with just a single MPI worker. This will work, but the ' + + 'experiments that we report in Plappert et al. (2018, https://arxiv.org/abs/1802.09464) ' + + 'were obtained with --num_cpu 19. This makes a significant difference and if you ' + + 'are looking to reproduce those results, be aware of this. Please also refer to ' + + 'https://github.com/openai/baselines/issues/314 for further details.') + logger.warn('****************') + logger.warn() + + dims = config.configure_dims(params) + policy = config.configure_ddpg(dims=dims, params=params, clip_return=clip_return) + # Load Learned Parameters + if logdir_tf: + import tensorflow as tf + saver = tf.train.Saver() + saver.restore(policy.sess, logdir_tf) + + rollout_params = { + 'exploit': False, + 'use_target_net': False, + 'use_demo_states': True, + 'compute_Q': False, + 'T': params['T'], + } + + eval_params = { + 'exploit': True, + 'use_target_net': params['test_with_polyak'], + 'use_demo_states': False, + 'compute_Q': True, + 'T': params['T'], + } + + for name in ['T', 'rollout_batch_size', 'gamma', 'noise_eps', 'random_eps']: + rollout_params[name] = params[name] + eval_params[name] = params[name] + + rollout_worker = RolloutWorker(params['make_env'], policy, dims, logger, **rollout_params) + rollout_worker.seed(rank_seed) + + evaluator = RolloutWorker(params['make_env'], policy, dims, logger, **eval_params) + evaluator.seed(rank_seed) + + # Log Directory for actions and qvalues + if not logdir_aq: + logdir_aq = os.path.join(logdir_tf, "ActionQvals") + if not os.path.exists(logdir_aq): + os.makedirs(logdir_aq) + clogger.info("Create Logdir to {}".format(logdir_aq)) + + test( + logdir=logdir, policy=policy, rollout_worker=rollout_worker, + evaluator=evaluator, n_epochs=n_epochs, n_test_rollouts=params['n_test_rollouts'], + n_cycles=params['n_cycles'], n_batches=params['n_batches'], + policy_save_interval=policy_save_interval, save_policies=save_policies, demo_file=demo_file, + logdir_aq=logdir_aq, + ) + + +@click.command() +@click.option('--env', type=str, default='FetchReach-v1', help='the name of the OpenAI Gym environment that you want to train on') +@click.option('--logdir', type=str, default=None, help='the path to where logs and policy pickles should go. If not specified, creates a folder in /tmp/') +@click.option('--n_epochs', type=int, default=50, help='the number of training epochs to run') +@click.option('--num_cpu', type=int, default=1, help='the number of CPU cores to use (using MPI)') +@click.option('--seed', type=int, default=0, help='the random seed used to seed both the environment and the training code') +@click.option('--policy_save_interval', type=int, default=5, help='the interval with which policy pickles are saved. If set to 0, only the best and latest policy will be pickled.') +@click.option('--replay_strategy', type=click.Choice(['future', 'none']), default='future', help='the HER replay strategy to be used. "future" uses HER, "none" disables HER.') +@click.option('--clip_return', type=int, default=1, help='whether or not returns should be clipped') +@click.option('--demo_file', type=str, default = 'PATH/TO/DEMO/DATA/FILE.npz', help='demo data file path') +@click.option('--logdir_tf', type=str, default=None, help='the path to save tf.variables.') +@click.option('--logdir_aq', type=str, default=None, help='the path to save tf.variables.') +def main(**kwargs): + clogger.info("Main Func @her.experiment.train") + launch(**kwargs) + + +if __name__ == '__main__': + main() diff --git a/baselines/her/experiment/train.py b/baselines/her/experiment/train.py old mode 100644 new mode 100755 index 82a11f0ad6..ea37343b4d --- a/baselines/her/experiment/train.py +++ b/baselines/her/experiment/train.py @@ -15,6 +15,12 @@ from subprocess import CalledProcessError +# -------------------------------------------------------------------------------------- +from baselines.custom_logger import CustomLoggerObject +clogger = CustomLoggerObject() +clogger.info("MyLogger is working!!") +# -------------------------------------------------------------------------------------- + def mpi_average(value): if value == []: @@ -24,33 +30,62 @@ def mpi_average(value): return mpi_moments(np.array(value))[0] -def train(policy, rollout_worker, evaluator, +def train(min_num, max_num, num_axis, reward_lambda, # nishimura + policy, rollout_worker, evaluator, n_epochs, n_test_rollouts, n_cycles, n_batches, policy_save_interval, - save_policies, demo_file, **kwargs): + save_policies, demo_file, logdir_init, **kwargs): rank = MPI.COMM_WORLD.Get_rank() latest_policy_path = os.path.join(logger.get_dir(), 'policy_latest.pkl') best_policy_path = os.path.join(logger.get_dir(), 'policy_best.pkl') periodic_policy_path = os.path.join(logger.get_dir(), 'policy_{}.pkl') + best_policy_grasp_path = os.path.join(logger.get_dir(), "grasp_dataset_on_best_policy.npy") # motoda + path_to_grasp_dataset = os.path.join(logger.get_dir(), "grasp_dataset_{}.npy") # motoda + + all_success_grasp_path = os.path.join(logger.get_dir(), "total_grasp_dataset.npy") # motoda + + # motoda -- + success_u = [] + init_success_u = [] + path_to_default_grasp_dataset = "model/initial_grasp_pose.npy" + if os.path.exists(path_to_default_grasp_dataset): + init_success_u = np.load(path_to_default_grasp_dataset) # Load Initial Grasp Pose set + init_success_u = (init_success_u.tolist()) + for tmp_suc in init_success_u: + success_u.append(tmp_suc[0:20]) + print ("Num of grasp : {} ".format(len (success_u))) + else: + print ("No initial grasp pose") + # --- + + # motoda -- + all_success_u = [] # Dumping grasp_pose + # -- logger.info("Training...") best_success_rate = -1 if policy.bc_loss == 1: policy.initDemoBuffer(demo_file) #initialize demo buffer if training with demonstrations for epoch in range(n_epochs): + clogger.info("Start: Epoch {}/{}".format(epoch, n_epochs)) # train rollout_worker.clear_history() + saved_success_u = [] for _ in range(n_cycles): - episode = rollout_worker.generate_rollouts() + episode, success_tmp = rollout_worker.generate_rollouts(min_num=min_num,num_axis=num_axis,reward_lambda=reward_lambda,success_u=success_u) # nishimura, 雑実装 + # clogger.info("Episode = {}".format(episode.keys())) + # for key in episode.keys(): + # clogger.info(" - {}: {}".format(key, episode[key].shape)) policy.store_episode(episode) for _ in range(n_batches): policy.train() policy.update_target_net() + saved_success_u += success_tmp # motoda # test evaluator.clear_history() for _ in range(n_test_rollouts): - evaluator.generate_rollouts() + evaluator.generate_rollouts(min_num=min_num,num_axis=num_axis,reward_lambda=reward_lambda) # nishimura, 雑実装 # record logs logger.record_tabular('epoch', epoch) @@ -71,10 +106,22 @@ def train(policy, rollout_worker, evaluator, logger.info('New best success rate: {}. Saving policy to {} ...'.format(best_success_rate, best_policy_path)) evaluator.save_policy(best_policy_path) evaluator.save_policy(latest_policy_path) + np.save(best_policy_grasp_path, success_u) if rank == 0 and policy_save_interval > 0 and epoch % policy_save_interval == 0 and save_policies: policy_path = periodic_policy_path.format(epoch) logger.info('Saving periodic policy to {} ...'.format(policy_path)) evaluator.save_policy(policy_path) + # -- motoda added + grasp_path = path_to_grasp_dataset.format(epoch) + logger.info('Saving grasp pose: {} grasps. Saving policy to {} ...'.format(len(saved_success_u), grasp_path)) + np.save(grasp_path, saved_success_u) + # -- + + # -- reset : grasp Pose ------- + # success_u = [] # reset (motoda) + # ----------------------------- + + success_u = success_u[-max_num:] # nishimura # make sure that different threads have different seeds local_uniform = np.random.uniform(size=(1,)) @@ -83,10 +130,17 @@ def train(policy, rollout_worker, evaluator, if rank != 0: assert local_uniform[0] != root_uniform[0] + all_success_u += saved_success_u # motoda + + # motoda -- + # Dumping the total success_pose + logger.info('Saving grasp pose: {} grasps. Saving policy to {} ...'.format(len(all_success_u), all_success_grasp_path)) + np.save(all_success_grasp_path, saved_success_u) + # -- def launch( - env, logdir, n_epochs, num_cpu, seed, replay_strategy, policy_save_interval, clip_return, - demo_file, override_params={}, save_policies=True + env, logdir, n_epochs, min_num, max_num, num_axis, reward_lambda, num_cpu, seed, replay_strategy, policy_save_interval, clip_return, + demo_file, logdir_tf=None, override_params={}, save_policies=True, logdir_init=None ): # Fork for multi-CPU MPI implementation. if num_cpu > 1: @@ -140,8 +194,26 @@ def launch( logger.warn('****************') logger.warn() + dims = config.configure_dims(params) policy = config.configure_ddpg(dims=dims, params=params, clip_return=clip_return) + clogger.info(policy.sess) + # Prepare for Saving Network + clogger.info("logdir_tf: {}".format(logdir_tf)) + if not logdir_tf == None: + clogger.info("Create tc.Saver()") + import tensorflow as tf + saver = tf.train.Saver() + + # motoda added -- + # Load Learned Parameters + if not logdir_init == None: + if logdir_tf == None: + import tensorflow as tf + saver = tf.train.Saver() + saver.restore(policy.sess, logdir_init) + clogger.info("Model was successflly loaded [logidr_tf={}]".format(logdir_init)) + # --------------- rollout_params = { 'exploit': False, @@ -170,23 +242,40 @@ def launch( evaluator.seed(rank_seed) train( + min_num=min_num, max_num=max_num, num_axis=num_axis, reward_lambda=reward_lambda, # nishimura logdir=logdir, policy=policy, rollout_worker=rollout_worker, evaluator=evaluator, n_epochs=n_epochs, n_test_rollouts=params['n_test_rollouts'], n_cycles=params['n_cycles'], n_batches=params['n_batches'], - policy_save_interval=policy_save_interval, save_policies=save_policies, demo_file=demo_file) + policy_save_interval=policy_save_interval, save_policies=save_policies, demo_file=demo_file, logdir_init=logdir_init) + + + # Save Trained Network + if logdir_tf: + clogger.info("Save tf.variables to {}".format(logdir_tf)) + clogger.info(policy.sess) + saver.save(policy.sess, logdir_tf) + clogger.info("Model was successflly saved [logidr_tf={}]".format(logdir_tf)) @click.command() @click.option('--env', type=str, default='FetchReach-v1', help='the name of the OpenAI Gym environment that you want to train on') @click.option('--logdir', type=str, default=None, help='the path to where logs and policy pickles should go. If not specified, creates a folder in /tmp/') @click.option('--n_epochs', type=int, default=50, help='the number of training epochs to run') +@click.option('--min_num', type=int, default=100,help='minimum number of success_u whether to run PCA') +@click.option('--max_num', type=int, default=10000,help='limit of success_u for PCA') +@click.option('--num_axis', type=int, default=5,help='number of principal components to calculate the reward function') +@click.option('--reward_lambda', type=float, default=1.,help='a weight for the second term of the reward function') @click.option('--num_cpu', type=int, default=1, help='the number of CPU cores to use (using MPI)') @click.option('--seed', type=int, default=0, help='the random seed used to seed both the environment and the training code') @click.option('--policy_save_interval', type=int, default=5, help='the interval with which policy pickles are saved. If set to 0, only the best and latest policy will be pickled.') @click.option('--replay_strategy', type=click.Choice(['future', 'none']), default='future', help='the HER replay strategy to be used. "future" uses HER, "none" disables HER.') @click.option('--clip_return', type=int, default=1, help='whether or not returns should be clipped') @click.option('--demo_file', type=str, default = 'PATH/TO/DEMO/DATA/FILE.npz', help='demo data file path') +@click.option('--logdir_tf', type=str, default=None, help='the path to save tf.variables.') +@click.option('--logdir_init', type=str, default='model/init', help='the path to load default paramater.') # There are meta data at model/init + def main(**kwargs): + clogger.info("Main Func @her.experiment.train") launch(**kwargs) diff --git a/baselines/her/her.py b/baselines/her/her.py old mode 100644 new mode 100755 diff --git a/baselines/her/normalizer.py b/baselines/her/normalizer.py old mode 100644 new mode 100755 diff --git a/baselines/her/replay_buffer.py b/baselines/her/replay_buffer.py old mode 100644 new mode 100755 diff --git a/baselines/her/rollout.py b/baselines/her/rollout.py old mode 100644 new mode 100755 index e33b92add1..dd5780519a --- a/baselines/her/rollout.py +++ b/baselines/her/rollout.py @@ -7,6 +7,13 @@ from baselines.her.util import convert_episode_to_batch_major, store_args +# -------------------------------------------------------------------------------------- +from baselines.custom_logger import CustomLoggerObject +clogger = CustomLoggerObject() +clogger.info("MyLogger is working!!") +# -------------------------------------------------------------------------------------- + + class RolloutWorker: @store_args @@ -61,10 +68,14 @@ def reset_all_rollouts(self): for i in range(self.rollout_batch_size): self.reset_rollout(i) - def generate_rollouts(self): + def generate_rollouts(self, min_num, num_axis, reward_lambda, success_u=[], is_train=True): # nishimura """Performs `rollout_batch_size` rollouts in parallel for time horizon `T` with the current policy acting on it accordingly. """ + + import sklearn + from sklearn.decomposition import PCA + self.reset_all_rollouts() # compute observations @@ -75,6 +86,8 @@ def generate_rollouts(self): # generate episodes obs, achieved_goals, acts, goals, successes = [], [], [], [], [] + q_vals = [] + fcs = [] info_values = [np.empty((self.T, self.rollout_batch_size, self.dims['info_' + key]), np.float32) for key in self.info_keys] Qs = [] for t in range(self.T): @@ -83,11 +96,15 @@ def generate_rollouts(self): compute_Q=self.compute_Q, noise_eps=self.noise_eps if not self.exploit else 0., random_eps=self.random_eps if not self.exploit else 0., - use_target_net=self.use_target_net) - + use_target_net=self.use_target_net,) + # clogger.info("compute_Q[{}, {}]: policy_output: {}".format(self.compute_Q, t, policy_output)) + if self.compute_Q: - u, Q = policy_output + u, Q, fc = policy_output Qs.append(Q) + q_vals.append(Q.copy()) + if fc.ndim == 1: + fc = fc.reshape(1,-1) else: u = policy_output @@ -95,18 +112,32 @@ def generate_rollouts(self): # The non-batched case should still have a reasonable shape. u = u.reshape(1, -1) + o_new = np.empty((self.rollout_batch_size, self.dims['o'])) ag_new = np.empty((self.rollout_batch_size, self.dims['g'])) success = np.zeros(self.rollout_batch_size) + # compute new states and observations for i in range(self.rollout_batch_size): + # -- nishimura 雑実装 + self.envs[i].num_axis = num_axis + self.envs[i].reward_lambda = reward_lambda + # -- try: # We fully ignore the reward here because it will have to be re-computed # for HER. curr_o_new, _, _, info = self.envs[i].step(u[i]) if 'is_success' in info: success[i] = info['is_success'] - o_new[i] = curr_o_new['observation'] + + if success[i] > 0: + success_u.append(u[i][0:20]) + if len(success_u)>=min_num: # nishimura + pca = PCA() + pca.fit(success_u) + self.envs[i].variance_ratio.append(pca.explained_variance_ratio_) + + o_new[i] = curr_o_new['observation'] ag_new[i] = curr_o_new['achieved_goal'] for idx, key in enumerate(self.info_keys): info_values[idx][t, i] = info[key] @@ -124,6 +155,8 @@ def generate_rollouts(self): achieved_goals.append(ag.copy()) successes.append(success.copy()) acts.append(u.copy()) + if self.compute_Q: + fcs.append(fc.copy()) goals.append(self.g.copy()) o[...] = o_new ag[...] = ag_new @@ -131,10 +164,21 @@ def generate_rollouts(self): achieved_goals.append(ag.copy()) self.initial_o[:] = o - episode = dict(o=obs, - u=acts, - g=goals, - ag=achieved_goals) + if is_train: + episode = dict(o=obs, + u=acts, + g=goals, + ag=achieved_goals + ) + else: + episode = dict(o=obs, + u=acts, + fc=fcs, + g=goals, + ag=achieved_goals, + q=q_vals, + ) + for key, value in zip(self.info_keys, info_values): episode['info_{}'.format(key)] = value @@ -147,7 +191,7 @@ def generate_rollouts(self): self.Q_history.append(np.mean(Qs)) self.n_episodes += self.rollout_batch_size - return convert_episode_to_batch_major(episode) + return convert_episode_to_batch_major(episode), success_u # motoda def clear_history(self): """Clears all histories that are used for statistics diff --git a/baselines/her/util.py b/baselines/her/util.py old mode 100644 new mode 100755 diff --git a/baselines/logger.py b/baselines/logger.py old mode 100644 new mode 100755 diff --git a/baselines/ppo1/README.md b/baselines/ppo1/README.md old mode 100644 new mode 100755 diff --git a/baselines/ppo1/__init__.py b/baselines/ppo1/__init__.py old mode 100644 new mode 100755 diff --git a/baselines/ppo1/cnn_policy.py b/baselines/ppo1/cnn_policy.py old mode 100644 new mode 100755 diff --git a/baselines/ppo1/mlp_policy.py b/baselines/ppo1/mlp_policy.py old mode 100644 new mode 100755 diff --git a/baselines/ppo1/pposgd_simple.py b/baselines/ppo1/pposgd_simple.py old mode 100644 new mode 100755 diff --git a/baselines/ppo1/run_atari.py b/baselines/ppo1/run_atari.py old mode 100644 new mode 100755 diff --git a/baselines/ppo1/run_humanoid.py b/baselines/ppo1/run_humanoid.py old mode 100644 new mode 100755 diff --git a/baselines/ppo1/run_mujoco.py b/baselines/ppo1/run_mujoco.py old mode 100644 new mode 100755 diff --git a/baselines/ppo1/run_robotics.py b/baselines/ppo1/run_robotics.py old mode 100644 new mode 100755 diff --git a/baselines/ppo2/README.md b/baselines/ppo2/README.md old mode 100644 new mode 100755 diff --git a/baselines/ppo2/__init__.py b/baselines/ppo2/__init__.py old mode 100644 new mode 100755 diff --git a/baselines/ppo2/defaults.py b/baselines/ppo2/defaults.py old mode 100644 new mode 100755 diff --git a/baselines/ppo2/microbatched_model.py b/baselines/ppo2/microbatched_model.py old mode 100644 new mode 100755 diff --git a/baselines/ppo2/model.py b/baselines/ppo2/model.py old mode 100644 new mode 100755 diff --git a/baselines/ppo2/ppo2.py b/baselines/ppo2/ppo2.py old mode 100644 new mode 100755 diff --git a/baselines/ppo2/runner.py b/baselines/ppo2/runner.py old mode 100644 new mode 100755 diff --git a/baselines/ppo2/test_microbatches.py b/baselines/ppo2/test_microbatches.py old mode 100644 new mode 100755 diff --git a/baselines/results_plotter.py b/baselines/results_plotter.py old mode 100644 new mode 100755 diff --git a/baselines/run.py b/baselines/run.py old mode 100644 new mode 100755 index c0298f3a43..8ef9cc5b18 --- a/baselines/run.py +++ b/baselines/run.py @@ -15,6 +15,14 @@ from baselines.common.vec_env.vec_normalize import VecNormalize + +# -------------------------------------------------------------------------------------- +from baselines.custom_logger import CustomLoggerObject +clogger = CustomLoggerObject() +clogger.info("MyLogger is working!!") +# -------------------------------------------------------------------------------------- + + try: from mpi4py import MPI except ImportError: diff --git a/baselines/trpo_mpi/README.md b/baselines/trpo_mpi/README.md old mode 100644 new mode 100755 diff --git a/baselines/trpo_mpi/__init__.py b/baselines/trpo_mpi/__init__.py old mode 100644 new mode 100755 diff --git a/baselines/trpo_mpi/defaults.py b/baselines/trpo_mpi/defaults.py old mode 100644 new mode 100755 diff --git a/baselines/trpo_mpi/trpo_mpi.py b/baselines/trpo_mpi/trpo_mpi.py old mode 100644 new mode 100755 diff --git a/docs/README.md b/docs/README.md new file mode 100755 index 0000000000..de5957c176 --- /dev/null +++ b/docs/README.md @@ -0,0 +1,147 @@ + [![Build status](https://travis-ci.org/openai/baselines.svg?branch=master)](https://travis-ci.org/openai/baselines) + +# Baselines + +OpenAI Baselines is a set of high-quality implementations of reinforcement learning algorithms. + +These algorithms will make it easier for the research community to replicate, refine, and identify new ideas, and will create good baselines to build research on top of. Our DQN implementation and its variants are roughly on par with the scores in published papers. We expect they will be used as a base around which new ideas can be added, and as a tool for comparing a new approach against existing ones. + +## Prerequisites +Baselines requires python3 (>=3.5) with the development headers. You'll also need system packages CMake, OpenMPI and zlib. Those can be installed as follows +### Ubuntu + +```bash +sudo apt-get update && sudo apt-get install cmake libopenmpi-dev python3-dev zlib1g-dev +``` + +### Mac OS X +Installation of system packages on Mac requires [Homebrew](https://brew.sh). With Homebrew installed, run the following: +```bash +brew install cmake openmpi +``` + +## Virtual environment +From the general python package sanity perspective, it is a good idea to use virtual environments (virtualenvs) to make sure packages from different projects do not interfere with each other. You can install virtualenv (which is itself a pip package) via +```bash +pip install virtualenv +``` +Virtualenvs are essentially folders that have copies of python executable and all python packages. +To create a virtualenv called venv with python3, one runs +```bash +virtualenv /path/to/venv --python=python3 +``` +To activate a virtualenv: +``` +. /path/to/venv/bin/activate +``` +More thorough tutorial on virtualenvs and options can be found [here](https://virtualenv.pypa.io/en/stable/) + + +## Installation +- Clone the repo and cd into it: + ```bash + git clone https://github.com/openai/baselines.git + cd baselines + ``` +- If you don't have TensorFlow installed already, install your favourite flavor of TensorFlow. In most cases, + ```bash + pip install tensorflow-gpu # if you have a CUDA-compatible gpu and proper drivers + ``` + or + ```bash + pip install tensorflow + ``` + should be sufficient. Refer to [TensorFlow installation guide](https://www.tensorflow.org/install/) + for more details. + +- Install baselines package + ```bash + pip install -e . + ``` + +### MuJoCo +Some of the baselines examples use [MuJoCo](http://www.mujoco.org) (multi-joint dynamics in contact) physics simulator, which is proprietary and requires binaries and a license (temporary 30-day license can be obtained from [www.mujoco.org](http://www.mujoco.org)). Instructions on setting up MuJoCo can be found [here](https://github.com/openai/mujoco-py) + +## Testing the installation +All unit tests in baselines can be run using pytest runner: +``` +pip install pytest +pytest +``` + +## Training models +Most of the algorithms in baselines repo are used as follows: +```bash +python -m baselines.run --alg= --env= [additional arguments] +``` +### Example 1. PPO with MuJoCo Humanoid +For instance, to train a fully-connected network controlling MuJoCo humanoid using PPO2 for 20M timesteps +```bash +python -m baselines.run --alg=ppo2 --env=Humanoid-v2 --network=mlp --num_timesteps=2e7 +``` +Note that for mujoco environments fully-connected network is default, so we can omit `--network=mlp` +The hyperparameters for both network and the learning algorithm can be controlled via the command line, for instance: +```bash +python -m baselines.run --alg=ppo2 --env=Humanoid-v2 --network=mlp --num_timesteps=2e7 --ent_coef=0.1 --num_hidden=32 --num_layers=3 --value_network=copy +``` +will set entropy coefficient to 0.1, and construct fully connected network with 3 layers with 32 hidden units in each, and create a separate network for value function estimation (so that its parameters are not shared with the policy network, but the structure is the same) + +See docstrings in [common/models.py](../baselines/common/models.py) for description of network parameters for each type of model, and +docstring for [baselines/ppo2/ppo2.py/learn()](../baselines/ppo2/ppo2.py#L152) for the description of the ppo2 hyperparamters. + +### Example 2. DQN on Atari +DQN with Atari is at this point a classics of benchmarks. To run the baselines implementation of DQN on Atari Pong: +``` +python -m baselines.run --alg=deepq --env=PongNoFrameskip-v4 --num_timesteps=1e6 +``` + +## Saving, loading and visualizing models +The algorithms serialization API is not properly unified yet; however, there is a simple method to save / restore trained models. +`--save_path` and `--load_path` command-line option loads the tensorflow state from a given path before training, and saves it after the training, respectively. +Let's imagine you'd like to train ppo2 on Atari Pong, save the model and then later visualize what has it learnt. +```bash +python -m baselines.run --alg=ppo2 --env=PongNoFrameskip-v4 --num_timesteps=2e7 --save_path=~/models/pong_20M_ppo2 +``` +This should get to the mean reward per episode about 20. To load and visualize the model, we'll do the following - load the model, train it for 0 steps, and then visualize: +```bash +python -m baselines.run --alg=ppo2 --env=PongNoFrameskip-v4 --num_timesteps=0 --load_path=~/models/pong_20M_ppo2 --play +``` + +*NOTE:* At the moment Mujoco training uses VecNormalize wrapper for the environment which is not being saved correctly; so loading the models trained on Mujoco will not work well if the environment is recreated. If necessary, you can work around that by replacing RunningMeanStd by TfRunningMeanStd in [baselines/common/vec_env/vec_normalize.py](../baselines/common/vec_env/vec_normalize.py#L12). This way, mean and std of environment normalizing wrapper will be saved in tensorflow variables and included in the model file; however, training is slower that way - hence not including it by default + +## Loading and vizualizing learning curves and other training metrics +See [here](docs/viz/viz.ipynb) for instructions on how to load and display the training data. + +## Subpackages + +- [A2C](../baselines/a2c) +- [ACER](../baselines/acer) +- [ACKTR](../baselines/acktr) +- [DDPG](../baselines/ddpg) +- [DQN](../baselines/deepq) +- [GAIL](../baselines/gail) +- [HER](../baselines/her) +- [PPO1](../baselines/ppo1) (obsolete version, left here temporarily) +- [PPO2](../baselines/ppo2) +- [TRPO](../baselines/trpo_mpi) + + + +## Benchmarks +Results of benchmarks on Mujoco (1M timesteps) and Atari (10M timesteps) are available +[here for Mujoco](https://htmlpreview.github.com/?https://github.com/openai/baselines/blob/master/benchmarks_mujoco1M.htm) +and +[here for Atari](https://htmlpreview.github.com/?https://github.com/openai/baselines/blob/master/benchmarks_atari10M.htm) +respectively. Note that these results may be not on the latest version of the code, particular commit hash with which results were obtained is specified on the benchmarks page. + +To cite this repository in publications: + + @misc{baselines, + author = {Dhariwal, Prafulla and Hesse, Christopher and Klimov, Oleg and Nichol, Alex and Plappert, Matthias and Radford, Alec and Schulman, John and Sidor, Szymon and Wu, Yuhuai and Zhokhov, Peter}, + title = {OpenAI Baselines}, + year = {2017}, + publisher = {GitHub}, + journal = {GitHub repository}, + howpublished = {\url{https://github.com/openai/baselines}}, + } + diff --git a/benchmarks_atari10M.htm b/docs/benchmarks_atari10M.htm old mode 100644 new mode 100755 similarity index 100% rename from benchmarks_atari10M.htm rename to docs/benchmarks_atari10M.htm diff --git a/benchmarks_mujoco1M.htm b/docs/benchmarks_mujoco1M.htm old mode 100644 new mode 100755 similarity index 100% rename from benchmarks_mujoco1M.htm rename to docs/benchmarks_mujoco1M.htm diff --git a/data/cartpole.gif b/docs/data/cartpole.gif old mode 100644 new mode 100755 similarity index 100% rename from data/cartpole.gif rename to docs/data/cartpole.gif diff --git a/data/fetchPickAndPlaceContrast.png b/docs/data/fetchPickAndPlaceContrast.png old mode 100644 new mode 100755 similarity index 100% rename from data/fetchPickAndPlaceContrast.png rename to docs/data/fetchPickAndPlaceContrast.png diff --git a/data/logo.jpg b/docs/data/logo.jpg old mode 100644 new mode 100755 similarity index 100% rename from data/logo.jpg rename to docs/data/logo.jpg diff --git a/docs/viz/viz.ipynb b/docs/viz/viz.ipynb old mode 100644 new mode 100755 diff --git a/gym-grasp/README.md b/gym-grasp/README.md new file mode 100644 index 0000000000..cd957b4579 --- /dev/null +++ b/gym-grasp/README.md @@ -0,0 +1,20 @@ +# gym_grasp + +## GraspBlock + + +# Installation + +```bash +cd gym-grasp +pip install -e . +``` + +# How To Use + +```python +import gym +import gym_grasp # This includes GraspBlock-v0 + +env = gym.make('GraspBlock-v0') +``` diff --git a/gym-grasp/__init__.py b/gym-grasp/__init__.py new file mode 100644 index 0000000000..e69de29bb2 diff --git a/gym-grasp/gym_grasp/__init__.py b/gym-grasp/gym_grasp/__init__.py new file mode 100644 index 0000000000..5fa8fec811 --- /dev/null +++ b/gym-grasp/gym_grasp/__init__.py @@ -0,0 +1,12 @@ +from gym.envs.registration import register + + +def _merge(a, b): + a.update(b) + return a + +register( + id='GraspBlock-v0', + entry_point='gym_grasp.envs:GraspBlockEnv', + max_episode_steps=100, +) \ No newline at end of file diff --git a/gym-grasp/gym_grasp/envs/README.md b/gym-grasp/gym_grasp/envs/README.md new file mode 100644 index 0000000000..5dbbfdab4a --- /dev/null +++ b/gym-grasp/gym_grasp/envs/README.md @@ -0,0 +1,54 @@ +# Robotics environments + +Details and documentation on these robotics environments are available in our [blog post](https://blog.openai.com/ingredients-for-robotics-research/), the accompanying [technical report](https://arxiv.org/abs/1802.09464), and the [Gym website](https://gym.openai.com/envs/#robotics). + +If you use these environments, please cite the following paper: + +``` +@misc{1802.09464, + Author = {Matthias Plappert and Marcin Andrychowicz and Alex Ray and Bob McGrew and Bowen Baker and Glenn Powell and Jonas Schneider and Josh Tobin and Maciek Chociej and Peter Welinder and Vikash Kumar and Wojciech Zaremba}, + Title = {Multi-Goal Reinforcement Learning: Challenging Robotics Environments and Request for Research}, + Year = {2018}, + Eprint = {arXiv:1802.09464}, +} +``` + +## Fetch environments + + +[FetchReach-v0](https://gym.openai.com/envs/FetchReach-v0/): Fetch has to move its end-effector to the desired goal position. + + + + +[FetchSlide-v0](https://gym.openai.com/envs/FetchSlide-v0/): Fetch has to hit a puck across a long table such that it slides and comes to rest on the desired goal. + + + + +[FetchPush-v0](https://gym.openai.com/envs/FetchPush-v0/): Fetch has to move a box by pushing it until it reaches a desired goal position. + + + + +[FetchPickAndPlace-v0](https://gym.openai.com/envs/FetchPickAndPlace-v0/): Fetch has to pick up a box from a table using its gripper and move it to a desired goal above the table. + +## Shadow Dexterous Hand environments + + +[HandReach-v0](https://gym.openai.com/envs/HandReach-v0/): ShadowHand has to reach with its thumb and a selected finger until they meet at a desired goal position above the palm. + + + + +[HandManipulateBlock-v0](https://gym.openai.com/envs/HandManipulateBlock-v0/): ShadowHand has to manipulate a block until it achieves a desired goal position and rotation. + + + + +[HandManipulateEgg-v0](https://gym.openai.com/envs/HandManipulateEgg-v0/): ShadowHand has to manipulate an egg until it achieves a desired goal position and rotation. + + + + +[HandManipulatePen-v0](https://gym.openai.com/envs/HandManipulatePen-v0/): ShadowHand has to manipulate a pen until it achieves a desired goal position and rotation. diff --git a/gym-grasp/gym_grasp/envs/__init__.py b/gym-grasp/gym_grasp/envs/__init__.py new file mode 100644 index 0000000000..a153f413f2 --- /dev/null +++ b/gym-grasp/gym_grasp/envs/__init__.py @@ -0,0 +1 @@ +from gym_grasp.envs.hand.grasp_block import GraspBlockEnv diff --git a/gym-grasp/gym_grasp/envs/assets/LICENSE.md b/gym-grasp/gym_grasp/envs/assets/LICENSE.md new file mode 100644 index 0000000000..22ce9010d0 --- /dev/null +++ b/gym-grasp/gym_grasp/envs/assets/LICENSE.md @@ -0,0 +1,222 @@ +# Fetch Robotics +The model of the [Fetch](http://fetchrobotics.com/platforms-research-development/) is based on [models provided by Fetch](https://github.com/fetchrobotics/fetch_ros/tree/indigo-devel/fetch_description). It was adapted and refined by OpenAI. + +# ShadowHand +The model of the [ShadowHand](https://www.shadowrobot.com/products/dexterous-hand/) is based on [models provided by ShadowRobot](https://github.com/shadow-robot/sr_common/tree/kinetic-devel/sr_description/hand/model), and on code used under the following license: + +(C) Vikash Kumar, CSE, UW. Licensed under Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with the License. You may obtain a copy of the License at http://www.apache.org/licenses/LICENSE-2.0. Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. 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We also recommend that a + file or class name and description of purpose be included on the + same "printed page" as the copyright notice for easier + identification within third-party archives. + + Copyright [yyyy] [name of copyright owner] + + Licensed under the Apache License, Version 2.0 (the "License"); + you may not use this file except in compliance with the License. + You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + + Unless required by applicable law or agreed to in writing, software + distributed under the License is distributed on an "AS IS" BASIS, + WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + See the License for the specific language governing permissions and + limitations under the License. + +Additional license notices: + + Sources : 1) Manipulator and Manipulation in High Dimensional Spaces. Vikash Kumar, Ph.D. Thesis, CSE, Univ. of Washington. 2016. + + Mujoco :: Advanced physics simulation engine + Source : www.roboti.us + Version : 1.40 + Released : 17Jan'17 + + Author :: Vikash Kumar + Contacts : vikash@openai.com + Last edits : 3Apr'17 diff --git a/gym-grasp/gym_grasp/envs/assets/fetch/pick_and_place.xml b/gym-grasp/gym_grasp/envs/assets/fetch/pick_and_place.xml new file mode 100644 index 0000000000..337032a832 --- /dev/null +++ b/gym-grasp/gym_grasp/envs/assets/fetch/pick_and_place.xml @@ -0,0 +1,35 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + diff --git a/gym-grasp/gym_grasp/envs/assets/fetch/push.xml b/gym-grasp/gym_grasp/envs/assets/fetch/push.xml new file mode 100644 index 0000000000..8e12db248c --- /dev/null +++ b/gym-grasp/gym_grasp/envs/assets/fetch/push.xml @@ -0,0 +1,32 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + \ No newline at end of file diff --git a/gym-grasp/gym_grasp/envs/assets/fetch/reach.xml b/gym-grasp/gym_grasp/envs/assets/fetch/reach.xml new file mode 100644 index 0000000000..c73d6249f3 --- /dev/null +++ b/gym-grasp/gym_grasp/envs/assets/fetch/reach.xml @@ -0,0 +1,26 @@ + + + + + + + + + + + + + + + + + + + + + + + + diff --git a/gym-grasp/gym_grasp/envs/assets/fetch/robot.xml b/gym-grasp/gym_grasp/envs/assets/fetch/robot.xml new file mode 100644 index 0000000000..9ee7723b5e --- /dev/null +++ b/gym-grasp/gym_grasp/envs/assets/fetch/robot.xml @@ -0,0 +1,123 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + diff --git a/gym-grasp/gym_grasp/envs/assets/fetch/shared.xml b/gym-grasp/gym_grasp/envs/assets/fetch/shared.xml new file mode 100644 index 0000000000..5d61fef70d --- /dev/null +++ b/gym-grasp/gym_grasp/envs/assets/fetch/shared.xml @@ -0,0 +1,66 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + diff --git a/gym-grasp/gym_grasp/envs/assets/fetch/slide.xml b/gym-grasp/gym_grasp/envs/assets/fetch/slide.xml new file mode 100644 index 0000000000..efbfb51bd0 --- /dev/null +++ b/gym-grasp/gym_grasp/envs/assets/fetch/slide.xml @@ -0,0 +1,32 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + diff --git a/gym-grasp/gym_grasp/envs/assets/hand/grasp_block.xml b/gym-grasp/gym_grasp/envs/assets/hand/grasp_block.xml new file mode 100644 index 0000000000..b271a2548d --- /dev/null +++ b/gym-grasp/gym_grasp/envs/assets/hand/grasp_block.xml @@ -0,0 +1,82 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + diff --git a/gym-grasp/gym_grasp/envs/assets/hand/manipulate_block.xml b/gym-grasp/gym_grasp/envs/assets/hand/manipulate_block.xml new file mode 100644 index 0000000000..83a6517e6c --- /dev/null +++ b/gym-grasp/gym_grasp/envs/assets/hand/manipulate_block.xml @@ -0,0 +1,41 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + diff --git a/gym-grasp/gym_grasp/envs/assets/hand/manipulate_egg.xml b/gym-grasp/gym_grasp/envs/assets/hand/manipulate_egg.xml new file mode 100644 index 0000000000..46d1dbba84 --- /dev/null +++ b/gym-grasp/gym_grasp/envs/assets/hand/manipulate_egg.xml @@ -0,0 +1,40 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + diff --git a/gym-grasp/gym_grasp/envs/assets/hand/manipulate_pen.xml b/gym-grasp/gym_grasp/envs/assets/hand/manipulate_pen.xml new file mode 100644 index 0000000000..20a6fb5e06 --- /dev/null +++ b/gym-grasp/gym_grasp/envs/assets/hand/manipulate_pen.xml @@ -0,0 +1,40 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + diff --git a/gym-grasp/gym_grasp/envs/assets/hand/reach.xml b/gym-grasp/gym_grasp/envs/assets/hand/reach.xml new file mode 100644 index 0000000000..71f6dfe621 --- /dev/null +++ b/gym-grasp/gym_grasp/envs/assets/hand/reach.xml @@ -0,0 +1,34 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + diff --git a/gym-grasp/gym_grasp/envs/assets/hand/robot.xml b/gym-grasp/gym_grasp/envs/assets/hand/robot.xml new file mode 100644 index 0000000000..dbb9e43448 --- /dev/null +++ b/gym-grasp/gym_grasp/envs/assets/hand/robot.xml @@ -0,0 +1,160 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + diff --git a/gym-grasp/gym_grasp/envs/assets/hand/robot_for_grasp.xml b/gym-grasp/gym_grasp/envs/assets/hand/robot_for_grasp.xml new file mode 100644 index 0000000000..a46cc3258b --- /dev/null +++ b/gym-grasp/gym_grasp/envs/assets/hand/robot_for_grasp.xml @@ -0,0 +1,165 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + diff --git a/gym-grasp/gym_grasp/envs/assets/hand/shared.xml b/gym-grasp/gym_grasp/envs/assets/hand/shared.xml new file mode 100644 index 0000000000..f27f265551 --- /dev/null +++ b/gym-grasp/gym_grasp/envs/assets/hand/shared.xml @@ -0,0 +1,254 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + 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b/gym-grasp/gym_grasp/envs/assets/textures/block_hidden.png new file mode 100644 index 0000000000..e08b8613c4 Binary files /dev/null and b/gym-grasp/gym_grasp/envs/assets/textures/block_hidden.png differ diff --git a/gym-grasp/gym_grasp/envs/fetch/__init__.py b/gym-grasp/gym_grasp/envs/fetch/__init__.py new file mode 100644 index 0000000000..e69de29bb2 diff --git a/gym-grasp/gym_grasp/envs/fetch/pick_and_place.py b/gym-grasp/gym_grasp/envs/fetch/pick_and_place.py new file mode 100644 index 0000000000..c6c5e7ea99 --- /dev/null +++ b/gym-grasp/gym_grasp/envs/fetch/pick_and_place.py @@ -0,0 +1,23 @@ +import os +from gym import utils +from gym.envs.robotics import fetch_env + + +# Ensure we get the path separator correct on windows +MODEL_XML_PATH = os.path.join('fetch', 'pick_and_place.xml') + + +class FetchPickAndPlaceEnv(fetch_env.FetchEnv, utils.EzPickle): + def __init__(self, reward_type='sparse'): + initial_qpos = { + 'robot0:slide0': 0.405, + 'robot0:slide1': 0.48, + 'robot0:slide2': 0.0, + 'object0:joint': [1.25, 0.53, 0.4, 1., 0., 0., 0.], + } + fetch_env.FetchEnv.__init__( + self, MODEL_XML_PATH, has_object=True, block_gripper=False, n_substeps=20, + gripper_extra_height=0.2, target_in_the_air=True, target_offset=0.0, + obj_range=0.15, target_range=0.15, distance_threshold=0.05, + initial_qpos=initial_qpos, reward_type=reward_type) + utils.EzPickle.__init__(self) diff --git a/gym-grasp/gym_grasp/envs/fetch/push.py b/gym-grasp/gym_grasp/envs/fetch/push.py new file mode 100644 index 0000000000..bde15ec00e --- /dev/null +++ b/gym-grasp/gym_grasp/envs/fetch/push.py @@ -0,0 +1,23 @@ +import os +from gym import utils +from gym.envs.robotics import fetch_env + + +# Ensure we get the path separator correct on windows +MODEL_XML_PATH = os.path.join('fetch', 'push.xml') + + +class FetchPushEnv(fetch_env.FetchEnv, utils.EzPickle): + def __init__(self, reward_type='sparse'): + initial_qpos = { + 'robot0:slide0': 0.405, + 'robot0:slide1': 0.48, + 'robot0:slide2': 0.0, + 'object0:joint': [1.25, 0.53, 0.4, 1., 0., 0., 0.], + } + fetch_env.FetchEnv.__init__( + self, MODEL_XML_PATH, has_object=True, block_gripper=True, n_substeps=20, + gripper_extra_height=0.0, target_in_the_air=False, target_offset=0.0, + obj_range=0.15, target_range=0.15, distance_threshold=0.05, + initial_qpos=initial_qpos, reward_type=reward_type) + utils.EzPickle.__init__(self) diff --git a/gym-grasp/gym_grasp/envs/fetch/reach.py b/gym-grasp/gym_grasp/envs/fetch/reach.py new file mode 100644 index 0000000000..cc3fc46c65 --- /dev/null +++ b/gym-grasp/gym_grasp/envs/fetch/reach.py @@ -0,0 +1,22 @@ +import os +from gym import utils +from gym.envs.robotics import fetch_env + + +# Ensure we get the path separator correct on windows +MODEL_XML_PATH = os.path.join('fetch', 'reach.xml') + + +class FetchReachEnv(fetch_env.FetchEnv, utils.EzPickle): + def __init__(self, reward_type='sparse'): + initial_qpos = { + 'robot0:slide0': 0.4049, + 'robot0:slide1': 0.48, + 'robot0:slide2': 0.0, + } + fetch_env.FetchEnv.__init__( + self, MODEL_XML_PATH, has_object=False, block_gripper=True, n_substeps=20, + gripper_extra_height=0.2, target_in_the_air=True, target_offset=0.0, + obj_range=0.15, target_range=0.15, distance_threshold=0.05, + initial_qpos=initial_qpos, reward_type=reward_type) + utils.EzPickle.__init__(self) diff --git a/gym-grasp/gym_grasp/envs/fetch/slide.py b/gym-grasp/gym_grasp/envs/fetch/slide.py new file mode 100644 index 0000000000..8c893b2b7d --- /dev/null +++ b/gym-grasp/gym_grasp/envs/fetch/slide.py @@ -0,0 +1,25 @@ +import os +import numpy as np + +from gym import utils +from gym.envs.robotics import fetch_env + + +# Ensure we get the path separator correct on windows +MODEL_XML_PATH = os.path.join('fetch', 'slide.xml') + + +class FetchSlideEnv(fetch_env.FetchEnv, utils.EzPickle): + def __init__(self, reward_type='sparse'): + initial_qpos = { + 'robot0:slide0': 0.05, + 'robot0:slide1': 0.48, + 'robot0:slide2': 0.0, + 'object0:joint': [1.7, 1.1, 0.4, 1., 0., 0., 0.], + } + fetch_env.FetchEnv.__init__( + self, MODEL_XML_PATH, has_object=True, block_gripper=True, n_substeps=20, + gripper_extra_height=-0.02, target_in_the_air=False, target_offset=np.array([0.4, 0.0, 0.0]), + obj_range=0.1, target_range=0.3, distance_threshold=0.05, + initial_qpos=initial_qpos, reward_type=reward_type) + utils.EzPickle.__init__(self) diff --git a/gym-grasp/gym_grasp/envs/fetch_env.py b/gym-grasp/gym_grasp/envs/fetch_env.py new file mode 100644 index 0000000000..4916c4bcaf --- /dev/null +++ b/gym-grasp/gym_grasp/envs/fetch_env.py @@ -0,0 +1,187 @@ +import numpy as np + +from gym.envs.robotics import rotations, robot_env, utils + + +def goal_distance(goal_a, goal_b): + assert goal_a.shape == goal_b.shape + return np.linalg.norm(goal_a - goal_b, axis=-1) + + +class FetchEnv(robot_env.RobotEnv): + """Superclass for all Fetch environments. + """ + + def __init__( + self, model_path, n_substeps, gripper_extra_height, block_gripper, + has_object, target_in_the_air, target_offset, obj_range, target_range, + distance_threshold, initial_qpos, reward_type, + ): + """Initializes a new Fetch environment. + + Args: + model_path (string): path to the environments XML file + n_substeps (int): number of substeps the simulation runs on every call to step + gripper_extra_height (float): additional height above the table when positioning the gripper + block_gripper (boolean): whether or not the gripper is blocked (i.e. not movable) or not + has_object (boolean): whether or not the environment has an object + target_in_the_air (boolean): whether or not the target should be in the air above the table or on the table surface + target_offset (float or array with 3 elements): offset of the target + obj_range (float): range of a uniform distribution for sampling initial object positions + target_range (float): range of a uniform distribution for sampling a target + distance_threshold (float): the threshold after which a goal is considered achieved + initial_qpos (dict): a dictionary of joint names and values that define the initial configuration + reward_type ('sparse' or 'dense'): the reward type, i.e. sparse or dense + """ + self.gripper_extra_height = gripper_extra_height + self.block_gripper = block_gripper + self.has_object = has_object + self.target_in_the_air = target_in_the_air + self.target_offset = target_offset + self.obj_range = obj_range + self.target_range = target_range + self.distance_threshold = distance_threshold + self.reward_type = reward_type + + super(FetchEnv, self).__init__( + model_path=model_path, n_substeps=n_substeps, n_actions=4, + initial_qpos=initial_qpos) + + # GoalEnv methods + # ---------------------------- + + def compute_reward(self, achieved_goal, goal, info): + # Compute distance between goal and the achieved goal. + d = goal_distance(achieved_goal, goal) + if self.reward_type == 'sparse': + return -(d > self.distance_threshold).astype(np.float32) + else: + return -d + + # RobotEnv methods + # ---------------------------- + + def _step_callback(self): + if self.block_gripper: + self.sim.data.set_joint_qpos('robot0:l_gripper_finger_joint', 0.) + self.sim.data.set_joint_qpos('robot0:r_gripper_finger_joint', 0.) + self.sim.forward() + + def _set_action(self, action): + assert action.shape == (4,) + action = action.copy() # ensure that we don't change the action outside of this scope + pos_ctrl, gripper_ctrl = action[:3], action[3] + + pos_ctrl *= 0.05 # limit maximum change in position + rot_ctrl = [1., 0., 1., 0.] # fixed rotation of the end effector, expressed as a quaternion + gripper_ctrl = np.array([gripper_ctrl, gripper_ctrl]) + assert gripper_ctrl.shape == (2,) + if self.block_gripper: + gripper_ctrl = np.zeros_like(gripper_ctrl) + action = np.concatenate([pos_ctrl, rot_ctrl, gripper_ctrl]) + + # Apply action to simulation. + utils.ctrl_set_action(self.sim, action) + utils.mocap_set_action(self.sim, action) + + def _get_obs(self): + # positions + grip_pos = self.sim.data.get_site_xpos('robot0:grip') + dt = self.sim.nsubsteps * self.sim.model.opt.timestep + grip_velp = self.sim.data.get_site_xvelp('robot0:grip') * dt + robot_qpos, robot_qvel = utils.robot_get_obs(self.sim) + if self.has_object: + object_pos = self.sim.data.get_site_xpos('object0') + # rotations + object_rot = rotations.mat2euler(self.sim.data.get_site_xmat('object0')) + # velocities + object_velp = self.sim.data.get_site_xvelp('object0') * dt + object_velr = self.sim.data.get_site_xvelr('object0') * dt + # gripper state + object_rel_pos = object_pos - grip_pos + object_velp -= grip_velp + else: + object_pos = object_rot = object_velp = object_velr = object_rel_pos = np.zeros(0) + gripper_state = robot_qpos[-2:] + gripper_vel = robot_qvel[-2:] * dt # change to a scalar if the gripper is made symmetric + + if not self.has_object: + achieved_goal = grip_pos.copy() + else: + achieved_goal = np.squeeze(object_pos.copy()) + obs = np.concatenate([ + grip_pos, object_pos.ravel(), object_rel_pos.ravel(), gripper_state, object_rot.ravel(), + object_velp.ravel(), object_velr.ravel(), grip_velp, gripper_vel, + ]) + + return { + 'observation': obs.copy(), + 'achieved_goal': achieved_goal.copy(), + 'desired_goal': self.goal.copy(), + } + + def _viewer_setup(self): + body_id = self.sim.model.body_name2id('robot0:gripper_link') + lookat = self.sim.data.body_xpos[body_id] + for idx, value in enumerate(lookat): + self.viewer.cam.lookat[idx] = value + self.viewer.cam.distance = 2.5 + self.viewer.cam.azimuth = 132. + self.viewer.cam.elevation = -14. + + def _render_callback(self): + # Visualize target. + sites_offset = (self.sim.data.site_xpos - self.sim.model.site_pos).copy() + site_id = self.sim.model.site_name2id('target0') + self.sim.model.site_pos[site_id] = self.goal - sites_offset[0] + self.sim.forward() + + def _reset_sim(self): + self.sim.set_state(self.initial_state) + + # Randomize start position of object. + if self.has_object: + object_xpos = self.initial_gripper_xpos[:2] + while np.linalg.norm(object_xpos - self.initial_gripper_xpos[:2]) < 0.1: + object_xpos = self.initial_gripper_xpos[:2] + self.np_random.uniform(-self.obj_range, self.obj_range, size=2) + object_qpos = self.sim.data.get_joint_qpos('object0:joint') + assert object_qpos.shape == (7,) + object_qpos[:2] = object_xpos + self.sim.data.set_joint_qpos('object0:joint', object_qpos) + + self.sim.forward() + return True + + def _sample_goal(self): + if self.has_object: + goal = self.initial_gripper_xpos[:3] + self.np_random.uniform(-self.target_range, self.target_range, size=3) + goal += self.target_offset + goal[2] = self.height_offset + if self.target_in_the_air and self.np_random.uniform() < 0.5: + goal[2] += self.np_random.uniform(0, 0.45) + else: + goal = self.initial_gripper_xpos[:3] + self.np_random.uniform(-0.15, 0.15, size=3) + return goal.copy() + + def _is_success(self, achieved_goal, desired_goal): + d = goal_distance(achieved_goal, desired_goal) + return (d < self.distance_threshold).astype(np.float32) + + def _env_setup(self, initial_qpos): + for name, value in initial_qpos.items(): + self.sim.data.set_joint_qpos(name, value) + utils.reset_mocap_welds(self.sim) + self.sim.forward() + + # Move end effector into position. + gripper_target = np.array([-0.498, 0.005, -0.431 + self.gripper_extra_height]) + self.sim.data.get_site_xpos('robot0:grip') + gripper_rotation = np.array([1., 0., 1., 0.]) + self.sim.data.set_mocap_pos('robot0:mocap', gripper_target) + self.sim.data.set_mocap_quat('robot0:mocap', gripper_rotation) + for _ in range(10): + self.sim.step() + + # Extract information for sampling goals. + self.initial_gripper_xpos = self.sim.data.get_site_xpos('robot0:grip').copy() + if self.has_object: + self.height_offset = self.sim.data.get_site_xpos('object0')[2] diff --git a/gym-grasp/gym_grasp/envs/hand/__init__.py b/gym-grasp/gym_grasp/envs/hand/__init__.py new file mode 100644 index 0000000000..e69de29bb2 diff --git a/gym-grasp/gym_grasp/envs/hand/grasp_block.py b/gym-grasp/gym_grasp/envs/hand/grasp_block.py new file mode 100644 index 0000000000..e1798a04d0 --- /dev/null +++ b/gym-grasp/gym_grasp/envs/hand/grasp_block.py @@ -0,0 +1,366 @@ +import os +import numpy as np +import random + +from gym import utils, error +# from gym.envs.robotics import rotations, hand_env +from gym_grasp.envs import rotations, hand_env +from gym.envs.robotics.utils import robot_get_obs + +try: + import mujoco_py +except ImportError as e: + raise error.DependencyNotInstalled("{}. (HINT: you need to install mujoco_py, and also perform the setup instructions here: https://github.com/openai/mujoco-py/.)".format(e)) + + +def quat_from_angle_and_axis(angle, axis): + assert axis.shape == (3,) + axis /= np.linalg.norm(axis) + quat = np.concatenate([[np.cos(angle / 2.)], np.sin(angle / 2.) * axis]) + quat /= np.linalg.norm(quat) + return quat + + +# Ensure we get the path separator correct on windows +MANIPULATE_BLOCK_XML = os.path.join('hand', 'manipulate_block.xml') +MANIPULATE_EGG_XML = os.path.join('hand', 'manipulate_egg.xml') +MANIPULATE_PEN_XML = os.path.join('hand', 'manipulate_pen.xml') +GRASP_BLOCK_XML = os.path.join('hand', 'grasp_block.xml') + + +class ManipulateEnv(hand_env.HandEnv, utils.EzPickle): + def __init__( + self, model_path, target_position, target_rotation, + target_position_range, reward_type, initial_qpos={}, + randomize_initial_position=True, randomize_initial_rotation=True, randomize_object=True, + distance_threshold=0.01, rotation_threshold=0.1, n_substeps=20, relative_control=False, + ignore_z_target_rotation=False, + target_id = 0, num_axis = 5, reward_lambda=1. + ): + """Initializes a new Hand manipulation environment. + + Args: + model_path (string): path to the environments XML file + target_position (string): the type of target position: + - ignore: target position is fully ignored, i.e. the object can be positioned arbitrarily + - fixed: target position is set to the initial position of the object + - random: target position is fully randomized according to target_position_range + target_rotation (string): the type of target rotation: + - ignore: target rotation is fully ignored, i.e. the object can be rotated arbitrarily + - fixed: target rotation is set to the initial rotation of the object + - xyz: fully randomized target rotation around the X, Y and Z axis + - z: fully randomized target rotation around the Z axis + - parallel: fully randomized target rotation around Z and axis-aligned rotation around X, Y + ignore_z_target_rotation (boolean): whether or not the Z axis of the target rotation is ignored + target_position_range (np.array of shape (3, 2)): range of the target_position randomization + reward_type ('sparse' or 'dense'): the reward type, i.e. sparse or dense + initial_qpos (dict): a dictionary of joint names and values that define the initial configuration + randomize_initial_position (boolean): whether or not to randomize the initial position of the object + randomize_initial_rotation (boolean): whether or not to randomize the initial rotation of the object + randomize_object (boolean) + distance_threshold (float, in meters): the threshold after which the position of a goal is considered achieved + rotation_threshold (float, in radians): the threshold after which the rotation of a goal is considered achieved + n_substeps (int): number of substeps the simulation runs on every call to step + relative_control (boolean): whether or not the hand is actuated in absolute joint positions or relative to the current state + target_id (int): target id + num_axis (int): the number of components + reward_lambda (float) : a weight for the second term of the reward function + """ + self.target_position = target_position + self.target_rotation = target_rotation + self.target_position_range = target_position_range + self.parallel_quats = [rotations.euler2quat(r) for r in rotations.get_parallel_rotations()] + self.randomize_initial_rotation = randomize_initial_rotation + self.randomize_initial_position = randomize_initial_position + self.distance_threshold = distance_threshold + self.rotation_threshold = rotation_threshold + self.reward_type = reward_type + self.ignore_z_target_rotation = ignore_z_target_rotation + + self.variance_ratio = [] + + self.object_list = ["box:joint", "apple:joint", "banana:joint", "beerbottle:joint", "book:joint", + "needle:joint", "pen:joint", "teacup:joint"] + self.target_id = target_id + self.num_axis = num_axis # the number of components + self.randomize_object = randomize_object # random target (boolean) + self.reward_lambda = reward_lambda # a weight for the second term of the reward function (float) + + if self.randomize_object == True: + self.object = self.object_list[random.randrange(0, 8, 1)] # in case of randomly selected target + else: + self.object = self.object_list[self.target_id] # target + + self.init_object_qpos = np.array([1, 0.87, 0.2, 1, 0, 0, 0]) + + assert self.target_position in ['ignore', 'fixed', 'random'] + assert self.target_rotation in ['ignore', 'fixed', 'xyz', 'z', 'parallel'] + + hand_env.HandEnv.__init__( + self, model_path, n_substeps=n_substeps, initial_qpos=initial_qpos, + relative_control=relative_control) + utils.EzPickle.__init__(self) + + def _get_achieved_goal(self): + # Object position and rotation. + object_qpos = self.sim.data.get_joint_qpos(self.object) + assert object_qpos.shape == (7,) + return object_qpos + + # def _randamize_target(self): + # self.sim.data.set_joint_qpos("target0:joint", [1, 0.87, 0.4, 1, 0, 0, 0]) + # # print("##### {} #####".format(self.sim.data.get_joint_qpos("target0:joint"))) + + def _goal_distance(self, goal_a, goal_b): + assert goal_a.shape == goal_b.shape + assert goal_a.shape[-1] == 7 + + d_pos = np.zeros_like(goal_a[..., 0]) + d_rot = np.zeros_like(goal_b[..., 0]) + if self.target_position != 'ignore': + delta_pos = goal_a[..., :3] - goal_b[..., :3] + d_pos = np.linalg.norm(delta_pos, axis=-1) + + if self.target_rotation != 'ignore': + quat_a, quat_b = goal_a[..., 3:], goal_b[..., 3:] + + if self.ignore_z_target_rotation: + # Special case: We want to ignore the Z component of the rotation. + # This code here assumes Euler angles with xyz convention. We first transform + # to euler, then set the Z component to be equal between the two, and finally + # transform back into quaternions. + euler_a = rotations.quat2euler(quat_a) + euler_b = rotations.quat2euler(quat_b) + euler_a[2] = euler_b[2] + quat_a = rotations.euler2quat(euler_a) + + # Subtract quaternions and extract angle between them. + quat_diff = rotations.quat_mul(quat_a, rotations.quat_conjugate(quat_b)) + angle_diff = 2 * np.arccos(np.clip(quat_diff[..., 0], -1., 1.)) + d_rot = angle_diff + assert d_pos.shape == d_rot.shape + return d_pos, d_rot + + # GoalEnv methods + # ---------------------------- + + def compute_reward(self, achieved_goal, goal, info): + if self.reward_type == 'sparse': + success = self._is_success(achieved_goal, goal).astype(np.float32) + return (success - 1.) + else: + d_pos, d_rot = self._goal_distance(achieved_goal, goal) + # We weigh the difference in position to avoid that `d_pos` (in meters) is completely + # dominated by `d_rot` (in radians). + + # -- nishimura + #reward = -(10. * d_pos) # d_pos : distance_error + reward = self._is_success(achieved_goal, goal)-1. # default + # -- + + # -- reward Contributed rate + if len(self.variance_ratio) > 0: + vr = self.variance_ratio[-1] + l = np.sum(vr[:(self.num_axis)]) + self.variance_ratio = [] + + reward -= self.reward_lambda*(1.-l) # nishimura + # -- + + return reward + + # RobotEnv methods + # ---------------------------- + + def _is_success(self, achieved_goal, desired_goal): + d_pos, d_rot = self._goal_distance(achieved_goal, desired_goal) + achieved_pos = (d_pos < self.distance_threshold).astype(np.float32) + achieved_rot = (d_rot < self.rotation_threshold).astype(np.float32) + achieved_both = achieved_pos * achieved_rot + return achieved_both + + def _env_setup(self, initial_qpos): + for name, value in initial_qpos.items(): + self.sim.data.set_joint_qpos(name, value) + self.sim.forward() + + def _reset_sim(self): + self.sim.set_state(self.initial_state) + self.sim.forward() + + # -- motoda + if self.randomize_object == True: + self.object = self.object_list[random.randrange(0, 8, 1)] # in case of randomly selected target + else: + self.object = self.object_list[self.target_id] # target + # -- + initial_qpos = self.init_object_qpos + initial_pos, initial_quat = initial_qpos[:3], initial_qpos[3:] + assert initial_qpos.shape == (7,) + assert initial_pos.shape == (3,) + assert initial_quat.shape == (4,) + initial_qpos = None + + # Randomization initial rotation. + if self.randomize_initial_rotation: + if self.target_rotation == 'z': + angle = self.np_random.uniform(-np.pi, np.pi) + axis = np.array([0., 0., 1.]) + offset_quat = quat_from_angle_and_axis(angle, axis) + initial_quat = rotations.quat_mul(initial_quat, offset_quat) + elif self.target_rotation == 'parallel': + angle = self.np_random.uniform(-np.pi, np.pi) + axis = np.array([0., 0., 1.]) + z_quat = quat_from_angle_and_axis(angle, axis) + parallel_quat = self.parallel_quats[self.np_random.randint(len(self.parallel_quats))] + offset_quat = rotations.quat_mul(z_quat, parallel_quat) + initial_quat = rotations.quat_mul(initial_quat, offset_quat) + elif self.target_rotation in ['xyz', 'ignore']: + angle = self.np_random.uniform(-np.pi, np.pi) + axis = self.np_random.uniform(-1., 1., size=3) + offset_quat = quat_from_angle_and_axis(angle, axis) + initial_quat = rotations.quat_mul(initial_quat, offset_quat) + elif self.target_rotation == 'fixed': + pass + else: + raise error.Error('Unknown target_rotation option "{}".'.format(self.target_rotation)) + + # Randomize initial position. + if self.randomize_initial_position: + if self.target_position != 'fixed': + initial_pos += self.np_random.normal(size=3, scale=0.005) + + initial_quat /= np.linalg.norm(initial_quat) + initial_qpos = np.concatenate([initial_pos, initial_quat]) + self.sim.data.set_joint_qpos(self.object, initial_qpos) + + def is_on_palm(): + self.sim.forward() + cube_middle_idx = self.sim.model.site_name2id('object:center') + cube_middle_pos = self.sim.data.site_xpos[cube_middle_idx] + is_on_palm = (cube_middle_pos[2] > 0.04) + return is_on_palm + + # Run the simulation for a bunch of timesteps to let everything settle in. + for _ in range(10): + self._set_action(np.zeros(21)) + try: + self.sim.step() + except mujoco_py.MujocoException: + return False + return is_on_palm() + + def _sample_goal(self): + # Select a goal for the object position. + target_pos = None + if self.target_position == 'random': + assert self.target_position_range.shape == (3, 2) + offset = self.np_random.uniform(self.target_position_range[:, 0], self.target_position_range[:, 1]) + assert offset.shape == (3,) + target_pos = self.sim.data.get_joint_qpos(self.object)[:3] + offset + elif self.target_position in ['ignore', 'fixed']: + target_pos = self.sim.data.get_joint_qpos(self.object)[:3] + else: + raise error.Error('Unknown target_position option "{}".'.format(self.target_position)) + assert target_pos is not None + assert target_pos.shape == (3,) + + # Select a goal for the object rotation. + target_quat = None + if self.target_rotation == 'z': + angle = self.np_random.uniform(-np.pi, np.pi) + axis = np.array([0., 0., 1.]) + target_quat = quat_from_angle_and_axis(angle, axis) + elif self.target_rotation == 'parallel': + angle = self.np_random.uniform(-np.pi, np.pi) + axis = np.array([0., 0., 1.]) + target_quat = quat_from_angle_and_axis(angle, axis) + parallel_quat = self.parallel_quats[self.np_random.randint(len(self.parallel_quats))] + target_quat = rotations.quat_mul(target_quat, parallel_quat) + elif self.target_rotation == 'xyz': + angle = self.np_random.uniform(-np.pi, np.pi) + axis = self.np_random.uniform(-1., 1., size=3) + target_quat = quat_from_angle_and_axis(angle, axis) + elif self.target_rotation in ['ignore', 'fixed']: + target_quat = self.sim.data.get_joint_qpos(self.object) + else: + raise error.Error('Unknown target_rotation option "{}".'.format(self.target_rotation)) + assert target_quat is not None + assert target_quat.shape == (4,) + + target_quat /= np.linalg.norm(target_quat) # normalized quaternion + goal = np.concatenate([target_pos, target_quat]) + return goal + + def _render_callback(self): + # Assign current state to target object but offset a bit so that the actual object + # is not obscured. + goal = self.goal.copy() + assert goal.shape == (7,) + if self.target_position == 'ignore': + # Move the object to the side since we do not care about it's position. + goal[0] += 0.15 + self.sim.data.set_joint_qpos('target:joint', goal) + self.sim.data.set_joint_qvel('target:joint', np.zeros(6)) + + if 'object_hidden' in self.sim.model.geom_names: + hidden_id = self.sim.model.geom_name2id('object_hidden') + self.sim.model.geom_rgba[hidden_id, 3] = 1. + self.sim.forward() + + def _get_obs(self): + robot_qpos, robot_qvel = robot_get_obs(self.sim) + object_qvel = self.sim.data.get_joint_qvel(self.object) + achieved_goal = self._get_achieved_goal().ravel() # this contains the object position + rotation + observation = np.concatenate([robot_qpos, robot_qvel, object_qvel, achieved_goal]) + return { + 'observation': observation.copy(), + 'achieved_goal': achieved_goal.copy(), + 'desired_goal': self.goal.ravel().copy(), + } + + +class HandBlockEnv(ManipulateEnv): + def __init__(self, target_position='random', target_rotation='xyz', reward_type='sparse'): + super(HandBlockEnv, self).__init__( + model_path=MANIPULATE_BLOCK_XML, target_position=target_position, + target_rotation=target_rotation, + target_position_range=np.array([(-0.04, 0.04), (-0.06, 0.02), (0.0, 0.06)]), + reward_type=reward_type) + + +class HandEggEnv(ManipulateEnv): + def __init__(self, target_position='random', target_rotation='xyz', reward_type='sparse'): + super(HandEggEnv, self).__init__( + model_path=MANIPULATE_EGG_XML, target_position=target_position, + target_rotation=target_rotation, + target_position_range=np.array([(-0.04, 0.04), (-0.06, 0.02), (0.0, 0.06)]), + reward_type=reward_type) + + +class HandPenEnv(ManipulateEnv): + def __init__(self, target_position='random', target_rotation='xyz', reward_type='sparse'): + super(HandPenEnv, self).__init__( + model_path=MANIPULATE_PEN_XML, target_position=target_position, + target_rotation=target_rotation, + target_position_range=np.array([(-0.04, 0.04), (-0.06, 0.02), (0.0, 0.06)]), + randomize_initial_rotation=False, reward_type=reward_type, + ignore_z_target_rotation=True, distance_threshold=0.05) + + +class GraspBlockEnv(ManipulateEnv): + def __init__(self, target_position='random', target_rotation='xyz', reward_type=None): + super(GraspBlockEnv, self).__init__( + model_path=GRASP_BLOCK_XML, target_position=target_position, + target_rotation=target_rotation, + target_position_range=np.array([(-0.025, 0.025), (-0.025, 0.025), (0.2, 0.25)]), + randomize_initial_position=False, reward_type=reward_type, + distance_threshold=0.05, + rotation_threshold=100.0, + randomize_object=False ,target_id = 0, num_axis = 5 + ) +''' +Object_list: + self.object_list = ["box:joint", "apple:joint", "banana:joint", "beerbottle:joint", "book:joint", + "needle:joint", "pen:joint", "teacup:joint"] +''' diff --git a/gym-grasp/gym_grasp/envs/hand/grasp_env.py b/gym-grasp/gym_grasp/envs/hand/grasp_env.py new file mode 100644 index 0000000000..89823864b2 --- /dev/null +++ b/gym-grasp/gym_grasp/envs/hand/grasp_env.py @@ -0,0 +1,27 @@ +#!/usr/bin/env python3 +""" +Displays robot fetch at a disco party. +""" +from mujoco_py import load_model_from_path, MjSim, MjViewer +import math +import os + +model = load_model_from_path("../assets/hand/grasp_block.xml") +sim = MjSim(model) + +viewer = MjViewer(sim) + +t = 0 + +while True: + viewer.render() + t += 1 + sim.step() + state = sim.get_state() + + state.qpos[1] = 0.1*math.sin(0.01*t) + state.qpos[0] = 0.05*math.cos(0.01*t) + + sim.set_state(state) + # if t > 100 and os.getenv('TESTING') is not None: + # break diff --git a/gym-grasp/gym_grasp/envs/hand/manipulate.py b/gym-grasp/gym_grasp/envs/hand/manipulate.py new file mode 100644 index 0000000000..de55f34827 --- /dev/null +++ b/gym-grasp/gym_grasp/envs/hand/manipulate.py @@ -0,0 +1,299 @@ +import os +import numpy as np + +from gym import utils, error +from gym.envs.robotics import rotations, hand_env +from gym.envs.robotics.utils import robot_get_obs + +try: + import mujoco_py +except ImportError as e: + raise error.DependencyNotInstalled("{}. (HINT: you need to install mujoco_py, and also perform the setup instructions here: https://github.com/openai/mujoco-py/.)".format(e)) + + +def quat_from_angle_and_axis(angle, axis): + assert axis.shape == (3,) + axis /= np.linalg.norm(axis) + quat = np.concatenate([[np.cos(angle / 2.)], np.sin(angle / 2.) * axis]) + quat /= np.linalg.norm(quat) + return quat + + +# Ensure we get the path separator correct on windows +MANIPULATE_BLOCK_XML = os.path.join('hand', 'manipulate_block.xml') +MANIPULATE_EGG_XML = os.path.join('hand', 'manipulate_egg.xml') +MANIPULATE_PEN_XML = os.path.join('hand', 'manipulate_pen.xml') + + +class ManipulateEnv(hand_env.HandEnv, utils.EzPickle): + def __init__( + self, model_path, target_position, target_rotation, + target_position_range, reward_type, initial_qpos={}, + randomize_initial_position=True, randomize_initial_rotation=True, + distance_threshold=0.01, rotation_threshold=0.1, n_substeps=20, relative_control=False, + ignore_z_target_rotation=False, + ): + """Initializes a new Hand manipulation environment. + + Args: + model_path (string): path to the environments XML file + target_position (string): the type of target position: + - ignore: target position is fully ignored, i.e. the object can be positioned arbitrarily + - fixed: target position is set to the initial position of the object + - random: target position is fully randomized according to target_position_range + target_rotation (string): the type of target rotation: + - ignore: target rotation is fully ignored, i.e. the object can be rotated arbitrarily + - fixed: target rotation is set to the initial rotation of the object + - xyz: fully randomized target rotation around the X, Y and Z axis + - z: fully randomized target rotation around the Z axis + - parallel: fully randomized target rotation around Z and axis-aligned rotation around X, Y + ignore_z_target_rotation (boolean): whether or not the Z axis of the target rotation is ignored + target_position_range (np.array of shape (3, 2)): range of the target_position randomization + reward_type ('sparse' or 'dense'): the reward type, i.e. sparse or dense + initial_qpos (dict): a dictionary of joint names and values that define the initial configuration + randomize_initial_position (boolean): whether or not to randomize the initial position of the object + randomize_initial_rotation (boolean): whether or not to randomize the initial rotation of the object + distance_threshold (float, in meters): the threshold after which the position of a goal is considered achieved + rotation_threshold (float, in radians): the threshold after which the rotation of a goal is considered achieved + n_substeps (int): number of substeps the simulation runs on every call to step + relative_control (boolean): whether or not the hand is actuated in absolute joint positions or relative to the current state + """ + self.target_position = target_position + self.target_rotation = target_rotation + self.target_position_range = target_position_range + self.parallel_quats = [rotations.euler2quat(r) for r in rotations.get_parallel_rotations()] + self.randomize_initial_rotation = randomize_initial_rotation + self.randomize_initial_position = randomize_initial_position + self.distance_threshold = distance_threshold + self.rotation_threshold = rotation_threshold + self.reward_type = reward_type + self.ignore_z_target_rotation = ignore_z_target_rotation + + assert self.target_position in ['ignore', 'fixed', 'random'] + assert self.target_rotation in ['ignore', 'fixed', 'xyz', 'z', 'parallel'] + + hand_env.HandEnv.__init__( + self, model_path, n_substeps=n_substeps, initial_qpos=initial_qpos, + relative_control=relative_control) + utils.EzPickle.__init__(self) + + def _get_achieved_goal(self): + # Object position and rotation. + object_qpos = self.sim.data.get_joint_qpos('object:joint') + assert object_qpos.shape == (7,) + return object_qpos + + def _goal_distance(self, goal_a, goal_b): + assert goal_a.shape == goal_b.shape + assert goal_a.shape[-1] == 7 + + d_pos = np.zeros_like(goal_a[..., 0]) + d_rot = np.zeros_like(goal_b[..., 0]) + if self.target_position != 'ignore': + delta_pos = goal_a[..., :3] - goal_b[..., :3] + d_pos = np.linalg.norm(delta_pos, axis=-1) + + if self.target_rotation != 'ignore': + quat_a, quat_b = goal_a[..., 3:], goal_b[..., 3:] + + if self.ignore_z_target_rotation: + # Special case: We want to ignore the Z component of the rotation. + # This code here assumes Euler angles with xyz convention. We first transform + # to euler, then set the Z component to be equal between the two, and finally + # transform back into quaternions. + euler_a = rotations.quat2euler(quat_a) + euler_b = rotations.quat2euler(quat_b) + euler_a[2] = euler_b[2] + quat_a = rotations.euler2quat(euler_a) + + # Subtract quaternions and extract angle between them. + quat_diff = rotations.quat_mul(quat_a, rotations.quat_conjugate(quat_b)) + angle_diff = 2 * np.arccos(np.clip(quat_diff[..., 0], -1., 1.)) + d_rot = angle_diff + assert d_pos.shape == d_rot.shape + return d_pos, d_rot + + # GoalEnv methods + # ---------------------------- + + def compute_reward(self, achieved_goal, goal, info): + if self.reward_type == 'sparse': + success = self._is_success(achieved_goal, goal).astype(np.float32) + return (success - 1.) + else: + d_pos, d_rot = self._goal_distance(achieved_goal, goal) + # We weigh the difference in position to avoid that `d_pos` (in meters) is completely + # dominated by `d_rot` (in radians). + return -(10. * d_pos + d_rot) + + # RobotEnv methods + # ---------------------------- + + def _is_success(self, achieved_goal, desired_goal): + d_pos, d_rot = self._goal_distance(achieved_goal, desired_goal) + achieved_pos = (d_pos < self.distance_threshold).astype(np.float32) + achieved_rot = (d_rot < self.rotation_threshold).astype(np.float32) + achieved_both = achieved_pos * achieved_rot + return achieved_both + + def _env_setup(self, initial_qpos): + for name, value in initial_qpos.items(): + self.sim.data.set_joint_qpos(name, value) + self.sim.forward() + + def _reset_sim(self): + self.sim.set_state(self.initial_state) + self.sim.forward() + + initial_qpos = self.sim.data.get_joint_qpos('object:joint').copy() + initial_pos, initial_quat = initial_qpos[:3], initial_qpos[3:] + assert initial_qpos.shape == (7,) + assert initial_pos.shape == (3,) + assert initial_quat.shape == (4,) + initial_qpos = None + + # Randomization initial rotation. + if self.randomize_initial_rotation: + if self.target_rotation == 'z': + angle = self.np_random.uniform(-np.pi, np.pi) + axis = np.array([0., 0., 1.]) + offset_quat = quat_from_angle_and_axis(angle, axis) + initial_quat = rotations.quat_mul(initial_quat, offset_quat) + elif self.target_rotation == 'parallel': + angle = self.np_random.uniform(-np.pi, np.pi) + axis = np.array([0., 0., 1.]) + z_quat = quat_from_angle_and_axis(angle, axis) + parallel_quat = self.parallel_quats[self.np_random.randint(len(self.parallel_quats))] + offset_quat = rotations.quat_mul(z_quat, parallel_quat) + initial_quat = rotations.quat_mul(initial_quat, offset_quat) + elif self.target_rotation in ['xyz', 'ignore']: + angle = self.np_random.uniform(-np.pi, np.pi) + axis = self.np_random.uniform(-1., 1., size=3) + offset_quat = quat_from_angle_and_axis(angle, axis) + initial_quat = rotations.quat_mul(initial_quat, offset_quat) + elif self.target_rotation == 'fixed': + pass + else: + raise error.Error('Unknown target_rotation option "{}".'.format(self.target_rotation)) + + # Randomize initial position. + if self.randomize_initial_position: + if self.target_position != 'fixed': + initial_pos += self.np_random.normal(size=3, scale=0.005) + + initial_quat /= np.linalg.norm(initial_quat) + initial_qpos = np.concatenate([initial_pos, initial_quat]) + self.sim.data.set_joint_qpos('object:joint', initial_qpos) + + def is_on_palm(): + self.sim.forward() + cube_middle_idx = self.sim.model.site_name2id('object:center') + cube_middle_pos = self.sim.data.site_xpos[cube_middle_idx] + is_on_palm = (cube_middle_pos[2] > 0.04) + return is_on_palm + + # Run the simulation for a bunch of timesteps to let everything settle in. + for _ in range(10): + self._set_action(np.zeros(20)) + try: + self.sim.step() + except mujoco_py.MujocoException: + return False + return is_on_palm() + + def _sample_goal(self): + # Select a goal for the object position. + target_pos = None + if self.target_position == 'random': + assert self.target_position_range.shape == (3, 2) + offset = self.np_random.uniform(self.target_position_range[:, 0], self.target_position_range[:, 1]) + assert offset.shape == (3,) + target_pos = self.sim.data.get_joint_qpos('object:joint')[:3] + offset + elif self.target_position in ['ignore', 'fixed']: + target_pos = self.sim.data.get_joint_qpos('object:joint')[:3] + else: + raise error.Error('Unknown target_position option "{}".'.format(self.target_position)) + assert target_pos is not None + assert target_pos.shape == (3,) + + # Select a goal for the object rotation. + target_quat = None + if self.target_rotation == 'z': + angle = self.np_random.uniform(-np.pi, np.pi) + axis = np.array([0., 0., 1.]) + target_quat = quat_from_angle_and_axis(angle, axis) + elif self.target_rotation == 'parallel': + angle = self.np_random.uniform(-np.pi, np.pi) + axis = np.array([0., 0., 1.]) + target_quat = quat_from_angle_and_axis(angle, axis) + parallel_quat = self.parallel_quats[self.np_random.randint(len(self.parallel_quats))] + target_quat = rotations.quat_mul(target_quat, parallel_quat) + elif self.target_rotation == 'xyz': + angle = self.np_random.uniform(-np.pi, np.pi) + axis = self.np_random.uniform(-1., 1., size=3) + target_quat = quat_from_angle_and_axis(angle, axis) + elif self.target_rotation in ['ignore', 'fixed']: + target_quat = self.sim.data.get_joint_qpos('object:joint') + else: + raise error.Error('Unknown target_rotation option "{}".'.format(self.target_rotation)) + assert target_quat is not None + assert target_quat.shape == (4,) + + target_quat /= np.linalg.norm(target_quat) # normalized quaternion + goal = np.concatenate([target_pos, target_quat]) + return goal + + def _render_callback(self): + # Assign current state to target object but offset a bit so that the actual object + # is not obscured. + goal = self.goal.copy() + assert goal.shape == (7,) + if self.target_position == 'ignore': + # Move the object to the side since we do not care about it's position. + goal[0] += 0.15 + self.sim.data.set_joint_qpos('target:joint', goal) + self.sim.data.set_joint_qvel('target:joint', np.zeros(6)) + + if 'object_hidden' in self.sim.model.geom_names: + hidden_id = self.sim.model.geom_name2id('object_hidden') + self.sim.model.geom_rgba[hidden_id, 3] = 1. + self.sim.forward() + + def _get_obs(self): + robot_qpos, robot_qvel = robot_get_obs(self.sim) + object_qvel = self.sim.data.get_joint_qvel('object:joint') + achieved_goal = self._get_achieved_goal().ravel() # this contains the object position + rotation + observation = np.concatenate([robot_qpos, robot_qvel, object_qvel, achieved_goal]) + return { + 'observation': observation.copy(), + 'achieved_goal': achieved_goal.copy(), + 'desired_goal': self.goal.ravel().copy(), + } + + +class HandBlockEnv(ManipulateEnv): + def __init__(self, target_position='random', target_rotation='xyz', reward_type='sparse'): + super(HandBlockEnv, self).__init__( + model_path=MANIPULATE_BLOCK_XML, target_position=target_position, + target_rotation=target_rotation, + target_position_range=np.array([(-0.04, 0.04), (-0.06, 0.02), (0.0, 0.06)]), + reward_type=reward_type) + + +class HandEggEnv(ManipulateEnv): + def __init__(self, target_position='random', target_rotation='xyz', reward_type='sparse'): + super(HandEggEnv, self).__init__( + model_path=MANIPULATE_EGG_XML, target_position=target_position, + target_rotation=target_rotation, + target_position_range=np.array([(-0.04, 0.04), (-0.06, 0.02), (0.0, 0.06)]), + reward_type=reward_type) + + +class HandPenEnv(ManipulateEnv): + def __init__(self, target_position='random', target_rotation='xyz', reward_type='sparse'): + super(HandPenEnv, self).__init__( + model_path=MANIPULATE_PEN_XML, target_position=target_position, + target_rotation=target_rotation, + target_position_range=np.array([(-0.04, 0.04), (-0.06, 0.02), (0.0, 0.06)]), + randomize_initial_rotation=False, reward_type=reward_type, + ignore_z_target_rotation=True, distance_threshold=0.05) diff --git a/gym-grasp/gym_grasp/envs/hand/reach.py b/gym-grasp/gym_grasp/envs/hand/reach.py new file mode 100644 index 0000000000..81ed9f9540 --- /dev/null +++ b/gym-grasp/gym_grasp/envs/hand/reach.py @@ -0,0 +1,149 @@ +import os +import numpy as np + +from gym import utils +from gym.envs.robotics import hand_env +from gym.envs.robotics.utils import robot_get_obs + + +FINGERTIP_SITE_NAMES = [ + 'robot0:S_fftip', + 'robot0:S_mftip', + 'robot0:S_rftip', + 'robot0:S_lftip', + 'robot0:S_thtip', +] + + +DEFAULT_INITIAL_QPOS = { + 'robot0:WRJ1': -0.16514339750464327, + 'robot0:WRJ0': -0.31973286565062153, + 'robot0:FFJ3': 0.14340512546557435, + 'robot0:FFJ2': 0.32028208333591573, + 'robot0:FFJ1': 0.7126053607727917, + 'robot0:FFJ0': 0.6705281001412586, + 'robot0:MFJ3': 0.000246444303701037, + 'robot0:MFJ2': 0.3152655251085491, + 'robot0:MFJ1': 0.7659800313729842, + 'robot0:MFJ0': 0.7323156897425923, + 'robot0:RFJ3': 0.00038520700007378114, + 'robot0:RFJ2': 0.36743546201985233, + 'robot0:RFJ1': 0.7119514095008576, + 'robot0:RFJ0': 0.6699446327514138, + 'robot0:LFJ4': 0.0525442258033891, + 'robot0:LFJ3': -0.13615534724474673, + 'robot0:LFJ2': 0.39872030433433003, + 'robot0:LFJ1': 0.7415570009679252, + 'robot0:LFJ0': 0.704096378652974, + 'robot0:THJ4': 0.003673823825070126, + 'robot0:THJ3': 0.5506291436028695, + 'robot0:THJ2': -0.014515151997119306, + 'robot0:THJ1': -0.0015229223564485414, + 'robot0:THJ0': -0.7894883021600622, +} + + +# Ensure we get the path separator correct on windows +MODEL_XML_PATH = os.path.join('hand', 'reach.xml') + + +def goal_distance(goal_a, goal_b): + assert goal_a.shape == goal_b.shape + return np.linalg.norm(goal_a - goal_b, axis=-1) + + +class HandReachEnv(hand_env.HandEnv, utils.EzPickle): + def __init__( + self, distance_threshold=0.01, n_substeps=20, relative_control=False, + initial_qpos=DEFAULT_INITIAL_QPOS, reward_type='sparse', + ): + self.distance_threshold = distance_threshold + self.reward_type = reward_type + + hand_env.HandEnv.__init__( + self, MODEL_XML_PATH, n_substeps=n_substeps, initial_qpos=initial_qpos, + relative_control=relative_control) + utils.EzPickle.__init__(self) + + def _get_achieved_goal(self): + goal = [self.sim.data.get_site_xpos(name) for name in FINGERTIP_SITE_NAMES] + return np.array(goal).flatten() + + # GoalEnv methods + # ---------------------------- + + def compute_reward(self, achieved_goal, goal, info): + d = goal_distance(achieved_goal, goal) + if self.reward_type == 'sparse': + return -(d > self.distance_threshold).astype(np.float32) + else: + return -d + + # RobotEnv methods + # ---------------------------- + + def _env_setup(self, initial_qpos): + for name, value in initial_qpos.items(): + self.sim.data.set_joint_qpos(name, value) + self.sim.forward() + + self.initial_goal = self._get_achieved_goal().copy() + self.palm_xpos = self.sim.data.body_xpos[self.sim.model.body_name2id('robot0:palm')].copy() + + def _get_obs(self): + robot_qpos, robot_qvel = robot_get_obs(self.sim) + achieved_goal = self._get_achieved_goal().ravel() + observation = np.concatenate([robot_qpos, robot_qvel, achieved_goal]) + return { + 'observation': observation.copy(), + 'achieved_goal': achieved_goal.copy(), + 'desired_goal': self.goal.copy(), + } + + def _sample_goal(self): + thumb_name = 'robot0:S_thtip' + finger_names = [name for name in FINGERTIP_SITE_NAMES if name != thumb_name] + finger_name = self.np_random.choice(finger_names) + + thumb_idx = FINGERTIP_SITE_NAMES.index(thumb_name) + finger_idx = FINGERTIP_SITE_NAMES.index(finger_name) + assert thumb_idx != finger_idx + + # Pick a meeting point above the hand. + meeting_pos = self.palm_xpos + np.array([0.0, -0.09, 0.05]) + meeting_pos += self.np_random.normal(scale=0.005, size=meeting_pos.shape) + + # Slightly move meeting goal towards the respective finger to avoid that they + # overlap. + goal = self.initial_goal.copy().reshape(-1, 3) + for idx in [thumb_idx, finger_idx]: + offset_direction = (meeting_pos - goal[idx]) + offset_direction /= np.linalg.norm(offset_direction) + goal[idx] = meeting_pos - 0.005 * offset_direction + + if self.np_random.uniform() < 0.1: + # With some probability, ask all fingers to move back to the origin. + # This avoids that the thumb constantly stays near the goal position already. + goal = self.initial_goal.copy() + return goal.flatten() + + def _is_success(self, achieved_goal, desired_goal): + d = goal_distance(achieved_goal, desired_goal) + return (d < self.distance_threshold).astype(np.float32) + + def _render_callback(self): + # Visualize targets. + sites_offset = (self.sim.data.site_xpos - self.sim.model.site_pos).copy() + goal = self.goal.reshape(5, 3) + for finger_idx in range(5): + site_name = 'target{}'.format(finger_idx) + site_id = self.sim.model.site_name2id(site_name) + self.sim.model.site_pos[site_id] = goal[finger_idx] - sites_offset[site_id] + + # Visualize finger positions. + achieved_goal = self._get_achieved_goal().reshape(5, 3) + for finger_idx in range(5): + site_name = 'finger{}'.format(finger_idx) + site_id = self.sim.model.site_name2id(site_name) + self.sim.model.site_pos[site_id] = achieved_goal[finger_idx] - sites_offset[site_id] + self.sim.forward() diff --git a/gym-grasp/gym_grasp/envs/hand_env.py b/gym-grasp/gym_grasp/envs/hand_env.py new file mode 100644 index 0000000000..1de155e7ed --- /dev/null +++ b/gym-grasp/gym_grasp/envs/hand_env.py @@ -0,0 +1,49 @@ +import os +import copy +import numpy as np + +import gym +from gym import error, spaces +from gym.utils import seeding +from gym_grasp.envs import robot_env + + +class HandEnv(robot_env.RobotEnv): + def __init__(self, model_path, n_substeps, initial_qpos, relative_control): + self.relative_control = relative_control + + super(HandEnv, self).__init__( + model_path=model_path, n_substeps=n_substeps, n_actions=21, + initial_qpos=initial_qpos) + + # RobotEnv methods + # ---------------------------- + + def _set_action(self, action): + assert action.shape == (21,) + + ctrlrange = self.sim.model.actuator_ctrlrange + actuation_range = (ctrlrange[:, 1] - ctrlrange[:, 0]) / 2. + if self.relative_control: + actuation_center = np.zeros_like(action) + for i in range(self.sim.data.ctrl.shape[0]): + actuation_center[i] = self.sim.data.get_joint_qpos( + self.sim.model.actuator_names[i].replace(':A_', ':')) + for joint_name in ['FF', 'MF', 'RF', 'LF']: + act_idx = self.sim.model.actuator_name2id( + 'robot0:A_{}J1'.format(joint_name)) + actuation_center[act_idx] += self.sim.data.get_joint_qpos( + 'robot0:{}J0'.format(joint_name)) + else: + actuation_center = (ctrlrange[:, 1] + ctrlrange[:, 0]) / 2. + self.sim.data.ctrl[:] = actuation_center + action * actuation_range + self.sim.data.ctrl[:] = np.clip(self.sim.data.ctrl, ctrlrange[:, 0], ctrlrange[:, 1]) + + def _viewer_setup(self): + body_id = self.sim.model.body_name2id('robot0:palm') + lookat = self.sim.data.body_xpos[body_id] + for idx, value in enumerate(lookat): + self.viewer.cam.lookat[idx] = value + self.viewer.cam.distance = 0.5 + self.viewer.cam.azimuth = 55. + self.viewer.cam.elevation = -25. diff --git a/gym-grasp/gym_grasp/envs/robot_env.py b/gym-grasp/gym_grasp/envs/robot_env.py new file mode 100644 index 0000000000..6d0714026a --- /dev/null +++ b/gym-grasp/gym_grasp/envs/robot_env.py @@ -0,0 +1,162 @@ +import os +import copy +import numpy as np + +import gym +from gym import error, spaces +from gym.utils import seeding + +try: + import mujoco_py +except ImportError as e: + raise error.DependencyNotInstalled("{}. (HINT: you need to install mujoco_py, and also perform the setup instructions here: https://github.com/openai/mujoco-py/.)".format(e)) + + +class RobotEnv(gym.GoalEnv): + def __init__(self, model_path, initial_qpos, n_actions, n_substeps): + if model_path.startswith('/'): + fullpath = model_path + else: + fullpath = os.path.join(os.path.dirname(__file__), 'assets', model_path) + if not os.path.exists(fullpath): + raise IOError('File {} does not exist'.format(fullpath)) + + model = mujoco_py.load_model_from_path(fullpath) + self.sim = mujoco_py.MjSim(model, nsubsteps=n_substeps) + self.viewer = None + + self.metadata = { + 'render.modes': ['human', 'rgb_array'], + 'video.frames_per_second': int(np.round(1.0 / self.dt)) + } + + self.seed() + self._env_setup(initial_qpos=initial_qpos) + self.initial_state = copy.deepcopy(self.sim.get_state()) + + self.goal = self._sample_goal() + obs = self._get_obs() + self.action_space = spaces.Box(-1., 1., shape=(n_actions,), dtype='float32') + self.observation_space = spaces.Dict(dict( + desired_goal=spaces.Box(-np.inf, np.inf, shape=obs['achieved_goal'].shape, dtype='float32'), + achieved_goal=spaces.Box(-np.inf, np.inf, shape=obs['achieved_goal'].shape, dtype='float32'), + observation=spaces.Box(-np.inf, np.inf, shape=obs['observation'].shape, dtype='float32'), + )) + + @property + def dt(self): + return self.sim.model.opt.timestep * self.sim.nsubsteps + + # Env methods + # ---------------------------- + + def seed(self, seed=None): + self.np_random, seed = seeding.np_random(seed) + return [seed] + + def step(self, action): + action = np.clip(action, self.action_space.low, self.action_space.high) + self._set_action(action) + self.sim.step() + self._step_callback() + obs = self._get_obs() + + done = False + info = { + 'is_success': self._is_success(obs['achieved_goal'], self.goal), + } + reward = self.compute_reward(obs['achieved_goal'], self.goal, info) + return obs, reward, done, info + + def reset(self): + # Attempt to reset the simulator. Since we randomize initial conditions, it + # is possible to get into a state with numerical issues (e.g. due to penetration or + # Gimbel lock) or we may not achieve an initial condition (e.g. an object is within the hand). + # In this case, we just keep randomizing until we eventually achieve a valid initial + # configuration. + did_reset_sim = False + while not did_reset_sim: + did_reset_sim = self._reset_sim() + self.goal = self._sample_goal().copy() + obs = self._get_obs() + return obs + + def close(self): + if self.viewer is not None: + # self.viewer.finish() + self.viewer = None + + def render(self, mode='human'): + self._render_callback() + if mode == 'rgb_array': + self._get_viewer().render() + # window size used for old mujoco-py: + width, height = 500, 500 + data = self._get_viewer().read_pixels(width, height, depth=False) + # original image is upside-down, so flip it + return data[::-1, :, :] + elif mode == 'human': + self._get_viewer().render() + + def _get_viewer(self): + if self.viewer is None: + self.viewer = mujoco_py.MjViewer(self.sim) + self._viewer_setup() + return self.viewer + + # Extension methods + # ---------------------------- + + def _reset_sim(self): + """Resets a simulation and indicates whether or not it was successful. + If a reset was unsuccessful (e.g. if a randomized state caused an error in the + simulation), this method should indicate such a failure by returning False. + In such a case, this method will be called again to attempt a the reset again. + """ + self.sim.set_state(self.initial_state) + self.sim.forward() + return True + + def _get_obs(self): + """Returns the observation. + """ + raise NotImplementedError() + + def _set_action(self, action): + """Applies the given action to the simulation. + """ + raise NotImplementedError() + + def _is_success(self, achieved_goal, desired_goal): + """Indicates whether or not the achieved goal successfully achieved the desired goal. + """ + raise NotImplementedError() + + def _sample_goal(self): + """Samples a new goal and returns it. + """ + raise NotImplementedError() + + def _env_setup(self, initial_qpos): + """Initial configuration of the environment. Can be used to configure initial state + and extract information from the simulation. + """ + pass + + def _viewer_setup(self): + """Initial configuration of the viewer. Can be used to set the camera position, + for example. + """ + pass + + def _render_callback(self): + """A custom callback that is called before rendering. Can be used + to implement custom visualizations. + """ + pass + + def _step_callback(self): + """A custom callback that is called after stepping the simulation. Can be used + to enforce additional constraints on the simulation state. + """ + pass diff --git a/gym-grasp/gym_grasp/envs/rotations.py b/gym-grasp/gym_grasp/envs/rotations.py new file mode 100644 index 0000000000..4aafb64a08 --- /dev/null +++ b/gym-grasp/gym_grasp/envs/rotations.py @@ -0,0 +1,369 @@ +# Copyright (c) 2009-2017, Matthew Brett and Christoph Gohlke +# All rights reserved. +# +# Redistribution and use in source and binary forms, with or without +# modification, are permitted provided that the following conditions are +# met: +# +# 1. Redistributions of source code must retain the above copyright notice, +# this list of conditions and the following disclaimer. +# +# 2. Redistributions in binary form must reproduce the above copyright +# notice, this list of conditions and the following disclaimer in the +# documentation and/or other materials provided with the distribution. +# +# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS +# IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, +# THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR +# PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR +# CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, +# EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, +# PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR +# PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF +# LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING +# NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS +# SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. + +# Many methods borrow heavily or entirely from transforms3d: +# https://github.com/matthew-brett/transforms3d +# They have mostly been modified to support batched operations. + +import numpy as np +import itertools + +''' +Rotations +========= + +Note: these have caused many subtle bugs in the past. +Be careful while updating these methods and while using them in clever ways. + +See MuJoCo documentation here: http://mujoco.org/book/modeling.html#COrientation + +Conventions +----------- + - All functions accept batches as well as individual rotations + - All rotation conventions match respective MuJoCo defaults + - All angles are in radians + - Matricies follow LR convention + - Euler Angles are all relative with 'xyz' axes ordering + - See specific representation for more information + +Representations +--------------- + +Euler + There are many euler angle frames -- here we will strive to use the default + in MuJoCo, which is eulerseq='xyz'. + This frame is a relative rotating frame, about x, y, and z axes in order. + Relative rotating means that after we rotate about x, then we use the + new (rotated) y, and the same for z. + +Quaternions + These are defined in terms of rotation (angle) about a unit vector (x, y, z) + We use the following convention: + q0 = cos(angle / 2) + q1 = sin(angle / 2) * x + q2 = sin(angle / 2) * y + q3 = sin(angle / 2) * z + This is also sometimes called qw, qx, qy, qz. + Note that quaternions are ambiguous, because we can represent a rotation by + angle about vector and -angle about vector <-x, -y, -z>. + To choose between these, we pick "first nonzero positive", where we + make the first nonzero element of the quaternion positive. + This can result in mismatches if you're converting an quaternion that is not + "first nonzero positive" to a different representation and back. + +Axis Angle + (Not currently implemented) + These are very straightforward. Rotation is angle about a unit vector. + +XY Axes + (Not currently implemented) + We are given x axis and y axis, and z axis is cross product of x and y. + +Z Axis + This is NOT RECOMMENDED. Defines a unit vector for the Z axis, + but rotation about this axis is not well defined. + Instead pick a fixed reference direction for another axis (e.g. X) + and calculate the other (e.g. Y = Z cross-product X), + then use XY Axes rotation instead. + +SO3 + (Not currently implemented) + While not supported by MuJoCo, this representation has a lot of nice features. + We expect to add support for these in the future. + +TODO / Missing +-------------- + - Rotation integration or derivatives (e.g. velocity conversions) + - More representations (SO3, etc) + - Random sampling (e.g. sample uniform random rotation) + - Performance benchmarks/measurements + - (Maybe) define everything as to/from matricies, for simplicity +''' + +# For testing whether a number is close to zero +_FLOAT_EPS = np.finfo(np.float64).eps +_EPS4 = _FLOAT_EPS * 4.0 + + +def euler2mat(euler): + """ Convert Euler Angles to Rotation Matrix. See rotation.py for notes """ + euler = np.asarray(euler, dtype=np.float64) + assert euler.shape[-1] == 3, "Invalid shaped euler {}".format(euler) + + ai, aj, ak = -euler[..., 2], -euler[..., 1], -euler[..., 0] + si, sj, sk = np.sin(ai), np.sin(aj), np.sin(ak) + ci, cj, ck = np.cos(ai), np.cos(aj), np.cos(ak) + cc, cs = ci * ck, ci * sk + sc, ss = si * ck, si * sk + + mat = np.empty(euler.shape[:-1] + (3, 3), dtype=np.float64) + mat[..., 2, 2] = cj * ck + mat[..., 2, 1] = sj * sc - cs + mat[..., 2, 0] = sj * cc + ss + mat[..., 1, 2] = cj * sk + mat[..., 1, 1] = sj * ss + cc + mat[..., 1, 0] = sj * cs - sc + mat[..., 0, 2] = -sj + mat[..., 0, 1] = cj * si + mat[..., 0, 0] = cj * ci + return mat + + +def euler2quat(euler): + """ Convert Euler Angles to Quaternions. See rotation.py for notes """ + euler = np.asarray(euler, dtype=np.float64) + assert euler.shape[-1] == 3, "Invalid shape euler {}".format(euler) + + ai, aj, ak = euler[..., 2] / 2, -euler[..., 1] / 2, euler[..., 0] / 2 + si, sj, sk = np.sin(ai), np.sin(aj), np.sin(ak) + ci, cj, ck = np.cos(ai), np.cos(aj), np.cos(ak) + cc, cs = ci * ck, ci * sk + sc, ss = si * ck, si * sk + + quat = np.empty(euler.shape[:-1] + (4,), dtype=np.float64) + quat[..., 0] = cj * cc + sj * ss + quat[..., 3] = cj * sc - sj * cs + quat[..., 2] = -(cj * ss + sj * cc) + quat[..., 1] = cj * cs - sj * sc + return quat + + +def mat2euler(mat): + """ Convert Rotation Matrix to Euler Angles. See rotation.py for notes """ + mat = np.asarray(mat, dtype=np.float64) + assert mat.shape[-2:] == (3, 3), "Invalid shape matrix {}".format(mat) + + cy = np.sqrt(mat[..., 2, 2] * mat[..., 2, 2] + mat[..., 1, 2] * mat[..., 1, 2]) + condition = cy > _EPS4 + euler = np.empty(mat.shape[:-1], dtype=np.float64) + euler[..., 2] = np.where(condition, + -np.arctan2(mat[..., 0, 1], mat[..., 0, 0]), + -np.arctan2(-mat[..., 1, 0], mat[..., 1, 1])) + euler[..., 1] = np.where(condition, + -np.arctan2(-mat[..., 0, 2], cy), + -np.arctan2(-mat[..., 0, 2], cy)) + euler[..., 0] = np.where(condition, + -np.arctan2(mat[..., 1, 2], mat[..., 2, 2]), + 0.0) + return euler + + +def mat2quat(mat): + """ Convert Rotation Matrix to Quaternion. See rotation.py for notes """ + mat = np.asarray(mat, dtype=np.float64) + assert mat.shape[-2:] == (3, 3), "Invalid shape matrix {}".format(mat) + + Qxx, Qyx, Qzx = mat[..., 0, 0], mat[..., 0, 1], mat[..., 0, 2] + Qxy, Qyy, Qzy = mat[..., 1, 0], mat[..., 1, 1], mat[..., 1, 2] + Qxz, Qyz, Qzz = mat[..., 2, 0], mat[..., 2, 1], mat[..., 2, 2] + # Fill only lower half of symmetric matrix + K = np.zeros(mat.shape[:-2] + (4, 4), dtype=np.float64) + K[..., 0, 0] = Qxx - Qyy - Qzz + K[..., 1, 0] = Qyx + Qxy + K[..., 1, 1] = Qyy - Qxx - Qzz + K[..., 2, 0] = Qzx + Qxz + K[..., 2, 1] = Qzy + Qyz + K[..., 2, 2] = Qzz - Qxx - Qyy + K[..., 3, 0] = Qyz - Qzy + K[..., 3, 1] = Qzx - Qxz + K[..., 3, 2] = Qxy - Qyx + K[..., 3, 3] = Qxx + Qyy + Qzz + K /= 3.0 + # TODO: vectorize this -- probably could be made faster + q = np.empty(K.shape[:-2] + (4,)) + it = np.nditer(q[..., 0], flags=['multi_index']) + while not it.finished: + # Use Hermitian eigenvectors, values for speed + vals, vecs = np.linalg.eigh(K[it.multi_index]) + # Select largest eigenvector, reorder to w,x,y,z quaternion + q[it.multi_index] = vecs[[3, 0, 1, 2], np.argmax(vals)] + # Prefer quaternion with positive w + # (q * -1 corresponds to same rotation as q) + if q[it.multi_index][0] < 0: + q[it.multi_index] *= -1 + it.iternext() + return q + + +def quat2euler(quat): + """ Convert Quaternion to Euler Angles. See rotation.py for notes """ + return mat2euler(quat2mat(quat)) + + +def subtract_euler(e1, e2): + assert e1.shape == e2.shape + assert e1.shape[-1] == 3 + q1 = euler2quat(e1) + q2 = euler2quat(e2) + q_diff = quat_mul(q1, quat_conjugate(q2)) + return quat2euler(q_diff) + + +def quat2mat(quat): + """ Convert Quaternion to Euler Angles. See rotation.py for notes """ + quat = np.asarray(quat, dtype=np.float64) + assert quat.shape[-1] == 4, "Invalid shape quat {}".format(quat) + + w, x, y, z = quat[..., 0], quat[..., 1], quat[..., 2], quat[..., 3] + Nq = np.sum(quat * quat, axis=-1) + s = 2.0 / Nq + X, Y, Z = x * s, y * s, z * s + wX, wY, wZ = w * X, w * Y, w * Z + xX, xY, xZ = x * X, x * Y, x * Z + yY, yZ, zZ = y * Y, y * Z, z * Z + + mat = np.empty(quat.shape[:-1] + (3, 3), dtype=np.float64) + mat[..., 0, 0] = 1.0 - (yY + zZ) + mat[..., 0, 1] = xY - wZ + mat[..., 0, 2] = xZ + wY + mat[..., 1, 0] = xY + wZ + mat[..., 1, 1] = 1.0 - (xX + zZ) + mat[..., 1, 2] = yZ - wX + mat[..., 2, 0] = xZ - wY + mat[..., 2, 1] = yZ + wX + mat[..., 2, 2] = 1.0 - (xX + yY) + return np.where((Nq > _FLOAT_EPS)[..., np.newaxis, np.newaxis], mat, np.eye(3)) + +def quat_conjugate(q): + inv_q = -q + inv_q[..., 0] *= -1 + return inv_q + +def quat_mul(q0, q1): + assert q0.shape == q1.shape + assert q0.shape[-1] == 4 + assert q1.shape[-1] == 4 + + w0 = q0[..., 0] + x0 = q0[..., 1] + y0 = q0[..., 2] + z0 = q0[..., 3] + + w1 = q1[..., 0] + x1 = q1[..., 1] + y1 = q1[..., 2] + z1 = q1[..., 3] + + w = w0 * w1 - x0 * x1 - y0 * y1 - z0 * z1 + x = w0 * x1 + x0 * w1 + y0 * z1 - z0 * y1 + y = w0 * y1 + y0 * w1 + z0 * x1 - x0 * z1 + z = w0 * z1 + z0 * w1 + x0 * y1 - y0 * x1 + q = np.array([w, x, y, z]) + if q.ndim == 2: + q = q.swapaxes(0, 1) + assert q.shape == q0.shape + return q + +def quat_rot_vec(q, v0): + q_v0 = np.array([0, v0[0], v0[1], v0[2]]) + q_v = quat_mul(q, quat_mul(q_v0, quat_conjugate(q))) + v = q_v[1:] + return v + +def quat_identity(): + return np.array([1, 0, 0, 0]) + +def quat2axisangle(quat): + theta = 0; + axis = np.array([0, 0, 1]); + sin_theta = np.linalg.norm(quat[1:]) + + if (sin_theta > 0.0001): + theta = 2 * np.arcsin(sin_theta) + theta *= 1 if quat[0] >= 0 else -1 + axis = quat[1:] / sin_theta + + return axis, theta + +def euler2point_euler(euler): + _euler = euler.copy() + if len(_euler.shape) < 2: + _euler = np.expand_dims(_euler,0) + assert(_euler.shape[1] == 3) + _euler_sin = np.sin(_euler) + _euler_cos = np.cos(_euler) + return np.concatenate([_euler_sin, _euler_cos], axis=-1) + +def point_euler2euler(euler): + _euler = euler.copy() + if len(_euler.shape) < 2: + _euler = np.expand_dims(_euler,0) + assert(_euler.shape[1] == 6) + angle = np.arctan(_euler[..., :3] / _euler[..., 3:]) + angle[_euler[..., 3:] < 0] += np.pi + return angle + +def quat2point_quat(quat): + # Should be in qw, qx, qy, qz + _quat = quat.copy() + if len(_quat.shape) < 2: + _quat = np.expand_dims(_quat, 0) + assert(_quat.shape[1] == 4) + angle = np.arccos(_quat[:,[0]]) * 2 + xyz = _quat[:, 1:] + xyz[np.squeeze(np.abs(np.sin(angle/2))) >= 1e-5] = (xyz / np.sin(angle / 2))[np.squeeze(np.abs(np.sin(angle/2))) >= 1e-5] + return np.concatenate([np.sin(angle),np.cos(angle), xyz], axis=-1) + +def point_quat2quat(quat): + _quat = quat.copy() + if len(_quat.shape) < 2: + _quat = np.expand_dims(_quat, 0) + assert(_quat.shape[1] == 5) + angle = np.arctan(_quat[:,[0]] / _quat[:,[1]]) + qw = np.cos(angle / 2) + + qxyz = _quat[:, 2:] + qxyz[np.squeeze(np.abs(np.sin(angle/2))) >= 1e-5] = (qxyz * np.sin(angle/2))[np.squeeze(np.abs(np.sin(angle/2))) >= 1e-5] + return np.concatenate([qw, qxyz], axis=-1) + +def normalize_angles(angles): + '''Puts angles in [-pi, pi] range.''' + angles = angles.copy() + if angles.size > 0: + angles = (angles + np.pi) % (2 * np.pi) - np.pi + assert -np.pi-1e-6 <= angles.min() and angles.max() <= np.pi+1e-6 + return angles + +def round_to_straight_angles(angles): + '''Returns closest angle modulo 90 degrees ''' + angles = np.round(angles / (np.pi / 2)) * (np.pi / 2) + return normalize_angles(angles) + +def get_parallel_rotations(): + mult90 = [0, np.pi/2, -np.pi/2, np.pi] + parallel_rotations = [] + for euler in itertools.product(mult90, repeat=3): + canonical = mat2euler(euler2mat(euler)) + canonical = np.round(canonical / (np.pi / 2)) + if canonical[0] == -2: + canonical[0] = 2 + if canonical[2] == -2: + canonical[2] = 2 + canonical *= np.pi / 2 + if all([(canonical != rot).any() for rot in parallel_rotations]): + parallel_rotations += [canonical] + assert len(parallel_rotations) == 24 + return parallel_rotations diff --git a/gym-grasp/gym_grasp/envs/utils.py b/gym-grasp/gym_grasp/envs/utils.py new file mode 100644 index 0000000000..a73e5f6052 --- /dev/null +++ b/gym-grasp/gym_grasp/envs/utils.py @@ -0,0 +1,96 @@ +import numpy as np + +from gym import error +try: + import mujoco_py +except ImportError as e: + raise error.DependencyNotInstalled("{}. (HINT: you need to install mujoco_py, and also perform the setup instructions here: https://github.com/openai/mujoco-py/.)".format(e)) + + +def robot_get_obs(sim): + """Returns all joint positions and velocities associated with + a robot. + """ + if sim.data.qpos is not None and sim.model.joint_names: + names = [n for n in sim.model.joint_names if n.startswith('robot')] + return ( + np.array([sim.data.get_joint_qpos(name) for name in names]), + np.array([sim.data.get_joint_qvel(name) for name in names]), + ) + return np.zeros(0), np.zeros(0) + + +def ctrl_set_action(sim, action): + """For torque actuators it copies the action into mujoco ctrl field. + For position actuators it sets the target relative to the current qpos. + """ + if sim.model.nmocap > 0: + _, action = np.split(action, (sim.model.nmocap * 7, )) + if sim.data.ctrl is not None: + for i in range(action.shape[0]): + if sim.model.actuator_biastype[i] == 0: + sim.data.ctrl[i] = action[i] + else: + idx = sim.model.jnt_qposadr[sim.model.actuator_trnid[i, 0]] + sim.data.ctrl[i] = sim.data.qpos[idx] + action[i] + + +def mocap_set_action(sim, action): + """The action controls the robot using mocaps. Specifically, bodies + on the robot (for example the gripper wrist) is controlled with + mocap bodies. In this case the action is the desired difference + in position and orientation (quaternion), in world coordinates, + of the of the target body. The mocap is positioned relative to + the target body according to the delta, and the MuJoCo equality + constraint optimizer tries to center the welded body on the mocap. + """ + if sim.model.nmocap > 0: + action, _ = np.split(action, (sim.model.nmocap * 7, )) + action = action.reshape(sim.model.nmocap, 7) + + pos_delta = action[:, :3] + quat_delta = action[:, 3:] + + reset_mocap2body_xpos(sim) + sim.data.mocap_pos[:] = sim.data.mocap_pos + pos_delta + sim.data.mocap_quat[:] = sim.data.mocap_quat + quat_delta + + +def reset_mocap_welds(sim): + """Resets the mocap welds that we use for actuation. + """ + if sim.model.nmocap > 0 and sim.model.eq_data is not None: + for i in range(sim.model.eq_data.shape[0]): + if sim.model.eq_type[i] == mujoco_py.const.EQ_WELD: + sim.model.eq_data[i, :] = np.array( + [0., 0., 0., 1., 0., 0., 0.]) + sim.forward() + + +def reset_mocap2body_xpos(sim): + """Resets the position and orientation of the mocap bodies to the same + values as the bodies they're welded to. + """ + + if (sim.model.eq_type is None or + sim.model.eq_obj1id is None or + sim.model.eq_obj2id is None): + return + for eq_type, obj1_id, obj2_id in zip(sim.model.eq_type, + sim.model.eq_obj1id, + sim.model.eq_obj2id): + if eq_type != mujoco_py.const.EQ_WELD: + continue + + mocap_id = sim.model.body_mocapid[obj1_id] + if mocap_id != -1: + # obj1 is the mocap, obj2 is the welded body + body_idx = obj2_id + else: + # obj2 is the mocap, obj1 is the welded body + mocap_id = sim.model.body_mocapid[obj2_id] + body_idx = obj1_id + + assert (mocap_id != -1) + sim.data.mocap_pos[mocap_id][:] = sim.data.body_xpos[body_idx] + sim.data.mocap_quat[mocap_id][:] = sim.data.body_xquat[body_idx] diff --git a/gym-grasp/setup.py b/gym-grasp/setup.py new file mode 100644 index 0000000000..16314b2f17 --- /dev/null +++ b/gym-grasp/setup.py @@ -0,0 +1,10 @@ +from setuptools import setup + +setup(name='gym_grasp', + version='0.0.1', + install_requires=['gym>=0.2.3', + 'mujoco_py>=1.50'], + package_data={'gym_grasp' : [ + 'envs/assets/hand/*.xml' + ]} +) diff --git a/mujoco-py b/mujoco-py new file mode 160000 index 0000000000..54367d181b --- /dev/null +++ b/mujoco-py @@ -0,0 +1 @@ +Subproject commit 54367d181b4335b42a0f094274a07b21352af9f2 diff --git a/projection/Dockerfile b/projection/Dockerfile new file mode 100644 index 0000000000..ec19fa6e41 --- /dev/null +++ b/projection/Dockerfile @@ -0,0 +1,24 @@ +FROM chainer/chainer:v4.5.0-python3 +MAINTAINER Yoshimura Naoya + +# Emacsのインストール +RUN apt-get update +RUN apt-get install emacs24-nox -y + + +# Install Chainer +RUN pip3 install jupyter \ + && jupyter notebook --generate-config +RUN echo 'alias python=python3' >> ~/.bashrc \ + && echo 'alias pip=pip3' >> ~/.bashrc +RUN pip3 install --upgrade pip + +# Install Python Module +COPY requirements.txt /root +RUN pip install -r /root/requirements.txt + + + +# Finish +RUN mkdir /root/work +WORKDIR /root/work diff --git a/projection/MAKE_CONTAINER.sh b/projection/MAKE_CONTAINER.sh new file mode 100644 index 0000000000..14a99f8ce0 --- /dev/null +++ b/projection/MAKE_CONTAINER.sh @@ -0,0 +1,18 @@ +mode=$1 + +if [ ${mode} = 0 ]; +then + DIR_CODE="/home/yoshimura/code708/synergy" + DIR_DATA="/home/yoshimura/code708/dataStore" + NAME="synergy" + IMAGE="yoshimura/synergy:v4.5.0" + PORT_NOTE=7088 + PORT_TFB=7086 + + docker run --runtime=nvidia -e NVIDIA_VISIBLE_DEVICES=0 \ + -v ${DIR_CODE}:/root/work \ + -v ${DIR_DATA}:/root/dataStore \ + --name ${NAME} \ + -p ${PORT_NOTE}:8888 -p ${PORT_TFB}:6006 \ + -it ${IMAGE} jupyter notebook --allow-root --ip 0.0.0.0 +fi diff --git a/projection/README.md b/projection/README.md new file mode 100644 index 0000000000..da5f8fcc85 --- /dev/null +++ b/projection/README.md @@ -0,0 +1,58 @@ +# Projection Network with Chainer +中枢神経系 (action) からPre-moter Nueronへの投射を行うネットワークの学習. + + +## Installation +### Requirements ++ chainer ++ cupy + +### Docker Setup +Fist, build docker image with this command. + +``` +$ docker build -t synergy/chainer:v4.5.0 . +``` + +And then make container, + +``` +docker run --runtime=nvidia -e NVIDIA_VISIBLE_DEVICES=${GPU} \ + -v ${DIR_code}:/root/work \ + -v ${DIR_DATA}:/root/dataStore \ + --name ${NAME} \ + -p ${PORT_NOTE} -p ${PORT_TFB} \ + -it ${IMAGE} jupyter notebook --allow-root --ip 0.0.0.0 +``` + +## Data Preparation +### Resampling +Use `utils/make_inputs.py`. After this, split data for`train/val/test` by yourself. + + +``` +python3 make_inputs.py \ + --path-in /root/dataStore/grasp_v1/episodes \ + --path-out /root/dataStore/grasp_v1/Inputs +``` + +## Training +note: Set correct paths! + + +``` +python3 run.py TRAIN \ + --path-data-train /root/dataStore/grasp_v1/Inputs/train \ + --path-data-val /root/dataStore/grasp_v1/Inputs/val \ + --path-model /root/dataStore/grasp_v1/Log/ChainerDenseNet.model \ + --path-log /root/dataStore/grasp_v1/Log/ \ + --gpu 0 \ + --batch-size 64 \ + --epoch 10 + +``` + + + +## Prediction (Generation) +TBA diff --git a/projection/dataset/default.py b/projection/dataset/default.py new file mode 100644 index 0000000000..c38028e2c6 --- /dev/null +++ b/projection/dataset/default.py @@ -0,0 +1,50 @@ +import os +import numpy as np +import h5py + + +from logging import getLogger, basicConfig, DEBUG +logger = getLogger(__name__) + +# Chainer +import chainer + +# --------------------------------------------------------- +class DefaultDataset(chainer.dataset.DatasetMixin): + """ Default Dataset Object for Multi-Class Classification + """ + + def __init__(self, file_list): + """ + Args. + ----- + - file_list :list of input files (+.h5) + """ + X, Y = [], [] + for path in file_list: + if not os.path.exists(path): + logger.warning("File does not exsists! [path={}]".format(path)) + continue + X_tmp, Y_tmp = self.load_file(path) + X.append(X_tmp) + Y.append(Y_tmp) + self.X = np.concatenate(X, axis=0) + self.Y = np.concatenate(Y, axis=0) + logger.info("Success: X={}, Y={}".format(self.X.shape, self.Y.shape)) + + + def load_file(self, path): + with h5py.File(path, 'r') as f: + X = np.array(f["fc"],) + Y = np.array(f['action/resampled'],) + + xshape, yshape = X.shape, Y.shape + X, Y = X.reshape((-1, xshape[-1])), Y.reshape((-1,yshape[-1])) + return X, Y + + + def __len__(self): + return len(self.X) + + def get_example(self, i): + return self.X[i], self.Y[i] diff --git a/projection/models/dense.py b/projection/models/dense.py new file mode 100644 index 0000000000..daac5806e7 --- /dev/null +++ b/projection/models/dense.py @@ -0,0 +1,52 @@ +import chainer +import chainer.links as L +import chainer.functions as F + +import numpy as np +import cupy as cp + + + +class DenseNet(chainer.Chain): + """ + Reference. + ---------- + - "Deep Convolutional and LSTM Recurrent Neural Networks for Multimodal Wearable Activity Recognition" + [www.mdpi.com/1424-8220/16/1/115/pdf] + - Baseline CNN + """ + """ + Args. + ----- + - n_in : int, Input dim (=X.shape[-1]) + - n_out : int, Output dim (=Y.shape[-1]) + """ + def __init__(self, n_in=None, n_out=None): + super(DenseNet, self,).__init__() + with self.init_scope(): + # FC + self.fc1 = L.Linear(n_in, 32) + self.fc2 = L.Linear(32, 32) + self.fc3 = L.Linear(32, n_out) + + def __call__(self, x): + # Full Connected + h1 = F.dropout(F.relu(self.fc1(x))) + h2 = F.dropout(F.relu(self.fc2(h1))) + h3 = F.tanh(self.fc3(h2)) + return h3 + + + def get_inter_layer(self, x): + h1 = F.relu(self.fc1(x)) + h2 = F.relu(self.fc2(h1)) + h3 = F.tanh(self.fc3(h2)) + + ret = { + "h1": h1, + "h2": h2, + "h3": h3, + } + + return h3, ret + diff --git a/projection/notebook/01_Resampling_of_Actions.ipynb b/projection/notebook/01_Resampling_of_Actions.ipynb new file mode 100644 index 0000000000..92ccfb1b66 --- /dev/null +++ b/projection/notebook/01_Resampling_of_Actions.ipynb @@ -0,0 +1,873 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 01: Critic Networkの出力の量子化" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 概要\n", + "+ Critic Networkはロボットハンドの各時間ごとの角度を出力する.\n", + "+ そのまま学習しても投射にならないため, 角度を曲げる, 伸ばす, そのままに変換する [-1,0,1,].\n", + "\n", + "### ToDo\n", + "+ シンプルに曲げる/曲げない/そのままでOKなのか？\n", + "+ 値の変化幅を決めるために, 各時刻毎の値の差分の分布を確認する." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 1-1: 差分の分布の確認" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(10, 2, 100, 21)\n" + ] + } + ], + "source": [ + "import os\n", + "import pandas as pd\n", + "import numpy as np\n", + "import h5py\n", + "\n", + "filename = os.path.join(\"/root/dataStore\", \"grasp_v1\", \"episodes\", \"epoch0.h5\")\n", + "with h5py.File(filename, 'r') as f:\n", + " A = np.array(f[\"action\"],)\n", + " \n", + "print(A.shape)" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(1980, 21)\n" + ] + } + ], + "source": [ + "%matplotlib inline\n", + "import matplotlib.pyplot as plt\n", + "import seaborn as sns\n", + "sns.set(\"notebook\", \"whitegrid\", font_scale=1.5)\n", + "\n", + "X = A[:,:,1:,:] - A[:,:,:-1,:]\n", + "X = X.reshape((-1,X.shape[-1],))\n", + "print(X.shape)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "row, col = 5, 4\n", + "\n", + "plt.figure(figsize=(8*col,5*row))\n", + "for no in range(20):\n", + " plt.subplot(row, col, no+1)\n", + " plt.hist(X[:, no]*(180./np.pi), density=True, bins=50, range=(-2,2), label=\"Angle {}\".format(no))\n", + " # plt.ylim([0,0.05])\n", + " #plt.xlim([-45.,45.])\n", + " #plt.xlim([-1,1.])\n", + " plt.ylim([0,0.5])\n", + " plt.legend()\n", + "\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 量子化\n", + "+ しきい値を勝手に設定して, [-1.0,1]に量子化を行う." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(10, 2, 100, 20)\n" + ] + } + ], + "source": [ + "import os\n", + "import pandas as pd\n", + "import numpy as np\n", + "import h5py\n", + "\n", + "filename = os.path.join(\"/root/dataStore\", \"tmp2\", \"episodes\", \"epoch0.h5\")\n", + "with h5py.File(filename, 'r') as f:\n", + " A = np.array(f[\"action\"],)\n", + "print(A.shape)\n", + "\n", + "X = A[:,:,1:,:] - A[:,:,:-1,:]" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(10, 2, 99, 20)\n" + ] + } + ], + "source": [ + "def resampling(X,th_min=-0.5, th_max=0.5):\n", + " \"\"\" 量子化の実行\n", + " \n", + " Args.\n", + " -----\n", + " - x: float\n", + " - th_min/th_max: float, threshhold [unit=degree]\n", + " \"\"\"\n", + " _X = X.copy()\n", + " _X[X < th_min] = -1.\n", + " _X[X > th_max] = 1.\n", + " _X[(X >= th_min) & (X<= th_max)] = 0\n", + " return _X\n", + "\n", + "\n", + "As = resampling(X*(180./np.pi), th_min=-1., th_max=1.)\n", + "print(As.shape)" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "%matplotlib inline\n", + "import matplotlib.pyplot as plt\n", + "import seaborn as sns\n", + "sns.set(\"notebook\", \"whitegrid\", font_scale=1.5)\n", + "\n", + "plt.figure(figsize=(8,8))\n", + "(labels, counts) = np.unique(As, return_counts=True)\n", + "plt.pie(counts, labels=labels, startangle=90, counterclock=False, autopct=\"%.2f%%\")\n", + "plt.legend(loc=\"lower right\")\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(1980, 20)\n", + "[[-1. -1. 1. ... -1. 1. 1.]\n", + " [ 0. 1. -1. ... 1. 1. 0.]\n", + " [ 1. 0. 0. ... 1. -1. -1.]\n", + " ...\n", + " [ 0. 0. 0. ... 0. 0. 0.]\n", + " [ 0. 0. 0. ... 0. 0. 0.]\n", + " [ 0. 0. 0. ... 0. 0. 0.]]\n", + "209\n" + ] + } + ], + "source": [ + "\"\"\" 組み合わせパターンの数え上げ\n", + "\"\"\"\n", + "\n", + "_As = As.reshape((-1, As.shape[-1]))\n", + "print(_As.shape,)\n", + "print(_As)\n", + "\n", + "combination = list(map(list, set(map(tuple,_As))))\n", + "print(len(combination))" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "scrolled": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(209, 20)\n" + ] + }, + { + "data": { + "image/png": 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\n", 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "%matplotlib inline\n", + "import matplotlib.pyplot as plt\n", + "import seaborn as sns\n", + "\n", + "\n", + "df_pattern = pd.DataFrame(labels).sort_values(by=list(range(20))).reset_index(drop=True)\n", + "df_pattern[\"counts\"] = counts\n", + "shape = df_pattern.shape\n", + "print(shape)\n", + "display(df_pattern.head())\n", + "\n", + "plt.figure(figsize=(shape[1]*0.4+3, shape[0]*0.4))\n", + "sns.heatmap(labels, annot=True, cmap=\"Blues\", square=True, cbar=False)\n", + "plt.ylabel(\"Motion Combination\")\n", + "plt.xlabel(\"Motor ID\")\n", + "plt.title(\"Motion Patern Map\")\n", + "\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 量子化済みデータを変換\n", + "+ [EpisodeNo, Batch, Sequence, env.action_space]\n", + "+ ==> [EpisodeNo, Batch, Sequence, env.action_space]\n", + "+ ==> [EpisodeNo, Batch, Sequence, env.action_space, 3]" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(10, 2, 100, 21)\n", + "(10, 2, 99, 21) (10, 2, 99, 21)\n" + ] + } + ], + "source": [ + "import os\n", + "import pandas as pd\n", + "import numpy as np\n", + "import h5py\n", + "\n", + "filename = os.path.join(\"/root/dataStore\", \"grasp_v1\", \"episodes\", \"epoch0.h5\")\n", + "with h5py.File(filename, 'r') as f:\n", + " A = np.array(f[\"action\"],)\n", + "print(A.shape)\n", + "\n", + "X = A[:,:,1:,:] - A[:,:,:-1,:]\n", + "As = resampling(X*(180./np.pi), th_min=-1., th_max=1.)\n", + "print(X.shape, As.shape)" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[10, 2, 99, 21, 3]\n", + "(41580, 3)\n", + "(10, 2, 99, 21, 3)\n" + ] + } + ], + "source": [ + "shape = list(As.shape) + [3]\n", + "print(shape)\n", + "As_onehot = np.eye(3)[As.ravel().astype(int)+1]\n", + "print(As_onehot.shape)\n", + "print(As_onehot.reshape(shape).shape)" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([-1., 1., 1., 1., 1., 1., -1., 0., -1., 1., -1., 1., 1.,\n", + " 1., 1., 1., -1., -1., -1., 1., 0.], dtype=float32)" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "As[0,0,0]" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[1., 0., 0.],\n", + " [0., 0., 1.],\n", + " [0., 0., 1.],\n", + " [0., 0., 1.],\n", + " [0., 0., 1.],\n", + " [0., 0., 1.],\n", + " [1., 0., 0.],\n", + " [0., 1., 0.],\n", + " [1., 0., 0.],\n", + " [0., 0., 1.],\n", + " [1., 0., 0.],\n", + " [0., 0., 1.],\n", + " [0., 0., 1.],\n", + " [0., 0., 1.],\n", + " [0., 0., 1.],\n", + " [0., 0., 1.],\n", + " [1., 0., 0.],\n", + " [1., 0., 0.],\n", + " [1., 0., 0.],\n", + " [0., 0., 1.],\n", + " [0., 1., 0.]])" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "As_onehot.reshape(shape)[0,0,0]" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[[[1., 1., 1., ..., 1., 1., 1.],\n", + " [1., 1., 1., ..., 1., 1., 1.],\n", + " [1., 1., 1., ..., 1., 1., 1.],\n", + " ...,\n", + " [1., 1., 1., ..., 1., 1., 1.],\n", + " [1., 1., 1., ..., 1., 1., 1.],\n", + " [1., 1., 1., ..., 1., 1., 1.]],\n", + "\n", + " [[1., 1., 1., ..., 1., 1., 1.],\n", + " [1., 1., 1., ..., 1., 1., 1.],\n", + " [1., 1., 1., ..., 1., 1., 1.],\n", + " ...,\n", + " [1., 1., 1., ..., 1., 1., 1.],\n", + " [1., 1., 1., ..., 1., 1., 1.],\n", + " [1., 1., 1., ..., 1., 1., 1.]]],\n", + "\n", + "\n", + " [[[1., 1., 1., ..., 1., 1., 1.],\n", + " [1., 1., 1., ..., 1., 1., 1.],\n", + " [1., 1., 1., ..., 1., 1., 1.],\n", + " ...,\n", + " [1., 1., 1., ..., 1., 1., 1.],\n", + " [1., 1., 1., ..., 1., 1., 1.],\n", + " [1., 1., 1., ..., 1., 1., 1.]],\n", + "\n", + " [[1., 1., 1., ..., 1., 1., 1.],\n", + " [1., 1., 1., ..., 1., 1., 1.],\n", + " [1., 1., 1., ..., 1., 1., 1.],\n", + " ...,\n", + " [1., 1., 1., ..., 1., 1., 1.],\n", + " [1., 1., 1., ..., 1., 1., 1.],\n", + " [1., 1., 1., ..., 1., 1., 1.]]],\n", + "\n", + "\n", + " [[[1., 1., 1., ..., 1., 1., 1.],\n", + " [1., 1., 1., ..., 1., 1., 1.],\n", + " [1., 1., 1., ..., 1., 1., 1.],\n", + " ...,\n", + " [1., 1., 1., ..., 1., 1., 1.],\n", + " [1., 1., 1., ..., 1., 1., 1.],\n", + " [1., 1., 1., ..., 1., 1., 1.]],\n", + "\n", + " [[1., 1., 1., ..., 1., 1., 1.],\n", + " [1., 1., 1., ..., 1., 1., 1.],\n", + " [1., 1., 1., ..., 1., 1., 1.],\n", + " ...,\n", + " [1., 1., 1., ..., 1., 1., 1.],\n", + " [1., 1., 1., ..., 1., 1., 1.],\n", + " [1., 1., 1., ..., 1., 1., 1.]]],\n", + "\n", + "\n", + " ...,\n", + "\n", + "\n", + " [[[1., 1., 1., ..., 1., 1., 1.],\n", + " [1., 1., 1., ..., 1., 1., 1.],\n", + " [1., 1., 1., ..., 1., 1., 1.],\n", + " ...,\n", + " [1., 1., 1., ..., 1., 1., 1.],\n", + " [1., 1., 1., ..., 1., 1., 1.],\n", + " [1., 1., 1., ..., 1., 1., 1.]],\n", + "\n", + " [[1., 1., 1., ..., 1., 1., 1.],\n", + " [1., 1., 1., ..., 1., 1., 1.],\n", + " [1., 1., 1., ..., 1., 1., 1.],\n", + " ...,\n", + " [1., 1., 1., ..., 1., 1., 1.],\n", + " [1., 1., 1., ..., 1., 1., 1.],\n", + " [1., 1., 1., ..., 1., 1., 1.]]],\n", + "\n", + "\n", + " [[[1., 1., 1., ..., 1., 1., 1.],\n", + " [1., 1., 1., ..., 1., 1., 1.],\n", + " [1., 1., 1., ..., 1., 1., 1.],\n", + " ...,\n", + " [1., 1., 1., ..., 1., 1., 1.],\n", + " [1., 1., 1., ..., 1., 1., 1.],\n", + " [1., 1., 1., ..., 1., 1., 1.]],\n", + "\n", + " [[1., 1., 1., ..., 1., 1., 1.],\n", + " [1., 1., 1., ..., 1., 1., 1.],\n", + " [1., 1., 1., ..., 1., 1., 1.],\n", + " ...,\n", + " [1., 1., 1., ..., 1., 1., 1.],\n", + " [1., 1., 1., ..., 1., 1., 1.],\n", + " [1., 1., 1., ..., 1., 1., 1.]]],\n", + "\n", + "\n", + " [[[1., 1., 1., ..., 1., 1., 1.],\n", + " [1., 1., 1., ..., 1., 1., 1.],\n", + " [1., 1., 1., ..., 1., 1., 1.],\n", + " ...,\n", + " [1., 1., 1., ..., 1., 1., 1.],\n", + " [1., 1., 1., ..., 1., 1., 1.],\n", + " [1., 1., 1., ..., 1., 1., 1.]],\n", + "\n", + " [[1., 1., 1., ..., 1., 1., 1.],\n", + " [1., 1., 1., ..., 1., 1., 1.],\n", + " [1., 1., 1., ..., 1., 1., 1.],\n", + " ...,\n", + " [1., 1., 1., ..., 1., 1., 1.],\n", + " [1., 1., 1., ..., 1., 1., 1.],\n", + " [1., 1., 1., ..., 1., 1., 1.]]]])" + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "As_onehot.reshape(shape).sum(axis=4)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.5.2" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/projection/notebook/02_PrepareDataset.ipynb b/projection/notebook/02_PrepareDataset.ipynb new file mode 100644 index 0000000000..5676e0c51f --- /dev/null +++ b/projection/notebook/02_PrepareDataset.ipynb @@ -0,0 +1,330 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# データセットの作成\n", + "+ chainer学習用のデータセットを作成.\n", + "+ shapeはできるだけ変更しないで, {中間出力, 関節角度(量子化済み＆量子化前),}" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import os\n", + "import sys\n", + "import numpy as np\n", + "import pandas as pd\n", + "import time\n", + "import threading\n", + "import h5py\n", + "\n", + "\n", + "from logging import getLogger, basicConfig, DEBUG, INFO\n", + "logger = getLogger(__name__)\n", + "LOG_FMT = \"{asctime} | {levelname:<5s} | {name} | {message}\"\n", + "basicConfig(level=INFO, format=LOG_FMT, style=\"{\")" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "def resampling(X,th_min=-1., th_max=1.):\n", + " \"\"\" 量子化の実行\n", + " \n", + " Args.\n", + " -----\n", + " - x: float\n", + " - th_min/th_max: float, threshhold [unit=degree]\n", + " \"\"\"\n", + " _X = X.copy()\n", + " _X[X < th_min] = -1.\n", + " _X[X > th_max] = 1.\n", + " _X[(X >= th_min) & (X<= th_max)] = 0\n", + " return _X" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "th_min= -0.5\n", + "th_max= 0.5\n", + "\n", + "def threading_clbk(ps):\n", + " (path_in, path_out,) = ps\n", + " \n", + " \n", + " logger.info(\"Start: Load from {}\".format(path_in))\n", + " # Load File\n", + " with h5py.File(path_in, 'r') as f:\n", + " A = np.array(f[\"action\"],)\n", + " FC = np.array(f[\"fc\"])\n", + " logger.info(\"Start: A={}, FC={} [from {}]\".format(A.shape, FC.shape, path_in))\n", + " \n", + " # 量子化 & Onehot Encoding\n", + " As = resampling(A,th_min=th_min, th_max=th_max)\n", + "\n", + " shape = list(As.shape) + [3]\n", + " As_onehot = np.eye(3)[As.ravel().astype(int)+1]\n", + " As_onehot = As_onehot.reshape(shape)\n", + " \n", + " # Write\n", + " with h5py.File(path_out, 'w') as f:\n", + " f.create_dataset(\"fc\", data=FC)\n", + " f.create_group('action')\n", + " f[\"action\"].create_dataset(\"raw\", data=A)\n", + " #f[\"action\"].create_dataset(\"resampled\", data=As)\n", + " f[\"action\"].create_dataset(\"onehot\", data=As_onehot)\n", + " logger.info(\"Finish: Write to {}\".format(path_out))\n", + " return True" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2019-01-09 09:50:04,795 | INFO | __main__ | Start: Load from /root/dataStore/grasp_v1/episodes/epoch0.h5\n", + "2019-01-09 09:50:04,799 | INFO | __main__ | Start: A=(10, 2, 100, 21), FC=(10, 2, 100, 256) [from /root/dataStore/grasp_v1/episodes/epoch0.h5]\n" + ] + }, + { + "ename": "OSError", + "evalue": "Unable to create file (unable to open file: name = '/root/dataStore/grasp_v1/Inputs/epoch0.h5', errno = 2, error message = 'No such file or directory', flags = 13, o_flags = 242)", + "output_type": "error", + "traceback": [ + "\u001b[0;31m\u001b[0m", + "\u001b[0;31mOSError\u001b[0mTraceback (most recent call last)", + "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[1;32m 7\u001b[0m \u001b[0mpath_out\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mos\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpath\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mjoin\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"/root/dataStore/grasp_v1/Inputs\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"epoch0.h5\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 8\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 9\u001b[0;31m \u001b[0mthreading_clbk\u001b[0m\u001b[0;34m(\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mpath_in\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpath_out\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", + "\u001b[0;32m\u001b[0m in \u001b[0;36mthreading_clbk\u001b[0;34m(ps)\u001b[0m\n\u001b[1;32m 21\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 22\u001b[0m \u001b[0;31m# Write\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 23\u001b[0;31m \u001b[0;32mwith\u001b[0m \u001b[0mh5py\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mFile\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpath_out\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'w'\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mas\u001b[0m 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\u001b[0mfcpl\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mmake_fcpl\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtrack_order\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtrack_order\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 394\u001b[0;31m swmr=swmr)\n\u001b[0m\u001b[1;32m 395\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 396\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mswmr_support\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m/usr/local/lib/python3.5/dist-packages/h5py/_hl/files.py\u001b[0m in \u001b[0;36mmake_fid\u001b[0;34m(name, mode, userblock_size, fapl, fcpl, swmr)\u001b[0m\n\u001b[1;32m 174\u001b[0m \u001b[0mfid\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mh5f\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcreate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mh5f\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mACC_EXCL\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfapl\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mfapl\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfcpl\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mfcpl\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 175\u001b[0m \u001b[0;32melif\u001b[0m \u001b[0mmode\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m'w'\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 176\u001b[0;31m \u001b[0mfid\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mh5f\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcreate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mh5f\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mACC_TRUNC\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfapl\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mfapl\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfcpl\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mfcpl\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 177\u001b[0m \u001b[0;32melif\u001b[0m \u001b[0mmode\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m'a'\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 178\u001b[0m \u001b[0;31m# Open in append mode (read/write).\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32mh5py/_objects.pyx\u001b[0m in \u001b[0;36mh5py._objects.with_phil.wrapper\u001b[0;34m()\u001b[0m\n", + "\u001b[0;32mh5py/_objects.pyx\u001b[0m in \u001b[0;36mh5py._objects.with_phil.wrapper\u001b[0;34m()\u001b[0m\n", + "\u001b[0;32mh5py/h5f.pyx\u001b[0m in \u001b[0;36mh5py.h5f.create\u001b[0;34m()\u001b[0m\n", + "\u001b[0;31mOSError\u001b[0m: Unable to create file (unable to open file: name = '/root/dataStore/grasp_v1/Inputs/epoch0.h5', errno = 2, error message = 'No such file or directory', flags = 13, o_flags = 242)" + ] + } + ], + "source": [ + "\"\"\" Apply for single episode\n", + "\"\"\"\n", + "import os\n", + "\n", + "\n", + "path_in = os.path.join(\"/root/dataStore/grasp_v1/episodes\", \"epoch0.h5\")\n", + "path_out = os.path.join(\"/root/dataStore/grasp_v1/Inputs\", \"epoch0.h5\")\n", + "\n", + "threading_clbk( (path_in, path_out,))" + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "['/root/dataStore/tmp2/episodes/epoch0.h5', '/root/dataStore/tmp2/episodes/epoch1.h5', '/root/dataStore/tmp2/episodes/epoch2.h5']\n" + ] + }, + { + "data": { + "text/plain": [ + "[('/root/dataStore/tmp2/episodes/epoch0.h5',\n", + " '/root/dataStore/tmp2/Inputs/epoch0.h5'),\n", + " ('/root/dataStore/tmp2/episodes/epoch1.h5',\n", + " '/root/dataStore/tmp2/Inputs/epoch1.h5'),\n", + " ('/root/dataStore/tmp2/episodes/epoch2.h5',\n", + " '/root/dataStore/tmp2/Inputs/epoch2.h5')]" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "\"\"\" Execute in paralel\n", + "\"\"\"\n", + "import glob\n", + "\n", + "dir_in = \"/root/dataStore/tmp2/episodes\"\n", + "dir_out = \"/root/dataStore/tmp2/Inputs\"\n", + "\n", + "file_list = list(glob.glob(os.path.join(dir_in, \"*.h5\")))\n", + "file_list.sort()\n", + "print(file_list)\n", + "\n", + "file_list = [(path_in, os.path.join(dir_out, path_in.split(\"/\")[-1])) for path_in in file_list]\n", + "display(file_list)" + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2018-12-27 07:55:41,312 | INFO | __main__ | Start Load OPP Dataset [3files]\n", + "2018-12-27 07:55:41,313 | INFO | __main__ | Start: Load from /root/dataStore/tmp2/episodes/epoch0.h5\n", + "2018-12-27 07:55:41,313 | INFO | __main__ | Start: Load from /root/dataStore/tmp2/episodes/epoch1.h5\n", + "2018-12-27 07:55:41,313 | INFO | __main__ | Start: Load from /root/dataStore/tmp2/episodes/epoch2.h5\n", + "2018-12-27 07:55:41,317 | INFO | __main__ | Start: A=(10, 2, 100, 20), FC=(10, 2, 100, 256) [from /root/dataStore/tmp2/episodes/epoch0.h5]\n", + "2018-12-27 07:55:41,320 | INFO | __main__ | Start: A=(10, 2, 100, 20), FC=(10, 2, 100, 256) [from /root/dataStore/tmp2/episodes/epoch1.h5]\n", + "2018-12-27 07:55:41,321 | INFO | __main__ | Start: A=(10, 2, 100, 20), FC=(10, 2, 100, 256) [from /root/dataStore/tmp2/episodes/epoch2.h5]\n", + "2018-12-27 07:55:41,326 | INFO | __main__ | Finish: Write to /root/dataStore/tmp2/Inputs/epoch0.h5\n", + "2018-12-27 07:55:41,330 | INFO | __main__ | Finish: Write to /root/dataStore/tmp2/Inputs/epoch1.h5\n", + "2018-12-27 07:55:41,333 | INFO | __main__ | Finish: Write to /root/dataStore/tmp2/Inputs/epoch2.h5\n", + "2018-12-27 07:55:41,335 | INFO | __main__ | Thread ... Finish!! [Results=3]\n", + "2018-12-27 07:55:41,336 | INFO | __main__ | Finish!!\n" + ] + } + ], + "source": [ + "# Load files using Threading\n", + "from concurrent.futures import ThreadPoolExecutor\n", + "\n", + "thread_list = []\n", + "max_worker = 5\n", + "logger.info(\"Start Load OPP Dataset [{}files]\".format(len(file_list))) \n", + "with ThreadPoolExecutor(max_workers=max_worker) as executor:\n", + " ret = executor.map(threading_clbk, file_list)\n", + "logger.info(\"Thread ... Finish!! [Results={}]\".format(len(list(ret))))\n", + "logger.info(\"Finish!!\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 作成したデータの確認" + ] + }, + { + "cell_type": "code", + "execution_count": 42, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(10, 2, 100, 20)\n" + ] + } + ], + "source": [ + "dir_in = \"/root/dataStore/tmp2/episodes\"\n", + "dir_out = \"/root/dataStore/tmp2/Inputs\"\n", + "\n", + "import os\n", + "import pandas as pd\n", + "import numpy as np\n", + "import h5py\n", + "\n", + "filename = os.path.join(\"/root/dataStore\", \"test\", \"Inputs\", \"test\", \"epoch8.h5\")\n", + "with h5py.File(filename, 'r') as f:\n", + " A = np.array(f[\"action/resampled\"],)\n", + "print(A.shape)" + ] + }, + { + "cell_type": "code", + "execution_count": 44, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[[[ 1., -1., 0., ..., 1., 1., -1.],\n", + " [ 0., -1., 0., ..., 0., 1., -1.],\n", + " [ 0., -1., -1., ..., -1., 1., -1.],\n", + " ...,\n", + " [-1., -1., 0., ..., -1., 1., -1.],\n", + " [-1., -1., 0., ..., -1., 1., -1.],\n", + " [-1., -1., 0., ..., -1., 1., -1.]],\n", + "\n", + " [[ 1., 1., 1., ..., 1., 1., -1.],\n", + " [ 1., 1., 1., ..., 1., 1., -1.],\n", + " [ 1., 1., 1., ..., 1., 1., -1.],\n", + " ...,\n", + " [ 1., 1., 1., ..., 1., 1., 1.],\n", + " [ 1., 1., 1., ..., 1., 1., 1.],\n", + " [ 1., 1., 1., ..., 1., 1., 1.]]],\n", + "\n", + "\n", + " [[[-1., 1., 1., ..., 1., 1., -1.],\n", + " [-1., 1., 1., ..., 0., 0., -1.],\n", + " [-1., 1., 1., ..., -1., 1., -1.],\n", + " ...,\n", + " [-1., 1., 0., ..., 0., 1., -1.],\n", + " [-1., 1., 0., ..., 0., 1., -1.],\n", + " [-1., 1., 0., ..., 0., 1., -1.]],\n", + "\n", + " [[ 1., 1., 1., ..., -1., 1., -1.],\n", + " [ 1., -1., 1., ..., -1., 1., -1.],\n", + " [ 1., -1., 0., ..., -1., 1., -1.],\n", + " ...,\n", + " [-1., -1., -1., ..., -1., 1., -1.],\n", + " [-1., -1., -1., ..., -1., 1., -1.],\n", + " [-1., -1., -1., ..., -1., 1., -1.]]]], dtype=float32)" + ] + }, + "execution_count": 44, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "A[:2]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.5.2" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/projection/requirements.txt b/projection/requirements.txt new file mode 100644 index 0000000000..27de39f3af --- /dev/null +++ b/projection/requirements.txt @@ -0,0 +1,9 @@ +matplotlib +numpy +scipy +sklearn +pandas +seaborn +librosa +jupyter +h5py \ No newline at end of file diff --git a/projection/run.py b/projection/run.py new file mode 100644 index 0000000000..3f8f90ca00 --- /dev/null +++ b/projection/run.py @@ -0,0 +1,294 @@ +import os +import argparse +import numpy as np +import pandas as pd +import h5py +import time +import glob + +import chainer +import chainer.links as L +import chainer.functions as F +from chainer.training import extensions +from chainer import serializers + +from logging import getLogger, basicConfig, DEBUG, INFO +logger = getLogger(__name__) +LOG_FMT = "{asctime} | {levelname:<5s} | {name} | {message}" +basicConfig(level=INFO, format=LOG_FMT, style="{") + +from utils.setup import reset_seed +reset_seed(0) + + +from dataset.default import DefaultDataset +from models.dense import DenseNet + +# ----------------------------------------------------------------------- +def make_parser(): + parser = argparse.ArgumentParser() + subparsers = parser.add_subparsers(title='Sub-Commands') + + # For TRAINING + train_parser = subparsers.add_parser('TRAIN') + train_parser.set_defaults(func=train) + train_parser.add_argument('--path-data-train', required=True, + help="path to an training data directory") + train_parser.add_argument('--path-data-val', required=True, + help="path to an test data directory") + train_parser.add_argument('--path-model', required=True, + help="path to save the trained model") + train_parser.add_argument('--path-log', required=True, + help="path to save log data.") + train_parser.add_argument('-G','--gpu', default=0, type=int, + help="Device identifier. {cpu=-1(default), gpu=0,..}") + train_parser.add_argument('-B','--batch-size', default=1024, type=int, + help="int, Batch size") + train_parser.add_argument('-E','--epochs', default=1, type=int, + help="Int, the number of training epochs") + # train_parser.add_argument('--debug', action='store_true', + # help="If you want run in debug mode, set this flag.",) + + + # For TEST + test_parser = subparsers.add_parser('TEST') + test_parser.set_defaults(func=test) + test_parser.add_argument('--path-data-test', required=True, + help="path to an test data directory") + test_parser.add_argument('--path-model', required=True, + help="path to save the trained model") + test_parser.add_argument('--path-log', required=True, + help="path to save log and resutls") + test_parser.add_argument('-G','--gpu', default=0, type=int, + help="Device identifier. {cpu=-1(default), gpu=0,..}") + test_parser.add_argument('-B','--batch-size', default=1024, type=int, + help="int, Batch size") + test_parser.add_argument('--debug', action='store_true', + help="If you want run in debug mode, set this flag.",) + # test_parser.add_argument('--data-type', default="TEST", + # help="Dataset Typem, {TRAIN, VAL, TEST(default)}",) + return parser + + + +# ----------------------------------------------------------------------- +def train(args, *, logger=getLogger(__name__+".train")): + """ Params + """ + MODEL_NAME = "DenseNet" + DIR_DATA_TRAIN = args.path_data_train + DIR_DATA_VAL = args.path_data_val + DIR_LOG = args.path_log + DIR_MODEL = args.path_model + gpu_id = int(args.gpu) + batch_size = int(args.batch_size) + n_epochs = int(args.epochs) + + + """ Load Training & Validation Data + """ + # Training Data + logger.info("Load Dataset") + ## Dataset + file_list_train = list(glob.glob(os.path.join(DIR_DATA_TRAIN, "*.h5"))) + file_list_train.sort() + file_list_val = list(glob.glob(os.path.join(DIR_DATA_VAL, "*.h5"))) + file_list_val.sort() + logger.info("- Train: {} files".format(len(file_list_train))) + logger.info("- Val: {} files".format(len(file_list_val))) + dataset_train = DefaultDataset(file_list_train) + dataset_val = DefaultDataset(file_list_val) + + # Iterator + iter_train = chainer.iterators.SerialIterator(dataset_train, batch_size, repeat=True, shuffle=True) + iter_val = chainer.iterators.SerialIterator(dataset_val, batch_size, repeat=False, shuffle=False) + + # Info + logger.info("- Train : X={}, Y={}".format(dataset_train.X.shape, dataset_train.Y.shape)) + logger.info("- Validation : X={}, Y={}".format(dataset_val.X.shape, dataset_val.Y.shape)) + + + """ Model + """ + model = DenseNet(n_in=dataset_train.X.shape[-1], n_out=dataset_train.Y.shape[-1]) + model = L.Classifier(model, lossfun=F.mean_squared_error) + model.compute_accuracy = False + if gpu_id >= 0: + model.to_gpu(gpu_id) + logger.info("Success: Building Model") + + + """ Trainer & Extention + """ + # Optimizer + optimizer = chainer.optimizers.Adam().setup(model) + # Update + updater = chainer.training.StandardUpdater(iter_train, optimizer,device=gpu_id) + # Trainer + trainer = chainer.training.Trainer(updater, (n_epochs, 'epoch'), out=DIR_LOG) + trainer.extend(extensions.LogReport()) + logger.info("Success: Build trainer") + + """ Extentions + """ + trainer.extend(extensions.Evaluator(iter_val, model, device=gpu_id), name='val') + trainer.extend(extensions.PrintReport(['epoch', 'elapsed_time', + 'main/loss', 'main/accuracy', + 'val/main/loss', 'val/main/accuracy', ])) + layers = ["fc1","fc2","fc3",] + for l in layers: + trainer.extend(extensions.ParameterStatistics(eval("model.predictor.{}".format(l)), + {'std': np.std,'max': np.max,'min':np.min,'mean': np.mean})) + trainer.extend(extensions.PlotReport(['{}/W/data/std'.format(l),], + x_key='epoch', file_name='std_{}.png'.format(l))) + trainer.extend(extensions.PlotReport(['{}/W/data/mean'.format(l),'{}/W/data/max'.format(l), '{}/W/data/min'.format(l)], + x_key='epoch', file_name='range_{}.png'.format(l))) + trainer.extend(extensions.PlotReport(['main/loss', 'val/main/loss',], x_key='epoch', file_name='loss.png')) + trainer.extend(extensions.PlotReport(['main/accuracy', 'val/main/accuracy'], x_key='epoch', file_name='accuracy.png')) + trainer.extend(extensions.dump_graph('main/loss')) + logger.info("Success: Build Extentions\n") + + """ Training & Save Model + """ + logger.info("Start Training") + trainer.run() + logger.info("Finish!!\n") + # Save + serializers.save_npz(DIR_MODEL, model) + logger.info("Save model") + + + +# ----------------------------------------------------------------------- +def test(args, *, logger=getLogger(__name__+".test")): + """ Params + """ + MODEL_NAME = "DenseNet" + DIR_DATA_TEST = args.path_data_test + DIR_LOG = args.path_log + DIR_MODEL = args.path_model + gpu_id = int(args.gpu) + batch_size = int(args.batch_size) + + + """ Load Training & Validation Data + """ + logger.info("Load Data ") + ## Dataset + file_list_test = list(glob.glob(os.path.join(DIR_DATA_TEST, "*.h5"))) + file_list_test.sort() + logger.info("- Test: {} files".format(len(file_list_test))) + dataset_test = DefaultDataset(file_list_test) + + # Iterator + iter_test = chainer.iterators.SerialIterator(dataset_test, batch_size, repeat=False, shuffle=False) + + # Info + logger.info("- Test : X={}, Y={}".format(dataset_test.X.shape, dataset_test.Y.shape)) + + + + """ Load Model + """ + model = DenseNet(n_in=dataset_test.X.shape[-1], n_out=dataset_test.Y.shape[-1]) + model = L.Classifier(model, lossfun=F.mean_squared_error) + model.compute_accuracy = True + chainer.serializers.load_npz(DIR_MODEL, model) + if gpu_id >= 0: + model.to_gpu(gpu_id) + logger.info("Success: Building Model") + + + """ Eval + """ + # Inference + from chainer.dataset import concat_examples + from chainer import cuda + iter_no, x_in, y_true, y_pred = 0, [], [], [] + y_inter = {} + + while True: + iter_no += 1 + if iter_no%1000 == 0: + logger.debug("Iteration: {}".format(iter_no)) + test_batch = iter_test.next() + _x, y_true_tmp = concat_examples(test_batch, device=gpu_id) + with chainer.using_config('train', False), chainer.using_config('enable_backprop', False): + # y_pred_tmp = model.predictor(_x).data + y_pred_tmp, y_inter_tmp = model.predictor.get_inter_layer(_x) + + x_in.append(cuda.to_cpu(_x)) + y_pred.append(cuda.to_cpu(y_pred_tmp.data)) + y_true.append(cuda.to_cpu(y_true_tmp)) + + for _y in y_inter_tmp: + if len(y_inter) == 0: + for key in y_inter_tmp.keys(): + y_inter[key] = [cuda.to_cpu(y_inter_tmp[key].data),] + else: + for key in y_inter_tmp.keys(): + _z = cuda.to_cpu(y_inter_tmp[key].data) + y_inter[key].append(_z) + + if iter_test.is_new_epoch: + iter_test.reset() + break + + + x_in = np.concatenate(x_in, axis=0) + y_pred = np.concatenate(y_pred, axis=0) + y_true = np.concatenate(y_true, axis=0) + logger.debug("- x_in, y_true={}, y_pred={}\n".format(x_in.shape, y_true.shape, y_pred.shape)) + for key in y_inter.keys(): + y_inter[key] = np.concatenate(y_inter[key], axis=0) + + + # Eval + df_in = pd.DataFrame(x_in) + df_in.columns = ["x_{}".format(c) for c in list(df_in.columns)] + df_pred = pd.DataFrame(y_pred) + df_pred.columns = ["pred_{}".format(c) for c in list(df_pred.columns)] + df_true = pd.DataFrame(y_true) + df_true.columns = ["true_{}".format(c) for c in list(df_true.columns)] + df_pred = pd.concat([df_in, df_pred,df_true], axis=1) + logger.info("df_pred = \n {}".format(df_pred.head())) + filename = os.path.join(DIR_LOG, "pred_detail.csv") + df_pred.to_csv(filename) + logger.info("Write results to {} [df_pred={}]".format(filename, df_pred.shape)) + + + # Save Internal State + filename = os.path.join(DIR_LOG, "pred_inernal.csv") + with h5py.File(filename, "w") as f: + # Intermidiate output after applying activation functions + f.create_group('post_act') + for key in y_inter.keys(): + f["post_act"].create_dataset(key, data=y_inter[key]) + + + # Summary + ## MSE + mse = np.mean((y_pred - y_true)**2) + mae = np.mean(np.absolute(y_pred - y_true)) + logger.info("=== Summary ===") + logger.info("MSE: {}".format(mse)) + logger.info("MAE: {}".format(mae)) + logger.info("===============") + + + + + +# ----------------------------------------------------------------------- +if __name__=='__main__': + parser = make_parser() + args = parser.parse_args() + print() + + + args_dict = vars(args) + logger.info(" Args:") + for key in args_dict.keys(): + logger.info(" - {:<15s}= {}".format(key, args_dict[key])) + print() + args.func(args) diff --git a/projection/utils/MAKE_CHAINER_INPUT.sh b/projection/utils/MAKE_CHAINER_INPUT.sh new file mode 100644 index 0000000000..8868c3137b --- /dev/null +++ b/projection/utils/MAKE_CHAINER_INPUT.sh @@ -0,0 +1,3 @@ +python3 make_inputs.py \ + --path-in /root/dataStore/grasp_v1/episodes \ + --path-out /root/dataStore/grasp_v1/Inputs diff --git a/projection/utils/make_inputs.py b/projection/utils/make_inputs.py new file mode 100644 index 0000000000..a903d7321f --- /dev/null +++ b/projection/utils/make_inputs.py @@ -0,0 +1,99 @@ +import os +import sys +import argparse +import numpy as np +import pandas as pd +import time +import threading +import h5py +import glob +from concurrent.futures import ThreadPoolExecutor + + +from logging import getLogger, basicConfig, DEBUG, INFO +logger = getLogger(__name__) +LOG_FMT = "{asctime} | {levelname:<5s} | {name} | {message}" +basicConfig(level=INFO, format=LOG_FMT, style="{") + + +# --------------------------------------------------------------------------------------------------- +def resampling(X,th_min=-1., th_max=1.): + """ 量子化の実行 + + Args. + ----- + - x: float [degree] + - th_min/th_max: float, threshhold [unit=degree] + """ + _X = X.copy() + _X[X < th_min] = -1. + _X[X > th_max] = 1. + _X[(X >= th_min) & (X<= th_max)] = 0 + return _X + + +# --------------------------------------------------------------------------------------------------- +def threading_clbk(ps): + (path_in, path_out,) = ps + + + logger.info("Start: Load from {}".format(path_in)) + # Load File + with h5py.File(path_in, 'r') as f: + A = np.array(f["action"],) + FC = np.array(f["fc"]) + logger.info("Start: A={}, FC={} [from {}]".format(A.shape, FC.shape, path_in)) + + # 量子化 & Onehot Encoding + As = resampling(A*(180./np.pi),) + + shape = list(As.shape) + [3] + As_onehot = np.eye(3)[As.ravel().astype(int)+1] + As_onehot = As_onehot.reshape(shape) + + # Write + with h5py.File(path_out, 'w') as f: + f.create_dataset("fc", data=FC) + f.create_group('action') + f["action"].create_dataset("raw", data=A) + f["action"].create_dataset("resampled", data=As) + # f["action"].create_dataset("onehot", data=As_onehot) + logger.info("Finish: Write to {}".format(path_out)) + return True + + +# -------------------------------------------------------------------------------------------------- +def main(args): + + dir_in = args.path_in # "/root/dataStore/tmp2/episodes" + dir_out = args.path_out # "/root/dataStore/tmp2/Inputs" + file_list = list(glob.glob(os.path.join(dir_in, "*.h5"))) + file_list.sort() + file_list = [(path_in, os.path.join(dir_out, path_in.split("/")[-1])) for path_in in file_list] + logger.info("Target Files: {}".format(len(file_list))) + + # Load files using Threading + thread_list = [] + max_worker = 5 + logger.info("Start Load OPP Dataset [{}files]".format(len(file_list))) + with ThreadPoolExecutor(max_workers=max_worker) as executor: + ret = executor.map(threading_clbk, file_list) + logger.info("Thread ... Finish!! [Results={}]".format(len(list(ret)))) + logger.info("Finish!!") + + +# -------------------------------------------------------------------------------------------------- +if __name__=='__main__': + parser = argparse.ArgumentParser() + parser.add_argument('--path-in', required=True, + help="path to a input dirctory") + parser.add_argument('--path-out', required=True, + help="path to a output dirctory") + + args = parser.parse_args() + args_dict = vars(args) + logger.info(" Args:") + for key in args_dict.keys(): + logger.info(" - {:<15s}= {}".format(key, args_dict[key])) + print() + main(args) diff --git a/projection/utils/setup.py b/projection/utils/setup.py new file mode 100644 index 0000000000..bd7cf4868a --- /dev/null +++ b/projection/utils/setup.py @@ -0,0 +1,15 @@ +import random +import numpy +import chainer + +from logging import getLogger, basicConfig, DEBUG, INFO + + +""" For Reproducibility +""" +def reset_seed(seed=0, *, logger=getLogger(__name__+'.reset_seed')): + random.seed(seed) + numpy.random.seed(seed) + if chainer.cuda.available: + chainer.cuda.cupy.random.seed(seed) + logger.info("Reset Seeds ... Done!") diff --git a/setup.cfg b/setup.cfg old mode 100644 new mode 100755 diff --git a/setup.py b/setup.py old mode 100644 new mode 100755