From 4bf419aa5a9b25bde5cd1e332b077f45fba190c1 Mon Sep 17 00:00:00 2001 From: Cheng Chi Date: Tue, 7 Mar 2023 16:07:15 -0500 Subject: [PATCH] code release --- .gitignore | 140 + README.md | 374 ++ conda_environment.yaml | 63 + conda_environment_macos.yaml | 55 + conda_environment_real.yaml | 71 + demo_real_robot.py | 160 + .../codecs/imagecodecs_numcodecs.py | 1386 ++++++ diffusion_policy/common/checkpoint_util.py | 59 + diffusion_policy/common/cv2_util.py | 150 + diffusion_policy/common/env_util.py | 23 + diffusion_policy/common/json_logger.py | 117 + diffusion_policy/common/nested_dict_util.py | 32 + diffusion_policy/common/normalize_util.py | 223 + .../common/pose_trajectory_interpolator.py | 208 + diffusion_policy/common/precise_sleep.py | 25 + diffusion_policy/common/pymunk_override.py | 248 + diffusion_policy/common/pymunk_util.py | 52 + diffusion_policy/common/pytorch_util.py | 82 + diffusion_policy/common/replay_buffer.py | 588 +++ .../common/robomimic_config_util.py | 47 + 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diffusion_policy/workspace/train_diffusion_transformer_lowdim_workspace.py create mode 100644 diffusion_policy/workspace/train_diffusion_unet_hybrid_workspace.py create mode 100644 diffusion_policy/workspace/train_diffusion_unet_image_workspace.py create mode 100644 diffusion_policy/workspace/train_diffusion_unet_lowdim_workspace.py create mode 100644 diffusion_policy/workspace/train_diffusion_unet_video_workspace.py create mode 100644 diffusion_policy/workspace/train_ibc_dfo_hybrid_workspace.py create mode 100644 diffusion_policy/workspace/train_ibc_dfo_lowdim_workspace.py create mode 100644 diffusion_policy/workspace/train_robomimic_image_workspace.py create mode 100644 diffusion_policy/workspace/train_robomimic_lowdim_workspace.py create mode 100644 eval_real_robot.py create mode 100644 media/multimodal_sim.png create mode 100644 media/teaser.png create mode 100644 multirun_metrics.py create mode 100644 pyrightconfig.json create mode 100644 ray_exec.py create mode 100644 ray_train_multirun.py create mode 100644 setup.py create mode 100644 tests/test_block_pushing.py create mode 100644 tests/test_cv2_util.py create mode 100644 tests/test_multi_realsense.py create mode 100644 tests/test_pose_trajectory_interpolator.py create mode 100644 tests/test_precise_sleep.py create mode 100644 tests/test_replay_buffer.py create mode 100644 tests/test_ring_buffer.py create mode 100644 tests/test_robomimic_image_runner.py create mode 100644 tests/test_robomimic_lowdim_runner.py create mode 100644 tests/test_shared_queue.py create mode 100644 tests/test_single_realsense.py create mode 100644 tests/test_timestamp_accumulator.py create mode 100644 train.py diff --git a/.gitignore b/.gitignore new file mode 100644 index 0000000..23dbd4e --- /dev/null +++ b/.gitignore @@ -0,0 +1,140 @@ +bin +logs +wandb +outputs +data +data_local +.vscode +_wandb + +**/.DS_Store + +fuse.cfg + +*.ai + +# Generation results +results/ + +ray/auth.json + +# Byte-compiled / optimized / DLL files +__pycache__/ +*.py[cod] +*$py.class + +# C extensions +*.so + +# Distribution / packaging +.Python +build/ +develop-eggs/ +dist/ +downloads/ +eggs/ +.eggs/ +lib/ +lib64/ +parts/ +sdist/ +var/ +wheels/ +pip-wheel-metadata/ +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 +*.py,cover +.hypothesis/ +.pytest_cache/ + +# Translations +*.mo +*.pot + +# Django stuff: +*.log +local_settings.py +db.sqlite3 +db.sqlite3-journal + +# Flask stuff: +instance/ +.webassets-cache + +# Scrapy stuff: +.scrapy + +# Sphinx documentation +docs/_build/ + +# PyBuilder +target/ + +# Jupyter Notebook +.ipynb_checkpoints + +# IPython +profile_default/ +ipython_config.py + +# pyenv +.python-version + +# pipenv +# According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control. +# However, in case of collaboration, if having platform-specific dependencies or dependencies +# having no cross-platform support, pipenv may install dependencies that don't work, or not +# install all needed dependencies. +#Pipfile.lock + +# PEP 582; used by e.g. github.com/David-OConnor/pyflow +__pypackages__/ + +# Celery stuff +celerybeat-schedule +celerybeat.pid + +# SageMath parsed files +*.sage.py + +# Spyder project settings +.spyderproject +.spyproject + +# Rope project settings +.ropeproject + +# mkdocs documentation +/site + +# mypy +.mypy_cache/ +.dmypy.json +dmypy.json + +# Pyre type checker +.pyre/ diff --git a/README.md b/README.md new file mode 100644 index 0000000..bb5e57a --- /dev/null +++ b/README.md @@ -0,0 +1,374 @@ +# Diffusion Policy + +[[Project page]](https://diffusion-policy.cs.columbia.edu/) +[[Paper]](https://diffusion-policy.cs.columbia.edu/#paper) +[[Data]](https://diffusion-policy.cs.columbia.edu/data/) +[[Colab (state)]](https://colab.research.google.com/drive/1gxdkgRVfM55zihY9TFLja97cSVZOZq2B?usp=sharing) +[[Colab (vision)]](https://colab.research.google.com/drive/18GIHeOQ5DyjMN8iIRZL2EKZ0745NLIpg?usp=sharing) + + +[Cheng Chi](http://cheng-chi.github.io/)1, +[Siyuan Feng](https://www.cs.cmu.edu/~sfeng/)2, +[Yilun Du](https://yilundu.github.io/)3, +[Zhenjia Xu](https://yilundu.github.io/)1, +[Eric Cousineau](https://www.eacousineau.com/)2, +[Benjamin Burchfiel](http://www.benburchfiel.com/)2, +[Shuran Song](https://www.cs.columbia.edu/~shurans/)1 + +1Columbia University, +2Toyota Research Institute, +3MIT + +drawing +drawing + +## ๐Ÿ› Try it out! +Our self-contained Google Colab notebooks is the easiest way to play with Diffusion Policy. We provide separate notebooks for [state-based environment](https://colab.research.google.com/drive/1gxdkgRVfM55zihY9TFLja97cSVZOZq2B?usp=sharing) and [vision-based environment](https://colab.research.google.com/drive/18GIHeOQ5DyjMN8iIRZL2EKZ0745NLIpg?usp=sharing). + +## ๐Ÿงพ Checkout our experiment logs! +For each experiment used to generate Table I,II and IV in the [paper](https://diffusion-policy.cs.columbia.edu/#paper), we provide: +1. A `config.yaml` that contains all parameters needed to reproduce the experiment. +2. Detailed training/eval `logs.json.txt` for every training step. +3. Checkpoints for the best `epoch=*-test_mean_score=*.ckpt` and last `latest.ckpt` epoch of each run. + +Experiment logs are hosted on our website as nested directories in format: +`https://diffusion-policy.cs.columbia.edu/data/experiments////` + +Within each experiment directory you may find: +``` +. +โ”œโ”€โ”€ config.yaml +โ”œโ”€โ”€ metrics +โ”‚ย ย  โ””โ”€โ”€ logs.json.txt +โ”œโ”€โ”€ train_0 +โ”‚ย ย  โ”œโ”€โ”€ checkpoints +โ”‚ย ย  โ”‚ย ย  โ”œโ”€โ”€ epoch=0300-test_mean_score=1.000.ckpt +โ”‚ย ย  โ”‚ย ย  โ””โ”€โ”€ latest.ckpt +โ”‚ย ย  โ””โ”€โ”€ logs.json.txt +โ”œโ”€โ”€ train_1 +โ”‚ย ย  โ”œโ”€โ”€ checkpoints +โ”‚ย ย  โ”‚ย ย  โ”œโ”€โ”€ epoch=0250-test_mean_score=1.000.ckpt +โ”‚ย ย  โ”‚ย ย  โ””โ”€โ”€ latest.ckpt +โ”‚ย ย  โ””โ”€โ”€ logs.json.txt +โ””โ”€โ”€ train_2 + โ”œโ”€โ”€ checkpoints + โ”‚ย ย  โ”œโ”€โ”€ epoch=0250-test_mean_score=1.000.ckpt + โ”‚ย ย  โ””โ”€โ”€ latest.ckpt + โ””โ”€โ”€ logs.json.txt +``` +The `metrics/logs.json.txt` file aggregates evaluation metrics from all 3 training runs every 50 epochs using `multirun_metrics.py`. The numbers reported in the paper correspond to `max` and `k_min_train_loss` aggregation keys. + +To download all files in a subdirectory, use: + +```console +$ wget --recursive --no-parent --no-host-directories --relative --reject="index.html*" https://diffusion-policy.cs.columbia.edu/data/experiments/low_dim/square_ph/diffusion_policy_cnn/ +``` + +## ๐Ÿ› ๏ธ Installation +### ๐Ÿ–ฅ๏ธ Simulation +To reproduce our simulation benchmark results, install our conda environment on a Linux machine with Nvidia GPU. On Ubuntu 20.04 you need to install the following apt packages for mujoco: +```console +sudo apt install -y libosmesa6-dev libgl1-mesa-glx libglfw3 patchelf +``` + +We recommend [Mambaforge](https://github.com/conda-forge/miniforge#mambaforge) instead of the standard anaconda distribution for faster installation: +```console +mamba env create -f conda_environment.yaml +``` + +but you can use conda as well: +```console +conda env create -f conda_environment.yaml +``` + +The `conda_environment_macos.yaml` file is only for development on MacOS and does not have full support for benchmarks. + +### ๐Ÿฆพ Real Robot +Hardware (for Push-T): +* 1x [UR5-CB3](https://www.universal-robots.com/cb3) or [UR5e](https://www.universal-robots.com/products/ur5-robot/) ([RTDE Interface](https://www.universal-robots.com/articles/ur/interface-communication/real-time-data-exchange-rtde-guide/) is required) +* 2x [RealSense D415](https://www.intelrealsense.com/depth-camera-d415/) +* 1x [3Dconnexion SpaceMouse](https://3dconnexion.com/us/product/spacemouse-wireless/) (for teleop) +* 1x [Millibar Robotics Manual Tool Changer](https://www.millibar.com/manual-tool-changer/) (only need robot side) +* 1x 3D printed [End effector](https://cad.onshape.com/documents/a818888644a15afa6cc68ee5/w/2885b48b018cda84f425beca/e/3e8771c2124cee024edd2fed?renderMode=0&uiState=63ffcba6631ca919895e64e5) +* 1x 3D printed [T-block](https://cad.onshape.com/documents/f1140134e38f6ed6902648d5/w/a78cf81827600e4ff4058d03/e/f35f57fb7589f72e05c76caf?renderMode=0&uiState=63ffcbc9af4a881b344898ee) +* USB-C cables and screws for RealSense + +Software: +* Ubuntu 20.04.3 (tested) +* Mujoco dependencies: +`sudo apt install libosmesa6-dev libgl1-mesa-glx libglfw3 patchelf` +* [RealSense SDK](https://github.com/IntelRealSense/librealsense/blob/master/doc/distribution_linux.md) +* Spacemouse dependencies: +`sudo apt apt install libspnav-dev spacenavd; sudo systemctl start spacenavd` +* Conda environment `mamba env create -f conda_environment_real.yaml` + +## ๐Ÿ–ฅ๏ธ Reproducing Simulation Benchmark Results +### Download Training Data +Under the repo root, create data subdirectory: +```console +[diffusion_policy]$ mkdir data && cd data +``` + +Download the corresponding zip file from [https://diffusion-policy.cs.columbia.edu/data/training/](https://diffusion-policy.cs.columbia.edu/data/training/) +```console +[data]$ wget https://diffusion-policy.cs.columbia.edu/data/training/pusht.zip +``` + +Extract training data: +```console +[data]$ unzip pusht.zip && rm -f pusht.zip && cd .. +``` + +Grab config file for the corresponding experiment: +```console +[diffusion_policy]$ wget -O image_pusht_diffusion_policy_cnn.yaml https://diffusion-policy.cs.columbia.edu/data/experiments/image/pusht/diffusion_policy_cnn/config.yaml +``` + +### Running for a single seed +Activate conda environment and login to [wandb](https://wandb.ai) (if you haven't already). +```console +[diffusion_policy]$ conda activate robodiff +(robodiff)[diffusion_policy]$ wandb login +``` + +Launch training with seed 42 on GPU 0. +```console +(robodiff)[diffusion_policy]$ python train.py --config-dir=. --config-name=image_pusht_diffusion_policy_cnn.yaml training.seed=42 training.device=cuda:0 hydra.run.dir='data/outputs/${now:%Y.%m.%d}/${now:%H.%M.%S}_${name}_${task_name}' +``` + +This will create a directory in format `data/outputs/yyyy.mm.dd/hh.mm.ss__` where configs, logs and checkpoints are written to. The policy will be evaluated every 50 epochs with the success rate logged as `test/mean_score` on wandb, as well as videos for some rollouts. +```console +(robodiff)[diffusion_policy]$ tree data/outputs/2023.03.01/20.02.03_train_diffusion_unet_hybrid_pusht_image -I wandb +data/outputs/2023.03.01/20.02.03_train_diffusion_unet_hybrid_pusht_image +โ”œโ”€โ”€ checkpoints +โ”‚ โ”œโ”€โ”€ epoch=0000-test_mean_score=0.134.ckpt +โ”‚ โ””โ”€โ”€ latest.ckpt +โ”œโ”€โ”€ .hydra +โ”‚ โ”œโ”€โ”€ config.yaml +โ”‚ โ”œโ”€โ”€ hydra.yaml +โ”‚ โ””โ”€โ”€ overrides.yaml +โ”œโ”€โ”€ logs.json.txt +โ”œโ”€โ”€ media +โ”‚ โ”œโ”€โ”€ 2k5u6wli.mp4 +โ”‚ โ”œโ”€โ”€ 2kvovxms.mp4 +โ”‚ โ”œโ”€โ”€ 2pxd9f6b.mp4 +โ”‚ โ”œโ”€โ”€ 2q5gjt5f.mp4 +โ”‚ โ”œโ”€โ”€ 2sawbf6m.mp4 +โ”‚ โ””โ”€โ”€ 538ubl79.mp4 +โ””โ”€โ”€ train.log + +3 directories, 13 files +``` + +### Running for multiple seeds +Launch local ray cluster. For large scale experiments, you might want to setup an [AWS cluster with autoscaling](https://docs.ray.io/en/master/cluster/vms/user-guides/launching-clusters/aws.html). All other commands remain the same. +```console +(robodiff)[diffusion_policy]$ export CUDA_VISIBLE_DEVICES=0,1,2 # select GPUs to be managed by the ray cluster +(robodiff)[diffusion_policy]$ ray start --head --num-gpus=3 +``` + +Launch a ray client which will start 3 training workers (3 seeds) and 1 metrics monitor worker. +```console +(robodiff)[diffusion_policy]$ python ray_train_multirun.py --config-dir=. --config-name=image_pusht_diffusion_policy_cnn.yaml --seeds=42,43,44 --monitor_key=test/mean_score -- multi_run.run_dir='data/outputs/${now:%Y.%m.%d}/${now:%H.%M.%S}_${name}_${task_name}' multi_run.wandb_name_base='${now:%Y.%m.%d-%H.%M.%S}_${name}_${task_name}' +``` + +In addition to the wandb log written by each training worker individually, the metrics monitor worker will log to wandb project `diffusion_policy_metrics` for the metrics aggregated from all 3 training runs. Local config, logs and checkpoints will be written to `data/outputs/yyyy.mm.dd/hh.mm.ss__` in a directory structure identical to our [training logs](https://diffusion-policy.cs.columbia.edu/data/experiments/): +```console +(robodiff)[diffusion_policy]$ tree data/outputs/2023.03.01/22.13.58_train_diffusion_unet_hybrid_pusht_image -I 'wandb|media' +data/outputs/2023.03.01/22.13.58_train_diffusion_unet_hybrid_pusht_image +โ”œโ”€โ”€ config.yaml +โ”œโ”€โ”€ metrics +โ”‚ โ”œโ”€โ”€ logs.json.txt +โ”‚ โ”œโ”€โ”€ metrics.json +โ”‚ โ””โ”€โ”€ metrics.log +โ”œโ”€โ”€ train_0 +โ”‚ โ”œโ”€โ”€ checkpoints +โ”‚ โ”‚ โ”œโ”€โ”€ epoch=0000-test_mean_score=0.174.ckpt +โ”‚ โ”‚ โ””โ”€โ”€ latest.ckpt +โ”‚ โ”œโ”€โ”€ logs.json.txt +โ”‚ โ””โ”€โ”€ train.log +โ”œโ”€โ”€ train_1 +โ”‚ โ”œโ”€โ”€ checkpoints +โ”‚ โ”‚ โ”œโ”€โ”€ epoch=0000-test_mean_score=0.131.ckpt +โ”‚ โ”‚ โ””โ”€โ”€ latest.ckpt +โ”‚ โ”œโ”€โ”€ logs.json.txt +โ”‚ โ””โ”€โ”€ train.log +โ””โ”€โ”€ train_2 + โ”œโ”€โ”€ checkpoints + โ”‚ โ”œโ”€โ”€ epoch=0000-test_mean_score=0.105.ckpt + โ”‚ โ””โ”€โ”€ latest.ckpt + โ”œโ”€โ”€ logs.json.txt + โ””โ”€โ”€ train.log + +7 directories, 16 files +``` + +## ๐Ÿฆพ Demo, Training and Eval on a Real Robot +Make sure your UR5 robot is running and accepting command from its network interface (emergency stop button within reach at all time), your RealSense cameras plugged in to your workstation (tested with `realsense-viewer`) and your SpaceMouse connected with the `spacenavd` deamon running (verify with `systemctl status spacenavd`). + +Start the demonstration collection script. Press "C" to start recording. Use SpaceMouse to move the robot. Press "S" to stop recording. +```console +(robodiff)[diffusion_policy]$ python demo_real_robot.py -o data/demo_pusht_real --robot_ip 192.168.0.204 +``` + +This should result in a demonstration dataset in `data/demo_pusht_real` with in the same structure as our example [real Push-T training dataset](https://diffusion-policy.cs.columbia.edu/data/training/pusht_real.zip). + +To train a Diffusion Policy, launch training with config: +```console +(robodiff)[diffusion_policy]$ python train.py --config-name=train_diffusion_unet_real_image_workspace task.dataset_path=data/demo_pusht_real +``` +Edit [`diffusion_policy/config/task/real_pusht_image.yaml`](./diffusion_policy/config/task/real_pusht_image.yaml) if your camera setup is different. + +Assuming the training has finished and you have a checkpoint at `data/outputs/blah/checkpoints/latest.ckpt`, launch the evaluation script with: +```console +python eval_real_robot.py -i data/outputs/blah/checkpoints/latest.ckpt -o data/eval_pusht_real --robot_ip 192.168.0.204 +``` +Press "C" to start evaluation (handing control over to the policy). Press "S" to stop the current episode. + +## ๐Ÿ—บ๏ธ Codebase Tutorial +This codebase is structured under the requirement that: +1. implementing `N` tasks and `M` methods will only require `O(N+M)` amount of code instead of `O(N*M)` +2. while retaining maximum flexibility. + +To achieve this requirement, we +1. maintained a simple unified interface between tasks and methods and +2. made the implementation of the tasks and the methods independent of each other. + +These design decisions come at the cost of code repetition between the tasks and the methods. However, we believe that the benefit of being able to add/modify task/methods without affecting the remainder and being able understand a task/method by reading the code linearly outweighs the cost of copying and pasting ๐Ÿ˜Š. + +### The Split +On the task side, we have: +* `Dataset`: adapts a (third-party) dataset to the interface. +* `EnvRunner`: executes a `Policy` that accepts the interface and produce logs and metrics. +* `config/task/.yaml`: contains all information needed to construct `Dataset` and `EnvRunner`. +* (optional) `Env`: an `gym==0.21.0` compatible class that encapsulates the task environment. + +On the policy side, we have: +* `Policy`: implements inference according to the interface and part of the training process. +* `Workspace`: manages the life-cycle of training and evaluation (interleaved) of a method. +* `config/.yaml`: contains all information needed to construct `Policy` and `Workspace`. + +### The Interface +#### Low Dim +A [`LowdimPolicy`](./diffusion_policy/policy/base_lowdim_policy.py) takes observation dictionary: +- `"obs":` Tensor of shape `(B,To,Do)` + +and predicts action dictionary: +- `"action": ` Tensor of shape `(B,Ta,Da)` + +A [`LowdimDataset`](./diffusion_policy/dataset/base_dataset.py) returns a sample of dictionary: +- `"obs":` Tensor of shape `(To, Do)` +- `"action":` Tensor of shape `(Ta, Da)` + +Its `get_normalizer` method returns a [`LinearNormalizer`](./diffusion_policy/model/common/normalizer.py) with keys `"obs","action"`. + +The `Policy` handles normalization on GPU with its copy of the `LinearNormalizer`. The parameters of the `LinearNormalizer` is saved as part of the `Policy`'s weights checkpoint. + +#### Image +A [`ImagePolicy`](./diffusion_policy/policy/base_image_policy.py) takes observation dictionary: +- `"key0":` Tensor of shape `(B,To,*)` +- `"key1":` Tensor of shape e.g. `(B,To,H,W,3)` ([0,1] float32) + +and predicts action dictionary: +- `"action": ` Tensor of shape `(B,Ta,Da)` + +A [`ImageDataset`](./diffusion_policy/dataset/base_dataset.py) returns a sample of dictionary: +- `"obs":` Dict of + - `"key0":` Tensor of shape `(To, *)` + - `"key1":` Tensor fo shape `(To,H,W,3)` +- `"action":` Tensor of shape `(Ta, Da)` + +Its `get_normalizer` method returns a [`LinearNormalizer`](./diffusion_policy/model/common/normalizer.py) with keys `"key0","key1","action"`. + +#### Example +``` +To = 3 +Ta = 4 +T = 6 +|o|o|o| +| | |a|a|a|a| +|o|o| +| |a|a|a|a|a| +| | | | |a|a| +``` +Terminology in the paper: `varname` in the codebase +- Observation Horizon: `To|n_obs_steps` +- Action Horizon: `Ta|n_action_steps` +- Prediction Horizon: `T|horizon` + +The classical (e.g. MDP) single step observation/action formulation is included as a special case where `To=1` and `Ta=1`. + +## ๐Ÿ”ฉ Key Components +### `Workspace` +A `Workspace` object encapsulates all states and code needed to run an experiment. +* Inherits from [`BaseWorkspace`](./diffusion_policy/workspace/base_workspace.py). +* A single `OmegaConf` config object generated by `hydra` should contain all information needed to construct the Workspace object and running experiments. This config correspond to `config/.yaml` + hydra overrides. +* The `run` method contains the entire pipeline for the experiment. +* Checkpoints happen at the `Workspace` level. All training states implemented as object attributes are automatically saved by the `save_checkpoint` method. +* All other states for the experiment should be implemented as local variables in the `run` method. + +The entrypoint for training is `train.py` which uses `@hydra.main` decorator. Read [hydra](https://hydra.cc/)'s official documentation for command line arguments and config overrides. For example, the argument `task=` will replace the `task` subtree of the config with the content of `config/task/.yaml`, thereby selecting the task to run for this experiment. + +### `Dataset` +A `Dataset` object: +* Inherits from `torch.utils.data.Dataset`. +* Returns a sample conforming to [the interface](#the-interface) depending on whether the task has Low Dim or Image observations. +* Has a method `get_normalizer` that returns a `LinearNormalizer` conforming to [the interface](#the-interface). + +Normalization is a very common source of bugs during project development. It is sometimes helpful to print out the specific `scale` and `bias` vectors used for each key in the `LinearNormalizer`. + +Most of our implementations of `Dataset` uses a combination of [`ReplayBuffer`](#replaybuffer) and [`SequenceSampler`](./diffusion_policy/common/sampler.py) to generate samples. Correctly handling padding at the beginning and the end of each demonstration episode according to `To` and `Ta` is important for good performance. Please read our [`SequenceSampler`](./diffusion_policy/common/sampler.py) before implementing your own sampling method. + +### `Policy` +A `Policy` object: +* Inherits from `BaseLowdimPolicy` or `BaseImagePolicy`. +* Has a method `predict_action` that given observation dict, predicts actions conforming to [the interface](#the-interface). +* Has a method `set_normalizer` that takes in a `LinearNormalizer` and handles observation/action normalization internally in the policy. +* (optional) Might has a method `compute_loss` that takes in a batch and returns the loss to be optimized. +* (optional) Usually each `Policy` class correspond to a `Workspace` class due to the differences of training and evaluation process between methods. + +### `EnvRunner` +A `EnvRunner` object abstracts away the subtle differences between different task environments. +* Has a method `run` that takes a `Policy` object for evaluation, and returns a dict of logs and metrics. Each value should be compatible with `wandb.log`. + +To maximize evaluation speed, we usually vectorize environments using our modification of [`gym.vector.AsyncVectorEnv`](./diffusion_policy/gym_util/async_vector_env.py) which runs each individual environment in a separate process (workaround python GIL). + +โš ๏ธ Since subprocesses are launched using `fork` on linux, you need to be specially careful for environments that creates its OpenGL context during initialization (e.g. robosuite) which, once inherited by the child process memory space, often causes obscure bugs like segmentation fault. As a workaround, you can provide a `dummy_env_fn` that constructs an environment without initializing OpenGL. + +### `ReplayBuffer` +The [`ReplayBuffer`](./diffusion_policy/common/replay_buffer.py) is a key data structure for storing a demonstration dataset both in-memory and on-disk with chunking and compression. It makes heavy use of the [`zarr`](https://zarr.readthedocs.io/en/stable/index.html) format but also has a `numpy` backend for lower access overhead. + +On disk, it can be stored as a nested directory (e.g. `data/pusht_cchi_v7_replay.zarr`) or a zip file (e.g. `data/robomimic/datasets/square/mh/image_abs.hdf5.zarr.zip`). + +Due to the relative small size of our datasets, it's often possible to store the entire image-based dataset in RAM with [`Jpeg2000` compression](./diffusion_policy/codecs/imagecodecs_numcodecs.py) which eliminates disk IO during training at the expense increasing of CPU workload. + +Example: +``` +data/pusht_cchi_v7_replay.zarr + โ”œโ”€โ”€ data + โ”‚ โ”œโ”€โ”€ action (25650, 2) float32 + โ”‚ โ”œโ”€โ”€ img (25650, 96, 96, 3) float32 + โ”‚ โ”œโ”€โ”€ keypoint (25650, 9, 2) float32 + โ”‚ โ”œโ”€โ”€ n_contacts (25650, 1) float32 + โ”‚ โ””โ”€โ”€ state (25650, 5) float32 + โ””โ”€โ”€ meta + โ””โ”€โ”€ episode_ends (206,) int64 +``` + +Each array in `data` stores one data field from all episodes concatenated along the first dimension (time). The `meta/episode_ends` array stores the end index for each episode along the fist dimension. + +## ๐Ÿฉน Adding a Task +Read and imitate: +* `diffusion_policy/dataset/pusht_image_dataset.py` +* `diffusion_policy/env_runner/pusht_image_runner.py` +* `diffusion_policy/config/task/pusht_image.yaml` + +Make sure that `shape_meta` correspond to input and output shapes for your task. Make sure `env_runner._target_` and `dataset._target_` point to the new classes you have added. When training, add `task=` to `train.py`'s arguments. + +## ๐Ÿฉน Adding a Method +Read and imitate: +* `diffusion_policy/workspace/train_diffusion_unet_image_workspace.py` +* `diffusion_policy/policy/diffusion_unet_image_policy.py` +* `diffusion_policy/config/train_diffusion_unet_image_workspace.yaml` + +Make sure your workspace yaml's `_target_` points to the new workspace class you created. diff --git a/conda_environment.yaml b/conda_environment.yaml new file mode 100644 index 0000000..97cbe9e --- /dev/null +++ b/conda_environment.yaml @@ -0,0 +1,63 @@ +name: robodiff +channels: + - pytorch + - pytorch3d + - nvidia + - conda-forge +dependencies: + - python=3.9 + - pip=22.2.2 + - cudatoolkit=11.6 + - pytorch=1.12.1 + - torchvision=0.13.1 + - pytorch3d=0.7.0 + - numpy=1.23.3 + - numba==0.56.4 + - scipy==1.9.1 + - py-opencv=4.6.0 + - cffi=1.15.1 + - ipykernel=6.16 + - matplotlib=3.6.1 + - zarr=2.12.0 + - numcodecs=0.10.2 + - h5py=3.7.0 + - hydra-core=1.2.0 + - einops=0.4.1 + - tqdm=4.64.1 + - dill=0.3.5.1 + - scikit-video=1.1.11 + - scikit-image=0.19.3 + - gym=0.21.0 + - pymunk=6.2.1 + - wandb=0.13.3 + - threadpoolctl=3.1.0 + - shapely=1.8.4 + - cython=0.29.32 + - imageio=2.22.0 + - imageio-ffmpeg=0.4.7 + - termcolor=2.0.1 + - tensorboard=2.10.1 + - tensorboardx=2.5.1 + - psutil=5.9.2 + - click=8.0.4 + - boto3=1.24.96 + - accelerate=0.13.2 + - datasets=2.6.1 + - diffusers=0.11.1 + - av=10.0.0 + - cmake=3.24.3 + # trick to force reinstall imagecodecs via pip + - imagecodecs==2022.8.8 + - pip: + - ray[default,tune]==2.2.0 + # requires mujoco py dependencies libosmesa6-dev libgl1-mesa-glx libglfw3 patchelf + - free-mujoco-py==2.1.6 + - pygame==2.1.2 + - pybullet-svl==3.1.6.4 + - robosuite @ https://github.com/cheng-chi/robosuite/archive/277ab9588ad7a4f4b55cf75508b44aa67ec171f0.tar.gz + - robomimic==0.2.0 + - pytorchvideo==0.1.5 + # pip package required for jpeg-xl + - imagecodecs==2022.9.26 + - r3m @ https://github.com/facebookresearch/r3m/archive/b2334e726887fa0206962d7984c69c5fb09cceab.tar.gz + - dm-control==1.0.9 diff --git a/conda_environment_macos.yaml b/conda_environment_macos.yaml new file mode 100644 index 0000000..0d26767 --- /dev/null +++ b/conda_environment_macos.yaml @@ -0,0 +1,55 @@ +name: robodiff +channels: + - pytorch + - conda-forge +dependencies: + - python=3.9 + - pip=22.2.2 + - pytorch=1.12.1 + - torchvision=0.13.1 + - numpy=1.23.3 + - numba==0.56.4 + - scipy==1.9.1 + - py-opencv=4.6.0 + - cffi=1.15.1 + - ipykernel=6.16 + - matplotlib=3.6.1 + - zarr=2.12.0 + - numcodecs=0.10.2 + - h5py=3.7.0 + - hydra-core=1.2.0 + - einops=0.4.1 + - tqdm=4.64.1 + - dill=0.3.5.1 + - scikit-video=1.1.11 + - scikit-image=0.19.3 + - gym=0.21.0 + - pymunk=6.2.1 + - wandb=0.13.3 + - threadpoolctl=3.1.0 + - shapely=1.8.4 + - cython=0.29.32 + - imageio=2.22.0 + - imageio-ffmpeg=0.4.7 + - termcolor=2.0.1 + - tensorboard=2.10.1 + - tensorboardx=2.5.1 + - psutil=5.9.2 + - click=8.0.4 + - boto3=1.24.96 + - accelerate=0.13.2 + - datasets=2.6.1 + - diffusers=0.11.1 + - av=10.0.0 + - cmake=3.24.3 + # trick to force reinstall imagecodecs via pip + - imagecodecs==2022.8.8 + - pip: + - ray[default,tune]==2.2.0 + - pygame==2.1.2 + - robomimic==0.2.0 + - pytorchvideo==0.1.5 + - atomics==1.0.2 + # No support for jpeg-xl for MacOS + - imagecodecs==2022.9.26 + - r3m @ https://github.com/facebookresearch/r3m/archive/b2334e726887fa0206962d7984c69c5fb09cceab.tar.gz diff --git a/conda_environment_real.yaml b/conda_environment_real.yaml new file mode 100644 index 0000000..1f84599 --- /dev/null +++ b/conda_environment_real.yaml @@ -0,0 +1,71 @@ +name: robodiff +channels: + - pytorch + - pytorch3d + - nvidia + - conda-forge +dependencies: + - python=3.9 + - pip=22.2.2 + - cudatoolkit=11.6 + - pytorch=1.12.1 + - torchvision=0.13.1 + - pytorch3d=0.7.0 + - numpy=1.23.3 + - numba==0.56.4 + - scipy==1.9.1 + - py-opencv=4.6.0 + - cffi=1.15.1 + - ipykernel=6.16 + - matplotlib=3.6.1 + - zarr=2.12.0 + - numcodecs=0.10.2 + - h5py=3.7.0 + - hydra-core=1.2.0 + - einops=0.4.1 + - tqdm=4.64.1 + - dill=0.3.5.1 + - scikit-video=1.1.11 + - scikit-image=0.19.3 + - gym=0.21.0 + - pymunk=6.2.1 + - wandb=0.13.3 + - threadpoolctl=3.1.0 + - shapely=1.8.4 + - cython=0.29.32 + - imageio=2.22.0 + - imageio-ffmpeg=0.4.7 + - termcolor=2.0.1 + - tensorboard=2.10.1 + - tensorboardx=2.5.1 + - psutil=5.9.2 + - click=8.0.4 + - boto3=1.24.96 + - accelerate=0.13.2 + - datasets=2.6.1 + - diffusers=0.11.1 + - av=10.0.0 + - cmake=3.24.3 + # trick to force reinstall imagecodecs via pip + - imagecodecs==2022.8.8 + - pip: + - ray[default,tune]==2.2.0 + # requires mujoco py dependencies libosmesa6-dev libgl1-mesa-glx libglfw3 patchelf + - free-mujoco-py==2.1.6 + - pygame==2.1.2 + - pybullet-svl==3.1.6.4 + - robosuite @ https://github.com/cheng-chi/robosuite/archive/277ab9588ad7a4f4b55cf75508b44aa67ec171f0.tar.gz + - robomimic==0.2.0 + - pytorchvideo==0.1.5 + # requires librealsense https://github.com/IntelRealSense/librealsense/blob/master/doc/distribution_linux.md + - pyrealsense2==2.51.1.4348 + # requires apt install libspnav-dev spacenavd; systemctl start spacenavd + - spnav @ https://github.com/cheng-chi/spnav/archive/c1c938ebe3cc542db4685e0d13850ff1abfdb943.tar.gz + - ur-rtde==1.5.5 + - atomics==1.0.2 + # pip package required for jpeg-xl + - imagecodecs==2022.9.26 + - r3m @ https://github.com/facebookresearch/r3m/archive/b2334e726887fa0206962d7984c69c5fb09cceab.tar.gz + - dm-control==1.0.9 + - pynput==1.7.6 + \ No newline at end of file diff --git a/demo_real_robot.py b/demo_real_robot.py new file mode 100644 index 0000000..846bade --- /dev/null +++ b/demo_real_robot.py @@ -0,0 +1,160 @@ +""" +Usage: +(robodiff)$ python demo_real_robot.py -o --robot_ip + +Robot movement: +Move your SpaceMouse to move the robot EEF (locked in xy plane). +Press SpaceMouse right button to unlock z axis. +Press SpaceMouse left button to enable rotation axes. + +Recording control: +Click the opencv window (make sure it's in focus). +Press "C" to start recording. +Press "S" to stop recording. +Press "Q" to exit program. +Press "Backspace" to delete the previously recorded episode. +""" + +# %% +import time +from multiprocessing.managers import SharedMemoryManager +import click +import cv2 +import numpy as np +import scipy.spatial.transform as st +from diffusion_policy.real_world.real_env import RealEnv +from diffusion_policy.real_world.spacemouse_shared_memory import Spacemouse +from diffusion_policy.common.precise_sleep import precise_wait +from diffusion_policy.real_world.keystroke_counter import ( + KeystrokeCounter, Key, KeyCode +) + +@click.command() +@click.option('--output', '-o', required=True, help="Directory to save demonstration dataset.") +@click.option('--robot_ip', '-ri', required=True, help="UR5's IP address e.g. 192.168.0.204") +@click.option('--vis_camera_idx', default=0, type=int, help="Which RealSense camera to visualize.") +@click.option('--init_joints', '-j', is_flag=True, default=False, help="Whether to initialize robot joint configuration in the beginning.") +@click.option('--frequency', '-f', default=10, type=float, help="Control frequency in Hz.") +@click.option('--command_latency', '-cl', default=0.01, type=float, help="Latency between receiving SapceMouse command to executing on Robot in Sec.") +def main(output, robot_ip, vis_camera_idx, init_joints, frequency, command_latency): + dt = 1/frequency + with SharedMemoryManager() as shm_manager: + with KeystrokeCounter() as key_counter, \ + Spacemouse(shm_manager=shm_manager) as sm, \ + RealEnv( + output_dir=output, + robot_ip=robot_ip, + # recording resolution + obs_image_resolution=(1280,720), + frequency=frequency, + init_joints=init_joints, + enable_multi_cam_vis=True, + record_raw_video=True, + # number of threads per camera view for video recording (H.264) + thread_per_video=3, + # video recording quality, lower is better (but slower). + video_crf=21, + shm_manager=shm_manager + ) as env: + cv2.setNumThreads(1) + + # realsense exposure + env.realsense.set_exposure(exposure=120, gain=0) + # realsense white balance + env.realsense.set_white_balance(white_balance=5900) + + time.sleep(1.0) + print('Ready!') + state = env.get_robot_state() + target_pose = state['TargetTCPPose'] + t_start = time.monotonic() + iter_idx = 0 + stop = False + is_recording = False + while not stop: + # calculate timing + t_cycle_end = t_start + (iter_idx + 1) * dt + t_sample = t_cycle_end - command_latency + t_command_target = t_cycle_end + dt + + # pump obs + obs = env.get_obs() + + # handle key presses + press_events = key_counter.get_press_events() + for key_stroke in press_events: + if key_stroke == KeyCode(char='q'): + # Exit program + stop = True + elif key_stroke == KeyCode(char='c'): + # Start recording + env.start_episode(t_start + (iter_idx + 2) * dt - time.monotonic() + time.time()) + key_counter.clear() + is_recording = True + print('Recording!') + elif key_stroke == KeyCode(char='s'): + # Stop recording + env.end_episode() + key_counter.clear() + is_recording = False + print('Stopped.') + elif key_stroke == Key.backspace: + # Delete the most recent recorded episode + if click.confirm('Are you sure to drop an episode?'): + env.drop_episode() + key_counter.clear() + is_recording = False + # delete + stage = key_counter[Key.space] + + # visualize + vis_img = obs[f'camera_{vis_camera_idx}'][-1,:,:,::-1].copy() + episode_id = env.replay_buffer.n_episodes + text = f'Episode: {episode_id}, Stage: {stage}' + if is_recording: + text += ', Recording!' + cv2.putText( + vis_img, + text, + (10,30), + fontFace=cv2.FONT_HERSHEY_SIMPLEX, + fontScale=1, + thickness=2, + color=(255,255,255) + ) + + cv2.imshow('default', vis_img) + cv2.pollKey() + + precise_wait(t_sample) + # get teleop command + sm_state = sm.get_motion_state_transformed() + # print(sm_state) + dpos = sm_state[:3] * (env.max_pos_speed / frequency) + drot_xyz = sm_state[3:] * (env.max_rot_speed / frequency) + + if not sm.is_button_pressed(0): + # translation mode + drot_xyz[:] = 0 + else: + dpos[:] = 0 + if not sm.is_button_pressed(1): + # 2D translation mode + dpos[2] = 0 + + drot = st.Rotation.from_euler('xyz', drot_xyz) + target_pose[:3] += dpos + target_pose[3:] = (drot * st.Rotation.from_rotvec( + target_pose[3:])).as_rotvec() + + # execute teleop command + env.exec_actions( + actions=[target_pose], + timestamps=[t_command_target-time.monotonic()+time.time()], + stages=[stage]) + precise_wait(t_cycle_end) + iter_idx += 1 + +# %% +if __name__ == '__main__': + main() diff --git a/diffusion_policy/codecs/imagecodecs_numcodecs.py b/diffusion_policy/codecs/imagecodecs_numcodecs.py new file mode 100644 index 0000000..cf55b97 --- /dev/null +++ b/diffusion_policy/codecs/imagecodecs_numcodecs.py @@ -0,0 +1,1386 @@ + +# imagecodecs/numcodecs.py + +# Copyright (c) 2021-2022, 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. +# +# 3. Neither the name of the copyright holder nor the names of its +# contributors may be used to endorse or promote products derived from +# this software without specific prior written permission. +# +# 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. + +"""Additional numcodecs implemented using imagecodecs.""" + +__version__ = '2022.9.26' + +__all__ = ('register_codecs',) + +import numpy +from numcodecs.abc import Codec +from numcodecs.registry import register_codec, get_codec + +import imagecodecs + + +def protective_squeeze(x: numpy.ndarray): + """ + Squeeze dim only if it's not the last dim. + Image dim expected to be *, H, W, C + """ + img_shape = x.shape[-3:] + if len(x.shape) > 3: + n_imgs = numpy.prod(x.shape[:-3]) + if n_imgs > 1: + img_shape = (-1,) + img_shape + return x.reshape(img_shape) + +def get_default_image_compressor(**kwargs): + if imagecodecs.JPEGXL: + # has JPEGXL + this_kwargs = { + 'effort': 3, + 'distance': 0.3, + # bug in libjxl, invalid codestream for non-lossless + # when decoding speed > 1 + 'decodingspeed': 1 + } + this_kwargs.update(kwargs) + return JpegXl(**this_kwargs) + else: + this_kwargs = { + 'level': 50 + } + this_kwargs.update(kwargs) + return Jpeg2k(**this_kwargs) + +class Aec(Codec): + """AEC codec for numcodecs.""" + + codec_id = 'imagecodecs_aec' + + def __init__( + self, bitspersample=None, flags=None, blocksize=None, rsi=None + ): + self.bitspersample = bitspersample + self.flags = flags + self.blocksize = blocksize + self.rsi = rsi + + def encode(self, buf): + return imagecodecs.aec_encode( + buf, + bitspersample=self.bitspersample, + flags=self.flags, + blocksize=self.blocksize, + rsi=self.rsi, + ) + + def decode(self, buf, out=None): + return imagecodecs.aec_decode( + buf, + bitspersample=self.bitspersample, + flags=self.flags, + blocksize=self.blocksize, + rsi=self.rsi, + out=_flat(out), + ) + + +class Apng(Codec): + """APNG codec for numcodecs.""" + + codec_id = 'imagecodecs_apng' + + def __init__(self, level=None, photometric=None, delay=None): + self.level = level + self.photometric = photometric + self.delay = delay + + def encode(self, buf): + buf = protective_squeeze(numpy.asarray(buf)) + return imagecodecs.apng_encode( + buf, + level=self.level, + photometric=self.photometric, + delay=self.delay, + ) + + def decode(self, buf, out=None): + return imagecodecs.apng_decode(buf, out=out) + + +class Avif(Codec): + """AVIF codec for numcodecs.""" + + codec_id = 'imagecodecs_avif' + + def __init__( + self, + level=None, + speed=None, + tilelog2=None, + bitspersample=None, + pixelformat=None, + numthreads=None, + index=None, + ): + self.level = level + self.speed = speed + self.tilelog2 = tilelog2 + self.bitspersample = bitspersample + self.pixelformat = pixelformat + self.numthreads = numthreads + self.index = index + + def encode(self, buf): + buf = protective_squeeze(numpy.asarray(buf)) + return imagecodecs.avif_encode( + buf, + level=self.level, + speed=self.speed, + tilelog2=self.tilelog2, + bitspersample=self.bitspersample, + pixelformat=self.pixelformat, + numthreads=self.numthreads, + ) + + def decode(self, buf, out=None): + return imagecodecs.avif_decode( + buf, index=self.index, numthreads=self.numthreads, out=out + ) + + +class Bitorder(Codec): + """Bitorder codec for numcodecs.""" + + codec_id = 'imagecodecs_bitorder' + + def encode(self, buf): + return imagecodecs.bitorder_encode(buf) + + def decode(self, buf, out=None): + return imagecodecs.bitorder_decode(buf, out=_flat(out)) + + +class Bitshuffle(Codec): + """Bitshuffle codec for numcodecs.""" + + codec_id = 'imagecodecs_bitshuffle' + + def __init__(self, itemsize=1, blocksize=0): + self.itemsize = itemsize + self.blocksize = blocksize + + def encode(self, buf): + return imagecodecs.bitshuffle_encode( + buf, itemsize=self.itemsize, blocksize=self.blocksize + ).tobytes() + + def decode(self, buf, out=None): + return imagecodecs.bitshuffle_decode( + buf, + itemsize=self.itemsize, + blocksize=self.blocksize, + out=_flat(out), + ) + + +class Blosc(Codec): + """Blosc codec for numcodecs.""" + + codec_id = 'imagecodecs_blosc' + + def __init__( + self, + level=None, + compressor=None, + typesize=None, + blocksize=None, + shuffle=None, + numthreads=None, + ): + self.level = level + self.compressor = compressor + self.typesize = typesize + self.blocksize = blocksize + self.shuffle = shuffle + self.numthreads = numthreads + + def encode(self, buf): + buf = protective_squeeze(numpy.asarray(buf)) + return imagecodecs.blosc_encode( + buf, + level=self.level, + compressor=self.compressor, + typesize=self.typesize, + blocksize=self.blocksize, + shuffle=self.shuffle, + numthreads=self.numthreads, + ) + + def decode(self, buf, out=None): + return imagecodecs.blosc_decode( + buf, numthreads=self.numthreads, out=_flat(out) + ) + + +class Blosc2(Codec): + """Blosc2 codec for numcodecs.""" + + codec_id = 'imagecodecs_blosc2' + + def __init__( + self, + level=None, + compressor=None, + typesize=None, + blocksize=None, + shuffle=None, + numthreads=None, + ): + self.level = level + self.compressor = compressor + self.typesize = typesize + self.blocksize = blocksize + self.shuffle = shuffle + self.numthreads = numthreads + + def encode(self, buf): + buf = protective_squeeze(numpy.asarray(buf)) + return imagecodecs.blosc2_encode( + buf, + level=self.level, + compressor=self.compressor, + typesize=self.typesize, + blocksize=self.blocksize, + shuffle=self.shuffle, + numthreads=self.numthreads, + ) + + def decode(self, buf, out=None): + return imagecodecs.blosc2_decode( + buf, numthreads=self.numthreads, out=_flat(out) + ) + + +class Brotli(Codec): + """Brotli codec for numcodecs.""" + + codec_id = 'imagecodecs_brotli' + + def __init__(self, level=None, mode=None, lgwin=None): + self.level = level + self.mode = mode + self.lgwin = lgwin + + def encode(self, buf): + return imagecodecs.brotli_encode( + buf, level=self.level, mode=self.mode, lgwin=self.lgwin + ) + + def decode(self, buf, out=None): + return imagecodecs.brotli_decode(buf, out=_flat(out)) + + +class ByteShuffle(Codec): + """ByteShuffle codec for numcodecs.""" + + codec_id = 'imagecodecs_byteshuffle' + + def __init__( + self, shape, dtype, axis=-1, dist=1, delta=False, reorder=False + ): + self.shape = tuple(shape) + self.dtype = numpy.dtype(dtype).str + self.axis = axis + self.dist = dist + self.delta = bool(delta) + self.reorder = bool(reorder) + + def encode(self, buf): + buf = protective_squeeze(numpy.asarray(buf)) + assert buf.shape == self.shape + assert buf.dtype == self.dtype + return imagecodecs.byteshuffle_encode( + buf, + axis=self.axis, + dist=self.dist, + delta=self.delta, + reorder=self.reorder, + ).tobytes() + + def decode(self, buf, out=None): + if not isinstance(buf, numpy.ndarray): + buf = numpy.frombuffer(buf, dtype=self.dtype).reshape(*self.shape) + return imagecodecs.byteshuffle_decode( + buf, + axis=self.axis, + dist=self.dist, + delta=self.delta, + reorder=self.reorder, + out=out, + ) + + +class Bz2(Codec): + """Bz2 codec for numcodecs.""" + + codec_id = 'imagecodecs_bz2' + + def __init__(self, level=None): + self.level = level + + def encode(self, buf): + return imagecodecs.bz2_encode(buf, level=self.level) + + def decode(self, buf, out=None): + return imagecodecs.bz2_decode(buf, out=_flat(out)) + + +class Cms(Codec): + """CMS codec for numcodecs.""" + + codec_id = 'imagecodecs_cms' + + def __init__(self, *args, **kwargs): + pass + + def encode(self, buf, out=None): + # return imagecodecs.cms_transform(buf) + raise NotImplementedError + + def decode(self, buf, out=None): + # return imagecodecs.cms_transform(buf) + raise NotImplementedError + + +class Deflate(Codec): + """Deflate codec for numcodecs.""" + + codec_id = 'imagecodecs_deflate' + + def __init__(self, level=None, raw=False): + self.level = level + self.raw = bool(raw) + + def encode(self, buf): + return imagecodecs.deflate_encode(buf, level=self.level, raw=self.raw) + + def decode(self, buf, out=None): + return imagecodecs.deflate_decode(buf, out=_flat(out), raw=self.raw) + + +class Delta(Codec): + """Delta codec for numcodecs.""" + + codec_id = 'imagecodecs_delta' + + def __init__(self, shape=None, dtype=None, axis=-1, dist=1): + self.shape = None if shape is None else tuple(shape) + self.dtype = None if dtype is None else numpy.dtype(dtype).str + self.axis = axis + self.dist = dist + + def encode(self, buf): + if self.shape is not None or self.dtype is not None: + buf = protective_squeeze(numpy.asarray(buf)) + assert buf.shape == self.shape + assert buf.dtype == self.dtype + return imagecodecs.delta_encode( + buf, axis=self.axis, dist=self.dist + ).tobytes() + + def decode(self, buf, out=None): + if self.shape is not None or self.dtype is not None: + buf = numpy.frombuffer(buf, dtype=self.dtype).reshape(*self.shape) + return imagecodecs.delta_decode( + buf, axis=self.axis, dist=self.dist, out=out + ) + + +class Float24(Codec): + """Float24 codec for numcodecs.""" + + codec_id = 'imagecodecs_float24' + + def __init__(self, byteorder=None, rounding=None): + self.byteorder = byteorder + self.rounding = rounding + + def encode(self, buf): + buf = protective_squeeze(numpy.asarray(buf)) + return imagecodecs.float24_encode( + buf, byteorder=self.byteorder, rounding=self.rounding + ) + + def decode(self, buf, out=None): + return imagecodecs.float24_decode( + buf, byteorder=self.byteorder, out=out + ) + + +class FloatPred(Codec): + """Floating Point Predictor codec for numcodecs.""" + + codec_id = 'imagecodecs_floatpred' + + def __init__(self, shape, dtype, axis=-1, dist=1): + self.shape = tuple(shape) + self.dtype = numpy.dtype(dtype).str + self.axis = axis + self.dist = dist + + def encode(self, buf): + buf = protective_squeeze(numpy.asarray(buf)) + assert buf.shape == self.shape + assert buf.dtype == self.dtype + return imagecodecs.floatpred_encode( + buf, axis=self.axis, dist=self.dist + ).tobytes() + + def decode(self, buf, out=None): + if not isinstance(buf, numpy.ndarray): + buf = numpy.frombuffer(buf, dtype=self.dtype).reshape(*self.shape) + return imagecodecs.floatpred_decode( + buf, axis=self.axis, dist=self.dist, out=out + ) + + +class Gif(Codec): + """GIF codec for numcodecs.""" + + codec_id = 'imagecodecs_gif' + + def encode(self, buf): + buf = protective_squeeze(numpy.asarray(buf)) + return imagecodecs.gif_encode(buf) + + def decode(self, buf, out=None): + return imagecodecs.gif_decode(buf, asrgb=False, out=out) + + +class Heif(Codec): + """HEIF codec for numcodecs.""" + + codec_id = 'imagecodecs_heif' + + def __init__( + self, + level=None, + bitspersample=None, + photometric=None, + compression=None, + numthreads=None, + index=None, + ): + self.level = level + self.bitspersample = bitspersample + self.photometric = photometric + self.compression = compression + self.numthreads = numthreads + self.index = index + + def encode(self, buf): + buf = protective_squeeze(numpy.asarray(buf)) + return imagecodecs.heif_encode( + buf, + level=self.level, + bitspersample=self.bitspersample, + photometric=self.photometric, + compression=self.compression, + numthreads=self.numthreads, + ) + + def decode(self, buf, out=None): + return imagecodecs.heif_decode( + buf, + index=self.index, + photometric=self.photometric, + numthreads=self.numthreads, + out=out, + ) + + +class Jetraw(Codec): + """Jetraw codec for numcodecs.""" + + codec_id = 'imagecodecs_jetraw' + + def __init__( + self, + shape, + identifier, + parameters=None, + verbosity=None, + errorbound=None, + ): + self.shape = shape + self.identifier = identifier + self.errorbound = errorbound + imagecodecs.jetraw_init(parameters, verbosity) + + def encode(self, buf): + return imagecodecs.jetraw_encode( + buf, identifier=self.identifier, errorbound=self.errorbound + ) + + def decode(self, buf, out=None): + if out is None: + out = numpy.empty(self.shape, numpy.uint16) + return imagecodecs.jetraw_decode(buf, out=out) + + +class Jpeg(Codec): + """JPEG codec for numcodecs.""" + + codec_id = 'imagecodecs_jpeg' + + def __init__( + self, + bitspersample=None, + tables=None, + header=None, + colorspace_data=None, + colorspace_jpeg=None, + level=None, + subsampling=None, + optimize=None, + smoothing=None, + ): + self.tables = tables + self.header = header + self.bitspersample = bitspersample + self.colorspace_data = colorspace_data + self.colorspace_jpeg = colorspace_jpeg + self.level = level + self.subsampling = subsampling + self.optimize = optimize + self.smoothing = smoothing + + def encode(self, buf): + buf = protective_squeeze(numpy.asarray(buf)) + return imagecodecs.jpeg_encode( + buf, + level=self.level, + colorspace=self.colorspace_data, + outcolorspace=self.colorspace_jpeg, + subsampling=self.subsampling, + optimize=self.optimize, + smoothing=self.smoothing, + ) + + def decode(self, buf, out=None): + out_shape = None + if out is not None: + out_shape = out.shape + out = protective_squeeze(out) + img = imagecodecs.jpeg_decode( + buf, + bitspersample=self.bitspersample, + tables=self.tables, + header=self.header, + colorspace=self.colorspace_jpeg, + outcolorspace=self.colorspace_data, + out=out, + ) + if out_shape is not None: + img = img.reshape(out_shape) + return img + + def get_config(self): + """Return dictionary holding configuration parameters.""" + config = dict(id=self.codec_id) + for key in self.__dict__: + if not key.startswith('_'): + value = getattr(self, key) + if value is not None and key in ('header', 'tables'): + import base64 + + value = base64.b64encode(value).decode() + config[key] = value + return config + + @classmethod + def from_config(cls, config): + """Instantiate codec from configuration object.""" + for key in ('header', 'tables'): + value = config.get(key, None) + if value is not None and isinstance(value, str): + import base64 + + config[key] = base64.b64decode(value.encode()) + return cls(**config) + + +class Jpeg2k(Codec): + """JPEG 2000 codec for numcodecs.""" + + codec_id = 'imagecodecs_jpeg2k' + + def __init__( + self, + level=None, + codecformat=None, + colorspace=None, + tile=None, + reversible=None, + bitspersample=None, + resolutions=None, + numthreads=None, + verbose=0, + ): + self.level = level + self.codecformat = codecformat + self.colorspace = colorspace + self.tile = None if tile is None else tuple(tile) + self.reversible = reversible + self.bitspersample = bitspersample + self.resolutions = resolutions + self.numthreads = numthreads + self.verbose = verbose + + def encode(self, buf): + buf = protective_squeeze(numpy.asarray(buf)) + return imagecodecs.jpeg2k_encode( + buf, + level=self.level, + codecformat=self.codecformat, + colorspace=self.colorspace, + tile=self.tile, + reversible=self.reversible, + bitspersample=self.bitspersample, + resolutions=self.resolutions, + numthreads=self.numthreads, + verbose=self.verbose, + ) + + def decode(self, buf, out=None): + return imagecodecs.jpeg2k_decode( + buf, verbose=self.verbose, numthreads=self.numthreads, out=out + ) + + +class JpegLs(Codec): + """JPEG LS codec for numcodecs.""" + + codec_id = 'imagecodecs_jpegls' + + def __init__(self, level=None): + self.level = level + + def encode(self, buf): + buf = protective_squeeze(numpy.asarray(buf)) + return imagecodecs.jpegls_encode(buf, level=self.level) + + def decode(self, buf, out=None): + return imagecodecs.jpegls_decode(buf, out=out) + + +class JpegXl(Codec): + """JPEG XL codec for numcodecs.""" + + codec_id = 'imagecodecs_jpegxl' + + def __init__( + self, + # encode + level=None, + effort=None, + distance=None, + lossless=None, + decodingspeed=None, + photometric=None, + planar=None, + usecontainer=None, + # decode + index=None, + keeporientation=None, + # both + numthreads=None, + ): + """ + Return JPEG XL image from numpy array. + Float must be in nominal range 0..1. + + Currently L, LA, RGB, RGBA images are supported in contig mode. + Extra channels are only supported for grayscale images in planar mode. + + Parameters + ---------- + level : Default to None, i.e. not overwriting lossess and decodingspeed options. + When < 0: Use lossless compression + When in [0,1,2,3,4]: Sets the decoding speed tier for the provided options. + Minimum is 0 (slowest to decode, best quality/density), and maximum + is 4 (fastest to decode, at the cost of some quality/density). + effort : Default to 3. + Sets encoder effort/speed level without affecting decoding speed. + Valid values are, from faster to slower speed: 1:lightning 2:thunder + 3:falcon 4:cheetah 5:hare 6:wombat 7:squirrel 8:kitten 9:tortoise. + Speed: lightning, thunder, falcon, cheetah, hare, wombat, squirrel, kitten, tortoise + control the encoder effort in ascending order. + This also affects memory usage: using lower effort will typically reduce memory + consumption during encoding. + lightning and thunder are fast modes useful for lossless mode (modular). + falcon disables all of the following tools. + cheetah enables coefficient reordering, context clustering, and heuristics for selecting DCT sizes and quantization steps. + hare enables Gaborish filtering, chroma from luma, and an initial estimate of quantization steps. + wombat enables error diffusion quantization and full DCT size selection heuristics. + squirrel (default) enables dots, patches, and spline detection, and full context clustering. + kitten optimizes the adaptive quantization for a psychovisual metric. + tortoise enables a more thorough adaptive quantization search. + distance : Default to 1.0 + Sets the distance level for lossy compression: target max butteraugli distance, + lower = higher quality. Range: 0 .. 15. 0.0 = mathematically lossless + (however, use JxlEncoderSetFrameLossless instead to use true lossless, + as setting distance to 0 alone is not the only requirement). + 1.0 = visually lossless. Recommended range: 0.5 .. 3.0. + lossess : Default to False. + Use lossess encoding. + decodingspeed : Default to 0. + Duplicate to level. [0,4] + photometric : Return JxlColorSpace value. + Default logic is quite complicated but works most of the time. + Accepted value: + int: [-1,3] + str: ['RGB', + 'WHITEISZERO', 'MINISWHITE', + 'BLACKISZERO', 'MINISBLACK', 'GRAY', + 'XYB', 'KNOWN'] + planar : Enable multi-channel mode. + Default to false. + usecontainer : + Forces the encoder to use the box-based container format (BMFF) + even when not necessary. + When using JxlEncoderUseBoxes, JxlEncoderStoreJPEGMetadata or + JxlEncoderSetCodestreamLevel with level 10, the encoder will + automatically also use the container format, it is not necessary + to use JxlEncoderUseContainer for those use cases. + By default this setting is disabled. + index : Selectively decode frames for animation. + Default to 0, decode all frames. + When set to > 0, decode that frame index only. + keeporientation : + Enables or disables preserving of as-in-bitstream pixeldata orientation. + Some images are encoded with an Orientation tag indicating that the + decoder must perform a rotation and/or mirroring to the encoded image data. + + If skip_reorientation is JXL_FALSE (the default): the decoder will apply + the transformation from the orientation setting, hence rendering the image + according to its specified intent. When producing a JxlBasicInfo, the decoder + will always set the orientation field to JXL_ORIENT_IDENTITY (matching the + returned pixel data) and also align xsize and ysize so that they correspond + to the width and the height of the returned pixel data. + + If skip_reorientation is JXL_TRUE: the decoder will skip applying the + transformation from the orientation setting, returning the image in + the as-in-bitstream pixeldata orientation. This may be faster to decode + since the decoder doesnt have to apply the transformation, but can + cause wrong display of the image if the orientation tag is not correctly + taken into account by the user. + + By default, this option is disabled, and the returned pixel data is + re-oriented according to the images Orientation setting. + threads : Default to 1. + If <= 0, use all cores. + If > 32, clipped to 32. + """ + + self.level = level + self.effort = effort + self.distance = distance + self.lossless = bool(lossless) + self.decodingspeed = decodingspeed + self.photometric = photometric + self.planar = planar + self.usecontainer = usecontainer + self.index = index + self.keeporientation = keeporientation + self.numthreads = numthreads + + def encode(self, buf): + # TODO: only squeeze all but last dim + buf = protective_squeeze(numpy.asarray(buf)) + return imagecodecs.jpegxl_encode( + buf, + level=self.level, + effort=self.effort, + distance=self.distance, + lossless=self.lossless, + decodingspeed=self.decodingspeed, + photometric=self.photometric, + planar=self.planar, + usecontainer=self.usecontainer, + numthreads=self.numthreads, + ) + + def decode(self, buf, out=None): + return imagecodecs.jpegxl_decode( + buf, + index=self.index, + keeporientation=self.keeporientation, + numthreads=self.numthreads, + out=out, + ) + + +class JpegXr(Codec): + """JPEG XR codec for numcodecs.""" + + codec_id = 'imagecodecs_jpegxr' + + def __init__( + self, + level=None, + photometric=None, + hasalpha=None, + resolution=None, + fp2int=None, + ): + self.level = level + self.photometric = photometric + self.hasalpha = hasalpha + self.resolution = resolution + self.fp2int = fp2int + + def encode(self, buf): + buf = protective_squeeze(numpy.asarray(buf)) + return imagecodecs.jpegxr_encode( + buf, + level=self.level, + photometric=self.photometric, + hasalpha=self.hasalpha, + resolution=self.resolution, + ) + + def decode(self, buf, out=None): + return imagecodecs.jpegxr_decode(buf, fp2int=self.fp2int, out=out) + + +class Lerc(Codec): + """LERC codec for numcodecs.""" + + codec_id = 'imagecodecs_lerc' + + def __init__(self, level=None, version=None, planar=None): + self.level = level + self.version = version + self.planar = bool(planar) + # TODO: support mask? + # self.mask = None + + def encode(self, buf): + buf = protective_squeeze(numpy.asarray(buf)) + return imagecodecs.lerc_encode( + buf, + level=self.level, + version=self.version, + planar=self.planar, + ) + + def decode(self, buf, out=None): + return imagecodecs.lerc_decode(buf, out=out) + + +class Ljpeg(Codec): + """LJPEG codec for numcodecs.""" + + codec_id = 'imagecodecs_ljpeg' + + def __init__(self, bitspersample=None): + self.bitspersample = bitspersample + + def encode(self, buf): + buf = protective_squeeze(numpy.asarray(buf)) + return imagecodecs.ljpeg_encode(buf, bitspersample=self.bitspersample) + + def decode(self, buf, out=None): + return imagecodecs.ljpeg_decode(buf, out=out) + + +class Lz4(Codec): + """LZ4 codec for numcodecs.""" + + codec_id = 'imagecodecs_lz4' + + def __init__(self, level=None, hc=False, header=True): + self.level = level + self.hc = hc + self.header = bool(header) + + def encode(self, buf): + return imagecodecs.lz4_encode( + buf, level=self.level, hc=self.hc, header=self.header + ) + + def decode(self, buf, out=None): + return imagecodecs.lz4_decode(buf, header=self.header, out=_flat(out)) + + +class Lz4f(Codec): + """LZ4F codec for numcodecs.""" + + codec_id = 'imagecodecs_lz4f' + + def __init__( + self, + level=None, + blocksizeid=False, + contentchecksum=None, + blockchecksum=None, + ): + self.level = level + self.blocksizeid = blocksizeid + self.contentchecksum = contentchecksum + self.blockchecksum = blockchecksum + + def encode(self, buf): + return imagecodecs.lz4f_encode( + buf, + level=self.level, + blocksizeid=self.blocksizeid, + contentchecksum=self.contentchecksum, + blockchecksum=self.blockchecksum, + ) + + def decode(self, buf, out=None): + return imagecodecs.lz4f_decode(buf, out=_flat(out)) + + +class Lzf(Codec): + """LZF codec for numcodecs.""" + + codec_id = 'imagecodecs_lzf' + + def __init__(self, header=True): + self.header = bool(header) + + def encode(self, buf): + return imagecodecs.lzf_encode(buf, header=self.header) + + def decode(self, buf, out=None): + return imagecodecs.lzf_decode(buf, header=self.header, out=_flat(out)) + + +class Lzma(Codec): + """LZMA codec for numcodecs.""" + + codec_id = 'imagecodecs_lzma' + + def __init__(self, level=None): + self.level = level + + def encode(self, buf): + return imagecodecs.lzma_encode(buf, level=self.level) + + def decode(self, buf, out=None): + return imagecodecs.lzma_decode(buf, out=_flat(out)) + + +class Lzw(Codec): + """LZW codec for numcodecs.""" + + codec_id = 'imagecodecs_lzw' + + def encode(self, buf): + return imagecodecs.lzw_encode(buf) + + def decode(self, buf, out=None): + return imagecodecs.lzw_decode(buf, out=_flat(out)) + + +class PackBits(Codec): + """PackBits codec for numcodecs.""" + + codec_id = 'imagecodecs_packbits' + + def __init__(self, axis=None): + self.axis = axis + + def encode(self, buf): + if not isinstance(buf, (bytes, bytearray)): + buf = protective_squeeze(numpy.asarray(buf)) + return imagecodecs.packbits_encode(buf, axis=self.axis) + + def decode(self, buf, out=None): + return imagecodecs.packbits_decode(buf, out=_flat(out)) + + +class Pglz(Codec): + """PGLZ codec for numcodecs.""" + + codec_id = 'imagecodecs_pglz' + + def __init__(self, header=True, strategy=None): + self.header = bool(header) + self.strategy = strategy + + def encode(self, buf): + return imagecodecs.pglz_encode( + buf, strategy=self.strategy, header=self.header + ) + + def decode(self, buf, out=None): + return imagecodecs.pglz_decode(buf, header=self.header, out=_flat(out)) + + +class Png(Codec): + """PNG codec for numcodecs.""" + + codec_id = 'imagecodecs_png' + + def __init__(self, level=None): + self.level = level + + def encode(self, buf): + buf = protective_squeeze(numpy.asarray(buf)) + return imagecodecs.png_encode(buf, level=self.level) + + def decode(self, buf, out=None): + return imagecodecs.png_decode(buf, out=out) + + +class Qoi(Codec): + """QOI codec for numcodecs.""" + + codec_id = 'imagecodecs_qoi' + + def __init__(self): + pass + + def encode(self, buf): + buf = protective_squeeze(numpy.asarray(buf)) + return imagecodecs.qoi_encode(buf) + + def decode(self, buf, out=None): + return imagecodecs.qoi_decode(buf, out=out) + + +class Rgbe(Codec): + """RGBE codec for numcodecs.""" + + codec_id = 'imagecodecs_rgbe' + + def __init__(self, header=False, shape=None, rle=None): + if not header and shape is None: + raise ValueError('must specify data shape if no header') + if shape and shape[-1] != 3: + raise ValueError('invalid shape') + self.shape = shape + self.header = bool(header) + self.rle = None if rle is None else bool(rle) + + def encode(self, buf): + buf = protective_squeeze(numpy.asarray(buf)) + return imagecodecs.rgbe_encode(buf, header=self.header, rle=self.rle) + + def decode(self, buf, out=None): + if out is None and not self.header: + out = numpy.empty(self.shape, numpy.float32) + return imagecodecs.rgbe_decode( + buf, header=self.header, rle=self.rle, out=out + ) + + +class Rcomp(Codec): + """Rcomp codec for numcodecs.""" + + codec_id = 'imagecodecs_rcomp' + + def __init__(self, shape, dtype, nblock=None): + self.shape = tuple(shape) + self.dtype = numpy.dtype(dtype).str + self.nblock = nblock + + def encode(self, buf): + return imagecodecs.rcomp_encode(buf, nblock=self.nblock) + + def decode(self, buf, out=None): + return imagecodecs.rcomp_decode( + buf, + shape=self.shape, + dtype=self.dtype, + nblock=self.nblock, + out=out, + ) + + +class Snappy(Codec): + """Snappy codec for numcodecs.""" + + codec_id = 'imagecodecs_snappy' + + def encode(self, buf): + return imagecodecs.snappy_encode(buf) + + def decode(self, buf, out=None): + return imagecodecs.snappy_decode(buf, out=_flat(out)) + + +class Spng(Codec): + """SPNG codec for numcodecs.""" + + codec_id = 'imagecodecs_spng' + + def __init__(self, level=None): + self.level = level + + def encode(self, buf): + buf = protective_squeeze(numpy.asarray(buf)) + return imagecodecs.spng_encode(buf, level=self.level) + + def decode(self, buf, out=None): + return imagecodecs.spng_decode(buf, out=out) + + +class Tiff(Codec): + """TIFF codec for numcodecs.""" + + codec_id = 'imagecodecs_tiff' + + def __init__(self, index=None, asrgb=None, verbose=0): + self.index = index + self.asrgb = bool(asrgb) + self.verbose = verbose + + def encode(self, buf): + # TODO: not implemented + buf = protective_squeeze(numpy.asarray(buf)) + return imagecodecs.tiff_encode(buf) + + def decode(self, buf, out=None): + return imagecodecs.tiff_decode( + buf, + index=self.index, + asrgb=self.asrgb, + verbose=self.verbose, + out=out, + ) + + +class Webp(Codec): + """WebP codec for numcodecs.""" + + codec_id = 'imagecodecs_webp' + + def __init__(self, level=None, lossless=None, method=None, hasalpha=None): + self.level = level + self.hasalpha = bool(hasalpha) + self.method = method + self.lossless = lossless + + def encode(self, buf): + buf = protective_squeeze(numpy.asarray(buf)) + return imagecodecs.webp_encode( + buf, level=self.level, lossless=self.lossless, method=self.method + ) + + def decode(self, buf, out=None): + return imagecodecs.webp_decode(buf, hasalpha=self.hasalpha, out=out) + + +class Xor(Codec): + """XOR codec for numcodecs.""" + + codec_id = 'imagecodecs_xor' + + def __init__(self, shape=None, dtype=None, axis=-1): + self.shape = None if shape is None else tuple(shape) + self.dtype = None if dtype is None else numpy.dtype(dtype).str + self.axis = axis + + def encode(self, buf): + if self.shape is not None or self.dtype is not None: + buf = protective_squeeze(numpy.asarray(buf)) + assert buf.shape == self.shape + assert buf.dtype == self.dtype + return imagecodecs.xor_encode(buf, axis=self.axis).tobytes() + + def decode(self, buf, out=None): + if self.shape is not None or self.dtype is not None: + buf = numpy.frombuffer(buf, dtype=self.dtype).reshape(*self.shape) + return imagecodecs.xor_decode(buf, axis=self.axis, out=_flat(out)) + + +class Zfp(Codec): + """ZFP codec for numcodecs.""" + + codec_id = 'imagecodecs_zfp' + + def __init__( + self, + shape=None, + dtype=None, + strides=None, + level=None, + mode=None, + execution=None, + numthreads=None, + chunksize=None, + header=True, + ): + if header: + self.shape = None + self.dtype = None + self.strides = None + elif shape is None or dtype is None: + raise ValueError('invalid shape or dtype') + else: + self.shape = tuple(shape) + self.dtype = numpy.dtype(dtype).str + self.strides = None if strides is None else tuple(strides) + self.level = level + self.mode = mode + self.execution = execution + self.numthreads = numthreads + self.chunksize = chunksize + self.header = bool(header) + + def encode(self, buf): + buf = protective_squeeze(numpy.asarray(buf)) + if not self.header: + assert buf.shape == self.shape + assert buf.dtype == self.dtype + return imagecodecs.zfp_encode( + buf, + level=self.level, + mode=self.mode, + execution=self.execution, + header=self.header, + numthreads=self.numthreads, + chunksize=self.chunksize, + ) + + def decode(self, buf, out=None): + if self.header: + return imagecodecs.zfp_decode(buf, out=out) + return imagecodecs.zfp_decode( + buf, + shape=self.shape, + dtype=numpy.dtype(self.dtype), + strides=self.strides, + numthreads=self.numthreads, + out=out, + ) + + +class Zlib(Codec): + """Zlib codec for numcodecs.""" + + codec_id = 'imagecodecs_zlib' + + def __init__(self, level=None): + self.level = level + + def encode(self, buf): + return imagecodecs.zlib_encode(buf, level=self.level) + + def decode(self, buf, out=None): + return imagecodecs.zlib_decode(buf, out=_flat(out)) + + +class Zlibng(Codec): + """Zlibng codec for numcodecs.""" + + codec_id = 'imagecodecs_zlibng' + + def __init__(self, level=None): + self.level = level + + def encode(self, buf): + return imagecodecs.zlibng_encode(buf, level=self.level) + + def decode(self, buf, out=None): + return imagecodecs.zlibng_decode(buf, out=_flat(out)) + + +class Zopfli(Codec): + """Zopfli codec for numcodecs.""" + + codec_id = 'imagecodecs_zopfli' + + def encode(self, buf): + return imagecodecs.zopfli_encode(buf) + + def decode(self, buf, out=None): + return imagecodecs.zopfli_decode(buf, out=_flat(out)) + + +class Zstd(Codec): + """ZStandard codec for numcodecs.""" + + codec_id = 'imagecodecs_zstd' + + def __init__(self, level=None): + self.level = level + + def encode(self, buf): + return imagecodecs.zstd_encode(buf, level=self.level) + + def decode(self, buf, out=None): + return imagecodecs.zstd_decode(buf, out=_flat(out)) + + +def _flat(out): + """Return numpy array as contiguous view of bytes if possible.""" + if out is None: + return None + view = memoryview(out) + if view.readonly or not view.contiguous: + return None + return view.cast('B') + + +def register_codecs(codecs=None, force=False, verbose=True): + """Register codecs in this module with numcodecs.""" + for name, cls in globals().items(): + if not hasattr(cls, 'codec_id') or name == 'Codec': + continue + if codecs is not None and cls.codec_id not in codecs: + continue + try: + try: + get_codec({'id': cls.codec_id}) + except TypeError: + # registered, but failed + pass + except ValueError: + # not registered yet + pass + else: + if not force: + if verbose: + log_warning( + f'numcodec {cls.codec_id!r} already registered' + ) + continue + if verbose: + log_warning(f'replacing registered numcodec {cls.codec_id!r}') + register_codec(cls) + + +def log_warning(msg, *args, **kwargs): + """Log message with level WARNING.""" + import logging + + logging.getLogger(__name__).warning(msg, *args, **kwargs) diff --git a/diffusion_policy/common/checkpoint_util.py b/diffusion_policy/common/checkpoint_util.py new file mode 100644 index 0000000..fbb0837 --- /dev/null +++ b/diffusion_policy/common/checkpoint_util.py @@ -0,0 +1,59 @@ +from typing import Optional, Dict +import os + +class TopKCheckpointManager: + def __init__(self, + save_dir, + monitor_key: str, + mode='min', + k=1, + format_str='epoch={epoch:03d}-train_loss={train_loss:.3f}.ckpt' + ): + assert mode in ['max', 'min'] + assert k >= 0 + + self.save_dir = save_dir + self.monitor_key = monitor_key + self.mode = mode + self.k = k + self.format_str = format_str + self.path_value_map = dict() + + def get_ckpt_path(self, data: Dict[str, float]) -> Optional[str]: + if self.k == 0: + return None + + value = data[self.monitor_key] + ckpt_path = os.path.join( + self.save_dir, self.format_str.format(**data)) + + if len(self.path_value_map) < self.k: + # under-capacity + self.path_value_map[ckpt_path] = value + return ckpt_path + + # at capacity + sorted_map = sorted(self.path_value_map.items(), key=lambda x: x[1]) + min_path, min_value = sorted_map[0] + max_path, max_value = sorted_map[-1] + + delete_path = None + if self.mode == 'max': + if value > min_value: + delete_path = min_path + else: + if value < max_value: + delete_path = max_path + + if delete_path is None: + return None + else: + del self.path_value_map[delete_path] + self.path_value_map[ckpt_path] = value + + if not os.path.exists(self.save_dir): + os.mkdir(self.save_dir) + + if os.path.exists(delete_path): + os.remove(delete_path) + return ckpt_path diff --git a/diffusion_policy/common/cv2_util.py b/diffusion_policy/common/cv2_util.py new file mode 100644 index 0000000..1d749bc --- /dev/null +++ b/diffusion_policy/common/cv2_util.py @@ -0,0 +1,150 @@ +from typing import Tuple +import math +import cv2 +import numpy as np + +def draw_reticle(img, u, v, label_color): + """ + Draws a reticle (cross-hair) on the image at the given position on top of + the original image. + @param img (In/Out) uint8 3 channel image + @param u X coordinate (width) + @param v Y coordinate (height) + @param label_color tuple of 3 ints for RGB color used for drawing. + """ + # Cast to int. + u = int(u) + v = int(v) + + white = (255, 255, 255) + cv2.circle(img, (u, v), 10, label_color, 1) + cv2.circle(img, (u, v), 11, white, 1) + cv2.circle(img, (u, v), 12, label_color, 1) + cv2.line(img, (u, v + 1), (u, v + 3), white, 1) + cv2.line(img, (u + 1, v), (u + 3, v), white, 1) + cv2.line(img, (u, v - 1), (u, v - 3), white, 1) + cv2.line(img, (u - 1, v), (u - 3, v), white, 1) + + +def draw_text( + img, + *, + text, + uv_top_left, + color=(255, 255, 255), + fontScale=0.5, + thickness=1, + fontFace=cv2.FONT_HERSHEY_SIMPLEX, + outline_color=(0, 0, 0), + line_spacing=1.5, +): + """ + Draws multiline with an outline. + """ + assert isinstance(text, str) + + uv_top_left = np.array(uv_top_left, dtype=float) + assert uv_top_left.shape == (2,) + + for line in text.splitlines(): + (w, h), _ = cv2.getTextSize( + text=line, + fontFace=fontFace, + fontScale=fontScale, + thickness=thickness, + ) + uv_bottom_left_i = uv_top_left + [0, h] + org = tuple(uv_bottom_left_i.astype(int)) + + if outline_color is not None: + cv2.putText( + img, + text=line, + org=org, + fontFace=fontFace, + fontScale=fontScale, + color=outline_color, + thickness=thickness * 3, + lineType=cv2.LINE_AA, + ) + cv2.putText( + img, + text=line, + org=org, + fontFace=fontFace, + fontScale=fontScale, + color=color, + thickness=thickness, + lineType=cv2.LINE_AA, + ) + + uv_top_left += [0, h * line_spacing] + + +def get_image_transform( + input_res: Tuple[int,int]=(1280,720), + output_res: Tuple[int,int]=(640,480), + bgr_to_rgb: bool=False): + + iw, ih = input_res + ow, oh = output_res + rw, rh = None, None + interp_method = cv2.INTER_AREA + + if (iw/ih) >= (ow/oh): + # input is wider + rh = oh + rw = math.ceil(rh / ih * iw) + if oh > ih: + interp_method = cv2.INTER_LINEAR + else: + rw = ow + rh = math.ceil(rw / iw * ih) + if ow > iw: + interp_method = cv2.INTER_LINEAR + + w_slice_start = (rw - ow) // 2 + w_slice = slice(w_slice_start, w_slice_start + ow) + h_slice_start = (rh - oh) // 2 + h_slice = slice(h_slice_start, h_slice_start + oh) + c_slice = slice(None) + if bgr_to_rgb: + c_slice = slice(None, None, -1) + + def transform(img: np.ndarray): + assert img.shape == ((ih,iw,3)) + # resize + img = cv2.resize(img, (rw, rh), interpolation=interp_method) + # crop + img = img[h_slice, w_slice, c_slice] + return img + return transform + +def optimal_row_cols( + n_cameras, + in_wh_ratio, + max_resolution=(1920, 1080) + ): + out_w, out_h = max_resolution + out_wh_ratio = out_w / out_h + + n_rows = np.arange(n_cameras,dtype=np.int64) + 1 + n_cols = np.ceil(n_cameras / n_rows).astype(np.int64) + cat_wh_ratio = in_wh_ratio * (n_cols / n_rows) + ratio_diff = np.abs(out_wh_ratio - cat_wh_ratio) + best_idx = np.argmin(ratio_diff) + best_n_row = n_rows[best_idx] + best_n_col = n_cols[best_idx] + best_cat_wh_ratio = cat_wh_ratio[best_idx] + + rw, rh = None, None + if best_cat_wh_ratio >= out_wh_ratio: + # cat is wider + rw = math.floor(out_w / best_n_col) + rh = math.floor(rw / in_wh_ratio) + else: + rh = math.floor(out_h / best_n_row) + rw = math.floor(rh * in_wh_ratio) + + # crop_resolution = (rw, rh) + return rw, rh, best_n_col, best_n_row diff --git a/diffusion_policy/common/env_util.py b/diffusion_policy/common/env_util.py new file mode 100644 index 0000000..a380f5a --- /dev/null +++ b/diffusion_policy/common/env_util.py @@ -0,0 +1,23 @@ +import cv2 +import numpy as np + + +def render_env_video(env, states, actions=None): + observations = states + imgs = list() + for i in range(len(observations)): + state = observations[i] + env.set_state(state) + if i == 0: + env.set_state(state) + img = env.render() + # draw action + if actions is not None: + action = actions[i] + coord = (action / 512 * 96).astype(np.int32) + cv2.drawMarker(img, coord, + color=(255,0,0), markerType=cv2.MARKER_CROSS, + markerSize=8, thickness=1) + imgs.append(img) + imgs = np.array(imgs) + return imgs diff --git a/diffusion_policy/common/json_logger.py b/diffusion_policy/common/json_logger.py new file mode 100644 index 0000000..ee9fb4a --- /dev/null +++ b/diffusion_policy/common/json_logger.py @@ -0,0 +1,117 @@ +from typing import Optional, Callable, Any, Sequence +import os +import copy +import json +import numbers +import pandas as pd + + +def read_json_log(path: str, + required_keys: Sequence[str]=tuple(), + **kwargs) -> pd.DataFrame: + """ + Read json-per-line file, with potentially incomplete lines. + kwargs passed to pd.read_json + """ + lines = list() + with open(path, 'r') as f: + while True: + # one json per line + line = f.readline() + if len(line) == 0: + # EOF + break + elif not line.endswith('\n'): + # incomplete line + break + is_relevant = False + for k in required_keys: + if k in line: + is_relevant = True + break + if is_relevant: + lines.append(line) + if len(lines) < 1: + return pd.DataFrame() + json_buf = f'[{",".join([line for line in (line.strip() for line in lines) if line])}]' + df = pd.read_json(json_buf, **kwargs) + return df + +class JsonLogger: + def __init__(self, path: str, + filter_fn: Optional[Callable[[str,Any],bool]]=None): + if filter_fn is None: + filter_fn = lambda k,v: isinstance(v, numbers.Number) + + # default to append mode + self.path = path + self.filter_fn = filter_fn + self.file = None + self.last_log = None + + def start(self): + # use line buffering + try: + self.file = file = open(self.path, 'r+', buffering=1) + except FileNotFoundError: + self.file = file = open(self.path, 'w+', buffering=1) + + # Move the pointer (similar to a cursor in a text editor) to the end of the file + pos = file.seek(0, os.SEEK_END) + + # Read each character in the file one at a time from the last + # character going backwards, searching for a newline character + # If we find a new line, exit the search + while pos > 0 and file.read(1) != "\n": + pos -= 1 + file.seek(pos, os.SEEK_SET) + # now the file pointer is at one past the last '\n' + # and pos is at the last '\n'. + last_line_end = file.tell() + + # find the start of second last line + pos = max(0, pos-1) + file.seek(pos, os.SEEK_SET) + while pos > 0 and file.read(1) != "\n": + pos -= 1 + file.seek(pos, os.SEEK_SET) + # now the file pointer is at one past the second last '\n' + last_line_start = file.tell() + + if last_line_start < last_line_end: + # has last line of json + last_line = file.readline() + self.last_log = json.loads(last_line) + + # remove the last incomplete line + file.seek(last_line_end) + file.truncate() + + def stop(self): + self.file.close() + self.file = None + + def __enter__(self): + self.start() + return self + + def __exit__(self, exc_type, exc_val, exc_tb): + self.stop() + + def log(self, data: dict): + filtered_data = dict( + filter(lambda x: self.filter_fn(*x), data.items())) + # save current as last log + self.last_log = filtered_data + for k, v in filtered_data.items(): + if isinstance(v, numbers.Integral): + filtered_data[k] = int(v) + elif isinstance(v, numbers.Number): + filtered_data[k] = float(v) + buf = json.dumps(filtered_data) + # ensure one line per json + buf = buf.replace('\n','') + '\n' + self.file.write(buf) + + def get_last_log(self): + return copy.deepcopy(self.last_log) diff --git a/diffusion_policy/common/nested_dict_util.py b/diffusion_policy/common/nested_dict_util.py new file mode 100644 index 0000000..b186f70 --- /dev/null +++ b/diffusion_policy/common/nested_dict_util.py @@ -0,0 +1,32 @@ +import functools + +def nested_dict_map(f, x): + """ + Map f over all leaf of nested dict x + """ + + if not isinstance(x, dict): + return f(x) + y = dict() + for key, value in x.items(): + y[key] = nested_dict_map(f, value) + return y + +def nested_dict_reduce(f, x): + """ + Map f over all values of nested dict x, and reduce to a single value + """ + if not isinstance(x, dict): + return x + + reduced_values = list() + for value in x.values(): + reduced_values.append(nested_dict_reduce(f, value)) + y = functools.reduce(f, reduced_values) + return y + + +def nested_dict_check(f, x): + bool_dict = nested_dict_map(f, x) + result = nested_dict_reduce(lambda x, y: x and y, bool_dict) + return result diff --git a/diffusion_policy/common/normalize_util.py b/diffusion_policy/common/normalize_util.py new file mode 100644 index 0000000..d6fc7b6 --- /dev/null +++ b/diffusion_policy/common/normalize_util.py @@ -0,0 +1,223 @@ +from diffusion_policy.model.common.normalizer import SingleFieldLinearNormalizer +from diffusion_policy.common.pytorch_util import dict_apply, dict_apply_reduce, dict_apply_split +import numpy as np + + +def get_range_normalizer_from_stat(stat, output_max=1, output_min=-1, range_eps=1e-7): + # -1, 1 normalization + input_max = stat['max'] + input_min = stat['min'] + input_range = input_max - input_min + ignore_dim = input_range < range_eps + input_range[ignore_dim] = output_max - output_min + scale = (output_max - output_min) / input_range + offset = output_min - scale * input_min + offset[ignore_dim] = (output_max + output_min) / 2 - input_min[ignore_dim] + + return SingleFieldLinearNormalizer.create_manual( + scale=scale, + offset=offset, + input_stats_dict=stat + ) + +def get_image_range_normalizer(): + scale = np.array([2], dtype=np.float32) + offset = np.array([-1], dtype=np.float32) + stat = { + 'min': np.array([0], dtype=np.float32), + 'max': np.array([1], dtype=np.float32), + 'mean': np.array([0.5], dtype=np.float32), + 'std': np.array([np.sqrt(1/12)], dtype=np.float32) + } + return SingleFieldLinearNormalizer.create_manual( + scale=scale, + offset=offset, + input_stats_dict=stat + ) + +def get_identity_normalizer_from_stat(stat): + scale = np.ones_like(stat['min']) + offset = np.zeros_like(stat['min']) + return SingleFieldLinearNormalizer.create_manual( + scale=scale, + offset=offset, + input_stats_dict=stat + ) + +def robomimic_abs_action_normalizer_from_stat(stat, rotation_transformer): + result = dict_apply_split( + stat, lambda x: { + 'pos': x[...,:3], + 'rot': x[...,3:6], + 'gripper': x[...,6:] + }) + + def get_pos_param_info(stat, output_max=1, output_min=-1, range_eps=1e-7): + # -1, 1 normalization + input_max = stat['max'] + input_min = stat['min'] + input_range = input_max - input_min + ignore_dim = input_range < range_eps + input_range[ignore_dim] = output_max - output_min + scale = (output_max - output_min) / input_range + offset = output_min - scale * input_min + offset[ignore_dim] = (output_max + output_min) / 2 - input_min[ignore_dim] + + return {'scale': scale, 'offset': offset}, stat + + def get_rot_param_info(stat): + example = rotation_transformer.forward(stat['mean']) + scale = np.ones_like(example) + offset = np.zeros_like(example) + info = { + 'max': np.ones_like(example), + 'min': np.full_like(example, -1), + 'mean': np.zeros_like(example), + 'std': np.ones_like(example) + } + return {'scale': scale, 'offset': offset}, info + + def get_gripper_param_info(stat): + example = stat['max'] + scale = np.ones_like(example) + offset = np.zeros_like(example) + info = { + 'max': np.ones_like(example), + 'min': np.full_like(example, -1), + 'mean': np.zeros_like(example), + 'std': np.ones_like(example) + } + return {'scale': scale, 'offset': offset}, info + + pos_param, pos_info = get_pos_param_info(result['pos']) + rot_param, rot_info = get_rot_param_info(result['rot']) + gripper_param, gripper_info = get_gripper_param_info(result['gripper']) + + param = dict_apply_reduce( + [pos_param, rot_param, gripper_param], + lambda x: np.concatenate(x,axis=-1)) + info = dict_apply_reduce( + [pos_info, rot_info, gripper_info], + lambda x: np.concatenate(x,axis=-1)) + + return SingleFieldLinearNormalizer.create_manual( + scale=param['scale'], + offset=param['offset'], + input_stats_dict=info + ) + + +def robomimic_abs_action_only_normalizer_from_stat(stat): + result = dict_apply_split( + stat, lambda x: { + 'pos': x[...,:3], + 'other': x[...,3:] + }) + + def get_pos_param_info(stat, output_max=1, output_min=-1, range_eps=1e-7): + # -1, 1 normalization + input_max = stat['max'] + input_min = stat['min'] + input_range = input_max - input_min + ignore_dim = input_range < range_eps + input_range[ignore_dim] = output_max - output_min + scale = (output_max - output_min) / input_range + offset = output_min - scale * input_min + offset[ignore_dim] = (output_max + output_min) / 2 - input_min[ignore_dim] + + return {'scale': scale, 'offset': offset}, stat + + + def get_other_param_info(stat): + example = stat['max'] + scale = np.ones_like(example) + offset = np.zeros_like(example) + info = { + 'max': np.ones_like(example), + 'min': np.full_like(example, -1), + 'mean': np.zeros_like(example), + 'std': np.ones_like(example) + } + return {'scale': scale, 'offset': offset}, info + + pos_param, pos_info = get_pos_param_info(result['pos']) + other_param, other_info = get_other_param_info(result['other']) + + param = dict_apply_reduce( + [pos_param, other_param], + lambda x: np.concatenate(x,axis=-1)) + info = dict_apply_reduce( + [pos_info, other_info], + lambda x: np.concatenate(x,axis=-1)) + + return SingleFieldLinearNormalizer.create_manual( + scale=param['scale'], + offset=param['offset'], + input_stats_dict=info + ) + + +def robomimic_abs_action_only_dual_arm_normalizer_from_stat(stat): + Da = stat['max'].shape[-1] + Dah = Da // 2 + result = dict_apply_split( + stat, lambda x: { + 'pos0': x[...,:3], + 'other0': x[...,3:Dah], + 'pos1': x[...,Dah:Dah+3], + 'other1': x[...,Dah+3:] + }) + + def get_pos_param_info(stat, output_max=1, output_min=-1, range_eps=1e-7): + # -1, 1 normalization + input_max = stat['max'] + input_min = stat['min'] + input_range = input_max - input_min + ignore_dim = input_range < range_eps + input_range[ignore_dim] = output_max - output_min + scale = (output_max - output_min) / input_range + offset = output_min - scale * input_min + offset[ignore_dim] = (output_max + output_min) / 2 - input_min[ignore_dim] + + return {'scale': scale, 'offset': offset}, stat + + + def get_other_param_info(stat): + example = stat['max'] + scale = np.ones_like(example) + offset = np.zeros_like(example) + info = { + 'max': np.ones_like(example), + 'min': np.full_like(example, -1), + 'mean': np.zeros_like(example), + 'std': np.ones_like(example) + } + return {'scale': scale, 'offset': offset}, info + + pos0_param, pos0_info = get_pos_param_info(result['pos0']) + pos1_param, pos1_info = get_pos_param_info(result['pos1']) + other0_param, other0_info = get_other_param_info(result['other0']) + other1_param, other1_info = get_other_param_info(result['other1']) + + param = dict_apply_reduce( + [pos0_param, other0_param, pos1_param, other1_param], + lambda x: np.concatenate(x,axis=-1)) + info = dict_apply_reduce( + [pos0_info, other0_info, pos1_info, other1_info], + lambda x: np.concatenate(x,axis=-1)) + + return SingleFieldLinearNormalizer.create_manual( + scale=param['scale'], + offset=param['offset'], + input_stats_dict=info + ) + + +def array_to_stats(arr: np.ndarray): + stat = { + 'min': np.min(arr, axis=0), + 'max': np.max(arr, axis=0), + 'mean': np.mean(arr, axis=0), + 'std': np.std(arr, axis=0) + } + return stat diff --git a/diffusion_policy/common/pose_trajectory_interpolator.py b/diffusion_policy/common/pose_trajectory_interpolator.py new file mode 100644 index 0000000..063943f --- /dev/null +++ b/diffusion_policy/common/pose_trajectory_interpolator.py @@ -0,0 +1,208 @@ +from typing import Union +import numbers +import numpy as np +import scipy.interpolate as si +import scipy.spatial.transform as st + +def rotation_distance(a: st.Rotation, b: st.Rotation) -> float: + return (b * a.inv()).magnitude() + +def pose_distance(start_pose, end_pose): + start_pose = np.array(start_pose) + end_pose = np.array(end_pose) + start_pos = start_pose[:3] + end_pos = end_pose[:3] + start_rot = st.Rotation.from_rotvec(start_pose[3:]) + end_rot = st.Rotation.from_rotvec(end_pose[3:]) + pos_dist = np.linalg.norm(end_pos - start_pos) + rot_dist = rotation_distance(start_rot, end_rot) + return pos_dist, rot_dist + +class PoseTrajectoryInterpolator: + def __init__(self, times: np.ndarray, poses: np.ndarray): + assert len(times) >= 1 + assert len(poses) == len(times) + if not isinstance(times, np.ndarray): + times = np.array(times) + if not isinstance(poses, np.ndarray): + poses = np.array(poses) + + if len(times) == 1: + # special treatement for single step interpolation + self.single_step = True + self._times = times + self._poses = poses + else: + self.single_step = False + assert np.all(times[1:] >= times[:-1]) + + pos = poses[:,:3] + rot = st.Rotation.from_rotvec(poses[:,3:]) + + self.pos_interp = si.interp1d(times, pos, + axis=0, assume_sorted=True) + self.rot_interp = st.Slerp(times, rot) + + @property + def times(self) -> np.ndarray: + if self.single_step: + return self._times + else: + return self.pos_interp.x + + @property + def poses(self) -> np.ndarray: + if self.single_step: + return self._poses + else: + n = len(self.times) + poses = np.zeros((n, 6)) + poses[:,:3] = self.pos_interp.y + poses[:,3:] = self.rot_interp(self.times).as_rotvec() + return poses + + def trim(self, + start_t: float, end_t: float + ) -> "PoseTrajectoryInterpolator": + assert start_t <= end_t + times = self.times + should_keep = (start_t < times) & (times < end_t) + keep_times = times[should_keep] + all_times = np.concatenate([[start_t], keep_times, [end_t]]) + # remove duplicates, Slerp requries strictly increasing x + all_times = np.unique(all_times) + # interpolate + all_poses = self(all_times) + return PoseTrajectoryInterpolator(times=all_times, poses=all_poses) + + def drive_to_waypoint(self, + pose, time, curr_time, + max_pos_speed=np.inf, + max_rot_speed=np.inf + ) -> "PoseTrajectoryInterpolator": + assert(max_pos_speed > 0) + assert(max_rot_speed > 0) + time = max(time, curr_time) + + curr_pose = self(curr_time) + pos_dist, rot_dist = pose_distance(curr_pose, pose) + pos_min_duration = pos_dist / max_pos_speed + rot_min_duration = rot_dist / max_rot_speed + duration = time - curr_time + duration = max(duration, max(pos_min_duration, rot_min_duration)) + assert duration >= 0 + last_waypoint_time = curr_time + duration + + # insert new pose + trimmed_interp = self.trim(curr_time, curr_time) + times = np.append(trimmed_interp.times, [last_waypoint_time], axis=0) + poses = np.append(trimmed_interp.poses, [pose], axis=0) + + # create new interpolator + final_interp = PoseTrajectoryInterpolator(times, poses) + return final_interp + + def schedule_waypoint(self, + pose, time, + max_pos_speed=np.inf, + max_rot_speed=np.inf, + curr_time=None, + last_waypoint_time=None + ) -> "PoseTrajectoryInterpolator": + assert(max_pos_speed > 0) + assert(max_rot_speed > 0) + if last_waypoint_time is not None: + assert curr_time is not None + + # trim current interpolator to between curr_time and last_waypoint_time + start_time = self.times[0] + end_time = self.times[-1] + assert start_time <= end_time + + if curr_time is not None: + if time <= curr_time: + # if insert time is earlier than current time + # no effect should be done to the interpolator + return self + # now, curr_time < time + start_time = max(curr_time, start_time) + + if last_waypoint_time is not None: + # if last_waypoint_time is earlier than start_time + # use start_time + if time <= last_waypoint_time: + end_time = curr_time + else: + end_time = max(last_waypoint_time, curr_time) + else: + end_time = curr_time + + end_time = min(end_time, time) + start_time = min(start_time, end_time) + # end time should be the latest of all times except time + # after this we can assume order (proven by zhenjia, due to the 2 min operations) + + # Constraints: + # start_time <= end_time <= time (proven by zhenjia) + # curr_time <= start_time (proven by zhenjia) + # curr_time <= time (proven by zhenjia) + + # time can't change + # last_waypoint_time can't change + # curr_time can't change + assert start_time <= end_time + assert end_time <= time + if last_waypoint_time is not None: + if time <= last_waypoint_time: + assert end_time == curr_time + else: + assert end_time == max(last_waypoint_time, curr_time) + + if curr_time is not None: + assert curr_time <= start_time + assert curr_time <= time + + trimmed_interp = self.trim(start_time, end_time) + # after this, all waypoints in trimmed_interp is within start_time and end_time + # and is earlier than time + + # determine speed + duration = time - end_time + end_pose = trimmed_interp(end_time) + pos_dist, rot_dist = pose_distance(pose, end_pose) + pos_min_duration = pos_dist / max_pos_speed + rot_min_duration = rot_dist / max_rot_speed + duration = max(duration, max(pos_min_duration, rot_min_duration)) + assert duration >= 0 + last_waypoint_time = end_time + duration + + # insert new pose + times = np.append(trimmed_interp.times, [last_waypoint_time], axis=0) + poses = np.append(trimmed_interp.poses, [pose], axis=0) + + # create new interpolator + final_interp = PoseTrajectoryInterpolator(times, poses) + return final_interp + + + def __call__(self, t: Union[numbers.Number, np.ndarray]) -> np.ndarray: + is_single = False + if isinstance(t, numbers.Number): + is_single = True + t = np.array([t]) + + pose = np.zeros((len(t), 6)) + if self.single_step: + pose[:] = self._poses[0] + else: + start_time = self.times[0] + end_time = self.times[-1] + t = np.clip(t, start_time, end_time) + + pose = np.zeros((len(t), 6)) + pose[:,:3] = self.pos_interp(t) + pose[:,3:] = self.rot_interp(t).as_rotvec() + + if is_single: + pose = pose[0] + return pose diff --git a/diffusion_policy/common/precise_sleep.py b/diffusion_policy/common/precise_sleep.py new file mode 100644 index 0000000..83ef62c --- /dev/null +++ b/diffusion_policy/common/precise_sleep.py @@ -0,0 +1,25 @@ +import time + +def precise_sleep(dt: float, slack_time: float=0.001, time_func=time.monotonic): + """ + Use hybrid of time.sleep and spinning to minimize jitter. + Sleep dt - slack_time seconds first, then spin for the rest. + """ + t_start = time_func() + if dt > slack_time: + time.sleep(dt - slack_time) + t_end = t_start + dt + while time_func() < t_end: + pass + return + +def precise_wait(t_end: float, slack_time: float=0.001, time_func=time.monotonic): + t_start = time_func() + t_wait = t_end - t_start + if t_wait > 0: + t_sleep = t_wait - slack_time + if t_sleep > 0: + time.sleep(t_sleep) + while time_func() < t_end: + pass + return diff --git a/diffusion_policy/common/pymunk_override.py b/diffusion_policy/common/pymunk_override.py new file mode 100644 index 0000000..78ecaa2 --- /dev/null +++ b/diffusion_policy/common/pymunk_override.py @@ -0,0 +1,248 @@ +# ---------------------------------------------------------------------------- +# pymunk +# Copyright (c) 2007-2016 Victor Blomqvist +# +# Permission is hereby granted, free of charge, to any person obtaining a copy +# of this software and associated documentation files (the "Software"), to deal +# in the Software without restriction, including without limitation the rights +# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +# copies of the Software, and to permit persons to whom the Software is +# furnished to do so, subject to the following conditions: +# +# The above copyright notice and this permission notice shall be included in +# all copies or substantial portions of the Software. +# +# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +# SOFTWARE. +# ---------------------------------------------------------------------------- + +"""This submodule contains helper functions to help with quick prototyping +using pymunk together with pygame. + +Intended to help with debugging and prototyping, not for actual production use +in a full application. The methods contained in this module is opinionated +about your coordinate system and not in any way optimized. +""" + +__docformat__ = "reStructuredText" + +__all__ = [ + "DrawOptions", + "get_mouse_pos", + "to_pygame", + "from_pygame", + "lighten", + "positive_y_is_up", +] + +from typing import List, Sequence, Tuple + +import pygame + +import numpy as np + +import pymunk +from pymunk.space_debug_draw_options import SpaceDebugColor +from pymunk.vec2d import Vec2d + +positive_y_is_up: bool = False +"""Make increasing values of y point upwards. + +When True:: + + y + ^ + | . (3, 3) + | + | . (2, 2) + | + +------ > x + +When False:: + + +------ > x + | + | . (2, 2) + | + | . (3, 3) + v + y + +""" + + +class DrawOptions(pymunk.SpaceDebugDrawOptions): + def __init__(self, surface: pygame.Surface) -> None: + """Draw a pymunk.Space on a pygame.Surface object. + + Typical usage:: + + >>> import pymunk + >>> surface = pygame.Surface((10,10)) + >>> space = pymunk.Space() + >>> options = pymunk.pygame_util.DrawOptions(surface) + >>> space.debug_draw(options) + + You can control the color of a shape by setting shape.color to the color + you want it drawn in:: + + >>> c = pymunk.Circle(None, 10) + >>> c.color = pygame.Color("pink") + + See pygame_util.demo.py for a full example + + Since pygame uses a coordiante system where y points down (in contrast + to many other cases), you either have to make the physics simulation + with Pymunk also behave in that way, or flip everything when you draw. + + The easiest is probably to just make the simulation behave the same + way as Pygame does. In that way all coordinates used are in the same + orientation and easy to reason about:: + + >>> space = pymunk.Space() + >>> space.gravity = (0, -1000) + >>> body = pymunk.Body() + >>> body.position = (0, 0) # will be positioned in the top left corner + >>> space.debug_draw(options) + + To flip the drawing its possible to set the module property + :py:data:`positive_y_is_up` to True. Then the pygame drawing will flip + the simulation upside down before drawing:: + + >>> positive_y_is_up = True + >>> body = pymunk.Body() + >>> body.position = (0, 0) + >>> # Body will be position in bottom left corner + + :Parameters: + surface : pygame.Surface + Surface that the objects will be drawn on + """ + self.surface = surface + super(DrawOptions, self).__init__() + + def draw_circle( + self, + pos: Vec2d, + angle: float, + radius: float, + outline_color: SpaceDebugColor, + fill_color: SpaceDebugColor, + ) -> None: + p = to_pygame(pos, self.surface) + + pygame.draw.circle(self.surface, fill_color.as_int(), p, round(radius), 0) + pygame.draw.circle(self.surface, light_color(fill_color).as_int(), p, round(radius-4), 0) + + circle_edge = pos + Vec2d(radius, 0).rotated(angle) + p2 = to_pygame(circle_edge, self.surface) + line_r = 2 if radius > 20 else 1 + # pygame.draw.lines(self.surface, outline_color.as_int(), False, [p, p2], line_r) + + def draw_segment(self, a: Vec2d, b: Vec2d, color: SpaceDebugColor) -> None: + p1 = to_pygame(a, self.surface) + p2 = to_pygame(b, self.surface) + + pygame.draw.aalines(self.surface, color.as_int(), False, [p1, p2]) + + def draw_fat_segment( + self, + a: Tuple[float, float], + b: Tuple[float, float], + radius: float, + outline_color: SpaceDebugColor, + fill_color: SpaceDebugColor, + ) -> None: + p1 = to_pygame(a, self.surface) + p2 = to_pygame(b, self.surface) + + r = round(max(1, radius * 2)) + pygame.draw.lines(self.surface, fill_color.as_int(), False, [p1, p2], r) + if r > 2: + orthog = [abs(p2[1] - p1[1]), abs(p2[0] - p1[0])] + if orthog[0] == 0 and orthog[1] == 0: + return + scale = radius / (orthog[0] * orthog[0] + orthog[1] * orthog[1]) ** 0.5 + orthog[0] = round(orthog[0] * scale) + orthog[1] = round(orthog[1] * scale) + points = [ + (p1[0] - orthog[0], p1[1] - orthog[1]), + (p1[0] + orthog[0], p1[1] + orthog[1]), + (p2[0] + orthog[0], p2[1] + orthog[1]), + (p2[0] - orthog[0], p2[1] - orthog[1]), + ] + pygame.draw.polygon(self.surface, fill_color.as_int(), points) + pygame.draw.circle( + self.surface, + fill_color.as_int(), + (round(p1[0]), round(p1[1])), + round(radius), + ) + pygame.draw.circle( + self.surface, + fill_color.as_int(), + (round(p2[0]), round(p2[1])), + round(radius), + ) + + def draw_polygon( + self, + verts: Sequence[Tuple[float, float]], + radius: float, + outline_color: SpaceDebugColor, + fill_color: SpaceDebugColor, + ) -> None: + ps = [to_pygame(v, self.surface) for v in verts] + ps += [ps[0]] + + radius = 2 + pygame.draw.polygon(self.surface, light_color(fill_color).as_int(), ps) + + if radius > 0: + for i in range(len(verts)): + a = verts[i] + b = verts[(i + 1) % len(verts)] + self.draw_fat_segment(a, b, radius, fill_color, fill_color) + + def draw_dot( + self, size: float, pos: Tuple[float, float], color: SpaceDebugColor + ) -> None: + p = to_pygame(pos, self.surface) + pygame.draw.circle(self.surface, color.as_int(), p, round(size), 0) + + +def get_mouse_pos(surface: pygame.Surface) -> Tuple[int, int]: + """Get position of the mouse pointer in pymunk coordinates.""" + p = pygame.mouse.get_pos() + return from_pygame(p, surface) + + +def to_pygame(p: Tuple[float, float], surface: pygame.Surface) -> Tuple[int, int]: + """Convenience method to convert pymunk coordinates to pygame surface + local coordinates. + + Note that in case positive_y_is_up is False, this function wont actually do + anything except converting the point to integers. + """ + if positive_y_is_up: + return round(p[0]), surface.get_height() - round(p[1]) + else: + return round(p[0]), round(p[1]) + + +def from_pygame(p: Tuple[float, float], surface: pygame.Surface) -> Tuple[int, int]: + """Convenience method to convert pygame surface local coordinates to + pymunk coordinates + """ + return to_pygame(p, surface) + + +def light_color(color: SpaceDebugColor): + color = np.minimum(1.2 * np.float32([color.r, color.g, color.b, color.a]), np.float32([255])) + color = SpaceDebugColor(r=color[0], g=color[1], b=color[2], a=color[3]) + return color diff --git a/diffusion_policy/common/pymunk_util.py b/diffusion_policy/common/pymunk_util.py new file mode 100644 index 0000000..4245994 --- /dev/null +++ b/diffusion_policy/common/pymunk_util.py @@ -0,0 +1,52 @@ +import pygame +import pymunk +import pymunk.pygame_util +import numpy as np + +COLLTYPE_DEFAULT = 0 +COLLTYPE_MOUSE = 1 +COLLTYPE_BALL = 2 + +def get_body_type(static=False): + body_type = pymunk.Body.DYNAMIC + if static: + body_type = pymunk.Body.STATIC + return body_type + + +def create_rectangle(space, + pos_x,pos_y,width,height, + density=3,static=False): + body = pymunk.Body(body_type=get_body_type(static)) + body.position = (pos_x,pos_y) + shape = pymunk.Poly.create_box(body,(width,height)) + shape.density = density + space.add(body,shape) + return body, shape + + +def create_rectangle_bb(space, + left, bottom, right, top, + **kwargs): + pos_x = (left + right) / 2 + pos_y = (top + bottom) / 2 + height = top - bottom + width = right - left + return create_rectangle(space, pos_x, pos_y, width, height, **kwargs) + +def create_circle(space, pos_x, pos_y, radius, density=3, static=False): + body = pymunk.Body(body_type=get_body_type(static)) + body.position = (pos_x, pos_y) + shape = pymunk.Circle(body, radius=radius) + shape.density = density + shape.collision_type = COLLTYPE_BALL + space.add(body, shape) + return body, shape + +def get_body_state(body): + state = np.zeros(6, dtype=np.float32) + state[:2] = body.position + state[2] = body.angle + state[3:5] = body.velocity + state[5] = body.angular_velocity + return state diff --git a/diffusion_policy/common/pytorch_util.py b/diffusion_policy/common/pytorch_util.py new file mode 100644 index 0000000..a9262d4 --- /dev/null +++ b/diffusion_policy/common/pytorch_util.py @@ -0,0 +1,82 @@ +from typing import Dict, Callable, List +import collections +import torch +import torch.nn as nn + +def dict_apply( + x: Dict[str, torch.Tensor], + func: Callable[[torch.Tensor], torch.Tensor] + ) -> Dict[str, torch.Tensor]: + result = dict() + for key, value in x.items(): + if isinstance(value, dict): + result[key] = dict_apply(value, func) + else: + result[key] = func(value) + return result + +def pad_remaining_dims(x, target): + assert x.shape == target.shape[:len(x.shape)] + return x.reshape(x.shape + (1,)*(len(target.shape) - len(x.shape))) + +def dict_apply_split( + x: Dict[str, torch.Tensor], + split_func: Callable[[torch.Tensor], Dict[str, torch.Tensor]] + ) -> Dict[str, torch.Tensor]: + results = collections.defaultdict(dict) + for key, value in x.items(): + result = split_func(value) + for k, v in result.items(): + results[k][key] = v + return results + +def dict_apply_reduce( + x: List[Dict[str, torch.Tensor]], + reduce_func: Callable[[List[torch.Tensor]], torch.Tensor] + ) -> Dict[str, torch.Tensor]: + result = dict() + for key in x[0].keys(): + result[key] = reduce_func([x_[key] for x_ in x]) + return result + + +def replace_submodules( + root_module: nn.Module, + predicate: Callable[[nn.Module], bool], + func: Callable[[nn.Module], nn.Module]) -> nn.Module: + """ + predicate: Return true if the module is to be replaced. + func: Return new module to use. + """ + if predicate(root_module): + return func(root_module) + + bn_list = [k.split('.') for k, m + in root_module.named_modules(remove_duplicate=True) + if predicate(m)] + for *parent, k in bn_list: + parent_module = root_module + if len(parent) > 0: + parent_module = root_module.get_submodule('.'.join(parent)) + if isinstance(parent_module, nn.Sequential): + src_module = parent_module[int(k)] + else: + src_module = getattr(parent_module, k) + tgt_module = func(src_module) + if isinstance(parent_module, nn.Sequential): + parent_module[int(k)] = tgt_module + else: + setattr(parent_module, k, tgt_module) + # verify that all BN are replaced + bn_list = [k.split('.') for k, m + in root_module.named_modules(remove_duplicate=True) + if predicate(m)] + assert len(bn_list) == 0 + return root_module + +def optimizer_to(optimizer, device): + for state in optimizer.state.values(): + for k, v in state.items(): + if isinstance(v, torch.Tensor): + state[k] = v.to(device=device) + return optimizer diff --git a/diffusion_policy/common/replay_buffer.py b/diffusion_policy/common/replay_buffer.py new file mode 100644 index 0000000..09dda9b --- /dev/null +++ b/diffusion_policy/common/replay_buffer.py @@ -0,0 +1,588 @@ +from typing import Union, Dict, Optional +import os +import math +import numbers +import zarr +import numcodecs +import numpy as np +from functools import cached_property + +def check_chunks_compatible(chunks: tuple, shape: tuple): + assert len(shape) == len(chunks) + for c in chunks: + assert isinstance(c, numbers.Integral) + assert c > 0 + +def rechunk_recompress_array(group, name, + chunks=None, chunk_length=None, + compressor=None, tmp_key='_temp'): + old_arr = group[name] + if chunks is None: + if chunk_length is not None: + chunks = (chunk_length,) + old_arr.chunks[1:] + else: + chunks = old_arr.chunks + check_chunks_compatible(chunks, old_arr.shape) + + if compressor is None: + compressor = old_arr.compressor + + if (chunks == old_arr.chunks) and (compressor == old_arr.compressor): + # no change + return old_arr + + # rechunk recompress + group.move(name, tmp_key) + old_arr = group[tmp_key] + n_copied, n_skipped, n_bytes_copied = zarr.copy( + source=old_arr, + dest=group, + name=name, + chunks=chunks, + compressor=compressor, + ) + del group[tmp_key] + arr = group[name] + return arr + +def get_optimal_chunks(shape, dtype, + target_chunk_bytes=2e6, + max_chunk_length=None): + """ + Common shapes + T,D + T,N,D + T,H,W,C + T,N,H,W,C + """ + itemsize = np.dtype(dtype).itemsize + # reversed + rshape = list(shape[::-1]) + if max_chunk_length is not None: + rshape[-1] = int(max_chunk_length) + split_idx = len(shape)-1 + for i in range(len(shape)-1): + this_chunk_bytes = itemsize * np.prod(rshape[:i]) + next_chunk_bytes = itemsize * np.prod(rshape[:i+1]) + if this_chunk_bytes <= target_chunk_bytes \ + and next_chunk_bytes > target_chunk_bytes: + split_idx = i + + rchunks = rshape[:split_idx] + item_chunk_bytes = itemsize * np.prod(rshape[:split_idx]) + this_max_chunk_length = rshape[split_idx] + next_chunk_length = min(this_max_chunk_length, math.ceil( + target_chunk_bytes / item_chunk_bytes)) + rchunks.append(next_chunk_length) + len_diff = len(shape) - len(rchunks) + rchunks.extend([1] * len_diff) + chunks = tuple(rchunks[::-1]) + # print(np.prod(chunks) * itemsize / target_chunk_bytes) + return chunks + + +class ReplayBuffer: + """ + Zarr-based temporal datastructure. + Assumes first dimention to be time. Only chunk in time dimension. + """ + def __init__(self, + root: Union[zarr.Group, + Dict[str,dict]]): + """ + Dummy constructor. Use copy_from* and create_from* class methods instead. + """ + assert('data' in root) + assert('meta' in root) + assert('episode_ends' in root['meta']) + for key, value in root['data'].items(): + assert(value.shape[0] == root['meta']['episode_ends'][-1]) + self.root = root + + # ============= create constructors =============== + @classmethod + def create_empty_zarr(cls, storage=None, root=None): + if root is None: + if storage is None: + storage = zarr.MemoryStore() + root = zarr.group(store=storage) + data = root.require_group('data', overwrite=False) + meta = root.require_group('meta', overwrite=False) + if 'episode_ends' not in meta: + episode_ends = meta.zeros('episode_ends', shape=(0,), dtype=np.int64, + compressor=None, overwrite=False) + return cls(root=root) + + @classmethod + def create_empty_numpy(cls): + root = { + 'data': dict(), + 'meta': { + 'episode_ends': np.zeros((0,), dtype=np.int64) + } + } + return cls(root=root) + + @classmethod + def create_from_group(cls, group, **kwargs): + if 'data' not in group: + # create from stratch + buffer = cls.create_empty_zarr(root=group, **kwargs) + else: + # already exist + buffer = cls(root=group, **kwargs) + return buffer + + @classmethod + def create_from_path(cls, zarr_path, mode='r', **kwargs): + """ + Open a on-disk zarr directly (for dataset larger than memory). + Slower. + """ + group = zarr.open(os.path.expanduser(zarr_path), mode) + return cls.create_from_group(group, **kwargs) + + # ============= copy constructors =============== + @classmethod + def copy_from_store(cls, src_store, store=None, keys=None, + chunks: Dict[str,tuple]=dict(), + compressors: Union[dict, str, numcodecs.abc.Codec]=dict(), + if_exists='replace', + **kwargs): + """ + Load to memory. + """ + src_root = zarr.group(src_store) + root = None + if store is None: + # numpy backend + meta = dict() + for key, value in src_root['meta'].items(): + if len(value.shape) == 0: + meta[key] = np.array(value) + else: + meta[key] = value[:] + + if keys is None: + keys = src_root['data'].keys() + data = dict() + for key in keys: + arr = src_root['data'][key] + data[key] = arr[:] + + root = { + 'meta': meta, + 'data': data + } + else: + root = zarr.group(store=store) + # copy without recompression + n_copied, n_skipped, n_bytes_copied = zarr.copy_store(source=src_store, dest=store, + source_path='/meta', dest_path='/meta', if_exists=if_exists) + data_group = root.create_group('data', overwrite=True) + if keys is None: + keys = src_root['data'].keys() + for key in keys: + value = src_root['data'][key] + cks = cls._resolve_array_chunks( + chunks=chunks, key=key, array=value) + cpr = cls._resolve_array_compressor( + compressors=compressors, key=key, array=value) + if cks == value.chunks and cpr == value.compressor: + # copy without recompression + this_path = '/data/' + key + n_copied, n_skipped, n_bytes_copied = zarr.copy_store( + source=src_store, dest=store, + source_path=this_path, dest_path=this_path, + if_exists=if_exists + ) + else: + # copy with recompression + n_copied, n_skipped, n_bytes_copied = zarr.copy( + source=value, dest=data_group, name=key, + chunks=cks, compressor=cpr, if_exists=if_exists + ) + buffer = cls(root=root) + return buffer + + @classmethod + def copy_from_path(cls, zarr_path, backend=None, store=None, keys=None, + chunks: Dict[str,tuple]=dict(), + compressors: Union[dict, str, numcodecs.abc.Codec]=dict(), + if_exists='replace', + **kwargs): + """ + Copy a on-disk zarr to in-memory compressed. + Recommended + """ + if backend == 'numpy': + print('backend argument is depreacted!') + store = None + group = zarr.open(os.path.expanduser(zarr_path), 'r') + return cls.copy_from_store(src_store=group.store, store=store, + keys=keys, chunks=chunks, compressors=compressors, + if_exists=if_exists, **kwargs) + + # ============= save methods =============== + def save_to_store(self, store, + chunks: Optional[Dict[str,tuple]]=dict(), + compressors: Union[str, numcodecs.abc.Codec, dict]=dict(), + if_exists='replace', + **kwargs): + + root = zarr.group(store) + if self.backend == 'zarr': + # recompression free copy + n_copied, n_skipped, n_bytes_copied = zarr.copy_store( + source=self.root.store, dest=store, + source_path='/meta', dest_path='/meta', if_exists=if_exists) + else: + meta_group = root.create_group('meta', overwrite=True) + # save meta, no chunking + for key, value in self.root['meta'].items(): + _ = meta_group.array( + name=key, + data=value, + shape=value.shape, + chunks=value.shape) + + # save data, chunk + data_group = root.create_group('data', overwrite=True) + for key, value in self.root['data'].items(): + cks = self._resolve_array_chunks( + chunks=chunks, key=key, array=value) + cpr = self._resolve_array_compressor( + compressors=compressors, key=key, array=value) + if isinstance(value, zarr.Array): + if cks == value.chunks and cpr == value.compressor: + # copy without recompression + this_path = '/data/' + key + n_copied, n_skipped, n_bytes_copied = zarr.copy_store( + source=self.root.store, dest=store, + source_path=this_path, dest_path=this_path, if_exists=if_exists) + else: + # copy with recompression + n_copied, n_skipped, n_bytes_copied = zarr.copy( + source=value, dest=data_group, name=key, + chunks=cks, compressor=cpr, if_exists=if_exists + ) + else: + # numpy + _ = data_group.array( + name=key, + data=value, + chunks=cks, + compressor=cpr + ) + return store + + def save_to_path(self, zarr_path, + chunks: Optional[Dict[str,tuple]]=dict(), + compressors: Union[str, numcodecs.abc.Codec, dict]=dict(), + if_exists='replace', + **kwargs): + store = zarr.DirectoryStore(os.path.expanduser(zarr_path)) + return self.save_to_store(store, chunks=chunks, + compressors=compressors, if_exists=if_exists, **kwargs) + + @staticmethod + def resolve_compressor(compressor='default'): + if compressor == 'default': + compressor = numcodecs.Blosc(cname='lz4', clevel=5, + shuffle=numcodecs.Blosc.NOSHUFFLE) + elif compressor == 'disk': + compressor = numcodecs.Blosc('zstd', clevel=5, + shuffle=numcodecs.Blosc.BITSHUFFLE) + return compressor + + @classmethod + def _resolve_array_compressor(cls, + compressors: Union[dict, str, numcodecs.abc.Codec], key, array): + # allows compressor to be explicitly set to None + cpr = 'nil' + if isinstance(compressors, dict): + if key in compressors: + cpr = cls.resolve_compressor(compressors[key]) + elif isinstance(array, zarr.Array): + cpr = array.compressor + else: + cpr = cls.resolve_compressor(compressors) + # backup default + if cpr == 'nil': + cpr = cls.resolve_compressor('default') + return cpr + + @classmethod + def _resolve_array_chunks(cls, + chunks: Union[dict, tuple], key, array): + cks = None + if isinstance(chunks, dict): + if key in chunks: + cks = chunks[key] + elif isinstance(array, zarr.Array): + cks = array.chunks + elif isinstance(chunks, tuple): + cks = chunks + else: + raise TypeError(f"Unsupported chunks type {type(chunks)}") + # backup default + if cks is None: + cks = get_optimal_chunks(shape=array.shape, dtype=array.dtype) + # check + check_chunks_compatible(chunks=cks, shape=array.shape) + return cks + + # ============= properties ================= + @cached_property + def data(self): + return self.root['data'] + + @cached_property + def meta(self): + return self.root['meta'] + + def update_meta(self, data): + # sanitize data + np_data = dict() + for key, value in data.items(): + if isinstance(value, np.ndarray): + np_data[key] = value + else: + arr = np.array(value) + if arr.dtype == object: + raise TypeError(f"Invalid value type {type(value)}") + np_data[key] = arr + + meta_group = self.meta + if self.backend == 'zarr': + for key, value in np_data.items(): + _ = meta_group.array( + name=key, + data=value, + shape=value.shape, + chunks=value.shape, + overwrite=True) + else: + meta_group.update(np_data) + + return meta_group + + @property + def episode_ends(self): + return self.meta['episode_ends'] + + def get_episode_idxs(self): + import numba + numba.jit(nopython=True) + def _get_episode_idxs(episode_ends): + result = np.zeros((episode_ends[-1],), dtype=np.int64) + for i in range(len(episode_ends)): + start = 0 + if i > 0: + start = episode_ends[i-1] + end = episode_ends[i] + for idx in range(start, end): + result[idx] = i + return result + return _get_episode_idxs(self.episode_ends) + + + @property + def backend(self): + backend = 'numpy' + if isinstance(self.root, zarr.Group): + backend = 'zarr' + return backend + + # =========== dict-like API ============== + def __repr__(self) -> str: + if self.backend == 'zarr': + return str(self.root.tree()) + else: + return super().__repr__() + + def keys(self): + return self.data.keys() + + def values(self): + return self.data.values() + + def items(self): + return self.data.items() + + def __getitem__(self, key): + return self.data[key] + + def __contains__(self, key): + return key in self.data + + # =========== our API ============== + @property + def n_steps(self): + if len(self.episode_ends) == 0: + return 0 + return self.episode_ends[-1] + + @property + def n_episodes(self): + return len(self.episode_ends) + + @property + def chunk_size(self): + if self.backend == 'zarr': + return next(iter(self.data.arrays()))[-1].chunks[0] + return None + + @property + def episode_lengths(self): + ends = self.episode_ends[:] + ends = np.insert(ends, 0, 0) + lengths = np.diff(ends) + return lengths + + def add_episode(self, + data: Dict[str, np.ndarray], + chunks: Optional[Dict[str,tuple]]=dict(), + compressors: Union[str, numcodecs.abc.Codec, dict]=dict()): + assert(len(data) > 0) + is_zarr = (self.backend == 'zarr') + + curr_len = self.n_steps + episode_length = None + for key, value in data.items(): + assert(len(value.shape) >= 1) + if episode_length is None: + episode_length = len(value) + else: + assert(episode_length == len(value)) + new_len = curr_len + episode_length + + for key, value in data.items(): + new_shape = (new_len,) + value.shape[1:] + # create array + if key not in self.data: + if is_zarr: + cks = self._resolve_array_chunks( + chunks=chunks, key=key, array=value) + cpr = self._resolve_array_compressor( + compressors=compressors, key=key, array=value) + arr = self.data.zeros(name=key, + shape=new_shape, + chunks=cks, + dtype=value.dtype, + compressor=cpr) + else: + # copy data to prevent modify + arr = np.zeros(shape=new_shape, dtype=value.dtype) + self.data[key] = arr + else: + arr = self.data[key] + assert(value.shape[1:] == arr.shape[1:]) + # same method for both zarr and numpy + if is_zarr: + arr.resize(new_shape) + else: + arr.resize(new_shape, refcheck=False) + # copy data + arr[-value.shape[0]:] = value + + # append to episode ends + episode_ends = self.episode_ends + if is_zarr: + episode_ends.resize(episode_ends.shape[0] + 1) + else: + episode_ends.resize(episode_ends.shape[0] + 1, refcheck=False) + episode_ends[-1] = new_len + + # rechunk + if is_zarr: + if episode_ends.chunks[0] < episode_ends.shape[0]: + rechunk_recompress_array(self.meta, 'episode_ends', + chunk_length=int(episode_ends.shape[0] * 1.5)) + + def drop_episode(self): + is_zarr = (self.backend == 'zarr') + episode_ends = self.episode_ends[:].copy() + assert(len(episode_ends) > 0) + start_idx = 0 + if len(episode_ends) > 1: + start_idx = episode_ends[-2] + for key, value in self.data.items(): + new_shape = (start_idx,) + value.shape[1:] + if is_zarr: + value.resize(new_shape) + else: + value.resize(new_shape, refcheck=False) + if is_zarr: + self.episode_ends.resize(len(episode_ends)-1) + else: + self.episode_ends.resize(len(episode_ends)-1, refcheck=False) + + def pop_episode(self): + assert(self.n_episodes > 0) + episode = self.get_episode(self.n_episodes-1, copy=True) + self.drop_episode() + return episode + + def extend(self, data): + self.add_episode(data) + + def get_episode(self, idx, copy=False): + idx = list(range(len(self.episode_ends)))[idx] + start_idx = 0 + if idx > 0: + start_idx = self.episode_ends[idx-1] + end_idx = self.episode_ends[idx] + result = self.get_steps_slice(start_idx, end_idx, copy=copy) + return result + + def get_episode_slice(self, idx): + start_idx = 0 + if idx > 0: + start_idx = self.episode_ends[idx-1] + end_idx = self.episode_ends[idx] + return slice(start_idx, end_idx) + + def get_steps_slice(self, start, stop, step=None, copy=False): + _slice = slice(start, stop, step) + + result = dict() + for key, value in self.data.items(): + x = value[_slice] + if copy and isinstance(value, np.ndarray): + x = x.copy() + result[key] = x + return result + + # =========== chunking ============= + def get_chunks(self) -> dict: + assert self.backend == 'zarr' + chunks = dict() + for key, value in self.data.items(): + chunks[key] = value.chunks + return chunks + + def set_chunks(self, chunks: dict): + assert self.backend == 'zarr' + for key, value in chunks.items(): + if key in self.data: + arr = self.data[key] + if value != arr.chunks: + check_chunks_compatible(chunks=value, shape=arr.shape) + rechunk_recompress_array(self.data, key, chunks=value) + + def get_compressors(self) -> dict: + assert self.backend == 'zarr' + compressors = dict() + for key, value in self.data.items(): + compressors[key] = value.compressor + return compressors + + def set_compressors(self, compressors: dict): + assert self.backend == 'zarr' + for key, value in compressors.items(): + if key in self.data: + arr = self.data[key] + compressor = self.resolve_compressor(value) + if compressor != arr.compressor: + rechunk_recompress_array(self.data, key, compressor=compressor) diff --git a/diffusion_policy/common/robomimic_config_util.py b/diffusion_policy/common/robomimic_config_util.py new file mode 100644 index 0000000..85768bb --- /dev/null +++ b/diffusion_policy/common/robomimic_config_util.py @@ -0,0 +1,47 @@ +from omegaconf import OmegaConf +from robomimic.config import config_factory +import robomimic.scripts.generate_paper_configs as gpc +from robomimic.scripts.generate_paper_configs import ( + modify_config_for_default_image_exp, + modify_config_for_default_low_dim_exp, + modify_config_for_dataset, +) + +def get_robomimic_config( + algo_name='bc_rnn', + hdf5_type='low_dim', + task_name='square', + dataset_type='ph' + ): + base_dataset_dir = '/tmp/null' + filter_key = None + + # decide whether to use low-dim or image training defaults + modifier_for_obs = modify_config_for_default_image_exp + if hdf5_type in ["low_dim", "low_dim_sparse", "low_dim_dense"]: + modifier_for_obs = modify_config_for_default_low_dim_exp + + algo_config_name = "bc" if algo_name == "bc_rnn" else algo_name + config = config_factory(algo_name=algo_config_name) + # turn into default config for observation modalities (e.g.: low-dim or rgb) + config = modifier_for_obs(config) + # add in config based on the dataset + config = modify_config_for_dataset( + config=config, + task_name=task_name, + dataset_type=dataset_type, + hdf5_type=hdf5_type, + base_dataset_dir=base_dataset_dir, + filter_key=filter_key, + ) + # add in algo hypers based on dataset + algo_config_modifier = getattr(gpc, f'modify_{algo_name}_config_for_dataset') + config = algo_config_modifier( + config=config, + task_name=task_name, + dataset_type=dataset_type, + hdf5_type=hdf5_type, + ) + return config + + diff --git a/diffusion_policy/common/robomimic_util.py b/diffusion_policy/common/robomimic_util.py new file mode 100644 index 0000000..24f25fb --- /dev/null +++ b/diffusion_policy/common/robomimic_util.py @@ -0,0 +1,177 @@ +import numpy as np +import copy + +import h5py +import robomimic.utils.obs_utils as ObsUtils +import robomimic.utils.file_utils as FileUtils +import robomimic.utils.env_utils as EnvUtils +from scipy.spatial.transform import Rotation + +from robomimic.config import config_factory + + +class RobomimicAbsoluteActionConverter: + def __init__(self, dataset_path, algo_name='bc'): + # default BC config + config = config_factory(algo_name=algo_name) + + # read config to set up metadata for observation modalities (e.g. detecting rgb observations) + # must ran before create dataset + ObsUtils.initialize_obs_utils_with_config(config) + + env_meta = FileUtils.get_env_metadata_from_dataset(dataset_path) + abs_env_meta = copy.deepcopy(env_meta) + abs_env_meta['env_kwargs']['controller_configs']['control_delta'] = False + + env = EnvUtils.create_env_from_metadata( + env_meta=env_meta, + render=False, + render_offscreen=False, + use_image_obs=False, + ) + assert len(env.env.robots) in (1, 2) + + abs_env = EnvUtils.create_env_from_metadata( + env_meta=abs_env_meta, + render=False, + render_offscreen=False, + use_image_obs=False, + ) + assert not abs_env.env.robots[0].controller.use_delta + + self.env = env + self.abs_env = abs_env + self.file = h5py.File(dataset_path, 'r') + + def __len__(self): + return len(self.file['data']) + + def convert_actions(self, + states: np.ndarray, + actions: np.ndarray) -> np.ndarray: + """ + Given state and delta action sequence + generate equivalent goal position and orientation for each step + keep the original gripper action intact. + """ + # in case of multi robot + # reshape (N,14) to (N,2,7) + # or (N,7) to (N,1,7) + stacked_actions = actions.reshape(*actions.shape[:-1],-1,7) + + env = self.env + # generate abs actions + action_goal_pos = np.zeros( + stacked_actions.shape[:-1]+(3,), + dtype=stacked_actions.dtype) + action_goal_ori = np.zeros( + stacked_actions.shape[:-1]+(3,), + dtype=stacked_actions.dtype) + action_gripper = stacked_actions[...,[-1]] + for i in range(len(states)): + _ = env.reset_to({'states': states[i]}) + + # taken from robot_env.py L#454 + for idx, robot in enumerate(env.env.robots): + # run controller goal generator + robot.control(stacked_actions[i,idx], policy_step=True) + + # read pos and ori from robots + controller = robot.controller + action_goal_pos[i,idx] = controller.goal_pos + action_goal_ori[i,idx] = Rotation.from_matrix( + controller.goal_ori).as_rotvec() + + stacked_abs_actions = np.concatenate([ + action_goal_pos, + action_goal_ori, + action_gripper + ], axis=-1) + abs_actions = stacked_abs_actions.reshape(actions.shape) + return abs_actions + + def convert_idx(self, idx): + file = self.file + demo = file[f'data/demo_{idx}'] + # input + states = demo['states'][:] + actions = demo['actions'][:] + + # generate abs actions + abs_actions = self.convert_actions(states, actions) + return abs_actions + + def convert_and_eval_idx(self, idx): + env = self.env + abs_env = self.abs_env + file = self.file + # first step have high error for some reason, not representative + eval_skip_steps = 1 + + demo = file[f'data/demo_{idx}'] + # input + states = demo['states'][:] + actions = demo['actions'][:] + + # generate abs actions + abs_actions = self.convert_actions(states, actions) + + # verify + robot0_eef_pos = demo['obs']['robot0_eef_pos'][:] + robot0_eef_quat = demo['obs']['robot0_eef_quat'][:] + + delta_error_info = self.evaluate_rollout_error( + env, states, actions, robot0_eef_pos, robot0_eef_quat, + metric_skip_steps=eval_skip_steps) + abs_error_info = self.evaluate_rollout_error( + abs_env, states, abs_actions, robot0_eef_pos, robot0_eef_quat, + metric_skip_steps=eval_skip_steps) + + info = { + 'delta_max_error': delta_error_info, + 'abs_max_error': abs_error_info + } + return abs_actions, info + + @staticmethod + def evaluate_rollout_error(env, + states, actions, + robot0_eef_pos, + robot0_eef_quat, + metric_skip_steps=1): + # first step have high error for some reason, not representative + + # evaluate abs actions + rollout_next_states = list() + rollout_next_eef_pos = list() + rollout_next_eef_quat = list() + obs = env.reset_to({'states': states[0]}) + for i in range(len(states)): + obs = env.reset_to({'states': states[i]}) + obs, reward, done, info = env.step(actions[i]) + obs = env.get_observation() + rollout_next_states.append(env.get_state()['states']) + rollout_next_eef_pos.append(obs['robot0_eef_pos']) + rollout_next_eef_quat.append(obs['robot0_eef_quat']) + rollout_next_states = np.array(rollout_next_states) + rollout_next_eef_pos = np.array(rollout_next_eef_pos) + rollout_next_eef_quat = np.array(rollout_next_eef_quat) + + next_state_diff = states[1:] - rollout_next_states[:-1] + max_next_state_diff = np.max(np.abs(next_state_diff[metric_skip_steps:])) + + next_eef_pos_diff = robot0_eef_pos[1:] - rollout_next_eef_pos[:-1] + next_eef_pos_dist = np.linalg.norm(next_eef_pos_diff, axis=-1) + max_next_eef_pos_dist = next_eef_pos_dist[metric_skip_steps:].max() + + next_eef_rot_diff = Rotation.from_quat(robot0_eef_quat[1:]) \ + * Rotation.from_quat(rollout_next_eef_quat[:-1]).inv() + next_eef_rot_dist = next_eef_rot_diff.magnitude() + max_next_eef_rot_dist = next_eef_rot_dist[metric_skip_steps:].max() + + info = { + 'state': max_next_state_diff, + 'pos': max_next_eef_pos_dist, + 'rot': max_next_eef_rot_dist + } + return info diff --git a/diffusion_policy/common/sampler.py b/diffusion_policy/common/sampler.py new file mode 100644 index 0000000..4accaa0 --- /dev/null +++ b/diffusion_policy/common/sampler.py @@ -0,0 +1,153 @@ +from typing import Optional +import numpy as np +import numba +from diffusion_policy.common.replay_buffer import ReplayBuffer + + +@numba.jit(nopython=True) +def create_indices( + episode_ends:np.ndarray, sequence_length:int, + episode_mask: np.ndarray, + pad_before: int=0, pad_after: int=0, + debug:bool=True) -> np.ndarray: + episode_mask.shape == episode_ends.shape + pad_before = min(max(pad_before, 0), sequence_length-1) + pad_after = min(max(pad_after, 0), sequence_length-1) + + indices = list() + for i in range(len(episode_ends)): + if not episode_mask[i]: + # skip episode + continue + start_idx = 0 + if i > 0: + start_idx = episode_ends[i-1] + end_idx = episode_ends[i] + episode_length = end_idx - start_idx + + min_start = -pad_before + max_start = episode_length - sequence_length + pad_after + + # range stops one idx before end + for idx in range(min_start, max_start+1): + buffer_start_idx = max(idx, 0) + start_idx + buffer_end_idx = min(idx+sequence_length, episode_length) + start_idx + start_offset = buffer_start_idx - (idx+start_idx) + end_offset = (idx+sequence_length+start_idx) - buffer_end_idx + sample_start_idx = 0 + start_offset + sample_end_idx = sequence_length - end_offset + if debug: + assert(start_offset >= 0) + assert(end_offset >= 0) + assert (sample_end_idx - sample_start_idx) == (buffer_end_idx - buffer_start_idx) + indices.append([ + buffer_start_idx, buffer_end_idx, + sample_start_idx, sample_end_idx]) + indices = np.array(indices) + return indices + + +def get_val_mask(n_episodes, val_ratio, seed=0): + val_mask = np.zeros(n_episodes, dtype=bool) + if val_ratio <= 0: + return val_mask + + # have at least 1 episode for validation, and at least 1 episode for train + n_val = min(max(1, round(n_episodes * val_ratio)), n_episodes-1) + rng = np.random.default_rng(seed=seed) + val_idxs = rng.choice(n_episodes, size=n_val, replace=False) + val_mask[val_idxs] = True + return val_mask + + +def downsample_mask(mask, max_n, seed=0): + # subsample training data + train_mask = mask + if (max_n is not None) and (np.sum(train_mask) > max_n): + n_train = int(max_n) + curr_train_idxs = np.nonzero(train_mask)[0] + rng = np.random.default_rng(seed=seed) + train_idxs_idx = rng.choice(len(curr_train_idxs), size=n_train, replace=False) + train_idxs = curr_train_idxs[train_idxs_idx] + train_mask = np.zeros_like(train_mask) + train_mask[train_idxs] = True + assert np.sum(train_mask) == n_train + return train_mask + +class SequenceSampler: + def __init__(self, + replay_buffer: ReplayBuffer, + sequence_length:int, + pad_before:int=0, + pad_after:int=0, + keys=None, + key_first_k=dict(), + episode_mask: Optional[np.ndarray]=None, + ): + """ + key_first_k: dict str: int + Only take first k data from these keys (to improve perf) + """ + + super().__init__() + assert(sequence_length >= 1) + if keys is None: + keys = list(replay_buffer.keys()) + + episode_ends = replay_buffer.episode_ends[:] + if episode_mask is None: + episode_mask = np.ones(episode_ends.shape, dtype=bool) + + if np.any(episode_mask): + indices = create_indices(episode_ends, + sequence_length=sequence_length, + pad_before=pad_before, + pad_after=pad_after, + episode_mask=episode_mask + ) + else: + indices = np.zeros((0,4), dtype=np.int64) + + # (buffer_start_idx, buffer_end_idx, sample_start_idx, sample_end_idx) + self.indices = indices + self.keys = list(keys) # prevent OmegaConf list performance problem + self.sequence_length = sequence_length + self.replay_buffer = replay_buffer + self.key_first_k = key_first_k + + def __len__(self): + return len(self.indices) + + def sample_sequence(self, idx): + buffer_start_idx, buffer_end_idx, sample_start_idx, sample_end_idx \ + = self.indices[idx] + result = dict() + for key in self.keys: + input_arr = self.replay_buffer[key] + # performance optimization, avoid small allocation if possible + if key not in self.key_first_k: + sample = input_arr[buffer_start_idx:buffer_end_idx] + else: + # performance optimization, only load used obs steps + n_data = buffer_end_idx - buffer_start_idx + k_data = min(self.key_first_k[key], n_data) + # fill value with Nan to catch bugs + # the non-loaded region should never be used + sample = np.full((n_data,) + input_arr.shape[1:], + fill_value=np.nan, dtype=input_arr.dtype) + try: + sample[:k_data] = input_arr[buffer_start_idx:buffer_start_idx+k_data] + except Exception as e: + import pdb; pdb.set_trace() + data = sample + if (sample_start_idx > 0) or (sample_end_idx < self.sequence_length): + data = np.zeros( + shape=(self.sequence_length,) + input_arr.shape[1:], + dtype=input_arr.dtype) + if sample_start_idx > 0: + data[:sample_start_idx] = sample[0] + if sample_end_idx < self.sequence_length: + data[sample_end_idx:] = sample[-1] + data[sample_start_idx:sample_end_idx] = sample + result[key] = data + return result diff --git a/diffusion_policy/common/timestamp_accumulator.py b/diffusion_policy/common/timestamp_accumulator.py new file mode 100644 index 0000000..e1eb285 --- /dev/null +++ b/diffusion_policy/common/timestamp_accumulator.py @@ -0,0 +1,222 @@ +from typing import List, Tuple, Optional, Dict +import math +import numpy as np + + +def get_accumulate_timestamp_idxs( + timestamps: List[float], + start_time: float, + dt: float, + eps:float=1e-5, + next_global_idx: Optional[int]=0, + allow_negative=False + ) -> Tuple[List[int], List[int], int]: + """ + For each dt window, choose the first timestamp in the window. + Assumes timestamps sorted. One timestamp might be chosen mulitple times due to dropped frames. + next_global_idx should start at 0 normally, and then use the returned next_global_idx. + However, when overwiting previous values are desired, set last_global_idx to None. + + Returns: + local_idxs: which index in the given timestamps array to chose from + global_idxs: the global index of each chosen timestamp + next_global_idx: used for next call. + """ + local_idxs = list() + global_idxs = list() + for local_idx, ts in enumerate(timestamps): + # add eps * dt to timestamps so that when ts == start_time + k * dt + # is always recorded as kth element (avoiding floating point errors) + global_idx = math.floor((ts - start_time) / dt + eps) + if (not allow_negative) and (global_idx < 0): + continue + if next_global_idx is None: + next_global_idx = global_idx + + n_repeats = max(0, global_idx - next_global_idx + 1) + for i in range(n_repeats): + local_idxs.append(local_idx) + global_idxs.append(next_global_idx + i) + next_global_idx += n_repeats + return local_idxs, global_idxs, next_global_idx + + +def align_timestamps( + timestamps: List[float], + target_global_idxs: List[int], + start_time: float, + dt: float, + eps:float=1e-5): + if isinstance(target_global_idxs, np.ndarray): + target_global_idxs = target_global_idxs.tolist() + assert len(target_global_idxs) > 0 + + local_idxs, global_idxs, _ = get_accumulate_timestamp_idxs( + timestamps=timestamps, + start_time=start_time, + dt=dt, + eps=eps, + next_global_idx=target_global_idxs[0], + allow_negative=True + ) + if len(global_idxs) > len(target_global_idxs): + # if more steps avaliable, truncate + global_idxs = global_idxs[:len(target_global_idxs)] + local_idxs = local_idxs[:len(target_global_idxs)] + + if len(global_idxs) == 0: + import pdb; pdb.set_trace() + + for i in range(len(target_global_idxs) - len(global_idxs)): + # if missing, repeat + local_idxs.append(len(timestamps)-1) + global_idxs.append(global_idxs[-1] + 1) + assert global_idxs == target_global_idxs + assert len(local_idxs) == len(global_idxs) + return local_idxs + + +class TimestampObsAccumulator: + def __init__(self, + start_time: float, + dt: float, + eps: float=1e-5): + self.start_time = start_time + self.dt = dt + self.eps = eps + self.obs_buffer = dict() + self.timestamp_buffer = None + self.next_global_idx = 0 + + def __len__(self): + return self.next_global_idx + + @property + def data(self): + if self.timestamp_buffer is None: + return dict() + result = dict() + for key, value in self.obs_buffer.items(): + result[key] = value[:len(self)] + return result + + @property + def actual_timestamps(self): + if self.timestamp_buffer is None: + return np.array([]) + return self.timestamp_buffer[:len(self)] + + @property + def timestamps(self): + if self.timestamp_buffer is None: + return np.array([]) + return self.start_time + np.arange(len(self)) * self.dt + + def put(self, data: Dict[str, np.ndarray], timestamps: np.ndarray): + """ + data: + key: T,* + """ + + local_idxs, global_idxs, self.next_global_idx = get_accumulate_timestamp_idxs( + timestamps=timestamps, + start_time=self.start_time, + dt=self.dt, + eps=self.eps, + next_global_idx=self.next_global_idx + ) + + if len(global_idxs) > 0: + if self.timestamp_buffer is None: + # first allocation + self.obs_buffer = dict() + for key, value in data.items(): + self.obs_buffer[key] = np.zeros_like(value) + self.timestamp_buffer = np.zeros( + (len(timestamps),), dtype=np.float64) + + this_max_size = global_idxs[-1] + 1 + if this_max_size > len(self.timestamp_buffer): + # reallocate + new_size = max(this_max_size, len(self.timestamp_buffer) * 2) + for key in list(self.obs_buffer.keys()): + new_shape = (new_size,) + self.obs_buffer[key].shape[1:] + self.obs_buffer[key] = np.resize(self.obs_buffer[key], new_shape) + self.timestamp_buffer = np.resize(self.timestamp_buffer, (new_size)) + + # write data + for key, value in self.obs_buffer.items(): + value[global_idxs] = data[key][local_idxs] + self.timestamp_buffer[global_idxs] = timestamps[local_idxs] + + +class TimestampActionAccumulator: + def __init__(self, + start_time: float, + dt: float, + eps: float=1e-5): + """ + Different from Obs accumulator, the action accumulator + allows overwriting previous values. + """ + self.start_time = start_time + self.dt = dt + self.eps = eps + self.action_buffer = None + self.timestamp_buffer = None + self.size = 0 + + def __len__(self): + return self.size + + @property + def actions(self): + if self.action_buffer is None: + return np.array([]) + return self.action_buffer[:len(self)] + + @property + def actual_timestamps(self): + if self.timestamp_buffer is None: + return np.array([]) + return self.timestamp_buffer[:len(self)] + + @property + def timestamps(self): + if self.timestamp_buffer is None: + return np.array([]) + return self.start_time + np.arange(len(self)) * self.dt + + def put(self, actions: np.ndarray, timestamps: np.ndarray): + """ + Note: timestamps is the time when the action will be issued, + not when the action will be completed (target_timestamp) + """ + + local_idxs, global_idxs, _ = get_accumulate_timestamp_idxs( + timestamps=timestamps, + start_time=self.start_time, + dt=self.dt, + eps=self.eps, + # allows overwriting previous actions + next_global_idx=None + ) + + if len(global_idxs) > 0: + if self.timestamp_buffer is None: + # first allocation + self.action_buffer = np.zeros_like(actions) + self.timestamp_buffer = np.zeros((len(actions),), dtype=np.float64) + + this_max_size = global_idxs[-1] + 1 + if this_max_size > len(self.timestamp_buffer): + # reallocate + new_size = max(this_max_size, len(self.timestamp_buffer) * 2) + new_shape = (new_size,) + self.action_buffer.shape[1:] + self.action_buffer = np.resize(self.action_buffer, new_shape) + self.timestamp_buffer = np.resize(self.timestamp_buffer, (new_size,)) + + # potentally rewrite old data (as expected) + self.action_buffer[global_idxs] = actions[local_idxs] + self.timestamp_buffer[global_idxs] = timestamps[local_idxs] + self.size = max(self.size, this_max_size) diff --git a/diffusion_policy/config/task/blockpush_lowdim_seed.yaml b/diffusion_policy/config/task/blockpush_lowdim_seed.yaml new file mode 100644 index 0000000..aa1e636 --- /dev/null +++ b/diffusion_policy/config/task/blockpush_lowdim_seed.yaml @@ -0,0 +1,34 @@ +name: blockpush_lowdim_seed + +obs_dim: 16 +action_dim: 2 +keypoint_dim: 2 +obs_eef_target: True + +env_runner: + _target_: diffusion_policy.env_runner.blockpush_lowdim_runner.BlockPushLowdimRunner + n_train: 6 + n_train_vis: 2 + train_start_seed: 0 + n_test: 50 + n_test_vis: 4 + test_start_seed: 100000 + max_steps: 350 + n_obs_steps: ${n_obs_steps} + n_action_steps: ${n_action_steps} + fps: 5 + past_action: ${past_action_visible} + abs_action: False + obs_eef_target: ${task.obs_eef_target} + n_envs: null + +dataset: + _target_: diffusion_policy.dataset.blockpush_lowdim_dataset.BlockPushLowdimDataset + zarr_path: data/block_pushing/multimodal_push_seed.zarr + horizon: ${horizon} + pad_before: ${eval:'${n_obs_steps}-1'} + pad_after: ${eval:'${n_action_steps}-1'} + obs_eef_target: ${task.obs_eef_target} + use_manual_normalizer: False + seed: 42 + val_ratio: 0.02 diff --git a/diffusion_policy/config/task/blockpush_lowdim_seed_abs.yaml b/diffusion_policy/config/task/blockpush_lowdim_seed_abs.yaml new file mode 100644 index 0000000..0649121 --- /dev/null +++ b/diffusion_policy/config/task/blockpush_lowdim_seed_abs.yaml @@ -0,0 +1,34 @@ +name: blockpush_lowdim_seed_abs + +obs_dim: 16 +action_dim: 2 +keypoint_dim: 2 +obs_eef_target: True + +env_runner: + _target_: diffusion_policy.env_runner.blockpush_lowdim_runner.BlockPushLowdimRunner + n_train: 6 + n_train_vis: 2 + train_start_seed: 0 + n_test: 50 + n_test_vis: 4 + test_start_seed: 100000 + max_steps: 350 + n_obs_steps: ${n_obs_steps} + n_action_steps: ${n_action_steps} + fps: 5 + past_action: ${past_action_visible} + abs_action: True + obs_eef_target: ${task.obs_eef_target} + n_envs: null + +dataset: + _target_: diffusion_policy.dataset.blockpush_lowdim_dataset.BlockPushLowdimDataset + zarr_path: data/block_pushing/multimodal_push_seed_abs.zarr + horizon: ${horizon} + pad_before: ${eval:'${n_obs_steps}-1'} + pad_after: ${eval:'${n_action_steps}-1'} + obs_eef_target: ${task.obs_eef_target} + use_manual_normalizer: False + seed: 42 + val_ratio: 0.02 diff --git a/diffusion_policy/config/task/can_image.yaml b/diffusion_policy/config/task/can_image.yaml new file mode 100644 index 0000000..10158bb --- /dev/null +++ b/diffusion_policy/config/task/can_image.yaml @@ -0,0 +1,64 @@ +name: can_image + +shape_meta: &shape_meta + # acceptable types: rgb, low_dim + obs: + agentview_image: + shape: [3, 84, 84] + type: rgb + robot0_eye_in_hand_image: + shape: [3, 84, 84] + type: rgb + robot0_eef_pos: + shape: [3] + # type default: low_dim + robot0_eef_quat: + shape: [4] + robot0_gripper_qpos: + shape: [2] + action: + shape: [7] + +task_name: &task_name can +dataset_type: &dataset_type ph +dataset_path: &dataset_path data/robomimic/datasets/${task.task_name}/${task.dataset_type}/image.hdf5 +abs_action: &abs_action False + +env_runner: + _target_: diffusion_policy.env_runner.robomimic_image_runner.RobomimicImageRunner + dataset_path: *dataset_path + shape_meta: *shape_meta + # costs 1GB per env + n_train: 6 + n_train_vis: 2 + train_start_idx: 0 + n_test: 50 + n_test_vis: 4 + test_start_seed: 100000 + # use python's eval function as resolver, single-quoted string as argument + max_steps: ${eval:'500 if "${task.dataset_type}" == "mh" else 400'} + n_obs_steps: ${n_obs_steps} + n_action_steps: ${n_action_steps} + render_obs_key: 'agentview_image' + fps: 10 + crf: 22 + past_action: ${past_action_visible} + abs_action: *abs_action + tqdm_interval_sec: 1.0 + n_envs: 28 +# evaluation at this config requires a 16 core 64GB instance. + +dataset: + _target_: diffusion_policy.dataset.robomimic_replay_image_dataset.RobomimicReplayImageDataset + shape_meta: *shape_meta + dataset_path: *dataset_path + horizon: ${horizon} + pad_before: ${eval:'${n_obs_steps}-1+${n_latency_steps}'} + pad_after: ${eval:'${n_action_steps}-1'} + n_obs_steps: ${dataset_obs_steps} + abs_action: *abs_action + rotation_rep: 'rotation_6d' + use_legacy_normalizer: False + use_cache: True + seed: 42 + val_ratio: 0.02 diff --git a/diffusion_policy/config/task/can_image_abs.yaml b/diffusion_policy/config/task/can_image_abs.yaml new file mode 100644 index 0000000..eee34dc --- /dev/null +++ b/diffusion_policy/config/task/can_image_abs.yaml @@ -0,0 +1,64 @@ +name: can_image + +shape_meta: &shape_meta + # acceptable types: rgb, low_dim + obs: + agentview_image: + shape: [3, 84, 84] + type: rgb + robot0_eye_in_hand_image: + shape: [3, 84, 84] + type: rgb + robot0_eef_pos: + shape: [3] + # type default: low_dim + robot0_eef_quat: + shape: [4] + robot0_gripper_qpos: + shape: [2] + action: + shape: [10] + +task_name: &task_name can +dataset_type: &dataset_type ph +dataset_path: &dataset_path data/robomimic/datasets/${task.task_name}/${task.dataset_type}/image_abs.hdf5 +abs_action: &abs_action True + +env_runner: + _target_: diffusion_policy.env_runner.robomimic_image_runner.RobomimicImageRunner + dataset_path: *dataset_path + shape_meta: *shape_meta + # costs 1GB per env + n_train: 6 + n_train_vis: 2 + train_start_idx: 0 + n_test: 50 + n_test_vis: 4 + test_start_seed: 100000 + # use python's eval function as resolver, single-quoted string as argument + max_steps: ${eval:'500 if "${task.dataset_type}" == "mh" else 400'} + n_obs_steps: ${n_obs_steps} + n_action_steps: ${n_action_steps} + render_obs_key: 'agentview_image' + fps: 10 + crf: 22 + past_action: ${past_action_visible} + abs_action: *abs_action + tqdm_interval_sec: 1.0 + n_envs: 28 +# evaluation at this config requires a 16 core 64GB instance. + +dataset: + _target_: diffusion_policy.dataset.robomimic_replay_image_dataset.RobomimicReplayImageDataset + shape_meta: *shape_meta + dataset_path: *dataset_path + horizon: ${horizon} + pad_before: ${eval:'${n_obs_steps}-1+${n_latency_steps}'} + pad_after: ${eval:'${n_action_steps}-1'} + n_obs_steps: ${dataset_obs_steps} + abs_action: *abs_action + rotation_rep: 'rotation_6d' + use_legacy_normalizer: False + use_cache: True + seed: 42 + val_ratio: 0.02 diff --git a/diffusion_policy/config/task/can_lowdim.yaml b/diffusion_policy/config/task/can_lowdim.yaml new file mode 100644 index 0000000..ba8d20f --- /dev/null +++ b/diffusion_policy/config/task/can_lowdim.yaml @@ -0,0 +1,45 @@ +name: can_lowdim + +obs_dim: 23 +action_dim: 7 +keypoint_dim: 3 + +obs_keys: &obs_keys ['object', 'robot0_eef_pos', 'robot0_eef_quat', 'robot0_gripper_qpos'] +task_name: &task_name can +dataset_type: &dataset_type ph +dataset_path: &dataset_path data/robomimic/datasets/${task.task_name}/${task.dataset_type}/low_dim.hdf5 +abs_action: &abs_action False + +env_runner: + _target_: diffusion_policy.env_runner.robomimic_lowdim_runner.RobomimicLowdimRunner + dataset_path: *dataset_path + obs_keys: *obs_keys + n_train: 6 + n_train_vis: 2 + train_start_idx: 0 + n_test: 50 + n_test_vis: 4 + test_start_seed: 100000 + # use python's eval function as resolver, single-quoted string as argument + max_steps: ${eval:'500 if "${task.dataset_type}" == "mh" else 400'} + n_obs_steps: ${n_obs_steps} + n_action_steps: ${n_action_steps} + n_latency_steps: ${n_latency_steps} + render_hw: [128,128] + fps: 10 + crf: 22 + past_action: ${past_action_visible} + abs_action: *abs_action + n_envs: 28 + +dataset: + _target_: diffusion_policy.dataset.robomimic_replay_lowdim_dataset.RobomimicReplayLowdimDataset + dataset_path: *dataset_path + horizon: ${horizon} + pad_before: ${eval:'${n_obs_steps}-1+${n_latency_steps}'} + pad_after: ${eval:'${n_action_steps}-1'} + obs_keys: *obs_keys + abs_action: *abs_action + use_legacy_normalizer: False + seed: 42 + val_ratio: 0.02 diff --git a/diffusion_policy/config/task/can_lowdim_abs.yaml b/diffusion_policy/config/task/can_lowdim_abs.yaml new file mode 100644 index 0000000..a9f723b --- /dev/null +++ b/diffusion_policy/config/task/can_lowdim_abs.yaml @@ -0,0 +1,46 @@ +name: can_lowdim + +obs_dim: 23 +action_dim: 10 +keypoint_dim: 3 + +obs_keys: &obs_keys ['object', 'robot0_eef_pos', 'robot0_eef_quat', 'robot0_gripper_qpos'] +task_name: &task_name can +dataset_type: &dataset_type ph +dataset_path: &dataset_path data/robomimic/datasets/${task.task_name}/${task.dataset_type}/low_dim_abs.hdf5 +abs_action: &abs_action True + +env_runner: + _target_: diffusion_policy.env_runner.robomimic_lowdim_runner.RobomimicLowdimRunner + dataset_path: *dataset_path + obs_keys: *obs_keys + n_train: 6 + n_train_vis: 2 + train_start_idx: 0 + n_test: 50 + n_test_vis: 4 + test_start_seed: 100000 + # use python's eval function as resolver, single-quoted string as argument + max_steps: ${eval:'500 if "${task.dataset_type}" == "mh" else 400'} + n_obs_steps: ${n_obs_steps} + n_action_steps: ${n_action_steps} + n_latency_steps: ${n_latency_steps} + render_hw: [128,128] + fps: 10 + crf: 22 + past_action: ${past_action_visible} + abs_action: *abs_action + n_envs: 28 + +dataset: + _target_: diffusion_policy.dataset.robomimic_replay_lowdim_dataset.RobomimicReplayLowdimDataset + dataset_path: *dataset_path + horizon: ${horizon} + pad_before: ${eval:'${n_obs_steps}-1+${n_latency_steps}'} + pad_after: ${eval:'${n_action_steps}-1'} + obs_keys: *obs_keys + abs_action: *abs_action + use_legacy_normalizer: False + rotation_rep: rotation_6d + seed: 42 + val_ratio: 0.02 diff --git a/diffusion_policy/config/task/kitchen_lowdim.yaml b/diffusion_policy/config/task/kitchen_lowdim.yaml new file mode 100644 index 0000000..a57d5d8 --- /dev/null +++ b/diffusion_policy/config/task/kitchen_lowdim.yaml @@ -0,0 +1,33 @@ +name: kitchen_lowdim + +obs_dim: 60 +action_dim: 9 +keypoint_dim: 3 + +dataset_dir: &dataset_dir data/kitchen + +env_runner: + _target_: diffusion_policy.env_runner.kitchen_lowdim_runner.KitchenLowdimRunner + dataset_dir: *dataset_dir + n_train: 6 + n_train_vis: 2 + train_start_seed: 0 + n_test: 50 + n_test_vis: 4 + test_start_seed: 100000 + max_steps: 280 + n_obs_steps: ${n_obs_steps} + n_action_steps: ${n_action_steps} + render_hw: [240, 360] + fps: 12.5 + past_action: ${past_action_visible} + n_envs: null + +dataset: + _target_: diffusion_policy.dataset.kitchen_lowdim_dataset.KitchenLowdimDataset + dataset_dir: *dataset_dir + horizon: ${horizon} + pad_before: ${eval:'${n_obs_steps}-1'} + pad_after: ${eval:'${n_action_steps}-1'} + seed: 42 + val_ratio: 0.02 diff --git a/diffusion_policy/config/task/kitchen_lowdim_abs.yaml b/diffusion_policy/config/task/kitchen_lowdim_abs.yaml new file mode 100644 index 0000000..ecd0691 --- /dev/null +++ b/diffusion_policy/config/task/kitchen_lowdim_abs.yaml @@ -0,0 +1,38 @@ +name: kitchen_lowdim + +obs_dim: 60 +action_dim: 9 +keypoint_dim: 3 + +abs_action: True +robot_noise_ratio: 0.1 + +env_runner: + _target_: diffusion_policy.env_runner.kitchen_lowdim_runner.KitchenLowdimRunner + dataset_dir: data/kitchen + n_train: 6 + n_train_vis: 2 + train_start_seed: 0 + n_test: 50 + n_test_vis: 4 + test_start_seed: 100000 + max_steps: 280 + n_obs_steps: ${n_obs_steps} + n_action_steps: ${n_action_steps} + render_hw: [240, 360] + fps: 12.5 + past_action: ${past_action_visible} + abs_action: ${task.abs_action} + robot_noise_ratio: ${task.robot_noise_ratio} + n_envs: null + +dataset: + _target_: diffusion_policy.dataset.kitchen_mjl_lowdim_dataset.KitchenMjlLowdimDataset + dataset_dir: data/kitchen/kitchen_demos_multitask + horizon: ${horizon} + pad_before: ${eval:'${n_obs_steps}-1'} + pad_after: ${eval:'${n_action_steps}-1'} + abs_action: ${task.abs_action} + robot_noise_ratio: ${task.robot_noise_ratio} + seed: 42 + val_ratio: 0.02 diff --git a/diffusion_policy/config/task/lift_image.yaml b/diffusion_policy/config/task/lift_image.yaml new file mode 100644 index 0000000..8ddde45 --- /dev/null +++ b/diffusion_policy/config/task/lift_image.yaml @@ -0,0 +1,64 @@ +name: lift_image + +shape_meta: &shape_meta + # acceptable types: rgb, low_dim + obs: + agentview_image: + shape: [3, 84, 84] + type: rgb + robot0_eye_in_hand_image: + shape: [3, 84, 84] + type: rgb + robot0_eef_pos: + shape: [3] + # type default: low_dim + robot0_eef_quat: + shape: [4] + robot0_gripper_qpos: + shape: [2] + action: + shape: [7] + +task_name: &task_name lift +dataset_type: &dataset_type ph +dataset_path: &dataset_path data/robomimic/datasets/${task.task_name}/${task.dataset_type}/image.hdf5 +abs_action: &abs_action False + +env_runner: + _target_: diffusion_policy.env_runner.robomimic_image_runner.RobomimicImageRunner + dataset_path: *dataset_path + shape_meta: *shape_meta + # costs 1GB per env + n_train: 6 + n_train_vis: 1 + train_start_idx: 0 + n_test: 50 + n_test_vis: 3 + test_start_seed: 100000 + # use python's eval function as resolver, single-quoted string as argument + max_steps: ${eval:'500 if "${task.dataset_type}" == "mh" else 400'} + n_obs_steps: ${n_obs_steps} + n_action_steps: ${n_action_steps} + render_obs_key: 'agentview_image' + fps: 10 + crf: 22 + past_action: ${past_action_visible} + abs_action: *abs_action + tqdm_interval_sec: 1.0 + n_envs: 28 +# evaluation at this config requires a 16 core 64GB instance. + +dataset: + _target_: diffusion_policy.dataset.robomimic_replay_image_dataset.RobomimicReplayImageDataset + shape_meta: *shape_meta + dataset_path: *dataset_path + horizon: ${horizon} + pad_before: ${eval:'${n_obs_steps}-1+${n_latency_steps}'} + pad_after: ${eval:'${n_action_steps}-1'} + n_obs_steps: ${dataset_obs_steps} + abs_action: *abs_action + rotation_rep: 'rotation_6d' + use_legacy_normalizer: False + use_cache: True + seed: 42 + val_ratio: 0.02 diff --git a/diffusion_policy/config/task/lift_image_abs.yaml b/diffusion_policy/config/task/lift_image_abs.yaml new file mode 100644 index 0000000..b25002f --- /dev/null +++ b/diffusion_policy/config/task/lift_image_abs.yaml @@ -0,0 +1,63 @@ +name: lift_image + +shape_meta: &shape_meta + # acceptable types: rgb, low_dim + obs: + agentview_image: + shape: [3, 84, 84] + type: rgb + robot0_eye_in_hand_image: + shape: [3, 84, 84] + type: rgb + robot0_eef_pos: + shape: [3] + # type default: low_dim + robot0_eef_quat: + shape: [4] + robot0_gripper_qpos: + shape: [2] + action: + shape: [10] + +task_name: &task_name lift +dataset_type: &dataset_type ph +dataset_path: &dataset_path data/robomimic/datasets/${task.task_name}/${task.dataset_type}/image_abs.hdf5 +abs_action: &abs_action True + +env_runner: + _target_: diffusion_policy.env_runner.robomimic_image_runner.RobomimicImageRunner + dataset_path: *dataset_path + shape_meta: *shape_meta + n_train: 6 + n_train_vis: 2 + train_start_idx: 0 + n_test: 50 + n_test_vis: 4 + test_start_seed: 100000 + # use python's eval function as resolver, single-quoted string as argument + max_steps: ${eval:'500 if "${task.dataset_type}" == "mh" else 400'} + n_obs_steps: ${n_obs_steps} + n_action_steps: ${n_action_steps} + render_obs_key: 'agentview_image' + fps: 10 + crf: 22 + past_action: ${past_action_visible} + abs_action: *abs_action + tqdm_interval_sec: 1.0 + n_envs: 28 +# evaluation at this config requires a 16 core 64GB instance. + +dataset: + _target_: diffusion_policy.dataset.robomimic_replay_image_dataset.RobomimicReplayImageDataset + shape_meta: *shape_meta + dataset_path: *dataset_path + horizon: ${horizon} + pad_before: ${eval:'${n_obs_steps}-1+${n_latency_steps}'} + pad_after: ${eval:'${n_action_steps}-1'} + n_obs_steps: ${dataset_obs_steps} + abs_action: *abs_action + rotation_rep: 'rotation_6d' + use_legacy_normalizer: False + use_cache: True + seed: 42 + val_ratio: 0.02 diff --git a/diffusion_policy/config/task/lift_lowdim.yaml b/diffusion_policy/config/task/lift_lowdim.yaml new file mode 100644 index 0000000..5b1b2d2 --- /dev/null +++ b/diffusion_policy/config/task/lift_lowdim.yaml @@ -0,0 +1,46 @@ +name: lift_lowdim + +obs_dim: 19 +action_dim: 7 +keypoint_dim: 3 + +obs_keys: &obs_keys ['object', 'robot0_eef_pos', 'robot0_eef_quat', 'robot0_gripper_qpos'] +task_name: &task_name lift +dataset_type: &dataset_type ph +dataset_path: &dataset_path data/robomimic/datasets/${task.task_name}/${task.dataset_type}/low_dim.hdf5 +abs_action: &abs_action False + +env_runner: + _target_: diffusion_policy.env_runner.robomimic_lowdim_runner.RobomimicLowdimRunner + dataset_path: *dataset_path + obs_keys: *obs_keys + n_train: 6 + n_train_vis: 2 + train_start_idx: 0 + n_test: 50 + n_test_vis: 4 + test_start_seed: 100000 + # use python's eval function as resolver, single-quoted string as argument + max_steps: ${eval:'500 if "${task.dataset_type}" == "mh" else 400'} + n_obs_steps: ${n_obs_steps} + n_action_steps: ${n_action_steps} + n_latency_steps: ${n_latency_steps} + render_hw: [128,128] + fps: 10 + crf: 22 + past_action: ${past_action_visible} + abs_action: *abs_action + tqdm_interval_sec: 1.0 + n_envs: 28 + +dataset: + _target_: diffusion_policy.dataset.robomimic_replay_lowdim_dataset.RobomimicReplayLowdimDataset + dataset_path: *dataset_path + horizon: ${horizon} + pad_before: ${eval:'${n_obs_steps}-1+${n_latency_steps}'} + pad_after: ${eval:'${n_action_steps}-1'} + obs_keys: *obs_keys + abs_action: *abs_action + use_legacy_normalizer: False + seed: 42 + val_ratio: 0.02 diff --git a/diffusion_policy/config/task/lift_lowdim_abs.yaml b/diffusion_policy/config/task/lift_lowdim_abs.yaml new file mode 100644 index 0000000..fe9d17b --- /dev/null +++ b/diffusion_policy/config/task/lift_lowdim_abs.yaml @@ -0,0 +1,47 @@ +name: lift_lowdim + +obs_dim: 19 +action_dim: 10 +keypoint_dim: 3 + +obs_keys: &obs_keys ['object', 'robot0_eef_pos', 'robot0_eef_quat', 'robot0_gripper_qpos'] +task_name: &task_name lift +dataset_type: &dataset_type ph +dataset_path: &dataset_path data/robomimic/datasets/${task.task_name}/${task.dataset_type}/low_dim_abs.hdf5 +abs_action: &abs_action True + +env_runner: + _target_: diffusion_policy.env_runner.robomimic_lowdim_runner.RobomimicLowdimRunner + dataset_path: *dataset_path + obs_keys: *obs_keys + n_train: 6 + n_train_vis: 2 + train_start_idx: 0 + n_test: 50 + n_test_vis: 3 + test_start_seed: 100000 + # use python's eval function as resolver, single-quoted string as argument + max_steps: ${eval:'500 if "${task.dataset_type}" == "mh" else 400'} + n_obs_steps: ${n_obs_steps} + n_action_steps: ${n_action_steps} + n_latency_steps: ${n_latency_steps} + render_hw: [128,128] + fps: 10 + crf: 22 + past_action: ${past_action_visible} + abs_action: *abs_action + tqdm_interval_sec: 1.0 + n_envs: 28 + +dataset: + _target_: diffusion_policy.dataset.robomimic_replay_lowdim_dataset.RobomimicReplayLowdimDataset + dataset_path: *dataset_path + horizon: ${horizon} + pad_before: ${eval:'${n_obs_steps}-1+${n_latency_steps}'} + pad_after: ${eval:'${n_action_steps}-1'} + obs_keys: *obs_keys + abs_action: *abs_action + use_legacy_normalizer: False + rotation_rep: rotation_6d + seed: 42 + val_ratio: 0.02 diff --git a/diffusion_policy/config/task/pusht_image.yaml b/diffusion_policy/config/task/pusht_image.yaml new file mode 100644 index 0000000..36b5dd3 --- /dev/null +++ b/diffusion_policy/config/task/pusht_image.yaml @@ -0,0 +1,40 @@ +name: pusht_image + +image_shape: &image_shape [3, 96, 96] +shape_meta: &shape_meta + # acceptable types: rgb, low_dim + obs: + image: + shape: *image_shape + type: rgb + agent_pos: + shape: [2] + type: low_dim + action: + shape: [2] + +env_runner: + _target_: diffusion_policy.env_runner.pusht_image_runner.PushTImageRunner + n_train: 6 + n_train_vis: 2 + train_start_seed: 0 + n_test: 50 + n_test_vis: 4 + legacy_test: True + test_start_seed: 100000 + max_steps: 300 + n_obs_steps: ${n_obs_steps} + n_action_steps: ${n_action_steps} + fps: 10 + past_action: ${past_action_visible} + n_envs: null + +dataset: + _target_: diffusion_policy.dataset.pusht_image_dataset.PushTImageDataset + zarr_path: data/pusht/pusht_cchi_v7_replay.zarr + horizon: ${horizon} + pad_before: ${eval:'${n_obs_steps}-1'} + pad_after: ${eval:'${n_action_steps}-1'} + seed: 42 + val_ratio: 0.02 + max_train_episodes: 90 diff --git a/diffusion_policy/config/task/pusht_lowdim.yaml b/diffusion_policy/config/task/pusht_lowdim.yaml new file mode 100644 index 0000000..3ec7eb5 --- /dev/null +++ b/diffusion_policy/config/task/pusht_lowdim.yaml @@ -0,0 +1,34 @@ +name: pusht_lowdim + +obs_dim: 20 # 9*2 keypoints + 2 state +action_dim: 2 +keypoint_dim: 2 + +env_runner: + _target_: diffusion_policy.env_runner.pusht_keypoints_runner.PushTKeypointsRunner + keypoint_visible_rate: ${keypoint_visible_rate} + n_train: 6 + n_train_vis: 2 + train_start_seed: 0 + n_test: 50 + n_test_vis: 4 + legacy_test: True + test_start_seed: 100000 + max_steps: 300 + n_obs_steps: ${n_obs_steps} + n_action_steps: ${n_action_steps} + n_latency_steps: ${n_latency_steps} + fps: 10 + agent_keypoints: False + past_action: ${past_action_visible} + n_envs: null + +dataset: + _target_: diffusion_policy.dataset.pusht_dataset.PushTLowdimDataset + zarr_path: data/pusht/pusht_cchi_v7_replay.zarr + horizon: ${horizon} + pad_before: ${eval:'${n_obs_steps}-1+${n_latency_steps}'} + pad_after: ${eval:'${n_action_steps}-1'} + seed: 42 + val_ratio: 0.02 + max_train_episodes: 90 diff --git a/diffusion_policy/config/task/real_pusht_image.yaml b/diffusion_policy/config/task/real_pusht_image.yaml new file mode 100644 index 0000000..a3f7c3f --- /dev/null +++ b/diffusion_policy/config/task/real_pusht_image.yaml @@ -0,0 +1,47 @@ +name: real_image + +image_shape: [3, 240, 320] +dataset_path: data/pusht_real/real_pusht_20230105 + +shape_meta: &shape_meta + # acceptable types: rgb, low_dim + obs: + # camera_0: + # shape: ${task.image_shape} + # type: rgb + camera_1: + shape: ${task.image_shape} + type: rgb + # camera_2: + # shape: ${task.image_shape} + # type: rgb + camera_3: + shape: ${task.image_shape} + type: rgb + # camera_4: + # shape: ${task.image_shape} + # type: rgb + robot_eef_pose: + shape: [2] + type: low_dim + action: + shape: [2] + +env_runner: + _target_: diffusion_policy.env_runner.real_pusht_image_runner.RealPushTImageRunner + +dataset: + _target_: diffusion_policy.dataset.real_pusht_image_dataset.RealPushTImageDataset + shape_meta: *shape_meta + dataset_path: ${task.dataset_path} + horizon: ${horizon} + pad_before: ${eval:'${n_obs_steps}-1+${n_latency_steps}'} + pad_after: ${eval:'${n_action_steps}-1'} + n_obs_steps: ${dataset_obs_steps} + n_latency_steps: ${n_latency_steps} + use_cache: True + seed: 42 + val_ratio: 0.00 + max_train_episodes: null + delta_action: False + diff --git a/diffusion_policy/config/task/square_image.yaml b/diffusion_policy/config/task/square_image.yaml new file mode 100644 index 0000000..827151b --- /dev/null +++ b/diffusion_policy/config/task/square_image.yaml @@ -0,0 +1,64 @@ +name: square_image + +shape_meta: &shape_meta + # acceptable types: rgb, low_dim + obs: + agentview_image: + shape: [3, 84, 84] + type: rgb + robot0_eye_in_hand_image: + shape: [3, 84, 84] + type: rgb + robot0_eef_pos: + shape: [3] + # type default: low_dim + robot0_eef_quat: + shape: [4] + robot0_gripper_qpos: + shape: [2] + action: + shape: [7] + +task_name: &task_name square +dataset_type: &dataset_type ph +dataset_path: &dataset_path data/robomimic/datasets/${task.task_name}/${task.dataset_type}/image.hdf5 +abs_action: &abs_action False + +env_runner: + _target_: diffusion_policy.env_runner.robomimic_image_runner.RobomimicImageRunner + dataset_path: *dataset_path + shape_meta: *shape_meta + # costs 1GB per env + n_train: 6 + n_train_vis: 2 + train_start_idx: 0 + n_test: 50 + n_test_vis: 4 + test_start_seed: 100000 + # use python's eval function as resolver, single-quoted string as argument + max_steps: ${eval:'500 if "${task.dataset_type}" == "mh" else 400'} + n_obs_steps: ${n_obs_steps} + n_action_steps: ${n_action_steps} + render_obs_key: 'agentview_image' + fps: 10 + crf: 22 + past_action: ${past_action_visible} + abs_action: *abs_action + tqdm_interval_sec: 1.0 + n_envs: 28 +# evaluation at this config requires a 16 core 64GB instance. + +dataset: + _target_: diffusion_policy.dataset.robomimic_replay_image_dataset.RobomimicReplayImageDataset + shape_meta: *shape_meta + dataset_path: *dataset_path + horizon: ${horizon} + pad_before: ${eval:'${n_obs_steps}-1+${n_latency_steps}'} + pad_after: ${eval:'${n_action_steps}-1'} + n_obs_steps: ${dataset_obs_steps} + abs_action: *abs_action + rotation_rep: 'rotation_6d' + use_legacy_normalizer: False + use_cache: True + seed: 42 + val_ratio: 0.02 diff --git a/diffusion_policy/config/task/square_image_abs.yaml b/diffusion_policy/config/task/square_image_abs.yaml new file mode 100644 index 0000000..d27a916 --- /dev/null +++ b/diffusion_policy/config/task/square_image_abs.yaml @@ -0,0 +1,64 @@ +name: square_image + +shape_meta: &shape_meta + # acceptable types: rgb, low_dim + obs: + agentview_image: + shape: [3, 84, 84] + type: rgb + robot0_eye_in_hand_image: + shape: [3, 84, 84] + type: rgb + robot0_eef_pos: + shape: [3] + # type default: low_dim + robot0_eef_quat: + shape: [4] + robot0_gripper_qpos: + shape: [2] + action: + shape: [10] + +task_name: &task_name square +dataset_type: &dataset_type ph +dataset_path: &dataset_path data/robomimic/datasets/${task.task_name}/${task.dataset_type}/image_abs.hdf5 +abs_action: &abs_action True + +env_runner: + _target_: diffusion_policy.env_runner.robomimic_image_runner.RobomimicImageRunner + dataset_path: *dataset_path + shape_meta: *shape_meta + # costs 1GB per env + n_train: 6 + n_train_vis: 2 + train_start_idx: 0 + n_test: 50 + n_test_vis: 4 + test_start_seed: 100000 + # use python's eval function as resolver, single-quoted string as argument + max_steps: ${eval:'500 if "${task.dataset_type}" == "mh" else 400'} + n_obs_steps: ${n_obs_steps} + n_action_steps: ${n_action_steps} + render_obs_key: 'agentview_image' + fps: 10 + crf: 22 + past_action: ${past_action_visible} + abs_action: *abs_action + tqdm_interval_sec: 1.0 + n_envs: 28 +# evaluation at this config requires a 16 core 64GB instance. + +dataset: + _target_: diffusion_policy.dataset.robomimic_replay_image_dataset.RobomimicReplayImageDataset + shape_meta: *shape_meta + dataset_path: *dataset_path + horizon: ${horizon} + pad_before: ${eval:'${n_obs_steps}-1+${n_latency_steps}'} + pad_after: ${eval:'${n_action_steps}-1'} + n_obs_steps: ${dataset_obs_steps} + abs_action: *abs_action + rotation_rep: 'rotation_6d' + use_legacy_normalizer: False + use_cache: True + seed: 42 + val_ratio: 0.02 diff --git a/diffusion_policy/config/task/square_lowdim.yaml b/diffusion_policy/config/task/square_lowdim.yaml new file mode 100644 index 0000000..f93e062 --- /dev/null +++ b/diffusion_policy/config/task/square_lowdim.yaml @@ -0,0 +1,46 @@ +name: square_lowdim + +obs_dim: 23 +action_dim: 7 +keypoint_dim: 3 + +obs_keys: &obs_keys ['object', 'robot0_eef_pos', 'robot0_eef_quat', 'robot0_gripper_qpos'] +task_name: &task_name square +dataset_type: &dataset_type ph +dataset_path: &dataset_path data/robomimic/datasets/${task.task_name}/${task.dataset_type}/low_dim.hdf5 +abs_action: &abs_action False + +env_runner: + _target_: diffusion_policy.env_runner.robomimic_lowdim_runner.RobomimicLowdimRunner + dataset_path: *dataset_path + obs_keys: *obs_keys + n_train: 6 + n_train_vis: 2 + train_start_idx: 0 + n_test: 50 + n_test_vis: 4 + test_start_seed: 100000 + # use python's eval function as resolver, single-quoted string as argument + max_steps: ${eval:'500 if "${task.dataset_type}" == "mh" else 400'} + n_obs_steps: ${n_obs_steps} + n_action_steps: ${n_action_steps} + n_latency_steps: ${n_latency_steps} + render_hw: [128,128] + fps: 10 + crf: 22 + past_action: ${past_action_visible} + abs_action: *abs_action + n_envs: 28 + +dataset: + _target_: diffusion_policy.dataset.robomimic_replay_lowdim_dataset.RobomimicReplayLowdimDataset + dataset_path: *dataset_path + horizon: ${horizon} + pad_before: ${eval:'${n_obs_steps}-1+${n_latency_steps}'} + pad_after: ${eval:'${n_action_steps}-1'} + obs_keys: *obs_keys + abs_action: *abs_action + use_legacy_normalizer: False + seed: 42 + val_ratio: 0.02 + max_train_episodes: null diff --git a/diffusion_policy/config/task/square_lowdim_abs.yaml b/diffusion_policy/config/task/square_lowdim_abs.yaml new file mode 100644 index 0000000..f69b3c8 --- /dev/null +++ b/diffusion_policy/config/task/square_lowdim_abs.yaml @@ -0,0 +1,47 @@ +name: square_lowdim + +obs_dim: 23 +action_dim: 10 +keypoint_dim: 3 + +obs_keys: &obs_keys ['object', 'robot0_eef_pos', 'robot0_eef_quat', 'robot0_gripper_qpos'] +task_name: &task_name square +dataset_type: &dataset_type ph +abs_action: &abs_action True +dataset_path: &dataset_path data/robomimic/datasets/${task.task_name}/${task.dataset_type}/low_dim_abs.hdf5 + + +env_runner: + _target_: diffusion_policy.env_runner.robomimic_lowdim_runner.RobomimicLowdimRunner + dataset_path: *dataset_path + obs_keys: *obs_keys + n_train: 6 + n_train_vis: 2 + train_start_idx: 0 + n_test: 50 + n_test_vis: 4 + test_start_seed: 100000 + # use python's eval function as resolver, single-quoted string as argument + max_steps: ${eval:'500 if "${task.dataset_type}" == "mh" else 400'} + n_obs_steps: ${n_obs_steps} + n_action_steps: ${n_action_steps} + n_latency_steps: ${n_latency_steps} + render_hw: [128,128] + fps: 10 + crf: 22 + past_action: ${past_action_visible} + abs_action: *abs_action + n_envs: 28 + +dataset: + _target_: diffusion_policy.dataset.robomimic_replay_lowdim_dataset.RobomimicReplayLowdimDataset + dataset_path: *dataset_path + horizon: ${horizon} + pad_before: ${eval:'${n_obs_steps}-1+${n_latency_steps}'} + pad_after: ${eval:'${n_action_steps}-1'} + obs_keys: *obs_keys + abs_action: *abs_action + use_legacy_normalizer: False + seed: 42 + val_ratio: 0.02 + max_train_episodes: null diff --git a/diffusion_policy/config/task/tool_hang_image.yaml b/diffusion_policy/config/task/tool_hang_image.yaml new file mode 100644 index 0000000..8a8b298 --- /dev/null +++ b/diffusion_policy/config/task/tool_hang_image.yaml @@ -0,0 +1,63 @@ +name: tool_hang_image + +shape_meta: &shape_meta + # acceptable types: rgb, low_dim + obs: + sideview_image: + shape: [3, 240, 240] + type: rgb + robot0_eye_in_hand_image: + shape: [3, 240, 240] + type: rgb + robot0_eef_pos: + shape: [3] + # type default: low_dim + robot0_eef_quat: + shape: [4] + robot0_gripper_qpos: + shape: [2] + action: + shape: [7] + +task_name: &task_name tool_hang +dataset_type: &dataset_type ph +dataset_path: &dataset_path data/robomimic/datasets/${task.task_name}/${task.dataset_type}/image.hdf5 +abs_action: &abs_action False + +env_runner: + _target_: diffusion_policy.env_runner.robomimic_image_runner.RobomimicImageRunner + dataset_path: *dataset_path + shape_meta: *shape_meta + # costs 1GB per env + n_train: 6 + n_train_vis: 2 + train_start_idx: 0 + n_test: 50 + n_test_vis: 4 + test_start_seed: 100000 + max_steps: 700 + n_obs_steps: ${n_obs_steps} + n_action_steps: ${n_action_steps} + render_obs_key: 'sideview_image' + fps: 10 + crf: 22 + past_action: ${past_action_visible} + abs_action: *abs_action + tqdm_interval_sec: 1.0 + n_envs: 28 +# evaluation at this config requires a 16 core 64GB instance. + +dataset: + _target_: diffusion_policy.dataset.robomimic_replay_image_dataset.RobomimicReplayImageDataset + shape_meta: *shape_meta + dataset_path: *dataset_path + horizon: ${horizon} + pad_before: ${eval:'${n_obs_steps}-1+${n_latency_steps}'} + pad_after: ${eval:'${n_action_steps}-1'} + n_obs_steps: ${dataset_obs_steps} + abs_action: *abs_action + rotation_rep: 'rotation_6d' + use_legacy_normalizer: False + use_cache: True + seed: 42 + val_ratio: 0.02 diff --git a/diffusion_policy/config/task/tool_hang_image_abs.yaml b/diffusion_policy/config/task/tool_hang_image_abs.yaml new file mode 100644 index 0000000..068bdc9 --- /dev/null +++ b/diffusion_policy/config/task/tool_hang_image_abs.yaml @@ -0,0 +1,63 @@ +name: tool_hang_image_abs + +shape_meta: &shape_meta + # acceptable types: rgb, low_dim + obs: + sideview_image: + shape: [3, 240, 240] + type: rgb + robot0_eye_in_hand_image: + shape: [3, 240, 240] + type: rgb + robot0_eef_pos: + shape: [3] + # type default: low_dim + robot0_eef_quat: + shape: [4] + robot0_gripper_qpos: + shape: [2] + action: + shape: [10] + +task_name: &task_name tool_hang +dataset_type: &dataset_type ph +dataset_path: &dataset_path data/robomimic/datasets/${task.task_name}/${task.dataset_type}/image_abs.hdf5 +abs_action: &abs_action True + +env_runner: + _target_: diffusion_policy.env_runner.robomimic_image_runner.RobomimicImageRunner + dataset_path: *dataset_path + shape_meta: *shape_meta + # costs 1GB per env + n_train: 6 + n_train_vis: 2 + train_start_idx: 0 + n_test: 50 + n_test_vis: 4 + test_start_seed: 100000 + max_steps: 700 + n_obs_steps: ${n_obs_steps} + n_action_steps: ${n_action_steps} + render_obs_key: 'sideview_image' + fps: 10 + crf: 22 + past_action: ${past_action_visible} + abs_action: *abs_action + tqdm_interval_sec: 1.0 + n_envs: 28 +# evaluation at this config requires a 16 core 64GB instance. + +dataset: + _target_: diffusion_policy.dataset.robomimic_replay_image_dataset.RobomimicReplayImageDataset + shape_meta: *shape_meta + dataset_path: *dataset_path + horizon: ${horizon} + pad_before: ${eval:'${n_obs_steps}-1+${n_latency_steps}'} + pad_after: ${eval:'${n_action_steps}-1'} + n_obs_steps: ${dataset_obs_steps} + abs_action: *abs_action + rotation_rep: 'rotation_6d' + use_legacy_normalizer: False + use_cache: True + seed: 42 + val_ratio: 0.02 diff --git a/diffusion_policy/config/task/tool_hang_lowdim.yaml b/diffusion_policy/config/task/tool_hang_lowdim.yaml new file mode 100644 index 0000000..3826439 --- /dev/null +++ b/diffusion_policy/config/task/tool_hang_lowdim.yaml @@ -0,0 +1,45 @@ +name: tool_hang_lowdim + +obs_dim: 53 +action_dim: 7 +keypoint_dim: 3 + +obs_keys: &obs_keys ['object', 'robot0_eef_pos', 'robot0_eef_quat', 'robot0_gripper_qpos'] +task_name: &task_name tool_hang +dataset_type: &dataset_type ph +dataset_path: &dataset_path data/robomimic/datasets/${task.task_name}/${task.dataset_type}/low_dim.hdf5 +abs_action: &abs_action False + +env_runner: + _target_: diffusion_policy.env_runner.robomimic_lowdim_runner.RobomimicLowdimRunner + dataset_path: *dataset_path + obs_keys: *obs_keys + n_train: 6 + n_train_vis: 2 + train_start_idx: 0 + n_test: 50 + n_test_vis: 4 + test_start_seed: 100000 + max_steps: 700 + n_obs_steps: ${n_obs_steps} + n_action_steps: ${n_action_steps} + n_latency_steps: ${n_latency_steps} + render_hw: [128,128] + fps: 10 + crf: 22 + past_action: ${past_action_visible} + abs_action: *abs_action + n_envs: 28 +# seed 42 will crash MuJoCo for some reason. + +dataset: + _target_: diffusion_policy.dataset.robomimic_replay_lowdim_dataset.RobomimicReplayLowdimDataset + dataset_path: *dataset_path + horizon: ${horizon} + pad_before: ${eval:'${n_obs_steps}-1+${n_latency_steps}'} + pad_after: ${eval:'${n_action_steps}-1'} + obs_keys: *obs_keys + abs_action: *abs_action + use_legacy_normalizer: False + seed: 42 + val_ratio: 0.02 diff --git a/diffusion_policy/config/task/tool_hang_lowdim_abs.yaml b/diffusion_policy/config/task/tool_hang_lowdim_abs.yaml new file mode 100644 index 0000000..bfdb34d --- /dev/null +++ b/diffusion_policy/config/task/tool_hang_lowdim_abs.yaml @@ -0,0 +1,46 @@ +name: tool_hang_lowdim + +obs_dim: 53 +action_dim: 10 +keypoint_dim: 3 + +obs_keys: &obs_keys ['object', 'robot0_eef_pos', 'robot0_eef_quat', 'robot0_gripper_qpos'] +task_name: &task_name tool_hang +dataset_type: &dataset_type ph +dataset_path: &dataset_path data/robomimic/datasets/${task.task_name}/${task.dataset_type}/low_dim_abs.hdf5 +abs_action: &abs_action True + +env_runner: + _target_: diffusion_policy.env_runner.robomimic_lowdim_runner.RobomimicLowdimRunner + dataset_path: *dataset_path + obs_keys: *obs_keys + n_train: 6 + n_train_vis: 2 + train_start_idx: 0 + n_test: 50 + n_test_vis: 4 + test_start_seed: 100000 + max_steps: 700 + n_obs_steps: ${n_obs_steps} + n_action_steps: ${n_action_steps} + n_latency_steps: ${n_latency_steps} + render_hw: [128,128] + fps: 10 + crf: 22 + past_action: ${past_action_visible} + abs_action: *abs_action + n_envs: 28 +# seed 42 will crash MuJoCo for some reason. + +dataset: + _target_: diffusion_policy.dataset.robomimic_replay_lowdim_dataset.RobomimicReplayLowdimDataset + dataset_path: *dataset_path + horizon: ${horizon} + pad_before: ${eval:'${n_obs_steps}-1+${n_latency_steps}'} + pad_after: ${eval:'${n_action_steps}-1'} + obs_keys: *obs_keys + abs_action: *abs_action + use_legacy_normalizer: False + rotation_rep: rotation_6d + seed: 42 + val_ratio: 0.02 diff --git a/diffusion_policy/config/task/transport_image.yaml b/diffusion_policy/config/task/transport_image.yaml new file mode 100644 index 0000000..c9c24cc --- /dev/null +++ b/diffusion_policy/config/task/transport_image.yaml @@ -0,0 +1,75 @@ +name: transport_image + +shape_meta: &shape_meta + # acceptable types: rgb, low_dim + obs: + shouldercamera0_image: + shape: [3, 84, 84] + type: rgb + robot0_eye_in_hand_image: + shape: [3, 84, 84] + type: rgb + robot0_eef_pos: + shape: [3] + # type default: low_dim + robot0_eef_quat: + shape: [4] + robot0_gripper_qpos: + shape: [2] + shouldercamera1_image: + shape: [3, 84, 84] + type: rgb + robot1_eye_in_hand_image: + shape: [3, 84, 84] + type: rgb + robot1_eef_pos: + shape: [3] + # type default: low_dim + robot1_eef_quat: + shape: [4] + robot1_gripper_qpos: + shape: [2] + action: + shape: [14] + +task_name: &task_name transport +dataset_type: &dataset_type ph +dataset_path: &dataset_path data/robomimic/datasets/${task.task_name}/${task.dataset_type}/image.hdf5 +abs_action: &abs_action False + +env_runner: + _target_: diffusion_policy.env_runner.robomimic_image_runner.RobomimicImageRunner + dataset_path: *dataset_path + shape_meta: *shape_meta + n_train: 6 + n_train_vis: 2 + train_start_idx: 0 + n_test: 50 + n_test_vis: 4 + test_start_seed: 100000 + max_steps: 700 + n_obs_steps: ${n_obs_steps} + n_action_steps: ${n_action_steps} + render_obs_key: 'shouldercamera0_image' + fps: 10 + crf: 22 + past_action: ${past_action_visible} + abs_action: *abs_action + tqdm_interval_sec: 1.0 + n_envs: 28 +# evaluation at this config requires a 16 core 64GB instance. + +dataset: + _target_: diffusion_policy.dataset.robomimic_replay_image_dataset.RobomimicReplayImageDataset + shape_meta: *shape_meta + dataset_path: *dataset_path + horizon: ${horizon} + pad_before: ${eval:'${n_obs_steps}-1+${n_latency_steps}'} + pad_after: ${eval:'${n_action_steps}-1'} + n_obs_steps: ${dataset_obs_steps} + abs_action: *abs_action + rotation_rep: 'rotation_6d' + use_legacy_normalizer: False + use_cache: True + seed: 42 + val_ratio: 0.02 diff --git a/diffusion_policy/config/task/transport_image_abs.yaml b/diffusion_policy/config/task/transport_image_abs.yaml new file mode 100644 index 0000000..74e76c6 --- /dev/null +++ b/diffusion_policy/config/task/transport_image_abs.yaml @@ -0,0 +1,75 @@ +name: transport_image + +shape_meta: &shape_meta + # acceptable types: rgb, low_dim + obs: + shouldercamera0_image: + shape: [3, 84, 84] + type: rgb + robot0_eye_in_hand_image: + shape: [3, 84, 84] + type: rgb + robot0_eef_pos: + shape: [3] + # type default: low_dim + robot0_eef_quat: + shape: [4] + robot0_gripper_qpos: + shape: [2] + shouldercamera1_image: + shape: [3, 84, 84] + type: rgb + robot1_eye_in_hand_image: + shape: [3, 84, 84] + type: rgb + robot1_eef_pos: + shape: [3] + # type default: low_dim + robot1_eef_quat: + shape: [4] + robot1_gripper_qpos: + shape: [2] + action: + shape: [20] + +task_name: &task_name transport +dataset_type: &dataset_type ph +dataset_path: &dataset_path data/robomimic/datasets/${task.task_name}/${task.dataset_type}/image_abs.hdf5 +abs_action: &abs_action True + +env_runner: + _target_: diffusion_policy.env_runner.robomimic_image_runner.RobomimicImageRunner + dataset_path: *dataset_path + shape_meta: *shape_meta + n_train: 6 + n_train_vis: 2 + train_start_idx: 0 + n_test: 50 + n_test_vis: 4 + test_start_seed: 100000 + max_steps: 700 + n_obs_steps: ${n_obs_steps} + n_action_steps: ${n_action_steps} + render_obs_key: 'shouldercamera0_image' + fps: 10 + crf: 22 + past_action: ${past_action_visible} + abs_action: *abs_action + tqdm_interval_sec: 1.0 + n_envs: 28 +# evaluation at this config requires a 16 core 64GB instance. + +dataset: + _target_: diffusion_policy.dataset.robomimic_replay_image_dataset.RobomimicReplayImageDataset + shape_meta: *shape_meta + dataset_path: *dataset_path + horizon: ${horizon} + pad_before: ${eval:'${n_obs_steps}-1+${n_latency_steps}'} + pad_after: ${eval:'${n_action_steps}-1'} + n_obs_steps: ${dataset_obs_steps} + abs_action: *abs_action + rotation_rep: 'rotation_6d' + use_legacy_normalizer: False + use_cache: True + seed: 42 + val_ratio: 0.02 diff --git a/diffusion_policy/config/task/transport_lowdim.yaml b/diffusion_policy/config/task/transport_lowdim.yaml new file mode 100644 index 0000000..99e279c --- /dev/null +++ b/diffusion_policy/config/task/transport_lowdim.yaml @@ -0,0 +1,49 @@ +name: transport_lowdim + +obs_dim: 59 # 41+(3+4+2)*2 +action_dim: 14 # 7*2 +keypoint_dim: 3 + +obs_keys: &obs_keys [ + 'object', + 'robot0_eef_pos', 'robot0_eef_quat', 'robot0_gripper_qpos', + 'robot1_eef_pos', 'robot1_eef_quat', 'robot1_gripper_qpos' +] +task_name: &task_name transport +dataset_type: &dataset_type ph +dataset_path: &dataset_path data/robomimic/datasets/${task.task_name}/${task.dataset_type}/low_dim.hdf5 +abs_action: &abs_action False + +env_runner: + _target_: diffusion_policy.env_runner.robomimic_lowdim_runner.RobomimicLowdimRunner + dataset_path: *dataset_path + obs_keys: *obs_keys + n_train: 6 + n_train_vis: 2 + train_start_idx: 0 + n_test: 50 + n_test_vis: 5 + test_start_seed: 100000 + max_steps: 700 + n_obs_steps: ${n_obs_steps} + n_action_steps: ${n_action_steps} + n_latency_steps: ${n_latency_steps} + render_hw: [128,128] + fps: 10 + crf: 22 + past_action: ${past_action_visible} + abs_action: *abs_action + n_envs: 28 +# evaluation at this config requires a 16 core 64GB instance. + +dataset: + _target_: diffusion_policy.dataset.robomimic_replay_lowdim_dataset.RobomimicReplayLowdimDataset + dataset_path: *dataset_path + horizon: ${horizon} + pad_before: ${eval:'${n_obs_steps}-1+${n_latency_steps}'} + pad_after: ${eval:'${n_action_steps}-1'} + obs_keys: *obs_keys + abs_action: *abs_action + use_legacy_normalizer: False + seed: 42 + val_ratio: 0.02 diff --git a/diffusion_policy/config/task/transport_lowdim_abs.yaml b/diffusion_policy/config/task/transport_lowdim_abs.yaml new file mode 100644 index 0000000..7cc3014 --- /dev/null +++ b/diffusion_policy/config/task/transport_lowdim_abs.yaml @@ -0,0 +1,49 @@ +name: transport_lowdim + +obs_dim: 59 # 41+(3+4+2)*2 +action_dim: 20 # 10*2 +keypoint_dim: 3 + +obs_keys: &obs_keys [ + 'object', + 'robot0_eef_pos', 'robot0_eef_quat', 'robot0_gripper_qpos', + 'robot1_eef_pos', 'robot1_eef_quat', 'robot1_gripper_qpos' +] +task_name: &task_name transport +dataset_type: &dataset_type ph +dataset_path: &dataset_path data/robomimic/datasets/${task.task_name}/${task.dataset_type}/low_dim_abs.hdf5 +abs_action: &abs_action True + +env_runner: + _target_: diffusion_policy.env_runner.robomimic_lowdim_runner.RobomimicLowdimRunner + dataset_path: *dataset_path + obs_keys: *obs_keys + n_train: 6 + n_train_vis: 2 + train_start_idx: 0 + n_test: 50 + n_test_vis: 4 + test_start_seed: 100000 + max_steps: 700 + n_obs_steps: ${n_obs_steps} + n_action_steps: ${n_action_steps} + n_latency_steps: ${n_latency_steps} + render_hw: [128,128] + fps: 10 + crf: 22 + past_action: ${past_action_visible} + abs_action: *abs_action + n_envs: 28 +# evaluation at this config requires a 16 core 64GB instance. + +dataset: + _target_: diffusion_policy.dataset.robomimic_replay_lowdim_dataset.RobomimicReplayLowdimDataset + dataset_path: *dataset_path + horizon: ${horizon} + pad_before: ${eval:'${n_obs_steps}-1+${n_latency_steps}'} + pad_after: ${eval:'${n_action_steps}-1'} + obs_keys: *obs_keys + abs_action: *abs_action + use_legacy_normalizer: False + seed: 42 + val_ratio: 0.02 diff --git a/diffusion_policy/config/train_bet_lowdim_workspace.yaml b/diffusion_policy/config/train_bet_lowdim_workspace.yaml new file mode 100644 index 0000000..63a3436 --- /dev/null +++ b/diffusion_policy/config/train_bet_lowdim_workspace.yaml @@ -0,0 +1,132 @@ +defaults: + - _self_ + - task: blockpush_lowdim_seed + +name: train_bet_lowdim +_target_: diffusion_policy.workspace.train_bet_lowdim_workspace.TrainBETLowdimWorkspace + +obs_dim: ${task.obs_dim} +action_dim: ${task.action_dim} +keypoint_dim: ${task.keypoint_dim} +task_name: ${task.name} +exp_name: "default" + +horizon: 3 +n_obs_steps: 3 +n_action_steps: 1 +n_latency_steps: 0 +past_action_visible: False +keypoint_visible_rate: 1.0 +obs_as_local_cond: False +obs_as_global_cond: False +pred_action_steps_only: False + +policy: + _target_: diffusion_policy.policy.bet_lowdim_policy.BETLowdimPolicy + + action_ae: + _target_: diffusion_policy.model.bet.action_ae.discretizers.k_means.KMeansDiscretizer + num_bins: 24 + action_dim: ${action_dim} + predict_offsets: True + + obs_encoding_net: + _target_: torch.nn.Identity + output_dim: ${obs_dim} + + state_prior: + _target_: diffusion_policy.model.bet.latent_generators.mingpt.MinGPT + + discrete_input: false + input_dim: ${obs_dim} + + vocab_size: ${policy.action_ae.num_bins} + + # Architecture details + n_layer: 4 + n_head: 4 + n_embd: 72 + + block_size: ${horizon} # Length of history/context + predict_offsets: True + offset_loss_scale: 1000.0 # actions are very small + focal_loss_gamma: 2.0 + action_dim: ${action_dim} + + horizon: ${horizon} + n_obs_steps: ${n_obs_steps} + n_action_steps: ${n_action_steps} + +dataloader: + batch_size: 256 + num_workers: 1 + shuffle: True + pin_memory: True + persistent_workers: False + +val_dataloader: + batch_size: 256 + num_workers: 1 + shuffle: False + pin_memory: True + persistent_workers: False + +optimizer: + learning_rate: 0.0001 # 1e-4 + weight_decay: 0.1 + betas: [0.9, 0.95] + +training: + device: "cuda:0" + seed: 42 + debug: False + resume: True + # optimization + lr_scheduler: cosine + lr_warmup_steps: 500 + num_epochs: 5000 + gradient_accumulate_every: 1 + grad_norm_clip: 1.0 + enable_normalizer: True + # training loop control + # in epochs + rollout_every: 50 + checkpoint_every: 50 + val_every: 1 + sample_every: 5 + # steps per epoch + max_train_steps: null + max_val_steps: null + # misc + tqdm_interval_sec: 1.0 + +logging: + project: diffusion_policy_debug + resume: True + mode: online + name: ${now:%Y.%m.%d-%H.%M.%S}_${name}_${task_name} + tags: ["${name}", "${task_name}", "${exp_name}"] + id: null + group: null + +checkpoint: + topk: + monitor_key: test_mean_score + mode: max + k: 5 + format_str: 'epoch={epoch:04d}-test_mean_score={test_mean_score:.3f}.ckpt' + save_last_ckpt: True + save_last_snapshot: False + +multi_run: + run_dir: data/outputs/${now:%Y.%m.%d}/${now:%H.%M.%S}_${name}_${task_name} + wandb_name_base: ${now:%Y.%m.%d-%H.%M.%S}_${name}_${task_name} + +hydra: + job: + override_dirname: ${name} + run: + dir: data/outputs/${now:%Y.%m.%d}/${now:%H.%M.%S}_${name}_${task_name} + sweep: + dir: data/outputs/${now:%Y.%m.%d}/${now:%H.%M.%S}_${name}_${task_name} + subdir: ${hydra.job.num} diff --git a/diffusion_policy/config/train_diffusion_transformer_hybrid_workspace.yaml b/diffusion_policy/config/train_diffusion_transformer_hybrid_workspace.yaml new file mode 100644 index 0000000..0a90039 --- /dev/null +++ b/diffusion_policy/config/train_diffusion_transformer_hybrid_workspace.yaml @@ -0,0 +1,141 @@ +defaults: + - _self_ + - task: lift_image_abs + +name: train_diffusion_transformer_hybrid +_target_: diffusion_policy.workspace.train_diffusion_transformer_hybrid_workspace.TrainDiffusionTransformerHybridWorkspace + +task_name: ${task.name} +shape_meta: ${task.shape_meta} +exp_name: "default" + +horizon: 10 +n_obs_steps: 2 +n_action_steps: 8 +n_latency_steps: 0 +dataset_obs_steps: ${n_obs_steps} +past_action_visible: False +keypoint_visible_rate: 1.0 +obs_as_cond: True + +policy: + _target_: diffusion_policy.policy.diffusion_transformer_hybrid_image_policy.DiffusionTransformerHybridImagePolicy + + shape_meta: ${shape_meta} + + noise_scheduler: + _target_: diffusers.schedulers.scheduling_ddpm.DDPMScheduler + num_train_timesteps: 100 + beta_start: 0.0001 + beta_end: 0.02 + beta_schedule: squaredcos_cap_v2 + variance_type: fixed_small # Yilun's paper uses fixed_small_log instead, but easy to cause Nan + clip_sample: True # required when predict_epsilon=False + prediction_type: epsilon # or sample + + horizon: ${horizon} + n_action_steps: ${eval:'${n_action_steps}+${n_latency_steps}'} + n_obs_steps: ${n_obs_steps} + num_inference_steps: 100 + + crop_shape: [76, 76] + obs_encoder_group_norm: True + eval_fixed_crop: True + + n_layer: 8 + n_cond_layers: 0 # >0: use transformer encoder for cond, otherwise use MLP + n_head: 4 + n_emb: 256 + p_drop_emb: 0.0 + p_drop_attn: 0.3 + causal_attn: True + time_as_cond: True # if false, use BERT like encoder only arch, time as input + obs_as_cond: ${obs_as_cond} + + # scheduler.step params + # predict_epsilon: True + +ema: + _target_: diffusion_policy.model.diffusion.ema_model.EMAModel + update_after_step: 0 + inv_gamma: 1.0 + power: 0.75 + min_value: 0.0 + max_value: 0.9999 + +dataloader: + batch_size: 64 + num_workers: 8 + shuffle: True + pin_memory: True + persistent_workers: False + +val_dataloader: + batch_size: 64 + num_workers: 8 + shuffle: False + pin_memory: True + persistent_workers: False + +optimizer: + transformer_weight_decay: 1.0e-3 + obs_encoder_weight_decay: 1.0e-6 + learning_rate: 1.0e-4 + betas: [0.9, 0.95] + +training: + device: "cuda:0" + seed: 42 + debug: False + resume: True + # optimization + lr_scheduler: cosine + # Transformer needs LR warmup + lr_warmup_steps: 1000 + num_epochs: 3050 + gradient_accumulate_every: 1 + # EMA destroys performance when used with BatchNorm + # replace BatchNorm with GroupNorm. + use_ema: True + # training loop control + # in epochs + rollout_every: 50 + checkpoint_every: 50 + val_every: 1 + sample_every: 5 + # steps per epoch + max_train_steps: null + max_val_steps: null + # misc + tqdm_interval_sec: 1.0 + +logging: + project: diffusion_policy_debug + resume: True + mode: online + name: ${now:%Y.%m.%d-%H.%M.%S}_${name}_${task_name} + tags: ["${name}", "${task_name}", "${exp_name}"] + id: null + group: null + +checkpoint: + topk: + monitor_key: test_mean_score + mode: max + k: 5 + format_str: 'epoch={epoch:04d}-test_mean_score={test_mean_score:.3f}.ckpt' + save_last_ckpt: True + save_last_snapshot: False + +multi_run: + run_dir: data/outputs/${now:%Y.%m.%d}/${now:%H.%M.%S}_${name}_${task_name} + wandb_name_base: ${now:%Y.%m.%d-%H.%M.%S}_${name}_${task_name} + +hydra: + job: + override_dirname: ${name} + run: + dir: data/outputs/${now:%Y.%m.%d}/${now:%H.%M.%S}_${name}_${task_name} + sweep: + dir: data/outputs/${now:%Y.%m.%d}/${now:%H.%M.%S}_${name}_${task_name} + subdir: ${hydra.job.num} diff --git a/diffusion_policy/config/train_diffusion_transformer_lowdim_kitchen_workspace.yaml b/diffusion_policy/config/train_diffusion_transformer_lowdim_kitchen_workspace.yaml new file mode 100644 index 0000000..937f230 --- /dev/null +++ b/diffusion_policy/config/train_diffusion_transformer_lowdim_kitchen_workspace.yaml @@ -0,0 +1,146 @@ +defaults: + - _self_ + - task: kitchen_lowdim_abs + +name: train_diffusion_transformer_lowdim +_target_: diffusion_policy.workspace.train_diffusion_transformer_lowdim_workspace.TrainDiffusionTransformerLowdimWorkspace + +obs_dim: ${task.obs_dim} +action_dim: ${task.action_dim} +task_name: ${task.name} +exp_name: "default" + +horizon: 16 +n_obs_steps: 4 +n_action_steps: 8 +n_latency_steps: 0 +past_action_visible: False +keypoint_visible_rate: 1.0 +obs_as_cond: True +pred_action_steps_only: False + +policy: + _target_: diffusion_policy.policy.diffusion_transformer_lowdim_policy.DiffusionTransformerLowdimPolicy + + model: + _target_: diffusion_policy.model.diffusion.transformer_for_diffusion.TransformerForDiffusion + input_dim: ${eval:'${action_dim} if ${obs_as_cond} else ${obs_dim} + ${action_dim}'} + output_dim: ${policy.model.input_dim} + horizon: ${horizon} + n_obs_steps: ${n_obs_steps} + cond_dim: ${eval:'${obs_dim} if ${obs_as_cond} else 0'} + + n_layer: 8 + n_head: 4 + n_emb: 768 + p_drop_emb: 0.0 + p_drop_attn: 0.1 + + causal_attn: True + time_as_cond: True # if false, use BERT like encoder only arch, time as input + obs_as_cond: ${obs_as_cond} + n_cond_layers: 0 # >0: use transformer encoder for cond, otherwise use MLP + + noise_scheduler: + _target_: diffusers.schedulers.scheduling_ddpm.DDPMScheduler + num_train_timesteps: 100 + beta_start: 0.0001 + beta_end: 0.02 + beta_schedule: squaredcos_cap_v2 + variance_type: fixed_small # Yilun's paper uses fixed_small_log instead, but easy to cause Nan + clip_sample: True # required when predict_epsilon=False + prediction_type: epsilon # or sample + + horizon: ${horizon} + obs_dim: ${obs_dim} + action_dim: ${action_dim} + n_action_steps: ${n_action_steps} + n_obs_steps: ${n_obs_steps} + num_inference_steps: 100 + obs_as_cond: ${obs_as_cond} + pred_action_steps_only: ${pred_action_steps_only} + + # scheduler.step params + # predict_epsilon: True + +ema: + _target_: diffusion_policy.model.diffusion.ema_model.EMAModel + update_after_step: 0 + inv_gamma: 1.0 + power: 0.75 + min_value: 0.0 + max_value: 0.9999 + +dataloader: + batch_size: 256 + num_workers: 1 + shuffle: True + pin_memory: True + persistent_workers: False + +val_dataloader: + batch_size: 256 + num_workers: 1 + shuffle: False + pin_memory: True + persistent_workers: False + +optimizer: + learning_rate: 1.0e-4 + weight_decay: 1.0e-3 + betas: [0.9, 0.95] + +training: + device: "cuda:0" + seed: 42 + debug: False + resume: True + # optimization + lr_scheduler: cosine + # Transformer needs LR warmup + lr_warmup_steps: 1000 + num_epochs: 5000 + gradient_accumulate_every: 1 + use_ema: True + # training loop control + # in epochs + rollout_every: 50 + checkpoint_every: 50 + val_every: 1 + sample_every: 5 + # steps per epoch + max_train_steps: null + max_val_steps: null + # misc + tqdm_interval_sec: 1.0 + +logging: + project: diffusion_policy_debug + resume: True + mode: online + name: ${now:%Y.%m.%d-%H.%M.%S}_${name}_${task_name} + tags: ["${name}", "${task_name}", "${exp_name}"] + id: null + group: null + +checkpoint: + topk: + monitor_key: test_mean_score + mode: max + k: 5 + format_str: 'epoch={epoch:04d}-test_mean_score={test_mean_score:.3f}.ckpt' + save_last_ckpt: True + save_last_snapshot: False + +multi_run: + run_dir: data/outputs/${now:%Y.%m.%d}/${now:%H.%M.%S}_${name}_${task_name} + wandb_name_base: ${now:%Y.%m.%d-%H.%M.%S}_${name}_${task_name} + +hydra: + job: + override_dirname: ${name} + run: + dir: data/outputs/${now:%Y.%m.%d}/${now:%H.%M.%S}_${name}_${task_name} + sweep: + dir: data/outputs/${now:%Y.%m.%d}/${now:%H.%M.%S}_${name}_${task_name} + subdir: ${hydra.job.num} diff --git a/diffusion_policy/config/train_diffusion_transformer_lowdim_pusht_workspace.yaml b/diffusion_policy/config/train_diffusion_transformer_lowdim_pusht_workspace.yaml new file mode 100644 index 0000000..ffedd86 --- /dev/null +++ b/diffusion_policy/config/train_diffusion_transformer_lowdim_pusht_workspace.yaml @@ -0,0 +1,146 @@ +defaults: + - _self_ + - task: pusht_lowdim + +name: train_diffusion_transformer_lowdim +_target_: diffusion_policy.workspace.train_diffusion_transformer_lowdim_workspace.TrainDiffusionTransformerLowdimWorkspace + +obs_dim: ${task.obs_dim} +action_dim: ${task.action_dim} +task_name: ${task.name} +exp_name: "default" + +horizon: 16 +n_obs_steps: 2 +n_action_steps: 8 +n_latency_steps: 0 +past_action_visible: False +keypoint_visible_rate: 1.0 +obs_as_cond: True +pred_action_steps_only: False + +policy: + _target_: diffusion_policy.policy.diffusion_transformer_lowdim_policy.DiffusionTransformerLowdimPolicy + + model: + _target_: diffusion_policy.model.diffusion.transformer_for_diffusion.TransformerForDiffusion + input_dim: ${eval:'${action_dim} if ${obs_as_cond} else ${obs_dim} + ${action_dim}'} + output_dim: ${policy.model.input_dim} + horizon: ${horizon} + n_obs_steps: ${n_obs_steps} + cond_dim: ${eval:'${obs_dim} if ${obs_as_cond} else 0'} + + n_layer: 8 + n_head: 4 + n_emb: 256 + p_drop_emb: 0.0 + p_drop_attn: 0.01 + + causal_attn: True + time_as_cond: True # if false, use BERT like encoder only arch, time as input + obs_as_cond: ${obs_as_cond} + n_cond_layers: 0 # >0: use transformer encoder for cond, otherwise use MLP + + noise_scheduler: + _target_: diffusers.schedulers.scheduling_ddpm.DDPMScheduler + num_train_timesteps: 100 + beta_start: 0.0001 + beta_end: 0.02 + beta_schedule: squaredcos_cap_v2 + variance_type: fixed_small # Yilun's paper uses fixed_small_log instead, but easy to cause Nan + clip_sample: True # required when predict_epsilon=False + prediction_type: epsilon # or sample + + horizon: ${horizon} + obs_dim: ${obs_dim} + action_dim: ${action_dim} + n_action_steps: ${n_action_steps} + n_obs_steps: ${n_obs_steps} + num_inference_steps: 100 + obs_as_cond: ${obs_as_cond} + pred_action_steps_only: ${pred_action_steps_only} + + # scheduler.step params + # predict_epsilon: True + +ema: + _target_: diffusion_policy.model.diffusion.ema_model.EMAModel + update_after_step: 0 + inv_gamma: 1.0 + power: 0.75 + min_value: 0.0 + max_value: 0.9999 + +dataloader: + batch_size: 256 + num_workers: 1 + shuffle: True + pin_memory: True + persistent_workers: False + +val_dataloader: + batch_size: 256 + num_workers: 1 + shuffle: False + pin_memory: True + persistent_workers: False + +optimizer: + learning_rate: 1.0e-4 + weight_decay: 1.0e-1 + betas: [0.9, 0.95] + +training: + device: "cuda:0" + seed: 42 + debug: False + resume: True + # optimization + lr_scheduler: cosine + # Transformer needs LR warmup + lr_warmup_steps: 1000 + num_epochs: 8000 + gradient_accumulate_every: 1 + use_ema: True + # training loop control + # in epochs + rollout_every: 50 + checkpoint_every: 50 + val_every: 1 + sample_every: 5 + # steps per epoch + max_train_steps: null + max_val_steps: null + # misc + tqdm_interval_sec: 1.0 + +logging: + project: diffusion_policy_debug + resume: True + mode: online + name: ${now:%Y.%m.%d-%H.%M.%S}_${name}_${task_name} + tags: ["${name}", "${task_name}", "${exp_name}"] + id: null + group: null + +checkpoint: + topk: + monitor_key: test_mean_score + mode: max + k: 5 + format_str: 'epoch={epoch:04d}-test_mean_score={test_mean_score:.3f}.ckpt' + save_last_ckpt: True + save_last_snapshot: False + +multi_run: + run_dir: data/outputs/${now:%Y.%m.%d}/${now:%H.%M.%S}_${name}_${task_name} + wandb_name_base: ${now:%Y.%m.%d-%H.%M.%S}_${name}_${task_name} + +hydra: + job: + override_dirname: ${name} + run: + dir: data/outputs/${now:%Y.%m.%d}/${now:%H.%M.%S}_${name}_${task_name} + sweep: + dir: data/outputs/${now:%Y.%m.%d}/${now:%H.%M.%S}_${name}_${task_name} + subdir: ${hydra.job.num} diff --git a/diffusion_policy/config/train_diffusion_transformer_lowdim_workspace.yaml b/diffusion_policy/config/train_diffusion_transformer_lowdim_workspace.yaml new file mode 100644 index 0000000..5f91d37 --- /dev/null +++ b/diffusion_policy/config/train_diffusion_transformer_lowdim_workspace.yaml @@ -0,0 +1,146 @@ +defaults: + - _self_ + - task: blockpush_lowdim_seed + +name: train_diffusion_transformer_lowdim +_target_: diffusion_policy.workspace.train_diffusion_transformer_lowdim_workspace.TrainDiffusionTransformerLowdimWorkspace + +obs_dim: ${task.obs_dim} +action_dim: ${task.action_dim} +task_name: ${task.name} +exp_name: "default" + +horizon: 5 +n_obs_steps: 3 +n_action_steps: 1 +n_latency_steps: 0 +past_action_visible: False +keypoint_visible_rate: 1.0 +obs_as_cond: True +pred_action_steps_only: False + +policy: + _target_: diffusion_policy.policy.diffusion_transformer_lowdim_policy.DiffusionTransformerLowdimPolicy + + model: + _target_: diffusion_policy.model.diffusion.transformer_for_diffusion.TransformerForDiffusion + input_dim: ${eval:'${action_dim} if ${obs_as_cond} else ${obs_dim} + ${action_dim}'} + output_dim: ${policy.model.input_dim} + horizon: ${horizon} + n_obs_steps: ${n_obs_steps} + cond_dim: ${eval:'${obs_dim} if ${obs_as_cond} else 0'} + + n_layer: 8 + n_head: 4 + n_emb: 256 + p_drop_emb: 0.0 + p_drop_attn: 0.3 + + causal_attn: True + time_as_cond: True # if false, use BERT like encoder only arch, time as input + obs_as_cond: ${obs_as_cond} + n_cond_layers: 0 # >0: use transformer encoder for cond, otherwise use MLP + + noise_scheduler: + _target_: diffusers.schedulers.scheduling_ddpm.DDPMScheduler + num_train_timesteps: 100 + beta_start: 0.0001 + beta_end: 0.02 + beta_schedule: squaredcos_cap_v2 + variance_type: fixed_small # Yilun's paper uses fixed_small_log instead, but easy to cause Nan + clip_sample: True # required when predict_epsilon=False + prediction_type: epsilon # or sample + + horizon: ${horizon} + obs_dim: ${obs_dim} + action_dim: ${action_dim} + n_action_steps: ${n_action_steps} + n_obs_steps: ${n_obs_steps} + num_inference_steps: 100 + obs_as_cond: ${obs_as_cond} + pred_action_steps_only: ${pred_action_steps_only} + + # scheduler.step params + # predict_epsilon: True + +ema: + _target_: diffusion_policy.model.diffusion.ema_model.EMAModel + update_after_step: 0 + inv_gamma: 1.0 + power: 0.75 + min_value: 0.0 + max_value: 0.9999 + +dataloader: + batch_size: 256 + num_workers: 1 + shuffle: True + pin_memory: True + persistent_workers: False + +val_dataloader: + batch_size: 256 + num_workers: 1 + shuffle: False + pin_memory: True + persistent_workers: False + +optimizer: + learning_rate: 1.0e-4 + weight_decay: 1.0e-3 + betas: [0.9, 0.95] + +training: + device: "cuda:0" + seed: 42 + debug: False + resume: True + # optimization + lr_scheduler: cosine + # Transformer needs LR warmup + lr_warmup_steps: 1000 + num_epochs: 5000 + gradient_accumulate_every: 1 + use_ema: True + # training loop control + # in epochs + rollout_every: 50 + checkpoint_every: 50 + val_every: 1 + sample_every: 5 + # steps per epoch + max_train_steps: null + max_val_steps: null + # misc + tqdm_interval_sec: 1.0 + +logging: + project: diffusion_policy_debug + resume: True + mode: online + name: ${now:%Y.%m.%d-%H.%M.%S}_${name}_${task_name} + tags: ["${name}", "${task_name}", "${exp_name}"] + id: null + group: null + +checkpoint: + topk: + monitor_key: test_mean_score + mode: max + k: 5 + format_str: 'epoch={epoch:04d}-test_mean_score={test_mean_score:.3f}.ckpt' + save_last_ckpt: True + save_last_snapshot: False + +multi_run: + run_dir: data/outputs/${now:%Y.%m.%d}/${now:%H.%M.%S}_${name}_${task_name} + wandb_name_base: ${now:%Y.%m.%d-%H.%M.%S}_${name}_${task_name} + +hydra: + job: + override_dirname: ${name} + run: + dir: data/outputs/${now:%Y.%m.%d}/${now:%H.%M.%S}_${name}_${task_name} + sweep: + dir: data/outputs/${now:%Y.%m.%d}/${now:%H.%M.%S}_${name}_${task_name} + subdir: ${hydra.job.num} diff --git a/diffusion_policy/config/train_diffusion_transformer_real_hybrid_workspace.yaml b/diffusion_policy/config/train_diffusion_transformer_real_hybrid_workspace.yaml new file mode 100644 index 0000000..69bfad0 --- /dev/null +++ b/diffusion_policy/config/train_diffusion_transformer_real_hybrid_workspace.yaml @@ -0,0 +1,144 @@ +defaults: + - _self_ + - task: real_pusht_image + +name: train_diffusion_transformer_hybrid +_target_: diffusion_policy.workspace.train_diffusion_transformer_hybrid_workspace.TrainDiffusionTransformerHybridWorkspace + +task_name: ${task.name} +shape_meta: ${task.shape_meta} +exp_name: "default" + +horizon: 16 +n_obs_steps: 2 +n_action_steps: 8 +n_latency_steps: 0 +dataset_obs_steps: ${n_obs_steps} +past_action_visible: False +keypoint_visible_rate: 1.0 +obs_as_cond: True + +policy: + _target_: diffusion_policy.policy.diffusion_transformer_hybrid_image_policy.DiffusionTransformerHybridImagePolicy + + shape_meta: ${shape_meta} + + noise_scheduler: + _target_: diffusers.schedulers.scheduling_ddim.DDIMScheduler + num_train_timesteps: 100 + beta_start: 0.0001 + beta_end: 0.02 + # beta_schedule is important + # this is the best we found + beta_schedule: squaredcos_cap_v2 + clip_sample: True + set_alpha_to_one: True + steps_offset: 0 + prediction_type: epsilon # or sample + + horizon: ${horizon} + n_action_steps: ${eval:'${n_action_steps}+${n_latency_steps}'} + n_obs_steps: ${n_obs_steps} + num_inference_steps: 8 + + crop_shape: [216, 288] # ch, cw 320x240 90% + obs_encoder_group_norm: True + eval_fixed_crop: True + + n_layer: 8 + n_cond_layers: 0 # >0: use transformer encoder for cond, otherwise use MLP + n_head: 4 + n_emb: 256 + p_drop_emb: 0.0 + p_drop_attn: 0.3 + causal_attn: True + time_as_cond: True # if false, use BERT like encoder only arch, time as input + obs_as_cond: ${obs_as_cond} + + # scheduler.step params + # predict_epsilon: True + +ema: + _target_: diffusion_policy.model.diffusion.ema_model.EMAModel + update_after_step: 0 + inv_gamma: 1.0 + power: 0.75 + min_value: 0.0 + max_value: 0.9999 + +dataloader: + batch_size: 64 + num_workers: 8 + shuffle: True + pin_memory: True + persistent_workers: True + +val_dataloader: + batch_size: 64 + num_workers: 8 + shuffle: False + pin_memory: True + persistent_workers: True + +optimizer: + transformer_weight_decay: 1.0e-3 + obs_encoder_weight_decay: 1.0e-6 + learning_rate: 1.0e-4 + betas: [0.9, 0.95] + +training: + device: "cuda:0" + seed: 42 + debug: False + resume: True + # optimization + lr_scheduler: cosine + # Transformer needs LR warmup + lr_warmup_steps: 500 + num_epochs: 600 + gradient_accumulate_every: 1 + # EMA destroys performance when used with BatchNorm + # replace BatchNorm with GroupNorm. + use_ema: True + # training loop control + # in epochs + rollout_every: 50 + checkpoint_every: 50 + val_every: 1 + sample_every: 5 + # steps per epoch + max_train_steps: null + max_val_steps: null + # misc + tqdm_interval_sec: 1.0 + +logging: + project: diffusion_policy_debug + resume: True + mode: online + name: ${now:%Y.%m.%d-%H.%M.%S}_${name}_${task_name} + tags: ["${name}", "${task_name}", "${exp_name}"] + id: null + group: null + +checkpoint: + topk: + monitor_key: train_loss + mode: min + k: 5 + format_str: 'epoch={epoch:04d}-train_loss={train_loss:.3f}.ckpt' + save_last_ckpt: True + save_last_snapshot: False + +multi_run: + run_dir: data/outputs/${now:%Y.%m.%d}/${now:%H.%M.%S}_${name}_${task_name} + wandb_name_base: ${now:%Y.%m.%d-%H.%M.%S}_${name}_${task_name} + +hydra: + job: + override_dirname: ${name} + run: + dir: data/outputs/${now:%Y.%m.%d}/${now:%H.%M.%S}_${name}_${task_name} + sweep: + dir: data/outputs/${now:%Y.%m.%d}/${now:%H.%M.%S}_${name}_${task_name} + subdir: ${hydra.job.num} diff --git a/diffusion_policy/config/train_diffusion_unet_ddim_hybrid_workspace.yaml b/diffusion_policy/config/train_diffusion_unet_ddim_hybrid_workspace.yaml new file mode 100644 index 0000000..414cbba --- /dev/null +++ b/diffusion_policy/config/train_diffusion_unet_ddim_hybrid_workspace.yaml @@ -0,0 +1,140 @@ +defaults: + - _self_ + - task: lift_image_abs + +name: train_diffusion_unet_hybrid +_target_: diffusion_policy.workspace.train_diffusion_unet_hybrid_workspace.TrainDiffusionUnetHybridWorkspace + +task_name: ${task.name} +shape_meta: ${task.shape_meta} +exp_name: "default" + +horizon: 16 +n_obs_steps: 2 +n_action_steps: 8 +n_latency_steps: 0 +dataset_obs_steps: ${n_obs_steps} +past_action_visible: False +keypoint_visible_rate: 1.0 +obs_as_global_cond: True + +policy: + _target_: diffusion_policy.policy.diffusion_unet_hybrid_image_policy.DiffusionUnetHybridImagePolicy + + shape_meta: ${shape_meta} + + noise_scheduler: + _target_: diffusers.schedulers.scheduling_ddim.DDIMScheduler + num_train_timesteps: 100 + beta_start: 0.0001 + beta_end: 0.02 + # beta_schedule is important + # this is the best we found + beta_schedule: squaredcos_cap_v2 + clip_sample: True + set_alpha_to_one: True + steps_offset: 0 + prediction_type: epsilon # or sample + + horizon: ${horizon} + n_action_steps: ${eval:'${n_action_steps}+${n_latency_steps}'} + n_obs_steps: ${n_obs_steps} + num_inference_steps: 8 + obs_as_global_cond: ${obs_as_global_cond} + # crop_shape: [76, 76] + crop_shape: null + diffusion_step_embed_dim: 128 + down_dims: [256,512,1024] + kernel_size: 5 + n_groups: 8 + cond_predict_scale: True + obs_encoder_group_norm: True + eval_fixed_crop: True + + # scheduler.step params + # predict_epsilon: True + +ema: + _target_: diffusion_policy.model.diffusion.ema_model.EMAModel + update_after_step: 0 + inv_gamma: 1.0 + power: 0.75 + min_value: 0.0 + max_value: 0.9999 + +dataloader: + batch_size: 64 + num_workers: 8 + shuffle: True + pin_memory: True + persistent_workers: False + +val_dataloader: + batch_size: 64 + num_workers: 8 + shuffle: False + pin_memory: True + persistent_workers: False + +optimizer: + _target_: torch.optim.AdamW + lr: 1.0e-4 + betas: [0.95, 0.999] + eps: 1.0e-8 + weight_decay: 1.0e-6 + +training: + device: "cuda:0" + seed: 42 + debug: False + resume: True + # optimization + lr_scheduler: cosine + lr_warmup_steps: 500 + num_epochs: 3000 + gradient_accumulate_every: 1 + # EMA destroys performance when used with BatchNorm + # replace BatchNorm with GroupNorm. + use_ema: True + # training loop control + # in epochs + rollout_every: 50 + checkpoint_every: 50 + val_every: 1 + sample_every: 5 + # steps per epoch + max_train_steps: null + max_val_steps: null + # misc + tqdm_interval_sec: 1.0 + +logging: + project: diffusion_policy_debug + resume: True + mode: online + name: ${now:%Y.%m.%d-%H.%M.%S}_${name}_${task_name} + tags: ["${name}", "${task_name}", "${exp_name}"] + id: null + group: null + +checkpoint: + topk: + monitor_key: test_mean_score + mode: max + k: 5 + format_str: 'epoch={epoch:04d}-test_mean_score={test_mean_score:.3f}.ckpt' + save_last_ckpt: True + save_last_snapshot: False + +multi_run: + run_dir: data/outputs/${now:%Y.%m.%d}/${now:%H.%M.%S}_${name}_${task_name} + wandb_name_base: ${now:%Y.%m.%d-%H.%M.%S}_${name}_${task_name} + +hydra: + job: + override_dirname: ${name} + run: + dir: data/outputs/${now:%Y.%m.%d}/${now:%H.%M.%S}_${name}_${task_name} + sweep: + dir: data/outputs/${now:%Y.%m.%d}/${now:%H.%M.%S}_${name}_${task_name} + subdir: ${hydra.job.num} diff --git a/diffusion_policy/config/train_diffusion_unet_ddim_lowdim_workspace.yaml b/diffusion_policy/config/train_diffusion_unet_ddim_lowdim_workspace.yaml new file mode 100644 index 0000000..90cd2f8 --- /dev/null +++ b/diffusion_policy/config/train_diffusion_unet_ddim_lowdim_workspace.yaml @@ -0,0 +1,146 @@ +defaults: + - _self_ + - task: pusht_lowdim + +name: train_diffusion_unet_lowdim +_target_: diffusion_policy.workspace.train_diffusion_unet_lowdim_workspace.TrainDiffusionUnetLowdimWorkspace + +obs_dim: ${task.obs_dim} +action_dim: ${task.action_dim} +keypoint_dim: ${task.keypoint_dim} +task_name: ${task.name} +exp_name: "default" + +horizon: 16 +n_obs_steps: 2 +n_action_steps: 8 +n_latency_steps: 0 +past_action_visible: False +keypoint_visible_rate: 1.0 +obs_as_local_cond: False +obs_as_global_cond: False +pred_action_steps_only: False + +policy: + _target_: diffusion_policy.policy.diffusion_unet_lowdim_policy.DiffusionUnetLowdimPolicy + + model: + _target_: diffusion_policy.model.diffusion.conditional_unet1d.ConditionalUnet1D + input_dim: "${eval: ${task.action_dim} if ${obs_as_local_cond} or ${obs_as_global_cond} else ${task.obs_dim} + ${task.action_dim}}" + local_cond_dim: "${eval: ${task.obs_dim} if ${obs_as_local_cond} else None}" + global_cond_dim: "${eval: ${task.obs_dim}*${n_obs_steps} if ${obs_as_global_cond} else None}" + diffusion_step_embed_dim: 256 + down_dims: [256, 512, 1024] + kernel_size: 5 + n_groups: 8 + cond_predict_scale: True + + noise_scheduler: + _target_: diffusers.schedulers.scheduling_ddim.DDIMScheduler + num_train_timesteps: 100 + beta_start: 0.0001 + beta_end: 0.02 + # beta_schedule is important + # this is the best we found + beta_schedule: squaredcos_cap_v2 + clip_sample: True + set_alpha_to_one: True + steps_offset: 0 + prediction_type: epsilon # or sample + + horizon: ${horizon} + obs_dim: ${obs_dim} + action_dim: ${action_dim} + n_action_steps: ${eval:'${n_action_steps}+${n_latency_steps}'} + n_obs_steps: ${n_obs_steps} + num_inference_steps: 8 + obs_as_local_cond: ${obs_as_local_cond} + obs_as_global_cond: ${obs_as_global_cond} + pred_action_steps_only: ${pred_action_steps_only} + oa_step_convention: True + + # scheduler.step params + # predict_epsilon: True + +ema: + _target_: diffusion_policy.model.diffusion.ema_model.EMAModel + update_after_step: 0 + inv_gamma: 1.0 + power: 0.75 + min_value: 0.0 + max_value: 0.9999 + +dataloader: + batch_size: 256 + num_workers: 1 + shuffle: True + pin_memory: True + persistent_workers: False + +val_dataloader: + batch_size: 256 + num_workers: 1 + shuffle: False + pin_memory: True + persistent_workers: False + +optimizer: + _target_: torch.optim.AdamW + lr: 1.0e-4 + betas: [0.95, 0.999] + eps: 1.0e-8 + weight_decay: 1.0e-6 + +training: + device: "cuda:0" + seed: 42 + debug: False + resume: True + # optimization + lr_scheduler: cosine + lr_warmup_steps: 500 + num_epochs: 8000 + gradient_accumulate_every: 1 + use_ema: True + # training loop control + # in epochs + rollout_every: 50 + checkpoint_every: 50 + val_every: 1 + sample_every: 5 + # steps per epoch + max_train_steps: null + max_val_steps: null + # misc + tqdm_interval_sec: 1.0 + +logging: + project: diffusion_policy_debug + resume: True + mode: online + name: ${now:%Y.%m.%d-%H.%M.%S}_${name}_${task_name} + tags: ["${name}", "${task_name}", "${exp_name}"] + id: null + group: null + +checkpoint: + topk: + monitor_key: test_mean_score + mode: max + k: 5 + format_str: 'epoch={epoch:04d}-test_mean_score={test_mean_score:.3f}.ckpt' + save_last_ckpt: True + save_last_snapshot: False + +multi_run: + run_dir: data/outputs/${now:%Y.%m.%d}/${now:%H.%M.%S}_${name}_${task_name} + wandb_name_base: ${now:%Y.%m.%d-%H.%M.%S}_${name}_${task_name} + +hydra: + job: + override_dirname: ${name} + run: + dir: data/outputs/${now:%Y.%m.%d}/${now:%H.%M.%S}_${name}_${task_name} + sweep: + dir: data/outputs/${now:%Y.%m.%d}/${now:%H.%M.%S}_${name}_${task_name} + subdir: ${hydra.job.num} diff --git a/diffusion_policy/config/train_diffusion_unet_hybrid_workspace.yaml b/diffusion_policy/config/train_diffusion_unet_hybrid_workspace.yaml new file mode 100644 index 0000000..9f9b1a6 --- /dev/null +++ b/diffusion_policy/config/train_diffusion_unet_hybrid_workspace.yaml @@ -0,0 +1,137 @@ +defaults: + - _self_ + - task: lift_image_abs + +name: train_diffusion_unet_hybrid +_target_: diffusion_policy.workspace.train_diffusion_unet_hybrid_workspace.TrainDiffusionUnetHybridWorkspace + +task_name: ${task.name} +shape_meta: ${task.shape_meta} +exp_name: "default" + +horizon: 16 +n_obs_steps: 2 +n_action_steps: 8 +n_latency_steps: 0 +dataset_obs_steps: ${n_obs_steps} +past_action_visible: False +keypoint_visible_rate: 1.0 +obs_as_global_cond: True + +policy: + _target_: diffusion_policy.policy.diffusion_unet_hybrid_image_policy.DiffusionUnetHybridImagePolicy + + shape_meta: ${shape_meta} + + noise_scheduler: + _target_: diffusers.schedulers.scheduling_ddpm.DDPMScheduler + num_train_timesteps: 100 + beta_start: 0.0001 + beta_end: 0.02 + beta_schedule: squaredcos_cap_v2 + variance_type: fixed_small # Yilun's paper uses fixed_small_log instead, but easy to cause Nan + clip_sample: True # required when predict_epsilon=False + prediction_type: epsilon # or sample + + horizon: ${horizon} + n_action_steps: ${eval:'${n_action_steps}+${n_latency_steps}'} + n_obs_steps: ${n_obs_steps} + num_inference_steps: 100 + obs_as_global_cond: ${obs_as_global_cond} + crop_shape: [76, 76] + # crop_shape: null + diffusion_step_embed_dim: 128 + down_dims: [512, 1024, 2048] + kernel_size: 5 + n_groups: 8 + cond_predict_scale: True + obs_encoder_group_norm: True + eval_fixed_crop: True + + # scheduler.step params + # predict_epsilon: True + +ema: + _target_: diffusion_policy.model.diffusion.ema_model.EMAModel + update_after_step: 0 + inv_gamma: 1.0 + power: 0.75 + min_value: 0.0 + max_value: 0.9999 + +dataloader: + batch_size: 64 + num_workers: 8 + shuffle: True + pin_memory: True + persistent_workers: False + +val_dataloader: + batch_size: 64 + num_workers: 8 + shuffle: False + pin_memory: True + persistent_workers: False + +optimizer: + _target_: torch.optim.AdamW + lr: 1.0e-4 + betas: [0.95, 0.999] + eps: 1.0e-8 + weight_decay: 1.0e-6 + +training: + device: "cuda:0" + seed: 42 + debug: False + resume: True + # optimization + lr_scheduler: cosine + lr_warmup_steps: 500 + num_epochs: 3050 + gradient_accumulate_every: 1 + # EMA destroys performance when used with BatchNorm + # replace BatchNorm with GroupNorm. + use_ema: True + # training loop control + # in epochs + rollout_every: 50 + checkpoint_every: 50 + val_every: 1 + sample_every: 5 + # steps per epoch + max_train_steps: null + max_val_steps: null + # misc + tqdm_interval_sec: 1.0 + +logging: + project: diffusion_policy_debug + resume: True + mode: online + name: ${now:%Y.%m.%d-%H.%M.%S}_${name}_${task_name} + tags: ["${name}", "${task_name}", "${exp_name}"] + id: null + group: null + +checkpoint: + topk: + monitor_key: test_mean_score + mode: max + k: 5 + format_str: 'epoch={epoch:04d}-test_mean_score={test_mean_score:.3f}.ckpt' + save_last_ckpt: True + save_last_snapshot: False + +multi_run: + run_dir: data/outputs/${now:%Y.%m.%d}/${now:%H.%M.%S}_${name}_${task_name} + wandb_name_base: ${now:%Y.%m.%d-%H.%M.%S}_${name}_${task_name} + +hydra: + job: + override_dirname: ${name} + run: + dir: data/outputs/${now:%Y.%m.%d}/${now:%H.%M.%S}_${name}_${task_name} + sweep: + dir: data/outputs/${now:%Y.%m.%d}/${now:%H.%M.%S}_${name}_${task_name} + subdir: ${hydra.job.num} diff --git a/diffusion_policy/config/train_diffusion_unet_image_pretrained_workspace.yaml b/diffusion_policy/config/train_diffusion_unet_image_pretrained_workspace.yaml new file mode 100644 index 0000000..122bea3 --- /dev/null +++ b/diffusion_policy/config/train_diffusion_unet_image_pretrained_workspace.yaml @@ -0,0 +1,149 @@ +defaults: + - _self_ + - task: lift_image_abs + +name: train_diffusion_unet_image +_target_: diffusion_policy.workspace.train_diffusion_unet_image_workspace.TrainDiffusionUnetImageWorkspace + +task_name: ${task.name} +shape_meta: ${task.shape_meta} +exp_name: "default" + +horizon: 16 +n_obs_steps: 2 +n_action_steps: 8 +n_latency_steps: 0 +dataset_obs_steps: ${n_obs_steps} +past_action_visible: False +keypoint_visible_rate: 1.0 +obs_as_global_cond: True + +policy: + _target_: diffusion_policy.policy.diffusion_unet_image_policy.DiffusionUnetImagePolicy + + shape_meta: ${shape_meta} + + noise_scheduler: + _target_: diffusers.schedulers.scheduling_ddpm.DDPMScheduler + num_train_timesteps: 100 + beta_start: 0.0001 + beta_end: 0.02 + beta_schedule: squaredcos_cap_v2 + variance_type: fixed_small # Yilun's paper uses fixed_small_log instead, but easy to cause Nan + clip_sample: True # required when predict_epsilon=False + prediction_type: epsilon # or sample + + obs_encoder: + _target_: diffusion_policy.model.vision.multi_image_obs_encoder.MultiImageObsEncoder + shape_meta: ${shape_meta} + rgb_model: + _target_: diffusion_policy.model.vision.model_getter.get_resnet + name: resnet18 + weights: IMAGENET1K_V1 # or r3m + resize_shape: [256, 256] + crop_shape: [224, 224] + random_crop: False + use_group_norm: False + share_rgb_model: True + imagenet_norm: True + + horizon: ${horizon} + n_action_steps: ${eval:'${n_action_steps}+${n_latency_steps}'} + n_obs_steps: ${n_obs_steps} + num_inference_steps: 100 + obs_as_global_cond: ${obs_as_global_cond} + # crop_shape: null + diffusion_step_embed_dim: 128 + down_dims: [512, 1024, 2048] + kernel_size: 5 + n_groups: 8 + cond_predict_scale: True + + # scheduler.step params + # predict_epsilon: True + +ema: + _target_: diffusion_policy.model.diffusion.ema_model.EMAModel + update_after_step: 0 + inv_gamma: 1.0 + power: 0.75 + min_value: 0.0 + max_value: 0.9999 + +dataloader: + batch_size: 64 + num_workers: 4 + shuffle: True + pin_memory: True + persistent_workers: False + +val_dataloader: + batch_size: 64 + num_workers: 4 + shuffle: False + pin_memory: True + persistent_workers: False + +optimizer: + _target_: torch.optim.AdamW + lr: 1.0e-4 + betas: [0.95, 0.999] + eps: 1.0e-8 + weight_decay: 1.0e-6 + +training: + device: "cuda:0" + seed: 42 + debug: False + resume: True + # optimization + lr_scheduler: cosine + lr_warmup_steps: 500 + num_epochs: 8000 + gradient_accumulate_every: 1 + # EMA destroys performance when used with BatchNorm + # replace BatchNorm with GroupNorm. + use_ema: True + freeze_encoder: True + # training loop control + # in epochs + rollout_every: 50 + checkpoint_every: 50 + val_every: 1 + sample_every: 5 + # steps per epoch + max_train_steps: null + max_val_steps: null + # misc + tqdm_interval_sec: 1.0 + +logging: + project: diffusion_policy_debug + resume: True + mode: online + name: ${now:%Y.%m.%d-%H.%M.%S}_${name}_${task_name} + tags: ["${name}", "${task_name}", "${exp_name}"] + id: null + group: null + +checkpoint: + topk: + monitor_key: test_mean_score + mode: max + k: 5 + format_str: 'epoch={epoch:04d}-test_mean_score={test_mean_score:.3f}.ckpt' + save_last_ckpt: True + save_last_snapshot: False + +multi_run: + run_dir: data/outputs/${now:%Y.%m.%d}/${now:%H.%M.%S}_${name}_${task_name} + wandb_name_base: ${now:%Y.%m.%d-%H.%M.%S}_${name}_${task_name} + +hydra: + job: + override_dirname: ${name} + run: + dir: data/outputs/${now:%Y.%m.%d}/${now:%H.%M.%S}_${name}_${task_name} + sweep: + dir: data/outputs/${now:%Y.%m.%d}/${now:%H.%M.%S}_${name}_${task_name} + subdir: ${hydra.job.num} diff --git a/diffusion_policy/config/train_diffusion_unet_image_workspace.yaml b/diffusion_policy/config/train_diffusion_unet_image_workspace.yaml new file mode 100644 index 0000000..f5636e3 --- /dev/null +++ b/diffusion_policy/config/train_diffusion_unet_image_workspace.yaml @@ -0,0 +1,150 @@ +defaults: + - _self_ + - task: lift_image_abs + +name: train_diffusion_unet_image +_target_: diffusion_policy.workspace.train_diffusion_unet_image_workspace.TrainDiffusionUnetImageWorkspace + +task_name: ${task.name} +shape_meta: ${task.shape_meta} +exp_name: "default" + +horizon: 16 +n_obs_steps: 2 +n_action_steps: 8 +n_latency_steps: 0 +dataset_obs_steps: ${n_obs_steps} +past_action_visible: False +keypoint_visible_rate: 1.0 +obs_as_global_cond: True + +policy: + _target_: diffusion_policy.policy.diffusion_unet_image_policy.DiffusionUnetImagePolicy + + shape_meta: ${shape_meta} + + noise_scheduler: + _target_: diffusers.schedulers.scheduling_ddpm.DDPMScheduler + num_train_timesteps: 100 + beta_start: 0.0001 + beta_end: 0.02 + beta_schedule: squaredcos_cap_v2 + variance_type: fixed_small # Yilun's paper uses fixed_small_log instead, but easy to cause Nan + clip_sample: True # required when predict_epsilon=False + prediction_type: epsilon # or sample + + obs_encoder: + _target_: diffusion_policy.model.vision.multi_image_obs_encoder.MultiImageObsEncoder + shape_meta: ${shape_meta} + rgb_model: + _target_: diffusion_policy.model.vision.model_getter.get_resnet + name: resnet18 + weights: null + resize_shape: null + crop_shape: [76, 76] + # constant center crop + random_crop: True + use_group_norm: True + share_rgb_model: False + imagenet_norm: True + + horizon: ${horizon} + n_action_steps: ${eval:'${n_action_steps}+${n_latency_steps}'} + n_obs_steps: ${n_obs_steps} + num_inference_steps: 100 + obs_as_global_cond: ${obs_as_global_cond} + # crop_shape: null + diffusion_step_embed_dim: 128 + down_dims: [512, 1024, 2048] + kernel_size: 5 + n_groups: 8 + cond_predict_scale: True + + # scheduler.step params + # predict_epsilon: True + +ema: + _target_: diffusion_policy.model.diffusion.ema_model.EMAModel + update_after_step: 0 + inv_gamma: 1.0 + power: 0.75 + min_value: 0.0 + max_value: 0.9999 + +dataloader: + batch_size: 64 + num_workers: 4 + shuffle: True + pin_memory: True + persistent_workers: False + +val_dataloader: + batch_size: 64 + num_workers: 4 + shuffle: False + pin_memory: True + persistent_workers: False + +optimizer: + _target_: torch.optim.AdamW + lr: 1.0e-4 + betas: [0.95, 0.999] + eps: 1.0e-8 + weight_decay: 1.0e-6 + +training: + device: "cuda:0" + seed: 42 + debug: False + resume: True + # optimization + lr_scheduler: cosine + lr_warmup_steps: 500 + num_epochs: 8000 + gradient_accumulate_every: 1 + # EMA destroys performance when used with BatchNorm + # replace BatchNorm with GroupNorm. + use_ema: True + freeze_encoder: False + # training loop control + # in epochs + rollout_every: 50 + checkpoint_every: 50 + val_every: 1 + sample_every: 5 + # steps per epoch + max_train_steps: null + max_val_steps: null + # misc + tqdm_interval_sec: 1.0 + +logging: + project: diffusion_policy_debug + resume: True + mode: online + name: ${now:%Y.%m.%d-%H.%M.%S}_${name}_${task_name} + tags: ["${name}", "${task_name}", "${exp_name}"] + id: null + group: null + +checkpoint: + topk: + monitor_key: test_mean_score + mode: max + k: 5 + format_str: 'epoch={epoch:04d}-test_mean_score={test_mean_score:.3f}.ckpt' + save_last_ckpt: True + save_last_snapshot: False + +multi_run: + run_dir: data/outputs/${now:%Y.%m.%d}/${now:%H.%M.%S}_${name}_${task_name} + wandb_name_base: ${now:%Y.%m.%d-%H.%M.%S}_${name}_${task_name} + +hydra: + job: + override_dirname: ${name} + run: + dir: data/outputs/${now:%Y.%m.%d}/${now:%H.%M.%S}_${name}_${task_name} + sweep: + dir: data/outputs/${now:%Y.%m.%d}/${now:%H.%M.%S}_${name}_${task_name} + subdir: ${hydra.job.num} diff --git a/diffusion_policy/config/train_diffusion_unet_lowdim_workspace.yaml b/diffusion_policy/config/train_diffusion_unet_lowdim_workspace.yaml new file mode 100644 index 0000000..7b4d35f --- /dev/null +++ b/diffusion_policy/config/train_diffusion_unet_lowdim_workspace.yaml @@ -0,0 +1,143 @@ +defaults: + - _self_ + - task: pusht_lowdim + +name: train_diffusion_unet_lowdim +_target_: diffusion_policy.workspace.train_diffusion_unet_lowdim_workspace.TrainDiffusionUnetLowdimWorkspace + +obs_dim: ${task.obs_dim} +action_dim: ${task.action_dim} +keypoint_dim: ${task.keypoint_dim} +task_name: ${task.name} +exp_name: "default" + +horizon: 16 +n_obs_steps: 2 +n_action_steps: 8 +n_latency_steps: 0 +past_action_visible: False +keypoint_visible_rate: 1.0 +obs_as_local_cond: False +obs_as_global_cond: True +pred_action_steps_only: False + +policy: + _target_: diffusion_policy.policy.diffusion_unet_lowdim_policy.DiffusionUnetLowdimPolicy + + model: + _target_: diffusion_policy.model.diffusion.conditional_unet1d.ConditionalUnet1D + input_dim: "${eval: ${task.action_dim} if ${obs_as_local_cond} or ${obs_as_global_cond} else ${task.obs_dim} + ${task.action_dim}}" + local_cond_dim: "${eval: ${task.obs_dim} if ${obs_as_local_cond} else None}" + global_cond_dim: "${eval: ${task.obs_dim}*${n_obs_steps} if ${obs_as_global_cond} else None}" + diffusion_step_embed_dim: 256 + down_dims: [256, 512, 1024] + kernel_size: 5 + n_groups: 8 + cond_predict_scale: True + + noise_scheduler: + _target_: diffusers.schedulers.scheduling_ddpm.DDPMScheduler + num_train_timesteps: 100 + beta_start: 0.0001 + beta_end: 0.02 + beta_schedule: squaredcos_cap_v2 + variance_type: fixed_small # Yilun's paper uses fixed_small_log instead, but easy to cause Nan + clip_sample: True # required when predict_epsilon=False + prediction_type: epsilon # or sample + + horizon: ${horizon} + obs_dim: ${obs_dim} + action_dim: ${action_dim} + n_action_steps: ${eval:'${n_action_steps}+${n_latency_steps}'} + n_obs_steps: ${n_obs_steps} + num_inference_steps: 100 + obs_as_local_cond: ${obs_as_local_cond} + obs_as_global_cond: ${obs_as_global_cond} + pred_action_steps_only: ${pred_action_steps_only} + oa_step_convention: True + + # scheduler.step params + # predict_epsilon: True + +ema: + _target_: diffusion_policy.model.diffusion.ema_model.EMAModel + update_after_step: 0 + inv_gamma: 1.0 + power: 0.75 + min_value: 0.0 + max_value: 0.9999 + +dataloader: + batch_size: 256 + num_workers: 1 + shuffle: True + pin_memory: True + persistent_workers: False + +val_dataloader: + batch_size: 256 + num_workers: 1 + shuffle: False + pin_memory: True + persistent_workers: False + +optimizer: + _target_: torch.optim.AdamW + lr: 1.0e-4 + betas: [0.95, 0.999] + eps: 1.0e-8 + weight_decay: 1.0e-6 + +training: + device: "cuda:0" + seed: 42 + debug: False + resume: True + # optimization + lr_scheduler: cosine + lr_warmup_steps: 500 + num_epochs: 5000 + gradient_accumulate_every: 1 + use_ema: True + # training loop control + # in epochs + rollout_every: 50 + checkpoint_every: 50 + val_every: 1 + sample_every: 5 + # steps per epoch + max_train_steps: null + max_val_steps: null + # misc + tqdm_interval_sec: 1.0 + +logging: + project: diffusion_policy_debug + resume: True + mode: online + name: ${now:%Y.%m.%d-%H.%M.%S}_${name}_${task_name} + tags: ["${name}", "${task_name}", "${exp_name}"] + id: null + group: null + +checkpoint: + topk: + monitor_key: test_mean_score + mode: max + k: 5 + format_str: 'epoch={epoch:04d}-test_mean_score={test_mean_score:.3f}.ckpt' + save_last_ckpt: True + save_last_snapshot: False + +multi_run: + run_dir: data/outputs/${now:%Y.%m.%d}/${now:%H.%M.%S}_${name}_${task_name} + wandb_name_base: ${now:%Y.%m.%d-%H.%M.%S}_${name}_${task_name} + +hydra: + job: + override_dirname: ${name} + run: + dir: data/outputs/${now:%Y.%m.%d}/${now:%H.%M.%S}_${name}_${task_name} + sweep: + dir: data/outputs/${now:%Y.%m.%d}/${now:%H.%M.%S}_${name}_${task_name} + subdir: ${hydra.job.num} diff --git a/diffusion_policy/config/train_diffusion_unet_real_hybrid_workspace.yaml b/diffusion_policy/config/train_diffusion_unet_real_hybrid_workspace.yaml new file mode 100644 index 0000000..5b32366 --- /dev/null +++ b/diffusion_policy/config/train_diffusion_unet_real_hybrid_workspace.yaml @@ -0,0 +1,141 @@ +defaults: + - _self_ + - task: real_pusht_image + +name: train_diffusion_unet_hybrid +_target_: diffusion_policy.workspace.train_diffusion_unet_hybrid_workspace.TrainDiffusionUnetHybridWorkspace + +task_name: ${task.name} +shape_meta: ${task.shape_meta} +exp_name: "default" + +horizon: 16 +n_obs_steps: 2 +n_action_steps: 8 +n_latency_steps: 0 +dataset_obs_steps: ${n_obs_steps} +past_action_visible: False +keypoint_visible_rate: 1.0 +obs_as_global_cond: True + +policy: + _target_: diffusion_policy.policy.diffusion_unet_hybrid_image_policy.DiffusionUnetHybridImagePolicy + + shape_meta: ${shape_meta} + + noise_scheduler: + _target_: diffusers.schedulers.scheduling_ddim.DDIMScheduler + num_train_timesteps: 100 + beta_start: 0.0001 + beta_end: 0.02 + # beta_schedule is important + # this is the best we found + beta_schedule: squaredcos_cap_v2 + clip_sample: True + set_alpha_to_one: True + steps_offset: 0 + prediction_type: epsilon # or sample + + horizon: ${horizon} + n_action_steps: ${eval:'${n_action_steps}+${n_latency_steps}'} + n_obs_steps: ${n_obs_steps} + num_inference_steps: 8 + obs_as_global_cond: ${obs_as_global_cond} + # crop_shape: [76, 76] # 84x84 90% + crop_shape: [216, 288] # ch, cw 320x240 90% + # crop_shape: null + diffusion_step_embed_dim: 128 + down_dims: [256,512,1024] + kernel_size: 5 + n_groups: 8 + cond_predict_scale: True + obs_encoder_group_norm: True + eval_fixed_crop: True + + # scheduler.step params + # predict_epsilon: True + +ema: + _target_: diffusion_policy.model.diffusion.ema_model.EMAModel + update_after_step: 0 + inv_gamma: 1.0 + power: 0.75 + min_value: 0.0 + max_value: 0.9999 + +dataloader: + batch_size: 64 + num_workers: 8 + shuffle: True + pin_memory: True + persistent_workers: True + +val_dataloader: + batch_size: 64 + num_workers: 8 + shuffle: False + pin_memory: True + persistent_workers: True + +optimizer: + _target_: torch.optim.AdamW + lr: 1.0e-4 + betas: [0.95, 0.999] + eps: 1.0e-8 + weight_decay: 1.0e-6 + +training: + device: "cuda:0" + seed: 42 + debug: False + resume: True + # optimization + lr_scheduler: cosine + lr_warmup_steps: 500 + num_epochs: 600 + gradient_accumulate_every: 1 + # EMA destroys performance when used with BatchNorm + # replace BatchNorm with GroupNorm. + use_ema: True + # training loop control + # in epochs + rollout_every: 50 + checkpoint_every: 50 + val_every: 1 + sample_every: 5 + # steps per epoch + max_train_steps: null + max_val_steps: null + # misc + tqdm_interval_sec: 1.0 + +logging: + project: diffusion_policy_debug + resume: True + mode: online + name: ${now:%Y.%m.%d-%H.%M.%S}_${name}_${task_name} + tags: ["${name}", "${task_name}", "${exp_name}"] + id: null + group: null + +checkpoint: + topk: + monitor_key: train_loss + mode: min + k: 5 + format_str: 'epoch={epoch:04d}-train_loss={train_loss:.3f}.ckpt' + save_last_ckpt: True + save_last_snapshot: False + +multi_run: + run_dir: data/outputs/${now:%Y.%m.%d}/${now:%H.%M.%S}_${name}_${task_name} + wandb_name_base: ${now:%Y.%m.%d-%H.%M.%S}_${name}_${task_name} + +hydra: + job: + override_dirname: ${name} + run: + dir: data/outputs/${now:%Y.%m.%d}/${now:%H.%M.%S}_${name}_${task_name} + sweep: + dir: data/outputs/${now:%Y.%m.%d}/${now:%H.%M.%S}_${name}_${task_name} + subdir: ${hydra.job.num} diff --git a/diffusion_policy/config/train_diffusion_unet_real_image_workspace.yaml b/diffusion_policy/config/train_diffusion_unet_real_image_workspace.yaml new file mode 100644 index 0000000..f6187ef --- /dev/null +++ b/diffusion_policy/config/train_diffusion_unet_real_image_workspace.yaml @@ -0,0 +1,152 @@ +defaults: + - _self_ + - task: real_pusht_image + +name: train_diffusion_unet_image +_target_: diffusion_policy.workspace.train_diffusion_unet_image_workspace.TrainDiffusionUnetImageWorkspace + +task_name: ${task.name} +shape_meta: ${task.shape_meta} +exp_name: "default" + +horizon: 16 +n_obs_steps: 2 +n_action_steps: 8 +n_latency_steps: 0 +dataset_obs_steps: ${n_obs_steps} +past_action_visible: False +keypoint_visible_rate: 1.0 +obs_as_global_cond: True + +policy: + _target_: diffusion_policy.policy.diffusion_unet_image_policy.DiffusionUnetImagePolicy + + shape_meta: ${shape_meta} + + noise_scheduler: + _target_: diffusers.schedulers.scheduling_ddim.DDIMScheduler + num_train_timesteps: 100 + beta_start: 0.0001 + beta_end: 0.02 + # beta_schedule is important + # this is the best we found + beta_schedule: squaredcos_cap_v2 + clip_sample: True + set_alpha_to_one: True + steps_offset: 0 + prediction_type: epsilon # or sample + + obs_encoder: + _target_: diffusion_policy.model.vision.multi_image_obs_encoder.MultiImageObsEncoder + shape_meta: ${shape_meta} + rgb_model: + _target_: diffusion_policy.model.vision.model_getter.get_resnet + name: resnet18 + weights: null + resize_shape: [240, 320] + crop_shape: [216, 288] # ch, cw 240x320 90% + random_crop: True + use_group_norm: True + share_rgb_model: False + imagenet_norm: True + + horizon: ${horizon} + n_action_steps: ${eval:'${n_action_steps}+${n_latency_steps}'} + n_obs_steps: ${n_obs_steps} + num_inference_steps: 100 + obs_as_global_cond: ${obs_as_global_cond} + # crop_shape: null + diffusion_step_embed_dim: 128 + down_dims: [512, 1024, 2048] + kernel_size: 5 + n_groups: 8 + cond_predict_scale: True + + # scheduler.step params + # predict_epsilon: True + +ema: + _target_: diffusion_policy.model.diffusion.ema_model.EMAModel + update_after_step: 0 + inv_gamma: 1.0 + power: 0.75 + min_value: 0.0 + max_value: 0.9999 + +dataloader: + batch_size: 64 + num_workers: 8 + shuffle: True + pin_memory: True + persistent_workers: True + +val_dataloader: + batch_size: 64 + num_workers: 8 + shuffle: False + pin_memory: True + persistent_workers: True + +optimizer: + _target_: torch.optim.AdamW + lr: 1.0e-4 + betas: [0.95, 0.999] + eps: 1.0e-8 + weight_decay: 1.0e-6 + +training: + device: "cuda:0" + seed: 42 + debug: False + resume: True + # optimization + lr_scheduler: cosine + lr_warmup_steps: 500 + num_epochs: 600 + gradient_accumulate_every: 1 + # EMA destroys performance when used with BatchNorm + # replace BatchNorm with GroupNorm. + use_ema: True + freeze_encoder: False + # training loop control + # in epochs + rollout_every: 50 + checkpoint_every: 50 + val_every: 1 + sample_every: 5 + # steps per epoch + max_train_steps: null + max_val_steps: null + # misc + tqdm_interval_sec: 1.0 + +logging: + project: diffusion_policy_debug + resume: True + mode: online + name: ${now:%Y.%m.%d-%H.%M.%S}_${name}_${task_name} + tags: ["${name}", "${task_name}", "${exp_name}"] + id: null + group: null + +checkpoint: + topk: + monitor_key: train_loss + mode: min + k: 5 + format_str: 'epoch={epoch:04d}-train_loss={train_loss:.3f}.ckpt' + save_last_ckpt: True + save_last_snapshot: False + +multi_run: + run_dir: data/outputs/${now:%Y.%m.%d}/${now:%H.%M.%S}_${name}_${task_name} + wandb_name_base: ${now:%Y.%m.%d-%H.%M.%S}_${name}_${task_name} + +hydra: + job: + override_dirname: ${name} + run: + dir: data/outputs/${now:%Y.%m.%d}/${now:%H.%M.%S}_${name}_${task_name} + sweep: + dir: data/outputs/${now:%Y.%m.%d}/${now:%H.%M.%S}_${name}_${task_name} + subdir: ${hydra.job.num} diff --git a/diffusion_policy/config/train_diffusion_unet_real_pretrained_workspace.yaml b/diffusion_policy/config/train_diffusion_unet_real_pretrained_workspace.yaml new file mode 100644 index 0000000..f129a1e --- /dev/null +++ b/diffusion_policy/config/train_diffusion_unet_real_pretrained_workspace.yaml @@ -0,0 +1,152 @@ +defaults: + - _self_ + - task: real_pusht_image + +name: train_diffusion_unet_image +_target_: diffusion_policy.workspace.train_diffusion_unet_image_workspace.TrainDiffusionUnetImageWorkspace + +task_name: ${task.name} +shape_meta: ${task.shape_meta} +exp_name: "default" + +horizon: 16 +n_obs_steps: 2 +n_action_steps: 8 +n_latency_steps: 0 +dataset_obs_steps: ${n_obs_steps} +past_action_visible: False +keypoint_visible_rate: 1.0 +obs_as_global_cond: True + +policy: + _target_: diffusion_policy.policy.diffusion_unet_image_policy.DiffusionUnetImagePolicy + + shape_meta: ${shape_meta} + + noise_scheduler: + _target_: diffusers.schedulers.scheduling_ddim.DDIMScheduler + num_train_timesteps: 100 + beta_start: 0.0001 + beta_end: 0.02 + # beta_schedule is important + # this is the best we found + beta_schedule: squaredcos_cap_v2 + clip_sample: True + set_alpha_to_one: True + steps_offset: 0 + prediction_type: epsilon # or sample + + obs_encoder: + _target_: diffusion_policy.model.vision.multi_image_obs_encoder.MultiImageObsEncoder + shape_meta: ${shape_meta} + rgb_model: + _target_: diffusion_policy.model.vision.model_getter.get_resnet + name: resnet18 + weights: IMAGENET1K_V1 # or r3m + resize_shape: [224,224] + crop_shape: null + random_crop: False + use_group_norm: False + share_rgb_model: True + imagenet_norm: True + + horizon: ${horizon} + n_action_steps: ${eval:'${n_action_steps}+${n_latency_steps}'} + n_obs_steps: ${n_obs_steps} + num_inference_steps: 100 + obs_as_global_cond: ${obs_as_global_cond} + # crop_shape: null + diffusion_step_embed_dim: 128 + down_dims: [512, 1024, 2048] + kernel_size: 5 + n_groups: 8 + cond_predict_scale: True + + # scheduler.step params + # predict_epsilon: True + +ema: + _target_: diffusion_policy.model.diffusion.ema_model.EMAModel + update_after_step: 0 + inv_gamma: 1.0 + power: 0.75 + min_value: 0.0 + max_value: 0.9999 + +dataloader: + batch_size: 64 + num_workers: 8 + shuffle: True + pin_memory: True + persistent_workers: True + +val_dataloader: + batch_size: 64 + num_workers: 8 + shuffle: False + pin_memory: True + persistent_workers: True + +optimizer: + _target_: torch.optim.AdamW + lr: 1.0e-4 + betas: [0.95, 0.999] + eps: 1.0e-8 + weight_decay: 1.0e-6 + +training: + device: "cuda:0" + seed: 42 + debug: False + resume: True + # optimization + lr_scheduler: cosine + lr_warmup_steps: 500 + num_epochs: 600 + gradient_accumulate_every: 1 + # EMA destroys performance when used with BatchNorm + # replace BatchNorm with GroupNorm. + use_ema: True + freeze_encoder: True + # training loop control + # in epochs + rollout_every: 50 + checkpoint_every: 50 + val_every: 1 + sample_every: 5 + # steps per epoch + max_train_steps: null + max_val_steps: null + # misc + tqdm_interval_sec: 1.0 + +logging: + project: diffusion_policy_debug + resume: True + mode: online + name: ${now:%Y.%m.%d-%H.%M.%S}_${name}_${task_name} + tags: ["${name}", "${task_name}", "${exp_name}"] + id: null + group: null + +checkpoint: + topk: + monitor_key: train_loss + mode: min + k: 5 + format_str: 'epoch={epoch:04d}-train_loss={train_loss:.3f}.ckpt' + save_last_ckpt: True + save_last_snapshot: False + +multi_run: + run_dir: data/outputs/${now:%Y.%m.%d}/${now:%H.%M.%S}_${name}_${task_name} + wandb_name_base: ${now:%Y.%m.%d-%H.%M.%S}_${name}_${task_name} + +hydra: + job: + override_dirname: ${name} + run: + dir: data/outputs/${now:%Y.%m.%d}/${now:%H.%M.%S}_${name}_${task_name} + sweep: + dir: data/outputs/${now:%Y.%m.%d}/${now:%H.%M.%S}_${name}_${task_name} + subdir: ${hydra.job.num} diff --git a/diffusion_policy/config/train_diffusion_unet_video_workspace.yaml b/diffusion_policy/config/train_diffusion_unet_video_workspace.yaml new file mode 100644 index 0000000..6abe7b6 --- /dev/null +++ b/diffusion_policy/config/train_diffusion_unet_video_workspace.yaml @@ -0,0 +1,128 @@ +defaults: + - _self_ + - task: lift_image_abs + +name: train_diffusion_unet_video +_target_: diffusion_policy.workspace.train_diffusion_unet_video_workspace.TrainDiffusionUnetVideoWorkspace + +task_name: ${task.name} +shape_meta: ${task.shape_meta} +exp_name: "default" + +horizon: 16 +n_obs_steps: 4 +n_action_steps: 8 +past_action_visible: False +keypoint_visible_rate: 1.0 +lowdim_as_global_cond: True + +policy: + _target_: diffusion_policy.policy.diffusion_unet_video_policy.DiffusionUnetVideoPolicy + + shape_meta: ${shape_meta} + + noise_scheduler: + _target_: diffusers.schedulers.scheduling_ddpm.DDPMScheduler + num_train_timesteps: 100 + beta_start: 0.0001 + beta_end: 0.02 + beta_schedule: squaredcos_cap_v2 + variance_type: fixed_small # Yilun's paper uses fixed_small_log instead, but easy to cause Nan + clip_sample: True # required when predict_epsilon=False + prediction_type: epsilon # or sample + + rgb_net: + _target_: diffusion_policy.model.obs_encoder.video_core.VideoCore + + backbone: + _target_: diffusion_policy.model.obs_encoder.video_core.VideoResNet + + norm_groups: 8 + input_channel: 3 + model_depth: 50 # ResNet 50 (18,34 not yet avaliable) + + pool: + _target_: diffusion_policy.model.ibc.global_avgpool.GlobalAvgpool + + dim: [2,3,4] + + horizon: ${horizon} + n_action_steps: ${n_action_steps} + n_obs_steps: ${n_obs_steps} + num_inference_steps: 100 + lowdim_as_global_cond: ${lowdim_as_global_cond} + diffusion_step_embed_dim: 128 + down_dims: [512, 1024, 2048] + kernel_size: 5 + n_groups: 8 + cond_predict_scale: True + + # TemporalAggregator Parameters + channel_mults: [1,1] + n_blocks_per_level: 1 + ta_kernel_size: 3 + ta_n_groups: 1 + + # scheduler.step params + # predict_epsilon: True + +ema: + _target_: diffusion_policy.model.diffusion.ema_model.EMAModel + update_after_step: 0 + inv_gamma: 1.0 + power: 0.75 + min_value: 0.0 + max_value: 0.9999 + +dataloader: + batch_size: 32 + num_workers: 1 + shuffle: True + pin_memory: True + +optimizer: + _target_: torch.optim.AdamW + lr: 0.0001 # 1e-4 + betas: [0.95, 0.999] + eps: 0.00000001 # 1e-8 + weight_decay: 0.000001 # 1e-6 + +training: + device: "cuda:0" + lr_scheduler: cosine + lr_warmup_steps: 500 + num_epochs: 1500 + gradient_accumulate_every: 1 + eval_every: 5000 + eval_first: False + val_every: 300 + use_ema: False + tqdm_interval_sec: 1.0 + seed: 42 + +logging: + project: diffusion_policy_debug + resume: True + mode: online + name: ${now:%Y.%m.%d-%H.%M.%S}_${name}_${task_name} + tags: ["${name}", "${task_name}", "${exp_name}"] + id: null + group: null + +checkpoint: + topk: + monitor_key: test_score + mode: max + k: 5 + format_str: 'epoch={epoch:03d}-test_score={test_score:.3f}.ckpt' + save_last_ckpt: False + save_last_snapshot: False + +hydra: + job: + override_dirname: ${name} + run: + dir: data/outputs/${now:%Y.%m.%d}/${now:%H.%M.%S}_${name}_${task_name} + sweep: + dir: data/outputs/${now:%Y.%m.%d}/${now:%H.%M.%S}_${name}_${task_name} + subdir: ${hydra.job.num} diff --git a/diffusion_policy/config/train_ibc_dfo_hybrid_workspace.yaml b/diffusion_policy/config/train_ibc_dfo_hybrid_workspace.yaml new file mode 100644 index 0000000..b6f5d47 --- /dev/null +++ b/diffusion_policy/config/train_ibc_dfo_hybrid_workspace.yaml @@ -0,0 +1,111 @@ +defaults: + - _self_ + - task: pusht_image + +name: train_ibc_dfo_hybrid +_target_: diffusion_policy.workspace.train_ibc_dfo_hybrid_workspace.TrainIbcDfoHybridWorkspace + +task_name: ${task.name} +shape_meta: ${task.shape_meta} +exp_name: "default" + +horizon: 2 +n_obs_steps: 2 +n_action_steps: 1 +n_latency_steps: 0 +dataset_obs_steps: ${n_obs_steps} +past_action_visible: False +keypoint_visible_rate: 1.0 + +policy: + _target_: diffusion_policy.policy.ibc_dfo_hybrid_image_policy.IbcDfoHybridImagePolicy + + shape_meta: ${shape_meta} + + horizon: ${horizon} + n_action_steps: ${eval:'${n_action_steps}+${n_latency_steps}'} + n_obs_steps: ${n_obs_steps} + dropout: 0.1 + train_n_neg: 1024 + pred_n_iter: 5 + pred_n_samples: 1024 + kevin_inference: False + andy_train: False + obs_encoder_group_norm: True + eval_fixed_crop: True + crop_shape: [84, 84] + +dataloader: + batch_size: 128 + num_workers: 8 + shuffle: True + pin_memory: True + persistent_workers: False + +val_dataloader: + batch_size: 128 + num_workers: 8 + shuffle: False + pin_memory: True + persistent_workers: False + +optimizer: + _target_: torch.optim.AdamW + lr: 1.0e-4 + betas: [0.95, 0.999] + eps: 1.0e-8 + weight_decay: 1.0e-6 + +training: + device: "cuda:0" + seed: 42 + debug: False + resume: True + # optimization + lr_scheduler: cosine + lr_warmup_steps: 500 + num_epochs: 3050 + gradient_accumulate_every: 1 + # training loop control + # in epochs + rollout_every: 50 + checkpoint_every: 50 + val_every: 1 + sample_every: 5 + sample_max_batch: 128 + # steps per epoch + max_train_steps: null + max_val_steps: null + # misc + tqdm_interval_sec: 1.0 + +logging: + project: diffusion_policy_debug + resume: True + mode: online + name: ${now:%Y.%m.%d-%H.%M.%S}_${name}_${task_name} + tags: ["${name}", "${task_name}", "${exp_name}"] + id: null + group: null + +checkpoint: + topk: + monitor_key: test_mean_score + mode: max + k: 5 + format_str: 'epoch={epoch:04d}-test_mean_score={test_mean_score:.3f}.ckpt' + save_last_ckpt: True + save_last_snapshot: False + +multi_run: + run_dir: data/outputs/${now:%Y.%m.%d}/${now:%H.%M.%S}_${name}_${task_name} + wandb_name_base: ${now:%Y.%m.%d-%H.%M.%S}_${name}_${task_name} + +hydra: + job: + override_dirname: ${name} + run: + dir: data/outputs/${now:%Y.%m.%d}/${now:%H.%M.%S}_${name}_${task_name} + sweep: + dir: data/outputs/${now:%Y.%m.%d}/${now:%H.%M.%S}_${name}_${task_name} + subdir: ${hydra.job.num} diff --git a/diffusion_policy/config/train_ibc_dfo_lowdim_workspace.yaml b/diffusion_policy/config/train_ibc_dfo_lowdim_workspace.yaml new file mode 100644 index 0000000..9e73a93 --- /dev/null +++ b/diffusion_policy/config/train_ibc_dfo_lowdim_workspace.yaml @@ -0,0 +1,109 @@ +defaults: + - _self_ + - task: pusht_lowdim + +name: train_ibc_dfo_lowdim +_target_: diffusion_policy.workspace.train_ibc_dfo_lowdim_workspace.TrainIbcDfoLowdimWorkspace + +obs_dim: ${task.obs_dim} +action_dim: ${task.action_dim} +keypoint_dim: ${task.keypoint_dim} +task_name: ${task.name} +exp_name: "default" + +horizon: 2 +n_obs_steps: 2 +n_action_steps: 1 +n_latency_steps: 0 +past_action_visible: False +keypoint_visible_rate: 1.0 + +policy: + _target_: diffusion_policy.policy.ibc_dfo_lowdim_policy.IbcDfoLowdimPolicy + + horizon: ${horizon} + obs_dim: ${obs_dim} + action_dim: ${action_dim} + n_action_steps: ${eval:'${n_action_steps}+${n_latency_steps}'} + n_obs_steps: ${n_obs_steps} + dropout: 0.1 + train_n_neg: 1024 + pred_n_iter: 5 + pred_n_samples: 1024 + kevin_inference: False + andy_train: False + +dataloader: + batch_size: 256 + num_workers: 1 + shuffle: True + pin_memory: True + persistent_workers: False + +val_dataloader: + batch_size: 256 + num_workers: 1 + shuffle: False + pin_memory: True + persistent_workers: False + +optimizer: + _target_: torch.optim.AdamW + lr: 1.0e-4 + betas: [0.95, 0.999] + eps: 1.0e-8 + weight_decay: 1.0e-6 + +training: + device: "cuda:0" + seed: 42 + debug: False + resume: True + # optimization + lr_scheduler: cosine + lr_warmup_steps: 500 + num_epochs: 5000 + gradient_accumulate_every: 1 + # training loop control + # in epochs + rollout_every: 50 + checkpoint_every: 50 + val_every: 1 + sample_every: 5 + sample_max_batch: 128 + # steps per epoch + max_train_steps: null + max_val_steps: null + # misc + tqdm_interval_sec: 1.0 + +logging: + project: diffusion_policy_debug + resume: True + mode: online + name: ${now:%Y.%m.%d-%H.%M.%S}_${name}_${task_name} + tags: ["${name}", "${task_name}", "${exp_name}"] + id: null + group: null + +checkpoint: + topk: + monitor_key: test_mean_score + mode: max + k: 5 + format_str: 'epoch={epoch:04d}-test_mean_score={test_mean_score:.3f}.ckpt' + save_last_ckpt: True + save_last_snapshot: False + +multi_run: + run_dir: data/outputs/${now:%Y.%m.%d}/${now:%H.%M.%S}_${name}_${task_name} + wandb_name_base: ${now:%Y.%m.%d-%H.%M.%S}_${name}_${task_name} + +hydra: + job: + override_dirname: ${name} + run: + dir: data/outputs/${now:%Y.%m.%d}/${now:%H.%M.%S}_${name}_${task_name} + sweep: + dir: data/outputs/${now:%Y.%m.%d}/${now:%H.%M.%S}_${name}_${task_name} + subdir: ${hydra.job.num} diff --git a/diffusion_policy/config/train_ibc_dfo_real_hybrid_workspace.yaml b/diffusion_policy/config/train_ibc_dfo_real_hybrid_workspace.yaml new file mode 100644 index 0000000..8255dc1 --- /dev/null +++ b/diffusion_policy/config/train_ibc_dfo_real_hybrid_workspace.yaml @@ -0,0 +1,111 @@ +defaults: + - _self_ + - task: real_pusht_image + +name: train_ibc_dfo_hybrid +_target_: diffusion_policy.workspace.train_ibc_dfo_hybrid_workspace.TrainIbcDfoHybridWorkspace + +task_name: ${task.name} +shape_meta: ${task.shape_meta} +exp_name: "default" + +horizon: 2 +n_obs_steps: 2 +n_action_steps: 1 +n_latency_steps: 1 +dataset_obs_steps: ${n_obs_steps} +past_action_visible: False +keypoint_visible_rate: 1.0 + +policy: + _target_: diffusion_policy.policy.ibc_dfo_hybrid_image_policy.IbcDfoHybridImagePolicy + + shape_meta: ${shape_meta} + + horizon: ${horizon} + n_action_steps: ${n_action_steps} + n_obs_steps: ${n_obs_steps} + dropout: 0.1 + train_n_neg: 256 + pred_n_iter: 3 + pred_n_samples: 1024 + kevin_inference: False + andy_train: False + obs_encoder_group_norm: True + eval_fixed_crop: True + crop_shape: [216, 288] # ch, cw 320x240 90% + +dataloader: + batch_size: 128 + num_workers: 8 + shuffle: True + pin_memory: True + persistent_workers: False + +val_dataloader: + batch_size: 128 + num_workers: 1 + shuffle: False + pin_memory: True + persistent_workers: False + +optimizer: + _target_: torch.optim.AdamW + lr: 1.0e-4 + betas: [0.95, 0.999] + eps: 1.0e-8 + weight_decay: 1.0e-6 + +training: + device: "cuda:0" + seed: 42 + debug: False + resume: True + # optimization + lr_scheduler: cosine + lr_warmup_steps: 500 + num_epochs: 1000 + gradient_accumulate_every: 1 + # training loop control + # in epochs + rollout_every: 50 + checkpoint_every: 5 + val_every: 1 + sample_every: 5 + sample_max_batch: 128 + # steps per epoch + max_train_steps: null + max_val_steps: null + # misc + tqdm_interval_sec: 1.0 + +logging: + project: diffusion_policy_debug + resume: True + mode: online + name: ${now:%Y.%m.%d-%H.%M.%S}_${name}_${task_name} + tags: ["${name}", "${task_name}", "${exp_name}"] + id: null + group: null + +checkpoint: + topk: + monitor_key: train_action_mse_error + mode: min + k: 5 + format_str: 'epoch={epoch:04d}-train_action_mse_error={train_action_mse_error:.3f}.ckpt' + save_last_ckpt: True + save_last_snapshot: False + +multi_run: + run_dir: data/outputs/${now:%Y.%m.%d}/${now:%H.%M.%S}_${name}_${task_name} + wandb_name_base: ${now:%Y.%m.%d-%H.%M.%S}_${name}_${task_name} + +hydra: + job: + override_dirname: ${name} + run: + dir: data/outputs/${now:%Y.%m.%d}/${now:%H.%M.%S}_${name}_${task_name} + sweep: + dir: data/outputs/${now:%Y.%m.%d}/${now:%H.%M.%S}_${name}_${task_name} + subdir: ${hydra.job.num} diff --git a/diffusion_policy/config/train_robomimic_image_workspace.yaml b/diffusion_policy/config/train_robomimic_image_workspace.yaml new file mode 100644 index 0000000..4cea6ab --- /dev/null +++ b/diffusion_policy/config/train_robomimic_image_workspace.yaml @@ -0,0 +1,92 @@ +defaults: + - _self_ + - task: lift_image + +name: train_robomimic_image +_target_: diffusion_policy.workspace.train_robomimic_image_workspace.TrainRobomimicImageWorkspace + +task_name: ${task.name} +shape_meta: ${task.shape_meta} +exp_name: "default" + +horizon: &horizon 10 +n_obs_steps: 1 +n_action_steps: 1 +n_latency_steps: 0 +dataset_obs_steps: *horizon +past_action_visible: False +keypoint_visible_rate: 1.0 + +policy: + _target_: diffusion_policy.policy.robomimic_image_policy.RobomimicImagePolicy + shape_meta: ${shape_meta} + algo_name: bc_rnn + obs_type: image + # oc.select resolver: key, default + task_name: ${oc.select:task.task_name,lift} + dataset_type: ${oc.select:task.dataset_type,ph} + crop_shape: [76,76] + +dataloader: + batch_size: 64 + num_workers: 16 + shuffle: True + pin_memory: True + persistent_workers: False + +val_dataloader: + batch_size: 64 + num_workers: 16 + shuffle: False + pin_memory: True + persistent_workers: False + +training: + device: "cuda:0" + seed: 42 + debug: False + resume: True + # optimization + num_epochs: 3050 + # training loop control + # in epochs + rollout_every: 50 + checkpoint_every: 50 + val_every: 1 + sample_every: 5 + # steps per epoch + max_train_steps: null + max_val_steps: null + # misc + tqdm_interval_sec: 1.0 + +logging: + project: diffusion_policy_debug + resume: True + mode: online + name: ${now:%Y.%m.%d-%H.%M.%S}_${name}_${task_name} + tags: ["${name}", "${task_name}", "${exp_name}"] + id: null + group: null + +checkpoint: + topk: + monitor_key: test_mean_score + mode: max + k: 5 + format_str: 'epoch={epoch:04d}-test_mean_score={test_mean_score:.3f}.ckpt' + save_last_ckpt: True + save_last_snapshot: False + +multi_run: + run_dir: data/outputs/${now:%Y.%m.%d}/${now:%H.%M.%S}_${name}_${task_name} + wandb_name_base: ${now:%Y.%m.%d-%H.%M.%S}_${name}_${task_name} + +hydra: + job: + override_dirname: ${name} + run: + dir: data/outputs/${now:%Y.%m.%d}/${now:%H.%M.%S}_${name}_${task_name} + sweep: + dir: data/outputs/${now:%Y.%m.%d}/${now:%H.%M.%S}_${name}_${task_name} + subdir: ${hydra.job.num} diff --git a/diffusion_policy/config/train_robomimic_lowdim_workspace.yaml b/diffusion_policy/config/train_robomimic_lowdim_workspace.yaml new file mode 100644 index 0000000..a657d1b --- /dev/null +++ b/diffusion_policy/config/train_robomimic_lowdim_workspace.yaml @@ -0,0 +1,92 @@ +defaults: + - _self_ + - task: pusht_lowdim + +name: train_robomimic_lowdim +_target_: diffusion_policy.workspace.train_robomimic_lowdim_workspace.TrainRobomimicLowdimWorkspace + +obs_dim: ${task.obs_dim} +action_dim: ${task.action_dim} +transition_dim: "${eval: ${task.obs_dim} + ${task.action_dim}}" +task_name: ${task.name} +exp_name: "default" + +horizon: 10 +n_obs_steps: 1 +n_action_steps: 1 +n_latency_steps: 0 +past_action_visible: False +keypoint_visible_rate: 1.0 + +policy: + _target_: diffusion_policy.policy.robomimic_lowdim_policy.RobomimicLowdimPolicy + action_dim: ${action_dim} + obs_dim: ${obs_dim} + algo_name: bc_rnn + obs_type: low_dim + # oc.select resolver: key, default + task_name: ${oc.select:task.task_name,lift} + dataset_type: ${oc.select:task.dataset_type,ph} + +dataloader: + batch_size: 256 + num_workers: 1 + shuffle: True + pin_memory: True + persistent_workers: False + +val_dataloader: + batch_size: 256 + num_workers: 1 + shuffle: False + pin_memory: True + persistent_workers: False + +training: + device: "cuda:0" + seed: 42 + debug: False + resume: True + # optimization + num_epochs: 5000 + # training loop control + # in epochs + rollout_every: 50 + checkpoint_every: 50 + val_every: 1 + # steps per epoch + max_train_steps: null + max_val_steps: null + # misc + tqdm_interval_sec: 1.0 + +logging: + project: diffusion_policy_debug + resume: True + mode: online + name: ${now:%Y.%m.%d-%H.%M.%S}_${name}_${task_name} + tags: ["${name}", "${task_name}", "${exp_name}"] + id: null + group: null + +checkpoint: + topk: + monitor_key: test_mean_score + mode: max + k: 5 + format_str: 'epoch={epoch:04d}-test_mean_score={test_mean_score:.3f}.ckpt' + save_last_ckpt: True + save_last_snapshot: False + +multi_run: + run_dir: data/outputs/${now:%Y.%m.%d}/${now:%H.%M.%S}_${name}_${task_name} + wandb_name_base: ${now:%Y.%m.%d-%H.%M.%S}_${name}_${task_name} + +hydra: + job: + override_dirname: ${name} + run: + dir: data/outputs/${now:%Y.%m.%d}/${now:%H.%M.%S}_${name}_${task_name} + sweep: + dir: data/outputs/${now:%Y.%m.%d}/${now:%H.%M.%S}_${name}_${task_name} + subdir: ${hydra.job.num} diff --git a/diffusion_policy/config/train_robomimic_real_image_workspace.yaml b/diffusion_policy/config/train_robomimic_real_image_workspace.yaml new file mode 100644 index 0000000..983976c --- /dev/null +++ b/diffusion_policy/config/train_robomimic_real_image_workspace.yaml @@ -0,0 +1,92 @@ +defaults: + - _self_ + - task: real_pusht_image + +name: train_robomimic_image +_target_: diffusion_policy.workspace.train_robomimic_image_workspace.TrainRobomimicImageWorkspace + +task_name: ${task.name} +shape_meta: ${task.shape_meta} +exp_name: "default" + +horizon: &horizon 10 +n_obs_steps: 1 +n_action_steps: 1 +n_latency_steps: 1 +dataset_obs_steps: *horizon +past_action_visible: False +keypoint_visible_rate: 1.0 + +policy: + _target_: diffusion_policy.policy.robomimic_image_policy.RobomimicImagePolicy + shape_meta: ${shape_meta} + algo_name: bc_rnn + obs_type: image + # oc.select resolver: key, default + task_name: ${oc.select:task.task_name,tool_hang} + dataset_type: ${oc.select:task.dataset_type,ph} + crop_shape: [216, 288] # ch, cw 320x240 90% + +dataloader: + batch_size: 32 + num_workers: 8 + shuffle: True + pin_memory: True + persistent_workers: True + +val_dataloader: + batch_size: 32 + num_workers: 1 + shuffle: False + pin_memory: True + persistent_workers: False + +training: + device: "cuda:0" + seed: 42 + debug: False + resume: True + # optimization + num_epochs: 1000 + # training loop control + # in epochs + rollout_every: 50 + checkpoint_every: 50 + val_every: 1 + sample_every: 5 + # steps per epoch + max_train_steps: null + max_val_steps: null + # misc + tqdm_interval_sec: 1.0 + +logging: + project: diffusion_policy_debug + resume: True + mode: online + name: ${now:%Y.%m.%d-%H.%M.%S}_${name}_${task_name} + tags: ["${name}", "${task_name}", "${exp_name}"] + id: null + group: null + +checkpoint: + topk: + monitor_key: train_loss + mode: min + k: 5 + format_str: 'epoch={epoch:04d}-train_loss={train_loss:.3f}.ckpt' + save_last_ckpt: True + save_last_snapshot: False + +multi_run: + run_dir: data/outputs/${now:%Y.%m.%d}/${now:%H.%M.%S}_${name}_${task_name} + wandb_name_base: ${now:%Y.%m.%d-%H.%M.%S}_${name}_${task_name} + +hydra: + job: + override_dirname: ${name} + run: + dir: data/outputs/${now:%Y.%m.%d}/${now:%H.%M.%S}_${name}_${task_name} + sweep: + dir: data/outputs/${now:%Y.%m.%d}/${now:%H.%M.%S}_${name}_${task_name} + subdir: ${hydra.job.num} diff --git a/diffusion_policy/dataset/base_dataset.py b/diffusion_policy/dataset/base_dataset.py new file mode 100644 index 0000000..72a39e8 --- /dev/null +++ b/diffusion_policy/dataset/base_dataset.py @@ -0,0 +1,51 @@ +from typing import Dict + +import torch +import torch.nn +from diffusion_policy.model.common.normalizer import LinearNormalizer + +class BaseLowdimDataset(torch.utils.data.Dataset): + def get_validation_dataset(self) -> 'BaseLowdimDataset': + # return an empty dataset by default + return BaseLowdimDataset() + + def get_normalizer(self, **kwargs) -> LinearNormalizer: + raise NotImplementedError() + + def get_all_actions(self) -> torch.Tensor: + raise NotImplementedError() + + def __len__(self) -> int: + return 0 + + def __getitem__(self, idx: int) -> Dict[str, torch.Tensor]: + """ + output: + obs: T, Do + action: T, Da + """ + raise NotImplementedError() + + +class BaseImageDataset(torch.utils.data.Dataset): + def get_validation_dataset(self) -> 'BaseLowdimDataset': + # return an empty dataset by default + return BaseImageDataset() + + def get_normalizer(self, **kwargs) -> LinearNormalizer: + raise NotImplementedError() + + def get_all_actions(self) -> torch.Tensor: + raise NotImplementedError() + + def __len__(self) -> int: + return 0 + + def __getitem__(self, idx: int) -> Dict[str, torch.Tensor]: + """ + output: + obs: + key: T, * + action: T, Da + """ + raise NotImplementedError() diff --git a/diffusion_policy/dataset/blockpush_lowdim_dataset.py b/diffusion_policy/dataset/blockpush_lowdim_dataset.py new file mode 100644 index 0000000..86242c2 --- /dev/null +++ b/diffusion_policy/dataset/blockpush_lowdim_dataset.py @@ -0,0 +1,126 @@ +from typing import Dict +import torch +import numpy as np +import copy +from diffusion_policy.common.pytorch_util import dict_apply +from diffusion_policy.common.replay_buffer import ReplayBuffer +from diffusion_policy.common.sampler import SequenceSampler, get_val_mask +from diffusion_policy.model.common.normalizer import LinearNormalizer, SingleFieldLinearNormalizer +from diffusion_policy.dataset.base_dataset import BaseLowdimDataset + +class BlockPushLowdimDataset(BaseLowdimDataset): + def __init__(self, + zarr_path, + horizon=1, + pad_before=0, + pad_after=0, + obs_key='obs', + action_key='action', + obs_eef_target=True, + use_manual_normalizer=False, + seed=42, + val_ratio=0.0 + ): + super().__init__() + self.replay_buffer = ReplayBuffer.copy_from_path( + zarr_path, keys=[obs_key, action_key]) + + val_mask = get_val_mask( + n_episodes=self.replay_buffer.n_episodes, + val_ratio=val_ratio, + seed=seed) + train_mask = ~val_mask + self.sampler = SequenceSampler( + replay_buffer=self.replay_buffer, + sequence_length=horizon, + pad_before=pad_before, + pad_after=pad_after, + episode_mask=train_mask) + self.obs_key = obs_key + self.action_key = action_key + self.obs_eef_target = obs_eef_target + self.use_manual_normalizer = use_manual_normalizer + self.train_mask = train_mask + self.horizon = horizon + self.pad_before = pad_before + self.pad_after = pad_after + + def get_validation_dataset(self): + val_set = copy.copy(self) + val_set.sampler = SequenceSampler( + replay_buffer=self.replay_buffer, + sequence_length=self.horizon, + pad_before=self.pad_before, + pad_after=self.pad_after, + episode_mask=~self.train_mask + ) + val_set.train_mask = ~self.train_mask + return val_set + + def get_normalizer(self, mode='limits', **kwargs): + data = self._sample_to_data(self.replay_buffer) + + normalizer = LinearNormalizer() + if not self.use_manual_normalizer: + normalizer.fit(data=data, last_n_dims=1, mode=mode, **kwargs) + else: + x = data['obs'] + stat = { + 'max': np.max(x, axis=0), + 'min': np.min(x, axis=0), + 'mean': np.mean(x, axis=0), + 'std': np.std(x, axis=0) + } + + is_x = np.zeros(stat['max'].shape, dtype=bool) + is_y = np.zeros_like(is_x) + is_x[[0,3,6,8,10,13]] = True + is_y[[1,4,7,9,11,14]] = True + is_rot = ~(is_x|is_y) + + def normalizer_with_masks(stat, masks): + global_scale = np.ones_like(stat['max']) + global_offset = np.zeros_like(stat['max']) + for mask in masks: + output_max = 1 + output_min = -1 + input_max = stat['max'][mask].max() + input_min = stat['min'][mask].min() + input_range = input_max - input_min + scale = (output_max - output_min) / input_range + offset = output_min - scale * input_min + global_scale[mask] = scale + global_offset[mask] = offset + return SingleFieldLinearNormalizer.create_manual( + scale=global_scale, + offset=global_offset, + input_stats_dict=stat + ) + + normalizer['obs'] = normalizer_with_masks(stat, [is_x, is_y, is_rot]) + normalizer['action'] = SingleFieldLinearNormalizer.create_fit( + data['action'], last_n_dims=1, mode=mode, **kwargs) + return normalizer + + def get_all_actions(self) -> torch.Tensor: + return torch.from_numpy(self.replay_buffer['action']) + + def __len__(self) -> int: + return len(self.sampler) + + def _sample_to_data(self, sample): + obs = sample[self.obs_key] # T, D_o + if not self.obs_eef_target: + obs[:,8:10] = 0 + data = { + 'obs': obs, + 'action': sample[self.action_key], # T, D_a + } + return data + + def __getitem__(self, idx: int) -> Dict[str, torch.Tensor]: + sample = self.sampler.sample_sequence(idx) + data = self._sample_to_data(sample) + + torch_data = dict_apply(data, torch.from_numpy) + return torch_data diff --git a/diffusion_policy/dataset/kitchen_lowdim_dataset.py b/diffusion_policy/dataset/kitchen_lowdim_dataset.py new file mode 100644 index 0000000..601e21c --- /dev/null +++ b/diffusion_policy/dataset/kitchen_lowdim_dataset.py @@ -0,0 +1,91 @@ +from typing import Dict +import torch +import numpy as np +import copy +import pathlib +from diffusion_policy.common.pytorch_util import dict_apply +from diffusion_policy.common.replay_buffer import ReplayBuffer +from diffusion_policy.common.sampler import SequenceSampler, get_val_mask +from diffusion_policy.model.common.normalizer import LinearNormalizer, SingleFieldLinearNormalizer +from diffusion_policy.dataset.base_dataset import BaseLowdimDataset + +class KitchenLowdimDataset(BaseLowdimDataset): + def __init__(self, + dataset_dir, + horizon=1, + pad_before=0, + pad_after=0, + seed=42, + val_ratio=0.0 + ): + super().__init__() + + data_directory = pathlib.Path(dataset_dir) + observations = np.load(data_directory / "observations_seq.npy") + actions = np.load(data_directory / "actions_seq.npy") + masks = np.load(data_directory / "existence_mask.npy") + + self.replay_buffer = ReplayBuffer.create_empty_numpy() + for i in range(len(masks)): + eps_len = int(masks[i].sum()) + obs = observations[i,:eps_len].astype(np.float32) + action = actions[i,:eps_len].astype(np.float32) + data = { + 'obs': obs, + 'action': action + } + self.replay_buffer.add_episode(data) + + val_mask = get_val_mask( + n_episodes=self.replay_buffer.n_episodes, + val_ratio=val_ratio, + seed=seed) + train_mask = ~val_mask + self.sampler = SequenceSampler( + replay_buffer=self.replay_buffer, + sequence_length=horizon, + pad_before=pad_before, + pad_after=pad_after, + episode_mask=train_mask) + + self.train_mask = train_mask + self.horizon = horizon + self.pad_before = pad_before + self.pad_after = pad_after + + def get_validation_dataset(self): + val_set = copy.copy(self) + val_set.sampler = SequenceSampler( + replay_buffer=self.replay_buffer, + sequence_length=self.horizon, + pad_before=self.pad_before, + pad_after=self.pad_after, + episode_mask=~self.train_mask + ) + val_set.train_mask = ~self.train_mask + return val_set + + def get_normalizer(self, mode='limits', **kwargs): + data = { + 'obs': self.replay_buffer['obs'], + 'action': self.replay_buffer['action'] + } + if 'range_eps' not in kwargs: + # to prevent blowing up dims that barely change + kwargs['range_eps'] = 5e-2 + normalizer = LinearNormalizer() + normalizer.fit(data=data, last_n_dims=1, mode=mode, **kwargs) + return normalizer + + def get_all_actions(self) -> torch.Tensor: + return torch.from_numpy(self.replay_buffer['action']) + + def __len__(self) -> int: + return len(self.sampler) + + def __getitem__(self, idx: int) -> Dict[str, torch.Tensor]: + sample = self.sampler.sample_sequence(idx) + data = sample + + torch_data = dict_apply(data, torch.from_numpy) + return torch_data diff --git a/diffusion_policy/dataset/kitchen_mjl_lowdim_dataset.py b/diffusion_policy/dataset/kitchen_mjl_lowdim_dataset.py new file mode 100644 index 0000000..e317381 --- /dev/null +++ b/diffusion_policy/dataset/kitchen_mjl_lowdim_dataset.py @@ -0,0 +1,112 @@ +from typing import Dict +import torch +import numpy as np +import copy +import pathlib +from tqdm import tqdm +from diffusion_policy.common.pytorch_util import dict_apply +from diffusion_policy.common.replay_buffer import ReplayBuffer +from diffusion_policy.common.sampler import SequenceSampler, get_val_mask +from diffusion_policy.model.common.normalizer import LinearNormalizer, SingleFieldLinearNormalizer +from diffusion_policy.dataset.base_dataset import BaseLowdimDataset +from diffusion_policy.env.kitchen.kitchen_util import parse_mjl_logs + +class KitchenMjlLowdimDataset(BaseLowdimDataset): + def __init__(self, + dataset_dir, + horizon=1, + pad_before=0, + pad_after=0, + abs_action=True, + robot_noise_ratio=0.0, + seed=42, + val_ratio=0.0 + ): + super().__init__() + + if not abs_action: + raise NotImplementedError() + + robot_pos_noise_amp = np.array([0.1 , 0.1 , 0.1 , 0.1 , 0.1 , 0.1 , 0.1 , 0.1 , + 0.1 , 0.005 , 0.005 , 0.0005, 0.0005, 0.0005, 0.0005, 0.0005, + 0.0005, 0.005 , 0.005 , 0.005 , 0.1 , 0.1 , 0.1 , 0.005 , + 0.005 , 0.005 , 0.1 , 0.1 , 0.1 , 0.005 ], dtype=np.float32) + rng = np.random.default_rng(seed=seed) + + data_directory = pathlib.Path(dataset_dir) + self.replay_buffer = ReplayBuffer.create_empty_numpy() + for i, mjl_path in enumerate(tqdm(list(data_directory.glob('*/*.mjl')))): + try: + data = parse_mjl_logs(str(mjl_path.absolute()), skipamount=40) + qpos = data['qpos'].astype(np.float32) + obs = np.concatenate([ + qpos[:,:9], + qpos[:,-21:], + np.zeros((len(qpos),30),dtype=np.float32) + ], axis=-1) + if robot_noise_ratio > 0: + # add observation noise to match real robot + noise = robot_noise_ratio * robot_pos_noise_amp * rng.uniform( + low=-1., high=1., size=(obs.shape[0], 30)) + obs[:,:30] += noise + episode = { + 'obs': obs, + 'action': data['ctrl'].astype(np.float32) + } + self.replay_buffer.add_episode(episode) + except Exception as e: + print(i, e) + + val_mask = get_val_mask( + n_episodes=self.replay_buffer.n_episodes, + val_ratio=val_ratio, + seed=seed) + train_mask = ~val_mask + self.sampler = SequenceSampler( + replay_buffer=self.replay_buffer, + sequence_length=horizon, + pad_before=pad_before, + pad_after=pad_after, + episode_mask=train_mask) + + self.train_mask = train_mask + self.horizon = horizon + self.pad_before = pad_before + self.pad_after = pad_after + + def get_validation_dataset(self): + val_set = copy.copy(self) + val_set.sampler = SequenceSampler( + replay_buffer=self.replay_buffer, + sequence_length=self.horizon, + pad_before=self.pad_before, + pad_after=self.pad_after, + episode_mask=~self.train_mask + ) + val_set.train_mask = ~self.train_mask + return val_set + + def get_normalizer(self, mode='limits', **kwargs): + data = { + 'obs': self.replay_buffer['obs'], + 'action': self.replay_buffer['action'] + } + if 'range_eps' not in kwargs: + # to prevent blowing up dims that barely change + kwargs['range_eps'] = 5e-2 + normalizer = LinearNormalizer() + normalizer.fit(data=data, last_n_dims=1, mode=mode, **kwargs) + return normalizer + + def get_all_actions(self) -> torch.Tensor: + return torch.from_numpy(self.replay_buffer['action']) + + def __len__(self) -> int: + return len(self.sampler) + + def __getitem__(self, idx: int) -> Dict[str, torch.Tensor]: + sample = self.sampler.sample_sequence(idx) + data = sample + + torch_data = dict_apply(data, torch.from_numpy) + return torch_data diff --git a/diffusion_policy/dataset/pusht_dataset.py b/diffusion_policy/dataset/pusht_dataset.py new file mode 100644 index 0000000..dc3ec1c --- /dev/null +++ b/diffusion_policy/dataset/pusht_dataset.py @@ -0,0 +1,97 @@ +from typing import Dict +import torch +import numpy as np +import copy +from diffusion_policy.common.pytorch_util import dict_apply +from diffusion_policy.common.replay_buffer import ReplayBuffer +from diffusion_policy.common.sampler import ( + SequenceSampler, get_val_mask, downsample_mask) +from diffusion_policy.model.common.normalizer import LinearNormalizer +from diffusion_policy.dataset.base_dataset import BaseLowdimDataset + +class PushTLowdimDataset(BaseLowdimDataset): + def __init__(self, + zarr_path, + horizon=1, + pad_before=0, + pad_after=0, + obs_key='keypoint', + state_key='state', + action_key='action', + seed=42, + val_ratio=0.0, + max_train_episodes=None + ): + super().__init__() + self.replay_buffer = ReplayBuffer.copy_from_path( + zarr_path, keys=[obs_key, state_key, action_key]) + + val_mask = get_val_mask( + n_episodes=self.replay_buffer.n_episodes, + val_ratio=val_ratio, + seed=seed) + train_mask = ~val_mask + train_mask = downsample_mask( + mask=train_mask, + max_n=max_train_episodes, + seed=seed) + + self.sampler = SequenceSampler( + replay_buffer=self.replay_buffer, + sequence_length=horizon, + pad_before=pad_before, + pad_after=pad_after, + episode_mask=train_mask + ) + self.obs_key = obs_key + self.state_key = state_key + self.action_key = action_key + self.train_mask = train_mask + self.horizon = horizon + self.pad_before = pad_before + self.pad_after = pad_after + + def get_validation_dataset(self): + val_set = copy.copy(self) + val_set.sampler = SequenceSampler( + replay_buffer=self.replay_buffer, + sequence_length=self.horizon, + pad_before=self.pad_before, + pad_after=self.pad_after, + episode_mask=~self.train_mask + ) + val_set.train_mask = ~self.train_mask + return val_set + + def get_normalizer(self, mode='limits', **kwargs): + data = self._sample_to_data(self.replay_buffer) + normalizer = LinearNormalizer() + normalizer.fit(data=data, last_n_dims=1, mode=mode, **kwargs) + return normalizer + + def get_all_actions(self) -> torch.Tensor: + return torch.from_numpy(self.replay_buffer[self.action_key]) + + def __len__(self) -> int: + return len(self.sampler) + + def _sample_to_data(self, sample): + keypoint = sample[self.obs_key] + state = sample[self.state_key] + agent_pos = state[:,:2] + obs = np.concatenate([ + keypoint.reshape(keypoint.shape[0], -1), + agent_pos], axis=-1) + + data = { + 'obs': obs, # T, D_o + 'action': sample[self.action_key], # T, D_a + } + return data + + def __getitem__(self, idx: int) -> Dict[str, torch.Tensor]: + sample = self.sampler.sample_sequence(idx) + data = self._sample_to_data(sample) + + torch_data = dict_apply(data, torch.from_numpy) + return torch_data diff --git a/diffusion_policy/dataset/pusht_image_dataset.py b/diffusion_policy/dataset/pusht_image_dataset.py new file mode 100644 index 0000000..f096a8f --- /dev/null +++ b/diffusion_policy/dataset/pusht_image_dataset.py @@ -0,0 +1,102 @@ +from typing import Dict +import torch +import numpy as np +import copy +from diffusion_policy.common.pytorch_util import dict_apply +from diffusion_policy.common.replay_buffer import ReplayBuffer +from diffusion_policy.common.sampler import ( + SequenceSampler, get_val_mask, downsample_mask) +from diffusion_policy.model.common.normalizer import LinearNormalizer +from diffusion_policy.dataset.base_dataset import BaseImageDataset +from diffusion_policy.common.normalize_util import get_image_range_normalizer + +class PushTImageDataset(BaseImageDataset): + def __init__(self, + zarr_path, + horizon=1, + pad_before=0, + pad_after=0, + seed=42, + val_ratio=0.0, + max_train_episodes=None + ): + + super().__init__() + self.replay_buffer = ReplayBuffer.copy_from_path( + zarr_path, keys=['img', 'state', 'action']) + val_mask = get_val_mask( + n_episodes=self.replay_buffer.n_episodes, + val_ratio=val_ratio, + seed=seed) + train_mask = ~val_mask + train_mask = downsample_mask( + mask=train_mask, + max_n=max_train_episodes, + seed=seed) + + self.sampler = SequenceSampler( + replay_buffer=self.replay_buffer, + sequence_length=horizon, + pad_before=pad_before, + pad_after=pad_after, + episode_mask=train_mask) + self.train_mask = train_mask + self.horizon = horizon + self.pad_before = pad_before + self.pad_after = pad_after + + def get_validation_dataset(self): + val_set = copy.copy(self) + val_set.sampler = SequenceSampler( + replay_buffer=self.replay_buffer, + sequence_length=self.horizon, + pad_before=self.pad_before, + pad_after=self.pad_after, + episode_mask=~self.train_mask + ) + val_set.train_mask = ~self.train_mask + return val_set + + def get_normalizer(self, mode='limits', **kwargs): + data = { + 'action': self.replay_buffer['action'], + 'agent_pos': self.replay_buffer['state'][...,:2] + } + normalizer = LinearNormalizer() + normalizer.fit(data=data, last_n_dims=1, mode=mode, **kwargs) + normalizer['image'] = get_image_range_normalizer() + return normalizer + + def __len__(self) -> int: + return len(self.sampler) + + def _sample_to_data(self, sample): + agent_pos = sample['state'][:,:2].astype(np.float32) # (agent_posx2, block_posex3) + image = np.moveaxis(sample['img'],-1,1)/255 + + data = { + 'obs': { + 'image': image, # T, 3, 96, 96 + 'agent_pos': agent_pos, # T, 2 + }, + 'action': sample['action'].astype(np.float32) # T, 2 + } + return data + + def __getitem__(self, idx: int) -> Dict[str, torch.Tensor]: + sample = self.sampler.sample_sequence(idx) + data = self._sample_to_data(sample) + torch_data = dict_apply(data, torch.from_numpy) + return torch_data + + +def test(): + import os + zarr_path = os.path.expanduser('~/dev/diffusion_policy/data/pusht/pusht_cchi_v7_replay.zarr') + dataset = PushTImageDataset(zarr_path, horizon=16) + + # from matplotlib import pyplot as plt + # normalizer = dataset.get_normalizer() + # nactions = normalizer['action'].normalize(dataset.replay_buffer['action']) + # diff = np.diff(nactions, axis=0) + # dists = np.linalg.norm(np.diff(nactions, axis=0), axis=-1) diff --git a/diffusion_policy/dataset/real_pusht_image_dataset.py b/diffusion_policy/dataset/real_pusht_image_dataset.py new file mode 100644 index 0000000..85017fa --- /dev/null +++ b/diffusion_policy/dataset/real_pusht_image_dataset.py @@ -0,0 +1,291 @@ +from typing import Dict, List +import torch +import numpy as np +import zarr +import os +import shutil +from filelock import FileLock +from threadpoolctl import threadpool_limits +from omegaconf import OmegaConf +import cv2 +import json +import hashlib +import copy +from diffusion_policy.common.pytorch_util import dict_apply +from diffusion_policy.dataset.base_dataset import BaseImageDataset +from diffusion_policy.model.common.normalizer import LinearNormalizer, SingleFieldLinearNormalizer +from diffusion_policy.common.replay_buffer import ReplayBuffer +from diffusion_policy.common.sampler import ( + SequenceSampler, get_val_mask, downsample_mask) +from diffusion_policy.real_world.real_data_conversion import real_data_to_replay_buffer +from diffusion_policy.common.normalize_util import ( + get_range_normalizer_from_stat, + get_image_range_normalizer, + get_identity_normalizer_from_stat, + array_to_stats +) + +class RealPushTImageDataset(BaseImageDataset): + def __init__(self, + shape_meta: dict, + dataset_path: str, + horizon=1, + pad_before=0, + pad_after=0, + n_obs_steps=None, + n_latency_steps=0, + use_cache=False, + seed=42, + val_ratio=0.0, + max_train_episodes=None, + delta_action=False, + ): + assert os.path.isdir(dataset_path) + + replay_buffer = None + if use_cache: + # fingerprint shape_meta + shape_meta_json = json.dumps(OmegaConf.to_container(shape_meta), sort_keys=True) + shape_meta_hash = hashlib.md5(shape_meta_json.encode('utf-8')).hexdigest() + cache_zarr_path = os.path.join(dataset_path, shape_meta_hash + '.zarr.zip') + cache_lock_path = cache_zarr_path + '.lock' + print('Acquiring lock on cache.') + with FileLock(cache_lock_path): + if not os.path.exists(cache_zarr_path): + # cache does not exists + try: + print('Cache does not exist. Creating!') + replay_buffer = _get_replay_buffer( + dataset_path=dataset_path, + shape_meta=shape_meta, + store=zarr.MemoryStore() + ) + print('Saving cache to disk.') + with zarr.ZipStore(cache_zarr_path) as zip_store: + replay_buffer.save_to_store( + store=zip_store + ) + except Exception as e: + shutil.rmtree(cache_zarr_path) + raise e + else: + print('Loading cached ReplayBuffer from Disk.') + with zarr.ZipStore(cache_zarr_path, mode='r') as zip_store: + replay_buffer = ReplayBuffer.copy_from_store( + src_store=zip_store, store=zarr.MemoryStore()) + print('Loaded!') + else: + replay_buffer = _get_replay_buffer( + dataset_path=dataset_path, + shape_meta=shape_meta, + store=zarr.MemoryStore() + ) + + if delta_action: + # replace action as relative to previous frame + actions = replay_buffer['action'][:] + # suport positions only at this time + assert actions.shape[1] <= 3 + actions_diff = np.zeros_like(actions) + episode_ends = replay_buffer.episode_ends[:] + for i in range(len(episode_ends)): + start = 0 + if i > 0: + start = episode_ends[i-1] + end = episode_ends[i] + # delta action is the difference between previous desired postion and the current + # it should be scheduled at the previous timestep for the curren timestep + # to ensure consistency with positional mode + actions_diff[start+1:end] = np.diff(actions[start:end], axis=0) + replay_buffer['action'][:] = actions_diff + + rgb_keys = list() + lowdim_keys = list() + obs_shape_meta = shape_meta['obs'] + for key, attr in obs_shape_meta.items(): + type = attr.get('type', 'low_dim') + if type == 'rgb': + rgb_keys.append(key) + elif type == 'low_dim': + lowdim_keys.append(key) + + key_first_k = dict() + if n_obs_steps is not None: + # only take first k obs from images + for key in rgb_keys + lowdim_keys: + key_first_k[key] = n_obs_steps + + val_mask = get_val_mask( + n_episodes=replay_buffer.n_episodes, + val_ratio=val_ratio, + seed=seed) + train_mask = ~val_mask + train_mask = downsample_mask( + mask=train_mask, + max_n=max_train_episodes, + seed=seed) + + sampler = SequenceSampler( + replay_buffer=replay_buffer, + sequence_length=horizon+n_latency_steps, + pad_before=pad_before, + pad_after=pad_after, + episode_mask=train_mask, + key_first_k=key_first_k) + + self.replay_buffer = replay_buffer + self.sampler = sampler + self.shape_meta = shape_meta + self.rgb_keys = rgb_keys + self.lowdim_keys = lowdim_keys + self.n_obs_steps = n_obs_steps + self.val_mask = val_mask + self.horizon = horizon + self.n_latency_steps = n_latency_steps + self.pad_before = pad_before + self.pad_after = pad_after + + def get_validation_dataset(self): + val_set = copy.copy(self) + val_set.sampler = SequenceSampler( + replay_buffer=self.replay_buffer, + sequence_length=self.horizon+self.n_latency_steps, + pad_before=self.pad_before, + pad_after=self.pad_after, + episode_mask=self.val_mask + ) + val_set.val_mask = ~self.val_mask + return val_set + + def get_normalizer(self, **kwargs) -> LinearNormalizer: + normalizer = LinearNormalizer() + + # action + normalizer['action'] = SingleFieldLinearNormalizer.create_fit( + self.replay_buffer['action']) + + # obs + for key in self.lowdim_keys: + normalizer[key] = SingleFieldLinearNormalizer.create_fit( + self.replay_buffer[key]) + + # image + for key in self.rgb_keys: + normalizer[key] = get_image_range_normalizer() + return normalizer + + def get_all_actions(self) -> torch.Tensor: + return torch.from_numpy(self.replay_buffer['action']) + + def __len__(self): + return len(self.sampler) + + def __getitem__(self, idx: int) -> Dict[str, torch.Tensor]: + threadpool_limits(1) + data = self.sampler.sample_sequence(idx) + + # to save RAM, only return first n_obs_steps of OBS + # since the rest will be discarded anyway. + # when self.n_obs_steps is None + # this slice does nothing (takes all) + T_slice = slice(self.n_obs_steps) + + obs_dict = dict() + for key in self.rgb_keys: + # move channel last to channel first + # T,H,W,C + # convert uint8 image to float32 + obs_dict[key] = np.moveaxis(data[key][T_slice],-1,1 + ).astype(np.float32) / 255. + # T,C,H,W + # save ram + del data[key] + for key in self.lowdim_keys: + obs_dict[key] = data[key][T_slice].astype(np.float32) + # save ram + del data[key] + + action = data['action'].astype(np.float32) + # handle latency by dropping first n_latency_steps action + # observations are already taken care of by T_slice + if self.n_latency_steps > 0: + action = action[self.n_latency_steps:] + + torch_data = { + 'obs': dict_apply(obs_dict, torch.from_numpy), + 'action': torch.from_numpy(action) + } + return torch_data + +def zarr_resize_index_last_dim(zarr_arr, idxs): + actions = zarr_arr[:] + actions = actions[...,idxs] + zarr_arr.resize(zarr_arr.shape[:-1] + (len(idxs),)) + zarr_arr[:] = actions + return zarr_arr + +def _get_replay_buffer(dataset_path, shape_meta, store): + # parse shape meta + rgb_keys = list() + lowdim_keys = list() + out_resolutions = dict() + lowdim_shapes = dict() + obs_shape_meta = shape_meta['obs'] + for key, attr in obs_shape_meta.items(): + type = attr.get('type', 'low_dim') + shape = tuple(attr.get('shape')) + if type == 'rgb': + rgb_keys.append(key) + c,h,w = shape + out_resolutions[key] = (w,h) + elif type == 'low_dim': + lowdim_keys.append(key) + lowdim_shapes[key] = tuple(shape) + if 'pose' in key: + assert tuple(shape) in [(2,),(6,)] + + action_shape = tuple(shape_meta['action']['shape']) + assert action_shape in [(2,),(6,)] + + # load data + cv2.setNumThreads(1) + with threadpool_limits(1): + replay_buffer = real_data_to_replay_buffer( + dataset_path=dataset_path, + out_store=store, + out_resolutions=out_resolutions, + lowdim_keys=lowdim_keys + ['action'], + image_keys=rgb_keys + ) + + # transform lowdim dimensions + if action_shape == (2,): + # 2D action space, only controls X and Y + zarr_arr = replay_buffer['action'] + zarr_resize_index_last_dim(zarr_arr, idxs=[0,1]) + + for key, shape in lowdim_shapes.items(): + if 'pose' in key and shape == (2,): + # only take X and Y + zarr_arr = replay_buffer[key] + zarr_resize_index_last_dim(zarr_arr, idxs=[0,1]) + + return replay_buffer + + +def test(): + import hydra + from omegaconf import OmegaConf + OmegaConf.register_new_resolver("eval", eval, replace=True) + + with hydra.initialize('../diffusion_policy/config'): + cfg = hydra.compose('train_robomimic_real_image_workspace') + OmegaConf.resolve(cfg) + dataset = hydra.utils.instantiate(cfg.task.dataset) + + from matplotlib import pyplot as plt + normalizer = dataset.get_normalizer() + nactions = normalizer['action'].normalize(dataset.replay_buffer['action'][:]) + diff = np.diff(nactions, axis=0) + dists = np.linalg.norm(np.diff(nactions, axis=0), axis=-1) + _ = plt.hist(dists, bins=100); plt.title('real action velocity') diff --git a/diffusion_policy/dataset/robomimic_replay_image_dataset.py b/diffusion_policy/dataset/robomimic_replay_image_dataset.py new file mode 100644 index 0000000..2728e9e --- /dev/null +++ b/diffusion_policy/dataset/robomimic_replay_image_dataset.py @@ -0,0 +1,373 @@ +from typing import Dict, List +import torch +import numpy as np +import h5py +from tqdm import tqdm +import zarr +import os +import shutil +import copy +import json +import hashlib +from filelock import FileLock +from threadpoolctl import threadpool_limits +import concurrent.futures +import multiprocessing +from omegaconf import OmegaConf +from diffusion_policy.common.pytorch_util import dict_apply +from diffusion_policy.dataset.base_dataset import BaseImageDataset, LinearNormalizer +from diffusion_policy.model.common.normalizer import LinearNormalizer, SingleFieldLinearNormalizer +from diffusion_policy.model.common.rotation_transformer import RotationTransformer +from diffusion_policy.codecs.imagecodecs_numcodecs import register_codecs, Jpeg2k +from diffusion_policy.common.replay_buffer import ReplayBuffer +from diffusion_policy.common.sampler import SequenceSampler, get_val_mask +from diffusion_policy.common.normalize_util import ( + robomimic_abs_action_only_normalizer_from_stat, + robomimic_abs_action_only_dual_arm_normalizer_from_stat, + get_range_normalizer_from_stat, + get_image_range_normalizer, + get_identity_normalizer_from_stat, + array_to_stats +) +register_codecs() + +class RobomimicReplayImageDataset(BaseImageDataset): + def __init__(self, + shape_meta: dict, + dataset_path: str, + horizon=1, + pad_before=0, + pad_after=0, + n_obs_steps=None, + abs_action=False, + rotation_rep='rotation_6d', # ignored when abs_action=False + use_legacy_normalizer=False, + use_cache=False, + seed=42, + val_ratio=0.0 + ): + rotation_transformer = RotationTransformer( + from_rep='axis_angle', to_rep=rotation_rep) + + replay_buffer = None + if use_cache: + cache_zarr_path = dataset_path + '.zarr.zip' + cache_lock_path = cache_zarr_path + '.lock' + print('Acquiring lock on cache.') + with FileLock(cache_lock_path): + if not os.path.exists(cache_zarr_path): + # cache does not exists + try: + print('Cache does not exist. Creating!') + # store = zarr.DirectoryStore(cache_zarr_path) + replay_buffer = _convert_robomimic_to_replay( + store=zarr.MemoryStore(), + shape_meta=shape_meta, + dataset_path=dataset_path, + abs_action=abs_action, + rotation_transformer=rotation_transformer) + print('Saving cache to disk.') + with zarr.ZipStore(cache_zarr_path) as zip_store: + replay_buffer.save_to_store( + store=zip_store + ) + except Exception as e: + shutil.rmtree(cache_zarr_path) + raise e + else: + print('Loading cached ReplayBuffer from Disk.') + with zarr.ZipStore(cache_zarr_path, mode='r') as zip_store: + replay_buffer = ReplayBuffer.copy_from_store( + src_store=zip_store, store=zarr.MemoryStore()) + print('Loaded!') + else: + replay_buffer = _convert_robomimic_to_replay( + store=zarr.MemoryStore(), + shape_meta=shape_meta, + dataset_path=dataset_path, + abs_action=abs_action, + rotation_transformer=rotation_transformer) + + rgb_keys = list() + lowdim_keys = list() + obs_shape_meta = shape_meta['obs'] + for key, attr in obs_shape_meta.items(): + type = attr.get('type', 'low_dim') + if type == 'rgb': + rgb_keys.append(key) + elif type == 'low_dim': + lowdim_keys.append(key) + + # for key in rgb_keys: + # replay_buffer[key].compressor.numthreads=1 + + key_first_k = dict() + if n_obs_steps is not None: + # only take first k obs from images + for key in rgb_keys + lowdim_keys: + key_first_k[key] = n_obs_steps + + val_mask = get_val_mask( + n_episodes=replay_buffer.n_episodes, + val_ratio=val_ratio, + seed=seed) + train_mask = ~val_mask + sampler = SequenceSampler( + replay_buffer=replay_buffer, + sequence_length=horizon, + pad_before=pad_before, + pad_after=pad_after, + episode_mask=train_mask, + key_first_k=key_first_k) + + self.replay_buffer = replay_buffer + self.sampler = sampler + self.shape_meta = shape_meta + self.rgb_keys = rgb_keys + self.lowdim_keys = lowdim_keys + self.abs_action = abs_action + self.n_obs_steps = n_obs_steps + self.train_mask = train_mask + self.horizon = horizon + self.pad_before = pad_before + self.pad_after = pad_after + self.use_legacy_normalizer = use_legacy_normalizer + + def get_validation_dataset(self): + val_set = copy.copy(self) + val_set.sampler = SequenceSampler( + replay_buffer=self.replay_buffer, + sequence_length=self.horizon, + pad_before=self.pad_before, + pad_after=self.pad_after, + episode_mask=~self.train_mask + ) + val_set.train_mask = ~self.train_mask + return val_set + + def get_normalizer(self, **kwargs) -> LinearNormalizer: + normalizer = LinearNormalizer() + + # action + stat = array_to_stats(self.replay_buffer['action']) + if self.abs_action: + if stat['mean'].shape[-1] > 10: + # dual arm + this_normalizer = robomimic_abs_action_only_dual_arm_normalizer_from_stat(stat) + else: + this_normalizer = robomimic_abs_action_only_normalizer_from_stat(stat) + + if self.use_legacy_normalizer: + this_normalizer = normalizer_from_stat(stat) + else: + # already normalized + this_normalizer = get_identity_normalizer_from_stat(stat) + normalizer['action'] = this_normalizer + + # obs + for key in self.lowdim_keys: + stat = array_to_stats(self.replay_buffer[key]) + + if key.endswith('pos'): + this_normalizer = get_range_normalizer_from_stat(stat) + elif key.endswith('quat'): + # quaternion is in [-1,1] already + this_normalizer = get_identity_normalizer_from_stat(stat) + elif key.endswith('qpos'): + this_normalizer = get_range_normalizer_from_stat(stat) + else: + raise RuntimeError('unsupported') + normalizer[key] = this_normalizer + + # image + for key in self.rgb_keys: + normalizer[key] = get_image_range_normalizer() + return normalizer + + def get_all_actions(self) -> torch.Tensor: + return torch.from_numpy(self.replay_buffer['action']) + + def __len__(self): + return len(self.sampler) + + def __getitem__(self, idx: int) -> Dict[str, torch.Tensor]: + threadpool_limits(1) + data = self.sampler.sample_sequence(idx) + + # to save RAM, only return first n_obs_steps of OBS + # since the rest will be discarded anyway. + # when self.n_obs_steps is None + # this slice does nothing (takes all) + T_slice = slice(self.n_obs_steps) + + obs_dict = dict() + for key in self.rgb_keys: + # move channel last to channel first + # T,H,W,C + # convert uint8 image to float32 + obs_dict[key] = np.moveaxis(data[key][T_slice],-1,1 + ).astype(np.float32) / 255. + # T,C,H,W + del data[key] + for key in self.lowdim_keys: + obs_dict[key] = data[key][T_slice].astype(np.float32) + del data[key] + + torch_data = { + 'obs': dict_apply(obs_dict, torch.from_numpy), + 'action': torch.from_numpy(data['action'].astype(np.float32)) + } + return torch_data + + +def _convert_actions(raw_actions, abs_action, rotation_transformer): + actions = raw_actions + if abs_action: + is_dual_arm = False + if raw_actions.shape[-1] == 14: + # dual arm + raw_actions = raw_actions.reshape(-1,2,7) + is_dual_arm = True + + pos = raw_actions[...,:3] + rot = raw_actions[...,3:6] + gripper = raw_actions[...,6:] + rot = rotation_transformer.forward(rot) + raw_actions = np.concatenate([ + pos, rot, gripper + ], axis=-1).astype(np.float32) + + if is_dual_arm: + raw_actions = raw_actions.reshape(-1,20) + actions = raw_actions + return actions + + +def _convert_robomimic_to_replay(store, shape_meta, dataset_path, abs_action, rotation_transformer, + n_workers=None, max_inflight_tasks=None): + if n_workers is None: + n_workers = multiprocessing.cpu_count() + if max_inflight_tasks is None: + max_inflight_tasks = n_workers * 5 + + # parse shape_meta + rgb_keys = list() + lowdim_keys = list() + # construct compressors and chunks + obs_shape_meta = shape_meta['obs'] + for key, attr in obs_shape_meta.items(): + shape = attr['shape'] + type = attr.get('type', 'low_dim') + if type == 'rgb': + rgb_keys.append(key) + elif type == 'low_dim': + lowdim_keys.append(key) + + root = zarr.group(store) + data_group = root.require_group('data', overwrite=True) + meta_group = root.require_group('meta', overwrite=True) + + with h5py.File(dataset_path) as file: + # count total steps + demos = file['data'] + episode_ends = list() + prev_end = 0 + for i in range(len(demos)): + demo = demos[f'demo_{i}'] + episode_length = demo['actions'].shape[0] + episode_end = prev_end + episode_length + prev_end = episode_end + episode_ends.append(episode_end) + n_steps = episode_ends[-1] + episode_starts = [0] + episode_ends[:-1] + _ = meta_group.array('episode_ends', episode_ends, + dtype=np.int64, compressor=None, overwrite=True) + + # save lowdim data + for key in tqdm(lowdim_keys + ['action'], desc="Loading lowdim data"): + data_key = 'obs/' + key + if key == 'action': + data_key = 'actions' + this_data = list() + for i in range(len(demos)): + demo = demos[f'demo_{i}'] + this_data.append(demo[data_key][:].astype(np.float32)) + this_data = np.concatenate(this_data, axis=0) + if key == 'action': + this_data = _convert_actions( + raw_actions=this_data, + abs_action=abs_action, + rotation_transformer=rotation_transformer + ) + assert this_data.shape == (n_steps,) + tuple(shape_meta['action']['shape']) + else: + assert this_data.shape == (n_steps,) + tuple(shape_meta['obs'][key]['shape']) + _ = data_group.array( + name=key, + data=this_data, + shape=this_data.shape, + chunks=this_data.shape, + compressor=None, + dtype=this_data.dtype + ) + + def img_copy(zarr_arr, zarr_idx, hdf5_arr, hdf5_idx): + try: + zarr_arr[zarr_idx] = hdf5_arr[hdf5_idx] + # make sure we can successfully decode + _ = zarr_arr[zarr_idx] + return True + except Exception as e: + return False + + with tqdm(total=n_steps*len(rgb_keys), desc="Loading image data", mininterval=1.0) as pbar: + # one chunk per thread, therefore no synchronization needed + with concurrent.futures.ThreadPoolExecutor(max_workers=n_workers) as executor: + futures = set() + for key in rgb_keys: + data_key = 'obs/' + key + shape = tuple(shape_meta['obs'][key]['shape']) + c,h,w = shape + this_compressor = Jpeg2k(level=50) + img_arr = data_group.require_dataset( + name=key, + shape=(n_steps,h,w,c), + chunks=(1,h,w,c), + compressor=this_compressor, + dtype=np.uint8 + ) + for episode_idx in range(len(demos)): + demo = demos[f'demo_{episode_idx}'] + hdf5_arr = demo['obs'][key] + for hdf5_idx in range(hdf5_arr.shape[0]): + if len(futures) >= max_inflight_tasks: + # limit number of inflight tasks + completed, futures = concurrent.futures.wait(futures, + return_when=concurrent.futures.FIRST_COMPLETED) + for f in completed: + if not f.result(): + raise RuntimeError('Failed to encode image!') + pbar.update(len(completed)) + + zarr_idx = episode_starts[episode_idx] + hdf5_idx + futures.add( + executor.submit(img_copy, + img_arr, zarr_idx, hdf5_arr, hdf5_idx)) + completed, futures = concurrent.futures.wait(futures) + for f in completed: + if not f.result(): + raise RuntimeError('Failed to encode image!') + pbar.update(len(completed)) + + replay_buffer = ReplayBuffer(root) + return replay_buffer + +def normalizer_from_stat(stat): + max_abs = np.maximum(stat['max'].max(), np.abs(stat['min']).max()) + scale = np.full_like(stat['max'], fill_value=1/max_abs) + offset = np.zeros_like(stat['max']) + return SingleFieldLinearNormalizer.create_manual( + scale=scale, + offset=offset, + input_stats_dict=stat + ) diff --git a/diffusion_policy/dataset/robomimic_replay_lowdim_dataset.py b/diffusion_policy/dataset/robomimic_replay_lowdim_dataset.py new file mode 100644 index 0000000..c642da9 --- /dev/null +++ b/diffusion_policy/dataset/robomimic_replay_lowdim_dataset.py @@ -0,0 +1,168 @@ +from typing import Dict, List +import torch +import numpy as np +import h5py +from tqdm import tqdm +import copy +from diffusion_policy.common.pytorch_util import dict_apply +from diffusion_policy.dataset.base_dataset import BaseLowdimDataset, LinearNormalizer +from diffusion_policy.model.common.normalizer import LinearNormalizer, SingleFieldLinearNormalizer +from diffusion_policy.model.common.rotation_transformer import RotationTransformer +from diffusion_policy.common.replay_buffer import ReplayBuffer +from diffusion_policy.common.sampler import ( + SequenceSampler, get_val_mask, downsample_mask) +from diffusion_policy.common.normalize_util import ( + robomimic_abs_action_only_normalizer_from_stat, + robomimic_abs_action_only_dual_arm_normalizer_from_stat, + get_identity_normalizer_from_stat, + array_to_stats +) + +class RobomimicReplayLowdimDataset(BaseLowdimDataset): + def __init__(self, + dataset_path: str, + horizon=1, + pad_before=0, + pad_after=0, + obs_keys: List[str]=[ + 'object', + 'robot0_eef_pos', + 'robot0_eef_quat', + 'robot0_gripper_qpos'], + abs_action=False, + rotation_rep='rotation_6d', + use_legacy_normalizer=False, + seed=42, + val_ratio=0.0, + max_train_episodes=None + ): + obs_keys = list(obs_keys) + rotation_transformer = RotationTransformer( + from_rep='axis_angle', to_rep=rotation_rep) + + replay_buffer = ReplayBuffer.create_empty_numpy() + with h5py.File(dataset_path) as file: + demos = file['data'] + for i in tqdm(range(len(demos)), desc="Loading hdf5 to ReplayBuffer"): + demo = demos[f'demo_{i}'] + episode = _data_to_obs( + raw_obs=demo['obs'], + raw_actions=demo['actions'][:].astype(np.float32), + obs_keys=obs_keys, + abs_action=abs_action, + rotation_transformer=rotation_transformer) + replay_buffer.add_episode(episode) + + val_mask = get_val_mask( + n_episodes=replay_buffer.n_episodes, + val_ratio=val_ratio, + seed=seed) + train_mask = ~val_mask + train_mask = downsample_mask( + mask=train_mask, + max_n=max_train_episodes, + seed=seed) + + sampler = SequenceSampler( + replay_buffer=replay_buffer, + sequence_length=horizon, + pad_before=pad_before, + pad_after=pad_after, + episode_mask=train_mask) + + self.replay_buffer = replay_buffer + self.sampler = sampler + self.abs_action = abs_action + self.train_mask = train_mask + self.horizon = horizon + self.pad_before = pad_before + self.pad_after = pad_after + self.use_legacy_normalizer = use_legacy_normalizer + + def get_validation_dataset(self): + val_set = copy.copy(self) + val_set.sampler = SequenceSampler( + replay_buffer=self.replay_buffer, + sequence_length=self.horizon, + pad_before=self.pad_before, + pad_after=self.pad_after, + episode_mask=~self.train_mask + ) + val_set.train_mask = ~self.train_mask + return val_set + + def get_normalizer(self, **kwargs) -> LinearNormalizer: + normalizer = LinearNormalizer() + + # action + stat = array_to_stats(self.replay_buffer['action']) + if self.abs_action: + if stat['mean'].shape[-1] > 10: + # dual arm + this_normalizer = robomimic_abs_action_only_dual_arm_normalizer_from_stat(stat) + else: + this_normalizer = robomimic_abs_action_only_normalizer_from_stat(stat) + + if self.use_legacy_normalizer: + this_normalizer = normalizer_from_stat(stat) + else: + # already normalized + this_normalizer = get_identity_normalizer_from_stat(stat) + normalizer['action'] = this_normalizer + + # aggregate obs stats + obs_stat = array_to_stats(self.replay_buffer['obs']) + + + normalizer['obs'] = normalizer_from_stat(obs_stat) + return normalizer + + def get_all_actions(self) -> torch.Tensor: + return torch.from_numpy(self.replay_buffer['action']) + + def __len__(self): + return len(self.sampler) + + def __getitem__(self, idx: int) -> Dict[str, torch.Tensor]: + data = self.sampler.sample_sequence(idx) + torch_data = dict_apply(data, torch.from_numpy) + return torch_data + +def normalizer_from_stat(stat): + max_abs = np.maximum(stat['max'].max(), np.abs(stat['min']).max()) + scale = np.full_like(stat['max'], fill_value=1/max_abs) + offset = np.zeros_like(stat['max']) + return SingleFieldLinearNormalizer.create_manual( + scale=scale, + offset=offset, + input_stats_dict=stat + ) + +def _data_to_obs(raw_obs, raw_actions, obs_keys, abs_action, rotation_transformer): + obs = np.concatenate([ + raw_obs[key] for key in obs_keys + ], axis=-1).astype(np.float32) + + if abs_action: + is_dual_arm = False + if raw_actions.shape[-1] == 14: + # dual arm + raw_actions = raw_actions.reshape(-1,2,7) + is_dual_arm = True + + pos = raw_actions[...,:3] + rot = raw_actions[...,3:6] + gripper = raw_actions[...,6:] + rot = rotation_transformer.forward(rot) + raw_actions = np.concatenate([ + pos, rot, gripper + ], axis=-1).astype(np.float32) + + if is_dual_arm: + raw_actions = raw_actions.reshape(-1,20) + + data = { + 'obs': obs, + 'action': raw_actions + } + return data diff --git a/diffusion_policy/env/block_pushing/assets/block.urdf b/diffusion_policy/env/block_pushing/assets/block.urdf new file mode 100644 index 0000000..10b44dd --- /dev/null +++ b/diffusion_policy/env/block_pushing/assets/block.urdf @@ -0,0 +1,31 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + diff --git a/diffusion_policy/env/block_pushing/assets/block2.urdf b/diffusion_policy/env/block_pushing/assets/block2.urdf new file mode 100644 index 0000000..36c081b --- /dev/null +++ b/diffusion_policy/env/block_pushing/assets/block2.urdf @@ -0,0 +1,31 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + diff --git a/diffusion_policy/env/block_pushing/assets/blocks/blue_cube.urdf b/diffusion_policy/env/block_pushing/assets/blocks/blue_cube.urdf new file mode 100644 index 0000000..089e804 --- /dev/null +++ b/diffusion_policy/env/block_pushing/assets/blocks/blue_cube.urdf @@ -0,0 +1,30 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + diff --git a/diffusion_policy/env/block_pushing/assets/blocks/cube.obj b/diffusion_policy/env/block_pushing/assets/blocks/cube.obj new file mode 100644 index 0000000..bc1d007 --- /dev/null +++ b/diffusion_policy/env/block_pushing/assets/blocks/cube.obj @@ -0,0 +1,446 @@ +# Blender v2.92.0 OBJ File: '' +# www.blender.org +mtllib cube.mtl +o square_blue_block_Cube.001 +v 0.000000 0.000000 -0.000000 +v 0.000000 0.038100 -0.000000 +v -0.014865 0.000000 0.018106 +v -0.016277 0.002032 0.019826 +v -0.015863 0.000595 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b/diffusion_policy/env/block_pushing/assets/blocks/yellow_pentagon.urdf @@ -0,0 +1,30 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + diff --git a/diffusion_policy/env/block_pushing/assets/insert.urdf b/diffusion_policy/env/block_pushing/assets/insert.urdf new file mode 100644 index 0000000..d912ff1 --- /dev/null +++ b/diffusion_policy/env/block_pushing/assets/insert.urdf @@ -0,0 +1,66 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + diff --git a/diffusion_policy/env/block_pushing/assets/plane.obj b/diffusion_policy/env/block_pushing/assets/plane.obj new file mode 100644 index 0000000..6062095 --- /dev/null +++ b/diffusion_policy/env/block_pushing/assets/plane.obj @@ -0,0 +1,18 @@ +# Blender v2.66 (sub 1) OBJ File: '' +# www.blender.org +mtllib plane.mtl +o Plane +v 15.000000 -15.000000 0.000000 +v 15.000000 15.000000 0.000000 +v -15.000000 15.000000 0.000000 +v -15.000000 -15.000000 0.000000 + +vt 15.000000 0.000000 +vt 15.000000 15.000000 +vt 0.000000 15.000000 +vt 0.000000 0.000000 + +usemtl Material +s off +f 1/1 2/2 3/3 +f 1/1 3/3 4/4 diff --git a/diffusion_policy/env/block_pushing/assets/suction/base.obj b/diffusion_policy/env/block_pushing/assets/suction/base.obj new file mode 100644 index 0000000..bdf1bb3 --- /dev/null +++ b/diffusion_policy/env/block_pushing/assets/suction/base.obj @@ -0,0 +1,396 @@ +# Object Export From Tinkercad Server 2015 + +mtllib obj.mtl + +o obj_0 +v 7.413 37.27 25 +v 7.413 37.27 0 +v 33.513 17.913 25 +v 35.107 14.542 25 +v 35.107 14.542 0 +v 33.513 17.913 0 +v -31.596 21.112 25 +v -31.596 21.112 0 +v -33.513 17.913 0 +v -33.513 17.913 25 +v 3.725 37.817 0 +v 3.725 37.817 25 +v -29.374 24.107 25 +v -29.374 24.107 0 +v 0 38 0 +v 11.031 36.364 25 +v 11.031 36.364 0 +v 14.542 35.107 25 +v 14.542 35.107 0 +v -37.27 -7.413 25 +v 17.913 33.513 25 +v -37.27 -7.413 0 +v -11.031 -36.364 25 +v 17.913 33.513 0 +v -36.364 -11.031 0 +v -14.542 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15 +f 47 46 15 +f 46 56 15 +f 56 62 15 +f 62 77 15 +f 77 90 15 +f 90 102 15 +f 102 108 15 +f 108 116 15 +f 116 38 15 +f 38 37 15 +f 37 42 15 +f 42 53 15 +f 53 65 15 +f 69 15 65 +f 81 15 69 +f 45 73 15 +f 101 111 15 +f 111 122 15 +f 122 32 15 +f 32 29 15 +f 29 35 15 +f 35 51 15 +f 51 85 15 +f 85 97 15 +f 97 105 15 +f 105 112 15 +f 112 121 15 +f 121 126 15 +f 126 25 15 +f 22 15 25 +f 45 15 22 +f 49 98 80 +f 49 80 52 +f 49 52 4 +f 49 4 3 +f 49 3 107 +f 49 107 103 +f 49 103 94 +f 49 94 82 +f 49 82 58 +f 49 58 21 +f 49 21 18 +f 49 18 16 +f 49 16 1 +f 49 1 12 +f 49 12 40 +f 40 39 93 +f 40 93 91 +f 40 91 84 +f 40 84 70 +f 40 70 66 +f 40 66 57 +f 40 57 27 +f 40 27 13 +f 40 13 7 +f 40 7 10 +f 40 10 127 +f 40 127 123 +f 40 123 118 +f 40 118 113 +f 63 40 113 +f 68 40 79 +f 64 40 68 +f 50 40 64 +f 41 40 50 +f 36 40 41 +f 33 40 36 +f 115 40 33 +f 106 40 115 +f 100 40 106 +f 88 40 100 +f 75 40 88 +f 60 40 75 +f 55 40 60 +f 43 40 55 +f 49 40 43 +f 99 79 40 +f 20 40 44 +f 28 40 20 +f 125 40 28 +f 120 40 125 +f 110 40 120 +f 104 40 110 +f 96 40 104 +f 83 40 96 +f 48 40 83 +f 34 40 48 +f 26 40 34 +f 23 40 26 +f 119 40 23 +f 109 40 119 +f 99 40 109 +f 63 44 40 +f 12 11 15 +f 12 15 40 +# 252 faces + + #end of obj_0 + diff --git a/diffusion_policy/env/block_pushing/assets/suction/cylinder.urdf b/diffusion_policy/env/block_pushing/assets/suction/cylinder.urdf new file mode 100644 index 0000000..d5779f3 --- /dev/null +++ b/diffusion_policy/env/block_pushing/assets/suction/cylinder.urdf @@ -0,0 +1,98 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + diff --git a/diffusion_policy/env/block_pushing/assets/suction/cylinder_real.urdf b/diffusion_policy/env/block_pushing/assets/suction/cylinder_real.urdf new file mode 100644 index 0000000..b4bb8b7 --- /dev/null +++ b/diffusion_policy/env/block_pushing/assets/suction/cylinder_real.urdf @@ -0,0 +1,98 @@ + + + + + + + + + + + + + + + + + + + + + + + + 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30 +v 0.492 4.977 30 +v 5.001 0.001 30 +v 4.977 0.492 30 +v 4.905 0.977 30 +v 4.786 1.453 30 +v 4.621 1.915 30 +v 4.411 2.358 30 +v 4.159 2.779 30 +v 3.866 3.173 30 +v 3.537 3.537 30 +v 3.173 3.866 30 +v -0.489 4.977 30 +v -0.974 4.905 30 +v -1.45 4.786 30 +v -1.912 4.621 30 +v -2.356 4.411 30 +v -2.776 4.159 30 +v -3.171 3.866 30 +v -3.534 3.537 30 +v -3.864 3.173 30 +v -4.156 2.779 30 +v -4.408 2.358 30 +v -4.618 1.915 30 +v -4.783 1.453 30 +v -4.903 0.977 30 +v -4.974 0.492 30 +v -4.999 0.001 30 +v -4.974 -0.489 30 +v -4.903 -0.974 30 +v -4.783 -1.45 30 +v -4.618 -1.912 30 +v -4.408 -2.356 30 +v -4.156 -2.776 30 +v -3.864 -3.171 30 +v -3.534 -3.534 30 +v -3.171 -3.864 30 +v -0.489 -4.974 30 +v -2.776 -4.156 30 +v -2.356 -4.408 30 +v -1.912 -4.618 30 +v -1.45 -4.783 30 +v -0.974 -4.902 30 +# 128 vertices + +g group_0_16089887 + +usemtl color_16089887 +s 0 + +f 2 3 1 +f 1 3 4 +f 1 4 5 +f 1 5 6 +f 1 6 7 +f 1 7 8 +f 9 10 8 +f 8 10 11 +f 8 11 12 +f 8 12 13 +f 8 13 14 +f 8 14 15 +f 8 15 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1379 1349 1350 +f 1350 1381 1379 +f 1387 1366 1367 +f 1380 1368 604 +f 606 604 1368 +f 1380 1389 1388 +f 1367 1380 1388 +f 192 748 177 +f 177 1381 184 +f 1379 1381 1382 +f 1383 1382 1381 +f 1383 1381 177 +f 1384 1352 1353 +f 1384 196 184 +f 1381 1352 1384 +f 184 1381 1384 +f 196 1384 1385 +f 1384 1353 1385 +f 1385 1354 1370 +f 1385 1353 1354 +f 1372 207 1370 +f 151 207 1372 +f 151 1373 156 +f 1373 151 1372 +f 142 1377 146 +f 1378 218 146 +f 1378 1386 218 +f 1378 1365 1386 +f 1386 1387 221 +f 1365 1366 1386 +f 1386 1366 1387 +f 1387 1367 1388 +f 223 1387 1388 +f 1388 231 223 +f 1390 1388 1389 +f 231 1388 47 +f 1390 47 1388 +f 1387 223 221 +# 2776 faces + + #end of obj_0 + diff --git a/diffusion_policy/env/block_pushing/assets/workspace.urdf b/diffusion_policy/env/block_pushing/assets/workspace.urdf new file mode 100644 index 0000000..0c888dc --- /dev/null +++ b/diffusion_policy/env/block_pushing/assets/workspace.urdf @@ -0,0 +1,30 @@ + + + + + + + + + + + + + + + + + + + + + + + + + diff --git a/diffusion_policy/env/block_pushing/assets/workspace_real.urdf b/diffusion_policy/env/block_pushing/assets/workspace_real.urdf new file mode 100644 index 0000000..2cc2d1d --- /dev/null +++ b/diffusion_policy/env/block_pushing/assets/workspace_real.urdf @@ -0,0 +1,29 @@ + + + + + + + + + + + + + + + + + + + + + + + + diff --git a/diffusion_policy/env/block_pushing/assets/zone.obj b/diffusion_policy/env/block_pushing/assets/zone.obj new file mode 100644 index 0000000..b985550 --- /dev/null +++ b/diffusion_policy/env/block_pushing/assets/zone.obj @@ -0,0 +1,64 @@ +# Object Export From Tinkercad Server 2015 + +mtllib obj.mtl + +o obj_0 +v 10 -10 20 +v 10 -10 0 +v 10 10 0 +v 10 10 20 +v 9.002 9.003 20 +v 9.002 -9.002 20 +v -10 10 0 +v -10 10 20 +v -9.003 9.003 20 +v -9.003 9.003 0 +v 9.002 9.003 0 +v 9.002 -9.002 0 +v -9.003 -9.002 0 +v -9.003 -9.002 20 +v -10 -10 0 +v -10 -10 20 +# 16 vertices + +g group_0_15277357 + +usemtl color_15277357 +s 0 + +f 1 2 3 +f 1 3 4 +f 4 5 6 +f 4 6 1 +f 9 10 11 +f 9 11 5 +f 6 12 13 +f 6 13 14 +f 10 9 14 +f 10 14 13 +f 7 10 13 +f 7 13 15 +f 4 8 5 +f 9 5 8 +f 8 7 15 +f 8 15 16 +f 10 7 11 +f 3 11 7 +f 11 3 12 +f 2 12 3 +f 14 16 6 +f 1 6 16 +f 16 15 2 +f 16 2 1 +f 9 8 14 +f 16 14 8 +f 7 8 3 +f 4 3 8 +f 2 15 12 +f 13 12 15 +f 12 6 5 +f 12 5 11 +# 32 faces + + #end of obj_0 + diff --git a/diffusion_policy/env/block_pushing/assets/zone.urdf b/diffusion_policy/env/block_pushing/assets/zone.urdf new file mode 100644 index 0000000..7cc2395 --- /dev/null +++ b/diffusion_policy/env/block_pushing/assets/zone.urdf @@ -0,0 +1,23 @@ + + + + + + + + + + + + + + + + + + + + + + + diff --git a/diffusion_policy/env/block_pushing/assets/zone2.urdf b/diffusion_policy/env/block_pushing/assets/zone2.urdf new file mode 100644 index 0000000..672edaf --- /dev/null +++ b/diffusion_policy/env/block_pushing/assets/zone2.urdf @@ -0,0 +1,23 @@ + + + + + + + + + + + + + + + + + + + + + + + diff --git a/diffusion_policy/env/block_pushing/block_pushing.py b/diffusion_policy/env/block_pushing/block_pushing.py new file mode 100644 index 0000000..8cc52ba --- /dev/null +++ b/diffusion_policy/env/block_pushing/block_pushing.py @@ -0,0 +1,1092 @@ +# coding=utf-8 +# Copyright 2022 The Reach ML Authors. +# +# 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. + +"""Simple block environments for the XArm.""" + +import collections +import enum +import math +import time +from typing import Dict, List, Optional, Tuple, Union + +import gym +from gym import spaces +from gym.envs import registration +from diffusion_policy.env.block_pushing.utils import utils_pybullet +from diffusion_policy.env.block_pushing.utils import xarm_sim_robot +from diffusion_policy.env.block_pushing.utils.pose3d import Pose3d +from diffusion_policy.env.block_pushing.utils.utils_pybullet import ObjState +from diffusion_policy.env.block_pushing.utils.utils_pybullet import XarmState +import numpy as np +from scipy.spatial import transform +import pybullet +import pybullet_utils.bullet_client as bullet_client + +import matplotlib.pyplot as plt + +BLOCK_URDF_PATH = "third_party/py/envs/assets/block.urdf" +PLANE_URDF_PATH = "third_party/bullet/examples/pybullet/gym/pybullet_data/" "plane.urdf" +WORKSPACE_URDF_PATH = "third_party/py/envs/assets/workspace.urdf" +ZONE_URDF_PATH = "third_party/py/envs/assets/zone.urdf" +INSERT_URDF_PATH = "third_party/py/envs/assets/insert.urdf" + +EFFECTOR_HEIGHT = 0.06 +EFFECTOR_DOWN_ROTATION = transform.Rotation.from_rotvec([0, math.pi, 0]) + +WORKSPACE_BOUNDS = np.array(((0.15, -0.5), (0.7, 0.5))) + +# Min/max bounds calculated from oracle data using: +# ibc/environments/board2d_dataset_statistics.ipynb +# to calculate [mean - 3 * std, mean + 3 * std] using the oracle data. +# pylint: disable=line-too-long +ACTION_MIN = np.array([-0.02547718, -0.02090043], np.float32) +ACTION_MAX = np.array([0.02869084, 0.04272365], np.float32) +EFFECTOR_TARGET_TRANSLATION_MIN = np.array( + [0.1774151772260666, -0.6287994794547558], np.float32 +) +EFFECTOR_TARGET_TRANSLATION_MAX = np.array( + [0.5654461532831192, 0.5441607423126698], np.float32 +) +EFFECTOR_TARGET_TO_BLOCK_TRANSLATION_MIN = np.array( + [-0.07369826920330524, -0.11395704373717308], np.float32 +) +EFFECTOR_TARGET_TO_BLOCK_TRANSLATION_MAX = np.array( + [0.10131562314927578, 0.19391131028532982], np.float32 +) +EFFECTOR_TARGET_TO_TARGET_TRANSLATION_MIN = np.array( + [-0.17813862301409245, -0.3309651017189026], np.float32 +) +EFFECTOR_TARGET_TO_TARGET_TRANSLATION_MAX = np.array( + [0.23726161383092403, 0.8404090404510498], np.float32 +) +BLOCK_ORIENTATION_COS_SIN_MIN = np.array( + [-2.0649861991405487, -0.6154364347457886], np.float32 +) +BLOCK_ORIENTATION_COS_SIN_MAX = np.array( + [1.6590178310871124, 1.8811014890670776], np.float32 +) +TARGET_ORIENTATION_COS_SIN_MIN = np.array( + [-1.0761439241468906, -0.8846937336493284], np.float32 +) +TARGET_ORIENTATION_COS_SIN_MAX = np.array( + [-0.8344330154359341, 0.8786859593819827], np.float32 +) + +# Hardcoded Pose joints to make sure we don't have surprises from using the +# IK solver on reset. The joint poses correspond to the Pose with: +# rotation = rotation3.Rotation3.from_axis_angle([0, 1, 0], math.pi) +# translation = np.array([0.3, -0.4, 0.07]) +INITIAL_JOINT_POSITIONS = np.array( + [ + -0.9254632489674508, + 0.6990770671568564, + -1.106629064060494, + 0.0006653351931553931, + 0.3987969742311386, + -4.063402065624296, + ] +) + +DEFAULT_CAMERA_POSE = (1.0, 0, 0.75) +DEFAULT_CAMERA_ORIENTATION = (np.pi / 4, np.pi, -np.pi / 2) +IMAGE_WIDTH = 320 +IMAGE_HEIGHT = 240 +CAMERA_INTRINSICS = ( + 0.803 * IMAGE_WIDTH, # fx + 0, + IMAGE_WIDTH / 2.0, # cx + 0, + 0.803 * IMAGE_WIDTH, # fy + IMAGE_HEIGHT / 2.0, # cy + 0, + 0, + 1, +) + +# "Realistic" visuals. +X_MIN_REAL = 0.15 +X_MAX_REAL = 0.6 +Y_MIN_REAL = -0.3048 +Y_MAX_REAL = 0.3048 +WORKSPACE_BOUNDS_REAL = np.array(((X_MIN_REAL, Y_MIN_REAL), (X_MAX_REAL, Y_MAX_REAL))) +WORKSPACE_URDF_PATH_REAL = "third_party/py/ibc/environments/assets/workspace_real.urdf" +CAMERA_POSE_REAL = (0.75, 0, 0.5) +CAMERA_ORIENTATION_REAL = (np.pi / 5, np.pi, -np.pi / 2) + +IMAGE_WIDTH_REAL = 320 +IMAGE_HEIGHT_REAL = 180 +CAMERA_INTRINSICS_REAL = ( + 0.803 * IMAGE_WIDTH_REAL, # fx + 0, + IMAGE_WIDTH_REAL / 2.0, # cx + 0, + 0.803 * IMAGE_WIDTH_REAL, # fy + IMAGE_HEIGHT_REAL / 2.0, # cy + 0, + 0, + 1, +) +# pylint: enable=line-too-long + + +def build_env_name(task, shared_memory, use_image_obs, use_normalized_env=False): + """Construct the env name from parameters.""" + if isinstance(task, str): + task = BlockTaskVariant[task] + env_name = "Block" + task.value + + if use_image_obs: + env_name = env_name + "Rgb" + if use_normalized_env: + env_name = env_name + "Normalized" + if shared_memory: + env_name = "Shared" + env_name + + env_name = env_name + "-v0" + + return env_name + + +class BlockTaskVariant(enum.Enum): + REACH = "Reach" + REACH_NORMALIZED = "ReachNormalized" + PUSH = "Push" + PUSH_NORMALIZED = "PushNormalized" + INSERT = "Insert" + + +def sleep_spin(sleep_time_sec): + """Spin wait sleep. Avoids time.sleep accuracy issues on Windows.""" + if sleep_time_sec <= 0: + return + t0 = time.perf_counter() + while time.perf_counter() - t0 < sleep_time_sec: + pass + + +class BlockPush(gym.Env): + """Simple XArm environment for block pushing.""" + + def __init__( + self, + control_frequency=10.0, + task=BlockTaskVariant.PUSH, + image_size=None, + shared_memory=False, + seed=None, + goal_dist_tolerance=0.01, + effector_height=None, + visuals_mode="default", + abs_action=False + ): + """Creates an env instance. + + Args: + control_frequency: Control frequency for the arm. Each env step will + advance the simulation by 1/control_frequency seconds. + task: enum for which task, see BlockTaskVariant enum. + image_size: Optional image size (height, width). If None, no image + observations will be used. + shared_memory: If True `pybullet.SHARED_MEMORY` is used to connect to + pybullet. Useful to debug. + seed: Optional seed for the environment. + goal_dist_tolerance: float, how far away from the goal to terminate. + effector_height: float, custom height for end effector. + visuals_mode: 'default' or 'real'. + """ + # pybullet.connect(pybullet.GUI) + # pybullet.resetDebugVisualizerCamera( + # cameraDistance=1.5, + # cameraYaw=0, + # cameraPitch=-40, + # cameraTargetPosition=[0.55, -0.35, 0.2], + # ) + if visuals_mode != "default" and visuals_mode != "real": + raise ValueError("visuals_mode must be `real` or `default`.") + self._task = task + self._connection_mode = pybullet.DIRECT + if shared_memory: + self._connection_mode = pybullet.SHARED_MEMORY + + self.goal_dist_tolerance = goal_dist_tolerance + + self.effector_height = effector_height or EFFECTOR_HEIGHT + + self._visuals_mode = visuals_mode + if visuals_mode == "default": + self._camera_pose = DEFAULT_CAMERA_POSE + self._camera_orientation = DEFAULT_CAMERA_ORIENTATION + self.workspace_bounds = WORKSPACE_BOUNDS + self._image_size = image_size + self._camera_instrinsics = CAMERA_INTRINSICS + self._workspace_urdf_path = WORKSPACE_URDF_PATH + else: + self._camera_pose = CAMERA_POSE_REAL + self._camera_orientation = CAMERA_ORIENTATION_REAL + self.workspace_bounds = WORKSPACE_BOUNDS_REAL + self._image_size = image_size + self._camera_instrinsics = CAMERA_INTRINSICS_REAL + self._workspace_urdf_path = WORKSPACE_URDF_PATH_REAL + + self.action_space = spaces.Box(low=-0.1, high=0.1, shape=(2,)) # x, y + self.observation_space = self._create_observation_space(image_size) + + self._rng = np.random.RandomState(seed=seed) + self._block_ids = None + self._previous_state = None + self._robot = None + self._workspace_uid = None + self._target_id = None + self._target_pose = None + self._target_effector_pose = None + self._pybullet_client = None + self.reach_target_translation = None + self._setup_pybullet_scene() + self._saved_state = None + + assert isinstance(self._pybullet_client, bullet_client.BulletClient) + self._control_frequency = control_frequency + self._step_frequency = ( + 1 / self._pybullet_client.getPhysicsEngineParameters()["fixedTimeStep"] + ) + + self._last_loop_time = None + self._last_loop_frame_sleep_time = None + if self._step_frequency % self._control_frequency != 0: + raise ValueError( + "Control frequency should be a multiple of the " + "configured Bullet TimeStep." + ) + self._sim_steps_per_step = int(self._step_frequency / self._control_frequency) + + self.rendered_img = None + self._abs_action = abs_action + + # Use saved_state and restore to make reset safe as no simulation state has + # been updated at this state, but the assets are now loaded. + self.save_state() + self.reset() + + @property + def pybullet_client(self): + return self._pybullet_client + + @property + def robot(self): + return self._robot + + @property + def workspace_uid(self): + return self._workspace_uid + + @property + def target_effector_pose(self): + return self._target_effector_pose + + @property + def target_pose(self): + return self._target_pose + + @property + def control_frequency(self): + return self._control_frequency + + @property + def connection_mode(self): + return self._connection_mode + + def save_state(self): + self._saved_state = self._pybullet_client.saveState() + + def set_goal_dist_tolerance(self, val): + self.goal_dist_tolerance = val + + def get_control_frequency(self): + return self._control_frequency + + def compute_state(self): + return self._compute_state() + + def get_goal_translation(self): + """Return the translation component of the goal (2D).""" + if self._task == BlockTaskVariant.REACH: + return np.concatenate([self.reach_target_translation, [0]]) + else: + return self._target_pose.translation if self._target_pose else None + + def get_obj_ids(self): + return self._block_ids + + def _setup_workspace_and_robot(self, end_effector="suction"): + self._pybullet_client.resetSimulation() + self._pybullet_client.configureDebugVisualizer(pybullet.COV_ENABLE_GUI, 0) + self._pybullet_client.setPhysicsEngineParameter(enableFileCaching=0) + self._pybullet_client.setGravity(0, 0, -9.8) + + utils_pybullet.load_urdf( + self._pybullet_client, PLANE_URDF_PATH, basePosition=[0, 0, -0.001] + ) + self._workspace_uid = utils_pybullet.load_urdf( + self._pybullet_client, + self._workspace_urdf_path, + basePosition=[0.35, 0, 0.0], + ) + + self._robot = xarm_sim_robot.XArmSimRobot( + self._pybullet_client, + initial_joint_positions=INITIAL_JOINT_POSITIONS, + end_effector=end_effector, + color="white" if self._visuals_mode == "real" else "default", + ) + + def _setup_pybullet_scene(self): + self._pybullet_client = bullet_client.BulletClient(self._connection_mode) + + # Temporarily disable rendering to speed up loading URDFs. + pybullet.configureDebugVisualizer(pybullet.COV_ENABLE_RENDERING, 0) + + self._setup_workspace_and_robot() + + if self._task == BlockTaskVariant.INSERT: + target_urdf_path = INSERT_URDF_PATH + else: + target_urdf_path = ZONE_URDF_PATH + + self._target_id = utils_pybullet.load_urdf( + self._pybullet_client, target_urdf_path, useFixedBase=True + ) + self._block_ids = [ + utils_pybullet.load_urdf( + self._pybullet_client, BLOCK_URDF_PATH, useFixedBase=False + ) + ] + + # Re-enable rendering. + pybullet.configureDebugVisualizer(pybullet.COV_ENABLE_RENDERING, 1) + + self.step_simulation_to_stabilize() + + def step_simulation_to_stabilize(self, nsteps=100): + for _ in range(nsteps): + self._pybullet_client.stepSimulation() + + def seed(self, seed=None): + self._rng = np.random.RandomState(seed=seed) + + def _set_robot_target_effector_pose(self, pose): + self._target_effector_pose = pose + self._robot.set_target_effector_pose(pose) + + def reset(self, reset_poses=True): + workspace_center_x = 0.4 + + if reset_poses: + self._pybullet_client.restoreState(self._saved_state) + + rotation = transform.Rotation.from_rotvec([0, math.pi, 0]) + translation = np.array([0.3, -0.4, self.effector_height]) + starting_pose = Pose3d(rotation=rotation, translation=translation) + self._set_robot_target_effector_pose(starting_pose) + + # Reset block pose. + block_x = workspace_center_x + self._rng.uniform(low=-0.1, high=0.1) + block_y = -0.2 + self._rng.uniform(low=-0.15, high=0.15) + block_translation = np.array([block_x, block_y, 0]) + block_sampled_angle = self._rng.uniform(math.pi) + block_rotation = transform.Rotation.from_rotvec([0, 0, block_sampled_angle]) + + self._pybullet_client.resetBasePositionAndOrientation( + self._block_ids[0], + block_translation.tolist(), + block_rotation.as_quat().tolist(), + ) + + # Reset target pose. + target_x = workspace_center_x + self._rng.uniform(low=-0.10, high=0.10) + target_y = 0.2 + self._rng.uniform(low=-0.15, high=0.15) + target_translation = np.array([target_x, target_y, 0.020]) + + target_sampled_angle = math.pi + self._rng.uniform( + low=-math.pi / 6, high=math.pi / 6 + ) + target_rotation = transform.Rotation.from_rotvec( + [0, 0, target_sampled_angle] + ) + + self._pybullet_client.resetBasePositionAndOrientation( + self._target_id, + target_translation.tolist(), + target_rotation.as_quat().tolist(), + ) + else: + ( + target_translation, + target_orientation_quat, + ) = self._pybullet_client.getBasePositionAndOrientation(self._target_id) + target_rotation = transform.Rotation.from_quat(target_orientation_quat) + target_translation = np.array(target_translation) + + self._target_pose = Pose3d( + rotation=target_rotation, translation=target_translation + ) + + if reset_poses: + self.step_simulation_to_stabilize() + + state = self._compute_state() + self._previous_state = state + + if self._task == BlockTaskVariant.REACH: + self._compute_reach_target(state) + + self._init_goal_distance = self._compute_goal_distance(state) + init_goal_eps = 1e-7 + assert self._init_goal_distance > init_goal_eps + self.best_fraction_reduced_goal_dist = 0.0 + + return state + + def _compute_goal_distance(self, state): + goal_translation = self.get_goal_translation() + if self._task != BlockTaskVariant.REACH: + goal_distance = np.linalg.norm( + state["block_translation"] - goal_translation[0:2] + ) + else: + goal_distance = np.linalg.norm( + state["effector_translation"] - goal_translation[0:2] + ) + return goal_distance + + def _compute_reach_target(self, state): + xy_block = state["block_translation"] + xy_target = state["target_translation"] + + xy_block_to_target = xy_target - xy_block + xy_dir_block_to_target = (xy_block_to_target) / np.linalg.norm( + xy_block_to_target + ) + self.reach_target_translation = xy_block + -1 * xy_dir_block_to_target * 0.05 + + def _compute_state(self): + effector_pose = self._robot.forward_kinematics() + block_position_and_orientation = ( + self._pybullet_client.getBasePositionAndOrientation(self._block_ids[0]) + ) + block_pose = Pose3d( + rotation=transform.Rotation.from_quat(block_position_and_orientation[1]), + translation=block_position_and_orientation[0], + ) + + def _yaw_from_pose(pose): + return np.array([pose.rotation.as_euler("xyz", degrees=False)[-1]]) + + obs = collections.OrderedDict( + block_translation=block_pose.translation[0:2], + block_orientation=_yaw_from_pose(block_pose), + effector_translation=effector_pose.translation[0:2], + effector_target_translation=self._target_effector_pose.translation[0:2], + target_translation=self._target_pose.translation[0:2], + target_orientation=_yaw_from_pose(self._target_pose), + ) + if self._image_size is not None: + obs["rgb"] = self._render_camera(self._image_size) + return obs + + def _step_robot_and_sim(self, action): + """Steps the robot and pybullet sim.""" + # Compute target_effector_pose by shifting the effector's pose by the + # action. + if self._abs_action: + target_effector_translation = np.array([action[0], action[1], 0]) + else: + target_effector_translation = np.array( + self._target_effector_pose.translation + ) + np.array([action[0], action[1], 0]) + + target_effector_translation[0:2] = np.clip( + target_effector_translation[0:2], + self.workspace_bounds[0], + self.workspace_bounds[1], + ) + target_effector_translation[-1] = self.effector_height + target_effector_pose = Pose3d( + rotation=EFFECTOR_DOWN_ROTATION, translation=target_effector_translation + ) + + self._set_robot_target_effector_pose(target_effector_pose) + + # Update sleep time dynamically to stay near real-time. + frame_sleep_time = 0 + if self._connection_mode == pybullet.SHARED_MEMORY: + cur_time = time.time() + if self._last_loop_time is not None: + # Calculate the total, non-sleeping time from the previous frame, this + # includes the actual step as well as any compute that happens in the + # caller thread (model inference, etc). + compute_time = ( + cur_time + - self._last_loop_time + - self._last_loop_frame_sleep_time * self._sim_steps_per_step + ) + # Use this to calculate the current frame's total sleep time to ensure + # that env.step runs at policy rate. This is an estimate since the + # previous frame's compute time may not match the current frame. + total_sleep_time = max((1 / self._control_frequency) - compute_time, 0) + # Now spread this out over the inner sim steps. This doesn't change + # control in any way, but makes the animation appear smooth. + frame_sleep_time = total_sleep_time / self._sim_steps_per_step + else: + # No estimate of the previous frame's compute, assume it is zero. + frame_sleep_time = 1 / self._step_frequency + + # Cache end of this loop time, to compute sleep time on next iteration. + self._last_loop_time = cur_time + self._last_loop_frame_sleep_time = frame_sleep_time + + for _ in range(self._sim_steps_per_step): + if self._connection_mode == pybullet.SHARED_MEMORY: + sleep_spin(frame_sleep_time) + self._pybullet_client.stepSimulation() + + def step(self, action): + self._step_robot_and_sim(action) + + state = self._compute_state() + + goal_distance = self._compute_goal_distance(state) + fraction_reduced_goal_distance = 1.0 - ( + goal_distance / self._init_goal_distance + ) + if fraction_reduced_goal_distance > self.best_fraction_reduced_goal_dist: + self.best_fraction_reduced_goal_dist = fraction_reduced_goal_distance + + done = False + reward = self.best_fraction_reduced_goal_dist + + # Terminate the episode if the block is close enough to the target. + if goal_distance < self.goal_dist_tolerance: + reward = 1.0 + done = True + + return state, reward, done, {} + + @property + def succeeded(self): + state = self._compute_state() + goal_distance = self._compute_goal_distance(state) + if goal_distance < self.goal_dist_tolerance: + return True + return False + + @property + def goal_distance(self): + state = self._compute_state() + return self._compute_goal_distance(state) + + def render(self, mode="rgb_array"): + if self._image_size is not None: + image_size = self._image_size + else: + # This allows rendering even for state-only obs, + # for visualization. + image_size = (IMAGE_HEIGHT, IMAGE_WIDTH) + + data = self._render_camera(image_size=(image_size[0], image_size[1])) + if mode == "human": + if self.rendered_img is None: + self.rendered_img = plt.imshow( + np.zeros((image_size[0], image_size[1], 4)) + ) + else: + self.rendered_img.set_data(data) + plt.draw() + plt.pause(0.00001) + return data + + def close(self): + self._pybullet_client.disconnect() + + def calc_camera_params(self, image_size): + # Mimic RealSense D415 camera parameters. + intrinsics = self._camera_instrinsics + + # Set default camera poses. + front_position = self._camera_pose + front_rotation = self._camera_orientation + front_rotation = self._pybullet_client.getQuaternionFromEuler(front_rotation) + # Default camera configs. + zrange = (0.01, 10.0) + + # OpenGL camera settings. + lookdir = np.float32([0, 0, 1]).reshape(3, 1) + updir = np.float32([0, -1, 0]).reshape(3, 1) + rotation = self._pybullet_client.getMatrixFromQuaternion(front_rotation) + rotm = np.float32(rotation).reshape(3, 3) + lookdir = (rotm @ lookdir).reshape(-1) + updir = (rotm @ updir).reshape(-1) + lookat = front_position + lookdir + focal_len = intrinsics[0] + znear, zfar = zrange + viewm = self._pybullet_client.computeViewMatrix(front_position, lookat, updir) + fovh = (image_size[0] / 2) / focal_len + fovh = 180 * np.arctan(fovh) * 2 / np.pi + + # Notes: 1) FOV is vertical FOV 2) aspect must be float + aspect_ratio = image_size[1] / image_size[0] + projm = self._pybullet_client.computeProjectionMatrixFOV( + fovh, aspect_ratio, znear, zfar + ) + + return viewm, projm, front_position, lookat, updir + + def _render_camera(self, image_size): + """Render RGB image with RealSense configuration.""" + viewm, projm, _, _, _ = self.calc_camera_params(image_size) + + # Render with OpenGL camera settings. + _, _, color, _, _ = self._pybullet_client.getCameraImage( + width=image_size[1], + height=image_size[0], + viewMatrix=viewm, + projectionMatrix=projm, + flags=pybullet.ER_SEGMENTATION_MASK_OBJECT_AND_LINKINDEX, + renderer=pybullet.ER_BULLET_HARDWARE_OPENGL, + ) + + # Get color image. + color_image_size = (image_size[0], image_size[1], 4) + color = np.array(color, dtype=np.uint8).reshape(color_image_size) + color = color[:, :, :3] # remove alpha channel + + return color.astype(np.uint8) + + def _create_observation_space(self, image_size): + pi2 = math.pi * 2 + + obs_dict = collections.OrderedDict( + block_translation=spaces.Box(low=-5, high=5, shape=(2,)), # x,y + block_orientation=spaces.Box(low=-pi2, high=pi2, shape=(1,)), # phi + effector_translation=spaces.Box( + low=self.workspace_bounds[0] - 0.1, # Small buffer for to IK noise. + high=self.workspace_bounds[1] + 0.1, + ), # x,y + effector_target_translation=spaces.Box( + low=self.workspace_bounds[0] - 0.1, # Small buffer for to IK noise. + high=self.workspace_bounds[1] + 0.1, + ), # x,y + target_translation=spaces.Box(low=-5, high=5, shape=(2,)), # x,y + target_orientation=spaces.Box( + low=-pi2, + high=pi2, + shape=(1,), + ), # theta + ) + if image_size is not None: + obs_dict["rgb"] = spaces.Box( + low=0, high=255, shape=(image_size[0], image_size[1], 3), dtype=np.uint8 + ) + return spaces.Dict(obs_dict) + + def get_pybullet_state(self): + """Save pybullet state of the scene. + + Returns: + dict containing 'robots', 'robot_end_effectors', 'targets', 'objects', + each containing a list of ObjState. + """ + state: Dict[str, List[ObjState]] = {} + + state["robots"] = [ + XarmState.get_bullet_state( + self._pybullet_client, + self.robot.xarm, + target_effector_pose=self._target_effector_pose, + goal_translation=self.get_goal_translation(), + ) + ] + + state["robot_end_effectors"] = [] + if self.robot.end_effector: + state["robot_end_effectors"].append( + ObjState.get_bullet_state( + self._pybullet_client, self.robot.end_effector + ) + ) + + state["targets"] = [] + if self._target_id: + state["targets"].append( + ObjState.get_bullet_state(self._pybullet_client, self._target_id) + ) + + state["objects"] = [] + for obj_id in self.get_obj_ids(): + state["objects"].append( + ObjState.get_bullet_state(self._pybullet_client, obj_id) + ) + + return state + + def set_pybullet_state(self, state): + """Restore pyullet state. + + WARNING: py_environment wrapper assumes environments aren't reset in their + constructor and will often reset the environment unintentionally. It is + always recommeneded that you call env.reset on the tfagents wrapper before + playback (replaying pybullet_state). + + Args: + state: dict containing 'robots', 'robot_end_effectors', 'targets', + 'objects', each containing a list of ObjState. + """ + + assert isinstance(state["robots"][0], XarmState) + xarm_state: XarmState = state["robots"][0] + xarm_state.set_bullet_state(self._pybullet_client, self.robot.xarm) + self._set_robot_target_effector_pose(xarm_state.target_effector_pose) + + def _set_state_safe(obj_state, obj_id): + if obj_state is not None: + assert obj_id is not None, "Cannot set state for missing object." + obj_state.set_bullet_state(self._pybullet_client, obj_id) + else: + assert obj_id is None, f"No state found for obj_id {obj_id}" + + robot_end_effectors = state["robot_end_effectors"] + _set_state_safe( + None if not robot_end_effectors else robot_end_effectors[0], + self.robot.end_effector, + ) + + targets = state["targets"] + _set_state_safe(None if not targets else targets[0], self._target_id) + + obj_ids = self.get_obj_ids() + assert len(state["objects"]) == len(obj_ids), "State length mismatch" + for obj_state, obj_id in zip(state["objects"], obj_ids): + _set_state_safe(obj_state, obj_id) + + self.reset(reset_poses=False) + + +class BlockPushNormalized(gym.Env): + """Simple XArm environment for block pushing, normalized state and actions.""" + + def __init__( + self, + control_frequency=10.0, + task=BlockTaskVariant.PUSH_NORMALIZED, + image_size=None, + shared_memory=False, + seed=None, + ): + """Creates an env instance. + + Args: + control_frequency: Control frequency for the arm. Each env step will + advance the simulation by 1/control_frequency seconds. + task: enum for which task, see BlockTaskVariant enum. + image_size: Optional image size (height, width). If None, no image + observations will be used. + shared_memory: If True `pybullet.SHARED_MEMORY` is used to connect to + pybullet. Useful to debug. + seed: Optional seed for the environment. + """ + # Map normalized task to unnormalized task. + if task == BlockTaskVariant.PUSH_NORMALIZED: + env_task = BlockTaskVariant.PUSH + elif task == BlockTaskVariant.REACH_NORMALIZED: + env_task = BlockTaskVariant.REACH + else: + raise ValueError("Unsupported task %s" % str(task)) + self._env = BlockPush( + control_frequency, env_task, image_size, shared_memory, seed + ) + self.action_space = spaces.Box(low=-1, high=1, shape=(2,)) + self.observation_space = spaces.Dict( + collections.OrderedDict( + effector_target_translation=spaces.Box(low=-1, high=1, shape=(2,)), + effector_target_to_block_translation=spaces.Box( + low=-1, high=1, shape=(2,) + ), + block_orientation_cos_sin=spaces.Box(low=-1, high=1, shape=(2,)), + effector_target_to_target_translation=spaces.Box( + low=-1, high=1, shape=(2,) + ), + target_orientation_cos_sin=spaces.Box(low=-1, high=1, shape=(2,)), + ) + ) + self.reset() + + def get_control_frequency(self): + return self._env.get_control_frequency() + + @property + def reach_target_translation(self): + return self._env.reach_target_translation + + def seed(self, seed=None): + self._env.seed(seed) + + def reset(self): + state = self._env.reset() + return self.calc_normalized_state(state) + + def step(self, action): + # The environment is normalized [mean-3*std, mean+3*std] -> [-1, 1]. + action = np.clip(action, a_min=-1.0, a_max=1.0) + state, reward, done, info = self._env.step( + self.calc_unnormalized_action(action) + ) + state = self.calc_normalized_state(state) + reward = reward * 100 # Keep returns in [0, 100] + return state, reward, done, info + + def render(self, mode="rgb_array"): + return self._env.render(mode) + + def close(self): + self._env.close() + + @staticmethod + def _normalize(values, values_min, values_max): + offset = (values_max + values_min) * 0.5 + scale = (values_max - values_min) * 0.5 + return (values - offset) / scale # [min, max] -> [-1, 1] + + @staticmethod + def _unnormalize(values, values_min, values_max): + offset = (values_max + values_min) * 0.5 + scale = (values_max - values_min) * 0.5 + return values * scale + offset # [-1, 1] -> [min, max] + + @classmethod + def calc_normalized_action(cls, action): + return cls._normalize(action, ACTION_MIN, ACTION_MAX) + + @classmethod + def calc_unnormalized_action(cls, norm_action): + return cls._unnormalize(norm_action, ACTION_MIN, ACTION_MAX) + + @classmethod + def calc_normalized_state(cls, state): + + effector_target_translation = cls._normalize( + state["effector_target_translation"], + EFFECTOR_TARGET_TRANSLATION_MIN, + EFFECTOR_TARGET_TRANSLATION_MAX, + ) + + effector_target_to_block_translation = cls._normalize( + state["block_translation"] - state["effector_target_translation"], + EFFECTOR_TARGET_TO_BLOCK_TRANSLATION_MIN, + EFFECTOR_TARGET_TO_BLOCK_TRANSLATION_MAX, + ) + ori = state["block_orientation"][0] + block_orientation_cos_sin = cls._normalize( + np.array([math.cos(ori), math.sin(ori)], np.float32), + BLOCK_ORIENTATION_COS_SIN_MIN, + BLOCK_ORIENTATION_COS_SIN_MAX, + ) + + effector_target_to_target_translation = cls._normalize( + state["target_translation"] - state["effector_target_translation"], + EFFECTOR_TARGET_TO_TARGET_TRANSLATION_MIN, + EFFECTOR_TARGET_TO_TARGET_TRANSLATION_MAX, + ) + ori = state["target_orientation"][0] + target_orientation_cos_sin = cls._normalize( + np.array([math.cos(ori), math.sin(ori)], np.float32), + TARGET_ORIENTATION_COS_SIN_MIN, + TARGET_ORIENTATION_COS_SIN_MAX, + ) + + # Note: We do not include effector_translation in the normalized state. + # This means the unnormalized -> normalized mapping is not invertable. + return collections.OrderedDict( + effector_target_translation=effector_target_translation, + effector_target_to_block_translation=effector_target_to_block_translation, + block_orientation_cos_sin=block_orientation_cos_sin, + effector_target_to_target_translation=effector_target_to_target_translation, + target_orientation_cos_sin=target_orientation_cos_sin, + ) + + @classmethod + def calc_unnormalized_state(cls, norm_state): + + effector_target_translation = cls._unnormalize( + norm_state["effector_target_translation"], + EFFECTOR_TARGET_TRANSLATION_MIN, + EFFECTOR_TARGET_TRANSLATION_MAX, + ) + # Note: normalized state does not include effector_translation state, this + # means this component will be missing (and is marked nan). + effector_translation = np.array([np.nan, np.nan], np.float32) + + effector_target_to_block_translation = cls._unnormalize( + norm_state["effector_target_to_block_translation"], + EFFECTOR_TARGET_TO_BLOCK_TRANSLATION_MIN, + EFFECTOR_TARGET_TO_BLOCK_TRANSLATION_MAX, + ) + block_translation = ( + effector_target_to_block_translation + effector_target_translation + ) + ori_cos_sin = cls._unnormalize( + norm_state["block_orientation_cos_sin"], + BLOCK_ORIENTATION_COS_SIN_MIN, + BLOCK_ORIENTATION_COS_SIN_MAX, + ) + block_orientation = np.array( + [math.atan2(ori_cos_sin[1], ori_cos_sin[0])], np.float32 + ) + + effector_target_to_target_translation = cls._unnormalize( + norm_state["effector_target_to_target_translation"], + EFFECTOR_TARGET_TO_TARGET_TRANSLATION_MIN, + EFFECTOR_TARGET_TO_TARGET_TRANSLATION_MAX, + ) + target_translation = ( + effector_target_to_target_translation + effector_target_translation + ) + ori_cos_sin = cls._unnormalize( + norm_state["target_orientation_cos_sin"], + TARGET_ORIENTATION_COS_SIN_MIN, + TARGET_ORIENTATION_COS_SIN_MAX, + ) + target_orientation = np.array( + [math.atan2(ori_cos_sin[1], ori_cos_sin[0])], np.float32 + ) + + return collections.OrderedDict( + block_translation=block_translation, + block_orientation=block_orientation, + effector_translation=effector_translation, + effector_target_translation=effector_target_translation, + target_translation=target_translation, + target_orientation=target_orientation, + ) + + def get_pybullet_state(self): + return self._env.get_pybullet_state() + + def set_pybullet_state(self, state): + return self._env.set_pybullet_state(state) + + @property + def pybullet_client(self): + return self._env.pybullet_client + + def calc_camera_params(self, image_size): + return self._env.calc_camera_params(image_size) + + def _compute_state(self): + return self.calc_normalized_state( + self._env._compute_state() + ) # pylint: disable=protected-access + + +# Make sure we only register once to allow us to reload the module in colab for +# debugging. +if "BlockPush-v0" in registration.registry.env_specs: + del registration.registry.env_specs["BlockInsert-v0"] + del registration.registry.env_specs["BlockPush-v0"] + del registration.registry.env_specs["BlockPushNormalized-v0"] + del registration.registry.env_specs["BlockPushRgbNormalized-v0"] + del registration.registry.env_specs["BlockReach-v0"] + del registration.registry.env_specs["BlockReachNormalized-v0"] + del registration.registry.env_specs["BlockReachRgbNormalized-v0"] + del registration.registry.env_specs["SharedBlockInsert-v0"] + del registration.registry.env_specs["SharedBlockPush-v0"] + del registration.registry.env_specs["SharedBlockReach-v0"] + +registration.register( + id="BlockInsert-v0", + entry_point=BlockPush, + kwargs=dict(task=BlockTaskVariant.INSERT), + max_episode_steps=50, +) +registration.register(id="BlockPush-v0", entry_point=BlockPush, max_episode_steps=100) +registration.register( + id="BlockPushNormalized-v0", + entry_point=BlockPushNormalized, + kwargs=dict(task=BlockTaskVariant.PUSH_NORMALIZED), + max_episode_steps=100, +) +registration.register( + id="BlockPushRgb-v0", + entry_point=BlockPush, + max_episode_steps=100, + kwargs=dict(image_size=(IMAGE_HEIGHT, IMAGE_WIDTH)), +) +registration.register( + id="BlockPushRgbNormalized-v0", + entry_point=BlockPushNormalized, + kwargs=dict( + task=BlockTaskVariant.PUSH_NORMALIZED, image_size=(IMAGE_HEIGHT, IMAGE_WIDTH) + ), + max_episode_steps=100, +) +registration.register( + id="BlockReach-v0", + entry_point=BlockPush, + kwargs=dict(task=BlockTaskVariant.REACH), + max_episode_steps=50, +) +registration.register( + id="BlockReachRgb-v0", + entry_point=BlockPush, + max_episode_steps=100, + kwargs=dict(task=BlockTaskVariant.REACH, image_size=(IMAGE_HEIGHT, IMAGE_WIDTH)), +) +registration.register( + id="BlockReachNormalized-v0", + entry_point=BlockPushNormalized, + kwargs=dict(task=BlockTaskVariant.REACH_NORMALIZED), + max_episode_steps=50, +) +registration.register( + id="BlockReachRgbNormalized-v0", + entry_point=BlockPushNormalized, + kwargs=dict( + task=BlockTaskVariant.REACH_NORMALIZED, image_size=(IMAGE_HEIGHT, IMAGE_WIDTH) + ), + max_episode_steps=50, +) + +registration.register( + id="SharedBlockInsert-v0", + entry_point=BlockPush, + kwargs=dict(task=BlockTaskVariant.INSERT, shared_memory=True), + max_episode_steps=50, +) +registration.register( + id="SharedBlockPush-v0", + entry_point=BlockPush, + kwargs=dict(shared_memory=True), + max_episode_steps=100, +) +registration.register( + id="SharedBlockPushNormalized-v0", + entry_point=BlockPushNormalized, + kwargs=dict(task=BlockTaskVariant.PUSH_NORMALIZED, shared_memory=True), + max_episode_steps=100, +) +registration.register( + id="SharedBlockReach-v0", + entry_point=BlockPush, + kwargs=dict(task=BlockTaskVariant.REACH, shared_memory=True), + max_episode_steps=50, +) diff --git a/diffusion_policy/env/block_pushing/block_pushing_discontinuous.py b/diffusion_policy/env/block_pushing/block_pushing_discontinuous.py new file mode 100644 index 0000000..06e9dc7 --- /dev/null +++ b/diffusion_policy/env/block_pushing/block_pushing_discontinuous.py @@ -0,0 +1,338 @@ +# coding=utf-8 +# Copyright 2022 The Reach ML Authors. +# +# 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. + +"""Discontinuous block pushing.""" +import collections +import enum +import math +from typing import List, Optional + +from gym import spaces +from gym.envs import registration +from diffusion_policy.env.block_pushing import block_pushing +from diffusion_policy.env.block_pushing.utils import utils_pybullet +from diffusion_policy.env.block_pushing.utils.pose3d import Pose3d +import numpy as np +from scipy.spatial import transform +import pybullet +import pybullet_utils.bullet_client as bullet_client + +ZONE2_URDF_PATH = "third_party/py/envs/assets/zone2.urdf" + +MIN_TARGET_DIST = 0.15 +NUM_RESET_ATTEMPTS = 1000 + + +def build_env_name(task, shared_memory, use_image_obs): + """Construct the env name from parameters.""" + del task + env_name = "BlockPushDiscontinuous" + + if use_image_obs: + env_name = env_name + "Rgb" + + if shared_memory: + env_name = "Shared" + env_name + + env_name = env_name + "-v0" + + return env_name + + +class BlockTaskVariant(enum.Enum): + REACH = "Reach" + REACH_NORMALIZED = "ReachNormalized" + PUSH = "Push" + PUSH_NORMALIZED = "PushNormalized" + INSERT = "Insert" + + +# pytype: skip-file +class BlockPushDiscontinuous(block_pushing.BlockPush): + """Discontinuous block pushing.""" + + def __init__( + self, + control_frequency=10.0, + task=BlockTaskVariant.PUSH, + image_size=None, + shared_memory=False, + seed=None, + goal_dist_tolerance=0.04, + ): + super(BlockPushDiscontinuous, self).__init__( + control_frequency=control_frequency, + task=task, + image_size=image_size, + shared_memory=shared_memory, + seed=seed, + goal_dist_tolerance=goal_dist_tolerance, + ) + + @property + def target_poses(self): + return self._target_poses + + def get_goal_translation(self): + """Return the translation component of the goal (2D).""" + if self._target_poses: + return [i.translation for i in self._target_poses] + else: + return None + + def _setup_pybullet_scene(self): + self._pybullet_client = bullet_client.BulletClient(self._connection_mode) + + # Temporarily disable rendering to speed up loading URDFs. + pybullet.configureDebugVisualizer(pybullet.COV_ENABLE_RENDERING, 0) + + self._setup_workspace_and_robot() + target_urdf_path = block_pushing.ZONE_URDF_PATH + + self._target_ids = [] + for _ in [block_pushing.ZONE_URDF_PATH, ZONE2_URDF_PATH]: + self._target_ids.append( + utils_pybullet.load_urdf( + self._pybullet_client, target_urdf_path, useFixedBase=True + ) + ) + self._block_ids = [ + utils_pybullet.load_urdf( + self._pybullet_client, block_pushing.BLOCK_URDF_PATH, useFixedBase=False + ) + ] + + # Re-enable rendering. + pybullet.configureDebugVisualizer(pybullet.COV_ENABLE_RENDERING, 1) + + self.step_simulation_to_stabilize() + + def _reset_target_poses(self, workspace_center_x): + """Resets target poses.""" + self._target_poses = [None for _ in range(len(self._target_ids))] + + def _reset_target_pose(idx, avoid=None): + def _get_random_translation(): + # Choose x,y randomly. + target_x = workspace_center_x + self._rng.uniform(low=-0.10, high=0.10) + # Fix ys for this environment. + if idx == 0: + target_y = 0 + else: + target_y = 0.4 + target_translation = np.array([target_x, target_y, 0.020]) + return target_translation + + if avoid is None: + target_translation = _get_random_translation() + else: + # Reject targets too close to `avoid`. + for _ in range(NUM_RESET_ATTEMPTS): + target_translation = _get_random_translation() + dist = np.linalg.norm(target_translation[0] - avoid[0]) + if dist > MIN_TARGET_DIST: + break + target_sampled_angle = math.pi + self._rng.uniform( + low=-math.pi / 6, high=math.pi / 6 + ) + target_rotation = transform.Rotation.from_rotvec( + [0, 0, target_sampled_angle] + ) + self._pybullet_client.resetBasePositionAndOrientation( + self._target_ids[idx], + target_translation.tolist(), + target_rotation.as_quat().tolist(), + ) + self._target_poses[idx] = Pose3d( + rotation=target_rotation, translation=target_translation + ) + + try_idx = 0 + while True: + # Choose the first target. + _reset_target_pose(0) + # Choose the second target, avoiding the first. + _reset_target_pose(1, avoid=self._target_poses[0].translation) + dist = np.linalg.norm( + self._target_poses[0].translation[0] + - self._target_poses[1].translation[0] + ) + if dist > MIN_TARGET_DIST: + break + try_idx += 1 + if try_idx >= NUM_RESET_ATTEMPTS: + raise ValueError("could not find matching target") + assert dist > MIN_TARGET_DIST + + def reset(self): + self._pybullet_client.restoreState(self._saved_state) + + rotation = transform.Rotation.from_rotvec([0, math.pi, 0]) + translation = np.array([0.3, -0.4, block_pushing.EFFECTOR_HEIGHT]) + starting_pose = Pose3d(rotation=rotation, translation=translation) + self._set_robot_target_effector_pose(starting_pose) + + workspace_center_x = 0.4 + + # Reset block pose. + block_x = workspace_center_x + self._rng.uniform(low=-0.1, high=0.1) + block_y = -0.2 + self._rng.uniform(low=-0.15, high=0.15) + block_translation = np.array([block_x, block_y, 0]) + block_sampled_angle = self._rng.uniform(math.pi) + block_rotation = transform.Rotation.from_rotvec([0, 0, block_sampled_angle]) + + self._pybullet_client.resetBasePositionAndOrientation( + self._block_ids[0], + block_translation.tolist(), + block_rotation.as_quat().tolist(), + ) + + # Reset target pose. + self._reset_target_poses(workspace_center_x) + + self.step_simulation_to_stabilize() + state = self._compute_state() + self._previous_state = state + self.min_dist_to_first_goal = np.inf + self.min_dist_to_second_goal = np.inf + self.steps = 0 + return state + + def _compute_goal_distance(self, state): + # Reward is 1. blocks is inside any target. + return np.mean([self.min_dist_to_first_goal, self.min_dist_to_second_goal]) + + def _compute_state(self): + effector_pose = self._robot.forward_kinematics() + block_position_and_orientation = ( + self._pybullet_client.getBasePositionAndOrientation(self._block_ids[0]) + ) + block_pose = Pose3d( + rotation=transform.Rotation.from_quat(block_position_and_orientation[1]), + translation=block_position_and_orientation[0], + ) + + def _yaw_from_pose(pose): + return np.array([pose.rotation.as_euler("xyz", degrees=False)[-1]]) + + obs = collections.OrderedDict( + block_translation=block_pose.translation[0:2], + block_orientation=_yaw_from_pose(block_pose), + effector_translation=effector_pose.translation[0:2], + effector_target_translation=self._target_effector_pose.translation[0:2], + target_translation=self._target_poses[0].translation[0:2], + target_orientation=_yaw_from_pose(self._target_poses[0]), + target2_translation=self._target_poses[1].translation[0:2], + target2_orientation=_yaw_from_pose(self._target_poses[1]), + ) + if self._image_size is not None: + obs["rgb"] = self._render_camera(self._image_size) + return obs + + def step(self, action): + self._step_robot_and_sim(action) + state = self._compute_state() + reward = self._get_reward(state) + done = False + if reward > 0.0: + done = True + # Cache so we can compute success. + self.state = state + return state, reward, done, {} + + def dist(self, state, target): + # Reward is 1. blocks is inside any target. + return np.linalg.norm( + state["block_translation"] - state["%s_translation" % target] + ) + + def _get_reward(self, state): + """Reward is 1.0 if agent hits both goals and stays at second.""" + # This also statefully updates these values. + self.min_dist_to_first_goal = min( + self.dist(state, "target"), self.min_dist_to_first_goal + ) + self.min_dist_to_second_goal = min( + self.dist(state, "target2"), self.min_dist_to_second_goal + ) + + def _reward(thresh): + reward_first = True if self.min_dist_to_first_goal < thresh else False + reward_second = True if self.min_dist_to_second_goal < thresh else False + return 1.0 if (reward_first and reward_second) else 0.0 + + reward = _reward(self.goal_dist_tolerance) + return reward + + @property + def succeeded(self): + thresh = self.goal_dist_tolerance + hit_first = True if self.min_dist_to_first_goal < thresh else False + hit_second = True if self.min_dist_to_first_goal < thresh else False + current_distance_to_second = self.dist(self.state, "target2") + still_at_second = True if current_distance_to_second < thresh else False + return hit_first and hit_second and still_at_second + + def _create_observation_space(self, image_size): + pi2 = math.pi * 2 + + obs_dict = collections.OrderedDict( + block_translation=spaces.Box(low=-5, high=5, shape=(2,)), # x,y + block_orientation=spaces.Box(low=-pi2, high=pi2, shape=(1,)), # phi + effector_translation=spaces.Box( + # Small buffer for to IK noise. + low=block_pushing.WORKSPACE_BOUNDS[0] - 0.1, + high=block_pushing.WORKSPACE_BOUNDS[1] + 0.1, + ), # x,y + effector_target_translation=spaces.Box( + # Small buffer for to IK noise. + low=block_pushing.WORKSPACE_BOUNDS[0] - 0.1, + high=block_pushing.WORKSPACE_BOUNDS[1] + 0.1, + ), # x,y + target_translation=spaces.Box(low=-5, high=5, shape=(2,)), # x,y + target_orientation=spaces.Box( + low=-pi2, + high=pi2, + shape=(1,), + ), # theta + target2_translation=spaces.Box(low=-5, high=5, shape=(2,)), # x,y + target2_orientation=spaces.Box( + low=-pi2, + high=pi2, + shape=(1,), + ), # theta + ) + if image_size is not None: + obs_dict["rgb"] = spaces.Box( + low=0, high=255, shape=(image_size[0], image_size[1], 3), dtype=np.uint8 + ) + return spaces.Dict(obs_dict) + + +if "BlockPushDiscontinuous-v0" in registration.registry.env_specs: + del registration.registry.env_specs["BlockPushDiscontinuous-v0"] + +registration.register( + id="BlockPushDiscontinuous-v0", + entry_point=BlockPushDiscontinuous, + max_episode_steps=200, +) + +registration.register( + id="BlockPushDiscontinuousRgb-v0", + entry_point=BlockPushDiscontinuous, + max_episode_steps=200, + kwargs=dict(image_size=(block_pushing.IMAGE_HEIGHT, block_pushing.IMAGE_WIDTH)), +) diff --git a/diffusion_policy/env/block_pushing/block_pushing_multimodal.py b/diffusion_policy/env/block_pushing/block_pushing_multimodal.py new file mode 100644 index 0000000..3ca9212 --- /dev/null +++ b/diffusion_policy/env/block_pushing/block_pushing_multimodal.py @@ -0,0 +1,802 @@ +# coding=utf-8 +# Copyright 2022 The Reach ML Authors. +# +# 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. + +"""Multimodal block environments for the XArm.""" + +import collections +import logging +import math +from typing import Dict, List, Optional, Union +import copy +import time + +from gym import spaces +from gym.envs import registration +from diffusion_policy.env.block_pushing import block_pushing +from diffusion_policy.env.block_pushing.utils import utils_pybullet +from diffusion_policy.env.block_pushing.utils.pose3d import Pose3d +from diffusion_policy.env.block_pushing.utils.utils_pybullet import ObjState +from diffusion_policy.env.block_pushing.utils.utils_pybullet import XarmState +import numpy as np +from scipy.spatial import transform +import pybullet +import pybullet_utils.bullet_client as bullet_client + +# pytype: skip-file +BLOCK2_URDF_PATH = "third_party/py/envs/assets/block2.urdf" +ZONE2_URDF_PATH = "third_party/py/envs/assets/zone2.urdf" + +# When resetting multiple targets, they should all be this far apart. +MIN_BLOCK_DIST = 0.1 +MIN_TARGET_DIST = 0.12 +# pylint: enable=line-too-long +NUM_RESET_ATTEMPTS = 1000 + +# Random movement of blocks +RANDOM_X_SHIFT = 0.1 +RANDOM_Y_SHIFT = 0.15 + +logging.basicConfig( + level="INFO", + format="%(asctime)s [%(levelname)s] %(message)s", + filemode="w", +) +logger = logging.getLogger() + + +def build_env_name(task, shared_memory, use_image_obs): + """Construct the env name from parameters.""" + del task + env_name = "BlockPushMultimodal" + + if use_image_obs: + env_name = env_name + "Rgb" + + if shared_memory: + env_name = "Shared" + env_name + + env_name = env_name + "-v0" + + return env_name + + +class BlockPushEventManager: + def __init__(self): + self.event_steps = { + 'REACH_0': -1, + 'REACH_1': -1, + 'TARGET_0_0': -1, + 'TARGET_0_1': -1, + 'TARGET_1_0': -1, + 'TARGET_1_1': -1 + } + + def reach(self, step, block_id): + key = f'REACH_{block_id}' + if self.event_steps[key] < 0: + self.event_steps[key] = step + + def target(self, step, block_id, target_id): + key = f'TARGET_{block_id}_{target_id}' + if self.event_steps[key] < 0: + self.event_steps[key] = step + + def reset(self): + for key in list(self.event_steps): + self.event_steps[key] = -1 + + def get_info(self): + return copy.deepcopy(self.event_steps) + +class BlockPushMultimodal(block_pushing.BlockPush): + """2 blocks, 2 targets.""" + + def __init__( + self, + control_frequency=10.0, + task=block_pushing.BlockTaskVariant.PUSH, + image_size=None, + shared_memory=False, + seed=None, + goal_dist_tolerance=0.05, + abs_action=False + ): + """Creates an env instance. + + Args: + control_frequency: Control frequency for the arm. Each env step will + advance the simulation by 1/control_frequency seconds. + task: enum for which task, see BlockTaskVariant enum. + image_size: Optional image size (height, width). If None, no image + observations will be used. + shared_memory: If True `pybullet.SHARED_MEMORY` is used to connect to + pybullet. Useful to debug. + seed: Optional seed for the environment. + goal_dist_tolerance: float, how far away from the goal to terminate. + """ + self._target_ids = None + self._target_poses = None + self._event_manager = BlockPushEventManager() + super(BlockPushMultimodal, self).__init__( + control_frequency=control_frequency, + task=task, + image_size=image_size, + shared_memory=shared_memory, + seed=seed, + goal_dist_tolerance=goal_dist_tolerance, + ) + self._init_distance = [-1.0, -1.0] + self._in_target = [[-1.0, -1.0], [-1.0, -1.0]] + self._first_move = [-1, -1] + self._step_num = 0 + self._abs_action = abs_action + + @property + def target_poses(self): + return self._target_poses + + def get_goal_translation(self): + """Return the translation component of the goal (2D).""" + if self._target_poses: + return [i.translation for i in self._target_poses] + else: + return None + + def _setup_pybullet_scene(self): + self._pybullet_client = bullet_client.BulletClient(self._connection_mode) + + # Temporarily disable rendering to speed up loading URDFs. + pybullet.configureDebugVisualizer(pybullet.COV_ENABLE_RENDERING, 0) + + self._setup_workspace_and_robot() + + self._target_ids = [ + utils_pybullet.load_urdf(self._pybullet_client, i, useFixedBase=True) + for i in [block_pushing.ZONE_URDF_PATH, ZONE2_URDF_PATH] + ] + self._block_ids = [] + for i in [block_pushing.BLOCK_URDF_PATH, BLOCK2_URDF_PATH]: + self._block_ids.append( + utils_pybullet.load_urdf(self._pybullet_client, i, useFixedBase=False) + ) + + # Re-enable rendering. + pybullet.configureDebugVisualizer(pybullet.COV_ENABLE_RENDERING, 1) + + self.step_simulation_to_stabilize() + + def _reset_block_poses(self, workspace_center_x): + """Resets block poses.""" + + # Helper for choosing random block position. + def _reset_block_pose(idx, add=0.0, avoid=None): + def _get_random_translation(): + block_x = ( + workspace_center_x + + add + + self._rng.uniform(low=-RANDOM_X_SHIFT, high=RANDOM_X_SHIFT) + ) + block_y = -0.2 + self._rng.uniform( + low=-RANDOM_Y_SHIFT, high=RANDOM_Y_SHIFT + ) + block_translation = np.array([block_x, block_y, 0]) + return block_translation + + if avoid is None: + block_translation = _get_random_translation() + else: + # Reject targets too close to `avoid`. + for _ in range(NUM_RESET_ATTEMPTS): + block_translation = _get_random_translation() + dist = np.linalg.norm(block_translation[0] - avoid[0]) + # print('block inner try_idx %d, dist %.3f' % (try_idx, dist)) + if dist > MIN_BLOCK_DIST: + break + block_sampled_angle = self._rng.uniform(math.pi) + block_rotation = transform.Rotation.from_rotvec([0, 0, block_sampled_angle]) + self._pybullet_client.resetBasePositionAndOrientation( + self._block_ids[idx], + block_translation.tolist(), + block_rotation.as_quat().tolist(), + ) + return block_translation + + # Reject targets too close to `avoid`. + for _ in range(NUM_RESET_ATTEMPTS): + # Reset first block. + b0_translation = _reset_block_pose(0) + # Reset second block away from first block. + b1_translation = _reset_block_pose(1, avoid=b0_translation) + dist = np.linalg.norm(b0_translation[0] - b1_translation[0]) + if dist > MIN_BLOCK_DIST: + break + else: + raise ValueError("could not find matching block") + assert dist > MIN_BLOCK_DIST + + def _reset_target_poses(self, workspace_center_x): + """Resets target poses.""" + + def _reset_target_pose(idx, add=0.0, avoid=None): + def _get_random_translation(): + # Choose x,y randomly. + target_x = ( + workspace_center_x + + add + + self._rng.uniform( + low=-0.05 * RANDOM_X_SHIFT, high=0.05 * RANDOM_X_SHIFT + ) + ) + target_y = 0.2 + self._rng.uniform( + low=-0.05 * RANDOM_Y_SHIFT, high=0.05 * RANDOM_Y_SHIFT + ) + target_translation = np.array([target_x, target_y, 0.020]) + return target_translation + + if avoid is None: + target_translation = _get_random_translation() + else: + # Reject targets too close to `avoid`. + for _ in range(NUM_RESET_ATTEMPTS): + target_translation = _get_random_translation() + dist = np.linalg.norm(target_translation[0] - avoid[0]) + # print('target inner try_idx %d, dist %.3f' % (try_idx, dist)) + if dist > MIN_TARGET_DIST: + break + target_sampled_angle = math.pi + self._rng.uniform( + low=-math.pi / 30, high=math.pi / 30 + ) + target_rotation = transform.Rotation.from_rotvec( + [0, 0, target_sampled_angle] + ) + self._pybullet_client.resetBasePositionAndOrientation( + self._target_ids[idx], + target_translation.tolist(), + target_rotation.as_quat().tolist(), + ) + self._target_poses[idx] = Pose3d( + rotation=target_rotation, translation=target_translation + ) + + if self._target_poses is None: + self._target_poses = [None for _ in range(len(self._target_ids))] + + for _ in range(NUM_RESET_ATTEMPTS): + # Choose the first target. + add = 0.12 * self._rng.choice([-1, 1]) + # Randomly flip the location of the targets. + _reset_target_pose(0, add=add) + _reset_target_pose(1, add=-add, avoid=self._target_poses[0].translation) + dist = np.linalg.norm( + self._target_poses[0].translation[0] + - self._target_poses[1].translation[0] + ) + if dist > MIN_TARGET_DIST: + break + else: + raise ValueError("could not find matching target") + assert dist > MIN_TARGET_DIST + + def _reset_object_poses(self, workspace_center_x, workspace_center_y): + # Reset block poses. + self._reset_block_poses(workspace_center_x) + + # Reset target poses. + self._reset_target_poses(workspace_center_x) + + self._init_distance = [-1.0, -1.0] + self._in_target = [[-1.0, -1.0], [-1.0, -1.0]] + self._step_num = 0 + + def reset(self, reset_poses=True): + workspace_center_x = 0.4 + workspace_center_y = 0.0 + + if reset_poses: + self._pybullet_client.restoreState(self._saved_state) + + rotation = transform.Rotation.from_rotvec([0, math.pi, 0]) + translation = np.array([0.3, -0.4, block_pushing.EFFECTOR_HEIGHT]) + starting_pose = Pose3d(rotation=rotation, translation=translation) + self._set_robot_target_effector_pose(starting_pose) + self._reset_object_poses(workspace_center_x, workspace_center_y) + + # else: + self._target_poses = [ + self._get_target_pose(idx) for idx in self._target_ids + ] + + if reset_poses: + self.step_simulation_to_stabilize() + + state = self._compute_state() + self._previous_state = state + self._event_manager.reset() + return state + + def _get_target_pose(self, idx): + ( + target_translation, + target_orientation_quat, + ) = self._pybullet_client.getBasePositionAndOrientation(idx) + target_rotation = transform.Rotation.from_quat(target_orientation_quat) + target_translation = np.array(target_translation) + return Pose3d(rotation=target_rotation, translation=target_translation) + + def _compute_reach_target(self, state): + xy_block = state["block_translation"] + xy_target = state["target_translation"] + + xy_block_to_target = xy_target - xy_block + xy_dir_block_to_target = (xy_block_to_target) / np.linalg.norm( + xy_block_to_target + ) + self.reach_target_translation = xy_block + -1 * xy_dir_block_to_target * 0.05 + + def _compute_state(self): + effector_pose = self._robot.forward_kinematics() + + def _get_block_pose(idx): + block_position_and_orientation = ( + self._pybullet_client.getBasePositionAndOrientation( + self._block_ids[idx] + ) + ) + block_pose = Pose3d( + rotation=transform.Rotation.from_quat( + block_position_and_orientation[1] + ), + translation=block_position_and_orientation[0], + ) + return block_pose + + block_poses = [_get_block_pose(i) for i in range(len(self._block_ids))] + + def _yaw_from_pose(pose): + return np.array([pose.rotation.as_euler("xyz", degrees=False)[-1] % np.pi]) + + obs = collections.OrderedDict( + block_translation=block_poses[0].translation[0:2], + block_orientation=_yaw_from_pose(block_poses[0]), + block2_translation=block_poses[1].translation[0:2], + block2_orientation=_yaw_from_pose(block_poses[1]), + effector_translation=effector_pose.translation[0:2], + effector_target_translation=self._target_effector_pose.translation[0:2], + target_translation=self._target_poses[0].translation[0:2], + target_orientation=_yaw_from_pose(self._target_poses[0]), + target2_translation=self._target_poses[1].translation[0:2], + target2_orientation=_yaw_from_pose(self._target_poses[1]), + ) + + for i in range(2): + new_distance = np.linalg.norm( + block_poses[i].translation[0:2] + ) # + np.linalg.norm(_yaw_from_pose(block_poses[i])) + if self._init_distance[i] == -1: + self._init_distance[i] = new_distance + else: + if self._init_distance[i] != 100: + if np.abs(new_distance - self._init_distance[i]) > 1e-3: + logger.info(f"Block {i} moved on step {self._step_num}") + self._event_manager.reach(step=self._step_num, block_id=i) + self._init_distance[i] = 100 + + self._step_num += 1 + if self._image_size is not None: + obs["rgb"] = self._render_camera(self._image_size) + return obs + + def step(self, action): + self._step_robot_and_sim(action) + + state = self._compute_state() + done = False + reward = self._get_reward(state) + if reward >= 0.5: + # Terminate the episode if both blocks are close enough to the targets. + done = True + + info = self._event_manager.get_info() + return state, reward, done, info + + def _step_robot_and_sim(self, action): + """Steps the robot and pybullet sim.""" + # Compute target_effector_pose by shifting the effector's pose by the + # action. + if self._abs_action: + target_effector_translation = np.array([action[0], action[1], 0]) + else: + target_effector_translation = np.array( + self._target_effector_pose.translation + ) + np.array([action[0], action[1], 0]) + + target_effector_translation[0:2] = np.clip( + target_effector_translation[0:2], + self.workspace_bounds[0], + self.workspace_bounds[1], + ) + target_effector_translation[-1] = self.effector_height + target_effector_pose = Pose3d( + rotation=block_pushing.EFFECTOR_DOWN_ROTATION, translation=target_effector_translation + ) + + self._set_robot_target_effector_pose(target_effector_pose) + + # Update sleep time dynamically to stay near real-time. + frame_sleep_time = 0 + if self._connection_mode == pybullet.SHARED_MEMORY: + cur_time = time.time() + if self._last_loop_time is not None: + # Calculate the total, non-sleeping time from the previous frame, this + # includes the actual step as well as any compute that happens in the + # caller thread (model inference, etc). + compute_time = ( + cur_time + - self._last_loop_time + - self._last_loop_frame_sleep_time * self._sim_steps_per_step + ) + # Use this to calculate the current frame's total sleep time to ensure + # that env.step runs at policy rate. This is an estimate since the + # previous frame's compute time may not match the current frame. + total_sleep_time = max((1 / self._control_frequency) - compute_time, 0) + # Now spread this out over the inner sim steps. This doesn't change + # control in any way, but makes the animation appear smooth. + frame_sleep_time = total_sleep_time / self._sim_steps_per_step + else: + # No estimate of the previous frame's compute, assume it is zero. + frame_sleep_time = 1 / self._step_frequency + + # Cache end of this loop time, to compute sleep time on next iteration. + self._last_loop_time = cur_time + self._last_loop_frame_sleep_time = frame_sleep_time + + for _ in range(self._sim_steps_per_step): + if self._connection_mode == pybullet.SHARED_MEMORY: + block_pushing.sleep_spin(frame_sleep_time) + self._pybullet_client.stepSimulation() + + def _get_reward(self, state): + # Reward is 1. if both blocks are inside targets, but not the same target. + targets = ["target", "target2"] + + def _block_target_dist(block, target): + return np.linalg.norm( + state["%s_translation" % block] - state["%s_translation" % target] + ) + + def _closest_target(block): + # Distances to all targets. + dists = [_block_target_dist(block, t) for t in targets] + # Which is closest. + closest_target = targets[np.argmin(dists)] + closest_dist = np.min(dists) + # Is it in the closest target? + in_target = closest_dist < self.goal_dist_tolerance + return closest_target, in_target + + blocks = ["block", "block2"] + + reward = 0.0 + + for t_i, t in enumerate(targets): + for b_i, b in enumerate(blocks): + if self._in_target[t_i][b_i] == -1: + dist = _block_target_dist(b, t) + if dist < self.goal_dist_tolerance: + self._in_target[t_i][b_i] = 0 + logger.info( + f"Block {b_i} entered target {t_i} on step {self._step_num}" + ) + self._event_manager.target(step=self._step_num, block_id=b_i, target_id=t_i) + reward += 0.49 + + b0_closest_target, b0_in_target = _closest_target("block") + b1_closest_target, b1_in_target = _closest_target("block2") + # reward = 0.0 + if b0_in_target and b1_in_target and (b0_closest_target != b1_closest_target): + reward = 0.51 + return reward + + def _compute_goal_distance(self, state): + blocks = ["block", "block2"] + + def _target_block_dist(target, block): + return np.linalg.norm( + state["%s_translation" % block] - state["%s_translation" % target] + ) + + def _closest_block_dist(target): + dists = [_target_block_dist(target, b) for b in blocks] + closest_dist = np.min(dists) + return closest_dist + + t0_closest_dist = _closest_block_dist("target") + t1_closest_dist = _closest_block_dist("target2") + return np.mean([t0_closest_dist, t1_closest_dist]) + + @property + def succeeded(self): + state = self._compute_state() + reward = self._get_reward(state) + if reward >= 0.5: + return True + return False + + def _create_observation_space(self, image_size): + pi2 = math.pi * 2 + + obs_dict = collections.OrderedDict( + block_translation=spaces.Box(low=-5, high=5, shape=(2,)), # x,y + block_orientation=spaces.Box(low=-pi2, high=pi2, shape=(1,)), # phi + block2_translation=spaces.Box(low=-5, high=5, shape=(2,)), # x,y + block2_orientation=spaces.Box(low=-pi2, high=pi2, shape=(1,)), # phi + effector_translation=spaces.Box( + low=block_pushing.WORKSPACE_BOUNDS[0] - 0.1, + high=block_pushing.WORKSPACE_BOUNDS[1] + 0.1, + ), # x,y + effector_target_translation=spaces.Box( + low=block_pushing.WORKSPACE_BOUNDS[0] - 0.1, + high=block_pushing.WORKSPACE_BOUNDS[1] + 0.1, + ), # x,y + target_translation=spaces.Box(low=-5, high=5, shape=(2,)), # x,y + target_orientation=spaces.Box( + low=-pi2, + high=pi2, + shape=(1,), + ), # theta + target2_translation=spaces.Box(low=-5, high=5, shape=(2,)), # x,y + target2_orientation=spaces.Box( + low=-pi2, + high=pi2, + shape=(1,), + ), # theta + ) + if image_size is not None: + obs_dict["rgb"] = spaces.Box( + low=0, high=255, shape=(image_size[0], image_size[1], 3), dtype=np.uint8 + ) + return spaces.Dict(obs_dict) + + def get_pybullet_state(self): + """Save pybullet state of the scene. + + Returns: + dict containing 'robots', 'robot_end_effectors', 'targets', 'objects', + each containing a list of ObjState. + """ + state: Dict[str, List[ObjState]] = {} + + state["robots"] = [ + XarmState.get_bullet_state( + self._pybullet_client, + self.robot.xarm, + target_effector_pose=self._target_effector_pose, + goal_translation=None, + ) + ] + + state["robot_end_effectors"] = [] + if self.robot.end_effector: + state["robot_end_effectors"].append( + ObjState.get_bullet_state( + self._pybullet_client, self.robot.end_effector + ) + ) + + state["targets"] = [] + if self._target_ids: + for target_id in self._target_ids: + state["targets"].append( + ObjState.get_bullet_state(self._pybullet_client, target_id) + ) + + state["objects"] = [] + for obj_id in self.get_obj_ids(): + state["objects"].append( + ObjState.get_bullet_state(self._pybullet_client, obj_id) + ) + + return state + + def set_pybullet_state(self, state): + """Restore pyullet state. + + WARNING: py_environment wrapper assumes environments aren't reset in their + constructor and will often reset the environment unintentionally. It is + always recommeneded that you call env.reset on the tfagents wrapper before + playback (replaying pybullet_state). + + Args: + state: dict containing 'robots', 'robot_end_effectors', 'targets', + 'objects', each containing a list of ObjState. + """ + + assert isinstance(state["robots"][0], XarmState) + xarm_state: XarmState = state["robots"][0] + xarm_state.set_bullet_state(self._pybullet_client, self.robot.xarm) + self._set_robot_target_effector_pose(xarm_state.target_effector_pose) + + def _set_state_safe(obj_state, obj_id): + if obj_state is not None: + assert obj_id is not None, "Cannot set state for missing object." + obj_state.set_bullet_state(self._pybullet_client, obj_id) + else: + assert obj_id is None, f"No state found for obj_id {obj_id}" + + robot_end_effectors = state["robot_end_effectors"] + _set_state_safe( + None if not robot_end_effectors else robot_end_effectors[0], + self.robot.end_effector, + ) + + for target_state, target_id in zip(state["targets"], self._target_ids): + _set_state_safe(target_state, target_id) + + obj_ids = self.get_obj_ids() + assert len(state["objects"]) == len(obj_ids), "State length mismatch" + for obj_state, obj_id in zip(state["objects"], obj_ids): + _set_state_safe(obj_state, obj_id) + + self.reset(reset_poses=False) + + +class BlockPushHorizontalMultimodal(BlockPushMultimodal): + def _reset_object_poses(self, workspace_center_x, workspace_center_y): + # Reset block poses. + self._reset_block_poses(workspace_center_y) + + # Reset target poses. + self._reset_target_poses(workspace_center_y) + + def _reset_block_poses(self, workspace_center_y): + """Resets block poses.""" + + # Helper for choosing random block position. + def _reset_block_pose(idx, add=0.0, avoid=None): + def _get_random_translation(): + block_x = 0.35 + 0.5 * self._rng.uniform( + low=-RANDOM_X_SHIFT, high=RANDOM_X_SHIFT + ) + block_y = ( + workspace_center_y + + add + + 0.5 * self._rng.uniform(low=-RANDOM_Y_SHIFT, high=RANDOM_Y_SHIFT) + ) + block_translation = np.array([block_x, block_y, 0]) + return block_translation + + if avoid is None: + block_translation = _get_random_translation() + else: + # Reject targets too close to `avoid`. + for _ in range(NUM_RESET_ATTEMPTS): + block_translation = _get_random_translation() + dist = np.linalg.norm(block_translation[0] - avoid[0]) + # print('block inner try_idx %d, dist %.3f' % (try_idx, dist)) + if dist > MIN_BLOCK_DIST: + break + block_sampled_angle = self._rng.uniform(math.pi) + block_rotation = transform.Rotation.from_rotvec([0, 0, block_sampled_angle]) + self._pybullet_client.resetBasePositionAndOrientation( + self._block_ids[idx], + block_translation.tolist(), + block_rotation.as_quat().tolist(), + ) + return block_translation + + # Reject targets too close to `avoid`. + for _ in range(NUM_RESET_ATTEMPTS): + # Reset first block. + add = 0.2 * self._rng.choice([-1, 1]) + b0_translation = _reset_block_pose(0, add=add) + # Reset second block away from first block. + b1_translation = _reset_block_pose(1, add=-add, avoid=b0_translation) + dist = np.linalg.norm(b0_translation[0] - b1_translation[0]) + if dist > MIN_BLOCK_DIST: + break + else: + raise ValueError("could not find matching block") + assert dist > MIN_BLOCK_DIST + + def _reset_target_poses(self, workspace_center_y): + """Resets target poses.""" + + def _reset_target_pose(idx, add=0.0, avoid=None): + def _get_random_translation(): + # Choose x,y randomly. + target_x = 0.5 + self._rng.uniform( + low=-0.05 * RANDOM_X_SHIFT, high=0.05 * RANDOM_X_SHIFT + ) + target_y = ( + workspace_center_y + + add + + self._rng.uniform( + low=-0.05 * RANDOM_Y_SHIFT, high=0.05 * RANDOM_Y_SHIFT + ) + ) + target_translation = np.array([target_x, target_y, 0.020]) + return target_translation + + if avoid is None: + target_translation = _get_random_translation() + else: + # Reject targets too close to `avoid`. + for _ in range(NUM_RESET_ATTEMPTS): + target_translation = _get_random_translation() + dist = np.linalg.norm(target_translation[0] - avoid[0]) + # print('target inner try_idx %d, dist %.3f' % (try_idx, dist)) + if dist > MIN_TARGET_DIST: + break + target_sampled_angle = math.pi + self._rng.uniform( + low=-math.pi / 30, high=math.pi / 30 + ) + target_rotation = transform.Rotation.from_rotvec( + [0, 0, target_sampled_angle] + ) + self._pybullet_client.resetBasePositionAndOrientation( + self._target_ids[idx], + target_translation.tolist(), + target_rotation.as_quat().tolist(), + ) + self._target_poses[idx] = Pose3d( + rotation=target_rotation, translation=target_translation + ) + + if self._target_poses is None: + self._target_poses = [None for _ in range(len(self._target_ids))] + + for _ in range(NUM_RESET_ATTEMPTS): + # Choose the first target. + add = 0.2 * self._rng.choice([-1, 1]) + # Randomly flip the location of the targets. + _reset_target_pose(0, add=add) + _reset_target_pose(1, add=-add, avoid=self._target_poses[0].translation) + dist = np.linalg.norm( + self._target_poses[0].translation[0] + - self._target_poses[1].translation[0] + ) + break + # if dist > MIN_TARGET_DIST: + # break + else: + raise ValueError("could not find matching target") + # assert dist > MIN_TARGET_DIST + + +if "BlockPushMultimodal-v0" in registration.registry.env_specs: + del registration.registry.env_specs["BlockPushMultimodal-v0"] + +registration.register( + id="BlockPushMultimodal-v0", entry_point=BlockPushMultimodal, max_episode_steps=350 +) + +registration.register( + id="BlockPushMultimodalFlipped-v0", + entry_point=BlockPushHorizontalMultimodal, + max_episode_steps=25, +) + +registration.register( + id="SharedBlockPushMultimodal-v0", + entry_point=BlockPushMultimodal, + kwargs=dict(shared_memory=True), + max_episode_steps=350, +) +registration.register( + id="BlockPushMultimodalRgb-v0", + entry_point=BlockPushMultimodal, + max_episode_steps=350, + kwargs=dict(image_size=(block_pushing.IMAGE_HEIGHT, block_pushing.IMAGE_WIDTH)), +) diff --git a/diffusion_policy/env/block_pushing/oracles/discontinuous_push_oracle.py b/diffusion_policy/env/block_pushing/oracles/discontinuous_push_oracle.py new file mode 100644 index 0000000..2fd074b --- /dev/null +++ b/diffusion_policy/env/block_pushing/oracles/discontinuous_push_oracle.py @@ -0,0 +1,70 @@ +# coding=utf-8 +# Copyright 2022 The Reach ML Authors. +# +# 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. + +"""Pushes to first target, waits, then pushes to second target.""" + +import diffusion_policy.env.block_pushing.oracles.oriented_push_oracle as oriented_push_oracle_module +import numpy as np +from tf_agents.trajectories import policy_step +from tf_agents.trajectories import time_step as ts +from tf_agents.typing import types + +# Only used for debug visualization. +import pybullet # pylint: disable=unused-import + + +class DiscontinuousOrientedPushOracle(oriented_push_oracle_module.OrientedPushOracle): + """Pushes to first target, waits, then pushes to second target.""" + + def __init__(self, env, goal_tolerance=0.04, wait=0): + super(DiscontinuousOrientedPushOracle, self).__init__(env) + self._countdown = 0 + self._wait = wait + self._goal_dist_tolerance = goal_tolerance + + def reset(self): + self.phase = "move_to_pre_block" + self._countdown = 0 + + def _action(self, time_step, policy_state): + if time_step.is_first(): + self.reset() + # Move to first target first. + self._current_target = "target" + self._has_switched = False + + def _block_target_dist(block, target): + dist = np.linalg.norm( + time_step.observation["%s_translation" % block] + - time_step.observation["%s_translation" % target] + ) + return dist + + d1 = _block_target_dist("block", "target") + if d1 < self._goal_dist_tolerance and not self._has_switched: + self._countdown = self._wait + # If first block has been pushed to first target, switch to second block. + self._has_switched = True + self._current_target = "target2" + + xy_delta = self._get_action_for_block_target( + time_step, block="block", target=self._current_target + ) + + if self._countdown > 0: + xy_delta = np.zeros_like(xy_delta) + self._countdown -= 1 + + return policy_step.PolicyStep(action=np.asarray(xy_delta, dtype=np.float32)) diff --git a/diffusion_policy/env/block_pushing/oracles/multimodal_push_oracle.py b/diffusion_policy/env/block_pushing/oracles/multimodal_push_oracle.py new file mode 100644 index 0000000..29a6386 --- /dev/null +++ b/diffusion_policy/env/block_pushing/oracles/multimodal_push_oracle.py @@ -0,0 +1,187 @@ +# coding=utf-8 +# Copyright 2022 The Reach ML Authors. +# +# 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. + +"""Oracle for multimodal pushing task.""" +import diffusion_policy.env.block_pushing.oracles.oriented_push_oracle as oriented_push_oracle_module +import numpy as np +from tf_agents.trajectories import policy_step +from tf_agents.trajectories import time_step as ts +from tf_agents.typing import types + +# Only used for debug visualization. +import pybullet # pylint: disable=unused-import + + +class MultimodalOrientedPushOracle(oriented_push_oracle_module.OrientedPushOracle): + """Oracle for multimodal pushing task.""" + + def __init__(self, env, goal_dist_tolerance=0.04, action_noise_std=0.0): + super(MultimodalOrientedPushOracle, self).__init__(env) + self._goal_dist_tolerance = goal_dist_tolerance + self._action_noise_std = action_noise_std + + def reset(self): + self.origin = None + self.first_preblock = None + self.phase = "move_to_pre_block" + + def _get_move_to_preblock(self, xy_pre_block, xy_ee): + max_step_velocity = 0.3 + # Go 5 cm away from the block, on the line between the block and target. + xy_delta_to_preblock = xy_pre_block - xy_ee + diff = np.linalg.norm(xy_delta_to_preblock) + if diff < 0.001: + self.phase = "move_to_block" + if self.first_preblock is None: + self.first_preblock = np.copy(xy_pre_block) + xy_delta = xy_delta_to_preblock + return xy_delta, max_step_velocity + + def _get_action_for_block_target(self, time_step, block="block", target="target"): + # Specifying this as velocity makes it independent of control frequency. + max_step_velocity = 0.35 + + info = self._get_action_info(time_step, block, target) + + if self.origin is None: + self.origin = np.copy(info.xy_ee) + + if self.phase == "move_to_pre_block": + xy_delta, max_step_velocity = self._get_move_to_preblock( + info.xy_pre_block, info.xy_ee + ) + + if self.phase == "return_to_first_preblock": + max_step_velocity = 0.3 + if self.first_preblock is None: + self.first_preblock = self.origin + # Return to the first preblock. + xy_delta_to_origin = self.first_preblock - info.xy_ee + diff = np.linalg.norm(xy_delta_to_origin) + if diff < 0.001: + self.phase = "return_to_origin" + xy_delta = xy_delta_to_origin + + if self.phase == "return_to_origin": + max_step_velocity = 0.3 + # Go 5 cm away from the block, on the line between the block and target. + xy_delta_to_origin = self.origin - info.xy_ee + diff = np.linalg.norm(xy_delta_to_origin) + if diff < 0.001: + self.phase = "move_to_pre_block" + xy_delta = xy_delta_to_origin + + if self.phase == "move_to_block": + xy_delta = self._get_move_to_block( + info.xy_delta_to_nexttoblock, + info.theta_threshold_to_orient, + info.theta_error, + ) + + if self.phase == "push_block": + xy_delta = self._get_push_block( + info.theta_error, + info.theta_threshold_to_orient, + info.xy_delta_to_touchingblock, + ) + + orient_circle_diameter = 0.025 + + if self.phase == "orient_block_left" or self.phase == "orient_block_right": + max_step_velocity = 0.15 + + if self.phase == "orient_block_left": + xy_delta = self._get_orient_block_left( + info.xy_dir_block_to_ee, + orient_circle_diameter, + info.xy_block, + info.xy_ee, + info.theta_error, + info.theta_threshold_flat_enough, + ) + + if self.phase == "orient_block_right": + xy_delta = self._get_orient_block_right( + info.xy_dir_block_to_ee, + orient_circle_diameter, + info.xy_block, + info.xy_ee, + info.theta_error, + info.theta_threshold_flat_enough, + ) + + if self._action_noise_std != 0.0: + xy_delta += self._np_random_state.randn(2) * self._action_noise_std + + max_step_distance = max_step_velocity * (1 / self._env.get_control_frequency()) + length = np.linalg.norm(xy_delta) + if length > max_step_distance: + xy_direction = xy_delta / length + xy_delta = xy_direction * max_step_distance + return xy_delta + + def _choose_goal_order(self): + """Chooses block->target order for multimodal pushing.""" + # Define all possible ((first_block, first_target), + # (second_block, second_target)). + possible_orders = [ + (("block", "target"), ("block2", "target2")), + (("block", "target2"), ("block2", "target")), + (("block2", "target"), ("block", "target2")), + (("block2", "target2"), ("block", "target")), + ] + # import pdb; pdb.set_trace() + # result = random.choice(possible_orders) + result = possible_orders[self._env._rng.choice(len(possible_orders))] + return result + + def _action(self, time_step, policy_state): + if time_step.is_first(): + self.reset() + ( + (self._first_block, self._first_target), + (self._second_block, self._second_target), + ) = self._choose_goal_order() + self._current_block, self._current_target = ( + self._first_block, + self._first_target, + ) + self._has_switched = False + + def _block_target_dist(block, target): + dist = np.linalg.norm( + time_step.observation["%s_translation" % block] + - time_step.observation["%s_translation" % target] + ) + return dist + + if ( + _block_target_dist(self._first_block, self._first_target) + < self._goal_dist_tolerance + and not self._has_switched + ): + # If first block has been pushed to first target, switch to second block. + self._current_block, self._current_target = ( + self._second_block, + self._second_target, + ) + self._has_switched = True + self.phase = "return_to_first_preblock" + + xy_delta = self._get_action_for_block_target( + time_step, block=self._current_block, target=self._current_target + ) + + return policy_step.PolicyStep(action=np.asarray(xy_delta, dtype=np.float32)) diff --git a/diffusion_policy/env/block_pushing/oracles/oriented_push_oracle.py b/diffusion_policy/env/block_pushing/oracles/oriented_push_oracle.py new file mode 100644 index 0000000..39c4c6a --- /dev/null +++ b/diffusion_policy/env/block_pushing/oracles/oriented_push_oracle.py @@ -0,0 +1,258 @@ +# coding=utf-8 +# Copyright 2022 The Reach ML Authors. +# +# 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. + +"""Oracle for pushing task which orients the block then pushes it.""" + +import diffusion_policy.env.block_pushing.oracles.pushing_info as pushing_info_module +import numpy as np +from tf_agents.policies import py_policy +from tf_agents.trajectories import policy_step +from tf_agents.trajectories import time_step as ts +from tf_agents.typing import types + +# Only used for debug visualization. +import pybullet # pylint: disable=unused-import + + +class OrientedPushOracle(py_policy.PyPolicy): + """Oracle for pushing task which orients the block then pushes it.""" + + def __init__(self, env, action_noise_std=0.0): + super(OrientedPushOracle, self).__init__( + env.time_step_spec(), env.action_spec() + ) + self._env = env + self._np_random_state = np.random.RandomState(0) + self.phase = "move_to_pre_block" + self._action_noise_std = action_noise_std + + def reset(self): + self.phase = "move_to_pre_block" + + def get_theta_from_vector(self, vector): + return np.arctan2(vector[1], vector[0]) + + def theta_to_rotation2d(self, theta): + r = np.array([[np.cos(theta), -np.sin(theta)], [np.sin(theta), np.cos(theta)]]) + return r + + def rotate(self, theta, xy_dir_block_to_ee): + rot_2d = self.theta_to_rotation2d(theta) + return rot_2d @ xy_dir_block_to_ee + + def _get_action_info(self, time_step, block, target): + xy_block = time_step.observation["%s_translation" % block][:2] + theta_block = time_step.observation["%s_orientation" % block] + xy_target = time_step.observation["%s_translation" % target][:2] + xy_ee = time_step.observation["effector_target_translation"][:2] + + xy_block_to_target = xy_target - xy_block + xy_dir_block_to_target = (xy_block_to_target) / np.linalg.norm( + xy_block_to_target + ) + theta_to_target = self.get_theta_from_vector(xy_dir_block_to_target) + + theta_error = theta_to_target - theta_block + # Block has 4-way symmetry. + while theta_error > np.pi / 4: + theta_error -= np.pi / 2.0 + while theta_error < -np.pi / 4: + theta_error += np.pi / 2.0 + + xy_pre_block = xy_block + -xy_dir_block_to_target * 0.05 + xy_nexttoblock = xy_block + -xy_dir_block_to_target * 0.03 + xy_touchingblock = xy_block + -xy_dir_block_to_target * 0.01 + xy_delta_to_nexttoblock = xy_nexttoblock - xy_ee + xy_delta_to_touchingblock = xy_touchingblock - xy_ee + + xy_block_to_ee = xy_ee - xy_block + xy_dir_block_to_ee = xy_block_to_ee / np.linalg.norm(xy_block_to_ee) + + theta_threshold_to_orient = 0.2 + theta_threshold_flat_enough = 0.03 + return pushing_info_module.PushingInfo( + xy_block=xy_block, + xy_ee=xy_ee, + xy_pre_block=xy_pre_block, + xy_delta_to_nexttoblock=xy_delta_to_nexttoblock, + xy_delta_to_touchingblock=xy_delta_to_touchingblock, + xy_dir_block_to_ee=xy_dir_block_to_ee, + theta_threshold_to_orient=theta_threshold_to_orient, + theta_threshold_flat_enough=theta_threshold_flat_enough, + theta_error=theta_error, + ) + + def _get_move_to_preblock(self, xy_pre_block, xy_ee): + max_step_velocity = 0.3 + # Go 5 cm away from the block, on the line between the block and target. + xy_delta_to_preblock = xy_pre_block - xy_ee + diff = np.linalg.norm(xy_delta_to_preblock) + if diff < 0.001: + self.phase = "move_to_block" + xy_delta = xy_delta_to_preblock + return xy_delta, max_step_velocity + + def _get_move_to_block( + self, xy_delta_to_nexttoblock, theta_threshold_to_orient, theta_error + ): + diff = np.linalg.norm(xy_delta_to_nexttoblock) + if diff < 0.001: + self.phase = "push_block" + # If need to re-oorient, then re-orient. + if theta_error > theta_threshold_to_orient: + self.phase = "orient_block_left" + if theta_error < -theta_threshold_to_orient: + self.phase = "orient_block_right" + # Otherwise, push into the block. + xy_delta = xy_delta_to_nexttoblock + return xy_delta + + def _get_push_block( + self, theta_error, theta_threshold_to_orient, xy_delta_to_touchingblock + ): + # If need to reorient, go back to move_to_pre_block, move_to_block first. + if theta_error > theta_threshold_to_orient: + self.phase = "move_to_pre_block" + if theta_error < -theta_threshold_to_orient: + self.phase = "move_to_pre_block" + xy_delta = xy_delta_to_touchingblock + return xy_delta + + def _get_orient_block_left( + self, + xy_dir_block_to_ee, + orient_circle_diameter, + xy_block, + xy_ee, + theta_error, + theta_threshold_flat_enough, + ): + xy_dir_block_to_ee = self.rotate(0.2, xy_dir_block_to_ee) + xy_block_to_ee = xy_dir_block_to_ee * orient_circle_diameter + xy_push_left_spot = xy_block + xy_block_to_ee + xy_delta = xy_push_left_spot - xy_ee + if theta_error < theta_threshold_flat_enough: + self.phase = "move_to_pre_block" + return xy_delta + + def _get_orient_block_right( + self, + xy_dir_block_to_ee, + orient_circle_diameter, + xy_block, + xy_ee, + theta_error, + theta_threshold_flat_enough, + ): + xy_dir_block_to_ee = self.rotate(-0.2, xy_dir_block_to_ee) + xy_block_to_ee = xy_dir_block_to_ee * orient_circle_diameter + xy_push_left_spot = xy_block + xy_block_to_ee + xy_delta = xy_push_left_spot - xy_ee + if theta_error > -theta_threshold_flat_enough: + self.phase = "move_to_pre_block" + return xy_delta + + def _get_action_for_block_target(self, time_step, block="block", target="target"): + # Specifying this as velocity makes it independent of control frequency. + max_step_velocity = 0.35 + info = self._get_action_info(time_step, block, target) + + if self.phase == "move_to_pre_block": + xy_delta, max_step_velocity = self._get_move_to_preblock( + info.xy_pre_block, info.xy_ee + ) + + if self.phase == "move_to_block": + xy_delta = self._get_move_to_block( + info.xy_delta_to_nexttoblock, + info.theta_threshold_to_orient, + info.theta_error, + ) + + if self.phase == "push_block": + xy_delta = self._get_push_block( + info.theta_error, + info.theta_threshold_to_orient, + info.xy_delta_to_touchingblock, + ) + + orient_circle_diameter = 0.025 + + if self.phase == "orient_block_left" or self.phase == "orient_block_right": + max_step_velocity = 0.15 + + if self.phase == "orient_block_left": + xy_delta = self._get_orient_block_left( + info.xy_dir_block_to_ee, + orient_circle_diameter, + info.xy_block, + info.xy_ee, + info.theta_error, + info.theta_threshold_flat_enough, + ) + + if self.phase == "orient_block_right": + xy_delta = self._get_orient_block_right( + info.xy_dir_block_to_ee, + orient_circle_diameter, + info.xy_block, + info.xy_ee, + info.theta_error, + info.theta_threshold_flat_enough, + ) + + if self._action_noise_std != 0.0: + xy_delta += self._np_random_state.randn(2) * self._action_noise_std + + max_step_distance = max_step_velocity * (1 / self._env.get_control_frequency()) + length = np.linalg.norm(xy_delta) + if length > max_step_distance: + xy_direction = xy_delta / length + xy_delta = xy_direction * max_step_distance + return xy_delta + + def _action(self, time_step, policy_state): + if time_step.is_first(): + self.reset() + xy_delta = self._get_action_for_block_target( + time_step, block="block", target="target" + ) + return policy_step.PolicyStep(action=np.asarray(xy_delta, dtype=np.float32)) + + +class OrientedPushNormalizedOracle(py_policy.PyPolicy): + """Oracle for pushing task which orients the block then pushes it.""" + + def __init__(self, env): + super(OrientedPushNormalizedOracle, self).__init__( + env.time_step_spec(), env.action_spec() + ) + self._oracle = OrientedPushOracle(env) + self._env = env + + def reset(self): + self._oracle.reset() + + def _action(self, time_step, policy_state): + time_step = time_step._asdict() + time_step["observation"] = self._env.calc_unnormalized_state( + time_step["observation"] + ) + step = self._oracle._action( + ts.TimeStep(**time_step), policy_state + ) # pylint: disable=protected-access + return policy_step.PolicyStep( + action=self._env.calc_normalized_action(step.action) + ) diff --git a/diffusion_policy/env/block_pushing/oracles/pushing_info.py b/diffusion_policy/env/block_pushing/oracles/pushing_info.py new file mode 100644 index 0000000..0c01cbb --- /dev/null +++ b/diffusion_policy/env/block_pushing/oracles/pushing_info.py @@ -0,0 +1,35 @@ +# coding=utf-8 +# Copyright 2022 The Reach ML Authors. +# +# 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. + +"""Dataclass holding info needed for pushing oracles.""" +import dataclasses +from typing import Any + + +@dataclasses.dataclass +class PushingInfo: + """Holds onto info necessary for pushing state machine.""" + + xy_block: Any = None + xy_ee: Any = None + xy_pre_block: Any = None + xy_delta_to_nexttoblock: Any = None + xy_delta_to_touchingblock: Any = None + xy_dir_block_to_ee: Any = None + theta_threshold_to_orient: Any = None + theta_threshold_flat_enough: Any = None + theta_error: Any = None + obstacle_poses: Any = None + distance_to_target: Any = None diff --git a/diffusion_policy/env/block_pushing/oracles/reach_oracle.py b/diffusion_policy/env/block_pushing/oracles/reach_oracle.py new file mode 100644 index 0000000..8b7746c --- /dev/null +++ b/diffusion_policy/env/block_pushing/oracles/reach_oracle.py @@ -0,0 +1,61 @@ +# coding=utf-8 +# Copyright 2022 The Reach ML Authors. +# +# 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. + +"""Reach oracle.""" +import numpy as np +from tf_agents.policies import py_policy +from tf_agents.trajectories import policy_step +from tf_agents.trajectories import time_step as ts +from tf_agents.typing import types + +# Only used for debug visualization. +import pybullet # pylint: disable=unused-import + + +class ReachOracle(py_policy.PyPolicy): + """Oracle for moving to a specific spot relative to the block and target.""" + + def __init__(self, env, block_pushing_oracles_action_std=0.0): + super(ReachOracle, self).__init__(env.time_step_spec(), env.action_spec()) + self._env = env + self._np_random_state = np.random.RandomState(0) + self._block_pushing_oracles_action_std = block_pushing_oracles_action_std + + def _action(self, time_step, policy_state): + + # Specifying this as velocity makes it independent of control frequency. + max_step_velocity = 0.2 + + xy_ee = time_step.observation["effector_target_translation"] + + # This should be observable from block and target translation, + # but re-using the computation from the env so that it's only done once, and + # used for reward / completion computation. + xy_pre_block = self._env.reach_target_translation + + xy_delta = xy_pre_block - xy_ee + + if self._block_pushing_oracles_action_std != 0.0: + xy_delta += ( + self._np_random_state.randn(2) * self._block_pushing_oracles_action_std + ) + + max_step_distance = max_step_velocity * (1 / self._env.get_control_frequency()) + length = np.linalg.norm(xy_delta) + if length > max_step_distance: + xy_direction = xy_delta / length + xy_delta = xy_direction * max_step_distance + + return policy_step.PolicyStep(action=np.asarray(xy_delta, dtype=np.float32)) diff --git a/diffusion_policy/env/block_pushing/utils/pose3d.py b/diffusion_policy/env/block_pushing/utils/pose3d.py new file mode 100644 index 0000000..a25beeb --- /dev/null +++ b/diffusion_policy/env/block_pushing/utils/pose3d.py @@ -0,0 +1,70 @@ +# coding=utf-8 +# Copyright 2022 The Reach ML Authors. +# +# 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. + +"""A simple 6DOF pose container. +""" + +import dataclasses +import numpy as np +from scipy.spatial import transform + + +class NoCopyAsDict(object): + """Base class for dataclasses. Avoids a copy in the asdict() call.""" + + def asdict(self): + """Replacement for dataclasses.asdict. + + TF Dataset does not handle dataclasses.asdict, which uses copy.deepcopy when + setting values in the output dict. This causes issues with tf.Dataset. + Instead, shallow copy contents. + + Returns: + dict containing contents of dataclass. + """ + return {k.name: getattr(self, k.name) for k in dataclasses.fields(self)} + + +@dataclasses.dataclass +class Pose3d(NoCopyAsDict): + """Simple container for translation and rotation.""" + + rotation: transform.Rotation + translation: np.ndarray + + @property + def vec7(self): + return np.concatenate([self.translation, self.rotation.as_quat()]) + + def serialize(self): + return { + "rotation": self.rotation.as_quat().tolist(), + "translation": self.translation.tolist(), + } + + @staticmethod + def deserialize(data): + return Pose3d( + rotation=transform.Rotation.from_quat(data["rotation"]), + translation=np.array(data["translation"]), + ) + + def __eq__(self, other): + return np.array_equal( + self.rotation.as_quat(), other.rotation.as_quat() + ) and np.array_equal(self.translation, other.translation) + + def __ne__(self, other): + return not self.__eq__(other) diff --git a/diffusion_policy/env/block_pushing/utils/utils_pybullet.py b/diffusion_policy/env/block_pushing/utils/utils_pybullet.py new file mode 100644 index 0000000..d9a664d --- /dev/null +++ b/diffusion_policy/env/block_pushing/utils/utils_pybullet.py @@ -0,0 +1,451 @@ +# coding=utf-8 +# Copyright 2022 The Reach ML Authors. +# +# 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. + +"""Assortment of utilities to interact with bullet within g3.""" +import dataclasses +import datetime +import getpass +import gzip +import json +import os +import time +from typing import Any, Dict, List, Optional, Tuple + +from absl import logging +from diffusion_policy.env.block_pushing.utils.pose3d import Pose3d +import numpy as np +from scipy.spatial import transform +import six + + +import pybullet +import pybullet_data +import pybullet_utils.bullet_client as bullet_client + +Vec3 = Tuple[float, float, float] +Vec4 = Tuple[float, float, float, float] +PYBULLET_STATE_VERSION = 2 # Basic versioning of serialized pybullet state. + + +# Note about rotation_to_matrix and matrix_to_rotation below: +# The abstractions below allow us to use older versions of scipy. +def rotation_to_matrix(rotation): + if hasattr(rotation, "as_dcm"): + return rotation.as_dcm() + else: + assert hasattr(rotation, "as_matrix") + return rotation.as_matrix() + + +def matrix_to_rotation(matrix): + if hasattr(transform.Rotation, "from_dcm"): + return transform.Rotation.from_dcm(matrix) + else: + assert hasattr(transform.Rotation, "from_matrix") + return transform.Rotation.from_matrix(matrix) + + +def load_urdf(pybullet_client, file_path, *args, **kwargs): + """Loads the given URDF filepath.""" + + # Handles most general file open case. + try: + if os.path.exists(file_path): + return pybullet_client.loadURDF(file_path, *args, **kwargs) + except pybullet_client.error: + pass + + try: + import pathlib + asset_path = str(pathlib.Path(__file__).parent.parent.joinpath('assets')) + if file_path.startswith("third_party/py/envs/assets/"): + pybullet_client.setAdditionalSearchPath(asset_path) + file_path = file_path[len("third_party/py/envs/assets/") :] + if file_path.startswith( + "third_party/bullet/examples/pybullet/gym/pybullet_data/" + ): + pybullet_client.setAdditionalSearchPath(pybullet_data.getDataPath()) + file_path = file_path[55:] + # logging.info("Loading URDF %s", file_path) + return pybullet_client.loadURDF(file_path, *args, **kwargs) + except pybullet.error: + raise FileNotFoundError("Cannot load the URDF file {}".format(file_path)) + + +def add_visual_sphere(client, center=(0, 0, 0), radius=0.1, rgba=(0.5, 0.5, 0.5, 0.5)): + """Add a sphere to bullet scene (visual only, no physics). + + Args: + client: pybullet client (or pybullet library handle). + center: Center of sphere. + radius: Sphere radius. + rgba: rgba color of sphere. + + Returns: + Unique integer bullet id of constructed object. + """ + vis_obj_id = client.createVisualShape( + client.GEOM_SPHERE, radius=radius, rgbaColor=rgba + ) + obj_id = client.createMultiBody( + baseCollisionShapeIndex=-1, baseVisualShapeIndex=vis_obj_id, basePosition=center + ) + return obj_id + + +def pybullet_mat_to_numpy_4x4(pybullet_matrix): + assert len(pybullet_matrix) == 16, "pybullet matrix should be len 16" + return np.transpose(np.reshape(np.array(pybullet_matrix, dtype=np.float64), (4, 4))) + + +def decompose_view_matrix(pybullet_view_matrix): + """Decompose view matrix into pos + quat format (assumes mat is rigid!).""" + # It would be MUCH better to use something from bullet, however pybullet does + # not expose all of the linear algebra library. + mat = pybullet_mat_to_numpy_4x4(pybullet_view_matrix) + + # View matrix is now: + # | R_11 R_12 R_13 t_1 | + # | R_21 R_22 R_23 t_2 | + # | R_31 R_32 R_33 t_3 | + # | 0 0 0 1 | + + # R is the inverse eye to target at orientation, and t is R * eye. + mat_view_to_world = np.linalg.inv(mat) + + # mat_view_to_world is the view to world transform, therefore the translation + # component of this matrix is simply the world space position (since mat * + # (0, 0, 0, 1)) is just copying the right column. + world_xyz_view = np.copy(mat_view_to_world[0:3, 3]) + + mat_view_to_world[0:3, 3] = 0 # Zero out the position change. + world_quat_view = matrix_to_rotation(mat_view_to_world).as_quat() + + return world_xyz_view, world_quat_view + + +def world_obj_to_view(world_xyz_obj, world_quat_obj, camera_view, client): + """Transform object into view space.""" + world_xyz_view, world_quat_view = decompose_view_matrix(camera_view) + view_xyz_world, view_quat_world = client.invertTransform( + world_xyz_view, world_quat_view + ) + view_xyz_obj, view_quat_obj = client.multiplyTransforms( + view_xyz_world, view_quat_world, world_xyz_obj, world_quat_obj + ) + + return view_xyz_obj, view_quat_obj + + +def image_xy_to_view_ray(xy, cam_width, cam_height, proj_mat_inv): + """Calculate view-space ray from pixel location.""" + # Recall (from http://www.songho.ca/opengl/gl_projectionmatrix.html): + # xyzw_clip = M_proj * xyzw_eye, and + # xyz_ndc = xyzw_clip[0:3] / xwzw_clip[3]. + xyz_ndc = np.array( + [2.0 * xy[0] / cam_width - 1.0, -(2.0 * xy[1] / cam_height - 1.0), 0] + ) # in [-1, 1] + xyzw_clip = np.concatenate([xyz_ndc, [1]]) + xyzw_eye = proj_mat_inv @ xyzw_clip + origin = np.zeros(3) + vec = xyzw_eye[:3] / max(np.linalg.norm(xyzw_eye[:3]), 1e-6) + return origin, vec + + +def view_ray_to_world_ray(origin, vec, view_mat_inv): + """Transform view-space ray into world space.""" + origin = view_mat_inv @ np.concatenate([origin, [1]]) + vec = view_mat_inv @ np.concatenate([vec, [0]]) + + return origin[:3], vec[:3] + + +def ray_to_plane_test(ray_origin, ray_vec, plane_origin, plane_normal): + """Perform a ray-plane intersection test.""" + ln = np.dot(plane_normal, ray_vec) + if abs(ln) < np.finfo(np.float32).eps: + return None + + # Solve for the intersection fraction t. + t = np.dot(plane_normal, plane_origin - ray_origin) / ln + if t >= 0: + return ray_origin + ray_vec * t + else: + return None + + +def get_workspace(env): + ( + workspace_origin, + workspace_quat, + ) = env.pybullet_client.getBasePositionAndOrientation(env.workspace_uid) + workspace_normal = rotation_to_matrix(transform.Rotation.from_quat(workspace_quat))[ + 2, 0:3 + ] + + return workspace_origin, workspace_normal + + +def reset_camera_pose(env, view_type): + """Reset camera pose to canonical frame.""" + p = env.pybullet_client + + if view_type == "POLICY": + camera_info = p.getDebugVisualizerCamera() + image_size = (camera_info[0], camera_info[1]) + + viewm, _, front_position, lookat, _ = env.calc_camera_params(image_size) + + euler = matrix_to_rotation(pybullet_mat_to_numpy_4x4(viewm)[0:3, 0:3]).as_euler( + "xyz", degrees=False + ) + pitch = euler[1] + yaw = -euler[2] + # The distance is a bit far away (the GL view has higher FOV). + distance = np.linalg.norm(front_position - lookat) * 0.6 + elif view_type == "TOP_DOWN": + workspace_origin, _ = get_workspace(env) + distance = 0.5 + lookat = workspace_origin + yaw = np.pi / 2 + # Note: pi/2 pitch results in gimble lock and pybullet doesn't support it. + pitch = -(np.pi / 2 - 1e-5) + else: + raise ValueError("unsupported view_type %s" % view_type) + p.resetDebugVisualizerCamera( + cameraDistance=distance, + cameraYaw=360 * yaw / (2.0 * np.pi), + cameraPitch=360 * pitch / (2.0 * np.pi), + cameraTargetPosition=lookat, + ) + + +def _lists_to_tuple(obj): + if isinstance(obj, list): + return tuple([_lists_to_tuple(v) for v in obj]) + else: + return obj + + +@dataclasses.dataclass +class ObjState: + """A container for storing pybullet object state.""" + + obj_id: int + + # base_pose: (xyz, quat). + base_pose: Tuple[Vec3, Vec4] + # base_vel: (vel, ang_vel). + base_vel: Tuple[Vec3, Vec3] + joint_info: Any + joint_state: Any + + @staticmethod + def get_bullet_state(client, obj_id): + """Read Pybullet internal state.""" + base_pose = client.getBasePositionAndOrientation(obj_id) + base_vel = client.getBaseVelocity(obj_id) + + joint_info = [] + joint_state = [] + for i in range(client.getNumJoints(obj_id)): + joint_state.append(client.getJointState(obj_id, i)) + joint_info.append(ObjState._get_joint_info(client, obj_id, i)) + + return ObjState( + obj_id=obj_id, + base_pose=base_pose, + base_vel=base_vel, + joint_info=tuple(joint_info), + joint_state=tuple(joint_state), + ) + + @staticmethod + def _get_joint_info(client, obj_id, joint_index): + ji = client.getJointInfo(obj_id, joint_index) + return tuple([v if not isinstance(v, bytes) else v.decode("utf-8") for v in ji]) + + def set_bullet_state(self, client, obj_id): + """Hard set the current bullet state.""" + xyz, quat = self.base_pose + client.resetBasePositionAndOrientation(obj_id, xyz, quat) + vel, ang_vel = self.base_vel + client.resetBaseVelocity(obj_id, vel, ang_vel) + + njoints = client.getNumJoints(obj_id) + if njoints != len(self.joint_info) or njoints != len(self.joint_state): + raise ValueError("Incorrect number of joint info state pairs.") + + for i, (joint_info, joint_state) in enumerate( + zip(self.joint_info, self.joint_state) + ): + joint_index = joint_info[0] + if joint_index != i: + raise ValueError("Joint index mismatch.") + + # Check that the current joint we're trying to restore state for has the + # same info as the state joint. + cur_joint_info = ObjState._get_joint_info(client, obj_id, joint_index) + if cur_joint_info != joint_info: + raise ValueError( + "joint_info mismatch %s vs %s (expected)" + % (str(cur_joint_info), str(joint_info)) + ) + joint_position = joint_state[0] + joint_velocity = joint_state[1] + client.resetJointState( + obj_id, i, targetValue=joint_position, targetVelocity=joint_velocity + ) + + def serialize(self): + return { + "obj_id": self.obj_id, + "base_pose": self.base_pose, + "base_vel": self.base_vel, + "joint_info": self.joint_info, + "joint_state": self.joint_state, + } + + @staticmethod + def deserialize(data): + return ObjState( + obj_id=_lists_to_tuple(data["obj_id"]), + base_pose=_lists_to_tuple(data["base_pose"]), + base_vel=_lists_to_tuple(data["base_vel"]), + joint_info=_lists_to_tuple(data["joint_info"]), + joint_state=_lists_to_tuple(data["joint_state"]), + ) + + +@dataclasses.dataclass +class XarmState(ObjState): + """A container for storing pybullet robot state.""" + + # The set point of the robot's controller. + target_effector_pose: Pose3d + goal_translation: Optional[Vec3] + + @staticmethod + def get_bullet_state(client, obj_id, target_effector_pose, goal_translation): + if goal_translation is not None: + goal_translation = tuple(goal_translation.tolist()) + return XarmState( + **dataclasses.asdict(ObjState.get_bullet_state(client, obj_id)), + target_effector_pose=target_effector_pose, + goal_translation=goal_translation + ) + + def serialize(self): + data = ObjState.serialize(self) + data["target_effector_pose"] = self.target_effector_pose.serialize() + if self.goal_translation is not None: + data["goal_translation"] = self.goal_translation + else: + data["goal_translation"] = [] + return data + + @staticmethod + def deserialize(data): + goal_translation = ( + None + if not data["goal_translation"] + else _lists_to_tuple(data["goal_translation"]) + ) + return XarmState( + obj_id=data["obj_id"], + base_pose=_lists_to_tuple(data["base_pose"]), + base_vel=_lists_to_tuple(data["base_vel"]), + joint_info=_lists_to_tuple(data["joint_info"]), + joint_state=_lists_to_tuple(data["joint_state"]), + goal_translation=goal_translation, + target_effector_pose=Pose3d.deserialize(data["target_effector_pose"]), + ) + + +def _serialize_pybullet_state(pybullet_state): + """Convert data to POD types.""" + if isinstance(pybullet_state, list): + return [_serialize_pybullet_state(entry) for entry in pybullet_state] + elif isinstance(pybullet_state, dict): + assert "_serialized_obj_name" not in pybullet_state + return { + key: _serialize_pybullet_state(value) + for key, value in pybullet_state.items() + } + elif isinstance(pybullet_state, (XarmState, ObjState)): + return { + "_serialized_obj_name": type(pybullet_state).__name__, + "_serialized_data": pybullet_state.serialize(), + } + elif isinstance(pybullet_state, int): + return pybullet_state + else: + raise ValueError( + "Unhandled type for object %s, type %s" + % (str(pybullet_state), type(pybullet_state)) + ) + + +def _deserialize_pybullet_state(state): + """Parse data from POD types.""" + if isinstance(state, list): + return [_deserialize_pybullet_state(item) for item in state] + elif isinstance(state, dict): + if "_serialized_obj_name" in state: + if state["_serialized_obj_name"] == XarmState.__name__: + return XarmState.deserialize(state["_serialized_data"]) + elif state["_serialized_obj_name"] == ObjState.__name__: + return ObjState.deserialize(state["_serialized_data"]) + else: + raise ValueError("Unsupported: %s" % state["_serialized_obj_name"]) + else: + return { + key: _deserialize_pybullet_state(value) for key, value in state.items() + } + elif isinstance(state, int): + return state + else: + raise ValueError("Unhandled type for object %s" % str(state)) + + +def write_pybullet_state(filename, pybullet_state, task, actions=None): + """Serialize pybullet state to json file.""" + import torch + data = { + "pybullet_state": _serialize_pybullet_state(pybullet_state), + "state_version": PYBULLET_STATE_VERSION, + "ts_ms": int(time.mktime(datetime.datetime.now().timetuple())) * 1000, + "user": getpass.getuser(), + "task": task, + "actions": actions if actions is not None else [], + } + torch.save(data, filename) + + +def read_pybullet_state(filename): + """Deserialize pybullet state from json file.""" + import torch + data = torch.load(filename) + + assert isinstance(data, dict) + + if data["state_version"] != PYBULLET_STATE_VERSION: + raise ValueError( + "incompatible state data (version %d, expected %d)" + % (data["state_version"], PYBULLET_STATE_VERSION) + ) + + data["pybullet_state"] = _deserialize_pybullet_state(data["pybullet_state"]) + return data diff --git a/diffusion_policy/env/block_pushing/utils/xarm_sim_robot.py b/diffusion_policy/env/block_pushing/utils/xarm_sim_robot.py new file mode 100644 index 0000000..600dd7e --- /dev/null +++ b/diffusion_policy/env/block_pushing/utils/xarm_sim_robot.py @@ -0,0 +1,230 @@ +# coding=utf-8 +# Copyright 2022 The Reach ML Authors. +# +# 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. + +"""XArm Robot Kinematics.""" +from diffusion_policy.env.block_pushing.utils import utils_pybullet +from diffusion_policy.env.block_pushing.utils.pose3d import Pose3d +import numpy as np +from scipy.spatial import transform +import pybullet + +XARM_URDF_PATH = ( + "third_party/bullet/examples/pybullet/gym/pybullet_data/" "xarm/xarm6_robot.urdf" +) +SUCTION_URDF_PATH = "third_party/py/envs/assets/suction/" "suction-head-long.urdf" +CYLINDER_URDF_PATH = "third_party/py/envs/assets/suction/" "cylinder.urdf" +CYLINDER_REAL_URDF_PATH = "third_party/py/envs/assets/suction/" "cylinder_real.urdf" +HOME_JOINT_POSITIONS = np.deg2rad([0, -20, -80, 0, 100, -30]) + + +class XArmSimRobot: + """A simulated PyBullet XArm robot, mostly for forward/inverse kinematics.""" + + def __init__( + self, + pybullet_client, + initial_joint_positions=HOME_JOINT_POSITIONS, + end_effector="none", + color="default", + ): + self._pybullet_client = pybullet_client + self.initial_joint_positions = initial_joint_positions + + if color == "default": + self.xarm = utils_pybullet.load_urdf( + pybullet_client, XARM_URDF_PATH, [0, 0, 0] + ) + else: + raise ValueError("Unrecognized xarm color %s" % color) + + # Get revolute joints of robot (skip fixed joints). + joints = [] + joint_indices = [] + for i in range(self._pybullet_client.getNumJoints(self.xarm)): + joint_info = self._pybullet_client.getJointInfo(self.xarm, i) + if joint_info[2] == pybullet.JOINT_REVOLUTE: + joints.append(joint_info[0]) + joint_indices.append(i) + # Note examples in pybullet do this, but it is not clear what the + # benefits are. + self._pybullet_client.changeDynamics( + self.xarm, i, linearDamping=0, angularDamping=0 + ) + + self._n_joints = len(joints) + self._joints = tuple(joints) + self._joint_indices = tuple(joint_indices) + + # Move robot to home joint configuration + self.reset_joints(self.initial_joint_positions) + self.effector_link = 6 + + if ( + end_effector == "suction" + or end_effector == "cylinder" + or end_effector == "cylinder_real" + ): + self.end_effector = self._setup_end_effector(end_effector) + else: + if end_effector != "none": + raise ValueError('end_effector "%s" is not supported.' % end_effector) + self.end_effector = None + + def _setup_end_effector(self, end_effector): + """Adds a suction or cylinder end effector.""" + pose = self.forward_kinematics() + if end_effector == "suction": + body = utils_pybullet.load_urdf( + self._pybullet_client, + SUCTION_URDF_PATH, + pose.translation, + pose.rotation.as_quat(), + ) + elif end_effector == "cylinder": + body = utils_pybullet.load_urdf( + self._pybullet_client, + CYLINDER_URDF_PATH, + pose.translation, + pose.rotation.as_quat(), + ) + elif end_effector == "cylinder_real": + body = utils_pybullet.load_urdf( + self._pybullet_client, + CYLINDER_REAL_URDF_PATH, + pose.translation, + pose.rotation.as_quat(), + ) + else: + raise ValueError('end_effector "%s" is not supported.' % end_effector) + + constraint_id = self._pybullet_client.createConstraint( + parentBodyUniqueId=self.xarm, + parentLinkIndex=6, + childBodyUniqueId=body, + childLinkIndex=-1, + jointType=pybullet.JOINT_FIXED, + jointAxis=(0, 0, 0), + parentFramePosition=(0, 0, 0), + childFramePosition=(0, 0, 0), + ) + self._pybullet_client.changeConstraint(constraint_id, maxForce=50) + + return body + + def reset_joints(self, joint_values): + """Sets the position of the Robot's joints. + + *Note*: This should only be used at the start while not running the + simulation resetJointState overrides all physics simulation. + + Args: + joint_values: Iterable with desired joint positions. + """ + for i in range(self._n_joints): + self._pybullet_client.resetJointState( + self.xarm, self._joints[i], joint_values[i] + ) + + def get_joints_measured(self): + joint_states = self._pybullet_client.getJointStates( + self.xarm, self._joint_indices + ) + joint_positions = np.array([state[0] for state in joint_states]) + joint_velocities = np.array([state[1] for state in joint_states]) + joint_torques = np.array([state[3] for state in joint_states]) + return joint_positions, joint_velocities, joint_torques + + def get_joint_positions(self): + joint_states = self._pybullet_client.getJointStates( + self.xarm, self._joint_indices + ) + joint_positions = np.array([state[0] for state in joint_states]) + return joint_positions + + def forward_kinematics(self): + """Forward kinematics.""" + effector_state = self._pybullet_client.getLinkState( + self.xarm, self.effector_link + ) + return Pose3d( + translation=np.array(effector_state[0]), + rotation=transform.Rotation.from_quat(effector_state[1]), + ) + + def inverse_kinematics( + self, world_effector_pose, max_iterations=100, residual_threshold=1e-10 + ): + """Inverse kinematics. + + Args: + world_effector_pose: Target Pose3d for the robot's end effector. + max_iterations: Refine the IK solution until the distance between target + and actual end effector position is below this threshold, or the + maxNumIterations is reached. Default is 20 iterations. + residual_threshold: Refine the IK solution until the distance between + target and actual end effector position is below this threshold, or the + maxNumIterations is reached. + + Returns: + Numpy array with required joint angles to reach the requested pose. + """ + return np.array( + self._pybullet_client.calculateInverseKinematics( + self.xarm, + self.effector_link, + world_effector_pose.translation, + world_effector_pose.rotation.as_quat(), # as_quat returns xyzw. + lowerLimits=[-17] * 6, + upperLimits=[17] * 6, + jointRanges=[17] * 6, + restPoses=[0, 0] + self.get_joint_positions()[2:].tolist(), + maxNumIterations=max_iterations, + residualThreshold=residual_threshold, + ) + ) + + def set_target_effector_pose(self, world_effector_pose): + target_joint_positions = self.inverse_kinematics(world_effector_pose) + self.set_target_joint_positions(target_joint_positions) + + def set_target_joint_velocities(self, target_joint_velocities): + self._pybullet_client.setJointMotorControlArray( + self.xarm, + self._joint_indices, + pybullet.VELOCITY_CONTROL, + targetVelocities=target_joint_velocities, + forces=[5 * 240.0] * 6, + ) + + def set_target_joint_positions(self, target_joint_positions): + self._pybullet_client.setJointMotorControlArray( + self.xarm, + self._joint_indices, + pybullet.POSITION_CONTROL, + targetPositions=target_joint_positions, + forces=[5 * 240.0] * 6, + ) + + def set_alpha_transparency(self, alpha): + visual_shape_data = self._pybullet_client.getVisualShapeData(self.xarm) + + for i in range(self._pybullet_client.getNumJoints(self.xarm)): + object_id, link_index, _, _, _, _, _, rgba_color = visual_shape_data[i] + assert object_id == self.xarm, "xarm id mismatch." + assert link_index == i, "Link visual data was returned out of order." + rgba_color = list(rgba_color[0:3]) + [alpha] + self._pybullet_client.changeVisualShape( + self.xarm, linkIndex=i, rgbaColor=rgba_color + ) diff --git a/diffusion_policy/env/kitchen/__init__.py b/diffusion_policy/env/kitchen/__init__.py new file mode 100644 index 0000000..f553f35 --- /dev/null +++ b/diffusion_policy/env/kitchen/__init__.py @@ -0,0 +1,30 @@ +"""Environments using kitchen and Franka robot.""" +from gym.envs.registration import register + +register( + id="kitchen-microwave-kettle-light-slider-v0", + entry_point="diffusion_policy.env.kitchen.v0:KitchenMicrowaveKettleLightSliderV0", + max_episode_steps=280, + reward_threshold=1.0, +) + +register( + id="kitchen-microwave-kettle-burner-light-v0", + entry_point="diffusion_policy.env.kitchen.v0:KitchenMicrowaveKettleBottomBurnerLightV0", + max_episode_steps=280, + reward_threshold=1.0, +) + +register( + id="kitchen-kettle-microwave-light-slider-v0", + entry_point="diffusion_policy.env.kitchen.v0:KitchenKettleMicrowaveLightSliderV0", + max_episode_steps=280, + reward_threshold=1.0, +) + +register( + id="kitchen-all-v0", + entry_point="diffusion_policy.env.kitchen.v0:KitchenAllV0", + max_episode_steps=280, + reward_threshold=1.0, +) diff --git a/diffusion_policy/env/kitchen/base.py b/diffusion_policy/env/kitchen/base.py new file mode 100644 index 0000000..61c9650 --- /dev/null +++ b/diffusion_policy/env/kitchen/base.py @@ -0,0 +1,153 @@ +import sys +import os +# hack to import adept envs +ADEPT_DIR = os.path.join(os.path.dirname(__file__), 'relay_policy_learning', 'adept_envs') +sys.path.append(ADEPT_DIR) + +import logging +import numpy as np +import adept_envs +from adept_envs.franka.kitchen_multitask_v0 import KitchenTaskRelaxV1 + +OBS_ELEMENT_INDICES = { + "bottom burner": np.array([11, 12]), + "top burner": np.array([15, 16]), + "light switch": np.array([17, 18]), + "slide cabinet": np.array([19]), + "hinge cabinet": np.array([20, 21]), + "microwave": np.array([22]), + "kettle": np.array([23, 24, 25, 26, 27, 28, 29]), +} +OBS_ELEMENT_GOALS = { + "bottom burner": np.array([-0.88, -0.01]), + "top burner": np.array([-0.92, -0.01]), + "light switch": np.array([-0.69, -0.05]), + "slide cabinet": np.array([0.37]), + "hinge cabinet": np.array([0.0, 1.45]), + "microwave": np.array([-0.75]), + "kettle": np.array([-0.23, 0.75, 1.62, 0.99, 0.0, 0.0, -0.06]), +} +BONUS_THRESH = 0.3 +logger = logging.getLogger() + + +class KitchenBase(KitchenTaskRelaxV1): + # A string of element names. The robot's task is then to modify each of + # these elements appropriately. + TASK_ELEMENTS = [] + ALL_TASKS = [ + "bottom burner", + "top burner", + "light switch", + "slide cabinet", + "hinge cabinet", + "microwave", + "kettle", + ] + REMOVE_TASKS_WHEN_COMPLETE = True + TERMINATE_ON_TASK_COMPLETE = True + TERMINATE_ON_WRONG_COMPLETE = False + COMPLETE_IN_ANY_ORDER = ( + True # This allows for the tasks to be completed in arbitrary order. + ) + + def __init__( + self, dataset_url=None, ref_max_score=None, ref_min_score=None, + use_abs_action=False, + **kwargs + ): + self.tasks_to_complete = list(self.TASK_ELEMENTS) + self.goal_masking = True + super(KitchenBase, self).__init__(use_abs_action=use_abs_action, **kwargs) + + def set_goal_masking(self, goal_masking=True): + """Sets goal masking for goal-conditioned approaches (like RPL).""" + self.goal_masking = goal_masking + + def _get_task_goal(self, task=None, actually_return_goal=False): + if task is None: + task = ["microwave", "kettle", "bottom burner", "light switch"] + new_goal = np.zeros_like(self.goal) + if self.goal_masking and not actually_return_goal: + return new_goal + for element in task: + element_idx = OBS_ELEMENT_INDICES[element] + element_goal = OBS_ELEMENT_GOALS[element] + new_goal[element_idx] = element_goal + + return new_goal + + def reset_model(self): + self.tasks_to_complete = list(self.TASK_ELEMENTS) + return super(KitchenBase, self).reset_model() + + def _get_reward_n_score(self, obs_dict): + reward_dict, score = super(KitchenBase, self)._get_reward_n_score(obs_dict) + reward = 0.0 + next_q_obs = obs_dict["qp"] + next_obj_obs = obs_dict["obj_qp"] + next_goal = self._get_task_goal( + task=self.TASK_ELEMENTS, actually_return_goal=True + ) # obs_dict['goal'] + idx_offset = len(next_q_obs) + completions = [] + all_completed_so_far = True + for element in self.tasks_to_complete: + element_idx = OBS_ELEMENT_INDICES[element] + distance = np.linalg.norm( + next_obj_obs[..., element_idx - idx_offset] - next_goal[element_idx] + ) + complete = distance < BONUS_THRESH + condition = ( + complete and all_completed_so_far + if not self.COMPLETE_IN_ANY_ORDER + else complete + ) + if condition: # element == self.tasks_to_complete[0]: + print("Task {} completed!".format(element)) + completions.append(element) + all_completed_so_far = all_completed_so_far and complete + if self.REMOVE_TASKS_WHEN_COMPLETE: + [self.tasks_to_complete.remove(element) for element in completions] + bonus = float(len(completions)) + reward_dict["bonus"] = bonus + reward_dict["r_total"] = bonus + score = bonus + return reward_dict, score + + def step(self, a, b=None): + obs, reward, done, env_info = super(KitchenBase, self).step(a, b=b) + if self.TERMINATE_ON_TASK_COMPLETE: + done = not self.tasks_to_complete + if self.TERMINATE_ON_WRONG_COMPLETE: + all_goal = self._get_task_goal(task=self.ALL_TASKS) + for wrong_task in list(set(self.ALL_TASKS) - set(self.TASK_ELEMENTS)): + element_idx = OBS_ELEMENT_INDICES[wrong_task] + distance = np.linalg.norm(obs[..., element_idx] - all_goal[element_idx]) + complete = distance < BONUS_THRESH + if complete: + done = True + break + env_info["completed_tasks"] = set(self.TASK_ELEMENTS) - set( + self.tasks_to_complete + ) + return obs, reward, done, env_info + + def get_goal(self): + """Loads goal state from dataset for goal-conditioned approaches (like RPL).""" + raise NotImplementedError + + def _split_data_into_seqs(self, data): + """Splits dataset object into list of sequence dicts.""" + seq_end_idxs = np.where(data["terminals"])[0] + start = 0 + seqs = [] + for end_idx in seq_end_idxs: + seqs.append( + dict( + states=data["observations"][start : end_idx + 1], + actions=data["actions"][start : end_idx + 1], + ) + ) + start = end_idx + 1 + return seqs diff --git a/diffusion_policy/env/kitchen/kitchen_lowdim_wrapper.py b/diffusion_policy/env/kitchen/kitchen_lowdim_wrapper.py new file mode 100644 index 0000000..c6908a4 --- /dev/null +++ b/diffusion_policy/env/kitchen/kitchen_lowdim_wrapper.py @@ -0,0 +1,49 @@ +from typing import List, Dict, Optional, Optional +import numpy as np +import gym +from gym.spaces import Box +from diffusion_policy.env.kitchen.base import KitchenBase + +class KitchenLowdimWrapper(gym.Env): + def __init__(self, + env: KitchenBase, + init_qpos: Optional[np.ndarray]=None, + init_qvel: Optional[np.ndarray]=None, + render_hw = (240,360) + ): + self.env = env + self.init_qpos = init_qpos + self.init_qvel = init_qvel + self.render_hw = render_hw + + @property + def action_space(self): + return self.env.action_space + + @property + def observation_space(self): + return self.env.observation_space + + def seed(self, seed=None): + return self.env.seed(seed) + + def reset(self): + if self.init_qpos is not None: + # reset anyway to be safe, not very expensive + _ = self.env.reset() + # start from known state + self.env.set_state(self.init_qpos, self.init_qvel) + obs = self.env._get_obs() + return obs + # obs, _, _, _ = self.env.step(np.zeros_like( + # self.action_space.sample())) + # return obs + else: + return self.env.reset() + + def render(self, mode='rgb_array'): + h, w = self.render_hw + return self.env.render(mode=mode, width=w, height=h) + + def step(self, a): + return self.env.step(a) diff --git a/diffusion_policy/env/kitchen/kitchen_util.py b/diffusion_policy/env/kitchen/kitchen_util.py new file mode 100644 index 0000000..59852c1 --- /dev/null +++ b/diffusion_policy/env/kitchen/kitchen_util.py @@ -0,0 +1,51 @@ +import struct +import numpy as np + +def parse_mjl_logs(read_filename, skipamount): + with open(read_filename, mode='rb') as file: + fileContent = file.read() + headers = struct.unpack('iiiiiii', fileContent[:28]) + nq = headers[0] + nv = headers[1] + nu = headers[2] + nmocap = headers[3] + nsensordata = headers[4] + nuserdata = headers[5] + name_len = headers[6] + name = struct.unpack(str(name_len) + 's', fileContent[28:28+name_len])[0] + rem_size = len(fileContent[28 + name_len:]) + num_floats = int(rem_size/4) + dat = np.asarray(struct.unpack(str(num_floats) + 'f', fileContent[28+name_len:])) + recsz = 1 + nq + nv + nu + 7*nmocap + nsensordata + nuserdata + if rem_size % recsz != 0: + print("ERROR") + else: + dat = np.reshape(dat, (int(len(dat)/recsz), recsz)) + dat = dat.T + + time = dat[0,:][::skipamount] - 0*dat[0, 0] + qpos = dat[1:nq + 1, :].T[::skipamount, :] + qvel = dat[nq+1:nq+nv+1,:].T[::skipamount, :] + ctrl = dat[nq+nv+1:nq+nv+nu+1,:].T[::skipamount,:] + mocap_pos = dat[nq+nv+nu+1:nq+nv+nu+3*nmocap+1,:].T[::skipamount, :] + mocap_quat = dat[nq+nv+nu+3*nmocap+1:nq+nv+nu+7*nmocap+1,:].T[::skipamount, :] + sensordata = dat[nq+nv+nu+7*nmocap+1:nq+nv+nu+7*nmocap+nsensordata+1,:].T[::skipamount,:] + userdata = dat[nq+nv+nu+7*nmocap+nsensordata+1:,:].T[::skipamount,:] + + data = dict(nq=nq, + nv=nv, + nu=nu, + nmocap=nmocap, + nsensordata=nsensordata, + name=name, + time=time, + qpos=qpos, + qvel=qvel, + ctrl=ctrl, + mocap_pos=mocap_pos, + mocap_quat=mocap_quat, + sensordata=sensordata, + userdata=userdata, + logName = read_filename + ) + return data diff --git a/diffusion_policy/env/kitchen/relay_policy_learning/adept_envs/.pylintrc b/diffusion_policy/env/kitchen/relay_policy_learning/adept_envs/.pylintrc new file mode 100644 index 0000000..9cda412 --- /dev/null +++ b/diffusion_policy/env/kitchen/relay_policy_learning/adept_envs/.pylintrc @@ -0,0 +1,433 @@ +[MASTER] + +# A comma-separated list of package or module names from where C extensions may +# be loaded. Extensions are loading into the active Python interpreter and may +# run arbitrary code. +extension-pkg-whitelist= + +# Add files or directories to the blacklist. They should be base names, not +# paths. +ignore=CVS + +# Add files or directories matching the regex patterns to the blacklist. The +# regex matches against base names, not paths. +ignore-patterns= + +# Python code to execute, usually for sys.path manipulation such as +# pygtk.require(). +#init-hook= + +# Use multiple processes to speed up Pylint. Specifying 0 will auto-detect the +# number of processors available to use. +jobs=1 + +# Control the amount of potential inferred values when inferring a single +# object. This can help the performance when dealing with large functions or +# complex, nested conditions. +limit-inference-results=100 + +# List of plugins (as comma separated values of python modules names) to load, +# usually to register additional checkers. +load-plugins= + +# Pickle collected data for later comparisons. +persistent=yes + +# Specify a configuration file. +#rcfile= + +# When enabled, pylint would attempt to guess common misconfiguration and emit +# user-friendly hints instead of false-positive error messages. +suggestion-mode=yes + +# Allow loading of arbitrary C extensions. Extensions are imported into the +# active Python interpreter and may run arbitrary code. +unsafe-load-any-extension=no + + +[MESSAGES CONTROL] + +# Only show warnings with the listed confidence levels. Leave empty to show +# all. Valid levels: HIGH, INFERENCE, INFERENCE_FAILURE, UNDEFINED. +confidence= + +# Disable the message, report, category or checker with the given id(s). You +# can either give multiple identifiers separated by comma (,) or put this +# option multiple times (only on the command line, not in the configuration +# file where it should appear only once). You can also use "--disable=all" to +# disable everything first and then reenable specific checks. For example, if +# you want to run only the similarities checker, you can use "--disable=all +# --enable=similarities". If you want to run only the classes checker, but have +# no Warning level messages displayed, use "--disable=all --enable=classes +# --disable=W". +disable=relative-beyond-top-level + + +[REPORTS] + +# Python expression which should return a note less than 10 (10 is the highest +# note). You have access to the variables errors warning, statement which +# respectively contain the number of errors / warnings messages and the total +# number of statements analyzed. This is used by the global evaluation report +# (RP0004). +evaluation=10.0 - ((float(5 * error + warning + refactor + convention) / statement) * 10) + +# Template used to display messages. This is a python new-style format string +# used to format the message information. See doc for all details. +#msg-template= + +# Set the output format. Available formats are text, parseable, colorized, json +# and msvs (visual studio). You can also give a reporter class, e.g. +# mypackage.mymodule.MyReporterClass. +output-format=text + +# Tells whether to display a full report or only the messages. +reports=no + +# Activate the evaluation score. +score=yes + + +[REFACTORING] + +# Maximum number of nested blocks for function / method body +max-nested-blocks=5 + +# Complete name of functions that never returns. When checking for +# inconsistent-return-statements if a never returning function is called then +# it will be considered as an explicit return statement and no message will be +# printed. +never-returning-functions=sys.exit + + +[LOGGING] + +# Format style used to check logging format string. `old` means using % +# formatting, while `new` is for `{}` formatting. +logging-format-style=old + +# Logging modules to check that the string format arguments are in logging +# function parameter format. +logging-modules=logging + + +[VARIABLES] + +# List of additional names supposed to be defined in builtins. Remember that +# you should avoid defining new builtins when possible. +additional-builtins= + +# Tells whether unused global variables should be treated as a violation. +allow-global-unused-variables=yes + +# List of strings which can identify a callback function by name. A callback +# name must start or end with one of those strings. +callbacks=cb_, + _cb + +# A regular expression matching the name of dummy variables (i.e. expected to +# not be used). +dummy-variables-rgx=_+$|(_[a-zA-Z0-9_]*[a-zA-Z0-9]+?$)|dummy|^ignored_|^unused_ + +# Argument names that match this expression will be ignored. Default to name +# with leading underscore. +ignored-argument-names=_.*|^ignored_|^unused_ + +# Tells whether we should check for unused import in __init__ files. +init-import=no + +# List of qualified module names which can have objects that can redefine +# builtins. +redefining-builtins-modules=six.moves,past.builtins,future.builtins,builtins,io + + +[FORMAT] + +# Expected format of line ending, e.g. empty (any line ending), LF or CRLF. +expected-line-ending-format= + +# Regexp for a line that is allowed to be longer than the limit. +ignore-long-lines=^\s*(# )??$ + +# Number of spaces of indent required inside a hanging or continued line. +indent-after-paren=4 + +# String used as indentation unit. This is usually " " (4 spaces) or "\t" (1 +# tab). +indent-string=' ' + +# Maximum number of characters on a single line. +max-line-length=80 + +# Maximum number of lines in a module +max-module-lines=99999 + +# List of optional constructs for which whitespace checking is disabled. `dict- +# separator` is used to allow tabulation in dicts, etc.: {1 : 1,\n222: 2}. +# `trailing-comma` allows a space between comma and closing bracket: (a, ). +# `empty-line` allows space-only lines. +no-space-check=trailing-comma, + dict-separator + +# Allow the body of a class to be on the same line as the declaration if body +# contains single statement. +single-line-class-stmt=no + +# Allow the body of an if to be on the same line as the test if there is no +# else. +single-line-if-stmt=no + + +[TYPECHECK] + +# List of decorators that produce context managers, such as +# contextlib.contextmanager. Add to this list to register other decorators that +# produce valid context managers. +contextmanager-decorators=contextlib.contextmanager + +# List of members which are set dynamically and missed by pylint inference +# system, and so shouldn't trigger E1101 when accessed. Python regular +# expressions are accepted. +generated-members= + +# Tells whether missing members accessed in mixin class should be ignored. A +# mixin class is detected if its name ends with "mixin" (case insensitive). +ignore-mixin-members=yes + +# Tells whether to warn about missing members when the owner of the attribute +# is inferred to be None. +ignore-none=yes + +# This flag controls whether pylint should warn about no-member and similar +# checks whenever an opaque object is returned when inferring. The inference +# can return multiple potential results while evaluating a Python object, but +# some branches might not be evaluated, which results in partial inference. In +# that case, it might be useful to still emit no-member and other checks for +# the rest of the inferred objects. +ignore-on-opaque-inference=yes + +# List of class names for which member attributes should not be checked (useful +# for classes with dynamically set attributes). This supports the use of +# qualified names. +ignored-classes=optparse.Values,thread._local,_thread._local + +# List of module names for which member attributes should not be checked +# (useful for modules/projects where namespaces are manipulated during runtime +# and thus existing member attributes cannot be deduced by static analysis. It +# supports qualified module names, as well as Unix pattern matching. +ignored-modules= + +# Show a hint with possible names when a member name was not found. The aspect +# of finding the hint is based on edit distance. +missing-member-hint=yes + +# The minimum edit distance a name should have in order to be considered a +# similar match for a missing member name. +missing-member-hint-distance=1 + +# The total number of similar names that should be taken in consideration when +# showing a hint for a missing member. +missing-member-max-choices=1 + + +[SIMILARITIES] + +# Ignore comments when computing similarities. +ignore-comments=yes + +# Ignore docstrings when computing similarities. +ignore-docstrings=yes + +# Ignore imports when computing similarities. +ignore-imports=no + +# Minimum lines number of a similarity. +min-similarity-lines=4 + + +[BASIC] + +# Naming style matching correct argument names +argument-naming-style=snake_case + +# Regular expression matching correct argument names. Overrides argument- +# naming-style +argument-rgx=^[a-z][a-z0-9_]*$ + +# Naming style matching correct attribute names +attr-naming-style=snake_case + +# Regular expression matching correct attribute names. Overrides attr-naming- +# style +attr-rgx=^_{0,2}[a-z][a-z0-9_]*$ + +# Bad variable names which should always be refused, separated by a comma +bad-names= + +# Naming style matching correct class attribute names +class-attribute-naming-style=any + +# Regular expression matching correct class attribute names. Overrides class- +# attribute-naming-style +class-attribute-rgx=^(_?[A-Z][A-Z0-9_]*|__[a-z0-9_]+__|_?[a-z][a-z0-9_]*)$ + +# Naming style matching correct class names +class-naming-style=PascalCase + +# Regular expression matching correct class names. Overrides class-naming-style +class-rgx=^_?[A-Z][a-zA-Z0-9]*$ + +# Naming style matching correct constant names +const-naming-style=UPPER_CASE + +# Regular expression matching correct constant names. Overrides const-naming- +# style +const-rgx=^(_?[A-Z][A-Z0-9_]*|__[a-z0-9_]+__|_?[a-z][a-z0-9_]*)$ + +# Minimum line length for functions/classes that require docstrings, shorter +# ones are exempt. +docstring-min-length=10 + +# Naming style matching correct function names +function-naming-style=snake_case + +# Regular expression matching correct function names. Overrides function- +# naming-style +function-rgx=^(?:(?PsetUp|tearDown|setUpModule|tearDownModule)|(?P_?[A-Z][a-zA-Z0-9]*)|(?P_?[a-z][a-z0-9_]*))$ + +# Good variable names which should always be accepted, separated by a comma +good-names=main, + _ + +# Include a hint for the correct naming format with invalid-name +include-naming-hint=no + +# Naming style matching correct inline iteration names +inlinevar-naming-style=any + +# Regular expression matching correct inline iteration names. Overrides +# inlinevar-naming-style +inlinevar-rgx=^[a-z][a-z0-9_]*$ + +# Naming style matching correct method names +method-naming-style=snake_case + +# Regular expression matching correct method names. Overrides method-naming- +# style +method-rgx=(?x)^(?:(?P_[a-z0-9_]+__|runTest|setUp|tearDown|setUpTestCase|tearDownTestCase|setupSelf|tearDownClass|setUpClass|(test|assert)_*[A-Z0-9][a-zA-Z0-9_]*|next)|(?P_{0,2}[A-Z][a-zA-Z0-9_]*)|(?P_{0,2}[a-z][a-z0-9_]*))$ + +# Naming style matching correct module names +module-naming-style=snake_case + +# Regular expression matching correct module names. Overrides module-naming- +# style +module-rgx=^(_?[a-z][a-z0-9_]*)|__init__|PRESUBMIT|PRESUBMIT_unittest$ + +# Colon-delimited sets of names that determine each other's naming style when +# the name regexes allow several styles. +name-group=function:method + +# Regular expression which should only match function or class names that do +# not require a docstring. +no-docstring-rgx=(__.*__|main) + +# List of decorators that produce properties, such as abc.abstractproperty. Add +# to this list to register other decorators that produce valid properties. +property-classes=abc.abstractproperty,google3.pyglib.function_utils.cached.property + +# Naming style matching correct variable names +variable-naming-style=snake_case + +# Regular expression matching correct variable names. Overrides variable- +# naming-style +variable-rgx=^[a-z][a-z0-9_]*$ + + +[SPELLING] + +# Limits count of emitted suggestions for spelling mistakes. +max-spelling-suggestions=4 + +# Spelling dictionary name. Available dictionaries: none. To make it working +# install python-enchant package.. +spelling-dict= + +# List of comma separated words that should not be checked. +spelling-ignore-words= + +# A path to a file that contains private dictionary; one word per line. +spelling-private-dict-file= + +# Tells whether to store unknown words to indicated private dictionary in +# --spelling-private-dict-file option instead of raising a message. +spelling-store-unknown-words=no + + +[MISCELLANEOUS] + +# List of note tags to take in consideration, separated by a comma. +notes=FIXME, + XXX, + TODO + + +[IMPORTS] + +# Allow wildcard imports from modules that define __all__. +allow-wildcard-with-all=no + +# Analyse import fallback blocks. This can be used to support both Python 2 and +# 3 compatible code, which means that the block might have code that exists +# only in one or another interpreter, leading to false positives when analysed. +analyse-fallback-blocks=no + +# Deprecated modules which should not be used, separated by a comma. +deprecated-modules=optparse,tkinter.tix + +# Create a graph of external dependencies in the given file (report RP0402 must +# not be disabled). +ext-import-graph= + +# Create a graph of every (i.e. internal and external) dependencies in the +# given file (report RP0402 must not be disabled). +import-graph= + +# Create a graph of internal dependencies in the given file (report RP0402 must +# not be disabled). +int-import-graph= + +# Force import order to recognize a module as part of the standard +# compatibility libraries. +known-standard-library= + +# Force import order to recognize a module as part of a third party library. +known-third-party=enchant + + +[CLASSES] + +# List of method names used to declare (i.e. assign) instance attributes. +defining-attr-methods=__init__, + __new__, + setUp + +# List of member names, which should be excluded from the protected access +# warning. +exclude-protected=_asdict, + _fields, + _replace, + _source, + _make + +# List of valid names for the first argument in a class method. +valid-classmethod-first-arg=cls + +# List of valid names for the first argument in a metaclass class method. +valid-metaclass-classmethod-first-arg=cls + + +[EXCEPTIONS] + +# Exceptions that will emit a warning when being caught. Defaults to +# "Exception". +overgeneral-exceptions=Exception diff --git a/diffusion_policy/env/kitchen/relay_policy_learning/adept_envs/.style.yapf b/diffusion_policy/env/kitchen/relay_policy_learning/adept_envs/.style.yapf new file mode 100644 index 0000000..29f83ff --- /dev/null +++ b/diffusion_policy/env/kitchen/relay_policy_learning/adept_envs/.style.yapf @@ -0,0 +1,323 @@ +[style] +# Align closing bracket with visual indentation. +align_closing_bracket_with_visual_indent=False + +# Allow dictionary keys to exist on multiple lines. For example: +# +# x = { +# ('this is the first element of a tuple', +# 'this is the second element of a tuple'): +# value, +# } +allow_multiline_dictionary_keys=False + +# Allow lambdas to be formatted on more than one line. +allow_multiline_lambdas=False + +# Allow splitting before a default / named assignment in an argument list. +allow_split_before_default_or_named_assigns=True + +# Allow splits before the dictionary value. +allow_split_before_dict_value=True + +# Let spacing indicate operator precedence. For example: +# +# a = 1 * 2 + 3 / 4 +# b = 1 / 2 - 3 * 4 +# c = (1 + 2) * (3 - 4) +# d = (1 - 2) / (3 + 4) +# e = 1 * 2 - 3 +# f = 1 + 2 + 3 + 4 +# +# will be formatted as follows to indicate precedence: +# +# a = 1*2 + 3/4 +# b = 1/2 - 3*4 +# c = (1+2) * (3-4) +# d = (1-2) / (3+4) +# e = 1*2 - 3 +# f = 1 + 2 + 3 + 4 +# +arithmetic_precedence_indication=False + +# Number of blank lines surrounding top-level function and class +# definitions. +blank_lines_around_top_level_definition=2 + +# Insert a blank line before a class-level docstring. +blank_line_before_class_docstring=False + +# Insert a blank line before a module docstring. +blank_line_before_module_docstring=False + +# Insert a blank line before a 'def' or 'class' immediately nested +# within another 'def' or 'class'. For example: +# +# class Foo: +# # <------ this blank line +# def method(): +# ... +blank_line_before_nested_class_or_def=True + +# Do not split consecutive brackets. Only relevant when +# dedent_closing_brackets is set. For example: +# +# call_func_that_takes_a_dict( +# { +# 'key1': 'value1', +# 'key2': 'value2', +# } +# ) +# +# would reformat to: +# +# call_func_that_takes_a_dict({ +# 'key1': 'value1', +# 'key2': 'value2', +# }) +coalesce_brackets=False + +# The column limit. +column_limit=80 + +# The style for continuation alignment. Possible values are: +# +# - SPACE: Use spaces for continuation alignment. This is default behavior. +# - FIXED: Use fixed number (CONTINUATION_INDENT_WIDTH) of columns +# (ie: CONTINUATION_INDENT_WIDTH/INDENT_WIDTH tabs) for continuation +# alignment. +# - LESS: Slightly left if cannot vertically align continuation lines with +# indent characters. +# - VALIGN-RIGHT: Vertically align continuation lines with indent +# characters. Slightly right (one more indent character) if cannot +# vertically align continuation lines with indent characters. +# +# For options FIXED, and VALIGN-RIGHT are only available when USE_TABS is +# enabled. +continuation_align_style=SPACE + +# Indent width used for line continuations. +continuation_indent_width=4 + +# Put closing brackets on a separate line, dedented, if the bracketed +# expression can't fit in a single line. Applies to all kinds of brackets, +# including function definitions and calls. For example: +# +# config = { +# 'key1': 'value1', +# 'key2': 'value2', +# } # <--- this bracket is dedented and on a separate line +# +# time_series = self.remote_client.query_entity_counters( +# entity='dev3246.region1', +# key='dns.query_latency_tcp', +# transform=Transformation.AVERAGE(window=timedelta(seconds=60)), +# start_ts=now()-timedelta(days=3), +# end_ts=now(), +# ) # <--- this bracket is dedented and on a separate line +dedent_closing_brackets=False + +# Disable the heuristic which places each list element on a separate line +# if the list is comma-terminated. +disable_ending_comma_heuristic=False + +# Place each dictionary entry onto its own line. +each_dict_entry_on_separate_line=True + +# The regex for an i18n comment. The presence of this comment stops +# reformatting of that line, because the comments are required to be +# next to the string they translate. +i18n_comment=#\..* + +# The i18n function call names. The presence of this function stops +# reformattting on that line, because the string it has cannot be moved +# away from the i18n comment. +i18n_function_call=N_, _ + +# Indent blank lines. +indent_blank_lines=False + +# Indent the dictionary value if it cannot fit on the same line as the +# dictionary key. For example: +# +# config = { +# 'key1': +# 'value1', +# 'key2': value1 + +# value2, +# } +indent_dictionary_value=False + +# The number of columns to use for indentation. +indent_width=4 + +# Join short lines into one line. E.g., single line 'if' statements. +join_multiple_lines=True + +# Do not include spaces around selected binary operators. For example: +# +# 1 + 2 * 3 - 4 / 5 +# +# will be formatted as follows when configured with "*,/": +# +# 1 + 2*3 - 4/5 +# +no_spaces_around_selected_binary_operators= + +# Use spaces around default or named assigns. +spaces_around_default_or_named_assign=False + +# Use spaces around the power operator. +spaces_around_power_operator=False + +# The number of spaces required before a trailing comment. +# This can be a single value (representing the number of spaces +# before each trailing comment) or list of values (representing +# alignment column values; trailing comments within a block will +# be aligned to the first column value that is greater than the maximum +# line length within the block). For example: +# +# With spaces_before_comment=5: +# +# 1 + 1 # Adding values +# +# will be formatted as: +# +# 1 + 1 # Adding values <-- 5 spaces between the end of the statement and comment +# +# With spaces_before_comment=15, 20: +# +# 1 + 1 # Adding values +# two + two # More adding +# +# longer_statement # This is a longer statement +# short # This is a shorter statement +# +# a_very_long_statement_that_extends_beyond_the_final_column # Comment +# short # This is a shorter statement +# +# will be formatted as: +# +# 1 + 1 # Adding values <-- end of line comments in block aligned to col 15 +# two + two # More adding +# +# longer_statement # This is a longer statement <-- end of line comments in block aligned to col 20 +# short # This is a shorter statement +# +# a_very_long_statement_that_extends_beyond_the_final_column # Comment <-- the end of line comments are aligned based on the line length +# short # This is a shorter statement +# +spaces_before_comment=2 + +# Insert a space between the ending comma and closing bracket of a list, +# etc. +space_between_ending_comma_and_closing_bracket=False + +# Split before arguments +split_all_comma_separated_values=False + +# Split before arguments if the argument list is terminated by a +# comma. +split_arguments_when_comma_terminated=False + +# Set to True to prefer splitting before '&', '|' or '^' rather than +# after. +split_before_bitwise_operator=False + +# Split before the closing bracket if a list or dict literal doesn't fit on +# a single line. +split_before_closing_bracket=True + +# Split before a dictionary or set generator (comp_for). For example, note +# the split before the 'for': +# +# foo = { +# variable: 'Hello world, have a nice day!' +# for variable in bar if variable != 42 +# } +split_before_dict_set_generator=False + +# Split before the '.' if we need to split a longer expression: +# +# foo = ('This is a really long string: {}, {}, {}, {}'.format(a, b, c, d)) +# +# would reformat to something like: +# +# foo = ('This is a really long string: {}, {}, {}, {}' +# .format(a, b, c, d)) +split_before_dot=False + +# Split after the opening paren which surrounds an expression if it doesn't +# fit on a single line. +split_before_expression_after_opening_paren=False + +# If an argument / parameter list is going to be split, then split before +# the first argument. +split_before_first_argument=False + +# Set to True to prefer splitting before 'and' or 'or' rather than +# after. +split_before_logical_operator=False + +# Split named assignments onto individual lines. +split_before_named_assigns=True + +# Set to True to split list comprehensions and generators that have +# non-trivial expressions and multiple clauses before each of these +# clauses. For example: +# +# result = [ +# a_long_var + 100 for a_long_var in xrange(1000) +# if a_long_var % 10] +# +# would reformat to something like: +# +# result = [ +# a_long_var + 100 +# for a_long_var in xrange(1000) +# if a_long_var % 10] +split_complex_comprehension=True + +# The penalty for splitting right after the opening bracket. +split_penalty_after_opening_bracket=30 + +# The penalty for splitting the line after a unary operator. +split_penalty_after_unary_operator=10000 + +# The penalty for splitting right before an if expression. +split_penalty_before_if_expr=0 + +# The penalty of splitting the line around the '&', '|', and '^' +# operators. +split_penalty_bitwise_operator=300 + +# The penalty for splitting a list comprehension or generator +# expression. +split_penalty_comprehension=2100 + +# The penalty for characters over the column limit. +split_penalty_excess_character=7000 + +# The penalty incurred by adding a line split to the unwrapped line. The +# more line splits added the higher the penalty. +split_penalty_for_added_line_split=30 + +# The penalty of splitting a list of "import as" names. For example: +# +# from a_very_long_or_indented_module_name_yada_yad import (long_argument_1, +# long_argument_2, +# long_argument_3) +# +# would reformat to something like: +# +# from a_very_long_or_indented_module_name_yada_yad import ( +# long_argument_1, long_argument_2, long_argument_3) +split_penalty_import_names=0 + +# The penalty of splitting the line around the 'and' and 'or' +# operators. +split_penalty_logical_operator=300 + +# Use the Tab character for indentation. +use_tabs=False + diff --git a/diffusion_policy/env/kitchen/relay_policy_learning/adept_envs/adept_envs/__init__.py b/diffusion_policy/env/kitchen/relay_policy_learning/adept_envs/adept_envs/__init__.py new file mode 100644 index 0000000..5e92a7b --- /dev/null +++ b/diffusion_policy/env/kitchen/relay_policy_learning/adept_envs/adept_envs/__init__.py @@ -0,0 +1,19 @@ +#!/usr/bin/python +# +# Copyright 2020 Google LLC +# +# 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. + +import adept_envs.franka + +from adept_envs.utils.configurable import global_config diff --git a/diffusion_policy/env/kitchen/relay_policy_learning/adept_envs/adept_envs/base_robot.py b/diffusion_policy/env/kitchen/relay_policy_learning/adept_envs/adept_envs/base_robot.py new file mode 100644 index 0000000..5c6f30f --- /dev/null +++ b/diffusion_policy/env/kitchen/relay_policy_learning/adept_envs/adept_envs/base_robot.py @@ -0,0 +1,151 @@ +#!/usr/bin/python +# +# Copyright 2020 Google LLC +# +# 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. + +import numpy as np +from collections import deque + +class BaseRobot(object): + """Base class for all robot classes.""" + + def __init__(self, + n_jnt, + n_obj, + pos_bounds=None, + vel_bounds=None, + calibration_path=None, + is_hardware=False, + device_name=None, + overlay=False, + calibration_mode=False, + observation_cache_maxsize=5): + """Create a new robot. + Args: + n_jnt: The number of dofs in the robot. + n_obj: The number of dofs in the object. + pos_bounds: (n_jnt, 2)-shape matrix denoting the min and max joint + position for each joint. + vel_bounds: (n_jnt, 2)-shape matrix denoting the min and max joint + velocity for each joint. + calibration_path: File path to the calibration configuration file to + use. + is_hardware: Whether to run on hardware or not. + device_name: The device path for the robot hardware. Only required + in legacy mode. + overlay: Whether to show a simulation overlay of the hardware. + calibration_mode: Start with motors disengaged. + """ + + assert n_jnt > 0 + assert n_obj >= 0 + + self._n_jnt = n_jnt + self._n_obj = n_obj + self._n_dofs = n_jnt + n_obj + + self._pos_bounds = None + if pos_bounds is not None: + pos_bounds = np.array(pos_bounds, dtype=np.float32) + assert pos_bounds.shape == (self._n_dofs, 2) + for low, high in pos_bounds: + assert low < high + self._pos_bounds = pos_bounds + self._vel_bounds = None + if vel_bounds is not None: + vel_bounds = np.array(vel_bounds, dtype=np.float32) + assert vel_bounds.shape == (self._n_dofs, 2) + for low, high in vel_bounds: + assert low < high + self._vel_bounds = vel_bounds + + self._is_hardware = is_hardware + self._device_name = device_name + self._calibration_path = calibration_path + self._overlay = overlay + self._calibration_mode = calibration_mode + self._observation_cache_maxsize = observation_cache_maxsize + + # Gets updated + self._observation_cache = deque([], maxlen=self._observation_cache_maxsize) + + + @property + def n_jnt(self): + return self._n_jnt + + @property + def n_obj(self): + return self._n_obj + + @property + def n_dofs(self): + return self._n_dofs + + @property + def pos_bounds(self): + return self._pos_bounds + + @property + def vel_bounds(self): + return self._vel_bounds + + @property + def is_hardware(self): + return self._is_hardware + + @property + def device_name(self): + return self._device_name + + @property + def calibration_path(self): + return self._calibration_path + + @property + def overlay(self): + return self._overlay + + @property + def has_obj(self): + return self._n_obj > 0 + + @property + def calibration_mode(self): + return self._calibration_mode + + @property + def observation_cache_maxsize(self): + return self._observation_cache_maxsize + + @property + def observation_cache(self): + return self._observation_cache + + + def clip_positions(self, positions): + """Clips the given joint positions to the position bounds. + + Args: + positions: The joint positions. + + Returns: + The bounded joint positions. + """ + if self.pos_bounds is None: + return positions + assert len(positions) == self.n_jnt or len(positions) == self.n_dofs + pos_bounds = self.pos_bounds[:len(positions)] + return np.clip(positions, pos_bounds[:, 0], pos_bounds[:, 1]) + diff --git a/diffusion_policy/env/kitchen/relay_policy_learning/adept_envs/adept_envs/franka/__init__.py b/diffusion_policy/env/kitchen/relay_policy_learning/adept_envs/adept_envs/franka/__init__.py new file mode 100644 index 0000000..528f344 --- /dev/null +++ b/diffusion_policy/env/kitchen/relay_policy_learning/adept_envs/adept_envs/franka/__init__.py @@ -0,0 +1,24 @@ +#!/usr/bin/python +# +# Copyright 2020 Google LLC +# +# 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. + +from gym.envs.registration import register + +# Relax the robot +register( + id='kitchen_relax-v1', + entry_point='adept_envs.franka.kitchen_multitask_v0:KitchenTaskRelaxV1', + max_episode_steps=280, +) \ No newline at end of file diff --git a/diffusion_policy/env/kitchen/relay_policy_learning/adept_envs/adept_envs/franka/assets/franka_kitchen_jntpos_act_ab.xml b/diffusion_policy/env/kitchen/relay_policy_learning/adept_envs/adept_envs/franka/assets/franka_kitchen_jntpos_act_ab.xml new file mode 100644 index 0000000..3441380 --- /dev/null +++ b/diffusion_policy/env/kitchen/relay_policy_learning/adept_envs/adept_envs/franka/assets/franka_kitchen_jntpos_act_ab.xml @@ -0,0 +1,94 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + diff --git a/diffusion_policy/env/kitchen/relay_policy_learning/adept_envs/adept_envs/franka/kitchen_multitask_v0.py b/diffusion_policy/env/kitchen/relay_policy_learning/adept_envs/adept_envs/franka/kitchen_multitask_v0.py new file mode 100644 index 0000000..30bdd33 --- /dev/null +++ b/diffusion_policy/env/kitchen/relay_policy_learning/adept_envs/adept_envs/franka/kitchen_multitask_v0.py @@ -0,0 +1,221 @@ +""" Kitchen environment for long horizon manipulation """ +#!/usr/bin/python +# +# Copyright 2020 Google LLC +# +# 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. + +import os +import numpy as np +from adept_envs import robot_env +from adept_envs.utils.configurable import configurable +from gym import spaces +from dm_control.mujoco import engine + +@configurable(pickleable=True) +class KitchenV0(robot_env.RobotEnv): + + CALIBRATION_PATHS = { + 'default': + os.path.join(os.path.dirname(__file__), 'robot/franka_config.xml') + } + # Converted to velocity actuation + ROBOTS = {'robot': 'adept_envs.franka.robot.franka_robot:Robot_VelAct'} + MODEl = os.path.join( + os.path.dirname(__file__), + '../franka/assets/franka_kitchen_jntpos_act_ab.xml') + N_DOF_ROBOT = 9 + N_DOF_OBJECT = 21 + + def __init__(self, + robot_params={}, frame_skip=40, + use_abs_action=False): + self.goal_concat = True + self.obs_dict = {} + self.robot_noise_ratio = 0.1 # 10% as per robot_config specs + self.goal = np.zeros((30,)) + self.use_abs_action = use_abs_action + if use_abs_action: + self.ROBOTS = {'robot': 'adept_envs.franka.robot.franka_robot:Robot_PosAct'} + + super().__init__( + self.MODEl, + robot=self.make_robot( + n_jnt=self.N_DOF_ROBOT, #root+robot_jnts + n_obj=self.N_DOF_OBJECT, + **robot_params), + frame_skip=frame_skip, + camera_settings=dict( + distance=4.5, + azimuth=-66, + elevation=-65, + ), + ) + self.init_qpos = self.sim.model.key_qpos[0].copy() + + # For the microwave kettle slide hinge + self.init_qpos = np.array([ 1.48388023e-01, -1.76848573e+00, 1.84390296e+00, -2.47685760e+00, + 2.60252026e-01, 7.12533105e-01, 1.59515394e+00, 4.79267505e-02, + 3.71350919e-02, -2.66279850e-04, -5.18043486e-05, 3.12877220e-05, + -4.51199853e-05, -3.90842156e-06, -4.22629655e-05, 6.28065475e-05, + 4.04984708e-05, 4.62730939e-04, -2.26906415e-04, -4.65501369e-04, + -6.44129196e-03, -1.77048263e-03, 1.08009684e-03, -2.69397440e-01, + 3.50383255e-01, 1.61944683e+00, 1.00618764e+00, 4.06395120e-03, + -6.62095997e-03, -2.68278933e-04]) + + self.init_qvel = self.sim.model.key_qvel[0].copy() + + self.act_mid = np.zeros(self.N_DOF_ROBOT) + self.act_amp = 2.0 * np.ones(self.N_DOF_ROBOT) + + act_lower = -1*np.ones((self.N_DOF_ROBOT,)) + act_upper = 1*np.ones((self.N_DOF_ROBOT,)) + if use_abs_action: + act_lower = act_lower * 8. + act_upper = act_upper * 8. + self.act_amp = np.ones(self.N_DOF_ROBOT) + + self.action_space = spaces.Box(act_lower, act_upper) + + obs_upper = 8. * np.ones(self.obs_dim) + obs_lower = -obs_upper + self.observation_space = spaces.Box(obs_lower, obs_upper) + + def _get_reward_n_score(self, obs_dict): + raise NotImplementedError() + + def step(self, a, b=None): + if not self.use_abs_action: + a = np.clip(a, -1.0, 1.0) + + if not self.initializing: + a = self.act_mid + a * self.act_amp # mean center and scale + else: + self.goal = self._get_task_goal() # update goal if init + + self.robot.step( + self, a, step_duration=self.skip * self.model.opt.timestep) + + # observations + obs = self._get_obs() + + #rewards + reward_dict, score = self._get_reward_n_score(self.obs_dict) + + # termination + done = False + + # finalize step + env_info = { + 'time': self.obs_dict['t'], + 'obs_dict': self.obs_dict, + 'rewards': reward_dict, + 'score': score, + # don't render every frame + # 'images': np.asarray(self.render(mode='rgb_array')) + } + # self.render() + return obs, reward_dict['r_total'], done, env_info + + def _get_obs(self): + t, qp, qv, obj_qp, obj_qv = self.robot.get_obs( + self, robot_noise_ratio=self.robot_noise_ratio) + + self.obs_dict = {} + self.obs_dict['t'] = t + self.obs_dict['qp'] = qp + self.obs_dict['qv'] = qv + self.obs_dict['obj_qp'] = obj_qp + self.obs_dict['obj_qv'] = obj_qv + self.obs_dict['goal'] = self.goal + if self.goal_concat: + return np.concatenate([self.obs_dict['qp'], self.obs_dict['obj_qp'], self.obs_dict['goal']]) + + def reset_model(self): + reset_pos = self.init_qpos[:].copy() + reset_vel = self.init_qvel[:].copy() + self.robot.reset(self, reset_pos, reset_vel) + self.sim.forward() + self.goal = self._get_task_goal() #sample a new goal on reset + return self._get_obs() + + def evaluate_success(self, paths): + # score + mean_score_per_rollout = np.zeros(shape=len(paths)) + for idx, path in enumerate(paths): + mean_score_per_rollout[idx] = np.mean(path['env_infos']['score']) + mean_score = np.mean(mean_score_per_rollout) + + # success percentage + num_success = 0 + num_paths = len(paths) + for path in paths: + num_success += bool(path['env_infos']['rewards']['bonus'][-1]) + success_percentage = num_success * 100.0 / num_paths + + # fuse results + return np.sign(mean_score) * ( + 1e6 * round(success_percentage, 2) + abs(mean_score)) + + def close_env(self): + self.robot.close() + + def set_goal(self, goal): + self.goal = goal + + def _get_task_goal(self): + return self.goal + + # Only include goal + @property + def goal_space(self): + len_obs = self.observation_space.low.shape[0] + env_lim = np.abs(self.observation_space.low[0]) + return spaces.Box(low=-env_lim, high=env_lim, shape=(len_obs//2,)) + + def convert_to_active_observation(self, observation): + return observation + +class KitchenTaskRelaxV1(KitchenV0): + """Kitchen environment with proper camera and goal setup""" + + def __init__(self, use_abs_action=False): + super(KitchenTaskRelaxV1, self).__init__( + use_abs_action=use_abs_action) + + def _get_reward_n_score(self, obs_dict): + reward_dict = {} + reward_dict['true_reward'] = 0. + reward_dict['bonus'] = 0. + reward_dict['r_total'] = 0. + score = 0. + return reward_dict, score + + def render(self, mode='human', width=1280, height=720, custom=True, **kwargs): + if custom: + camera = engine.MovableCamera(self.sim, height, width) + if 'distance' not in kwargs: + kwargs['distance'] = 2.2 + if 'lookat' not in kwargs: + kwargs['lookat'] = [-0.2, .5, 2.] + if 'azimuth' not in kwargs: + kwargs['azimuth'] = 70 + if 'elevation' not in kwargs: + kwargs['elevation'] = -35 + camera.set_pose(**kwargs) + img = camera.render() + return img + else: + return super(KitchenTaskRelaxV1, self).render( + mode=mode, width=width, height=height, **kwargs) + diff --git a/diffusion_policy/env/kitchen/relay_policy_learning/adept_envs/adept_envs/franka/robot/franka_config.xml b/diffusion_policy/env/kitchen/relay_policy_learning/adept_envs/adept_envs/franka/robot/franka_config.xml new file mode 100644 index 0000000..aeb4f49 --- /dev/null +++ b/diffusion_policy/env/kitchen/relay_policy_learning/adept_envs/adept_envs/franka/robot/franka_config.xml @@ -0,0 +1,59 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + diff --git a/diffusion_policy/env/kitchen/relay_policy_learning/adept_envs/adept_envs/franka/robot/franka_robot.py b/diffusion_policy/env/kitchen/relay_policy_learning/adept_envs/adept_envs/franka/robot/franka_robot.py new file mode 100644 index 0000000..087b9dd --- /dev/null +++ b/diffusion_policy/env/kitchen/relay_policy_learning/adept_envs/adept_envs/franka/robot/franka_robot.py @@ -0,0 +1,264 @@ +#!/usr/bin/python +# +# Copyright 2020 Google LLC +# +# 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. + +import os, getpass +import numpy as np +from termcolor import cprint +import time +import copy +import click + +from adept_envs import base_robot +from adept_envs.utils.config import (get_config_root_node, read_config_from_node) + +# obervations structure +from collections import namedtuple +observation = namedtuple('observation', ['time', 'qpos_robot', 'qvel_robot', 'qpos_object', 'qvel_object']) + + + +franka_interface = '' + +class Robot(base_robot.BaseRobot): + + """ + Abstracts away the differences between the robot_simulation and robot_hardware + + """ + + def __init__(self, *args, **kwargs): + super(Robot, self).__init__(*args, **kwargs) + global franka_interface + + # Read robot configurations + self._read_specs_from_config(robot_configs=self.calibration_path) + + + # Robot: Handware + if self.is_hardware: + if franka_interface == '': + raise NotImplementedError() + from handware.franka import franka + + # initialize franka + self.franka_interface = franka() + franka_interface = self.franka_interface + cprint("Initializing %s Hardware (Status:%d)" % (self.robot_name, self.franka.okay(self.robot_hardware_dof)), 'white', 'on_grey') + else: + self.franka_interface = franka_interface + cprint("Reusing previours Franka session", 'white', 'on_grey') + + # Robot: Simulation + else: + self.robot_name = "Franka" + cprint("Initializing %s sim" % self.robot_name, 'white', 'on_grey') + + # Robot's time + self.time_start = time.time() + self.time = time.time()-self.time_start + self.time_render = -1 # time of rendering + + + # read specs from the calibration file + def _read_specs_from_config(self, robot_configs): + root, root_name = get_config_root_node(config_file_name=robot_configs) + self.robot_name = root_name[0] + self.robot_mode = np.zeros(self.n_dofs, dtype=int) + self.robot_mj_dof = np.zeros(self.n_dofs, dtype=int) + self.robot_hardware_dof = np.zeros(self.n_dofs, dtype=int) + self.robot_scale = np.zeros(self.n_dofs, dtype=float) + self.robot_offset = np.zeros(self.n_dofs, dtype=float) + self.robot_pos_bound = np.zeros([self.n_dofs, 2], dtype=float) + self.robot_vel_bound = np.zeros([self.n_dofs, 2], dtype=float) + self.robot_pos_noise_amp = np.zeros(self.n_dofs, dtype=float) + self.robot_vel_noise_amp = np.zeros(self.n_dofs, dtype=float) + + print("Reading configurations for %s" % self.robot_name) + for i in range(self.n_dofs): + self.robot_mode[i] = read_config_from_node(root, "qpos"+str(i), "mode", int) + self.robot_mj_dof[i] = read_config_from_node(root, "qpos"+str(i), "mj_dof", int) + self.robot_hardware_dof[i] = read_config_from_node(root, "qpos"+str(i), "hardware_dof", int) + self.robot_scale[i] = read_config_from_node(root, "qpos"+str(i), "scale", float) + self.robot_offset[i] = read_config_from_node(root, "qpos"+str(i), "offset", float) + self.robot_pos_bound[i] = read_config_from_node(root, "qpos"+str(i), "pos_bound", float) + self.robot_vel_bound[i] = read_config_from_node(root, "qpos"+str(i), "vel_bound", float) + self.robot_pos_noise_amp[i] = read_config_from_node(root, "qpos"+str(i), "pos_noise_amp", float) + self.robot_vel_noise_amp[i] = read_config_from_node(root, "qpos"+str(i), "vel_noise_amp", float) + + + # convert to hardware space + def _de_calib(self, qp_mj, qv_mj=None): + qp_ad = (qp_mj-self.robot_offset)/self.robot_scale + if qv_mj is not None: + qv_ad = qv_mj/self.robot_scale + return qp_ad, qv_ad + else: + return qp_ad + + # convert to mujoco space + def _calib(self, qp_ad, qv_ad): + qp_mj = qp_ad* self.robot_scale + self.robot_offset + qv_mj = qv_ad* self.robot_scale + return qp_mj, qv_mj + + + # refresh the observation cache + def _observation_cache_refresh(self, env): + for _ in range(self.observation_cache_maxsize): + self.get_obs(env, sim_mimic_hardware=False) + + # get past observation + def get_obs_from_cache(self, env, index=-1): + assert (index>=0 and index=-self.observation_cache_maxsize), \ + "cache index out of bound. (cache size is %2d)"%self.observation_cache_maxsize + obs = self.observation_cache[index] + if self.has_obj: + return obs.time, obs.qpos_robot, obs.qvel_robot, obs.qpos_object, obs.qvel_object + else: + return obs.time, obs.qpos_robot, obs.qvel_robot + + + # get observation + def get_obs(self, env, robot_noise_ratio=1, object_noise_ratio=1, sim_mimic_hardware=True): + if self.is_hardware: + raise NotImplementedError() + + else: + #Gather simulated observation + qp = env.sim.data.qpos[:self.n_jnt].copy() + qv = env.sim.data.qvel[:self.n_jnt].copy() + if self.has_obj: + qp_obj = env.sim.data.qpos[-self.n_obj:].copy() + qv_obj = env.sim.data.qvel[-self.n_obj:].copy() + else: + qp_obj = None + qv_obj = None + self.time = env.sim.data.time + + # Simulate observation noise + if not env.initializing: + qp += robot_noise_ratio*self.robot_pos_noise_amp[:self.n_jnt]*env.np_random.uniform(low=-1., high=1., size=self.n_jnt) + qv += robot_noise_ratio*self.robot_vel_noise_amp[:self.n_jnt]*env.np_random.uniform(low=-1., high=1., size=self.n_jnt) + if self.has_obj: + qp_obj += robot_noise_ratio*self.robot_pos_noise_amp[-self.n_obj:]*env.np_random.uniform(low=-1., high=1., size=self.n_obj) + qv_obj += robot_noise_ratio*self.robot_vel_noise_amp[-self.n_obj:]*env.np_random.uniform(low=-1., high=1., size=self.n_obj) + + # cache observations + obs = observation(time=self.time, qpos_robot=qp, qvel_robot=qv, qpos_object=qp_obj, qvel_object=qv_obj) + self.observation_cache.append(obs) + + if self.has_obj: + return obs.time, obs.qpos_robot, obs.qvel_robot, obs.qpos_object, obs.qvel_object + else: + return obs.time, obs.qpos_robot, obs.qvel_robot + + + # enforce position specs. + def ctrl_position_limits(self, ctrl_position): + ctrl_feasible_position = np.clip(ctrl_position, self.robot_pos_bound[:self.n_jnt, 0], self.robot_pos_bound[:self.n_jnt, 1]) + return ctrl_feasible_position + + + # step the robot env + def step(self, env, ctrl_desired, step_duration, sim_override=False): + + # Populate observation cache during startup + if env.initializing: + self._observation_cache_refresh(env) + + # enforce velocity limits + ctrl_feasible = self.ctrl_velocity_limits(ctrl_desired, step_duration) + + # enforce position limits + ctrl_feasible = self.ctrl_position_limits(ctrl_feasible) + + # Send controls to the robot + if self.is_hardware and (not sim_override): + raise NotImplementedError() + else: + env.do_simulation(ctrl_feasible, int(step_duration/env.sim.model.opt.timestep)) # render is folded in here + + # Update current robot state on the overlay + if self.overlay: + env.sim.data.qpos[self.n_jnt:2*self.n_jnt] = env.desired_pose.copy() + env.sim.forward() + + # synchronize time + if self.is_hardware: + time_now = (time.time()-self.time_start) + time_left_in_step = step_duration - (time_now-self.time) + if(time_left_in_step>0.0001): + time.sleep(time_left_in_step) + return 1 + + + def reset(self, env, reset_pose, reset_vel, overlay_mimic_reset_pose=True, sim_override=False): + reset_pose = self.clip_positions(reset_pose) + + if self.is_hardware: + raise NotImplementedError() + else: + env.sim.reset() + env.sim.data.qpos[:self.n_jnt] = reset_pose[:self.n_jnt].copy() + env.sim.data.qvel[:self.n_jnt] = reset_vel[:self.n_jnt].copy() + if self.has_obj: + env.sim.data.qpos[-self.n_obj:] = reset_pose[-self.n_obj:].copy() + env.sim.data.qvel[-self.n_obj:] = reset_vel[-self.n_obj:].copy() + env.sim.forward() + + if self.overlay: + env.sim.data.qpos[self.n_jnt:2*self.n_jnt] = env.desired_pose[:self.n_jnt].copy() + env.sim.forward() + + # refresh observation cache before exit + self._observation_cache_refresh(env) + + + def close(self): + if self.is_hardware: + cprint("Closing Franka hardware... ", 'white', 'on_grey', end='', flush=True) + status = 0 + raise NotImplementedError() + cprint("Closed (Status: {})".format(status), 'white', 'on_grey', flush=True) + else: + cprint("Closing Franka sim", 'white', 'on_grey', flush=True) + + +class Robot_PosAct(Robot): + + # enforce velocity sepcs. + # ALERT: This depends on previous observation. This is not ideal as it breaks MDP addumptions. Be careful + def ctrl_velocity_limits(self, ctrl_position, step_duration): + last_obs = self.observation_cache[-1] + ctrl_desired_vel = (ctrl_position-last_obs.qpos_robot[:self.n_jnt])/step_duration + + ctrl_feasible_vel = np.clip(ctrl_desired_vel, self.robot_vel_bound[:self.n_jnt, 0], self.robot_vel_bound[:self.n_jnt, 1]) + ctrl_feasible_position = last_obs.qpos_robot[:self.n_jnt] + ctrl_feasible_vel*step_duration + return ctrl_feasible_position + + +class Robot_VelAct(Robot): + + # enforce velocity sepcs. + # ALERT: This depends on previous observation. This is not ideal as it breaks MDP addumptions. Be careful + def ctrl_velocity_limits(self, ctrl_velocity, step_duration): + last_obs = self.observation_cache[-1] + + ctrl_feasible_vel = np.clip(ctrl_velocity, self.robot_vel_bound[:self.n_jnt, 0], self.robot_vel_bound[:self.n_jnt, 1]) + ctrl_feasible_position = last_obs.qpos_robot[:self.n_jnt] + ctrl_feasible_vel*step_duration + return ctrl_feasible_position + diff --git a/diffusion_policy/env/kitchen/relay_policy_learning/adept_envs/adept_envs/mujoco_env.py b/diffusion_policy/env/kitchen/relay_policy_learning/adept_envs/adept_envs/mujoco_env.py new file mode 100644 index 0000000..e8cd9ec --- /dev/null +++ b/diffusion_policy/env/kitchen/relay_policy_learning/adept_envs/adept_envs/mujoco_env.py @@ -0,0 +1,202 @@ +"""Base environment for MuJoCo-based environments.""" + +#!/usr/bin/python +# +# Copyright 2020 Google LLC +# +# 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. + + +import collections +import os +import time +from typing import Dict, Optional + +import gym +from gym import spaces +from gym.utils import seeding +import numpy as np + +from adept_envs.simulation.sim_robot import MujocoSimRobot, RenderMode + +DEFAULT_RENDER_SIZE = 480 + +USE_DM_CONTROL = True + + +class MujocoEnv(gym.Env): + """Superclass for all MuJoCo environments.""" + + def __init__(self, + model_path: str, + frame_skip: int, + camera_settings: Optional[Dict] = None, + use_dm_backend: Optional[bool] = None, + ): + """Initializes a new MuJoCo environment. + + Args: + model_path: The path to the MuJoCo XML file. + frame_skip: The number of simulation steps per environment step. On + hardware this influences the duration of each environment step. + camera_settings: Settings to initialize the simulation camera. This + can contain the keys `distance`, `azimuth`, and `elevation`. + use_dm_backend: A boolean to switch between mujoco-py and dm_control. + """ + self._seed() + if not os.path.isfile(model_path): + raise IOError( + '[MujocoEnv]: Model path does not exist: {}'.format(model_path)) + self.frame_skip = frame_skip + + self.sim_robot = MujocoSimRobot( + model_path, + use_dm_backend=use_dm_backend or USE_DM_CONTROL, + camera_settings=camera_settings) + self.sim = self.sim_robot.sim + self.model = self.sim_robot.model + self.data = self.sim_robot.data + + self.metadata = { + 'render.modes': ['human', 'rgb_array', 'depth_array'], + 'video.frames_per_second': int(np.round(1.0 / self.dt)) + } + self.mujoco_render_frames = False + + self.init_qpos = self.data.qpos.ravel().copy() + self.init_qvel = self.data.qvel.ravel().copy() + observation, _reward, done, _info = self.step(np.zeros(self.model.nu)) + assert not done + + bounds = self.model.actuator_ctrlrange.copy() + act_upper = bounds[:, 1] + act_lower = bounds[:, 0] + + # Define the action and observation spaces. + # HACK: MJRL is still using gym 0.9.x so we can't provide a dtype. + try: + self.action_space = spaces.Box( + act_lower, act_upper, dtype=np.float32) + if isinstance(observation, collections.Mapping): + self.observation_space = spaces.Dict({ + k: spaces.Box(-np.inf, np.inf, shape=v.shape, dtype=np.float32) for k, v in observation.items()}) + else: + self.obs_dim = np.sum([o.size for o in observation]) if type(observation) is tuple else observation.size + self.observation_space = spaces.Box( + -np.inf, np.inf, observation.shape, dtype=np.float32) + + except TypeError: + # Fallback case for gym 0.9.x + self.action_space = spaces.Box(act_lower, act_upper) + assert not isinstance(observation, collections.Mapping), 'gym 0.9.x does not support dictionary observation.' + self.obs_dim = np.sum([o.size for o in observation]) if type(observation) is tuple else observation.size + self.observation_space = spaces.Box( + -np.inf, np.inf, observation.shape) + + def seed(self, seed=None): # Compatibility with new gym + return self._seed(seed) + + def _seed(self, seed=None): + self.np_random, seed = seeding.np_random(seed) + return [seed] + + # methods to override: + # ---------------------------- + + def reset_model(self): + """Reset the robot degrees of freedom (qpos and qvel). + + Implement this in each subclass. + """ + raise NotImplementedError + + # ----------------------------- + + def reset(self): # compatibility with new gym + return self._reset() + + def _reset(self): + self.sim.reset() + self.sim.forward() + ob = self.reset_model() + return ob + + def set_state(self, qpos, qvel): + assert qpos.shape == (self.model.nq,) and qvel.shape == (self.model.nv,) + # we are directly manipulating mujoco state here + data = self.sim.data # MjData + for i in range(self.model.nq): + data.qpos[i] = qpos[i] + for i in range(self.model.nv): + data.qvel[i] = qvel[i] + # state = np.concatenate([self.data.qpos, self.data.qvel, self.data.act]) + # self.sim.set_state(state) + self.sim.forward() + + @property + def dt(self): + return self.model.opt.timestep * self.frame_skip + + def do_simulation(self, ctrl, n_frames): + for i in range(self.model.nu): + self.sim.data.ctrl[i] = ctrl[i] + + for _ in range(n_frames): + self.sim.step() + + # TODO(michaelahn): Remove this; render should be called separately. + if self.mujoco_render_frames is True: + self.mj_render() + + def render(self, + mode='human', + width=DEFAULT_RENDER_SIZE, + height=DEFAULT_RENDER_SIZE, + camera_id=-1): + """Renders the environment. + + Args: + mode: The type of rendering to use. + - 'human': Renders to a graphical window. + - 'rgb_array': Returns the RGB image as an np.ndarray. + - 'depth_array': Returns the depth image as an np.ndarray. + width: The width of the rendered image. This only affects offscreen + rendering. + height: The height of the rendered image. This only affects + offscreen rendering. + camera_id: The ID of the camera to use. By default, this is the free + camera. If specified, only affects offscreen rendering. + """ + if mode == 'human': + self.sim_robot.renderer.render_to_window() + elif mode == 'rgb_array': + assert width and height + return self.sim_robot.renderer.render_offscreen( + width, height, mode=RenderMode.RGB, camera_id=camera_id) + elif mode == 'depth_array': + assert width and height + return self.sim_robot.renderer.render_offscreen( + width, height, mode=RenderMode.DEPTH, camera_id=camera_id) + else: + raise NotImplementedError(mode) + + def close(self): + self.sim_robot.close() + + def mj_render(self): + """Backwards compatibility with MJRL.""" + self.render(mode='human') + + def state_vector(self): + state = self.sim.get_state() + return np.concatenate([state.qpos.flat, state.qvel.flat]) \ No newline at end of file diff --git a/diffusion_policy/env/kitchen/relay_policy_learning/adept_envs/adept_envs/robot_env.py b/diffusion_policy/env/kitchen/relay_policy_learning/adept_envs/adept_envs/robot_env.py new file mode 100644 index 0000000..76f11fa --- /dev/null +++ b/diffusion_policy/env/kitchen/relay_policy_learning/adept_envs/adept_envs/robot_env.py @@ -0,0 +1,166 @@ +"""Base class for robotics environments.""" + +#!/usr/bin/python +# +# Copyright 2020 Google LLC +# +# 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. + +import importlib +import inspect +import os +from typing import Dict, Optional + +import numpy as np + + +from adept_envs import mujoco_env +from adept_envs.base_robot import BaseRobot +from adept_envs.utils.configurable import import_class_from_path +from adept_envs.utils.constants import MODELS_PATH + + +class RobotEnv(mujoco_env.MujocoEnv): + """Base environment for all adept robots.""" + + # Mapping of robot name to fully qualified class path. + # e.g. 'robot': 'adept_envs.dclaw.robot.Robot' + # Subclasses should override this to specify the Robot classes they support. + ROBOTS = {} + + # Mapping of device path to the calibration file to use. If the device path + # is not found, the 'default' key is used. + # This can be overriden by subclasses. + CALIBRATION_PATHS = {} + + def __init__(self, + model_path: str, + robot: BaseRobot, + frame_skip: int, + camera_settings: Optional[Dict] = None): + """Initializes a robotics environment. + + Args: + model_path: The path to the model to run. Relative paths will be + interpreted as relative to the 'adept_models' folder. + robot: The Robot object to use. + frame_skip: The number of simulation steps per environment step. On + hardware this influences the duration of each environment step. + camera_settings: Settings to initialize the simulation camera. This + can contain the keys `distance`, `azimuth`, and `elevation`. + """ + self._robot = robot + + # Initial pose for first step. + self.desired_pose = np.zeros(self.n_jnt) + + if not model_path.startswith('/'): + model_path = os.path.abspath(os.path.join(MODELS_PATH, model_path)) + + self.remote_viz = None + + try: + from adept_envs.utils.remote_viz import RemoteViz + self.remote_viz = RemoteViz(model_path) + except ImportError: + pass + + + self._initializing = True + super(RobotEnv, self).__init__( + model_path, frame_skip, camera_settings=camera_settings) + self._initializing = False + + + @property + def robot(self): + return self._robot + + @property + def n_jnt(self): + return self._robot.n_jnt + + @property + def n_obj(self): + return self._robot.n_obj + + @property + def skip(self): + """Alias for frame_skip. Needed for MJRL.""" + return self.frame_skip + + @property + def initializing(self): + return self._initializing + + def close_env(self): + if self._robot is not None: + self._robot.close() + + def make_robot(self, + n_jnt, + n_obj=0, + is_hardware=False, + device_name=None, + legacy=False, + **kwargs): + """Creates a new robot for the environment. + + Args: + n_jnt: The number of joints in the robot. + n_obj: The number of object joints in the robot environment. + is_hardware: Whether to run on hardware or not. + device_name: The device path for the robot hardware. + legacy: If true, runs using direct dynamixel communication rather + than DDS. + kwargs: See BaseRobot for other parameters. + + Returns: + A Robot object. + """ + if not self.ROBOTS: + raise NotImplementedError('Subclasses must override ROBOTS.') + + if is_hardware and not device_name: + raise ValueError('Must provide device name if running on hardware.') + + robot_name = 'dds_robot' if not legacy and is_hardware else 'robot' + if robot_name not in self.ROBOTS: + raise KeyError("Unsupported robot '{}', available: {}".format( + robot_name, list(self.ROBOTS.keys()))) + + cls = import_class_from_path(self.ROBOTS[robot_name]) + + calibration_path = None + if self.CALIBRATION_PATHS: + if not device_name: + calibration_name = 'default' + elif device_name not in self.CALIBRATION_PATHS: + print('Device "{}" not in CALIBRATION_PATHS; using default.' + .format(device_name)) + calibration_name = 'default' + else: + calibration_name = device_name + + calibration_path = self.CALIBRATION_PATHS[calibration_name] + if not os.path.isfile(calibration_path): + raise OSError('Could not find calibration file at: {}'.format( + calibration_path)) + + return cls( + n_jnt, + n_obj, + is_hardware=is_hardware, + device_name=device_name, + calibration_path=calibration_path, + **kwargs) diff --git a/diffusion_policy/env/kitchen/relay_policy_learning/adept_envs/adept_envs/simulation/module.py b/diffusion_policy/env/kitchen/relay_policy_learning/adept_envs/adept_envs/simulation/module.py new file mode 100644 index 0000000..a1284c7 --- /dev/null +++ b/diffusion_policy/env/kitchen/relay_policy_learning/adept_envs/adept_envs/simulation/module.py @@ -0,0 +1,126 @@ +#!/usr/bin/python +# +# Copyright 2020 Google LLC +# +# 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. + +"""Module for caching Python modules related to simulation.""" + +import sys + +_MUJOCO_PY_MODULE = None + +_DM_MUJOCO_MODULE = None +_DM_VIEWER_MODULE = None +_DM_RENDER_MODULE = None + +_GLFW_MODULE = None + + +def get_mujoco_py(): + """Returns the mujoco_py module.""" + global _MUJOCO_PY_MODULE + if _MUJOCO_PY_MODULE: + return _MUJOCO_PY_MODULE + try: + import mujoco_py + # Override the warning function. + from mujoco_py.builder import cymj + cymj.set_warning_callback(_mj_warning_fn) + except ImportError: + print( + 'Failed to import mujoco_py. Ensure that mujoco_py (using MuJoCo ' + 'v1.50) is installed.', + file=sys.stderr) + sys.exit(1) + _MUJOCO_PY_MODULE = mujoco_py + return mujoco_py + + +def get_mujoco_py_mjlib(): + """Returns the mujoco_py mjlib module.""" + + class MjlibDelegate: + """Wrapper that forwards mjlib calls.""" + + def __init__(self, lib): + self._lib = lib + + def __getattr__(self, name: str): + if name.startswith('mj'): + return getattr(self._lib, '_' + name) + raise AttributeError(name) + + return MjlibDelegate(get_mujoco_py().cymj) + + +def get_dm_mujoco(): + """Returns the DM Control mujoco module.""" + global _DM_MUJOCO_MODULE + if _DM_MUJOCO_MODULE: + return _DM_MUJOCO_MODULE + try: + from dm_control import mujoco + except ImportError: + print( + 'Failed to import dm_control.mujoco. Ensure that dm_control (using ' + 'MuJoCo v2.00) is installed.', + file=sys.stderr) + sys.exit(1) + _DM_MUJOCO_MODULE = mujoco + return mujoco + + +def get_dm_viewer(): + """Returns the DM Control viewer module.""" + global _DM_VIEWER_MODULE + if _DM_VIEWER_MODULE: + return _DM_VIEWER_MODULE + try: + from dm_control import viewer + except ImportError: + print( + 'Failed to import dm_control.viewer. Ensure that dm_control (using ' + 'MuJoCo v2.00) is installed.', + file=sys.stderr) + sys.exit(1) + _DM_VIEWER_MODULE = viewer + return viewer + + +def get_dm_render(): + """Returns the DM Control render module.""" + global _DM_RENDER_MODULE + if _DM_RENDER_MODULE: + return _DM_RENDER_MODULE + try: + try: + from dm_control import _render + render = _render + except ImportError: + print('Warning: DM Control is out of date.') + from dm_control import render + except ImportError: + print( + 'Failed to import dm_control.render. Ensure that dm_control (using ' + 'MuJoCo v2.00) is installed.', + file=sys.stderr) + sys.exit(1) + _DM_RENDER_MODULE = render + return render + + +def _mj_warning_fn(warn_data: bytes): + """Warning function override for mujoco_py.""" + print('WARNING: Mujoco simulation is unstable (has NaNs): {}'.format( + warn_data.decode())) diff --git a/diffusion_policy/env/kitchen/relay_policy_learning/adept_envs/adept_envs/simulation/renderer.py b/diffusion_policy/env/kitchen/relay_policy_learning/adept_envs/adept_envs/simulation/renderer.py new file mode 100644 index 0000000..28b9de2 --- /dev/null +++ b/diffusion_policy/env/kitchen/relay_policy_learning/adept_envs/adept_envs/simulation/renderer.py @@ -0,0 +1,293 @@ +#!/usr/bin/python +# +# Copyright 2020 Google LLC +# +# 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. + +"""Module for viewing Physics objects in the DM Control viewer.""" + +import abc +import enum +import sys +from typing import Dict, Optional + +import numpy as np + +from adept_envs.simulation import module + +# Default window dimensions. +DEFAULT_WINDOW_WIDTH = 1024 +DEFAULT_WINDOW_HEIGHT = 768 + +DEFAULT_WINDOW_TITLE = 'MuJoCo Viewer' + +_MAX_RENDERBUFFER_SIZE = 2048 + + +class RenderMode(enum.Enum): + """Rendering modes for offscreen rendering.""" + RGB = 0 + DEPTH = 1 + SEGMENTATION = 2 + + +class Renderer(abc.ABC): + """Base interface for rendering simulations.""" + + def __init__(self, camera_settings: Optional[Dict] = None): + self._camera_settings = camera_settings + + @abc.abstractmethod + def close(self): + """Cleans up any resources being used by the renderer.""" + + @abc.abstractmethod + def render_to_window(self): + """Renders the simulation to a window.""" + + @abc.abstractmethod + def render_offscreen(self, + width: int, + height: int, + mode: RenderMode = RenderMode.RGB, + camera_id: int = -1) -> np.ndarray: + """Renders the camera view as a NumPy array of pixels. + + Args: + width: The viewport width (pixels). + height: The viewport height (pixels). + mode: The rendering mode. + camera_id: The ID of the camera to render from. By default, uses + the free camera. + + Returns: + A NumPy array of the pixels. + """ + + def _update_camera(self, camera): + """Updates the given camera to move to the initial settings.""" + if not self._camera_settings: + return + distance = self._camera_settings.get('distance') + azimuth = self._camera_settings.get('azimuth') + elevation = self._camera_settings.get('elevation') + lookat = self._camera_settings.get('lookat') + + if distance is not None: + camera.distance = distance + if azimuth is not None: + camera.azimuth = azimuth + if elevation is not None: + camera.elevation = elevation + if lookat is not None: + camera.lookat[:] = lookat + + +class MjPyRenderer(Renderer): + """Class for rendering mujoco_py simulations.""" + + def __init__(self, sim, **kwargs): + assert isinstance(sim, module.get_mujoco_py().MjSim), \ + 'MjPyRenderer takes a mujoco_py MjSim object.' + super().__init__(**kwargs) + self._sim = sim + self._onscreen_renderer = None + self._offscreen_renderer = None + + def render_to_window(self): + """Renders the simulation to a window.""" + if not self._onscreen_renderer: + self._onscreen_renderer = module.get_mujoco_py().MjViewer(self._sim) + self._update_camera(self._onscreen_renderer.cam) + + self._onscreen_renderer.render() + + def render_offscreen(self, + width: int, + height: int, + mode: RenderMode = RenderMode.RGB, + camera_id: int = -1) -> np.ndarray: + """Renders the camera view as a NumPy array of pixels. + + Args: + width: The viewport width (pixels). + height: The viewport height (pixels). + mode: The rendering mode. + camera_id: The ID of the camera to render from. By default, uses + the free camera. + + Returns: + A NumPy array of the pixels. + """ + if not self._offscreen_renderer: + self._offscreen_renderer = module.get_mujoco_py() \ + .MjRenderContextOffscreen(self._sim) + + # Update the camera configuration for the free-camera. + if camera_id == -1: + self._update_camera(self._offscreen_renderer.cam) + + self._offscreen_renderer.render(width, height, camera_id) + if mode == RenderMode.RGB: + data = self._offscreen_renderer.read_pixels( + width, height, depth=False) + # Original image is upside-down, so flip it + return data[::-1, :, :] + elif mode == RenderMode.DEPTH: + data = self._offscreen_renderer.read_pixels( + width, height, depth=True)[1] + # Original image is upside-down, so flip it + return data[::-1, :] + else: + raise NotImplementedError(mode) + + def close(self): + """Cleans up any resources being used by the renderer.""" + + +class DMRenderer(Renderer): + """Class for rendering DM Control Physics objects.""" + + def __init__(self, physics, **kwargs): + assert isinstance(physics, module.get_dm_mujoco().Physics), \ + 'DMRenderer takes a DM Control Physics object.' + super().__init__(**kwargs) + self._physics = physics + self._window = None + + # Set the camera to lookat the center of the geoms. (mujoco_py does + # this automatically. + if 'lookat' not in self._camera_settings: + self._camera_settings['lookat'] = [ + np.median(self._physics.data.geom_xpos[:, i]) for i in range(3) + ] + + def render_to_window(self): + """Renders the Physics object to a window. + + The window continuously renders the Physics in a separate thread. + + This function is a no-op if the window was already created. + """ + if not self._window: + self._window = DMRenderWindow() + self._window.load_model(self._physics) + self._update_camera(self._window.camera) + self._window.run_frame() + + def render_offscreen(self, + width: int, + height: int, + mode: RenderMode = RenderMode.RGB, + camera_id: int = -1) -> np.ndarray: + """Renders the camera view as a NumPy array of pixels. + + Args: + width: The viewport width (pixels). + height: The viewport height (pixels). + mode: The rendering mode. + camera_id: The ID of the camera to render from. By default, uses + the free camera. + + Returns: + A NumPy array of the pixels. + """ + mujoco = module.get_dm_mujoco() + # TODO(michaelahn): Consider caching the camera. + camera = mujoco.Camera( + physics=self._physics, + height=height, + width=width, + camera_id=camera_id) + + # Update the camera configuration for the free-camera. + if camera_id == -1: + self._update_camera( + camera._render_camera, # pylint: disable=protected-access + ) + + image = camera.render( + depth=(mode == RenderMode.DEPTH), + segmentation=(mode == RenderMode.SEGMENTATION)) + camera._scene.free() # pylint: disable=protected-access + return image + + def close(self): + """Cleans up any resources being used by the renderer.""" + if self._window: + self._window.close() + self._window = None + + +class DMRenderWindow: + """Class that encapsulates a graphical window.""" + + def __init__(self, + width: int = DEFAULT_WINDOW_WIDTH, + height: int = DEFAULT_WINDOW_HEIGHT, + title: str = DEFAULT_WINDOW_TITLE): + """Creates a graphical render window. + + Args: + width: The width of the window. + height: The height of the window. + title: The title of the window. + """ + dmv = module.get_dm_viewer() + self._viewport = dmv.renderer.Viewport(width, height) + self._window = dmv.gui.RenderWindow(width, height, title) + self._viewer = dmv.viewer.Viewer(self._viewport, self._window.mouse, + self._window.keyboard) + self._draw_surface = None + self._renderer = dmv.renderer.NullRenderer() + + @property + def camera(self): + return self._viewer._camera._camera + + def close(self): + self._viewer.deinitialize() + self._renderer.release() + self._draw_surface.free() + self._window.close() + + def load_model(self, physics): + """Loads the given Physics object to render.""" + self._viewer.deinitialize() + + self._draw_surface = module.get_dm_render().Renderer( + max_width=_MAX_RENDERBUFFER_SIZE, max_height=_MAX_RENDERBUFFER_SIZE) + self._renderer = module.get_dm_viewer().renderer.OffScreenRenderer( + physics.model, self._draw_surface) + + self._viewer.initialize(physics, self._renderer, touchpad=False) + + def run_frame(self): + """Renders one frame of the simulation. + + NOTE: This is extremely slow at the moment. + """ + glfw = module.get_dm_viewer().gui.glfw_gui.glfw + glfw_window = self._window._context.window + if glfw.window_should_close(glfw_window): + sys.exit(0) + + self._viewport.set_size(*self._window.shape) + self._viewer.render() + pixels = self._renderer.pixels + + with self._window._context.make_current() as ctx: + ctx.call(self._window._update_gui_on_render_thread, glfw_window, + pixels) + self._window._mouse.process_events() + self._window._keyboard.process_events() diff --git a/diffusion_policy/env/kitchen/relay_policy_learning/adept_envs/adept_envs/simulation/sim_robot.py b/diffusion_policy/env/kitchen/relay_policy_learning/adept_envs/adept_envs/simulation/sim_robot.py new file mode 100644 index 0000000..195d524 --- /dev/null +++ b/diffusion_policy/env/kitchen/relay_policy_learning/adept_envs/adept_envs/simulation/sim_robot.py @@ -0,0 +1,135 @@ +#!/usr/bin/python +# +# Copyright 2020 Google LLC +# +# 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. + +"""Module for loading MuJoCo models.""" + +import os +from typing import Dict, Optional + +from adept_envs.simulation import module +from adept_envs.simulation.renderer import DMRenderer, MjPyRenderer, RenderMode + + +class MujocoSimRobot: + """Class that encapsulates a MuJoCo simulation. + + This class exposes methods that are agnostic to the simulation backend. + Two backends are supported: + 1. mujoco_py - MuJoCo v1.50 + 2. dm_control - MuJoCo v2.00 + """ + + def __init__(self, + model_file: str, + use_dm_backend: bool = False, + camera_settings: Optional[Dict] = None): + """Initializes a new simulation. + + Args: + model_file: The MuJoCo XML model file to load. + use_dm_backend: If True, uses DM Control's Physics (MuJoCo v2.0) as + the backend for the simulation. Otherwise, uses mujoco_py (MuJoCo + v1.5) as the backend. + camera_settings: Settings to initialize the renderer's camera. This + can contain the keys `distance`, `azimuth`, and `elevation`. + """ + self._use_dm_backend = use_dm_backend + + if not os.path.isfile(model_file): + raise ValueError( + '[MujocoSimRobot] Invalid model file path: {}'.format( + model_file)) + + if self._use_dm_backend: + dm_mujoco = module.get_dm_mujoco() + if model_file.endswith('.mjb'): + self.sim = dm_mujoco.Physics.from_binary_path(model_file) + else: + self.sim = dm_mujoco.Physics.from_xml_path(model_file) + self.model = self.sim.model + self._patch_mjlib_accessors(self.model, self.sim.data) + self.renderer = DMRenderer( + self.sim, camera_settings=camera_settings) + else: # Use mujoco_py + mujoco_py = module.get_mujoco_py() + self.model = mujoco_py.load_model_from_path(model_file) + self.sim = mujoco_py.MjSim(self.model) + self.renderer = MjPyRenderer( + self.sim, camera_settings=camera_settings) + + self.data = self.sim.data + + def close(self): + """Cleans up any resources being used by the simulation.""" + self.renderer.close() + + def save_binary(self, path: str): + """Saves the loaded model to a binary .mjb file.""" + if os.path.exists(path): + raise ValueError( + '[MujocoSimRobot] Path already exists: {}'.format(path)) + if not path.endswith('.mjb'): + path = path + '.mjb' + if self._use_dm_backend: + self.model.save_binary(path) + else: + with open(path, 'wb') as f: + f.write(self.model.get_mjb()) + + def get_mjlib(self): + """Returns an object that exposes the low-level MuJoCo API.""" + if self._use_dm_backend: + return module.get_dm_mujoco().wrapper.mjbindings.mjlib + else: + return module.get_mujoco_py_mjlib() + + def _patch_mjlib_accessors(self, model, data): + """Adds accessors to the DM Control objects to support mujoco_py API.""" + assert self._use_dm_backend + mjlib = self.get_mjlib() + + def name2id(type_name, name): + obj_id = mjlib.mj_name2id(model.ptr, + mjlib.mju_str2Type(type_name.encode()), + name.encode()) + if obj_id < 0: + raise ValueError('No {} with name "{}" exists.'.format( + type_name, name)) + return obj_id + + if not hasattr(model, 'body_name2id'): + model.body_name2id = lambda name: name2id('body', name) + + if not hasattr(model, 'geom_name2id'): + model.geom_name2id = lambda name: name2id('geom', name) + + if not hasattr(model, 'site_name2id'): + model.site_name2id = lambda name: name2id('site', name) + + if not hasattr(model, 'joint_name2id'): + model.joint_name2id = lambda name: name2id('joint', name) + + if not hasattr(model, 'actuator_name2id'): + model.actuator_name2id = lambda name: name2id('actuator', name) + + if not hasattr(model, 'camera_name2id'): + model.camera_name2id = lambda name: name2id('camera', name) + + if not hasattr(data, 'body_xpos'): + data.body_xpos = data.xpos + + if not hasattr(data, 'body_xquat'): + data.body_xquat = data.xquat diff --git a/diffusion_policy/env/kitchen/relay_policy_learning/adept_envs/adept_envs/utils/config.py b/diffusion_policy/env/kitchen/relay_policy_learning/adept_envs/adept_envs/utils/config.py new file mode 100644 index 0000000..e6aea13 --- /dev/null +++ b/diffusion_policy/env/kitchen/relay_policy_learning/adept_envs/adept_envs/utils/config.py @@ -0,0 +1,99 @@ +#!/usr/bin/python +# +# Copyright 2020 Google LLC +# +# 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. + +import numpy as np +try: + import cElementTree as ET +except ImportError: + try: + # Python 2.5 need to import a different module + import xml.etree.cElementTree as ET + except ImportError: + exit_err("Failed to import cElementTree from any known place") + +CONFIG_XML_DATA = """ + + + + + +""" + + +# Read config from root +def read_config_from_node(root_node, parent_name, child_name, dtype=int): + # find parent + parent_node = root_node.find(parent_name) + if parent_node == None: + quit("Parent %s not found" % parent_name) + + # get child data + child_data = parent_node.get(child_name) + if child_data == None: + quit("Child %s not found" % child_name) + + config_val = np.array(child_data.split(), dtype=dtype) + return config_val + + +# get config frlom file or string +def get_config_root_node(config_file_name=None, config_file_data=None): + try: + # get root + if config_file_data is None: + config_file_content = open(config_file_name, "r") + config = ET.parse(config_file_content) + root_node = config.getroot() + else: + root_node = ET.fromstring(config_file_data) + + # get root data + root_data = root_node.get('name') + root_name = np.array(root_data.split(), dtype=str) + except: + quit("ERROR: Unable to process config file %s" % config_file_name) + + return root_node, root_name + + +# Read config from config_file +def read_config_from_xml(config_file_name, parent_name, child_name, dtype=int): + root_node, root_name = get_config_root_node( + config_file_name=config_file_name) + return read_config_from_node(root_node, parent_name, child_name, dtype) + + +# tests +if __name__ == '__main__': + print("Read config and parse -------------------------") + root, root_name = get_config_root_node(config_file_data=CONFIG_XML_DATA) + print("Root:name \t", root_name) + print("limit:low \t", read_config_from_node(root, "limits", "low", float)) + print("limit:high \t", read_config_from_node(root, "limits", "high", float)) + print("scale:joint \t", read_config_from_node(root, "scale", "joint", + float)) + print("data:type \t", read_config_from_node(root, "data", "type", str)) + + # read straight from xml (dum the XML data as duh.xml for this test) + root, root_name = get_config_root_node(config_file_name="duh.xml") + print("Read from xml --------------------------------") + print("limit:low \t", read_config_from_xml("duh.xml", "limits", "low", + float)) + print("limit:high \t", + read_config_from_xml("duh.xml", "limits", "high", float)) + print("scale:joint \t", + read_config_from_xml("duh.xml", "scale", "joint", float)) + print("data:type \t", read_config_from_xml("duh.xml", "data", "type", str)) diff --git a/diffusion_policy/env/kitchen/relay_policy_learning/adept_envs/adept_envs/utils/configurable.py b/diffusion_policy/env/kitchen/relay_policy_learning/adept_envs/adept_envs/utils/configurable.py new file mode 100644 index 0000000..6685a50 --- /dev/null +++ b/diffusion_policy/env/kitchen/relay_policy_learning/adept_envs/adept_envs/utils/configurable.py @@ -0,0 +1,163 @@ +#!/usr/bin/python +# +# Copyright 2020 Google LLC +# +# 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. + +import importlib +import inspect +import os + +from gym.envs.registration import registry as gym_registry + + +def import_class_from_path(class_path): + """Given 'path.to.module:object', imports and returns the object.""" + module_path, class_name = class_path.split(":") + module = importlib.import_module(module_path) + return getattr(module, class_name) + + +class ConfigCache(object): + """Configuration class to store constructor arguments. + + This is used to store parameters to pass to Gym environments at init time. + """ + + def __init__(self): + self._configs = {} + self._default_config = {} + + def set_default_config(self, config): + """Sets the default configuration used for all RobotEnv envs.""" + self._default_config = dict(config) + + def set_config(self, cls_or_env_id, config): + """Sets the configuration for the given environment within a context. + + Args: + cls_or_env_id (Class | str): A class type or Gym environment ID to + configure. + config (dict): The configuration parameters. + """ + config_key = self._get_config_key(cls_or_env_id) + self._configs[config_key] = dict(config) + + def get_config(self, cls_or_env_id): + """Returns the configuration for the given env name. + + Args: + cls_or_env_id (Class | str): A class type or Gym environment ID to + get the configuration of. + """ + config_key = self._get_config_key(cls_or_env_id) + config = dict(self._default_config) + config.update(self._configs.get(config_key, {})) + return config + + def clear_config(self, cls_or_env_id): + """Clears the configuration for the given ID.""" + config_key = self._get_config_key(cls_or_env_id) + if config_key in self._configs: + del self._configs[config_key] + + def _get_config_key(self, cls_or_env_id): + if inspect.isclass(cls_or_env_id): + return cls_or_env_id + env_id = cls_or_env_id + assert isinstance(env_id, str) + if env_id not in gym_registry.env_specs: + raise ValueError("Unregistered environment name {}.".format(env_id)) + entry_point = gym_registry.env_specs[env_id]._entry_point + if callable(entry_point): + return entry_point + else: + return import_class_from_path(entry_point) + + +# Global robot config. +global_config = ConfigCache() + + +def configurable(config_id=None, pickleable=False, config_cache=global_config): + """Class decorator to allow injection of constructor arguments. + + This allows constructor arguments to be passed via ConfigCache. + Example usage: + + @configurable() + class A: + def __init__(b=None, c=2, d='Wow'): + ... + + global_config.set_config(A, {'b': 10, 'c': 20}) + a = A() # b=10, c=20, d='Wow' + a = A(b=30) # b=30, c=20, d='Wow' + + Args: + config_id: ID of the config to use. This defaults to the class type. + pickleable: Whether this class is pickleable. If true, causes the pickle + state to include the config and constructor arguments. + config_cache: The ConfigCache to use to read config data from. Uses + the global ConfigCache by default. + """ + def cls_decorator(cls): + assert inspect.isclass(cls) + + # Overwrite the class constructor to pass arguments from the config. + base_init = cls.__init__ + def __init__(self, *args, **kwargs): + + config = config_cache.get_config(config_id or type(self)) + # Allow kwargs to override the config. + kwargs = {**config, **kwargs} + + # print('Initializing {} with params: {}'.format(type(self).__name__, + # kwargs)) + + if pickleable: + self._pkl_env_args = args + self._pkl_env_kwargs = kwargs + + base_init(self, *args, **kwargs) + cls.__init__ = __init__ + + # If the class is pickleable, overwrite the state methods to save + # the constructor arguments and config. + if pickleable: + # Use same pickle keys as gym.utils.ezpickle for backwards compat. + PKL_ARGS_KEY = '_ezpickle_args' + PKL_KWARGS_KEY = '_ezpickle_kwargs' + + def __getstate__(self): + return { + PKL_ARGS_KEY: self._pkl_env_args, + PKL_KWARGS_KEY: self._pkl_env_kwargs, + } + cls.__getstate__ = __getstate__ + + def __setstate__(self, data): + saved_args = data[PKL_ARGS_KEY] + saved_kwargs = data[PKL_KWARGS_KEY] + + # Override the saved state with the current config. + config = config_cache.get_config(config_id or type(self)) + # Allow kwargs to override the config. + kwargs = {**saved_kwargs, **config} + + inst = type(self)(*saved_args, **kwargs) + self.__dict__.update(inst.__dict__) + cls.__setstate__ = __setstate__ + + return cls + return cls_decorator diff --git a/diffusion_policy/env/kitchen/relay_policy_learning/adept_envs/adept_envs/utils/constants.py b/diffusion_policy/env/kitchen/relay_policy_learning/adept_envs/adept_envs/utils/constants.py new file mode 100644 index 0000000..9c63fb7 --- /dev/null +++ b/diffusion_policy/env/kitchen/relay_policy_learning/adept_envs/adept_envs/utils/constants.py @@ -0,0 +1,23 @@ +#!/usr/bin/python +# +# Copyright 2020 Google LLC +# +# 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. + +import os + +ENVS_ROOT_PATH = os.path.abspath(os.path.join( + os.path.dirname(os.path.abspath(__file__)), + "../../")) + +MODELS_PATH = os.path.abspath(os.path.join(ENVS_ROOT_PATH, "../adept_models/")) diff --git a/diffusion_policy/env/kitchen/relay_policy_learning/adept_envs/adept_envs/utils/parse_demos.py b/diffusion_policy/env/kitchen/relay_policy_learning/adept_envs/adept_envs/utils/parse_demos.py new file mode 100644 index 0000000..01f9c36 --- /dev/null +++ b/diffusion_policy/env/kitchen/relay_policy_learning/adept_envs/adept_envs/utils/parse_demos.py @@ -0,0 +1,221 @@ +#!/usr/bin/python +# +# Copyright 2020 Google LLC +# +# 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. + +import click +import glob +import pickle +import numpy as np +from parse_mjl import parse_mjl_logs, viz_parsed_mjl_logs +from mjrl.utils.gym_env import GymEnv +import adept_envs +import time as timer +import skvideo.io +import gym + +# headless renderer +render_buffer = [] # rendering buffer + + +def viewer(env, + mode='initialize', + filename='video', + frame_size=(640, 480), + camera_id=0, + render=None): + if render == 'onscreen': + env.mj_render() + + elif render == 'offscreen': + + global render_buffer + if mode == 'initialize': + render_buffer = [] + mode = 'render' + + if mode == 'render': + curr_frame = env.render(mode='rgb_array') + render_buffer.append(curr_frame) + + if mode == 'save': + skvideo.io.vwrite(filename, np.asarray(render_buffer)) + print("\noffscreen buffer saved", filename) + + elif render == 'None': + pass + + else: + print("unknown render: ", render) + + +# view demos (physics ignored) +def render_demos(env, data, filename='demo_rendering.mp4', render=None): + FPS = 30 + render_skip = max(1, round(1. / \ + (FPS * env.sim.model.opt.timestep * env.frame_skip))) + t0 = timer.time() + + viewer(env, mode='initialize', render=render) + for i_frame in range(data['ctrl'].shape[0]): + env.sim.data.qpos[:] = data['qpos'][i_frame].copy() + env.sim.data.qvel[:] = data['qvel'][i_frame].copy() + env.sim.forward() + if i_frame % render_skip == 0: + viewer(env, mode='render', render=render) + print(i_frame, end=', ', flush=True) + + viewer(env, mode='save', filename=filename, render=render) + print("time taken = %f" % (timer.time() - t0)) + + +# playback demos and get data(physics respected) +def gather_training_data(env, data, filename='demo_playback.mp4', render=None): + env = env.env + FPS = 30 + render_skip = max(1, round(1. / \ + (FPS * env.sim.model.opt.timestep * env.frame_skip))) + t0 = timer.time() + + # initialize + env.reset() + init_qpos = data['qpos'][0].copy() + init_qvel = data['qvel'][0].copy() + act_mid = env.act_mid + act_rng = env.act_amp + + # prepare env + env.sim.data.qpos[:] = init_qpos + env.sim.data.qvel[:] = init_qvel + env.sim.forward() + viewer(env, mode='initialize', render=render) + + # step the env and gather data + path_obs = None + for i_frame in range(data['ctrl'].shape[0] - 1): + # Reset every time step + # if i_frame % 1 == 0: + # qp = data['qpos'][i_frame].copy() + # qv = data['qvel'][i_frame].copy() + # env.sim.data.qpos[:] = qp + # env.sim.data.qvel[:] = qv + # env.sim.forward() + + obs = env._get_obs() + + # Construct the action + # ctrl = (data['qpos'][i_frame + 1][:9] - obs[:9]) / (env.skip * env.model.opt.timestep) + ctrl = (data['ctrl'][i_frame] - obs[:9])/(env.skip*env.model.opt.timestep) + act = (ctrl - act_mid) / act_rng + act = np.clip(act, -0.999, 0.999) + next_obs, reward, done, env_info = env.step(act) + if path_obs is None: + path_obs = obs + path_act = act + else: + path_obs = np.vstack((path_obs, obs)) + path_act = np.vstack((path_act, act)) + + # render when needed to maintain FPS + if i_frame % render_skip == 0: + viewer(env, mode='render', render=render) + print(i_frame, end=', ', flush=True) + + # finalize + if render: + viewer(env, mode='save', filename=filename, render=render) + + t1 = timer.time() + print("time taken = %f" % (t1 - t0)) + + # note that are one step away from + return path_obs, path_act, init_qpos, init_qvel + + +# MAIN ========================================================= +@click.command(help="parse tele-op demos") +@click.option('--env', '-e', type=str, help='gym env name', required=True) +@click.option( + '--demo_dir', + '-d', + type=str, + help='directory with tele-op logs', + required=True) +@click.option( + '--skip', + '-s', + type=int, + help='number of frames to skip (1:no skip)', + default=1) +@click.option('--graph', '-g', type=bool, help='plot logs', default=False) +@click.option('--save_logs', '-l', type=bool, help='save logs', default=False) +@click.option( + '--view', '-v', type=str, help='render/playback', default='render') +@click.option( + '--render', '-r', type=str, help='onscreen/offscreen', default='onscreen') +def main(env, demo_dir, skip, graph, save_logs, view, render): + + gym_env = gym.make(env) + paths = [] + print("Scanning demo_dir: " + demo_dir + "=========") + for ind, file in enumerate(glob.glob(demo_dir + "*.mjl")): + + # process logs + print("processing: " + file, end=': ') + + data = parse_mjl_logs(file, skip) + + print("log duration %0.2f" % (data['time'][-1] - data['time'][0])) + + # plot logs + if (graph): + print("plotting: " + file) + viz_parsed_mjl_logs(data) + + # save logs + if (save_logs): + pickle.dump(data, open(file[:-4] + ".pkl", 'wb')) + + # render logs to video + if view == 'render': + render_demos( + gym_env, + data, + filename=data['logName'][:-4] + '_demo_render.mp4', + render=render) + + # playback logs and gather data + elif view == 'playback': + try: + obs, act,init_qpos, init_qvel = gather_training_data(gym_env, data,\ + filename=data['logName'][:-4]+'_playback.mp4', render=render) + except Exception as e: + print(e) + continue + path = { + 'observations': obs, + 'actions': act, + 'goals': obs, + 'init_qpos': init_qpos, + 'init_qvel': init_qvel + } + paths.append(path) + # accept = input('accept demo?') + # if accept == 'n': + # continue + pickle.dump(path, open(demo_dir + env + str(ind) + "_path.pkl", 'wb')) + print(demo_dir + env + file + "_path.pkl") + +if __name__ == '__main__': + main() \ No newline at end of file diff --git a/diffusion_policy/env/kitchen/relay_policy_learning/adept_envs/adept_envs/utils/quatmath.py b/diffusion_policy/env/kitchen/relay_policy_learning/adept_envs/adept_envs/utils/quatmath.py new file mode 100644 index 0000000..bae531a --- /dev/null +++ b/diffusion_policy/env/kitchen/relay_policy_learning/adept_envs/adept_envs/utils/quatmath.py @@ -0,0 +1,180 @@ +#!/usr/bin/python +# +# Copyright 2020 Google LLC +# +# 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. + +import numpy as np +# For testing whether a number is close to zero +_FLOAT_EPS = np.finfo(np.float64).eps +_EPS4 = _FLOAT_EPS * 4.0 + + +def mulQuat(qa, qb): + res = np.zeros(4) + res[0] = qa[0]*qb[0] - qa[1]*qb[1] - qa[2]*qb[2] - qa[3]*qb[3] + res[1] = qa[0]*qb[1] + qa[1]*qb[0] + qa[2]*qb[3] - qa[3]*qb[2] + res[2] = qa[0]*qb[2] - qa[1]*qb[3] + qa[2]*qb[0] + qa[3]*qb[1] + res[3] = qa[0]*qb[3] + qa[1]*qb[2] - qa[2]*qb[1] + qa[3]*qb[0] + return res + +def negQuat(quat): + return np.array([quat[0], -quat[1], -quat[2], -quat[3]]) + +def quat2Vel(quat, dt=1): + axis = quat[1:].copy() + sin_a_2 = np.sqrt(np.sum(axis**2)) + axis = axis/(sin_a_2+1e-8) + speed = 2*np.arctan2(sin_a_2, quat[0])/dt + return speed, axis + +def quatDiff2Vel(quat1, quat2, dt): + neg = negQuat(quat1) + diff = mulQuat(quat2, neg) + return quat2Vel(diff, dt) + + +def axis_angle2quat(axis, angle): + c = np.cos(angle/2) + s = np.sin(angle/2) + return np.array([c, s*axis[0], s*axis[1], s*axis[2]]) + +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 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)) \ No newline at end of file diff --git a/diffusion_policy/env/kitchen/relay_policy_learning/adept_models/.gitignore b/diffusion_policy/env/kitchen/relay_policy_learning/adept_models/.gitignore new file mode 100644 index 0000000..b8e8667 --- /dev/null +++ b/diffusion_policy/env/kitchen/relay_policy_learning/adept_models/.gitignore @@ -0,0 +1,8 @@ +# General +.DS_Store +*.swp +*.profraw + +# Editors +.vscode +.idea diff --git a/diffusion_policy/env/kitchen/relay_policy_learning/adept_models/CONTRIBUTING.public.md b/diffusion_policy/env/kitchen/relay_policy_learning/adept_models/CONTRIBUTING.public.md new file mode 100644 index 0000000..db177d4 --- /dev/null +++ b/diffusion_policy/env/kitchen/relay_policy_learning/adept_models/CONTRIBUTING.public.md @@ -0,0 +1,28 @@ +# How to Contribute + +We'd love to accept your patches and contributions to this project. 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Based on code in the ROBEL suite +https://github.com/google-research/robel + +## Disclaimer + +This is not an official Google product. + diff --git a/diffusion_policy/env/kitchen/relay_policy_learning/adept_models/__init__.py b/diffusion_policy/env/kitchen/relay_policy_learning/adept_models/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/diffusion_policy/env/kitchen/relay_policy_learning/adept_models/kitchen/assets/backwall_asset.xml b/diffusion_policy/env/kitchen/relay_policy_learning/adept_models/kitchen/assets/backwall_asset.xml new file mode 100644 index 0000000..9e1e39d --- /dev/null +++ b/diffusion_policy/env/kitchen/relay_policy_learning/adept_models/kitchen/assets/backwall_asset.xml @@ -0,0 +1,21 @@ + + + + + + + + + + + + + + + + + + + + + diff --git a/diffusion_policy/env/kitchen/relay_policy_learning/adept_models/kitchen/assets/backwall_chain.xml b/diffusion_policy/env/kitchen/relay_policy_learning/adept_models/kitchen/assets/backwall_chain.xml new file mode 100644 index 0000000..b76b0da --- /dev/null +++ b/diffusion_policy/env/kitchen/relay_policy_learning/adept_models/kitchen/assets/backwall_chain.xml @@ -0,0 +1,16 @@ + + + + + + + + + + + + + + + + diff --git a/diffusion_policy/env/kitchen/relay_policy_learning/adept_models/kitchen/assets/counters_asset.xml b/diffusion_policy/env/kitchen/relay_policy_learning/adept_models/kitchen/assets/counters_asset.xml new file mode 100644 index 0000000..c3e28f8 --- /dev/null +++ b/diffusion_policy/env/kitchen/relay_policy_learning/adept_models/kitchen/assets/counters_asset.xml @@ -0,0 +1,32 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + diff --git a/diffusion_policy/env/kitchen/relay_policy_learning/adept_models/kitchen/assets/counters_chain.xml b/diffusion_policy/env/kitchen/relay_policy_learning/adept_models/kitchen/assets/counters_chain.xml new file mode 100644 index 0000000..83e1791 --- /dev/null +++ b/diffusion_policy/env/kitchen/relay_policy_learning/adept_models/kitchen/assets/counters_chain.xml @@ -0,0 +1,48 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + diff --git a/diffusion_policy/env/kitchen/relay_policy_learning/adept_models/kitchen/assets/hingecabinet_asset.xml b/diffusion_policy/env/kitchen/relay_policy_learning/adept_models/kitchen/assets/hingecabinet_asset.xml new file mode 100644 index 0000000..8202810 --- /dev/null +++ b/diffusion_policy/env/kitchen/relay_policy_learning/adept_models/kitchen/assets/hingecabinet_asset.xml @@ -0,0 +1,25 @@ + + + + + + + + + + + + + + + + + + + + + + + + + diff --git a/diffusion_policy/env/kitchen/relay_policy_learning/adept_models/kitchen/assets/hingecabinet_chain.xml b/diffusion_policy/env/kitchen/relay_policy_learning/adept_models/kitchen/assets/hingecabinet_chain.xml new file mode 100644 index 0000000..7f935d3 --- /dev/null +++ b/diffusion_policy/env/kitchen/relay_policy_learning/adept_models/kitchen/assets/hingecabinet_chain.xml @@ -0,0 +1,49 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + diff --git a/diffusion_policy/env/kitchen/relay_policy_learning/adept_models/kitchen/assets/kettle_asset.xml b/diffusion_policy/env/kitchen/relay_policy_learning/adept_models/kitchen/assets/kettle_asset.xml new file mode 100644 index 0000000..dbe8e9b --- /dev/null +++ b/diffusion_policy/env/kitchen/relay_policy_learning/adept_models/kitchen/assets/kettle_asset.xml @@ -0,0 +1,23 @@ + + + + + + + + + + + + + + + + + + + + + + + diff --git a/diffusion_policy/env/kitchen/relay_policy_learning/adept_models/kitchen/assets/kettle_chain.xml b/diffusion_policy/env/kitchen/relay_policy_learning/adept_models/kitchen/assets/kettle_chain.xml new file mode 100644 index 0000000..fb5f224 --- /dev/null +++ b/diffusion_policy/env/kitchen/relay_policy_learning/adept_models/kitchen/assets/kettle_chain.xml @@ -0,0 +1,20 @@ + + + + + + + + + + + + + + + + + + + + diff --git a/diffusion_policy/env/kitchen/relay_policy_learning/adept_models/kitchen/assets/microwave_asset.xml b/diffusion_policy/env/kitchen/relay_policy_learning/adept_models/kitchen/assets/microwave_asset.xml new file mode 100644 index 0000000..cc651ee --- /dev/null +++ b/diffusion_policy/env/kitchen/relay_policy_learning/adept_models/kitchen/assets/microwave_asset.xml @@ -0,0 +1,31 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + diff --git a/diffusion_policy/env/kitchen/relay_policy_learning/adept_models/kitchen/assets/microwave_chain.xml b/diffusion_policy/env/kitchen/relay_policy_learning/adept_models/kitchen/assets/microwave_chain.xml new file mode 100644 index 0000000..fd88ab3 --- /dev/null +++ b/diffusion_policy/env/kitchen/relay_policy_learning/adept_models/kitchen/assets/microwave_chain.xml @@ -0,0 +1,28 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + diff --git a/diffusion_policy/env/kitchen/relay_policy_learning/adept_models/kitchen/assets/oven_asset.xml b/diffusion_policy/env/kitchen/relay_policy_learning/adept_models/kitchen/assets/oven_asset.xml new file mode 100644 index 0000000..ef1184e --- /dev/null +++ b/diffusion_policy/env/kitchen/relay_policy_learning/adept_models/kitchen/assets/oven_asset.xml @@ -0,0 +1,50 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + diff --git a/diffusion_policy/env/kitchen/relay_policy_learning/adept_models/kitchen/assets/oven_chain.xml b/diffusion_policy/env/kitchen/relay_policy_learning/adept_models/kitchen/assets/oven_chain.xml new file mode 100644 index 0000000..f96f8c7 --- /dev/null +++ b/diffusion_policy/env/kitchen/relay_policy_learning/adept_models/kitchen/assets/oven_chain.xml @@ -0,0 +1,115 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + 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