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feat/codec-parameters
20 Commits
| Author | SHA1 | Message | Date | |
|---|---|---|---|---|
 Khalil Meftah
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8a3d64033f |
Reward models refactor (#3142)
* feat(rewards): add RewardModelConfig and PreTrainedRewardModel base classes * refactor(rewards): migrate Classifier from policies/sac/reward_model/ to rewards/classifier/ * refactor(rewards): migrate SARM from policies/sarm/ to rewards/sarm/ * refactor(rewards): add rewards/factory.py and remove reward model code from policies/factory.py * refactor(rewards): update imports and delete old reward model locations * test(rewards): add reward model tests and update existing test imports * fix(rewards): restore full Classifier and SARM implementations * test(rewards): restore missing CUDA and mixed precision classifier processor tests * refactor(lerobot_train.py): remove rabc specific configuration and replace it with a generic samplerweight class in lerobot_train * refactor(lerobot_train.py): add missing sampling weight script * linter + missing files * add testing for sampl weighter * revert some useless changes, improve typing * update docs * add automatic detection of the progress path * remove type exp * improve comment * fix: move rabc.py to rewards/sarm/ and update import paths * refactor(imports): update reward model imports to new module structure * refactor(imports): update reward model imports to reflect new module structure * refactor(imports): conditionally import pandas based on availability * feat(configs): add reward_model field to TrainPipelineConfig and Hub fields to RewardModelConfig * refactor(policies): remove reward model branches from policy factory and __init__ * refactor(rewards): expand __init__ facade and fix SARMConfig __post_init__ crash * feat(train): route reward model training through rewards/factory instead of policies/factory * refactor(train): streamline reward model training logic * fix(rewards): ensure FileNotFoundError is raised for missing config_file * refactor(train): update __get_path_fields__ to include reward_model for config loading * refactor(classifier): remove redundant input normalization in predict_reward method * fix(train): raise ValueError for non-trainable reward models in train function * refactor(pretrained_rm): add model card template * refactor(tests): reward models * refactor(sarm): update reset method and remove unused action prediction methods * refactor(wandb): differentiate tags for reward model and policy training in cfg_to_group function * fix(train): raise ValueError for PEFT usage in reward model training * refactor(rewards): enhance RewardModelConfig with device handling and delta indices properties --------- Co-authored-by: Michel Aractingi <michel.aractingi@huggingface.co> |
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Steven Palma
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df0763a2bc | feat(dependencies): minimal default tag install (#3362) | ||
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Pepijn
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919184d6f8 |
feat(envs): lazy env init + AsyncVectorEnv as default for n_envs > 1 (#3274)
* docs(benchmarks): add benchmark integration guide and standardize benchmark docs Add a comprehensive guide for adding new benchmarks to LeRobot, and refactor the existing LIBERO and Meta-World docs to follow the new standardized template. Made-with: Cursor * refactor(envs): move dispatch logic from factory into EnvConfig subclasses Replace hardcoded if/elif chains in factory.py with create_envs() and get_env_processors() methods on EnvConfig. New benchmarks now only need to register a config subclass — no factory.py edits required. Net -23 lines: factory.py shrinks from ~200 to ~70 lines of logic. Made-with: Cursor * docs(benchmarks): clean up adding-benchmarks guide for clarity Rewrite for simpler language, better structure, and easier navigation. Move quick-reference table to the top, fold eval explanation into architecture section, condense the doc template to a bulleted outline. Made-with: Cursor * fix link * fix task count * fix: enable SmolVLA eval on LIBERO with custom camera mappings - Thread camera_name_mapping from LiberoEnv config through to gym envs - Sync features_map with camera_name_mapping in LiberoEnv.__post_init__ - Fix render() to use first available camera instead of hardcoded "image" - Handle non-dict final_info in rollout by falling back to info["is_success"] - Add use_peft legacy field to SmolVLAConfig for checkpoint compat - Add defaults to GR00TN15Config init=False fields for transformers 5.3 Made-with: Cursor * fix: use direct AutoresetMode import for gymnasium compat Made-with: Cursor * fix: handle gymnasium < 1.0 without AutoresetMode Made-with: Cursor * refactor: revert policy changes, keep env-only camera mapping fixes - Revert GR00T N1.5 default_factory/default changes (transformers compat) - Revert SmolVLA use_peft legacy field - Apply ruff formatting fixes - camera_name_mapping stays entirely in env/eval layer (no policy changes) Made-with: Cursor * Update docs/source/env_processor.mdx Co-authored-by: Khalil Meftah <khalil.meftah@huggingface.co> Signed-off-by: Pepijn <138571049+pkooij@users.noreply.github.com> * feat(envs): lazy env init + AsyncVectorEnv as default for n_envs > 1 LiberoEnv and MetaworldEnv previously allocated GPU resources (EGL context, OpenGL framebuffer) in __init__, before AsyncVectorEnv's fork(). Worker processes inherited stale GPU handles, causing EGL_BAD_CONTEXT crashes on first render. Fix: defer OffScreenRenderEnv / MT1 construction to _ensure_env(), called on first reset() or step() inside the worker subprocess. Each worker creates its own clean context after fork(). Also fixes lerobot_eval.py:170 (add_envs_task TODO): replace with env.call("task") which works with both SyncVectorEnv and AsyncVectorEnv. AsyncVectorEnv is now the default for n_envs > 1; auto-downgraded to SyncVectorEnv when n_envs=1 (no benefit, less overhead). Expected speedup: ~15-20x for LIBERO Spatial with batch_size=50. Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com> * fix: close envs between tasks to prevent worker process accumulation eval_policy_all never closed environments after each task completed, causing AsyncVectorEnv worker processes to accumulate (N_tasks × n_envs). This led to OOM, BrokenPipeError and EOFError on multi-task benchmarks. Also fixes: - AsyncVectorEnv compat in envs/utils.py (use get_attr/call instead of .envs) - Tuple task handling in tokenizer_processor and lerobot_eval - _LazyAsyncVectorEnv for deferred worker spawning in LIBERO Made-with: Cursor * fix(eval): use task_description instead of task for language conditioning env.call("task") returns the LIBERO task name with underscores (e.g. "pick_up_the_black_bowl_...") instead of the natural language description ("pick up the black bowl ..."). The VLM tokenizes these completely differently, causing 0.0 reward across all episodes. Made-with: Cursor * docs: update adding_benchmarks for async env changes - Replace add_envs_task reference with env.call("task_description") - Update use_async_envs default to True - Add note about lazy GPU init for AsyncVectorEnv compatibility Made-with: Cursor * feat(eval): batch_size=auto + faster env loading - batch_size=0 (default) auto-tunes based on CPU cores, capped by n_episodes and 64. Removes the need for users to guess the right value. The old batch_size > n_episodes error is replaced by silently clamping to n_episodes. - _LazyAsyncVectorEnv accepts pre-computed spaces so only one temp env is created per suite (not per task). For libero_spatial (10 tasks) this avoids 9 redundant LiberoEnv instantiations during env setup. Made-with: Cursor * docs: add evaluation guide and update benchmarks doc - New docs/source/evaluation.mdx covering lerobot-eval usage, batch_size auto-tuning, AsyncVectorEnv performance, tuning tips, output format, multi-task evaluation, and programmatic usage. - Add evaluation page to _toctree.yml under Benchmarks section. - Update adding_benchmarks.mdx to reference batch_size auto default and link to the evaluation guide. Made-with: Cursor * docs(evaluation): remove benchmark table, rename section header Made-with: Cursor * perf(eval): shared memory, observation passthrough, task prefetch - AsyncVectorEnv now uses shared_memory=True for zero-copy observation transfer - LiberoEnvConfig.gym_kwargs passes observation_height/width to the env - eval_policy_all prefetches next task's workers while current task runs Made-with: Cursor * style: ruff format Made-with: Cursor * chore: revert env_processor.mdx changes (not part of this PR) Made-with: Cursor * ci(benchmarks): add isolated integration tests for libero and metaworld Each benchmark gets its own Docker image (lerobot[libero] / lerobot[metaworld] only) so incompatible dep trees cannot collide. A 1-episode smoke eval runs per benchmark on GPU runners. Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com> * ci(benchmarks): pin action hashes and use uv sync --locked Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com> * ci(benchmarks): trigger only on envs/ or lerobot_eval.py changes Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com> * fix(ci): set LIBERO_DATA_FOLDER to bypass interactive stdin prompt libero/__init__.py calls input() to ask about a custom dataset path, which raises EOFError when stdin is closed inside Docker. Setting LIBERO_DATA_FOLDER skips the prompt entirely. Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com> * docs(benchmarks): add CI smoke test step to adding_benchmarks guide Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com> * fix(ci): pre-create libero config in Dockerfile to bypass stdin prompt libero/__init__.py calls input() when ~/.libero/config.yaml is missing. We write the config at image build time (without importing libero) so the prompt never fires at runtime. Also trigger CI on pyproject.toml changes. Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com> * fix(ci): use shell to create libero config instead of multiline python -c The multiline RUN python -c "..." was being parsed as Dockerfile instructions. Use printf to write ~/.libero/config.yaml directly. Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com> * fix(ci): point libero config to bundled package init_files The config was pointing to /tmp/libero_init which doesn't exist. Use importlib.util.find_spec to locate the hf-libero package directory and write paths to the actual bundled bddl_files/init_files/assets. Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com> * fix(ci): add smolvla extra to benchmark Dockerfiles num2words (required by SmolVLM processor) is declared in lerobot[smolvla], not lerobot[libero/metaworld]. Install both extras together. Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com> * fix(eval): render_frame covers _LazyAsyncVectorEnv isinstance(env, AsyncVectorEnv) silently skipped _LazyAsyncVectorEnv, causing video rendering to produce no frames on the default async path. Switch to hasattr(env, "call") so any async-compatible env (including _LazyAsyncVectorEnv) hits the call("render") branch. Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com> * refactor(envs): remove unused _get_sub_env_attr helper _get_sub_env_attr was defined but never called anywhere in the codebase. _sub_env_has_attr (its sibling) is kept — it is actively used in utils.py. Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com> * chore: apply prettier formatting to docs Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com> * docs(env_processor): remove deprecated add_envs_task from pipeline example add_envs_task is replaced by env.call("task_description") in this PR. Remove it from the pipeline walkthrough and renumber the steps (8→7). Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com> * refactor(envs): remove __del__ from _LazyAsyncVectorEnv __del__ is unreliable as a cleanup mechanism. close() is already called explicitly in the eval loop's finally block, so the finalizer is redundant. Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com> * fix(eval): prefetch next task's workers after close to avoid GPU memory overlap Previously, next task's AsyncVectorEnv workers were spawned while the current task was still running, causing both tasks' GPU contexts to coexist. Moving the prefetch start into the finally block (after env.close()) ensures workers for task N+1 only spin up once task N has released GPU memory. Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com> * refactor(envs): move _LazyAsyncVectorEnv to utils and apply to metaworld _LazyAsyncVectorEnv lived in libero.py but metaworld had the same OOM problem: all tasks' AsyncVectorEnv workers were spawned eagerly, wasting GPU memory for tasks not yet running. Move the class to envs/utils.py so both environments share it, then apply the same is_async + lazy wrapping pattern in create_metaworld_envs. Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com> * chore: remove out-of-scope benchmark/CI/docs files from PR Benchmark CI workflow, Dockerfiles, benchmark docs, evaluation smoke-test doc, and dispatch tests belong in a separate PR. Scope this PR to the async env init changes only. Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com> * chore: restore adding_benchmarks + test_dispatch, drop env_processor changes - Restore docs/source/adding_benchmarks.mdx (belongs in this PR) - Restore tests/envs/test_dispatch.py (belongs in this PR) - Revert docs/source/env_processor.mdx to main (out of scope for this PR) Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com> * docs(adding_benchmarks): remove CI smoke test step (coming in separate PR) Step 7 (Dockerfile + benchmark_tests.yml CI job) and its table rows are out of scope for this PR. The CI infrastructure will be added on top in a follow-up PR. Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com> * refactor(envs): remove unused add_envs_task Replaced by env.call("task_description") in lerobot_eval.py. No callers remain in the codebase. Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com> * style: fix prettier formatting in env_processor.mdx Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com> * fix(eval): catch AttributeError and NotImplementedError explicitly for task description Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com> * fix(envs): use forkserver context and close envs in test to prevent deadlock AsyncVectorEnv with default fork context leaks worker processes between test_policy parametrized cases; subsequent env creation deadlocks because new forked workers inherit stale pipe FDs from previous test's leaked workers. - configs.py: pass context="forkserver" to AsyncVectorEnv (matches _LazyAsyncVectorEnv) - test_policies.py: call close_envs(envs) at end of test_policy to clean up workers Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com> * fix(envs): default use_async_envs=False in create_envs and make_env Tests that call make_env(n_envs=2) without passing use_async_envs were getting AsyncVectorEnv, whose forked workers can't resolve gym namespaces registered at runtime. Default to False (sync) so existing tests pass. lerobot_eval.py explicitly passes cfg.eval.use_async_envs, so the CLI async behaviour (controlled by EvalConfig.use_async_envs) is unchanged. Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com> --------- Signed-off-by: Pepijn <138571049+pkooij@users.noreply.github.com> Co-authored-by: Khalil Meftah <khalil.meftah@huggingface.co> Co-authored-by: Claude Sonnet 4.6 <noreply@anthropic.com> |
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Steven Palma
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a07b1d76f1 | chore(dependecies): untangle dependecies across internal modules (#3149) | ||
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Steven Palma
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e489ba24fc |
feat(dependencies): require Python 3.12+ as minimum version (#3023)
* feat(dependecies): upgrade to python3.12 * fix(test): processor regex message * fix(test): processor regex message * fix(dependecies): resolve all tags in python 3.12 * fix(dependecies): add more hints to faster resolve * chore(dependecies): remove cli tag huggingface-hub dep * refactor(policy): update eagle for python3.12 * chore(docs): update policy creation for python 3.12 * chore(test): skip failing tests in macos |
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Jade Choghari
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b18cef2e26 |
feat(dataset): add subtask support (#2860)
* add subtask * remove folder * add docs * update doc * add testing * update test * update constant naming + doc * more docs |
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Kartik
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fc296548cb |
feat(envs): Add NVIDIA IsaacLab-Arena Lerobot (#2699)
* adding Isaaclab Arena from collab
* adding into lerobot-eval
* minor modification
* added bash script for env setup
* setups
* fix applauncher not getting the arguments
* data conversion, train and eval smolvla
* fixed imports
* clean-up
* added test suits & clean up - wip
* fixed video recording
* clean-up
* hub integration working
* clean-up
* added kwargs
* Revert "added kwargs"
This reverts commit 9b445356385d0707655cf04d02be058b25138119.
* added kwargs
* clean-up
* cleaned unused function
* added logging
* docs
* cleaned up IsaaclabArenaEnv
* clean-up
* clean-up
* clean up
* added tests
* minor clean-up
* fix: support for state based envs
* feat(envs): Add NVIDIA IsaacLab Arena integration with LeRobot for policy evaluation at scale
* feat(envs): Add IsaacLab Arena integration for policy evaluation
Integrate NVIDIA IsaacLab Arena with LeRobot to enable GPU-accelerated
simulation through the EnvHub infrastructure.
This enables:
- Training imitation learning policies (PI0, SmolVLA, etc.)
- Evaluating trained policies in with IsaacLab Arena
The implementation adds:
- IsaaclabArenaEnv config with Arena-specific parameters
- IsaaclabArenaProcessorStep for observation processing
- Hub loading from nvkartik/isaaclab-arena-envs repository
- Video recording support
Available environments include GR1 microwave manipulation, Galileo
pick-and-place, G1 loco-manipulation, and button pressing tasks.
Datasets: nvkartik/Arena-GR1-Manipulation-Task
Policies: nvkartik/pi05-arena-gr1-microwave,
nvkartik/smolvla-arena-gr1-microwave
* added isaaclab arena wrapper and corresponding tests
* added error handling
* renamed wrapper file: isaaclab_arena to isaaclab
* added extra kwarg changes
* adjustments for hub envs
* correct class name in test file
* fixed parsing of env_kwargs
* tested end to end
* removed unused code
* refactor design
* shifted IsaacLab to hub
* removed IsaacLab tests
* docs: Add LW-BenchHub evaluation instructions
* docs: Add LW-BenchHub evaluation instructions
* docs diet
* minor edits to texts
* IL Arena commit hash
* update links
* minor edits
* fix numpy version after install of lerobot
* links update
* valideated on vanilla brev
* docs: Add LW-BenchHub evaluation instructions
* remove kwargs from all make_env calls
* remove kwargs from all make_env calls
* fix LW table and indentations
* remove environment list from docs
* docs: Update lw-benchhub eval config in envhub docs
* removing kwargs
* removed extra line
* ensure pinocchio install for lightwheel + add lightwheel website link
* remove env_kwargs
* no default empty value for hub_path
* not using assert method
* remove env_processor defaults
* revert and adding default "" value for hub_path
* pinning down packages versions
* explicit None value for hub_path
* Update src/lerobot/configs/eval.py
Co-authored-by: Jade Choghari <chogharijade@gmail.com>
Signed-off-by: Lior Ben Horin <liorbenhorin@gmail.com>
* corrected formatting
* corrected job_name var in config
* updated docs and namespace
* updated namespace
* updated docs
* updated docs
* added hardware requirements
* updated docs
---------
Signed-off-by: Lior Ben Horin <liorbenhorin@gmail.com>
Co-authored-by: lbenhorin <lbenhorin@nvidia.com>
Co-authored-by: Lior Ben Horin <liorbenhorin@gmail.com>
Co-authored-by: Jade Choghari <chogharijade@gmail.com>
Co-authored-by: Steven Palma <imstevenpmwork@ieee.org>
Co-authored-by: tianheng.wu <tianheng.wu@lightwheel.ai>
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Jade Choghari
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6e86a69dcd |
feat(envs): add envs pre-post processor (#2474)
* more changes * working changes * more changes * more fixes * fix style * more * clean * put axis-1 * more fixes * more styling fixes: * iterate on review: * more changes * add env processor * style * more changes * add docs * fix imports * fix test, add to train * Update src/lerobot/envs/factory.py Co-authored-by: Michel Aractingi <michel.aractingi@huggingface.co> Signed-off-by: Jade Choghari <chogharijade@gmail.com> * iterate on review --------- Signed-off-by: Jade Choghari <chogharijade@gmail.com> Co-authored-by: jade.choghari@huggingface.co <“chogharijade@gmail.com”> Co-authored-by: Michel Aractingi <michel.aractingi@huggingface.co> |
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Steven Palma
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6c28ef894a | chore(docs): add missing license headers (#2140) | ||
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Pepijn
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abde7be3b3 |
Add OpenPi, Pi0 and Pi0.5 (#1910)
* initial commit * change device in test * do detailed import * adhere to python 3.11 syntax * fix autodocstring * additionally * do same in other files * add model. prefix to all keys in state dict * use dummy stats * add pi05 * also shorten action_steps * fix test * all test pass! and fix tokenizer max length between 05 and 0 * remove test * fix transformer dependency * fix test * split pi0 and pi05 policy in seperate files * fix test * fix push to hub test * add some comments, license and readme * remove warning in config * add pi05 to factory * remove check * rename action_horizon to chunk_size * clean up padding of state and action (more in line with lerobot pi0) * add openpi image transforms for training and add more flexibility to _preprocess_images similar to lerobot pi0 * fix key match from pytorch state dict (similar keys to openpi implementation now) * also for pi05 * update to python 3.11 * revert to openpi transformer replace python 3.11 * fix(modeling pi0): nit warning message * use safeauto_docstring * fix: remove unused param * fix from pretrained * add preprocess tests * also compile forward method * Do not add model prefix to normalization * use same name for action and state dim as lerobot pi0 and remove fixed image keys * load from pretrained_path * temp: hardcode base model * fix override self.pretrained_path = None overwrite * rename to loss * remove additional image augmentations, lerobot dataset already does this * Add docs * put tests in test folder * Add test to instatiate all base models * go back to python 3.10 * update docs * adapt docs pi05 * change docs: finetune base model options * minor docs fixes and dependencies * remove todo * cast float64 to float32 for mps * skip if no transformers * fix tests * add new models to modelcard * add back init * fix circular input * feat: only run pi test on GPU * remove require_nightly_gpu * replace decorator test_pi0_openpi * rename action_dim, state_dim to max_action_dim, max_state_dim * fix doc and constants * cleanup tests * fix from pretrained * fix tests * add comment pi0 pi05 tests, add image features to pi0 pi05 hub tests * fix, state is included in language not in flow head * Move test to specific folder * and paligemma task with newline * remove add_special_tokens, not needed * feedback pr * Remove previous pi0 and rename pi0_openpi and pi05_openpi * Add Quantile stats to LeRobotDataset (#1985) * - Add RunningQuantileStats class for efficient histogram-based quantile computation - Integrate quantile parameters (compute_quantiles, quantiles) into LeRobotDataset - Support quantile computation during episode collection and aggregation - Add comprehensive function-based test suite (24 tests) for quantile functionality - Maintain full backward compatibility with existing stats computation - Enable configurable quantiles (default: [0.01, 0.99]) for robust normalization * style fixes, make quantiles computation by default to new datasets * fix tests * - Added DEFAULT_QUANTILES=[0.01, 0.10, 0.50, 0.90, 0.99] to be computed for each features instead of being chosen by the user - Fortified tests. * - add helper functions to reshape stats - add missing test for quantiles * - Add QUANTILE normalization mode to normalize the data with the 1st and 99th percentiles. - Add QUANTILE10 normalization mode to normalize the data with the 10th and 90th percentiles. * style fixes * Added missing lisence * Simplify compute_stats * - added script `augment_dataset_quantile_stats.py` so that we can add quantile stats to existing v3 datasets that dont have quatniles - modified quantile computation instead of using the edge for the value, interpolate the values in the bin * rename pi0/pi05 files * Remove open pi patch and use custom transformer branch for now * renaming * fix * Revert "fix" This reverts commit |
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Adil Zouitine
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2d3a605b3c |
Revert feat(normalization): add validation for empty features in NormalizerProcessorStep and UnnormalizerProcessorStep (#2087)
Revert "feat(normalization): add validation for empty features in NormalizerProcessorStep and UnnormalizerProcessorStep (#2087)"
This reverts commit
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Adil Zouitine
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f173265354 |
feat(normalization): add validation for empty features in NormalizerProcessorStep and UnnormalizerProcessorStep (#2087)
* feat(normalization): add validation for empty features in NormalizerProcessorStep and UnnormalizerProcessorStep * refactor(normalization): streamline feature reconstruction logic in _NormalizationMixin * refactor(tests): remove unused preprocessor initialization in test_act_backbone_lr --------- Co-authored-by: Pepijn <138571049+pkooij@users.noreply.github.com> |
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Steven Palma
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bbcf66bd82 | chore: enable simplify in ruff lint (#2085) | ||
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Steven Palma
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c378a325f0 | chore: enable pyugrade ruff lint (#2084) | ||
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Steven Palma
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c5b5955c5a | chore: replace hard-coded next values with constants throughout all the source code (#2056) | ||
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Steven Palma
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d2782cf66b |
chore: replace hard-coded action values with constants throughout all the source code (#2055)
* chore: replace hard-coded 'action' values with constants throughout all the source code * chore(tests): replace hard-coded action values with constants throughout all the test code |
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Steven Palma
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43d878a102 |
chore: replace hard-coded obs values with constants throughout all the source code (#2037)
* chore: replace hard-coded OBS values with constants throughout all the source code * chore(tests): replace hard-coded OBS values with constants throughout all the test code |
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Steven Palma
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7cf04a5ec3 | chore: move constants to utils (#2016) | ||
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Adil Zouitine
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78b866116f |
feat(processors): use pipelines across the codebase (#1452)
* Refactor observation preprocessing to use a modular pipeline system
- Introduced `RobotPipeline` and `ObservationProcessor` for handling observation transformations.
- Updated `preprocess_observation` to maintain backward compatibility while leveraging the new pipeline.
- Added tests for the new processing components and ensured they match the original functionality.
- Removed hardcoded logic in favor of a more flexible, composable architecture.
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* Refactor observation processing and improve modularity
- Updated `ObservationProcessor` to enhance the modular design for processing observations.
- Cleaned up imports and improved code readability by removing unnecessary lines and comments.
- Ensured backward compatibility while integrating new processing components.
- Added tests to validate the functionality of the updated processing architecture.
* Remove redundant tests for None observation and serialization methods in `test_observation_processor.py` to streamline the test suite and improve maintainability.
* Refactor processing architecture to use RobotProcessor
- Replaced instances of RobotPipeline with RobotProcessor across the codebase for improved modularity and clarity.
- Introduced ProcessorStepRegistry for better management of processing steps.
- Updated relevant documentation and tests to reflect the new processing structure.
- Enhanced the save/load functionality to support the new processor design.
- Added a model card template for RobotProcessor to facilitate sharing and documentation.
* Add RobotProcessor tutorial to documentation
- Introduced a new tutorial on using RobotProcessor for preprocessing robot data.
- Added a section in the table of contents for easy navigation to the new tutorial.
- The tutorial covers key concepts, real-world scenarios, and practical examples for effective use of the RobotProcessor pipeline.
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* Add normalization processor and related components
- Introduced `NormalizationProcessor` to handle both observation normalization and action unnormalization.
- Added `ObservationNormalizer` and `ActionUnnormalizer` classes for specific normalization tasks.
- Updated `__init__.py` to include the new `NormalizationProcessor` in the module exports.
- Enhanced `ObservationProcessor` with registration in the `ProcessorStepRegistry` for better modularity.
- Created `RenameProcessor` for renaming keys in observations, improving flexibility in data processing.
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* Enhance processing architecture with new components
- Added `RenameProcessor` to facilitate key renaming in observations, improving data handling flexibility.
- Updated `__init__.py` to include `RenameProcessor` in module exports.
- Refactored `NormalizationProcessor` and `ObservationNormalizer` to use `rsplit` for better key handling.
- Introduced comprehensive tests for `NormalizationProcessor` and `RenameProcessor` to ensure functionality and robustness.
* chore (docs): add docstring for processor
* fix (test): test factory
* fix(test): policies
* Update tests/processor/test_observation_processor.py
Co-authored-by: Copilot <175728472+Copilot@users.noreply.github.com>
Signed-off-by: Adil Zouitine <adilzouitinegm@gmail.com>
* chore(test): add suggestion made by copilot regarding numpy test
* fix(test): import issue
* Refactor normalization components and update tests
- Renamed `ObservationNormalizer` to `NormalizerProcessor` and `ActionUnnormalizer` to `UnnormalizerProcessor` for clarity.
- Consolidated normalization logic for both observations and actions into `NormalizerProcessor` and `UnnormalizerProcessor`.
- Updated tests to reflect the new class names and ensure proper functionality of normalization and unnormalization processes.
- Enhanced handling of missing statistics in normalization processes.
* chore (docstrin):Improve docstring for NormalizerProcessor
* feat (device processor): Implement device processor
* chore (batch handling): Enhance processing components with batch conversion utilities
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* fix(test): linting issue
* chore (output format): improves output format
* chore (type): add typing for multiprocess envs
* feat (overrides): Implement support for loading processors with parameter overrides
- Added the ability to provide non-serializable objects when loading processors from saved configurations using the `overrides` parameter.
- Enhanced error handling for invalid override keys and instantiation errors.
- Updated documentation and examples to illustrate the usage of overrides for both registered and unregistered steps.
- Added comprehensive tests to validate the new functionality and ensure backward compatibility.
* chore(normalization): addressing comments from copilot
* chore(learner): nit comment from copilot
* feat(pipeline): Enhance step_through method to support both tuple and dict inputs
* refactor(pipeline): Simplify observation and padding data handling in batch transitions
* Apply suggestions from code review
Co-authored-by: Simon Alibert <75076266+aliberts@users.noreply.github.com>
Signed-off-by: Adil Zouitine <adilzouitinegm@gmail.com>
* [pre-commit.ci] auto fixes from pre-commit.com hooks
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* refactor(pipeline): Introduce ComplementaryDataProcessor for handling complementary data in transitions
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* refactor(pipeline): Transition from tuple to dictionary format for EnvTransition
- Updated the EnvTransition structure to use a dictionary format instead of a tuple, enhancing readability and maintainability.
- Replaced instances of TransitionIndex with TransitionKey for accessing transition components.
- Adjusted related processing functions and tests to accommodate the new dictionary format, ensuring consistent handling of transitions across the codebase.
* refactor(observation_processor): Improve observation processing by using constants and simplifying pixel handling
- Introduced constants for observation keys to enhance readability.
- Streamlined the handling of the "pixels" key by copying observations first and processing images more clearly.
- Updated the environment state and agent position assignments to use the new constants, improving maintainability.
* feat(pipeline): Add hook unregistration functionality and enhance documentation
- Implemented methods to unregister before, after, and reset hooks in the RobotProcessor class, allowing for more flexible hook management.
- Enhanced documentation to clarify hook execution semantics and the implications of modifying transitions within hooks.
- Added comprehensive tests to verify the correct behavior of hook registration and unregistration, including error handling for non-existent hooks.
* refactor(pipeline): Clarify hook behavior and improve documentation
- Updated the RobotProcessor class to ensure hooks are strictly for observation and do not modify transitions, enhancing clarity and maintainability.
- Refactored hook registration methods to reflect the new behavior, ensuring they accept only functions that do not return modified transitions.
- Enhanced documentation to clearly outline the purpose of hooks and their execution semantics.
- Added tests to verify that hooks are not executed during the step_through method while ensuring they function correctly during the __call__ method.
* feat(pipeline): Add __repr__ method to RobotProcessor for improved readability
- Implemented a __repr__ method in the RobotProcessor class to provide a clear string representation of the processor, including step names and optional parameters like name and seed.
- Added comprehensive tests to validate the __repr__ output for various scenarios, including empty processors, single and multiple steps, custom names, and seed values.
- Ensured that the representation handles long lists of steps with truncation for better readability.
* chore(pipeline): Move _CFG_NAME along other class member
* refactor(pipeline): Utilize get_safe_torch_device for device assignment
- Replaced direct torch.device instantiation with get_safe_torch_device to ensure safe device handling.
- This change enhances code readability and maintains consistency in device management across the RobotProcessor class.
* refactor(pipeline): Enhance state filename generation and profiling method
- Updated state filename generation to use the registry name when available, improving clarity in saved files.
- Modified the profile_steps method to include a warmup_runs parameter, allowing for more controlled performance profiling.
- Ensured consistent conditions during profiling by deep copying transitions for each run, enhancing accuracy in timing results.
* chore(doc): address pip install commant lerobot that not exist yet
* feat(pipeline): Enhance configuration filename handling and state file naming
- Introduced support for custom configuration filenames in the `save_pretrained` method, allowing users to specify a filename instead of the default.
- Improved state file naming to include step indices, preventing conflicts when multiple processors of the same type are saved.
- Added automatic detection for configuration files when loading from a directory, with error handling for multiple files.
- Updated tests to validate new features, including custom filenames and automatic config detection.
* refactor(pipeline): Improve state file naming conventions for clarity and uniqueness
- Enhanced state file naming to include the processor's sanitized name, ensuring uniqueness when multiple processors are saved in the same directory.
- Updated tests to reflect changes in state file naming, verifying that filenames now include the processor name and step indices to prevent conflicts.
- Added a new test to validate state file naming when using multiple processors, ensuring distinct filenames for each processor's state files.
* docs(pipeline): Add clarification for repo name sanitization process
* Feat/pipeline add feature contract (#1637)
* Add feature contract to pipelinestep and pipeline
* Add tests
* Add processor tests
* PR feedback
* encorperate pr feedback
* type in doc
* oops
* docs(pipeline): Clarify transition handling and hook behavior
- Updated documentation to specify that hooks always receive transitions in EnvTransition format, ensuring consistent behavior across input formats.
- Refactored the step_through method to yield only EnvTransition objects, regardless of the input format, and updated related tests to reflect this change.
- Enhanced test assertions to verify the structure of results and the correctness of processing steps.
* refactor(pipeline): Remove to() method for device management
- Eliminated the to() method from RobotProcessor, which was responsible for moving tensor states to specified devices.
- Removed associated unit tests that validated the functionality of the to() method across various scenarios.
- Streamlined the pipeline code by focusing on other device management strategies.
* refactor(pipeline): Remove model card generation and streamline processor methods
- Eliminated the _generate_model_card method from RobotProcessor, which was responsible for generating README.md files from a template.
- Updated save_pretrained method to remove model card generation, focusing on serialization of processor definitions and parameters.
- Added default implementations for get_config, state_dict, load_state_dict, reset, and feature_contract methods in various processor classes to enhance consistency and usability.
* refactor(observation): Streamline observation preprocessing and remove unused processor methods
- Updated the `preprocess_observation` function to enhance image handling and ensure proper tensor formatting.
- Removed the `RobotProcessor` and associated transition handling from the `rollout` function, simplifying the observation processing flow.
- Integrated direct calls to `preprocess_observation` for improved clarity and efficiency in the evaluation script.
* refactor(pipeline): Rename parameters for clarity and enhance save/load functionality
- Updated parameter names in the save_pretrained and from_pretrained methods for improved readability, changing destination_path to save_directory and source to pretrained_model_name_or_path.
- Enhanced the save_pretrained method to ensure directory creation and file handling is consistent with the new parameter names.
- Streamlined the loading process in from_pretrained to utilize loaded_config for better clarity and maintainability.
* refactor(pipeline): minor improvements (#1684)
* chore(pipeline): remove unused features + device torch + envtransition keys
* refactor(pipeline): ImageProcessor & StateProcessor are both implemented directly in VanillaObservationPRocessor
* refactor(pipeline): RenameProcessor now inherits from ObservationProcessor + remove unused code
* test(pipeline): fix broken test after refactors
* docs(pipeline): update docstrings VanillaObservationProcessor
* chore(pipeline): move None check to base pipeline classes
* feat(processors): Introduce processors for various policy types
- Added `make_processor` function to create processor instances for different policy types, including `tdmpc`, `diffusion`, `act`, `vqbet`, `pi0`, `pi0fast`, `sac`, and `reward_classifier`.
- Implemented corresponding processor files for each policy type, encapsulating normalization and unnormalization steps.
- Updated existing policies to remove direct normalization dependencies, enhancing modularity and clarity.
- Enhanced test coverage to validate the integration of new processors with existing policy configurations.
* refactor(learner): Remove normalization from cached image features retrieval
- Simplified the retrieval of observation features by removing the normalization step from the `get_cached_image_features` method calls.
- This change enhances clarity and aligns with the recent updates to policy processors.
* refactor(policies): Remove unnormalization step from action predictions
- Eliminated the unnormalization of actions in both `TDMPCPolicy` and `VQBeTPolicy` classes to streamline action prediction.
- This change improves code clarity and aligns with recent updates to policy processors.
* feat(train): Integrate preprocessor into training pipeline
* refactor(train): Update preprocessor initialization to include dataset statistics
* refactor(policies): Enhance processor creation and add NaN detection hook
* feat(record): Integrate RobotProcessor into recording loop and update policy handling
- Added support for RobotProcessor in the record_loop function to enhance data processing capabilities.
- Updated the logic to reset both policy and processor when provided, ensuring proper state management.
- Modified action prediction to utilize the processor, improving the overall functionality of the recording process.
- Adjusted the save_checkpoint function to include preprocessor state saving, enhancing checkpointing capabilities.
* feat(migration): Add script for migrating policy models with normalization layers
* [pre-commit.ci] auto fixes from pre-commit.com hooks
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* [pre-commit.ci] auto fixes from pre-commit.com hooks
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* feat(migrate): Enhance migration script to create preprocessor and postprocessor for policy models
- Updated the migration script to generate both a preprocessor and a postprocessor, improving the handling of normalization for training and inference.
- Added functionality to convert features to PolicyFeature objects, ensuring compatibility with the new processor architecture.
- Refined the extraction and removal of normalization statistics and layers, streamlining the migration process.
- Improved error handling for missing mandatory configuration fields during model instantiation.
* feat(migrate): Add model card generation and saving to migration script
- Implemented functionality to generate and save a model card for the migrated model, including metadata such as dataset repository ID, license, and tags.
- Enhanced the script to push the model card to the hub if requested, improving model documentation and accessibility.
- Refactored the saving process to ensure the model card is saved locally and uploaded correctly when pushing to the hub.
* feat(processor): Introduce ToBatchProcessor for handling observation batching
- Added ToBatchProcessor to ensure observations have proper batch dimensions for model processing.
- Implemented functionality to add batch dimensions to state and image observations as needed.
- Created comprehensive unit tests to validate the processor's behavior with various tensor dimensions and types.
- Ensured compatibility with existing transition keys and maintained the integrity of non-observation data.
* feat(processors): Add ToBatchProcessor to multiple policy processors
- Integrated ToBatchProcessor into various policy processors to handle observation batching.
- Updated make functions for act, diffusion, pi0, pi0fast, sac, smolvla, tdmpc, and vqbet processors to include the new batching functionality.
- Ensured consistency across all processor implementations for improved data handling.
* refactor(factory): Remove unused imports and NaN detection hook from processor creation
* feat(batch_processor): Enhance ToBatchProcessor to handle action batching
- Updated ToBatchProcessor to add batch dimensions to actions in addition to observations.
- Implemented separate methods for processing observations and actions, improving code readability.
- Added comprehensive unit tests to validate action batching functionality across various tensor dimensions and types.
* [pre-commit.ci] auto fixes from pre-commit.com hooks
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* feat(factory): Enhance make_processor to support preprocessor and postprocessor configuration
- Introduced ProcessorConfigKwargs TypedDict for better type safety in processor configuration.
- Updated make_processor to accept preprocessor and postprocessor configuration filenames, improving flexibility in processor instantiation.
- Refactored the loading of pretrained processors to utilize the new configuration options.
* refactor(factory): Clean up imports in factory.py
- Removed unused import of IdentityProcessor to streamline the code.
* feat(migrate): Extend load_model_from_hub to include train configuration
- Updated load_model_from_hub to return the train configuration alongside the model state_dict and config.
- Modified main function to handle the additional train configuration when loading models from both the hub and local paths.
- Adjusted dataset_repo_id extraction to utilize the train configuration for improved accuracy.
* refactor(record): Rename processor parameters and update processing logic
- Renamed `processor` to `preprocessor` and added `postprocessor` parameter for clarity.
- Updated the `record_loop` and `predict_action` functions to utilize the new preprocessor and postprocessor, enhancing the processing flow.
- Ensured compatibility with existing functionality while improving code readability.
* feat(batch_processor): Add task field processing to ToBatchProcessor
- Enhanced ToBatchProcessor to wrap string tasks in a list, adding batch dimensions for compatibility with model inference.
- Implemented a new method for processing complementary data, ensuring that task values are correctly handled as either strings or lists of strings.
- Added comprehensive unit tests to validate task processing, including edge cases and in-place mutation of complementary data.
* feat(normalization): Implement IDENTITY mode for normalization and unnormalization
- Enhanced NormalizerProcessor and UnnormalizerProcessor to support IDENTITY mode, allowing features to bypass normalization when specified.
- Updated processing logic to check normalization modes and handle missing statistics gracefully.
- Added comprehensive unit tests to validate IDENTITY mode functionality for both observations and actions, ensuring correct behavior across various scenarios.
- Improved error handling for unsupported normalization modes.
* fix(rebase): remove residual normalization layer:
* refactor(diffusion): remove normalization layer from input processing
* refactor(normalization): Remove unused state dict transformation methods and streamline imports
- Eliminated the _transform_state_dict_keys and _load_as_safetensor methods from PI0Policy, simplifying the model loading process.
- Cleaned up imports in modeling_pi0.py by removing log_model_loading_keys and init_logging.
- Updated TDMPCPolicy and VQBeTPolicy to handle action removal from batches during offline evaluation.
- Introduced hotswap_stats function in normalize_processor.py to update normalization statistics dynamically, with corresponding tests to ensure functionality.
* refactor(normalization): Clean up imports in normalize_processor.py
* feat(batch_processor): Add feature_contract method to ToBatchProcessor
- Introduced feature_contract method that returns features without modification, maintaining the no-op behavior of the processor.
- This addition enhances the flexibility of the ToBatchProcessor for future feature processing needs.
* fix(dependencies): Update transformers dependency constraint to allow only versions up to 4.52.0
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* feature(pipeline): port tokenizer pipeline for VLA (#1645)
* feat(tokenizer): Introduce TokenizerProcessor for text tokenization
- Added TokenizerProcessor class to handle tokenization of task strings using Hugging Face's AutoTokenizer.
- Supports both string and list inputs, with customizable parameters for task key, output key, and tokenization settings.
- Implemented comprehensive unit tests to validate functionality, including handling of various input scenarios and integration with RobotProcessor.
- Updated types.py to include LANGUAGE feature type and modified __init__.py to register the new processor.
* feat(language): Enhance language processing in TokenizerProcessor
- Added OBS_LANGUAGE constant to define the observation language key.
- Updated TokenizerProcessor to store tokenized task data in the observation dictionary, ensuring compatibility with the new language feature.
- Introduced Pi0NewLineProcessor to append newlines to tasks for proper tokenization.
- Modified tests to validate the integration of language tokens and attention masks in the observation structure.
* feat(tokenizer): Add padding configuration to TokenizerProcessor
- Introduced `padding_side` parameter to the TokenizerProcessor for customizable padding direction.
- Updated the `make_pi0_processor` function to include the new padding configuration.
- Enhanced unit tests to validate the functionality of the `padding_side` parameter in various scenarios.
* feat(processor): Add state management methods to Pi0NewLineProcessor
* feat(normalization): Track normalization and unnormalization info in complementary data
- Updated NormalizerProcessor and UnnormalizerProcessor to accept additional parameters for tracking normalization modes.
- Enhanced the __call__ methods to store normalization and unnormalization information in the complementary data of transitions.
- Added unit tests to verify the correct tracking of normalization info, including scenarios with missing stats and selective normalization keys.
* feat(factory): Add preprocessor and postprocessor overrides to ProcessorConfigKwargs
- Updated ProcessorConfigKwargs to include optional overrides for preprocessor and postprocessor configurations.
- Enhanced the make_processor function to utilize the new overrides, allowing for more flexible processor initialization.
* feat(processors): Integrate RenameProcessor into various processor configurations
- Added RenameProcessor to the input steps of multiple processor functions, including make_act_processor, make_diffusion_processor, make_pi0_processor, make_sac_processor, make_tdmpc_processor, make_vqbet_processor, and make_smolvla_processor.
- Consolidated normalization features from input and output into a single NormalizerProcessor for improved efficiency.
- Updated the input steps to ensure compatibility with the new RenameProcessor integration.
* feat(smolvla): Refactor language processing and introduce new line processor (#1658)
- Removed the prepare_language method and directly accessed language tokens and masks from the batch using the OBS_LANGUAGE constant.
- Added SmolVLANewLineProcessor to ensure tasks end with a newline, enhancing tokenization compatibility.
- Updated the make_smolvla_processor function to include the new line processor and tokenizer processor for improved input handling.
* feture(policies): add device processor (#1659)
* feat(processors): Integrate DeviceProcessor into multiple processor configurations
- Added DeviceProcessor to the input and output steps of various processor functions, including make_act_processor, make_diffusion_processor, make_pi0_processor, make_pi0fast_processor, make_sac_processor, make_tdmpc_processor, make_vqbet_processor, and make_smolvla_processor.
- Enhanced the DeviceProcessor class with state management methods and ensured compatibility with existing processor pipelines.
- Introduced unit tests for DeviceProcessor to validate functionality across different scenarios, including CPU and CUDA operations.
* [pre-commit.ci] auto fixes from pre-commit.com hooks
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* refactor(pipeline): Remove to() method for device management
- Eliminated the to() method from RobotProcessor, which was responsible for moving tensor states to specified devices.
- Removed associated unit tests that validated the functionality of the to() method across various scenarios.
- Streamlined the pipeline code by focusing on other device management strategies.
* feat(processor): Enhance DeviceProcessor with float dtype conversion
- Added support for optional float dtype conversion in DeviceProcessor, allowing tensors to be converted to specified floating-point types while preserving non-float types.
- Implemented validation for float dtype input and updated the processor's configuration methods to include float dtype.
- Refactored tensor processing logic to streamline device movement and dtype conversion.
- Introduced comprehensive unit tests to validate the new float dtype functionality across various scenarios.
* feat(policies): Add new line processors and update module exports
* feat(processor): Enhance batch and device processors to handle index and task_index fields
- Added logic to ToBatchProcessor for unsqueezing 0D tensors for index and task_index fields, ensuring they are processed as 1D tensors.
- Updated DeviceProcessor to process index and task_index fields in complementary data, preserving their tensor types and ensuring non-tensor fields remain unchanged.
- Enhanced unit tests to validate the correct handling of index and task_index fields across various scenarios, including device compatibility and dtype preservation.
* refactor(processors): Standardize processor naming conventions
- Updated processor names across various files to use a consistent "robot_preprocessor" and "robot_postprocessor" format.
- Modified the make_processor functions in factory, act, diffusion, pi0, pi0fast, sac, smolvla, tdmpc, and vqbet to reflect the new naming scheme.
- Enhanced the pipeline configuration to align with the updated processor names, improving clarity and maintainability.
* refactor(factory): Update processor configuration and type hints
- Changed return type of get_policy_class to type[PreTrainedPolicy] for improved type safety.
- Enhanced make_processor function to utilize dataset_stats in processor creation for better flexibility.
- Updated ProcessorConfigKwargs to include dataset_stats, allowing for more comprehensive processor configurations.
- Streamlined processor initialization by removing unnecessary kwargs and ensuring clarity in processor type handling.
* refactor(factory, pi0fast): Update processor function names and parameters
- Renamed make_pi0_processor to make_pi0fast_processor for clarity and consistency.
- Updated parameter names in the factory's make_processor function to use pretrained_model_name_or_path instead of source, enhancing readability and alignment with naming conventions.
* fix(train.py) push postprocessor with preprocessor
- Add preprocesser policy overrides for device and rename_map
- Add rename_map to DatasetRecordConfig (record.py)
* refactor(device_processor): Update device handling and improve type hints
- Changed device attribute type from torch.device to str for better clarity.
- Introduced a private _device attribute to store the actual torch.device instance.
- Updated tests to conditionally check for CUDA availability, ensuring compatibility across different environments.
- Refactored device-related assertions in tests to use a consistent approach for device type verification.
* [pre-commit.ci] auto fixes from pre-commit.com hooks
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* test(tokenizer_processor): Add require_package decorator for transformers
- Introduced @require_package("transformers") decorator in multiple test functions to ensure the transformers package is available before running tests.
- This change enhances test reliability by preventing failures due to missing dependencies.
* refactor(migrate_policy_normalization): Enhance preprocessor and postprocessor structure
- Introduced RenameProcessor in the preprocessor to handle renaming features.
- Combined input and output features in a single NormalizerProcessor for improved efficiency.
- Updated RobotProcessor initialization to clarify step naming for preprocessor and postprocessor.
- Added DeviceProcessor to both preprocessor and postprocessor for better device management.
* Integrate pipeline and add phone teleop (#1681)
* Add normalization processor and related components
- Introduced `NormalizationProcessor` to handle both observation normalization and action unnormalization.
- Added `ObservationNormalizer` and `ActionUnnormalizer` classes for specific normalization tasks.
- Updated `__init__.py` to include the new `NormalizationProcessor` in the module exports.
- Enhanced `ObservationProcessor` with registration in the `ProcessorStepRegistry` for better modularity.
- Created `RenameProcessor` for renaming keys in observations, improving flexibility in data processing.
* [pre-commit.ci] auto fixes from pre-commit.com hooks
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* Enhance processing architecture with new components
- Added `RenameProcessor` to facilitate key renaming in observations, improving data handling flexibility.
- Updated `__init__.py` to include `RenameProcessor` in module exports.
- Refactored `NormalizationProcessor` and `ObservationNormalizer` to use `rsplit` for better key handling.
- Introduced comprehensive tests for `NormalizationProcessor` and `RenameProcessor` to ensure functionality and robustness.
* chore (docs): add docstring for processor
* fix (test): test factory
* fix(test): policies
* Update tests/processor/test_observation_processor.py
Co-authored-by: Copilot <175728472+Copilot@users.noreply.github.com>
Signed-off-by: Adil Zouitine <adilzouitinegm@gmail.com>
* chore(test): add suggestion made by copilot regarding numpy test
* fix(test): import issue
* Refactor normalization components and update tests
- Renamed `ObservationNormalizer` to `NormalizerProcessor` and `ActionUnnormalizer` to `UnnormalizerProcessor` for clarity.
- Consolidated normalization logic for both observations and actions into `NormalizerProcessor` and `UnnormalizerProcessor`.
- Updated tests to reflect the new class names and ensure proper functionality of normalization and unnormalization processes.
- Enhanced handling of missing statistics in normalization processes.
* chore (docstrin):Improve docstring for NormalizerProcessor
* feat (device processor): Implement device processor
* chore (batch handling): Enhance processing components with batch conversion utilities
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* fix(test): linting issue
* chore (output format): improves output format
* chore (type): add typing for multiprocess envs
* feat (overrides): Implement support for loading processors with parameter overrides
- Added the ability to provide non-serializable objects when loading processors from saved configurations using the `overrides` parameter.
- Enhanced error handling for invalid override keys and instantiation errors.
- Updated documentation and examples to illustrate the usage of overrides for both registered and unregistered steps.
- Added comprehensive tests to validate the new functionality and ensure backward compatibility.
* chore(normalization): addressing comments from copilot
* chore(learner): nit comment from copilot
* feat(pipeline): Enhance step_through method to support both tuple and dict inputs
* refactor(pipeline): Simplify observation and padding data handling in batch transitions
* Apply suggestions from code review
Co-authored-by: Simon Alibert <75076266+aliberts@users.noreply.github.com>
Signed-off-by: Adil Zouitine <adilzouitinegm@gmail.com>
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* refactor(pipeline): Introduce ComplementaryDataProcessor for handling complementary data in transitions
* fix(ci): temporary fix on dataset deps version
* feat(processors): Introduce processors for various policy types
- Added `make_processor` function to create processor instances for different policy types, including `tdmpc`, `diffusion`, `act`, `vqbet`, `pi0`, `pi0fast`, `sac`, and `reward_classifier`.
- Implemented corresponding processor files for each policy type, encapsulating normalization and unnormalization steps.
- Updated existing policies to remove direct normalization dependencies, enhancing modularity and clarity.
- Enhanced test coverage to validate the integration of new processors with existing policy configurations.
* refactor(learner): Remove normalization from cached image features retrieval
- Simplified the retrieval of observation features by removing the normalization step from the `get_cached_image_features` method calls.
- This change enhances clarity and aligns with the recent updates to policy processors.
* refactor(policies): Remove unnormalization step from action predictions
- Eliminated the unnormalization of actions in both `TDMPCPolicy` and `VQBeTPolicy` classes to streamline action prediction.
- This change improves code clarity and aligns with recent updates to policy processors.
* feat(train): Integrate preprocessor into training pipeline
* refactor(train): Update preprocessor initialization to include dataset statistics
* refactor(policies): Enhance processor creation and add NaN detection hook
* refactor(train): Update memory pinning logic for mps compatibility
* feat: initial commit phone teleop
* ugly delta control
* use quaternion
* Refactor observation preprocessing to use a modular pipeline system
- Introduced `RobotPipeline` and `ObservationProcessor` for handling observation transformations.
- Updated `preprocess_observation` to maintain backward compatibility while leveraging the new pipeline.
- Added tests for the new processing components and ensured they match the original functionality.
- Removed hardcoded logic in favor of a more flexible, composable architecture.
* [pre-commit.ci] auto fixes from pre-commit.com hooks
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* Refactor observation processing and improve modularity
- Updated `ObservationProcessor` to enhance the modular design for processing observations.
- Cleaned up imports and improved code readability by removing unnecessary lines and comments.
- Ensured backward compatibility while integrating new processing components.
- Added tests to validate the functionality of the updated processing architecture.
* Remove redundant tests for None observation and serialization methods in `test_observation_processor.py` to streamline the test suite and improve maintainability.
* Refactor processing architecture to use RobotProcessor
- Replaced instances of RobotPipeline with RobotProcessor across the codebase for improved modularity and clarity.
- Introduced ProcessorStepRegistry for better management of processing steps.
- Updated relevant documentation and tests to reflect the new processing structure.
- Enhanced the save/load functionality to support the new processor design.
- Added a model card template for RobotProcessor to facilitate sharing and documentation.
* Add RobotProcessor tutorial to documentation
- Introduced a new tutorial on using RobotProcessor for preprocessing robot data.
- Added a section in the table of contents for easy navigation to the new tutorial.
- The tutorial covers key concepts, real-world scenarios, and practical examples for effective use of the RobotProcessor pipeline.
* [pre-commit.ci] auto fixes from pre-commit.com hooks
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* Add normalization processor and related components
- Introduced `NormalizationProcessor` to handle both observation normalization and action unnormalization.
- Added `ObservationNormalizer` and `ActionUnnormalizer` classes for specific normalization tasks.
- Updated `__init__.py` to include the new `NormalizationProcessor` in the module exports.
- Enhanced `ObservationProcessor` with registration in the `ProcessorStepRegistry` for better modularity.
- Created `RenameProcessor` for renaming keys in observations, improving flexibility in data processing.
* [pre-commit.ci] auto fixes from pre-commit.com hooks
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* Enhance processing architecture with new components
- Added `RenameProcessor` to facilitate key renaming in observations, improving data handling flexibility.
- Updated `__init__.py` to include `RenameProcessor` in module exports.
- Refactored `NormalizationProcessor` and `ObservationNormalizer` to use `rsplit` for better key handling.
- Introduced comprehensive tests for `NormalizationProcessor` and `RenameProcessor` to ensure functionality and robustness.
* chore (docs): add docstring for processor
* fix (test): test factory
* fix(test): policies
* Update tests/processor/test_observation_processor.py
Co-authored-by: Copilot <175728472+Copilot@users.noreply.github.com>
Signed-off-by: Adil Zouitine <adilzouitinegm@gmail.com>
* chore(test): add suggestion made by copilot regarding numpy test
* fix(test): import issue
* Refactor normalization components and update tests
- Renamed `ObservationNormalizer` to `NormalizerProcessor` and `ActionUnnormalizer` to `UnnormalizerProcessor` for clarity.
- Consolidated normalization logic for both observations and actions into `NormalizerProcessor` and `UnnormalizerProcessor`.
- Updated tests to reflect the new class names and ensure proper functionality of normalization and unnormalization processes.
- Enhanced handling of missing statistics in normalization processes.
* chore (docstrin):Improve docstring for NormalizerProcessor
* feat (device processor): Implement device processor
* chore (batch handling): Enhance processing components with batch conversion utilities
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* fix(test): linting issue
* chore (output format): improves output format
* chore (type): add typing for multiprocess envs
* feat (overrides): Implement support for loading processors with parameter overrides
- Added the ability to provide non-serializable objects when loading processors from saved configurations using the `overrides` parameter.
- Enhanced error handling for invalid override keys and instantiation errors.
- Updated documentation and examples to illustrate the usage of overrides for both registered and unregistered steps.
- Added comprehensive tests to validate the new functionality and ensure backward compatibility.
* chore(normalization): addressing comments from copilot
* chore(learner): nit comment from copilot
* feat(pipeline): Enhance step_through method to support both tuple and dict inputs
* refactor(pipeline): Simplify observation and padding data handling in batch transitions
* Apply suggestions from code review
Co-authored-by: Simon Alibert <75076266+aliberts@users.noreply.github.com>
Signed-off-by: Adil Zouitine <adilzouitinegm@gmail.com>
* [pre-commit.ci] auto fixes from pre-commit.com hooks
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* refactor(pipeline): Introduce ComplementaryDataProcessor for handling complementary data in transitions
* [pre-commit.ci] auto fixes from pre-commit.com hooks
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* refactor(pipeline): Transition from tuple to dictionary format for EnvTransition
- Updated the EnvTransition structure to use a dictionary format instead of a tuple, enhancing readability and maintainability.
- Replaced instances of TransitionIndex with TransitionKey for accessing transition components.
- Adjusted related processing functions and tests to accommodate the new dictionary format, ensuring consistent handling of transitions across the codebase.
* refactor(observation_processor): Improve observation processing by using constants and simplifying pixel handling
- Introduced constants for observation keys to enhance readability.
- Streamlined the handling of the "pixels" key by copying observations first and processing images more clearly.
- Updated the environment state and agent position assignments to use the new constants, improving maintainability.
* feat(pipeline): Add hook unregistration functionality and enhance documentation
- Implemented methods to unregister before, after, and reset hooks in the RobotProcessor class, allowing for more flexible hook management.
- Enhanced documentation to clarify hook execution semantics and the implications of modifying transitions within hooks.
- Added comprehensive tests to verify the correct behavior of hook registration and unregistration, including error handling for non-existent hooks.
* refactor(pipeline): Clarify hook behavior and improve documentation
- Updated the RobotProcessor class to ensure hooks are strictly for observation and do not modify transitions, enhancing clarity and maintainability.
- Refactored hook registration methods to reflect the new behavior, ensuring they accept only functions that do not return modified transitions.
- Enhanced documentation to clearly outline the purpose of hooks and their execution semantics.
- Added tests to verify that hooks are not executed during the step_through method while ensuring they function correctly during the __call__ method.
* feat(pipeline): Add __repr__ method to RobotProcessor for improved readability
- Implemented a __repr__ method in the RobotProcessor class to provide a clear string representation of the processor, including step names and optional parameters like name and seed.
- Added comprehensive tests to validate the __repr__ output for various scenarios, including empty processors, single and multiple steps, custom names, and seed values.
- Ensured that the representation handles long lists of steps with truncation for better readability.
* chore(pipeline): Move _CFG_NAME along other class member
* refactor(pipeline): Utilize get_safe_torch_device for device assignment
- Replaced direct torch.device instantiation with get_safe_torch_device to ensure safe device handling.
- This change enhances code readability and maintains consistency in device management across the RobotProcessor class.
* refactor(pipeline): Enhance state filename generation and profiling method
- Updated state filename generation to use the registry name when available, improving clarity in saved files.
- Modified the profile_steps method to include a warmup_runs parameter, allowing for more controlled performance profiling.
- Ensured consistent conditions during profiling by deep copying transitions for each run, enhancing accuracy in timing results.
* chore(doc): address pip install commant lerobot that not exist yet
* feat(pipeline): Enhance configuration filename handling and state file naming
- Introduced support for custom configuration filenames in the `save_pretrained` method, allowing users to specify a filename instead of the default.
- Improved state file naming to include step indices, preventing conflicts when multiple processors of the same type are saved.
- Added automatic detection for configuration files when loading from a directory, with error handling for multiple files.
- Updated tests to validate new features, including custom filenames and automatic config detection.
* refactor(pipeline): Improve state file naming conventions for clarity and uniqueness
- Enhanced state file naming to include the processor's sanitized name, ensuring uniqueness when multiple processors are saved in the same directory.
- Updated tests to reflect changes in state file naming, verifying that filenames now include the processor name and step indices to prevent conflicts.
- Added a new test to validate state file naming when using multiple processors, ensuring distinct filenames for each processor's state files.
* docs(pipeline): Add clarification for repo name sanitization process
* feat(processors): Introduce processors for various policy types
- Added `make_processor` function to create processor instances for different policy types, including `tdmpc`, `diffusion`, `act`, `vqbet`, `pi0`, `pi0fast`, `sac`, and `reward_classifier`.
- Implemented corresponding processor files for each policy type, encapsulating normalization and unnormalization steps.
- Updated existing policies to remove direct normalization dependencies, enhancing modularity and clarity.
- Enhanced test coverage to validate the integration of new processors with existing policy configurations.
* refactor(learner): Remove normalization from cached image features retrieval
- Simplified the retrieval of observation features by removing the normalization step from the `get_cached_image_features` method calls.
- This change enhances clarity and aligns with the recent updates to policy processors.
* refactor(policies): Remove unnormalization step from action predictions
- Eliminated the unnormalization of actions in both `TDMPCPolicy` and `VQBeTPolicy` classes to streamline action prediction.
- This change improves code clarity and aligns with recent updates to policy processors.
* feat(train): Integrate preprocessor into training pipeline
* refactor(train): Update preprocessor initialization to include dataset statistics
* refactor(policies): Enhance processor creation and add NaN detection hook
* feat(record): Integrate RobotProcessor into recording loop and update policy handling
- Added support for RobotProcessor in the record_loop function to enhance data processing capabilities.
- Updated the logic to reset both policy and processor when provided, ensuring proper state management.
- Modified action prediction to utilize the processor, improving the overall functionality of the recording process.
- Adjusted the save_checkpoint function to include preprocessor state saving, enhancing checkpointing capabilities.
* feat(migration): Add script for migrating policy models with normalization layers
* [pre-commit.ci] auto fixes from pre-commit.com hooks
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* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* feat(migrate): Enhance migration script to create preprocessor and postprocessor for policy models
- Updated the migration script to generate both a preprocessor and a postprocessor, improving the handling of normalization for training and inference.
- Added functionality to convert features to PolicyFeature objects, ensuring compatibility with the new processor architecture.
- Refined the extraction and removal of normalization statistics and layers, streamlining the migration process.
- Improved error handling for missing mandatory configuration fields during model instantiation.
* feat(migrate): Add model card generation and saving to migration script
- Implemented functionality to generate and save a model card for the migrated model, including metadata such as dataset repository ID, license, and tags.
- Enhanced the script to push the model card to the hub if requested, improving model documentation and accessibility.
- Refactored the saving process to ensure the model card is saved locally and uploaded correctly when pushing to the hub.
* feat(processor): Introduce ToBatchProcessor for handling observation batching
- Added ToBatchProcessor to ensure observations have proper batch dimensions for model processing.
- Implemented functionality to add batch dimensions to state and image observations as needed.
- Created comprehensive unit tests to validate the processor's behavior with various tensor dimensions and types.
- Ensured compatibility with existing transition keys and maintained the integrity of non-observation data.
* feat(processors): Add ToBatchProcessor to multiple policy processors
- Integrated ToBatchProcessor into various policy processors to handle observation batching.
- Updated make functions for act, diffusion, pi0, pi0fast, sac, smolvla, tdmpc, and vqbet processors to include the new batching functionality.
- Ensured consistency across all processor implementations for improved data handling.
* refactor(factory): Remove unused imports and NaN detection hook from processor creation
* feat(batch_processor): Enhance ToBatchProcessor to handle action batching
- Updated ToBatchProcessor to add batch dimensions to actions in addition to observations.
- Implemented separate methods for processing observations and actions, improving code readability.
- Added comprehensive unit tests to validate action batching functionality across various tensor dimensions and types.
* [pre-commit.ci] auto fixes from pre-commit.com hooks
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* feat(factory): Enhance make_processor to support preprocessor and postprocessor configuration
- Introduced ProcessorConfigKwargs TypedDict for better type safety in processor configuration.
- Updated make_processor to accept preprocessor and postprocessor configuration filenames, improving flexibility in processor instantiation.
- Refactored the loading of pretrained processors to utilize the new configuration options.
* refactor(factory): Clean up imports in factory.py
- Removed unused import of IdentityProcessor to streamline the code.
* feat(migrate): Extend load_model_from_hub to include train configuration
- Updated load_model_from_hub to return the train configuration alongside the model state_dict and config.
- Modified main function to handle the additional train configuration when loading models from both the hub and local paths.
- Adjusted dataset_repo_id extraction to utilize the train configuration for improved accuracy.
* refactor(record): Rename processor parameters and update processing logic
- Renamed `processor` to `preprocessor` and added `postprocessor` parameter for clarity.
- Updated the `record_loop` and `predict_action` functions to utilize the new preprocessor and postprocessor, enhancing the processing flow.
- Ensured compatibility with existing functionality while improving code readability.
* feat(batch_processor): Add task field processing to ToBatchProcessor
- Enhanced ToBatchProcessor to wrap string tasks in a list, adding batch dimensions for compatibility with model inference.
- Implemented a new method for processing complementary data, ensuring that task values are correctly handled as either strings or lists of strings.
- Added comprehensive unit tests to validate task processing, including edge cases and in-place mutation of complementary data.
* feat(normalization): Implement IDENTITY mode for normalization and unnormalization
- Enhanced NormalizerProcessor and UnnormalizerProcessor to support IDENTITY mode, allowing features to bypass normalization when specified.
- Updated processing logic to check normalization modes and handle missing statistics gracefully.
- Added comprehensive unit tests to validate IDENTITY mode functionality for both observations and actions, ensuring correct behavior across various scenarios.
- Improved error handling for unsupported normalization modes.
* fix(rebase): remove residual normalization layer:
* refactor(diffusion): remove normalization layer from input processing
* Add debug + calib
* cleanup
* Add pipeline
* fix int
* Add record example
* nit
* Add feature contract to pipelinestep and pipeline
* Add tests
* Add processor tests
* PR feedback
* encorperate pr feedback
* type in doc
* oops
* cleaned up steps and integrated pipeline with feature_contract
* refactor steps and robot to pipeline
* cleanup pipeline
* cleanup code further
* make it run
* feat(processors): Introduce processors for various policy types
- Added `make_processor` function to create processor instances for different policy types, including `tdmpc`, `diffusion`, `act`, `vqbet`, `pi0`, `pi0fast`, `sac`, and `reward_classifier`.
- Implemented corresponding processor files for each policy type, encapsulating normalization and unnormalization steps.
- Updated existing policies to remove direct normalization dependencies, enhancing modularity and clarity.
- Enhanced test coverage to validate the integration of new processors with existing policy configurations.
* refactor(learner): Remove normalization from cached image features retrieval
- Simplified the retrieval of observation features by removing the normalization step from the `get_cached_image_features` method calls.
- This change enhances clarity and aligns with the recent updates to policy processors.
* refactor(policies): Remove unnormalization step from action predictions
- Eliminated the unnormalization of actions in both `TDMPCPolicy` and `VQBeTPolicy` classes to streamline action prediction.
- This change improves code clarity and aligns with recent updates to policy processors.
* feat(train): Integrate preprocessor into training pipeline
* refactor(train): Update preprocessor initialization to include dataset statistics
* refactor(policies): Enhance processor creation and add NaN detection hook
* feat(record): Integrate RobotProcessor into recording loop and update policy handling
- Added support for RobotProcessor in the record_loop function to enhance data processing capabilities.
- Updated the logic to reset both policy and processor when provided, ensuring proper state management.
- Modified action prediction to utilize the processor, improving the overall functionality of the recording process.
- Adjusted the save_checkpoint function to include preprocessor state saving, enhancing checkpointing capabilities.
* feat(migration): Add script for migrating policy models with normalization layers
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* feat(migrate): Enhance migration script to create preprocessor and postprocessor for policy models
- Updated the migration script to generate both a preprocessor and a postprocessor, improving the handling of normalization for training and inference.
- Added functionality to convert features to PolicyFeature objects, ensuring compatibility with the new processor architecture.
- Refined the extraction and removal of normalization statistics and layers, streamlining the migration process.
- Improved error handling for missing mandatory configuration fields during model instantiation.
* feat(migrate): Add model card generation and saving to migration script
- Implemented functionality to generate and save a model card for the migrated model, including metadata such as dataset repository ID, license, and tags.
- Enhanced the script to push the model card to the hub if requested, improving model documentation and accessibility.
- Refactored the saving process to ensure the model card is saved locally and uploaded correctly when pushing to the hub.
* feat(processor): Introduce ToBatchProcessor for handling observation batching
- Added ToBatchProcessor to ensure observations have proper batch dimensions for model processing.
- Implemented functionality to add batch dimensions to state and image observations as needed.
- Created comprehensive unit tests to validate the processor's behavior with various tensor dimensions and types.
- Ensured compatibility with existing transition keys and maintained the integrity of non-observation data.
* feat(processors): Add ToBatchProcessor to multiple policy processors
- Integrated ToBatchProcessor into various policy processors to handle observation batching.
- Updated make functions for act, diffusion, pi0, pi0fast, sac, smolvla, tdmpc, and vqbet processors to include the new batching functionality.
- Ensured consistency across all processor implementations for improved data handling.
* refactor(factory): Remove unused imports and NaN detection hook from processor creation
* feat(batch_processor): Enhance ToBatchProcessor to handle action batching
- Updated ToBatchProcessor to add batch dimensions to actions in addition to observations.
- Implemented separate methods for processing observations and actions, improving code readability.
- Added comprehensive unit tests to validate action batching functionality across various tensor dimensions and types.
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* feat(factory): Enhance make_processor to support preprocessor and postprocessor configuration
- Introduced ProcessorConfigKwargs TypedDict for better type safety in processor configuration.
- Updated make_processor to accept preprocessor and postprocessor configuration filenames, improving flexibility in processor instantiation.
- Refactored the loading of pretrained processors to utilize the new configuration options.
* refactor(factory): Clean up imports in factory.py
- Removed unused import of IdentityProcessor to streamline the code.
* feat(migrate): Extend load_model_from_hub to include train configuration
- Updated load_model_from_hub to return the train configuration alongside the model state_dict and config.
- Modified main function to handle the additional train configuration when loading models from both the hub and local paths.
- Adjusted dataset_repo_id extraction to utilize the train configuration for improved accuracy.
* refactor(record): Rename processor parameters and update processing logic
- Renamed `processor` to `preprocessor` and added `postprocessor` parameter for clarity.
- Updated the `record_loop` and `predict_action` functions to utilize the new preprocessor and postprocessor, enhancing the processing flow.
- Ensured compatibility with existing functionality while improving code readability.
* feat(batch_processor): Add task field processing to ToBatchProcessor
- Enhanced ToBatchProcessor to wrap string tasks in a list, adding batch dimensions for compatibility with model inference.
- Implemented a new method for processing complementary data, ensuring that task values are correctly handled as either strings or lists of strings.
- Added comprehensive unit tests to validate task processing, including edge cases and in-place mutation of complementary data.
* feat(normalization): Implement IDENTITY mode for normalization and unnormalization
- Enhanced NormalizerProcessor and UnnormalizerProcessor to support IDENTITY mode, allowing features to bypass normalization when specified.
- Updated processing logic to check normalization modes and handle missing statistics gracefully.
- Added comprehensive unit tests to validate IDENTITY mode functionality for both observations and actions, ensuring correct behavior across various scenarios.
- Improved error handling for unsupported normalization modes.
* fix(rebase): remove residual normalization layer:
* refactor(diffusion): remove normalization layer from input processing
* refactor(normalization): Remove unused state dict transformation methods and streamline imports
- Eliminated the _transform_state_dict_keys and _load_as_safetensor methods from PI0Policy, simplifying the model loading process.
- Cleaned up imports in modeling_pi0.py by removing log_model_loading_keys and init_logging.
- Updated TDMPCPolicy and VQBeTPolicy to handle action removal from batches during offline evaluation.
- Introduced hotswap_stats function in normalize_processor.py to update normalization statistics dynamically, with corresponding tests to ensure functionality.
* refactor(normalization): Clean up imports in normalize_processor.py
* feat(batch_processor): Add feature_contract method to ToBatchProcessor
- Introduced feature_contract method that returns features without modification, maintaining the no-op behavior of the processor.
- This addition enhances the flexibility of the ToBatchProcessor for future feature processing needs.
* fix(dependencies): Update transformers dependency constraint to allow only versions up to 4.52.0
* [pre-commit.ci] auto fixes from pre-commit.com hooks
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* feat(tokenizer): Introduce TokenizerProcessor for text tokenization
- Added TokenizerProcessor class to handle tokenization of task strings using Hugging Face's AutoTokenizer.
- Supports both string and list inputs, with customizable parameters for task key, output key, and tokenization settings.
- Implemented comprehensive unit tests to validate functionality, including handling of various input scenarios and integration with RobotProcessor.
- Updated types.py to include LANGUAGE feature type and modified __init__.py to register the new processor.
* feat(language): Enhance language processing in TokenizerProcessor
- Added OBS_LANGUAGE constant to define the observation language key.
- Updated TokenizerProcessor to store tokenized task data in the observation dictionary, ensuring compatibility with the new language feature.
- Introduced Pi0NewLineProcessor to append newlines to tasks for proper tokenization.
- Modified tests to validate the integration of language tokens and attention masks in the observation structure.
* feat(tokenizer): Add padding configuration to TokenizerProcessor
- Introduced `padding_side` parameter to the TokenizerProcessor for customizable padding direction.
- Updated the `make_pi0_processor` function to include the new padding configuration.
- Enhanced unit tests to validate the functionality of the `padding_side` parameter in various scenarios.
* feat(processor): Add state management methods to Pi0NewLineProcessor
* feat(normalization): Track normalization and unnormalization info in complementary data
- Updated NormalizerProcessor and UnnormalizerProcessor to accept additional parameters for tracking normalization modes.
- Enhanced the __call__ methods to store normalization and unnormalization information in the complementary data of transitions.
- Added unit tests to verify the correct tracking of normalization info, including scenarios with missing stats and selective normalization keys.
* feat(factory): Add preprocessor and postprocessor overrides to ProcessorConfigKwargs
- Updated ProcessorConfigKwargs to include optional overrides for preprocessor and postprocessor configurations.
- Enhanced the make_processor function to utilize the new overrides, allowing for more flexible processor initialization.
* feat(processors): Integrate RenameProcessor into various processor configurations
- Added RenameProcessor to the input steps of multiple processor functions, including make_act_processor, make_diffusion_processor, make_pi0_processor, make_sac_processor, make_tdmpc_processor, make_vqbet_processor, and make_smolvla_processor.
- Consolidated normalization features from input and output into a single NormalizerProcessor for improved efficiency.
- Updated the input steps to ensure compatibility with the new RenameProcessor integration.
* Do some todos and cleanup
* change feature_contract to dataset_features
* use one method for conversion pipeline output to add_frame dict and use base processors where possible
* Add back in and use record_loop
* update todo
* rename to_dataset_frame
* feat(smolvla): Refactor language processing and introduce new line processor (#1658)
- Removed the prepare_language method and directly accessed language tokens and masks from the batch using the OBS_LANGUAGE constant.
- Added SmolVLANewLineProcessor to ensure tasks end with a newline, enhancing tokenization compatibility.
- Updated the make_smolvla_processor function to include the new line processor and tokenizer processor for improved input handling.
* feat(processors): Integrate DeviceProcessor into multiple processor configurations
- Added DeviceProcessor to the input and output steps of various processor functions, including make_act_processor, make_diffusion_processor, make_pi0_processor, make_pi0fast_processor, make_sac_processor, make_tdmpc_processor, make_vqbet_processor, and make_smolvla_processor.
- Enhanced the DeviceProcessor class with state management methods and ensured compatibility with existing processor pipelines.
- Introduced unit tests for DeviceProcessor to validate functionality across different scenarios, including CPU and CUDA operations.
* [pre-commit.ci] auto fixes from pre-commit.com hooks
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* fix
* fix reference frame
* refactor(pipeline): Remove to() method for device management
- Eliminated the to() method from RobotProcessor, which was responsible for moving tensor states to specified devices.
- Removed associated unit tests that validated the functionality of the to() method across various scenarios.
- Streamlined the pipeline code by focusing on other device management strategies.
* feat(processor): Enhance DeviceProcessor with float dtype conversion
- Added support for optional float dtype conversion in DeviceProcessor, allowing tensors to be converted to specified floating-point types while preserving non-float types.
- Implemented validation for float dtype input and updated the processor's configuration methods to include float dtype.
- Refactored tensor processing logic to streamline device movement and dtype conversion.
- Introduced comprehensive unit tests to validate the new float dtype functionality across various scenarios.
* update data visualization
* update teleop example
* fix record bugs
* Add replay
* Not code
* feature(pipeline): port tokenizer pipeline for VLA (#1645)
* feat(tokenizer): Introduce TokenizerProcessor for text tokenization
- Added TokenizerProcessor class to handle tokenization of task strings using Hugging Face's AutoTokenizer.
- Supports both string and list inputs, with customizable parameters for task key, output key, and tokenization settings.
- Implemented comprehensive unit tests to validate functionality, including handling of various input scenarios and integration with RobotProcessor.
- Updated types.py to include LANGUAGE feature type and modified __init__.py to register the new processor.
* feat(language): Enhance language processing in TokenizerProcessor
- Added OBS_LANGUAGE constant to define the observation language key.
- Updated TokenizerProcessor to store tokenized task data in the observation dictionary, ensuring compatibility with the new language feature.
- Introduced Pi0NewLineProcessor to append newlines to tasks for proper tokenization.
- Modified tests to validate the integration of language tokens and attention masks in the observation structure.
* feat(tokenizer): Add padding configuration to TokenizerProcessor
- Introduced `padding_side` parameter to the TokenizerProcessor for customizable padding direction.
- Updated the `make_pi0_processor` function to include the new padding configuration.
- Enhanced unit tests to validate the functionality of the `padding_side` parameter in various scenarios.
* feat(processor): Add state management methods to Pi0NewLineProcessor
* feat(normalization): Track normalization and unnormalization info in complementary data
- Updated NormalizerProcessor and UnnormalizerProcessor to accept additional parameters for tracking normalization modes.
- Enhanced the __call__ methods to store normalization and unnormalization information in the complementary data of transitions.
- Added unit tests to verify the correct tracking of normalization info, including scenarios with missing stats and selective normalization keys.
* feat(factory): Add preprocessor and postprocessor overrides to ProcessorConfigKwargs
- Updated ProcessorConfigKwargs to include optional overrides for preprocessor and postprocessor configurations.
- Enhanced the make_processor function to utilize the new overrides, allowing for more flexible processor initialization.
* feat(processors): Integrate RenameProcessor into various processor configurations
- Added RenameProcessor to the input steps of multiple processor functions, including make_act_processor, make_diffusion_processor, make_pi0_processor, make_sac_processor, make_tdmpc_processor, make_vqbet_processor, and make_smolvla_processor.
- Consolidated normalization features from input and output into a single NormalizerProcessor for improved efficiency.
- Updated the input steps to ensure compatibility with the new RenameProcessor integration.
* feat(smolvla): Refactor language processing and introduce new line processor (#1658)
- Removed the prepare_language method and directly accessed language tokens and masks from the batch using the OBS_LANGUAGE constant.
- Added SmolVLANewLineProcessor to ensure tasks end with a newline, enhancing tokenization compatibility.
- Updated the make_smolvla_processor function to include the new line processor and tokenizer processor for improved input handling.
* feture(policies): add device processor (#1659)
* feat(processors): Integrate DeviceProcessor into multiple processor configurations
- Added DeviceProcessor to the input and output steps of various processor functions, including make_act_processor, make_diffusion_processor, make_pi0_processor, make_pi0fast_processor, make_sac_processor, make_tdmpc_processor, make_vqbet_processor, and make_smolvla_processor.
- Enhanced the DeviceProcessor class with state management methods and ensured compatibility with existing processor pipelines.
- Introduced unit tests for DeviceProcessor to validate functionality across different scenarios, including CPU and CUDA operations.
* [pre-commit.ci] auto fixes from pre-commit.com hooks
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* refactor(pipeline): Remove to() method for device management
- Eliminated the to() method from RobotProcessor, which was responsible for moving tensor states to specified devices.
- Removed associated unit tests that validated the functionality of the to() method across various scenarios.
- Streamlined the pipeline code by focusing on other device management strategies.
* feat(processor): Enhance DeviceProcessor with float dtype conversion
- Added support for optional float dtype conversion in DeviceProcessor, allowing tensors to be converted to specified floating-point types while preserving non-float types.
- Implemented validation for float dtype input and updated the processor's configuration methods to include float dtype.
- Refactored tensor processing logic to streamline device movement and dtype conversion.
- Introduced comprehensive unit tests to validate the new float dtype functionality across various scenarios.
* feat(policies): Add new line processors and update module exports
* feat(processor): Enhance batch and device processors to handle index and task_index fields
- Added logic to ToBatchProcessor for unsqueezing 0D tensors for index and task_index fields, ensuring they are processed as 1D tensors.
- Updated DeviceProcessor to process index and task_index fields in complementary data, preserving their tensor types and ensuring non-tensor fields remain unchanged.
- Enhanced unit tests to validate the correct handling of index and task_index fields across various scenarios, including device compatibility and dtype preservation.
* Add eval script
* fix `q_curr` in InverseKinematicsEEToJoints to the IK solution
* feat(processors): Introduce processors for various policy types
- Added `make_processor` function to create processor instances for different policy types, including `tdmpc`, `diffusion`, `act`, `vqbet`, `pi0`, `pi0fast`, `sac`, and `reward_classifier`.
- Implemented corresponding processor files for each policy type, encapsulating normalization and unnormalization steps.
- Updated existing policies to remove direct normalization dependencies, enhancing modularity and clarity.
- Enhanced test coverage to validate the integration of new processors with existing policy configurations.
* refactor(learner): Remove normalization from cached image features retrieval
- Simplified the retrieval of observation features by removing the normalization step from the `get_cached_image_features` method calls.
- This change enhances clarity and aligns with the recent updates to policy processors.
* refactor(policies): Remove unnormalization step from action predictions
- Eliminated the unnormalization of actions in both `TDMPCPolicy` and `VQBeTPolicy` classes to streamline action prediction.
- This change improves code clarity and aligns with recent updates to policy processors.
* feat(train): Integrate preprocessor into training pipeline
* refactor(train): Update preprocessor initialization to include dataset statistics
* refactor(policies): Enhance processor creation and add NaN detection hook
* feat(record): Integrate RobotProcessor into recording loop and update policy handling
- Added support for RobotProcessor in the record_loop function to enhance data processing capabilities.
- Updated the logic to reset both policy and processor when provided, ensuring proper state management.
- Modified action prediction to utilize the processor, improving the overall functionality of the recording process.
- Adjusted the save_checkpoint function to include preprocessor state saving, enhancing checkpointing capabilities.
* feat(migration): Add script for migrating policy models with normalization layers
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* feat(migrate): Enhance migration script to create preprocessor and postprocessor for policy models
- Updated the migration script to generate both a preprocessor and a postprocessor, improving the handling of normalization for training and inference.
- Added functionality to convert features to PolicyFeature objects, ensuring compatibility with the new processor architecture.
- Refined the extraction and removal of normalization statistics and layers, streamlining the migration process.
- Improved error handling for missing mandatory configuration fields during model instantiation.
* feat(migrate): Add model card generation and saving to migration script
- Implemented functionality to generate and save a model card for the migrated model, including metadata such as dataset repository ID, license, and tags.
- Enhanced the script to push the model card to the hub if requested, improving model documentation and accessibility.
- Refactored the saving process to ensure the model card is saved locally and uploaded correctly when pushing to the hub.
* feat(processor): Introduce ToBatchProcessor for handling observation batching
- Added ToBatchProcessor to ensure observations have proper batch dimensions for model processing.
- Implemented functionality to add batch dimensions to state and image observations as needed.
- Created comprehensive unit tests to validate the processor's behavior with various tensor dimensions and types.
- Ensured compatibility with existing transition keys and maintained the integrity of non-observation data.
* feat(processors): Add ToBatchProcessor to multiple policy processors
- Integrated ToBatchProcessor into various policy processors to handle observation batching.
- Updated make functions for act, diffusion, pi0, pi0fast, sac, smolvla, tdmpc, and vqbet processors to include the new batching functionality.
- Ensured consistency across all processor implementations for improved data handling.
* refactor(factory): Remove unused imports and NaN detection hook from processor creation
* feat(batch_processor): Enhance ToBatchProcessor to handle action batching
- Updated ToBatchProcessor to add batch dimensions to actions in addition to observations.
- Implemented separate methods for processing observations and actions, improving code readability.
- Added comprehensive unit tests to validate action batching functionality across various tensor dimensions and types.
* [pre-commit.ci] auto fixes from pre-commit.com hooks
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* feat(factory): Enhance make_processor to support preprocessor and postprocessor configuration
- Introduced ProcessorConfigKwargs TypedDict for better type safety in processor configuration.
- Updated make_processor to accept preprocessor and postprocessor configuration filenames, improving flexibility in processor instantiation.
- Refactored the loading of pretrained processors to utilize the new configuration options.
* refactor(factory): Clean up imports in factory.py
- Removed unused import of IdentityProcessor to streamline the code.
* feat(migrate): Extend load_model_from_hub to include train configuration
- Updated load_model_from_hub to return the train configuration alongside the model state_dict and config.
- Modified main function to handle the additional train configuration when loading models from both the hub and local paths.
- Adjusted dataset_repo_id extraction to utilize the train configuration for improved accuracy.
* refactor(record): Rename processor parameters and update processing logic
- Renamed `processor` to `preprocessor` and added `postprocessor` parameter for clarity.
- Updated the `record_loop` and `predict_action` functions to utilize the new preprocessor and postprocessor, enhancing the processing flow.
- Ensured compatibility with existing functionality while improving code readability.
* feat(batch_processor): Add task field processing to ToBatchProcessor
- Enhanced ToBatchProcessor to wrap string tasks in a list, adding batch dimensions for compatibility with model inference.
- Implemented a new method for processing complementary data, ensuring that task values are correctly handled as either strings or lists of strings.
- Added comprehensive unit tests to validate task processing, including edge cases and in-place mutation of complementary data.
* feat(normalization): Implement IDENTITY mode for normalization and unnormalization
- Enhanced NormalizerProcessor and UnnormalizerProcessor to support IDENTITY mode, allowing features to bypass normalization when specified.
- Updated processing logic to check normalization modes and handle missing statistics gracefully.
- Added comprehensive unit tests to validate IDENTITY mode functionality for both observations and actions, ensuring correct behavior across various scenarios.
- Improved error handling for unsupported normalization modes.
* fix(rebase): remove residual normalization layer:
* refactor(diffusion): remove normalization layer from input processing
* refactor(normalization): Remove unused state dict transformation methods and streamline imports
- Eliminated the _transform_state_dict_keys and _load_as_safetensor methods from PI0Policy, simplifying the model loading process.
- Cleaned up imports in modeling_pi0.py by removing log_model_loading_keys and init_logging.
- Updated TDMPCPolicy and VQBeTPolicy to handle action removal from batches during offline evaluation.
- Introduced hotswap_stats function in normalize_processor.py to update normalization statistics dynamically, with corresponding tests to ensure functionality.
* refactor(normalization): Clean up imports in normalize_processor.py
* feat(batch_processor): Add feature_contract method to ToBatchProcessor
- Introduced feature_contract method that returns features without modification, maintaining the no-op behavior of the processor.
- This addition enhances the flexibility of the ToBatchProcessor for future feature processing needs.
* fix(dependencies): Update transformers dependency constraint to allow only versions up to 4.52.0
* [pre-commit.ci] auto fixes from pre-commit.com hooks
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* feature(pipeline): port tokenizer pipeline for VLA (#1645)
* feat(tokenizer): Introduce TokenizerProcessor for text tokenization
- Added TokenizerProcessor class to handle tokenization of task strings using Hugging Face's AutoTokenizer.
- Supports both string and list inputs, with customizable parameters for task key, output key, and tokenization settings.
- Implemented comprehensive unit tests to validate functionality, including handling of various input scenarios and integration with RobotProcessor.
- Updated types.py to include LANGUAGE feature type and modified __init__.py to register the new processor.
* feat(language): Enhance language processing in TokenizerProcessor
- Added OBS_LANGUAGE constant to define the observation language key.
- Updated TokenizerProcessor to store tokenized task data in the observation dictionary, ensuring compatibility with the new language feature.
- Introduced Pi0NewLineProcessor to append newlines to tasks for proper tokenization.
- Modified tests to validate the integration of language tokens and attention masks in the observation structure.
* feat(tokenizer): Add padding configuration to TokenizerProcessor
- Introduced `padding_side` parameter to the TokenizerProcessor for customizable padding direction.
- Updated the `make_pi0_processor` function to include the new padding configuration.
- Enhanced unit tests to validate the functionality of the `padding_side` parameter in various scenarios.
* feat(processor): Add state management methods to Pi0NewLineProcessor
* feat(normalization): Track normalization and unnormalization info in complementary data
- Updated NormalizerProcessor and UnnormalizerProcessor to accept additional parameters for tracking normalization modes.
- Enhanced the __call__ methods to store normalization and unnormalization information in the complementary data of transitions.
- Added unit tests to verify the correct tracking of normalization info, including scenarios with missing stats and selective normalization keys.
* feat(factory): Add preprocessor and postprocessor overrides to ProcessorConfigKwargs
- Updated ProcessorConfigKwargs to include optional overrides for preprocessor and postprocessor configurations.
- Enhanced the make_processor function to utilize the new overrides, allowing for more flexible processor initialization.
* feat(processors): Integrate RenameProcessor into various processor configurations
- Added RenameProcessor to the input steps of multiple processor functions, including make_act_processor, make_diffusion_processor, make_pi0_processor, make_sac_processor, make_tdmpc_processor, make_vqbet_processor, and make_smolvla_processor.
- Consolidated normalization features from input and output into a single NormalizerProcessor for improved efficiency.
- Updated the input steps to ensure compatibility with the new RenameProcessor integration.
* feat(smolvla): Refactor language processing and introduce new line processor (#1658)
- Removed the prepare_language method and directly accessed language tokens and masks from the batch using the OBS_LANGUAGE constant.
- Added SmolVLANewLineProcessor to ensure tasks end with a newline, enhancing tokenization compatibility.
- Updated the make_smolvla_processor function to include the new line processor and tokenizer processor for improved input handling.
* feture(policies): add device processor (#1659)
* feat(processors): Integrate DeviceProcessor into multiple processor configurations
- Added DeviceProcessor to the input and output steps of various processor functions, including make_act_processor, make_diffusion_processor, make_pi0_processor, make_pi0fast_processor, make_sac_processor, make_tdmpc_processor, make_vqbet_processor, and make_smolvla_processor.
- Enhanced the DeviceProcessor class with state management methods and ensured compatibility with existing processor pipelines.
- Introduced unit tests for DeviceProcessor to validate functionality across different scenarios, including CPU and CUDA operations.
* [pre-commit.ci] auto fixes from pre-commit.com hooks
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* refactor(pipeline): Remove to() method for device management
- Eliminated the to() method from RobotProcessor, which was responsible for moving tensor states to specified devices.
- Removed associated unit tests that validated the functionality of the to() method across various scenarios.
- Streamlined the pipeline code by focusing on other device management strategies.
* feat(processor): Enhance DeviceProcessor with float dtype conversion
- Added support for optional float dtype conversion in DeviceProcessor, allowing tensors to be converted to specified floating-point types while preserving non-float types.
- Implemented validation for float dtype input and updated the processor's configuration methods to include float dtype.
- Refactored tensor processing logic to streamline device movement and dtype conversion.
- Introduced comprehensive unit tests to validate the new float dtype functionality across various scenarios.
* feat(policies): Add new line processors and update module exports
* feat(processor): Enhance batch and device processors to handle index and task_index fields
- Added logic to ToBatchProcessor for unsqueezing 0D tensors for index and task_index fields, ensuring they are processed as 1D tensors.
- Updated DeviceProcessor to process index and task_index fields in complementary data, preserving their tensor types and ensuring non-tensor fields remain unchanged.
- Enhanced unit tests to validate the correct handling of index and task_index fields across various scenarios, including device compatibility and dtype preservation.
* refactor(processors): Standardize processor naming conventions
- Updated processor names across various files to use a consistent "robot_preprocessor" and "robot_postprocessor" format.
- Modified the make_processor functions in factory, act, diffusion, pi0, pi0fast, sac, smolvla, tdmpc, and vqbet to reflect the new naming scheme.
- Enhanced the pipeline configuration to align with the updated processor names, improving clarity and maintainability.
* refactor(factory): Update processor configuration and type hints
- Changed return type of get_policy_class to type[PreTrainedPolicy] for improved type safety.
- Enhanced make_processor function to utilize dataset_stats in processor creation for better flexibility.
- Updated ProcessorConfigKwargs to include dataset_stats, allowing for more comprehensive processor configurations.
- Streamlined processor initialization by removing unnecessary kwargs and ensuring clarity in processor type handling.
* Fix eval and android gripper
* add some tests
* refactor(factory, pi0fast): Update processor function names and parameters
- Renamed make_pi0_processor to make_pi0fast_processor for clarity and consistency.
- Updated parameter names in the factory's make_processor function to use pretrained_model_name_or_path instead of source, enhancing readability and alignment with naming conventions.
* fix(train.py) push postprocessor with preprocessor
- Add preprocesser policy overrides for device and rename_map
- Add rename_map to DatasetRecordConfig (record.py)
* Cleanup pr
* fix more git diff pr issues
* add path as type in save_pretrained
* small nit
* [pre-commit.ci] auto fixes from pre-commit.com hooks
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* rename test file
* fix: make dataset_features/feature_contract is optional
* fix tests
* Encorperate pr feedback
* clean up record.py
* add ascii art, fix normal record
* remove merge issues
* fix merge
* remove features
* Add feedback PR
* fix last 4 tests
* remove features check
* rename to transform_features
* add transform_features
* fix lekiwi eval and update eval api example
---------
Signed-off-by: Adil Zouitine <adilzouitinegm@gmail.com>
Signed-off-by: Pepijn <138571049+pkooij@users.noreply.github.com>
Co-authored-by: Adil Zouitine <adilzouitinegm@gmail.com>
Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Co-authored-by: Copilot <175728472+Copilot@users.noreply.github.com>
Co-authored-by: Simon Alibert <75076266+aliberts@users.noreply.github.com>
Co-authored-by: Michel Aractingi <michel.aractingi@huggingface.co>
* refactor(TokenizerProcessor): improve dependency handling and observation management
- Updated TokenizerProcessor to conditionally import AutoTokenizer based on the availability of the transformers library, enhancing flexibility.
- Modified tokenizer attribute type to Any to accommodate scenarios where transformers may not be installed.
- Improved observation handling by using a more concise approach to manage the transition dictionary, ensuring compatibility with existing data structures.
- Added error handling for missing transformers library, providing clear guidance for users on installation requirements.
* feat(dependencies): Add scipy as a required dependency
- Included `scipy>=1.15.2` in the project dependencies to enhance functionality and support for scientific computing tasks.
* feat(policies): convert save_policy_to_safetensors with pipeline
* refactor(normalization): remove Normalize and Unnormalize classes
- Deleted the Normalize and Unnormalize classes from the normalization module to streamline the codebase.
- Updated tests to ensure compatibility with the removal of these classes, focusing on the new NormalizerProcessor and UnnormalizerProcessor implementations.
- Enhanced the handling of normalization statistics and improved overall code clarity.
* refactor(factory): streamline processor loading by removing unused comments
- Removed commented-out code related to loading pretrained processors in the make_processor function.
- This change enhances code clarity and maintains focus on the current implementation.
* feat(DeviceProcessor): Enhance tensor processing with device detection and float dtype conversion
- Improved the _process_tensor method to preserve GPU placement for tensors already on a GPU, facilitating multi-GPU training scenarios.
- Introduced a new _detect_device method in TokenizerProcessor to ensure tokenized tensors match the device of existing tensors in transitions.
- Added comprehensive unit tests to validate the functionality of device detection and float dtype conversion across various scenarios.
* feat(tests): Add comprehensive tests for various policy processors
- Introduced new test files for ACT, Classifier, Diffusion, PI0, SAC, SmolVLA, TDMPC, and VQBeT policy processors.
- Each test file includes unit tests to validate functionality, including handling of batch sizes, device management, and data type conversions.
- Enhanced test coverage to ensure robustness and reliability of processor implementations across different scenarios.
* refactor(train): Remove unnecessary tensor device handling in training loop
* Refactor`gym_manipulator.py` using the universal pipeline (#1650)
* Migrate gym_manipulator to use the pipeline
Added get_teleop_events function to capture relevant events from teleop devices unrelated to actions
* Added the capability to record a dataset
* Added the replay functionality with the pipeline
* Refactored `actor.py` to use the pipeline
* [pre-commit.ci] auto fixes from pre-commit.com hooks
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* RL works at this commit - fixed actor.py and bugs in gym_manipulator
* change folder structure to reduce the size of gym_manip
* Refactored hilserl config
* Remove dataset and mode from HilSerlEnvConfig to a GymManipulatorConfig to reduce verbose of configs during training
* format docs
* removed get_teleop_events from abc
* Refactor environment configuration and processing pipeline for GymHIL support. Removed device attribute from HILSerlRobotEnvConfig, added DummyTeleopDevice for simulation, and updated processor creation to accommodate GymHIL environments.
* Improved typing for HILRobotEnv config and GymManipulator config
* [pre-commit.ci] auto fixes from pre-commit.com hooks
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* Migrated `gym_manipulator` to use a more modular structure similar to phone teleop
* Refactor gripper handling and transition processing in HIL and robot kinematic processors
- Updated gripper position handling to use a consistent key format across processors
- Improved the EEReferenceAndDelta class to handle reference joint positions.
- Added support for discrete gripper actions in the GripperVelocityToJoint processor.
- Refactored the gym manipulator to improve modularity and clarity in processing steps.
* Added delta_action_processor mapping wrapper
* Added missing file delta_action_processor and improved imports in `gym_manipulator`
* nit
* Added missing file joint_observation_processor
* Enhance processing architecture with new teleoperation processors
- Introduced `AddTeleopActionAsComplimentaryData` and `AddTeleopEventsAsInfo` for integrating teleoperator actions and events into transitions.
- Added `Torch2NumpyActionProcessor` and `Numpy2TorchActionProcessor` for seamless conversion between PyTorch tensors and NumPy arrays.
- Updated `__init__.py` to include new processors in module exports, improving modularity and clarity in the processing pipeline.
- GymHIL is now fully supported with HIL using the pipeline
* Refactor configuration structure for gym_hil integration
- Renamed sections for better readability, such as changing "Gym Wrappers Configuration" to "Processor Configuration."
- Enhanced documentation with clear examples for dataset collection and policy evaluation configurations.
* Enhance reset configuration and teleoperation event handling
- Added `terminate_on_success` parameter to `ResetConfig` and `InterventionActionProcessor` for controlling episode termination behavior upon success detection.
- Updated documentation to clarify the impact of `terminate_on_success` on data collection for reward classifier training.
- Refactored teleoperation event handling to use `TeleopEvents` constants for improved readability and maintainability across various modules.
* fix(keyboard teleop), delta action keys
* Added transform features and feature contract
* Added transform features for image crop
* Enum for TeleopEvents
* Update tranform_features delta action proc
---------
Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
* Remove HILEnvConfig references
* chore(processor): Add default names for preprocessor and postprocessor in constants
- Introduced `PREPROCESSOR_DEFAULT_NAME` and `POSTPROCESSOR_DEFAULT_NAME` constants for consistent naming across various processor implementations.
- Updated processor creation in multiple policy files to utilize these constants, enhancing code readability and maintainability.
- Modified the training script to load and save the preprocessor and postprocessor using the new constants.
* feat(processor): multiple improvements to the pipeline porting (#1749)
* [Port codebase pipeline] General fixes for RL and scripts (#1748)
* Refactor dataset configuration in documentation and codebase
- Updated dataset configuration keys from `dataset_root` to `root` and `num_episodes` to `num_episodes_to_record` for consistency.
- Adjusted replay episode handling by renaming `episode` to `replay_episode`.
- Enhanced documentation
- added specific processor to transform from policy actions to delta actions
* Added Robot action to tensor processor
Added new processor script for dealing with gym specific action processing
* removed RobotAction2Tensor processor; imrpoved choosing observations in actor
* nit in delta action
* added missing reset functions to kinematics
* Adapt teleoperate and replay to pipeline similar to record
* refactor(processors): move to inheritance (#1750)
* fix(teleoperator): improvements phone implementation (#1752)
* fix(teleoperator): protect shared state in phone implementation
* refactor(teleop): separate classes in phone
* fix: solve breaking changes (#1753)
* refactor(policies): multiple improvements (#1754)
* refactor(processor): simpler logic in device processor (#1755)
* refactor(processor): euclidean distance in delta action processor (#1757)
* refactor(processor): improvements to joint observations processor migration (#1758)
* refactor(processor): improvements to tokenizer migration (#1759)
* refactor(processor): improvements to tokenizer migration
* fix(tests): tokenizer tests regression from #1750
* fix(processors): fix float comparison and config in hil processors (#1760)
* chore(teleop): remove unnecessary callbacks in KeyboardEndEffectorTeleop (#1761)
* refactor(processor): improvements normalize pipeline migration (#1756)
* refactor(processor): several improvements normalize processor step
* refactor(processor): more improvements normalize processor
* refactor(processor): more changes to normalizer
* refactor(processor): take a different approach to DRY
* refactor(processor): final design
* chore(record): revert comment and continue deleted (#1764)
* refactor(examples): pipeline phone examples (#1769)
* refactor(examples): phone teleop + teleop script
* refactor(examples): phone replay + replay
* chore(examples): rename phone example files & folders
* feat(processor): fix improvements to the pipeline porting (#1796)
* refactor(processor): enhance tensor device handling in normalization process (#1795)
* refactor(tests): remove unsupported device detection test for complementary data (#1797)
* chore(tests): update ToBatchProcessor test (#1798)
* refactor(tests): remove in-place mutation tests for actions and complementary data in batch processor
* test(tests): add tests for action and task processing in batch processor
* add names for android and ios phone (#1799)
* use _tensor_stats in normalize processor (#1800)
* fix(normalize_processor): correct device reference for tensor epsilon handling (#1801)
* add point 5 add missing feature contracts (#1806)
* Fix PR comments 1452 (#1807)
* use key to determine image
* Address rest of PR comments
* use PolicyFeatures in transform_features
---------
Co-authored-by: Pepijn <138571049+pkooij@users.noreply.github.com>
---------
Co-authored-by: Michel Aractingi <michel.aractingi@huggingface.co>
Co-authored-by: Adil Zouitine <adilzouitinegm@gmail.com>
Co-authored-by: Pepijn <138571049+pkooij@users.noreply.github.com>
* refactor(constants, processor): standardize action and observation keys across multiple files (#1808)
- Added new constants for truncated and done states in constants.py.
- Updated references to action and observation keys in pipeline_features.py, converters.py, hil_processor.py, tokenizer_processor.py, and robot_kinematic_processor.py to use the new constants for improved readability and maintainability.
* refactor(processor): improve processor pipeline typing with generic type (#1810)
* refactor(processor): introduce generic type for to_output
- Always return `TOutput`
- Remove `_prepare_transition`, so `__call__` now always returns `TOutput`
- Update tests accordingly
- This refactor paves the way for adding settings for `to_transition` and `to_output` in `make_processor` and the post-processor
* refactor(processor): consolidate ProcessorKwargs usage across policies
- Removed the ProcessorTypes module and integrated ProcessorKwargs directly into the processor pipeline.
- Updated multiple policy files to utilize the new ProcessorKwargs structure for preprocessor and postprocessor arguments.
- Simplified the handling of processor kwargs by initializing them to empty dictionaries when not provided.
* refactor(converters): implement unified tensor conversion function (#1830)
- Introduced `to_tensor` function using `singledispatch` to handle various input types, including scalars, arrays, and dictionaries, converting them to PyTorch tensors.
- Replaced previous tensor conversion logic in `gym_action_processor`, `normalize_processor`, and `test_converters` with the new `to_tensor` function for improved readability and maintainability.
- Updated tests to cover new functionality and ensure correct tensor conversion behavior.
* Revert "refactor(converters): implement unified tensor conversion function (#…" (#1840)
This reverts commit
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Adil Zouitine
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feat(pipeline): universal processor for LeRobot (#1431)
* Refactor observation preprocessing to use a modular pipeline system - Introduced `RobotPipeline` and `ObservationProcessor` for handling observation transformations. - Updated `preprocess_observation` to maintain backward compatibility while leveraging the new pipeline. - Added tests for the new processing components and ensured they match the original functionality. - Removed hardcoded logic in favor of a more flexible, composable architecture. * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * Refactor observation processing and improve modularity - Updated `ObservationProcessor` to enhance the modular design for processing observations. - Cleaned up imports and improved code readability by removing unnecessary lines and comments. - Ensured backward compatibility while integrating new processing components. - Added tests to validate the functionality of the updated processing architecture. * Remove redundant tests for None observation and serialization methods in `test_observation_processor.py` to streamline the test suite and improve maintainability. * Refactor processing architecture to use RobotProcessor - Replaced instances of RobotPipeline with RobotProcessor across the codebase for improved modularity and clarity. - Introduced ProcessorStepRegistry for better management of processing steps. - Updated relevant documentation and tests to reflect the new processing structure. - Enhanced the save/load functionality to support the new processor design. - Added a model card template for RobotProcessor to facilitate sharing and documentation. * Add RobotProcessor tutorial to documentation - Introduced a new tutorial on using RobotProcessor for preprocessing robot data. - Added a section in the table of contents for easy navigation to the new tutorial. - The tutorial covers key concepts, real-world scenarios, and practical examples for effective use of the RobotProcessor pipeline. * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * Add normalization processor and related components - Introduced `NormalizationProcessor` to handle both observation normalization and action unnormalization. - Added `ObservationNormalizer` and `ActionUnnormalizer` classes for specific normalization tasks. - Updated `__init__.py` to include the new `NormalizationProcessor` in the module exports. - Enhanced `ObservationProcessor` with registration in the `ProcessorStepRegistry` for better modularity. - Created `RenameProcessor` for renaming keys in observations, improving flexibility in data processing. * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * Enhance processing architecture with new components - Added `RenameProcessor` to facilitate key renaming in observations, improving data handling flexibility. - Updated `__init__.py` to include `RenameProcessor` in module exports. - Refactored `NormalizationProcessor` and `ObservationNormalizer` to use `rsplit` for better key handling. - Introduced comprehensive tests for `NormalizationProcessor` and `RenameProcessor` to ensure functionality and robustness. * chore (docs): add docstring for processor * fix (test): test factory * fix(test): policies * Update tests/processor/test_observation_processor.py Co-authored-by: Copilot <175728472+Copilot@users.noreply.github.com> Signed-off-by: Adil Zouitine <adilzouitinegm@gmail.com> * chore(test): add suggestion made by copilot regarding numpy test * fix(test): import issue * Refactor normalization components and update tests - Renamed `ObservationNormalizer` to `NormalizerProcessor` and `ActionUnnormalizer` to `UnnormalizerProcessor` for clarity. - Consolidated normalization logic for both observations and actions into `NormalizerProcessor` and `UnnormalizerProcessor`. - Updated tests to reflect the new class names and ensure proper functionality of normalization and unnormalization processes. - Enhanced handling of missing statistics in normalization processes. * chore (docstrin):Improve docstring for NormalizerProcessor * feat (device processor): Implement device processor * chore (batch handling): Enhance processing components with batch conversion utilities * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * fix(test): linting issue * chore (output format): improves output format * chore (type): add typing for multiprocess envs * feat (overrides): Implement support for loading processors with parameter overrides - Added the ability to provide non-serializable objects when loading processors from saved configurations using the `overrides` parameter. - Enhanced error handling for invalid override keys and instantiation errors. - Updated documentation and examples to illustrate the usage of overrides for both registered and unregistered steps. - Added comprehensive tests to validate the new functionality and ensure backward compatibility. * chore(normalization): addressing comments from copilot * chore(learner): nit comment from copilot * feat(pipeline): Enhance step_through method to support both tuple and dict inputs * refactor(pipeline): Simplify observation and padding data handling in batch transitions * Apply suggestions from code review Co-authored-by: Simon Alibert <75076266+aliberts@users.noreply.github.com> Signed-off-by: Adil Zouitine <adilzouitinegm@gmail.com> * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * refactor(pipeline): Introduce ComplementaryDataProcessor for handling complementary data in transitions * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * refactor(pipeline): Transition from tuple to dictionary format for EnvTransition - Updated the EnvTransition structure to use a dictionary format instead of a tuple, enhancing readability and maintainability. - Replaced instances of TransitionIndex with TransitionKey for accessing transition components. - Adjusted related processing functions and tests to accommodate the new dictionary format, ensuring consistent handling of transitions across the codebase. * refactor(observation_processor): Improve observation processing by using constants and simplifying pixel handling - Introduced constants for observation keys to enhance readability. - Streamlined the handling of the "pixels" key by copying observations first and processing images more clearly. - Updated the environment state and agent position assignments to use the new constants, improving maintainability. * feat(pipeline): Add hook unregistration functionality and enhance documentation - Implemented methods to unregister before, after, and reset hooks in the RobotProcessor class, allowing for more flexible hook management. - Enhanced documentation to clarify hook execution semantics and the implications of modifying transitions within hooks. - Added comprehensive tests to verify the correct behavior of hook registration and unregistration, including error handling for non-existent hooks. * refactor(pipeline): Clarify hook behavior and improve documentation - Updated the RobotProcessor class to ensure hooks are strictly for observation and do not modify transitions, enhancing clarity and maintainability. - Refactored hook registration methods to reflect the new behavior, ensuring they accept only functions that do not return modified transitions. - Enhanced documentation to clearly outline the purpose of hooks and their execution semantics. - Added tests to verify that hooks are not executed during the step_through method while ensuring they function correctly during the __call__ method. * feat(pipeline): Add __repr__ method to RobotProcessor for improved readability - Implemented a __repr__ method in the RobotProcessor class to provide a clear string representation of the processor, including step names and optional parameters like name and seed. - Added comprehensive tests to validate the __repr__ output for various scenarios, including empty processors, single and multiple steps, custom names, and seed values. - Ensured that the representation handles long lists of steps with truncation for better readability. * chore(pipeline): Move _CFG_NAME along other class member * refactor(pipeline): Utilize get_safe_torch_device for device assignment - Replaced direct torch.device instantiation with get_safe_torch_device to ensure safe device handling. - This change enhances code readability and maintains consistency in device management across the RobotProcessor class. * refactor(pipeline): Enhance state filename generation and profiling method - Updated state filename generation to use the registry name when available, improving clarity in saved files. - Modified the profile_steps method to include a warmup_runs parameter, allowing for more controlled performance profiling. - Ensured consistent conditions during profiling by deep copying transitions for each run, enhancing accuracy in timing results. * chore(doc): address pip install commant lerobot that not exist yet * feat(pipeline): Enhance configuration filename handling and state file naming - Introduced support for custom configuration filenames in the `save_pretrained` method, allowing users to specify a filename instead of the default. - Improved state file naming to include step indices, preventing conflicts when multiple processors of the same type are saved. - Added automatic detection for configuration files when loading from a directory, with error handling for multiple files. - Updated tests to validate new features, including custom filenames and automatic config detection. * refactor(pipeline): Improve state file naming conventions for clarity and uniqueness - Enhanced state file naming to include the processor's sanitized name, ensuring uniqueness when multiple processors are saved in the same directory. - Updated tests to reflect changes in state file naming, verifying that filenames now include the processor name and step indices to prevent conflicts. - Added a new test to validate state file naming when using multiple processors, ensuring distinct filenames for each processor's state files. * docs(pipeline): Add clarification for repo name sanitization process * Feat/pipeline add feature contract (#1637) * Add feature contract to pipelinestep and pipeline * Add tests * Add processor tests * PR feedback * encorperate pr feedback * type in doc * oops * docs(pipeline): Clarify transition handling and hook behavior - Updated documentation to specify that hooks always receive transitions in EnvTransition format, ensuring consistent behavior across input formats. - Refactored the step_through method to yield only EnvTransition objects, regardless of the input format, and updated related tests to reflect this change. - Enhanced test assertions to verify the structure of results and the correctness of processing steps. * refactor(pipeline): Remove to() method for device management - Eliminated the to() method from RobotProcessor, which was responsible for moving tensor states to specified devices. - Removed associated unit tests that validated the functionality of the to() method across various scenarios. - Streamlined the pipeline code by focusing on other device management strategies. * refactor(pipeline): Remove model card generation and streamline processor methods - Eliminated the _generate_model_card method from RobotProcessor, which was responsible for generating README.md files from a template. - Updated save_pretrained method to remove model card generation, focusing on serialization of processor definitions and parameters. - Added default implementations for get_config, state_dict, load_state_dict, reset, and feature_contract methods in various processor classes to enhance consistency and usability. * refactor(observation): Streamline observation preprocessing and remove unused processor methods - Updated the `preprocess_observation` function to enhance image handling and ensure proper tensor formatting. - Removed the `RobotProcessor` and associated transition handling from the `rollout` function, simplifying the observation processing flow. - Integrated direct calls to `preprocess_observation` for improved clarity and efficiency in the evaluation script. * refactor(pipeline): Rename parameters for clarity and enhance save/load functionality - Updated parameter names in the save_pretrained and from_pretrained methods for improved readability, changing destination_path to save_directory and source to pretrained_model_name_or_path. - Enhanced the save_pretrained method to ensure directory creation and file handling is consistent with the new parameter names. - Streamlined the loading process in from_pretrained to utilize loaded_config for better clarity and maintainability. * refactor(pipeline): minor improvements (#1684) * chore(pipeline): remove unused features + device torch + envtransition keys * refactor(pipeline): ImageProcessor & StateProcessor are both implemented directly in VanillaObservationPRocessor * refactor(pipeline): RenameProcessor now inherits from ObservationProcessor + remove unused code * test(pipeline): fix broken test after refactors * docs(pipeline): update docstrings VanillaObservationProcessor * chore(pipeline): move None check to base pipeline classes --------- Signed-off-by: Adil Zouitine <adilzouitinegm@gmail.com> Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com> Co-authored-by: Copilot <175728472+Copilot@users.noreply.github.com> Co-authored-by: Simon Alibert <75076266+aliberts@users.noreply.github.com> Co-authored-by: Pepijn <138571049+pkooij@users.noreply.github.com> Co-authored-by: Steven Palma <imstevenpmwork@ieee.org> |