tangger/lerobot
1
0
Fork 1
Code Issues Pull Requests Actions 1 Packages Projects Releases Wiki Activity

Compare commits

merge into: tangger:fix_path
Branches Tags
tangger:main tangger:robot_client tangger:realman-dual-yehao tangger:mindbotv1 tangger:realman-dual-arm tangger:lerobot-xh tangger:realman-dual-xh tangger:realman-dual-arm-bak tangger:realman-single-arm tangger:recovered-commit tangger:realman-dual tangger:depth tangger:realman tangger:smolvla_doc tangger:user/aliberts/2025_02_25_refactor_robots tangger:user/michel-aractingi/2025-05-20-hil-rebase-robots tangger:user/fracapuano/async-inf tangger:my-fix-based-on-pr-1175 tangger:pre-commit-ci-update-config tangger:user/aliberts/2025_04_03_add_hope_jr tangger:user/michel-aractingi/2025-06-02-docs-for-hil-serl tangger:user/aliberts/2025_06_02_cap_pymunk tangger:hf-papers tangger:user/fracapuano/2025_05_29_dataset_streaming_delta_timesteps tangger:user/rcadene/2025_04_11_dataset_v3 tangger:user/fracapuano/2025_05_21_dataset_streaming tangger:origin/fix/record_policy_action_dict tangger:revert-1160-mshukor-patch-1 tangger:refactor/updated_api_docs_rebased tangger:user/adil-zouitine/2025-1-7-port-hil-serl-new tangger:test/robot_refactor_experiments tangger:user/aliberts/2025_04_19_refactor_cameras tangger:user/pepijn/2025_05_05_lekiwi_dual_teleop tangger:user/azouitine/2025-04-24-hot-fix-ci tangger:user/fracapuano/2024-04-24-mocking-robots tangger:user/fracapuano/2025-04-23-fixing-mock-robot tangger:user/fracapuano/2025-04-23-predicting-chunks tangger:user/michel-aractingi/tmp-hil-serl-rebase tangger:user/michel-aractingi/tmp-port-hil-serl-new tangger:test/add_cameras_di_tests_no_tmp_connect tangger:test/add_cameras_di_tests tangger:test/add_cameras_patch_tests_no_tmp_connect tangger:test/add_cameras_patch_tests tangger:user/mrussi/2025_01_09_hope_junior tangger:2025_04_11_vla_eval tangger:user/mrussi/2025_04_12_hopejr tangger:user/azouitine/2025-4-8-softmax-grasp-critic tangger:user/michel_aractingi/2024-04-04-grasp-critic tangger:user/aliberts/2025_04_08_fix_install_instruction tangger:user/rcadene/2025_02_19_port_openx tangger:feat/add_rerun tangger:user/michel_adil/dump_hil_serl tangger:user/pepijn/2025_03_18_fix_rotation_so100_docs tangger:user/pepijn/2025_03_11_weighted_sampling tangger:user/pepijn/2025_01_27_steps_assembly_intructions tangger:user/pepijn/2025_03_20_dana_workshop tangger:torchcodec-cpu tangger:user/pepijn/2025_03_11_focal_loss tangger:user/aliberts/2025_03_10_new_feetech_calibration tangger:user/aliberts/2025_03_08_register_configs tangger:fix/lint_warnings tangger:feat/autopolicy tangger:chore/bump_pythonversion_precommit tangger:qgallouedec-patch-1 tangger:user/aliberts/2025_02_27_fix_pr_style_bot tangger:user/mshukor/2025_02_25_accelerate tangger:user/michel-aractingi/2024-02-21-hilserl-threading-to-mp tangger:user/rcadene/2025_02_17_visualize_inference tangger:user/michel-aractingi/2024-02-18-hilserl-cache-embedding tangger:user/rcadene/2025_02_17_streaming tangger:user/rcadene/2025_02_07_improve_pi0 tangger:user/rcadene/2025_01_28_aloha_hdf5 tangger:user/pepijn/2025_01_24_save_autocorrect_calibration_to_json tangger:user/michel-aractingi/2025-01-21-server-client-arch tangger:user/michel-aractingi/2025-01-18-port-rlds-example tangger:user/adil-zouitine/2025-1-7-port-hil-serl tangger:user/michel-aractingi/2024-11-27-port-hil-serl tangger:user/michel-aractingi/2024-11-26-Maniskill-support tangger:user/rcadene/2024_11_26_hope_junior tangger:user/rcadene/2024_12_03_manage_dataset tangger:temp_branch_hil_serl tangger:user/michel-aractingi/2024-12-07-soft-actor-critic tangger:user/michel-aractingi/2024-12-06-sac-implementation tangger:user/rcadene/2024_11_27_reachy2_viz_inference tangger:tdmpc23 tangger:user/michel-aractingi/2024-11-20-new-tdmpc2-impl tangger:user/michel-aractingi/2024-10-15-control-sim tangger:user/rcadene/2024_10_24_feetech_dataset_v2 tangger:user/rcadene/2024_09_18_async_inference tangger:user/rcadene/2024_09_10_train_aloha_debug tangger:user/aliberts/2024_09_10_fix_docker_dev tangger:user/michel-aractingi/2024-09-02/move-make-optimizer-to-policy tangger:user/michel-aractingi/2024-08-26-tdmpc2-implementation tangger:user/jmoss/2024_08_19_add_hiwonder_motors tangger:user/rcadene/2024_08_24_edit_dataset_remove_episodes tangger:user/rcadene/2024_08_22_control_aloha tangger:Cadene-patch-1 tangger:user/rcadene/2024_08_06_improve_visualize_dataset_inference tangger:user/rcadene/2024_07_16_control_robot_v2_aloha tangger:user/aliberts/2024_07_26_remove_dataset_artifacts tangger:user/rcadene/2024_07_26_control_robot_v2_train tangger:user/rcadene/2024_07_29_control_robot_linux tangger:user/rcadene/2024_07_27_nightly tangger:user/rcadene/2024_07_09_bill_of_materials tangger:user/rcadene/2024_07_16_vqbet_multi_images tangger:user/youliang/2024_06_20_oxe_data_format_to_hg tangger:user/rcadene/2024_06_22_control_robot_other tangger:thomwolf_2024_06_18_fix_normalization tangger:thomwolf_2024_06_12_deep_dive_ACT tangger:user/rcadene/2024_06_01_custom_visualize_dataset tangger:thomwolf_2024_06_04_simple_gym tangger:user/marinabar/2024_06_10_evaluation_stats tangger:user/rcadene/2024_06_07_faster_act tangger:user/rcadene/2024_06_08_add_data_augmentation tangger:user/rcadene/2024_06_03_reachy2 tangger:user/rcadene/2024_06_03_fix_delta_timestamps tangger:thomwolf_2024_06_04_nans tangger:thomwolf_2024_06_04_nan_fixes tangger:user/aliberts/2024_05_06_add_coverage tangger:fix_aloha_conversion_nan tangger:user/rcadene/2024_05_20_dora_parquet_format_viz tangger:thom_arm tangger:thom-act tangger:alexander-soare/add_drop_last_keyframes tangger:user/aliberts/2024_05_20_add_gym_dora tangger:user/aliberts/2024_05_14_compare_policies tangger:user/aliberts/2024_05_12_add_metrics_logging_config tangger:thom-fixes tangger:user/rcadene/2024_05_08_test tangger:user/aliberts/2024_05_07_dev_docker tangger:origin/user/rcadene/2024_04_30_refactor_push_dataset_to_hub_video tangger:user/rcadene/2024_04_30_refactor_push_dataset_to_hub_video tangger:qgallouedec/macos_ci tangger:qgallouedec/use_pretrainedconfig tangger:qgallouedec/move_update_optim_schedul_outside_policy tangger:user/rcadene/2024_04_20_mobile_aloha_rerun tangger:thom-proposals tangger:user/aliberts/2024_03_31_fix_simxarm_render tangger:user/rcadene/2024_03_31_pretrained_models_aloha_simxarm tangger:user/aliberts/2024_03_28_package_envs tangger:user/aliberts/2024_03_26_add_ci_tests tangger:user/rcadene/2024_03_25_readme tangger:user/aliberts/2024_03_22_fix_simxarm tangger:user/rcadene/2024_03_22_pretrained tangger:user/aliberts/2024_03_15_notebook_example tangger:user/aliberts/2024_03_16_update_torchrl tangger:user/rcadene/2024_03_14_video_dataset tangger:fix_path tangger:user/alexander/multistep_policy_rollout tangger:user/rcadene/2024_03_11_aloha_load_pretrained_from_act_repo tangger:user/rcadene/2024_03_08_act_policy tangger:user/rcadene/2024_03_04_sbatch_hopper tangger:user/rcadene/2024_03_04_diffusion_pretrained_from_repo hangX:lerobot-xh hangX:realman-dual-arm hangX:realman-single-arm hangX:recovered-commit hangX:realman-dual hangX:depth hangX:realman hangX:smolvla_doc hangX:user/aliberts/2025_02_25_refactor_robots hangX:user/michel-aractingi/2025-05-20-hil-rebase-robots hangX:user/fracapuano/async-inf hangX:main hangX:my-fix-based-on-pr-1175 hangX:pre-commit-ci-update-config hangX:user/aliberts/2025_04_03_add_hope_jr hangX:user/michel-aractingi/2025-06-02-docs-for-hil-serl hangX:user/aliberts/2025_06_02_cap_pymunk hangX:hf-papers hangX:user/fracapuano/2025_05_29_dataset_streaming_delta_timesteps hangX:user/rcadene/2025_04_11_dataset_v3 hangX:user/fracapuano/2025_05_21_dataset_streaming hangX:origin/fix/record_policy_action_dict hangX:revert-1160-mshukor-patch-1 hangX:refactor/updated_api_docs_rebased hangX:user/adil-zouitine/2025-1-7-port-hil-serl-new hangX:test/robot_refactor_experiments hangX:user/aliberts/2025_04_19_refactor_cameras hangX:user/pepijn/2025_05_05_lekiwi_dual_teleop hangX:user/azouitine/2025-04-24-hot-fix-ci hangX:user/fracapuano/2024-04-24-mocking-robots hangX:user/fracapuano/2025-04-23-fixing-mock-robot hangX:user/fracapuano/2025-04-23-predicting-chunks hangX:user/michel-aractingi/tmp-hil-serl-rebase hangX:user/michel-aractingi/tmp-port-hil-serl-new hangX:test/add_cameras_di_tests_no_tmp_connect hangX:test/add_cameras_di_tests hangX:test/add_cameras_patch_tests_no_tmp_connect hangX:test/add_cameras_patch_tests hangX:user/mrussi/2025_01_09_hope_junior hangX:2025_04_11_vla_eval hangX:user/mrussi/2025_04_12_hopejr hangX:user/azouitine/2025-4-8-softmax-grasp-critic hangX:user/michel_aractingi/2024-04-04-grasp-critic hangX:user/aliberts/2025_04_08_fix_install_instruction hangX:user/rcadene/2025_02_19_port_openx hangX:feat/add_rerun hangX:user/michel_adil/dump_hil_serl hangX:user/pepijn/2025_03_18_fix_rotation_so100_docs hangX:user/pepijn/2025_03_11_weighted_sampling hangX:user/pepijn/2025_01_27_steps_assembly_intructions hangX:user/pepijn/2025_03_20_dana_workshop hangX:torchcodec-cpu hangX:user/pepijn/2025_03_11_focal_loss hangX:user/aliberts/2025_03_10_new_feetech_calibration hangX:user/aliberts/2025_03_08_register_configs hangX:fix/lint_warnings hangX:feat/autopolicy hangX:chore/bump_pythonversion_precommit hangX:qgallouedec-patch-1 hangX:user/aliberts/2025_02_27_fix_pr_style_bot hangX:user/mshukor/2025_02_25_accelerate hangX:user/michel-aractingi/2024-02-21-hilserl-threading-to-mp hangX:user/rcadene/2025_02_17_visualize_inference hangX:user/michel-aractingi/2024-02-18-hilserl-cache-embedding hangX:user/rcadene/2025_02_17_streaming hangX:user/rcadene/2025_02_07_improve_pi0 hangX:user/rcadene/2025_01_28_aloha_hdf5 hangX:user/pepijn/2025_01_24_save_autocorrect_calibration_to_json hangX:user/michel-aractingi/2025-01-21-server-client-arch hangX:user/michel-aractingi/2025-01-18-port-rlds-example hangX:user/adil-zouitine/2025-1-7-port-hil-serl hangX:user/michel-aractingi/2024-11-27-port-hil-serl hangX:user/michel-aractingi/2024-11-26-Maniskill-support hangX:user/rcadene/2024_11_26_hope_junior hangX:user/rcadene/2024_12_03_manage_dataset hangX:temp_branch_hil_serl hangX:user/michel-aractingi/2024-12-07-soft-actor-critic hangX:user/michel-aractingi/2024-12-06-sac-implementation hangX:user/rcadene/2024_11_27_reachy2_viz_inference hangX:tdmpc23 hangX:user/michel-aractingi/2024-11-20-new-tdmpc2-impl hangX:user/michel-aractingi/2024-10-15-control-sim hangX:user/rcadene/2024_10_24_feetech_dataset_v2 hangX:user/rcadene/2024_09_18_async_inference hangX:user/rcadene/2024_09_10_train_aloha_debug hangX:user/aliberts/2024_09_10_fix_docker_dev hangX:user/michel-aractingi/2024-09-02/move-make-optimizer-to-policy hangX:user/michel-aractingi/2024-08-26-tdmpc2-implementation hangX:user/jmoss/2024_08_19_add_hiwonder_motors hangX:user/rcadene/2024_08_24_edit_dataset_remove_episodes hangX:user/rcadene/2024_08_22_control_aloha hangX:Cadene-patch-1 hangX:user/rcadene/2024_08_06_improve_visualize_dataset_inference hangX:user/rcadene/2024_07_16_control_robot_v2_aloha hangX:user/aliberts/2024_07_26_remove_dataset_artifacts hangX:user/rcadene/2024_07_26_control_robot_v2_train hangX:user/rcadene/2024_07_29_control_robot_linux hangX:user/rcadene/2024_07_27_nightly hangX:user/rcadene/2024_07_09_bill_of_materials hangX:user/rcadene/2024_07_16_vqbet_multi_images hangX:user/youliang/2024_06_20_oxe_data_format_to_hg hangX:user/rcadene/2024_06_22_control_robot_other hangX:thomwolf_2024_06_18_fix_normalization hangX:thomwolf_2024_06_12_deep_dive_ACT hangX:user/rcadene/2024_06_01_custom_visualize_dataset hangX:thomwolf_2024_06_04_simple_gym hangX:user/marinabar/2024_06_10_evaluation_stats hangX:user/rcadene/2024_06_07_faster_act hangX:user/rcadene/2024_06_08_add_data_augmentation hangX:user/rcadene/2024_06_03_reachy2 hangX:user/rcadene/2024_06_03_fix_delta_timestamps hangX:thomwolf_2024_06_04_nans hangX:thomwolf_2024_06_04_nan_fixes hangX:user/aliberts/2024_05_06_add_coverage hangX:fix_aloha_conversion_nan hangX:user/rcadene/2024_05_20_dora_parquet_format_viz hangX:thom_arm hangX:thom-act hangX:alexander-soare/add_drop_last_keyframes hangX:user/aliberts/2024_05_20_add_gym_dora hangX:user/aliberts/2024_05_14_compare_policies hangX:user/aliberts/2024_05_12_add_metrics_logging_config hangX:thom-fixes hangX:user/rcadene/2024_05_08_test hangX:user/aliberts/2024_05_07_dev_docker hangX:origin/user/rcadene/2024_04_30_refactor_push_dataset_to_hub_video hangX:user/rcadene/2024_04_30_refactor_push_dataset_to_hub_video hangX:qgallouedec/macos_ci hangX:qgallouedec/use_pretrainedconfig hangX:qgallouedec/move_update_optim_schedul_outside_policy hangX:user/rcadene/2024_04_20_mobile_aloha_rerun hangX:thom-proposals hangX:user/aliberts/2024_03_31_fix_simxarm_render hangX:user/rcadene/2024_03_31_pretrained_models_aloha_simxarm hangX:user/aliberts/2024_03_28_package_envs hangX:user/aliberts/2024_03_26_add_ci_tests hangX:user/rcadene/2024_03_25_readme hangX:user/aliberts/2024_03_22_fix_simxarm hangX:user/rcadene/2024_03_22_pretrained hangX:user/aliberts/2024_03_15_notebook_example hangX:user/aliberts/2024_03_16_update_torchrl hangX:user/rcadene/2024_03_14_video_dataset hangX:fix_path hangX:user/alexander/multistep_policy_rollout hangX:user/rcadene/2024_03_11_aloha_load_pretrained_from_act_repo hangX:user/rcadene/2024_03_08_act_policy hangX:user/rcadene/2024_03_04_sbatch_hopper hangX:user/rcadene/2024_03_04_diffusion_pretrained_from_repo
...
pull from: hangX:user/rcadene/2024_03_22_pretrained
Branches Tags
hangX:lerobot-xh hangX:realman-dual-arm hangX:realman-single-arm hangX:recovered-commit hangX:realman-dual hangX:depth hangX:realman hangX:smolvla_doc hangX:user/aliberts/2025_02_25_refactor_robots hangX:user/michel-aractingi/2025-05-20-hil-rebase-robots hangX:user/fracapuano/async-inf hangX:main hangX:my-fix-based-on-pr-1175 hangX:pre-commit-ci-update-config hangX:user/aliberts/2025_04_03_add_hope_jr hangX:user/michel-aractingi/2025-06-02-docs-for-hil-serl hangX:user/aliberts/2025_06_02_cap_pymunk hangX:hf-papers hangX:user/fracapuano/2025_05_29_dataset_streaming_delta_timesteps hangX:user/rcadene/2025_04_11_dataset_v3 hangX:user/fracapuano/2025_05_21_dataset_streaming hangX:origin/fix/record_policy_action_dict hangX:revert-1160-mshukor-patch-1 hangX:refactor/updated_api_docs_rebased hangX:user/adil-zouitine/2025-1-7-port-hil-serl-new hangX:test/robot_refactor_experiments hangX:user/aliberts/2025_04_19_refactor_cameras hangX:user/pepijn/2025_05_05_lekiwi_dual_teleop hangX:user/azouitine/2025-04-24-hot-fix-ci hangX:user/fracapuano/2024-04-24-mocking-robots hangX:user/fracapuano/2025-04-23-fixing-mock-robot hangX:user/fracapuano/2025-04-23-predicting-chunks hangX:user/michel-aractingi/tmp-hil-serl-rebase hangX:user/michel-aractingi/tmp-port-hil-serl-new hangX:test/add_cameras_di_tests_no_tmp_connect hangX:test/add_cameras_di_tests hangX:test/add_cameras_patch_tests_no_tmp_connect hangX:test/add_cameras_patch_tests hangX:user/mrussi/2025_01_09_hope_junior hangX:2025_04_11_vla_eval hangX:user/mrussi/2025_04_12_hopejr hangX:user/azouitine/2025-4-8-softmax-grasp-critic hangX:user/michel_aractingi/2024-04-04-grasp-critic hangX:user/aliberts/2025_04_08_fix_install_instruction hangX:user/rcadene/2025_02_19_port_openx hangX:feat/add_rerun hangX:user/michel_adil/dump_hil_serl hangX:user/pepijn/2025_03_18_fix_rotation_so100_docs hangX:user/pepijn/2025_03_11_weighted_sampling hangX:user/pepijn/2025_01_27_steps_assembly_intructions hangX:user/pepijn/2025_03_20_dana_workshop hangX:torchcodec-cpu hangX:user/pepijn/2025_03_11_focal_loss hangX:user/aliberts/2025_03_10_new_feetech_calibration hangX:user/aliberts/2025_03_08_register_configs hangX:fix/lint_warnings hangX:feat/autopolicy hangX:chore/bump_pythonversion_precommit hangX:qgallouedec-patch-1 hangX:user/aliberts/2025_02_27_fix_pr_style_bot hangX:user/mshukor/2025_02_25_accelerate hangX:user/michel-aractingi/2024-02-21-hilserl-threading-to-mp hangX:user/rcadene/2025_02_17_visualize_inference hangX:user/michel-aractingi/2024-02-18-hilserl-cache-embedding hangX:user/rcadene/2025_02_17_streaming hangX:user/rcadene/2025_02_07_improve_pi0 hangX:user/rcadene/2025_01_28_aloha_hdf5 hangX:user/pepijn/2025_01_24_save_autocorrect_calibration_to_json hangX:user/michel-aractingi/2025-01-21-server-client-arch hangX:user/michel-aractingi/2025-01-18-port-rlds-example hangX:user/adil-zouitine/2025-1-7-port-hil-serl hangX:user/michel-aractingi/2024-11-27-port-hil-serl hangX:user/michel-aractingi/2024-11-26-Maniskill-support hangX:user/rcadene/2024_11_26_hope_junior hangX:user/rcadene/2024_12_03_manage_dataset hangX:temp_branch_hil_serl hangX:user/michel-aractingi/2024-12-07-soft-actor-critic hangX:user/michel-aractingi/2024-12-06-sac-implementation hangX:user/rcadene/2024_11_27_reachy2_viz_inference hangX:tdmpc23 hangX:user/michel-aractingi/2024-11-20-new-tdmpc2-impl hangX:user/michel-aractingi/2024-10-15-control-sim hangX:user/rcadene/2024_10_24_feetech_dataset_v2 hangX:user/rcadene/2024_09_18_async_inference hangX:user/rcadene/2024_09_10_train_aloha_debug hangX:user/aliberts/2024_09_10_fix_docker_dev hangX:user/michel-aractingi/2024-09-02/move-make-optimizer-to-policy hangX:user/michel-aractingi/2024-08-26-tdmpc2-implementation hangX:user/jmoss/2024_08_19_add_hiwonder_motors hangX:user/rcadene/2024_08_24_edit_dataset_remove_episodes hangX:user/rcadene/2024_08_22_control_aloha hangX:Cadene-patch-1 hangX:user/rcadene/2024_08_06_improve_visualize_dataset_inference hangX:user/rcadene/2024_07_16_control_robot_v2_aloha hangX:user/aliberts/2024_07_26_remove_dataset_artifacts hangX:user/rcadene/2024_07_26_control_robot_v2_train hangX:user/rcadene/2024_07_29_control_robot_linux hangX:user/rcadene/2024_07_27_nightly hangX:user/rcadene/2024_07_09_bill_of_materials hangX:user/rcadene/2024_07_16_vqbet_multi_images hangX:user/youliang/2024_06_20_oxe_data_format_to_hg hangX:user/rcadene/2024_06_22_control_robot_other hangX:thomwolf_2024_06_18_fix_normalization hangX:thomwolf_2024_06_12_deep_dive_ACT hangX:user/rcadene/2024_06_01_custom_visualize_dataset hangX:thomwolf_2024_06_04_simple_gym hangX:user/marinabar/2024_06_10_evaluation_stats hangX:user/rcadene/2024_06_07_faster_act hangX:user/rcadene/2024_06_08_add_data_augmentation hangX:user/rcadene/2024_06_03_reachy2 hangX:user/rcadene/2024_06_03_fix_delta_timestamps hangX:thomwolf_2024_06_04_nans hangX:thomwolf_2024_06_04_nan_fixes hangX:user/aliberts/2024_05_06_add_coverage hangX:fix_aloha_conversion_nan hangX:user/rcadene/2024_05_20_dora_parquet_format_viz hangX:thom_arm hangX:thom-act hangX:alexander-soare/add_drop_last_keyframes hangX:user/aliberts/2024_05_20_add_gym_dora hangX:user/aliberts/2024_05_14_compare_policies hangX:user/aliberts/2024_05_12_add_metrics_logging_config hangX:thom-fixes hangX:user/rcadene/2024_05_08_test hangX:user/aliberts/2024_05_07_dev_docker hangX:origin/user/rcadene/2024_04_30_refactor_push_dataset_to_hub_video hangX:user/rcadene/2024_04_30_refactor_push_dataset_to_hub_video hangX:qgallouedec/macos_ci hangX:qgallouedec/use_pretrainedconfig hangX:qgallouedec/move_update_optim_schedul_outside_policy hangX:user/rcadene/2024_04_20_mobile_aloha_rerun hangX:thom-proposals hangX:user/aliberts/2024_03_31_fix_simxarm_render hangX:user/rcadene/2024_03_31_pretrained_models_aloha_simxarm hangX:user/aliberts/2024_03_28_package_envs hangX:user/aliberts/2024_03_26_add_ci_tests hangX:user/rcadene/2024_03_25_readme hangX:user/aliberts/2024_03_22_fix_simxarm hangX:user/rcadene/2024_03_22_pretrained hangX:user/aliberts/2024_03_15_notebook_example hangX:user/aliberts/2024_03_16_update_torchrl hangX:user/rcadene/2024_03_14_video_dataset hangX:fix_path hangX:user/alexander/multistep_policy_rollout hangX:user/rcadene/2024_03_11_aloha_load_pretrained_from_act_repo hangX:user/rcadene/2024_03_08_act_policy hangX:user/rcadene/2024_03_04_sbatch_hopper hangX:user/rcadene/2024_03_04_diffusion_pretrained_from_repo tangger:main tangger:robot_client tangger:realman-dual-yehao tangger:mindbotv1 tangger:realman-dual-arm tangger:lerobot-xh tangger:realman-dual-xh tangger:realman-dual-arm-bak tangger:realman-single-arm tangger:recovered-commit tangger:realman-dual tangger:depth tangger:realman tangger:smolvla_doc tangger:user/aliberts/2025_02_25_refactor_robots tangger:user/michel-aractingi/2025-05-20-hil-rebase-robots tangger:user/fracapuano/async-inf tangger:my-fix-based-on-pr-1175 tangger:pre-commit-ci-update-config tangger:user/aliberts/2025_04_03_add_hope_jr tangger:user/michel-aractingi/2025-06-02-docs-for-hil-serl tangger:user/aliberts/2025_06_02_cap_pymunk tangger:hf-papers tangger:user/fracapuano/2025_05_29_dataset_streaming_delta_timesteps tangger:user/rcadene/2025_04_11_dataset_v3 tangger:user/fracapuano/2025_05_21_dataset_streaming tangger:origin/fix/record_policy_action_dict tangger:revert-1160-mshukor-patch-1 tangger:refactor/updated_api_docs_rebased tangger:user/adil-zouitine/2025-1-7-port-hil-serl-new tangger:test/robot_refactor_experiments tangger:user/aliberts/2025_04_19_refactor_cameras tangger:user/pepijn/2025_05_05_lekiwi_dual_teleop tangger:user/azouitine/2025-04-24-hot-fix-ci tangger:user/fracapuano/2024-04-24-mocking-robots tangger:user/fracapuano/2025-04-23-fixing-mock-robot tangger:user/fracapuano/2025-04-23-predicting-chunks tangger:user/michel-aractingi/tmp-hil-serl-rebase tangger:user/michel-aractingi/tmp-port-hil-serl-new tangger:test/add_cameras_di_tests_no_tmp_connect tangger:test/add_cameras_di_tests tangger:test/add_cameras_patch_tests_no_tmp_connect tangger:test/add_cameras_patch_tests tangger:user/mrussi/2025_01_09_hope_junior tangger:2025_04_11_vla_eval tangger:user/mrussi/2025_04_12_hopejr tangger:user/azouitine/2025-4-8-softmax-grasp-critic tangger:user/michel_aractingi/2024-04-04-grasp-critic tangger:user/aliberts/2025_04_08_fix_install_instruction tangger:user/rcadene/2025_02_19_port_openx tangger:feat/add_rerun tangger:user/michel_adil/dump_hil_serl tangger:user/pepijn/2025_03_18_fix_rotation_so100_docs tangger:user/pepijn/2025_03_11_weighted_sampling tangger:user/pepijn/2025_01_27_steps_assembly_intructions tangger:user/pepijn/2025_03_20_dana_workshop tangger:torchcodec-cpu tangger:user/pepijn/2025_03_11_focal_loss tangger:user/aliberts/2025_03_10_new_feetech_calibration tangger:user/aliberts/2025_03_08_register_configs tangger:fix/lint_warnings tangger:feat/autopolicy tangger:chore/bump_pythonversion_precommit tangger:qgallouedec-patch-1 tangger:user/aliberts/2025_02_27_fix_pr_style_bot tangger:user/mshukor/2025_02_25_accelerate tangger:user/michel-aractingi/2024-02-21-hilserl-threading-to-mp tangger:user/rcadene/2025_02_17_visualize_inference tangger:user/michel-aractingi/2024-02-18-hilserl-cache-embedding tangger:user/rcadene/2025_02_17_streaming tangger:user/rcadene/2025_02_07_improve_pi0 tangger:user/rcadene/2025_01_28_aloha_hdf5 tangger:user/pepijn/2025_01_24_save_autocorrect_calibration_to_json tangger:user/michel-aractingi/2025-01-21-server-client-arch tangger:user/michel-aractingi/2025-01-18-port-rlds-example tangger:user/adil-zouitine/2025-1-7-port-hil-serl tangger:user/michel-aractingi/2024-11-27-port-hil-serl tangger:user/michel-aractingi/2024-11-26-Maniskill-support tangger:user/rcadene/2024_11_26_hope_junior tangger:user/rcadene/2024_12_03_manage_dataset tangger:temp_branch_hil_serl tangger:user/michel-aractingi/2024-12-07-soft-actor-critic tangger:user/michel-aractingi/2024-12-06-sac-implementation tangger:user/rcadene/2024_11_27_reachy2_viz_inference tangger:tdmpc23 tangger:user/michel-aractingi/2024-11-20-new-tdmpc2-impl tangger:user/michel-aractingi/2024-10-15-control-sim tangger:user/rcadene/2024_10_24_feetech_dataset_v2 tangger:user/rcadene/2024_09_18_async_inference tangger:user/rcadene/2024_09_10_train_aloha_debug tangger:user/aliberts/2024_09_10_fix_docker_dev tangger:user/michel-aractingi/2024-09-02/move-make-optimizer-to-policy tangger:user/michel-aractingi/2024-08-26-tdmpc2-implementation tangger:user/jmoss/2024_08_19_add_hiwonder_motors tangger:user/rcadene/2024_08_24_edit_dataset_remove_episodes tangger:user/rcadene/2024_08_22_control_aloha tangger:Cadene-patch-1 tangger:user/rcadene/2024_08_06_improve_visualize_dataset_inference tangger:user/rcadene/2024_07_16_control_robot_v2_aloha tangger:user/aliberts/2024_07_26_remove_dataset_artifacts tangger:user/rcadene/2024_07_26_control_robot_v2_train tangger:user/rcadene/2024_07_29_control_robot_linux tangger:user/rcadene/2024_07_27_nightly tangger:user/rcadene/2024_07_09_bill_of_materials tangger:user/rcadene/2024_07_16_vqbet_multi_images tangger:user/youliang/2024_06_20_oxe_data_format_to_hg tangger:user/rcadene/2024_06_22_control_robot_other tangger:thomwolf_2024_06_18_fix_normalization tangger:thomwolf_2024_06_12_deep_dive_ACT tangger:user/rcadene/2024_06_01_custom_visualize_dataset tangger:thomwolf_2024_06_04_simple_gym tangger:user/marinabar/2024_06_10_evaluation_stats tangger:user/rcadene/2024_06_07_faster_act tangger:user/rcadene/2024_06_08_add_data_augmentation tangger:user/rcadene/2024_06_03_reachy2 tangger:user/rcadene/2024_06_03_fix_delta_timestamps tangger:thomwolf_2024_06_04_nans tangger:thomwolf_2024_06_04_nan_fixes tangger:user/aliberts/2024_05_06_add_coverage tangger:fix_aloha_conversion_nan tangger:user/rcadene/2024_05_20_dora_parquet_format_viz tangger:thom_arm tangger:thom-act tangger:alexander-soare/add_drop_last_keyframes tangger:user/aliberts/2024_05_20_add_gym_dora tangger:user/aliberts/2024_05_14_compare_policies tangger:user/aliberts/2024_05_12_add_metrics_logging_config tangger:thom-fixes tangger:user/rcadene/2024_05_08_test tangger:user/aliberts/2024_05_07_dev_docker tangger:origin/user/rcadene/2024_04_30_refactor_push_dataset_to_hub_video tangger:user/rcadene/2024_04_30_refactor_push_dataset_to_hub_video tangger:qgallouedec/macos_ci tangger:qgallouedec/use_pretrainedconfig tangger:qgallouedec/move_update_optim_schedul_outside_policy tangger:user/rcadene/2024_04_20_mobile_aloha_rerun tangger:thom-proposals tangger:user/aliberts/2024_03_31_fix_simxarm_render tangger:user/rcadene/2024_03_31_pretrained_models_aloha_simxarm tangger:user/aliberts/2024_03_28_package_envs tangger:user/aliberts/2024_03_26_add_ci_tests tangger:user/rcadene/2024_03_25_readme tangger:user/aliberts/2024_03_22_fix_simxarm tangger:user/rcadene/2024_03_22_pretrained tangger:user/aliberts/2024_03_15_notebook_example tangger:user/aliberts/2024_03_16_update_torchrl tangger:user/rcadene/2024_03_14_video_dataset tangger:fix_path tangger:user/alexander/multistep_policy_rollout tangger:user/rcadene/2024_03_11_aloha_load_pretrained_from_act_repo tangger:user/rcadene/2024_03_08_act_policy tangger:user/rcadene/2024_03_04_sbatch_hopper tangger:user/rcadene/2024_03_04_diffusion_pretrained_from_repo

60 Commits
fix_path ... user/rcade

Author SHA1 Message Date
Cadene
b1e4b7967e WIP 2024-03-22 00:13:39 +00:00
Remi Cadene
70d7b99d09 add pretrained 2024-03-22 00:47:30 +01:00
Remi
b633748987 Merge pull request #41 from alexander-soare/fix_pusht_diffusion 
Fixes issues with PushT diffusion
 2024-03-22 00:05:40 +01:00
Alexander Soare
41912b962b remove TODO 2024-03-21 13:51:26 +00:00
Alexander Soare
98361073ef cpu poetry lock 2024-03-21 12:02:24 +00:00
Alexander Soare
48df15ed26 add cpu dep 2024-03-21 11:58:28 +00:00
Alexander Soare
b562f89c3b update deps 2024-03-21 11:42:45 +00:00
Alexander Soare
4e10cd306b revert changes to default.yaml 2024-03-21 10:27:07 +00:00
Alexander Soare
72d3c3120b Merge remote-tracking branch 'upstream/main' into fix_pusht_diffusion 2024-03-21 10:20:52 +00:00
Alexander Soare
acf1174447 ready for review 2024-03-21 10:18:50 +00:00
Simon Alibert
1bd50122be Merge pull request #40 from huggingface/user/aliberts/2024_03_20_enable_mps_device 
Enable mps backend for Apple silicon devices
 2024-03-20 19:33:12 +01:00
Remi
2b0221052a Merge pull request #39 from huggingface/user/rcadene/2024_03_20_update_stats_v1.1 
Update stats for Pusht and Aloha datasets (v1.0 -> v1.1)
 2024-03-20 18:41:15 +01:00
Simon Alibert
4631d36c05 Add get_safe_torch_device in policies 2024-03-20 18:38:55 +01:00
Cadene
f23a53c3e4 update stats.pth 2024-03-20 17:36:13 +00:00
Cadene
82e6e01651 v1.1 2024-03-20 17:34:00 +00:00
Alexander Soare
d323993569 backup wip 2024-03-20 15:01:27 +00:00
Remi
ec536ef0fa Merge pull request #26 from Cadene/user/alexander-soare/multistep_policy_and_serial_env 
Incorporate SerialEnv and introduct multistep policy logic
 2024-03-20 15:55:27 +01:00
Remi
3910c48e43 Merge pull request #37 from Cadene/user/rcadene/2024_03_19_improve_readme 
Improve README/LICENSE
 2024-03-20 15:50:22 +01:00
Alexander Soare
4b7ec81dde remove abstracmethods, fix online training 2024-03-20 14:49:41 +00:00
Cadene
98a816f0f8 WIP 2024-03-20 14:47:27 +00:00
Cadene
45a4a02b7e WIP 2024-03-20 10:19:55 +00:00
Cadene
8bed0fc465 WIP 2024-03-20 10:10:00 +00:00
Alexander Soare
32e3f71dd1 backup wip 2024-03-20 09:49:16 +00:00
Alexander Soare
5332766a82 revision 2024-03-20 09:45:45 +00:00
Alexander Soare
b1ec3da035 remove internal rendering hooks 2024-03-20 09:23:23 +00:00
Alexander Soare
d16f6a93b3 Merge remote-tracking branch 'upstream/main' into user/alexander-soare/multistep_policy_and_serial_env 2024-03-20 09:01:45 +00:00
Alexander Soare
52e149fbfd Only save video frames in first rollout 2024-03-20 08:32:11 +00:00
Alexander Soare
4f1955edfd Clear action queue when environment is reset 2024-03-20 08:31:06 +00:00
Alexander Soare
c5010fee9a fix seeding 2024-03-20 08:21:33 +00:00
Alexander Soare
18fa88475b Move reset_warning_issued flag to class attribute 2024-03-20 08:09:38 +00:00
Alexander Soare
b54cdc9a0f break_when_any_done==True for batch_size==1 2024-03-19 19:08:25 +00:00
Alexander Soare
46ac87d2a6 ready for review 2024-03-19 18:59:08 +00:00
Alexander Soare
896a11f60e backup wip 2024-03-19 18:50:04 +00:00
Remi
2d5abbbd6f Merge pull request #36 from Cadene/user/rcadene/2024_03_19_replay_buffer_folder 
Add replay_buffer directory and dataset versioning
 2024-03-19 18:04:29 +01:00
Cadene
7d5d99e036 Address more comments 2024-03-19 16:53:07 +00:00
Cadene
b420ab88f4 version naming conventions 2024-03-19 16:44:19 +00:00
Cadene
e799dc5e3f Improve mock_dataset 2024-03-19 16:38:07 +00:00
Cadene
10034e85c4 Aloha done 2024-03-19 16:03:42 +00:00
Alexander Soare
ea17f4ce50 backup wip 2024-03-19 16:02:09 +00:00
Cadene
6a1a29386a Add replay_buffer directory in pusht datasets + aloha (WIP) 2024-03-19 15:49:45 +00:00
Alexander Soare
88347965c2 revert dp changes, make act and tdmpc batch friendly 2024-03-18 19:18:21 +00:00
Alexander Soare
09ddd9bf92 Merge branch 'main' into user/alexander-soare/multistep_policy_and_serial_env 2024-03-18 18:27:50 +00:00
Remi
099a465367 Merge pull request #35 from Cadene/switch_between_train_and_eval 
Switch between train and eval modes
 2024-03-18 13:21:16 +01:00
Alexander Soare
8e346b379d switch between train and eval 2024-03-18 09:45:17 +00:00
Alexander Soare
bae7e7b41c Merge remote-tracking branch 'origin/main' into user/alexander-soare/multistep_policy_and_serial_env 2024-03-15 14:06:53 +00:00
Remi
75cc10198f Merge pull request #31 from Cadene/disable_wandb_artifact 
Fix wandb artifact name and add disable option
 2024-03-15 15:06:16 +01:00
Alexander Soare
3124f71ebd Merge remote-tracking branch 'origin/main' into user/alexander-soare/multistep_policy_and_serial_env 2024-03-15 14:04:23 +00:00
Alexander Soare
4ecfd17f9e fix wandb artifact name and add disable option 2024-03-15 13:56:55 +00:00
Remi
58d1787ee3 Merge pull request #30 from Cadene/user/rcadene/2024_03_15_fix_path 
Use Path type instead of str for data_dir
 2024-03-15 14:41:33 +01:00
Cadene
b752833f3f fix download 2024-03-15 13:19:18 +00:00
Alexander Soare
a45896dc8d Merge remote-tracking branch 'origin/main' into user/alexander-soare/multistep_policy_and_serial_env 2024-03-15 13:05:35 +00:00
Alexander Soare
a222c88c99 Merge branch 'user/alexander-soare/train_pusht' into user/alexander-soare/multistep_policy_and_serial_env 2024-03-14 16:06:21 +00:00
Alexander Soare
736bc969ca Merge branch 'main' into user/alexander-soare/train_pusht 2024-03-14 16:05:11 +00:00
Alexander Soare
4822d63dbe Merge branch 'main' into user/alexander-soare/multistep_policy_and_serial_env 2024-03-14 16:04:40 +00:00
Alexander Soare
ba91976944 wip: still needs batch logic for act and tdmp 2024-03-14 15:24:10 +00:00
Alexander Soare
98484ac68e ready for review 2024-03-12 21:59:01 +00:00
Alexander Soare
9512d1d2f3 Merge branch 'main' into user/alexander-soare/train_pusht 2024-03-12 19:41:27 +00:00
Alexander Soare
87fcc536f9 wip - still need to verify full training run 2024-03-11 18:45:21 +00:00
Alexander Soare
304355c917 Merge remote-tracking branch 'origin/main' into train_pusht 2024-03-11 15:37:37 +00:00
Alexander Soare
2a01487494 early training loss as expected 2024-03-11 13:34:04 +00:00
107 changed files with 1554 additions and 572 deletions
Expand all files Collapse all files

358
.github/poetry/cpu/poetry.lock generated vendored
View File

@@ -1,4 +1,4 @@
# This file is automatically @generated by Poetry 1.8.1 and should not be changed by hand.
# This file is automatically @generated by Poetry 1.8.2 and should not be changed by hand.
[[package]]
name = "absl-py"
@@ -44,56 +44,56 @@ files = [
[[package]]
name = "av"
version = "11.0.0"
version = "12.0.0"
description = "Pythonic bindings for FFmpeg's libraries."
optional = false
python-versions = ">=3.8"
files = [
{file = "av-11.0.0-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:a01f13b37eb6d181e03bbbbda29093fe2d68f10755795188220acdc89560ec27"},
{file = "av-11.0.0-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:b2236faee1b5d71dff3cdef81ef6eec22cc8b71dbfb45eb037e6437fe80f24e7"},
{file = "av-11.0.0-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:40543a08e5c84aecd2bc84da5d43548743201897f0ba21bf5ae3a4dcddefca2b"},
{file = "av-11.0.0-cp310-cp310-manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:2907376884d956376aaf3bc1905fa4e0dcb9ba4e0d183e519392a19d89317d1b"},
{file = "av-11.0.0-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:e8d5581dcdc81cd601e3ce036809f14da82c46ff187bcefe981ec819390e0ab0"},
{file = "av-11.0.0-cp310-cp310-win_amd64.whl", hash = "sha256:150490f2a62cfa470f3cb60f3a0060ff93afd807e2b7b3b0eeeb5a992eb8d67b"},
{file = "av-11.0.0-cp311-cp311-macosx_10_9_x86_64.whl", hash = "sha256:d9bac0de62f09e2cb4e2132b5a46a89bc31c898189aa285b484c17351d991afe"},
{file = "av-11.0.0-cp311-cp311-macosx_11_0_arm64.whl", hash = "sha256:2122ff8bdace4ce50207920f37de472517921e2ca1f0503464f748fdb8e20506"},
{file = "av-11.0.0-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:527d840697fee6ad4cf47eba987eaf30cd76bd96b2d20eaa907e166b9b8065c8"},
{file = "av-11.0.0-cp311-cp311-manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:abeaedddfca9101886eb6fc47318c5f5ece8480d330d73aacf6917d7421981a2"},
{file = "av-11.0.0-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:13790fbb889b955baf885fe3761e923e85537ef414173465ec293177cedb7b99"},
{file = "av-11.0.0-cp311-cp311-win_amd64.whl", hash = "sha256:fc27e27f52480287f44226ad4ae3eb53346bf027959d0f00a9154530bd98b371"},
{file = "av-11.0.0-cp312-cp312-macosx_10_9_x86_64.whl", hash = "sha256:892583e2c6b8c2500e5d24310f499caefcdaa2e48c8f7169ad41041aaaf4da11"},
{file = "av-11.0.0-cp312-cp312-macosx_11_0_arm64.whl", hash = "sha256:6943679d70a9f4de974049e7ae2cf0b20afe0d7ddab650526c02a6cf9adcd08f"},
{file = "av-11.0.0-cp312-cp312-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:e6d73b038ccf1df5c16bc643eee5c694fb7732e09375e2f4903c1f4ce90dfb72"},
{file = "av-11.0.0-cp312-cp312-manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:c83422db3333e97b9680700df5185139352fc3a568b14179da3bdcbeb2f0e91b"},
{file = "av-11.0.0-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:8413900f6a3639e0088c018a3a516a1656d4d16799e7aa759a16ddf3bd268e2b"},
{file = "av-11.0.0-cp312-cp312-win_amd64.whl", hash = "sha256:908e49ee336223801d8f2f7dca5a1deb64e9d8256138b8e7a79013b682a6ebb5"},
{file = "av-11.0.0-cp38-cp38-macosx_10_9_x86_64.whl", hash = "sha256:82411ae4a562da07b76028d2f349fb0e6a86aa78ad2b18d2d7bf5b06b17fba14"},
{file = "av-11.0.0-cp38-cp38-macosx_11_0_arm64.whl", hash = "sha256:621104bd63e38fa4eca554da3722b1aac329619de39152f27eec8999acc72342"},
{file = "av-11.0.0-cp38-cp38-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:442878990c094455a16c10127edcc54bc4e78d355e6a13ad2a27608b0ecda38f"},
{file = "av-11.0.0-cp38-cp38-manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:658199c92987dc72511f5ee8ade62faef6234b7a04c8b5788de99e366be5e073"},
{file = "av-11.0.0-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:ad4b381665c49267b46f87297573898b85e5c41384750fee2e70267fbc4ba318"},
{file = "av-11.0.0-cp38-cp38-win_amd64.whl", hash = "sha256:60de14f71293e36ca4e297cc8a8460f0cf74f38a201694f3c6fc7f40301582f2"},
{file = "av-11.0.0-cp39-cp39-macosx_10_9_x86_64.whl", hash = "sha256:a90f04af96374dab94028a7471597bdfcf03083338b9be2eb8ca4805a8ec7ab5"},
{file = "av-11.0.0-cp39-cp39-macosx_11_0_arm64.whl", hash = "sha256:8821ab2d23e4cb5c8abea6b08d2b1bfceca6af2d88fab1d1dc1b3ec7b34933c7"},
{file = "av-11.0.0-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:9a92342ed307eeaf9509a6b0f3bafd4337c4880c851b50acc18df48c625b63b6"},
{file = "av-11.0.0-cp39-cp39-manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:bbe3502975bc844f5d432c1f24d331bf6ef3e05532ebf06f7ed08b60719b8ea5"},
{file = "av-11.0.0-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:c278b3a4fd111b4c9190abe6b1a5ca358d5f91e851d470b62577b957e0187b09"},
{file = "av-11.0.0-cp39-cp39-win_amd64.whl", hash = "sha256:478aa1d54fbc3058ea65ff41086b6adbe1326b456a027d2f3b59dbe60b4ac2ca"},
{file = "av-11.0.0-pp310-pypy310_pp73-macosx_10_9_x86_64.whl", hash = "sha256:e8df10bb2d56a981d02a8a0b41491912b76dad06305d174a2575ef55ad451100"},
{file = "av-11.0.0-pp310-pypy310_pp73-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:b30c51e597785a89241bd61865faff2dbd3327856a8285a1e120dbf60e18348b"},
{file = "av-11.0.0-pp310-pypy310_pp73-manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:a8b8bd92edb096699b306e7b090ad096925ca3bdae6f89656f023fa2a2da627d"},
{file = "av-11.0.0-pp310-pypy310_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:9383af733abfc44f6fc29307a6c922fbf671ee343dc97b78b74eac6a2346a46d"},
{file = "av-11.0.0-pp310-pypy310_pp73-win_amd64.whl", hash = "sha256:a9df4a60579198b560f641cdfe4c2139948a70193ddc096b275f2cf6d94e3e04"},
{file = "av-11.0.0-pp38-pypy38_pp73-macosx_10_9_x86_64.whl", hash = "sha256:8ae5f7ae0a7093fb813686d4aa4c554531f80a28480427f5c155da51b747eff0"},
{file = "av-11.0.0-pp38-pypy38_pp73-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:50fb7d606f8236891d773c701d5650b93af8dbf78eeaac36fc7e1f7f64a9d664"},
{file = "av-11.0.0-pp38-pypy38_pp73-manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:543e0f9bf6ff02dedbe66d906fbc89c8907c80a8ea7413fc3fed68ce4a6e9b44"},
{file = "av-11.0.0-pp38-pypy38_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:daa279c884457ab194ce78bdd89c0aa391af733da95fb3258d4c6eb8c258299a"},
{file = "av-11.0.0-pp39-pypy39_pp73-macosx_10_9_x86_64.whl", hash = "sha256:1aacc21f4cf96447117a61edfb776afb73186750a5e08a21484ddfc3599aefb5"},
{file = "av-11.0.0-pp39-pypy39_pp73-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:2568b38eef777b916a5d02e42b8f67f92e12023531239ddd32e1ca4f3cdf8c5b"},
{file = "av-11.0.0-pp39-pypy39_pp73-manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:747c6d347e27c59cc2e78c9c505d23cd88eceff0cc9386be73693ae9009a577c"},
{file = "av-11.0.0-pp39-pypy39_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:4bbd8f4941b9d3450eff40003b9b9d904667aec7ab085fa31f0f9bca32d755e0"},
{file = "av-11.0.0-pp39-pypy39_pp73-win_amd64.whl", hash = "sha256:f39c1244ba0cf185b2722aeec116b8a98a2ee5728ce687cec0bda60ee0360dfc"},
{file = "av-11.0.0.tar.gz", hash = "sha256:48223f000a252070f8e700ff634bb7fb3aa1b7bc7e450373029fbdd6f369ac31"},
{file = "av-12.0.0-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:b9d0890553951f76c479a9f2bb952aebae902b1c7d52feea614d37e1cd728a44"},
{file = "av-12.0.0-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:5d7f229a253c2e3fea9682c09c5ae179bd6d5d2da38d89eb7f29ef7bed10cb2f"},
{file = "av-12.0.0-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:61b3555d143aacf02e0446f6030319403538eba4dc713c18dfa653a2a23e7f9c"},
{file = "av-12.0.0-cp310-cp310-manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:607e13b2c2b26159a37525d7b6f647a32ce78711fccff23d146d3e255ffa115f"},
{file = "av-12.0.0-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:39f0b4cfb89f4f06b339c766f92648e798a96747d4163f2fa78660d1ab1f1b5e"},
{file = "av-12.0.0-cp310-cp310-win_amd64.whl", hash = "sha256:41dcb8c269fa58a56edf3a3c814c32a0c69586827f132b4e395a951b0ce14fad"},
{file = "av-12.0.0-cp311-cp311-macosx_10_9_x86_64.whl", hash = "sha256:4fa78fbe0e4469226512380180063116105048c66cb12e18ab4b518466c57e6c"},
{file = "av-12.0.0-cp311-cp311-macosx_11_0_arm64.whl", hash = "sha256:60a869be1d6af916e65ea461cb93922f5db0698655ed7a7eae7c3ecd4af4debb"},
{file = "av-12.0.0-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:df61811cc551c186f0a0e530d97b8b139453534d0f92c1790a923f666522ceda"},
{file = "av-12.0.0-cp311-cp311-manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:99cd2fc53091ebfb9a2fa9dd3580267f5bd1c040d0efd99fbc1a162576b271cb"},
{file = "av-12.0.0-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:7a6d4f1e261df48932128e6495772faa4cc23f5dd1512eec73daab82ad9f3240"},
{file = "av-12.0.0-cp311-cp311-win_amd64.whl", hash = "sha256:6aec88e41a498b1e01e2dce5371557e20f9a51aae0c16decc5924ec0be2e22b6"},
{file = "av-12.0.0-cp312-cp312-macosx_10_9_x86_64.whl", hash = "sha256:90eb8f2d548e96cbc6f78e89c911cdb15a3d80fd944f31111660ce45939cd037"},
{file = "av-12.0.0-cp312-cp312-macosx_11_0_arm64.whl", hash = "sha256:d7f3a02910e77d750dbd516256a16db15030e5371530ff5a5ae902dc03d9005d"},
{file = "av-12.0.0-cp312-cp312-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:e2477cc51526aa50575313d66e5e8ad7ab944588469be5e557b360ed572ae536"},
{file = "av-12.0.0-cp312-cp312-manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:a2f47149d3ca6deb79f3e515b8bef50e27ebdb160813e6d67dba77278d2a7883"},
{file = "av-12.0.0-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:3306e4a3ce8b5bfcc3075793d4ed3a2df69179d8fba22cb944a6164dc235dfb6"},
{file = "av-12.0.0-cp312-cp312-win_amd64.whl", hash = "sha256:dc1b742e7f6df1b499fb960bd6697d1dd8e7ada7484a041a8c20e70a87225f53"},
{file = "av-12.0.0-cp38-cp38-macosx_10_9_x86_64.whl", hash = "sha256:0183be6889e835e1b074b4037bfce4fd44671c606cf1c4ab92ea2f271b544aec"},
{file = "av-12.0.0-cp38-cp38-macosx_11_0_arm64.whl", hash = "sha256:57337f20b208292ec8d3b11e4d289d8688a43d728174850a81b865d3253fff2c"},
{file = "av-12.0.0-cp38-cp38-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:0ec915e8f6521545a38566eefc281042ee504ea3cee0618d8558e4920588b3b2"},
{file = "av-12.0.0-cp38-cp38-manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:33ad5c0a23c45b72bd6bd47f3b2c1adcd2935ee3d0b6178ed66bba62b964ff31"},
{file = "av-12.0.0-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:bfc3a652b12c93120514d56cf025da47442c5ba51530cdf7ba3660257dbb0de1"},
{file = "av-12.0.0-cp38-cp38-win_amd64.whl", hash = "sha256:037f793dd1ef4a1f57f090191a7f803ad10ec82da0d04ea26bbe0b8a145fe927"},
{file = "av-12.0.0-cp39-cp39-macosx_10_9_x86_64.whl", hash = "sha256:fc532376aa264722fae55063abd1871d17a563dc895978e142c8ecfcdeb3a2e8"},
{file = "av-12.0.0-cp39-cp39-macosx_11_0_arm64.whl", hash = "sha256:abf0c4bc40a0af8a30f4cd96f3be6f19fbce0f21222d7fcec148e085127153f7"},
{file = "av-12.0.0-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:81cedd1c072fbebf606724c406b1a1b00adc711f1dfd2bc04c633ce39d8439d8"},
{file = "av-12.0.0-cp39-cp39-manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:02d60f48be9f15dcda37d50f3ce8d7249d9a455643d4322dd3449986bacfc628"},
{file = "av-12.0.0-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:5d2619e4c26d661eecfc404f7d739d8b35f0dcef353fabe61512e030254b7031"},
{file = "av-12.0.0-cp39-cp39-win_amd64.whl", hash = "sha256:1892cc91c888d101777d5432d54e0554c11d1c3a2c65d02a2cae0a2256a8fbb9"},
{file = "av-12.0.0-pp310-pypy310_pp73-macosx_10_9_x86_64.whl", hash = "sha256:4819e3ef6c3a44ef6f75907229133a1ee7f688245b2cf49b6b8e969a81ca72c9"},
{file = "av-12.0.0-pp310-pypy310_pp73-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:bb16bb314cf1503b0250fc46b2c455ee196584231101be0123f4f78638227b62"},
{file = "av-12.0.0-pp310-pypy310_pp73-manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:f3e6a62bda9a1e144feeb59bbee046d7a2d98399634a30f57e4990197313c158"},
{file = "av-12.0.0-pp310-pypy310_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:e08175ffbafa3a70c7b2f81083e160e34122a208cdf70f150b8f5d02c2de6965"},
{file = "av-12.0.0-pp310-pypy310_pp73-win_amd64.whl", hash = "sha256:e1d255be317b7c1ebdc4dae98935b9f3869161112dc829c625e54f90d8bdd7ab"},
{file = "av-12.0.0-pp38-pypy38_pp73-macosx_10_9_x86_64.whl", hash = "sha256:17964b36e08435910aabd5b3f7dca12f99536902529767d276026bc08f94ced7"},
{file = "av-12.0.0-pp38-pypy38_pp73-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:d2d5f78de29edee06ddcdd4c2b759914575492d6a0cd4de2ce31ee63a4953eff"},
{file = "av-12.0.0-pp38-pypy38_pp73-manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:309b32bc97158d0f0c19e273b8e17a855a86806b7194aebc23bd497326cff11f"},
{file = "av-12.0.0-pp38-pypy38_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:c409c71bd9c7c2f8d018c822f36b1447cfa96eca158381a96f3319bb0ff6e79e"},
{file = "av-12.0.0-pp39-pypy39_pp73-macosx_10_9_x86_64.whl", hash = "sha256:08fc5eaef60a257d622998626e233bf3ff90d2f817f6695d6a27e0ffcfe9dcff"},
{file = "av-12.0.0-pp39-pypy39_pp73-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:746ab0eff8a7a21a6c6d16e6b6e61709527eba2ad1a524d92a01bb60d02a3df7"},
{file = "av-12.0.0-pp39-pypy39_pp73-manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:013b3ac3de3aa1c137af0cedafd364fd1c7524ab3e1cd53e04564fd1632ac04d"},
{file = "av-12.0.0-pp39-pypy39_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:0fa55923527648f51ac005e44fe2797ebc67f53ad4850e0194d3753761ee33a2"},
{file = "av-12.0.0-pp39-pypy39_pp73-win_amd64.whl", hash = "sha256:35d514f4dee0cf67e9e6b2a65fb4a28f98da88e71e8c7f7960bd04625d9fe965"},
{file = "av-12.0.0.tar.gz", hash = "sha256:bcf21ebb722d4538b4099e5a78f730d78814dd70003511c185941dba5651b14d"},
]
[[package]]
@@ -614,6 +614,16 @@ files = [
[package.dependencies]
six = ">=1.4.0"
[[package]]
name = "egl-probe"
version = "1.0.2"
description = ""
optional = false
python-versions = "*"
files = [
{file = "egl_probe-1.0.2.tar.gz", hash = "sha256:29bdca7b08da1e060cfb42cd46af8300a7ac4f3b1b2eeb16e545ea16d9a5ac93"},
]
[[package]]
name = "einops"
version = "0.7.0"
@@ -705,13 +715,13 @@ typing = ["typing-extensions (>=4.8)"]
[[package]]
name = "fsspec"
version = "2024.2.0"
version = "2024.3.1"
description = "File-system specification"
optional = false
python-versions = ">=3.8"
files = [
{file = "fsspec-2024.2.0-py3-none-any.whl", hash = "sha256:817f969556fa5916bc682e02ca2045f96ff7f586d45110fcb76022063ad2c7d8"},
{file = "fsspec-2024.2.0.tar.gz", hash = "sha256:b6ad1a679f760dda52b1168c859d01b7b80648ea6f7f7c7f5a8a91dc3f3ecb84"},
{file = "fsspec-2024.3.1-py3-none-any.whl", hash = "sha256:918d18d41bf73f0e2b261824baeb1b124bcf771767e3a26425cd7dec3332f512"},
{file = "fsspec-2024.3.1.tar.gz", hash = "sha256:f39780e282d7d117ffb42bb96992f8a90795e4d0fb0f661a70ca39fe9c43ded9"},
]
[package.extras]
@@ -810,6 +820,72 @@ files = [
[package.extras]
preview = ["glfw-preview"]
[[package]]
name = "grpcio"
version = "1.62.1"
description = "HTTP/2-based RPC framework"
optional = false
python-versions = ">=3.7"
files = [
{file = "grpcio-1.62.1-cp310-cp310-linux_armv7l.whl", hash = "sha256:179bee6f5ed7b5f618844f760b6acf7e910988de77a4f75b95bbfaa8106f3c1e"},
{file = "grpcio-1.62.1-cp310-cp310-macosx_12_0_universal2.whl", hash = "sha256:48611e4fa010e823ba2de8fd3f77c1322dd60cb0d180dc6630a7e157b205f7ea"},
{file = "grpcio-1.62.1-cp310-cp310-manylinux_2_17_aarch64.whl", hash = "sha256:b2a0e71b0a2158aa4bce48be9f8f9eb45cbd17c78c7443616d00abbe2a509f6d"},
{file = "grpcio-1.62.1-cp310-cp310-manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:fbe80577c7880911d3ad65e5ecc997416c98f354efeba2f8d0f9112a67ed65a5"},
{file = "grpcio-1.62.1-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:58f6c693d446964e3292425e1d16e21a97a48ba9172f2d0df9d7b640acb99243"},
{file = "grpcio-1.62.1-cp310-cp310-musllinux_1_1_i686.whl", hash = "sha256:77c339403db5a20ef4fed02e4d1a9a3d9866bf9c0afc77a42234677313ea22f3"},
{file = "grpcio-1.62.1-cp310-cp310-musllinux_1_1_x86_64.whl", hash = "sha256:b5a4ea906db7dec694098435d84bf2854fe158eb3cd51e1107e571246d4d1d70"},
{file = "grpcio-1.62.1-cp310-cp310-win32.whl", hash = "sha256:4187201a53f8561c015bc745b81a1b2d278967b8de35f3399b84b0695e281d5f"},
{file = "grpcio-1.62.1-cp310-cp310-win_amd64.whl", hash = "sha256:844d1f3fb11bd1ed362d3fdc495d0770cfab75761836193af166fee113421d66"},
{file = "grpcio-1.62.1-cp311-cp311-linux_armv7l.whl", hash = "sha256:833379943d1728a005e44103f17ecd73d058d37d95783eb8f0b28ddc1f54d7b2"},
{file = "grpcio-1.62.1-cp311-cp311-macosx_10_10_universal2.whl", hash = "sha256:c7fcc6a32e7b7b58f5a7d27530669337a5d587d4066060bcb9dee7a8c833dfb7"},
{file = "grpcio-1.62.1-cp311-cp311-manylinux_2_17_aarch64.whl", hash = "sha256:fa7d28eb4d50b7cbe75bb8b45ed0da9a1dc5b219a0af59449676a29c2eed9698"},
{file = "grpcio-1.62.1-cp311-cp311-manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:48f7135c3de2f298b833be8b4ae20cafe37091634e91f61f5a7eb3d61ec6f660"},
{file = "grpcio-1.62.1-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:71f11fd63365ade276c9d4a7b7df5c136f9030e3457107e1791b3737a9b9ed6a"},
{file = "grpcio-1.62.1-cp311-cp311-musllinux_1_1_i686.whl", hash = "sha256:4b49fd8fe9f9ac23b78437da94c54aa7e9996fbb220bac024a67469ce5d0825f"},
{file = "grpcio-1.62.1-cp311-cp311-musllinux_1_1_x86_64.whl", hash = "sha256:482ae2ae78679ba9ed5752099b32e5fe580443b4f798e1b71df412abf43375db"},
{file = "grpcio-1.62.1-cp311-cp311-win32.whl", hash = "sha256:1faa02530b6c7426404372515fe5ddf66e199c2ee613f88f025c6f3bd816450c"},
{file = "grpcio-1.62.1-cp311-cp311-win_amd64.whl", hash = "sha256:5bd90b8c395f39bc82a5fb32a0173e220e3f401ff697840f4003e15b96d1befc"},
{file = "grpcio-1.62.1-cp312-cp312-linux_armv7l.whl", hash = "sha256:b134d5d71b4e0837fff574c00e49176051a1c532d26c052a1e43231f252d813b"},
{file = "grpcio-1.62.1-cp312-cp312-macosx_10_10_universal2.whl", hash = "sha256:d1f6c96573dc09d50dbcbd91dbf71d5cf97640c9427c32584010fbbd4c0e0037"},
{file = "grpcio-1.62.1-cp312-cp312-manylinux_2_17_aarch64.whl", hash = "sha256:359f821d4578f80f41909b9ee9b76fb249a21035a061a327f91c953493782c31"},
{file = "grpcio-1.62.1-cp312-cp312-manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:a485f0c2010c696be269184bdb5ae72781344cb4e60db976c59d84dd6354fac9"},
{file = "grpcio-1.62.1-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:b50b09b4dc01767163d67e1532f948264167cd27f49e9377e3556c3cba1268e1"},
{file = "grpcio-1.62.1-cp312-cp312-musllinux_1_1_i686.whl", hash = "sha256:3227c667dccbe38f2c4d943238b887bac588d97c104815aecc62d2fd976e014b"},
{file = "grpcio-1.62.1-cp312-cp312-musllinux_1_1_x86_64.whl", hash = "sha256:3952b581eb121324853ce2b191dae08badb75cd493cb4e0243368aa9e61cfd41"},
{file = "grpcio-1.62.1-cp312-cp312-win32.whl", hash = "sha256:83a17b303425104d6329c10eb34bba186ffa67161e63fa6cdae7776ff76df73f"},
{file = "grpcio-1.62.1-cp312-cp312-win_amd64.whl", hash = "sha256:6696ffe440333a19d8d128e88d440f91fb92c75a80ce4b44d55800e656a3ef1d"},
{file = "grpcio-1.62.1-cp37-cp37m-linux_armv7l.whl", hash = "sha256:e3393b0823f938253370ebef033c9fd23d27f3eae8eb9a8f6264900c7ea3fb5a"},
{file = "grpcio-1.62.1-cp37-cp37m-macosx_10_10_universal2.whl", hash = "sha256:83e7ccb85a74beaeae2634f10eb858a0ed1a63081172649ff4261f929bacfd22"},
{file = "grpcio-1.62.1-cp37-cp37m-manylinux_2_17_aarch64.whl", hash = "sha256:882020c87999d54667a284c7ddf065b359bd00251fcd70279ac486776dbf84ec"},
{file = "grpcio-1.62.1-cp37-cp37m-manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:a10383035e864f386fe096fed5c47d27a2bf7173c56a6e26cffaaa5a361addb1"},
{file = "grpcio-1.62.1-cp37-cp37m-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:960edebedc6b9ada1ef58e1c71156f28689978188cd8cff3b646b57288a927d9"},
{file = "grpcio-1.62.1-cp37-cp37m-musllinux_1_1_i686.whl", hash = "sha256:23e2e04b83f347d0aadde0c9b616f4726c3d76db04b438fd3904b289a725267f"},
{file = "grpcio-1.62.1-cp37-cp37m-musllinux_1_1_x86_64.whl", hash = "sha256:978121758711916d34fe57c1f75b79cdfc73952f1481bb9583399331682d36f7"},
{file = "grpcio-1.62.1-cp37-cp37m-win_amd64.whl", hash = "sha256:9084086190cc6d628f282e5615f987288b95457292e969b9205e45b442276407"},
{file = "grpcio-1.62.1-cp38-cp38-linux_armv7l.whl", hash = "sha256:22bccdd7b23c420a27fd28540fb5dcbc97dc6be105f7698cb0e7d7a420d0e362"},
{file = "grpcio-1.62.1-cp38-cp38-macosx_10_10_universal2.whl", hash = "sha256:8999bf1b57172dbc7c3e4bb3c732658e918f5c333b2942243f10d0d653953ba9"},
{file = "grpcio-1.62.1-cp38-cp38-manylinux_2_17_aarch64.whl", hash = "sha256:d9e52558b8b8c2f4ac05ac86344a7417ccdd2b460a59616de49eb6933b07a0bd"},
{file = "grpcio-1.62.1-cp38-cp38-manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:1714e7bc935780bc3de1b3fcbc7674209adf5208ff825799d579ffd6cd0bd505"},
{file = "grpcio-1.62.1-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:c8842ccbd8c0e253c1f189088228f9b433f7a93b7196b9e5b6f87dba393f5d5d"},
{file = "grpcio-1.62.1-cp38-cp38-musllinux_1_1_i686.whl", hash = "sha256:1f1e7b36bdff50103af95a80923bf1853f6823dd62f2d2a2524b66ed74103e49"},
{file = "grpcio-1.62.1-cp38-cp38-musllinux_1_1_x86_64.whl", hash = "sha256:bba97b8e8883a8038606480d6b6772289f4c907f6ba780fa1f7b7da7dfd76f06"},
{file = "grpcio-1.62.1-cp38-cp38-win32.whl", hash = "sha256:a7f615270fe534548112a74e790cd9d4f5509d744dd718cd442bf016626c22e4"},
{file = "grpcio-1.62.1-cp38-cp38-win_amd64.whl", hash = "sha256:e6c8c8693df718c5ecbc7babb12c69a4e3677fd11de8886f05ab22d4e6b1c43b"},
{file = "grpcio-1.62.1-cp39-cp39-linux_armv7l.whl", hash = "sha256:73db2dc1b201d20ab7083e7041946910bb991e7e9761a0394bbc3c2632326483"},
{file = "grpcio-1.62.1-cp39-cp39-macosx_10_10_universal2.whl", hash = "sha256:407b26b7f7bbd4f4751dbc9767a1f0716f9fe72d3d7e96bb3ccfc4aace07c8de"},
{file = "grpcio-1.62.1-cp39-cp39-manylinux_2_17_aarch64.whl", hash = "sha256:f8de7c8cef9261a2d0a62edf2ccea3d741a523c6b8a6477a340a1f2e417658de"},
{file = "grpcio-1.62.1-cp39-cp39-manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:9bd5c8a1af40ec305d001c60236308a67e25419003e9bb3ebfab5695a8d0b369"},
{file = "grpcio-1.62.1-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:be0477cb31da67846a33b1a75c611f88bfbcd427fe17701b6317aefceee1b96f"},
{file = "grpcio-1.62.1-cp39-cp39-musllinux_1_1_i686.whl", hash = "sha256:60dcd824df166ba266ee0cfaf35a31406cd16ef602b49f5d4dfb21f014b0dedd"},
{file = "grpcio-1.62.1-cp39-cp39-musllinux_1_1_x86_64.whl", hash = "sha256:973c49086cabab773525f6077f95e5a993bfc03ba8fc32e32f2c279497780585"},
{file = "grpcio-1.62.1-cp39-cp39-win32.whl", hash = "sha256:12859468e8918d3bd243d213cd6fd6ab07208195dc140763c00dfe901ce1e1b4"},
{file = "grpcio-1.62.1-cp39-cp39-win_amd64.whl", hash = "sha256:b7209117bbeebdfa5d898205cc55153a51285757902dd73c47de498ad4d11332"},
{file = "grpcio-1.62.1.tar.gz", hash = "sha256:6c455e008fa86d9e9a9d85bb76da4277c0d7d9668a3bfa70dbe86e9f3c759947"},
]
[package.extras]
protobuf = ["grpcio-tools (>=1.62.1)"]
[[package]]
name = "gym"
version = "0.26.2"
@@ -1012,13 +1088,13 @@ setuptools = "*"
[[package]]
name = "importlib-metadata"
version = "7.0.2"
version = "7.1.0"
description = "Read metadata from Python packages"
optional = false
python-versions = ">=3.8"
files = [
{file = "importlib_metadata-7.0.2-py3-none-any.whl", hash = "sha256:f4bc4c0c070c490abf4ce96d715f68e95923320370efb66143df00199bb6c100"},
{file = "importlib_metadata-7.0.2.tar.gz", hash = "sha256:198f568f3230878cb1b44fbd7975f87906c22336dba2e4a7f05278c281fbd792"},
{file = "importlib_metadata-7.1.0-py3-none-any.whl", hash = "sha256:30962b96c0c223483ed6cc7280e7f0199feb01a0e40cfae4d4450fc6fab1f570"},
{file = "importlib_metadata-7.1.0.tar.gz", hash = "sha256:b78938b926ee8d5f020fc4772d487045805a55ddbad2ecf21c6d60938dc7fcd2"},
]
[package.dependencies]
@@ -1027,17 +1103,17 @@ zipp = ">=0.5"
[package.extras]
docs = ["furo", "jaraco.packaging (>=9.3)", "jaraco.tidelift (>=1.4)", "rst.linker (>=1.9)", "sphinx (>=3.5)", "sphinx-lint"]
perf = ["ipython"]
testing = ["flufl.flake8", "importlib-resources (>=1.3)", "packaging", "pyfakefs", "pytest (>=6)", "pytest-checkdocs (>=2.4)", "pytest-cov", "pytest-enabler (>=2.2)", "pytest-mypy", "pytest-perf (>=0.9.2)", "pytest-ruff (>=0.2.1)"]
testing = ["flufl.flake8", "importlib-resources (>=1.3)", "jaraco.test (>=5.4)", "packaging", "pyfakefs", "pytest (>=6)", "pytest-checkdocs (>=2.4)", "pytest-cov", "pytest-enabler (>=2.2)", "pytest-mypy", "pytest-perf (>=0.9.2)", "pytest-ruff (>=0.2.1)"]
[[package]]
name = "importlib-resources"
version = "6.3.0"
version = "6.3.2"
description = "Read resources from Python packages"
optional = false
python-versions = ">=3.8"
files = [
{file = "importlib_resources-6.3.0-py3-none-any.whl", hash = "sha256:783407aa1cd05550e3aa123e8f7cfaebee35ffa9cb0242919e2d1e4172222705"},
{file = "importlib_resources-6.3.0.tar.gz", hash = "sha256:166072a97e86917a9025876f34286f549b9caf1d10b35a1b372bffa1600c6569"},
{file = "importlib_resources-6.3.2-py3-none-any.whl", hash = "sha256:f41f4098b16cd140a97d256137cfd943d958219007990b2afb00439fc623f580"},
{file = "importlib_resources-6.3.2.tar.gz", hash = "sha256:963eb79649252b0160c1afcfe5a1d3fe3ad66edd0a8b114beacffb70c0674223"},
]
[package.extras]
@@ -1254,6 +1330,21 @@ html5 = ["html5lib"]
htmlsoup = ["BeautifulSoup4"]
source = ["Cython (>=3.0.7)"]
[[package]]
name = "markdown"
version = "3.6"
description = "Python implementation of John Gruber's Markdown."
optional = false
python-versions = ">=3.8"
files = [
{file = "Markdown-3.6-py3-none-any.whl", hash = "sha256:48f276f4d8cfb8ce6527c8f79e2ee29708508bf4d40aa410fbc3b4ee832c850f"},
{file = "Markdown-3.6.tar.gz", hash = "sha256:ed4f41f6daecbeeb96e576ce414c41d2d876daa9a16cb35fa8ed8c2ddfad0224"},
]
[package.extras]
docs = ["mdx-gh-links (>=0.2)", "mkdocs (>=1.5)", "mkdocs-gen-files", "mkdocs-literate-nav", "mkdocs-nature (>=0.6)", "mkdocs-section-index", "mkdocstrings[python]"]
testing = ["coverage", "pyyaml"]
[[package]]
name = "markupsafe"
version = "2.1.5"
@@ -1459,32 +1550,32 @@ setuptools = "*"
[[package]]
name = "numba"
version = "0.59.0"
version = "0.59.1"
description = "compiling Python code using LLVM"
optional = false
python-versions = ">=3.9"
files = [
{file = "numba-0.59.0-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:8d061d800473fb8fef76a455221f4ad649a53f5e0f96e3f6c8b8553ee6fa98fa"},
{file = "numba-0.59.0-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:c086a434e7d3891ce5dfd3d1e7ee8102ac1e733962098578b507864120559ceb"},
{file = "numba-0.59.0-cp310-cp310-manylinux2014_aarch64.manylinux_2_17_aarch64.whl", hash = "sha256:9e20736bf62e61f8353fb71b0d3a1efba636c7a303d511600fc57648b55823ed"},
{file = "numba-0.59.0-cp310-cp310-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:e86e6786aec31d2002122199486e10bbc0dc40f78d76364cded375912b13614c"},
{file = "numba-0.59.0-cp310-cp310-win_amd64.whl", hash = "sha256:0307ee91b24500bb7e64d8a109848baf3a3905df48ce142b8ac60aaa406a0400"},
{file = "numba-0.59.0-cp311-cp311-macosx_10_9_x86_64.whl", hash = "sha256:d540f69a8245fb714419c2209e9af6104e568eb97623adc8943642e61f5d6d8e"},
{file = "numba-0.59.0-cp311-cp311-macosx_11_0_arm64.whl", hash = "sha256:1192d6b2906bf3ff72b1d97458724d98860ab86a91abdd4cfd9328432b661e31"},
{file = "numba-0.59.0-cp311-cp311-manylinux2014_aarch64.manylinux_2_17_aarch64.whl", hash = "sha256:90efb436d3413809fcd15298c6d395cb7d98184350472588356ccf19db9e37c8"},
{file = "numba-0.59.0-cp311-cp311-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:cd3dac45e25d927dcb65d44fb3a973994f5add2b15add13337844afe669dd1ba"},
{file = "numba-0.59.0-cp311-cp311-win_amd64.whl", hash = "sha256:753dc601a159861808cc3207bad5c17724d3b69552fd22768fddbf302a817a4c"},
{file = "numba-0.59.0-cp312-cp312-macosx_10_9_x86_64.whl", hash = "sha256:ce62bc0e6dd5264e7ff7f34f41786889fa81a6b860662f824aa7532537a7bee0"},
{file = "numba-0.59.0-cp312-cp312-macosx_11_0_arm64.whl", hash = "sha256:8cbef55b73741b5eea2dbaf1b0590b14977ca95a13a07d200b794f8f6833a01c"},
{file = "numba-0.59.0-cp312-cp312-manylinux2014_aarch64.manylinux_2_17_aarch64.whl", hash = "sha256:70d26ba589f764be45ea8c272caa467dbe882b9676f6749fe6f42678091f5f21"},
{file = "numba-0.59.0-cp312-cp312-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:e125f7d69968118c28ec0eed9fbedd75440e64214b8d2eac033c22c04db48492"},
{file = "numba-0.59.0-cp312-cp312-win_amd64.whl", hash = "sha256:4981659220b61a03c1e557654027d271f56f3087448967a55c79a0e5f926de62"},
{file = "numba-0.59.0-cp39-cp39-macosx_10_9_x86_64.whl", hash = "sha256:fe4d7562d1eed754a7511ed7ba962067f198f86909741c5c6e18c4f1819b1f47"},
{file = "numba-0.59.0-cp39-cp39-macosx_11_0_arm64.whl", hash = "sha256:6feb1504bb432280f900deaf4b1dadcee68812209500ed3f81c375cbceab24dc"},
{file = "numba-0.59.0-cp39-cp39-manylinux2014_aarch64.manylinux_2_17_aarch64.whl", hash = "sha256:944faad25ee23ea9dda582bfb0189fb9f4fc232359a80ab2a028b94c14ce2b1d"},
{file = "numba-0.59.0-cp39-cp39-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:5516a469514bfae52a9d7989db4940653a5cbfac106f44cb9c50133b7ad6224b"},
{file = "numba-0.59.0-cp39-cp39-win_amd64.whl", hash = "sha256:32bd0a41525ec0b1b853da244808f4e5333867df3c43c30c33f89cf20b9c2b63"},
{file = "numba-0.59.0.tar.gz", hash = "sha256:12b9b064a3e4ad00e2371fc5212ef0396c80f41caec9b5ec391c8b04b6eaf2a8"},
{file = "numba-0.59.1-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:97385a7f12212c4f4bc28f648720a92514bee79d7063e40ef66c2d30600fd18e"},
{file = "numba-0.59.1-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:0b77aecf52040de2a1eb1d7e314497b9e56fba17466c80b457b971a25bb1576d"},
{file = "numba-0.59.1-cp310-cp310-manylinux2014_aarch64.manylinux_2_17_aarch64.whl", hash = "sha256:3476a4f641bfd58f35ead42f4dcaf5f132569c4647c6f1360ccf18ee4cda3990"},
{file = "numba-0.59.1-cp310-cp310-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:525ef3f820931bdae95ee5379c670d5c97289c6520726bc6937a4a7d4230ba24"},
{file = "numba-0.59.1-cp310-cp310-win_amd64.whl", hash = "sha256:990e395e44d192a12105eca3083b61307db7da10e093972ca285c85bef0963d6"},
{file = "numba-0.59.1-cp311-cp311-macosx_10_9_x86_64.whl", hash = "sha256:43727e7ad20b3ec23ee4fc642f5b61845c71f75dd2825b3c234390c6d8d64051"},
{file = "numba-0.59.1-cp311-cp311-macosx_11_0_arm64.whl", hash = "sha256:411df625372c77959570050e861981e9d196cc1da9aa62c3d6a836b5cc338966"},
{file = "numba-0.59.1-cp311-cp311-manylinux2014_aarch64.manylinux_2_17_aarch64.whl", hash = "sha256:2801003caa263d1e8497fb84829a7ecfb61738a95f62bc05693fcf1733e978e4"},
{file = "numba-0.59.1-cp311-cp311-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:dd2842fac03be4e5324ebbbd4d2d0c8c0fc6e0df75c09477dd45b288a0777389"},
{file = "numba-0.59.1-cp311-cp311-win_amd64.whl", hash = "sha256:0594b3dfb369fada1f8bb2e3045cd6c61a564c62e50cf1f86b4666bc721b3450"},
{file = "numba-0.59.1-cp312-cp312-macosx_10_9_x86_64.whl", hash = "sha256:1cce206a3b92836cdf26ef39d3a3242fec25e07f020cc4feec4c4a865e340569"},
{file = "numba-0.59.1-cp312-cp312-macosx_11_0_arm64.whl", hash = "sha256:8c8b4477763cb1fbd86a3be7050500229417bf60867c93e131fd2626edb02238"},
{file = "numba-0.59.1-cp312-cp312-manylinux2014_aarch64.manylinux_2_17_aarch64.whl", hash = "sha256:7d80bce4ef7e65bf895c29e3889ca75a29ee01da80266a01d34815918e365835"},
{file = "numba-0.59.1-cp312-cp312-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:f7ad1d217773e89a9845886401eaaab0a156a90aa2f179fdc125261fd1105096"},
{file = "numba-0.59.1-cp312-cp312-win_amd64.whl", hash = "sha256:5bf68f4d69dd3a9f26a9b23548fa23e3bcb9042e2935257b471d2a8d3c424b7f"},
{file = "numba-0.59.1-cp39-cp39-macosx_10_9_x86_64.whl", hash = "sha256:4e0318ae729de6e5dbe64c75ead1a95eb01fabfe0e2ebed81ebf0344d32db0ae"},
{file = "numba-0.59.1-cp39-cp39-macosx_11_0_arm64.whl", hash = "sha256:0f68589740a8c38bb7dc1b938b55d1145244c8353078eea23895d4f82c8b9ec1"},
{file = "numba-0.59.1-cp39-cp39-manylinux2014_aarch64.manylinux_2_17_aarch64.whl", hash = "sha256:649913a3758891c77c32e2d2a3bcbedf4a69f5fea276d11f9119677c45a422e8"},
{file = "numba-0.59.1-cp39-cp39-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:9712808e4545270291d76b9a264839ac878c5eb7d8b6e02c970dc0ac29bc8187"},
{file = "numba-0.59.1-cp39-cp39-win_amd64.whl", hash = "sha256:8d51ccd7008a83105ad6a0082b6a2b70f1142dc7cfd76deb8c5a862367eb8c86"},
{file = "numba-0.59.1.tar.gz", hash = "sha256:76f69132b96028d2774ed20415e8c528a34e3299a40581bae178f0994a2f370b"},
]
[package.dependencies]
@@ -2310,6 +2401,30 @@ urllib3 = ">=1.21.1,<3"
socks = ["PySocks (>=1.5.6,!=1.5.7)"]
use-chardet-on-py3 = ["chardet (>=3.0.2,<6)"]
[[package]]
name = "robomimic"
version = "0.2.0"
description = "robomimic: A Modular Framework for Robot Learning from Demonstration"
optional = false
python-versions = ">=3"
files = [
{file = "robomimic-0.2.0.tar.gz", hash = "sha256:ee3bb5cf9c3e1feead6b57b43c5db738fd0a8e0c015fdf6419808af8fffdc463"},
]
[package.dependencies]
egl_probe = ">=1.0.1"
h5py = "*"
imageio = "*"
imageio-ffmpeg = "*"
numpy = ">=1.13.3"
psutil = "*"
tensorboard = "*"
tensorboardX = "*"
termcolor = "*"
torch = "*"
torchvision = "*"
tqdm = "*"
[[package]]
name = "safetensors"
version = "0.4.2"
@@ -2534,13 +2649,13 @@ test = ["asv", "gmpy2", "hypothesis", "mpmath", "pooch", "pytest", "pytest-cov",
[[package]]
name = "sentry-sdk"
version = "1.42.0"
version = "1.43.0"
description = "Python client for Sentry (https://sentry.io)"
optional = false
python-versions = "*"
files = [
{file = "sentry-sdk-1.42.0.tar.gz", hash = "sha256:4a8364b8f7edbf47f95f7163e48334c96100d9c098f0ae6606e2e18183c223e6"},
{file = "sentry_sdk-1.42.0-py2.py3-none-any.whl", hash = "sha256:a654ee7e497a3f5f6368b36d4f04baeab1fe92b3105f7f6965d6ef0de35a9ba4"},
{file = "sentry-sdk-1.43.0.tar.gz", hash = "sha256:41df73af89d22921d8733714fb0fc5586c3461907e06688e6537d01a27e0e0f6"},
{file = "sentry_sdk-1.43.0-py2.py3-none-any.whl", hash = "sha256:8d768724839ca18d7b4c7463ef7528c40b7aa2bfbf7fe554d5f9a7c044acfd36"},
]
[package.dependencies]
@@ -2554,6 +2669,7 @@ asyncpg = ["asyncpg (>=0.23)"]
beam = ["apache-beam (>=2.12)"]
bottle = ["bottle (>=0.12.13)"]
celery = ["celery (>=3)"]
celery-redbeat = ["celery-redbeat (>=2)"]
chalice = ["chalice (>=1.16.0)"]
clickhouse-driver = ["clickhouse-driver (>=0.2.0)"]
django = ["django (>=1.8)"]
@@ -2798,9 +2914,58 @@ files = [
[package.dependencies]
mpmath = ">=0.19"
[[package]]
name = "tensorboard"
version = "2.16.2"
description = "TensorBoard lets you watch Tensors Flow"
optional = false
python-versions = ">=3.9"
files = [
{file = "tensorboard-2.16.2-py3-none-any.whl", hash = "sha256:9f2b4e7dad86667615c0e5cd072f1ea8403fc032a299f0072d6f74855775cc45"},
]
[package.dependencies]
absl-py = ">=0.4"
grpcio = ">=1.48.2"
markdown = ">=2.6.8"
numpy = ">=1.12.0"
protobuf = ">=3.19.6,<4.24.0 || >4.24.0"
setuptools = ">=41.0.0"
six = ">1.9"
tensorboard-data-server = ">=0.7.0,<0.8.0"
werkzeug = ">=1.0.1"
[[package]]
name = "tensorboard-data-server"
version = "0.7.2"
description = "Fast data loading for TensorBoard"
optional = false
python-versions = ">=3.7"
files = [
{file = "tensorboard_data_server-0.7.2-py3-none-any.whl", hash = "sha256:7e0610d205889588983836ec05dc098e80f97b7e7bbff7e994ebb78f578d0ddb"},
{file = "tensorboard_data_server-0.7.2-py3-none-macosx_10_9_x86_64.whl", hash = "sha256:9fe5d24221b29625dbc7328b0436ca7fc1c23de4acf4d272f1180856e32f9f60"},
{file = "tensorboard_data_server-0.7.2-py3-none-manylinux_2_31_x86_64.whl", hash = "sha256:ef687163c24185ae9754ed5650eb5bc4d84ff257aabdc33f0cc6f74d8ba54530"},
]
[[package]]
name = "tensorboardx"
version = "2.6.2.2"
description = "TensorBoardX lets you watch Tensors Flow without Tensorflow"
optional = false
python-versions = "*"
files = [
{file = "tensorboardX-2.6.2.2-py2.py3-none-any.whl", hash = "sha256:160025acbf759ede23fd3526ae9d9bfbfd8b68eb16c38a010ebe326dc6395db8"},
{file = "tensorboardX-2.6.2.2.tar.gz", hash = "sha256:c6476d7cd0d529b0b72f4acadb1269f9ed8b22f441e87a84f2a3b940bb87b666"},
]
[package.dependencies]
numpy = "*"
packaging = "*"
protobuf = ">=3.20"
[[package]]
name = "tensordict"
version = "0.4.0+6a56ecd"
version = "0.4.0+b4c91e8"
description = ""
optional = false
python-versions = "*"
@@ -2821,7 +2986,7 @@ tests = ["pytest", "pytest-benchmark", "pytest-instafail", "pytest-rerunfailures
type = "git"
url = "https://github.com/pytorch/tensordict"
reference = "HEAD"
resolved_reference = "6a56ecd728757feee387f946b7da66dd452b739b"
resolved_reference = "b4c91e8828c538ca0a50d8383fd99311a9afb078"
[[package]]
name = "termcolor"
@@ -3084,6 +3249,23 @@ perf = ["orjson"]
reports = ["pydantic (>=2.0.0)"]
sweeps = ["sweeps (>=0.2.0)"]
[[package]]
name = "werkzeug"
version = "3.0.1"
description = "The comprehensive WSGI web application library."
optional = false
python-versions = ">=3.8"
files = [
{file = "werkzeug-3.0.1-py3-none-any.whl", hash = "sha256:90a285dc0e42ad56b34e696398b8122ee4c681833fb35b8334a095d82c56da10"},
{file = "werkzeug-3.0.1.tar.gz", hash = "sha256:507e811ecea72b18a404947aded4b3390e1db8f826b494d76550ef45bb3b1dcc"},
]
[package.dependencies]
MarkupSafe = ">=2.1.1"
[package.extras]
watchdog = ["watchdog (>=2.3)"]
[[package]]
name = "zarr"
version = "2.17.1"
@@ -3107,13 +3289,13 @@ jupyter = ["ipytree (>=0.2.2)", "ipywidgets (>=8.0.0)", "notebook"]
[[package]]
name = "zipp"
version = "3.18.0"
version = "3.18.1"
description = "Backport of pathlib-compatible object wrapper for zip files"
optional = false
python-versions = ">=3.8"
files = [
{file = "zipp-3.18.0-py3-none-any.whl", hash = "sha256:c1bb803ed69d2cce2373152797064f7e79bc43f0a3748eb494096a867e0ebf79"},
{file = "zipp-3.18.0.tar.gz", hash = "sha256:df8d042b02765029a09b157efd8e820451045890acc30f8e37dd2f94a060221f"},
{file = "zipp-3.18.1-py3-none-any.whl", hash = "sha256:206f5a15f2af3dbaee80769fb7dc6f249695e940acca08dfb2a4769fe61e538b"},
{file = "zipp-3.18.1.tar.gz", hash = "sha256:2884ed22e7d8961de1c9a05142eb69a247f120291bc0206a00a7642f09b5b715"},
]
[package.extras]
@@ -3123,4 +3305,4 @@ testing = ["big-O", "jaraco.functools", "jaraco.itertools", "more-itertools", "p
[metadata]
lock-version = "2.0"
python-versions = "^3.10"
content-hash = "4aa6a1e3f29560dd4a1c24d493ee1154089da4aa8d2190ad1f786c125ab2b735"
content-hash = "cbd9aedcb3a24417b85124fb94db706dd6ca0a90dfb610b0aebdcd3aa2a0333c"

1
.github/poetry/cpu/pyproject.toml vendored
View File

@@ -51,6 +51,7 @@ torchvision = {version = "^0.17.1", source = "torch-cpu"}
h5py = "^3.10.0"
dm = "^1.3"
dm-control = "^1.0.16"
robomimic = "0.2.0"
huggingface-hub = "^0.21.4"

204
LICENSE
View File

@@ -276,3 +276,207 @@ AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.
## Some of lerobot's code is derived from DETR, which is subject to the following copyright notice:
Apache License
Version 2.0, January 2004
http://www.apache.org/licenses/
TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION
1. Definitions.
"License" shall mean the terms and conditions for use, reproduction,
and distribution as defined by Sections 1 through 9 of this document.
"Licensor" shall mean the copyright owner or entity authorized by
the copyright owner that is granting the License.
"Legal Entity" shall mean the union of the acting entity and all
other entities that control, are controlled by, or are under common
control with that entity. For the purposes of this definition,
"control" means (i) the power, direct or indirect, to cause the
direction or management of such entity, whether by contract or
otherwise, or (ii) ownership of fifty percent (50%) or more of the
outstanding shares, or (iii) beneficial ownership of such entity.
"You" (or "Your") shall mean an individual or Legal Entity
exercising permissions granted by this License.
"Source" form shall mean the preferred form for making modifications,
including but not limited to software source code, documentation
source, and configuration files.
"Object" form shall mean any form resulting from mechanical
transformation or translation of a Source form, including but
not limited to compiled object code, generated documentation,
and conversions to other media types.
"Work" shall mean the work of authorship, whether in Source or
Object form, made available under the License, as indicated by a
copyright notice that is included in or attached to the work
(an example is provided in the Appendix below).
"Derivative Works" shall mean any work, whether in Source or Object
form, that is based on (or derived from) the Work and for which the
editorial revisions, annotations, elaborations, or other modifications
represent, as a whole, an original work of authorship. For the purposes
of this License, Derivative Works shall not include works that remain
separable from, or merely link (or bind by name) to the interfaces of,
the Work and Derivative Works thereof.
"Contribution" shall mean any work of authorship, including
the original version of the Work and any modifications or additions
to that Work or Derivative Works thereof, that is intentionally
submitted to Licensor for inclusion in the Work by the copyright owner
or by an individual or Legal Entity authorized to submit on behalf of
the copyright owner. For the purposes of this definition, "submitted"
means any form of electronic, verbal, or written communication sent
to the Licensor or its representatives, including but not limited to
communication on electronic mailing lists, source code control systems,
and issue tracking systems that are managed by, or on behalf of, the
Licensor for the purpose of discussing and improving the Work, but
excluding communication that is conspicuously marked or otherwise
designated in writing by the copyright owner as "Not a Contribution."
"Contributor" shall mean Licensor and any individual or Legal Entity
on behalf of whom a Contribution has been received by Licensor and
subsequently incorporated within the Work.
2. Grant of Copyright License. Subject to the terms and conditions of
this License, each Contributor hereby grants to You a perpetual,
worldwide, non-exclusive, no-charge, royalty-free, irrevocable
copyright license to reproduce, prepare Derivative Works of,
publicly display, publicly perform, sublicense, and distribute the
Work and such Derivative Works in Source or Object form.
3. Grant of Patent License. Subject to the terms and conditions of
this License, each Contributor hereby grants to You a perpetual,
worldwide, non-exclusive, no-charge, royalty-free, irrevocable
(except as stated in this section) patent license to make, have made,
use, offer to sell, sell, import, and otherwise transfer the Work,
where such license applies only to those patent claims licensable
by such Contributor that are necessarily infringed by their
Contribution(s) alone or by combination of their Contribution(s)
with the Work to which such Contribution(s) was submitted. If You
institute patent litigation against any entity (including a
cross-claim or counterclaim in a lawsuit) alleging that the Work
or a Contribution incorporated within the Work constitutes direct
or contributory patent infringement, then any patent licenses
granted to You under this License for that Work shall terminate
as of the date such litigation is filed.
4. Redistribution. You may reproduce and distribute copies of the
Work or Derivative Works thereof in any medium, with or without
modifications, and in Source or Object form, provided that You
meet the following conditions:
(a) You must give any other recipients of the Work or
Derivative Works a copy of this License; and
(b) You must cause any modified files to carry prominent notices
stating that You changed the files; and
(c) You must retain, in the Source form of any Derivative Works
that You distribute, all copyright, patent, trademark, and
attribution notices from the Source form of the Work,
excluding those notices that do not pertain to any part of
the Derivative Works; and
(d) If the Work includes a "NOTICE" text file as part of its
distribution, then any Derivative Works that You distribute must
include a readable copy of the attribution notices contained
within such NOTICE file, excluding those notices that do not
pertain to any part of the Derivative Works, in at least one
of the following places: within a NOTICE text file distributed
as part of the Derivative Works; within the Source form or
documentation, if provided along with the Derivative Works; or,
within a display generated by the Derivative Works, if and
wherever such third-party notices normally appear. The contents
of the NOTICE file are for informational purposes only and
do not modify the License. You may add Your own attribution
notices within Derivative Works that You distribute, alongside
or as an addendum to the NOTICE text from the Work, provided
that such additional attribution notices cannot be construed
as modifying the License.
You may add Your own copyright statement to Your modifications and
may provide additional or different license terms and conditions
for use, reproduction, or distribution of Your modifications, or
for any such Derivative Works as a whole, provided Your use,
reproduction, and distribution of the Work otherwise complies with
the conditions stated in this License.
5. Submission of Contributions. Unless You explicitly state otherwise,
any Contribution intentionally submitted for inclusion in the Work
by You to the Licensor shall be under the terms and conditions of
this License, without any additional terms or conditions.
Notwithstanding the above, nothing herein shall supersede or modify
the terms of any separate license agreement you may have executed
with Licensor regarding such Contributions.
6. Trademarks. This License does not grant permission to use the trade
names, trademarks, service marks, or product names of the Licensor,
except as required for reasonable and customary use in describing the
origin of the Work and reproducing the content of the NOTICE file.
7. Disclaimer of Warranty. Unless required by applicable law or
agreed to in writing, Licensor provides the Work (and each
Contributor provides its Contributions) on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or
implied, including, without limitation, any warranties or conditions
of TITLE, NON-INFRINGEMENT, MERCHANTABILITY, or FITNESS FOR A
PARTICULAR PURPOSE. You are solely responsible for determining the
appropriateness of using or redistributing the Work and assume any
risks associated with Your exercise of permissions under this License.
8. Limitation of Liability. In no event and under no legal theory,
whether in tort (including negligence), contract, or otherwise,
unless required by applicable law (such as deliberate and grossly
negligent acts) or agreed to in writing, shall any Contributor be
liable to You for damages, including any direct, indirect, special,
incidental, or consequential damages of any character arising as a
result of this License or out of the use or inability to use the
Work (including but not limited to damages for loss of goodwill,
work stoppage, computer failure or malfunction, or any and all
other commercial damages or losses), even if such Contributor
has been advised of the possibility of such damages.
9. Accepting Warranty or Additional Liability. While redistributing
the Work or Derivative Works thereof, You may choose to offer,
and charge a fee for, acceptance of support, warranty, indemnity,
or other liability obligations and/or rights consistent with this
License. However, in accepting such obligations, You may act only
on Your own behalf and on Your sole responsibility, not on behalf
of any other Contributor, and only if You agree to indemnify,
defend, and hold each Contributor harmless for any liability
incurred by, or claims asserted against, such Contributor by reason
of your accepting any such warranty or additional liability.
END OF TERMS AND CONDITIONS
APPENDIX: How to apply the Apache License to your work.
To apply the Apache License to your work, attach the following
boilerplate notice, with the fields enclosed by brackets "[]"
replaced with your own identifying information. (Don't include
the brackets!) The text should be enclosed in the appropriate
comment syntax for the file format. We also recommend that a
file or class name and description of purpose be included on the
same "printed page" as the copyright notice for easier
identification within third-party archives.
Copyright 2020 - present, Facebook, Inc
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License.

72
README.md
View File

@@ -1,4 +1,21 @@
# LeRobot
# Le Robot
#### State-of-the-art machine learning for real-world robotics
Le Robot aims to provide models, datasets, and tools for real-world robotics in PyTorch. The goal is to lower the barrier for entry to robotics so that everyone can contribute and benefit from sharing datasets and pretrained models.
Le Robot contains state-of-the-art approaches that have been shown to transfer to the real-world with a focus on imitation learning and reinforcement learning.
Le Robot already provides a set of pretrained models, datasets with human collected demonstrations, and simulated environments so that everyone can get started. In the coming weeks, the plan is to add more and more supports for real-world robotics on the most affordable and capable robots out there.
Le Robot is built upon [TorchRL](https://github.com/pytorch/rl) which provides abstractions and utilities for Reinforcement Learning.
## Acknowledgment
- Our ACT policy and ALOHA environment are adapted from [ALOHA](https://tonyzhaozh.github.io/aloha/)
- Our Diffusion policy and Pusht environment are adapted from [Diffusion Policy](https://diffusion-policy.cs.columbia.edu/)
- Our TDMPC policy and Simxarm environment are adapted from [FOWM](https://www.yunhaifeng.com/FOWM/)
## Installation
@@ -138,7 +155,7 @@ git lfs pull
When adding a new dataset, mock it with
```
python tests/scripts/mock_dataset.py --in-data-dir data/<dataset_id> --out-data-dir tests/data/<dataset_id>
python tests/scripts/mock_dataset.py --in-data-dir data/$DATASET --out-data-dir tests/data/$DATASET
```
Run tests
@@ -148,24 +165,61 @@ DATA_DIR="tests/data" pytest -sx tests
**Datasets**
To add a pytorch rl dataset to the hub, first login and use a token generated from [huggingface settings](https://huggingface.co/settings/tokens) with write access:
To add a dataset to the hub, first login and use a token generated from [huggingface settings](https://huggingface.co/settings/tokens) with write access:
```
huggingface-cli login --token $HUGGINGFACE_TOKEN --add-to-git-credential
huggingface-cli login --token ${HUGGINGFACE_TOKEN} --add-to-git-credential
```
Then you can upload it to the hub with:
```
HF_HUB_ENABLE_HF_TRANSFER=1 huggingface-cli upload --repo-type dataset $HF_USER/$DATASET data/$DATASET
HF_HUB_ENABLE_HF_TRANSFER=1 huggingface-cli upload $HF_USER/$DATASET data/$DATASET \
--repo-type dataset \
--revision v1.0
```
You will need to set the corresponding version as a default argument in your dataset class:
```python
version: str | None = "v1.0",
```
See: [`lerobot/common/datasets/pusht.py`](https://github.com/Cadene/lerobot/blob/main/lerobot/common/datasets/pusht.py)
For instance, for [cadene/pusht](https://huggingface.co/datasets/cadene/pusht), we used:
```
HF_USER=cadene
DATASET=pusht
```
If you want to improve an existing dataset, you can download it locally with:
```
mkdir -p data/$DATASET
HF_HUB_ENABLE_HF_TRANSFER=1 huggingface-cli download ${HF_USER}/$DATASET \
--repo-type dataset \
--local-dir data/$DATASET \
--local-dir-use-symlinks=False \
--revision v1.0
```
## Acknowledgment
- Our Diffusion policy and Pusht environment are adapted from [Diffusion Policy](https://diffusion-policy.cs.columbia.edu/)
- Our TDMPC policy and Simxarm environment are adapted from [FOWM](https://www.yunhaifeng.com/FOWM/)
- Our ACT policy and ALOHA environment are adapted from [ALOHA](https://tonyzhaozh.github.io/aloha/)
Iterate on your code and dataset with:
```
DATA_DIR=data python train.py
```
Upload a new version (v2.0 or v1.1 if the changes are respectively more or less significant):
```
HF_HUB_ENABLE_HF_TRANSFER=1 huggingface-cli upload $HF_USER/$DATASET data/$DATASET \
--repo-type dataset \
--revision v1.1 \
--delete "*"
```
Then you will need to set the corresponding version as a default argument in your dataset class:
```python
version: str | None = "v1.1",
```
See: [`lerobot/common/datasets/pusht.py`](https://github.com/Cadene/lerobot/blob/main/lerobot/common/datasets/pusht.py)
Finally, you might want to mock the dataset if you need to update the unit tests as well:
```
python tests/scripts/mock_dataset.py --in-data-dir data/$DATASET --out-data-dir tests/data/$DATASET
```

67
examples/pretrained.py Normal file
View File

@@ -0,0 +1,67 @@
import logging
from pathlib import Path
import torch
from huggingface_hub import snapshot_download
from omegaconf import OmegaConf
from tensordict.nn import TensorDictModule
from lerobot.common.datasets.factory import make_offline_buffer
from lerobot.common.envs.factory import make_env
from lerobot.common.logger import log_output_dir
from lerobot.common.policies.factory import make_policy
from lerobot.common.utils import get_safe_torch_device, init_logging, set_seed
from lerobot.scripts.eval import eval_policy
folder = Path(snapshot_download("lerobot/diffusion_policy_pusht_image", revision="v1.0"))
cfg = OmegaConf.load(folder / "config.yaml")
cfg.policy.pretrained_model_path = folder / "model.pt"
cfg.eval_episodes = 1
cfg.episode_length = 50
# cfg.device = "cpu"
out_dir = "tmp/"
if out_dir is None:
raise NotImplementedError()
init_logging()
# Check device is available
get_safe_torch_device(cfg.device, log=True)
torch.backends.cudnn.benchmark = True
torch.backends.cuda.matmul.allow_tf32 = True
set_seed(cfg.seed)
log_output_dir(out_dir)
logging.info("make_offline_buffer")
offline_buffer = make_offline_buffer(cfg)
logging.info("make_env")
env = make_env(cfg, transform=offline_buffer.transform)
if cfg.policy.pretrained_model_path:
policy = make_policy(cfg)
policy = TensorDictModule(
policy,
in_keys=["observation", "step_count"],
out_keys=["action"],
)
else:
# when policy is None, rollout a random policy
policy = None
metrics = eval_policy(
env,
policy=policy,
save_video=True,
video_dir=Path(out_dir) / "eval",
fps=cfg.env.fps,
max_steps=cfg.env.episode_length,
num_episodes=cfg.eval_episodes,
)
print(metrics)
logging.info("End of eval")

22
lerobot/common/datasets/abstract.py
View File

@@ -19,6 +19,7 @@ class AbstractExperienceReplay(TensorDictReplayBuffer):
def __init__(
self,
dataset_id: str,
version: str | None = None,
batch_size: int = None,
*,
shuffle: bool = True,
@@ -31,8 +32,15 @@ class AbstractExperienceReplay(TensorDictReplayBuffer):
transform: "torchrl.envs.Transform" = None,
):
self.dataset_id = dataset_id
self.version = version
self.shuffle = shuffle
self.root = root
if self.root is not None and self.version is not None:
logging.warning(
f"The version of the dataset ({self.version}) is not enforced when root is provided ({self.root})."
)
storage = self._download_or_load_dataset()
super().__init__(
@@ -49,9 +57,9 @@ class AbstractExperienceReplay(TensorDictReplayBuffer):
@property
def stats_patterns(self) -> dict:
return {
("observation", "state"): "b c -> 1 c",
("observation", "image"): "b c h w -> 1 c 1 1",
("action",): "b c -> 1 c",
("observation", "state"): "b c -> c",
("observation", "image"): "b c h w -> c 1 1",
("action",): "b c -> c",
}
@property
@@ -96,10 +104,14 @@ class AbstractExperienceReplay(TensorDictReplayBuffer):
def _download_or_load_dataset(self) -> torch.StorageBase:
if self.root is None:
self.data_dir = snapshot_download(repo_id=f"cadene/{self.dataset_id}", repo_type="dataset")
self.data_dir = Path(
snapshot_download(
repo_id=f"cadene/{self.dataset_id}", repo_type="dataset", revision=self.version
)
)
else:
self.data_dir = self.root / self.dataset_id
return TensorStorage(TensorDict.load_memmap(self.data_dir))
return TensorStorage(TensorDict.load_memmap(self.data_dir / "replay_buffer"))
def _compute_stats(self, num_batch=100, batch_size=32):
rb = TensorDictReplayBuffer(

8
lerobot/common/datasets/aloha.py
View File

@@ -84,6 +84,7 @@ class AlohaExperienceReplay(AbstractExperienceReplay):
def __init__(
self,
dataset_id: str,
version: str | None = "v1.1",
batch_size: int = None,
*,
shuffle: bool = True,
@@ -99,6 +100,7 @@ class AlohaExperienceReplay(AbstractExperienceReplay):
super().__init__(
dataset_id,
version,
batch_size,
shuffle=shuffle,
root=root,
@@ -113,11 +115,11 @@ class AlohaExperienceReplay(AbstractExperienceReplay):
@property
def stats_patterns(self) -> dict:
d = {
("observation", "state"): "b c -> 1 c",
("action",): "b c -> 1 c",
("observation", "state"): "b c -> c",
("action",): "b c -> c",
}
for cam in CAMERAS[self.dataset_id]:
d[("observation", "image", cam)] = "b c h w -> 1 c 1 1"
d[("observation", "image", cam)] = "b c h w -> c 1 1"
return d
@property

2
lerobot/common/datasets/pusht.py
View File

@@ -87,6 +87,7 @@ class PushtExperienceReplay(AbstractExperienceReplay):
def __init__(
self,
dataset_id: str,
version: str | None = "v1.1",
batch_size: int = None,
*,
shuffle: bool = True,
@@ -100,6 +101,7 @@ class PushtExperienceReplay(AbstractExperienceReplay):
):
super().__init__(
dataset_id,
version,
batch_size,
shuffle=shuffle,
root=root,

2
lerobot/common/datasets/simxarm.py
View File

@@ -40,6 +40,7 @@ class SimxarmExperienceReplay(AbstractExperienceReplay):
def __init__(
self,
dataset_id: str,
version: str | None = None,
batch_size: int = None,
*,
shuffle: bool = True,
@@ -53,6 +54,7 @@ class SimxarmExperienceReplay(AbstractExperienceReplay):
):
super().__init__(
dataset_id,
version,
batch_size,
shuffle=shuffle,
root=root,

30
lerobot/common/envs/abstract.py
View File

@@ -1,4 +1,3 @@
import abc
from collections import deque
from typing import Optional
@@ -27,7 +26,6 @@ class AbstractEnv(EnvBase):
self.image_size = image_size
self.num_prev_obs = num_prev_obs
self.num_prev_action = num_prev_action
self._rendering_hooks = []
if pixels_only:
assert from_pixels
@@ -45,36 +43,20 @@ class AbstractEnv(EnvBase):
raise NotImplementedError()
# self._prev_action_queue = deque(maxlen=self.num_prev_action)
def register_rendering_hook(self, func):
self._rendering_hooks.append(func)
def call_rendering_hooks(self):
for func in self._rendering_hooks:
func(self)
def reset_rendering_hooks(self):
self._rendering_hooks = []
@abc.abstractmethod
def render(self, mode="rgb_array", width=640, height=480):
raise NotImplementedError()
raise NotImplementedError("Abstract method")
@abc.abstractmethod
def _reset(self, tensordict: Optional[TensorDict] = None):
raise NotImplementedError()
raise NotImplementedError("Abstract method")
@abc.abstractmethod
def _step(self, tensordict: TensorDict):
raise NotImplementedError()
raise NotImplementedError("Abstract method")
@abc.abstractmethod
def _make_env(self):
raise NotImplementedError()
raise NotImplementedError("Abstract method")
@abc.abstractmethod
def _make_spec(self):
raise NotImplementedError()
raise NotImplementedError("Abstract method")
@abc.abstractmethod
def _set_seed(self, seed: Optional[int]):
raise NotImplementedError()
raise NotImplementedError("Abstract method")

120
lerobot/common/envs/aloha/env.py
View File

@@ -35,6 +35,8 @@ _has_gym = importlib.util.find_spec("gym") is not None
class AlohaEnv(AbstractEnv):
_reset_warning_issued = False
def __init__(
self,
task,
@@ -120,91 +122,77 @@ class AlohaEnv(AbstractEnv):
return obs
def _reset(self, tensordict: Optional[TensorDict] = None):
td = tensordict
if td is None or td.is_empty():
# we need to handle seed iteration, since self._env.reset() rely an internal _seed.
self._current_seed += 1
self.set_seed(self._current_seed)
if tensordict is not None and not AlohaEnv._reset_warning_issued:
logging.warning(f"{self.__class__.__name__}._reset ignores the provided tensordict.")
AlohaEnv._reset_warning_issued = True
# TODO(rcadene): do not use global variable for this
if "sim_transfer_cube" in self.task:
BOX_POSE[0] = sample_box_pose() # used in sim reset
elif "sim_insertion" in self.task:
BOX_POSE[0] = np.concatenate(sample_insertion_pose()) # used in sim reset
# we need to handle seed iteration, since self._env.reset() rely an internal _seed.
self._current_seed += 1
self.set_seed(self._current_seed)
raw_obs = self._env.reset()
# TODO(rcadene): add assert
# assert self._current_seed == self._env._seed
# TODO(rcadene): do not use global variable for this
if "sim_transfer_cube" in self.task:
BOX_POSE[0] = sample_box_pose() # used in sim reset
elif "sim_insertion" in self.task:
BOX_POSE[0] = np.concatenate(sample_insertion_pose()) # used in sim reset
obs = self._format_raw_obs(raw_obs.observation)
raw_obs = self._env.reset()
# TODO(rcadene): add assert
# assert self._current_seed == self._env._seed
if self.num_prev_obs > 0:
stacked_obs = {}
if "image" in obs:
self._prev_obs_image_queue = deque(
[obs["image"]["top"]] * (self.num_prev_obs + 1), maxlen=(self.num_prev_obs + 1)
)
stacked_obs["image"] = {"top": torch.stack(list(self._prev_obs_image_queue))}
if "state" in obs:
self._prev_obs_state_queue = deque(
[obs["state"]] * (self.num_prev_obs + 1), maxlen=(self.num_prev_obs + 1)
)
stacked_obs["state"] = torch.stack(list(self._prev_obs_state_queue))
obs = stacked_obs
obs = self._format_raw_obs(raw_obs.observation)
td = TensorDict(
{
"observation": TensorDict(obs, batch_size=[]),
"done": torch.tensor([False], dtype=torch.bool),
},
batch_size=[],
)
else:
raise NotImplementedError()
if self.num_prev_obs > 0:
stacked_obs = {}
if "image" in obs:
self._prev_obs_image_queue = deque(
[obs["image"]["top"]] * (self.num_prev_obs + 1), maxlen=(self.num_prev_obs + 1)
)
stacked_obs["image"] = {"top": torch.stack(list(self._prev_obs_image_queue))}
if "state" in obs:
self._prev_obs_state_queue = deque(
[obs["state"]] * (self.num_prev_obs + 1), maxlen=(self.num_prev_obs + 1)
)
stacked_obs["state"] = torch.stack(list(self._prev_obs_state_queue))
obs = stacked_obs
td = TensorDict(
{
"observation": TensorDict(obs, batch_size=[]),
"done": torch.tensor([False], dtype=torch.bool),
},
batch_size=[],
)
self.call_rendering_hooks()
return td
def _step(self, tensordict: TensorDict):
td = tensordict
action = td["action"].numpy()
# step expects shape=(4,) so we pad if necessary
assert action.ndim == 1
# TODO(rcadene): add info["is_success"] and info["success"] ?
sum_reward = 0
if action.ndim == 1:
action = einops.repeat(action, "c -> t c", t=self.frame_skip)
else:
if self.frame_skip > 1:
raise NotImplementedError()
_, reward, _, raw_obs = self._env.step(action)
num_action_steps = action.shape[0]
for i in range(num_action_steps):
_, reward, discount, raw_obs = self._env.step(action[i])
del discount # not used
# TODO(rcadene): add an enum
success = done = reward == 4
obs = self._format_raw_obs(raw_obs)
# TOOD(rcadene): add an enum
success = done = reward == 4
sum_reward += reward
obs = self._format_raw_obs(raw_obs)
if self.num_prev_obs > 0:
stacked_obs = {}
if "image" in obs:
self._prev_obs_image_queue.append(obs["image"]["top"])
stacked_obs["image"] = {"top": torch.stack(list(self._prev_obs_image_queue))}
if "state" in obs:
self._prev_obs_state_queue.append(obs["state"])
stacked_obs["state"] = torch.stack(list(self._prev_obs_state_queue))
obs = stacked_obs
self.call_rendering_hooks()
if self.num_prev_obs > 0:
stacked_obs = {}
if "image" in obs:
self._prev_obs_image_queue.append(obs["image"]["top"])
stacked_obs["image"] = {"top": torch.stack(list(self._prev_obs_image_queue))}
if "state" in obs:
self._prev_obs_state_queue.append(obs["state"])
stacked_obs["state"] = torch.stack(list(self._prev_obs_state_queue))
obs = stacked_obs
td = TensorDict(
{
"observation": TensorDict(obs, batch_size=[]),
"reward": torch.tensor([sum_reward], dtype=torch.float32),
# succes and done are true when coverage > self.success_threshold in env
"reward": torch.tensor([reward], dtype=torch.float32),
# success and done are true when coverage > self.success_threshold in env
"done": torch.tensor([done], dtype=torch.bool),
"success": torch.tensor([success], dtype=torch.bool),
},

67
lerobot/common/envs/factory.py
View File

@@ -1,14 +1,18 @@
from torchrl.envs import SerialEnv
from torchrl.envs.transforms import Compose, StepCounter, Transform, TransformedEnv
def make_env(cfg, transform=None):
"""
Note: The returned environment is wrapped in a torchrl.SerialEnv with cfg.rollout_batch_size underlying
environments. The env therefore returns batches.`
"""
kwargs = {
"frame_skip": cfg.env.action_repeat,
"from_pixels": cfg.env.from_pixels,
"pixels_only": cfg.env.pixels_only,
"image_size": cfg.env.image_size,
# TODO(rcadene): do we want a specific eval_env_seed?
"seed": cfg.seed,
"num_prev_obs": cfg.n_obs_steps - 1,
}
@@ -31,43 +35,30 @@ def make_env(cfg, transform=None):
else:
raise ValueError(cfg.env.name)
env = clsfunc(**kwargs)
def _make_env(seed):
nonlocal kwargs
kwargs["seed"] = seed
env = clsfunc(**kwargs)
# limit rollout to max_steps
env = TransformedEnv(env, StepCounter(max_steps=cfg.env.episode_length))
# limit rollout to max_steps
env = TransformedEnv(env, StepCounter(max_steps=cfg.env.episode_length))
if transform is not None:
# useful to add normalization
if isinstance(transform, Compose):
for tf in transform:
env.append_transform(tf.clone())
elif isinstance(transform, Transform):
env.append_transform(transform.clone())
else:
raise NotImplementedError()
if transform is not None:
# useful to add normalization
if isinstance(transform, Compose):
for tf in transform:
env.append_transform(tf.clone())
elif isinstance(transform, Transform):
env.append_transform(transform.clone())
else:
raise NotImplementedError()
return env
return env
# def make_env(env_name, frame_skip, device, is_test=False):
# env = GymEnv(
# env_name,
# frame_skip=frame_skip,
# from_pixels=True,
# pixels_only=False,
# device=device,
# )
# env = TransformedEnv(env)
# env.append_transform(NoopResetEnv(noops=30, random=True))
# if not is_test:
# env.append_transform(EndOfLifeTransform())
# env.append_transform(RewardClipping(-1, 1))
# env.append_transform(ToTensorImage())
# env.append_transform(GrayScale())
# env.append_transform(Resize(84, 84))
# env.append_transform(CatFrames(N=4, dim=-3))
# env.append_transform(RewardSum())
# env.append_transform(StepCounter(max_steps=4500))
# env.append_transform(DoubleToFloat())
# env.append_transform(VecNorm(in_keys=["pixels"]))
# return env
return SerialEnv(
cfg.rollout_batch_size,
create_env_fn=_make_env,
create_env_kwargs=[
{"seed": env_seed} for env_seed in range(cfg.seed, cfg.seed + cfg.rollout_batch_size)
],
)

131
lerobot/common/envs/pusht/env.py
View File

@@ -1,8 +1,10 @@
import importlib
import logging
from collections import deque
from typing import Optional
import einops
import cv2
import numpy as np
import torch
from tensordict import TensorDict
from torchrl.data.tensor_specs import (
@@ -20,6 +22,8 @@ _has_gym = importlib.util.find_spec("gym") is not None
class PushtEnv(AbstractEnv):
_reset_warning_issued = False
def __init__(
self,
task="pusht",
@@ -57,12 +61,30 @@ class PushtEnv(AbstractEnv):
self._env = PushTImageEnv(render_size=self.image_size)
def render(self, mode="rgb_array", width=384, height=384):
def render(self, mode="rgb_array", width=96, height=96, with_marker=True):
"""
with_marker adds a cursor showing the targeted action for the controller.
"""
if width != height:
raise NotImplementedError()
tmp = self._env.render_size
self._env.render_size = width
out = self._env.render(mode)
if width != self._env.render_size:
self._env.render_cache = None
self._env.render_size = width
out = self._env.render(mode).copy()
if with_marker and self._env.latest_action is not None:
action = np.array(self._env.latest_action)
coord = (action / 512 * self._env.render_size).astype(np.int32)
marker_size = int(8 / 96 * self._env.render_size)
thickness = int(1 / 96 * self._env.render_size)
cv2.drawMarker(
out,
coord,
color=(255, 0, 0),
markerType=cv2.MARKER_CROSS,
markerSize=marker_size,
thickness=thickness,
)
self._env.render_size = tmp
return out
@@ -80,80 +102,67 @@ class PushtEnv(AbstractEnv):
return obs
def _reset(self, tensordict: Optional[TensorDict] = None):
td = tensordict
if td is None or td.is_empty():
# we need to handle seed iteration, since self._env.reset() rely an internal _seed.
self._current_seed += 1
self.set_seed(self._current_seed)
raw_obs = self._env.reset()
assert self._current_seed == self._env._seed
if tensordict is not None and not PushtEnv._reset_warning_issued:
logging.warning(f"{self.__class__.__name__}._reset ignores the provided tensordict.")
PushtEnv._reset_warning_issued = True
obs = self._format_raw_obs(raw_obs)
# we need to handle seed iteration, since self._env.reset() rely an internal _seed.
self._current_seed += 1
self.set_seed(self._current_seed)
raw_obs = self._env.reset()
assert self._current_seed == self._env._seed
if self.num_prev_obs > 0:
stacked_obs = {}
if "image" in obs:
self._prev_obs_image_queue = deque(
[obs["image"]] * (self.num_prev_obs + 1), maxlen=(self.num_prev_obs + 1)
)
stacked_obs["image"] = torch.stack(list(self._prev_obs_image_queue))
if "state" in obs:
self._prev_obs_state_queue = deque(
[obs["state"]] * (self.num_prev_obs + 1), maxlen=(self.num_prev_obs + 1)
)
stacked_obs["state"] = torch.stack(list(self._prev_obs_state_queue))
obs = stacked_obs
obs = self._format_raw_obs(raw_obs)
td = TensorDict(
{
"observation": TensorDict(obs, batch_size=[]),
"done": torch.tensor([False], dtype=torch.bool),
},
batch_size=[],
)
else:
raise NotImplementedError()
if self.num_prev_obs > 0:
stacked_obs = {}
if "image" in obs:
self._prev_obs_image_queue = deque(
[obs["image"]] * (self.num_prev_obs + 1), maxlen=(self.num_prev_obs + 1)
)
stacked_obs["image"] = torch.stack(list(self._prev_obs_image_queue))
if "state" in obs:
self._prev_obs_state_queue = deque(
[obs["state"]] * (self.num_prev_obs + 1), maxlen=(self.num_prev_obs + 1)
)
stacked_obs["state"] = torch.stack(list(self._prev_obs_state_queue))
obs = stacked_obs
td = TensorDict(
{
"observation": TensorDict(obs, batch_size=[]),
"done": torch.tensor([False], dtype=torch.bool),
},
batch_size=[],
)
self.call_rendering_hooks()
return td
def _step(self, tensordict: TensorDict):
td = tensordict
action = td["action"].numpy()
# step expects shape=(4,) so we pad if necessary
assert action.ndim == 1
# TODO(rcadene): add info["is_success"] and info["success"] ?
sum_reward = 0
if action.ndim == 1:
action = einops.repeat(action, "c -> t c", t=self.frame_skip)
else:
if self.frame_skip > 1:
raise NotImplementedError()
raw_obs, reward, done, info = self._env.step(action)
num_action_steps = action.shape[0]
for i in range(num_action_steps):
raw_obs, reward, done, info = self._env.step(action[i])
sum_reward += reward
obs = self._format_raw_obs(raw_obs)
obs = self._format_raw_obs(raw_obs)
if self.num_prev_obs > 0:
stacked_obs = {}
if "image" in obs:
self._prev_obs_image_queue.append(obs["image"])
stacked_obs["image"] = torch.stack(list(self._prev_obs_image_queue))
if "state" in obs:
self._prev_obs_state_queue.append(obs["state"])
stacked_obs["state"] = torch.stack(list(self._prev_obs_state_queue))
obs = stacked_obs
self.call_rendering_hooks()
if self.num_prev_obs > 0:
stacked_obs = {}
if "image" in obs:
self._prev_obs_image_queue.append(obs["image"])
stacked_obs["image"] = torch.stack(list(self._prev_obs_image_queue))
if "state" in obs:
self._prev_obs_state_queue.append(obs["state"])
stacked_obs["state"] = torch.stack(list(self._prev_obs_state_queue))
obs = stacked_obs
td = TensorDict(
{
"observation": TensorDict(obs, batch_size=[]),
"reward": torch.tensor([sum_reward], dtype=torch.float32),
# succes and done are true when coverage > self.success_threshold in env
"reward": torch.tensor([reward], dtype=torch.float32),
# success and done are true when coverage > self.success_threshold in env
"done": torch.tensor([done], dtype=torch.bool),
"success": torch.tensor([done], dtype=torch.bool),
},

15
lerobot/common/envs/pusht/pusht_image_env.py
View File

@@ -1,4 +1,3 @@
import cv2
import numpy as np
from gym import spaces
@@ -28,20 +27,6 @@ class PushTImageEnv(PushTEnv):
img_obs = np.moveaxis(img, -1, 0)
obs = {"image": img_obs, "agent_pos": agent_pos}
# draw action
if self.latest_action is not None:
action = np.array(self.latest_action)
coord = (action / 512 * 96).astype(np.int32)
marker_size = int(8 / 96 * self.render_size)
thickness = int(1 / 96 * self.render_size)
cv2.drawMarker(
img,
coord,
color=(255, 0, 0),
markerType=cv2.MARKER_CROSS,
markerSize=marker_size,
thickness=thickness,
)
self.render_cache = img
return obs

3
lerobot/common/envs/simxarm.py
View File

@@ -118,7 +118,6 @@ class SimxarmEnv(AbstractEnv):
else:
raise NotImplementedError()
self.call_rendering_hooks()
return td
def _step(self, tensordict: TensorDict):
@@ -152,8 +151,6 @@ class SimxarmEnv(AbstractEnv):
stacked_obs["state"] = torch.stack(list(self._prev_obs_state_queue))
obs = stacked_obs
self.call_rendering_hooks()
td = TensorDict(
{
"observation": self._format_raw_obs(raw_obs),

6
lerobot/common/logger.py
View File

@@ -30,6 +30,7 @@ class Logger:
self._model_dir = self._log_dir / "models"
self._buffer_dir = self._log_dir / "buffers"
self._save_model = cfg.save_model
self._disable_wandb_artifact = cfg.wandb.disable_artifact
self._save_buffer = cfg.save_buffer
self._group = cfg_to_group(cfg)
self._seed = cfg.seed
@@ -71,9 +72,10 @@ class Logger:
self._model_dir.mkdir(parents=True, exist_ok=True)
fp = self._model_dir / f"{str(identifier)}.pt"
policy.save(fp)
if self._wandb:
if self._wandb and not self._disable_wandb_artifact:
# note wandb artifact does not accept ":" in its name
artifact = self._wandb.Artifact(
self._group + "-" + str(self._seed) + "-" + str(identifier),
self._group.replace(":", "_") + "-" + str(self._seed) + "-" + str(identifier),
type="model",
)
artifact.add_file(fp)

70
lerobot/common/policies/abstract.py Normal file
View File

@@ -0,0 +1,70 @@
from collections import deque
import torch
from torch import Tensor, nn
class AbstractPolicy(nn.Module):
"""Base policy which all policies should be derived from.
The forward method should generally not be overriden as it plays the role of handling multi-step policies. See its
documentation for more information.
"""
def __init__(self, n_action_steps: int | None):
"""
n_action_steps: Sets the cache size for storing action trajectories. If None, it is assumed that a single
action is returned by `select_actions` and that doesn't have a horizon dimension. The `forward` method then
adds that dimension.
"""
super().__init__()
self.n_action_steps = n_action_steps
self.clear_action_queue()
def update(self, replay_buffer, step):
"""One step of the policy's learning algorithm."""
raise NotImplementedError("Abstract method")
def save(self, fp):
torch.save(self.state_dict(), fp)
def load(self, fp):
d = torch.load(fp)
self.load_state_dict(d)
def select_actions(self, observation) -> Tensor:
"""Select an action (or trajectory of actions) based on an observation during rollout.
If n_action_steps was provided at initialization, this should return a (batch_size, n_action_steps, *) tensor of
actions. Otherwise if n_actions_steps is None, this should return a (batch_size, *) tensor of actions.
"""
raise NotImplementedError("Abstract method")
def clear_action_queue(self):
"""This should be called whenever the environment is reset."""
if self.n_action_steps is not None:
self._action_queue = deque([], maxlen=self.n_action_steps)
def forward(self, *args, **kwargs) -> Tensor:
"""Inference step that makes multi-step policies compatible with their single-step environments.
WARNING: In general, this should not be overriden.
Consider a "policy" that observes the environment then charts a course of N actions to take. To make this fit
into the formalism of a TorchRL environment, we view it as being effectively a policy that (1) makes an
observation and prepares a queue of actions, (2) consumes that queue when queried, regardless of the environment
observation, (3) repopulates the action queue when empty. This method handles the aforementioned logic so that
the subclass doesn't have to.
This method effectively wraps the `select_actions` method of the subclass. The following assumptions are made:
1. The `select_actions` method returns a Tensor of actions with shape (B, H, *) where B is the batch size, H is
the action trajectory horizon and * is the action dimensions.
2. Prior to the `select_actions` method being called, theres is an `n_action_steps` instance attribute defined.
"""
if self.n_action_steps is None:
return self.select_actions(*args, **kwargs)
if len(self._action_queue) == 0:
# `select_actions` returns a (batch_size, n_action_steps, *) tensor, but the queue effectively has shape
# (n_action_steps, batch_size, *), hence the transpose.
self._action_queue.extend(self.select_actions(*args, **kwargs).transpose(0, 1))
return self._action_queue.popleft()

23
lerobot/common/policies/act/policy.py
View File

@@ -2,11 +2,12 @@ import logging
import time
import torch
import torch.nn as nn
import torch.nn.functional as F # noqa: N812
import torchvision.transforms as transforms
from lerobot.common.policies.abstract import AbstractPolicy
from lerobot.common.policies.act.detr_vae import build
from lerobot.common.utils import get_safe_torch_device
def build_act_model_and_optimizer(cfg):
@@ -40,12 +41,12 @@ def kl_divergence(mu, logvar):
return total_kld, dimension_wise_kld, mean_kld
class ActionChunkingTransformerPolicy(nn.Module):
class ActionChunkingTransformerPolicy(AbstractPolicy):
def __init__(self, cfg, device, n_action_steps=1):
super().__init__()
super().__init__(n_action_steps)
self.cfg = cfg
self.n_action_steps = n_action_steps
self.device = device
self.device = get_safe_torch_device(device)
self.model, self.optimizer = build_act_model_and_optimizer(cfg)
self.kl_weight = self.cfg.kl_weight
logging.info(f"KL Weight {self.kl_weight}")
@@ -147,16 +148,15 @@ class ActionChunkingTransformerPolicy(nn.Module):
return loss
@torch.no_grad()
def forward(self, observation, step_count):
def select_actions(self, observation, step_count):
if observation["image"].shape[0] != 1:
raise NotImplementedError("Batch size > 1 not handled")
# TODO(rcadene): remove unused step_count
del step_count
self.eval()
# TODO(rcadene): remove unsqueeze hack to add bsize=1
observation["image", "top"] = observation["image", "top"].unsqueeze(0)
# observation["state"] = observation["state"].unsqueeze(0)
# TODO(rcadene): remove hack
# add 1 camera dimension
observation["image", "top"] = observation["image", "top"].unsqueeze(1)
@@ -180,11 +180,8 @@ class ActionChunkingTransformerPolicy(nn.Module):
# exp_weights = torch.from_numpy(exp_weights).cuda().unsqueeze(dim=1)
# raw_action = (actions_for_curr_step * exp_weights).sum(dim=0, keepdim=True)
# remove bsize=1
action = action.squeeze(0)
# take first predicted action or n first actions
action = action[0] if self.n_action_steps == 1 else action[: self.n_action_steps]
action = action[: self.n_action_steps]
return action
def _forward(self, qpos, image, actions=None, is_pad=None):

44
lerobot/common/policies/diffusion/diffusion_unet_image_policy.py
View File

@@ -1,3 +1,44 @@
"""Code from the original diffusion policy project.
Notes on how to load a checkpoint from the original repository:
In the original repository, run the eval and use a breakpoint to extract the policy weights.
```
torch.save(policy.state_dict(), "weights.pt")
```
In this repository, add a breakpoint somewhere after creating an equivalent policy and load in the weights:
```
loaded = torch.load("weights.pt")
aligned = {}
their_prefix = "obs_encoder.obs_nets.image.backbone"
our_prefix = "obs_encoder.key_model_map.image.backbone"
aligned.update({our_prefix + k.removeprefix(their_prefix): v for k, v in loaded.items() if k.startswith(their_prefix)})
their_prefix = "obs_encoder.obs_nets.image.pool"
our_prefix = "obs_encoder.key_model_map.image.pool"
aligned.update({our_prefix + k.removeprefix(their_prefix): v for k, v in loaded.items() if k.startswith(their_prefix)})
their_prefix = "obs_encoder.obs_nets.image.nets.3"
our_prefix = "obs_encoder.key_model_map.image.out"
aligned.update({our_prefix + k.removeprefix(their_prefix): v for k, v in loaded.items() if k.startswith(their_prefix)})
aligned.update({k: v for k, v in loaded.items() if k.startswith('model.')})
# Note: here you are loading into the ema model.
missing_keys, unexpected_keys = policy.ema_diffusion.load_state_dict(aligned, strict=False)
assert all('_dummy_variable' in k for k in missing_keys)
assert len(unexpected_keys) == 0
```
Then in that same runtime you can also save the weights with the new aligned state_dict:
```
policy.save("weights.pt")
```
Now you can remove the breakpoint and extra code and load in the weights just like with any other lerobot checkpoint.
"""
from typing import Dict
import torch
@@ -190,11 +231,10 @@ class DiffusionUnetImagePolicy(BaseImagePolicy):
# run sampling
nsample = self.conditional_sample(
cond_data, cond_mask, local_cond=local_cond, global_cond=global_cond, **self.kwargs
cond_data, cond_mask, local_cond=local_cond, global_cond=global_cond
)
action_pred = nsample[..., :action_dim]
# get action
start = n_obs_steps - 1
end = start + self.n_action_steps

55
lerobot/common/policies/diffusion/model/multi_image_obs_encoder.py
View File

@@ -1,15 +1,40 @@
import copy
from typing import Dict, Tuple, Union
from typing import Dict, Optional, Tuple, Union
import torch
import torch.nn as nn
import torchvision
from robomimic.models.base_nets import ResNet18Conv, SpatialSoftmax
from lerobot.common.policies.diffusion.model.crop_randomizer import CropRandomizer
from lerobot.common.policies.diffusion.model.module_attr_mixin import ModuleAttrMixin
from lerobot.common.policies.diffusion.pytorch_utils import replace_submodules
class RgbEncoder(nn.Module):
"""Following `VisualCore` from Robomimic 0.2.0."""
def __init__(self, input_shape, relu=True, pretrained=False, num_keypoints=32):
"""
input_shape: channel-first input shape (C, H, W)
resnet_name: a timm model name.
pretrained: whether to use timm pretrained weights.
relu: whether to use relu as a final step.
num_keypoints: Number of keypoints for SpatialSoftmax (default value of 32 matches PushT Image).
"""
super().__init__()
self.backbone = ResNet18Conv(input_channel=input_shape[0], pretrained=pretrained)
# Figure out the feature map shape.
with torch.inference_mode():
feat_map_shape = tuple(self.backbone(torch.zeros(size=(1, *input_shape))).shape[1:])
self.pool = SpatialSoftmax(feat_map_shape, num_kp=num_keypoints)
self.out = nn.Linear(num_keypoints * 2, num_keypoints * 2)
self.relu = nn.ReLU() if relu else nn.Identity()
def forward(self, x):
return self.relu(self.out(torch.flatten(self.pool(self.backbone(x)), start_dim=1)))
class MultiImageObsEncoder(ModuleAttrMixin):
def __init__(
self,
@@ -24,7 +49,7 @@ class MultiImageObsEncoder(ModuleAttrMixin):
share_rgb_model: bool = False,
# renormalize rgb input with imagenet normalization
# assuming input in [0,1]
imagenet_norm: bool = False,
norm_mean_std: Optional[tuple[float, float]] = None,
):
"""
Assumes rgb input: B,C,H,W
@@ -98,10 +123,9 @@ class MultiImageObsEncoder(ModuleAttrMixin):
this_normalizer = torchvision.transforms.CenterCrop(size=(h, w))
# configure normalizer
this_normalizer = nn.Identity()
if imagenet_norm:
# TODO(rcadene): move normalizer to dataset and env
if norm_mean_std is not None:
this_normalizer = torchvision.transforms.Normalize(
mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]
mean=norm_mean_std[0], std=norm_mean_std[1]
)
this_transform = nn.Sequential(this_resizer, this_randomizer, this_normalizer)
@@ -124,6 +148,17 @@ class MultiImageObsEncoder(ModuleAttrMixin):
def forward(self, obs_dict):
batch_size = None
features = []
# process lowdim input
for key in self.low_dim_keys:
data = obs_dict[key]
if batch_size is None:
batch_size = data.shape[0]
else:
assert batch_size == data.shape[0]
assert data.shape[1:] == self.key_shape_map[key]
features.append(data)
# process rgb input
if self.share_rgb_model:
# pass all rgb obs to rgb model
@@ -161,16 +196,6 @@ class MultiImageObsEncoder(ModuleAttrMixin):
feature = self.key_model_map[key](img)
features.append(feature)
# process lowdim input
for key in self.low_dim_keys:
data = obs_dict[key]
if batch_size is None:
batch_size = data.shape[0]
else:
assert batch_size == data.shape[0]
assert data.shape[1:] == self.key_shape_map[key]
features.append(data)
# concatenate all features
result = torch.cat(features, dim=-1)
return result

49
lerobot/common/policies/diffusion/policy.py
View File

@@ -1,16 +1,18 @@
import copy
import logging
import time
import hydra
import torch
import torch.nn as nn
from lerobot.common.policies.abstract import AbstractPolicy
from lerobot.common.policies.diffusion.diffusion_unet_image_policy import DiffusionUnetImagePolicy
from lerobot.common.policies.diffusion.model.lr_scheduler import get_scheduler
from lerobot.common.policies.diffusion.model.multi_image_obs_encoder import MultiImageObsEncoder
from lerobot.common.policies.diffusion.model.multi_image_obs_encoder import MultiImageObsEncoder, RgbEncoder
from lerobot.common.utils import get_safe_torch_device
class DiffusionPolicy(nn.Module):
class DiffusionPolicy(AbstractPolicy):
def __init__(
self,
cfg,
@@ -34,11 +36,14 @@ class DiffusionPolicy(nn.Module):
# parameters passed to step
**kwargs,
):
super().__init__()
super().__init__(n_action_steps)
self.cfg = cfg
noise_scheduler = hydra.utils.instantiate(cfg_noise_scheduler)
rgb_model = hydra.utils.instantiate(cfg_rgb_model)
rgb_model_input_shape = copy.deepcopy(shape_meta.obs.image.shape)
if cfg_obs_encoder.crop_shape is not None:
rgb_model_input_shape[1:] = cfg_obs_encoder.crop_shape
rgb_model = RgbEncoder(input_shape=rgb_model_input_shape, **cfg_rgb_model)
obs_encoder = MultiImageObsEncoder(
rgb_model=rgb_model,
**cfg_obs_encoder,
@@ -62,15 +67,16 @@ class DiffusionPolicy(nn.Module):
**kwargs,
)
self.device = torch.device(cfg_device)
if torch.cuda.is_available() and cfg_device == "cuda":
self.diffusion.cuda()
self.device = get_safe_torch_device(cfg_device)
self.diffusion.to(self.device)
self.ema_diffusion = None
self.ema = None
if self.cfg.use_ema:
self.ema_diffusion = copy.deepcopy(self.diffusion)
self.ema = hydra.utils.instantiate(
cfg_ema,
model=copy.deepcopy(self.diffusion),
model=self.ema_diffusion,
)
self.optimizer = hydra.utils.instantiate(
@@ -93,21 +99,22 @@ class DiffusionPolicy(nn.Module):
)
@torch.no_grad()
def forward(self, observation, step_count):
def select_actions(self, observation, step_count):
"""
Note: this uses the ema model weights if self.training == False, otherwise the non-ema model weights.
"""
# TODO(rcadene): remove unused step_count
del step_count
# TODO(rcadene): remove unsqueeze hack to add bsize=1
observation["image"] = observation["image"].unsqueeze(0)
observation["state"] = observation["state"].unsqueeze(0)
obs_dict = {
"image": observation["image"],
"agent_pos": observation["state"],
}
out = self.diffusion.predict_action(obs_dict)
action = out["action"].squeeze(0)
if self.training:
out = self.diffusion.predict_action(obs_dict)
else:
out = self.ema_diffusion.predict_action(obs_dict)
action = out["action"]
return action
def update(self, replay_buffer, step):
@@ -196,4 +203,10 @@ class DiffusionPolicy(nn.Module):
def load(self, fp):
d = torch.load(fp)
self.load_state_dict(d)
missing_keys, unexpected_keys = self.load_state_dict(d, strict=False)
if len(missing_keys) > 0:
assert all(k.startswith("ema_diffusion.") for k in missing_keys)
logging.warning(
"DiffusionPolicy.load expected ema parameters in loaded state dict but none were found."
)
assert len(unexpected_keys) == 0

3
lerobot/common/policies/factory.py
View File

@@ -1,4 +1,7 @@
def make_policy(cfg):
if cfg.policy.name != "diffusion" and cfg.rollout_batch_size > 1:
raise NotImplementedError("Only diffusion policy supports rollout_batch_size > 1 for the time being.")
if cfg.policy.name == "tdmpc":
from lerobot.common.policies.tdmpc.policy import TDMPC

24
lerobot/common/policies/tdmpc/policy.py
View File

@@ -9,6 +9,8 @@ import torch
import torch.nn as nn
import lerobot.common.policies.tdmpc.helper as h
from lerobot.common.policies.abstract import AbstractPolicy
from lerobot.common.utils import get_safe_torch_device
FIRST_FRAME = 0
@@ -85,17 +87,18 @@ class TOLD(nn.Module):
return torch.min(Q1, Q2) if return_type == "min" else (Q1 + Q2) / 2
class TDMPC(nn.Module):
class TDMPC(AbstractPolicy):
"""Implementation of TD-MPC learning + inference."""
def __init__(self, cfg, device):
super().__init__()
super().__init__(None)
self.action_dim = cfg.action_dim
self.cfg = cfg
self.device = torch.device(device)
self.device = get_safe_torch_device(device)
self.std = h.linear_schedule(cfg.std_schedule, 0)
self.model = TOLD(cfg).cuda() if torch.cuda.is_available() and device == "cuda" else TOLD(cfg)
self.model = TOLD(cfg)
self.model.to(self.device)
self.model_target = deepcopy(self.model)
self.optim = torch.optim.Adam(self.model.parameters(), lr=self.cfg.lr)
self.pi_optim = torch.optim.Adam(self.model._pi.parameters(), lr=self.cfg.lr)
@@ -124,20 +127,19 @@ class TDMPC(nn.Module):
self.model_target.load_state_dict(d["model_target"])
@torch.no_grad()
def forward(self, observation, step_count):
t0 = step_count.item() == 0
def select_actions(self, observation, step_count):
if observation["image"].shape[0] != 1:
raise NotImplementedError("Batch size > 1 not handled")
# TODO(rcadene): remove unsqueeze hack...
if observation["image"].ndim == 3:
observation["image"] = observation["image"].unsqueeze(0)
observation["state"] = observation["state"].unsqueeze(0)
t0 = step_count.item() == 0
obs = {
# TODO(rcadene): remove contiguous hack...
"rgb": observation["image"].contiguous(),
"state": observation["state"].contiguous(),
}
action = self.act(obs, t0=t0, step=self.step.item())
# Note: unsqueeze needed because `act` still uses non-batch logic.
action = self.act(obs, t0=t0, step=self.step.item()).unsqueeze(0)
return action
@torch.no_grad()

20
lerobot/common/utils.py
View File

@@ -6,6 +6,26 @@ import numpy as np
import torch
def get_safe_torch_device(cfg_device: str, log: bool = False) -> torch.device:
match cfg_device:
case "cuda":
assert torch.cuda.is_available()
device = torch.device("cuda")
case "mps":
assert torch.backends.mps.is_available()
device = torch.device("mps")
case "cpu":
device = torch.device("cpu")
if log:
logging.warning("Using CPU, this will be slow.")
case _:
device = torch.device(cfg_device)
if log:
logging.warning(f"Using custom {cfg_device} device.")
return device
def set_seed(seed):
"""Set seed for reproducibility."""
random.seed(seed)

5
lerobot/configs/default.yaml
View File

@@ -10,6 +10,9 @@ hydra:
name: default
seed: 1337
# batch size for TorchRL SerialEnv. Each underlying env will get the seed = seed + env_index
# NOTE: only diffusion policy supports rollout_batch_size > 1
rollout_batch_size: 1
device: cuda # cpu
prefetch: 4
eval_freq: ???
@@ -30,5 +33,7 @@ policy: ???
wandb:
enable: true
# Set to true to disable saving an artifact despite save_model == True
disable_artifact: false
project: lerobot
notes: ""

25
lerobot/configs/policy/diffusion.yaml
View File

@@ -12,6 +12,7 @@ shape_meta:
action:
shape: [2]
seed: 100000
horizon: 16
n_obs_steps: 2
n_action_steps: 8
@@ -21,12 +22,12 @@ past_action_visible: False
keypoint_visible_rate: 1.0
obs_as_global_cond: True
eval_episodes: 1
eval_freq: 10000
save_freq: 100000
eval_episodes: 50
eval_freq: 5000
save_freq: 5000
log_freq: 250
offline_steps: 1344000
offline_steps: 200000
online_steps: 0
offline_prioritized_sampler: true
@@ -42,8 +43,8 @@ policy:
num_inference_steps: 100
obs_as_global_cond: ${obs_as_global_cond}
# crop_shape: null
diffusion_step_embed_dim: 256 # before 128
down_dims: [256, 512, 1024] # before [512, 1024, 2048]
diffusion_step_embed_dim: 128
down_dims: [512, 1024, 2048]
kernel_size: 5
n_groups: 8
cond_predict_scale: True
@@ -76,17 +77,17 @@ noise_scheduler:
obs_encoder:
shape_meta: ${shape_meta}
# resize_shape: null
# crop_shape: [76, 76]
crop_shape: [84, 84]
# constant center crop
# random_crop: True
random_crop: True
use_group_norm: True
share_rgb_model: False
imagenet_norm: True
norm_mean_std: [0.5, 0.5] # for PushT the original impl normalizes to [-1, 1] (maybe not the case for robomimic envs)
rgb_model:
_target_: lerobot.common.policies.diffusion.pytorch_utils.get_resnet
name: resnet18
weights: null
pretrained: false
num_keypoints: 32
relu: true
ema:
_target_: lerobot.common.policies.diffusion.model.ema_model.EMAModel

98
lerobot/scripts/eval.py
View File

@@ -3,6 +3,7 @@ import threading
import time
from pathlib import Path
import einops
import hydra
import imageio
import numpy as np
@@ -10,12 +11,14 @@ import torch
import tqdm
from tensordict.nn import TensorDictModule
from torchrl.envs import EnvBase
from torchrl.envs.batched_envs import BatchedEnvBase
from lerobot.common.datasets.factory import make_offline_buffer
from lerobot.common.envs.factory import make_env
from lerobot.common.logger import log_output_dir
from lerobot.common.policies.abstract import AbstractPolicy
from lerobot.common.policies.factory import make_policy
from lerobot.common.utils import init_logging, set_seed
from lerobot.common.utils import get_safe_torch_device, init_logging, set_seed
def write_video(video_path, stacked_frames, fps):
@@ -23,8 +26,8 @@ def write_video(video_path, stacked_frames, fps):
def eval_policy(
env: EnvBase,
policy: TensorDictModule = None,
env: BatchedEnvBase,
policy: AbstractPolicy,
num_episodes: int = 10,
max_steps: int = 30,
save_video: bool = False,
@@ -32,59 +35,82 @@ def eval_policy(
fps: int = 15,
return_first_video: bool = False,
):
if policy is not None:
policy.eval()
start = time.time()
sum_rewards = []
max_rewards = []
successes = []
threads = []
for i in tqdm.tqdm(range(num_episodes)):
ep_frames = []
if save_video or (return_first_video and i == 0):
threads = [] # for video saving threads
episode_counter = 0 # for saving the correct number of videos
def render_frame(env):
# TODO(alexander-soare): if num_episodes is not evenly divisible by the batch size, this will do more work than
# needed as I'm currently taking a ceil.
for i in tqdm.tqdm(range(-(-num_episodes // env.batch_size[0]))):
ep_frames = []
def maybe_render_frame(env: EnvBase, _):
if save_video or (return_first_video and i == 0): # noqa: B023
ep_frames.append(env.render()) # noqa: B023
env.register_rendering_hook(render_frame)
with torch.inference_mode():
# TODO(alexander-soare): When `break_when_any_done == False` this rolls out for max_steps even when all
# envs are done the first time. But we only use the first rollout. This is a waste of compute.
if policy is not None:
policy.clear_action_queue()
rollout = env.rollout(
max_steps=max_steps,
policy=policy,
auto_cast_to_device=True,
callback=maybe_render_frame,
break_when_any_done=env.batch_size[0] == 1,
)
# print(", ".join([f"{x:.3f}" for x in rollout["next", "reward"][:,0].tolist()]))
ep_sum_reward = rollout["next", "reward"].sum()
ep_max_reward = rollout["next", "reward"].max()
ep_success = rollout["next", "success"].any()
sum_rewards.append(ep_sum_reward.item())
max_rewards.append(ep_max_reward.item())
successes.append(ep_success.item())
# Figure out where in each rollout sequence the first done condition was encountered (results after this won't
# be included).
# Note: this assumes that the shape of the done key is (batch_size, max_steps, 1).
# Note: this relies on a property of argmax: that it returns the first occurrence as a tiebreaker.
rollout_steps = rollout["next", "done"].shape[1]
done_indices = torch.argmax(rollout["next", "done"].to(int), axis=1) # (batch_size, rollout_steps)
mask = (torch.arange(rollout_steps) <= done_indices).unsqueeze(-1) # (batch_size, rollout_steps, 1)
batch_sum_reward = einops.reduce((rollout["next", "reward"] * mask), "b n 1 -> b", "sum")
batch_max_reward = einops.reduce((rollout["next", "reward"] * mask), "b n 1 -> b", "max")
batch_success = einops.reduce((rollout["next", "success"] * mask), "b n 1 -> b", "any")
sum_rewards.extend(batch_sum_reward.tolist())
max_rewards.extend(batch_max_reward.tolist())
successes.extend(batch_success.tolist())
if save_video or (return_first_video and i == 0):
stacked_frames = np.stack(ep_frames)
batch_stacked_frames = np.stack(ep_frames) # (t, b, *)
batch_stacked_frames = batch_stacked_frames.transpose(
1, 0, *range(2, batch_stacked_frames.ndim)
) # (b, t, *)
if save_video:
video_dir.mkdir(parents=True, exist_ok=True)
video_path = video_dir / f"eval_episode_{i}.mp4"
thread = threading.Thread(
target=write_video,
args=(str(video_path), stacked_frames, fps),
)
thread.start()
threads.append(thread)
for stacked_frames, done_index in zip(
batch_stacked_frames, done_indices.flatten().tolist(), strict=False
):
if episode_counter >= num_episodes:
continue
video_dir.mkdir(parents=True, exist_ok=True)
video_path = video_dir / f"eval_episode_{episode_counter}.mp4"
thread = threading.Thread(
target=write_video,
args=(str(video_path), stacked_frames[:done_index], fps),
)
thread.start()
threads.append(thread)
episode_counter += 1
if return_first_video and i == 0:
first_video = stacked_frames.transpose(0, 3, 1, 2)
env.reset_rendering_hooks()
first_video = batch_stacked_frames[0].transpose(0, 3, 1, 2)
for thread in threads:
thread.join()
info = {
"avg_sum_reward": np.nanmean(sum_rewards),
"avg_max_reward": np.nanmean(max_rewards),
"pc_success": np.nanmean(successes) * 100,
"avg_sum_reward": np.nanmean(sum_rewards[:num_episodes]),
"avg_max_reward": np.nanmean(max_rewards[:num_episodes]),
"pc_success": np.nanmean(successes[:num_episodes]) * 100,
"eval_s": time.time() - start,
"eval_ep_s": (time.time() - start) / num_episodes,
}
@@ -104,10 +130,8 @@ def eval(cfg: dict, out_dir=None):
init_logging()
if cfg.device == "cuda":
assert torch.cuda.is_available()
else:
logging.warning("Using CPU, this will be slow.")
# Check device is available
get_safe_torch_device(cfg.device, log=True)
torch.backends.cudnn.benchmark = True
torch.backends.cuda.matmul.allow_tf32 = True
@@ -138,7 +162,7 @@ def eval(cfg: dict, out_dir=None):
save_video=True,
video_dir=Path(out_dir) / "eval",
fps=cfg.env.fps,
max_steps=cfg.env.episode_length // cfg.n_action_steps,
max_steps=cfg.env.episode_length,
num_episodes=cfg.eval_episodes,
)
print(metrics)

78
lerobot/scripts/train.py
View File

@@ -12,7 +12,7 @@ from lerobot.common.datasets.factory import make_offline_buffer
from lerobot.common.envs.factory import make_env
from lerobot.common.logger import Logger, log_output_dir
from lerobot.common.policies.factory import make_policy
from lerobot.common.utils import format_big_number, init_logging, set_seed
from lerobot.common.utils import format_big_number, get_safe_torch_device, init_logging, set_seed
from lerobot.scripts.eval import eval_policy
@@ -112,13 +112,13 @@ def train(cfg: dict, out_dir=None, job_name=None):
raise NotImplementedError()
if job_name is None:
raise NotImplementedError()
if cfg.online_steps > 0:
assert cfg.rollout_batch_size == 1, "rollout_batch_size > 1 not supported for online training steps"
init_logging()
if cfg.device == "cuda":
assert torch.cuda.is_available()
else:
logging.warning("Using CPU, this will be slow.")
# Check device is available
get_safe_torch_device(cfg.device, log=True)
torch.backends.cudnn.benchmark = True
torch.backends.cuda.matmul.allow_tf32 = True
@@ -155,11 +155,7 @@ def train(cfg: dict, out_dir=None, job_name=None):
num_learnable_params = sum(p.numel() for p in policy.parameters() if p.requires_grad)
num_total_params = sum(p.numel() for p in policy.parameters())
td_policy = TensorDictModule(
policy,
in_keys=["observation", "step_count"],
out_keys=["action"],
)
td_policy = TensorDictModule(policy, in_keys=["observation", "step_count"], out_keys=["action"])
# log metrics to terminal and wandb
logger = Logger(out_dir, job_name, cfg)
@@ -174,24 +170,15 @@ def train(cfg: dict, out_dir=None, job_name=None):
logging.info(f"{num_learnable_params=} ({format_big_number(num_learnable_params)})")
logging.info(f"{num_total_params=} ({format_big_number(num_total_params)})")
step = 0 # number of policy update (forward + backward + optim)
is_offline = True
for offline_step in range(cfg.offline_steps):
if offline_step == 0:
logging.info("Start offline training on a fixed dataset")
# TODO(rcadene): is it ok if step_t=0 = 0 and not 1 as previously done?
train_info = policy.update(offline_buffer, step)
if step % cfg.log_freq == 0:
log_train_info(logger, train_info, step, cfg, offline_buffer, is_offline)
if step > 0 and step % cfg.eval_freq == 0:
# Note: this helper will be used in offline and online training loops.
def _maybe_eval_and_maybe_save(step):
if step % cfg.eval_freq == 0:
logging.info(f"Eval policy at step {step}")
eval_info, first_video = eval_policy(
env,
td_policy,
num_episodes=cfg.eval_episodes,
max_steps=cfg.env.episode_length // cfg.n_action_steps,
max_steps=cfg.env.episode_length,
return_first_video=True,
video_dir=Path(out_dir) / "eval",
save_video=True,
@@ -201,11 +188,27 @@ def train(cfg: dict, out_dir=None, job_name=None):
logger.log_video(first_video, step, mode="eval")
logging.info("Resume training")
if step > 0 and cfg.save_model and step % cfg.save_freq == 0:
logging.info(f"Checkpoint policy at step {step}")
if cfg.save_model and step % cfg.save_freq == 0:
logging.info(f"Checkpoint policy after step {step}")
logger.save_model(policy, identifier=step)
logging.info("Resume training")
step = 0 # number of policy update (forward + backward + optim)
is_offline = True
for offline_step in range(cfg.offline_steps):
if offline_step == 0:
logging.info("Start offline training on a fixed dataset")
# TODO(rcadene): is it ok if step_t=0 = 0 and not 1 as previously done?
policy.train()
train_info = policy.update(offline_buffer, step)
if step % cfg.log_freq == 0:
log_train_info(logger, train_info, step, cfg, offline_buffer, is_offline)
# Note: _maybe_eval_and_maybe_save happens **after** the `step`th training update has completed, so we pass in
# step + 1.
_maybe_eval_and_maybe_save(step + 1)
step += 1
demo_buffer = offline_buffer if cfg.policy.balanced_sampling else None
@@ -217,11 +220,11 @@ def train(cfg: dict, out_dir=None, job_name=None):
# TODO: add configurable number of rollout? (default=1)
with torch.no_grad():
rollout = env.rollout(
max_steps=cfg.env.episode_length // cfg.n_action_steps,
max_steps=cfg.env.episode_length,
policy=td_policy,
auto_cast_to_device=True,
)
assert len(rollout) <= cfg.env.episode_length // cfg.n_action_steps
assert len(rollout) <= cfg.env.episode_length
# set same episode index for all time steps contained in this rollout
rollout["episode"] = torch.tensor([env_step] * len(rollout), dtype=torch.int)
online_buffer.extend(rollout)
@@ -247,24 +250,9 @@ def train(cfg: dict, out_dir=None, job_name=None):
train_info.update(rollout_info)
log_train_info(logger, train_info, step, cfg, offline_buffer, is_offline)
if step > 0 and step % cfg.eval_freq == 0:
logging.info(f"Eval policy at step {step}")
eval_info, first_video = eval_policy(
env,
td_policy,
num_episodes=cfg.eval_episodes,
max_steps=cfg.env.episode_length // cfg.n_action_steps,
return_first_video=True,
)
log_eval_info(logger, eval_info, step, cfg, offline_buffer, is_offline)
if cfg.wandb.enable:
logger.log_video(first_video, step, mode="eval")
logging.info("Resume training")
if step > 0 and cfg.save_model and step % cfg.save_freq == 0:
logging.info(f"Checkpoint policy at step {step}")
logger.save_model(policy, identifier=step)
logging.info("Resume training")
# Note: _maybe_eval_and_maybe_save happens **after** the `step`th training update has completed, so we pass
# in step + 1.
_maybe_eval_and_maybe_save(step + 1)
step += 1
online_step += 1

365
poetry.lock generated
View File

@@ -1,4 +1,4 @@
# This file is automatically @generated by Poetry 1.8.1 and should not be changed by hand.
# This file is automatically @generated by Poetry 1.8.2 and should not be changed by hand.
[[package]]
name = "absl-py"
@@ -44,56 +44,56 @@ files = [
[[package]]
name = "av"
version = "11.0.0"
version = "12.0.0"
description = "Pythonic bindings for FFmpeg's libraries."
optional = false
python-versions = ">=3.8"
files = [
{file = "av-11.0.0-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:a01f13b37eb6d181e03bbbbda29093fe2d68f10755795188220acdc89560ec27"},
{file = "av-11.0.0-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:b2236faee1b5d71dff3cdef81ef6eec22cc8b71dbfb45eb037e6437fe80f24e7"},
{file = "av-11.0.0-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:40543a08e5c84aecd2bc84da5d43548743201897f0ba21bf5ae3a4dcddefca2b"},
{file = "av-11.0.0-cp310-cp310-manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:2907376884d956376aaf3bc1905fa4e0dcb9ba4e0d183e519392a19d89317d1b"},
{file = "av-11.0.0-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:e8d5581dcdc81cd601e3ce036809f14da82c46ff187bcefe981ec819390e0ab0"},
{file = "av-11.0.0-cp310-cp310-win_amd64.whl", hash = "sha256:150490f2a62cfa470f3cb60f3a0060ff93afd807e2b7b3b0eeeb5a992eb8d67b"},
{file = "av-11.0.0-cp311-cp311-macosx_10_9_x86_64.whl", hash = "sha256:d9bac0de62f09e2cb4e2132b5a46a89bc31c898189aa285b484c17351d991afe"},
{file = "av-11.0.0-cp311-cp311-macosx_11_0_arm64.whl", hash = "sha256:2122ff8bdace4ce50207920f37de472517921e2ca1f0503464f748fdb8e20506"},
{file = "av-11.0.0-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:527d840697fee6ad4cf47eba987eaf30cd76bd96b2d20eaa907e166b9b8065c8"},
{file = "av-11.0.0-cp311-cp311-manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:abeaedddfca9101886eb6fc47318c5f5ece8480d330d73aacf6917d7421981a2"},
{file = "av-11.0.0-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:13790fbb889b955baf885fe3761e923e85537ef414173465ec293177cedb7b99"},
{file = "av-11.0.0-cp311-cp311-win_amd64.whl", hash = "sha256:fc27e27f52480287f44226ad4ae3eb53346bf027959d0f00a9154530bd98b371"},
{file = "av-11.0.0-cp312-cp312-macosx_10_9_x86_64.whl", hash = "sha256:892583e2c6b8c2500e5d24310f499caefcdaa2e48c8f7169ad41041aaaf4da11"},
{file = "av-11.0.0-cp312-cp312-macosx_11_0_arm64.whl", hash = "sha256:6943679d70a9f4de974049e7ae2cf0b20afe0d7ddab650526c02a6cf9adcd08f"},
{file = "av-11.0.0-cp312-cp312-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:e6d73b038ccf1df5c16bc643eee5c694fb7732e09375e2f4903c1f4ce90dfb72"},
{file = "av-11.0.0-cp312-cp312-manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:c83422db3333e97b9680700df5185139352fc3a568b14179da3bdcbeb2f0e91b"},
{file = "av-11.0.0-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:8413900f6a3639e0088c018a3a516a1656d4d16799e7aa759a16ddf3bd268e2b"},
{file = "av-11.0.0-cp312-cp312-win_amd64.whl", hash = "sha256:908e49ee336223801d8f2f7dca5a1deb64e9d8256138b8e7a79013b682a6ebb5"},
{file = "av-11.0.0-cp38-cp38-macosx_10_9_x86_64.whl", hash = "sha256:82411ae4a562da07b76028d2f349fb0e6a86aa78ad2b18d2d7bf5b06b17fba14"},
{file = "av-11.0.0-cp38-cp38-macosx_11_0_arm64.whl", hash = "sha256:621104bd63e38fa4eca554da3722b1aac329619de39152f27eec8999acc72342"},
{file = "av-11.0.0-cp38-cp38-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:442878990c094455a16c10127edcc54bc4e78d355e6a13ad2a27608b0ecda38f"},
{file = "av-11.0.0-cp38-cp38-manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:658199c92987dc72511f5ee8ade62faef6234b7a04c8b5788de99e366be5e073"},
{file = "av-11.0.0-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:ad4b381665c49267b46f87297573898b85e5c41384750fee2e70267fbc4ba318"},
{file = "av-11.0.0-cp38-cp38-win_amd64.whl", hash = "sha256:60de14f71293e36ca4e297cc8a8460f0cf74f38a201694f3c6fc7f40301582f2"},
{file = "av-11.0.0-cp39-cp39-macosx_10_9_x86_64.whl", hash = "sha256:a90f04af96374dab94028a7471597bdfcf03083338b9be2eb8ca4805a8ec7ab5"},
{file = "av-11.0.0-cp39-cp39-macosx_11_0_arm64.whl", hash = "sha256:8821ab2d23e4cb5c8abea6b08d2b1bfceca6af2d88fab1d1dc1b3ec7b34933c7"},
{file = "av-11.0.0-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:9a92342ed307eeaf9509a6b0f3bafd4337c4880c851b50acc18df48c625b63b6"},
{file = "av-11.0.0-cp39-cp39-manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:bbe3502975bc844f5d432c1f24d331bf6ef3e05532ebf06f7ed08b60719b8ea5"},
{file = "av-11.0.0-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:c278b3a4fd111b4c9190abe6b1a5ca358d5f91e851d470b62577b957e0187b09"},
{file = "av-11.0.0-cp39-cp39-win_amd64.whl", hash = "sha256:478aa1d54fbc3058ea65ff41086b6adbe1326b456a027d2f3b59dbe60b4ac2ca"},
{file = "av-11.0.0-pp310-pypy310_pp73-macosx_10_9_x86_64.whl", hash = "sha256:e8df10bb2d56a981d02a8a0b41491912b76dad06305d174a2575ef55ad451100"},
{file = "av-11.0.0-pp310-pypy310_pp73-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:b30c51e597785a89241bd61865faff2dbd3327856a8285a1e120dbf60e18348b"},
{file = "av-11.0.0-pp310-pypy310_pp73-manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:a8b8bd92edb096699b306e7b090ad096925ca3bdae6f89656f023fa2a2da627d"},
{file = "av-11.0.0-pp310-pypy310_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:9383af733abfc44f6fc29307a6c922fbf671ee343dc97b78b74eac6a2346a46d"},
{file = "av-11.0.0-pp310-pypy310_pp73-win_amd64.whl", hash = "sha256:a9df4a60579198b560f641cdfe4c2139948a70193ddc096b275f2cf6d94e3e04"},
{file = "av-11.0.0-pp38-pypy38_pp73-macosx_10_9_x86_64.whl", hash = "sha256:8ae5f7ae0a7093fb813686d4aa4c554531f80a28480427f5c155da51b747eff0"},
{file = "av-11.0.0-pp38-pypy38_pp73-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:50fb7d606f8236891d773c701d5650b93af8dbf78eeaac36fc7e1f7f64a9d664"},
{file = "av-11.0.0-pp38-pypy38_pp73-manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:543e0f9bf6ff02dedbe66d906fbc89c8907c80a8ea7413fc3fed68ce4a6e9b44"},
{file = "av-11.0.0-pp38-pypy38_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:daa279c884457ab194ce78bdd89c0aa391af733da95fb3258d4c6eb8c258299a"},
{file = "av-11.0.0-pp39-pypy39_pp73-macosx_10_9_x86_64.whl", hash = "sha256:1aacc21f4cf96447117a61edfb776afb73186750a5e08a21484ddfc3599aefb5"},
{file = "av-11.0.0-pp39-pypy39_pp73-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:2568b38eef777b916a5d02e42b8f67f92e12023531239ddd32e1ca4f3cdf8c5b"},
{file = "av-11.0.0-pp39-pypy39_pp73-manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:747c6d347e27c59cc2e78c9c505d23cd88eceff0cc9386be73693ae9009a577c"},
{file = "av-11.0.0-pp39-pypy39_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:4bbd8f4941b9d3450eff40003b9b9d904667aec7ab085fa31f0f9bca32d755e0"},
{file = "av-11.0.0-pp39-pypy39_pp73-win_amd64.whl", hash = "sha256:f39c1244ba0cf185b2722aeec116b8a98a2ee5728ce687cec0bda60ee0360dfc"},
{file = "av-11.0.0.tar.gz", hash = "sha256:48223f000a252070f8e700ff634bb7fb3aa1b7bc7e450373029fbdd6f369ac31"},
{file = "av-12.0.0-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:b9d0890553951f76c479a9f2bb952aebae902b1c7d52feea614d37e1cd728a44"},
{file = "av-12.0.0-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:5d7f229a253c2e3fea9682c09c5ae179bd6d5d2da38d89eb7f29ef7bed10cb2f"},
{file = "av-12.0.0-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:61b3555d143aacf02e0446f6030319403538eba4dc713c18dfa653a2a23e7f9c"},
{file = "av-12.0.0-cp310-cp310-manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:607e13b2c2b26159a37525d7b6f647a32ce78711fccff23d146d3e255ffa115f"},
{file = "av-12.0.0-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:39f0b4cfb89f4f06b339c766f92648e798a96747d4163f2fa78660d1ab1f1b5e"},
{file = "av-12.0.0-cp310-cp310-win_amd64.whl", hash = "sha256:41dcb8c269fa58a56edf3a3c814c32a0c69586827f132b4e395a951b0ce14fad"},
{file = "av-12.0.0-cp311-cp311-macosx_10_9_x86_64.whl", hash = "sha256:4fa78fbe0e4469226512380180063116105048c66cb12e18ab4b518466c57e6c"},
{file = "av-12.0.0-cp311-cp311-macosx_11_0_arm64.whl", hash = "sha256:60a869be1d6af916e65ea461cb93922f5db0698655ed7a7eae7c3ecd4af4debb"},
{file = "av-12.0.0-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:df61811cc551c186f0a0e530d97b8b139453534d0f92c1790a923f666522ceda"},
{file = "av-12.0.0-cp311-cp311-manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:99cd2fc53091ebfb9a2fa9dd3580267f5bd1c040d0efd99fbc1a162576b271cb"},
{file = "av-12.0.0-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:7a6d4f1e261df48932128e6495772faa4cc23f5dd1512eec73daab82ad9f3240"},
{file = "av-12.0.0-cp311-cp311-win_amd64.whl", hash = "sha256:6aec88e41a498b1e01e2dce5371557e20f9a51aae0c16decc5924ec0be2e22b6"},
{file = "av-12.0.0-cp312-cp312-macosx_10_9_x86_64.whl", hash = "sha256:90eb8f2d548e96cbc6f78e89c911cdb15a3d80fd944f31111660ce45939cd037"},
{file = "av-12.0.0-cp312-cp312-macosx_11_0_arm64.whl", hash = "sha256:d7f3a02910e77d750dbd516256a16db15030e5371530ff5a5ae902dc03d9005d"},
{file = "av-12.0.0-cp312-cp312-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:e2477cc51526aa50575313d66e5e8ad7ab944588469be5e557b360ed572ae536"},
{file = "av-12.0.0-cp312-cp312-manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:a2f47149d3ca6deb79f3e515b8bef50e27ebdb160813e6d67dba77278d2a7883"},
{file = "av-12.0.0-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:3306e4a3ce8b5bfcc3075793d4ed3a2df69179d8fba22cb944a6164dc235dfb6"},
{file = "av-12.0.0-cp312-cp312-win_amd64.whl", hash = "sha256:dc1b742e7f6df1b499fb960bd6697d1dd8e7ada7484a041a8c20e70a87225f53"},
{file = "av-12.0.0-cp38-cp38-macosx_10_9_x86_64.whl", hash = "sha256:0183be6889e835e1b074b4037bfce4fd44671c606cf1c4ab92ea2f271b544aec"},
{file = "av-12.0.0-cp38-cp38-macosx_11_0_arm64.whl", hash = "sha256:57337f20b208292ec8d3b11e4d289d8688a43d728174850a81b865d3253fff2c"},
{file = "av-12.0.0-cp38-cp38-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:0ec915e8f6521545a38566eefc281042ee504ea3cee0618d8558e4920588b3b2"},
{file = "av-12.0.0-cp38-cp38-manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:33ad5c0a23c45b72bd6bd47f3b2c1adcd2935ee3d0b6178ed66bba62b964ff31"},
{file = "av-12.0.0-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:bfc3a652b12c93120514d56cf025da47442c5ba51530cdf7ba3660257dbb0de1"},
{file = "av-12.0.0-cp38-cp38-win_amd64.whl", hash = "sha256:037f793dd1ef4a1f57f090191a7f803ad10ec82da0d04ea26bbe0b8a145fe927"},
{file = "av-12.0.0-cp39-cp39-macosx_10_9_x86_64.whl", hash = "sha256:fc532376aa264722fae55063abd1871d17a563dc895978e142c8ecfcdeb3a2e8"},
{file = "av-12.0.0-cp39-cp39-macosx_11_0_arm64.whl", hash = "sha256:abf0c4bc40a0af8a30f4cd96f3be6f19fbce0f21222d7fcec148e085127153f7"},
{file = "av-12.0.0-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:81cedd1c072fbebf606724c406b1a1b00adc711f1dfd2bc04c633ce39d8439d8"},
{file = "av-12.0.0-cp39-cp39-manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:02d60f48be9f15dcda37d50f3ce8d7249d9a455643d4322dd3449986bacfc628"},
{file = "av-12.0.0-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:5d2619e4c26d661eecfc404f7d739d8b35f0dcef353fabe61512e030254b7031"},
{file = "av-12.0.0-cp39-cp39-win_amd64.whl", hash = "sha256:1892cc91c888d101777d5432d54e0554c11d1c3a2c65d02a2cae0a2256a8fbb9"},
{file = "av-12.0.0-pp310-pypy310_pp73-macosx_10_9_x86_64.whl", hash = "sha256:4819e3ef6c3a44ef6f75907229133a1ee7f688245b2cf49b6b8e969a81ca72c9"},
{file = "av-12.0.0-pp310-pypy310_pp73-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:bb16bb314cf1503b0250fc46b2c455ee196584231101be0123f4f78638227b62"},
{file = "av-12.0.0-pp310-pypy310_pp73-manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:f3e6a62bda9a1e144feeb59bbee046d7a2d98399634a30f57e4990197313c158"},
{file = "av-12.0.0-pp310-pypy310_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:e08175ffbafa3a70c7b2f81083e160e34122a208cdf70f150b8f5d02c2de6965"},
{file = "av-12.0.0-pp310-pypy310_pp73-win_amd64.whl", hash = "sha256:e1d255be317b7c1ebdc4dae98935b9f3869161112dc829c625e54f90d8bdd7ab"},
{file = "av-12.0.0-pp38-pypy38_pp73-macosx_10_9_x86_64.whl", hash = "sha256:17964b36e08435910aabd5b3f7dca12f99536902529767d276026bc08f94ced7"},
{file = "av-12.0.0-pp38-pypy38_pp73-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:d2d5f78de29edee06ddcdd4c2b759914575492d6a0cd4de2ce31ee63a4953eff"},
{file = "av-12.0.0-pp38-pypy38_pp73-manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:309b32bc97158d0f0c19e273b8e17a855a86806b7194aebc23bd497326cff11f"},
{file = "av-12.0.0-pp38-pypy38_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:c409c71bd9c7c2f8d018c822f36b1447cfa96eca158381a96f3319bb0ff6e79e"},
{file = "av-12.0.0-pp39-pypy39_pp73-macosx_10_9_x86_64.whl", hash = "sha256:08fc5eaef60a257d622998626e233bf3ff90d2f817f6695d6a27e0ffcfe9dcff"},
{file = "av-12.0.0-pp39-pypy39_pp73-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:746ab0eff8a7a21a6c6d16e6b6e61709527eba2ad1a524d92a01bb60d02a3df7"},
{file = "av-12.0.0-pp39-pypy39_pp73-manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:013b3ac3de3aa1c137af0cedafd364fd1c7524ab3e1cd53e04564fd1632ac04d"},
{file = "av-12.0.0-pp39-pypy39_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:0fa55923527648f51ac005e44fe2797ebc67f53ad4850e0194d3753761ee33a2"},
{file = "av-12.0.0-pp39-pypy39_pp73-win_amd64.whl", hash = "sha256:35d514f4dee0cf67e9e6b2a65fb4a28f98da88e71e8c7f7960bd04625d9fe965"},
{file = "av-12.0.0.tar.gz", hash = "sha256:bcf21ebb722d4538b4099e5a78f730d78814dd70003511c185941dba5651b14d"},
]
[[package]]
@@ -604,6 +604,16 @@ files = [
[package.dependencies]
six = ">=1.4.0"
[[package]]
name = "egl-probe"
version = "1.0.2"
description = ""
optional = false
python-versions = "*"
files = [
{file = "egl_probe-1.0.2.tar.gz", hash = "sha256:29bdca7b08da1e060cfb42cd46af8300a7ac4f3b1b2eeb16e545ea16d9a5ac93"},
]
[[package]]
name = "einops"
version = "0.7.0"
@@ -658,13 +668,13 @@ typing = ["typing-extensions (>=4.8)"]
[[package]]
name = "fsspec"
version = "2024.2.0"
version = "2024.3.1"
description = "File-system specification"
optional = false
python-versions = ">=3.8"
files = [
{file = "fsspec-2024.2.0-py3-none-any.whl", hash = "sha256:817f969556fa5916bc682e02ca2045f96ff7f586d45110fcb76022063ad2c7d8"},
{file = "fsspec-2024.2.0.tar.gz", hash = "sha256:b6ad1a679f760dda52b1168c859d01b7b80648ea6f7f7c7f5a8a91dc3f3ecb84"},
{file = "fsspec-2024.3.1-py3-none-any.whl", hash = "sha256:918d18d41bf73f0e2b261824baeb1b124bcf771767e3a26425cd7dec3332f512"},
{file = "fsspec-2024.3.1.tar.gz", hash = "sha256:f39780e282d7d117ffb42bb96992f8a90795e4d0fb0f661a70ca39fe9c43ded9"},
]
[package.extras]
@@ -763,6 +773,72 @@ files = [
[package.extras]
preview = ["glfw-preview"]
[[package]]
name = "grpcio"
version = "1.62.1"
description = "HTTP/2-based RPC framework"
optional = false
python-versions = ">=3.7"
files = [
{file = "grpcio-1.62.1-cp310-cp310-linux_armv7l.whl", hash = "sha256:179bee6f5ed7b5f618844f760b6acf7e910988de77a4f75b95bbfaa8106f3c1e"},
{file = "grpcio-1.62.1-cp310-cp310-macosx_12_0_universal2.whl", hash = "sha256:48611e4fa010e823ba2de8fd3f77c1322dd60cb0d180dc6630a7e157b205f7ea"},
{file = "grpcio-1.62.1-cp310-cp310-manylinux_2_17_aarch64.whl", hash = "sha256:b2a0e71b0a2158aa4bce48be9f8f9eb45cbd17c78c7443616d00abbe2a509f6d"},
{file = "grpcio-1.62.1-cp310-cp310-manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:fbe80577c7880911d3ad65e5ecc997416c98f354efeba2f8d0f9112a67ed65a5"},
{file = "grpcio-1.62.1-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:58f6c693d446964e3292425e1d16e21a97a48ba9172f2d0df9d7b640acb99243"},
{file = "grpcio-1.62.1-cp310-cp310-musllinux_1_1_i686.whl", hash = "sha256:77c339403db5a20ef4fed02e4d1a9a3d9866bf9c0afc77a42234677313ea22f3"},
{file = "grpcio-1.62.1-cp310-cp310-musllinux_1_1_x86_64.whl", hash = "sha256:b5a4ea906db7dec694098435d84bf2854fe158eb3cd51e1107e571246d4d1d70"},
{file = "grpcio-1.62.1-cp310-cp310-win32.whl", hash = "sha256:4187201a53f8561c015bc745b81a1b2d278967b8de35f3399b84b0695e281d5f"},
{file = "grpcio-1.62.1-cp310-cp310-win_amd64.whl", hash = "sha256:844d1f3fb11bd1ed362d3fdc495d0770cfab75761836193af166fee113421d66"},
{file = "grpcio-1.62.1-cp311-cp311-linux_armv7l.whl", hash = "sha256:833379943d1728a005e44103f17ecd73d058d37d95783eb8f0b28ddc1f54d7b2"},
{file = "grpcio-1.62.1-cp311-cp311-macosx_10_10_universal2.whl", hash = "sha256:c7fcc6a32e7b7b58f5a7d27530669337a5d587d4066060bcb9dee7a8c833dfb7"},
{file = "grpcio-1.62.1-cp311-cp311-manylinux_2_17_aarch64.whl", hash = "sha256:fa7d28eb4d50b7cbe75bb8b45ed0da9a1dc5b219a0af59449676a29c2eed9698"},
{file = "grpcio-1.62.1-cp311-cp311-manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:48f7135c3de2f298b833be8b4ae20cafe37091634e91f61f5a7eb3d61ec6f660"},
{file = "grpcio-1.62.1-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:71f11fd63365ade276c9d4a7b7df5c136f9030e3457107e1791b3737a9b9ed6a"},
{file = "grpcio-1.62.1-cp311-cp311-musllinux_1_1_i686.whl", hash = "sha256:4b49fd8fe9f9ac23b78437da94c54aa7e9996fbb220bac024a67469ce5d0825f"},
{file = "grpcio-1.62.1-cp311-cp311-musllinux_1_1_x86_64.whl", hash = "sha256:482ae2ae78679ba9ed5752099b32e5fe580443b4f798e1b71df412abf43375db"},
{file = "grpcio-1.62.1-cp311-cp311-win32.whl", hash = "sha256:1faa02530b6c7426404372515fe5ddf66e199c2ee613f88f025c6f3bd816450c"},
{file = "grpcio-1.62.1-cp311-cp311-win_amd64.whl", hash = "sha256:5bd90b8c395f39bc82a5fb32a0173e220e3f401ff697840f4003e15b96d1befc"},
{file = "grpcio-1.62.1-cp312-cp312-linux_armv7l.whl", hash = "sha256:b134d5d71b4e0837fff574c00e49176051a1c532d26c052a1e43231f252d813b"},
{file = "grpcio-1.62.1-cp312-cp312-macosx_10_10_universal2.whl", hash = "sha256:d1f6c96573dc09d50dbcbd91dbf71d5cf97640c9427c32584010fbbd4c0e0037"},
{file = "grpcio-1.62.1-cp312-cp312-manylinux_2_17_aarch64.whl", hash = "sha256:359f821d4578f80f41909b9ee9b76fb249a21035a061a327f91c953493782c31"},
{file = "grpcio-1.62.1-cp312-cp312-manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:a485f0c2010c696be269184bdb5ae72781344cb4e60db976c59d84dd6354fac9"},
{file = "grpcio-1.62.1-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:b50b09b4dc01767163d67e1532f948264167cd27f49e9377e3556c3cba1268e1"},
{file = "grpcio-1.62.1-cp312-cp312-musllinux_1_1_i686.whl", hash = "sha256:3227c667dccbe38f2c4d943238b887bac588d97c104815aecc62d2fd976e014b"},
{file = "grpcio-1.62.1-cp312-cp312-musllinux_1_1_x86_64.whl", hash = "sha256:3952b581eb121324853ce2b191dae08badb75cd493cb4e0243368aa9e61cfd41"},
{file = "grpcio-1.62.1-cp312-cp312-win32.whl", hash = "sha256:83a17b303425104d6329c10eb34bba186ffa67161e63fa6cdae7776ff76df73f"},
{file = "grpcio-1.62.1-cp312-cp312-win_amd64.whl", hash = "sha256:6696ffe440333a19d8d128e88d440f91fb92c75a80ce4b44d55800e656a3ef1d"},
{file = "grpcio-1.62.1-cp37-cp37m-linux_armv7l.whl", hash = "sha256:e3393b0823f938253370ebef033c9fd23d27f3eae8eb9a8f6264900c7ea3fb5a"},
{file = "grpcio-1.62.1-cp37-cp37m-macosx_10_10_universal2.whl", hash = "sha256:83e7ccb85a74beaeae2634f10eb858a0ed1a63081172649ff4261f929bacfd22"},
{file = "grpcio-1.62.1-cp37-cp37m-manylinux_2_17_aarch64.whl", hash = "sha256:882020c87999d54667a284c7ddf065b359bd00251fcd70279ac486776dbf84ec"},
{file = "grpcio-1.62.1-cp37-cp37m-manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:a10383035e864f386fe096fed5c47d27a2bf7173c56a6e26cffaaa5a361addb1"},
{file = "grpcio-1.62.1-cp37-cp37m-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:960edebedc6b9ada1ef58e1c71156f28689978188cd8cff3b646b57288a927d9"},
{file = "grpcio-1.62.1-cp37-cp37m-musllinux_1_1_i686.whl", hash = "sha256:23e2e04b83f347d0aadde0c9b616f4726c3d76db04b438fd3904b289a725267f"},
{file = "grpcio-1.62.1-cp37-cp37m-musllinux_1_1_x86_64.whl", hash = "sha256:978121758711916d34fe57c1f75b79cdfc73952f1481bb9583399331682d36f7"},
{file = "grpcio-1.62.1-cp37-cp37m-win_amd64.whl", hash = "sha256:9084086190cc6d628f282e5615f987288b95457292e969b9205e45b442276407"},
{file = "grpcio-1.62.1-cp38-cp38-linux_armv7l.whl", hash = "sha256:22bccdd7b23c420a27fd28540fb5dcbc97dc6be105f7698cb0e7d7a420d0e362"},
{file = "grpcio-1.62.1-cp38-cp38-macosx_10_10_universal2.whl", hash = "sha256:8999bf1b57172dbc7c3e4bb3c732658e918f5c333b2942243f10d0d653953ba9"},
{file = "grpcio-1.62.1-cp38-cp38-manylinux_2_17_aarch64.whl", hash = "sha256:d9e52558b8b8c2f4ac05ac86344a7417ccdd2b460a59616de49eb6933b07a0bd"},
{file = "grpcio-1.62.1-cp38-cp38-manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:1714e7bc935780bc3de1b3fcbc7674209adf5208ff825799d579ffd6cd0bd505"},
{file = "grpcio-1.62.1-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:c8842ccbd8c0e253c1f189088228f9b433f7a93b7196b9e5b6f87dba393f5d5d"},
{file = "grpcio-1.62.1-cp38-cp38-musllinux_1_1_i686.whl", hash = "sha256:1f1e7b36bdff50103af95a80923bf1853f6823dd62f2d2a2524b66ed74103e49"},
{file = "grpcio-1.62.1-cp38-cp38-musllinux_1_1_x86_64.whl", hash = "sha256:bba97b8e8883a8038606480d6b6772289f4c907f6ba780fa1f7b7da7dfd76f06"},
{file = "grpcio-1.62.1-cp38-cp38-win32.whl", hash = "sha256:a7f615270fe534548112a74e790cd9d4f5509d744dd718cd442bf016626c22e4"},
{file = "grpcio-1.62.1-cp38-cp38-win_amd64.whl", hash = "sha256:e6c8c8693df718c5ecbc7babb12c69a4e3677fd11de8886f05ab22d4e6b1c43b"},
{file = "grpcio-1.62.1-cp39-cp39-linux_armv7l.whl", hash = "sha256:73db2dc1b201d20ab7083e7041946910bb991e7e9761a0394bbc3c2632326483"},
{file = "grpcio-1.62.1-cp39-cp39-macosx_10_10_universal2.whl", hash = "sha256:407b26b7f7bbd4f4751dbc9767a1f0716f9fe72d3d7e96bb3ccfc4aace07c8de"},
{file = "grpcio-1.62.1-cp39-cp39-manylinux_2_17_aarch64.whl", hash = "sha256:f8de7c8cef9261a2d0a62edf2ccea3d741a523c6b8a6477a340a1f2e417658de"},
{file = "grpcio-1.62.1-cp39-cp39-manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:9bd5c8a1af40ec305d001c60236308a67e25419003e9bb3ebfab5695a8d0b369"},
{file = "grpcio-1.62.1-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:be0477cb31da67846a33b1a75c611f88bfbcd427fe17701b6317aefceee1b96f"},
{file = "grpcio-1.62.1-cp39-cp39-musllinux_1_1_i686.whl", hash = "sha256:60dcd824df166ba266ee0cfaf35a31406cd16ef602b49f5d4dfb21f014b0dedd"},
{file = "grpcio-1.62.1-cp39-cp39-musllinux_1_1_x86_64.whl", hash = "sha256:973c49086cabab773525f6077f95e5a993bfc03ba8fc32e32f2c279497780585"},
{file = "grpcio-1.62.1-cp39-cp39-win32.whl", hash = "sha256:12859468e8918d3bd243d213cd6fd6ab07208195dc140763c00dfe901ce1e1b4"},
{file = "grpcio-1.62.1-cp39-cp39-win_amd64.whl", hash = "sha256:b7209117bbeebdfa5d898205cc55153a51285757902dd73c47de498ad4d11332"},
{file = "grpcio-1.62.1.tar.gz", hash = "sha256:6c455e008fa86d9e9a9d85bb76da4277c0d7d9668a3bfa70dbe86e9f3c759947"},
]
[package.extras]
protobuf = ["grpcio-tools (>=1.62.1)"]
[[package]]
name = "gym"
version = "0.26.2"
@@ -1038,13 +1114,13 @@ setuptools = "*"
[[package]]
name = "importlib-metadata"
version = "7.0.2"
version = "7.1.0"
description = "Read metadata from Python packages"
optional = false
python-versions = ">=3.8"
files = [
{file = "importlib_metadata-7.0.2-py3-none-any.whl", hash = "sha256:f4bc4c0c070c490abf4ce96d715f68e95923320370efb66143df00199bb6c100"},
{file = "importlib_metadata-7.0.2.tar.gz", hash = "sha256:198f568f3230878cb1b44fbd7975f87906c22336dba2e4a7f05278c281fbd792"},
{file = "importlib_metadata-7.1.0-py3-none-any.whl", hash = "sha256:30962b96c0c223483ed6cc7280e7f0199feb01a0e40cfae4d4450fc6fab1f570"},
{file = "importlib_metadata-7.1.0.tar.gz", hash = "sha256:b78938b926ee8d5f020fc4772d487045805a55ddbad2ecf21c6d60938dc7fcd2"},
]
[package.dependencies]
@@ -1053,7 +1129,7 @@ zipp = ">=0.5"
[package.extras]
docs = ["furo", "jaraco.packaging (>=9.3)", "jaraco.tidelift (>=1.4)", "rst.linker (>=1.9)", "sphinx (>=3.5)", "sphinx-lint"]
perf = ["ipython"]
testing = ["flufl.flake8", "importlib-resources (>=1.3)", "packaging", "pyfakefs", "pytest (>=6)", "pytest-checkdocs (>=2.4)", "pytest-cov", "pytest-enabler (>=2.2)", "pytest-mypy", "pytest-perf (>=0.9.2)", "pytest-ruff (>=0.2.1)"]
testing = ["flufl.flake8", "importlib-resources (>=1.3)", "jaraco.test (>=5.4)", "packaging", "pyfakefs", "pytest (>=6)", "pytest-checkdocs (>=2.4)", "pytest-cov", "pytest-enabler (>=2.2)", "pytest-mypy", "pytest-perf (>=0.9.2)", "pytest-ruff (>=0.2.1)"]
[[package]]
name = "iniconfig"
@@ -1265,6 +1341,21 @@ html5 = ["html5lib"]
htmlsoup = ["BeautifulSoup4"]
source = ["Cython (>=3.0.7)"]
[[package]]
name = "markdown"
version = "3.6"
description = "Python implementation of John Gruber's Markdown."
optional = false
python-versions = ">=3.8"
files = [
{file = "Markdown-3.6-py3-none-any.whl", hash = "sha256:48f276f4d8cfb8ce6527c8f79e2ee29708508bf4d40aa410fbc3b4ee832c850f"},
{file = "Markdown-3.6.tar.gz", hash = "sha256:ed4f41f6daecbeeb96e576ce414c41d2d876daa9a16cb35fa8ed8c2ddfad0224"},
]
[package.extras]
docs = ["mdx-gh-links (>=0.2)", "mkdocs (>=1.5)", "mkdocs-gen-files", "mkdocs-literate-nav", "mkdocs-nature (>=0.6)", "mkdocs-section-index", "mkdocstrings[python]"]
testing = ["coverage", "pyyaml"]
[[package]]
name = "markupsafe"
version = "2.1.5"
@@ -1468,32 +1559,32 @@ setuptools = "*"
[[package]]
name = "numba"
version = "0.59.0"
version = "0.59.1"
description = "compiling Python code using LLVM"
optional = false
python-versions = ">=3.9"
files = [
{file = "numba-0.59.0-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:8d061d800473fb8fef76a455221f4ad649a53f5e0f96e3f6c8b8553ee6fa98fa"},
{file = "numba-0.59.0-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:c086a434e7d3891ce5dfd3d1e7ee8102ac1e733962098578b507864120559ceb"},
{file = "numba-0.59.0-cp310-cp310-manylinux2014_aarch64.manylinux_2_17_aarch64.whl", hash = "sha256:9e20736bf62e61f8353fb71b0d3a1efba636c7a303d511600fc57648b55823ed"},
{file = "numba-0.59.0-cp310-cp310-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:e86e6786aec31d2002122199486e10bbc0dc40f78d76364cded375912b13614c"},
{file = "numba-0.59.0-cp310-cp310-win_amd64.whl", hash = "sha256:0307ee91b24500bb7e64d8a109848baf3a3905df48ce142b8ac60aaa406a0400"},
{file = "numba-0.59.0-cp311-cp311-macosx_10_9_x86_64.whl", hash = "sha256:d540f69a8245fb714419c2209e9af6104e568eb97623adc8943642e61f5d6d8e"},
{file = "numba-0.59.0-cp311-cp311-macosx_11_0_arm64.whl", hash = "sha256:1192d6b2906bf3ff72b1d97458724d98860ab86a91abdd4cfd9328432b661e31"},
{file = "numba-0.59.0-cp311-cp311-manylinux2014_aarch64.manylinux_2_17_aarch64.whl", hash = "sha256:90efb436d3413809fcd15298c6d395cb7d98184350472588356ccf19db9e37c8"},
{file = "numba-0.59.0-cp311-cp311-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:cd3dac45e25d927dcb65d44fb3a973994f5add2b15add13337844afe669dd1ba"},
{file = "numba-0.59.0-cp311-cp311-win_amd64.whl", hash = "sha256:753dc601a159861808cc3207bad5c17724d3b69552fd22768fddbf302a817a4c"},
{file = "numba-0.59.0-cp312-cp312-macosx_10_9_x86_64.whl", hash = "sha256:ce62bc0e6dd5264e7ff7f34f41786889fa81a6b860662f824aa7532537a7bee0"},
{file = "numba-0.59.0-cp312-cp312-macosx_11_0_arm64.whl", hash = "sha256:8cbef55b73741b5eea2dbaf1b0590b14977ca95a13a07d200b794f8f6833a01c"},
{file = "numba-0.59.0-cp312-cp312-manylinux2014_aarch64.manylinux_2_17_aarch64.whl", hash = "sha256:70d26ba589f764be45ea8c272caa467dbe882b9676f6749fe6f42678091f5f21"},
{file = "numba-0.59.0-cp312-cp312-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:e125f7d69968118c28ec0eed9fbedd75440e64214b8d2eac033c22c04db48492"},
{file = "numba-0.59.0-cp312-cp312-win_amd64.whl", hash = "sha256:4981659220b61a03c1e557654027d271f56f3087448967a55c79a0e5f926de62"},
{file = "numba-0.59.0-cp39-cp39-macosx_10_9_x86_64.whl", hash = "sha256:fe4d7562d1eed754a7511ed7ba962067f198f86909741c5c6e18c4f1819b1f47"},
{file = "numba-0.59.0-cp39-cp39-macosx_11_0_arm64.whl", hash = "sha256:6feb1504bb432280f900deaf4b1dadcee68812209500ed3f81c375cbceab24dc"},
{file = "numba-0.59.0-cp39-cp39-manylinux2014_aarch64.manylinux_2_17_aarch64.whl", hash = "sha256:944faad25ee23ea9dda582bfb0189fb9f4fc232359a80ab2a028b94c14ce2b1d"},
{file = "numba-0.59.0-cp39-cp39-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:5516a469514bfae52a9d7989db4940653a5cbfac106f44cb9c50133b7ad6224b"},
{file = "numba-0.59.0-cp39-cp39-win_amd64.whl", hash = "sha256:32bd0a41525ec0b1b853da244808f4e5333867df3c43c30c33f89cf20b9c2b63"},
{file = "numba-0.59.0.tar.gz", hash = "sha256:12b9b064a3e4ad00e2371fc5212ef0396c80f41caec9b5ec391c8b04b6eaf2a8"},
{file = "numba-0.59.1-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:97385a7f12212c4f4bc28f648720a92514bee79d7063e40ef66c2d30600fd18e"},
{file = "numba-0.59.1-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:0b77aecf52040de2a1eb1d7e314497b9e56fba17466c80b457b971a25bb1576d"},
{file = "numba-0.59.1-cp310-cp310-manylinux2014_aarch64.manylinux_2_17_aarch64.whl", hash = "sha256:3476a4f641bfd58f35ead42f4dcaf5f132569c4647c6f1360ccf18ee4cda3990"},
{file = "numba-0.59.1-cp310-cp310-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:525ef3f820931bdae95ee5379c670d5c97289c6520726bc6937a4a7d4230ba24"},
{file = "numba-0.59.1-cp310-cp310-win_amd64.whl", hash = "sha256:990e395e44d192a12105eca3083b61307db7da10e093972ca285c85bef0963d6"},
{file = "numba-0.59.1-cp311-cp311-macosx_10_9_x86_64.whl", hash = "sha256:43727e7ad20b3ec23ee4fc642f5b61845c71f75dd2825b3c234390c6d8d64051"},
{file = "numba-0.59.1-cp311-cp311-macosx_11_0_arm64.whl", hash = "sha256:411df625372c77959570050e861981e9d196cc1da9aa62c3d6a836b5cc338966"},
{file = "numba-0.59.1-cp311-cp311-manylinux2014_aarch64.manylinux_2_17_aarch64.whl", hash = "sha256:2801003caa263d1e8497fb84829a7ecfb61738a95f62bc05693fcf1733e978e4"},
{file = "numba-0.59.1-cp311-cp311-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:dd2842fac03be4e5324ebbbd4d2d0c8c0fc6e0df75c09477dd45b288a0777389"},
{file = "numba-0.59.1-cp311-cp311-win_amd64.whl", hash = "sha256:0594b3dfb369fada1f8bb2e3045cd6c61a564c62e50cf1f86b4666bc721b3450"},
{file = "numba-0.59.1-cp312-cp312-macosx_10_9_x86_64.whl", hash = "sha256:1cce206a3b92836cdf26ef39d3a3242fec25e07f020cc4feec4c4a865e340569"},
{file = "numba-0.59.1-cp312-cp312-macosx_11_0_arm64.whl", hash = "sha256:8c8b4477763cb1fbd86a3be7050500229417bf60867c93e131fd2626edb02238"},
{file = "numba-0.59.1-cp312-cp312-manylinux2014_aarch64.manylinux_2_17_aarch64.whl", hash = "sha256:7d80bce4ef7e65bf895c29e3889ca75a29ee01da80266a01d34815918e365835"},
{file = "numba-0.59.1-cp312-cp312-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:f7ad1d217773e89a9845886401eaaab0a156a90aa2f179fdc125261fd1105096"},
{file = "numba-0.59.1-cp312-cp312-win_amd64.whl", hash = "sha256:5bf68f4d69dd3a9f26a9b23548fa23e3bcb9042e2935257b471d2a8d3c424b7f"},
{file = "numba-0.59.1-cp39-cp39-macosx_10_9_x86_64.whl", hash = "sha256:4e0318ae729de6e5dbe64c75ead1a95eb01fabfe0e2ebed81ebf0344d32db0ae"},
{file = "numba-0.59.1-cp39-cp39-macosx_11_0_arm64.whl", hash = "sha256:0f68589740a8c38bb7dc1b938b55d1145244c8353078eea23895d4f82c8b9ec1"},
{file = "numba-0.59.1-cp39-cp39-manylinux2014_aarch64.manylinux_2_17_aarch64.whl", hash = "sha256:649913a3758891c77c32e2d2a3bcbedf4a69f5fea276d11f9119677c45a422e8"},
{file = "numba-0.59.1-cp39-cp39-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:9712808e4545270291d76b9a264839ac878c5eb7d8b6e02c970dc0ac29bc8187"},
{file = "numba-0.59.1-cp39-cp39-win_amd64.whl", hash = "sha256:8d51ccd7008a83105ad6a0082b6a2b70f1142dc7cfd76deb8c5a862367eb8c86"},
{file = "numba-0.59.1.tar.gz", hash = "sha256:76f69132b96028d2774ed20415e8c528a34e3299a40581bae178f0994a2f370b"},
]
[package.dependencies]
@@ -2460,6 +2551,30 @@ urllib3 = ">=1.21.1,<3"
socks = ["PySocks (>=1.5.6,!=1.5.7)"]
use-chardet-on-py3 = ["chardet (>=3.0.2,<6)"]
[[package]]
name = "robomimic"
version = "0.2.0"
description = "robomimic: A Modular Framework for Robot Learning from Demonstration"
optional = false
python-versions = ">=3"
files = [
{file = "robomimic-0.2.0.tar.gz", hash = "sha256:ee3bb5cf9c3e1feead6b57b43c5db738fd0a8e0c015fdf6419808af8fffdc463"},
]
[package.dependencies]
egl_probe = ">=1.0.1"
h5py = "*"
imageio = "*"
imageio-ffmpeg = "*"
numpy = ">=1.13.3"
psutil = "*"
tensorboard = "*"
tensorboardX = "*"
termcolor = "*"
torch = "*"
torchvision = "*"
tqdm = "*"
[[package]]
name = "safetensors"
version = "0.4.2"
@@ -2684,13 +2799,13 @@ test = ["asv", "gmpy2", "hypothesis", "mpmath", "pooch", "pytest", "pytest-cov",
[[package]]
name = "sentry-sdk"
version = "1.41.0"
version = "1.43.0"
description = "Python client for Sentry (https://sentry.io)"
optional = false
python-versions = "*"
files = [
{file = "sentry-sdk-1.41.0.tar.gz", hash = "sha256:4f2d6c43c07925d8cd10dfbd0970ea7cb784f70e79523cca9dbcd72df38e5a46"},
{file = "sentry_sdk-1.41.0-py2.py3-none-any.whl", hash = "sha256:be4f8f4b29a80b6a3b71f0f31487beb9e296391da20af8504498a328befed53f"},
{file = "sentry-sdk-1.43.0.tar.gz", hash = "sha256:41df73af89d22921d8733714fb0fc5586c3461907e06688e6537d01a27e0e0f6"},
{file = "sentry_sdk-1.43.0-py2.py3-none-any.whl", hash = "sha256:8d768724839ca18d7b4c7463ef7528c40b7aa2bfbf7fe554d5f9a7c044acfd36"},
]
[package.dependencies]
@@ -2704,6 +2819,7 @@ asyncpg = ["asyncpg (>=0.23)"]
beam = ["apache-beam (>=2.12)"]
bottle = ["bottle (>=0.12.13)"]
celery = ["celery (>=3)"]
celery-redbeat = ["celery-redbeat (>=2)"]
chalice = ["chalice (>=1.16.0)"]
clickhouse-driver = ["clickhouse-driver (>=0.2.0)"]
django = ["django (>=1.8)"]
@@ -2714,6 +2830,7 @@ grpcio = ["grpcio (>=1.21.1)"]
httpx = ["httpx (>=0.16.0)"]
huey = ["huey (>=2)"]
loguru = ["loguru (>=0.5)"]
openai = ["openai (>=1.0.0)", "tiktoken (>=0.3.0)"]
opentelemetry = ["opentelemetry-distro (>=0.35b0)"]
opentelemetry-experimental = ["opentelemetry-distro (>=0.40b0,<1.0)", "opentelemetry-instrumentation-aiohttp-client (>=0.40b0,<1.0)", "opentelemetry-instrumentation-django (>=0.40b0,<1.0)", "opentelemetry-instrumentation-fastapi (>=0.40b0,<1.0)", "opentelemetry-instrumentation-flask (>=0.40b0,<1.0)", "opentelemetry-instrumentation-requests (>=0.40b0,<1.0)", "opentelemetry-instrumentation-sqlite3 (>=0.40b0,<1.0)", "opentelemetry-instrumentation-urllib (>=0.40b0,<1.0)"]
pure-eval = ["asttokens", "executing", "pure-eval"]
@@ -2829,18 +2946,18 @@ test = ["pytest"]
[[package]]
name = "setuptools"
version = "69.1.1"
version = "69.2.0"
description = "Easily download, build, install, upgrade, and uninstall Python packages"
optional = false
python-versions = ">=3.8"
files = [
{file = "setuptools-69.1.1-py3-none-any.whl", hash = "sha256:02fa291a0471b3a18b2b2481ed902af520c69e8ae0919c13da936542754b4c56"},
{file = "setuptools-69.1.1.tar.gz", hash = "sha256:5c0806c7d9af348e6dd3777b4f4dbb42c7ad85b190104837488eab9a7c945cf8"},
{file = "setuptools-69.2.0-py3-none-any.whl", hash = "sha256:c21c49fb1042386df081cb5d86759792ab89efca84cf114889191cd09aacc80c"},
{file = "setuptools-69.2.0.tar.gz", hash = "sha256:0ff4183f8f42cd8fa3acea16c45205521a4ef28f73c6391d8a25e92893134f2e"},
]
[package.extras]
docs = ["furo", "jaraco.packaging (>=9.3)", "jaraco.tidelift (>=1.4)", "pygments-github-lexers (==0.0.5)", "rst.linker (>=1.9)", "sphinx (<7.2.5)", "sphinx (>=3.5)", "sphinx-favicon", "sphinx-inline-tabs", "sphinx-lint", "sphinx-notfound-page (>=1,<2)", "sphinx-reredirects", "sphinxcontrib-towncrier"]
testing = ["build[virtualenv]", "filelock (>=3.4.0)", "flake8-2020", "ini2toml[lite] (>=0.9)", "jaraco.develop (>=7.21)", "jaraco.envs (>=2.2)", "jaraco.path (>=3.2.0)", "packaging (>=23.2)", "pip (>=19.1)", "pytest (>=6)", "pytest-checkdocs (>=2.4)", "pytest-cov", "pytest-enabler (>=2.2)", "pytest-home (>=0.5)", "pytest-mypy (>=0.9.1)", "pytest-perf", "pytest-ruff (>=0.2.1)", "pytest-timeout", "pytest-xdist", "tomli-w (>=1.0.0)", "virtualenv (>=13.0.0)", "wheel"]
testing = ["build[virtualenv]", "filelock (>=3.4.0)", "importlib-metadata", "ini2toml[lite] (>=0.9)", "jaraco.develop (>=7.21)", "jaraco.envs (>=2.2)", "jaraco.path (>=3.2.0)", "mypy (==1.9)", "packaging (>=23.2)", "pip (>=19.1)", "pytest (>=6)", "pytest-checkdocs (>=2.4)", "pytest-cov", "pytest-enabler (>=2.2)", "pytest-home (>=0.5)", "pytest-mypy (>=0.9.1)", "pytest-perf", "pytest-ruff (>=0.2.1)", "pytest-timeout", "pytest-xdist (>=3)", "tomli", "tomli-w (>=1.0.0)", "virtualenv (>=13.0.0)", "wheel"]
testing-integration = ["build[virtualenv] (>=1.0.3)", "filelock (>=3.4.0)", "jaraco.envs (>=2.2)", "jaraco.path (>=3.2.0)", "packaging (>=23.2)", "pytest", "pytest-enabler", "pytest-xdist", "tomli", "virtualenv (>=13.0.0)", "wheel"]
[[package]]
@@ -2947,9 +3064,58 @@ files = [
[package.dependencies]
mpmath = ">=0.19"
[[package]]
name = "tensorboard"
version = "2.16.2"
description = "TensorBoard lets you watch Tensors Flow"
optional = false
python-versions = ">=3.9"
files = [
{file = "tensorboard-2.16.2-py3-none-any.whl", hash = "sha256:9f2b4e7dad86667615c0e5cd072f1ea8403fc032a299f0072d6f74855775cc45"},
]
[package.dependencies]
absl-py = ">=0.4"
grpcio = ">=1.48.2"
markdown = ">=2.6.8"
numpy = ">=1.12.0"
protobuf = ">=3.19.6,<4.24.0 || >4.24.0"
setuptools = ">=41.0.0"
six = ">1.9"
tensorboard-data-server = ">=0.7.0,<0.8.0"
werkzeug = ">=1.0.1"
[[package]]
name = "tensorboard-data-server"
version = "0.7.2"
description = "Fast data loading for TensorBoard"
optional = false
python-versions = ">=3.7"
files = [
{file = "tensorboard_data_server-0.7.2-py3-none-any.whl", hash = "sha256:7e0610d205889588983836ec05dc098e80f97b7e7bbff7e994ebb78f578d0ddb"},
{file = "tensorboard_data_server-0.7.2-py3-none-macosx_10_9_x86_64.whl", hash = "sha256:9fe5d24221b29625dbc7328b0436ca7fc1c23de4acf4d272f1180856e32f9f60"},
{file = "tensorboard_data_server-0.7.2-py3-none-manylinux_2_31_x86_64.whl", hash = "sha256:ef687163c24185ae9754ed5650eb5bc4d84ff257aabdc33f0cc6f74d8ba54530"},
]
[[package]]
name = "tensorboardx"
version = "2.6.2.2"
description = "TensorBoardX lets you watch Tensors Flow without Tensorflow"
optional = false
python-versions = "*"
files = [
{file = "tensorboardX-2.6.2.2-py2.py3-none-any.whl", hash = "sha256:160025acbf759ede23fd3526ae9d9bfbfd8b68eb16c38a010ebe326dc6395db8"},
{file = "tensorboardX-2.6.2.2.tar.gz", hash = "sha256:c6476d7cd0d529b0b72f4acadb1269f9ed8b22f441e87a84f2a3b940bb87b666"},
]
[package.dependencies]
numpy = "*"
packaging = "*"
protobuf = ">=3.20"
[[package]]
name = "tensordict"
version = "0.4.0+551331d"
version = "0.4.0+b4c91e8"
description = ""
optional = false
python-versions = "*"
@@ -2970,7 +3136,7 @@ tests = ["pytest", "pytest-benchmark", "pytest-instafail", "pytest-rerunfailures
type = "git"
url = "https://github.com/pytorch/tensordict"
reference = "HEAD"
resolved_reference = "ed22554d6860731610df784b2f5d09f31d3dbc7a"
resolved_reference = "b4c91e8828c538ca0a50d8383fd99311a9afb078"
[[package]]
name = "termcolor"
@@ -3288,6 +3454,23 @@ perf = ["orjson"]
reports = ["pydantic (>=2.0.0)"]
sweeps = ["sweeps (>=0.2.0)"]
[[package]]
name = "werkzeug"
version = "3.0.1"
description = "The comprehensive WSGI web application library."
optional = false
python-versions = ">=3.8"
files = [
{file = "werkzeug-3.0.1-py3-none-any.whl", hash = "sha256:90a285dc0e42ad56b34e696398b8122ee4c681833fb35b8334a095d82c56da10"},
{file = "werkzeug-3.0.1.tar.gz", hash = "sha256:507e811ecea72b18a404947aded4b3390e1db8f826b494d76550ef45bb3b1dcc"},
]
[package.dependencies]
MarkupSafe = ">=2.1.1"
[package.extras]
watchdog = ["watchdog (>=2.3)"]
[[package]]
name = "zarr"
version = "2.17.1"
@@ -3311,20 +3494,20 @@ jupyter = ["ipytree (>=0.2.2)", "ipywidgets (>=8.0.0)", "notebook"]
[[package]]
name = "zipp"
version = "3.17.0"
version = "3.18.1"
description = "Backport of pathlib-compatible object wrapper for zip files"
optional = false
python-versions = ">=3.8"
files = [
{file = "zipp-3.17.0-py3-none-any.whl", hash = "sha256:0e923e726174922dce09c53c59ad483ff7bbb8e572e00c7f7c46b88556409f31"},
{file = "zipp-3.17.0.tar.gz", hash = "sha256:84e64a1c28cf7e91ed2078bb8cc8c259cb19b76942096c8d7b84947690cabaf0"},
{file = "zipp-3.18.1-py3-none-any.whl", hash = "sha256:206f5a15f2af3dbaee80769fb7dc6f249695e940acca08dfb2a4769fe61e538b"},
{file = "zipp-3.18.1.tar.gz", hash = "sha256:2884ed22e7d8961de1c9a05142eb69a247f120291bc0206a00a7642f09b5b715"},
]
[package.extras]
docs = ["furo", "jaraco.packaging (>=9.3)", "jaraco.tidelift (>=1.4)", "rst.linker (>=1.9)", "sphinx (<7.2.5)", "sphinx (>=3.5)", "sphinx-lint"]
testing = ["big-O", "jaraco.functools", "jaraco.itertools", "more-itertools", "pytest (>=6)", "pytest-black (>=0.3.7)", "pytest-checkdocs (>=2.4)", "pytest-cov", "pytest-enabler (>=2.2)", "pytest-ignore-flaky", "pytest-mypy (>=0.9.1)", "pytest-ruff"]
docs = ["furo", "jaraco.packaging (>=9.3)", "jaraco.tidelift (>=1.4)", "rst.linker (>=1.9)", "sphinx (>=3.5)", "sphinx-lint"]
testing = ["big-O", "jaraco.functools", "jaraco.itertools", "more-itertools", "pytest (>=6)", "pytest-checkdocs (>=2.4)", "pytest-cov", "pytest-enabler (>=2.2)", "pytest-ignore-flaky", "pytest-mypy", "pytest-ruff (>=0.2.1)"]
[metadata]
lock-version = "2.0"
python-versions = "^3.10"
content-hash = "ee86b84a795e6a3e9c2d79f244a87b55589adbe46d549ac38adf48be27c04cf9"
content-hash = "1a45c808e1c48bcbf4319d4cf6876771b7d50f40a5a8968a8b7f3af36192bf34"

1
pyproject.toml
View File

@@ -51,6 +51,7 @@ torchvision = "^0.17.1"
h5py = "^3.10.0"
dm-control = "1.0.14"
huggingface-hub = {extras = ["hf-transfer"], version = "^0.21.4"}
robomimic = "0.2.0"
[tool.poetry.group.dev.dependencies]

0
tests/data/aloha_sim_insertion_human/action.memmap → tests/data/aloha_sim_insertion_human/replay_buffer/action.memmap
View File

0
tests/data/aloha_sim_insertion_human/episode.memmap → tests/data/aloha_sim_insertion_human/replay_buffer/episode.memmap
View File

0
tests/data/aloha_sim_insertion_human/frame_id.memmap → tests/data/aloha_sim_insertion_human/replay_buffer/frame_id.memmap
View File

0
tests/data/aloha_sim_insertion_human/meta.json → tests/data/aloha_sim_insertion_human/replay_buffer/meta.json
View File

0
tests/data/aloha_sim_insertion_human/next/done.memmap → tests/data/aloha_sim_insertion_human/replay_buffer/next/done.memmap
View File

0
tests/data/aloha_sim_insertion_human/next/meta.json → tests/data/aloha_sim_insertion_human/replay_buffer/next/meta.json
View File

0
tests/data/aloha_sim_insertion_human/next/observation/image/meta.json → tests/data/aloha_sim_insertion_human/replay_buffer/next/observation/image/meta.json
View File

0
tests/data/aloha_sim_insertion_human/next/observation/image/top.memmap → tests/data/aloha_sim_insertion_human/replay_buffer/next/observation/image/top.memmap
View File

0
tests/data/aloha_sim_insertion_human/next/observation/meta.json → tests/data/aloha_sim_insertion_human/replay_buffer/next/observation/meta.json
View File

0
tests/data/aloha_sim_insertion_human/next/observation/state.memmap → tests/data/aloha_sim_insertion_human/replay_buffer/next/observation/state.memmap
View File

0
tests/data/aloha_sim_insertion_human/observation/image/meta.json → tests/data/aloha_sim_insertion_human/replay_buffer/observation/image/meta.json
View File

0
tests/data/aloha_sim_insertion_human/observation/image/top.memmap → tests/data/aloha_sim_insertion_human/replay_buffer/observation/image/top.memmap
View File

0
tests/data/aloha_sim_insertion_human/observation/meta.json → tests/data/aloha_sim_insertion_human/replay_buffer/observation/meta.json
View File

0
tests/data/aloha_sim_insertion_human/observation/state.memmap → tests/data/aloha_sim_insertion_human/replay_buffer/observation/state.memmap
View File

BIN
tests/data/aloha_sim_insertion_human/stats.pth
View File

Binary file not shown.

0
tests/data/aloha_sim_insertion_scripted/action.memmap → tests/data/aloha_sim_insertion_scripted/replay_buffer/action.memmap
View File

0
tests/data/aloha_sim_insertion_scripted/episode.memmap → tests/data/aloha_sim_insertion_scripted/replay_buffer/episode.memmap
View File

0
tests/data/aloha_sim_insertion_scripted/frame_id.memmap → tests/data/aloha_sim_insertion_scripted/replay_buffer/frame_id.memmap
View File

0
tests/data/aloha_sim_insertion_scripted/meta.json → tests/data/aloha_sim_insertion_scripted/replay_buffer/meta.json
View File

0
tests/data/aloha_sim_insertion_scripted/next/done.memmap → tests/data/aloha_sim_insertion_scripted/replay_buffer/next/done.memmap
View File

0
tests/data/aloha_sim_insertion_scripted/next/meta.json → tests/data/aloha_sim_insertion_scripted/replay_buffer/next/meta.json
View File

0
tests/data/aloha_sim_insertion_scripted/next/observation/image/meta.json → tests/data/aloha_sim_insertion_scripted/replay_buffer/next/observation/image/meta.json
View File

0
tests/data/aloha_sim_insertion_scripted/next/observation/image/top.memmap → tests/data/aloha_sim_insertion_scripted/replay_buffer/next/observation/image/top.memmap
View File

0
tests/data/aloha_sim_insertion_scripted/next/observation/meta.json → tests/data/aloha_sim_insertion_scripted/replay_buffer/next/observation/meta.json
View File

0
tests/data/aloha_sim_insertion_scripted/next/observation/state.memmap → tests/data/aloha_sim_insertion_scripted/replay_buffer/next/observation/state.memmap
View File

0
tests/data/aloha_sim_insertion_scripted/observation/image/meta.json → tests/data/aloha_sim_insertion_scripted/replay_buffer/observation/image/meta.json
View File

0
tests/data/aloha_sim_insertion_scripted/observation/image/top.memmap → tests/data/aloha_sim_insertion_scripted/replay_buffer/observation/image/top.memmap
View File

0
tests/data/aloha_sim_insertion_scripted/observation/meta.json → tests/data/aloha_sim_insertion_scripted/replay_buffer/observation/meta.json
View File

0
tests/data/aloha_sim_insertion_scripted/observation/state.memmap → tests/data/aloha_sim_insertion_scripted/replay_buffer/observation/state.memmap
View File

BIN
tests/data/aloha_sim_insertion_scripted/stats.pth
View File

Binary file not shown.

0
tests/data/aloha_sim_transfer_cube_human/action.memmap → tests/data/aloha_sim_transfer_cube_human/replay_buffer/action.memmap
View File

0
tests/data/aloha_sim_transfer_cube_human/episode.memmap → tests/data/aloha_sim_transfer_cube_human/replay_buffer/episode.memmap
View File

0
tests/data/aloha_sim_transfer_cube_human/frame_id.memmap → tests/data/aloha_sim_transfer_cube_human/replay_buffer/frame_id.memmap
View File

0
tests/data/aloha_sim_transfer_cube_human/meta.json → tests/data/aloha_sim_transfer_cube_human/replay_buffer/meta.json
View File

0
tests/data/aloha_sim_transfer_cube_human/next/done.memmap → tests/data/aloha_sim_transfer_cube_human/replay_buffer/next/done.memmap
View File

0
tests/data/aloha_sim_transfer_cube_human/next/meta.json → tests/data/aloha_sim_transfer_cube_human/replay_buffer/next/meta.json
View File

0
tests/data/aloha_sim_transfer_cube_human/next/observation/image/meta.json → tests/data/aloha_sim_transfer_cube_human/replay_buffer/next/observation/image/meta.json
View File

0
tests/data/aloha_sim_transfer_cube_human/next/observation/image/top.memmap → tests/data/aloha_sim_transfer_cube_human/replay_buffer/next/observation/image/top.memmap
View File

0
tests/data/aloha_sim_transfer_cube_human/next/observation/meta.json → tests/data/aloha_sim_transfer_cube_human/replay_buffer/next/observation/meta.json
View File

0
tests/data/aloha_sim_transfer_cube_human/next/observation/state.memmap → tests/data/aloha_sim_transfer_cube_human/replay_buffer/next/observation/state.memmap
View File

0
tests/data/aloha_sim_transfer_cube_human/observation/image/meta.json → tests/data/aloha_sim_transfer_cube_human/replay_buffer/observation/image/meta.json
View File

0
tests/data/aloha_sim_transfer_cube_human/observation/image/top.memmap → tests/data/aloha_sim_transfer_cube_human/replay_buffer/observation/image/top.memmap
View File

0
tests/data/aloha_sim_transfer_cube_human/observation/meta.json → tests/data/aloha_sim_transfer_cube_human/replay_buffer/observation/meta.json
View File

0
tests/data/aloha_sim_transfer_cube_human/observation/state.memmap → tests/data/aloha_sim_transfer_cube_human/replay_buffer/observation/state.memmap
View File

BIN
tests/data/aloha_sim_transfer_cube_human/stats.pth
View File

Binary file not shown.

0
tests/data/aloha_sim_transfer_cube_scripted/action.memmap → tests/data/aloha_sim_transfer_cube_scripted/replay_buffer/action.memmap
View File

0
tests/data/aloha_sim_transfer_cube_scripted/episode.memmap → tests/data/aloha_sim_transfer_cube_scripted/replay_buffer/episode.memmap
View File

0
tests/data/aloha_sim_transfer_cube_scripted/frame_id.memmap → tests/data/aloha_sim_transfer_cube_scripted/replay_buffer/frame_id.memmap
View File

0
tests/data/aloha_sim_transfer_cube_scripted/meta.json → tests/data/aloha_sim_transfer_cube_scripted/replay_buffer/meta.json
View File

0
tests/data/aloha_sim_transfer_cube_scripted/next/done.memmap → tests/data/aloha_sim_transfer_cube_scripted/replay_buffer/next/done.memmap
View File

0
tests/data/aloha_sim_transfer_cube_scripted/next/meta.json → tests/data/aloha_sim_transfer_cube_scripted/replay_buffer/next/meta.json
View File

0
tests/data/aloha_sim_transfer_cube_scripted/next/observation/image/meta.json → tests/data/aloha_sim_transfer_cube_scripted/replay_buffer/next/observation/image/meta.json
View File

0
tests/data/aloha_sim_transfer_cube_scripted/next/observation/image/top.memmap → tests/data/aloha_sim_transfer_cube_scripted/replay_buffer/next/observation/image/top.memmap
View File

0
tests/data/aloha_sim_transfer_cube_scripted/next/observation/meta.json → tests/data/aloha_sim_transfer_cube_scripted/replay_buffer/next/observation/meta.json
View File

0
tests/data/aloha_sim_transfer_cube_scripted/next/observation/state.memmap → tests/data/aloha_sim_transfer_cube_scripted/replay_buffer/next/observation/state.memmap
View File

0
tests/data/aloha_sim_transfer_cube_scripted/observation/image/meta.json → tests/data/aloha_sim_transfer_cube_scripted/replay_buffer/observation/image/meta.json
View File

0
tests/data/aloha_sim_transfer_cube_scripted/observation/image/top.memmap → tests/data/aloha_sim_transfer_cube_scripted/replay_buffer/observation/image/top.memmap
View File

0
tests/data/aloha_sim_transfer_cube_scripted/observation/meta.json → tests/data/aloha_sim_transfer_cube_scripted/replay_buffer/observation/meta.json
View File

0
tests/data/aloha_sim_transfer_cube_scripted/observation/state.memmap → tests/data/aloha_sim_transfer_cube_scripted/replay_buffer/observation/state.memmap
View File

BIN
tests/data/aloha_sim_transfer_cube_scripted/stats.pth
View File

Binary file not shown.

0
tests/data/pusht/action.memmap → tests/data/pusht/replay_buffer/action.memmap
View File

0
tests/data/pusht/episode.memmap → tests/data/pusht/replay_buffer/episode.memmap
View File

0
tests/data/pusht/frame_id.memmap → tests/data/pusht/replay_buffer/frame_id.memmap
View File

0
tests/data/pusht/meta.json → tests/data/pusht/replay_buffer/meta.json
View File

0
tests/data/pusht/next/done.memmap → tests/data/pusht/replay_buffer/next/done.memmap
View File

0
tests/data/pusht/next/meta.json → tests/data/pusht/replay_buffer/next/meta.json
View File

0
tests/data/pusht/next/observation/image.memmap → tests/data/pusht/replay_buffer/next/observation/image.memmap
View File

0
tests/data/pusht/next/observation/meta.json → tests/data/pusht/replay_buffer/next/observation/meta.json
View File

0
tests/data/pusht/next/observation/state.memmap → tests/data/pusht/replay_buffer/next/observation/state.memmap
View File

0
tests/data/pusht/next/reward.memmap → tests/data/pusht/replay_buffer/next/reward.memmap
View File

Some files were not shown because too many files have changed in this diff Show More