From 3e786caf94ea2167db13991480a9fa8714a29122 Mon Sep 17 00:00:00 2001 From: Sebastian Peralta Date: Tue, 12 Dec 2023 05:04:44 -0500 Subject: [PATCH] updated readme, started inference server --- .coverage | Bin 53248 -> 53248 bytes .gitmodules | 6 + README.md | 94 ++++++++++++-- demo_out/.gitkeep | 0 demo_out/test_0.png | Bin 0 -> 84513 bytes demo_out/test_1.png | Bin 0 -> 84513 bytes demo_out/test_2.png | Bin 0 -> 84513 bytes demo_out/test_3.png | Bin 0 -> 88228 bytes demo_out/test_4.png | Bin 0 -> 99452 bytes demo_out/test_5.png | Bin 0 -> 99452 bytes poetry.lock | 2 +- robo_transformers/inference.py | 121 +++++++++++++++++++ robo_transformers/rt1/__init__.py | 0 robo_transformers/{ => rt1}/rt1_inference.py | 106 +++++++++++++--- tests/test_rt1_policy.py | 8 +- third_party/rt1/robotics_transformer | 1 + third_party/t2r/tensor2robot | 1 + 17 files changed, 309 insertions(+), 30 deletions(-) create mode 100644 .gitmodules create mode 100644 demo_out/.gitkeep create mode 100644 demo_out/test_0.png create mode 100644 demo_out/test_1.png create mode 100644 demo_out/test_2.png create mode 100644 demo_out/test_3.png create mode 100644 demo_out/test_4.png create mode 100644 demo_out/test_5.png create mode 100644 robo_transformers/inference.py create mode 100644 robo_transformers/rt1/__init__.py rename robo_transformers/{ => rt1}/rt1_inference.py (53%) create mode 160000 third_party/rt1/robotics_transformer create mode 160000 third_party/t2r/tensor2robot diff --git a/.coverage b/.coverage index afdf496e228b588ed74f1aa1350d15c19142ef02..bb6998138d26c1e1fa69c204c2949beee5656103 100644 GIT binary patch delta 469 zcmZozz}&Eac>{|B7c)Nx1OGbyaDI->f&#zz*nF8;7#ig#Z}wN!(k)5NEzmE@Ps)!k zDN4*MPRlRKO)V{|B8zcW(2L8311qBxKvjsA;Ff^)9=JQu&jW0Q(-ep!XJJBAeuCW diff --git a/.gitmodules b/.gitmodules new file mode 100644 index 0000000..19b1708 --- /dev/null +++ b/.gitmodules @@ -0,0 +1,6 @@ +[submodule "third_party/t2r/tensor2robot"] + path = third_party/t2r/tensor2robot + url = https://github.com/sebbyjp/tensor2robot.git +[submodule "third_party/rt1/robotics_transformer"] + path = third_party/rt1/robotics_transformer + url = https://www.github.com/google-research/robotics_transformer.git diff --git a/README.md b/README.md index 942f77e..2137d7c 100644 --- a/README.md +++ b/README.md @@ -1,19 +1,91 @@ [![Code Coverage](https://codecov.io/gh/sebbyjp/dgl_ros/branch/code_cov/graph/badge.svg?token=9225d677-c4f2-4607-a9dd-8c22446f13bc)](https://codecov.io/gh/sebbyjp/dgl_ros) # Library for Robotic Transformers. RT-1 and RT-X-1. -# Instructions: -- Clone this repo: `https://github.com/sebbyjp/robo_transformers.git --recurse-submodules` -- Run `pip install poetry` -- Run `poetry build .` -- Run inference `python robo_transformers/rt1_inference.py` -## Download RT-1-X model from the Open-X Embodiment paper. +## Install: + +Requirements: +- python3.11 + +Clone this repo: +`git clone https://github.com/sebbyjp/robo_transformers.git --recurse-submodules` + +`cd robo_transformers` + +Use poetry + +`pip install poetry` + +Create the requirements.txt: + +`poetry install && poetry export` + +**Install dependencies:** + +- *Optional*: Activate a virtual environment: + + - `poetry config virtualenvs.in-project true` + - `poetry env use .venv/bin/activate` (Run `poetry env info` to find the proper path) + +`python3 -m pip install -r requirements.txt` + +## Run Inference On Demo Images. +`python robo_transformers/inference/rt1/rt1_inference.py` + +### Optional +To install the checkpoints from the robotics_transformer git repo, you will need git-lfs +- Install git-lfs (use brew or apt if on unix), then + +`cd third_party/rt1/robotics_transformer && git lfs install` +`git lfs pull https://www.github.com/google-research/robotics_transformer.git ` + +### Optional: Download RT-1-X model from the Open-X Embodiment paper. - Install gsutil: `pip install gsutil` - Run: `gsutil -m cp -r gs://gdm-robotics-open-x-embodiment/open_x_embodiment_and_rt_x_oss/rt_1_x_tf_trained_for_002272480_step.zip ./checkpoints/` - Unzip: `cd checkpoints && unzip rt_1_x_tf_trained_for_002272480_step.zip` -## Optional -To install the checkpoints from the robotics_transformer git repo, you will need git-lfs -- Install git-lfs (use brew or apt if on unix) -- Run `git lfs install` -- Run `git lfs clone https://www.github.com/google-research/robotics_transformer.git ` \ No newline at end of file +## Notes +`action, next_policy_state = model.act(time_step, curr_policy_state)` +### policy state is internal state of network: +In this case it is a 6-frame window of past observations,actions and the index in time. +``` +{'action_tokens': ArraySpec(shape=(6, 11, 1, 1), dtype=dtype('int32'), name='action_tokens'), + 'image': ArraySpec(shape=(6, 256, 320, 3), dtype=dtype('uint8'), name='image'), + 'step_num': ArraySpec(shape=(1, 1, 1, 1), dtype=dtype('int32'), name='step_num'), + 't': ArraySpec(shape=(1, 1, 1, 1), dtype=dtype('int32'), name='t')} + ``` + + +### time_step is the input from the environment: +``` +{'discount': BoundedArraySpec(shape=(), dtype=dtype('float32'), name='discount', minimum=0.0, maximum=1.0), + 'observation': {'base_pose_tool_reached': ArraySpec(shape=(7,), dtype=dtype('float32'), name='base_pose_tool_reached'), + 'gripper_closed': ArraySpec(shape=(1,), dtype=dtype('float32'), name='gripper_closed'), + 'gripper_closedness_commanded': ArraySpec(shape=(1,), dtype=dtype('float32'), name='gripper_closedness_commanded'), + 'height_to_bottom': ArraySpec(shape=(1,), dtype=dtype('float32'), name='height_to_bottom'), + 'image': ArraySpec(shape=(256, 320, 3), dtype=dtype('uint8'), name='image'), + 'natural_language_embedding': ArraySpec(shape=(512,), dtype=dtype('float32'), name='natural_language_embedding'), + 'natural_language_instruction': ArraySpec(shape=(), dtype=dtype('O'), name='natural_language_instruction'), + 'orientation_box': ArraySpec(shape=(2, 3), dtype=dtype('float32'), name='orientation_box'), + 'orientation_start': ArraySpec(shape=(4,), dtype=dtype('float32'), name='orientation_in_camera_space'), + 'robot_orientation_positions_box': ArraySpec(shape=(3, 3), dtype=dtype('float32'), name='robot_orientation_positions_box'), + 'rotation_delta_to_go': ArraySpec(shape=(3,), dtype=dtype('float32'), name='rotation_delta_to_go'), + 'src_rotation': ArraySpec(shape=(4,), dtype=dtype('float32'), name='transform_camera_robot'), + 'vector_to_go': ArraySpec(shape=(3,), dtype=dtype('float32'), name='vector_to_go'), + 'workspace_bounds': ArraySpec(shape=(3, 3), dtype=dtype('float32'), name='workspace_bounds')}, + 'reward': ArraySpec(shape=(), dtype=dtype('float32'), name='reward'), + 'step_type': ArraySpec(shape=(), dtype=dtype('int32'), name='step_type')} + ``` + +### action: +``` +{'base_displacement_vector': BoundedArraySpec(shape=(2,), dtype=dtype('float32'), name='base_displacement_vector', minimum=-1.0, maximum=1.0), + 'base_displacement_vertical_rotation': BoundedArraySpec(shape=(1,), dtype=dtype('float32'), name='base_displacement_vertical_rotation', minimum=-3.1415927410125732, maximum=3.1415927410125732), + 'gripper_closedness_action': BoundedArraySpec(shape=(1,), dtype=dtype('float32'), name='gripper_closedness_action', minimum=-1.0, maximum=1.0), + 'rotation_delta': BoundedArraySpec(shape=(3,), dtype=dtype('float32'), name='rotation_delta', minimum=-1.5707963705062866, maximum=1.5707963705062866), + 'terminate_episode': BoundedArraySpec(shape=(3,), dtype=dtype('int32'), name='terminate_episode', minimum=0, maximum=1), + 'world_vector': BoundedArraySpec(shape=(3,), dtype=dtype('float32'), name='world_vector', minimum=-1.0, maximum=1.0)} + ``` + + ## TODO: + - Render action, policy_state, observation specs in something prettier like pandas data frame. \ No newline at end of file diff --git a/demo_out/.gitkeep b/demo_out/.gitkeep new file mode 100644 index 0000000..e69de29 diff --git a/demo_out/test_0.png b/demo_out/test_0.png new file mode 100644 index 0000000000000000000000000000000000000000..e83afd62c88d697c505c6768955b1dc6096137db GIT binary patch literal 84513 zcmWifcQ{+`8^&W*jFMQ@(jc~?B5Ksu7)7bo8r7Pych%lOjM^o!+EO)2tM(qDX7E+J z5gJva_Drq%`TcRub*}T@dp*xN@BM!6`$>3gpnaYG7Cis}xc*QFVGICJs9jwEAm!DY zM-}qvSI>Zl2zArI{JIN5jk|^KZS?g{eDjmug|qy&Np{U=z%;d5^*Vj`+}SLBi3z>D zdTh8x=lh5?o^?6pHBH0d^*jM%sN=idI3P!)7~qWsHb_ztI-Tp+(h3RvJgZns&uirx zTjE$)3xD-&Iyan4B~?8mw&)OgbIKc zx<9qgT+9qE*Bi;Xu!2-=)l2qZ0*Y?aRr1X|v8GJwBl)863bCXM5<-Kq%BMfT~LzY?~c{M23fX 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type from a sensor or web interface. A camera image, a point cloud, etc.''' + pass + +class InternalState(object): + ''' The internal state of the agent. This is produced internally in contrast to Observation.''' + pass + +# This is different than the Observer in tf-agents. That observer simply records from the environment. +# This Observer is responsible from taking in data from the environment and converting it into an Observation. +# Essentially it is a tf-agents observer + a post-processor. + +# In the case of RT1, the Observer would take in a camera image and a natural language instruction and convert it into an Observation. +class Observer(object): + def observe(self, srcs: list[Src]) -> Observation: + pass + +class Rt1Observer(Observer): + def observe(self, srcs: list[Src(PIL.Image), Src(str)]) -> Observation: + pass + + +def inference( + model: any, + internal_state: dict, + observation: dict, + supervision: dict, + config: dict, +) -> dict: + """Infer action from observation. + + Args: + cgn (CGN): ContactGraspNet model + pcd (np.ndarray): point cloud + threshold (float, optional): Success threshol. Defaults to 0.5. + visualize (bool, optional): Whether or not to visualize output. Defaults to False. + max_grasps (int, optional): Maximum grasps. Zero means unlimited. Defaults to 0. + obj_mask (np.ndarray, optional): Object mask. Defaults to None. + + Returns: + tuple[np.ndarray, np.ndarray, np.ndarray]: The grasps, confidence and indices of the points used for inference. + """ + # cgn.eval() + # pcd = torch.Tensor(pcd).to(dtype=torch.float32).to(cgn.device) + # if pcd.shape[0] > 20000: + # downsample_idxs = np.array(random.sample(range(pcd.shape[0] - 1), 20000)) + # else: + # downsample_idxs = np.arange(pcd.shape[0]) + # pcd = pcd[downsample_idxs, :] + + # batch = torch.zeros(pcd.shape[0]).to(dtype=torch.int64).to(cgn.device) + # fps_idxs = farthest_point_sample(pcd, batch, 2048 / pcd.shape[0]) + + # if obj_mask is not None: + # obj_mask = torch.Tensor(obj_mask[downsample_idxs]) + # obj_mask = obj_mask[fps_idxs] + # else: + # obj_mask = torch.ones(fps_idxs.shape[0]) + # points, pred_grasps, confidence, pred_widths, _, _ = cgn( + # pcd[:, 3:], + # pcd_poses=pcd[:, :3], + # batch=batch, + # idxs=fps_idxs, + # gripper_depth=gripper_depth, + # gripper_width=gripper_width, + # ) + + # sig = torch.nn.Sigmoid() + # confidence = sig(confidence) + # confidence = confidence.reshape(-1) + # pred_grasps = ( + # torch.flatten(pred_grasps, start_dim=0, end_dim=1).detach().cpu().numpy() + # ) + + # confidence = ( + # obj_mask.detach().cpu().numpy() * confidence.detach().cpu().numpy() + # ).reshape(-1) + # pred_widths = ( + # torch.flatten(pred_widths, start_dim=0, end_dim=1).detach().cpu().numpy() + # ) + # points = torch.flatten(points, start_dim=0, end_dim=1).detach().cpu().numpy() + + # success_mask = (confidence > threshold).nonzero()[0] + # if len(success_mask) == 0: + # print("failed to find successful grasps") + # return None, None, None + + # success_grasps = pred_grasps[success_mask] + # success_confidence = confidence[success_mask] + # print("Found {} grasps".format(success_grasps.shape[0])) + # if max_grasps > 0 and success_grasps.shape[0] > max_grasps: + # success_grasps = success_grasps[:max_grasps] + # success_confidence = success_confidence[:max_grasps] + # if visualize: + # visualize_grasps( + # pcd.detach().cpu().numpy(), + # success_grasps, + # gripper_depth=gripper_depth, + # gripper_width=gripper_width, + # ) + # return success_grasps, success_confidence, downsample_idxs \ No newline at end of file diff --git a/robo_transformers/rt1/__init__.py b/robo_transformers/rt1/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/robo_transformers/rt1_inference.py b/robo_transformers/rt1/rt1_inference.py similarity index 53% rename from robo_transformers/rt1_inference.py rename to robo_transformers/rt1/rt1_inference.py index f89f141..ebef341 100644 --- a/robo_transformers/rt1_inference.py +++ b/robo_transformers/rt1/rt1_inference.py @@ -36,16 +36,17 @@ def load_rt1( if not verbose: absl.logging.set_verbosity(absl.logging.ERROR) print('Loading RT-1 from checkpoint: {}...'.format(checkpoint_path)) - rt1_policy: LoadedPolicy = LoadedPolicy(model_path=checkpoint_path, - load_specs_from_pbtxt=load_specs_from_pbtxt, - use_tf_function=use_tf_function, - batch_time_steps=batch_time_steps) + rt1_policy: LoadedPolicy = LoadedPolicy( + model_path=checkpoint_path, + load_specs_from_pbtxt=load_specs_from_pbtxt, + use_tf_function=use_tf_function, + batch_time_steps=batch_time_steps) absl.logging.set_verbosity(absl.logging.WARN) print('RT-1 loaded.') return rt1_policy -def text_embed(input: str, batch_size: int = 1) -> tf.Tensor: +def embed_text(input: str, batch_size: int = 1) -> tf.Tensor: '''Embeds a string using the Universal Sentence Encoder. Args: @@ -60,7 +61,7 @@ def text_embed(input: str, batch_size: int = 1) -> tf.Tensor: (batch_size, 512)) -def demo_imgs(batch_size: int = 1) -> list[tf.Tensor]: +def get_demo_imgs(batch_size: int = 1) -> list[tf.Tensor]: '''Loads a demo video from the ./demo_vids/ directory. Returns: @@ -81,19 +82,94 @@ def demo_imgs(batch_size: int = 1) -> list[tf.Tensor]: return imgs -def run_on_test_imgs(batch_size: int = 1, rt1_policy: tf_agents.policies.tf_policy.TFPolicy = None): +# Dumbed down representations of the actual specs. + + +class RT1InternalState(object): + + def __init__(self, + batch_size: int = 1, + nframes: int = 6, + imgs: np.ndarray = None, + t: int = 5, + step_num: int = 5): + if imgs is None: + self.imgs = tf.zeros((batch_size, 6, HEIGHT, WIDTH, 3), + dtype=np.uint8) + self.batch_size = batch_size + self.nframes = nframes + self.t = tf.constant(t, + shape=(batch_size, nframes, 1, 1, 1, 1), + dtype=tf.int32) + self.step_num = tf.constant(step_num, + shape=(batch_size, nframes, 1, 1, 1, 1), + dtype=tf.int32) + + +class RT1InferenceObservation(object): + + def __init__(self, + img: np.ndarray = None, + instructions: str = "pick up the block"): + if img is None: + self.imgs = tf.zeros((HEIGHT, WIDTH, 3), dtype=np.uint8) + + +def inference(instructions: list[str], + imgs: np.ndarray, + batch_size: int = 1, + reward: np.ndarray = None, + rt1_policy: tf_agents.policies.tf_policy.TFPolicy = None, + rt1_curr_state=None): + # Actually just run from the beginning instead of inventing state if we don't have it. if rt1_policy is None: rt1_policy = load_rt1() - instructions = text_embed(["pick up the block"], batch_size) - imgs = demo_imgs(batch_size) + if rt1_curr_state is None: + state = rt1_policy.get_initial_state(batch_size) + # Pretend that we have some state from a previous timestep. + state['image'] = tf.tile(tf.reshape(imgs[0], (1, 1, HEIGHT, WIDTH, 3)), + [1, 6, 1, 1, 1]) + # Pretend this is timestep 5 out of 6. + state['t'] = tf.constant(5, + shape=(batch_size, 1, 1, 1, 1), + dtype=tf.int32) + state['step_num'] = tf.constant(5, + shape=(batch_size, 1, 1, 1, 1), + dtype=tf.int32) + + # Start with zeros in all fields since actual spec has a lot more state + # than we care to give. + observation = tf_agents.specs.zero_spec_nest(tf_agents.specs.from_spec( + rt1_policy.time_step_spec.observation), + outer_dims=(batch_size,)) + observation['image'] = imgs[-1] + observation['natural_language_embedding'] = instructions[-1] + + time_step = ts.transition(observation, + reward=reward * tf.ones( + (batch_size,), dtype=tf.float32), + outer_dims=(batch_size,)) + action, state, _ = rt1_policy.action(time_step, state) + + # View the results. + pprint(action) + # TODO(speralta): convert this to the dummed down specs. + return action, state + + +# TODO(speralta): Use the inference method above to run on the demo images. +def run_on_demo_imgs(batch_size: int = 1, + rt1_policy: tf_agents.policies.tf_policy.TFPolicy = None): + instructions = embed_text(["pick up the block"], batch_size) + imgs = get_demo_imgs(batch_size) observation = tf_agents.specs.zero_spec_nest(tf_agents.specs.from_spec( rt1_policy.time_step_spec.observation), outer_dims=(batch_size,)) - # Give a small reward for the first observation which is far from the block. - reward = 0.25 + # Give a small reward for the first observation which is far from the block. + reward = 0.25 observation['image'] = imgs[0] observation['natural_language_embedding'] = instructions @@ -113,7 +189,8 @@ def run_on_test_imgs(batch_size: int = 1, rt1_policy: tf_agents.policies.tf_poli # Simulate the first image, far from the block. action, state, _ = rt1_policy.action(time_step, state) time_step = ts.transition(observation, - reward= reward * tf.ones((batch_size,), dtype=tf.float32), + reward=reward * tf.ones( + (batch_size,), dtype=tf.float32), outer_dims=(batch_size,)) # Simulate the second image, closer to the block. @@ -129,8 +206,9 @@ def run_on_test_imgs(batch_size: int = 1, rt1_policy: tf_agents.policies.tf_poli for i in range(6): img_out = Image.fromarray(state['image'][0, i, :, :, :].numpy().astype( np.uint8)) - img_out.save('test_imgs/test_{}.png'.format(i)) + img_out.save('demo_out/test_{}.png'.format(i)) if __name__ == '__main__': - run_on_test_imgs() + + run_on_demo_imgs() diff --git a/tests/test_rt1_policy.py b/tests/test_rt1_policy.py index 2cddd9f..f374e05 100644 --- a/tests/test_rt1_policy.py +++ b/tests/test_rt1_policy.py @@ -1,18 +1,18 @@ -from robo_transformers.rt1_inference import run_on_test_imgs, load_rt1 +from robo_transformers.rt1.rt1_inference import run_on_demo_imgs, load_rt1 from importlib.resources import files checkpoints_dir = files("robotics_transformer").joinpath("trained_checkpoints/") def test_rt_1_policy_main(): policy = load_rt1(checkpoints_dir.joinpath('rt1main')) - assert run_on_test_imgs(rt1_policy=policy) is None + assert run_on_demo_imgs(rt1_policy=policy) is None def test_rt_1_policy_1_simreal(): policy = load_rt1(checkpoints_dir.joinpath('rt1simreal')) - assert run_on_test_imgs(rt1_policy=policy) is None + assert run_on_demo_imgs(rt1_policy=policy) is None def test_rt_1_policy_1_multirobot(): policy = load_rt1(checkpoints_dir.joinpath('rt1multirobot')) - assert run_on_test_imgs(rt1_policy=policy) is None + assert run_on_demo_imgs(rt1_policy=policy) is None if __name__ == '__main__': test_rt_1_policy_main() diff --git a/third_party/rt1/robotics_transformer b/third_party/rt1/robotics_transformer new file mode 160000 index 0000000..4569641 --- /dev/null +++ b/third_party/rt1/robotics_transformer @@ -0,0 +1 @@ +Subproject commit 4569641b8111f3f402c32d8e24becd2a6e952ecc diff --git a/third_party/t2r/tensor2robot b/third_party/t2r/tensor2robot new file mode 160000 index 0000000..0f1a82c --- /dev/null +++ b/third_party/t2r/tensor2robot @@ -0,0 +1 @@ +Subproject commit 0f1a82c19131eaedb285aaaaf44b2dc3d64706a0