From 61eb153dbc3562f9d3ca5efde3ebc1c41054e31c Mon Sep 17 00:00:00 2001 From: Florian Vahl <7vahl@informatik.uni-hamburg.de> Date: Thu, 9 Jan 2025 16:54:21 +0100 Subject: [PATCH 01/10] Add first version of ros webots runner --- ddlitlab2024/dataset/pytorch.py | 30 +- ddlitlab2024/ml/inference/ros.py | 276 ++++++++ ddlitlab2024/utils/utils.py | 26 + poetry.lock | 1037 +++++++++++++----------------- pyproject.toml | 2 +- 5 files changed, 739 insertions(+), 632 deletions(-) create mode 100644 ddlitlab2024/ml/inference/ros.py diff --git a/ddlitlab2024/dataset/pytorch.py b/ddlitlab2024/dataset/pytorch.py index 2c8186c..feb6f6c 100755 --- a/ddlitlab2024/dataset/pytorch.py +++ b/ddlitlab2024/dataset/pytorch.py @@ -14,9 +14,9 @@ from ddlitlab2024 import DB_PATH from ddlitlab2024.dataset import logger -from ddlitlab2024.dataset.models import JointStates, RobotState +from ddlitlab2024.dataset.models import RobotState from ddlitlab2024.ml.model.encoder.imu import IMUEncoder -from ddlitlab2024.utils.utils import quats_to_5d +from ddlitlab2024.utils.utils import JOINT_NAMES_ORDER, quats_to_5d def connect_to_db(data_base_path: str | Path = DB_PATH, worker_id: int | None = None) -> sqlite3.Connection: @@ -78,30 +78,6 @@ def __init__( self.trajectory_stride = trajectory_stride self.num_joints = num_joints - # Define the naming and default ordering of the joints - self.joint_names = [ - JointStates.head_pan.name, - JointStates.head_tilt.name, - JointStates.l_ankle_pitch.name, - JointStates.l_ankle_roll.name, - JointStates.l_elbow.name, - JointStates.l_hip_pitch.name, - JointStates.l_hip_roll.name, - JointStates.l_hip_yaw.name, - JointStates.l_knee.name, - JointStates.l_shoulder_pitch.name, - JointStates.l_shoulder_roll.name, - JointStates.r_ankle_pitch.name, - JointStates.r_ankle_roll.name, - JointStates.r_elbow.name, - JointStates.r_hip_pitch.name, - JointStates.r_hip_roll.name, - JointStates.r_hip_yaw.name, - JointStates.r_knee.name, - JointStates.r_shoulder_pitch.name, - JointStates.r_shoulder_roll.name, - ] - # Print out metadata cursor = self.db_connection.cursor() cursor.execute("SELECT team_name, start_time, location, original_file FROM Recording") @@ -142,7 +118,7 @@ def query_joint_data( ) # Convert to numpy array, keep only the joint angle columns in alphabetical order - raw_joint_data = raw_joint_data[self.joint_names].to_numpy(dtype=np.float32) + raw_joint_data = raw_joint_data[JOINT_NAMES_ORDER].to_numpy(dtype=np.float32) assert raw_joint_data.shape[1] == self.num_joints, "The number of joints is not correct" diff --git a/ddlitlab2024/ml/inference/ros.py b/ddlitlab2024/ml/inference/ros.py new file mode 100644 index 0000000..35d0f8f --- /dev/null +++ b/ddlitlab2024/ml/inference/ros.py @@ -0,0 +1,276 @@ +import time + +import cv2 +import numpy as np +import rclpy +import torch +import torch.nn.functional as F # noqa +from bitbots_msgs.msg import JointCommand +from bitbots_tf_buffer import Buffer +from cv_bridge import CvBridge +from diffusers.schedulers.scheduling_ddim import DDIMScheduler +from ema_pytorch import EMA +from game_controller_hl_interfaces.msg import GameState +from rclpy.duration import Duration +from rclpy.node import Node +from rclpy.time import Time +from sensor_msgs.msg import Image, JointState + +from ddlitlab2024.dataset.pytorch import Normalizer +from ddlitlab2024.ml.model import End2EndDiffusionTransformer +from ddlitlab2024.ml.model.encoder.image import ImageEncoderType, SequenceEncoderType +from ddlitlab2024.ml.model.encoder.imu import IMUEncoder +from ddlitlab2024.utils.utils import JOINT_NAMES_ORDER + +# Check if CUDA is available and set the device +device = torch.device("cuda" if torch.cuda.is_available() else "cpu") + + +class Inference(Node): + def __init__(self, node_name, context): + super().__init__(node_name, context=context) + # Activate sim time + self.get_logger().info("Activate sim time") + self.set_parameters( + [rclpy.parameter.Parameter("use_sim_time", rclpy.Parameter.Type.BOOL, True)], + ) + + # Params + self.sample_rate = 50 # Hz + hidden_dim = 256 + self.action_context_length = 100 + self.trajectory_prediction_length = 10 + train_denoising_timesteps = 1000 + self.inference_denosing_timesteps = 30 + self.image_context_length = 10 + self.imu_context_length = 100 + self.joint_state_context_length = 100 + self.num_joints = 20 + checkpoint = "/homes/17vahl/ddlitlab2024/ddlitlab2024/ml/training/trajectory_transformer_model_fixed_norm.pth" + + # Subscribe to all the input topics + self.joint_state_sub = self.create_subscription(JointState, "/joint_states", self.joint_state_callback, 10) + self.img_sub = self.create_subscription(Image, "/camera/image_proc", self.img_callback, 10) + self.gamestate_sub = self.create_subscription(GameState, "/gamestate", self.gamestate_callback, 10) + + # Publisher for the output topic + self.joint_state_pub = self.create_publisher(JointCommand, "/DynamixelController/command", 10) + + # Image embedding buffer + self.latest_image = None + self.image_embeddings = [] + + # IMU buffer + self.imu_data = [] + + # Joint state buffer + self.latest_joint_state = None + self.joint_state_data = [] + + # Joint command buffer + self.joint_command_data = [] + + # Gamestate + self.latest_game_state = None + + # Add default values to the buffers + self.image_embeddings = [torch.zeros(3, 480, 480)] * self.image_context_length + self.imu_data = [torch.tensor([0.0, 0.0, 0.0, 1.0])] * self.imu_context_length + self.joint_state_data = [torch.zeros(len(JOINT_NAMES_ORDER))] * self.joint_state_context_length + self.joint_command_data = [torch.zeros(self.num_joints)] * self.action_context_length + + # TF buffer to estimate imu similarly to the way we fixed the dataset + self.tf_buffer = Buffer(self, Duration(seconds=10)) + self.cv_bridge = CvBridge() + self.rate = self.create_rate(self.sample_rate) + + # Load model + self.get_logger().info("Load model") + self.model = End2EndDiffusionTransformer( + num_joints=self.num_joints, + hidden_dim=hidden_dim, + use_action_history=True, + num_action_history_encoder_layers=2, + max_action_context_length=self.action_context_length, + use_imu=True, + imu_orientation_embedding_method=IMUEncoder.OrientationEmbeddingMethod.QUATERNION, + num_imu_encoder_layers=2, + max_imu_context_length=self.imu_context_length, + use_joint_states=True, + joint_state_encoder_layers=2, + max_joint_state_context_length=self.joint_state_context_length, + use_images=True, + image_sequence_encoder_type=SequenceEncoderType.TRANSFORMER, + image_encoder_type=ImageEncoderType.RESNET18, + num_image_sequence_encoder_layers=1, + max_image_context_length=self.image_context_length, + num_decoder_layers=4, + trajectory_prediction_length=self.trajectory_prediction_length, + ).to(device) + + self.og_model = self.model + + self.normalizer = Normalizer(self.model.mean, self.model.std) + self.model = EMA(self.model) + self.model.load_state_dict(torch.load(checkpoint, weights_only=True)) + self.model.eval() + print(self.normalizer.mean) + + # Create diffusion noise scheduler + self.get_logger().info("Create diffusion noise scheduler") + self.scheduler = DDIMScheduler(beta_schedule="squaredcos_cap_v2", clip_sample=False) + self.scheduler.config["num_train_timesteps"] = train_denoising_timesteps + self.scheduler.set_timesteps(self.inference_denosing_timesteps) + + # Create control timer to run inference at a fixed rate + interval = 1 / self.sample_rate # * self.trajectory_prediction_length + self.create_timer(interval, self.step) + + def joint_state_callback(self, msg: JointState): + self.latest_joint_state = msg + + def img_callback(self, msg: Image): + self.latest_image = msg + + def gamestate_callback(self, msg: GameState): + self.latest_game_state = msg + + def step(self): + self.get_logger().info("Step") + + # First we want to fill the buffers + if self.latest_joint_state is not None: + # Joint names are not in the correct order, so we need to reorder them + joint_state = torch.zeros(len(JOINT_NAMES_ORDER)) + for i, joint_name in enumerate(JOINT_NAMES_ORDER): + idx = self.latest_joint_state.name.index(joint_name) + joint_state[i] = self.latest_joint_state.position[idx] + self.get_logger().info("Storing joint state") + self.joint_state_data.append(joint_state) + + self.get_logger().info("Calculating image embeddings") + if self.latest_image is not None: + # Here we don't just want to put the image in the buffer, but calculate the embedding first + # But for now the model dos not support the direct use of embeddings so we + # calculate them every timestep for the whole sequence. + # This is not efficient and should be changed in the future TODO + + # Deserialize the image + img = self.cv_bridge.imgmsg_to_cv2(self.latest_image, desired_encoding="rgb8") + + # Resize the image + img = cv2.resize(img, (480, 480)) + + # Make chw from hwc + img = np.moveaxis(img, -1, 0) + + # Convert the image to a tensor + img = torch.tensor(img, dtype=torch.float32) + + self.image_embeddings.append(img) + + self.get_logger().info("Calculating IMU data") + # Due to a bug in the recordings of the bit-bots we can not use the imu data directly, + # but instead need to derive it from the tf tree + imu_transform = self.tf_buffer.lookup_transform("base_footprint", "base_link", Time()) + + self.get_logger().info("Storing IMU data") + + # Store imu data as np array in the form wxyz + self.imu_data.append( + torch.tensor( + [ + imu_transform.transform.rotation.x, + imu_transform.transform.rotation.y, + imu_transform.transform.rotation.z, + imu_transform.transform.rotation.w, + ] + ) + ) + + print(self.imu_data[-1]) + print(len(self.imu_data)) + print(self.joint_state_data[-1]) + + # Remove the oldest data from the buffers + self.joint_state_data = self.joint_state_data[-self.joint_state_context_length :] + self.image_embeddings = self.image_embeddings[-self.image_context_length :] + self.imu_data = self.imu_data[-self.imu_context_length :] + self.joint_command_data = self.joint_command_data[-self.action_context_length :] + + # Prepare the data for inference + batch = { + "joint_state": (torch.stack(list(self.joint_state_data), dim=0).unsqueeze(0).to(device) + 3 * np.pi) + % (2 * np.pi), + "image_data": torch.stack(list(self.image_embeddings), dim=0).unsqueeze(0).to(device), + "rotation": torch.stack(list(self.imu_data), dim=0).unsqueeze(0).to(device), + "joint_command_history": torch.stack(list(self.joint_command_data), dim=0).unsqueeze(0).to(device), + } + + # Perform the denoising process + trajectory = torch.randn(1, self.trajectory_prediction_length, self.num_joints).to(device) + + self.get_logger().info("Performing denoising process") + + ## Perform the embedding of the conditioning + embedded_input = self.og_model.encode_input_data(batch) + + self.scheduler.set_timesteps(self.inference_denosing_timesteps) + for t in self.scheduler.timesteps: + with torch.no_grad(): + # Predict the noise residual + noise_pred = self.og_model.forward_with_context( + embedded_input, trajectory, torch.tensor([t], device=device) + ) + + # Update the trajectory based on the predicted noise and the current step of the denoising process + trajectory = self.scheduler.step(noise_pred, t, trajectory).prev_sample + + # Undo the normalization + trajectory = self.normalizer.denormalize(trajectory) + + self.get_logger().info("Publishing trajectory") + + # Store the trajectory in the joint command buffer (action history) + # self.joint_command_data.append(trajectory[0, 0].cpu()) + + # Publish the trajectory + # self.joint_state_pub.publish( + # JointCommand( + # joint_names=JOINT_NAMES_ORDER, + # velocities=[-1.0] * len(JOINT_NAMES_ORDER), + # accelerations=[-1.0] * len(JOINT_NAMES_ORDER), + # max_currents=[-1.0] * len(JOINT_NAMES_ORDER), + # positions=trajectory[0, 0].cpu().numpy() - np.pi, + # ) + # ) + + # Store the trajectory in the joint command buffer (action history) + for i in range(self.trajectory_prediction_length): + self.joint_command_data.append(trajectory[0, i].cpu()) + + # Publish the trajectory one by one + for i in range(self.trajectory_prediction_length): + time.sleep(1 / self.sample_rate) + self.joint_state_pub.publish( + JointCommand( + joint_names=JOINT_NAMES_ORDER, + velocities=[-1.0] * len(JOINT_NAMES_ORDER), + accelerations=[-1.0] * len(JOINT_NAMES_ORDER), + max_currents=[-1.0] * len(JOINT_NAMES_ORDER), + positions=trajectory[0, i].cpu().numpy() - np.pi, + ) + ) + + +def main(args=None): + rclpy.init(args=args) + node = Inference("inference", None) + executor = rclpy.executors.MultiThreadedExecutor() + executor.add_node(node) + executor.spin() + rclpy.shutdown() + + +if __name__ == "__main__": + main() diff --git a/ddlitlab2024/utils/utils.py b/ddlitlab2024/utils/utils.py index 1ff8eea..ab5e7f6 100644 --- a/ddlitlab2024/utils/utils.py +++ b/ddlitlab2024/utils/utils.py @@ -3,6 +3,8 @@ import numpy as np from transforms3d.quaternions import quat2axangle +from ddlitlab2024.dataset.models import JointStates + CAMELCASE_TO_SNAKECASE_REGEX = re.compile(r"(? str: Convert a camelCase string to snake_case. """ return CAMELCASE_TO_SNAKECASE_REGEX.sub("_", name).lower() + + +JOINT_NAMES_ORDER = [ + JointStates.head_pan.name, + JointStates.head_tilt.name, + JointStates.l_ankle_pitch.name, + JointStates.l_ankle_roll.name, + JointStates.l_elbow.name, + JointStates.l_hip_pitch.name, + JointStates.l_hip_roll.name, + JointStates.l_hip_yaw.name, + JointStates.l_knee.name, + JointStates.l_shoulder_pitch.name, + JointStates.l_shoulder_roll.name, + JointStates.r_ankle_pitch.name, + JointStates.r_ankle_roll.name, + JointStates.r_elbow.name, + JointStates.r_hip_pitch.name, + JointStates.r_hip_roll.name, + JointStates.r_hip_yaw.name, + JointStates.r_knee.name, + JointStates.r_shoulder_pitch.name, + JointStates.r_shoulder_roll.name, +] diff --git a/poetry.lock b/poetry.lock index 7fbaaab..30587d8 100644 --- a/poetry.lock +++ b/poetry.lock @@ -2,14 +2,14 @@ [[package]] name = "certifi" -version = "2024.8.30" +version = "2024.12.14" description = "Python package for providing Mozilla's CA Bundle." category = "main" optional = false python-versions = ">=3.6" files = [ - {file = "certifi-2024.8.30-py3-none-any.whl", hash = "sha256:922820b53db7a7257ffbda3f597266d435245903d80737e34f8a45ff3e3230d8"}, - {file = "certifi-2024.8.30.tar.gz", hash = "sha256:bec941d2aa8195e248a60b31ff9f0558284cf01a52591ceda73ea9afffd69fd9"}, + {file = "certifi-2024.12.14-py3-none-any.whl", hash = "sha256:1275f7a45be9464efc1173084eaa30f866fe2e47d389406136d332ed4967ec56"}, + {file = "certifi-2024.12.14.tar.gz", hash = "sha256:b650d30f370c2b724812bee08008be0c4163b163ddaec3f2546c1caf65f191db"}, ] [[package]] @@ -94,117 +94,104 @@ pycparser = "*" [[package]] name = "charset-normalizer" -version = "3.4.0" +version = "3.4.1" description = "The Real First Universal Charset Detector. 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"8750884ed6e3dcefcad638665088cf825e20a570a2f65311951ae9162c55c12b" diff --git a/pyproject.toml b/pyproject.toml index 7d48cd4..1f14e99 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -15,7 +15,7 @@ python = "^3.10" diffusers = "^0.31.0" ema-pytorch = "^0.7.3" matplotlib = "^3.9.2" -numpy = "^2.1.2" +numpy = "^1.9.2" pandas = "^2.2.3" rich = "^13.9.3" sqlalchemy = "^2.0.36" From 15bd46707bf97e645f9a1dc1735371ce2ddab501 Mon Sep 17 00:00:00 2001 From: Florian Vahl <7vahl@informatik.uni-hamburg.de> Date: Sun, 12 Jan 2025 14:42:26 +0100 Subject: [PATCH 02/10] Current wip --- ddlitlab2024/ml/inference/ros.py | 105 ++++++++++++++++++++++--------- 1 file changed, 74 insertions(+), 31 deletions(-) diff --git a/ddlitlab2024/ml/inference/ros.py b/ddlitlab2024/ml/inference/ros.py index 35d0f8f..9d8b544 100644 --- a/ddlitlab2024/ml/inference/ros.py +++ b/ddlitlab2024/ml/inference/ros.py @@ -22,6 +22,15 @@ from ddlitlab2024.ml.model.encoder.imu import IMUEncoder from ddlitlab2024.utils.utils import JOINT_NAMES_ORDER +from ddlitlab2024.dataset.pytorch import DDLITLab2024Dataset, Normalizer, worker_init_fn +from ddlitlab2024.ml import logger +from ddlitlab2024.ml.model import End2EndDiffusionTransformer +from ddlitlab2024.ml.model.encoder.image import ImageEncoderType, SequenceEncoderType +from ddlitlab2024.ml.model.encoder.imu import IMUEncoder + +from torch.utils.data import DataLoader + + # Check if CUDA is available and set the device device = torch.device("cuda" if torch.cuda.is_available() else "cpu") @@ -46,7 +55,7 @@ def __init__(self, node_name, context): self.imu_context_length = 100 self.joint_state_context_length = 100 self.num_joints = 20 - checkpoint = "/homes/17vahl/ddlitlab2024/ddlitlab2024/ml/training/trajectory_transformer_model_fixed_norm.pth" + checkpoint = "/homes/17vahl/ddlitlab2024/ddlitlab2024/ml/training/trajectory_transformer_model_500_epoch_xmas.pth" # Subscribe to all the input topics self.joint_state_sub = self.create_subscription(JointState, "/joint_states", self.joint_state_callback, 10) @@ -74,10 +83,42 @@ def __init__(self, node_name, context): self.latest_game_state = None # Add default values to the buffers - self.image_embeddings = [torch.zeros(3, 480, 480)] * self.image_context_length - self.imu_data = [torch.tensor([0.0, 0.0, 0.0, 1.0])] * self.imu_context_length - self.joint_state_data = [torch.zeros(len(JOINT_NAMES_ORDER))] * self.joint_state_context_length - self.joint_command_data = [torch.zeros(self.num_joints)] * self.action_context_length + #self.image_embeddings = [torch.zeros(3, 480, 480)] * self.image_context_length + #self.imu_data = [torch.tensor([0.0, 0.0, 0.0, 1.0])] * self.imu_context_length + #self.joint_state_data = [torch.zeros(len(JOINT_NAMES_ORDER))] * self.joint_state_context_length + #self.joint_command_data = [torch.zeros(self.num_joints)] * self.action_context_length + + # Create Dataset object + dataset = DDLITLab2024Dataset( + num_joints=self.num_joints, + num_frames_video=self.image_context_length, + num_samples_joint_trajectory_future=self.trajectory_prediction_length, + num_samples_joint_trajectory=self.action_context_length, + num_samples_imu=self.imu_context_length, + num_samples_joint_states=self.joint_state_context_length, + ) + + # Create DataLoader object + num_workers = 5 + dataloader = DataLoader( + dataset, + batch_size=1, + shuffle=True, + collate_fn=DDLITLab2024Dataset.collate_fn, + persistent_workers=num_workers > 1, + num_workers=num_workers, + worker_init_fn=worker_init_fn, + ) + + dataloader = iter(dataloader) + + batch = next(dataloader) + + # Add the data from the dataset to the buffers + self.image_embeddings = [x for x in batch.image_data.squeeze(0)] + self.imu_data = [x for x in batch.rotation.squeeze(0)] + self.joint_state_data = [x for x in batch.joint_state.squeeze(0)] + self.joint_command_data = [x for x in batch.joint_command_history.squeeze(0)] # TF buffer to estimate imu similarly to the way we fixed the dataset self.tf_buffer = Buffer(self, Duration(seconds=10)) @@ -204,9 +245,11 @@ def step(self): % (2 * np.pi), "image_data": torch.stack(list(self.image_embeddings), dim=0).unsqueeze(0).to(device), "rotation": torch.stack(list(self.imu_data), dim=0).unsqueeze(0).to(device), - "joint_command_history": torch.stack(list(self.joint_command_data), dim=0).unsqueeze(0).to(device), + "joint_command_history": (torch.stack(list(self.joint_state_data), dim=0).unsqueeze(0).to(device) + 3 * np.pi) + % (2 * np.pi) # torch.stack(list(self.joint_command_data), dim=0).unsqueeze(0).to(device), } + # Perform the denoising process trajectory = torch.randn(1, self.trajectory_prediction_length, self.num_joints).to(device) @@ -232,35 +275,35 @@ def step(self): self.get_logger().info("Publishing trajectory") # Store the trajectory in the joint command buffer (action history) - # self.joint_command_data.append(trajectory[0, 0].cpu()) + self.joint_command_data.append(trajectory[0, -1].cpu()) # Publish the trajectory - # self.joint_state_pub.publish( - # JointCommand( - # joint_names=JOINT_NAMES_ORDER, - # velocities=[-1.0] * len(JOINT_NAMES_ORDER), - # accelerations=[-1.0] * len(JOINT_NAMES_ORDER), - # max_currents=[-1.0] * len(JOINT_NAMES_ORDER), - # positions=trajectory[0, 0].cpu().numpy() - np.pi, - # ) - # ) + self.joint_state_pub.publish( + JointCommand( + joint_names=JOINT_NAMES_ORDER, + velocities=[-1.0] * len(JOINT_NAMES_ORDER), + accelerations=[-1.0] * len(JOINT_NAMES_ORDER), + max_currents=[-1.0] * len(JOINT_NAMES_ORDER), + positions=trajectory[0, -1].cpu().numpy() - np.pi, + ) + ) # Store the trajectory in the joint command buffer (action history) - for i in range(self.trajectory_prediction_length): - self.joint_command_data.append(trajectory[0, i].cpu()) - - # Publish the trajectory one by one - for i in range(self.trajectory_prediction_length): - time.sleep(1 / self.sample_rate) - self.joint_state_pub.publish( - JointCommand( - joint_names=JOINT_NAMES_ORDER, - velocities=[-1.0] * len(JOINT_NAMES_ORDER), - accelerations=[-1.0] * len(JOINT_NAMES_ORDER), - max_currents=[-1.0] * len(JOINT_NAMES_ORDER), - positions=trajectory[0, i].cpu().numpy() - np.pi, - ) - ) + #for i in range(self.trajectory_prediction_length): + # self.joint_command_data.append(trajectory[0, i].cpu()) + + ## Publish the trajectory one by one + #for i in range(self.trajectory_prediction_length): + # time.sleep(1 / self.sample_rate) + # self.joint_state_pub.publish( + # JointCommand( + # joint_names=JOINT_NAMES_ORDER, + # velocities=[-1.0] * len(JOINT_NAMES_ORDER), + # accelerations=[-1.0] * len(JOINT_NAMES_ORDER), + # max_currents=[-1.0] * len(JOINT_NAMES_ORDER), + # positions=trajectory[0, i].cpu().numpy() - np.pi, + # ) + # ) def main(args=None): From 3afb18c4c037ca1068c4094d3167ef88b5dd7c74 Mon Sep 17 00:00:00 2001 From: Florian Vahl Date: Sun, 12 Jan 2025 15:56:58 +0100 Subject: [PATCH 03/10] Fix pe buffer performace --- ddlitlab2024/ml/model/misc.py | 10 +++++----- 1 file changed, 5 insertions(+), 5 deletions(-) diff --git a/ddlitlab2024/ml/model/misc.py b/ddlitlab2024/ml/model/misc.py index 91a3162..6c7da36 100644 --- a/ddlitlab2024/ml/model/misc.py +++ b/ddlitlab2024/ml/model/misc.py @@ -48,12 +48,12 @@ def __init__(self, d_model, max_len): :param max_len: The maximum length of the input sequences. """ super().__init__() - pe = torch.zeros(max_len, d_model) + self.register_buffer('pe', torch.zeros(1, max_len, d_model), persistent=False) + position = torch.arange(0, max_len, dtype=torch.float).unsqueeze(1) div_term = torch.exp(torch.arange(0, d_model, 2).float() * (-np.log(10000.0) / d_model)) - pe[:, 0::2] = torch.sin(position * div_term) - pe[:, 1::2] = torch.cos(position * div_term) - self.pe = pe.unsqueeze(0) + self.pe[0, :, 0::2] = torch.sin(position * div_term) + self.pe[0, :, 1::2] = torch.cos(position * div_term) def forward(self, x): """ @@ -62,4 +62,4 @@ def forward(self, x): :param x: The input tensor. :return: The input tensor with the positional encoding added. """ - return x + self.pe[:, : x.size(1)].to(x.device) + return x + self.pe[:, : x.size(1)] From ea60f24785df5b05e312a5c3101a17991f671f83 Mon Sep 17 00:00:00 2001 From: Florian Vahl Date: Sun, 12 Jan 2025 19:16:46 +0100 Subject: [PATCH 04/10] State of the first goal video --- ddlitlab2024/ml/inference/ros.py | 273 ++++++++---------- .../ml/inference/trajectory_player.py | 88 ++++++ ddlitlab2024/ml/model/misc.py | 2 +- 3 files changed, 211 insertions(+), 152 deletions(-) create mode 100644 ddlitlab2024/ml/inference/trajectory_player.py diff --git a/ddlitlab2024/ml/inference/ros.py b/ddlitlab2024/ml/inference/ros.py index 9d8b544..88a5b2a 100644 --- a/ddlitlab2024/ml/inference/ros.py +++ b/ddlitlab2024/ml/inference/ros.py @@ -1,4 +1,6 @@ import time +from threading import Lock +from typing import Optional import cv2 import numpy as np @@ -11,10 +13,14 @@ from diffusers.schedulers.scheduling_ddim import DDIMScheduler from ema_pytorch import EMA from game_controller_hl_interfaces.msg import GameState +from profilehooks import profile +from rclpy.callback_groups import MutuallyExclusiveCallbackGroup from rclpy.duration import Duration +from rclpy.executors import MultiThreadedExecutor from rclpy.node import Node from rclpy.time import Time from sensor_msgs.msg import Image, JointState +from trajectory_msgs.msg import JointTrajectory, JointTrajectoryPoint from ddlitlab2024.dataset.pytorch import Normalizer from ddlitlab2024.ml.model import End2EndDiffusionTransformer @@ -22,15 +28,6 @@ from ddlitlab2024.ml.model.encoder.imu import IMUEncoder from ddlitlab2024.utils.utils import JOINT_NAMES_ORDER -from ddlitlab2024.dataset.pytorch import DDLITLab2024Dataset, Normalizer, worker_init_fn -from ddlitlab2024.ml import logger -from ddlitlab2024.ml.model import End2EndDiffusionTransformer -from ddlitlab2024.ml.model.encoder.image import ImageEncoderType, SequenceEncoderType -from ddlitlab2024.ml.model.encoder.imu import IMUEncoder - -from torch.utils.data import DataLoader - - # Check if CUDA is available and set the device device = torch.device("cuda" if torch.cuda.is_available() else "cpu") @@ -50,20 +47,24 @@ def __init__(self, node_name, context): self.action_context_length = 100 self.trajectory_prediction_length = 10 train_denoising_timesteps = 1000 - self.inference_denosing_timesteps = 30 + self.inference_denosing_timesteps = 10 self.image_context_length = 10 self.imu_context_length = 100 self.joint_state_context_length = 100 self.num_joints = 20 - checkpoint = "/homes/17vahl/ddlitlab2024/ddlitlab2024/ml/training/trajectory_transformer_model_500_epoch_xmas.pth" + checkpoint = "/home/florian/ddlitlab/ddlitlab_repo/ddlitlab2024/ml/training/trajectory_transformer_model_500_epoch_xmas.pth" # Subscribe to all the input topics self.joint_state_sub = self.create_subscription(JointState, "/joint_states", self.joint_state_callback, 10) self.img_sub = self.create_subscription(Image, "/camera/image_proc", self.img_callback, 10) self.gamestate_sub = self.create_subscription(GameState, "/gamestate", self.gamestate_callback, 10) + self.motor_command_sub = self.create_subscription( + JointCommand, "/DynamixelController/command", self.motor_command_callback, 10 + ) # Publisher for the output topic - self.joint_state_pub = self.create_publisher(JointCommand, "/DynamixelController/command", 10) + # self.joint_state_pub = self.create_publisher(JointCommand, "/DynamixelController/command", 10) + self.trajectory_pub = self.create_publisher(JointTrajectory, "/traj", 10) # Image embedding buffer self.latest_image = None @@ -73,52 +74,23 @@ def __init__(self, node_name, context): self.imu_data = [] # Joint state buffer - self.latest_joint_state = None + self.latest_joint_state: Optional[JointState] = None self.joint_state_data = [] # Joint command buffer + self.latest_motor_command: Optional[JointCommand] = None self.joint_command_data = [] # Gamestate self.latest_game_state = None # Add default values to the buffers - #self.image_embeddings = [torch.zeros(3, 480, 480)] * self.image_context_length - #self.imu_data = [torch.tensor([0.0, 0.0, 0.0, 1.0])] * self.imu_context_length - #self.joint_state_data = [torch.zeros(len(JOINT_NAMES_ORDER))] * self.joint_state_context_length - #self.joint_command_data = [torch.zeros(self.num_joints)] * self.action_context_length + self.image_embeddings = [torch.randn(3, 480, 480)] * self.image_context_length + self.imu_data = [torch.randn(4)] * self.imu_context_length + self.joint_state_data = [torch.randn(len(JOINT_NAMES_ORDER))] * self.joint_state_context_length + self.joint_command_data = [torch.randn(self.num_joints)] * self.action_context_length - # Create Dataset object - dataset = DDLITLab2024Dataset( - num_joints=self.num_joints, - num_frames_video=self.image_context_length, - num_samples_joint_trajectory_future=self.trajectory_prediction_length, - num_samples_joint_trajectory=self.action_context_length, - num_samples_imu=self.imu_context_length, - num_samples_joint_states=self.joint_state_context_length, - ) - - # Create DataLoader object - num_workers = 5 - dataloader = DataLoader( - dataset, - batch_size=1, - shuffle=True, - collate_fn=DDLITLab2024Dataset.collate_fn, - persistent_workers=num_workers > 1, - num_workers=num_workers, - worker_init_fn=worker_init_fn, - ) - - dataloader = iter(dataloader) - - batch = next(dataloader) - - # Add the data from the dataset to the buffers - self.image_embeddings = [x for x in batch.image_data.squeeze(0)] - self.imu_data = [x for x in batch.rotation.squeeze(0)] - self.joint_state_data = [x for x in batch.joint_state.squeeze(0)] - self.joint_command_data = [x for x in batch.joint_command_history.squeeze(0)] + self.data_lock = Lock() # TF buffer to estimate imu similarly to the way we fixed the dataset self.tf_buffer = Buffer(self, Duration(seconds=10)) @@ -164,8 +136,12 @@ def __init__(self, node_name, context): self.scheduler.set_timesteps(self.inference_denosing_timesteps) # Create control timer to run inference at a fixed rate - interval = 1 / self.sample_rate # * self.trajectory_prediction_length - self.create_timer(interval, self.step) + interval = 1 / self.sample_rate * self.trajectory_prediction_length + # We want to run the inference in a separate thread to not block the callbacks, but we also want to make sure + # that the inference is not running multiple times in parallel + self.create_timer(interval, self.step, callback_group=MutuallyExclusiveCallbackGroup()) + interval = 1 / self.sample_rate + self.create_timer(interval, self.update_buffers) def joint_state_callback(self, msg: JointState): self.latest_joint_state = msg @@ -176,88 +152,99 @@ def img_callback(self, msg: Image): def gamestate_callback(self, msg: GameState): self.latest_game_state = msg - def step(self): - self.get_logger().info("Step") - - # First we want to fill the buffers - if self.latest_joint_state is not None: - # Joint names are not in the correct order, so we need to reorder them - joint_state = torch.zeros(len(JOINT_NAMES_ORDER)) - for i, joint_name in enumerate(JOINT_NAMES_ORDER): - idx = self.latest_joint_state.name.index(joint_name) - joint_state[i] = self.latest_joint_state.position[idx] - self.get_logger().info("Storing joint state") - self.joint_state_data.append(joint_state) - - self.get_logger().info("Calculating image embeddings") - if self.latest_image is not None: - # Here we don't just want to put the image in the buffer, but calculate the embedding first - # But for now the model dos not support the direct use of embeddings so we - # calculate them every timestep for the whole sequence. - # This is not efficient and should be changed in the future TODO - - # Deserialize the image - img = self.cv_bridge.imgmsg_to_cv2(self.latest_image, desired_encoding="rgb8") - - # Resize the image - img = cv2.resize(img, (480, 480)) - - # Make chw from hwc - img = np.moveaxis(img, -1, 0) - - # Convert the image to a tensor - img = torch.tensor(img, dtype=torch.float32) - - self.image_embeddings.append(img) - - self.get_logger().info("Calculating IMU data") - # Due to a bug in the recordings of the bit-bots we can not use the imu data directly, - # but instead need to derive it from the tf tree - imu_transform = self.tf_buffer.lookup_transform("base_footprint", "base_link", Time()) - - self.get_logger().info("Storing IMU data") - - # Store imu data as np array in the form wxyz - self.imu_data.append( - torch.tensor( - [ - imu_transform.transform.rotation.x, - imu_transform.transform.rotation.y, - imu_transform.transform.rotation.z, - imu_transform.transform.rotation.w, - ] + def motor_command_callback(self, msg: JointCommand): + self.latest_motor_command = msg + + def update_buffers(self): + with self.data_lock: + # First we want to fill the buffers + if self.latest_joint_state is not None: + # Joint names are not in the correct order, so we need to reorder them + joint_state = torch.zeros(len(JOINT_NAMES_ORDER)) + for i, joint_name in enumerate(JOINT_NAMES_ORDER): + idx = self.latest_joint_state.name.index(joint_name) + joint_state[i] = self.latest_joint_state.position[idx] + self.joint_state_data.append(joint_state) + + if self.latest_motor_command is not None: + # Joint names are not in the correct order, so we need to reorder them + joint_state = torch.zeros(len(JOINT_NAMES_ORDER)) + for i, joint_name in enumerate(JOINT_NAMES_ORDER): + idx = self.latest_motor_command.joint_names.index(joint_name) + joint_state[i] = self.latest_motor_command.positions[idx] + self.joint_command_data.append(joint_state) + + if self.latest_image is not None: + # Here we don't just want to put the image in the buffer, but calculate the embedding first + # But for now the model dos not support the direct use of embeddings so we + # calculate them every timestep for the whole sequence. + # This is not efficient and should be changed in the future TODO + + # Deserialize the image + img = self.cv_bridge.imgmsg_to_cv2(self.latest_image, desired_encoding="rgb8") + + # Resize the image + img = cv2.resize(img, (480, 480)) + + # Make chw from hwc + img = np.moveaxis(img, -1, 0) + + # Convert the image to a tensor + img = torch.tensor(img, dtype=torch.float32) + + self.image_embeddings.append(img) + + # Due to a bug in the recordings of the bit-bots we can not use the imu data directly, + # but instead need to derive it from the tf tree + imu_transform = self.tf_buffer.lookup_transform("base_footprint", "base_link", Time()) + + # Store imu data as np array in the form wxyz + self.imu_data.append( + torch.tensor( + [ + imu_transform.transform.rotation.x, + imu_transform.transform.rotation.y, + imu_transform.transform.rotation.z, + imu_transform.transform.rotation.w, + ] + ) ) - ) - print(self.imu_data[-1]) - print(len(self.imu_data)) - print(self.joint_state_data[-1]) + # Remove the oldest data from the buffers + self.joint_state_data = self.joint_state_data[-self.joint_state_context_length :] + self.image_embeddings = self.image_embeddings[-self.image_context_length :] + self.imu_data = self.imu_data[-self.imu_context_length :] + self.joint_command_data = self.joint_command_data[-self.action_context_length :] - # Remove the oldest data from the buffers - self.joint_state_data = self.joint_state_data[-self.joint_state_context_length :] - self.image_embeddings = self.image_embeddings[-self.image_context_length :] - self.imu_data = self.imu_data[-self.imu_context_length :] - self.joint_command_data = self.joint_command_data[-self.action_context_length :] + @profile + def step(self): + self.get_logger().info("Step") # Prepare the data for inference - batch = { - "joint_state": (torch.stack(list(self.joint_state_data), dim=0).unsqueeze(0).to(device) + 3 * np.pi) - % (2 * np.pi), - "image_data": torch.stack(list(self.image_embeddings), dim=0).unsqueeze(0).to(device), - "rotation": torch.stack(list(self.imu_data), dim=0).unsqueeze(0).to(device), - "joint_command_history": (torch.stack(list(self.joint_state_data), dim=0).unsqueeze(0).to(device) + 3 * np.pi) - % (2 * np.pi) # torch.stack(list(self.joint_command_data), dim=0).unsqueeze(0).to(device), - } - + with self.data_lock: + batch = { + "joint_state": (torch.stack(list(self.joint_state_data), dim=0).unsqueeze(0).to(device) + 3 * np.pi) + % (2 * np.pi), + "image_data": torch.stack(list(self.image_embeddings), dim=0).unsqueeze(0).to(device), + "rotation": torch.stack(list(self.imu_data), dim=0).unsqueeze(0).to(device), + "joint_command_history": ( + torch.stack(list(self.joint_command_data), dim=0).unsqueeze(0).to(device) + 3 * np.pi + ) + % (2 * np.pi), # torch.stack(list(self.joint_command_data), dim=0).unsqueeze(0).to(device), + } # Perform the denoising process trajectory = torch.randn(1, self.trajectory_prediction_length, self.num_joints).to(device) - self.get_logger().info("Performing denoising process") + start_ros_time = self.get_clock().now() ## Perform the embedding of the conditioning + start = time.time() embedded_input = self.og_model.encode_input_data(batch) + # print("Time for embedding: ", time.time() - start) + # Denoise the trajectory + start = time.time() self.scheduler.set_timesteps(self.inference_denosing_timesteps) for t in self.scheduler.timesteps: with torch.no_grad(): @@ -269,47 +256,31 @@ def step(self): # Update the trajectory based on the predicted noise and the current step of the denoising process trajectory = self.scheduler.step(noise_pred, t, trajectory).prev_sample + # print("Time for forward: ", time.time() - start) + # Undo the normalization trajectory = self.normalizer.denormalize(trajectory) - self.get_logger().info("Publishing trajectory") - - # Store the trajectory in the joint command buffer (action history) - self.joint_command_data.append(trajectory[0, -1].cpu()) - # Publish the trajectory - self.joint_state_pub.publish( - JointCommand( - joint_names=JOINT_NAMES_ORDER, - velocities=[-1.0] * len(JOINT_NAMES_ORDER), - accelerations=[-1.0] * len(JOINT_NAMES_ORDER), - max_currents=[-1.0] * len(JOINT_NAMES_ORDER), - positions=trajectory[0, -1].cpu().numpy() - np.pi, - ) - ) - - # Store the trajectory in the joint command buffer (action history) - #for i in range(self.trajectory_prediction_length): - # self.joint_command_data.append(trajectory[0, i].cpu()) - - ## Publish the trajectory one by one - #for i in range(self.trajectory_prediction_length): - # time.sleep(1 / self.sample_rate) - # self.joint_state_pub.publish( - # JointCommand( - # joint_names=JOINT_NAMES_ORDER, - # velocities=[-1.0] * len(JOINT_NAMES_ORDER), - # accelerations=[-1.0] * len(JOINT_NAMES_ORDER), - # max_currents=[-1.0] * len(JOINT_NAMES_ORDER), - # positions=trajectory[0, i].cpu().numpy() - np.pi, - # ) - # ) + trajectory_msg = JointTrajectory() + trajectory_msg.header.stamp = Time.to_msg(start_ros_time) + trajectory_msg.joint_names = JOINT_NAMES_ORDER + trajectory_msg.points = [] + for i in range(self.trajectory_prediction_length): + point = JointTrajectoryPoint() + point.positions = trajectory[0, i].cpu().numpy() - np.pi + point.time_from_start = Duration(nanoseconds=int(1e9 / self.sample_rate * i)).to_msg() + point.velocities = [3.0] * 2 + [-1.0] * (len(JOINT_NAMES_ORDER) - 2) + point.accelerations = [-1.0] * len(JOINT_NAMES_ORDER) + point.effort = [-1.0] * len(JOINT_NAMES_ORDER) + trajectory_msg.points.append(point) + self.trajectory_pub.publish(trajectory_msg) def main(args=None): rclpy.init(args=args) node = Inference("inference", None) - executor = rclpy.executors.MultiThreadedExecutor() + executor = MultiThreadedExecutor(num_threads=5) executor.add_node(node) executor.spin() rclpy.shutdown() diff --git a/ddlitlab2024/ml/inference/trajectory_player.py b/ddlitlab2024/ml/inference/trajectory_player.py new file mode 100644 index 0000000..1fbe5e7 --- /dev/null +++ b/ddlitlab2024/ml/inference/trajectory_player.py @@ -0,0 +1,88 @@ +from typing import Optional + +import rclpy +import torch.nn.functional as F # noqa +from bitbots_msgs.msg import JointCommand +from rclpy.callback_groups import MutuallyExclusiveCallbackGroup +from rclpy.duration import Duration +from rclpy.node import Node +from rclpy.time import Time +from trajectory_msgs.msg import JointTrajectory, JointTrajectoryPoint + + +class TrajectoryPlayer(Node): + def __init__(self, node_name, context): + super().__init__(node_name, context=context) + # Activate sim time + self.get_logger().info("Activate sim time") + self.set_parameters( + [rclpy.parameter.Parameter("use_sim_time", rclpy.Parameter.Type.BOOL, True)], + ) + + # Subscribers + self.trajectory_subscriber = self.create_subscription( + JointTrajectory, + "traj", + self.trajectory_callback, + 10, + callback_group=MutuallyExclusiveCallbackGroup(), + ) + + self.joint_command_publisher = self.create_publisher(JointCommand, "DynamixelController/command", 10) + + # Current trajectory + self.current_trajectory = None + + self.create_timer(1 / 50, self.timer_callback) + + def trajectory_callback(self, msg: JointTrajectory): + self.current_trajectory = msg + + def timer_callback(self): + if self.current_trajectory is None: + self.get_logger().info("No trajectory available") + return + + # Get the current time + current_time = self.get_clock().now() + + # Find the current point based on the current time, the time the trajectory created and the time of the points + current_point: Optional[JointTrajectoryPoint] = None + for i, point in enumerate(self.current_trajectory.points): + time_of_point = Time.from_msg(self.current_trajectory.header.stamp) + Duration.from_msg( + point.time_from_start + ) + if time_of_point < current_time: + current_point = point + a = i + + print( + f"Using point {a} at time {time_of_point} as current point of the " + f"trajectory starting at {Time.from_msg(self.current_trajectory.header.stamp)}" + ) + + if current_point is None: + self.get_logger().info("Trajectory is in the future") + return + + # Publish the current point + self.joint_command_publisher.publish( + JointCommand( + joint_names=self.current_trajectory.joint_names, + velocities=current_point.velocities, + accelerations=current_point.accelerations, + max_currents=current_point.effort, + positions=current_point.positions, + ) + ) + + +def main(args=None): + rclpy.init(args=args) + node = TrajectoryPlayer("traj_player", None) + rclpy.spin(node) + rclpy.shutdown() + + +if __name__ == "__main__": + main() diff --git a/ddlitlab2024/ml/model/misc.py b/ddlitlab2024/ml/model/misc.py index 6c7da36..dc213eb 100644 --- a/ddlitlab2024/ml/model/misc.py +++ b/ddlitlab2024/ml/model/misc.py @@ -48,7 +48,7 @@ def __init__(self, d_model, max_len): :param max_len: The maximum length of the input sequences. """ super().__init__() - self.register_buffer('pe', torch.zeros(1, max_len, d_model), persistent=False) + self.register_buffer("pe", torch.zeros(1, max_len, d_model), persistent=False) position = torch.arange(0, max_len, dtype=torch.float).unsqueeze(1) div_term = torch.exp(torch.arange(0, d_model, 2).float() * (-np.log(10000.0) / d_model)) From 8995a9c66e8ca2a540d3a804efed51f8c1b3df1e Mon Sep 17 00:00:00 2001 From: Florian Vahl <7vahl@informatik.uni-hamburg.de> Date: Thu, 30 Jan 2025 11:36:09 +0100 Subject: [PATCH 05/10] Fix lock file --- poetry.lock | 671 +++++++++++++++++++++++++++++----------------------- 1 file changed, 369 insertions(+), 302 deletions(-) diff --git a/poetry.lock b/poetry.lock index ee13a10..9af5be4 100644 --- a/poetry.lock +++ b/poetry.lock @@ -1,9 +1,10 @@ -# This file is automatically @generated by Poetry 1.8.5 and should not be changed by hand. +# This file is automatically @generated by Poetry and should not be changed by hand. [[package]] name = "certifi" version = "2024.12.14" description = "Python package for providing Mozilla's CA Bundle." +category = "main" optional = false python-versions = ">=3.6" files = [ @@ -15,6 +16,7 @@ files = [ name = "cffi" version = "1.17.1" description = "Foreign Function Interface for Python calling C code." +category = "main" optional = false python-versions = ">=3.8" files = [ @@ -94,6 +96,7 @@ pycparser = "*" name = "charset-normalizer" version = "3.4.1" description = "The Real First Universal Charset Detector. Open, modern and actively maintained alternative to Chardet." +category = "main" optional = false python-versions = ">=3.7" files = [ @@ -195,6 +198,7 @@ files = [ name = "colorama" version = "0.4.6" description = "Cross-platform colored terminal text." +category = "main" optional = false python-versions = "!=3.0.*,!=3.1.*,!=3.2.*,!=3.3.*,!=3.4.*,!=3.5.*,!=3.6.*,>=2.7" files = [ @@ -206,6 +210,7 @@ files = [ name = "contourpy" version = "1.3.1" description = "Python library for calculating contours of 2D quadrilateral grids" +category = "main" optional = false python-versions = ">=3.10" files = [ @@ -279,6 +284,7 @@ test-no-images = ["pytest", "pytest-cov", "pytest-rerunfailures", "pytest-xdist" name = "cycler" version = "0.12.1" description = "Composable style cycles" +category = "main" optional = false python-versions = ">=3.8" files = [ @@ -294,6 +300,7 @@ tests = ["pytest", "pytest-cov", "pytest-xdist"] name = "diffusers" version = "0.31.0" description = "State-of-the-art diffusion in PyTorch and JAX." +category = "main" optional = false python-versions = ">=3.8.0" files = [ @@ -324,6 +331,7 @@ training = ["Jinja2", "accelerate (>=0.31.0)", "datasets", "peft (>=0.6.0)", "pr name = "docstring-parser" version = "0.16" description = "Parse Python docstrings in reST, Google and Numpydoc format" +category = "main" optional = false python-versions = ">=3.6,<4.0" files = [ @@ -335,6 +343,7 @@ files = [ name = "ema-pytorch" version = "0.7.7" description = "Easy way to keep track of exponential moving average version of your pytorch module" +category = "main" optional = false python-versions = "*" files = [ @@ -349,6 +358,7 @@ torch = ">=2.0" name = "exceptiongroup" version = "1.2.2" description = "Backport of PEP 654 (exception groups)" +category = "dev" optional = false python-versions = ">=3.7" files = [ @@ -361,77 +371,79 @@ test = ["pytest (>=6)"] [[package]] name = "filelock" -version = "3.16.1" +version = "3.17.0" description = "A platform independent file lock." 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"tabulate" version = "0.9.0" description = "Pretty-print tabular data" +category = "main" optional = false python-versions = ">=3.7" files = [ @@ -2086,6 +2139,7 @@ widechars = ["wcwidth"] name = "tomli" version = "2.2.1" description = "A lil' TOML parser" +category = "dev" optional = false python-versions = ">=3.8" files = [ @@ -2127,6 +2181,7 @@ files = [ name = "torch" version = "2.5.1" description = "Tensors and Dynamic neural networks in Python with strong GPU acceleration" +category = "main" optional = false python-versions = ">=3.8.0" files = [ @@ -2179,6 +2234,7 @@ optree = ["optree (>=0.12.0)"] name = "torchinfo" version = "1.8.0" description = "Model summary in PyTorch, based off of the original torchsummary." +category = "main" optional = false python-versions = ">=3.7" files = [ @@ -2190,6 +2246,7 @@ files = [ name = "torchvision" version = "0.20.1" description = "image and video datasets and models for torch deep learning" +category = "main" optional = false python-versions = ">=3.8" files = [ @@ -2213,7 +2270,7 @@ files = [ [package.dependencies] numpy = "*" -pillow = ">=5.3.0,<8.3.dev0 || >=8.4.dev0" +pillow = ">=5.3.0,<8.3.0 || >=8.4.0" torch = "2.5.1" [package.extras] @@ -2224,6 +2281,7 @@ scipy = ["scipy"] name = "tqdm" version = "4.67.1" description = "Fast, Extensible Progress Meter" +category = "main" optional = false python-versions = ">=3.7" files = [ @@ -2245,6 +2303,7 @@ telegram = ["requests"] name = "transforms3d" version = "0.4.2" description = "Functions for 3D coordinate transformations" +category = "main" optional = false python-versions = ">=3.6" files = [ @@ -2259,6 +2318,7 @@ numpy = ">=1.15" name = "triton" version = "3.1.0" description = "A language and compiler for custom Deep Learning operations" +category = "main" optional = false python-versions = "*" files = [ @@ -2281,6 +2341,7 @@ tutorials = ["matplotlib", "pandas", "tabulate"] name = "typed-argument-parser" version = "1.10.1" description = "Typed Argument Parser" +category = "main" optional = false python-versions = ">=3.8" files = [ @@ -2301,6 +2362,7 @@ dev-no-pydantic = ["flake8", "pytest", "pytest-cov"] name = "typing-extensions" version = "4.12.2" description = "Backported and Experimental Type Hints for Python 3.8+" +category = "main" optional = false python-versions = ">=3.8" files = [ @@ -2312,6 +2374,7 @@ files = [ name = "typing-inspect" version = "0.9.0" description = "Runtime inspection utilities for typing module." +category = "main" optional = false python-versions = "*" files = [ @@ -2325,19 +2388,21 @@ typing-extensions = ">=3.7.4" [[package]] name = "tzdata" -version = "2024.2" +version = "2025.1" description = "Provider of IANA time zone data" +category = "main" optional = false python-versions = ">=2" files = [ - {file = "tzdata-2024.2-py2.py3-none-any.whl", hash = "sha256:a48093786cdcde33cad18c2555e8532f34422074448fbc874186f0abd79565cd"}, - {file = "tzdata-2024.2.tar.gz", hash = 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["cffi (>=1.11)"] [metadata] lock-version = "2.0" python-versions = "^3.10" -content-hash = "097be830a3467a430f32244e584660848ee00c55836932109387750be450f1bc" +content-hash = "b0e47b6832d86e4c1ac75745bc3c5b6683955eec0c916f4d6027b81ad494ba0e" From bb8ec7d5d051332cb60898f7e5a7743d669c0be8 Mon Sep 17 00:00:00 2001 From: Florian Vahl <7vahl@informatik.uni-hamburg.de> Date: Thu, 30 Jan 2025 11:39:31 +0100 Subject: [PATCH 06/10] Fix formatting --- ddlitlab2024/dataset/vizualization.ipynb | 6 +++--- ddlitlab2024/ml/inference/ros.py | 9 ++++++--- 2 files changed, 9 insertions(+), 6 deletions(-) diff --git a/ddlitlab2024/dataset/vizualization.ipynb b/ddlitlab2024/dataset/vizualization.ipynb index 8926670..97ef3b9 100644 --- a/ddlitlab2024/dataset/vizualization.ipynb +++ b/ddlitlab2024/dataset/vizualization.ipynb @@ -6,11 +6,11 @@ "metadata": {}, "outputs": [], "source": [ - "import numpy as np\n", - "import pandas as pd\n", - "import matplotlib.pyplot as plt\n", "import sqlite3\n", "\n", + "import matplotlib.pyplot as plt\n", + "import pandas as pd\n", + "\n", "con = sqlite3.connect(\"./db.sqlite3\")" ] }, diff --git a/ddlitlab2024/ml/inference/ros.py b/ddlitlab2024/ml/inference/ros.py index 88a5b2a..02c2c67 100644 --- a/ddlitlab2024/ml/inference/ros.py +++ b/ddlitlab2024/ml/inference/ros.py @@ -52,7 +52,10 @@ def __init__(self, node_name, context): self.imu_context_length = 100 self.joint_state_context_length = 100 self.num_joints = 20 - checkpoint = "/home/florian/ddlitlab/ddlitlab_repo/ddlitlab2024/ml/training/trajectory_transformer_model_500_epoch_xmas.pth" + checkpoint = ( + "/home/florian/ddlitlab/ddlitlab_repo/ddlitlab2024/ml/training/" + "trajectory_transformer_model_500_epoch_xmas.pth" + ) # Subscribe to all the input topics self.joint_state_sub = self.create_subscription(JointState, "/joint_states", self.joint_state_callback, 10) @@ -241,7 +244,7 @@ def step(self): ## Perform the embedding of the conditioning start = time.time() embedded_input = self.og_model.encode_input_data(batch) - # print("Time for embedding: ", time.time() - start) + print("Time for embedding: ", time.time() - start) # Denoise the trajectory start = time.time() @@ -256,7 +259,7 @@ def step(self): # Update the trajectory based on the predicted noise and the current step of the denoising process trajectory = self.scheduler.step(noise_pred, t, trajectory).prev_sample - # print("Time for forward: ", time.time() - start) + print("Time for forward: ", time.time() - start) # Undo the normalization trajectory = self.normalizer.denormalize(trajectory) From 21a0134afdc53bc1d8594d360fba75d03956fb16 Mon Sep 17 00:00:00 2001 From: Florian Vahl <7vahl@informatik.uni-hamburg.de> Date: Thu, 30 Jan 2025 12:17:17 +0100 Subject: [PATCH 07/10] Add TODO --- ddlitlab2024/ml/inference/ros.py | 4 +++- 1 file changed, 3 insertions(+), 1 deletion(-) diff --git a/ddlitlab2024/ml/inference/ros.py b/ddlitlab2024/ml/inference/ros.py index 02c2c67..5620cd9 100644 --- a/ddlitlab2024/ml/inference/ros.py +++ b/ddlitlab2024/ml/inference/ros.py @@ -273,7 +273,9 @@ def step(self): point = JointTrajectoryPoint() point.positions = trajectory[0, i].cpu().numpy() - np.pi point.time_from_start = Duration(nanoseconds=int(1e9 / self.sample_rate * i)).to_msg() - point.velocities = [3.0] * 2 + [-1.0] * (len(JOINT_NAMES_ORDER) - 2) + point.velocities = [3.0] * 2 + [-1.0] * ( + len(JOINT_NAMES_ORDER) - 2 + ) # TODO remove if interpolation is added point.accelerations = [-1.0] * len(JOINT_NAMES_ORDER) point.effort = [-1.0] * len(JOINT_NAMES_ORDER) trajectory_msg.points.append(point) From f719f53d4c19e3f17fff28259c11faaa4d5ee3a7 Mon Sep 17 00:00:00 2001 From: Florian Vahl <7vahl@informatik.uni-hamburg.de> Date: Thu, 30 Jan 2025 12:39:06 +0100 Subject: [PATCH 08/10] Move joint states to better place --- ddlitlab2024/dataset/models.py | 25 ++++++++++++++++++ ddlitlab2024/dataset/pytorch.py | 6 ++--- ddlitlab2024/ml/inference/ros.py | 23 ++++++++-------- .../ml/inference/trajectory_player.py | 4 ++- ddlitlab2024/utils/utils.py | 26 ------------------- 5 files changed, 43 insertions(+), 41 deletions(-) diff --git a/ddlitlab2024/dataset/models.py b/ddlitlab2024/dataset/models.py index 9f7ae31..bc69965 100644 --- a/ddlitlab2024/dataset/models.py +++ b/ddlitlab2024/dataset/models.py @@ -196,6 +196,31 @@ class JointStates(Base): Index("idx_recording_stamp_joint_state", "recording_id", asc("stamp")), ) + def get_ordered_joint_names(self) -> list[str]: + return [ + JointStates.head_pan.name, + JointStates.head_tilt.name, + JointStates.l_ankle_pitch.name, + JointStates.l_ankle_roll.name, + JointStates.l_elbow.name, + JointStates.l_hip_pitch.name, + JointStates.l_hip_roll.name, + JointStates.l_hip_yaw.name, + JointStates.l_knee.name, + JointStates.l_shoulder_pitch.name, + JointStates.l_shoulder_roll.name, + JointStates.r_ankle_pitch.name, + JointStates.r_ankle_roll.name, + JointStates.r_elbow.name, + JointStates.r_hip_pitch.name, + JointStates.r_hip_roll.name, + JointStates.r_hip_yaw.name, + JointStates.r_knee.name, + JointStates.r_shoulder_pitch.name, + JointStates.r_shoulder_roll.name, + # TODO add new NAO joints + ] + class JointCommands(Base): __tablename__ = "JointCommands" diff --git a/ddlitlab2024/dataset/pytorch.py b/ddlitlab2024/dataset/pytorch.py index bd97314..ba02b79 100755 --- a/ddlitlab2024/dataset/pytorch.py +++ b/ddlitlab2024/dataset/pytorch.py @@ -15,9 +15,9 @@ from ddlitlab2024 import DB_PATH from ddlitlab2024.dataset import logger -from ddlitlab2024.dataset.models import RobotState +from ddlitlab2024.dataset.models import RobotState, JointStates from ddlitlab2024.ml.model.encoder.imu import IMUEncoder -from ddlitlab2024.utils.utils import JOINT_NAMES_ORDER, quats_to_5d +from ddlitlab2024.utils.utils import quats_to_5d def connect_to_db(data_base_path: str | Path = DB_PATH, worker_id: int | None = None) -> sqlite3.Connection: @@ -119,7 +119,7 @@ def query_joint_data( ) # Convert to numpy array, keep only the joint angle columns in alphabetical order - raw_joint_data = raw_joint_data[JOINT_NAMES_ORDER].to_numpy(dtype=np.float32) + raw_joint_data = raw_joint_data[JointStates.get_ordered_joint_names()].to_numpy(dtype=np.float32) assert raw_joint_data.shape[1] == self.num_joints, "The number of joints is not correct" diff --git a/ddlitlab2024/ml/inference/ros.py b/ddlitlab2024/ml/inference/ros.py index 5620cd9..c17d032 100644 --- a/ddlitlab2024/ml/inference/ros.py +++ b/ddlitlab2024/ml/inference/ros.py @@ -22,11 +22,12 @@ from sensor_msgs.msg import Image, JointState from trajectory_msgs.msg import JointTrajectory, JointTrajectoryPoint +from ddlitlab2024 import DEFAULT_RESAMPLE_RATE_HZ from ddlitlab2024.dataset.pytorch import Normalizer +from ddlitlab2024.dataset.models import JointStates from ddlitlab2024.ml.model import End2EndDiffusionTransformer from ddlitlab2024.ml.model.encoder.image import ImageEncoderType, SequenceEncoderType from ddlitlab2024.ml.model.encoder.imu import IMUEncoder -from ddlitlab2024.utils.utils import JOINT_NAMES_ORDER # Check if CUDA is available and set the device device = torch.device("cuda" if torch.cuda.is_available() else "cpu") @@ -42,7 +43,7 @@ def __init__(self, node_name, context): ) # Params - self.sample_rate = 50 # Hz + self.sample_rate = DEFAULT_RESAMPLE_RATE_HZ hidden_dim = 256 self.action_context_length = 100 self.trajectory_prediction_length = 10 @@ -90,7 +91,7 @@ def __init__(self, node_name, context): # Add default values to the buffers self.image_embeddings = [torch.randn(3, 480, 480)] * self.image_context_length self.imu_data = [torch.randn(4)] * self.imu_context_length - self.joint_state_data = [torch.randn(len(JOINT_NAMES_ORDER))] * self.joint_state_context_length + self.joint_state_data = [torch.randn(len(JointStates.get_ordered_joint_names()))] * self.joint_state_context_length self.joint_command_data = [torch.randn(self.num_joints)] * self.action_context_length self.data_lock = Lock() @@ -163,16 +164,16 @@ def update_buffers(self): # First we want to fill the buffers if self.latest_joint_state is not None: # Joint names are not in the correct order, so we need to reorder them - joint_state = torch.zeros(len(JOINT_NAMES_ORDER)) - for i, joint_name in enumerate(JOINT_NAMES_ORDER): + joint_state = torch.zeros(len(JointStates.get_ordered_joint_names())) + for i, joint_name in enumerate(JointStates.get_ordered_joint_names()): idx = self.latest_joint_state.name.index(joint_name) joint_state[i] = self.latest_joint_state.position[idx] self.joint_state_data.append(joint_state) if self.latest_motor_command is not None: # Joint names are not in the correct order, so we need to reorder them - joint_state = torch.zeros(len(JOINT_NAMES_ORDER)) - for i, joint_name in enumerate(JOINT_NAMES_ORDER): + joint_state = torch.zeros(len(JointStates.get_ordered_joint_names())) + for i, joint_name in enumerate(JointStates.get_ordered_joint_names()): idx = self.latest_motor_command.joint_names.index(joint_name) joint_state[i] = self.latest_motor_command.positions[idx] self.joint_command_data.append(joint_state) @@ -267,17 +268,17 @@ def step(self): # Publish the trajectory trajectory_msg = JointTrajectory() trajectory_msg.header.stamp = Time.to_msg(start_ros_time) - trajectory_msg.joint_names = JOINT_NAMES_ORDER + trajectory_msg.joint_names = JointStates.get_ordered_joint_names() trajectory_msg.points = [] for i in range(self.trajectory_prediction_length): point = JointTrajectoryPoint() point.positions = trajectory[0, i].cpu().numpy() - np.pi point.time_from_start = Duration(nanoseconds=int(1e9 / self.sample_rate * i)).to_msg() point.velocities = [3.0] * 2 + [-1.0] * ( - len(JOINT_NAMES_ORDER) - 2 + len(JointStates.get_ordered_joint_names()) - 2 ) # TODO remove if interpolation is added - point.accelerations = [-1.0] * len(JOINT_NAMES_ORDER) - point.effort = [-1.0] * len(JOINT_NAMES_ORDER) + point.accelerations = [-1.0] * len(JointStates.get_ordered_joint_names()) + point.effort = [-1.0] * len(JointStates.get_ordered_joint_names()) trajectory_msg.points.append(point) self.trajectory_pub.publish(trajectory_msg) diff --git a/ddlitlab2024/ml/inference/trajectory_player.py b/ddlitlab2024/ml/inference/trajectory_player.py index 1fbe5e7..df9f73c 100644 --- a/ddlitlab2024/ml/inference/trajectory_player.py +++ b/ddlitlab2024/ml/inference/trajectory_player.py @@ -9,6 +9,8 @@ from rclpy.time import Time from trajectory_msgs.msg import JointTrajectory, JointTrajectoryPoint +from ddlitlab2024 import DEFAULT_RESAMPLE_RATE_HZ + class TrajectoryPlayer(Node): def __init__(self, node_name, context): @@ -33,7 +35,7 @@ def __init__(self, node_name, context): # Current trajectory self.current_trajectory = None - self.create_timer(1 / 50, self.timer_callback) + self.create_timer(1 / DEFAULT_RESAMPLE_RATE_HZ, self.timer_callback) def trajectory_callback(self, msg: JointTrajectory): self.current_trajectory = msg diff --git a/ddlitlab2024/utils/utils.py b/ddlitlab2024/utils/utils.py index ab5e7f6..1ff8eea 100644 --- a/ddlitlab2024/utils/utils.py +++ b/ddlitlab2024/utils/utils.py @@ -3,8 +3,6 @@ import numpy as np from transforms3d.quaternions import quat2axangle -from ddlitlab2024.dataset.models import JointStates - CAMELCASE_TO_SNAKECASE_REGEX = re.compile(r"(? str: Convert a camelCase string to snake_case. """ return CAMELCASE_TO_SNAKECASE_REGEX.sub("_", name).lower() - - -JOINT_NAMES_ORDER = [ - JointStates.head_pan.name, - JointStates.head_tilt.name, - JointStates.l_ankle_pitch.name, - JointStates.l_ankle_roll.name, - JointStates.l_elbow.name, - JointStates.l_hip_pitch.name, - JointStates.l_hip_roll.name, - JointStates.l_hip_yaw.name, - JointStates.l_knee.name, - JointStates.l_shoulder_pitch.name, - JointStates.l_shoulder_roll.name, - JointStates.r_ankle_pitch.name, - JointStates.r_ankle_roll.name, - JointStates.r_elbow.name, - JointStates.r_hip_pitch.name, - JointStates.r_hip_roll.name, - JointStates.r_hip_yaw.name, - JointStates.r_knee.name, - JointStates.r_shoulder_pitch.name, - JointStates.r_shoulder_roll.name, -] From af99d8b6ef66dbbb1b23c892f5d66eb65e3d1518 Mon Sep 17 00:00:00 2001 From: Florian Vahl <7vahl@informatik.uni-hamburg.de> Date: Thu, 30 Jan 2025 12:40:36 +0100 Subject: [PATCH 09/10] Merge lock file --- poetry.lock | 216 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<7vahl@informatik.uni-hamburg.de> Date: Thu, 30 Jan 2025 12:42:17 +0100 Subject: [PATCH 10/10] Apply formatting --- ddlitlab2024/dataset/pytorch.py | 2 +- ddlitlab2024/ml/inference/ros.py | 6 ++++-- 2 files changed, 5 insertions(+), 3 deletions(-) diff --git a/ddlitlab2024/dataset/pytorch.py b/ddlitlab2024/dataset/pytorch.py index ba02b79..6fd12cb 100755 --- a/ddlitlab2024/dataset/pytorch.py +++ b/ddlitlab2024/dataset/pytorch.py @@ -15,7 +15,7 @@ from ddlitlab2024 import DB_PATH from ddlitlab2024.dataset import logger -from ddlitlab2024.dataset.models import RobotState, JointStates +from ddlitlab2024.dataset.models import JointStates, RobotState from ddlitlab2024.ml.model.encoder.imu import IMUEncoder from ddlitlab2024.utils.utils import quats_to_5d diff --git a/ddlitlab2024/ml/inference/ros.py b/ddlitlab2024/ml/inference/ros.py index c17d032..b52406b 100644 --- a/ddlitlab2024/ml/inference/ros.py +++ b/ddlitlab2024/ml/inference/ros.py @@ -23,8 +23,8 @@ from trajectory_msgs.msg import JointTrajectory, JointTrajectoryPoint from ddlitlab2024 import DEFAULT_RESAMPLE_RATE_HZ -from ddlitlab2024.dataset.pytorch import Normalizer from ddlitlab2024.dataset.models import JointStates +from ddlitlab2024.dataset.pytorch import Normalizer from ddlitlab2024.ml.model import End2EndDiffusionTransformer from ddlitlab2024.ml.model.encoder.image import ImageEncoderType, SequenceEncoderType from ddlitlab2024.ml.model.encoder.imu import IMUEncoder @@ -91,7 +91,9 @@ def __init__(self, node_name, context): # Add default values to the buffers self.image_embeddings = [torch.randn(3, 480, 480)] * self.image_context_length self.imu_data = [torch.randn(4)] * self.imu_context_length - self.joint_state_data = [torch.randn(len(JointStates.get_ordered_joint_names()))] * self.joint_state_context_length + self.joint_state_data = [ + torch.randn(len(JointStates.get_ordered_joint_names())) + ] * self.joint_state_context_length self.joint_command_data = [torch.randn(self.num_joints)] * self.action_context_length self.data_lock = Lock()