diff --git a/LICENSE b/LICENSE index a8ff93b..899c9b1 100644 --- a/LICENSE +++ b/LICENSE @@ -26,5 +26,6 @@ This code builds upon following open-source code-bases. Please visit the URLs to 8) https://github.com/unitreerobotics/unitree_dds_wrapper 9) https://github.com/tonyzhaozh/act 10) https://github.com/facebookresearch/detr +11) https://github.com/Dingry/BunnyVisionPro ------------------ diff --git a/README.md b/README.md index 5dd493a..f81257c 100644 --- a/README.md +++ b/README.md @@ -1,13 +1,38 @@ -# Video Demo +# πŸ“Ί Video Demo -# Introduction -This repository implements teleoperation of the humanoid robot Unitree H1_2 using Apple Vision Pro. - - - -# Prerequisites +# 0. πŸ“– Introduction +This repository implements teleoperation of the **Unitree humanoid robot** using **Apple Vision Pro**. + +Here are the robots that will be supported, + + + + + + + + + + + + + + + + + + + + + + +
🤖 Robot ⚪ Status
G1(29DoF) + Dex3-1 ✅ Completed
G1(23DoF) ⏱ In Progress
H1(Arm 4DoF) ⏱ In Progress
H1_2(Arm 7DoF) + Inspire ⏱ In Progress
+ + + +# 1. πŸ“¦ Prerequisites We tested our code on Ubuntu 20.04 and Ubuntu 22.04, other operating systems may be configured differently. @@ -15,7 +40,7 @@ For more information, you can refer to [Official Documentation ](https://support -## inverse kinematics +## 1.1 🦾 inverse kinematics ```bash conda create -n tv python=3.8 @@ -26,7 +51,7 @@ pip install meshcat pip install casadi ``` -## unitree_dds_wrapper +## 1.2 πŸ•ΉοΈ unitree_dds_wrapper ```bash # Install the Python version of the unitree_dds_wrapper. @@ -35,11 +60,13 @@ cd unitree_dds_wrapper/python pip install -e . ``` +> p.s. This is a temporary version, and it will be replaced with [unitree_sdk2_python](https://github.com/unitreerobotics/unitree_sdk2_python) in the future. -## TeleVision and Apple Vision Pro configuration -### basic +# 2. πŸ› οΈ TeleVision and Apple Vision Pro configuration + +## 2.1 πŸ“₯ basic ```bash cd ~ @@ -49,13 +76,7 @@ pip install -r requirements.txt cd act/detr && pip install -e . ``` -### Isaac Gym - -If you want to try teleoperation example in a simulated environment (`teleop_hand.py`): -1. Download Isaac Gym: https://developer.nvidia.com/isaac-gym/download -2. Extracting to the current directory, go to the `IsaacGym_Preview_4_Package/isaacgym/python` directory and execute the command: `pip install -e .` - -### Local streaming +## 2.2 πŸ”Œ Local streaming **Apple** does not allow WebXR on non-https connections. To test the application locally, we need to create a self-signed certificate and install it on the client. You need a ubuntu machine and a router. Connect the VisionPro and the ubuntu machine to the same router. @@ -66,7 +87,9 @@ If you want to try teleoperation example in a simulated environment (`teleop_han ifconfig | grep inet ``` -Suppose the local ip address of the ubuntu machine is `192.168.123.2` +Suppose the local ip address of the **Host machine** is `192.168.123.2` + +> p.s. you can use `ifconfig` command to check your **Host machine** ip address. 3. create certificate: @@ -96,44 +119,70 @@ Copy the `rootCA.pem` via AirDrop to VisionPro and install it. Settings > General > About > Certificate Trust Settings. Under "Enable full trust for root certificates", turn on trust for the certificate. -settings > Apps > Safari > Advanced > Feature Flags > Enable WebXR Related Features +Settings > Apps > Safari > Advanced > Feature Flags > Enable WebXR Related Features. -6. open the browser on Safari on VisionPro and go to https://192.168.123.2:8012?ws=wss://192.168.123.2:8012 +## 2.3 πŸ”Ž Test environment -7. Click `Enter VR` and `Allow` to start the VR session. +This step is to verify that the environment is installed correctly. -### Simulation Teleoperation Example -1. After setup up streaming with either local or network streaming following the above instructions, you can try teleoperating two robot hands in Issac Gym: +1. Download Isaac Gym: https://developer.nvidia.com/isaac-gym/download + + Extracting to the current directory, go to the `IsaacGym_Preview_4_Package/isaacgym/python` directory and execute the command: ```bash - cd teleop && python teleop_hand.py + pip install -e . ``` -2. Go to your vuer site on VisionPro, click `Enter VR` and `Allow` to enter immersive environment. +2. After setup up streaming with local following the above instructions, you can try teleoperating two robot hands in Issac Gym: -3. See your hands in 3D! + ```bash + cd teleop + python teleop_test_gym.py + ``` + +3. Wear your Apple Vision Pro device. + +4. Open Safari on Apple Vision Pro and visit: https://192.168.123.2:8012?ws=wss://192.168.123.2:8012 + + > p.s. This IP address should match the IP address of your **Host machine**. + +5. Click `Enter VR` and `Allow` to start the VR session. + +6. See your hands in 3D!
+# 3. πŸš€ Usage + +Please read the [Official Documentation ](https://support.unitree.com/home/zh/Teleoperation) at least once before starting this program. +## 3.1 πŸ–ΌοΈ Image Server - +Copy `image_server.py` in the `avp_teleoperate/teleop/image_server` directory to the **PC2** of Unitree Robot (G1/H1/H1_2/etc.), and execute the following command **in the PC2**: +```bash +# Now located in Unitree Robot PC2 +python image_server.py +# You can see the terminal output as follows: +# Image server has started, waiting for client connections... +# Image Resolution: width is x, height is x +``` +After image service is started, you can use `image_client.py` **in the Host** terminal to test whether the communication is successful: -# Usage +```bash +python image_client.py +``` -Please read the [Official Documentation ](https://support.unitree.com/home/zh/Teleoperation) at least once before starting this program. +## 3.2 βœ‹ Inspire hands Server (optional) -## Dexterous hands service +You can refer to [Dexterous Hand Development](https://support.unitree.com/home/zh/H1_developer/Dexterous_hand) to configure related environments and compile control programs. First, use [this URL](https://oss-global-cdn.unitree.com/static/0a8335f7498548d28412c31ea047d4be.zip) to download the dexterous hand control interface program. Copy it to PC of Unitree H1_2. -You can refer to [Dexterous Hand Development](https://support.unitree.com/home/zh/H1_developer/Dexterous_hand) to configure related environments and compile control programs. First, use [this URL](https://oss-global-cdn.unitree.com/static/0a8335f7498548d28412c31ea047d4be.zip) to download the dexterous hand control interface program and copy it to PC of Unitree H1_2. On Unitree H1_2's PC, execute command: +On Unitree H1_2's PC, execute command: ```bash sudo apt install libboost-all-dev libspdlog-dev @@ -149,59 +198,82 @@ sudo ./inspire_hand -s /dev/ttyUSB0 If two hands open and close continuously, it indicates success. Once successful, close the `./h1_hand_example` program in Terminal 2. +## 3.3 πŸš€ Start + +> ![Warning](https://img.shields.io/badge/Warning-Important-red) +> +> 1. Everyone must keep a safe distance from the robot to prevent any potential danger! +> +> 2. Please make sure to read the [Official Documentation](https://support.unitree.com/home/zh/Teleoperation) at least once before running this program. -## Image Server +It's best to have two operators to run this program, referred to as **Operator A** and **Operator B**. -Copy `image_server.py` in the `avp_teleoperate/teleop/image_server` directory to the PC of Unitree H1_2, and execute the following command **in the PC**: +Now, **Operator B** execute the following command on **Host machine** : ```bash -sudo python image_server.py +python teleop_hand_and_arm.py ``` -After image service is started, you can use `image_client.py` **in the Host** terminal to test whether the communication is successful: +And then, **Operator A** -```bash -python image_client.py -``` +1. Wear your Apple Vision Pro device. -## Start +2. Open Safari on Apple Vision Pro and visit : https://192.168.123.2:8012?ws=wss://192.168.123.2:8012 -> Warning : All persons must maintain an adequate safety distance from the robot to avoid danger! + > p.s. This IP address should match the IP address of your **Host machine**. -```bash -python teleop_hand_and_arm.py -``` +3. Click `Enter VR` and `Allow` to start the VR session. + +When host terminal outputs "Please enter the start signal (enter 'r' to start the subsequent program):", **Operator B** can start teleoperation program by pressing the **r** key in the terminal. + +At this time, **Operator A** can remotely control the robot's arms and dexterous hands. +Next, **Operator B** can press **s** key to begin recording data in the 'record image' window that opens, and press **s** again to stop. This can be repeated as necessary. +## 3.4 πŸ”š Exit -# Codebase Tutorial +To exit the program, **Operator B** can press the **q** key in the 'record image' window. -The overall structure of the code remains the same as TeleVision, and we only focus on the modified file directories related to Unitree Robot. +> ![Warning](https://img.shields.io/badge/Warning-Important-red) +> +> To avoid damaging the robot, **Operator A** need to make sure the robot's arms are in a natural down position before exiting. - avp_teleoperate/ - β”‚ - β”œβ”€β”€ act [Documents Related to ACT Policy for Imitation Learning] - β”‚ - β”œβ”€β”€ assets [Storage of robot URDF-related files] - β”‚ - β”œβ”€β”€ scripts - β”‚ - β”œβ”€β”€ teleop - β”‚ β”œβ”€β”€ image_server/ [Image Transfer Server and Client Code] - β”‚ β”‚ β”œβ”€β”€ image_client.py [Client (only used to test whether the image stream service is OK, not used for teleoperation)] - β”‚ β”‚ β”œβ”€β”€ image_server.py [Capture images from binocular cameras and send via network (performed on Unitree H1_2)] - β”‚ β”‚ - β”‚ β”œβ”€β”€ robot_control/ [Storage of IK solver, arm and hand control related documents] - β”‚ β”‚ β”œβ”€β”€ robot_arm_ik.py [Inverse kinematics of the arm] - β”‚ β”‚ β”œβ”€β”€ robot_arm.py [Control dual arm joints and lock the others] - β”‚ β”‚ β”œβ”€β”€ robot_hand.py [Control hand joints] - β”‚ β”‚ - β”‚ │──teleop_hand_and_arm.py [Startup execution code for teleoperation] - | |β€”β€”teleop_hand.py [Can be used for testing the environment configuration] + + +# 4. πŸ—ΊοΈ Codebase Tutorial + +``` +avp_teleoperate/ +β”‚ +β”œβ”€β”€ assets [Storage of robot URDF-related files] +β”‚ +β”œβ”€β”€ teleop +β”‚ β”œβ”€β”€ image_server +β”‚ β”‚ β”œβ”€β”€ image_client.py [Used to receive image data from the robot image server] +β”‚ β”‚ β”œβ”€β”€ image_server.py [Capture images from cameras and send via network (Running on robot's on-board computer)] +β”‚ β”‚ +β”‚ β”œβ”€β”€ open_television +β”‚ β”‚ β”œβ”€β”€ television.py [Using Vuer to capture wrist and hand data from apple vision pro] +β”‚ β”‚ β”œβ”€β”€ tv_wrapper.py [Post-processing of captured data] +β”‚ β”‚ +β”‚ β”œβ”€β”€ robot_control +β”‚ β”‚ β”œβ”€β”€ robot_arm_ik.py [Inverse kinematics of the arm] +β”‚ β”‚ β”œβ”€β”€ robot_arm.py [Control dual arm joints and lock the others] +β”‚ β”‚ β”œβ”€β”€ robot_hand_inspire.py [Control inspire hand joints] +β”‚ β”‚ β”œβ”€β”€ robot_hand_unitree.py [Control unitree hand joints] +β”‚ β”‚ +β”‚ β”œβ”€β”€ utils +β”‚ β”‚ β”œβ”€β”€ episode_writer.py [Used to record data for imitation learning] +β”‚ β”‚ β”œβ”€β”€ mat_tool.py [Some small math tools] +β”‚ β”‚ β”œβ”€β”€ weighted_moving_filter.py [For filtering joint data] +β”‚ β”‚ +β”‚ │──teleop_hand_and_arm.py [Startup execution code for teleoperation] +| |β€”β€”teleop_test_gym.py [Can be used to verify that the environment is installed correctly] +``` -# Acknowledgement +# 5. πŸ™ Acknowledgement This code builds upon following open-source code-bases. Please visit the URLs to see the respective LICENSES: @@ -214,4 +286,5 @@ This code builds upon following open-source code-bases. Please visit the URLs to 7) https://github.com/zeromq/pyzmq 8) https://github.com/unitreerobotics/unitree_dds_wrapper 9) https://github.com/tonyzhaozh/act -10) https://github.com/facebookresearch/detr \ No newline at end of file +10) https://github.com/facebookresearch/detr +11) https://github.com/Dingry/BunnyVisionPro \ No newline at end of file diff --git a/act/LICENSE b/act/LICENSE deleted file mode 100644 index 35e5f5e..0000000 --- a/act/LICENSE +++ /dev/null @@ -1,21 +0,0 @@ -MIT License - -Copyright (c) 2023 Tony Z. Zhao - -Permission is hereby granted, free of charge, to any person obtaining a copy -of this software and associated documentation files (the "Software"), to deal -in the Software without restriction, including without limitation the rights -to use, copy, modify, merge, publish, distribute, sublicense, and/or sell -copies of the Software, and to permit persons to whom the Software is -furnished to do so, subject to the following conditions: - -The above copyright notice and this permission notice shall be included in all -copies or substantial portions of the Software. - -THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR -IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, -FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE -AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER -LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, -OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE -SOFTWARE. diff --git a/act/README.md b/act/README.md deleted file mode 100644 index 1f58040..0000000 --- a/act/README.md +++ /dev/null @@ -1,8 +0,0 @@ -This part of the codebase is modified from ACT https://github.com/tonyzhaozh/act under MIT License. - - @article{zhao2023learning, - title={Learning fine-grained bimanual manipulation with low-cost hardware}, - author={Zhao, Tony Z and Kumar, Vikash and Levine, Sergey and Finn, Chelsea}, - journal={arXiv preprint arXiv:2304.13705}, - year={2023} - } \ No newline at end of file diff --git a/act/conda_env.yaml b/act/conda_env.yaml deleted file mode 100644 index 0f44d6b..0000000 --- a/act/conda_env.yaml +++ /dev/null @@ -1,23 +0,0 @@ -name: aloha -channels: - - pytorch - - nvidia - - conda-forge -dependencies: - - python=3.9 - - pip=23.0.1 - - pytorch=2.0.0 - - torchvision=0.15.0 - - pytorch-cuda=11.8 - - pyquaternion=0.9.9 - - pyyaml=6.0 - - rospkg=1.5.0 - - pexpect=4.8.0 - - mujoco=2.3.3 - - dm_control=1.0.9 - - py-opencv=4.7.0 - - matplotlib=3.7.1 - - einops=0.6.0 - - packaging=23.0 - - h5py=3.8.0 - - ipython=8.12.0 diff --git a/act/detr/LICENSE b/act/detr/LICENSE deleted file mode 100644 index b1395e9..0000000 --- a/act/detr/LICENSE +++ /dev/null @@ -1,201 +0,0 @@ - Apache License - Version 2.0, January 2004 - http://www.apache.org/licenses/ - - TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION - - 1. 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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. diff --git a/act/detr/README.md b/act/detr/README.md deleted file mode 100644 index 500b1b8..0000000 --- a/act/detr/README.md +++ /dev/null @@ -1,9 +0,0 @@ -This part of the codebase is modified from DETR https://github.com/facebookresearch/detr under APACHE 2.0. - - @article{Carion2020EndtoEndOD, - title={End-to-End Object Detection with Transformers}, - author={Nicolas Carion and Francisco Massa and Gabriel Synnaeve and Nicolas Usunier and Alexander Kirillov and Sergey Zagoruyko}, - journal={ArXiv}, - year={2020}, - volume={abs/2005.12872} - } \ No newline at end of file diff --git a/act/detr/main.py b/act/detr/main.py deleted file mode 100644 index ca17adf..0000000 --- a/act/detr/main.py +++ /dev/null @@ -1,128 +0,0 @@ -# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved -import argparse -from pathlib import Path - -import numpy as np -import torch -from .models import build_ACT_model, build_CNNMLP_model - -import IPython -e = IPython.embed - -def get_args_parser(): - parser = argparse.ArgumentParser('Set transformer detector', add_help=False) - parser.add_argument('--lr', default=1e-4, type=float) # will be overridden - parser.add_argument('--lr_backbone', default=1e-5, type=float) # will be overridden - parser.add_argument('--batch_size', default=2, type=int) # not used - parser.add_argument('--weight_decay', default=1e-4, type=float) - parser.add_argument('--epochs', default=300, type=int) # not used - parser.add_argument('--lr_drop', default=200, type=int) # not used - parser.add_argument('--clip_max_norm', default=0.1, type=float, # not used - help='gradient clipping max norm') - parser.add_argument('--qpos_noise_std', action='store', default=0, type=float, help='lr', required=False) - - - # Model parameters - # * Backbone - parser.add_argument('--backbone', default='resnet18', type=str, # will be overridden - help="Name of the convolutional backbone to use") - parser.add_argument('--dilation', action='store_true', - help="If true, we replace stride with dilation in the last convolutional block (DC5)") - parser.add_argument('--position_embedding', default='sine', type=str, choices=('sine', 'learned'), - help="Type of positional embedding to use on top of the image features") - parser.add_argument('--camera_names', default=[], type=list, # will be overridden - help="A list of camera names") - - # * Transformer - parser.add_argument('--enc_layers', default=4, type=int, # will be overridden - help="Number of encoding layers in the transformer") - parser.add_argument('--dec_layers', default=6, type=int, # will be overridden - help="Number of decoding layers in the transformer") - parser.add_argument('--dim_feedforward', default=2048, type=int, # will be overridden - help="Intermediate size of the feedforward layers in the transformer blocks") - parser.add_argument('--hidden_dim', default=256, type=int, # will be overridden - help="Size of the embeddings (dimension of the transformer)") - parser.add_argument('--dropout', default=0.1, type=float, - help="Dropout applied in the transformer") - parser.add_argument('--nheads', default=8, type=int, # will be overridden - help="Number of attention heads inside the transformer's attentions") - parser.add_argument('--num_queries', default=400, type=int, # will be overridden - help="Number of query slots") - parser.add_argument('--pre_norm', action='store_true') - - # * Segmentation - parser.add_argument('--masks', action='store_true', - help="Train segmentation head if the flag is provided") - - # repeat args in imitate_episodes just to avoid error. Will not be used - parser.add_argument('--eval', action='store_true') - parser.add_argument('--onscreen_render', action='store_true') - # parser.add_argument('--ckpt_dir', action='store', type=str, help='ckpt_dir', required=True) - parser.add_argument('--policy_class', action='store', type=str, help='policy_class, capitalize', required=True) - # parser.add_argument('--task_name', action='store', type=str, help='task_name', required=True) - parser.add_argument('--seed', action='store', type=int, help='seed', required=True) - parser.add_argument('--num_epochs', action='store', type=int, help='num_epochs', required=True) - parser.add_argument('--kl_weight', action='store', type=int, help='KL Weight', required=False) - parser.add_argument('--chunk_size', action='store', type=int, help='chunk_size', required=False) - parser.add_argument('--temporal_agg', action='store_true') - - parser.add_argument('--save_jit', action='store_true') - parser.add_argument('--ckpt_dir', default='/home/cxx/h1_hardware/data/logs', type=str, # will be overridden - help='ckpt_dir') - - parser.add_argument('--no_wandb', action='store_true') - parser.add_argument('--resumeid', action='store', type=str, help='resume id', required=False) - parser.add_argument('--resume_ckpt', action='store', type=str, help='resume ckpt', required=False) - parser.add_argument('--taskid', action='store', type=str, help='task id', required=True) - parser.add_argument('--exptid', action='store', type=str, help='experiment id', required=True) - parser.add_argument('--source', choices=['self', 'ssd'], default='self') - - - return parser - - -def build_ACT_model_and_optimizer(args_override): - parser = argparse.ArgumentParser('DETR training and evaluation script', parents=[get_args_parser()]) - args = parser.parse_args() - - for k, v in args_override.items(): - setattr(args, k, v) - - model = build_ACT_model(args) - model.cuda() - - param_dicts = [ - {"params": [p for n, p in model.named_parameters() if "backbone" not in n and p.requires_grad]}, - { - "params": [p for n, p in model.named_parameters() if "backbone" in n and p.requires_grad], - "lr": args.lr_backbone, - }, - ] - optimizer = torch.optim.AdamW(param_dicts, lr=args.lr, - weight_decay=args.weight_decay) - - return model, optimizer - - -def build_CNNMLP_model_and_optimizer(args_override): - parser = argparse.ArgumentParser('DETR training and evaluation script', parents=[get_args_parser()]) - args = parser.parse_args() - - for k, v in args_override.items(): - setattr(args, k, v) - - model = build_CNNMLP_model(args) - model.cuda() - - param_dicts = [ - {"params": [p for n, p in model.named_parameters() if "backbone" not in n and p.requires_grad]}, - { - "params": [p for n, p in model.named_parameters() if "backbone" in n and p.requires_grad], - "lr": args.lr_backbone, - }, - ] - optimizer = torch.optim.AdamW(param_dicts, lr=args.lr, - weight_decay=args.weight_decay) - - return model, optimizer - diff --git a/act/detr/models/__init__.py b/act/detr/models/__init__.py deleted file mode 100644 index cc78db1..0000000 --- a/act/detr/models/__init__.py +++ /dev/null @@ -1,9 +0,0 @@ -# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved -from .detr_vae import build as build_vae -from .detr_vae import build_cnnmlp as build_cnnmlp - -def build_ACT_model(args): - return build_vae(args) - -def build_CNNMLP_model(args): - return build_cnnmlp(args) \ No newline at end of file diff --git a/act/detr/models/backbone.py b/act/detr/models/backbone.py deleted file mode 100644 index 1ded145..0000000 --- a/act/detr/models/backbone.py +++ /dev/null @@ -1,144 +0,0 @@ -# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved -""" -Backbone modules. -""" -from collections import OrderedDict - -import torch -import torch.nn.functional as F -import torchvision -from torch import nn -from torchvision.models._utils import IntermediateLayerGetter -from typing import Dict, List - -from util.misc import NestedTensor, is_main_process - -from .position_encoding import build_position_encoding - -import IPython -e = IPython.embed - -class FrozenBatchNorm2d(torch.nn.Module): - """ - BatchNorm2d where the batch statistics and the affine parameters are fixed. - - Copy-paste from torchvision.misc.ops with added eps before rqsrt, - without which any other policy_models than torchvision.policy_models.resnet[18,34,50,101] - produce nans. - """ - - def __init__(self, n): - super(FrozenBatchNorm2d, self).__init__() - self.register_buffer("weight", torch.ones(n)) - self.register_buffer("bias", torch.zeros(n)) - self.register_buffer("running_mean", torch.zeros(n)) - self.register_buffer("running_var", torch.ones(n)) - - def _load_from_state_dict(self, state_dict, prefix, local_metadata, strict, - missing_keys, unexpected_keys, error_msgs): - num_batches_tracked_key = prefix + 'num_batches_tracked' - if num_batches_tracked_key in state_dict: - del state_dict[num_batches_tracked_key] - - super(FrozenBatchNorm2d, self)._load_from_state_dict( - state_dict, prefix, local_metadata, strict, - missing_keys, unexpected_keys, error_msgs) - - def forward(self, x): - # move reshapes to the beginning - # to make it fuser-friendly - w = self.weight.reshape(1, -1, 1, 1) - b = self.bias.reshape(1, -1, 1, 1) - rv = self.running_var.reshape(1, -1, 1, 1) - rm = self.running_mean.reshape(1, -1, 1, 1) - eps = 1e-5 - scale = w * (rv + eps).rsqrt() - bias = b - rm * scale - return x * scale + bias - - -class BackboneBase(nn.Module): - - def __init__(self, backbone: nn.Module, train_backbone: bool, num_channels: int, return_interm_layers: bool): - super().__init__() - # for name, parameter in backbone.named_parameters(): # only train later layers # TODO do we want this? - # if not train_backbone or 'layer2' not in name and 'layer3' not in name and 'layer4' not in name: - # parameter.requires_grad_(False) - if return_interm_layers: - return_layers = {"layer1": "0", "layer2": "1", "layer3": "2", "layer4": "3"} - else: - return_layers = {'layer4': "0"} - self.body = IntermediateLayerGetter(backbone, return_layers=return_layers) - self.num_channels = num_channels - - def forward(self, tensor): - xs = self.body(tensor) - return xs - # out: Dict[str, NestedTensor] = {} - # for name, x in xs.items(): - # m = tensor_list.mask - # assert m is not None - # mask = F.interpolate(m[None].float(), size=x.shape[-2:]).to(torch.bool)[0] - # out[name] = NestedTensor(x, mask) - # return out - - -class Backbone(BackboneBase): - """ResNet backbone with frozen BatchNorm.""" - def __init__(self, name: str, - train_backbone: bool, - return_interm_layers: bool, - dilation: bool): - backbone = getattr(torchvision.models, name)( - replace_stride_with_dilation=[False, False, dilation], - pretrained=is_main_process(), norm_layer=FrozenBatchNorm2d) # pretrained # TODO do we want frozen batch_norm?? - num_channels = 512 if name in ('resnet18', 'resnet34') else 2048 - super().__init__(backbone, train_backbone, num_channels, return_interm_layers) - -class DINOv2BackBone(nn.Module): - def __init__(self) -> None: - super().__init__() - self.body = torch.hub.load('facebookresearch/dinov2', 'dinov2_vits14') - self.body.eval() - self.num_channels = 384 - - @torch.no_grad() - def forward(self, tensor): - xs = self.body.forward_features(tensor)["x_norm_patchtokens"] - od = OrderedDict() - od["0"] = xs.reshape(xs.shape[0], 22, 16, 384).permute(0, 3, 2, 1) - return od - -class Joiner(nn.Sequential): - def __init__(self, backbone, position_embedding): - super().__init__(backbone, position_embedding) - - # def forward (self, tensor): - # xs = self[0](tensor) - # pos = self[1](xs) - # return xs, pos - - def forward(self, tensor_list: NestedTensor): - xs = self[0](tensor_list) - out: List[NestedTensor] = [] - pos = [] - for name, x in xs.items(): - out.append(x) - # position encoding - pos.append(self[1](x).to(x.dtype)) - - return out, pos - - -def build_backbone(args): - position_embedding = build_position_encoding(args) - train_backbone = args.lr_backbone > 0 - return_interm_layers = args.masks - if args.backbone == 'dino_v2': - backbone = DINOv2BackBone() - else: - assert args.backbone in ['resnet18', 'resnet34'] - backbone = Backbone(args.backbone, train_backbone, return_interm_layers, args.dilation) - model = Joiner(backbone, position_embedding) - model.num_channels = backbone.num_channels - return model diff --git a/act/detr/models/detr_vae.py b/act/detr/models/detr_vae.py deleted file mode 100644 index b3b3ee0..0000000 --- a/act/detr/models/detr_vae.py +++ /dev/null @@ -1,302 +0,0 @@ -# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved -""" -DETR model and criterion classes. -""" -import torch -from torch import nn -from torch.autograd import Variable -from .backbone import build_backbone -from .transformer import build_transformer, TransformerEncoder, TransformerEncoderLayer - -import numpy as np -import time - -import IPython -e = IPython.embed - - -def reparametrize(mu, logvar): - std = logvar.div(2).exp() - eps = Variable(std.data.new(std.size()).normal_()) - return mu + std * eps - - -def get_sinusoid_encoding_table(n_position, d_hid): - def get_position_angle_vec(position): - return [position / np.power(10000, 2 * (hid_j // 2) / d_hid) for hid_j in range(d_hid)] - - sinusoid_table = np.array([get_position_angle_vec(pos_i) for pos_i in range(n_position)]) - sinusoid_table[:, 0::2] = np.sin(sinusoid_table[:, 0::2]) # dim 2i - sinusoid_table[:, 1::2] = np.cos(sinusoid_table[:, 1::2]) # dim 2i+1 - - return torch.FloatTensor(sinusoid_table).unsqueeze(0) - - -class DETRVAE(nn.Module): - """ This is the DETR module that performs object detection """ - def __init__(self, backbones, transformer, encoder, state_dim, action_dim, num_queries, camera_names): - """ Initializes the model. - Parameters: - backbones: torch module of the backbone to be used. See backbone.py - transformer: torch module of the transformer architecture. See transformer.py - state_dim: robot state dimension of the environment - num_queries: number of object queries, ie detection slot. This is the maximal number of objects - DETR can detect in a single image. For COCO, we recommend 100 queries. - aux_loss: True if auxiliary decoding losses (loss at each decoder layer) are to be used. - """ - super().__init__() - self.num_queries = num_queries - self.camera_names = camera_names - self.transformer = transformer - self.encoder = encoder - hidden_dim = transformer.d_model - self.action_head = nn.Linear(hidden_dim, action_dim) - self.is_pad_head = nn.Linear(hidden_dim, 1) - self.query_embed = nn.Embedding(num_queries, hidden_dim) - if backbones is not None: - self.input_proj = nn.Conv2d(backbones[0].num_channels, hidden_dim, kernel_size=1) - self.backbones = nn.ModuleList(backbones) - self.input_proj_robot_state = nn.Linear(state_dim, hidden_dim) - else: - raise NotImplementedError - # input_dim = 14 + 7 # robot_state + env_state - self.input_proj_robot_state = nn.Linear(state_dim, hidden_dim) - self.input_proj_env_state = nn.Linear(7, hidden_dim) - self.pos = torch.nn.Embedding(2, hidden_dim) - self.backbones = None - - # encoder extra parameters - self.latent_dim = 32 # final size of latent z # TODO tune - self.cls_embed = nn.Embedding(1, hidden_dim) # extra cls token embedding - self.encoder_action_proj = nn.Linear(action_dim, hidden_dim) # project action to embedding - self.encoder_joint_proj = nn.Linear(state_dim, hidden_dim) # project qpos to embedding - self.latent_proj = nn.Linear(hidden_dim, self.latent_dim*2) # project hidden state to latent std, var - self.register_buffer('pos_table', get_sinusoid_encoding_table(1+1+num_queries, hidden_dim)) # [CLS], qpos, a_seq - - # decoder extra parameters - self.latent_out_proj = nn.Linear(self.latent_dim, hidden_dim) # project latent sample to embedding - self.additional_pos_embed = nn.Embedding(2, hidden_dim) # learned position embedding for proprio and latent - - def forward(self, qpos, image, env_state, actions=None, is_pad=None): - """ - qpos: batch, qpos_dim - image: batch, num_cam, channel, height, width - env_state: None - actions: batch, seq, action_dim - """ - is_training = actions is not None # train or val - bs, _ = qpos.shape - ### Obtain latent z from action sequence - if is_training: - # project action sequence to embedding dim, and concat with a CLS token - action_embed = self.encoder_action_proj(actions) # (bs, seq, hidden_dim) - qpos_embed = self.encoder_joint_proj(qpos) # (bs, hidden_dim) - qpos_embed = torch.unsqueeze(qpos_embed, axis=1) # (bs, 1, hidden_dim) - cls_embed = self.cls_embed.weight # (1, hidden_dim) - cls_embed = torch.unsqueeze(cls_embed, axis=0).repeat(bs, 1, 1) # (bs, 1, hidden_dim) - encoder_input = torch.cat([cls_embed, qpos_embed, action_embed], axis=1) # (bs, seq+1, hidden_dim) - encoder_input = encoder_input.permute(1, 0, 2) # (seq+1, bs, hidden_dim) - # do not mask cls token - cls_joint_is_pad = torch.full((bs, 2), False).to(qpos.device) # False: not a padding - is_pad = torch.cat([cls_joint_is_pad, is_pad], axis=1) # (bs, seq+1) - # obtain position embedding - pos_embed = self.pos_table.clone().detach() - pos_embed = pos_embed.permute(1, 0, 2) # (seq+1, 1, hidden_dim) - # query model - encoder_output = self.encoder(encoder_input, pos=pos_embed, src_key_padding_mask=is_pad) - encoder_output = encoder_output[0] # take cls output only - latent_info = self.latent_proj(encoder_output) - mu = latent_info[:, :self.latent_dim] - logvar = latent_info[:, self.latent_dim:] - latent_sample = reparametrize(mu, logvar) - latent_input = self.latent_out_proj(latent_sample) - else: - mu = logvar = None - latent_sample = torch.zeros([bs, self.latent_dim], dtype=torch.float32).to(qpos.device) - latent_input = self.latent_out_proj(latent_sample) - - if self.backbones is not None: - # Image observation features and position embeddings - all_cam_features = [] - all_cam_pos = [] - featuress, poss = self.backbones[0](image.flatten(0, 1)) # HARDCODED - featuress = featuress[0].view(image.shape[0], 2, 384, 16, 22) # take the last layer feature - pos = poss[0] - for cam_id, cam_name in enumerate(self.camera_names): - # start = time.time() - # import ipdb; ipdb.set_trace() - features = featuress[:, cam_id] # HARDCODED - # features, pos = self.backbones[cam_id](image[:, cam_id]) # HARDCODED - # print("Time for 1 backbone: ", time.time() - start, image.shape) - # features = features[0] # take the last layer feature - # pos = pos[0] - all_cam_features.append(self.input_proj(features)) - all_cam_pos.append(pos/2+ cam_id - 0.5) - # break - - # for cam_id, cam_name in enumerate(self.camera_names): - # features, pos = self.backbones[0](image[:, cam_id]) # HARDCODED - # features = features[0] # take the last layer feature - # pos = pos[0] - # all_cam_features.append(self.input_proj(features)) - # all_cam_pos.append(pos) - - # proprioception features - proprio_input = self.input_proj_robot_state(qpos) - # fold camera dimension into width dimension - src = torch.cat(all_cam_features, axis=3) - pos = torch.cat(all_cam_pos, axis=3) - hs = self.transformer(src, None, self.query_embed.weight, pos, latent_input, proprio_input, self.additional_pos_embed.weight)[0] - else: - raise NotImplementedError - qpos = self.input_proj_robot_state(qpos) - env_state = self.input_proj_env_state(env_state) - transformer_input = torch.cat([qpos, env_state], axis=1) # seq length = 2 - hs = self.transformer(transformer_input, None, self.query_embed.weight, self.pos.weight)[0] - a_hat = self.action_head(hs) - is_pad_hat = self.is_pad_head(hs) - return a_hat, is_pad_hat, [mu, logvar] - - - -class CNNMLP(nn.Module): - def __init__(self, backbones, state_dim, camera_names): - """ Initializes the model. - Parameters: - backbones: torch module of the backbone to be used. See backbone.py - transformer: torch module of the transformer architecture. See transformer.py - state_dim: robot state dimension of the environment - num_queries: number of object queries, ie detection slot. This is the maximal number of objects - DETR can detect in a single image. For COCO, we recommend 100 queries. - aux_loss: True if auxiliary decoding losses (loss at each decoder layer) are to be used. - """ - super().__init__() - self.camera_names = camera_names - self.action_head = nn.Linear(1000, state_dim) # TODO add more - if backbones is not None: - self.backbones = nn.ModuleList(backbones) - backbone_down_projs = [] - for backbone in backbones: - down_proj = nn.Sequential( - nn.Conv2d(backbone.num_channels, 128, kernel_size=5), - nn.Conv2d(128, 64, kernel_size=5), - nn.Conv2d(64, 32, kernel_size=5) - ) - backbone_down_projs.append(down_proj) - self.backbone_down_projs = nn.ModuleList(backbone_down_projs) - - mlp_in_dim = 768 * len(backbones) + 14 - self.mlp = mlp(input_dim=mlp_in_dim, hidden_dim=1024, output_dim=14, hidden_depth=2) - else: - raise NotImplementedError - - def forward(self, qpos, image, env_state, actions=None): - """ - qpos: batch, qpos_dim - image: batch, num_cam, channel, height, width - env_state: None - actions: batch, seq, action_dim - """ - is_training = actions is not None # train or val - bs, _ = qpos.shape - # Image observation features and position embeddings - all_cam_features = [] - for cam_id, cam_name in enumerate(self.camera_names): - features, pos = self.backbones[cam_id](image[:, cam_id]) - features = features[0] # take the last layer feature - pos = pos[0] # not used - all_cam_features.append(self.backbone_down_projs[cam_id](features)) - # flatten everything - flattened_features = [] - for cam_feature in all_cam_features: - flattened_features.append(cam_feature.reshape([bs, -1])) - flattened_features = torch.cat(flattened_features, axis=1) # 768 each - features = torch.cat([flattened_features, qpos], axis=1) # qpos: 14 - a_hat = self.mlp(features) - return a_hat - - -def mlp(input_dim, hidden_dim, output_dim, hidden_depth): - if hidden_depth == 0: - mods = [nn.Linear(input_dim, output_dim)] - else: - mods = [nn.Linear(input_dim, hidden_dim), nn.ReLU(inplace=True)] - for i in range(hidden_depth - 1): - mods += [nn.Linear(hidden_dim, hidden_dim), nn.ReLU(inplace=True)] - mods.append(nn.Linear(hidden_dim, output_dim)) - trunk = nn.Sequential(*mods) - return trunk - - -def build_encoder(args): - d_model = args.hidden_dim # 256 - dropout = args.dropout # 0.1 - nhead = args.nheads # 8 - dim_feedforward = args.dim_feedforward # 2048 - num_encoder_layers = args.enc_layers # 4 # TODO shared with VAE decoder - normalize_before = args.pre_norm # False - activation = "relu" - - encoder_layer = TransformerEncoderLayer(d_model, nhead, dim_feedforward, - dropout, activation, normalize_before) - encoder_norm = nn.LayerNorm(d_model) if normalize_before else None - encoder = TransformerEncoder(encoder_layer, num_encoder_layers, encoder_norm) - - return encoder - - -def build(args): - state_dim = args.state_dim - action_dim = args.action_dim - - # From state - # backbone = None # from state for now, no need for conv nets - # From image - backbones = [] - # backbone = build_backbone(args) - # backbones.append(backbone) - # for _ in args.camera_names: - backbone = build_backbone(args) - backbones.append(backbone) - - transformer = build_transformer(args) - - encoder = build_encoder(args) - - model = DETRVAE( - backbones, - transformer, - encoder, - state_dim=state_dim, - action_dim=action_dim, - num_queries=args.num_queries, - camera_names=args.camera_names, - ) - - n_parameters = sum(p.numel() for p in model.parameters() if p.requires_grad) - print("number of parameters: %.2fM" % (n_parameters/1e6,)) - - return model - -def build_cnnmlp(args): - state_dim = 14 # TODO hardcode - - # From state - # backbone = None # from state for now, no need for conv nets - # From image - backbones = [] - for _ in args.camera_names: - backbone = build_backbone(args) - backbones.append(backbone) - - model = CNNMLP( - backbones, - state_dim=state_dim, - camera_names=args.camera_names, - ) - - n_parameters = sum(p.numel() for p in model.parameters() if p.requires_grad) - print("number of parameters: %.2fM" % (n_parameters/1e6,)) - - return model - diff --git a/act/detr/models/position_encoding.py b/act/detr/models/position_encoding.py deleted file mode 100644 index 209d917..0000000 --- a/act/detr/models/position_encoding.py +++ /dev/null @@ -1,93 +0,0 @@ -# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved -""" -Various positional encodings for the transformer. -""" -import math -import torch -from torch import nn - -from util.misc import NestedTensor - -import IPython -e = IPython.embed - -class PositionEmbeddingSine(nn.Module): - """ - This is a more standard version of the position embedding, very similar to the one - used by the Attention is all you need paper, generalized to work on images. - """ - def __init__(self, num_pos_feats=64, temperature=10000, normalize=False, scale=None): - super().__init__() - self.num_pos_feats = num_pos_feats - self.temperature = temperature - self.normalize = normalize - if scale is not None and normalize is False: - raise ValueError("normalize should be True if scale is passed") - if scale is None: - scale = 2 * math.pi - self.scale = scale - - def forward(self, tensor): - x = tensor - # mask = tensor_list.mask - # assert mask is not None - # not_mask = ~mask - - not_mask = torch.ones_like(x[0, [0]]) - y_embed = not_mask.cumsum(1, dtype=torch.float32) - x_embed = not_mask.cumsum(2, dtype=torch.float32) - if self.normalize: - eps = 1e-6 - y_embed = y_embed / (y_embed[:, -1:, :] + eps) * self.scale - x_embed = x_embed / (x_embed[:, :, -1:] + eps) * self.scale - - dim_t = torch.arange(self.num_pos_feats, dtype=torch.float32, device=x.device) - dim_t = self.temperature ** (2 * (dim_t // 2) / self.num_pos_feats) - - pos_x = x_embed[:, :, :, None] / dim_t - pos_y = y_embed[:, :, :, None] / dim_t - pos_x = torch.stack((pos_x[:, :, :, 0::2].sin(), pos_x[:, :, :, 1::2].cos()), dim=4).flatten(3) - pos_y = torch.stack((pos_y[:, :, :, 0::2].sin(), pos_y[:, :, :, 1::2].cos()), dim=4).flatten(3) - pos = torch.cat((pos_y, pos_x), dim=3).permute(0, 3, 1, 2) - return pos - - -class PositionEmbeddingLearned(nn.Module): - """ - Absolute pos embedding, learned. - """ - def __init__(self, num_pos_feats=256): - super().__init__() - self.row_embed = nn.Embedding(50, num_pos_feats) - self.col_embed = nn.Embedding(50, num_pos_feats) - self.reset_parameters() - - def reset_parameters(self): - nn.init.uniform_(self.row_embed.weight) - nn.init.uniform_(self.col_embed.weight) - - def forward(self, tensor_list: NestedTensor): - x = tensor_list.tensors - h, w = x.shape[-2:] - i = torch.arange(w, device=x.device) - j = torch.arange(h, device=x.device) - x_emb = self.col_embed(i) - y_emb = self.row_embed(j) - pos = torch.cat([ - x_emb.unsqueeze(0).repeat(h, 1, 1), - y_emb.unsqueeze(1).repeat(1, w, 1), - ], dim=-1).permute(2, 0, 1).unsqueeze(0).repeat(x.shape[0], 1, 1, 1) - return pos - - -def build_position_encoding(args): - N_steps = args.hidden_dim // 2 - if args.position_embedding in ('v2', 'sine'): - # TODO find a better way of exposing other arguments - position_embedding = PositionEmbeddingSine(N_steps, normalize=True) - elif args.position_embedding in ('v3', 'learned'): - position_embedding = PositionEmbeddingLearned(N_steps) - else: - raise ValueError(f"not supported {args.position_embedding}") - - return position_embedding diff --git a/act/detr/models/transformer.py b/act/detr/models/transformer.py deleted file mode 100644 index f38afd0..0000000 --- a/act/detr/models/transformer.py +++ /dev/null @@ -1,314 +0,0 @@ -# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved -""" -DETR Transformer class. - -Copy-paste from torch.nn.Transformer with modifications: - * positional encodings are passed in MHattention - * extra LN at the end of encoder is removed - * decoder returns a stack of activations from all decoding layers -""" -import copy -from typing import Optional, List - -import torch -import torch.nn.functional as F -from torch import nn, Tensor - -import IPython -e = IPython.embed - -class Transformer(nn.Module): - - def __init__(self, d_model=512, nhead=8, num_encoder_layers=6, - num_decoder_layers=6, dim_feedforward=2048, dropout=0.1, - activation="relu", normalize_before=False, - return_intermediate_dec=False): - super().__init__() - - encoder_layer = TransformerEncoderLayer(d_model, nhead, dim_feedforward, - dropout, activation, normalize_before) - encoder_norm = nn.LayerNorm(d_model) if normalize_before else None - self.encoder = TransformerEncoder(encoder_layer, num_encoder_layers, encoder_norm) - - decoder_layer = TransformerDecoderLayer(d_model, nhead, dim_feedforward, - dropout, activation, normalize_before) - decoder_norm = nn.LayerNorm(d_model) - self.decoder = TransformerDecoder(decoder_layer, num_decoder_layers, decoder_norm, - return_intermediate=return_intermediate_dec) - - self._reset_parameters() - - self.d_model = d_model - self.nhead = nhead - - def _reset_parameters(self): - for p in self.parameters(): - if p.dim() > 1: - nn.init.xavier_uniform_(p) - - def forward(self, src, mask, query_embed, pos_embed, latent_input=None, proprio_input=None, additional_pos_embed=None): - # TODO flatten only when input has H and W - if len(src.shape) == 4: # has H and W - # flatten NxCxHxW to HWxNxC - bs, c, h, w = src.shape - src = src.flatten(2).permute(2, 0, 1) - pos_embed = pos_embed.flatten(2).permute(2, 0, 1).repeat(1, bs, 1) - query_embed = query_embed.unsqueeze(1).repeat(1, bs, 1) - # mask = mask.flatten(1) - - additional_pos_embed = additional_pos_embed.unsqueeze(1).repeat(1, bs, 1) # seq, bs, dim - pos_embed = torch.cat([additional_pos_embed, pos_embed], axis=0) - - addition_input = torch.stack([latent_input, proprio_input], axis=0) - src = torch.cat([addition_input, src], axis=0) - else: - assert len(src.shape) == 3 - # flatten NxHWxC to HWxNxC - bs, hw, c = src.shape - src = src.permute(1, 0, 2) - pos_embed = pos_embed.unsqueeze(1).repeat(1, bs, 1) - query_embed = query_embed.unsqueeze(1).repeat(1, bs, 1) - - tgt = torch.zeros_like(query_embed) - memory = self.encoder(src, src_key_padding_mask=mask, pos=pos_embed) - hs = self.decoder(tgt, memory, memory_key_padding_mask=mask, - pos=pos_embed, query_pos=query_embed) - hs = hs.transpose(1, 2) - return hs - -class TransformerEncoder(nn.Module): - - def __init__(self, encoder_layer, num_layers, norm=None): - super().__init__() - self.layers = _get_clones(encoder_layer, num_layers) - self.num_layers = num_layers - self.norm = norm - - def forward(self, src, - mask: Optional[Tensor] = None, - src_key_padding_mask: Optional[Tensor] = None, - pos: Optional[Tensor] = None): - output = src - - for layer in self.layers: - output = layer(output, src_mask=mask, - src_key_padding_mask=src_key_padding_mask, pos=pos) - - if self.norm is not None: - output = self.norm(output) - - return output - - -class TransformerDecoder(nn.Module): - - def __init__(self, decoder_layer, num_layers, norm=None, return_intermediate=False): - super().__init__() - self.layers = _get_clones(decoder_layer, num_layers) - self.num_layers = num_layers - self.norm = norm - self.return_intermediate = return_intermediate - - def forward(self, tgt, memory, - tgt_mask: Optional[Tensor] = None, - memory_mask: Optional[Tensor] = None, - tgt_key_padding_mask: Optional[Tensor] = None, - memory_key_padding_mask: Optional[Tensor] = None, - pos: Optional[Tensor] = None, - query_pos: Optional[Tensor] = None): - output = tgt - - intermediate = [] - - for layer in self.layers: - output = layer(output, memory, tgt_mask=tgt_mask, - memory_mask=memory_mask, - tgt_key_padding_mask=tgt_key_padding_mask, - memory_key_padding_mask=memory_key_padding_mask, - pos=pos, query_pos=query_pos) - if self.return_intermediate: - intermediate.append(self.norm(output)) - - if self.norm is not None: - output = self.norm(output) - if self.return_intermediate: - intermediate.pop() - intermediate.append(output) - - if self.return_intermediate: - return torch.stack(intermediate) - - return output.unsqueeze(0) - - -class TransformerEncoderLayer(nn.Module): - - def __init__(self, d_model, nhead, dim_feedforward=2048, dropout=0.1, - activation="relu", normalize_before=False): - super().__init__() - self.self_attn = nn.MultiheadAttention(d_model, nhead, dropout=dropout) - # Implementation of Feedforward model - self.linear1 = nn.Linear(d_model, dim_feedforward) - self.dropout = nn.Dropout(dropout) - self.linear2 = nn.Linear(dim_feedforward, d_model) - - self.norm1 = nn.LayerNorm(d_model) - self.norm2 = nn.LayerNorm(d_model) - self.dropout1 = nn.Dropout(dropout) - self.dropout2 = nn.Dropout(dropout) - - self.activation = _get_activation_fn(activation) - self.normalize_before = normalize_before - - def with_pos_embed(self, tensor, pos: Optional[Tensor]): - return tensor if pos is None else tensor + pos - - def forward_post(self, - src, - src_mask: Optional[Tensor] = None, - src_key_padding_mask: Optional[Tensor] = None, - pos: Optional[Tensor] = None): - q = k = self.with_pos_embed(src, pos) - src2 = self.self_attn(q, k, value=src, attn_mask=src_mask, - key_padding_mask=src_key_padding_mask)[0] - src = src + self.dropout1(src2) - src = self.norm1(src) - src2 = self.linear2(self.dropout(self.activation(self.linear1(src)))) - src = src + self.dropout2(src2) - src = self.norm2(src) - return src - - def forward_pre(self, src, - src_mask: Optional[Tensor] = None, - src_key_padding_mask: Optional[Tensor] = None, - pos: Optional[Tensor] = None): - src2 = self.norm1(src) - q = k = self.with_pos_embed(src2, pos) - src2 = self.self_attn(q, k, value=src2, attn_mask=src_mask, - key_padding_mask=src_key_padding_mask)[0] - src = src + self.dropout1(src2) - src2 = self.norm2(src) - src2 = self.linear2(self.dropout(self.activation(self.linear1(src2)))) - src = src + self.dropout2(src2) - return src - - def forward(self, src, - src_mask: Optional[Tensor] = None, - src_key_padding_mask: Optional[Tensor] = None, - pos: Optional[Tensor] = None): - if self.normalize_before: - return self.forward_pre(src, src_mask, src_key_padding_mask, pos) - return self.forward_post(src, src_mask, src_key_padding_mask, pos) - - -class TransformerDecoderLayer(nn.Module): - - def __init__(self, d_model, nhead, dim_feedforward=2048, dropout=0.1, - activation="relu", normalize_before=False): - super().__init__() - self.self_attn = nn.MultiheadAttention(d_model, nhead, dropout=dropout) - self.multihead_attn = nn.MultiheadAttention(d_model, nhead, dropout=dropout) - # Implementation of Feedforward model - self.linear1 = nn.Linear(d_model, dim_feedforward) - self.dropout = nn.Dropout(dropout) - self.linear2 = nn.Linear(dim_feedforward, d_model) - - self.norm1 = nn.LayerNorm(d_model) - self.norm2 = nn.LayerNorm(d_model) - self.norm3 = nn.LayerNorm(d_model) - self.dropout1 = nn.Dropout(dropout) - self.dropout2 = nn.Dropout(dropout) - self.dropout3 = nn.Dropout(dropout) - - self.activation = _get_activation_fn(activation) - self.normalize_before = normalize_before - - def with_pos_embed(self, tensor, pos: Optional[Tensor]): - return tensor if pos is None else tensor + pos - - def forward_post(self, tgt, memory, - tgt_mask: Optional[Tensor] = None, - memory_mask: Optional[Tensor] = None, - tgt_key_padding_mask: Optional[Tensor] = None, - memory_key_padding_mask: Optional[Tensor] = None, - pos: Optional[Tensor] = None, - query_pos: Optional[Tensor] = None): - q = k = self.with_pos_embed(tgt, query_pos) - tgt2 = self.self_attn(q, k, value=tgt, attn_mask=tgt_mask, - key_padding_mask=tgt_key_padding_mask)[0] - tgt = tgt + self.dropout1(tgt2) - tgt = self.norm1(tgt) - tgt2 = self.multihead_attn(query=self.with_pos_embed(tgt, query_pos), - key=self.with_pos_embed(memory, pos), - value=memory, attn_mask=memory_mask, - key_padding_mask=memory_key_padding_mask)[0] - tgt = tgt + self.dropout2(tgt2) - tgt = self.norm2(tgt) - tgt2 = self.linear2(self.dropout(self.activation(self.linear1(tgt)))) - tgt = tgt + self.dropout3(tgt2) - tgt = self.norm3(tgt) - return tgt - - def forward_pre(self, tgt, memory, - tgt_mask: Optional[Tensor] = None, - memory_mask: Optional[Tensor] = None, - tgt_key_padding_mask: Optional[Tensor] = None, - memory_key_padding_mask: Optional[Tensor] = None, - pos: Optional[Tensor] = None, - query_pos: Optional[Tensor] = None): - tgt2 = self.norm1(tgt) - q = k = self.with_pos_embed(tgt2, query_pos) - tgt2 = self.self_attn(q, k, value=tgt2, attn_mask=tgt_mask, - key_padding_mask=tgt_key_padding_mask)[0] - tgt = tgt + self.dropout1(tgt2) - tgt2 = self.norm2(tgt) - tgt2 = self.multihead_attn(query=self.with_pos_embed(tgt2, query_pos), - key=self.with_pos_embed(memory, pos), - value=memory, attn_mask=memory_mask, - key_padding_mask=memory_key_padding_mask)[0] - tgt = tgt + self.dropout2(tgt2) - tgt2 = self.norm3(tgt) - tgt2 = self.linear2(self.dropout(self.activation(self.linear1(tgt2)))) - tgt = tgt + self.dropout3(tgt2) - return tgt - - def forward(self, tgt, memory, - tgt_mask: Optional[Tensor] = None, - memory_mask: Optional[Tensor] = None, - tgt_key_padding_mask: Optional[Tensor] = None, - memory_key_padding_mask: Optional[Tensor] = None, - pos: Optional[Tensor] = None, - query_pos: Optional[Tensor] = None): - if self.normalize_before: - return self.forward_pre(tgt, memory, tgt_mask, memory_mask, - tgt_key_padding_mask, memory_key_padding_mask, pos, query_pos) - return self.forward_post(tgt, memory, tgt_mask, memory_mask, - tgt_key_padding_mask, memory_key_padding_mask, pos, query_pos) - - -def _get_clones(module, N): - return nn.ModuleList([copy.deepcopy(module) for i in range(N)]) - - -def build_transformer(args): - return Transformer( - d_model=args.hidden_dim, - dropout=args.dropout, - nhead=args.nheads, - dim_feedforward=args.dim_feedforward, - num_encoder_layers=args.enc_layers, - num_decoder_layers=args.dec_layers, - normalize_before=args.pre_norm, - return_intermediate_dec=True, - ) - - -def _get_activation_fn(activation): - """Return an activation function given a string""" - if activation == "relu": - return F.relu - if activation == "gelu": - return F.gelu - if activation == "glu": - return F.glu - raise RuntimeError(F"activation should be relu/gelu, not {activation}.") diff --git a/act/detr/setup.py b/act/detr/setup.py deleted file mode 100644 index 55d18c0..0000000 --- a/act/detr/setup.py +++ /dev/null @@ -1,10 +0,0 @@ -from distutils.core import setup -from setuptools import find_packages - -setup( - name='detr', - version='0.0.0', - packages=find_packages(), - license='MIT License', - long_description=open('README.md').read(), -) \ No newline at end of file diff --git a/act/detr/util/__init__.py b/act/detr/util/__init__.py deleted file mode 100644 index 168f997..0000000 --- a/act/detr/util/__init__.py +++ /dev/null @@ -1 +0,0 @@ -# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved diff --git a/act/detr/util/box_ops.py b/act/detr/util/box_ops.py deleted file mode 100644 index 9c088e5..0000000 --- a/act/detr/util/box_ops.py +++ /dev/null @@ -1,88 +0,0 @@ -# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved -""" -Utilities for bounding box manipulation and GIoU. -""" -import torch -from torchvision.ops.boxes import box_area - - -def box_cxcywh_to_xyxy(x): - x_c, y_c, w, h = x.unbind(-1) - b = [(x_c - 0.5 * w), (y_c - 0.5 * h), - (x_c + 0.5 * w), (y_c + 0.5 * h)] - return torch.stack(b, dim=-1) - - -def box_xyxy_to_cxcywh(x): - x0, y0, x1, y1 = x.unbind(-1) - b = [(x0 + x1) / 2, (y0 + y1) / 2, - (x1 - x0), (y1 - y0)] - return torch.stack(b, dim=-1) - - -# modified from torchvision to also return the union -def box_iou(boxes1, boxes2): - area1 = box_area(boxes1) - area2 = box_area(boxes2) - - lt = torch.max(boxes1[:, None, :2], boxes2[:, :2]) # [N,M,2] - rb = torch.min(boxes1[:, None, 2:], boxes2[:, 2:]) # [N,M,2] - - wh = (rb - lt).clamp(min=0) # [N,M,2] - inter = wh[:, :, 0] * wh[:, :, 1] # [N,M] - - union = area1[:, None] + area2 - inter - - iou = inter / union - return iou, union - - -def generalized_box_iou(boxes1, boxes2): - """ - Generalized IoU from https://giou.stanford.edu/ - - The boxes should be in [x0, y0, x1, y1] format - - Returns a [N, M] pairwise matrix, where N = len(boxes1) - and M = len(boxes2) - """ - # degenerate boxes gives inf / nan results - # so do an early check - assert (boxes1[:, 2:] >= boxes1[:, :2]).all() - assert (boxes2[:, 2:] >= boxes2[:, :2]).all() - iou, union = box_iou(boxes1, boxes2) - - lt = torch.min(boxes1[:, None, :2], boxes2[:, :2]) - rb = torch.max(boxes1[:, None, 2:], boxes2[:, 2:]) - - wh = (rb - lt).clamp(min=0) # [N,M,2] - area = wh[:, :, 0] * wh[:, :, 1] - - return iou - (area - union) / area - - -def masks_to_boxes(masks): - """Compute the bounding boxes around the provided masks - - The masks should be in format [N, H, W] where N is the number of masks, (H, W) are the spatial dimensions. - - Returns a [N, 4] tensors, with the boxes in xyxy format - """ - if masks.numel() == 0: - return torch.zeros((0, 4), device=masks.device) - - h, w = masks.shape[-2:] - - y = torch.arange(0, h, dtype=torch.float) - x = torch.arange(0, w, dtype=torch.float) - y, x = torch.meshgrid(y, x) - - x_mask = (masks * x.unsqueeze(0)) - x_max = x_mask.flatten(1).max(-1)[0] - x_min = x_mask.masked_fill(~(masks.bool()), 1e8).flatten(1).min(-1)[0] - - y_mask = (masks * y.unsqueeze(0)) - y_max = y_mask.flatten(1).max(-1)[0] - y_min = y_mask.masked_fill(~(masks.bool()), 1e8).flatten(1).min(-1)[0] - - return torch.stack([x_min, y_min, x_max, y_max], 1) diff --git a/act/detr/util/misc.py b/act/detr/util/misc.py deleted file mode 100644 index dfa9fb5..0000000 --- a/act/detr/util/misc.py +++ /dev/null @@ -1,468 +0,0 @@ -# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved -""" -Misc functions, including distributed helpers. - -Mostly copy-paste from torchvision references. -""" -import os -import subprocess -import time -from collections import defaultdict, deque -import datetime -import pickle -from packaging import version -from typing import Optional, List - -import torch -import torch.distributed as dist -from torch import Tensor - -# needed due to empty tensor bug in pytorch and torchvision 0.5 -import torchvision -if version.parse(torchvision.__version__) < version.parse('0.7'): - from torchvision.ops import _new_empty_tensor - from torchvision.ops.misc import _output_size - - -class SmoothedValue(object): - """Track a series of values and provide access to smoothed values over a - window or the global series average. - """ - - def __init__(self, window_size=20, fmt=None): - if fmt is None: - fmt = "{median:.4f} ({global_avg:.4f})" - self.deque = deque(maxlen=window_size) - self.total = 0.0 - self.count = 0 - self.fmt = fmt - - def update(self, value, n=1): - self.deque.append(value) - self.count += n - self.total += value * n - - def synchronize_between_processes(self): - """ - Warning: does not synchronize the deque! - """ - if not is_dist_avail_and_initialized(): - return - t = torch.tensor([self.count, self.total], dtype=torch.float64, device='cuda') - dist.barrier() - dist.all_reduce(t) - t = t.tolist() - self.count = int(t[0]) - self.total = t[1] - - @property - def median(self): - d = torch.tensor(list(self.deque)) - return d.median().item() - - @property - def avg(self): - d = torch.tensor(list(self.deque), dtype=torch.float32) - return d.mean().item() - - @property - def global_avg(self): - return self.total / self.count - - @property - def max(self): - return max(self.deque) - - @property - def value(self): - return self.deque[-1] - - def __str__(self): - return self.fmt.format( - median=self.median, - avg=self.avg, - global_avg=self.global_avg, - max=self.max, - value=self.value) - - -def all_gather(data): - """ - Run all_gather on arbitrary picklable data (not necessarily tensors) - Args: - data: any picklable object - Returns: - list[data]: list of data gathered from each rank - """ - world_size = get_world_size() - if world_size == 1: - return [data] - - # serialized to a Tensor - buffer = pickle.dumps(data) - storage = torch.ByteStorage.from_buffer(buffer) - tensor = torch.ByteTensor(storage).to("cuda") - - # obtain Tensor size of each rank - local_size = torch.tensor([tensor.numel()], device="cuda") - size_list = [torch.tensor([0], device="cuda") for _ in range(world_size)] - dist.all_gather(size_list, local_size) - size_list = [int(size.item()) for size in size_list] - max_size = max(size_list) - - # receiving Tensor from all ranks - # we pad the tensor because torch all_gather does not support - # gathering tensors of different shapes - tensor_list = [] - for _ in size_list: - tensor_list.append(torch.empty((max_size,), dtype=torch.uint8, device="cuda")) - if local_size != max_size: - padding = torch.empty(size=(max_size - local_size,), dtype=torch.uint8, device="cuda") - tensor = torch.cat((tensor, padding), dim=0) - dist.all_gather(tensor_list, tensor) - - data_list = [] - for size, tensor in zip(size_list, tensor_list): - buffer = tensor.cpu().numpy().tobytes()[:size] - data_list.append(pickle.loads(buffer)) - - return data_list - - -def reduce_dict(input_dict, average=True): - """ - Args: - input_dict (dict): all the values will be reduced - average (bool): whether to do average or sum - Reduce the values in the dictionary from all processes so that all processes - have the averaged results. Returns a dict with the same fields as - input_dict, after reduction. - """ - world_size = get_world_size() - if world_size < 2: - return input_dict - with torch.no_grad(): - names = [] - values = [] - # sort the keys so that they are consistent across processes - for k in sorted(input_dict.keys()): - names.append(k) - values.append(input_dict[k]) - values = torch.stack(values, dim=0) - dist.all_reduce(values) - if average: - values /= world_size - reduced_dict = {k: v for k, v in zip(names, values)} - return reduced_dict - - -class MetricLogger(object): - def __init__(self, delimiter="\t"): - self.meters = defaultdict(SmoothedValue) - self.delimiter = delimiter - - def update(self, **kwargs): - for k, v in kwargs.items(): - if isinstance(v, torch.Tensor): - v = v.item() - assert isinstance(v, (float, int)) - self.meters[k].update(v) - - def __getattr__(self, attr): - if attr in self.meters: - return self.meters[attr] - if attr in self.__dict__: - return self.__dict__[attr] - raise AttributeError("'{}' object has no attribute '{}'".format( - type(self).__name__, attr)) - - def __str__(self): - loss_str = [] - for name, meter in self.meters.items(): - loss_str.append( - "{}: {}".format(name, str(meter)) - ) - return self.delimiter.join(loss_str) - - def synchronize_between_processes(self): - for meter in self.meters.values(): - meter.synchronize_between_processes() - - def add_meter(self, name, meter): - self.meters[name] = meter - - def log_every(self, iterable, print_freq, header=None): - i = 0 - if not header: - header = '' - start_time = time.time() - end = time.time() - iter_time = SmoothedValue(fmt='{avg:.4f}') - data_time = SmoothedValue(fmt='{avg:.4f}') - space_fmt = ':' + str(len(str(len(iterable)))) + 'd' - if torch.cuda.is_available(): - log_msg = self.delimiter.join([ - header, - '[{0' + space_fmt + '}/{1}]', - 'eta: {eta}', - '{meters}', - 'time: {time}', - 'data: {data}', - 'max mem: {memory:.0f}' - ]) - else: - log_msg = self.delimiter.join([ - header, - '[{0' + space_fmt + '}/{1}]', - 'eta: {eta}', - '{meters}', - 'time: {time}', - 'data: {data}' - ]) - MB = 1024.0 * 1024.0 - for obj in iterable: - data_time.update(time.time() - end) - yield obj - iter_time.update(time.time() - end) - if i % print_freq == 0 or i == len(iterable) - 1: - eta_seconds = iter_time.global_avg * (len(iterable) - i) - eta_string = str(datetime.timedelta(seconds=int(eta_seconds))) - if torch.cuda.is_available(): - print(log_msg.format( - i, len(iterable), eta=eta_string, - meters=str(self), - time=str(iter_time), data=str(data_time), - memory=torch.cuda.max_memory_allocated() / MB)) - else: - print(log_msg.format( - i, len(iterable), eta=eta_string, - meters=str(self), - time=str(iter_time), data=str(data_time))) - i += 1 - end = time.time() - total_time = time.time() - start_time - total_time_str = str(datetime.timedelta(seconds=int(total_time))) - print('{} Total time: {} ({:.4f} s / it)'.format( - header, total_time_str, total_time / len(iterable))) - - -def get_sha(): - cwd = os.path.dirname(os.path.abspath(__file__)) - - def _run(command): - return subprocess.check_output(command, cwd=cwd).decode('ascii').strip() - sha = 'N/A' - diff = "clean" - branch = 'N/A' - try: - sha = _run(['git', 'rev-parse', 'HEAD']) - subprocess.check_output(['git', 'diff'], cwd=cwd) - diff = _run(['git', 'diff-index', 'HEAD']) - diff = "has uncommited changes" if diff else "clean" - branch = _run(['git', 'rev-parse', '--abbrev-ref', 'HEAD']) - except Exception: - pass - message = f"sha: {sha}, status: {diff}, branch: {branch}" - return message - - -def collate_fn(batch): - batch = list(zip(*batch)) - batch[0] = nested_tensor_from_tensor_list(batch[0]) - return tuple(batch) - - -def _max_by_axis(the_list): - # type: (List[List[int]]) -> List[int] - maxes = the_list[0] - for sublist in the_list[1:]: - for index, item in enumerate(sublist): - maxes[index] = max(maxes[index], item) - return maxes - - -class NestedTensor(object): - def __init__(self, tensors, mask: Optional[Tensor]): - self.tensors = tensors - self.mask = mask - - def to(self, device): - # type: (Device) -> NestedTensor # noqa - cast_tensor = self.tensors.to(device) - mask = self.mask - if mask is not None: - assert mask is not None - cast_mask = mask.to(device) - else: - cast_mask = None - return NestedTensor(cast_tensor, cast_mask) - - def decompose(self): - return self.tensors, self.mask - - def __repr__(self): - return str(self.tensors) - - -def nested_tensor_from_tensor_list(tensor_list: List[Tensor]): - # TODO make this more general - if tensor_list[0].ndim == 3: - if torchvision._is_tracing(): - # nested_tensor_from_tensor_list() does not export well to ONNX - # call _onnx_nested_tensor_from_tensor_list() instead - return _onnx_nested_tensor_from_tensor_list(tensor_list) - - # TODO make it support different-sized images - max_size = _max_by_axis([list(img.shape) for img in tensor_list]) - # min_size = tuple(min(s) for s in zip(*[img.shape for img in tensor_list])) - batch_shape = [len(tensor_list)] + max_size - b, c, h, w = batch_shape - dtype = tensor_list[0].dtype - device = tensor_list[0].device - tensor = torch.zeros(batch_shape, dtype=dtype, device=device) - mask = torch.ones((b, h, w), dtype=torch.bool, device=device) - for img, pad_img, m in zip(tensor_list, tensor, mask): - pad_img[: img.shape[0], : img.shape[1], : img.shape[2]].copy_(img) - m[: img.shape[1], :img.shape[2]] = False - else: - raise ValueError('not supported') - return NestedTensor(tensor, mask) - - -# _onnx_nested_tensor_from_tensor_list() is an implementation of -# nested_tensor_from_tensor_list() that is supported by ONNX tracing. -@torch.jit.unused -def _onnx_nested_tensor_from_tensor_list(tensor_list: List[Tensor]) -> NestedTensor: - max_size = [] - for i in range(tensor_list[0].dim()): - max_size_i = torch.max(torch.stack([img.shape[i] for img in tensor_list]).to(torch.float32)).to(torch.int64) - max_size.append(max_size_i) - max_size = tuple(max_size) - - # work around for - # pad_img[: img.shape[0], : img.shape[1], : img.shape[2]].copy_(img) - # m[: img.shape[1], :img.shape[2]] = False - # which is not yet supported in onnx - padded_imgs = [] - padded_masks = [] - for img in tensor_list: - padding = [(s1 - s2) for s1, s2 in zip(max_size, tuple(img.shape))] - padded_img = torch.nn.functional.pad(img, (0, padding[2], 0, padding[1], 0, padding[0])) - padded_imgs.append(padded_img) - - m = torch.zeros_like(img[0], dtype=torch.int, device=img.device) - padded_mask = torch.nn.functional.pad(m, (0, padding[2], 0, padding[1]), "constant", 1) - padded_masks.append(padded_mask.to(torch.bool)) - - tensor = torch.stack(padded_imgs) - mask = torch.stack(padded_masks) - - return NestedTensor(tensor, mask=mask) - - -def setup_for_distributed(is_master): - """ - This function disables printing when not in master process - """ - import builtins as __builtin__ - builtin_print = __builtin__.print - - def print(*args, **kwargs): - force = kwargs.pop('force', False) - if is_master or force: - builtin_print(*args, **kwargs) - - __builtin__.print = print - - -def is_dist_avail_and_initialized(): - if not dist.is_available(): - return False - if not dist.is_initialized(): - return False - return True - - -def get_world_size(): - if not is_dist_avail_and_initialized(): - return 1 - return dist.get_world_size() - - -def get_rank(): - if not is_dist_avail_and_initialized(): - return 0 - return dist.get_rank() - - -def is_main_process(): - return get_rank() == 0 - - -def save_on_master(*args, **kwargs): - if is_main_process(): - torch.save(*args, **kwargs) - - -def init_distributed_mode(args): - if 'RANK' in os.environ and 'WORLD_SIZE' in os.environ: - args.rank = int(os.environ["RANK"]) - args.world_size = int(os.environ['WORLD_SIZE']) - args.gpu = int(os.environ['LOCAL_RANK']) - elif 'SLURM_PROCID' in os.environ: - args.rank = int(os.environ['SLURM_PROCID']) - args.gpu = args.rank % torch.cuda.device_count() - else: - print('Not using distributed mode') - args.distributed = False - return - - args.distributed = True - - torch.cuda.set_device(args.gpu) - args.dist_backend = 'nccl' - print('| distributed init (rank {}): {}'.format( - args.rank, args.dist_url), flush=True) - torch.distributed.init_process_group(backend=args.dist_backend, init_method=args.dist_url, - world_size=args.world_size, rank=args.rank) - torch.distributed.barrier() - setup_for_distributed(args.rank == 0) - - -@torch.no_grad() -def accuracy(output, target, topk=(1,)): - """Computes the precision@k for the specified values of k""" - if target.numel() == 0: - return [torch.zeros([], device=output.device)] - maxk = max(topk) - batch_size = target.size(0) - - _, pred = output.topk(maxk, 1, True, True) - pred = pred.t() - correct = pred.eq(target.view(1, -1).expand_as(pred)) - - res = [] - for k in topk: - correct_k = correct[:k].view(-1).float().sum(0) - res.append(correct_k.mul_(100.0 / batch_size)) - return res - - -def interpolate(input, size=None, scale_factor=None, mode="nearest", align_corners=None): - # type: (Tensor, Optional[List[int]], Optional[float], str, Optional[bool]) -> Tensor - """ - Equivalent to nn.functional.interpolate, but with support for empty batch sizes. - This will eventually be supported natively by PyTorch, and this - class can go away. - """ - if version.parse(torchvision.__version__) < version.parse('0.7'): - if input.numel() > 0: - return torch.nn.functional.interpolate( - input, size, scale_factor, mode, align_corners - ) - - output_shape = _output_size(2, input, size, scale_factor) - output_shape = list(input.shape[:-2]) + list(output_shape) - return _new_empty_tensor(input, output_shape) - else: - return torchvision.ops.misc.interpolate(input, size, scale_factor, mode, align_corners) diff --git a/act/detr/util/plot_utils.py b/act/detr/util/plot_utils.py deleted file mode 100644 index 0f24bed..0000000 --- a/act/detr/util/plot_utils.py +++ /dev/null @@ -1,107 +0,0 @@ -""" -Plotting utilities to visualize training logs. -""" -import torch -import pandas as pd -import numpy as np -import seaborn as sns -import matplotlib.pyplot as plt - -from pathlib import Path, PurePath - - -def plot_logs(logs, fields=('class_error', 'loss_bbox_unscaled', 'mAP'), ewm_col=0, log_name='log.txt'): - ''' - Function to plot specific fields from training log(s). Plots both training and test results. - - :: Inputs - logs = list containing Path objects, each pointing to individual dir with a log file - - fields = which results to plot from each log file - plots both training and test for each field. - - ewm_col = optional, which column to use as the exponential weighted smoothing of the plots - - log_name = optional, name of log file if different than default 'log.txt'. - - :: Outputs - matplotlib plots of results in fields, color coded for each log file. - - solid lines are training results, dashed lines are test results. - - ''' - func_name = "plot_utils.py::plot_logs" - - # verify logs is a list of Paths (list[Paths]) or single Pathlib object Path, - # convert single Path to list to avoid 'not iterable' error - - if not isinstance(logs, list): - if isinstance(logs, PurePath): - logs = [logs] - print(f"{func_name} info: logs param expects a list argument, converted to list[Path].") - else: - raise ValueError(f"{func_name} - invalid argument for logs parameter.\n \ - Expect list[Path] or single Path obj, received {type(logs)}") - - # Quality checks - verify valid dir(s), that every item in list is Path object, and that log_name exists in each dir - for i, dir in enumerate(logs): - if not isinstance(dir, PurePath): - raise ValueError(f"{func_name} - non-Path object in logs argument of {type(dir)}: \n{dir}") - if not dir.exists(): - raise ValueError(f"{func_name} - invalid directory in logs argument:\n{dir}") - # verify log_name exists - fn = Path(dir / log_name) - if not fn.exists(): - print(f"-> missing {log_name}. Have you gotten to Epoch 1 in training?") - print(f"--> full path of missing log file: {fn}") - return - - # load log file(s) and plot - dfs = [pd.read_json(Path(p) / log_name, lines=True) for p in logs] - - fig, axs = plt.subplots(ncols=len(fields), figsize=(16, 5)) - - for df, color in zip(dfs, sns.color_palette(n_colors=len(logs))): - for j, field in enumerate(fields): - if field == 'mAP': - coco_eval = pd.DataFrame( - np.stack(df.test_coco_eval_bbox.dropna().values)[:, 1] - ).ewm(com=ewm_col).mean() - axs[j].plot(coco_eval, c=color) - else: - df.interpolate().ewm(com=ewm_col).mean().plot( - y=[f'train_{field}', f'test_{field}'], - ax=axs[j], - color=[color] * 2, - style=['-', '--'] - ) - for ax, field in zip(axs, fields): - ax.legend([Path(p).name for p in logs]) - ax.set_title(field) - - -def plot_precision_recall(files, naming_scheme='iter'): - if naming_scheme == 'exp_id': - # name becomes exp_id - names = [f.parts[-3] for f in files] - elif naming_scheme == 'iter': - names = [f.stem for f in files] - else: - raise ValueError(f'not supported {naming_scheme}') - fig, axs = plt.subplots(ncols=2, figsize=(16, 5)) - for f, color, name in zip(files, sns.color_palette("Blues", n_colors=len(files)), names): - data = torch.load(f) - # precision is n_iou, n_points, n_cat, n_area, max_det - precision = data['precision'] - recall = data['params'].recThrs - scores = data['scores'] - # take precision for all classes, all areas and 100 detections - precision = precision[0, :, :, 0, -1].mean(1) - scores = scores[0, :, :, 0, -1].mean(1) - prec = precision.mean() - rec = data['recall'][0, :, 0, -1].mean() - print(f'{naming_scheme} {name}: mAP@50={prec * 100: 05.1f}, ' + - f'score={scores.mean():0.3f}, ' + - f'f1={2 * prec * rec / (prec + rec + 1e-8):0.3f}' - ) - axs[0].plot(recall, precision, c=color) - axs[1].plot(recall, scores, c=color) - - axs[0].set_title('Precision / Recall') - axs[0].legend(names) - axs[1].set_title('Scores / Recall') - axs[1].legend(names) - return fig, axs diff --git a/act/imitate_episodes.py b/act/imitate_episodes.py deleted file mode 100644 index f771972..0000000 --- a/act/imitate_episodes.py +++ /dev/null @@ -1,367 +0,0 @@ -import torch -import numpy as np -import os -import pickle -import argparse -import matplotlib.pyplot as plt -from copy import deepcopy -from tqdm import tqdm -from einops import rearrange - -# from .constants import DT -# from .constants import PUPPET_GRIPPER_JOINT_OPEN -from utils import load_data # data functions -from utils import compute_dict_mean, set_seed, detach_dict, parse_id, find_all_ckpt # helper functions -from policy import ACTPolicy, CNNMLPPolicy -# from .visualize_episodes import save_videos -import wandb - -# from sim_env import BOX_POSE -# from constants import SIM_TASK_CONFIGS -import IPython -e = IPython.embed -import time -from itertools import repeat - -def repeater(data_loader): - epoch = 0 - for loader in repeat(data_loader): - for data in loader: - yield data - print(f'Epoch {epoch} done') - epoch += 1 - -from pathlib import Path - - -def main(args): - set_seed(1) - # command line parameters - is_eval = args['eval'] - policy_class = args['policy_class'] - onscreen_render = args['onscreen_render'] - # task_name = args['task_name'] - batch_size_train = args['batch_size'] - batch_size_val = args['batch_size'] - num_epochs = args['num_epochs'] - - # get task parameters - # is_sim = task_name[:4] == 'sim_' - task_dir, task_name = parse_id(RECORD_DIR, args['taskid']) - dataset_dir = (Path(task_dir) / 'processed').resolve() - ckpt_dir = (LOG_DIR / task_name / args['exptid']).resolve() - print("*"*20) - print(f"Task name: {task_name}") - print("*"*20) - - # print(f"Checkpoint dir: {ckpt_dir}") - # task_config = SIM_TASK_CONFIGS[task_name] - # dataset_dir = task_config['dataset_dir'] - # ckpt_dir = task_config['ckpt_dir'] - # num_episodes = task_config['num_episodes'] - # episode_len = task_config['episode_len'] - camera_names = ['left', 'right'] - - # fixed parameters - state_dim = 26 - action_dim = 28 - lr_backbone = 1e-5 - backbone = 'dino_v2' - if policy_class == 'ACT': - enc_layers = 4 - dec_layers = 7 - nheads = 8 - policy_config = {'lr': args['lr'], - 'num_queries': args['chunk_size'], - 'kl_weight': args['kl_weight'], - 'hidden_dim': args['hidden_dim'], - 'dim_feedforward': args['dim_feedforward'], - 'lr_backbone': lr_backbone, - 'backbone': backbone, - 'enc_layers': enc_layers, - 'dec_layers': dec_layers, - 'nheads': nheads, - 'camera_names': camera_names, - 'state_dim': state_dim, - 'action_dim': action_dim, - 'qpos_noise_std': args['qpos_noise_std'], - } - elif policy_class == 'CNNMLP': - policy_config = {'lr': args['lr'], 'lr_backbone': lr_backbone, 'backbone' : backbone, 'num_queries': 1, - 'camera_names': camera_names,} - else: - raise NotImplementedError - - config = { - 'num_epochs': num_epochs, - 'ckpt_dir': ckpt_dir, - # 'episode_len': episode_len, - 'state_dim': state_dim, - 'action_dim': action_dim, - 'lr': args['lr'], - 'policy_class': policy_class, - 'onscreen_render': onscreen_render, - 'policy_config': policy_config, - # 'task_name': task_name, - 'seed': args['seed'], - 'temporal_agg': args['temporal_agg'], - 'camera_names': camera_names, - # 'real_robot': not is_sim - 'resumeid': args['resumeid'], - 'resume_ckpt': args['resume_ckpt'], - 'task_name': task_name, - 'exptid': args['exptid'], - } - mode = "disabled" if args["no_wandb"] or args["save_jit"] else "online" - wandb.init(project="television", name=args['exptid'], group=task_name, entity="cxx", mode=mode, dir="../data/logs") - wandb.config.update(config) - train_dataloader, val_dataloader, stats, _ = load_data(dataset_dir, camera_names, batch_size_train, batch_size_val) - - # save dataset stats - if not os.path.isdir(ckpt_dir): - os.makedirs(ckpt_dir) - stats_path = os.path.join(ckpt_dir, f'dataset_stats.pkl') - with open(stats_path, 'wb') as f: - pickle.dump(stats, f) - - if args['save_jit']: - save_jit(config) - return - - best_ckpt_info = train_bc(train_dataloader, val_dataloader, config) - best_epoch, min_val_loss, best_state_dict = best_ckpt_info - - # save best checkpoint - ckpt_path = os.path.join(ckpt_dir, f'policy_best.ckpt') - torch.save(best_state_dict, ckpt_path) - print(f'Best ckpt, val loss {min_val_loss:.6f} @ epoch{best_epoch}') - wandb.finish() - -def make_policy(policy_class, policy_config): - if policy_class == 'ACT': - policy = ACTPolicy(policy_config) - elif policy_class == 'CNNMLP': - policy = CNNMLPPolicy(policy_config) - else: - raise NotImplementedError - return policy - - -def make_optimizer(policy_class, policy): - if policy_class == 'ACT': - optimizer = policy.configure_optimizers() - elif policy_class == 'CNNMLP': - optimizer = policy.configure_optimizers() - else: - raise NotImplementedError - return optimizer - - -def get_image(ts, camera_names): - curr_images = [] - for cam_name in camera_names: - curr_image = rearrange(ts.observation['images'][cam_name], 'h w c -> c h w') - curr_images.append(curr_image) - curr_image = np.stack(curr_images, axis=0) - curr_image = torch.from_numpy(curr_image / 255.0).float().cuda().unsqueeze(0) - return curr_image - - -def forward_pass(data, policy): - image_data, qpos_data, action_data, is_pad = data - image_data, qpos_data, action_data, is_pad = image_data.cuda(), qpos_data.cuda(), action_data.cuda(), is_pad.cuda() - return policy(qpos_data, image_data, action_data, is_pad) # TODO remove None - - - -def train_bc(train_dataloader, val_dataloader, config): - num_epochs = config['num_epochs'] - ckpt_dir = config['ckpt_dir'] - seed = config['seed'] - policy_class = config['policy_class'] - policy_config = config['policy_config'] - - set_seed(seed) - - policy = make_policy(policy_class, policy_config) - policy.cuda() - optimizer = make_optimizer(policy_class, policy) - - if config['resumeid']: - exp_dir, exp_name = parse_id((LOG_DIR / config['task_name']).resolve(), config['resumeid']) - policy, _, _ = load_ckpt(policy, exp_dir, config['resume_ckpt']) - # if config['resume_ckpt']: - # ckpt_name = f"policy_epoch_{config['resume_ckpt']}_seed_0.ckpt" - # else: - # ckpt_name, _ = find_all_ckpt(exp_dir)#f"policy_last.ckpt" - # resume_path = (Path(exp_dir) / ckpt_name).resolve() - # print(f"Resuming from {resume_path}") - # checkpoint = torch.load(resume_path) - # policy.load_state_dict(checkpoint) - - # train_history = [] - # validation_history = [] - min_val_loss = np.inf - best_ckpt_info = None - - train_dataloader = repeater(train_dataloader) - for epoch in tqdm(range(num_epochs)): - print(f'\nEpoch {epoch}') - if epoch % 500 == 0: - # validation - with torch.inference_mode(): - policy.eval() - validation_dicts = [] - for batch_idx, data in enumerate(val_dataloader): - forward_dict = forward_pass(data, policy) - validation_dicts.append(forward_dict) - if batch_idx > 20: - break - - validation_summary = compute_dict_mean(validation_dicts) - - epoch_val_loss = validation_summary['loss'] - if epoch_val_loss < min_val_loss: - min_val_loss = epoch_val_loss - best_ckpt_info = (epoch, min_val_loss, deepcopy(policy.state_dict())) - for k in list(validation_summary.keys()): - validation_summary[f'val/{k}'] = validation_summary.pop(k) - wandb.log(validation_summary, step=epoch) - print(f'Val loss: {epoch_val_loss:.5f}') - summary_string = '' - for k, v in validation_summary.items(): - summary_string += f'{k}: {v.item():.3f} ' - print(summary_string) - - # training - policy.train() - optimizer.zero_grad() - - data = next(train_dataloader) - forward_dict = forward_pass(data, policy) - # backward - loss = forward_dict['loss'] - loss.backward() - optimizer.step() - optimizer.zero_grad() - - epoch_summary = detach_dict(forward_dict) - - # epoch_summary = compute_dict_mean(train_history[(batch_idx+1)*epoch:(batch_idx+1)*(epoch+1)]) - epoch_train_loss = epoch_summary['loss'] - print(f'Train loss: {epoch_train_loss:.5f}') - summary_string = '' - for k, v in epoch_summary.items(): - summary_string += f'{k}: {v.item():.3f} ' - print(summary_string) - wandb.log(epoch_summary, step=epoch) - - if epoch % 1000 == 0 and epoch >= 1000: - ckpt_path = os.path.join(ckpt_dir, f'policy_epoch_{epoch}_seed_{seed}.ckpt') - torch.save(policy.state_dict(), ckpt_path) - # plot_history(train_history, validation_history, epoch, ckpt_dir, seed) - - ckpt_path = os.path.join(ckpt_dir, f'policy_last.ckpt') - torch.save(policy.state_dict(), ckpt_path) - - best_epoch, min_val_loss, best_state_dict = best_ckpt_info - ckpt_path = os.path.join(ckpt_dir, f'policy_epoch_{best_epoch}_seed_{seed}.ckpt') - torch.save(best_state_dict, ckpt_path) - print(f'Training finished:\nSeed {seed}, val loss {min_val_loss:.6f} at epoch {best_epoch}') - - # save training curves - # plot_history(train_history, validation_history, num_epochs, ckpt_dir, seed) - - return best_ckpt_info - - -def plot_history(train_history, validation_history, num_epochs, ckpt_dir, seed): - # save training curves - for key in train_history[0]: - plot_path = os.path.join(ckpt_dir, f'train_val_{key}_seed_{seed}.png') - plt.figure() - train_values = [summary[key].item() for summary in train_history] - val_values = [summary[key].item() for summary in validation_history] - plt.plot(np.linspace(0, num_epochs-1, len(train_history)), train_values, label='train') - plt.plot(np.linspace(0, num_epochs-1, len(validation_history)), val_values, label='validation') - # plt.ylim([-0.1, 1]) - plt.tight_layout() - plt.legend() - plt.title(key) - plt.savefig(plot_path) - print(f'Saved plots to {ckpt_dir}') - -def load_ckpt(policy, exp_dir, ckpt_name): - if ckpt_name: - epoch = ckpt_name - ckpt_name = f"policy_epoch_{ckpt_name}_seed_0.ckpt" - else: - ckpt_name, epoch = find_all_ckpt(exp_dir)#f"policy_last.ckpt" - resume_path = (Path(exp_dir) / ckpt_name).resolve() - print("*"*20) - print(f"Resuming from {resume_path}") - print("*"*20) - policy.load_state_dict(torch.load(resume_path)) - return policy, ckpt_name, epoch - -def save_jit(config): - # ckpt_dir = config['ckpt_dir'] - policy_class = config['policy_class'] - policy_config = config['policy_config'] - - exp_dir, exp_name = parse_id((LOG_DIR / config['task_name']).resolve(), config['exptid']) - - policy = make_policy(policy_class, policy_config) - policy.cuda() - - policy, ckpt_name, epoch = load_ckpt(policy, exp_dir, config['resume_ckpt']) - - policy.eval() - image_data = torch.rand((1, 2, 3, 480, 640), device='cuda') - qpos_data = torch.rand((1, config['state_dim']), device='cuda') - input_data = (qpos_data, image_data) - - traced_policy = torch.jit.trace(policy, input_data) - save_path = os.path.join(exp_dir, f"traced_jit_{epoch}.pt") - traced_policy.save(save_path) - print("Saved traced actor at ", save_path) - - new_policy = torch.jit.load(save_path) - -if __name__ == '__main__': - parser = argparse.ArgumentParser() - parser.add_argument('--eval', action='store_true') - parser.add_argument('--onscreen_render', action='store_true') - parser.add_argument('--policy_class', action='store', type=str, help='policy_class, capitalize', required=True) - # parser.add_argument('--task_name', action='store', type=str, help='task_name', required=True) - parser.add_argument('--batch_size', action='store', type=int, help='batch_size', required=True) - parser.add_argument('--seed', action='store', type=int, help='seed', required=True) - parser.add_argument('--num_epochs', action='store', type=int, help='num_epochs', required=True) - parser.add_argument('--lr', action='store', type=float, help='lr', required=True) - parser.add_argument('--qpos_noise_std', action='store', default=0, type=float, help='lr', required=False) - - # for ACT - parser.add_argument('--kl_weight', action='store', type=int, help='KL Weight', required=False) - parser.add_argument('--chunk_size', action='store', type=int, help='chunk_size', required=False) - parser.add_argument('--hidden_dim', action='store', type=int, help='hidden_dim', required=False) - parser.add_argument('--dim_feedforward', action='store', type=int, help='dim_feedforward', required=False) - parser.add_argument('--temporal_agg', action='store_true') - parser.add_argument('--save_jit', action='store_true') - parser.add_argument('--no_wandb', action='store_true') - parser.add_argument('--resumeid', action='store', default="", type=str, help='resume id', required=False) - parser.add_argument('--resume_ckpt', action='store', default="", type=str, help='resume ckpt', required=False) - parser.add_argument('--taskid', action='store', type=str, help='task id', required=True) - parser.add_argument('--exptid', action='store', type=str, help='experiment id', required=True) - parser.add_argument('--source', choices=['self', 'ssd'], default='self') - args = vars(parser.parse_args()) - - if args['source'] == 'self': - current_dir = Path(__file__).parent.resolve() - else: - current_dir = Path("/media/cxx/Extreme Pro/human2robot/data/").resolve() - DATA_DIR = (current_dir.parent / 'data/').resolve() - RECORD_DIR = (DATA_DIR / 'recordings/').resolve() - LOG_DIR = (DATA_DIR / 'logs/').resolve() - # print(f"\nDATA dir: {DATA_DIR}") - - main(args) diff --git a/act/policy.py b/act/policy.py deleted file mode 100644 index ef6061d..0000000 --- a/act/policy.py +++ /dev/null @@ -1,115 +0,0 @@ -import torch.nn as nn -from torch.nn import functional as F -from torchvision.transforms import v2 -import torch - -from detr.main import build_ACT_model_and_optimizer, build_CNNMLP_model_and_optimizer -import IPython -e = IPython.embed - -class ACTPolicy(nn.Module): - def __init__(self, args_override): - super().__init__() - model, optimizer = build_ACT_model_and_optimizer(args_override) - self.model = model # CVAE decoder - self.optimizer = optimizer - self.kl_weight = args_override['kl_weight'] - self.qpos_noise_std = args_override['qpos_noise_std'] - print(f'KL Weight {self.kl_weight}') - - def __call__(self, qpos, image, actions=None, is_pad=None): - env_state = None - # normalize = transforms.Normalize(mean=[0.485, 0.456, 0.406], - # std=[0.229, 0.224, 0.225]) - patch_h = 16 - patch_w = 22 - if actions is not None: # training time - # transform = v2.Compose([ - # v2.ColorJitter(brightness=0.5, contrast=0.5, saturation=0.5, hue=0.5), - # v2.RandomPerspective(distortion_scale=0.5), - # v2.RandomAffine(degrees=10, translate=(0.1,0.1), scale=(0.9,1.1)), - # v2.GaussianBlur(kernel_size=(9,9), sigma=(0.1,2.0)), - # v2.Normalize( - # mean=[0.485, 0.456, 0.406], - # std=[0.229, 0.224, 0.225]) - # ]) - transform = v2.Compose([ - v2.ColorJitter(brightness=0.5, contrast=0.5, saturation=0.5, hue=0.5), - v2.RandomPerspective(distortion_scale=0.5), - v2.RandomAffine(degrees=10, translate=(0.1,0.1), scale=(0.9,1.1)), - v2.GaussianBlur(kernel_size=(9,9), sigma=(0.1,2.0)), - v2.Resize((patch_h * 14, patch_w * 14)), - # v2.CenterCrop((patch_h * 14, patch_w * 14)), - v2.Normalize(mean=(0.485, 0.456, 0.406), std=(0.229, 0.224, 0.225)), - ]) - qpos += (self.qpos_noise_std**0.5)*torch.randn_like(qpos) - else: # inference time - transform = v2.Compose([ - v2.Resize((patch_h * 14, patch_w * 14)), - # v2.CenterCrop((patch_h * 14, patch_w * 14)), - v2.Normalize(mean=(0.485, 0.456, 0.406), std=(0.229, 0.224, 0.225)) - ]) - - image = transform(image) - if actions is not None: # training time - actions = actions[:, :self.model.num_queries] - is_pad = is_pad[:, :self.model.num_queries] - - a_hat, is_pad_hat, (mu, logvar) = self.model(qpos, image, env_state, actions, is_pad) - total_kld, dim_wise_kld, mean_kld = kl_divergence(mu, logvar) - loss_dict = dict() - all_l1 = F.l1_loss(actions, a_hat, reduction='none') - l1 = (all_l1 * ~is_pad.unsqueeze(-1)).mean() - loss_dict['l1'] = l1 - loss_dict['kl'] = total_kld[0] - loss_dict['loss'] = loss_dict['l1'] + loss_dict['kl'] * self.kl_weight - return loss_dict - else: # inference time - a_hat, _, (_, _) = self.model(qpos, image, env_state) # no action, sample from prior - return a_hat - - def configure_optimizers(self): - return self.optimizer - - -class CNNMLPPolicy(nn.Module): - def __init__(self, args_override): - super().__init__() - model, optimizer = build_CNNMLP_model_and_optimizer(args_override) - self.model = model # decoder - self.optimizer = optimizer - - def __call__(self, qpos, image, actions=None, is_pad=None): - env_state = None # TODO - normalize = transforms.Normalize(mean=[0.485, 0.456, 0.406], - std=[0.229, 0.224, 0.225]) - image = normalize(image) - if actions is not None: # training time - actions = actions[:, 0] - a_hat = self.model(qpos, image, env_state, actions) - mse = F.mse_loss(actions, a_hat) - loss_dict = dict() - loss_dict['mse'] = mse - loss_dict['loss'] = loss_dict['mse'] - return loss_dict - else: # inference time - a_hat = self.model(qpos, image, env_state) # no action, sample from prior - return a_hat - - def configure_optimizers(self): - return self.optimizer - -def kl_divergence(mu, logvar): - batch_size = mu.size(0) - assert batch_size != 0 - if mu.data.ndimension() == 4: - mu = mu.view(mu.size(0), mu.size(1)) - if logvar.data.ndimension() == 4: - logvar = logvar.view(logvar.size(0), logvar.size(1)) - - klds = -0.5 * (1 + logvar - mu.pow(2) - logvar.exp()) - total_kld = klds.sum(1).mean(0, True) - dimension_wise_kld = klds.mean(0) - mean_kld = klds.mean(1).mean(0, True) - - return total_kld, dimension_wise_kld, mean_kld diff --git a/act/utils.py b/act/utils.py deleted file mode 100644 index f4de4ab..0000000 --- a/act/utils.py +++ /dev/null @@ -1,286 +0,0 @@ -import numpy as np -import torch -import os -import h5py -from torch.utils.data import TensorDataset, DataLoader -import time -import IPython -e = IPython.embed -from pathlib import Path - -class EpisodicDataset(torch.utils.data.Dataset): - def __init__(self, episode_ids, dataset_dir, camera_names, norm_stats, episode_len, history_stack=0): - super(EpisodicDataset).__init__() - self.episode_ids = episode_ids - self.dataset_dir = dataset_dir - self.camera_names = camera_names - self.norm_stats = norm_stats - self.is_sim = None - self.max_pad_len = 200 - action_str = 'qpos_action' - - self.history_stack = history_stack - - self.dataset_paths = [] - self.roots = [] - self.is_sims = [] - self.original_action_shapes = [] - - self.states = [] - self.image_dict = dict() - for cam_name in self.camera_names: - self.image_dict[cam_name] = [] - self.actions = [] - - for i, episode_id in enumerate(self.episode_ids): - self.dataset_paths.append(os.path.join(self.dataset_dir, f'processed_episode_{episode_id}.hdf5')) - root = h5py.File(self.dataset_paths[i], 'r') - self.roots.append(root) - self.is_sims.append(root.attrs['sim']) - self.original_action_shapes.append(root[action_str].shape) - - self.states.append(np.array(root['observation.state'])) - for cam_name in self.camera_names: - self.image_dict[cam_name].append(root[f'observation.image.{cam_name}']) - self.actions.append(np.array(root[action_str])) - - self.is_sim = self.is_sims[0] - - self.episode_len = episode_len - self.cumulative_len = np.cumsum(self.episode_len) - - # self.__getitem__(0) # initialize self.is_sim - - # def __len__(self): - # return len(self.episode_ids) - - def _locate_transition(self, index): - assert index < self.cumulative_len[-1] - episode_index = np.argmax(self.cumulative_len > index) # argmax returns first True index - start_ts = index - (self.cumulative_len[episode_index] - self.episode_len[episode_index]) - return episode_index, start_ts - - def __getitem__(self, ts_index): - sample_full_episode = False # hardcode - - index, start_ts = self._locate_transition(ts_index) - - original_action_shape = self.original_action_shapes[index] - episode_len = original_action_shape[0] - - if sample_full_episode: - start_ts = 0 - else: - start_ts = np.random.choice(episode_len) - - # get observation at start_ts only - qpos = self.states[index][start_ts] - # qvel = root['/observations/qvel'][start_ts] - - if self.history_stack > 0: - last_indices = np.maximum(0, np.arange(start_ts-self.history_stack, start_ts)).astype(int) - last_action = self.actions[index][last_indices, :] - - image_dict = dict() - for cam_name in self.camera_names: - image_dict[cam_name] = self.image_dict[cam_name][index][start_ts] - # get all actions after and including start_ts - all_time_action = self.actions[index][:] - - all_time_action_padded = np.zeros((self.max_pad_len+original_action_shape[0], original_action_shape[1]), dtype=np.float32) - all_time_action_padded[:episode_len] = all_time_action - all_time_action_padded[episode_len:] = all_time_action[-1] - - padded_action = all_time_action_padded[start_ts:start_ts+self.max_pad_len] - real_len = episode_len - start_ts - - is_pad = np.zeros(self.max_pad_len) - is_pad[real_len:] = 1 - - # new axis for different cameras - all_cam_images = [] - for cam_name in self.camera_names: - all_cam_images.append(image_dict[cam_name]) - all_cam_images = np.stack(all_cam_images, axis=0) - - # construct observations - image_data = torch.from_numpy(all_cam_images) - qpos_data = torch.from_numpy(qpos).float() - action_data = torch.from_numpy(padded_action).float() - is_pad = torch.from_numpy(is_pad).bool() - if self.history_stack > 0: - last_action_data = torch.from_numpy(last_action).float() - - # normalize image and change dtype to float - image_data = image_data / 255.0 - action_data = (action_data - self.norm_stats["action_mean"]) / self.norm_stats["action_std"] - qpos_data = (qpos_data - self.norm_stats["qpos_mean"]) / self.norm_stats["qpos_std"] - if self.history_stack > 0: - last_action_data = (last_action_data - self.norm_stats['action_mean']) / self.norm_stats['action_std'] - qpos_data = torch.cat((qpos_data, last_action_data.flatten())) - # print(f"qpos_data: {qpos_data.shape}, action_data: {action_data.shape}, image_data: {image_data.shape}, is_pad: {is_pad.shape}") - return image_data, qpos_data, action_data, is_pad - - -def get_norm_stats(dataset_dir, num_episodes): - action_str = 'qpos_action' - all_qpos_data = [] - all_action_data = [] - all_episode_len = [] - for episode_idx in range(num_episodes): - dataset_path = os.path.join(dataset_dir, f'processed_episode_{episode_idx}.hdf5') - with h5py.File(dataset_path, 'r') as root: - qpos = root['observation.state'][()] - action = root[action_str][()] - all_qpos_data.append(torch.from_numpy(qpos)) - all_action_data.append(torch.from_numpy(action)) - all_episode_len.append(len(qpos)) - all_qpos_data = torch.cat(all_qpos_data) - all_action_data = torch.cat(all_action_data) - all_action_data = all_action_data - - # normalize action data - action_mean = all_action_data.mean(dim=0, keepdim=True) # (episode, timstep, action_dim) - action_std = all_action_data.std(dim=0, keepdim=True) - action_std = torch.clip(action_std, 1e-2, np.inf) # clipping - - # normalize qpos data - qpos_mean = all_qpos_data.mean(dim=0, keepdim=True) - qpos_std = all_qpos_data.std(dim=0, keepdim=True) - qpos_std = torch.clip(qpos_std, 1e-2, np.inf) # clipping - - stats = {"action_mean": action_mean.numpy().squeeze(), "action_std": action_std.numpy().squeeze(), - "qpos_mean": qpos_mean.numpy().squeeze(), "qpos_std": qpos_std.numpy().squeeze(), - "example_qpos": qpos} - - return stats, all_episode_len - -def find_all_processed_episodes(path): - episodes = [f for f in os.listdir(path)] - return episodes - -def BatchSampler(batch_size, episode_len_l, sample_weights=None): - sample_probs = np.array(sample_weights) / np.sum(sample_weights) if sample_weights is not None else None - sum_dataset_len_l = np.cumsum([0] + [np.sum(episode_len) for episode_len in episode_len_l]) - while True: - batch = [] - for _ in range(batch_size): - episode_idx = np.random.choice(len(episode_len_l), p=sample_probs) - step_idx = np.random.randint(sum_dataset_len_l[episode_idx], sum_dataset_len_l[episode_idx + 1]) - batch.append(step_idx) - yield batch - -def load_data(dataset_dir, camera_names, batch_size_train, batch_size_val): - print(f'\nData from: {dataset_dir}\n') - - all_eps = find_all_processed_episodes(dataset_dir) - num_episodes = len(all_eps) - - # obtain train test split - train_ratio = 0.99 - shuffled_indices = np.random.permutation(num_episodes) - train_indices = shuffled_indices[:int(train_ratio * num_episodes)] - val_indices = shuffled_indices[int(train_ratio * num_episodes):] - print(f"Train episodes: {len(train_indices)}, Val episodes: {len(val_indices)}") - # obtain normalization stats for qpos and action - norm_stats, all_episode_len = get_norm_stats(dataset_dir, num_episodes) - - train_episode_len_l = [all_episode_len[i] for i in train_indices] - val_episode_len_l = [all_episode_len[i] for i in val_indices] - batch_sampler_train = BatchSampler(batch_size_train, train_episode_len_l) - batch_sampler_val = BatchSampler(batch_size_val, val_episode_len_l, None) - - # construct dataset and dataloader - train_dataset = EpisodicDataset(train_indices, dataset_dir, camera_names, norm_stats, train_episode_len_l) - val_dataset = EpisodicDataset(val_indices, dataset_dir, camera_names, norm_stats, val_episode_len_l) - train_dataloader = DataLoader(train_dataset, batch_sampler=batch_sampler_train, pin_memory=True, num_workers=24, prefetch_factor=2) - val_dataloader = DataLoader(val_dataset, batch_sampler=batch_sampler_val, pin_memory=True, num_workers=16, prefetch_factor=2) - - return train_dataloader, val_dataloader, norm_stats, train_dataset.is_sim - -def sample_box_pose(): - x_range = [0.0, 0.2] - y_range = [0.4, 0.6] - z_range = [0.05, 0.05] - - ranges = np.vstack([x_range, y_range, z_range]) - cube_position = np.random.uniform(ranges[:, 0], ranges[:, 1]) - - cube_quat = np.array([1, 0, 0, 0]) - return np.concatenate([cube_position, cube_quat]) - -def sample_insertion_pose(): - # Peg - x_range = [0.1, 0.2] - y_range = [0.4, 0.6] - z_range = [0.05, 0.05] - - ranges = np.vstack([x_range, y_range, z_range]) - peg_position = np.random.uniform(ranges[:, 0], ranges[:, 1]) - - peg_quat = np.array([1, 0, 0, 0]) - peg_pose = np.concatenate([peg_position, peg_quat]) - - # Socket - x_range = [-0.2, -0.1] - y_range = [0.4, 0.6] - z_range = [0.05, 0.05] - - ranges = np.vstack([x_range, y_range, z_range]) - socket_position = np.random.uniform(ranges[:, 0], ranges[:, 1]) - - socket_quat = np.array([1, 0, 0, 0]) - socket_pose = np.concatenate([socket_position, socket_quat]) - - return peg_pose, socket_pose - -### helper functions - -def compute_dict_mean(epoch_dicts): - result = {k: None for k in epoch_dicts[0]} - num_items = len(epoch_dicts) - for k in result: - value_sum = 0 - for epoch_dict in epoch_dicts: - value_sum += epoch_dict[k] - result[k] = value_sum / num_items - return result - -def detach_dict(d): - new_d = dict() - for k, v in d.items(): - new_d[k] = v.detach() - return new_d - -def set_seed(seed): - torch.manual_seed(seed) - np.random.seed(seed) - -def parse_id(base_dir, prefix): - base_path = Path(base_dir) - # Ensure the base path exists and is a directory - if not 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-0.00916589517 0.0306019988 -0.00917101093 + endloop + endfacet + + facet normal 0 0 0 + outer loop + vertex -0.00699999975 0.0323942304 0.00900000054 + vertex -0.00916589517 0.0306019988 -0.00917101093 + vertex -0.00975675788 0.0298723429 0.00917101093 + endloop + endfacet + + facet normal 0 0 0 + outer loop + vertex -0.00699999975 0.0323942304 0.00900000054 + vertex -0.00975675788 0.0298723429 0.00917101093 + vertex -0.0018080502 0.0242999997 0.0148906345 + endloop + endfacet + + facet normal 0 0 0 + outer loop + vertex -0.00699999975 0.0323942304 0.00900000054 + vertex -0.0018080502 0.0242999997 0.0148906345 + vertex -0.0043192273 0.033158794 0.00943301339 + endloop + endfacet + +endsolid AssimpScene diff --git a/assets/unitree_hand/unitree_dex3.yml b/assets/unitree_hand/unitree_dex3.yml new file mode 100644 index 0000000..4e2ea51 --- /dev/null +++ b/assets/unitree_hand/unitree_dex3.yml @@ -0,0 +1,48 @@ +left: + type: vector + urdf_path: unitree_hand/unitree_dex3_left.urdf + + # Target refers to the retargeting target, which is the robot hand + # target_joint_names: + target_joint_names: + [ + "left_hand_zero_joint", + "left_hand_one_joint", + "left_hand_two_joint", + "left_hand_three_joint", + "left_hand_four_joint", + "left_hand_five_joint", + "left_hand_six_joint", + ] + wrist_link_name: None + target_origin_link_names: ["base_link_thumb","base_link","base_link"] + target_task_link_names: ["thumb_tip","index_tip","middle_tip"] + target_link_human_indices: [[0, 0, 0], [4, 9, 14]] + + scaling_factor: 1.4 + + # A smaller alpha means stronger filtering, i.e. more smooth but also larger latency + low_pass_alpha: 0.2 + +right: + type: vector + urdf_path: unitree_hand/unitree_dex3_right.urdf + + # Target refers to the retargeting target, which is the robot hand + target_joint_names: + [ + "right_hand_zero_joint", + "right_hand_one_joint", + "right_hand_two_joint", + "right_hand_three_joint", + "right_hand_four_joint", + "right_hand_five_joint", + "right_hand_six_joint", + ] + wrist_link_name: None + target_origin_link_names: ["base_link_thumb","base_link","base_link"] + target_task_link_names: ["thumb_tip", "index_tip", "middle_tip"] + target_link_human_indices: [[0, 0, 0], [4, 9, 14]] + scaling_factor: 1.4 + # A smaller alpha means stronger filtering, i.e. more smooth but also larger latency + low_pass_alpha: 0.2 diff --git a/assets/unitree_hand/unitree_dex3_left.urdf b/assets/unitree_hand/unitree_dex3_left.urdf new file mode 100644 index 0000000..f4609b3 --- /dev/null +++ b/assets/unitree_hand/unitree_dex3_left.urdf @@ -0,0 +1,400 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + \ No newline at end of file diff --git a/assets/unitree_hand/unitree_dex3_right.urdf b/assets/unitree_hand/unitree_dex3_right.urdf new file mode 100644 index 0000000..2f0554e --- /dev/null +++ b/assets/unitree_hand/unitree_dex3_right.urdf @@ -0,0 +1,400 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + \ No newline at end of file diff --git a/scripts/deploy_sim.py b/scripts/deploy_sim.py deleted file mode 100644 index 294661a..0000000 --- a/scripts/deploy_sim.py +++ /dev/null @@ -1,150 +0,0 @@ -from isaacgym import gymapi -from isaacgym import gymutil - -import math -import numpy as np -import matplotlib.pyplot as plt - -from replay_demo import Player - -from pathlib import Path -import h5py -from tqdm import tqdm -import time -import yaml -import pickle -import torch -import cv2 -from collections import deque -import argparse -import sys -sys.path.append("../") -from act.utils import parse_id -# from act.imitate_episodes import RECORD_DIR, DATA_DIR, LOG_DIR - -from pathlib import Path -current_dir = Path(__file__).parent.resolve() -DATA_DIR = (current_dir.parent / 'data/').resolve() -RECORD_DIR = (DATA_DIR / 'recordings/').resolve() -LOG_DIR = (DATA_DIR / 'logs/').resolve() -# print(f"\nDATA dir: {DATA_DIR}") - -def get_norm_stats(data_path): - # norm_stats = { - # "action_mean": np.array([]), "action_std": np.array([]), - # "qpos_mean": np.array([]), "qpos_std": np.array([]), - # } - with open(data_path, "rb") as f: - norm_stats = pickle.load(f) - return norm_stats - - -def load_policy(policy_path, device): - policy = torch.jit.load(policy_path, map_location=device) - return policy - - -def normalize_input(state, left_img, right_img, norm_stats, last_action_data=None): - # import ipdb; ipdb.set_trace() - # left_img = cv2.resize(left_img, (308, 224)) - # right_img = cv2.resize(right_img, (308, 224)) - image_data = torch.from_numpy(np.stack([left_img, right_img], axis=0)) / 255.0 - qpos_data = (torch.from_numpy(state) - norm_stats["qpos_mean"]) / norm_stats["qpos_std"] - image_data = image_data.view((1, 2, 3, 480, 640)).to(device='cuda') - qpos_data = qpos_data.view((1, 26)).to(device='cuda') - - if last_action_data is not None: - last_action_data = torch.from_numpy(last_action_data).to(device='cuda').view((1, -1)).to(torch.float) - qpos_data = torch.cat((qpos_data, last_action_data), dim=1) - return (qpos_data, image_data) - - -def merge_act(actions_for_curr_step, k = 0.01): - actions_populated = np.all(actions_for_curr_step != 0, axis=1) - actions_for_curr_step = actions_for_curr_step[actions_populated] - - exp_weights = np.exp(-k * np.arange(actions_for_curr_step.shape[0])) - exp_weights = (exp_weights / exp_weights.sum()).reshape((-1, 1)) - raw_action = (actions_for_curr_step * exp_weights).sum(axis=0) - - return raw_action - - -if __name__ == '__main__': - parser = argparse.ArgumentParser('Set transformer detector', add_help=False) - parser.add_argument('--taskid', action='store', type=str, help='task id', required=True) - parser.add_argument('--exptid', action='store', type=str, help='experiment id', required=True) - parser.add_argument('--resume_ckpt', action='store', type=str, help='resume checkpoint', required=True) - args = vars(parser.parse_args()) - - episode_name = "processed_episode_0.hdf5" - task_dir, task_name = parse_id(RECORD_DIR, args['taskid']) - episode_path = (Path(task_dir) / 'processed' / episode_name).resolve() - exp_path, _ = parse_id((Path(LOG_DIR) / task_name).resolve(), args['exptid']) - - norm_stat_path = Path(exp_path) / "dataset_stats.pkl" - policy_path = Path(exp_path) / f"traced_jit_{args['resume_ckpt']}.pt" - - temporal_agg = True - action_dim = 28 - - chunk_size = 60 - device = "cuda" - - data = h5py.File(str(episode_path), 'r') - actions = np.array(data['qpos_action']) - left_imgs = np.array(data['observation.image.left']) - right_imgs = np.array(data['observation.image.right']) - states = np.array(data['observation.state']) - init_action = np.array(data.attrs['init_action']) - data.close() - timestamps = states.shape[0] - - norm_stats = get_norm_stats(norm_stat_path) - policy = load_policy(policy_path, device) - policy.cuda() - policy.eval() - - history_stack = 0 - if history_stack > 0: - last_action_queue = deque(maxlen=history_stack) - for i in range(history_stack): - last_action_queue.append(actions[0]) - else: - last_action_queue = None - last_action_data = None - player = Player(dt=1/30) - - if temporal_agg: - all_time_actions = np.zeros([timestamps, timestamps+chunk_size, action_dim]) - else: - num_actions_exe = chunk_size - - try: - output = None - act_index = 0 - for t in tqdm(range(timestamps)): - if history_stack > 0: - last_action_data = np.array(last_action_queue) - - data = normalize_input(states[t], left_imgs[t], right_imgs[t], norm_stats, last_action_data) - - if temporal_agg: - output = policy(*data)[0].detach().cpu().numpy() # (1,chuck_size,action_dim) - all_time_actions[[t], t:t+chunk_size] = output - act = merge_act(all_time_actions[:, t]) - else: - if output is None or act_index == num_actions_exe-1: - print("Inference...") - output = policy(*data)[0].detach().cpu().numpy() - act_index = 0 - act = output[act_index] - act_index += 1 - # import ipdb; ipdb.set_trace() - if history_stack > 0: - last_action_queue.append(act) - act = act * norm_stats["action_std"] + norm_stats["action_mean"] - player.step(act, left_imgs[t], right_imgs[t]) - except KeyboardInterrupt: - player.end() - exit() \ No newline at end of file diff --git a/scripts/plot_action.py b/scripts/plot_action.py deleted file mode 100644 index 28cecf4..0000000 --- a/scripts/plot_action.py +++ /dev/null @@ -1,33 +0,0 @@ -import math -import numpy as np -import matplotlib.pyplot as plt - -from pathlib import Path -import h5py -from tqdm import tqdm -import time -import yaml -import pickle - - -if __name__ == '__main__': - - root = "../data/recordings" - exp_name = "00-can-sorting" - episode_name = "processed_episode_0.hdf5" - episode_path = Path(root) / exp_name / "processed" / episode_name - - data = h5py.File(str(episode_path), 'r') - actions = np.array(data['qpos_action']) - data.close() - timestamps = actions.shape[0] - action_dim = actions.shape[1] - - plot_num = np.ceil(np.sqrt(action_dim)).astype(int) - plt.subplot(plot_num, plot_num, 1) - - for i in range(action_dim): - plt.subplot(plot_num, plot_num, i+1) - plt.plot(np.arange(timestamps), actions[:,i]) - - plt.show() diff --git a/scripts/post_process.py b/scripts/post_process.py deleted file mode 100644 index 35c6f3b..0000000 --- a/scripts/post_process.py +++ /dev/null @@ -1,207 +0,0 @@ -import h5py -import numpy as np -import pyzed.sl as sl -import time -import cv2 -import matplotlib.pyplot as plt -import tqdm -import torch -from torch.utils.data import Dataset -import os -import multiprocessing -from numpy.lib.stride_tricks import as_strided - -from pytransform3d import rotations -import concurrent.futures -from pathlib import Path -import argparse - -def load_svo(path, crop_size_h=240, crop_size_w=320): - input_file = path + ".svo" - # import ipdb; ipdb.set_trace() - print(input_file) - crop_size_h = crop_size_h - crop_size_w = crop_size_w - init_parameters = sl.InitParameters() - init_parameters.set_from_svo_file(input_file) - - zed = sl.Camera() - err = zed.open(init_parameters) - left_image = sl.Mat() - right_image = sl.Mat() - - nb_frames = zed.get_svo_number_of_frames() - print("Total image frames: ", nb_frames) - - cropped_img_shape = (720-crop_size_h, 1280-2*crop_size_w) - left_imgs = np.zeros((nb_frames, 3, cropped_img_shape[0], cropped_img_shape[1]), dtype=np.uint8) - right_imgs = np.zeros((nb_frames, 3, cropped_img_shape[0], cropped_img_shape[1]), dtype=np.uint8) - timestamps = np.zeros((nb_frames, ), dtype=np.int64) - cnt = 0 - while True: - if zed.grab() == sl.ERROR_CODE.SUCCESS: - zed.retrieve_image(left_image, sl.VIEW.LEFT) - zed.retrieve_image(right_image, sl.VIEW.RIGHT) - - timestamps[cnt] = zed.get_timestamp(sl.TIME_REFERENCE.IMAGE).get_milliseconds() - # import ipdb; ipdb.set_trace() - left_imgs[cnt] = cv2.cvtColor(left_image.get_data()[crop_size_h:, crop_size_w:-crop_size_w], cv2.COLOR_BGRA2RGB).transpose(2, 0, 1) - right_imgs[cnt] = cv2.cvtColor(right_image.get_data()[crop_size_h:, crop_size_w:-crop_size_w], cv2.COLOR_BGRA2RGB).transpose(2, 0, 1) - cnt += 1 - if cnt % 100 == 0: - print(f"{cnt/nb_frames*100:.2f}%") - # plt.imsave(f"left_img_{cnt}.png", left_imgs[cnt-1].transpose(1, 2, 0)) - elif zed.grab() == sl.ERROR_CODE.END_OF_SVOFILE_REACHED: - break - # print delta mean and std for img_timstamps - delta = np.diff(timestamps)[:-1] - print("img timestamps delta mean: ", np.mean(delta)) - print("img timestamps delta std: ", np.std(delta)) - return left_imgs[10:-10], right_imgs[10:-10], timestamps[10:-10] - -def load_hdf5(path, offset=10): # offset 10ms - input_file = path + ".hdf5" - file = h5py.File(input_file, 'r') - print(f"Total hdf5_frames: {file['/obs/timestamp'].shape[0]}") - # print(file["/obs/timestamp"].shape) - # print(file["/obs/qpos"].shape) - # print(file["/obs/qvel"].shape) - # print(file["/action/joint_pos"].shape) - # print("keys: ", list(file.keys())) - timestamps = np.array(file["/obs/timestamp"][:] * 1000, dtype=np.int64) - offset - states = np.array(file["/obs/qpos"][:]) - actions = np.array(file["/action/joint_pos"][:]) - cmds = np.array(file["/action/cmd"][:]) - - return timestamps, states, actions, cmds - -def match_timestamps(candidate, ref): - closest_indices = [] - # candidate = np.sort(candidate) - for t in ref: - idx = np.searchsorted(candidate, t, side="left") - if idx > 0 and (idx == len(candidate) or np.fabs(t - candidate[idx-1]) < np.fabs(t - candidate[idx])): - closest_indices.append(idx-1) - else: - closest_indices.append(idx) - # print("closest_indices: ", len(closest_indices)) - return np.array(closest_indices) - -def find_all_episodes(path): - episodes = [os.path.join(path, f) for f in os.listdir(path) if f.startswith('episode') and f.endswith('.svo')] - episodes = [os.path.basename(ep).split(".")[0] for ep in episodes] - return episodes - -def create_chunks(data, chunk_size): - N, F = data.shape - if chunk_size > N: - raise ValueError("chunk_size cannot be greater than N.") - - stride0, stride1 = data.strides - new_shape = (N - chunk_size + 1, chunk_size, F) - new_strides = (stride0, stride0, stride1) - - return as_strided(data, shape=new_shape, strides=new_strides) - -def process_episode(file_name, ep): - left_imgs, right_imgs, img_timestamps = load_svo(file_name) - hdf5_timestamps, states, actions, cmds = load_hdf5(file_name) - closest_indices = match_timestamps(candidate=hdf5_timestamps, ref=img_timestamps) - - timesteps = len(closest_indices) - qpos_actions = actions[closest_indices] - cmds = cmds[closest_indices] - - # save_video(left_imgs, file_name + ".mp4") - path = os.path.dirname(file_name) - all_data_path = os.path.join(path, "processed") - os.makedirs(all_data_path, exist_ok=True) - - with h5py.File(all_data_path + f"/processed_{ep}.hdf5", 'w') as hf: - start = time.time() - hf.create_dataset('observation.image.left', data=left_imgs) - hf.create_dataset('observation.image.right', data=right_imgs) - hf.create_dataset('cmds', data=cmds.astype(np.float32)) - hf.create_dataset('observation.state', data=states[closest_indices].astype(np.float32)) - hf.create_dataset('qpos_action', data=qpos_actions.astype(np.float32)) - hf.attrs['sim'] = False - hf.attrs['init_action'] = cmds[0].astype(np.float32) - - print("Time to save dataset: ", time.time() - start) - -def process_all_episodes(all_eps, path): - results = [] - with concurrent.futures.ProcessPoolExecutor() as executor: - future_to_ep = {executor.submit(process_episode, os.path.join(path, ep), ep): ep for ep in all_eps} - for future in concurrent.futures.as_completed(future_to_ep): - ep = future_to_ep[future] - try: - result = future.result() - results.append(result) - except Exception as e: - print(f"Episode {ep} generated an exception: {e}") - return results - -def save_video(left_imgs, path): - _, height, width= left_imgs[0].shape - print(f"width: {width}, height: {height}") - fourcc = cv2.VideoWriter_fourcc(*'mp4v') - video_writer = cv2.VideoWriter(path, fourcc, 60, (width, height)) - - for img in left_imgs: - # print(img.shape) - img_bgr = cv2.cvtColor(img.transpose(1, 2, 0), cv2.COLOR_RGB2BGR) - video_writer.write(img_bgr) - - video_writer.release() - -def find_all_processed_episodes(path): - episodes = [f for f in os.listdir(path)] - return episodes - - -if __name__ == "__main__": - parser = argparse.ArgumentParser() - parser.add_argument('--save_video', action='store_true', default=False) - args = parser.parse_args() - - root = "../data/recordings" - folder_name = "00-can-sorting" - - - path = os.path.join(root, folder_name) - - all_eps = find_all_episodes(path) - - if args.save_video: - file_name = path + "/" + all_eps[0] - print("saving video for file: ", file_name) - left_imgs, right_imgs, img_timestamps = load_svo(file_name) - os.makedirs(os.path.join(path, "videos"), exist_ok=True) - save_video(left_imgs, os.path.join(path, "videos", "sample.mp4")) - else: - for ep in all_eps: - file_name = path + "/" + ep - process_episode(file_name, ep) - print('processed file', file_name) - - # print len - folder_path = Path(root) / folder_name / "processed" - - episodes = find_all_processed_episodes(folder_path) - num_episodes = len(episodes) - lens = [] - - for episode in episodes: - episode_path = folder_path / episode - - data = h5py.File(str(episode_path), 'r') - lens.append(data['qpos_action'].shape[0]) - data.close() - - lens = np.array(lens) - episodes = np.array(episodes) - print(lens[np.argsort(lens)]) - print(episodes[np.argsort(lens)]) - # results = process_all_episodes(all_eps, path) - # print(len(results)) diff --git a/scripts/replay_demo.py b/scripts/replay_demo.py deleted file mode 100644 index 0275230..0000000 --- a/scripts/replay_demo.py +++ /dev/null @@ -1,175 +0,0 @@ -from isaacgym import gymapi -from isaacgym import gymutil - -import math -import numpy as np -import matplotlib.pyplot as plt - -from pytransform3d import rotations - -from pathlib import Path -import h5py -from tqdm import tqdm -import time -import yaml -import torch -from torchvision.transforms import v2 -from sklearn.decomposition import PCA - -class Player: - def __init__(self, dt=1/60): - self.dt = dt - self.head_mat = None - self.left_wrist_mat = None - self.right_wrist_mat = None - self.left_hand_pos = None - self.right_hand_pos = None - - # initialize gym - self.gym = gymapi.acquire_gym() - - # configure sim - sim_params = gymapi.SimParams() - sim_params.dt = dt - sim_params.substeps = 2 - sim_params.up_axis = gymapi.UP_AXIS_Z - sim_params.gravity = gymapi.Vec3(0.0, 0.0, -9.81) - sim_params.physx.solver_type = 1 - sim_params.physx.num_position_iterations = 4 - sim_params.physx.num_velocity_iterations = 1 - sim_params.physx.max_gpu_contact_pairs = 8388608 - sim_params.physx.contact_offset = 0.002 - sim_params.physx.friction_offset_threshold = 0.001 - sim_params.physx.friction_correlation_distance = 0.0005 - sim_params.physx.rest_offset = 0.0 - sim_params.physx.use_gpu = True - sim_params.use_gpu_pipeline = False - - self.sim = self.gym.create_sim(0, 0, gymapi.SIM_PHYSX, sim_params) - if self.sim is None: - print("*** Failed to create sim") - quit() - - plane_params = gymapi.PlaneParams() - plane_params.distance = 0.0 - plane_params.normal = gymapi.Vec3(0.0, 0.0, 1.0) - self.gym.add_ground(self.sim, plane_params) - - robot_asset_root = "../assets" - robot_asset_file = 'h1_inspire/urdf/h1_inspire.urdf' - asset_options = gymapi.AssetOptions() - asset_options.fix_base_link = True - asset_options.default_dof_drive_mode = gymapi.DOF_MODE_POS - robot_asset = self.gym.load_asset(self.sim, robot_asset_root, robot_asset_file, asset_options) - dof = self.gym.get_asset_dof_count(robot_asset) - - # set up the env grid - num_envs = 1 - num_per_row = int(math.sqrt(num_envs)) - env_spacing = 1.25 - env_lower = gymapi.Vec3(-env_spacing, 0.0, -env_spacing) - env_upper = gymapi.Vec3(env_spacing, env_spacing, env_spacing) - np.random.seed(17) - self.env = self.gym.create_env(self.sim, env_lower, env_upper, num_per_row) - - # robot - pose = gymapi.Transform() - pose.p = gymapi.Vec3(-0.8, 0, 1.1) - pose.r = gymapi.Quat(0, 0, 0, 1) - self.robot_handle = self.gym.create_actor(self.env, robot_asset, pose, 'robot', 1, 1) - self.gym.set_actor_dof_states(self.env, self.robot_handle, np.zeros(dof, gymapi.DofState.dtype), - gymapi.STATE_ALL) - - # create default viewer - self.viewer = self.gym.create_viewer(self.sim, gymapi.CameraProperties()) - if self.viewer is None: - print("*** Failed to create viewer") - quit() - cam_pos = gymapi.Vec3(1, 1, 2) - cam_target = gymapi.Vec3(0, 0, 1) - self.gym.viewer_camera_look_at(self.viewer, None, cam_pos, cam_target) - - plt.figure(figsize=(12, 6)) - plt.ion() - - def step(self, action, left_img, right_img): - qpos = self.convert_h1_qpos(action) - states = np.zeros(qpos.shape, dtype=gymapi.DofState.dtype) - states['pos'] = qpos - self.gym.set_actor_dof_states(self.env, self.robot_handle, states, gymapi.STATE_POS) - - # step the physics - self.gym.simulate(self.sim) - self.gym.step_graphics(self.sim) - - left_img = left_img.transpose((1, 2, 0)) - right_img = right_img.transpose((1, 2, 0)) - img = np.concatenate((left_img, right_img), axis=1) - - plt.cla() - plt.title('VisionPro View') - plt.imshow(img, aspect='equal') - plt.pause(0.001) - - self.gym.draw_viewer(self.viewer, self.sim, True) - self.gym.sync_frame_time(self.sim) - - def end(self): - self.gym.destroy_viewer(self.viewer) - self.gym.destroy_sim(self.sim) - plt.close() - - def convert_h1_qpos(self, action): - ''' - left_arm_indices = [13, 14, 15, 16, 17, 18, 19] - right_arm_indices = [32, 33, 34, 35, 36, 37, 38] - left_hand_indices = [20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31] - right_hand_indices = [39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50] - ''' - qpos = np.zeros(51) - qpos[13:20] = action[0:7] - - # left hand actions - qpos[20:22] = action[7] - qpos[22:24] = action[8] - qpos[24:26] = action[9] - qpos[26:28] = action[10] - qpos[28] = action[11] - qpos[29:32] = action[12] * np.array([1, 1.6, 2.4]) - - qpos[32:39] = action[13:20] - - # right hand actions - qpos[39:41] = action[20] - qpos[41:43] = action[21] - qpos[43:45] = action[22] - qpos[45:47] = action[23] - qpos[47] = action[24] - qpos[48:51] = action[25] * np.array([1, 1.6, 2.4]) - - return qpos - -if __name__ == '__main__': - - root = "../data/recordings/" - # folder_name = "00-coke_can_insert-2024_05_26-23_50_58/processed" - folder_name = "00-can-sorting/processed" - episode_name = "processed_episode_0.hdf5" - episode_path = Path(root) / folder_name / episode_name - - data = h5py.File(str(episode_path), 'r') - actions = np.array(data['qpos_action'])[::2] - left_imgs = np.array(data['observation.image.left'])[::2] # 30hz - right_imgs = np.array(data['observation.image.right'])[::2] - data.close() - - timestamps = actions.shape[0] - - player = Player(1/30) - - try: - for t in tqdm(range(timestamps)): - player.step(actions[t], left_imgs[t, :], right_imgs[t, :]) - except KeyboardInterrupt: - player.end() - exit() diff --git a/teleop/Preprocessor.py b/teleop/Preprocessor.py deleted file mode 100644 index 3376d1d..0000000 --- a/teleop/Preprocessor.py +++ /dev/null @@ -1,146 +0,0 @@ -import math -import numpy as np - -from constants_vuer import grd_yup2grd_zup, hand2inspire_l_arm, hand2inspire_r_arm,hand2inspire_l_finger,hand2inspire_r_finger,hand2inspire -from motion_utils import mat_update, fast_mat_inv - - -class VuerPreprocessor: - def __init__(self): - self.vuer_head_mat = np.array([[1, 0, 0, 0], - [0, 1, 0, 1.5], - [0, 0, 1, -0.2], - [0, 0, 0, 1]]) - self.vuer_right_wrist_mat = np.array([[1, 0, 0, 0.5], - [0, 1, 0, 1], - [0, 0, 1, -0.5], - [0, 0, 0, 1]]) - self.vuer_left_wrist_mat = np.array([[1, 0, 0, -0.5], - [0, 1, 0, 1], - [0, 0, 1, -0.5], - [0, 0, 0, 1]]) - - def process(self, tv): - self.vuer_head_mat = mat_update(self.vuer_head_mat, tv.head_matrix.copy()) - self.vuer_right_wrist_mat = mat_update(self.vuer_right_wrist_mat, tv.right_hand.copy()) - self.vuer_left_wrist_mat = mat_update(self.vuer_left_wrist_mat, tv.left_hand.copy()) - - # change of basis - head_mat = grd_yup2grd_zup @ self.vuer_head_mat @ fast_mat_inv(grd_yup2grd_zup) - right_wrist_mat = grd_yup2grd_zup @ self.vuer_right_wrist_mat @ fast_mat_inv(grd_yup2grd_zup) - left_wrist_mat = grd_yup2grd_zup @ self.vuer_left_wrist_mat @ fast_mat_inv(grd_yup2grd_zup) - - rel_left_wrist_mat = left_wrist_mat @ hand2inspire_l_arm - rel_left_wrist_mat[0:3, 3] = rel_left_wrist_mat[0:3, 3] - head_mat[0:3, 3] - - rel_right_wrist_mat = right_wrist_mat @ hand2inspire_r_arm # wTr = wTh @ hTr - rel_right_wrist_mat[0:3, 3] = rel_right_wrist_mat[0:3, 3] - head_mat[0:3, 3] - - # homogeneous - left_fingers = np.concatenate([tv.left_landmarks.copy().T, np.ones((1, tv.left_landmarks.shape[0]))]) - right_fingers = np.concatenate([tv.right_landmarks.copy().T, np.ones((1, tv.right_landmarks.shape[0]))]) - - # change of basis - left_fingers = grd_yup2grd_zup @ left_fingers - right_fingers = grd_yup2grd_zup @ right_fingers - - rel_left_fingers = fast_mat_inv(left_wrist_mat) @ left_fingers - rel_right_fingers = fast_mat_inv(right_wrist_mat) @ right_fingers - rel_left_fingers = (hand2inspire_l_finger.T @ rel_left_fingers)[0:3, :].T - rel_right_fingers = (hand2inspire_r_finger.T @ rel_right_fingers)[0:3, :].T - - return head_mat, rel_left_wrist_mat, rel_right_wrist_mat, rel_left_fingers, rel_right_fingers - - def get_hand_gesture(self, tv): - self.vuer_right_wrist_mat = mat_update(self.vuer_right_wrist_mat, tv.right_hand.copy()) - self.vuer_left_wrist_mat = mat_update(self.vuer_left_wrist_mat, tv.left_hand.copy()) - - # change of basis - right_wrist_mat = grd_yup2grd_zup @ self.vuer_right_wrist_mat @ fast_mat_inv(grd_yup2grd_zup) - left_wrist_mat = grd_yup2grd_zup @ self.vuer_left_wrist_mat @ fast_mat_inv(grd_yup2grd_zup) - - left_fingers = np.concatenate([tv.left_landmarks.copy().T, np.ones((1, tv.left_landmarks.shape[0]))]) - right_fingers = np.concatenate([tv.right_landmarks.copy().T, np.ones((1, tv.right_landmarks.shape[0]))]) - - # change of basis - left_fingers = grd_yup2grd_zup @ left_fingers - right_fingers = grd_yup2grd_zup @ right_fingers - - rel_left_fingers = fast_mat_inv(left_wrist_mat) @ left_fingers - rel_right_fingers = fast_mat_inv(right_wrist_mat) @ right_fingers - rel_left_fingers = (hand2inspire_l_finger.T @ rel_left_fingers)[0:3, :].T - rel_right_fingers = (hand2inspire_r_finger.T @ rel_right_fingers)[0:3, :].T - all_fingers = np.concatenate([rel_left_fingers, rel_right_fingers], axis=0) - - return all_fingers - - -class VuerPreprocessorLegacy: - def __init__(self): - self.vuer_head_mat = np.array([[1, 0, 0, 0], - [0, 1, 0, 1.5], - [0, 0, 1, -0.2], - [0, 0, 0, 1]]) - self.vuer_right_wrist_mat = np.array([[1, 0, 0, 0.5], - [0, 1, 0, 1], - [0, 0, 1, -0.5], - [0, 0, 0, 1]]) - self.vuer_left_wrist_mat = np.array([[1, 0, 0, -0.5], - [0, 1, 0, 1], - [0, 0, 1, -0.5], - [0, 0, 0, 1]]) - - def process(self, tv): - self.vuer_head_mat = mat_update(self.vuer_head_mat, tv.head_matrix.copy()) - self.vuer_right_wrist_mat = mat_update(self.vuer_right_wrist_mat, tv.right_hand.copy()) - self.vuer_left_wrist_mat = mat_update(self.vuer_left_wrist_mat, tv.left_hand.copy()) - - # change of basis - head_mat = grd_yup2grd_zup @ self.vuer_head_mat @ fast_mat_inv(grd_yup2grd_zup) - right_wrist_mat = grd_yup2grd_zup @ self.vuer_right_wrist_mat @ fast_mat_inv(grd_yup2grd_zup) - left_wrist_mat = grd_yup2grd_zup @ self.vuer_left_wrist_mat @ fast_mat_inv(grd_yup2grd_zup) - - rel_left_wrist_mat = left_wrist_mat @ hand2inspire - rel_left_wrist_mat[0:3, 3] = rel_left_wrist_mat[0:3, 3] - head_mat[0:3, 3] - - rel_right_wrist_mat = right_wrist_mat @ hand2inspire # wTr = wTh @ hTr - rel_right_wrist_mat[0:3, 3] = rel_right_wrist_mat[0:3, 3] - head_mat[0:3, 3] - - # homogeneous - left_fingers = np.concatenate([tv.left_landmarks.copy().T, np.ones((1, tv.left_landmarks.shape[0]))]) - right_fingers = np.concatenate([tv.right_landmarks.copy().T, np.ones((1, tv.right_landmarks.shape[0]))]) - - # change of basis - left_fingers = grd_yup2grd_zup @ left_fingers - right_fingers = grd_yup2grd_zup @ right_fingers - - rel_left_fingers = fast_mat_inv(left_wrist_mat) @ left_fingers - rel_right_fingers = fast_mat_inv(right_wrist_mat) @ right_fingers - rel_left_fingers = (hand2inspire.T @ rel_left_fingers)[0:3, :].T - rel_right_fingers = (hand2inspire.T @ rel_right_fingers)[0:3, :].T - - return head_mat, rel_left_wrist_mat, rel_right_wrist_mat, rel_left_fingers, rel_right_fingers - - def get_hand_gesture(self, tv): - self.vuer_right_wrist_mat = mat_update(self.vuer_right_wrist_mat, tv.right_hand.copy()) - self.vuer_left_wrist_mat = mat_update(self.vuer_left_wrist_mat, tv.left_hand.copy()) - - # change of basis - right_wrist_mat = grd_yup2grd_zup @ self.vuer_right_wrist_mat @ fast_mat_inv(grd_yup2grd_zup) - left_wrist_mat = grd_yup2grd_zup @ self.vuer_left_wrist_mat @ fast_mat_inv(grd_yup2grd_zup) - - left_fingers = np.concatenate([tv.left_landmarks.copy().T, np.ones((1, tv.left_landmarks.shape[0]))]) - right_fingers = np.concatenate([tv.right_landmarks.copy().T, np.ones((1, tv.right_landmarks.shape[0]))]) - - # change of basis - left_fingers = grd_yup2grd_zup @ left_fingers - right_fingers = grd_yup2grd_zup @ right_fingers - - rel_left_fingers = fast_mat_inv(left_wrist_mat) @ left_fingers - rel_right_fingers = fast_mat_inv(right_wrist_mat) @ right_fingers - rel_left_fingers = (hand2inspire.T @ rel_left_fingers)[0:3, :].T - rel_right_fingers = (hand2inspire.T @ rel_right_fingers)[0:3, :].T - all_fingers = np.concatenate([rel_left_fingers, rel_right_fingers], axis=0) - - return all_fingers - diff --git a/teleop/TeleVision.py b/teleop/TeleVision.py deleted file mode 100644 index 76d90d0..0000000 --- a/teleop/TeleVision.py +++ /dev/null @@ -1,247 +0,0 @@ -import time -from vuer import Vuer -from vuer.events import ClientEvent -from vuer.schemas import ImageBackground, group, Hands, WebRTCStereoVideoPlane, DefaultScene -from multiprocessing import Array, Process, shared_memory, Queue, Manager, Event, Semaphore -import numpy as np -import asyncio -from webrtc.zed_server import * - -class OpenTeleVision: - def __init__(self, img_shape, shm_name, queue, toggle_streaming, stream_mode="image", cert_file="./cert.pem", key_file="./key.pem", ngrok=False): - # self.app=Vuer() - self.img_shape = (img_shape[0], 2*img_shape[1], 3) - self.img_height, self.img_width = img_shape[:2] - - if ngrok: - self.app = Vuer(host='0.0.0.0', queries=dict(grid=False), queue_len=3) - else: - self.app = Vuer(host='0.0.0.0', cert=cert_file, key=key_file, queries=dict(grid=False), queue_len=3) - - self.app.add_handler("HAND_MOVE")(self.on_hand_move) - self.app.add_handler("CAMERA_MOVE")(self.on_cam_move) - if stream_mode == "image": - existing_shm = shared_memory.SharedMemory(name=shm_name) - self.img_array = np.ndarray((self.img_shape[0], self.img_shape[1], 3), dtype=np.uint8, buffer=existing_shm.buf) - self.app.spawn(start=False)(self.main_image) - elif stream_mode == "webrtc": - self.app.spawn(start=False)(self.main_webrtc) - else: - raise ValueError("stream_mode must be either 'webrtc' or 'image'") - - self.left_hand_shared = Array('d', 16, lock=True) - self.right_hand_shared = Array('d', 16, lock=True) - self.left_landmarks_shared = Array('d', 75, lock=True) - self.right_landmarks_shared = Array('d', 75, lock=True) - - self.head_matrix_shared = Array('d', 16, lock=True) - self.aspect_shared = Value('d', 1.0, lock=True) - if stream_mode == "webrtc": - # webrtc server - if Args.verbose: - logging.basicConfig(level=logging.DEBUG) - else: - logging.basicConfig(level=logging.INFO) - Args.img_shape = img_shape - # Args.shm_name = shm_name - Args.fps = 60 - - ssl_context = ssl.SSLContext() - ssl_context.load_cert_chain(cert_file, key_file) - - app = web.Application() - cors = aiohttp_cors.setup(app, defaults={ - "*": aiohttp_cors.ResourceOptions( - allow_credentials=True, - expose_headers="*", - allow_headers="*", - allow_methods="*", - ) - }) - rtc = RTC(img_shape, queue, toggle_streaming, 60) - app.on_shutdown.append(on_shutdown) - cors.add(app.router.add_get("/", index)) - cors.add(app.router.add_get("/client.js", javascript)) - cors.add(app.router.add_post("/offer", rtc.offer)) - - self.webrtc_process = Process(target=web.run_app, args=(app,), kwargs={"host": "0.0.0.0", "port": 8080, "ssl_context": ssl_context}) - self.webrtc_process.daemon = True - self.webrtc_process.start() - # web.run_app(app, host="0.0.0.0", port=8080, ssl_context=ssl_context) - - self.process = Process(target=self.run) - self.process.daemon = True - self.process.start() - - - def run(self): - self.app.run() - - async def on_cam_move(self, event, session, fps=60): - # only intercept the ego camera. - # if event.key != "ego": - # return - try: - # with self.head_matrix_shared.get_lock(): # Use the lock to ensure thread-safe updates - # self.head_matrix_shared[:] = event.value["camera"]["matrix"] - # with self.aspect_shared.get_lock(): - # self.aspect_shared.value = event.value['camera']['aspect'] - self.head_matrix_shared[:] = event.value["camera"]["matrix"] - self.aspect_shared.value = event.value['camera']['aspect'] - except: - pass - # self.head_matrix = np.array(event.value["camera"]["matrix"]).reshape(4, 4, order="F") - # print(np.array(event.value["camera"]["matrix"]).reshape(4, 4, order="F")) - # print("camera moved", event.value["matrix"].shape, event.value["matrix"]) - - async def on_hand_move(self, event, session, fps=60): - try: - # with self.left_hand_shared.get_lock(): # Use the lock to ensure thread-safe updates - # self.left_hand_shared[:] = event.value["leftHand"] - # with self.right_hand_shared.get_lock(): - # self.right_hand_shared[:] = event.value["rightHand"] - # with self.left_landmarks_shared.get_lock(): - # self.left_landmarks_shared[:] = np.array(event.value["leftLandmarks"]).flatten() - # with self.right_landmarks_shared.get_lock(): - # self.right_landmarks_shared[:] = np.array(event.value["rightLandmarks"]).flatten() - self.left_hand_shared[:] = event.value["leftHand"] - self.right_hand_shared[:] = event.value["rightHand"] - self.left_landmarks_shared[:] = np.array(event.value["leftLandmarks"]).flatten() - self.right_landmarks_shared[:] = np.array(event.value["rightLandmarks"]).flatten() - except: - pass - - async def main_webrtc(self, session, fps=60): - session.set @ DefaultScene(frameloop="always") - session.upsert @ Hands(fps=fps, stream=True, key="hands", showLeft=False, showRight=False) - session.upsert @ WebRTCStereoVideoPlane( - src="https://192.168.8.102:8080/offer", - key="zed", - aspect=1.33334, - height = 8, - position=[0, -2, -0.2], - ) - while True: - await asyncio.sleep(1) - - async def main_image(self, session, fps=60): - session.upsert @ Hands(fps=fps, stream=True, key="hands", showLeft=False, showRight=False) - while True: - # aspect = self.aspect_shared.value - display_image = self.img_array - - # session.upsert( - # ImageBackground( - # # Can scale the images down. - # display_image[:self.img_height], - # # 'jpg' encoding is significantly faster than 'png'. - # format="jpeg", - # quality=80, - # key="left-image", - # interpolate=True, - # # fixed=True, - # aspect=1.778, - # distanceToCamera=2, - # position=[0, -0.5, -2], - # rotation=[0, 0, 0], - # ), - # to="bgChildren", - # ) - - session.upsert( - [ImageBackground( - # Can scale the images down. - display_image[::2, :self.img_width], - # display_image[:self.img_height:2, ::2], - # 'jpg' encoding is significantly faster than 'png'. - format="jpeg", - quality=80, - key="left-image", - interpolate=True, - # fixed=True, - aspect=1.66667, - # distanceToCamera=0.5, - height = 8, - position=[0, -1, 3], - # rotation=[0, 0, 0], - layers=1, - alphaSrc="./vinette.jpg" - ), - ImageBackground( - # Can scale the images down. - display_image[::2, self.img_width:], - # display_image[self.img_height::2, ::2], - # 'jpg' encoding is significantly faster than 'png'. - format="jpeg", - quality=80, - key="right-image", - interpolate=True, - # fixed=True, - aspect=1.66667, - # distanceToCamera=0.5, - height = 8, - position=[0, -1, 3], - # rotation=[0, 0, 0], - layers=2, - alphaSrc="./vinette.jpg" - )], - to="bgChildren", - ) - await asyncio.sleep(0.03) - - @property - def left_hand(self): - # with self.left_hand_shared.get_lock(): - # return np.array(self.left_hand_shared[:]).reshape(4, 4, order="F") - return np.array(self.left_hand_shared[:]).reshape(4, 4, order="F") - - - @property - def right_hand(self): - # with self.right_hand_shared.get_lock(): - # return np.array(self.right_hand_shared[:]).reshape(4, 4, order="F") - return np.array(self.right_hand_shared[:]).reshape(4, 4, order="F") - - - @property - def left_landmarks(self): - # with self.left_landmarks_shared.get_lock(): - # return np.array(self.left_landmarks_shared[:]).reshape(25, 3) - return np.array(self.left_landmarks_shared[:]).reshape(25, 3) - - @property - def right_landmarks(self): - # with self.right_landmarks_shared.get_lock(): - # return np.array(self.right_landmarks_shared[:]).reshape(25, 3) - return np.array(self.right_landmarks_shared[:]).reshape(25, 3) - - @property - def head_matrix(self): - # with self.head_matrix_shared.get_lock(): - # return np.array(self.head_matrix_shared[:]).reshape(4, 4, order="F") - return np.array(self.head_matrix_shared[:]).reshape(4, 4, order="F") - - @property - def aspect(self): - # with self.aspect_shared.get_lock(): - # return float(self.aspect_shared.value) - return float(self.aspect_shared.value) - - -if __name__ == "__main__": - resolution = (720, 1280) - crop_size_w = 340 # (resolution[1] - resolution[0]) // 2 - crop_size_h = 270 - resolution_cropped = (resolution[0] - crop_size_h, resolution[1] - 2 * crop_size_w) # 450 * 600 - img_shape = (2 * resolution_cropped[0], resolution_cropped[1], 3) # 900 * 600 - img_height, img_width = resolution_cropped[:2] # 450 * 600 - shm = shared_memory.SharedMemory(create=True, size=np.prod(img_shape) * np.uint8().itemsize) - shm_name = shm.name - img_array = np.ndarray((img_shape[0], img_shape[1], 3), dtype=np.uint8, buffer=shm.buf) - - tv = OpenTeleVision(resolution_cropped, cert_file="../cert.pem", key_file="../key.pem") - while True: - # print(tv.left_landmarks) - # print(tv.left_hand) - # tv.modify_shared_image(random=True) - time.sleep(1) diff --git a/teleop/constants_vuer.py b/teleop/constants_vuer.py deleted file mode 100644 index 7f03639..0000000 --- a/teleop/constants_vuer.py +++ /dev/null @@ -1,35 +0,0 @@ -import numpy as np - -tip_indices = [4, 9, 14, 19, 24] - -hand2inspire_l_arm = np.array([[1, 0, 0, 0], - [0, 0, -1, 0], - [0, 1, 0, 0], - [0, 0, 0, 1]]) - -hand2inspire_r_arm = np.array([[1, 0, 0, 0], - [0, 0, 1, 0], - [0, -1, 0, 0], - [0, 0, 0, 1]]) - -hand2inspire_l_finger = np.array([[0, -1, 0, 0], - [0, 0, -1, 0], - [1, 0, 0, 0], - [0, 0, 0, 1]]) - -hand2inspire_r_finger = np.array([[0, -1, 0, 0], - [0, 0, -1, 0], - [1, 0, 0, 0], - [0, 0, 0, 1]]) - -grd_yup2grd_zup = np.array([[0, 0, -1, 0], - [-1, 0, 0, 0], - [0, 1, 0, 0], - [0, 0, 0, 1]]) - -# legacy -hand2inspire = np.array([[0, -1, 0, 0], - [0, 0, -1, 0], - [1, 0, 0, 0], - [0, 0, 0, 1]]) - diff --git a/teleop/dynamixel/active_cam.py b/teleop/dynamixel/active_cam.py deleted file mode 100644 index 53c22cf..0000000 --- a/teleop/dynamixel/active_cam.py +++ /dev/null @@ -1,105 +0,0 @@ -import os -import time -from dataclasses import dataclass -from typing import Dict, Optional, Sequence, Tuple -import numpy as np -from .agent import Agent -from .dynamixel_robot import DynamixelRobot -# from agent import Agent -# from dynamixel_robot import DynamixelRobot - - -@dataclass -class DynamixelRobotConfig: - joint_ids: Sequence[int] - """The joint ids of dynamixel robot. Usually (1, 2, 3 ...).""" - - joint_offsets: Sequence[float] - """The joint offsets of robot. There needs to be a joint offset for each joint_id and should be a multiple of pi/2.""" - - joint_signs: Sequence[int] - """The joint signs is -1 for all dynamixel""" - - gripper_config: Tuple[int, int, int] - """reserved for later work""" - - # it will run after init and work as init check - def __post_init__(self): - assert len(self.joint_ids) == len(self.joint_offsets) - assert len(self.joint_ids) == len(self.joint_signs) - - def make_robot( - self, port: str = "/dev/ttyUSB0", start_joints: Optional[np.ndarray] = None - ) -> DynamixelRobot: - return DynamixelRobot( - joint_ids=self.joint_ids, - joint_offsets=list(self.joint_offsets), - real=True, - joint_signs=list(self.joint_signs), - port=port, - gripper_config=self.gripper_config, - start_joints=start_joints, - ) - -# Can put multi robot into the dic, note that the calibration info shoule be put here -PORT_CONFIG_MAP: Dict[str, DynamixelRobotConfig] = { - #! for camera mounta - "/dev/serial/by-id/usb-FTDI_USB__-__Serial_Converter_FT8IT033-if00-port0": DynamixelRobotConfig( - joint_ids=(1, 2), - joint_offsets=( - 2*np.pi/2, - 2*np.pi/2, - ), - joint_signs=(-1, -1), - gripper_config=None, - ), - -} - -# general we only input port into the class, other info is stored in the dic -class DynamixelAgent(Agent): - def __init__( - self, - port: str, - dynamixel_config: Optional[DynamixelRobotConfig] = None, - start_joints: Optional[np.ndarray] = None, - cap_num: int = 42, - ): - #! init dynamixel robot setting - # use the config to make the robot - if dynamixel_config is not None: - self._robot = dynamixel_config.make_robot( - port=port, start_joints=start_joints - ) - # find the info auto - else: - # check port - assert os.path.exists(port), port - assert port in PORT_CONFIG_MAP, f"Port {port} not in config map" - - # use port to gain config - config = PORT_CONFIG_MAP[port] - self._robot = config.make_robot(port=port, start_joints=start_joints) - - def act(self, obs: Dict[str, np.ndarray]) -> np.ndarray: - return self._robot.get_joint_state() - -if __name__ == "__main__": - agent = DynamixelAgent(port="/dev/serial/by-id/usb-FTDI_USB__-__Serial_Converter_FT8IST6E-if00-port0") - - agent._robot.set_torque_mode(True) - - min_radians = -1.57 - max_radians = 1.57 - interval = 0.1 - - current_radian = 0 - while current_radian <= max_radians: - action = agent.act(1) - print("now action : ", [f"{x:.3f}" for x in action]) - command = [0, 0] - agent._robot.command_joint_state([0, current_radian]) - time.sleep(0.1) - true_value = agent._robot._driver.get_joints() - print("true value : ", [f"{x:.3f}" for x in true_value]) - current_radian += interval diff --git a/teleop/dynamixel/agent.py b/teleop/dynamixel/agent.py deleted file mode 100644 index fa8bff7..0000000 --- a/teleop/dynamixel/agent.py +++ /dev/null @@ -1,42 +0,0 @@ -from typing import Any, Dict, Protocol - -import numpy as np - -class Agent(Protocol): - def act(self, obs: Dict[str, Any]) -> np.ndarray: - """Returns an action given an observation. - - Args: - obs: observation from the environment. - - Returns: - action: action to take on the environment. - """ - raise NotImplementedError - - -class DummyAgent(Agent): - def __init__(self, num_dofs: int): - self.num_dofs = num_dofs - - def act(self, obs: Dict[str, Any]) -> np.ndarray: - return np.zeros(self.num_dofs) - - -class BimanualAgent(Agent): - def __init__(self, agent_left: Agent, agent_right: Agent): - self.agent_left = agent_left - self.agent_right = agent_right - - def act(self, obs: Dict[str, Any]) -> np.ndarray: - left_obs = {} - right_obs = {} - for key, val in obs.items(): - L = val.shape[0] - half_dim = L // 2 - assert L == half_dim * 2, f"{key} must be even, something is wrong" - left_obs[key] = val[:half_dim] - right_obs[key] = val[half_dim:] - return np.concatenate( - [self.agent_left.act(left_obs), self.agent_right.act(right_obs)] - ) diff --git a/teleop/dynamixel/driver.py b/teleop/dynamixel/driver.py deleted file mode 100644 index dc189c2..0000000 --- a/teleop/dynamixel/driver.py +++ /dev/null @@ -1,287 +0,0 @@ -import time -from threading import Event, Lock, Thread -from typing import Protocol, Sequence - -import numpy as np -#! dynamixel_sdk -from dynamixel_sdk.group_sync_read import GroupSyncRead -from dynamixel_sdk.group_sync_write import GroupSyncWrite -from dynamixel_sdk.packet_handler import PacketHandler -from dynamixel_sdk.port_handler import PortHandler -from dynamixel_sdk.robotis_def import ( - COMM_SUCCESS, - DXL_HIBYTE, - DXL_HIWORD, - DXL_LOBYTE, - DXL_LOWORD, -) - -# Constants -ADDR_TORQUE_ENABLE = 64 -ADDR_GOAL_POSITION = 116 -LEN_GOAL_POSITION = 4 -#! rewrite -ADDR_PRESENT_POSITION = 132 -ADDR_PRESENT_POSITION = 140 #Position Trajectory(140) -LEN_PRESENT_POSITION = 4 -TORQUE_ENABLE = 1 -TORQUE_DISABLE = 0 - - -class DynamixelDriverProtocol(Protocol): - def set_joints(self, joint_angles: Sequence[float]): - """Set the joint angles for the Dynamixel servos. - - Args: - joint_angles (Sequence[float]): A list of joint angles. - """ - ... - - def torque_enabled(self) -> bool: - """Check if torque is enabled for the Dynamixel servos. - - Returns: - bool: True if torque is enabled, False if it is disabled. - """ - ... - - def set_torque_mode(self, enable: bool): - """Set the torque mode for the Dynamixel servos. - - Args: - enable (bool): True to enable torque, False to disable. - """ - ... - - def get_joints(self) -> np.ndarray: - """Get the current joint angles in radians. - - Returns: - np.ndarray: An array of joint angles. - """ - ... - - def close(self): - """Close the driver.""" - - -class FakeDynamixelDriver(DynamixelDriverProtocol): - def __init__(self, ids: Sequence[int]): - self._ids = ids - self._joint_angles = np.zeros(len(ids), dtype=int) - self._torque_enabled = False - - def set_joints(self, joint_angles: Sequence[float]): - if len(joint_angles) != len(self._ids): - raise ValueError( - "The length of joint_angles must match the number of servos" - ) - if not self._torque_enabled: - raise RuntimeError("Torque must be enabled to set joint angles") - self._joint_angles = np.array(joint_angles) - - def torque_enabled(self) -> bool: - return self._torque_enabled - - def set_torque_mode(self, enable: bool): - self._torque_enabled = enable - - def get_joints(self) -> np.ndarray: - return self._joint_angles.copy() - - def close(self): - pass - - -class DynamixelDriver(DynamixelDriverProtocol): - def __init__( - self, ids: Sequence[int], port: str = "/dev/ttyUSB0", baudrate: int = 57600 - ): - """Initialize the DynamixelDriver class. - - Args: - ids (Sequence[int]): A list of IDs for the Dynamixel servos. - port (str): The USB port to connect to the arm. - baudrate (int): The baudrate for communication. - """ - self._ids = ids - self._joint_angles = None - #! - self._lock = Lock() - - #! do not need to know the detail now - # Initialize the port handler, packet handler, and group sync read/write - self._portHandler = PortHandler(port) - self._packetHandler = PacketHandler(2.0) - - self._groupSyncRead = GroupSyncRead( - self._portHandler, - self._packetHandler, - ADDR_PRESENT_POSITION, - LEN_PRESENT_POSITION, - ) - self._groupSyncWrite = GroupSyncWrite( - self._portHandler, - self._packetHandler, - ADDR_GOAL_POSITION, - LEN_GOAL_POSITION, - ) - - # Open the port and set the baudrate - if not self._portHandler.openPort(): - raise RuntimeError("Failed to open the port") - - if not self._portHandler.setBaudRate(baudrate): - raise RuntimeError(f"Failed to change the baudrate, {baudrate}") - - # Add parameters for each Dynamixel servo to the group sync read - for dxl_id in self._ids: - if not self._groupSyncRead.addParam(dxl_id): - raise RuntimeError( - f"Failed to add parameter for Dynamixel with ID {dxl_id}" - ) - - # Disable torque for each Dynamixel servo - self._torque_enabled = False - try: - self.set_torque_mode(self._torque_enabled) - except Exception as e: - print(f"port: {port}, {e}") - - # control the thread - self._stop_thread = Event() - - self._start_reading_thread() - - def set_joints(self, joint_angles: Sequence[float]): - if len(joint_angles) != len(self._ids): - raise ValueError( - "The length of joint_angles must match the number of servos" - ) - if not self._torque_enabled: - raise RuntimeError("Torque must be enabled to set joint angles") - - for dxl_id, angle in zip(self._ids, joint_angles): - # Convert the angle to the appropriate value for the servo - position_value = int(angle * 2048 / np.pi) - - # Allocate goal position value into byte array - param_goal_position = [ - DXL_LOBYTE(DXL_LOWORD(position_value)), - DXL_HIBYTE(DXL_LOWORD(position_value)), - DXL_LOBYTE(DXL_HIWORD(position_value)), - DXL_HIBYTE(DXL_HIWORD(position_value)), - ] - - # Add goal position value to the Syncwrite parameter storage - dxl_addparam_result = self._groupSyncWrite.addParam( - dxl_id, param_goal_position - ) - if not dxl_addparam_result: - raise RuntimeError( - f"Failed to set joint angle for Dynamixel with ID {dxl_id}" - ) - - # Syncwrite goal position - dxl_comm_result = self._groupSyncWrite.txPacket() - if dxl_comm_result != COMM_SUCCESS: - raise RuntimeError("Failed to syncwrite goal position") - - # Clear syncwrite parameter storage - self._groupSyncWrite.clearParam() - - def torque_enabled(self) -> bool: - return self._torque_enabled - - def set_torque_mode(self, enable: bool): - torque_value = TORQUE_ENABLE if enable else TORQUE_DISABLE - with self._lock: - for dxl_id in self._ids: - dxl_comm_result, dxl_error = self._packetHandler.write1ByteTxRx( - self._portHandler, dxl_id, ADDR_TORQUE_ENABLE, torque_value - ) - if dxl_comm_result != COMM_SUCCESS or dxl_error != 0: - print(dxl_comm_result) - print(dxl_error) - raise RuntimeError( - f"Failed to set torque mode for Dynamixel with ID {dxl_id}" - ) - - self._torque_enabled = enable - - def _start_reading_thread(self): - self._reading_thread = Thread(target=self._read_joint_angles) - # self._reading_thread.daemon = True - # self._reading_thread.start() - - def _read_joint_angles(self): - # Continuously read joint angles and update the joint_angles array - while not self._stop_thread.is_set(): - time.sleep(0.001) - with self._lock: - _joint_angles = np.zeros(len(self._ids), dtype=int) - dxl_comm_result = self._groupSyncRead.txRxPacket() - if dxl_comm_result != COMM_SUCCESS: - print(f"warning, comm failed: {dxl_comm_result}") - continue - for i, dxl_id in enumerate(self._ids): - if self._groupSyncRead.isAvailable( - dxl_id, ADDR_PRESENT_POSITION, LEN_PRESENT_POSITION - ): - angle = self._groupSyncRead.getData( - dxl_id, ADDR_PRESENT_POSITION, LEN_PRESENT_POSITION - ) - angle = np.int32(np.uint32(angle)) - _joint_angles[i] = angle - else: - raise RuntimeError( - f"Failed to get joint angles for Dynamixel with ID {dxl_id}" - ) - self._joint_angles = _joint_angles - # self._groupSyncRead.clearParam() # TODO what does this do? should i add it - - def get_joints(self) -> np.ndarray: - # Return a copy of the joint_angles array to avoid race conditions - while self._joint_angles is None: - time.sleep(0.1) - with self._lock: - _j = self._joint_angles.copy() - return _j / 2048.0 * np.pi - - def close(self): - self._stop_thread.set() - self._reading_thread.join() - self._portHandler.closePort() - - -def main(): - # Set the port, baudrate, and servo IDs - ids = [1,2] - - # Create a DynamixelDriver instance - try: - driver = DynamixelDriver(ids) - except FileNotFoundError: - driver = DynamixelDriver(ids, port="/dev/cu.usbserial-FT7WBMUB") - - driver.set_torque_mode(True) - driver.set_torque_mode(False) - - # Print the joint angles - try: - while True: - joint_angles = driver.get_joints() - print(f"Joint angles for IDs {ids}: {joint_angles}") - # print(f"Joint angles for IDs {ids[1]}: {joint_angles[1]}") - except KeyboardInterrupt: - driver.close() - - # driver.set_torque_mode(True) - # joint_angles = driver.get_joints() - # print(f"Joint angles for IDs {ids}: {joint_angles}") - # driver.set_joints([6, 6]) - # driver.set_joints([7, 8]) - - -if __name__ == "__main__": - main() diff --git a/teleop/dynamixel/dynamixel_robot.py b/teleop/dynamixel/dynamixel_robot.py deleted file mode 100644 index 5f5dddd..0000000 --- a/teleop/dynamixel/dynamixel_robot.py +++ /dev/null @@ -1,161 +0,0 @@ -from typing import Dict, Optional, Sequence, Tuple - -import numpy as np - -# from robots.robot import Robot -import sys -from .robot import Robot -from .driver import DynamixelDriver,DynamixelDriverProtocol,FakeDynamixelDriver -# from robot import Robot -# from driver import DynamixelDriver,DynamixelDriverProtocol,FakeDynamixelDriver - - -class DynamixelRobot(Robot): - """A class representing a UR robot.""" - - def __init__( - self, - joint_ids: Sequence[int], - joint_offsets: Optional[Sequence[float]] = None, - joint_signs: Optional[Sequence[int]] = None, - real: bool = False, - port: str = "/dev/ttyUSB0", - baudrate: int = 57600, - gripper_config: Optional[Tuple[int, float, float]] = None, - start_joints: Optional[np.ndarray] = None, - ): - - - print(f"attempting to connect to port: {port}") - - # used to set gripper, reserved for later - self.gripper_open_close: Optional[Tuple[float, float]] - - if gripper_config is not None: - assert joint_offsets is not None - assert joint_signs is not None - - joint_ids = tuple(joint_ids) + (gripper_config[0],) - joint_offsets = tuple(joint_offsets) + (0.0,) - joint_signs = tuple(joint_signs) + (1,) - self.gripper_open_close = ( - gripper_config[1] * np.pi / 180, - gripper_config[2] * np.pi / 180, - ) - else: - self.gripper_open_close = None - - # all the self variable will start with _ - - # set joint config - self._joint_ids = joint_ids - self._driver: DynamixelDriverProtocol - - if joint_offsets is None: - self._joint_offsets = np.zeros(len(joint_ids)) - else: - self._joint_offsets = np.array(joint_offsets) - - if joint_signs is None: - self._joint_signs = np.ones(len(joint_ids)) - else: - self._joint_signs = np.array(joint_signs) - - # check - assert len(self._joint_ids) == len(self._joint_offsets), ( - f"joint_ids: {len(self._joint_ids)}, " - f"joint_offsets: {len(self._joint_offsets)}" - ) - assert len(self._joint_ids) == len(self._joint_signs), ( - f"joint_ids: {len(self._joint_ids)}, " - f"joint_signs: {len(self._joint_signs)}" - ) - assert np.all( - np.abs(self._joint_signs) == 1 - ), f"joint_signs: {self._joint_signs}" - - # when called in gello_agent, real == True, used to do test - if real: - self._driver = DynamixelDriver(joint_ids, port=port, baudrate=baudrate) - # we dault set the torque_mode(False) - self._driver.set_torque_mode(False) - else: - self._driver = FakeDynamixelDriver(joint_ids) - - self._torque_on = False - self._last_pos = None - self._alpha = 0.99 - - #! this part might important! need to check if the robot start at different joints - if start_joints is not None: - # loop through all joints and add +- 2pi to the joint offsets to get the closest to start joints - new_joint_offsets = [] - current_joints = self.get_joint_state() - assert current_joints.shape == start_joints.shape - - if gripper_config is not None: - current_joints = current_joints[:-1] - start_joints = start_joints[:-1] - - for c_joint, s_joint, joint_offset in zip( - current_joints, start_joints, self._joint_offsets - ): - new_joint_offsets.append( - np.pi * 2 * np.round((s_joint - c_joint) / (2 * np.pi)) - + joint_offset - ) - - if gripper_config is not None: - new_joint_offsets.append(self._joint_offsets[-1]) - self._joint_offsets = np.array(new_joint_offsets) - - - #! might change to @property - def num_dofs(self) -> int: - return len(self._joint_ids) - - #! now the joint num is different - def get_joint_state(self) -> np.ndarray: - pos = (self._driver.get_joints() - self._joint_offsets) * self._joint_signs - assert len(pos) == self.num_dofs() - - if self.gripper_open_close is not None: - # map pos to [0, 1] - g_pos = (pos[-1] - self.gripper_open_close[0]) / ( - self.gripper_open_close[1] - self.gripper_open_close[0] - ) - g_pos = min(max(0, g_pos), 1) - pos[-1] = g_pos - - if self._last_pos is None: - self._last_pos = pos - else: - # exponential smoothing - pos = self._last_pos * (1 - self._alpha) + pos * self._alpha - self._last_pos = pos - - new_pos = np.append(pos, 0) - return new_pos - - def map_to_valid_range(self, radians_array): - mapped_radians = np.mod(radians_array, 2 * np.pi) - return mapped_radians - - def command_joint_state(self, joint_state: np.ndarray) -> None: - # print("command : ", [f"{x:.3f}" for x in joint_state]) - set_value = (joint_state + self._joint_offsets).tolist() - # print("_set value : ", [f"{x:.3f}" for x in set_value]) - set_value = self.map_to_valid_range(set_value) - # print("set value : ", [f"{x:.3f}" for x in set_value]) - self._driver.set_joints(set_value) - - def set_torque_mode(self, mode: bool): - if mode == self._torque_on: - return - self._driver.set_torque_mode(mode) - self._torque_on = mode - - def get_observations(self) -> Dict[str, np.ndarray]: - return {"joint_state": self.get_joint_state()} - - diff --git a/teleop/dynamixel/robot.py b/teleop/dynamixel/robot.py deleted file mode 100644 index b4a6e96..0000000 --- a/teleop/dynamixel/robot.py +++ /dev/null @@ -1,128 +0,0 @@ -from abc import abstractmethod -from typing import Dict, Protocol - -import numpy as np - - -class Robot(Protocol): - """Robot protocol. - - A protocol for a robot that can be controlled. - """ - - @abstractmethod - def num_dofs(self) -> int: - """Get the number of joints of the robot. - - Returns: - int: The number of joints of the robot. - """ - raise NotImplementedError - - @abstractmethod - def get_joint_state(self) -> np.ndarray: - """Get the current state of the leader robot. - - Returns: - T: The current state of the leader robot. - """ - raise NotImplementedError - - @abstractmethod - def command_joint_state(self, joint_state: np.ndarray) -> None: - """Command the leader robot to a given state. - - Args: - joint_state (np.ndarray): The state to command the leader robot to. - """ - raise NotImplementedError - - @abstractmethod - def get_observations(self) -> Dict[str, np.ndarray]: - """Get the current observations of the robot. - - This is to extract all the information that is available from the robot, - such as joint positions, joint velocities, etc. This may also include - information from additional sensors, such as cameras, force sensors, etc. - - Returns: - Dict[str, np.ndarray]: A dictionary of observations. - """ - raise NotImplementedError - - -class PrintRobot(Robot): - """A robot that prints the commanded joint state.""" - - def __init__(self, num_dofs: int, dont_print: bool = False): - self._num_dofs = num_dofs - self._joint_state = np.zeros(num_dofs) - self._dont_print = dont_print - - def num_dofs(self) -> int: - return self._num_dofs - - def get_joint_state(self) -> np.ndarray: - return self._joint_state - - def command_joint_state(self, joint_state: np.ndarray) -> None: - assert len(joint_state) == (self._num_dofs), ( - f"Expected joint state of length {self._num_dofs}, " - f"got {len(joint_state)}." - ) - self._joint_state = joint_state - if not self._dont_print: - print(self._joint_state) - - def get_observations(self) -> Dict[str, np.ndarray]: - joint_state = self.get_joint_state() - pos_quat = np.zeros(7) - return { - "joint_positions": joint_state, - "joint_velocities": joint_state, - "ee_pos_quat": pos_quat, - "gripper_position": np.array(0), - } - - -class BimanualRobot(Robot): - def __init__(self, robot_l: Robot, robot_r: Robot): - self._robot_l = robot_l - self._robot_r = robot_r - - def num_dofs(self) -> int: - return self._robot_l.num_dofs() + self._robot_r.num_dofs() - - def get_joint_state(self) -> np.ndarray: - return np.concatenate( - (self._robot_l.get_joint_state(), self._robot_r.get_joint_state()) - ) - - def command_joint_state(self, joint_state: np.ndarray) -> None: - self._robot_l.command_joint_state(joint_state[: self._robot_l.num_dofs()]) - self._robot_r.command_joint_state(joint_state[self._robot_l.num_dofs() :]) - - def get_observations(self) -> Dict[str, np.ndarray]: - l_obs = self._robot_l.get_observations() - r_obs = self._robot_r.get_observations() - assert l_obs.keys() == r_obs.keys() - return_obs = {} - for k in l_obs.keys(): - try: - return_obs[k] = np.concatenate((l_obs[k], r_obs[k])) - except Exception as e: - print(e) - print(k) - print(l_obs[k]) - print(r_obs[k]) - raise RuntimeError() - - return return_obs - - -def main(): - pass - - -if __name__ == "__main__": - main() diff --git a/teleop/image_server/image_client.py b/teleop/image_server/image_client.py index 55ae572..45e4375 100644 --- a/teleop/image_server/image_client.py +++ b/teleop/image_server/image_client.py @@ -1,42 +1,154 @@ import cv2 import zmq -import pickle -import zlib +import numpy as np +import time +import struct +from collections import deque +from multiprocessing import shared_memory -def start_client(): - # set ZeroMQ context and socket - context = zmq.Context() - socket = context.socket(zmq.PULL) - socket.connect("tcp://192.168.123.162:5555") - print("The client is connected and waiting to receive data...") +class ImageClient: + def __init__(self, img_shape = None, img_shm_name = None, image_show = False, server_address = "192.168.123.164", port = 5555, Unit_Test = False): + self.running = True + self._image_show = image_show + self._server_address = server_address + self._port = port - while True: - try: - compressed_data = b'' - while True: - chunk = socket.recv() - compressed_data += chunk - if len(chunk) < 60000: - break + self.enable_shm = False + if img_shape is not None and img_shm_name is not None: + self._image_shm = shared_memory.SharedMemory(name=img_shm_name) + self._img_array = np.ndarray(img_shape, dtype = np.uint8, buffer = self._image_shm.buf) + self.enable_shm = True + + # Performance evaluation parameters + self._enable_performance_eval = Unit_Test + if self._enable_performance_eval: + self._init_performance_metrics() + + def _init_performance_metrics(self): + self._frame_count = 0 # Total frames received + self._last_frame_id = -1 # Last received frame ID + + # Real-time FPS calculation using a time window + self._time_window = 1.0 # Time window size (in seconds) + self._frame_times = deque() # Timestamps of frames received within the time window + + # Data transmission quality metrics + self._latencies = deque() # Latencies of frames within the time window + self._lost_frames = 0 # Total lost frames + self._total_frames = 0 # Expected total frames based on frame IDs + + def _update_performance_metrics(self, timestamp, frame_id, receive_time): + # Update latency + latency = receive_time - timestamp + self._latencies.append(latency) + + # Remove latencies outside the time window + while self._latencies and self._frame_times and self._latencies[0] < receive_time - self._time_window: + self._latencies.popleft() + + # Update frame times + self._frame_times.append(receive_time) + # Remove timestamps outside the time window + while self._frame_times and self._frame_times[0] < receive_time - self._time_window: + self._frame_times.popleft() - data = zlib.decompress(compressed_data) - frame_data = pickle.loads(data) + # Update frame counts for lost frame calculation + expected_frame_id = self._last_frame_id + 1 if self._last_frame_id != -1 else frame_id + if frame_id != expected_frame_id: + lost = frame_id - expected_frame_id + if lost < 0: + print(f"[Image Client] Received out-of-order frame ID: {frame_id}") + else: + self._lost_frames += lost + print(f"[Image Client] Detected lost frames: {lost}, Expected frame ID: {expected_frame_id}, Received frame ID: {frame_id}") + self._last_frame_id = frame_id + self._total_frames = frame_id + 1 + + self._frame_count += 1 + + def _print_performance_metrics(self, receive_time): + if self._frame_count % 30 == 0: + # Calculate real-time FPS + real_time_fps = len(self._frame_times) / self._time_window if self._time_window > 0 else 0 + + # Calculate latency metrics + if self._latencies: + avg_latency = sum(self._latencies) / len(self._latencies) + max_latency = max(self._latencies) + min_latency = min(self._latencies) + jitter = max_latency - min_latency + else: + avg_latency = max_latency = min_latency = jitter = 0 + + # Calculate lost frame rate + lost_frame_rate = (self._lost_frames / self._total_frames) * 100 if self._total_frames > 0 else 0 + + print(f"[Image Client] Real-time FPS: {real_time_fps:.2f}, Avg Latency: {avg_latency*1000:.2f} ms, Max Latency: {max_latency*1000:.2f} ms, \ + Min Latency: {min_latency*1000:.2f} ms, Jitter: {jitter*1000:.2f} ms, Lost Frame Rate: {lost_frame_rate:.2f}%") + + def _close(self): + self._socket.close() + self._context.term() + if self._image_show: + cv2.destroyAllWindows() + print("Image client has been closed.") + + + def receive_process(self): + # Set up ZeroMQ context and socket + self._context = zmq.Context() + self._socket = self._context.socket(zmq.SUB) + self._socket.connect(f"tcp://{self._server_address}:{self._port}") + self._socket.setsockopt_string(zmq.SUBSCRIBE, "") + + print("\nImage client has started, waiting to receive data...") + try: + while self.running: + # Receive message + message = self._socket.recv() + receive_time = time.time() - # decode and display the image - frame = cv2.imdecode(frame_data, cv2.IMREAD_COLOR) + if self._enable_performance_eval: + header_size = struct.calcsize('dI') + try: + # Attempt to extract header and image data + header = message[:header_size] + jpg_bytes = message[header_size:] + timestamp, frame_id = struct.unpack('dI', header) + except struct.error as e: + print(f"[Image Client] Error unpacking header: {e}, discarding message.") + continue + else: + # No header, entire message is image data + jpg_bytes = message + # Decode image + np_img = np.frombuffer(jpg_bytes, dtype=np.uint8) + current_image = cv2.imdecode(np_img, cv2.IMREAD_COLOR) + if current_image is None: + print("[Image Client] Failed to decode image.") + continue - # convert BGR to RGB - frame_rgb = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB) + if self.enable_shm: + np.copyto(self._img_array, np.array(current_image)) + + if self._image_show: + cv2.imshow('Image Client Stream', current_image) + if cv2.waitKey(1) & 0xFF == ord('q'): + self.running = False - cv2.imshow('Video Stream', frame_rgb) + if self._enable_performance_eval: + self._update_performance_metrics(timestamp, frame_id, receive_time) + self._print_performance_metrics(receive_time) - if cv2.waitKey(1) & 0xFF == ord('q'): - break + except KeyboardInterrupt: + print("Image client interrupted by user.") except Exception as e: - print(f"An error occurred while receiving data: {e}") - break - context.term() - cv2.destroyAllWindows() + print(f"[Image Client] An error occurred while receiving data: {e}") + finally: + self._close() if __name__ == "__main__": - start_client() + # Initialize the client with performance evaluation enabled + # client = ImageClient(image_show = True, server_address='127.0.0.1', Unit_Test=True) # local test + client = ImageClient(image_show = True, server_address='192.168.123.164', Unit_Test=False) # deployment test + client.receive_process() \ No newline at end of file diff --git a/teleop/image_server/image_server.py b/teleop/image_server/image_server.py index 0f5fb9b..6723b23 100644 --- a/teleop/image_server/image_server.py +++ b/teleop/image_server/image_server.py @@ -1,48 +1,96 @@ import cv2 import zmq -import pickle -import zlib - -def start_server(): - # camera init - cap = cv2.VideoCapture(0, cv2.CAP_V4L2) - cap.set(cv2.CAP_PROP_FOURCC, cv2.VideoWriter.fourcc('M', 'J', 'P', 'G')) - cap.set(cv2.CAP_PROP_FRAME_WIDTH, 2560) - cap.set(cv2.CAP_PROP_FRAME_HEIGHT, 720) - cap.set(cv2.CAP_PROP_FPS, 30) - - # set ZeroMQ context and socket - context = zmq.Context() - socket = context.socket(zmq.PUSH) - socket.setsockopt(zmq.SNDHWM, 1) - socket.bind("tcp://*:5555") - print("The server has started, waiting for client connections...") - - while True: - ret, frame = cap.read() - if not ret: - print("frame read is error") - break - - # encoding image - ret2, frame = cv2.imencode('.jpg', frame) - if not ret2: - continue - - # Compressing data using pickle and zlib - data = pickle.dumps(frame) - compressed_data = zlib.compress(data) - - # sending data in pieces - chunk_size = 60000 - num_chunks = len(compressed_data) // chunk_size + 1 - for i in range(num_chunks): - start = i * chunk_size - end = start + chunk_size - chunk = compressed_data[start:end] - socket.send(chunk) - cap.release() - context.term() +import time +import struct +from collections import deque + +class ImageServer: + def __init__(self, fps = 30, port = 5555, Unit_Test = False): + self.fps = fps + self.port = port + self.enable_performance_eval = Unit_Test + + # initiate binocular camera: 480 * (640 * 2) + self.cap = cv2.VideoCapture(0, cv2.CAP_V4L2) + self.cap.set(cv2.CAP_PROP_FOURCC, cv2.VideoWriter.fourcc('M', 'J', 'P', 'G')) + self.cap.set(cv2.CAP_PROP_FRAME_WIDTH, 640 * 2) + self.cap.set(cv2.CAP_PROP_FRAME_HEIGHT, 480) + self.cap.set(cv2.CAP_PROP_FPS, self.fps) + + # set ZeroMQ context and socket + self.context = zmq.Context() + self.socket = self.context.socket(zmq.PUB) + self.socket.bind(f"tcp://*:{self.port}") + + if self.enable_performance_eval: + self._init_performance_metrics() + + print("Image server has started, waiting for client connections...") + print(f"Image Resolution: width is {self.cap.get(cv2.CAP_PROP_FRAME_WIDTH)}, height is {self.cap.get(cv2.CAP_PROP_FRAME_HEIGHT)}\n") + + def _init_performance_metrics(self): + self.frame_count = 0 # Total frames sent + self.time_window = 1.0 # Time window for FPS calculation (in seconds) + self.frame_times = deque() # Timestamps of frames sent within the time window + self.start_time = time.time() # Start time of the streaming + + def _update_performance_metrics(self, current_time): + # Add current time to frame times deque + self.frame_times.append(current_time) + # Remove timestamps outside the time window + while self.frame_times and self.frame_times[0] < current_time - self.time_window: + self.frame_times.popleft() + # Increment frame count + self.frame_count += 1 + + def _print_performance_metrics(self, current_time): + if self.frame_count % 30 == 0: + elapsed_time = current_time - self.start_time + real_time_fps = len(self.frame_times) / self.time_window + print(f"[Image Server] Real-time FPS: {real_time_fps:.2f}, Total frames sent: {self.frame_count}, Elapsed time: {elapsed_time:.2f} sec") + + def _close(self): + self.cap.release() + self.socket.close() + self.context.term() + print("[Image Server] The server has been closed.") + + def send_process(self): + try: + while True: + ret, frame = self.cap.read() + if not ret: + print("[Image Server] Frame read is error.") + break + + ret, buffer = cv2.imencode('.jpg', frame) + if not ret: + print("[Image Server] Frame imencode is failed.") + continue + + jpg_bytes = buffer.tobytes() + + if self.enable_performance_eval: + timestamp = time.time() + frame_id = self.frame_count + header = struct.pack('dI', timestamp, frame_id) # 8-byte double, 4-byte unsigned int + message = header + jpg_bytes + else: + message = jpg_bytes + + self.socket.send(message) + + if self.enable_performance_eval: + current_time = time.time() + self._update_performance_metrics(current_time) + self._print_performance_metrics(current_time) + + except KeyboardInterrupt: + print("[Image Server] Interrupted by user.") + finally: + self._close() if __name__ == "__main__": - start_server() + # server = ImageServer(Unit_Test = True) # test + server = ImageServer(Unit_Test = False) # deployment + server.send_process() \ No newline at end of file diff --git a/teleop/open_television/constants.py b/teleop/open_television/constants.py new file mode 100644 index 0000000..0d771ed --- /dev/null +++ b/teleop/open_television/constants.py @@ -0,0 +1,40 @@ +import numpy as np + + +T_to_unitree_left_wrist = np.array([[1, 0, 0, 0], + [0, 0, -1, 0], + [0, 1, 0, 0], + [0, 0, 0, 1]]) + +T_to_unitree_right_wrist = np.array([[1, 0, 0, 0], + [0, 0, 1, 0], + [0, -1, 0, 0], + [0, 0, 0, 1]]) + +T_to_unitree_hand = np.array([[0, 0, 1, 0], + [-1,0, 0, 0], + [0, -1,0, 0], + [0, 0, 0, 1]]) + +T_robot_openxr = np.array([[0, 0, -1, 0], + [-1, 0, 0, 0], + [0, 1, 0, 0], + [0, 0, 0, 1]]) + + + +const_head_vuer_mat = np.array([[1, 0, 0, 0], + [0, 1, 0, 1.5], + [0, 0, 1, -0.2], + [0, 0, 0, 1]]) + +const_right_wrist_vuer_mat = np.array([[1, 0, 0, 0.5], + [0, 1, 0, 1], + [0, 0, 1, -0.5], + [0, 0, 0, 1]]) + +const_left_wrist_vuer_mat = np.array([[1, 0, 0, -0.5], + [0, 1, 0, 1], + [0, 0, 1, -0.5], + [0, 0, 0, 1]]) + diff --git a/teleop/open_television/television.py b/teleop/open_television/television.py new file mode 100644 index 0000000..2d0a0db --- /dev/null +++ b/teleop/open_television/television.py @@ -0,0 +1,149 @@ +import time +from vuer import Vuer +from vuer.schemas import ImageBackground, Hands +from multiprocessing import Array, Process, shared_memory +import numpy as np +import asyncio +import cv2 + +from multiprocessing import context +Value = context._default_context.Value + + +class TeleVision: + def __init__(self, img_shape, img_shm_name, cert_file="./cert.pem", key_file="./key.pem", ngrok=False): + self.img_height = img_shape[0] + self.img_width = img_shape[1] // 2 + + if ngrok: + self.vuer = Vuer(host='0.0.0.0', queries=dict(grid=False), queue_len=3) + else: + self.vuer = Vuer(host='0.0.0.0', cert=cert_file, key=key_file, queries=dict(grid=False), queue_len=3) + + self.vuer.add_handler("HAND_MOVE")(self.on_hand_move) + self.vuer.add_handler("CAMERA_MOVE")(self.on_cam_move) + + existing_shm = shared_memory.SharedMemory(name=img_shm_name) + self.img_array = np.ndarray(img_shape, dtype=np.uint8, buffer=existing_shm.buf) + self.vuer.spawn(start=False)(self.main_image) + # self.vuer.spawn(start=False)(self.main_image_stereo) + + self.left_hand_shared = Array('d', 16, lock=True) + self.right_hand_shared = Array('d', 16, lock=True) + self.left_landmarks_shared = Array('d', 75, lock=True) + self.right_landmarks_shared = Array('d', 75, lock=True) + + self.head_matrix_shared = Array('d', 16, lock=True) + self.aspect_shared = Value('d', 1.0, lock=True) + + self.process = Process(target=self.vuer_run) + self.process.daemon = True + self.process.start() + + + def vuer_run(self): + self.vuer.run() + + async def on_cam_move(self, event, session, fps=60): + try: + self.head_matrix_shared[:] = event.value["camera"]["matrix"] + self.aspect_shared.value = event.value['camera']['aspect'] + except: + pass + + async def on_hand_move(self, event, session, fps=60): + try: + self.left_hand_shared[:] = event.value["leftHand"] + self.right_hand_shared[:] = event.value["rightHand"] + self.left_landmarks_shared[:] = np.array(event.value["leftLandmarks"]).flatten() + self.right_landmarks_shared[:] = np.array(event.value["rightLandmarks"]).flatten() + except: + pass + + async def main_image(self, session, fps=60): + session.upsert @ Hands(fps=fps, stream=True, key="hands", showLeft=False, showRight=False) + while True: + display_image = cv2.cvtColor(self.img_array, cv2.COLOR_BGR2RGB) + aspect_ratio = self.img_width / self.img_height + session.upsert( + [ + ImageBackground( + display_image[:, :self.img_width], + aspect=1.778, + height=1, + distanceToCamera=1, + # The underlying rendering engine supported a layer binary bitmask for both objects and the camera. + # Below we set the two image planes, left and right, to layers=1 and layers=2. + # Note that these two masks are associated with left eye’s camera and the right eye’s camera. + layers=1, + format="jpeg", + quality=50, + key="background-left", + interpolate=True, + ), + ImageBackground( + display_image[:, self.img_width:], + aspect=1.778, + height=1, + distanceToCamera=1, + layers=2, + format="jpeg", + quality=50, + key="background-right", + interpolate=True, + ), + ], + to="bgChildren", + ) + # 'jpeg' encoding should give you about 30fps with a 16ms wait in-between. + await asyncio.sleep(0.016 * 2) + + @property + def left_hand(self): + return np.array(self.left_hand_shared[:]).reshape(4, 4, order="F") + + + @property + def right_hand(self): + return np.array(self.right_hand_shared[:]).reshape(4, 4, order="F") + + + @property + def left_landmarks(self): + return np.array(self.left_landmarks_shared[:]).reshape(25, 3) + + @property + def right_landmarks(self): + return np.array(self.right_landmarks_shared[:]).reshape(25, 3) + + @property + def head_matrix(self): + return np.array(self.head_matrix_shared[:]).reshape(4, 4, order="F") + + @property + def aspect(self): + return float(self.aspect_shared.value) + +if __name__ == '__main__': + import os + import sys + current_dir = os.path.dirname(os.path.abspath(__file__)) + parent_dir = os.path.dirname(current_dir) + sys.path.append(parent_dir) + import threading + from image_server.image_client import ImageClient + + # image + img_shape = (480, 640 * 2, 3) + img_shm = shared_memory.SharedMemory(create=True, size=np.prod(img_shape) * np.uint8().itemsize) + img_array = np.ndarray(img_shape, dtype=np.uint8, buffer=img_shm.buf) + img_client = ImageClient(img_shape = img_shape, img_shm_name = img_shm.name) + image_receive_thread = threading.Thread(target=img_client.receive_process, daemon=True) + image_receive_thread.start() + + # television + tv = TeleVision(img_shape, img_shm.name) + print("vuer unit test program running...") + print("you can press ^C to interrupt program.") + while True: + time.sleep(0.03) \ No newline at end of file diff --git a/teleop/open_television/tv_wrapper.py b/teleop/open_television/tv_wrapper.py new file mode 100644 index 0000000..d4ce189 --- /dev/null +++ b/teleop/open_television/tv_wrapper.py @@ -0,0 +1,147 @@ +import numpy as np +from teleop.open_television.television import TeleVision +from teleop.open_television.constants import * +from teleop.utils.mat_tool import mat_update, fast_mat_inv + +""" +(basis) OpenXR Convention : y up, z back, x right. +(basis) Robot Convention : z up, y left, x front. +p.s. Vuer's all raw data follows OpenXR Convention, WORLD coordinate. + +under (basis) Robot Convention, wrist's initial pose convention: + + # (Left Wrist) XR/AppleVisionPro Convention: + - the x-axis pointing from wrist toward middle. + - the y-axis pointing from index toward pinky. + - the z-axis pointing from palm toward back of the hand. + + # (Right Wrist) XR/AppleVisionPro Convention: + - the x-axis pointing from wrist toward middle. + - the y-axis pointing from pinky toward index. + - the z-axis pointing from palm toward back of the hand. + + # (Left Wrist URDF) Unitree Convention: + - the x-axis pointing from wrist toward middle. + - the y-axis pointing from palm toward back of the hand. + - the z-axis pointing from pinky toward index. + + # (Right Wrist URDF) Unitree Convention: + - the x-axis pointing from wrist toward middle. + - the y-axis pointing from back of the hand toward palm. + - the z-axis pointing from pinky toward index. + +under (basis) Robot Convention, hand's initial pose convention: + + # (Left Hand) XR/AppleVisionPro Convention: + - the x-axis pointing from wrist toward middle. + - the y-axis pointing from index toward pinky. + - the z-axis pointing from palm toward back of the hand. + + # (Right Hand) XR/AppleVisionPro Convention: + - the x-axis pointing from wrist toward middle. + - the y-axis pointing from pinky toward index. + - the z-axis pointing from palm toward back of the hand. + + # (Left Hand URDF) Unitree Convention: + - The x-axis pointing from palm toward back of the hand. + - The y-axis pointing from middle toward wrist. + - The z-axis pointing from pinky toward index. + + # (Right Hand URDF) Unitree Convention: + - The x-axis pointing from palm toward back of the hand. + - The y-axis pointing from middle toward wrist. + - The z-axis pointing from index toward pinky. + + p.s. From website: https://registry.khronos.org/OpenXR/specs/1.1/man/html/openxr.html. + You can find **(Left/Right Wrist) XR/AppleVisionPro Convention** related information like this below: + "The wrist joint is located at the pivot point of the wrist, which is location invariant when twisting the hand without moving the forearm. + The backward (+Z) direction is parallel to the line from wrist joint to middle finger metacarpal joint, and points away from the finger tips. + The up (+Y) direction points out towards back of the hand and perpendicular to the skin at wrist. + The X direction is perpendicular to the Y and Z directions and follows the right hand rule." + Note: The above context is of course under **(basis) OpenXR Convention**. + + p.s. **(Wrist/Hand URDF) Unitree Convention** information come from URDF files. +""" + +class TeleVisionWrapper: + def __init__(self, img_shape, img_shm_name): + self.tv = TeleVision(img_shape, img_shm_name) + + def get_data(self): + + # --------------------------------wrist------------------------------------- + + # TeleVision obtains a basis coordinate that is OpenXR Convention + head_vuer_mat = mat_update(const_head_vuer_mat, self.tv.head_matrix.copy()) + left_wrist_vuer_mat = mat_update(const_left_wrist_vuer_mat, self.tv.left_hand.copy()) + right_wrist_vuer_mat = mat_update(const_right_wrist_vuer_mat, self.tv.right_hand.copy()) + + # Change basis convention: VuerMat ((basis) OpenXR Convention) to WristMat ((basis) Robot Convention) + # p.s. WristMat = T_{robot}_{openxr} * VuerMat * T_{robot}_{openxr}^T + # Reason for right multiply fast_mat_inv(T_robot_openxr): + # This is similarity transformation: B = PAP^{-1}, that is B ~ A + # For example: + # - For a pose data T_r under the Robot Convention, left-multiplying WristMat means: + # - WristMat * T_r ==> T_{robot}_{openxr} * VuerMat * T_{openxr}_{robot} * T_r + # - First, transform to the OpenXR Convention (The function of T_{openxr}_{robot}) + # - then, apply the rotation VuerMat in the OpenXR Convention (The function of VuerMat) + # - finally, transform back to the Robot Convention (The function of T_{robot}_{openxr}) + # This results in the same rotation effect under the Robot Convention as in the OpenXR Convention. + head_mat = T_robot_openxr @ head_vuer_mat @ fast_mat_inv(T_robot_openxr) + left_wrist_mat = T_robot_openxr @ left_wrist_vuer_mat @ fast_mat_inv(T_robot_openxr) + right_wrist_mat = T_robot_openxr @ right_wrist_vuer_mat @ fast_mat_inv(T_robot_openxr) + + # Change wrist convention: WristMat ((Left Wrist) XR/AppleVisionPro Convention) to UnitreeWristMat((Left Wrist URDF) Unitree Convention) + # Reason for right multiply (T_to_unitree_left_wrist) : Rotate 90 degrees counterclockwise about its own x-axis. + # Reason for right multiply (T_to_unitree_right_wrist): Rotate 90 degrees clockwise about its own x-axis. + unitree_left_wrist = left_wrist_mat @ T_to_unitree_left_wrist + unitree_right_wrist = right_wrist_mat @ T_to_unitree_right_wrist + + # Transfer from WORLD to HEAD coordinate (translation only). + unitree_left_wrist[0:3, 3] = unitree_left_wrist[0:3, 3] - head_mat[0:3, 3] + unitree_right_wrist[0:3, 3] = unitree_right_wrist[0:3, 3] - head_mat[0:3, 3] + + # --------------------------------hand------------------------------------- + + # Homogeneous, [xyz] to [xyz1] + # p.s. np.concatenate([25,3]^T,(1,25)) ==> hand_vuer_mat.shape is (4,25) + # Now under (basis) OpenXR Convention, mat shape like this: + # x0 x1 x2 Β·Β·Β· x23 x24 + # y0 y1 y1 Β·Β·Β· y23 y24 + # z0 z1 z2 Β·Β·Β· z23 z24 + # 1 1 1 Β·Β·Β· 1 1 + left_hand_vuer_mat = np.concatenate([self.tv.left_landmarks.copy().T, np.ones((1, self.tv.left_landmarks.shape[0]))]) + right_hand_vuer_mat = np.concatenate([self.tv.right_landmarks.copy().T, np.ones((1, self.tv.right_landmarks.shape[0]))]) + + # Change basis convention: from (basis) OpenXR Convention to (basis) Robot Convention + # Just a change of basis for 3D points. No rotation, only translation. No need to right-multiply fast_mat_inv(T_robot_openxr). + left_hand_mat = T_robot_openxr @ left_hand_vuer_mat + right_hand_mat = T_robot_openxr @ right_hand_vuer_mat + + # Transfer from WORLD to WRIST coordinate. (this process under (basis) Robot Convention) + # p.s. HandMat_WristBased = WristMat_{wrold}_{wrist}^T * HandMat_{wrold} + # HandMat_WristBased = WristMat_{wrist}_{wrold} * HandMat_{wrold}, that is HandMat_{wrist} + left_hand_mat_wb = fast_mat_inv(left_wrist_mat) @ left_hand_mat + right_hand_mat_wb = fast_mat_inv(right_wrist_mat) @ right_hand_mat + # Change hand convention: HandMat ((Left Hand) XR/AppleVisionPro Convention) to UnitreeHandMat((Left Hand URDF) Unitree Convention) + # Reason for left multiply : T_to_unitree_hand @ left_hand_mat_wb ==> (4,4) @ (4,25) ==> (4,25), (4,25)[0:3, :] ==> (3,25), (3,25).T ==> (25,3) + # Now under (Left Hand URDF) Unitree Convention, mat shape like this: + # [x0, y0, z0] + # [x1, y1, z1] + # Β·Β·Β· + # [x23,y23,z23] + # [x24,y24,z24] + unitree_left_hand = (T_to_unitree_hand @ left_hand_mat_wb)[0:3, :].T + unitree_right_hand = (T_to_unitree_hand @ right_hand_mat_wb)[0:3, :].T + + # --------------------------------offset------------------------------------- + + head_rmat = head_mat[:3, :3] + # The origin of the coordinate for IK Solve is the WAIST joint motor. You can use teleop/robot_control/robot_arm_ik.py Unit_Test to check it. + # The origin of the coordinate of unitree_left_wrist is HEAD. So it is necessary to translate the origin of unitree_left_wrist from HEAD to WAIST. + unitree_left_wrist[0, 3] +=0.20 + unitree_right_wrist[0,3] +=0.20 + unitree_left_wrist[2, 3] +=0.45 + unitree_right_wrist[2,3] +=0.45 + + return head_rmat, unitree_left_wrist, unitree_right_wrist, unitree_left_hand, unitree_right_hand \ No newline at end of file diff --git a/teleop/robot_control/hand_retargeting.py b/teleop/robot_control/hand_retargeting.py new file mode 100644 index 0000000..294eb81 --- /dev/null +++ b/teleop/robot_control/hand_retargeting.py @@ -0,0 +1,22 @@ +from dex_retargeting.retargeting_config import RetargetingConfig +from pathlib import Path +import yaml +from enum import Enum + +class HandType(Enum): + INSPIRE_HAND = "../assets/inspire_hand/inspire_hand.yml" + UNITREE_DEX3 = "../assets/unitree_hand/unitree_dex3.yml" + +class HandRetargeting: + def __init__(self, hand_type: HandType): + RetargetingConfig.set_default_urdf_dir('../assets') + + config_file_path = hand_type.value + + with Path(config_file_path).open('r') as f: + self.cfg = yaml.safe_load(f) + + left_retargeting_config = RetargetingConfig.from_dict(self.cfg['left']) + right_retargeting_config = RetargetingConfig.from_dict(self.cfg['right']) + self.left_retargeting = left_retargeting_config.build() + self.right_retargeting = right_retargeting_config.build() \ No newline at end of file diff --git a/teleop/robot_control/robot_arm.py b/teleop/robot_control/robot_arm.py index 6751601..2466efe 100644 --- a/teleop/robot_control/robot_arm.py +++ b/teleop/robot_control/robot_arm.py @@ -5,35 +5,23 @@ import time from unitree_dds_wrapper.idl import unitree_hg from unitree_dds_wrapper.publisher import Publisher from unitree_dds_wrapper.subscription import Subscription -from unitree_dds_wrapper.utils.crc import crc32 import struct from enum import IntEnum -import copy kTopicLowCommand = "rt/lowcmd" kTopicLowState = "rt/lowstate" -kNumMotors = 35 - - - -class MotorCommand: - def __init__(self): - self.q_ref = np.zeros(kNumMotors) - self.dq_ref = np.zeros(kNumMotors) - self.tau_ff = np.zeros(kNumMotors) - self.kp = np.zeros(kNumMotors) - self.kd = np.zeros(kNumMotors) +G1_29_Num_Motors = 35 + class MotorState: def __init__(self): - self.q = np.zeros(kNumMotors) - self.dq = np.zeros(kNumMotors) + self.q = None + self.dq = None -class BaseState: +class G1_29_LowState: def __init__(self): - self.omega = np.zeros(3) - self.rpy = np.zeros(3) + self.motor_state = [MotorState() for _ in range(G1_29_Num_Motors)] class DataBuffer: def __init__(self): @@ -48,86 +36,77 @@ class DataBuffer: with self.lock: self.data = data -np.set_printoptions(linewidth=240) - -class H1ArmController: +class G1_29_ArmController: def __init__(self): - print("Initialize H1ArmController...") - self.q_desList = np.zeros(kNumMotors) - self.q_tau_ff = np.zeros(kNumMotors) - self.msg = unitree_hg.msg.dds_.LowCmd_() - self.__packFmtHGLowCmd = '<2B2x' + 'B3x5fI' * 35 + '5I' + print("Initialize G1_29_ArmController...") + self.q_target = np.zeros(14) + self.tauff_target = np.zeros(14) + self.msg = unitree_hg.msg.dds_.LowCmd_() + self.msg.mode_machine = 3 # g1 is 3, h1_2 is 4 + self.__packFmtHGLowCmd = '<2B2x' + 'B3x5fI' * 35 + '5I' self.msg.head = [0xFE, 0xEF] + self.lowcmd_publisher = Publisher(unitree_hg.msg.dds_.LowCmd_, kTopicLowCommand) self.lowstate_subscriber = Subscription(unitree_hg.msg.dds_.LowState_, kTopicLowState) - self.motor_state_buffer = DataBuffer() - self.motor_command_buffer = DataBuffer() - self.base_state_buffer = DataBuffer() - - self.kp_low = 140.0 - self.kd_low = 7.5 - - self.kp_high = 200.0 - self.kd_high = 5.0 - - self.kp_wrist = 35.0 - self.kd_wrist = 6.0 - - self.control_dt = 0.01 - self.hip_pitch_init_pos = -0.5 - self.knee_init_pos = 1.0 - self.ankle_init_pos = -0.5 - self.shoulder_pitch_init_pos = -1.4 - self.time = 0.0 - self.init_duration = 10.0 - self.report_dt = 0.1 - self.ratio = 0.0 - self.q_target = [] - while not self.lowstate_subscriber.msg: - print("lowstate_subscriber is not ok! Please check dds.") - time.sleep(0.01) - - for id in JointIndex: - self.msg.motor_cmd[id].q = self.lowstate_subscriber.msg.motor_state[id].q - self.q_target.append(self.msg.motor_cmd[id].q) - print(f"Init q_pose is :{self.q_target}") - duration = 1000 - init_q = np.array([self.lowstate_subscriber.msg.motor_state[id].q for id in JointIndex]) - print("Lock Leg...") - for i in range(duration): - time.sleep(0.001) - q_t = init_q + (self.q_target - init_q) * i / duration - for i, id in enumerate(JointIndex): - self.msg.motor_cmd[id].mode = 1 - if id not in JointArmIndex: - self.msg.motor_cmd[id].kp = 200 - self.msg.motor_cmd[id].kd = 5 - self.msg.motor_cmd[id].q = q_t[i] - self.pre_communication() - self.lowcmd_publisher.msg = self.msg - self.lowcmd_publisher.write() - print("Lock Leg OK!") + self.lowstate_buffer = DataBuffer() + + self.kp_high = 100.0 + self.kd_high = 3.0 + self.kp_low = 80.0 + self.kd_low = 3.0 + self.kp_wrist = 40.0 + self.kd_wrist = 1.5 + + self.all_motor_q = None + self.arm_velocity_limit = 20.0 + self.control_dt = 1.0 / 250.0 - self.report_rpy_thread = threading.Thread(target=self.SubscribeState) - self.report_rpy_thread.start() + self._speed_gradual_max = False + self._gradual_start_time = None + self._gradual_time = None - self.control_thread = threading.Thread(target=self.Control) - self.control_thread.start() + self.subscribe_thread = threading.Thread(target=self._subscribe_motor_state) + self.subscribe_thread.daemon = True + self.subscribe_thread.start() - self.command_writer_thread = threading.Thread(target=self.LowCommandWriter) - self.command_writer_thread.start() - print("Initialize H1ArmController OK!") + while not self.lowstate_buffer.GetData(): + time.sleep(0.01) + print("Waiting to subscribe dds...") + + self.all_motor_q = self.get_current_motor_q() + print(f"Current all body motor state q:\n{self.all_motor_q} \n") + print(f"Current two arms motor state q:\n{self.get_current_dual_arm_q()}\n") + print("Lock all joints except two arms...\n") + for id in G1_29_JointIndex: + self.msg.motor_cmd[id].mode = 1 + if id in G1_29_JointArmIndex: + if self._Is_wrist_motor(id): + self.msg.motor_cmd[id].kp = self.kp_wrist + self.msg.motor_cmd[id].kd = self.kd_wrist + else: + self.msg.motor_cmd[id].kp = self.kp_low + self.msg.motor_cmd[id].kd = self.kd_low + else: + if self._Is_weak_motor(id): + self.msg.motor_cmd[id].kp = self.kp_low + self.msg.motor_cmd[id].kd = self.kd_low + else: + self.msg.motor_cmd[id].kp = self.kp_high + self.msg.motor_cmd[id].kd = self.kd_high + self.msg.motor_cmd[id].q = self.all_motor_q[id] + self.pre_communication() + self.lowcmd_publisher.msg = self.msg + self.lowcmd_publisher.write() + print("Lock OK!\n") - def LowStateHandler(self, message): - low_state = message - self.RecordMotorState(low_state) - self.RecordBaseState(low_state) + self.publish_thread = threading.Thread(target=self._ctrl_motor_state) + self.ctrl_lock = threading.Lock() + self.publish_thread.daemon = True + self.publish_thread.start() - def SetMotorPose(self,q_desList,q_tau_ff): - self.q_desList = q_desList - self.q_tau_ff = q_tau_ff + print("Initialize G1_29_ArmController OK!\n") def __Trans(self, packData): calcData = [] @@ -162,9 +141,6 @@ class H1ArmController: return crc - def pre_communication(self): - self.__pack_crc() - def __pack_crc(self): origData = [] origData.append(self.msg.mode_pr) @@ -185,166 +161,231 @@ class H1ArmController: calcdata = self.__Trans(calcdata) self.msg.crc = self.__Crc32(calcdata) - def LowCommandWriter(self): - while True: - mc_tmp_ptr = self.motor_command_buffer.GetData() - if mc_tmp_ptr: - for i in JointArmIndex: - self.msg.motor_cmd[i].tau = mc_tmp_ptr.tau_ff[i] - self.msg.motor_cmd[i].q = mc_tmp_ptr.q_ref[i] - self.msg.motor_cmd[i].dq = mc_tmp_ptr.dq_ref[i] - self.msg.motor_cmd[i].kp = mc_tmp_ptr.kp[i] - self.msg.motor_cmd[i].kd = mc_tmp_ptr.kd[i] - self.pre_communication() - self.lowcmd_publisher.msg = self.msg - self.lowcmd_publisher.write() - time.sleep(0.002) - - def Control(self): + def pre_communication(self): + self.__pack_crc() + + def clip_arm_q_target(self, target_q, velocity_limit): + current_q = self.get_current_dual_arm_q() + delta = target_q - current_q + motion_scale = np.max(np.abs(delta)) / (velocity_limit * self.control_dt) + cliped_arm_q_target = current_q + delta / max(motion_scale, 1.0) + return cliped_arm_q_target + + def _ctrl_motor_state(self): while True: - ms_tmp_ptr = self.motor_state_buffer.GetData() - if ms_tmp_ptr: - tem_q_desList = copy.deepcopy(self.q_desList) - tem_q_tau_ff = copy.deepcopy(self.q_tau_ff) - motor_command_tmp = MotorCommand() - self.time += self.control_dt - self.time = min(max(self.time, 0.0), self.init_duration) - self.ratio = self.time / self.init_duration - for i in range(kNumMotors): - if self.IsWeakMotor(i): - motor_command_tmp.kp[i] = self.kp_low - motor_command_tmp.kd[i] = self.kd_low - elif self.IsWristMotor(i): - motor_command_tmp.kp[i] = self.kp_wrist - motor_command_tmp.kd[i] = self.kd_wrist - else: - motor_command_tmp.kp[i] = self.kp_high - motor_command_tmp.kd[i] = self.kd_high - motor_command_tmp.dq_ref[i] = 0.0 - motor_command_tmp.tau_ff[i] = tem_q_tau_ff[i] - q_des = tem_q_desList[i] - - q_des = (q_des - ms_tmp_ptr.q[i]) * self.ratio + ms_tmp_ptr.q[i] - motor_command_tmp.q_ref[i] = q_des - self.motor_command_buffer.SetData(motor_command_tmp) - time.sleep(0.002) + start_time = time.time() + + with self.ctrl_lock: + arm_q_target = self.q_target + arm_tauff_target = self.tauff_target + + cliped_arm_q_target = self.clip_arm_q_target(arm_q_target, velocity_limit = self.arm_velocity_limit) + + for idx, id in enumerate(G1_29_JointArmIndex): + self.msg.motor_cmd[id].q = cliped_arm_q_target[idx] + self.msg.motor_cmd[id].dq = 0 + self.msg.motor_cmd[id].tau = arm_tauff_target[idx] + + self.pre_communication() + self.lowcmd_publisher.msg = self.msg + self.lowcmd_publisher.write() + + if self._speed_gradual_max is True: + t_elapsed = start_time - self._gradual_start_time + self.arm_velocity_limit = 20.0 + (10.0 * min(1.0, t_elapsed / 5.0)) + + current_time = time.time() + all_t_elapsed = current_time - start_time + sleep_time = max(0, (self.control_dt - all_t_elapsed)) + time.sleep(sleep_time) + # print(f"arm_velocity_limit:{self.arm_velocity_limit}") + # print(f"sleep_time:{sleep_time}") + + def ctrl_dual_arm(self, q_target, tauff_target): + '''Set control target values q & tau of the left and right arm motors.''' + with self.ctrl_lock: + self.q_target = q_target + self.tauff_target = tauff_target - def GetMotorState(self): - ms_tmp_ptr = self.motor_state_buffer.GetData() - if ms_tmp_ptr: - return ms_tmp_ptr.q[13:27],ms_tmp_ptr.dq[13:27] - else: - return None,None - - def SubscribeState(self): + def get_current_motor_q(self): + '''Return current state q of all body motors.''' + return np.array([self.lowstate_buffer.GetData().motor_state[id].q for id in G1_29_JointIndex]) + + def get_current_dual_arm_q(self): + '''Return current state q of the left and right arm motors.''' + return np.array([self.lowstate_buffer.GetData().motor_state[id].q for id in G1_29_JointArmIndex]) + + def get_current_dual_arm_dq(self): + '''Return current state dq of the left and right arm motors.''' + return np.array([self.lowstate_buffer.GetData().motor_state[id].dq for id in G1_29_JointArmIndex]) + + def _subscribe_motor_state(self): while True: if self.lowstate_subscriber.msg: - self.LowStateHandler(self.lowstate_subscriber.msg) + lowstate = G1_29_LowState() + for id in range(G1_29_Num_Motors): + lowstate.motor_state[id].q = self.lowstate_subscriber.msg.motor_state[id].q + lowstate.motor_state[id].dq = self.lowstate_subscriber.msg.motor_state[id].dq + self.lowstate_buffer.SetData(lowstate) time.sleep(0.002) - def RecordMotorState(self, msg): - ms_tmp = MotorState() - for i in range(kNumMotors): - ms_tmp.q[i] = msg.motor_state[i].q - ms_tmp.dq[i] = msg.motor_state[i].dq - self.motor_state_buffer.SetData(ms_tmp) + def speed_gradual_max(self, t = 5.0): + '''Parameter t is the total time required for arms velocity to gradually increase to its maximum value, in seconds. The default is 5.0.''' + self._gradual_start_time = time.time() + self._gradual_time = t + self._speed_gradual_max = True - def RecordBaseState(self, msg): - bs_tmp = BaseState() - bs_tmp.omega = msg.imu_state.gyroscope - bs_tmp.rpy = msg.imu_state.rpy - self.base_state_buffer.SetData(bs_tmp) + def speed_instant_max(self): + '''set arms velocity to the maximum value immediately, instead of gradually increasing.''' + self.arm_velocity_limit = 30.0 - def IsWeakMotor(self, motor_index): + def _Is_weak_motor(self, motor_index): weak_motors = [ - JointIndex.kLeftAnkle, - JointIndex.kRightAnkle, + G1_29_JointIndex.kLeftAnklePitch, + G1_29_JointIndex.kRightAnklePitch, # Left arm - JointIndex.kLeftShoulderPitch, - JointIndex.kLeftShoulderRoll, - JointIndex.kLeftShoulderYaw, - JointIndex.kLeftElbowPitch, + G1_29_JointIndex.kLeftShoulderPitch, + G1_29_JointIndex.kLeftShoulderRoll, + G1_29_JointIndex.kLeftShoulderYaw, + G1_29_JointIndex.kLeftElbow, # Right arm - JointIndex.kRightShoulderPitch, - JointIndex.kRightShoulderRoll, - JointIndex.kRightShoulderYaw, - JointIndex.kRightElbowPitch, + G1_29_JointIndex.kRightShoulderPitch, + G1_29_JointIndex.kRightShoulderRoll, + G1_29_JointIndex.kRightShoulderYaw, + G1_29_JointIndex.kRightElbow, ] return motor_index in weak_motors - def IsWristMotor(self, motor_index): + def _Is_wrist_motor(self, motor_index): wrist_motors = [ - JointIndex.kLeftElbowRoll, - JointIndex.kLeftWristPitch, - JointIndex.kLeftWristyaw, - JointIndex.kRightElbowRoll, - JointIndex.kRightWristPitch, - JointIndex.kRightWristYaw, + G1_29_JointIndex.kLeftWristRoll, + G1_29_JointIndex.kLeftWristPitch, + G1_29_JointIndex.kLeftWristyaw, + G1_29_JointIndex.kRightWristRoll, + G1_29_JointIndex.kRightWristPitch, + G1_29_JointIndex.kRightWristYaw, ] return motor_index in wrist_motors -class JointArmIndex(IntEnum): +class G1_29_JointArmIndex(IntEnum): # Left arm - kLeftShoulderPitch = 13 - kLeftShoulderRoll = 14 - kLeftShoulderYaw = 15 - kLeftElbowPitch = 16 - kLeftElbowRoll = 17 - kLeftWristPitch = 18 - kLeftWristyaw = 19 + kLeftShoulderPitch = 15 + kLeftShoulderRoll = 16 + kLeftShoulderYaw = 17 + kLeftElbow = 18 + kLeftWristRoll = 19 + kLeftWristPitch = 20 + kLeftWristyaw = 21 # Right arm - kRightShoulderPitch = 20 - kRightShoulderRoll = 21 - kRightShoulderYaw = 22 - kRightElbowPitch = 23 - kRightElbowRoll = 24 - kRightWristPitch = 25 - kRightWristYaw = 26 - -class JointIndex(IntEnum): + kRightShoulderPitch = 22 + kRightShoulderRoll = 23 + kRightShoulderYaw = 24 + kRightElbow = 25 + kRightWristRoll = 26 + kRightWristPitch = 27 + kRightWristYaw = 28 + +class G1_29_JointIndex(IntEnum): # Left leg - kLeftHipYaw = 0 + kLeftHipPitch = 0 kLeftHipRoll = 1 - kLeftHipPitch = 2 + kLeftHipYaw = 2 kLeftKnee = 3 - kLeftAnkle = 4 + kLeftAnklePitch = 4 kLeftAnkleRoll = 5 # Right leg - kRightHipYaw = 6 + kRightHipPitch = 6 kRightHipRoll = 7 - kRightHipPitch = 8 + kRightHipYaw = 8 kRightKnee = 9 - kRightAnkle = 10 + kRightAnklePitch = 10 kRightAnkleRoll = 11 kWaistYaw = 12 + kWaistRoll = 13 + kWaistPitch = 14 # Left arm - kLeftShoulderPitch = 13 - kLeftShoulderRoll = 14 - kLeftShoulderYaw = 15 - kLeftElbowPitch = 16 - kLeftElbowRoll = 17 - kLeftWristPitch = 18 - kLeftWristyaw = 19 + kLeftShoulderPitch = 15 + kLeftShoulderRoll = 16 + kLeftShoulderYaw = 17 + kLeftElbow = 18 + kLeftWristRoll = 19 + kLeftWristPitch = 20 + kLeftWristyaw = 21 # Right arm - kRightShoulderPitch = 20 - kRightShoulderRoll = 21 - kRightShoulderYaw = 22 - kRightElbowPitch = 23 - kRightElbowRoll = 24 - kRightWristPitch = 25 - kRightWristYaw = 26 - - kNotUsedJoint = 27 - kNotUsedJoint1 = 28 - kNotUsedJoint2 = 29 - kNotUsedJoint3 = 30 - kNotUsedJoint4 = 31 - kNotUsedJoint5 = 32 - kNotUsedJoint6 = 33 - kNotUsedJoint7 = 34 + kRightShoulderPitch = 22 + kRightShoulderRoll = 23 + kRightShoulderYaw = 24 + kRightElbow = 25 + kRightWristRoll = 26 + kRightWristPitch = 27 + kRightWristYaw = 28 + + # not used + kNotUsedJoint0 = 29 + kNotUsedJoint1 = 30 + kNotUsedJoint2 = 31 + kNotUsedJoint3 = 32 + kNotUsedJoint4 = 33 + kNotUsedJoint5 = 34 + +if __name__ == "__main__": + from robot_arm_ik import G1_29_ArmIK + import pinocchio as pin + + arm_ik = G1_29_ArmIK(Unit_Test = True, Visualization = False) + g1arm = G1_29_ArmController() + + # initial positon + L_tf_target = pin.SE3( + pin.Quaternion(1, 0, 0, 0), + np.array([0.25, +0.2, 0.1]), + ) + + R_tf_target = pin.SE3( + pin.Quaternion(1, 0, 0, 0), + np.array([0.25, -0.2, 0.1]), + ) + + rotation_speed = 0.005 # Rotation speed in radians per iteration + q_target = np.zeros(35) + tauff_target = np.zeros(35) + + user_input = input("Please enter the start signal (enter 's' to start the subsequent program): \n") + if user_input.lower() == 's': + step = 0 + g1arm.speed_gradual_max() + while True: + if step <= 120: + angle = rotation_speed * step + L_quat = pin.Quaternion(np.cos(angle / 2), 0, np.sin(angle / 2), 0) # y axis + R_quat = pin.Quaternion(np.cos(angle / 2), 0, 0, np.sin(angle / 2)) # z axis + + L_tf_target.translation += np.array([0.001, 0.001, 0.001]) + R_tf_target.translation += np.array([0.001, -0.001, 0.001]) + else: + angle = rotation_speed * (240 - step) + L_quat = pin.Quaternion(np.cos(angle / 2), 0, np.sin(angle / 2), 0) # y axis + R_quat = pin.Quaternion(np.cos(angle / 2), 0, 0, np.sin(angle / 2)) # z axis + + L_tf_target.translation -= np.array([0.001, 0.001, 0.001]) + R_tf_target.translation -= np.array([0.001, -0.001, 0.001]) + + L_tf_target.rotation = L_quat.toRotationMatrix() + R_tf_target.rotation = R_quat.toRotationMatrix() + + current_lr_arm_q = g1arm.get_current_dual_arm_q() + current_lr_arm_dq = g1arm.get_current_dual_arm_dq() + + sol_q, sol_tauff = arm_ik.solve_ik(L_tf_target.homogeneous, R_tf_target.homogeneous, current_lr_arm_q, current_lr_arm_dq) + + g1arm.ctrl_dual_arm(sol_q, sol_tauff) + step += 1 + if step > 240: + step = 0 + time.sleep(0.01) \ No newline at end of file diff --git a/teleop/robot_control/robot_arm_ik.py b/teleop/robot_control/robot_arm_ik.py index 04279af..123f912 100644 --- a/teleop/robot_control/robot_arm_ik.py +++ b/teleop/robot_control/robot_arm_ik.py @@ -9,20 +9,24 @@ from pinocchio.visualize import MeshcatVisualizer import os import sys -current_dir = os.path.dirname(os.path.abspath(__file__)) -parent_dir = os.path.dirname(current_dir) -sys.path.append(parent_dir) +parent2_dir = os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(__file__)))) +sys.path.append(parent2_dir) -from robot_control.joint_smooth import WeightedMovingFilter +from teleop.utils.weighted_moving_filter import WeightedMovingFilter -class Arm_IK: - def __init__(self): +class G1_29_ArmIK: + def __init__(self, Unit_Test = False, Visualization = False): np.set_printoptions(precision=5, suppress=True, linewidth=200) - self.robot = pin.RobotWrapper.BuildFromURDF('../assets/g1/g1_body29_hand14.urdf', '../assets/g1/') - # self.robot = pin.RobotWrapper.BuildFromURDF('../../assets/g1/g1_body29_hand14.urdf', '../../assets/g1/') # for test + self.Unit_Test = Unit_Test + self.Visualization = Visualization - self.mixed_jointsToLockIDs = [ + if not self.Unit_Test: + self.robot = pin.RobotWrapper.BuildFromURDF('../assets/g1/g1_body29_hand14.urdf', '../assets/g1/') + else: + self.robot = pin.RobotWrapper.BuildFromURDF('../../assets/g1/g1_body29_hand14.urdf', '../../assets/g1/') # for test + + self.mixed_jointsToLockIDs = [ "left_hip_pitch_joint" , "left_hip_roll_joint" , "left_hip_yaw_joint" , @@ -77,50 +81,11 @@ class Arm_IK: pin.FrameType.OP_FRAME) ) - self.joint_smooth_filter = WeightedMovingFilter(np.array([0.4, 0.3, 0.2, 0.1]), 14) - - self.init_data = np.zeros(self.reduced_robot.model.nq) - - # # Initialize the Meshcat visualizer for visualization - # self.vis = MeshcatVisualizer(self.reduced_robot.model, self.reduced_robot.collision_model, self.reduced_robot.visual_model) - # self.vis.initViewer(open=True) - # self.vis.loadViewerModel("pinocchio") - # self.vis.displayFrames(True, frame_ids=[49, 81, 101, 102], axis_length = 0.15, axis_width = 5) - # self.vis.display(pin.neutral(self.reduced_robot.model)) - - # # for i in range(self.reduced_robot.model.nframes): - # # frame = self.reduced_robot.model.frames[i] - # # frame_id = self.reduced_robot.model.getFrameId(frame.name) - # # print(f"Frame ID: {frame_id}, Name: {frame.name}") - - # # Enable the display of end effector target frames with short axis lengths and greater width. - # frame_viz_names = ['L_ee_target', 'R_ee_target'] - # FRAME_AXIS_POSITIONS = ( - # np.array([[0, 0, 0], [1, 0, 0], - # [0, 0, 0], [0, 1, 0], - # [0, 0, 0], [0, 0, 1]]).astype(np.float32).T - # ) - # FRAME_AXIS_COLORS = ( - # np.array([[1, 0, 0], [1, 0.6, 0], - # [0, 1, 0], [0.6, 1, 0], - # [0, 0, 1], [0, 0.6, 1]]).astype(np.float32).T - # ) - # axis_length = 0.1 - # axis_width = 10 - # for frame_viz_name in frame_viz_names: - # self.vis.viewer[frame_viz_name].set_object( - # mg.LineSegments( - # mg.PointsGeometry( - # position=axis_length * FRAME_AXIS_POSITIONS, - # color=FRAME_AXIS_COLORS, - # ), - # mg.LineBasicMaterial( - # linewidth=axis_width, - # vertexColors=True, - # ), - # ) - # ) - + # for i in range(self.reduced_robot.model.nframes): + # frame = self.reduced_robot.model.frames[i] + # frame_id = self.reduced_robot.model.getFrameId(frame.name) + # print(f"Frame ID: {frame_id}, Name: {frame.name}") + # Creating Casadi models and data for symbolic computing self.cmodel = cpin.Model(self.reduced_robot.model) self.cdata = self.cmodel.createData() @@ -134,17 +99,24 @@ class Arm_IK: # Get the hand joint ID and define the error function self.L_hand_id = self.reduced_robot.model.getFrameId("L_ee") self.R_hand_id = self.reduced_robot.model.getFrameId("R_ee") - self.error = casadi.Function( - "error", + + self.translational_error = casadi.Function( + "translational_error", + [self.cq, self.cTf_l, self.cTf_r], + [ + casadi.vertcat( + self.cdata.oMf[self.L_hand_id].translation - self.cTf_l[:3,3], + self.cdata.oMf[self.R_hand_id].translation - self.cTf_r[:3,3] + ) + ], + ) + self.rotational_error = casadi.Function( + "rotational_error", [self.cq, self.cTf_l, self.cTf_r], [ casadi.vertcat( - cpin.log6( - self.cdata.oMf[self.L_hand_id].inverse() * cpin.SE3(self.cTf_l) - ).vector, - cpin.log6( - self.cdata.oMf[self.R_hand_id].inverse() * cpin.SE3(self.cTf_r) - ).vector + cpin.log3(self.cdata.oMf[self.L_hand_id].rotation @ self.cTf_l[:3,:3].T), + cpin.log3(self.cdata.oMf[self.R_hand_id].rotation @ self.cTf_r[:3,:3].T) ) ], ) @@ -155,8 +127,9 @@ class Arm_IK: self.var_q_last = self.opti.parameter(self.reduced_robot.model.nq) # for smooth self.param_tf_l = self.opti.parameter(4, 4) self.param_tf_r = self.opti.parameter(4, 4) - self.totalcost = casadi.sumsqr(self.error(self.var_q, self.param_tf_l, self.param_tf_r)) - self.regularization = casadi.sumsqr(self.var_q) + self.translational_cost = casadi.sumsqr(self.translational_error(self.var_q, self.param_tf_l, self.param_tf_r)) + self.rotation_cost = casadi.sumsqr(self.rotational_error(self.var_q, self.param_tf_l, self.param_tf_r)) + self.regularization_cost = casadi.sumsqr(self.var_q) self.smooth_cost = casadi.sumsqr(self.var_q - self.var_q_last) # Setting optimization constraints and goals @@ -165,20 +138,60 @@ class Arm_IK: self.var_q, self.reduced_robot.model.upperPositionLimit) ) - # self.opti.minimize(20 * self.totalcost + 0.01 * self.regularization) - self.opti.minimize(20 * self.totalcost + 0.01* self.regularization + 0.1 * self.smooth_cost) # for smooth + self.opti.minimize(50 * self.translational_cost + self.rotation_cost + 0.02 * self.regularization_cost + 0.1 * self.smooth_cost) opts = { 'ipopt':{ 'print_level':0, 'max_iter':50, - 'tol':1e-4 + 'tol':1e-6 }, - 'print_time':False + 'print_time':False,# print or not + 'calc_lam_p':False # https://github.com/casadi/casadi/wiki/FAQ:-Why-am-I-getting-%22NaN-detected%22in-my-optimization%3F } self.opti.solver("ipopt", opts) - def adjust_pose(self, human_left_pose, human_right_pose, human_arm_length=0.60, robot_arm_length=0.75): + self.init_data = np.zeros(self.reduced_robot.model.nq) + self.smooth_filter = WeightedMovingFilter(np.array([0.4, 0.3, 0.2, 0.1]), 14) + self.vis = None + + if self.Visualization: + # Initialize the Meshcat visualizer for visualization + self.vis = MeshcatVisualizer(self.reduced_robot.model, self.reduced_robot.collision_model, self.reduced_robot.visual_model) + self.vis.initViewer(open=True) + self.vis.loadViewerModel("pinocchio") + self.vis.displayFrames(True, frame_ids=[101, 102], axis_length = 0.15, axis_width = 5) + self.vis.display(pin.neutral(self.reduced_robot.model)) + + # Enable the display of end effector target frames with short axis lengths and greater width. + frame_viz_names = ['L_ee_target', 'R_ee_target'] + FRAME_AXIS_POSITIONS = ( + np.array([[0, 0, 0], [1, 0, 0], + [0, 0, 0], [0, 1, 0], + [0, 0, 0], [0, 0, 1]]).astype(np.float32).T + ) + FRAME_AXIS_COLORS = ( + np.array([[1, 0, 0], [1, 0.6, 0], + [0, 1, 0], [0.6, 1, 0], + [0, 0, 1], [0, 0.6, 1]]).astype(np.float32).T + ) + axis_length = 0.1 + axis_width = 10 + for frame_viz_name in frame_viz_names: + self.vis.viewer[frame_viz_name].set_object( + mg.LineSegments( + mg.PointsGeometry( + position=axis_length * FRAME_AXIS_POSITIONS, + color=FRAME_AXIS_COLORS, + ), + mg.LineBasicMaterial( + linewidth=axis_width, + vertexColors=True, + ), + ) + ) + # If the robot arm is not the same size as your arm :) + def scale_arms(self, human_left_pose, human_right_pose, human_arm_length=0.60, robot_arm_length=0.75): scale_factor = robot_arm_length / human_arm_length robot_left_pose = human_left_pose.copy() robot_right_pose = human_right_pose.copy() @@ -186,74 +199,111 @@ class Arm_IK: robot_right_pose[:3, 3] *= scale_factor return robot_left_pose, robot_right_pose - def ik_fun(self, left_pose, right_pose, motorstate=None, motorV=None): - if motorstate is not None: - self.init_data = motorstate + def solve_ik(self, left_wrist, right_wrist, current_lr_arm_motor_q = None, current_lr_arm_motor_dq = None): + if current_lr_arm_motor_q is not None: + self.init_data = current_lr_arm_motor_q self.opti.set_initial(self.var_q, self.init_data) - # left_pose, right_pose = self.adjust_pose(left_pose, right_pose) - - # self.vis.viewer['L_ee_target'].set_transform(left_pose) # for visualization - # self.vis.viewer['R_ee_target'].set_transform(right_pose) # for visualization + # left_wrist, right_wrist = self.scale_arms(left_wrist, right_wrist) + if self.Visualization: + self.vis.viewer['L_ee_target'].set_transform(left_wrist) # for visualization + self.vis.viewer['R_ee_target'].set_transform(right_wrist) # for visualization - self.opti.set_value(self.param_tf_l, left_pose) - self.opti.set_value(self.param_tf_r, right_pose) + self.opti.set_value(self.param_tf_l, left_wrist) + self.opti.set_value(self.param_tf_r, right_wrist) self.opti.set_value(self.var_q_last, self.init_data) # for smooth try: - # sol = self.opti.solve() - sol = self.opti.solve_limited() + sol = self.opti.solve() + # sol = self.opti.solve_limited() + sol_q = self.opti.value(self.var_q) + self.smooth_filter.add_data(sol_q) + sol_q = self.smooth_filter.filtered_data - self.joint_smooth_filter.add_data(sol_q) - sol_q = self.joint_smooth_filter.filtered_data + if current_lr_arm_motor_dq is not None: + v = current_lr_arm_motor_dq * 0.0 + else: + v = (sol_q - self.init_data) * 0.0 - # self.vis.display(sol_q) # for visualization self.init_data = sol_q - if motorV is not None: - v =motorV * 0.0 - else: - v = (sol_q-self.init_data ) * 0.0 + sol_tauff = pin.rnea(self.reduced_robot.model, self.reduced_robot.data, sol_q, v, np.zeros(self.reduced_robot.model.nv)) - tau_ff = pin.rnea(self.reduced_robot.model, self.reduced_robot.data, sol_q, v, np.zeros(self.reduced_robot.model.nv)) + if self.Visualization: + self.vis.display(sol_q) # for visualization - return sol_q, tau_ff ,True + return sol_q, sol_tauff except Exception as e: print(f"ERROR in convergence, plotting debug info.{e}") - # sol_q = self.opti.debug.value(self.var_q) # return original value - return sol_q, '',False + + sol_q = self.opti.debug.value(self.var_q) + self.smooth_filter.add_data(sol_q) + sol_q = self.smooth_filter.filtered_data + + if current_lr_arm_motor_dq is not None: + v = current_lr_arm_motor_dq * 0.0 + else: + v = (sol_q - self.init_data) * 0.0 + + self.init_data = sol_q + + sol_tauff = pin.rnea(self.reduced_robot.model, self.reduced_robot.data, sol_q, v, np.zeros(self.reduced_robot.model.nv)) + + print(f"sol_q:{sol_q} \nmotorstate: \n{current_lr_arm_motor_q} \nleft_pose: \n{left_wrist} \nright_pose: \n{right_wrist}") + if self.Visualization: + self.vis.display(sol_q) # for visualization + + # return sol_q, sol_tauff + return current_lr_arm_motor_q, np.zeros(self.reduced_robot.model.nv) if __name__ == "__main__": - arm_ik = Arm_IK() + arm_ik = G1_29_ArmIK(Unit_Test = True, Visualization = True) # initial positon L_tf_target = pin.SE3( pin.Quaternion(1, 0, 0, 0), - np.array([0.23, +0.2, 0.2]), + np.array([0.25, +0.25, 0.1]), ) R_tf_target = pin.SE3( pin.Quaternion(1, 0, 0, 0), - np.array([0.23, -0.2, 0.2]), + np.array([0.25, -0.25, 0.1]), ) - rotation_speed = 0.005 # Rotation speed in radians per iteration + rotation_speed = 0.005 + noise_amplitude_translation = 0.001 + noise_amplitude_rotation = 0.1 - user_input = input("Please enter the start signal (enter 's' to start the subsequent program):") + user_input = input("Please enter the start signal (enter 's' to start the subsequent program):\n") if user_input.lower() == 's': - - for i in range(150): - angle = rotation_speed * i - L_quat = pin.Quaternion(np.cos(angle / 2), 0, np.sin(angle / 2), 0) # y axis - R_quat = pin.Quaternion(np.cos(angle / 2), 0, 0, np.sin(angle / 2)) # z axis - - L_tf_target.translation += np.array([0.001, 0.001, 0.001]) - R_tf_target.translation += np.array([0.001, -0.001, 0.001]) - L_tf_target.rotation = L_quat.toRotationMatrix() - R_tf_target.rotation = R_quat.toRotationMatrix() - - arm_ik.ik_fun(L_tf_target.homogeneous, R_tf_target.homogeneous) - time.sleep(0.05) \ No newline at end of file + step = 0 + while True: + # Apply rotation noise with bias towards y and z axes + rotation_noise_L = pin.Quaternion( + np.cos(np.random.normal(0, noise_amplitude_rotation) / 2),0,np.random.normal(0, noise_amplitude_rotation / 2),0).normalized() # y bias + + rotation_noise_R = pin.Quaternion( + np.cos(np.random.normal(0, noise_amplitude_rotation) / 2),0,0,np.random.normal(0, noise_amplitude_rotation / 2)).normalized() # z bias + + if step <= 120: + angle = rotation_speed * step + L_tf_target.rotation = (rotation_noise_L * pin.Quaternion(np.cos(angle / 2), 0, np.sin(angle / 2), 0)).toRotationMatrix() # y axis + R_tf_target.rotation = (rotation_noise_R * pin.Quaternion(np.cos(angle / 2), 0, 0, np.sin(angle / 2))).toRotationMatrix() # z axis + L_tf_target.translation += (np.array([0.001, 0.001, 0.001]) + np.random.normal(0, noise_amplitude_translation, 3)) + R_tf_target.translation += (np.array([0.001, -0.001, 0.001]) + np.random.normal(0, noise_amplitude_translation, 3)) + else: + angle = rotation_speed * (240 - step) + L_tf_target.rotation = (rotation_noise_L * pin.Quaternion(np.cos(angle / 2), 0, np.sin(angle / 2), 0)).toRotationMatrix() # y axis + R_tf_target.rotation = (rotation_noise_R * pin.Quaternion(np.cos(angle / 2), 0, 0, np.sin(angle / 2))).toRotationMatrix() # z axis + L_tf_target.translation -= (np.array([0.001, 0.001, 0.001]) + np.random.normal(0, noise_amplitude_translation, 3)) + R_tf_target.translation -= (np.array([0.001, -0.001, 0.001]) + np.random.normal(0, noise_amplitude_translation, 3)) + + arm_ik.solve_ik(L_tf_target.homogeneous, R_tf_target.homogeneous) + + step += 1 + if step > 240: + step = 0 + time.sleep(0.1) \ No newline at end of file diff --git a/teleop/robot_control/robot_hand.py b/teleop/robot_control/robot_hand_inspire.py similarity index 68% rename from teleop/robot_control/robot_hand.py rename to teleop/robot_control/robot_hand_inspire.py index 8ef3080..b452be1 100644 --- a/teleop/robot_control/robot_hand.py +++ b/teleop/robot_control/robot_hand_inspire.py @@ -1,32 +1,30 @@ - +# this file is legacy, need to fix. from unitree_dds_wrapper.idl import unitree_go from unitree_dds_wrapper.publisher import Publisher from unitree_dds_wrapper.subscription import Subscription +from avp_teleoperate.teleop.open_television.constants import inspire_tip_indices +from teleop.robot_control.hand_retargeting import HandRetargeting, HandType import numpy as np import threading import time +inspire_tip_indices = [4, 9, 14, 19, 24] -class H1HandController: +class InspireController: def __init__(self): self.cmd = unitree_go.msg.dds_.MotorCmds_() self.state = unitree_go.msg.dds_.MotorStates_() - self.labels = { - "open": np.ones(6), - "close": np.zeros(6), - "half": np.full(6, 0.5) - } self.lock = threading.Lock() - self.init_dds() - - def init_dds(self): self.handcmd = Publisher(unitree_go.msg.dds_.MotorCmds_, "rt/inspire/cmd") self.handstate = Subscription(unitree_go.msg.dds_.MotorStates_, "rt/inspire/state") self.cmd.cmds = [unitree_go.msg.dds_.MotorCmd_() for _ in range(12)] self.state.states = [unitree_go.msg.dds_.MotorState_() for _ in range(12)] - self.report_rpy_thread = threading.Thread(target=self.subscribe_state) - self.report_rpy_thread.start() + self.subscribe_state_thread = threading.Thread(target=self.subscribe_state) + self.subscribe_state_thread.daemon = True + self.subscribe_state_thread.start() + + self.hand_retargeting = HandRetargeting(HandType.INSPIRE_HAND) def subscribe_state(self): while True: @@ -34,23 +32,14 @@ class H1HandController: self.state = self.handstate.msg time.sleep(0.01) - def ctrl(self, label): - if label in self.labels: - self._ctrl(self.labels[label], self.labels[label]) - else: - print(f"Invalid label: {label}") - - def crtl(self,right_angles,left_angles): - self._ctrl(right_angles,left_angles) - - def _ctrl(self, right_angles, left_angles): + def ctrl(self, left_angles, right_angles): for i in range(6): self.cmd.cmds[i].q = right_angles[i] self.cmd.cmds[i+6].q = left_angles[i] self.handcmd.msg.cmds = self.cmd.cmds self.handcmd.write() - def get_hand_state(self): + def get_current_dual_hand_q(self): with self.lock: q = np.array([self.state.states[i].q for i in range(12)]) return q diff --git a/teleop/robot_control/robot_hand_unitree.py b/teleop/robot_control/robot_hand_unitree.py new file mode 100644 index 0000000..db845e6 --- /dev/null +++ b/teleop/robot_control/robot_hand_unitree.py @@ -0,0 +1,97 @@ +from unitree_dds_wrapper.robots.trihand.trihand_pub_cmd import UnitreeTrihand as trihand_pub +from unitree_dds_wrapper.robots.trihand.trihand_sub_state import UnitreeTrihand as trihand_sub + +import numpy as np +import time +from multiprocessing import Array +from teleop.robot_control.hand_retargeting import HandRetargeting, HandType +import threading + +unitree_tip_indices = [4, 9, 14] + +class Dex3_1_Controller: + def __init__(self, fps = 100.0): + self.dex3_pub = trihand_pub() + + kp = np.full(7, 1.5) + kd = np.full(7, 0.2) + q = np.full(7,0.0) + dq = np.full(7,0.0) + tau = np.full(7,0.0) + self.dex3_pub.left_hand.kp = kp + self.dex3_pub.left_hand.kd = kd + self.dex3_pub.left_hand.q = q + self.dex3_pub.left_hand.dq = dq + self.dex3_pub.left_hand.tau = tau + + self.dex3_pub.right_hand.kp = kp + self.dex3_pub.right_hand.kd = kd + self.dex3_pub.right_hand.q = q + self.dex3_pub.right_hand.dq = dq + self.dex3_pub.right_hand.tau = tau + + self.dual_hand_state_array = [0.0] * 14 + self.lr_hand_state_lock = threading.Lock() + # self.dual_hand_state_array = Array('d', 14, lock=True) + + self.sub_state = trihand_sub() + self.sub_state.wait_for_connection() + + self.subscribe_state_thread = threading.Thread(target=self.subscribe_state) + self.subscribe_state_thread.daemon = True + self.subscribe_state_thread.start() + + self.hand_retargeting = HandRetargeting(HandType.UNITREE_DEX3) + + self.running = True + self.fps = fps + + print("UnitreeDex3 Controller init ok.\n") + + def subscribe_state(self): + while True: + lq,rq= self.sub_state.sub() + with self.lr_hand_state_lock: + self.dual_hand_state_array[:] = np.concatenate((lq, rq)) + # self.dual_hand_state_array[:] = np.concatenate((lq,rq)) + time.sleep(0.002) + + + def ctrl(self, left_angles, right_angles): + """set current left, right hand motor state target q""" + self.dex3_pub.left_hand.q = left_angles + self.dex3_pub.right_hand.q = right_angles + self.dex3_pub.pub() + # print("hand ctrl publish ok.") + + def get_current_dual_hand_q(self): + """return current left, right hand motor state q""" + with self.lr_hand_state_lock: + return self.dual_hand_state_array[:].copy() + + def control_process(self, left_hand_array, right_hand_array, dual_hand_state_array = None, dual_hand_aciton_array = None): + left_qpos = np.full(7, 0) + right_qpos = np.full(7, 0) + try: + while self.running: + start_time = time.time() + left_hand_mat = np.array(left_hand_array[:]).reshape(25, 3).copy() + right_hand_mat = np.array(right_hand_array[:]).reshape(25, 3).copy() + + is_initial = np.all(left_hand_mat == 0.0) + if not is_initial: + left_qpos = self.hand_retargeting.left_retargeting.retarget(left_hand_mat[unitree_tip_indices])[[0,1,2,3,4,5,6]] + right_qpos = self.hand_retargeting.right_retargeting.retarget(right_hand_mat[unitree_tip_indices])[[0,1,2,3,4,5,6]] + + self.ctrl(left_qpos, right_qpos) + + if dual_hand_state_array and dual_hand_aciton_array: + dual_hand_state_array[:] = self.get_current_dual_hand_q() + dual_hand_aciton_array[:] = np.concatenate((left_qpos, right_qpos)) + + current_time = time.time() + time_elapsed = current_time - start_time + sleep_time = max(0, (1 / self.fps) - time_elapsed) + time.sleep(sleep_time) + finally: + print("Dex3_1_Controller has been closed.") \ No newline at end of file diff --git a/teleop/teleop_active_cam.py b/teleop/teleop_active_cam.py deleted file mode 100644 index 69952eb..0000000 --- a/teleop/teleop_active_cam.py +++ /dev/null @@ -1,86 +0,0 @@ -import math -import numpy as np - -np.set_printoptions(precision=2, suppress=True) -import matplotlib.pyplot as plt -from scipy.spatial.transform import Rotation as R -from pytransform3d import rotations - -import time -import cv2 -from constants_vuer import * -from TeleVision import OpenTeleVision -import pyzed.sl as sl -from dynamixel.active_cam import DynamixelAgent -from multiprocessing import Array, Process, shared_memory, Queue, Manager, Event, Semaphore - -resolution = (720, 1280) -crop_size_w = 1 -crop_size_h = 0 -resolution_cropped = (resolution[0] - crop_size_h, resolution[1] - 2 * crop_size_w) - -agent = DynamixelAgent(port="/dev/serial/by-id/usb-FTDI_USB__-__Serial_Converter_FT8IT033-if00-port0") -agent._robot.set_torque_mode(True) - -# Create a Camera object -zed = sl.Camera() - -# Create a InitParameters object and set configuration parameters -init_params = sl.InitParameters() -init_params.camera_resolution = sl.RESOLUTION.HD720 # Use HD720 opr HD1200 video mode, depending on camera type. -init_params.camera_fps = 60 # Set fps at 60 - -# Open the camera -err = zed.open(init_params) -if err != sl.ERROR_CODE.SUCCESS: - print("Camera Open : " + repr(err) + ". Exit program.") - exit() - -# Capture 50 frames and stop -i = 0 -image_left = sl.Mat() -image_right = sl.Mat() -runtime_parameters = sl.RuntimeParameters() - -img_shape = (resolution_cropped[0], 2 * resolution_cropped[1], 3) -img_height, img_width = resolution_cropped[:2] -shm = shared_memory.SharedMemory(create=True, size=np.prod(img_shape) * np.uint8().itemsize) -img_array = np.ndarray((img_shape[0], img_shape[1], 3), dtype=np.uint8, buffer=shm.buf) -image_queue = Queue() -toggle_streaming = Event() -tv = OpenTeleVision(resolution_cropped, shm.name, image_queue, toggle_streaming) - -while True: - start = time.time() - - head_mat = grd_yup2grd_zup[:3, :3] @ tv.head_matrix[:3, :3] @ grd_yup2grd_zup[:3, :3].T - if np.sum(head_mat) == 0: - head_mat = np.eye(3) - head_rot = rotations.quaternion_from_matrix(head_mat[0:3, 0:3]) - try: - ypr = rotations.euler_from_quaternion(head_rot, 2, 1, 0, False) - # print(ypr) - # agent._robot.command_joint_state([0., 0.4]) - agent._robot.command_joint_state(ypr[:2]) - # print("success") - except: - # print("failed") - # exit() - pass - - if zed.grab(runtime_parameters) == sl.ERROR_CODE.SUCCESS: - zed.retrieve_image(image_left, sl.VIEW.LEFT) - zed.retrieve_image(image_right, sl.VIEW.RIGHT) - timestamp = zed.get_timestamp(sl.TIME_REFERENCE.CURRENT) # Get the timestamp at the time the image was captured - # print("Image resolution: {0} x {1} || Image timestamp: {2}\n".format(image.get_width(), image.get_height(), - # timestamp.get_milliseconds())) - - bgr = np.hstack((image_left.numpy()[crop_size_h:, crop_size_w:-crop_size_w], - image_right.numpy()[crop_size_h:, crop_size_w:-crop_size_w])) - rgb = cv2.cvtColor(bgr, cv2.COLOR_BGRA2RGB) - - np.copyto(img_array, rgb) - - end = time.time() - # print(1/(end-start)) -zed.close() \ No newline at end of file diff --git a/teleop/teleop_hand_and_arm.py b/teleop/teleop_hand_and_arm.py index 293f64a..75eed7a 100644 --- a/teleop/teleop_hand_and_arm.py +++ b/teleop/teleop_hand_and_arm.py @@ -1,19 +1,9 @@ import numpy as np - -from TeleVision import OpenTeleVision -from Preprocessor import VuerPreprocessor -from constants_vuer import tip_indices -from dex_retargeting.retargeting_config import RetargetingConfig - -from pathlib import Path import time -import yaml -from multiprocessing import Process, shared_memory, Queue, Manager, Event, Lock - +import argparse import cv2 -import zmq -import pickle -import zlib +from multiprocessing import Process, shared_memory, Array +import threading import os import sys @@ -21,146 +11,170 @@ current_dir = os.path.dirname(os.path.abspath(__file__)) parent_dir = os.path.dirname(current_dir) sys.path.append(parent_dir) -from robot_control.robot_hand import H1HandController -from teleop.robot_control.robot_arm import H1ArmController -from teleop.robot_control.robot_arm_ik import Arm_IK - - -def image_receiver(image_queue, resolution, crop_size_w, crop_size_h): - context = zmq.Context() - socket = context.socket(zmq.PULL) - socket.connect("tcp://192.168.123.162:5555") - - while True: - compressed_data = b'' - while True: - chunk = socket.recv() - compressed_data += chunk - if len(chunk) < 60000: - break - data = zlib.decompress(compressed_data) - frame_data = pickle.loads(data) - - # Decode and display the image - frame = cv2.imdecode(frame_data, cv2.IMREAD_COLOR) - sm.write_image(frame) - # Control receiving frequency - time.sleep(0.01) - -class SharedMemoryImage: - def __init__(self, img_shape): - self.resolution = img_shape#(720, 1280) - self.crop_size_w = 0 - self.crop_size_h = 0 - self.resolution_cropped = (self.resolution[0]-self.crop_size_h, self.resolution[1] - 2 * self.crop_size_w) - - self.img_shape = (self.resolution_cropped[0], 2 * self.resolution_cropped[1], 3) - self.img_height, self.img_width = self.resolution_cropped[:2] - - self.shm = shared_memory.SharedMemory(create=True, size=np.prod(self.img_shape) * np.uint8().itemsize) - self.img_array = np.ndarray((self.img_shape[0], self.img_shape[1], 3), dtype=np.uint8, buffer=self.shm.buf) - self.lock = Lock() - - def write_image(self, image): - with self.lock: - np.copyto(self.img_array, image) - - def read_image(self): - with self.lock: - image_copy = self.img_array.copy() - return image_copy - - def cleanup(self): - self.shm.close() - self.shm.unlink() - -class VuerTeleop: - def __init__(self, config_file_path): - self.resolution = (480,640) #(720, 1280) - self.crop_size_w = 0 - self.crop_size_h = 0 - self.resolution_cropped = (self.resolution[0]-self.crop_size_h, self.resolution[1]-2*self.crop_size_w) - - self.img_shape = (self.resolution_cropped[0], 2 * self.resolution_cropped[1], 3) - self.img_height, self.img_width = self.resolution_cropped[:2] - - self.shm = shared_memory.SharedMemory(create=True, size=np.prod(self.img_shape) * np.uint8().itemsize) - self.img_array = np.ndarray((self.img_shape[0], self.img_shape[1], 3), dtype=np.uint8, buffer=self.shm.buf) - image_queue = Queue() - toggle_streaming = Event() - self.tv = OpenTeleVision(self.resolution_cropped, self.shm.name, image_queue, toggle_streaming) - self.processor = VuerPreprocessor() - - RetargetingConfig.set_default_urdf_dir('../assets') - with Path(config_file_path).open('r') as f: - cfg = yaml.safe_load(f) - left_retargeting_config = RetargetingConfig.from_dict(cfg['left']) - right_retargeting_config = RetargetingConfig.from_dict(cfg['right']) - self.left_retargeting = left_retargeting_config.build() - self.right_retargeting = right_retargeting_config.build() - - def step(self): - head_mat, left_wrist_mat, right_wrist_mat, left_hand_mat, right_hand_mat = self.processor.process(self.tv) - head_rmat = head_mat[:3, :3] - - left_wrist_mat[2, 3] +=0.45 - right_wrist_mat[2,3] +=0.45 - left_wrist_mat[0, 3] +=0.20 - right_wrist_mat[0,3] +=0.20 - - left_qpos = self.left_retargeting.retarget(left_hand_mat[tip_indices])[[4, 5, 6, 7, 10, 11, 8, 9, 0, 1, 2, 3]] - right_qpos = self.right_retargeting.retarget(right_hand_mat[tip_indices])[[4, 5, 6, 7, 10, 11, 8, 9, 0, 1, 2, 3]] - - return head_rmat, left_wrist_mat, right_wrist_mat, left_qpos, right_qpos +from teleop.open_television.tv_wrapper import TeleVisionWrapper +from teleop.robot_control.robot_arm import G1_29_ArmController +from teleop.robot_control.robot_arm_ik import G1_29_ArmIK +from teleop.robot_control.robot_hand_unitree import Dex3_1_Controller +from teleop.image_server.image_client import ImageClient +from teleop.utils.episode_writer import EpisodeWriter if __name__ == '__main__': - manager = Manager() - image_queue = manager.Queue() - teleoperator = VuerTeleop('inspire_hand.yml') - h1hand = H1HandController() - h1arm = H1ArmController() - arm_ik = Arm_IK() - sm = SharedMemoryImage((480,640)) - image_process = Process(target=image_receiver, args=(sm, teleoperator.resolution, teleoperator.crop_size_w, teleoperator.crop_size_h)) - image_process.start() - + parser = argparse.ArgumentParser() + parser.add_argument('--record', type=bool, default=True, help='save data or not') + parser.add_argument('--task_dir', type=str, default='data', help='path to save data') + parser.add_argument('--frequency', type=int, default=30.0, help='save data\'s frequency') + args = parser.parse_args() + print(f"args:{args}\n") + + # image + img_shape = (480, 640 * 2, 3) + img_shm = shared_memory.SharedMemory(create=True, size=np.prod(img_shape) * np.uint8().itemsize) + img_array = np.ndarray(img_shape, dtype=np.uint8, buffer=img_shm.buf) + img_client = ImageClient(img_shape = img_shape, img_shm_name = img_shm.name) + image_receive_thread = threading.Thread(target=img_client.receive_process, daemon=True) + image_receive_thread.daemon = True + image_receive_thread.start() + + # television and arm + tv_wrapper = TeleVisionWrapper(img_shape, img_shm.name) + arm_ctrl = G1_29_ArmController() + arm_ik = G1_29_ArmIK() + + # hand + hand_ctrl = Dex3_1_Controller() + left_hand_array = Array('d', 75, lock=True) + right_hand_array = Array('d', 75, lock=True) + dual_hand_state_array = Array('d', 14, lock=True) # current left, right hand state data + dual_hand_aciton_array = Array('d', 14, lock=True) # current left and right hand action data to be controlled + hand_control_process = Process(target=hand_ctrl.control_process, args=(left_hand_array, right_hand_array, dual_hand_state_array, dual_hand_aciton_array)) + hand_control_process.daemon = True + hand_control_process.start() + + if args.record: + recorder = EpisodeWriter(task_dir=args.task_dir, frequency=args.frequency) + try: - user_input = input("Please enter the start signal (enter 's' to start the subsequent program):") - if user_input.lower() == 's': - while True: - armstate = None - armv = None - frame = sm.read_image() - np.copyto(teleoperator.img_array, np.array(frame)) - handstate = h1hand.get_hand_state() - - q_poseList=np.zeros(35) - q_tau_ff=np.zeros(35) - armstate,armv = h1arm.GetMotorState() - - head_rmat, left_pose, right_pose, left_qpos, right_qpos = teleoperator.step() - sol_q ,tau_ff, flag = arm_ik.ik_fun(left_pose, right_pose, armstate,armv) - - if flag: - q_poseList[13:27] = sol_q - q_tau_ff[13:27] = tau_ff - else: - q_poseList[13:27] = armstate - q_tau_ff = np.zeros(35) - - h1arm.SetMotorPose(q_poseList, q_tau_ff) - - if right_pose is not None and left_qpos is not None: - # 4,5: index 6,7: middle, 0,1: pinky, 2,3: ring, 8,9: thumb - right_angles = [1.7 - right_qpos[i] for i in [4, 6, 2, 0]] - right_angles.append(1.2 - right_qpos[8]) - right_angles.append(0.5 - right_qpos[9]) - - left_angles = [1.7- left_qpos[i] for i in [4, 6, 2, 0]] - left_angles.append(1.2 - left_qpos[8]) - left_angles.append(0.5 - left_qpos[9]) - h1hand.crtl(right_angles,left_angles) + user_input = input("Please enter the start signal (enter 'r' to start the subsequent program):\n") + if user_input.lower() == 'r': + arm_ctrl.speed_gradual_max() + if args.record: + press_key_s_count = 0 + + running = True + recording = False + while running: + start_time = time.time() + head_rmat, left_wrist, right_wrist, left_hand, right_hand = tv_wrapper.get_data() + + # send hand skeleton data to hand_ctrl.control_process + left_hand_array[:] = left_hand.flatten() + right_hand_array[:] = right_hand.flatten() + + # get current arm motor data. solve ik using motor data and wrist pose, then use ik results to control arms. + time_ik_start = time.time() + current_lr_arm_q = arm_ctrl.get_current_dual_arm_q() + current_lr_arm_dq = arm_ctrl.get_current_dual_arm_dq() + sol_q, sol_tauff = arm_ik.solve_ik(left_wrist, right_wrist, current_lr_arm_q, current_lr_arm_dq) + time_ik_end = time.time() + # print(f"ik:\t{round(time_ik_end - time_ik_start, 6)}") + arm_ctrl.ctrl_dual_arm(sol_q, sol_tauff) + + # record data + if args.record: + current_image = img_array.copy() + left_image = current_image[:, :640] + right_image = current_image[:, 640:] + left_arm_state = current_lr_arm_q[:7] + right_arm_state = current_lr_arm_q[-7:] + left_hand_state = dual_hand_state_array[:7] + right_hand_state = dual_hand_state_array[-7:] + left_arm_action = sol_q[:7] + right_arm_action = sol_q[-7:] + left_hand_action = dual_hand_aciton_array[:7] + right_hand_action = dual_hand_aciton_array[-7:] + + cv2.imshow("record image", current_image) + key = cv2.waitKey(1) & 0xFF + if key == ord('q'): + running = False + elif key == ord('s'): + press_key_s_count += 1 + if press_key_s_count % 2 == 1: + print("Start recording...") + recording = True + recorder.create_episode() + else: + print("End recording...") + recording = False + recorder.save_episode() + + if recording: + colors = {} + depths = {} + colors[f"color_{0}"] = left_image + colors[f"color_{1}"] = right_image + states = { + "left_arm": { + "qpos": left_arm_state.tolist(), # numpy.array -> list + "qvel": [], + "torque": [], + }, + "right_arm": { + "qpos": right_arm_state.tolist(), + "qvel": [], + "torque": [], + }, + "left_hand": { + "qpos": left_hand_state, # Array returns a list after slicing + "qvel": [], + "torque": [], + }, + "right_hand": { + "qpos": right_hand_state, + "qvel": [], + "torque": [], + }, + "body": None, + } + actions = { + "left_arm": { + "qpos": left_arm_action.tolist(), + "qvel": [], + "torque": [], + }, + "right_arm": { + "qpos": right_arm_action.tolist(), + "qvel": [], + "torque": [], + }, + "left_hand": { + "qpos": left_hand_action, + "qvel": [], + "torque": [], + }, + "right_hand": { + "qpos": right_hand_action, + "qvel": [], + "torque": [], + }, + "body": None, + } + recorder.add_item(colors=colors, depths=depths, states=states, actions=actions) + + current_time = time.time() + time_elapsed = current_time - start_time + sleep_time = max(0, (1 / float(args.frequency)) - time_elapsed) + time.sleep(sleep_time) + # print(f"main process sleep: {sleep_time}") except KeyboardInterrupt: + img_shm.unlink() + img_shm.close() + print("KeyboardInterrupt, exiting program...") exit(0) + finally: + img_shm.unlink() + img_shm.close() + print("Finally, exiting program...") + exit(0) \ No newline at end of file diff --git a/teleop/teleop_hand.py b/teleop/teleop_test_gym.py similarity index 75% rename from teleop/teleop_hand.py rename to teleop/teleop_test_gym.py index 9dce152..252a9b7 100644 --- a/teleop/teleop_hand.py +++ b/teleop/teleop_test_gym.py @@ -1,61 +1,21 @@ +# TODO: this file need to be fixed. from isaacgym import gymapi -from isaacgym import gymutil from isaacgym import gymtorch - import math import numpy as np import torch - -from TeleVision import OpenTeleVision -from Preprocessor import VuerPreprocessorLegacy as VuerPreprocessor -from constants_vuer import tip_indices -from dex_retargeting.retargeting_config import RetargetingConfig +import time from pytransform3d import rotations -from pathlib import Path -import argparse -import time -import yaml -from multiprocessing import Array, Process, shared_memory, Queue, Manager, Event, Semaphore - -class VuerTeleop: - def __init__(self, config_file_path): - self.resolution = (720, 1280) - self.crop_size_w = 0 - self.crop_size_h = 0 - self.resolution_cropped = (self.resolution[0]-self.crop_size_h, self.resolution[1]-2*self.crop_size_w) - - self.img_shape = (self.resolution_cropped[0], 2 * self.resolution_cropped[1], 3) - self.img_height, self.img_width = self.resolution_cropped[:2] - - self.shm = shared_memory.SharedMemory(create=True, size=np.prod(self.img_shape) * np.uint8().itemsize) - self.img_array = np.ndarray((self.img_shape[0], self.img_shape[1], 3), dtype=np.uint8, buffer=self.shm.buf) - image_queue = Queue() - toggle_streaming = Event() - self.tv = OpenTeleVision(self.resolution_cropped, self.shm.name, image_queue, toggle_streaming) - self.processor = VuerPreprocessor() - - RetargetingConfig.set_default_urdf_dir('../assets') - with Path(config_file_path).open('r') as f: - cfg = yaml.safe_load(f) - left_retargeting_config = RetargetingConfig.from_dict(cfg['left']) - right_retargeting_config = RetargetingConfig.from_dict(cfg['right']) - self.left_retargeting = left_retargeting_config.build() - self.right_retargeting = right_retargeting_config.build() - - def step(self): - head_mat, left_wrist_mat, right_wrist_mat, left_hand_mat, right_hand_mat = self.processor.process(self.tv) - - head_rmat = head_mat[:3, :3] - - left_pose = np.concatenate([left_wrist_mat[:3, 3] + np.array([-0.6, 0, 1.6]), - rotations.quaternion_from_matrix(left_wrist_mat[:3, :3])[[1, 2, 3, 0]]]) - right_pose = np.concatenate([right_wrist_mat[:3, 3] + np.array([-0.6, 0, 1.6]), - rotations.quaternion_from_matrix(right_wrist_mat[:3, :3])[[1, 2, 3, 0]]]) - left_qpos = self.left_retargeting.retarget(left_hand_mat[tip_indices])[[4, 5, 6, 7, 10, 11, 8, 9, 0, 1, 2, 3]] - right_qpos = self.right_retargeting.retarget(right_hand_mat[tip_indices])[[4, 5, 6, 7, 10, 11, 8, 9, 0, 1, 2, 3]] - - return head_rmat, left_pose, right_pose, left_qpos, right_qpos +import os +import sys +current_dir = os.path.dirname(os.path.abspath(__file__)) +parent_dir = os.path.dirname(current_dir) +sys.path.append(parent_dir) + +from teleop.open_television.tv_wrapper import TeleVisionWrapper +from multiprocessing import Process, shared_memory, Array +from teleop.robot_control.robot_hand_unitree import Dex3_1_Controller class Sim: def __init__(self, @@ -104,8 +64,8 @@ class Sim: cube_asset = self.gym.create_box(self.sim, 0.05, 0.05, 0.05, cube_asset_options) asset_root = "../assets" - left_asset_path = "inspire_hand/inspire_hand_left.urdf" - right_asset_path = "inspire_hand/inspire_hand_right.urdf" + left_asset_path = "unitree_hand/unitree_dex3_left.urdf" + right_asset_path = "unitree_hand/unitree_dex3_right.urdf" asset_options = gymapi.AssetOptions() asset_options.fix_base_link = True asset_options.default_dof_drive_mode = gymapi.DOF_MODE_POS @@ -252,15 +212,36 @@ class Sim: if __name__ == '__main__': - teleoperator = VuerTeleop('inspire_hand.yml') + # image and television + img_shape = (720, 2560, 3) + img_shm = shared_memory.SharedMemory(create=True, size=np.prod(img_shape) * np.uint8().itemsize) + img_array = np.ndarray(img_shape, dtype=np.uint8, buffer=img_shm.buf) + tv_wrapper = TeleVisionWrapper(img_shape, img_shm.name) + # hand + left_hand_array = Array('d', 75, lock=True) + right_hand_array = Array('d', 75, lock=True) + lr_hand_state_array = Array('d', 14, lock=True) # current left, right hand state data + lr_hand_aciton_array = Array('d', 14, lock=True) # current left and right hand action data to be controlled + hand_ctrl = Dex3_1_Controller(lr_hand_state_array, lr_hand_aciton_array) + hand_control_process = Process(target=hand_ctrl.control_process, args=(left_hand_array, right_hand_array)) + hand_control_process.daemon = True + hand_control_process.start() simulator = Sim() try: while True: - head_rmat, left_pose, right_pose, left_qpos, right_qpos = teleoperator.step() - left_img, right_img = simulator.step(head_rmat, left_pose, right_pose, left_qpos, right_qpos) - np.copyto(teleoperator.img_array, np.hstack((left_img, right_img))) - + user_input = input("Please enter the start signal (enter 's' to start the subsequent program):") + if user_input.lower() == 's': + while True: + head_rmat, left_wrist, right_wrist, left_hand, right_hand = tv_wrapper.get_data() + left_pose = np.concatenate([left_wrist[:3, 3] + np.array([-0.6, 0, 1.6]), + rotations.quaternion_from_matrix(left_wrist[:3, :3])[[1, 2, 3, 0]]]) + right_pose = np.concatenate([right_wrist[:3, 3] + np.array([-0.6, 0, 1.6]), + rotations.quaternion_from_matrix(right_wrist[:3, :3])[[1, 2, 3, 0]]]) + left_hand_array[:] = left_hand.flatten() + right_hand_array[:] = right_hand.flatten() + left_img, right_img = simulator.step(head_rmat, left_pose, right_pose, lr_hand_aciton_array[:7], lr_hand_aciton_array[-7:]) + np.copyto(img_array, np.hstack((left_img, right_img))) except KeyboardInterrupt: simulator.end() exit(0) diff --git a/teleop/utils/episode_writer.py b/teleop/utils/episode_writer.py new file mode 100755 index 0000000..7d3e50f --- /dev/null +++ b/teleop/utils/episode_writer.py @@ -0,0 +1,142 @@ +import os +import cv2 +import json +import datetime +import numpy as np +import time + + +class EpisodeWriter(object): + def __init__(self, task_dir, frequency=30, image_size=[640, 480]): + """ + image_size: [width, height] + """ + print("==> EpisodeWriter initializing...\n") + self.task_dir = task_dir + self.frequency = frequency + self.image_size = image_size + + self.data = {} + self.episode_data = [] + self.item_id = -1 + self.episode_id = -1 + if os.path.exists(self.task_dir): + episode_dirs = [episode_dir for episode_dir in os.listdir(self.task_dir) if 'episode_' in episode_dir] + episode_last = sorted(episode_dirs)[-1] if len(episode_dirs) > 0 else None + self.episode_id = 0 if episode_last is None else int(episode_last.split('_')[-1]) + print(f"==> task_dir directory already exist, now self.episode_id is：{self.episode_id}\n") + else: + os.makedirs(self.task_dir) + print(f"==> episode directory does not exist, now create one.\n") + self.data_info() + self.text_desc() + print("==> EpisodeWriter initialized successfully.\n") + + def data_info(self, version='1.0.0', date=None, author=None): + self.info = { + "version": "1.0.0" if version is None else version, + "date": "" if date is None else datetime.date.today().strftime('%Y-%m-%d'), + "author": "unitree" if author is None else author, + "image": {"width":self.image_size[0], "height":self.image_size[1], "fps":self.frequency}, + "depth": {"width":self.image_size[0], "height":self.image_size[1], "fps":self.frequency}, + "audio": {"sample_rate": 16000, "channels": 1, "format":"PCM", "bits":16}, # PCM_S16 + "joint_names":{ + "left_arm": ['kLeftShoulderPitch' ,'kLeftShoulderRoll', 'kLeftShoulderYaw', 'kLeftElbow', 'kLeftWristRoll', 'kLeftWristPitch', 'kLeftWristyaw'], + "left_hand": [], + "right_arm": [], + "right_hand": [], + "body": [], + }, + + "tactile_names": { + "left_hand": [], + "right_hand": [], + }, + } + def text_desc(self): + self.text = { + "goal": "Pick up the red cup on the table.", + "desc": "Pick up the cup from the table and place it in another position. The operation should be smooth and the water in the cup should not spill out", + "steps":"step1: searching for cups. step2: go to the target location. step3: pick up the cup", + } + + + def create_episode(self): + """ + Create a new episode, each episode needs to specify the episode_id. + text: Text descriptions of operation goals, steps, etc. The text description of each episode is the same. + goal: operation goal + desc: description + steps: operation steps + """ + + self.item_id = -1 + self.episode_data = [] + self.episode_id = self.episode_id + 1 + + self.episode_dir = os.path.join(self.task_dir, f"episode_{str(self.episode_id).zfill(4)}") + self.color_dir = os.path.join(self.episode_dir, 'colors') + self.depth_dir = os.path.join(self.episode_dir, 'depths') + self.audio_dir = os.path.join(self.episode_dir, 'audios') + self.json_path = os.path.join(self.episode_dir, 'data.json') + os.makedirs(self.episode_dir, exist_ok=True) + os.makedirs(self.color_dir, exist_ok=True) + os.makedirs(self.depth_dir, exist_ok=True) + os.makedirs(self.audio_dir, exist_ok=True) + + def add_item(self, colors, depths=None, states=None, actions=None, tactiles=None, audios=None, log = True): + self.item_id += 1 + item_data = { + 'idx': self.item_id, + 'colors': {"color_0": None}, + 'depths': {"depth_0": None}, + 'states': {'left_arm':{}, 'right_arm':{}, 'left_hand':{}, 'right_hand':{}, 'body': {}}, + 'actions': {'left_arm':{}, 'right_arm':{}, 'left_hand':{}, 'right_hand':{}, 'body': {}}, + 'tactiles': {'left_hand':[], 'right_hand':[]}, + 'audios': { "mic0": None}, + } + + # save images + if colors is not None: + for idx, (_, color) in enumerate(colors.items()): + color_key = f'color_{idx}' + color_name = f'{str(self.item_id).zfill(6)}_{color_key}.jpg' + cv2.imwrite(os.path.join(self.color_dir, color_name), color) + item_data['colors'][color_key] = os.path.join('colors', color_name) + + # save depths + if depths is not None: + for idx, (_, depth) in enumerate(depths.items()): + depth_key = f'depth_{idx}' + depth_name = f'{str(self.item_id).zfill(6)}_{depth_key}.png' + cv2.imwrite(os.path.join(self.depth_dir, depth_name), depth) + item_data['depths'][depth_key] = os.path.join('depths', depth_name) + + # save audios + if audios is not None: + for mic, audio in audios.items(): + audio_name = f'audio_{str(self.item_id).zfill(6)}_{mic}.npy' + np.save(os.path.join(self.audio_dir, audio_name), audio.astype(np.int16)) + item_data['audios'][mic] = os.path.join('audios', audio_name) + + item_data['states'] = states + item_data['actions'] = actions + item_data['tactiles'] = tactiles + + self.episode_data.append(item_data) + + if log: + curent_record_time = time.time() + print(f"==> episode_id:{self.episode_id} item_id:{self.item_id} current_time:{curent_record_time}") + + + def save_episode(self): + """ + with open("./hmm.json",'r',encoding='utf-8') as json_file: + model=json.load(json_file) + """ + self.data['info'] = self.info + self.data['text'] = self.text + self.data['data'] = self.episode_data + with open(self.json_path,'w',encoding='utf-8') as jsonf: + jsonf.write(json.dumps(self.data, indent=4, ensure_ascii=False)) \ No newline at end of file diff --git a/teleop/motion_utils.py b/teleop/utils/mat_tool.py similarity index 94% rename from teleop/motion_utils.py rename to teleop/utils/mat_tool.py index fc68166..d3ad531 100644 --- a/teleop/motion_utils.py +++ b/teleop/utils/mat_tool.py @@ -11,4 +11,4 @@ def fast_mat_inv(mat): ret = np.eye(4) ret[:3, :3] = mat[:3, :3].T ret[:3, 3] = -mat[:3, :3].T @ mat[:3, 3] - return ret + return ret \ No newline at end of file diff --git a/teleop/robot_control/joint_smooth.py b/teleop/utils/weighted_moving_filter.py similarity index 90% rename from teleop/robot_control/joint_smooth.py rename to teleop/utils/weighted_moving_filter.py index d8a1ae2..c0c9769 100644 --- a/teleop/robot_control/joint_smooth.py +++ b/teleop/utils/weighted_moving_filter.py @@ -24,6 +24,10 @@ class WeightedMovingFilter: def add_data(self, new_data): assert len(new_data) == self._data_size + + if len(self._data_queue) > 0 and np.array_equal(new_data, self._data_queue[-1]): + return # skip duplicate data + if len(self._data_queue) >= self._window_size: self._data_queue.pop(0) @@ -61,7 +65,7 @@ def visualize_filter_comparison(filter_params, steps): # col0 should not 2b filtered plt.subplot(len(filter_params), 2, idx * 2 + 1) - plt.plot(filtered_data[:, 0], label=f'Filtered (Window {len(weights)})') + plt.plot(filtered_data[:, 0], label=f'Filtered (Window {filter._window_size})') plt.plot(original_data[:, 0], 'r--', label='Original', alpha=0.5) plt.title(f'Joint 1 - Should not to be filtered.') plt.xlabel('Step') @@ -70,9 +74,9 @@ def visualize_filter_comparison(filter_params, steps): # col13 should 2b filtered plt.subplot(len(filter_params), 2, idx * 2 + 2) - plt.plot(filtered_data[:, 13], label=f'Filtered (Window {len(weights)})') + plt.plot(filtered_data[:, 13], label=f'Filtered (Window {filter._window_size})') plt.plot(original_data[:, 13], 'r--', label='Original', alpha=0.5) - plt.title(f'Joint 13 - Window {len(weights)}, Weights {weights}') + plt.title(f'Joint 13 - Window {filter._window_size}, Weights {weights}') plt.xlabel('Step') plt.ylabel('Value') plt.legend() diff --git a/teleop/webrtc/client.js b/teleop/webrtc/client.js deleted file mode 100644 index b4e8c6c..0000000 --- a/teleop/webrtc/client.js +++ /dev/null @@ -1,77 +0,0 @@ -var pc = null; - -function negotiate() { - pc.addTransceiver('video', { direction: 'recvonly' }); - pc.addTransceiver('audio', { direction: 'recvonly' }); - return pc.createOffer().then((offer) => { - return pc.setLocalDescription(offer); - }).then(() => { - // wait for ICE gathering to complete - return new Promise((resolve) => { - if (pc.iceGatheringState === 'complete') { - resolve(); - } else { - const checkState = () => { - if (pc.iceGatheringState === 'complete') { - pc.removeEventListener('icegatheringstatechange', checkState); - resolve(); - } - }; - pc.addEventListener('icegatheringstatechange', checkState); - } - }); - }).then(() => { - var offer = pc.localDescription; - return fetch('/offer', { - body: JSON.stringify({ - sdp: offer.sdp, - type: offer.type, - }), - headers: { - 'Content-Type': 'application/json' - }, - method: 'POST' - }); - }).then((response) => { - return response.json(); - }).then((answer) => { - console.log(">>>", answer); - return pc.setRemoteDescription(answer); - }).catch((e) => { - alert(e); - }); -} - -function start() { - var config = { - sdpSemantics: 'unified-plan' - }; - - if (document.getElementById('use-stun').checked) { - config.iceServers = [{ urls: ['stun:stun.l.google.com:19302'] }]; - } - - pc = new RTCPeerConnection(config); - - // connect audio / video - pc.addEventListener('track', (evt) => { - if (evt.track.kind == 'video') { - document.getElementById('video').srcObject = evt.streams[0]; - } else { - document.getElementById('audio').srcObject = evt.streams[0]; - } - }); - - document.getElementById('start').style.display = 'none'; - negotiate(); - document.getElementById('stop').style.display = 'inline-block'; -} - -function stop() { - document.getElementById('stop').style.display = 'none'; - - // close peer connection - setTimeout(() => { - pc.close(); - }, 500); -} \ No newline at end of file diff --git a/teleop/webrtc/index.html b/teleop/webrtc/index.html deleted file mode 100644 index 159ff99..0000000 --- a/teleop/webrtc/index.html +++ /dev/null @@ -1,43 +0,0 @@ - - - - - - WebRTC webcam - - - - -
- - -
- - - -
-

Media

- - - -
- - - - \ No newline at end of file diff --git a/teleop/webrtc/orig_webcam_example.py b/teleop/webrtc/orig_webcam_example.py deleted file mode 100644 index a486be1..0000000 --- a/teleop/webrtc/orig_webcam_example.py +++ /dev/null @@ -1,164 +0,0 @@ -import argparse -import asyncio -import json -import logging -import os -import platform -import ssl - -from aiohttp import web -from aiortc import RTCPeerConnection, RTCSessionDescription -from aiortc.contrib.media import MediaPlayer, MediaRelay -from aiortc.rtcrtpsender import RTCRtpSender - -ROOT = os.path.dirname(__file__) - - -relay = None -webcam = None - - -def create_local_tracks(play_from, decode): - global relay, webcam - - if play_from: - player = MediaPlayer(play_from, decode=decode) - return player.audio, player.video - else: - options = {"framerate": "30", "video_size": "640x480"} - if relay is None: - if platform.system() == "Darwin": - webcam = MediaPlayer( - "default:none", format="avfoundation", options=options - ) - elif platform.system() == "Windows": - webcam = MediaPlayer( - "video=Integrated Camera", format="dshow", options=options - ) - else: - webcam = MediaPlayer("/dev/video0", format="v4l2", options=options) - relay = MediaRelay() - return None, relay.subscribe(webcam.video) - - -def force_codec(pc, sender, forced_codec): - kind = forced_codec.split("/")[0] - codecs = RTCRtpSender.getCapabilities(kind).codecs - transceiver = next(t for t in pc.getTransceivers() if t.sender == sender) - transceiver.setCodecPreferences( - [codec for codec in codecs if codec.mimeType == forced_codec] - ) - - -async def index(request): - content = open(os.path.join(ROOT, "index.html"), "r").read() - return web.Response(content_type="text/html", text=content) - - -async def javascript(request): - content = open(os.path.join(ROOT, "client.js"), "r").read() - return web.Response(content_type="application/javascript", text=content) - - -async def offer(request): - params = await request.json() - offer = RTCSessionDescription(sdp=params["sdp"], type=params["type"]) - - pc = RTCPeerConnection() - pcs.add(pc) - - @pc.on("connectionstatechange") - async def on_connectionstatechange(): - print("Connection state is %s" % pc.connectionState) - if pc.connectionState == "failed": - await pc.close() - pcs.discard(pc) - - # open media source - audio, video = create_local_tracks( - args.play_from, decode=not args.play_without_decoding - ) - - if audio: - audio_sender = pc.addTrack(audio) - if args.audio_codec: - force_codec(pc, audio_sender, args.audio_codec) - elif args.play_without_decoding: - raise Exception("You must specify the audio codec using --audio-codec") - - if video: - video_sender = pc.addTrack(video) - if args.video_codec: - force_codec(pc, video_sender, args.video_codec) - elif args.play_without_decoding: - raise Exception("You must specify the video codec using --video-codec") - - await pc.setRemoteDescription(offer) - - answer = await pc.createAnswer() - await pc.setLocalDescription(answer) - - return web.Response( - content_type="application/json", - text=json.dumps( - {"sdp": pc.localDescription.sdp, "type": pc.localDescription.type} - ), - ) - - -pcs = set() - - -async def on_shutdown(app): - # close peer connections - coros = [pc.close() for pc in pcs] - await asyncio.gather(*coros) - pcs.clear() - - -if __name__ == "__main__": - parser = argparse.ArgumentParser(description="WebRTC webcam demo") - parser.add_argument("--cert-file", help="SSL certificate file (for HTTPS)") - parser.add_argument("--key-file", help="SSL key file (for HTTPS)") - parser.add_argument("--play-from", help="Read the media from a file and sent it.") - parser.add_argument( - "--play-without-decoding", - help=( - "Read the media without decoding it (experimental). " - "For now it only works with an MPEGTS container with only H.264 video." - ), - action="store_true", - ) - parser.add_argument( - "--host", default="0.0.0.0", help="Host for HTTP server (default: 0.0.0.0)" - ) - parser.add_argument( - "--port", type=int, default=8080, help="Port for HTTP server (default: 8080)" - ) - parser.add_argument("--verbose", "-v", action="count") - parser.add_argument( - "--audio-codec", help="Force a specific audio codec (e.g. audio/opus)" - ) - parser.add_argument( - "--video-codec", help="Force a specific video codec (e.g. video/H264)" - ) - - args = parser.parse_args() - - if args.verbose: - logging.basicConfig(level=logging.DEBUG) - else: - logging.basicConfig(level=logging.INFO) - - if args.cert_file: - ssl_context = ssl.SSLContext() - ssl_context.load_cert_chain(args.cert_file, args.key_file) - else: - ssl_context = None - - app = web.Application() - app.on_shutdown.append(on_shutdown) - app.router.add_get("/", index) - app.router.add_get("/client.js", javascript) - app.router.add_post("/offer", offer) - web.run_app(app, host=args.host, port=args.port, ssl_context=ssl_context) \ No newline at end of file diff --git a/teleop/webrtc/webcam.py b/teleop/webrtc/webcam.py deleted file mode 100644 index b95c0f6..0000000 --- a/teleop/webrtc/webcam.py +++ /dev/null @@ -1,206 +0,0 @@ -import argparse -import asyncio -import json -import logging -import os -import ssl - -import cv2 -import numpy as np -import time -from pyzed import sl -from aiortc import MediaStreamTrack -import av - -from aiohttp import web -from aiortc import RTCPeerConnection, RTCSessionDescription -from aiortc.rtcrtpsender import RTCRtpSender - -ROOT = os.path.dirname(__file__) - - -# relay = None -# webcam = None - -left_mat = sl.Mat() -right_mat = sl.Mat() - -class VideoTransformTrack(MediaStreamTrack): - kind = "video" - - def __init__(self, camera): - super().__init__() - self.camera = camera - self.runtime_parameters = sl.RuntimeParameters() - self._direction = "sendonly" - self.start_time = time.time() - self.timescale = 1000 # Use a timescale of 1000 for milliseconds - self.frame_index = 0 - - async def recv(self): - frame = await self.get_frame() - if frame is not None: - # Timestamp is calculated based on the frame index and the frame rate of the video - timestamp = int((time.time() - self.start_time) * self.timescale) - # Create an aiortc VideoFrame, setting timestamp and time_base accordingly - video_frame = av.VideoFrame.from_ndarray(frame, format="bgr24") - video_frame.pts = timestamp - video_frame.time_base = self.timescale - return video_frame - else: - return None - - async def get_frame(self): - if self.camera.grab(self.runtime_parameters) == sl.ERROR_CODE.SUCCESS: - self.camera.retrieve_image(left_mat, sl.VIEW.LEFT) - self.camera.retrieve_image(right_mat, sl.VIEW.RIGHT) - # Convert to BGR format as required by aiortc - bgra = np.vstack((left_mat.numpy(), right_mat.numpy())) - frame = cv2.cvtColor(bgra, cv2.COLOR_BGRA2BGR) - return frame - else: - return None - - -def create_local_tracks(play_from, decode): - camera = sl.Camera() - init_params = sl.InitParameters(camera_resolution=sl.RESOLUTION.HD720, camera_fps=30) - if camera.open(init_params) == sl.ERROR_CODE.SUCCESS: - print("ZED Camera successfully opened.") - return None, VideoTransformTrack(camera) # Assuming you only want video track - else: - print("Failed to open ZED camera") - return None, None - - -def force_codec(pc, sender, forced_codec): - kind = forced_codec.split("/")[0] - codecs = RTCRtpSender.getCapabilities(kind).codecs - transceiver = next(t for t in pc.getTransceivers() if t.sender == sender) - transceiver.setCodecPreferences( - [codec for codec in codecs if codec.mimeType == forced_codec] - ) - - -async def index(request): - content = open(os.path.join(ROOT, "index.html"), "r").read() - return web.Response(content_type="text/html", text=content) - - -async def javascript(request): - content = open(os.path.join(ROOT, "client.js"), "r").read() - return web.Response(content_type="application/javascript", text=content) - - -async def offer(request): - params = await request.json() - offer = RTCSessionDescription(sdp=params["sdp"], type=params["type"]) - - pc = RTCPeerConnection() - pcs.add(pc) - - @pc.on('icecandidate') - async def on_ice_candidate(event): - if event.candidate: - if event.candidate.type == 'host': - # Handle or signal only the host candidate - print(f"Host candidate: {event.candidate}") - else: - # Ignore non-host candidates - pass - - @pc.on("connectionstatechange") - async def on_connectionstatechange(): - print("Connection state is %s" % pc.connectionState) - if pc.connectionState == "failed": - await pc.close() - pcs.discard(pc) - - # open media source - audio, video = create_local_tracks( - args.play_from, decode=not args.play_without_decoding - ) - - if audio: - audio_sender = pc.addTrack(audio) - if args.audio_codec: - force_codec(pc, audio_sender, args.audio_codec) - elif args.play_without_decoding: - raise Exception("You must specify the audio codec using --audio-codec") - - if video: - video_sender = pc.addTrack(video) - if args.video_codec: - force_codec(pc, video_sender, args.video_codec) - elif args.play_without_decoding: - raise Exception("You must specify the video codec using --video-codec") - - await pc.setRemoteDescription(offer) - - answer = await pc.createAnswer() - await pc.setLocalDescription(answer) - - return web.Response( - content_type="application/json", - text=json.dumps( - {"sdp": pc.localDescription.sdp, "type": pc.localDescription.type} - ), - ) - - -pcs = set() - - -async def on_shutdown(app): - # close peer connections - coros = [pc.close() for pc in pcs] - await asyncio.gather(*coros) - pcs.clear() - - -if __name__ == "__main__": - parser = argparse.ArgumentParser(description="WebRTC webcam demo") - parser.add_argument("--cert-file", help="SSL certificate file (for HTTPS)") - parser.add_argument("--key-file", help="SSL key file (for HTTPS)") - parser.add_argument("--play-from", help="Read the media from a file and sent it.") - parser.add_argument( - "--play-without-decoding", - help=( - "Read the media without decoding it (experimental). " - "For now it only works with an MPEGTS container with only H.264 video." - ), - action="store_true", - ) - parser.add_argument( - "--host", default="0.0.0.0", help="Host for HTTP server (default: 0.0.0.0)" - ) - parser.add_argument( - "--port", type=int, default=8080, help="Port for HTTP server (default: 8080)" - ) - parser.add_argument("--verbose", "-v", action="count") - parser.add_argument( - "--audio-codec", help="Force a specific audio codec (e.g. audio/opus)" - ) - parser.add_argument( - "--video-codec", help="Force a specific video codec (e.g. video/H264)" - ) - - args = parser.parse_args() - - if args.verbose: - logging.basicConfig(level=logging.DEBUG) - else: - logging.basicConfig(level=logging.INFO) - - if args.cert_file: - ssl_context = ssl.SSLContext() - ssl_context.load_cert_chain(args.cert_file, args.key_file) - else: - ssl_context = None - - app = web.Application() - app.on_shutdown.append(on_shutdown) - app.router.add_get("/", index) - app.router.add_get("/client.js", javascript) - app.router.add_post("/offer", offer) - web.run_app(app, host=args.host, port=args.port, ssl_context=ssl_context) \ No newline at end of file diff --git a/teleop/webrtc/webcam_server.py b/teleop/webrtc/webcam_server.py deleted file mode 100644 index 8b5c49c..0000000 --- a/teleop/webrtc/webcam_server.py +++ /dev/null @@ -1,206 +0,0 @@ -import asyncio -import json -import logging -import os -import platform -import ssl - -import aiohttp_cors -from aiohttp import web -from aiortc import RTCPeerConnection, RTCSessionDescription -from aiortc.contrib.media import MediaPlayer, MediaRelay -from aiortc.rtcrtpsender import RTCRtpSender -from multiprocessing import Process, Array, Value, shared_memory - -ROOT = os.path.dirname(__file__) - -relay = None -webcam = None - - -from aiortc import MediaStreamTrack -from av import VideoFrame -import numpy as np -import time - -class ZedVideoTrack(MediaStreamTrack): - - kind = "video" - - def __init__(self, img_shape, shm_name, fps): - super().__init__() # Initialize base class - self.img_shape = (2*img_shape[0], img_shape[1], 3) - self.img_height, self.img_width = img_shape[:2] - existing_shm = shared_memory.SharedMemory(name=shm_name) - self.img_array = np.ndarray((self.img_shape[0], self.img_shape[1], 3), dtype=np.uint8, buffer=existing_shm.buf) - - self.frame_interval = 1 / fps - self._last_frame_time = time.time() - - async def recv(self): - """ - This method is called when a new frame is needed. - """ - now = time.time() - wait_time = self._last_frame_time + self.frame_interval - now - if wait_time > 0: - await asyncio.sleep(wait_time) - self._last_frame_time = time.time() - - frame = self.img_array # Assuming this is an async function to fetch a frame - av_frame = VideoFrame.from_ndarray(frame, format='bgr24') # Convert numpy array to AVFrame - av_frame.pts = None - av_frame.time_base = None - return av_frame - -def create_local_tracks(play_from, decode): - global relay, webcam - - if play_from: - player = MediaPlayer(play_from, decode=decode) - return player.audio, player.video - else: - options = {"framerate": "30", "video_size": "640x480"} - if relay is None: - if platform.system() == "Darwin": - webcam = MediaPlayer( - "default:none", format="avfoundation", options=options - ) - elif platform.system() == "Windows": - webcam = MediaPlayer( - "video=Integrated Camera", format="dshow", options=options - ) - else: - webcam = MediaPlayer("/dev/video0", format=None, options=options) - relay = MediaRelay() - return None, relay.subscribe(webcam.video) - - -def force_codec(pc, sender, forced_codec): - kind = forced_codec.split("/")[0] - codecs = RTCRtpSender.getCapabilities(kind).codecs - transceiver = next(t for t in pc.getTransceivers() if t.sender == sender) - transceiver.setCodecPreferences( - [codec for codec in codecs if codec.mimeType == forced_codec] - ) - - -async def index(request): - content = open(os.path.join(ROOT, "index.html"), "r").read() - return web.Response(content_type="text/html", text=content) - - -async def javascript(request): - content = open(os.path.join(ROOT, "client.js"), "r").read() - return web.Response(content_type="application/javascript", text=content) - - -async def offer(request): - params = await request.json() - offer = RTCSessionDescription(sdp=params["sdp"], type=params["type"]) - - pc = RTCPeerConnection() - pcs.add(pc) - - @pc.on("connectionstatechange") - async def on_connectionstatechange(): - print("Connection state is %s" % pc.connectionState) - if pc.connectionState == "failed": - await pc.close() - pcs.discard(pc) - - # open media source - audio, video = create_local_tracks( - Args.play_from, decode=not Args.play_without_decoding - ) - - if audio: - audio_sender = pc.addTrack(audio) - if Args.audio_codec: - force_codec(pc, audio_sender, Args.audio_codec) - elif Args.play_without_decoding: - raise Exception("You must specify the audio codec using --audio-codec") - - if video: - video_sender = pc.addTrack(video) - if Args.video_codec: - force_codec(pc, video_sender, Args.video_codec) - elif Args.play_without_decoding: - raise Exception("You must specify the video codec using --video-codec") - - await pc.setRemoteDescription(offer) - - answer = await pc.createAnswer() - await pc.setLocalDescription(answer) - - return web.Response( - content_type="application/json", - text=json.dumps( - {"sdp": pc.localDescription.sdp, "type": pc.localDescription.type} - ), - ) - - -pcs = set() - - -async def on_shutdown(app): - # close peer connections - coros = [pc.close() for pc in pcs] - await asyncio.gather(*coros) - pcs.clear() - - -from params_proto import ParamsProto, Proto, Flag - - -class Args(ParamsProto): - description = "WebRTC webcam demo" - cert_file = Proto(help="SSL certificate file (for HTTPS)") - key_file = Proto(help="SSL key file (for HTTPS)") - - host = Proto(default="0.0.0.0", help="Host for HTTP server (default: 0.0.0.0)") - port = Proto(default=8080, dtype=int, help="Port for HTTP server (default: 8080)") - - play_from = Proto(help="Read the media from a file and send it.") - play_without_decoding = Flag( - "Read the media without decoding it (experimental). " - "For now it only works with an MPEGTS container with only H.264 video." - ) - - audio_codec = Proto(help="Force a specific audio codec (e.g. audio/opus)") - video_codec = Proto(help="Force a specific video codec (e.g. video/H264)") - - verbose = Flag() - - -if __name__ == '__main__': - - if Args.verbose: - logging.basicConfig(level=logging.DEBUG) - else: - logging.basicConfig(level=logging.INFO) - - if Args.cert_file: - print("Using SSL certificate file: %s" % Args.cert_file) - ssl_context = ssl.SSLContext() - ssl_context.load_cert_chain(Args.cert_file, Args.key_file) - else: - ssl_context = None - - app = web.Application() - cors = aiohttp_cors.setup(app, defaults={ - "*": aiohttp_cors.ResourceOptions( - allow_credentials=True, - expose_headers="*", - allow_headers="*", - allow_methods="*", - ) - }) - - app.on_shutdown.append(on_shutdown) - cors.add(app.router.add_get("/", index)) - cors.add(app.router.add_get("/client.js", javascript)) - cors.add(app.router.add_post("/offer", offer)) - - web.run_app(app, host=Args.host, port=Args.port, ssl_context=ssl_context) \ No newline at end of file diff --git a/teleop/webrtc/zed_server.py b/teleop/webrtc/zed_server.py deleted file mode 100644 index b036168..0000000 --- a/teleop/webrtc/zed_server.py +++ /dev/null @@ -1,195 +0,0 @@ -import asyncio -import json -import logging -import os -import platform -import ssl - -import aiohttp_cors -from aiohttp import web -from aiortc import RTCPeerConnection, RTCSessionDescription -from aiortc.contrib.media import MediaPlayer, MediaRelay -from aiortc.rtcrtpsender import RTCRtpSender -from multiprocessing import Process, Array, Value, shared_memory - -ROOT = os.path.dirname(__file__) - -relay = None -webcam = None - - -from aiortc import MediaStreamTrack -from av import VideoFrame -import numpy as np -import time - -class ZedVideoTrack(MediaStreamTrack): - kind = "video" - def __init__(self, queue, toggle_streaming, fps): - super().__init__() # Initialize base class - # self.img_shape = (2*img_shape[0], img_shape[1], 3) - # self.img_height, self.img_width = img_shape[:2] - # self.shm_name = shm_name - # existing_shm = shared_memory.SharedMemory(name=shm_name) - # self.img_array = np.ndarray((self.img_shape[0], self.img_shape[1], 3), dtype=np.uint8, buffer=existing_shm.buf) - self.img_queue = queue - self.toggle_streaming = toggle_streaming - self.streaming_started = False - self.timescale = 1000 # Use a timescale of 1000 for milliseconds - # self.frame_interval = 1 / fps - self._last_frame_time = time.time() - self.start_time = time.time() - - async def recv(self): - """ - This method is called when a new frame is needed. - """ - # now = time.time() - # wait_time = self._last_frame_time + self.frame_interval - now - # if wait_time > 0: - # await asyncio.sleep(wait_time) - # self._last_frame_time = time.time() - # start = time.time() - if not self.streaming_started: - self.toggle_streaming.set() - self.streaming_started = True - frame = self.img_queue.get() - # self.sem.release() - # print("Time to get frame: ", time.time() - start, self.img_queue.qsize()) - # frame = self.img_array.copy() # Assuming this is an async function to fetch a frame - # frame = np.random.randint(0, 256, size=(480, 640, 3), dtype=np.uint8) - # print("recv") - # start = time.time() - av_frame = VideoFrame.from_ndarray(frame, format='rgb24') # Convert numpy array to AVFrame - timestamp = int((time.time() - self.start_time) * self.timescale) - av_frame.pts = timestamp - av_frame.time_base = self.timescale - # print("Time to process frame: ", time.time() - start) - return av_frame - - -def force_codec(pc, sender, forced_codec): - kind = forced_codec.split("/")[0] - codecs = RTCRtpSender.getCapabilities(kind).codecs - transceiver = next(t for t in pc.getTransceivers() if t.sender == sender) - transceiver.setCodecPreferences( - [codec for codec in codecs if codec.mimeType == forced_codec] - ) - - -async def index(request): - content = open(os.path.join(ROOT, "index.html"), "r").read() - return web.Response(content_type="text/html", text=content) - - -async def javascript(request): - content = open(os.path.join(ROOT, "client.js"), "r").read() - return web.Response(content_type="application/javascript", text=content) - -class RTC(): - def __init__(self, img_shape, img_queue, toggle_streaming, fps) -> None: - self.img_shape = img_shape - self.img_queue = img_queue - self.fps = fps - self.toggle_streaming = toggle_streaming - - async def offer(self, request): - params = await request.json() - offer = RTCSessionDescription(sdp=params["sdp"], type=params["type"]) - - pc = RTCPeerConnection() - pcs.add(pc) - - @pc.on("connectionstatechange") - async def on_connectionstatechange(): - print("Connection state is %s" % pc.connectionState) - if pc.connectionState == "failed": - await pc.close() - pcs.discard(pc) - - # open media source - zed_track = ZedVideoTrack(self.img_queue, self.toggle_streaming, self.fps) - video_sender = pc.addTrack(zed_track) - # if Args.video_codec: - force_codec(pc, video_sender, "video/H264") - # elif Args.play_without_decoding: - # raise Exception("You must specify the video codec using --video-codec") - - await pc.setRemoteDescription(offer) - - answer = await pc.createAnswer() - await pc.setLocalDescription(answer) - - return web.Response( - content_type="application/json", - text=json.dumps( - {"sdp": pc.localDescription.sdp, "type": pc.localDescription.type} - ), - ) - - -pcs = set() - -async def on_shutdown(app): - # close peer connections - coros = [pc.close() for pc in pcs] - await asyncio.gather(*coros) - pcs.clear() - - -from params_proto import ParamsProto, Proto, Flag - - -class Args(ParamsProto): - description = "WebRTC webcam demo" - cert_file = Proto(help="SSL certificate file (for HTTPS)") - key_file = Proto(help="SSL key file (for HTTPS)") - - host = Proto(default="0.0.0.0", help="Host for HTTP server (default: 0.0.0.0)") - port = Proto(default=8080, dtype=int, help="Port for HTTP server (default: 8080)") - - play_from = Proto(help="Read the media from a file and send it.") - play_without_decoding = Flag( - "Read the media without decoding it (experimental). " - "For now it only works with an MPEGTS container with only H.264 video." - ) - - audio_codec = Proto(help="Force a specific audio codec (e.g. audio/opus)") - video_codec = Proto(help="Force a specific video codec (e.g. video/H264)") - img_shape = Proto(help="") - shm_name = Proto(help="") - fps = Proto(help="") - - verbose = Flag() - - -if __name__ == '__main__': - - if Args.verbose: - logging.basicConfig(level=logging.DEBUG) - else: - logging.basicConfig(level=logging.INFO) - - if Args.cert_file: - print("Using SSL certificate file: %s" % Args.cert_file) - ssl_context = ssl.SSLContext() - ssl_context.load_cert_chain(Args.cert_file, Args.key_file) - else: - ssl_context = None - - app = web.Application() - cors = aiohttp_cors.setup(app, defaults={ - "*": aiohttp_cors.ResourceOptions( - allow_credentials=True, - expose_headers="*", - allow_headers="*", - allow_methods="*", - ) - }) - rtc = RTC((960, 640), queue) - app.on_shutdown.append(on_shutdown) - cors.add(app.router.add_get("/", index)) - cors.add(app.router.add_get("/client.js", javascript)) - cors.add(app.router.add_post("/offer", rtc.offer)) - - web.run_app(app, host=Args.host, port=Args.port, ssl_context=ssl_context) \ No newline at end of file