[release] major version update for g1 : code refactoring and optimization, etc

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
 
 ------------------

README.md

@@ -1,13 +1,38 @@
 
-# Video Demo
+# 📺 Video Demo
 <img src="./img/1.webp" autoplay loop="loop" style="width: 49%" controls></img><img src="./img/2.webp" autoplay loop="loop" style="width: 49%" controls></img>
 
-# Introduction
-This repository implements teleoperation of the humanoid robot Unitree H1_2 using Apple Vision Pro.
-
-
-
-# Prerequisites
+# 0. 📖 Introduction
+This repository implements teleoperation of the **Unitree humanoid robot** using **Apple Vision Pro**.
+
+Here are the robots that will be supported,
+
+<table>
+  <tr>
+    <th style="text-align: center;"> &#129302; Robot </th>
+    <th style="text-align: center;"> &#9898; Status </th>
+  </tr>
+  <tr>
+    <td style="text-align: center;"> G1(29DoF) + Dex3-1 </td>
+    <td style="text-align: center;"> &#9989; Completed </td>
+  </tr>
+  <tr>
+    <td style="text-align: center;"> G1(23DoF) </td>
+    <td style="text-align: center;"> &#9201; In Progress </td>
+  </tr>
+  <tr>
+    <td style="text-align: center;"> H1(Arm 4DoF) </td>
+    <td style="text-align: center;"> &#9201; In Progress </td>
+  </tr>
+  <tr>
+    <td style="text-align: center;"> H1_2(Arm 7DoF) + Inspire </td>
+    <td style="text-align: center;"> &#9201; In Progress </td>
+  </tr>
+</table>
+
+
+
+# 1. 📦 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 🕹️ 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. 🛠️ TeleVision and Apple Vision Pro configuration
+
+## 2.1 📥 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 🔌 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.
+
+## 2.3 🔎 Test environment
+
+This step is to verify that the environment is installed correctly.
+
+1. Download Isaac Gym: https://developer.nvidia.com/isaac-gym/download
 
-6. open the browser on Safari on VisionPro and go to https://192.168.123.2:8012?ws=wss://192.168.123.2:8012
+    Extracting to the current directory, go to the `IsaacGym_Preview_4_Package/isaacgym/python` directory and execute the command:
 
-7. Click `Enter VR` and `Allow` to start the VR session.
+    ```bash
+    pip install -e .
+    ```
 
-### 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:
+2. After setup up streaming with local following the above instructions, you can try teleoperating two robot hands in Issac Gym:
 
     ```bash
-    cd teleop && python teleop_hand.py
+    cd teleop
+    python teleop_test_gym.py
     ```
 
-2. Go to your vuer site on VisionPro, click `Enter VR` and `Allow` to enter immersive environment.
+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.
 
-3. See your hands in 3D!
+6. See your hands in 3D!
 
 <div style="center">
   <img src="https://oss-global-cdn.unitree.com/static/d079e884c3074e8495f81e78c9586f7f_2556x1179.jpg" width="50%">
 </div>
 
 
+# 3. 🚀 Usage
 
+Please read the  [Official Documentation ](https://support.unitree.com/home/zh/Teleoperation) at least once before starting this program.
 
 
-<!-- <p style="text-align: center;">
-  <img src="https://doc-cdn.unitree.com/static/2024/7/25/4b1b2327d4774abfbe8ef1c084d81cd7_2686x1627.png"  style="display: block; margin: auto; width: 30%;">
-</p> -->
+## 3.1 🖼️ 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
+```
 
-# Usage
+After image service is started, you can use `image_client.py` **in the Host** terminal to test whether the communication is successful:
 
-Please read the  [Official Documentation ](https://support.unitree.com/home/zh/Teleoperation) at least once before starting this program.
+```bash
+python image_client.py
+```
+
+## 3.2 ✋ 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 🚀 Start
 
-## Image Server
+> ![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.
 
-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**:
+It's best to have two operators to run this program, referred to as **Operator A** and **Operator B**.
+
+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.
 
-# Codebase Tutorial
+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.
 
-The overall structure of the code remains the same as TeleVision, and we only focus on the modified file directories related to Unitree Robot.
+## 3.4 🔚 Exit
 
+To exit the program, **Operator B** can press the **q** key in the 'record image'  window.  
+
+>  ![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.
+
+
+
+# 4. 🗺️ Codebase Tutorial
+
+```
 avp_teleoperate/
 │
-    ├── act                       [Documents Related to ACT Policy for Imitation Learning]
-    │
 ├── assets                    [Storage of robot URDF-related files]
 │
-    ├── scripts
-    │
 ├── teleop
-    │   ├── image_server/         [Image Transfer Server and Client Code]
-    │   │     ├── image_client.py [Client (only used to test whether the image stream service is OK, not used for teleoperation)]
-    │   │     ├── image_server.py [Capture images from binocular cameras and send via network (performed on Unitree H1_2)]
+│   ├── image_server
+│   │     ├── image_client.py [Used to receive image data from the robot image server]
+│   │     ├── image_server.py [Capture images from cameras and send via network (Running on robot's on-board computer)]
+│   │
+│   ├── open_television
+│   │      ├── television.py    [Using Vuer to capture wrist and hand data from apple vision pro]  
+│   │      ├── tv_wrapper.py    [Post-processing of captured data]
 │   │
-    │   ├── robot_control/          [Storage of IK solver, arm and hand control related documents]
+│   ├── robot_control
 │   │      ├── robot_arm_ik.py        [Inverse kinematics of the arm]  
 │   │      ├── robot_arm.py           [Control dual arm joints and lock the others]
-    │   │      ├── robot_hand.py    [Control hand joints]
+│   │      ├── robot_hand_inspire.py  [Control inspire hand joints]
+│   │      ├── robot_hand_unitree.py  [Control unitree hand joints]
+│   │
+│   ├── utils
+│   │      ├── episode_writer.py          [Used to record data for imitation learning]  
+│   │      ├── mat_tool.py                [Some small math tools]
+│   │      ├── weighted_moving_filter.py  [For filtering joint data]
 │   │
 │   │──teleop_hand_and_arm.py   [Startup execution code for teleoperation]
-    |   |——teleop_hand.py           [Can be used for testing the environment configuration]
+|   |——teleop_test_gym.py       [Can be used to verify that the environment is installed correctly]
+```
 
 
 
-# Acknowledgement
+# 5. 🙏 Acknowledgement
 
 This code builds upon following open-source code-bases. Please visit the URLs to see the respective LICENSES:
 
@@ -215,3 +287,4 @@ 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

act/LICENSE

@@ -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.

act/README.md

@@ -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}
-    }

act/conda_env.yaml

@@ -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

act/detr/LICENSE

@@ -1,201 +0,0 @@
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act/detr/README.md

@@ -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}
-    }

act/detr/main.py

@@ -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
-

act/detr/models/__init__.py

@@ -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)

act/detr/models/backbone.py

@@ -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

act/detr/models/detr_vae.py

@@ -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
-

act/detr/models/position_encoding.py

@@ -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

act/detr/models/transformer.py

@@ -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}.")

act/detr/setup.py

@@ -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(),
-)

act/detr/util/__init__.py

@@ -1 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved

act/detr/util/box_ops.py

@@ -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)

act/detr/util/misc.py

@@ -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)

act/detr/util/plot_utils.py

@@ -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

act/imitate_episodes.py

@@ -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)

act/policy.py

@@ -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

act/utils.py

@@ -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 base_path.exists() or not base_path.is_dir():
-        raise ValueError(f"The provided base directory does not exist or is not a directory: \n{base_path}")
-
-    # Loop through all subdirectories of the base path
-    for subfolder in base_path.iterdir():
-        if subfolder.is_dir() and subfolder.name.startswith(prefix):
-            return str(subfolder), subfolder.name
-    
-    # If no matching subfolder is found
-    return None, None
-
-def find_all_ckpt(base_dir, prefix="policy_epoch_"):
-    base_path = Path(base_dir)
-    # Ensure the base path exists and is a directory
-    if not base_path.exists() or not base_path.is_dir():
-        raise ValueError("The provided base directory does not exist or is not a directory.")
-
-    ckpt_files = []
-    for file in base_path.iterdir():
-        if file.is_file() and file.name.startswith(prefix):
-            ckpt_files.append(file.name)
-    # find latest ckpt
-    ckpt_files = sorted(ckpt_files, key=lambda x: int(x.split(prefix)[-1].split('_')[0]), reverse=True)
-    epoch = int(ckpt_files[0].split(prefix)[-1].split('_')[0])
-    return ckpt_files[0], epoch

assets/inspire_hand/inspire_hand_left.urdf

@@ -0,0 +1,819 @@
+<robot
+  name="h1">
+
+    <link name="L_hand_base_link">
+      <inertial>
+        <origin
+          xyz="-0.002551 -0.066047 -0.0019357"
+          rpy="0 0 0" />
+        <mass
+          value="0.14143" />
+        <inertia
+          ixx="0.0001234"
+          ixy="2.1995E-06"
+          ixz="-1.7694E-06"
+          iyy="8.3835E-05"
+          iyz="1.5968E-06"
+          izz="7.7231E-05" />
+      </inertial>
+      <visual>
+        <origin
+          xyz="0 0 0"
+          rpy="0 0 0" />
+        <geometry>
+          <mesh filename="./meshes/L_hand_base_link.STL" />
+        </geometry>
+        <material name="">
+          <color rgba="0.1 0.1 0.1 1" />
+        </material>
+      </visual>
+      <collision>
+        <origin
+          xyz="0 0 0"
+          rpy="0 0 0" />
+        <geometry>
+          <mesh filename="./meshes/L_hand_base_link.STL" />
+        </geometry>
+      </collision>
+    </link>
+
+    <link name="L_thumb_proximal_base">
+      <inertial>
+        <origin
+          xyz="0.0048817 0.00038782 -0.00722"
+          rpy="0 0 0" />
+        <mass
+          value="0.0018869" />
+        <inertia
+          ixx="5.5158E-08"
+          ixy="-1.1803E-08"
+          ixz="-4.6743E-09"
+          iyy="8.2164E-08"
+          iyz="-1.3521E-09"
+          izz="6.7434E-08" />
+      </inertial>
+      <visual>
+        <origin
+          xyz="0 0 0"
+          rpy="0 0 0" />
+        <geometry>
+          <mesh filename="./meshes/Link11_L.STL" />
+        </geometry>
+        <material name="">
+          <color rgba="0.1 0.1 0.1 1" />
+        </material>
+      </visual>
+      <collision>
+        <origin
+          xyz="0 0 0"
+          rpy="0 0 0" />
+        <geometry>
+          <mesh filename="./meshes/Link11_L.STL" />
+        </geometry>
+      </collision>
+    </link>
+
+    <joint
+      name="L_thumb_proximal_yaw_joint"
+      type="revolute">
+      <origin
+        xyz="-0.01696 -0.0691 0.02045"
+        rpy="1.5708 -1.5708 0" />
+      <parent
+        link="L_hand_base_link" />
+      <child
+        link="L_thumb_proximal_base" />
+      <axis
+        xyz="0 0 1" />
+      <limit
+        lower="-0.1"
+        upper="1.3"
+        effort="1"
+        velocity="0.5" />
+    </joint>
+
+    <link name="L_thumb_proximal">
+      <inertial>
+        <origin
+          xyz="0.021936 -0.01279 -0.0080386"
+          rpy="0 0 0" />
+        <mass
+          value="0.0066101" />
+        <inertia
+          ixx="1.5693E-06"
+          ixy="7.8339E-07"
+          ixz="8.5959E-10"
+          iyy="1.7356E-06"
+          iyz="1.0378E-09"
+          izz="2.787E-06" />
+      </inertial>
+      <visual>
+        <origin
+          xyz="0 0 0"
+          rpy="0 0 0" />
+        <geometry>
+          <mesh filename="./meshes/Link12_L.STL" />
+        </geometry>
+        <material name="">
+          <color rgba="0.1 0.1 0.1 1" />
+        </material>
+      </visual>
+      <collision>
+        <origin
+          xyz="0 0 0"
+          rpy="0 0 0" />
+        <geometry>
+          <mesh filename="./meshes/Link12_L.STL" />
+        </geometry>
+      </collision>
+    </link>
+
+    <joint
+      name="L_thumb_proximal_pitch_joint"
+      type="revolute">
+      <origin
+        xyz="0.0099867 0.0098242 -0.0089"
+        rpy="-1.5708 0 0.16939" />
+      <parent
+        link="L_thumb_proximal_base" />
+      <child
+        link="L_thumb_proximal" />
+      <axis
+        xyz="0 0 -1" />
+      <limit
+        lower="0.0"
+        upper="0.5"
+        effort="1"
+        velocity="0.5" />
+    </joint>
+
+    <link name="L_thumb_intermediate">
+      <inertial>
+        <origin
+          xyz="0.0095531 0.0016282 -0.0072002"
+          rpy="0 0 0" />
+        <mass
+          value="0.0037844" />
+        <inertia
+          ixx="3.6981E-07"
+          ixy="9.8603E-08"
+          ixz="-2.8173E-12"
+          iyy="3.2395E-07"
+          iyz="-2.8028E-12"
+          izz="4.6532E-07" />
+      </inertial>
+      <visual>
+        <origin
+          xyz="0 0 0"
+          rpy="0 0 0" />
+        <geometry>
+          <mesh filename="./meshes/Link13_L.STL" />
+        </geometry>
+        <material name="">
+          <color rgba="0.1 0.1 0.1 1" />
+        </material>
+      </visual>
+      <collision>
+        <origin
+          xyz="0 0 0"
+          rpy="0 0 0" />
+        <geometry>
+          <mesh filename="./meshes/Link13_L.STL" />
+        </geometry>
+      </collision>
+    </link>
+
+    <joint
+      name="L_thumb_intermediate_joint"
+      type="revolute">
+      <origin
+        xyz="0.04407 -0.034553 -0.0008"
+        rpy="0 0 0" />
+      <parent
+        link="L_thumb_proximal" />
+      <child
+        link="L_thumb_intermediate" />
+      <axis
+        xyz="0 0 -1" />
+      <limit
+        lower="0"
+        upper="0.8"
+        effort="1"
+        velocity="0.5" />
+      <mimic 
+        joint="L_thumb_proximal_pitch_joint"
+        multiplier="1.6"
+        offset="0" />
+    </joint>
+
+    <link name="L_thumb_distal">
+      <inertial>
+        <origin
+          xyz="0.0092888 -0.004953 -0.0060033"
+          rpy="0 0 0" />
+        <mass
+          value="0.003344" />
+        <inertia
+          ixx="1.3632E-07"
+          ixy="5.6787E-08"
+          ixz="-9.1939E-11"
+          iyy="1.4052E-07"
+          iyz="1.2145E-10"
+          izz="2.0026E-07" />
+      </inertial>
+      <visual>
+        <origin
+          xyz="0 0 0"
+          rpy="0 0 0" />
+        <geometry>
+          <mesh filename="./meshes/Link14_L.STL" />
+        </geometry>
+        <material name="">
+          <color rgba="0.1 0.1 0.1 1" />
+        </material>
+      </visual>
+      <collision>
+        <origin
+          xyz="0 0 0"
+          rpy="0 0 0" />
+        <geometry>
+          <mesh filename="./meshes/Link14_L.STL" />
+        </geometry>
+      </collision>
+    </link>
+
+    <joint
+      name="L_thumb_distal_joint"
+      type="revolute">
+      <origin
+        xyz="0.020248 -0.010156 -0.0012"
+        rpy="0 0 0" />
+      <parent
+        link="L_thumb_intermediate" />
+      <child
+        link="L_thumb_distal" />
+      <axis
+        xyz="0 0 -1" />
+      <limit
+        lower="0"
+        upper="1.2"
+        effort="1"
+        velocity="0.5" />
+      <mimic 
+        joint="L_thumb_proximal_pitch_joint"
+        multiplier="2.4"
+        offset="0" />
+    </joint>
+
+    <link name="L_index_proximal">
+      <inertial>
+        <origin
+          xyz="0.0012971 -0.011934 -0.0059998"
+          rpy="0 0 0" />
+        <mass
+          value="0.0042405" />
+        <inertia
+          ixx="6.6215E-07"
+          ixy="1.8442E-08"
+          ixz="1.3746E-12"
+          iyy="2.1167E-07"
+          iyz="-1.4773E-11"
+          izz="6.9402E-07" />
+      </inertial>
+      <visual>
+        <origin
+          xyz="0 0 0"
+          rpy="0 0 0" />
+        <geometry>
+          <mesh filename="./meshes/Link15_L.STL" />
+        </geometry>
+        <material name="">
+          <color rgba="0.1 0.1 0.1 1" />
+        </material>
+      </visual>
+      <collision>
+        <origin
+          xyz="0 0 0"
+          rpy="0 0 0" />
+        <geometry>
+          <mesh filename="./meshes/Link15_L.STL" />
+        </geometry>
+      </collision>
+    </link>
+
+    <link name="L_thumb_tip">
+    <visual>
+      <origin
+        xyz="0 0 0"
+        rpy="0 0 0" />
+      <geometry>
+        <sphere radius="0.005"/>
+      </geometry>
+      <material name="green">
+        <color rgba="0 1 0 1" />
+      </material>
+    </visual>
+  </link>
+  <joint name="L_thumb_tip_joint" type="fixed">
+    <parent link="L_thumb_distal"/>
+    <child link="L_thumb_tip"/>
+    <origin rpy="0 0 0" xyz="0.015 -0.013 -0.004"/>
+  </joint>
+
+    <joint
+      name="L_index_proximal_joint"
+      type="revolute">
+      <origin
+        xyz="0.00028533 -0.13653 0.032268"
+        rpy="-0.034907 0 0" />
+      <parent
+        link="L_hand_base_link" />
+      <child
+        link="L_index_proximal" />
+      <axis
+        xyz="0 0 -1" />
+      <limit
+        lower="0"
+        upper="1.7"
+        effort="1"
+        velocity="0.5" />
+    </joint>
+
+    <link name="L_index_intermediate">
+      <inertial>
+        <origin
+          xyz="0.0021753 -0.019567 -0.005"
+          rpy="0 0 0" />
+        <mass
+          value="0.0045682" />
+        <inertia
+          ixx="7.6284E-07"
+          ixy="-8.063E-08"
+          ixz="3.6797E-13"
+          iyy="9.4308E-08"
+          iyz="1.5743E-13"
+          izz="7.8176E-07" />
+      </inertial>
+      <visual>
+        <origin
+          xyz="0 0 0"
+          rpy="0 0 0" />
+        <geometry>
+          <mesh filename="./meshes/Link16_L.STL" />
+        </geometry>
+        <material name="">
+          <color rgba="0.1 0.1 0.1 1" />
+        </material>
+      </visual>
+      <collision>
+        <origin
+          xyz="0 0 0"
+          rpy="0 0 0" />
+        <geometry>
+          <mesh filename="./meshes/Link16_L.STL" />
+        </geometry>
+      </collision>
+    </link>
+
+    <joint
+      name="L_index_intermediate_joint"
+      type="revolute">
+      <origin
+        xyz="-0.0024229 -0.032041 -0.001"
+        rpy="0 0 0" />
+      <parent
+        link="L_index_proximal" />
+      <child
+        link="L_index_intermediate" />
+      <axis
+        xyz="0 0 -1" />
+      <limit
+        lower="0"
+        upper="1.7"
+        effort="1"
+        velocity="0.5" />
+      <mimic 
+        joint="L_index_proximal_joint"
+        multiplier="1"
+        offset="0" />
+    </joint>
+
+    <link name="L_index_tip">
+    <visual>
+      <origin
+        xyz="0 0 0"
+        rpy="0 0 0" />
+      <geometry>
+        <sphere radius="0.005"/>
+      </geometry>
+      <material name="green">
+        <color rgba="0 1 0 1" />
+      </material>
+    </visual>
+  </link>
+  <joint name="L_index_tip_joint" type="fixed">
+    <parent link="L_index_intermediate"/>
+    <child link="L_index_tip"/>
+    <origin rpy="0 0 0" xyz="-0.005 -0.04 -0.004"/>
+  </joint>
+
+    <link name="L_middle_proximal">
+      <inertial>
+        <origin
+          xyz="0.0012971 -0.011934 -0.0059999"
+          rpy="0 0 0" />
+        <mass
+          value="0.0042405" />
+        <inertia
+          ixx="6.6215E-07"
+          ixy="1.8442E-08"
+          ixz="1.2299E-12"
+          iyy="2.1167E-07"
+          iyz="-1.4484E-11"
+          izz="6.9402E-07" />
+      </inertial>
+      <visual>
+        <origin
+          xyz="0 0 0"
+          rpy="0 0 0" />
+        <geometry>
+          <mesh filename="./meshes/Link17_L.STL" />
+        </geometry>
+        <material name="">
+          <color rgba="0.1 0.1 0.1 1" />
+        </material>
+      </visual>
+      <collision>
+        <origin
+          xyz="0 0 0"
+          rpy="0 0 0" />
+        <geometry>
+          <mesh filename="./meshes/Link17_L.STL" />
+        </geometry>
+      </collision>
+    </link>
+
+    <joint
+      name="L_middle_proximal_joint"
+      type="revolute">
+      <origin
+        xyz="0.00028533 -0.1371 0.01295"
+        rpy="0 0 0" />
+      <parent
+        link="L_hand_base_link" />
+      <child
+        link="L_middle_proximal" />
+      <axis
+        xyz="0 0 -1" />
+      <limit
+        lower="0"
+        upper="1.7"
+        effort="1"
+        velocity="0.5" />
+    </joint>
+
+    <link name="L_middle_intermediate">
+      <inertial>
+        <origin
+          xyz="0.001921 -0.020796 -0.0049999"
+          rpy="0 0 0" />
+        <mass
+          value="0.0050397" />
+        <inertia
+          ixx="9.5823E-07"
+          ixy="-1.1425E-07"
+          ixz="-2.4186E-12"
+          iyy="1.0646E-07"
+          iyz="3.6974E-12"
+          izz="9.8385E-07" />
+      </inertial>
+      <visual>
+        <origin
+          xyz="0 0 0"
+          rpy="0 0 0" />
+        <geometry>
+          <mesh filename="./meshes/Link18_L.STL" />
+        </geometry>
+        <material name="">
+          <color rgba="0.1 0.1 0.1 1" />
+        </material>
+      </visual>
+      <collision>
+        <origin
+          xyz="0 0 0"
+          rpy="0 0 0" />
+        <geometry>
+          <mesh filename="./meshes/Link18_L.STL" />
+        </geometry>
+      </collision>
+    </link>
+    
+    <joint
+      name="L_middle_intermediate_joint"
+      type="revolute">
+      <origin
+        xyz="-0.0024229 -0.032041 -0.001"
+        rpy="0 0 0" />
+      <parent
+        link="L_middle_proximal" />
+      <child
+        link="L_middle_intermediate" />
+      <axis
+        xyz="0 0 -1" />
+      <limit
+        lower="0"
+        upper="1.7"
+        effort="1"
+        velocity="0.5" />
+      <mimic 
+        joint="L_middle_proximal_joint"
+        multiplier="1"
+        offset="0" />
+    </joint>
+
+    <link name="L_middle_tip">
+    <visual>
+      <origin
+        xyz="0 0 0"
+        rpy="0 0 0" />
+      <geometry>
+        <sphere radius="0.005"/>
+      </geometry>
+      <material name="green">
+        <color rgba="0 1 0 1" />
+      </material>
+    </visual>
+  </link>
+  <joint name="L_middle_tip_joint" type="fixed">
+    <parent link="L_middle_intermediate"/>
+    <child link="L_middle_tip"/>
+    <origin rpy="0 0 0" xyz="-0.005 -0.045 -0.004"/>
+  </joint>
+
+    <link name="L_ring_proximal">
+      <inertial>
+        <origin
+          xyz="0.0012971 -0.011934 -0.0059999"
+          rpy="0 0 0" />
+        <mass
+          value="0.0042405" />
+        <inertia
+          ixx="6.6215E-07"
+          ixy="1.8442E-08"
+          ixz="9.6052E-13"
+          iyy="2.1167E-07"
+          iyz="-1.4124E-11"
+          izz="6.9402E-07" />
+      </inertial>
+      <visual>
+        <origin
+          xyz="0 0 0"
+          rpy="0 0 0" />
+        <geometry>
+          <mesh filename="./meshes/Link19_L.STL" />
+        </geometry>
+        <material name="">
+          <color rgba="0.1 0.1 0.1 1" />
+        </material>
+      </visual>
+      <collision>
+        <origin
+          xyz="0 0 0"
+          rpy="0 0 0" />
+        <geometry>
+          <mesh filename="./meshes/Link19_L.STL" />
+        </geometry>
+      </collision>
+    </link>
+
+    <joint
+      name="L_ring_proximal_joint"
+      type="revolute">
+      <origin
+        xyz="0.00028533 -0.13691 -0.0062872"
+        rpy="0.05236 0 0" />
+      <parent
+        link="L_hand_base_link" />
+      <child
+        link="L_ring_proximal" />
+      <axis
+        xyz="0 0 -1" />
+      <limit
+        lower="0"
+        upper="1.7"
+        effort="1"
+        velocity="0.5" />
+    </joint>
+
+    <link name="L_ring_intermediate">
+      <inertial>
+        <origin
+          xyz="0.0021753 -0.019567 -0.005"
+          rpy="0 0 0" />
+        <mass
+          value="0.0045682" />
+        <inertia
+          ixx="7.6285E-07"
+          ixy="-8.0631E-08"
+          ixz="3.3472E-14"
+          iyy="9.4308E-08"
+          iyz="-4.4773E-13"
+          izz="7.8176E-07" />
+      </inertial>
+      <visual>
+        <origin
+          xyz="0 0 0"
+          rpy="0 0 0" />
+        <geometry>
+          <mesh filename="./meshes/Link20_L.STL" />
+        </geometry>
+        <material name="">
+          <color rgba="0.1 0.1 0.1 1" />
+        </material>
+      </visual>
+      <collision>
+        <origin
+          xyz="0 0 0"
+          rpy="0 0 0" />
+        <geometry>
+          <mesh filename="./meshes/Link20_L.STL" />
+        </geometry>
+      </collision>
+    </link>
+
+    <joint
+      name="L_ring_intermediate_joint"
+      type="revolute">
+      <origin
+        xyz="-0.0024229 -0.032041 -0.001"
+        rpy="0 0 0" />
+      <parent
+        link="L_ring_proximal" />
+      <child
+        link="L_ring_intermediate" />
+      <axis
+        xyz="0 0 -1" />
+      <limit
+        lower="0"
+        upper="1.7"
+        effort="1"
+        velocity="0.5" />
+      <mimic 
+        joint="L_ring_proximal_joint"
+        multiplier="1"
+        offset="0" />
+    </joint>
+
+    <link name="L_ring_tip">
+    <visual>
+      <origin
+        xyz="0 0 0"
+        rpy="0 0 0" />
+      <geometry>
+        <sphere radius="0.005"/>
+      </geometry>
+      <material name="green">
+        <color rgba="0 1 0 1" />
+      </material>
+    </visual>
+  </link>
+  <joint name="L_ring_tip_joint" type="fixed">
+    <parent link="L_ring_intermediate"/>
+    <child link="L_ring_tip"/>
+    <origin rpy="0 0 0" xyz="-0.002 -0.04 -0.004"/>
+  </joint>
+
+    <link name="L_pinky_proximal">
+      <inertial>
+        <origin
+          xyz="0.0012971 -0.011934 -0.0059999"
+          rpy="0 0 0" />
+        <mass
+          value="0.0042405" />
+        <inertia
+          ixx="6.6215E-07"
+          ixy="1.8442E-08"
+          ixz="1.0279E-12"
+          iyy="2.1167E-07"
+          iyz="-1.4277E-11"
+          izz="6.9402E-07" />
+      </inertial>
+      <visual>
+        <origin
+          xyz="0 0 0"
+          rpy="0 0 0" />
+        <geometry>
+          <mesh filename="./meshes/Link21_L.STL" />
+        </geometry>
+        <material name="">
+          <color rgba="0.1 0.1 0.1 1" />
+        </material>
+      </visual>
+      <collision>
+        <origin
+          xyz="0 0 0"
+          rpy="0 0 0" />
+        <geometry>
+          <mesh filename="./meshes/Link21_L.STL" />
+        </geometry>
+      </collision>
+    </link>
+
+    <joint
+      name="L_pinky_proximal_joint"
+      type="revolute">
+      <origin
+        xyz="0.00028533 -0.13571 -0.025488"
+        rpy="0.10472 0 0" />
+      <parent
+        link="L_hand_base_link" />
+      <child
+        link="L_pinky_proximal" />
+      <axis
+        xyz="0 0 -1" />
+      <limit
+        lower="0"
+        upper="1.7"
+        effort="1"
+        velocity="0.5" />
+    </joint>
+
+    <link name="L_pinky_intermediate">
+      <inertial>
+        <origin
+          xyz="0.0024788 -0.016208 -0.0050001"
+          rpy="0 0 0" />
+        <mass
+          value="0.0036036" />
+        <inertia
+          ixx="4.3923E-07"
+          ixy="-4.1355E-08"
+          ixz="1.2263E-12"
+          iyy="7.0315E-08"
+          iyz="3.1311E-12"
+          izz="4.4881E-07" />
+      </inertial>
+      <visual>
+        <origin
+          xyz="0 0 0"
+          rpy="0 0 0" />
+        <geometry>
+          <mesh filename="./meshes/Link22_L.STL" />
+        </geometry>
+        <material name="">
+          <color rgba="0.1 0.1 0.1 1" />
+        </material>
+      </visual>
+      <collision>
+        <origin
+          xyz="0 0 0"
+          rpy="0 0 0" />
+        <geometry>
+          <mesh filename="./meshes/Link22_L.STL" />
+        </geometry>
+      </collision>
+    </link>
+
+    <joint
+      name="L_pinky_intermediate_joint"
+      type="revolute">
+      <origin
+        xyz="-0.0024229 -0.032041 -0.001"
+        rpy="0 0 0" />
+      <parent
+        link="L_pinky_proximal" />
+      <child
+        link="L_pinky_intermediate" />
+      <axis
+        xyz="0 0 -1" />
+      <limit
+        lower="0"
+        upper="1.7"
+        effort="1"
+        velocity="0.5" />
+      <mimic 
+        joint="L_pinky_proximal_joint"
+        multiplier="1"
+        offset="0" />
+    </joint>
+
+    <link name="L_pinky_tip">
+    <visual>
+      <origin
+        xyz="0 0 0"
+        rpy="0 0 0" />
+      <geometry>
+        <sphere radius="0.005"/>
+      </geometry>
+      <material name="green">
+        <color rgba="0 1 0 1" />
+      </material>
+    </visual>
+  </link>
+  <joint name="L_pinky_tip_joint" type="fixed">
+    <parent link="L_pinky_intermediate"/>
+    <child link="L_pinky_tip"/>
+    <origin rpy="0 0 0" xyz="-0.002 -0.032 -0.004"/>
+  </joint>
+
+</robot>

assets/inspire_hand/inspire_hand_right.urdf

@@ -0,0 +1,830 @@
+<robot
+  name="h1">
+
+  <mujoco>
+    <compiler meshdir="./meshes" discardvisual="false" />
+  </mujoco>
+
+
+  <!-- [CAUTION] uncomment when convert to mujoco -->
+  <!-- <link name="world"></link>
+  <joint name="floating_base_joint" type="floating">
+    <parent link="world" />
+    <child link="pelvis" />
+  </joint> -->
+
+  <link name="R_hand_base_link">
+    <inertial>
+      <origin
+        xyz="-0.0025264 -0.066047 0.0019598"
+        rpy="0 0 0" />
+      <mass
+        value="0.14143" />
+      <inertia
+        ixx="0.00012281"
+        ixy="2.1711E-06"
+        ixz="1.7709E-06"
+        iyy="8.3832E-05"
+        iyz="-1.6551E-06"
+        izz="7.6663E-05" />
+    </inertial>
+    <visual>
+      <origin
+        xyz="0 0 0"
+        rpy="0 0 0" />
+      <geometry>
+        <mesh filename="./meshes/R_hand_base_link.STL" />
+      </geometry>
+      <material name="">
+        <color rgba="0.1 0.1 0.1 1" />
+      </material>
+    </visual>
+    <collision>
+      <origin
+        xyz="0 0 0"
+        rpy="0 0 0" />
+      <geometry>
+        <mesh filename="./meshes/R_hand_base_link.STL" />
+      </geometry>
+    </collision>
+  </link>
+
+  <link name="R_thumb_proximal_base">
+    <inertial>
+      <origin
+        xyz="-0.0048064 0.0009382 -0.00757"
+        rpy="0 0 0" />
+      <mass
+        value="0.0018869" />
+      <inertia
+        ixx="5.816E-08"
+        ixy="1.4539E-08"
+        ixz="4.491E-09"
+        iyy="7.9161E-08"
+        iyz="-1.8727E-09"
+        izz="6.7433E-08" />
+    </inertial>
+    <visual>
+      <origin
+        xyz="0 0 0"
+        rpy="0 0 0" />
+      <geometry>
+        <mesh filename="./meshes/Link11_R.STL" />
+      </geometry>
+      <material name="">
+        <color rgba="0.1 0.1 0.1 1" />
+      </material>
+    </visual>
+    <collision>
+      <origin
+        xyz="0 0 0"
+        rpy="0 0 0" />
+      <geometry>
+        <mesh filename="./meshes/Link11_R.STL" />
+      </geometry>
+    </collision>
+  </link>
+
+  <joint
+    name="R_thumb_proximal_yaw_joint"
+    type="revolute">
+    <origin
+      xyz="-0.01696 -0.0691 -0.02045"
+      rpy="1.5708 -1.5708 0" />
+    <parent
+      link="R_hand_base_link" />
+    <child
+      link="R_thumb_proximal_base" />
+    <axis
+      xyz="0 0 -1" />
+    <limit
+      lower="-0.1"
+      upper="1.3"
+      effort="1"
+      velocity="0.5" />
+  </joint>
+
+  <link name="R_thumb_proximal">
+    <inertial>
+      <origin
+        xyz="0.021932 0.012785 -0.0080386"
+        rpy="0 0 0" />
+      <mass
+        value="0.0066075" />
+      <inertia
+        ixx="1.5686E-06"
+        ixy="-7.8296E-07"
+        ixz="8.9143E-10"
+        iyy="1.7353E-06"
+        iyz="-1.0191E-09"
+        izz="2.786E-06" />
+    </inertial>
+    <visual>
+      <origin
+        xyz="0 0 0"
+        rpy="0 0 0" />
+      <geometry>
+        <mesh filename="./meshes/Link12_R.STL" />
+      </geometry>
+      <material name="">
+        <color rgba="0.1 0.1 0.1 1" />
+      </material>
+    </visual>
+    <collision>
+      <origin
+        xyz="0 0 0"
+        rpy="0 0 0" />
+      <geometry>
+        <mesh filename="./meshes/Link12_R.STL" />
+      </geometry>
+    </collision>
+  </link>
+
+  <joint
+    name="R_thumb_proximal_pitch_joint"
+    type="revolute">
+    <origin
+      xyz="-0.0088099 0.010892 -0.00925"
+      rpy="1.5708 0 2.8587" />
+    <parent
+      link="R_thumb_proximal_base" />
+    <child
+      link="R_thumb_proximal" />
+    <axis
+      xyz="0 0 1" />
+    <limit
+      lower="0.0"
+      upper="0.5"
+      effort="1"
+      velocity="0.5" />
+  </joint>
+
+  <link name="R_thumb_intermediate">
+    <inertial>
+      <origin
+        xyz="0.0095544 -0.0016282 -0.0071997"
+        rpy="0 0 0" />
+      <mass
+        value="0.0037847" />
+      <inertia
+        ixx="3.6981E-07"
+        ixy="-9.8581E-08"
+        ixz="-4.7469E-12"
+        iyy="3.2394E-07"
+        iyz="1.0939E-12"
+        izz="4.6531E-07" />
+    </inertial>
+    <visual>
+      <origin
+        xyz="0 0 0"
+        rpy="0 0 0" />
+      <geometry>
+        <mesh filename="./meshes/Link13_R.STL" />
+      </geometry>
+      <material name="">
+        <color rgba="0.1 0.1 0.1 1" />
+      </material>
+    </visual>
+    <collision>
+      <origin
+        xyz="0 0 0"
+        rpy="0 0 0" />
+      <geometry>
+        <mesh filename="./meshes/Link13_R.STL" />
+      </geometry>
+    </collision>
+  </link>
+
+  <joint
+    name="R_thumb_intermediate_joint"
+    type="revolute">
+    <origin
+      xyz="0.04407 0.034553 -0.0008"
+      rpy="0 0 0" />
+    <parent
+      link="R_thumb_proximal" />
+    <child
+      link="R_thumb_intermediate" />
+    <axis
+      xyz="0 0 1" />
+    <limit
+      lower="0"
+      upper="0.8"
+      effort="1"
+      velocity="0.5" />
+    <mimic 
+      joint="R_thumb_proximal_pitch_joint"
+      multiplier="1.6"
+      offset="0" />
+  </joint>
+
+  <link name="R_thumb_distal">
+    <inertial>
+      <origin
+        xyz="0.0092888 0.0049529 -0.0060033"
+        rpy="0 0 0" />
+      <mass
+        value="0.0033441" />
+      <inertia
+        ixx="1.3632E-07"
+        ixy="-5.6788E-08"
+        ixz="-9.2764E-11"
+        iyy="1.4052E-07"
+        iyz="-1.2283E-10"
+        izz="2.0026E-07" />
+    </inertial>
+    <visual>
+      <origin
+        xyz="0 0 0"
+        rpy="0 0 0" />
+      <geometry>
+        <mesh filename="./meshes/Link14_R.STL" />
+      </geometry>
+      <material name="">
+        <color rgba="0.1 0.1 0.1 1" />
+      </material>
+    </visual>
+    <collision>
+      <origin
+        xyz="0 0 0"
+        rpy="0 0 0" />
+      <geometry>
+        <mesh filename="./meshes/Link14_R.STL" />
+      </geometry>
+    </collision>
+  </link>
+
+  <joint
+    name="R_thumb_distal_joint"
+    type="revolute">
+    <origin
+      xyz="0.020248 0.010156 -0.0012"
+      rpy="0 0 0" />
+    <parent
+      link="R_thumb_intermediate" />
+    <child
+      link="R_thumb_distal" />
+    <axis
+      xyz="0 0 1" />
+    <limit
+      lower="0"
+      upper="1.2"
+      effort="1"
+      velocity="0.5" />
+    <mimic 
+      joint="R_thumb_proximal_pitch_joint"
+      multiplier="2.4"
+      offset="0" />
+  </joint>
+
+  <link name="R_thumb_tip">
+    <visual>
+      <origin
+        xyz="0 0 0"
+        rpy="0 0 0" />
+      <geometry>
+        <sphere radius="0.005"/>
+      </geometry>
+      <material name="green">
+        <color rgba="0 1 0 1" />
+      </material>
+    </visual>
+  </link>
+  <joint name="R_thumb_tip_joint" type="fixed">
+    <parent link="R_thumb_distal"/>
+    <child link="R_thumb_tip"/>
+    <origin rpy="0 0 0" xyz="0.015 0.013 -0.004"/>
+  </joint>
+
+  <link name="R_index_proximal">
+    <inertial>
+      <origin
+        xyz="0.0012259 0.011942 -0.0060001"
+        rpy="0 0 0" />
+      <mass
+        value="0.0042403" />
+      <inertia
+        ixx="6.6232E-07"
+        ixy="-1.5775E-08"
+        ixz="1.8515E-12"
+        iyy="2.1146E-07"
+        iyz="-5.0828E-12"
+        izz="6.9398E-07" />
+    </inertial>
+    <visual>
+      <origin
+        xyz="0 0 0"
+        rpy="0 0 0" />
+      <geometry>
+        <mesh filename="./meshes/Link15_R.STL" />
+      </geometry>
+      <material name="">
+        <color rgba="0.1 0.1 0.1 1" />
+      </material>
+    </visual>
+    <collision>
+      <origin
+        xyz="0 0 0"
+        rpy="0 0 0" />
+      <geometry>
+        <mesh filename="./meshes/Link15_R.STL" />
+      </geometry>
+    </collision>
+  </link>
+
+  <joint
+    name="R_index_proximal_joint"
+    type="revolute">
+    <origin
+      xyz="0.00028533 -0.13653 -0.032268"
+      rpy="-3.1067 0 0" />
+    <parent
+      link="R_hand_base_link" />
+    <child
+      link="R_index_proximal" />
+    <axis
+      xyz="0 0 1" />
+    <limit
+      lower="0"
+      upper="1.7"
+      effort="1"
+      velocity="0.5" />
+  </joint>
+
+  <link name="R_index_intermediate">
+    <inertial>
+      <origin
+        xyz="0.0019697 0.019589 -0.005"
+        rpy="0 0 0" />
+      <mass
+        value="0.0045683" />
+      <inertia
+        ixx="7.6111E-07"
+        ixy="8.7637E-08"
+        ixz="-3.7751E-13"
+        iyy="9.6076E-08"
+        iyz="9.9444E-13"
+        izz="7.8179E-07" />
+    </inertial>
+    <visual>
+      <origin
+        xyz="0 0 0"
+        rpy="0 0 0" />
+      <geometry>
+        <mesh filename="./meshes/Link16_R.STL" />
+      </geometry>
+      <material name="">
+        <color rgba="0.1 0.1 0.1 1" />
+      </material>
+    </visual>
+    <collision>
+      <origin
+        xyz="0 0 0"
+        rpy="0 0 0" />
+      <geometry>
+        <mesh filename="./meshes/Link16_R.STL" />
+      </geometry>
+    </collision>
+  </link>
+
+  <joint
+    name="R_index_intermediate_joint"
+    type="revolute">
+    <origin
+      xyz="-0.0026138 0.032026 -0.001"
+      rpy="0 0 0" />
+    <parent
+      link="R_index_proximal" />
+    <child
+      link="R_index_intermediate" />
+    <axis
+      xyz="0 0 1" />
+    <limit
+      lower="0"
+      upper="1.7"
+      effort="1"
+      velocity="0.5" />
+    <mimic 
+      joint="R_index_proximal_joint"
+      multiplier="1"
+      offset="0" />
+  </joint>
+
+  <link name="R_index_tip">
+    <visual>
+      <origin
+        xyz="0 0 0"
+        rpy="0 0 0" />
+      <geometry>
+        <sphere radius="0.005"/>
+      </geometry>
+      <material name="green">
+        <color rgba="0 1 0 1" />
+      </material>
+    </visual>
+  </link>
+  <joint name="R_index_tip_joint" type="fixed">
+    <parent link="R_index_intermediate"/>
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+    type="revolute">
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+    <child link="R_pinky_tip"/>
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