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jianglongyejianglongye
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fix: fix issues and pin dep versions
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‎.gitignore‎

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@@ -111,7 +111,7 @@ celerybeat.pid
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# Environments
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.env
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.venv
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env/
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# env/
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venv/
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ENV/
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env.bak/

‎README.md‎

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@@ -22,14 +22,14 @@ To set up the environment, follow these steps:
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conda create -n cgf python=3.10 -y && conda activate cgf
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conda install pytorch==1.13.1 torchvision==0.14.1 torchaudio==0.13.1 pytorch-cuda=11.7 -c pytorch -c nvidia -y
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pip install six numpy==1.23.1 tqdm pyyaml scipy opencv-python trimesh einops lxml transforms3d viser fvcore
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pip install six numpy==1.23.1 tqdm pyyaml scipy opencv-python trimesh einops lxml transforms3d fvcore viser==0.1.34
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pip install git+https://github.com/hassony2/chumpy.git
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pip install git+https://github.com/hassony2/manopth
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pip install --no-index --no-cache-dir pytorch3d -f https://dl.fbaipublicfiles.com/pytorch3d/packaging/wheels/py310_cu117_pyt1131/download.html
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# the above command is for pytorch 1.13.1, CUDA 11.7, python 3.10, if you encounter any issues, please try install pytorch3d from source:
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# pip install git+https://github.com/facebookresearch/pytorch3d.git
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pip install git+https://github.com/facebookresearch/pytorch3d.git@v0.7.3
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# installing pytorch3d from source is usually more robust, for pytorch 1.13.1, CUDA 11.7, python 3.10, the following command *may* work:
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# pip install --no-index --no-cache-dir pytorch3d -f https://dl.fbaipublicfiles.com/pytorch3d/packaging/wheels/py310_cu117_pyt1131/download.html
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pip install git+https://github.com/UM-ARM-Lab/pytorch_kinematics
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pip install git+https://github.com/UM-ARM-Lab/pytorch_kinematics.git@v0.6.1
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```
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## Data Preparation

‎cgf/transformation.py‎

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import numpy as np
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import torch
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import torch.nn.functional as F
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# fix for chumpy
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np.bool = np.bool_
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np.int = np.int_
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np.float = np.float_
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np.complex = np.complex_
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np.object = np.object_
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np.str = np.str_
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from manopth.manolayer import ManoLayer
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from scipy.spatial.transform import Rotation as Rot
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"""
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if isinstance(quaternions, np.ndarray) or isinstance(quaternions, list) or isinstance(quaternions, tuple):
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quaternions = np.asarray(quaternions)
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r, i, j, k = quaternions[..., 0], quaternions[..., 1], quaternions[..., 2], quaternions[..., 3]
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r, i, j, k = (
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quaternions[..., 0],
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quaternions[..., 1],
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quaternions[..., 2],
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quaternions[..., 3],
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)
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two_s = 2.0 / (quaternions * quaternions).sum(-1)
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matrices = np.stack(
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[
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def __init__(self, side, mano_root):
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with warnings.catch_warnings():
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warnings.simplefilter("ignore", UserWarning)
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self.mano_layer = ManoLayer(flat_hand_mean=False, ncomps=45, side=side, mano_root=mano_root, use_pca=True)
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self.mano_layer = ManoLayer(
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flat_hand_mean=False,
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ncomps=45,
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side=side,
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mano_root=mano_root,
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use_pca=True,
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)
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def transform_pose(self, pose, betas, tf_mat=None, quat=None, tl=None):
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assert tf_mat is not None or (quat is not None and tl is not None), "incorrect params"

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