id int64 0 190k | prompt stringlengths 21 13.4M | docstring stringlengths 1 12k ⌀ |
|---|---|---|
14,331 | import numpy as np
import trimesh
from trimesh.proximity import closest_point
from .mesh_eval import compute_similarity_transform
The provided code snippet includes necessary dependencies for implementing the `keypoint_accel_error` function. Write a Python function `def keypoint_accel_error(gt, pred, mask=None)` to so... | Computes acceleration error: Note that for each frame that is not visible, three entries in the acceleration error should be zero'd out. Args: gt (Nx14x3). pred (Nx14x3). mask (N). Returns: error_accel (N-2). |
14,332 | import numpy as np
import trimesh
from trimesh.proximity import closest_point
from .mesh_eval import compute_similarity_transform
def compute_similarity_transform(source_points,
target_points,
return_tform=False):
"""Computes a similarity transform ... | Computes per vertex error (PVE). Args: verts_gt (N x verts_num x 3). verts_pred (N x verts_num x 3). alignment (str, optional): method to align the prediction with the groundtruth. Supported options are: - ``'none'``: no alignment will be applied - ``'scale'``: align in the least-square sense in scale - ``'procrustes'`... |
14,333 | import numpy as np
import trimesh
from trimesh.proximity import closest_point
from .mesh_eval import compute_similarity_transform
def compute_similarity_transform(source_points,
target_points,
return_tform=False):
"""Computes a similarity transform ... | Calculate the Percentage of Correct Keypoints (3DPCK) w. or w/o rigid alignment. Paper ref: `Monocular 3D Human Pose Estimation In The Wild Using Improved CNN Supervision' 3DV'2017. <https://arxiv.org/pdf/1611.09813>`__ . Note: - batch_size: N - num_keypoints: K - keypoint_dims: C Args: pred (np.ndarray[N, K, C]): Pred... |
14,334 | import numpy as np
import trimesh
from trimesh.proximity import closest_point
from .mesh_eval import compute_similarity_transform
def compute_similarity_transform(source_points,
target_points,
return_tform=False):
"""Computes a similarity transform ... | Calculate the Area Under the Curve (3DAUC) computed for a range of 3DPCK thresholds. Paper ref: `Monocular 3D Human Pose Estimation In The Wild Using Improved CNN Supervision' 3DV'2017. <https://arxiv.org/pdf/1611.09813>`__ . This implementation is derived from mpii_compute_3d_pck.m, which is provided as part of the MP... |
14,335 | import numpy as np
import trimesh
from trimesh.proximity import closest_point
from .mesh_eval import compute_similarity_transform
def compute_similarity_transform(source_points,
target_points,
return_tform=False):
"""Computes a similarity transform ... | This script computes the reconstruction error between an input mesh and a ground truth mesh. Args: groundtruth_vertices (np.ndarray[N,3]): Ground truth vertices. grundtruth_landmark_points (np.ndarray[7,3]): Ground truth annotations. predicted_mesh_vertices (np.ndarray[M,3]): Predicted vertices. predicted_mesh_faces (n... |
14,336 | from mmcv.utils import Registry
POST_PROCESSING = Registry('post_processing')
The provided code snippet includes necessary dependencies for implementing the `build_post_processing` function. Write a Python function `def build_post_processing(cfg)` to solve the following problem:
Build post processing function.
Here i... | Build post processing function. |
14,337 | import math
import warnings
import numpy as np
import torch
from ..builder import POST_PROCESSING
def smoothing_factor(t_e, cutoff):
r = 2 * math.pi * cutoff * t_e
return r / (r + 1) | null |
14,338 | import math
import warnings
import numpy as np
import torch
from ..builder import POST_PROCESSING
def exponential_smoothing(a, x, x_prev):
return a * x + (1 - a) * x_prev | null |
14,339 | import copy
import math
from typing import Optional
import numpy as np
import torch
import torch.nn.functional as F
from mmcv.runner import load_checkpoint
from torch import Tensor, nn
from mmhuman3d.utils.transforms import (
aa_to_rotmat,
rot6d_to_rotmat,
rotmat_to_aa,
rotmat_to_rot6d,
)
from ..builder... | null |
14,340 | import copy
import math
from typing import Optional
import numpy as np
import torch
import torch.nn.functional as F
from mmcv.runner import load_checkpoint
from torch import Tensor, nn
from mmhuman3d.utils.transforms import (
aa_to_rotmat,
rot6d_to_rotmat,
rotmat_to_aa,
rotmat_to_rot6d,
)
from ..builder... | null |
14,341 | import copy
import math
from typing import Optional
import numpy as np
import torch
import torch.nn.functional as F
from mmcv.runner import load_checkpoint
from torch import Tensor, nn
from mmhuman3d.utils.transforms import (
aa_to_rotmat,
rot6d_to_rotmat,
rotmat_to_aa,
rotmat_to_rot6d,
)
from ..builder... | Return an activation function given a string. |
14,342 | import warnings
from typing import Iterable, List, Optional, Tuple, Union
import numpy as np
import torch
from mmhuman3d.utils.transforms import ee_to_rotmat, rotmat_to_ee
The provided code snippet includes necessary dependencies for implementing the `convert_K_4x4_to_3x3` function. Write a Python function `def conver... | Convert opencv 4x4 intrinsic matrix to 3x3. Args: K (Union[torch.Tensor, np.ndarray]): Input 4x4 intrinsic matrix, left mm defined. for perspective: [[fx, 0, px, 0], [0, fy, py, 0], [0, 0, 0, 1], [0, 0, 1, 0]] for orthographics: [[fx, 0, 0, px], [0, fy, 0, py], [0, 0, 1, 0], [0, 0, 0, 1]] is_perspective (bool, optional... |
14,343 | from typing import Tuple, Union
import numpy as np
import torch
from .convert_convention import convert_camera_matrix
def convert_camera_matrix(
K: Optional[Union[torch.Tensor, np.ndarray]] = None,
R: Optional[Union[torch.Tensor, np.ndarray]] = None,
T: Optional[Union[torch.Tensor, np.ndarray]] = None,
... | Convert perspective to weakperspective intrinsic matrix. Args: K (Union[torch.Tensor, np.ndarray]): input intrinsic matrix, shape should be (batch, 4, 4) or (batch, 3, 3). zmean (Union[torch.Tensor, np.ndarray, int, float]): zmean for object. shape should be (batch, ) or singleton number. resolution (Union[int, Tuple[i... |
14,344 | import numpy as np
from mmhuman3d.utils.transforms import aa_to_rotmat, rotmat_to_aa
def aa_to_rotmat(
axis_angle: Union[torch.Tensor, numpy.ndarray]
) -> Union[torch.Tensor, numpy.ndarray]:
"""
Convert axis_angle to rotation matrixs.
Args:
axis_angle (Union[torch.Tensor, numpy.ndarray]): input... | Transform body model parameters to camera frame. Args: global_orient (numpy.ndarray): shape (3, ). Only global_orient and transl needs to be updated in the rigid transformation transl (numpy.ndarray): shape (3, ). pelvis (numpy.ndarray): shape (3, ). 3D joint location of pelvis This is necessary to eliminate the offset... |
14,345 | import numpy as np
from mmhuman3d.utils.transforms import aa_to_rotmat, rotmat_to_aa
def aa_to_rotmat(
axis_angle: Union[torch.Tensor, numpy.ndarray]
) -> Union[torch.Tensor, numpy.ndarray]:
"""
Convert axis_angle to rotation matrixs.
Args:
axis_angle (Union[torch.Tensor, numpy.ndarray]): input... | Transform body model parameters to camera frame by batch. Args: global_orient (np.ndarray): shape (N, 3). Only global_orient and transl needs to be updated in the rigid transformation transl (np.ndarray): shape (N, 3). pelvis (np.ndarray): shape (N, 3). 3D joint location of pelvis This is necessary to eliminate the off... |
14,346 | from typing import Optional
import numpy as np
import torch
from smplx import SMPL as _SMPL
from smplx.lbs import (
batch_rigid_transform,
blend_shapes,
transform_mat,
vertices2joints,
)
from mmhuman3d.core.conventions.keypoints_mapping import (
convert_kps,
get_keypoint_num,
)
from mmhuman3d.co... | null |
14,347 | from typing import Optional
import numpy as np
import torch
from smplx import SMPL as _SMPL
from smplx.lbs import (
batch_rigid_transform,
blend_shapes,
transform_mat,
vertices2joints,
)
from mmhuman3d.core.conventions.keypoints_mapping import (
convert_kps,
get_keypoint_num,
)
from mmhuman3d.co... | null |
14,348 | import torch
import torch.nn as nn
from .utils import weighted_loss
The provided code snippet includes necessary dependencies for implementing the `smooth_l1_loss` function. Write a Python function `def smooth_l1_loss(pred, target, beta=1.0)` to solve the following problem:
Smooth L1 loss. Args: pred (torch.Tensor): T... | Smooth L1 loss. Args: pred (torch.Tensor): The prediction. target (torch.Tensor): The learning target of the prediction. beta (float, optional): The threshold in the piecewise function. Defaults to 1.0. Returns: torch.Tensor: Calculated loss |
14,349 | import torch
import torch.nn as nn
from .utils import weighted_loss
The provided code snippet includes necessary dependencies for implementing the `l1_loss` function. Write a Python function `def l1_loss(pred, target)` to solve the following problem:
L1 loss. Args: pred (torch.Tensor): The prediction. target (torch.Te... | L1 loss. Args: pred (torch.Tensor): The prediction. target (torch.Tensor): The learning target of the prediction. Returns: torch.Tensor: Calculated loss |
14,350 | import functools
import torch
import torch.nn.functional as F
def weight_reduce_loss(loss, weight=None, reduction='mean', avg_factor=None):
"""Apply element-wise weight and reduce loss.
Args:
loss (Tensor): Element-wise loss.
weight (Tensor): Element-wise weights.
reduction (str): Same a... | Create a weighted version of a given loss function. To use this decorator, the loss function must have the signature like `loss_func(pred, target, **kwargs)`. The function only needs to compute element-wise loss without any reduction. This decorator will add weight and reduction arguments to the function. The decorated... |
14,351 | import functools
import torch
import torch.nn.functional as F
The provided code snippet includes necessary dependencies for implementing the `convert_to_one_hot` function. Write a Python function `def convert_to_one_hot(targets: torch.Tensor, classes) -> torch.Tensor` to solve the following problem:
This function conv... | This function converts target class indices to one-hot vectors, given the number of classes. Args: targets (Tensor): The ground truth label of the prediction with shape (N, 1) classes (int): the number of classes. Returns: Tensor: Processed loss values. |
14,352 | import torch
import torch.nn as nn
import torch.nn.functional as F
from .utils import weighted_loss
def gmof(x, sigma):
"""Geman-McClure error function."""
x_squared = x**2
sigma_squared = sigma**2
return (sigma_squared * x_squared) / (sigma_squared + x_squared)
def mse_loss(pred, target):
"""Warppe... | Extended MSE Loss with GMOF. |
14,353 | from typing import Optional, Union
import torch
import torch.distributed as dist
import torch.nn.functional as F
from mmcv.runner import get_dist_info
from torch.nn.modules.loss import _Loss
from .utils import weighted_loss
The provided code snippet includes necessary dependencies for implementing the `bmc_loss_md` fu... | Args: pred (torch.Tensor): The prediction. Shape should be (N, L). target (torch.Tensor): The learning target of the prediction. noise_var (torch.Tensor): Noise var of ground truth distribution. all_gather (bool): Whether gather tensors across all sub-processes. Only used in DDP training scheme. loss_mse_weight (float,... |
14,354 | from mmcv.utils import Registry
from .balanced_mse_loss import BMCLossMD
from .cross_entropy_loss import CrossEntropyLoss
from .gan_loss import GANLoss
from .mse_loss import KeypointMSELoss, MSELoss
from .prior_loss import (
CameraPriorLoss,
JointPriorLoss,
LimbLengthLoss,
MaxMixturePrior,
PoseRegLo... | Build loss. |
14,355 | import torch
import torch.nn as nn
import torch.nn.functional as F
from .utils import weight_reduce_loss
def weight_reduce_loss(loss, weight=None, reduction='mean', avg_factor=None):
"""Apply element-wise weight and reduce loss.
Args:
loss (Tensor): Element-wise loss.
weight (Tensor): Element-... | Calculate the CrossEntropy loss. Args: pred (torch.Tensor): The prediction with shape (N, C), C is the number of classes. label (torch.Tensor): The learning label of the prediction. weight (torch.Tensor, optional): Sample-wise loss weight. reduction (str, optional): The method used to reduce the loss. avg_factor (int, ... |
14,356 | import torch
import torch.nn as nn
import torch.nn.functional as F
from .utils import weight_reduce_loss
def _expand_onehot_labels(labels, label_weights, label_channels, ignore_index):
"""Expand onehot labels to match the size of prediction."""
bin_labels = labels.new_full((labels.size(0), label_channels), 0)
... | Calculate the binary CrossEntropy loss. Args: pred (torch.Tensor): The prediction with shape (N, 1). label (torch.Tensor): The learning label of the prediction. weight (torch.Tensor, optional): Sample-wise loss weight. reduction (str, optional): The method used to reduce the loss. Options are "none", "mean" and "sum". ... |
14,357 | import torch
import torch.nn as nn
import torch.nn.functional as F
from .utils import weight_reduce_loss
The provided code snippet includes necessary dependencies for implementing the `mask_cross_entropy` function. Write a Python function `def mask_cross_entropy(pred, target, ... | Calculate the CrossEntropy loss for masks. Args: pred (torch.Tensor): The prediction with shape (N, C, *), C is the number of classes. The trailing * indicates arbitrary shape. target (torch.Tensor): The learning label of the prediction. label (torch.Tensor): ``label`` indicates the class label of the mask correspondin... |
14,358 | import torch
import torch.nn as nn
The provided code snippet includes necessary dependencies for implementing the `rotation_distance_loss` function. Write a Python function `def rotation_distance_loss(pred, target, epsilon)` to solve the following problem:
Warpper of rotation distance loss.
Here is the function:
def... | Warpper of rotation distance loss. |
14,359 | from mmcv.utils import Registry
from .temporal_encoder import TemporalGRUEncoder
NECKS = Registry('necks')
NECKS.register_module(name='TemporalGRUEncoder', module=TemporalGRUEncoder)
The provided code snippet includes necessary dependencies for implementing the `build_neck` function. Write a Python function `def build... | Build neck. |
14,360 | from mmcv.utils import Registry
from .pose_discriminator import (
FullPoseDiscriminator,
PoseDiscriminator,
ShapeDiscriminator,
SMPLDiscriminator,
)
DISCRIMINATORS = Registry('discriminators')
DISCRIMINATORS.register_module(
name='ShapeDiscriminator', module=ShapeDiscriminator)
DISCRIMINATORS.regist... | Build discriminator. |
14,361 | from mmcv.utils import Registry
from .smplify import SMPLify
from .smplifyx import SMPLifyX
REGISTRANTS = Registry('registrants')
REGISTRANTS.register_module(name='SMPLify', module=SMPLify)
REGISTRANTS.register_module(name='SMPLifyX', module=SMPLifyX)
The provided code snippet includes necessary dependencies for imple... | Build registrant. |
14,362 | import numpy as np
import torch
import torch.cuda.comm
import torch.nn as nn
from mmcv.runner.base_module import BaseModule
from torch.nn import functional as F
from mmhuman3d.core.conventions.keypoints_mapping import get_flip_pairs
The provided code snippet includes necessary dependencies for implementing the `norm_h... | Normalize heatmap. Args: norm_type (str): type of normalization. Currently only 'softmax' is supported heatmap (torch.Tensor): model output heatmap with shape (Bx29xF^2) where F^2 refers to number of squared feature channels F Returns: heatmap (torch.Tensor): normalized heatmap according to specified type with shape (B... |
14,363 | import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
from mmcv.runner.base_module import BaseModule
from torch.nn.modules.utils import _pair
from mmhuman3d.utils.geometry import rot6d_to_rotmat
The provided code snippet includes necessary dependencies for implementing the `interpolate`... | Args: feat (torch.Tensor): [B, C, H, W] image features uv (torch.Tensor): [B, 2, N] uv coordinates in the image plane, range [-1, 1] Returns: samples[:, :, :, 0] (torch.Tensor): [B, C, N] image features at the uv coordinates |
14,364 | import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
from mmcv.runner.base_module import BaseModule
from torch.nn.modules.utils import _pair
from mmhuman3d.utils.geometry import rot6d_to_rotmat
def _softmax(tensor, temperature, dim=-1):
return F.softmax(tensor * temperature, dim=dim... | Softargmax layer for heatmaps. |
14,365 | import math
import numpy as np
import scipy
import torch
import torch.cuda.comm
import torch.nn as nn
from mmcv.runner.base_module import BaseModule
from torch.nn import functional as F
from mmhuman3d.core.conventions.keypoints_mapping.flame import (
FLAME_73_KEYPOINTS,
)
from mmhuman3d.core.conventions.keypoints_m... | Get attention modules. Args: config_path (str): Attention config path. module_keys (list): Model name. img_feature_dim (dict): Image feature dimension. hidden_feat_dim (int): Attention feature dimension. n_iter (int): Number of iterations. num_attention_heads (int, optional): Defaults to 1. Returns: Attention modules |
14,366 | from mmcv.utils import Registry
from .cliff_head import CliffHead
from .expose_head import ExPoseBodyHead, ExPoseFaceHead, ExPoseHandHead
from .hmr_head import HMRHead
from .hybrik_head import HybrIKHead
from .pare_head import PareHead
from .pymafx_head import PyMAFXHead, Regressor
HEADS = Registry('heads')
HEADS.regis... | Build head. |
14,367 | import json
import math
import sys
from io import open
import torch
from torch import nn
from .modeling_utils import PretrainedConfig, PreTrainedModel
The provided code snippet includes necessary dependencies for implementing the `gelu` function. Write a Python function `def gelu(x)` to solve the following problem:
Im... | Implementation of the gelu activation function. For information: OpenAI GPT's gelu is slightly different (and gives slightly different results): 0.5 * x * (1 + torch.tanh(math.sqrt(2 / math.pi) * ( x + 0.044715 * torch.pow(x, 3)))) Also see https://arxiv.org/abs/1606.08415 |
14,368 | import json
import math
import sys
from io import open
import torch
from torch import nn
from .modeling_utils import PretrainedConfig, PreTrainedModel
def swish(x):
return x * torch.sigmoid(x) | null |
14,369 | from abc import ABCMeta, abstractmethod
from typing import Optional, Tuple, Union
import torch
import torch.nn.functional as F
import mmhuman3d.core.visualization.visualize_smpl as visualize_smpl
from mmhuman3d.core.conventions.keypoints_mapping import get_keypoint_idx
from mmhuman3d.models.utils import FitsDict
from m... | Set requies_grad for all the networks. Args: nets (nn.Module | list[nn.Module]): A list of networks or a single network. requires_grad (bool): Whether the networks require gradients or not |
14,370 | from abc import ABCMeta
from typing import Optional, Union
import torch
import torch.nn as nn
from mmhuman3d.core.conventions.keypoints_mapping.flame import (
FLAME_73_KEYPOINTS,
)
from mmhuman3d.core.conventions.keypoints_mapping.mano import (
MANO_RIGHT_REORDER_KEYPOINTS,
)
from mmhuman3d.models.body_models.s... | null |
14,371 | from abc import ABCMeta, abstractmethod
from typing import Optional, Tuple, Union
import torch
import torch.nn.functional as F
import mmhuman3d.core.visualization.visualize_smpl as visualize_smpl
from mmhuman3d.core.conventions.keypoints_mapping import get_keypoint_idx
from mmhuman3d.models.utils import FitsDict
from m... | Set requies_grad for all the networks. Args: nets (nn.Module | list[nn.Module]): A list of networks or a single network. requires_grad (bool): Whether the networks require gradients or not |
14,372 | from abc import ABCMeta, abstractmethod
from typing import Optional, Union
import torch
import torch.nn as nn
import torch.nn.functional as F
from mmhuman3d.core.conventions.keypoints_mapping import (
get_keypoint_idx,
get_keypoint_idxs_by_part,
)
from mmhuman3d.utils.geometry import (
batch_rodrigues,
... | Set requies_grad for all the networks. Args: nets (nn.Module | list[nn.Module]): A list of networks or a single network. requires_grad (bool): Whether the networks require gradients or not |
14,373 | from abc import ABCMeta, abstractmethod
from typing import Optional, Union
import torch
import torch.nn as nn
import torch.nn.functional as F
from mmhuman3d.core.conventions.keypoints_mapping import (
get_keypoint_idx,
get_keypoint_idxs_by_part,
)
from mmhuman3d.utils.geometry import (
batch_rodrigues,
... | aa2rotmat. |
14,374 | from mmcv.cnn import MODELS as MMCV_MODELS
from mmcv.utils import Registry
from .cliff_mesh_estimator import CliffImageBodyModelEstimator
from .expressive_mesh_estimator import SMPLXImageBodyModelEstimator
from .hybrik import HybrIK_trainer
from .mesh_estimator import ImageBodyModelEstimator, VideoBodyModelEstimator
fr... | null |
14,375 | from abc import ABCMeta
import torch
from mmhuman3d.data.datasets.pipelines.hybrik_transforms import heatmap2coord
from mmhuman3d.utils.transforms import rotmat_to_quat
from ..backbones.builder import build_backbone
from ..body_models.builder import build_body_model
from ..heads.builder import build_head
from ..losses.... | Set requies_grad for all the networks. Args: nets (nn.Module | list[nn.Module]): A list of networks or a single network. requires_grad (bool): Whether the networks require gradients or not |
14,376 | import os
from typing import List
import numpy as np
import torch
import torch.nn.functional as F
from smplx.utils import find_joint_kin_chain
from mmhuman3d.core.conventions.keypoints_mapping import (
get_keypoint_idx,
get_keypoint_idxs_by_part,
)
from mmhuman3d.utils.geometry import weak_perspective_projectio... | Get the transformation of points on the cropped image to the points on the original image. |
14,377 | import os
from typing import List
import numpy as np
import torch
import torch.nn.functional as F
from smplx.utils import find_joint_kin_chain
from mmhuman3d.core.conventions.keypoints_mapping import (
get_keypoint_idx,
get_keypoint_idxs_by_part,
)
from mmhuman3d.utils.geometry import weak_perspective_projectio... | Concat images of different size. |
14,378 | import os
from typing import List
import numpy as np
import torch
import torch.nn.functional as F
from smplx.utils import find_joint_kin_chain
from mmhuman3d.core.conventions.keypoints_mapping import (
get_keypoint_idx,
get_keypoint_idxs_by_part,
)
from mmhuman3d.utils.geometry import weak_perspective_projectio... | Flip function. Flip rotmat. |
14,379 | import os
from typing import List
import numpy as np
import torch
import torch.nn.functional as F
from smplx.utils import find_joint_kin_chain
from mmhuman3d.core.conventions.keypoints_mapping import (
get_keypoint_idx,
get_keypoint_idxs_by_part,
)
from mmhuman3d.utils.geometry import weak_perspective_projectio... | Computes the absolute rotation of a joint from the kinematic chain. |
14,380 | import os
from typing import List
import numpy as np
import torch
import torch.nn.functional as F
from smplx.utils import find_joint_kin_chain
from mmhuman3d.core.conventions.keypoints_mapping import (
get_keypoint_idx,
get_keypoint_idxs_by_part,
)
from mmhuman3d.utils.geometry import weak_perspective_projectio... | Get partial mesh of SMPL. Returns: part_vert_faces |
14,381 | from __future__ import absolute_import, division, print_function
import torch
from mmhuman3d.utils.transforms import aa_to_rotmat
def batch_get_pelvis_orient_svd(rel_pose_skeleton, rel_rest_pose, parents,
children, dtype):
"""Get pelvis orientation svd for batch data.
Args:
... | Applies inverse kinematics transform to joints in a batch. Args: pose_skeleton (torch.tensor): Locations of estimated pose skeleton with shape (Bx29x3) global_orient (torch.tensor|none): Tensor of global rotation matrices with shape (Bx1x3x3) phis (torch.tensor): Rotation on bone axis parameters with shape (Bx23x2) res... |
14,382 | from typing import Union
import numpy
import torch
from mmhuman3d.core.conventions.joints_mapping.standard_joint_angles import (
TRANSFORMATION_AA_TO_SJA,
TRANSFORMATION_SJA_TO_AA,
)
from .logger import get_root_logger
class Compose:
def __init__(self, transforms: list):
"""Composes several transfor... | Convert axis_angle to quaternions. Args: axis_angle (Union[torch.Tensor, numpy.ndarray]): input shape should be (..., 3). ndim of input is unlimited. Returns: Union[torch.Tensor, numpy.ndarray]: shape would be (..., 4). |
14,383 | from typing import Union
import numpy
import torch
from mmhuman3d.core.conventions.joints_mapping.standard_joint_angles import (
TRANSFORMATION_AA_TO_SJA,
TRANSFORMATION_SJA_TO_AA,
)
from .logger import get_root_logger
class Compose:
def __init__(self, transforms: list):
"""Composes several transfor... | Convert rotation matrixs to quaternions. Args: matrix (Union[torch.Tensor, numpy.ndarray]): input shape should be (..., 3, 3). ndim of input is unlimited. Returns: Union[torch.Tensor, numpy.ndarray]: shape would be (..., 4). |
14,384 | from typing import Union
import numpy
import torch
from mmhuman3d.core.conventions.joints_mapping.standard_joint_angles import (
TRANSFORMATION_AA_TO_SJA,
TRANSFORMATION_SJA_TO_AA,
)
from .logger import get_root_logger
class Compose:
def __init__(self, transforms: list):
"""Composes several transfor... | Convert rotation matrixs to rotation 6d representations. Args: matrix (Union[torch.Tensor, numpy.ndarray]): input shape should be (..., 3, 3). ndim of input is unlimited. Returns: Union[torch.Tensor, numpy.ndarray]: shape would be (..., 6). [1] Zhou, Y., Barnes, C., Lu, J., Yang, J., & Li, H. On the Continuity of Rotat... |
14,385 | from typing import Union
import numpy
import torch
from mmhuman3d.core.conventions.joints_mapping.standard_joint_angles import (
TRANSFORMATION_AA_TO_SJA,
TRANSFORMATION_SJA_TO_AA,
)
from .logger import get_root_logger
class Compose:
def __init__(self, transforms: list):
"""Composes several transfor... | Convert quaternions to axis angles. Args: quaternions (Union[torch.Tensor, numpy.ndarray]): input shape should be (..., 3). ndim of input is unlimited. Returns: Union[torch.Tensor, numpy.ndarray]: shape would be (..., 3). |
14,386 | from typing import Union
import numpy
import torch
from mmhuman3d.core.conventions.joints_mapping.standard_joint_angles import (
TRANSFORMATION_AA_TO_SJA,
TRANSFORMATION_SJA_TO_AA,
)
from .logger import get_root_logger
class Compose:
def __init__(self, transforms: list):
"""Composes several transfor... | Convert quaternions to rotation matrixs. Args: quaternions (Union[torch.Tensor, numpy.ndarray]): input shape should be (..., 3). ndim of input is unlimited. Returns: Union[torch.Tensor, numpy.ndarray]: shape would be (..., 3, 3). |
14,387 | from typing import Union
import numpy
import torch
from mmhuman3d.core.conventions.joints_mapping.standard_joint_angles import (
TRANSFORMATION_AA_TO_SJA,
TRANSFORMATION_SJA_TO_AA,
)
from .logger import get_root_logger
class Compose:
def __init__(self, transforms: list):
"""Composes several transfor... | Convert rotation 6d representations to rotation matrixs. Args: rotation_6d (Union[torch.Tensor, numpy.ndarray]): input shape should be (..., 6). ndim of input is unlimited. Returns: Union[torch.Tensor, numpy.ndarray]: shape would be (..., 3, 3). [1] Zhou, Y., Barnes, C., Lu, J., Yang, J., & Li, H. On the Continuity of ... |
14,388 | from typing import Union
import numpy
import torch
from mmhuman3d.core.conventions.joints_mapping.standard_joint_angles import (
TRANSFORMATION_AA_TO_SJA,
TRANSFORMATION_SJA_TO_AA,
)
from .logger import get_root_logger
class Compose:
def __init__(self, transforms: list):
"""Composes several transfor... | Convert axis angles to euler angle. Args: axis_angle (Union[torch.Tensor, numpy.ndarray]): input shape should be (..., 3). ndim of input is unlimited. convention (str, optional): Convention string of three letters from {“x”, “y”, and “z”}. Defaults to 'xyz'. Returns: Union[torch.Tensor, numpy.ndarray]: shape would be (... |
14,389 | from typing import Union
import numpy
import torch
from mmhuman3d.core.conventions.joints_mapping.standard_joint_angles import (
TRANSFORMATION_AA_TO_SJA,
TRANSFORMATION_SJA_TO_AA,
)
from .logger import get_root_logger
class Compose:
def __init__(self, transforms: list):
"""Composes several transfor... | Convert axis angles to rotation 6d representations. Args: axis_angle (Union[torch.Tensor, numpy.ndarray]): input shape should be (..., 3). ndim of input is unlimited. Returns: Union[torch.Tensor, numpy.ndarray]: shape would be (..., 6). [1] Zhou, Y., Barnes, C., Lu, J., Yang, J., & Li, H. On the Continuity of Rotation ... |
14,390 | from typing import Union
import numpy
import torch
from mmhuman3d.core.conventions.joints_mapping.standard_joint_angles import (
TRANSFORMATION_AA_TO_SJA,
TRANSFORMATION_SJA_TO_AA,
)
from .logger import get_root_logger
class Compose:
def __init__(self, transforms: list):
"""Composes several transfor... | Convert euler angles to axis angles. Args: euler_angle (Union[torch.Tensor, numpy.ndarray]): input shape should be (..., 3). ndim of input is unlimited. convention (str, optional): Convention string of three letters from {“x”, “y”, and “z”}. Defaults to 'xyz'. Returns: Union[torch.Tensor, numpy.ndarray]: shape would be... |
14,391 | from typing import Union
import numpy
import torch
from mmhuman3d.core.conventions.joints_mapping.standard_joint_angles import (
TRANSFORMATION_AA_TO_SJA,
TRANSFORMATION_SJA_TO_AA,
)
from .logger import get_root_logger
class Compose:
def __init__(self, transforms: list):
"""Composes several transfor... | Convert euler angles to quaternions. Args: euler_angle (Union[torch.Tensor, numpy.ndarray]): input shape should be (..., 3). ndim of input is unlimited. convention (str, optional): Convention string of three letters from {“x”, “y”, and “z”}. Defaults to 'xyz'. Returns: Union[torch.Tensor, numpy.ndarray]: shape would be... |
14,392 | from typing import Union
import numpy
import torch
from mmhuman3d.core.conventions.joints_mapping.standard_joint_angles import (
TRANSFORMATION_AA_TO_SJA,
TRANSFORMATION_SJA_TO_AA,
)
from .logger import get_root_logger
class Compose:
def __init__(self, transforms: list):
"""Composes several transfor... | Convert euler angles to rotation 6d representation. Args: euler_angle (Union[torch.Tensor, numpy.ndarray]): input shape should be (..., 3). ndim of input is unlimited. convention (str, optional): Convention string of three letters from {“x”, “y”, and “z”}. Defaults to 'xyz'. Returns: Union[torch.Tensor, numpy.ndarray]:... |
14,393 | from typing import Union
import numpy
import torch
from mmhuman3d.core.conventions.joints_mapping.standard_joint_angles import (
TRANSFORMATION_AA_TO_SJA,
TRANSFORMATION_SJA_TO_AA,
)
from .logger import get_root_logger
class Compose:
def __init__(self, transforms: list):
"""Composes several transfor... | Convert quaternions to euler angles. Args: quaternions (Union[torch.Tensor, numpy.ndarray]): input shape should be (..., 4). ndim of input is unlimited. convention (str, optional): Convention string of three letters from {“x”, “y”, and “z”}. Defaults to 'xyz'. Returns: Union[torch.Tensor, numpy.ndarray]: shape would be... |
14,394 | from typing import Union
import numpy
import torch
from mmhuman3d.core.conventions.joints_mapping.standard_joint_angles import (
TRANSFORMATION_AA_TO_SJA,
TRANSFORMATION_SJA_TO_AA,
)
from .logger import get_root_logger
class Compose:
def __init__(self, transforms: list):
"""Composes several transfor... | Convert quaternions to rotation 6d representations. Args: quaternions (Union[torch.Tensor, numpy.ndarray]): input shape should be (..., 4). ndim of input is unlimited. Returns: Union[torch.Tensor, numpy.ndarray]: shape would be (..., 6). [1] Zhou, Y., Barnes, C., Lu, J., Yang, J., & Li, H. On the Continuity of Rotation... |
14,395 | from typing import Union
import numpy
import torch
from mmhuman3d.core.conventions.joints_mapping.standard_joint_angles import (
TRANSFORMATION_AA_TO_SJA,
TRANSFORMATION_SJA_TO_AA,
)
from .logger import get_root_logger
class Compose:
def __init__(self, transforms: list):
"""Composes several transfor... | Convert rotation 6d representations to axis angles. Args: rotation_6d (Union[torch.Tensor, numpy.ndarray]): input shape should be (..., 6). ndim of input is unlimited. Returns: Union[torch.Tensor, numpy.ndarray]: shape would be (..., 3). [1] Zhou, Y., Barnes, C., Lu, J., Yang, J., & Li, H. On the Continuity of Rotation... |
14,396 | from typing import Union
import numpy
import torch
from mmhuman3d.core.conventions.joints_mapping.standard_joint_angles import (
TRANSFORMATION_AA_TO_SJA,
TRANSFORMATION_SJA_TO_AA,
)
from .logger import get_root_logger
class Compose:
def __init__(self, transforms: list):
"""Composes several transfor... | Convert rotation 6d representations to euler angles. Args: rotation_6d (Union[torch.Tensor, numpy.ndarray]): input shape should be (..., 6). ndim of input is unlimited. Returns: Union[torch.Tensor, numpy.ndarray]: shape would be (..., 3). [1] Zhou, Y., Barnes, C., Lu, J., Yang, J., & Li, H. On the Continuity of Rotatio... |
14,397 | from typing import Union
import numpy
import torch
from mmhuman3d.core.conventions.joints_mapping.standard_joint_angles import (
TRANSFORMATION_AA_TO_SJA,
TRANSFORMATION_SJA_TO_AA,
)
from .logger import get_root_logger
class Compose:
def __init__(self, transforms: list):
"""Composes several transfor... | Convert rotation 6d representations to quaternions. Args: rotation (Union[torch.Tensor, numpy.ndarray]): input shape should be (..., 6). ndim of input is unlimited. Returns: Union[torch.Tensor, numpy.ndarray]: shape would be (..., 4). [1] Zhou, Y., Barnes, C., Lu, J., Yang, J., & Li, H. On the Continuity of Rotation Re... |
14,398 | from typing import Union
import numpy
import torch
from mmhuman3d.core.conventions.joints_mapping.standard_joint_angles import (
TRANSFORMATION_AA_TO_SJA,
TRANSFORMATION_SJA_TO_AA,
)
from .logger import get_root_logger
class Compose:
def __init__(self, transforms: list):
"""Composes several transfor... | Convert axis-angles to standard joint angles. Args: axis_angle (Union[torch.Tensor, numpy.ndarray]): input shape should be (..., 21, 3), ndim of input is unlimited. R_t (Union[torch.Tensor, numpy.ndarray]): input shape should be (..., 21, 3, 3). Transformation matrices from original axis-angle coordinate system to stan... |
14,399 | from typing import Union
import numpy
import torch
from mmhuman3d.core.conventions.joints_mapping.standard_joint_angles import (
TRANSFORMATION_AA_TO_SJA,
TRANSFORMATION_SJA_TO_AA,
)
from .logger import get_root_logger
class Compose:
def __init__(self, transforms: list):
"""Composes several transfor... | Convert standard joint angles to axis angles. Args: sja (Union[torch.Tensor, numpy.ndarray]): input shape should be (..., 21, 3). ndim of input is unlimited. R_t (Union[torch.Tensor, numpy.ndarray]): input shape should be (..., 21, 3, 3). Transformation matrices from original axis-angle coordinate system to standard jo... |
14,400 | from typing import Union
import numpy
import torch
from mmhuman3d.core.conventions.joints_mapping.standard_joint_angles import (
TRANSFORMATION_AA_TO_SJA,
TRANSFORMATION_SJA_TO_AA,
)
from .logger import get_root_logger
The provided code snippet includes necessary dependencies for implementing the `make_homegen... | Appends a row of [0,0,0,1] to a batch size x 3 x 4 Tensor. Parameters ---------- :param input: A tensor of dimensions batch size x 3 x 4 :return: A tensor batch size x 4 x 4 (appended with 0,0,0,1) |
14,401 | from typing import Union
import numpy
import torch
from mmhuman3d.core.conventions.joints_mapping.standard_joint_angles import (
TRANSFORMATION_AA_TO_SJA,
TRANSFORMATION_SJA_TO_AA,
)
from .logger import get_root_logger
The provided code snippet includes necessary dependencies for implementing the `make_homegen... | Appends a row of [0,0,0,1] to a 3 x 4 Tensor. Parameters ---------- :param input: A tensor of dimensions 3 x 4 :return: A tensor batch size x 4 x 4 (appended with 0,0,0,1) |
14,402 | import copy
import os
from typing import Iterable, Optional, Union
import numpy as np
import torch
from pytorch3d.renderer.cameras import CamerasBase
from mmhuman3d.core.cameras import build_cameras
from mmhuman3d.core.conventions.cameras.convert_convention import (
convert_camera_matrix,
convert_world_view,
)
... | Convert opencv calibration smpl poses&transl parameters to model based poses&transl or verts. Args: R (Union[np.ndarray, torch.Tensor]): (frame, 3, 3) T (Union[np.ndarray, torch.Tensor]): [(frame, 3) K (Optional[Union[np.ndarray, torch.Tensor]], optional): (frame, 3, 3) or (frame, 4, 4). Defaults to None. resolution (O... |
14,403 | import copy
import os
from typing import Iterable, Optional, Union
import numpy as np
import torch
from pytorch3d.renderer.cameras import CamerasBase
from mmhuman3d.core.cameras import build_cameras
from mmhuman3d.core.conventions.cameras.convert_convention import (
convert_camera_matrix,
convert_world_view,
)
... | Project 3d points to image. Args: points3d (Union[np.ndarray, torch.Tensor]): shape could be (..., 3). cameras (CamerasBase): pytorch3d cameras or mmhuman3d cameras. resolution (Iterable[int]): (height, width) for rectangle or width for square. K (Union[torch.Tensor, np.ndarray], optional): intrinsic matrix. Defaults t... |
14,404 | import copy
import os
from typing import Iterable, Optional, Union
import numpy as np
import torch
from pytorch3d.renderer.cameras import CamerasBase
from mmhuman3d.core.cameras import build_cameras
from mmhuman3d.core.conventions.cameras.convert_convention import (
convert_camera_matrix,
convert_world_view,
)
... | vector: B x N x C h_vector: B x N x (C + 1) |
14,405 | from mmcv.utils import collect_env as collect_base_env
from mmcv.utils import get_git_hash
import mmhuman3d
The provided code snippet includes necessary dependencies for implementing the `collect_env` function. Write a Python function `def collect_env()` to solve the following problem:
Collect the information of the r... | Collect the information of the running environments. |
14,406 | import warnings
from typing import List, Optional, Union
import torch
from pytorch3d.io import IO
from pytorch3d.io import load_objs_as_meshes as _load_objs_as_meshes
from pytorch3d.io import save_obj
from pytorch3d.renderer import TexturesUV, TexturesVertex
from pytorch3d.structures import (
Meshes,
Pointcloud... | Join `meshes` as a scene each batch. Only for Pytorch3D `meshes`. The Meshes must share the same batch size, and topology could be different. They must all be on the same device. If `include_textures` is true, the textures should be the same type, all be None is not accepted. If `include_textures` is False, textures ar... |
14,407 | import warnings
from typing import List, Optional, Union
import torch
from pytorch3d.io import IO
from pytorch3d.io import load_objs_as_meshes as _load_objs_as_meshes
from pytorch3d.io import save_obj
from pytorch3d.renderer import TexturesUV, TexturesVertex
from pytorch3d.structures import (
Meshes,
Pointcloud... | Convert PyTorch3D vertex color `Meshes` to `PointClouds`. Args: meshes (Meshes): input meshes. include_textures (bool, optional): Whether include colors. Require the texture of input meshes is vertex color. Defaults to True. alpha (float, optional): transparency. Defaults to 1.0. Returns: Pointclouds: output pointcloud... |
14,408 | import warnings
from typing import List, Optional, Union
import torch
from pytorch3d.io import IO
from pytorch3d.io import load_objs_as_meshes as _load_objs_as_meshes
from pytorch3d.io import save_obj
from pytorch3d.renderer import TexturesUV, TexturesVertex
from pytorch3d.structures import (
Meshes,
Pointcloud... | Convert a Pytorch3D meshes's textures from TexturesUV to TexturesVertex. Args: meshes (Meshes): input Meshes. Returns: Meshes: converted Meshes. |
14,409 | import warnings
from typing import List, Optional, Union
import torch
from pytorch3d.io import IO
from pytorch3d.io import load_objs_as_meshes as _load_objs_as_meshes
from pytorch3d.io import save_obj
from pytorch3d.renderer import TexturesUV, TexturesVertex
from pytorch3d.structures import (
Meshes,
Pointcloud... | null |
14,410 | import warnings
from typing import List, Optional, Union
import torch
from pytorch3d.io import IO
from pytorch3d.io import load_objs_as_meshes as _load_objs_as_meshes
from pytorch3d.io import save_obj
from pytorch3d.renderer import TexturesUV, TexturesVertex
from pytorch3d.structures import (
Meshes,
Pointcloud... | null |
14,411 | import colorsys
import os
from collections import defaultdict
from contextlib import contextmanager
from functools import partial
from pathlib import Path
import mmcv
import numpy as np
from mmcv import Timer
from scipy import interpolate
from mmhuman3d.core.post_processing import build_post_processing
The provided co... | Prepare frames from input_path. Args: input_path (str, optional): Defaults to None. Raises: ValueError: check the input path. Returns: List[np.ndarray]: prepared frames |
14,412 | import glob
import json
import os
import shutil
import string
import subprocess
import sys
from pathlib import Path
from typing import Iterable, List, Optional, Tuple, Union
import numpy as np
from mmhuman3d.utils.path_utils import check_input_path, prepare_output_path
def pad_for_libx264(image_array):
"""Pad zeros... | Convert an array to a video directly, gif not supported. Args: image_array (np.ndarray): shape should be (f * h * w * 3). output_path (str): output video file path. fps (Union[int, float, optional): fps. Defaults to 30. resolution (Optional[Union[Tuple[int, int], Tuple[float, float]]], optional): (height, width) of the... |
14,413 | import glob
import json
import os
import shutil
import string
import subprocess
import sys
from pathlib import Path
from typing import Iterable, List, Optional, Tuple, Union
import numpy as np
from mmhuman3d.utils.path_utils import check_input_path, prepare_output_path
class vid_info_reader(object):
def __init__(se... | Convert a video to a gif file. Args: input_path (str): video file path. output_path (str): gif file path. resolution (Optional[Union[Tuple[int, int], Tuple[float, float]]], optional): (height, width) of the output video. Defaults to None. fps (Union[float, int], optional): frames per second. Defaults to 15. disable_log... |
14,414 | import glob
import json
import os
import shutil
import string
import subprocess
import sys
from pathlib import Path
from typing import Iterable, List, Optional, Tuple, Union
import numpy as np
from mmhuman3d.utils.path_utils import check_input_path, prepare_output_path
def prepare_output_path(output_path: str,
... | Convert series of images to a video, similar to images_to_video, but provide more suitable parameters. Args: input_folder (str): input image folder. output_path (str): output gif file path. remove_raw_file (bool, optional): whether remove raw images. Defaults to False. img_format (str, optional): format to name the ima... |
14,415 | import glob
import json
import os
import shutil
import string
import subprocess
import sys
from pathlib import Path
from typing import Iterable, List, Optional, Tuple, Union
import numpy as np
from mmhuman3d.utils.path_utils import check_input_path, prepare_output_path
def prepare_output_path(output_path: str,
... | Convert a gif file to a video. Args: input_path (str): input gif file path. output_path (str): output video file path. fps (int, optional): fps. Defaults to 30. remove_raw_file (bool, optional): whether remove original input file. Defaults to False. down_sample_scale (Union[int, float], optional): down sample scale. De... |
14,416 | import glob
import json
import os
import shutil
import string
import subprocess
import sys
from pathlib import Path
from typing import Iterable, List, Optional, Tuple, Union
import numpy as np
from mmhuman3d.utils.path_utils import check_input_path, prepare_output_path
def prepare_output_path(output_path: str,
... | Convert a gif file to a folder of images. Args: input_path (str): input gif file path. output_folder (str): output folder to save the images. fps (int, optional): fps. Defaults to 30. img_format (str, optional): output image name format. Defaults to '%06d.png'. resolution (Optional[Union[Tuple[int, int], Tuple[float, f... |
14,417 | import glob
import json
import os
import shutil
import string
import subprocess
import sys
from pathlib import Path
from typing import Iterable, List, Optional, Tuple, Union
import numpy as np
from mmhuman3d.utils.path_utils import check_input_path, prepare_output_path
class vid_info_reader(object):
def __init__(se... | Spatially or temporally crop a video or gif file. Args: input_path (str): input video or gif file path. output_path (str): output video or gif file path. box (Iterable[int], optional): [x, y of the crop region left. corner and width and height]. Defaults to [0, 0, 100, 100]. resolution (Optional[Union[Tuple[int, int], ... |
14,418 | import glob
import json
import os
import shutil
import string
import subprocess
import sys
from pathlib import Path
from typing import Iterable, List, Optional, Tuple, Union
import numpy as np
from mmhuman3d.utils.path_utils import check_input_path, prepare_output_path
class vid_info_reader(object):
def __init__(se... | Temporally crop a video/gif into another video/gif. Args: input_path (str): input video or gif file path. output_path (str): output video of gif file path. start (int, optional): start frame index. Defaults to 0. end (int, optional): end frame index. Exclusive. Could be positive int or negative int or None. If None, al... |
14,419 | import glob
import json
import os
import shutil
import string
import subprocess
import sys
from pathlib import Path
from typing import Iterable, List, Optional, Tuple, Union
import numpy as np
from mmhuman3d.utils.path_utils import check_input_path, prepare_output_path
def prepare_output_path(output_path: str,
... | Spatially concat some videos as an array video. Args: input_path_list (list): input video or gif file list. output_path (str): output video or gif file path. array (List[int], optional): line number and column number of the video array]. Defaults to [1, 1]. direction (str, optional): [choose in 'h' or 'v', represent ho... |
14,420 | import glob
import json
import os
import shutil
import string
import subprocess
import sys
from pathlib import Path
from typing import Iterable, List, Optional, Tuple, Union
import numpy as np
from mmhuman3d.utils.path_utils import check_input_path, prepare_output_path
def prepare_output_path(output_path: str,
... | Concat no matter videos or gifs into a temporal sequence, and save as a new video or gif file. Args: input_path_list (List[str]): list of input video paths. output_path (str): output video file path. resolution (Optional[Union[Tuple[int, int], Tuple[float, float]]] , optional): (height, width) of output]. Defaults to (... |
14,421 | import glob
import json
import os
import shutil
import string
import subprocess
import sys
from pathlib import Path
from typing import Iterable, List, Optional, Tuple, Union
import numpy as np
from mmhuman3d.utils.path_utils import check_input_path, prepare_output_path
class vid_info_reader(object):
def __init__(se... | Compress a video file. Args: input_path (str): input video file path. output_path (str): output video file path. compress_rate (int, optional): compress rate, influents the bit rate. Defaults to 1. down_sample_scale (Union[float, int], optional): spatial down sample scale. Defaults to 1. fps (int, optional): Frames per... |
14,422 | from collections import OrderedDict
import torch.distributed as dist
from mmcv.runner import OptimizerHook
from torch._utils import (
_flatten_dense_tensors,
_take_tensors,
_unflatten_dense_tensors,
)
def _allreduce_coalesced(tensors, world_size, bucket_size_mb=-1):
if bucket_size_mb > 0:
bucket... | null |
14,423 | from functools import partial
import torch
def multi_apply(func, *args, **kwargs):
pfunc = partial(func, **kwargs) if kwargs else func
map_results = map(pfunc, *args)
return tuple(map(list, zip(*map_results))) | null |
14,424 | from functools import partial
import torch
def torch_to_numpy(x):
assert isinstance(x, torch.Tensor)
return x.detach().cpu().numpy() | null |
14,425 | from typing import Optional, Tuple, Union
import numpy as np
import torch
from mmhuman3d.core.conventions.keypoints_mapping import KEYPOINTS_FACTORY
from mmhuman3d.core.conventions.keypoints_mapping.human_data import (
HUMAN_DATA_LIMBS_INDEX,
HUMAN_DATA_PALETTE,
)
The provided code snippet includes necessary d... | Process gt 2D keypoints and apply transforms. |
14,426 | import numpy as np
import torch
from einops.einops import rearrange
from torch.nn import functional as F
The provided code snippet includes necessary dependencies for implementing the `rot6d_to_rotmat` function. Write a Python function `def rot6d_to_rotmat(x)` to solve the following problem:
Convert 6D rotation repres... | Convert 6D rotation representation to 3x3 rotation matrix. Based on Zhou et al., "On the Continuity of Rotation Representations in Neural Networks", CVPR 2019 Input: (B,6) Batch of 6-D rotation representations Output: (B,3,3) Batch of corresponding rotation matrices |
14,427 | import numpy as np
import torch
from einops.einops import rearrange
from torch.nn import functional as F
def quaternion_to_angle_axis(quaternion: torch.Tensor) -> torch.Tensor:
"""
This function is borrowed from https://github.com/kornia/kornia
Convert quaternion vector to angle axis of rotation.
Adapte... | This function is borrowed from https://github.com/kornia/kornia Convert 3x4 rotation matrix to Rodrigues vector Args: rotation_matrix (Tensor): rotation matrix. Returns: Tensor: Rodrigues vector transformation. Shape: - Input: :math:`(N, 3, 4)` - Output: :math:`(N, 3)` Example: >>> input = torch.rand(2, 3, 4) # Nx3x4 >... |
14,428 | import numpy as np
import torch
from einops.einops import rearrange
from torch.nn import functional as F
def estimate_translation_np(S,
joints_2d,
joints_conf,
focal_length=5000,
img_size=224):
"""Find ca... | Find camera translation that brings 3D joints S closest to 2D the corresponding joints_2d. Input: S: (B, 49, 3) 3D joint locations joints: (B, 49, 3) 2D joint locations and confidence Returns: (B, 3) camera translation vectors |
14,429 | import numpy as np
import torch
from einops.einops import rearrange
from torch.nn import functional as F
def perspective_projection(points, rotation, translation, focal_length,
camera_center):
"""This function computes the perspective projection of a set of points.
Input:
poin... | Perform orthographic projection of 3D points using the camera parameters, return projected 2D points in image plane. Notes: batch size: B point number: N Args: points_3d (Tensor([B, N, 3])): 3D points. camera (Tensor([B, 3])): camera parameters with the 3 channel as (scale, translation_x, translation_y) Returns: points... |
14,430 | import numpy as np
import torch
from einops.einops import rearrange
from torch.nn import functional as F
The provided code snippet includes necessary dependencies for implementing the `weak_perspective_projection` function. Write a Python function `def weak_perspective_projection(points, scale, translation)` to solve ... | This function computes the weak perspective projection of a set of points. Input: points (bs, N, 3): 3D points scale (bs,1): scalar translation (bs, 2): point 2D translation |
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