repo stringlengths 1 99 | file stringlengths 13 215 | code stringlengths 12 59.2M | file_length int64 12 59.2M | avg_line_length float64 3.82 1.48M | max_line_length int64 12 2.51M | extension_type stringclasses 1
value |
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ROMP | ROMP-master/romp/lib/models/balanced_dataparallel.py | '''
borrowed from https://github.com/xingyizhou/CenterNet/blob/819e0d0dde02f7b8cb0644987a8d3a370aa8206a/src/lib/models/scatter_gather.py
'''
import torch
from torch.autograd import Variable
from torch.nn.parallel._functions import Scatter, Gather
from torch.nn.modules import Module
from torch.nn.parallel.scatter_gathe... | 6,785 | 42.5 | 132 | py |
ROMP | ROMP-master/romp/lib/models/base.py | from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import os,sys
import torch
import torch.nn as nn
import config
from config import args
from utils i... | 3,554 | 33.852941 | 118 | py |
ROMP | ROMP-master/romp/lib/models/romp_model.py | from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import torch
import torch.nn as nn
import sys, os
root_dir = os.path.join(os.path.dirname(__file__),'..')
if root_dir not in sys.path:
sys.path.insert(0, root_dir)
from models.base import Base
from models.... | 5,420 | 39.155556 | 137 | py |
ROMP | ROMP-master/romp/lib/models/basic_modules.py | from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import os
import logging
import torch
import torch.nn as nn
import sys, os
from config import args
... | 10,785 | 31.684848 | 93 | py |
ROMP | ROMP-master/romp/lib/models/CoordConv.py | import torch
from torch import nn
def get_3Dcoord_maps_halfz(size, z_base):
range_arr = torch.arange(size, dtype=torch.float32)
z_len = len(z_base)
Z_map = z_base.reshape(1,z_len,1,1,1).repeat(1,1,size,size,1)
Y_map = range_arr.reshape(1,1,size,1,1).repeat(1,z_len,1,size,1) / size * 2 -1
X_map = ra... | 5,101 | 33.013333 | 101 | py |
ROMP | ROMP-master/romp/lib/models/hrnet_32.py | from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import os,sys
import torch
import torch.nn as nn
import sys, os
root_dir = os.path.join(os.path.dir... | 8,881 | 38.127753 | 127 | py |
ROMP | ROMP-master/romp/lib/models/resnet_50.py | from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import torch
import torch.nn as nn
import torchvision.models.resnet as resnet
import torchvision.transforms.functional as F
import sys, os
root_dir = os.path.join(os.path.dirname(__file__),'..')
if root_dir not ... | 5,569 | 37.680556 | 128 | py |
ROMP | ROMP-master/romp/lib/models/build.py | import sys, os
import torch
import torch.nn as nn
from config import args
from models.hrnet_32 import HigherResolutionNet
from models.resnet_50 import ResNet_50
from models.romp_model import ROMP
from models.bev_model import BEV
Backbones = {'hrnet': HigherResolutionNet, 'resnet': ResNet_50}
Heads = {1: ROMP, 6:BEV}
... | 1,017 | 27.277778 | 63 | py |
ROMP | ROMP-master/romp/lib/models/bev_model.py | from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import torch
import torch.nn as nn
import torch.nn.functional as F
import sys, os
from models.base import Base
from models.CoordConv import get_coord_maps, get_3Dcoord_maps, get_3Dcoord_maps_halfz
from mod... | 12,898 | 52.970711 | 170 | py |
ROMP | ROMP-master/romp/lib/visualization/visualization.py | import numpy as np
import torch
import cv2
import torch.nn.functional as F
import trimesh
import matplotlib
matplotlib.use('agg')
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
import math
import copy
import os, sys
import pytorch3d.renderer as pyr
import constants
import config
from config i... | 22,272 | 53.192214 | 195 | py |
ROMP | ROMP-master/romp/lib/visualization/renderer_pt3d.py | # -*- coding: utf-8 -*-
# brought from https://github.com/mkocabas/VIBE/blob/master/lib/utils/renderer.py
import sys, os
import json
import torch
from torch import nn
import pickle
# Data structures and functions for rendering
from pytorch3d.structures import Meshes, join_meshes_as_scene
from pytorch3d.renderer import ... | 6,380 | 39.386076 | 151 | py |
ROMP | ROMP-master/romp/lib/visualization/vedo_visualizer.py | import os,sys
import vedo
from vedo import *
import vtk
from vtk.util.numpy_support import vtk_to_numpy
import numpy as np
import pickle
import cv2
import torch
import config
import constants
from config import args
from utils.temporal_optimization import OneEuroFilter
from utils.projection import convert_cam_to_3d_tr... | 10,446 | 46.058559 | 214 | py |
ROMP | ROMP-master/romp/lib/visualization/renderer_pyrd.py | import pyrender
import trimesh
import numpy as np
import torch
class Renderer(object):
def __init__(self, focal_length=600, height=512, width=512,**kwargs):
self.renderer = pyrender.OffscreenRenderer(height, width)
self.camera_center = np.array([width / 2., height / 2.])
self.focal_length ... | 4,135 | 39.54902 | 114 | py |
ROMP | ROMP-master/romp/lib/visualization/vis_platform/vis_server_py36_o3d9.py | '''
Brought from https://github.com/zju3dv/EasyMocap/blob/master/easymocap/socket/o3d.py
'''
import sys, os
root_dir = os.path.join(os.path.dirname(__file__),'..')
if root_dir not in sys.path:
sys.path.insert(0, root_dir)
import open3d as o3d
from visualization.vis_utils_py36_o3d9 import Timer, CritRange, Config... | 9,467 | 36.275591 | 118 | py |
ROMP | ROMP-master/romp/lib/visualization/vis_platform/vis_server_o3d13.py | '''
Brought from https://github.com/zju3dv/EasyMocap/blob/master/easymocap/socket/o3d.py
'''
import os,sys
import open3d as o3d
from .vis_utils_o3d13 import Timer, get_rgb_01, CritRange, Config, Vector3dVector, Vector2iVector, get_uvs,\
load_mesh, create_mesh, create_mesh_with_uvma... | 10,037 | 37.756757 | 118 | py |
ROMP | ROMP-master/romp/lib/utils/demo_utils.py | import cv2
import keyboard
import imageio
import torch
import numpy as np
import random
from transforms3d.axangles import axangle2mat
import pickle
from PIL import Image
import torchvision
import time
import os,sys
import config
import constants
from config import args
from utils import save_obj
def get_video_bn... | 3,613 | 30.982301 | 148 | py |
ROMP | ROMP-master/romp/lib/utils/cam_utils.py | import torch
import torch.nn.functional as F
import numpy as np
import cv2
import sys, os
import config
import constants
from config import args
tan_fov = np.tan(np.radians(args().FOV/2.))
cam3dmap_anchor = torch.from_numpy(constants.get_cam3dmap_anchor(args().FOV,args().centermap_size)).float()
scale_num = len(cam3dm... | 7,450 | 38.215789 | 176 | py |
ROMP | ROMP-master/romp/lib/utils/center_utils.py | import torch
import constants
from config import args
import numpy as np
from .cam_utils import convert_cam_params_to_centermap_coords
def denormalize_center(center, size=args().centermap_size):
center = (center+1)/2*size
center[center<1] = 1
center[center>size - 1] = size - 1
if isinstance(center, np... | 1,230 | 36.30303 | 73 | py |
ROMP | ROMP-master/romp/lib/utils/train_utils.py | import sys,os
import random
import torch
import numpy as np
import logging
def justify_detection_state(detection_flag, reorganize_idx):
if detection_flag.sum() == 0:
detection_flag = False
else:
reorganize_idx = reorganize_idx[detection_flag.bool()].long()
detection_flag = True
retu... | 8,553 | 36.030303 | 108 | py |
ROMP | ROMP-master/romp/lib/utils/rot_6D.py | import torch
from torch.nn import functional as F
import numpy as np
def rot6D_to_angular(rot6D):
batch_size = rot6D.shape[0]
pred_rotmat = rot6d_to_rotmat(rot6D).view(batch_size, -1, 3, 3)
pose = rotation_matrix_to_angle_axis(
pred_rotmat.reshape(-1, 3, 3)).reshape(batch_size, -1)
return pose
... | 21,235 | 36.387324 | 126 | py |
ROMP | ROMP-master/romp/lib/utils/projection.py | import torch
import numpy as np
import sys, os
import constants
from config import args
from utils.cam_utils import denormalize_cam_params_to_trans
def filter_out_incorrect_trans(kp_3ds, trans, kp_2ds, thresh=20, focal_length=args().focal_length, center_offset=torch.Tensor([args().input_size, args().input_size])/2.):... | 7,285 | 46.311688 | 183 | py |
ROMP | ROMP-master/romp/lib/utils/util.py | #encoding=utf-8
import h5py
import torch
import numpy as np
import json
import torch.nn.functional as F
import cv2
import math
import hashlib
import shutil
import pickle
import yaml
import csv
import platform
import os,sys
import glob
from io import BytesIO
from scipy.spatial.transform import Rotation as R
TAG_CHAR = ... | 26,620 | 30.318824 | 129 | py |
ROMP | ROMP-master/romp/lib/utils/augments.py | import imgaug as ia
import imgaug.augmenters as iaa
from imgaug.augmenters import compute_paddings_to_reach_aspect_ratio, Crop, Pad
from imgaug.augmentables import Keypoint, KeypointsOnImage
import random
import cv2
import numpy as np
ia.seed(1)
import random
import math
import numpy as np
import torch
from PIL impor... | 19,215 | 38.136456 | 155 | py |
ROMP | ROMP-master/romp/lib/smpl_family/smpl_regressor.py | import sys,os
import torch
import torch.nn as nn
import config
import numpy as np
from .smpl import SMPL
from config import args
class SMPLR(nn.Module):
def __init__(self, use_gender=False):
super(SMPLR, self).__init__()
model_path = os.path.join(config.model_dir,'parameters','smpl')
self.... | 1,010 | 37.884615 | 89 | py |
ROMP | ROMP-master/romp/lib/smpl_family/smpl_wrapper.py | import torch
import torch.nn as nn
import numpy as np
import sys, os
import config
from config import args
import constants
from smpl_family.smpl import SMPL
from utils.projection import vertices_kp3d_projection
from utils.rot_6D import rot6D_to_angular
class SMPLWrapper(nn.Module):
def __init__(self):
s... | 2,999 | 57.823529 | 165 | py |
ROMP | ROMP-master/romp/lib/smpl_family/smpla.py | import torch
from smpl_family.smpl import SMPL
import torch.nn as nn
class SMPLA_parser(nn.Module):
def __init__(self, smpla_path, smil_path, baby_thresh=0.8):
super(SMPLA_parser, self).__init__()
self.smil_model = SMPL(smil_path, model_type='smpl')
self.smpla_model = SMPL(smpla_path, model... | 1,390 | 50.518519 | 175 | py |
ROMP | ROMP-master/romp/lib/smpl_family/smpl.py | from __future__ import absolute_import
from __future__ import print_function
from __future__ import division
import os,sys
import os.path as osp
import pickle
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
class VertexJointSelector(nn.Module):
def __init__(self, extra_joint... | 15,028 | 39.618919 | 143 | py |
ROMP | ROMP-master/romp/lib/smpl_family/smpl_wrapper_relative.py | import torch
import torch.nn as nn
import numpy as np
import logging
import sys, os
import config
from config import args
import constants
from smpl_family.smpla import SMPLA_parser
from utils.projection import vertices_kp3d_projection
from utils.rot_6D import rot6D_to_angular
import torch.nn.functional as F
class SM... | 4,277 | 50.542169 | 145 | py |
ROMP | ROMP-master/romp/lib/tracking/tracker.py | import numpy as np
from numba import jit
from collections import deque
import itertools
import os
import os.path as osp
import time
import torch
import cv2
import torch.nn.functional as F
import sys, os
from tracking.tracking_utils.utils import *
from tracking.tracking_utils.log import logger
from tracking.tracking_ut... | 10,867 | 36.09215 | 136 | py |
ROMP | ROMP-master/romp/lib/tracking/tracking_utils/utils.py | import glob
import random
import time
import os
import os.path as osp
import cv2
import matplotlib.pyplot as plt
import numpy as np
import torch
import torch.nn.functional as F
from torchvision.ops import nms
#import maskrcnn_benchmark.layers.nms as nms
# Set printoptions
torch.set_printoptions(linewidth=1320, precis... | 16,654 | 36.343049 | 117 | py |
ROMP | ROMP-master/romp/predict/webcam.py | import sys
whether_set_yml = ['configs_yml' in input_arg for input_arg in sys.argv]
if sum(whether_set_yml)==0:
default_webcam_configs_yml = "--configs_yml=configs/webcam.yml"
print('No configs_yml is set, set it to the default {}'.format(default_webcam_configs_yml))
sys.argv.append(default_webcam_configs_... | 8,507 | 47.896552 | 131 | py |
ROMP | ROMP-master/romp/predict/image.py | import sys
whether_set_yml = ['configs_yml' in input_arg for input_arg in sys.argv]
if sum(whether_set_yml)==0:
default_webcam_configs_yml = "--configs_yml=configs/image.yml"
print('No configs_yml is set, set it to the default {}'.format(default_webcam_configs_yml))
sys.argv.append(default_webcam_configs_y... | 3,820 | 47.367089 | 134 | py |
ROMP | ROMP-master/romp/predict/video.py | import sys
whether_set_yml = ['configs_yml' in input_arg for input_arg in sys.argv]
if sum(whether_set_yml)==0:
default_webcam_configs_yml = "--configs_yml=configs/video.yml"
print('No configs_yml is set, set it to the default {}'.format(default_webcam_configs_yml))
sys.argv.append(default_webcam_configs_y... | 11,234 | 53.275362 | 140 | py |
ROMP | ROMP-master/romp/predict/base_predictor.py | from ..base import *
from utils.projection import convert_cam_to_3d_trans
from utils.demo_utils import save_meshes, get_video_bn, Time_counter
import platform
from utils.util import save_result_dict_tonpz
from dataset.internet import img_preprocess
from torch.cuda.amp import autocast
class Predictor(Base):
def __i... | 5,089 | 54.326087 | 156 | py |
ROMP | ROMP-master/trace/base.py | import sys, os, cv2
import numpy as np
import time, datetime
import logging
import copy, random, itertools
from prettytable import PrettyTable
import pickle
import torch
import torch.nn as nn
from torch.utils.tensorboard import SummaryWriter
from torch.utils.data import Dataset, DataLoader, ConcatDataset
#from pudb im... | 15,186 | 60.735772 | 166 | py |
ROMP | ROMP-master/trace/track.py | from base import *
from utils.demo_utils import video2frame
import glob
import os
import joblib
import lap
from tracker.byte_tracker_3dcenter import Tracker
from visualization.BEV_visualizer import Renderer, convert_front_view_to_bird_view_video
def linear_assignment(cost_matrix, thresh):
if cost_matrix.size == ... | 36,781 | 61.342373 | 512 | py |
ROMP | ROMP-master/trace/train_video.py |
from base import *
from lib.models.build import build_temporal_model
from lib.datasets.mixed_dataset import MixedDataset, SingleVideoDataset
from lib.utils.video_utils import ordered_organize_frame_outputs_to_clip
from lib.loss_funcs import Loss, Learnable_Loss
from lib import config
np.set_printoptions(precision=2, s... | 19,555 | 53.778711 | 239 | py |
ROMP | ROMP-master/trace/lib/config.py | import os,sys
import argparse
import math
import numpy as np
import torch
import yaml
import logging
import time
import platform
currentfile = os.path.abspath(__file__)
code_dir = currentfile.replace('config.py','')
project_dir = currentfile.replace(os.path.sep+os.path.join('trace', 'lib', 'config.py'), '')
source_di... | 31,413 | 75.995098 | 210 | py |
ROMP | ROMP-master/trace/lib/tracker/byte_tracker_3dcenter_combined.py | import numpy as np
from collections import deque
import os
import os.path as osp
import copy
import torch
import torch.nn.functional as F
from lib.tracker.kalman_filter_3dcenter import KalmanFilter
from lib.tracker import matching
from lib.tracker.basetrack import BaseTrack, TrackState
from lib.config import args
cla... | 20,490 | 39.818725 | 154 | py |
ROMP | ROMP-master/trace/lib/evaluation/smpl.py | from __future__ import absolute_import
from __future__ import print_function
from __future__ import division
import os,sys
import os.path as osp
import pickle
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
#from .utils import time_cost
class VertexJointSelector(nn.Module):
... | 16,455 | 40.450882 | 173 | py |
ROMP | ROMP-master/trace/lib/evaluation/eval_ds_utils.py | import sys,os
import torch
import numpy as np
def cmup_evaluation_act_wise(results,imgpaths,action_names):
actions = []
action_results = []
for imgpath in imgpaths:
actions.append(os.path.basename(imgpath).split('-')[0].split('_')[1])
for action_name in action_names:
action_idx = np.wh... | 3,015 | 39.756757 | 94 | py |
ROMP | ROMP-master/trace/lib/evaluation/eval_3DMPB.py | import os
import numpy as np
import cv2
import torch
from itertools import product
from evaluation.smpl import SMPL, SMPLA_parser
from evaluation.evaluation_matrix import compute_error_accel_np
import glob
import json
import tqdm
from utils.rotation_transform import angle_axis_to_rotation_matrix, rotation_matrix_to_ang... | 21,509 | 43.719335 | 176 | py |
ROMP | ROMP-master/trace/lib/evaluation/__init__.py | import sys, os
lib_dir = os.path.dirname(__file__)
root_dir = os.path.join(lib_dir.replace(os.path.basename(lib_dir), ''))
if root_dir not in sys.path:
sys.path.insert(0, root_dir)
from .evaluation_matrix import compute_error_verts, compute_similarity_transform, compute_similarity_transform_torch, \
... | 613 | 54.818182 | 141 | py |
ROMP | ROMP-master/trace/lib/evaluation/evaluation_matrix.py | import os,sys
import torch
import numpy as np
sys.path.append(os.path.abspath(__file__).replace('evaluation/evaluation_matrix.py',''))
import config
import constants
from smplx import SMPL
# Part of the codes are brought from https://github.com/mkocabas/VIBE/blob/master/lib/utils/eval_utils.py
def compute_accel(joints... | 20,249 | 35.160714 | 173 | py |
ROMP | ROMP-master/trace/lib/maps_utils/matching.py | import torch
import numpy as np
from config import args
from utils.center_utils import process_gt_center, parse_gt_center3d
from maps_utils.matching_utils import greedy_matching_kp2ds
def match_with_kp2ds(meta_data, outputs, cfg):
#print(outputs['pj2d'].shape, meta_data['full_kp2d'].shape, meta_data['valid_masks'... | 8,637 | 47.52809 | 139 | py |
ROMP | ROMP-master/trace/lib/maps_utils/target_generators.py | #Brought from https://github.com/HRNet/HigherHRNet-Human-Pose-Estimation/blob/master/lib/datasets/target_generators/target_generators.py
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import numpy as np
import torch
class HeatmapGenerator():
def __ini... | 6,287 | 39.050955 | 136 | py |
ROMP | ROMP-master/trace/lib/maps_utils/relative_parser.py | from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import numpy as np
import torch
import torch.nn.functional as F
import sys, os
import constants
from config import args
def parse_age_cls_results(age_probs):
age_preds = torch.ones_like(age_probs).long()*-... | 2,203 | 45.893617 | 115 | py |
ROMP | ROMP-master/trace/lib/maps_utils/centermap.py | import torch
import sys,os
import numpy as np
from config import args
class CenterMap(object):
def __init__(self,style='heatmap_adaptive_scale'):
self.style=style
self.size = args().centermap_size
self.max_person = args().max_person
self.shrink_scale = float(args().input_size//self.... | 23,676 | 42.846296 | 165 | py |
ROMP | ROMP-master/trace/lib/maps_utils/suppress_duplication.py | import torch
from config import args
from utils.rot_6D import rot6D_to_angular
from loss_funcs.params_loss import batch_smpl_pose_l2_error
def suppressing_silimar_mesh_and_2D_center(params_preds, pred_batch_ids, pred_czyxs, top_score,rot_dim=6, center2D_thresh=5, pose_thresh=2.5): # center2D_thresh=5, pose_thresh=2.5... | 2,680 | 57.282609 | 214 | py |
ROMP | ROMP-master/trace/lib/maps_utils/result_parser.py | import os,sys
import torch
import torch.nn as nn
import numpy as np
import logging
import config
from config import args
import constants
if not args().learn_relative:
from smpl_family.smpl_wrapper import SMPLWrapper
else:
from smpl_family.smpl_wrapper_relative import SMPLWrapper
from maps_utils.centermap im... | 15,769 | 54.528169 | 178 | py |
ROMP | ROMP-master/trace/lib/maps_utils/debug_utils.py | import torch
import numpy as np
def print_dict(td):
keys = collect_keyname(td)
print(keys)
def get_size(item):
if isinstance(item, list) or isinstance(item, tuple):
return len(item)
elif isinstance(item, torch.Tensor) or isinstance(item, np.ndarray):
return item.shape
else:
... | 566 | 23.652174 | 72 | py |
ROMP | ROMP-master/trace/lib/maps_utils/temp_result_parser.py | import os
import sys
import torch
import torch.nn as nn
import numpy as np
import logging
import config
from config import args
import constants
from smpl_family.smpl_wrapper_relative_temp import SMPLWrapper
from maps_utils.centermap import CenterMap
from maps_utils.debug_utils import print_dict
from maps_utils.match... | 11,728 | 48.910638 | 142 | py |
ROMP | ROMP-master/trace/lib/loss_funcs/matching.py | import torch
import numpy as np
import lap
def linear_assignment(cost_matrix, thresh=100.):
if cost_matrix.size == 0:
return np.empty((0, 2), dtype=int), tuple(range(cost_matrix.shape[0])), tuple(range(cost_matrix.shape[1]))
matches, unmatched_a, unmatched_b = [], [], []
cost, x, y = lap.lapjv(cost... | 2,701 | 38.735294 | 144 | py |
ROMP | ROMP-master/trace/lib/loss_funcs/params_loss.py | from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import torch
import torch.nn as nn
import sys, os
root_dir = os.path.join(os.path.dirname(__file__),'..')
if root_dir not in sys.path:
sys.path.insert(0, root_dir)
import time
import pickle
import numpy as... | 2,932 | 40.9 | 120 | py |
ROMP | ROMP-master/trace/lib/loss_funcs/learnable_loss.py | from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from asyncio import constants
import torch
import torch.nn as nn
import torch.nn.functional as F
import sys, os
root_dir = os.path.join(os.path.dirname(__file__),'..')
if root_dir not in sys.path:
sys.path.... | 4,810 | 47.59596 | 153 | py |
ROMP | ROMP-master/trace/lib/loss_funcs/maps_loss.py | from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import torch
import torch.nn as nn
import sys
import os
import config
import time
import pickle
import numpy as np
from utils.center_utils import denormalize_center
DEFAULT_DTYPE = torch.float32
def motion_o... | 15,169 | 37.308081 | 158 | py |
ROMP | ROMP-master/trace/lib/loss_funcs/keypoints_loss.py | from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import torch
import torch.nn as nn
import sys, os
import constants
import cv2
import time
import pickle
import numpy as np
import torch.nn.functional as F
from evaluation import compute_error_verts, compute_si... | 5,773 | 42.413534 | 124 | py |
ROMP | ROMP-master/trace/lib/loss_funcs/relative_loss.py | from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import torch
import torch.nn as nn
import sys, os
import time
import pickle
import numpy as np
import config
import constants
from config import args
from utils.cam_utils import convert_scale_to_depth
def m... | 9,686 | 44.055814 | 145 | py |
ROMP | ROMP-master/trace/lib/loss_funcs/video_loss.py | from numpy import result_type
import torch
import numpy as np
import itertools
from config import args
from utils.cam_utils import denormalize_cam_params_to_trans
from utils.projection import perspective_projection, perspective_projection_withfovs
from loss_funcs.keypoints_loss import batch_kp_2d_l2_loss
from utils.ro... | 24,692 | 47.994048 | 187 | py |
ROMP | ROMP-master/trace/lib/loss_funcs/prior_loss.py | from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import torch
import torch.nn as nn
import sys
import os
from config import args
import time
import pickle
import numpy as np
DEFAULT_DTYPE = torch.float32
class Interperlation_penalty(nn.Module):
def __i... | 15,023 | 38.641161 | 129 | py |
ROMP | ROMP-master/trace/lib/loss_funcs/calc_loss.py | from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import torch
import torch.nn as nn
import numpy as np
from config import args
import constants
from utils.center_utils import denormalize_center
from loss_funcs.params_loss import batch_smpl_pose_l2_error, bat... | 17,575 | 60.670175 | 207 | py |
ROMP | ROMP-master/trace/lib/models/video_base.py | from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import os,sys
import torch
import torch.nn as nn
import numpy as np
import config
from config import args
from utils import print_dict
if args().model_precision=='fp16':
from torch.cuda.amp import autocast... | 7,980 | 49.834395 | 174 | py |
ROMP | ROMP-master/trace/lib/models/base.py | from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import os,sys
import torch
import torch.nn as nn
import config
from config import args
from maps_utils.debug_utils import print_dict
if args().model_precision=='fp16':
from torch.cuda.amp import autocast
... | 5,705 | 38.625 | 118 | py |
ROMP | ROMP-master/trace/lib/models/tempGRU.py | import os
import torch
import os.path as osp
import torch.nn as nn
import torch.nn.functional as F
class TemporalEncoder(nn.Module):
def __init__(
self,
with_gru=False,
input_size=128,
out_size=[6],
n_gru_layers=1,
hidden_size=128):
s... | 1,511 | 30.5 | 92 | py |
ROMP | ROMP-master/trace/lib/models/basic_modules.py | from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import os
import logging
import torch
import torch.nn as nn
import sys, os
from config import args
BN_MOMENTUM = 0.1
logger = logging.getLogger(__name__)
def conv3x3(in_planes, out_planes, stride=1):
""... | 10,675 | 31.748466 | 93 | py |
ROMP | ROMP-master/trace/lib/models/GRU.py | import torch
import torch.nn as nn
import torch.nn.functional as F
class ConvGRUCell(nn.Module):
def __init__(self, hidden_dim=128, input_dim=128+128, kernel_size=3):
super(ConvGRUCell, self).__init__()
self.convz = nn.Conv2d(hidden_dim+input_dim, hidden_dim, kernel_size, padding=1)
self.co... | 13,611 | 37.891429 | 137 | py |
ROMP | ROMP-master/trace/lib/models/CoordConv.py | import torch
from torch import nn
def get_3Dcoord_maps_halfz(size, z_base):
range_arr = torch.arange(size, dtype=torch.float32)
z_len = len(z_base)
Z_map = z_base.reshape(1,z_len,1,1,1).repeat(1,1,size,size,1)
Y_map = range_arr.reshape(1,1,size,1,1).repeat(1,z_len,1,size,1) / size * 2 -1
X_map = ra... | 5,470 | 33.19375 | 101 | py |
ROMP | ROMP-master/trace/lib/models/TempRegressor.py | import torch
from torch import nn
from functools import partial
from einops.layers.torch import Rearrange, Reduce
class PreNormResidual(nn.Module):
def __init__(self, dim, fn):
super().__init__()
self.fn = fn
self.norm = nn.LayerNorm(dim)
def forward(self, x):
return self.fn(s... | 3,083 | 32.16129 | 100 | py |
ROMP | ROMP-master/trace/lib/models/hrnet_32.py | from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import os,sys
import torch
import torch.nn as nn
import sys, os
from models.basic_modules import Ba... | 8,829 | 38.419643 | 127 | py |
ROMP | ROMP-master/trace/lib/models/TemporalRegressor.py | # Modified from https://github.com/lucidrains/vit-pytorch/blob/main/vit_pytorch/vit.py
import torch
from torch import nn
import numpy as np
import torch.nn.functional as F
from einops import rearrange
import math
class Residual(nn.Module):
def __init__(self, fn):
super().__init__()
self.fn = fn
... | 8,899 | 34.6 | 150 | py |
ROMP | ROMP-master/trace/lib/models/modelv6.py | from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import torch
import torch.nn as nn
import torch.nn.functional as F
import sys, os
from models.base import Base
from models.CoordConv import get_coord_maps, get_3Dcoord_maps, get_3Dcoord_maps_halfz
from mod... | 19,327 | 53.909091 | 170 | py |
ROMP | ROMP-master/trace/lib/models/resnet_50.py | from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import torch
import torch.nn as nn
import torchvision.models.resnet as resnet
import torchvision.transforms.functional as F
import sys, os
from utils import BHWC_to_BCHW, copy_state_dict
from models.CoordConv im... | 5,551 | 37.825175 | 131 | py |
ROMP | ROMP-master/trace/lib/models/TempTracker.py | import torch
import numpy as np
from config import args
from lib.tracker.byte_tracker_3dcenter_combined import Tracker
from utils.cam_utils import denormalize_cam_params_to_trans
from utils.smooth_filter import OneEuroFilter
def resize2tracking_format(pred_batch_ids, pred_czyxs, clip_length, seq_masks, image_repeat=ar... | 37,239 | 56.647059 | 174 | py |
ROMP | ROMP-master/trace/lib/models/trace.py | from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import torch
import torch.nn as nn
import torch.nn.functional as F
import numpy as np
import logging
import copy
import config
from config import args
import cv2
from models.video_base import VideoBase
from map... | 60,368 | 64.193305 | 305 | py |
ROMP | ROMP-master/trace/lib/models/EProPnP6DoFSolver.py | import torch
from torch import nn
from epropnp.epropnp import EProPnP6DoF
from epropnp.levenberg_marquardt import LMSolver, RSLMSolver
from epropnp.camera import PerspectiveCamera
from epropnp.cost_fun import AdaptiveHuberPnPCost
def prepare_camera_mats(fovs, length, device):
cam_mats = torch.zeros(length, 3, 3).... | 2,698 | 46.350877 | 126 | py |
ROMP | ROMP-master/trace/lib/models/modelv1.py | from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import torch
import torch.nn as nn
import sys, os
from models.base import Base
from models.CoordConv import get_coord_maps
from models.basic_modules import BasicBlock,Bottleneck
import config
from config impo... | 5,309 | 39.227273 | 137 | py |
ROMP | ROMP-master/trace/lib/models/build.py | import sys, os
import torch
import torch.nn as nn
from models.hrnet_32 import HigherResolutionNet
from models.resnet_50 import ResNet_50
from models.modelv1 import ROMP
from models.modelv6 import BEV
from models.trace import TROMPv2
Backbones = {'hrnet': HigherResolutionNet, 'resnet': ResNet_50}
Heads = {1: ROMP, 6:BE... | 1,179 | 27.780488 | 63 | py |
ROMP | ROMP-master/trace/lib/models/deform_conv/setup.py | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required b... | 1,785 | 29.793103 | 74 | py |
ROMP | ROMP-master/trace/lib/models/deform_conv/functions/deform_pool.py | import torch
from torch.autograd import Function
from .. import deform_pool_cuda
class DeformRoIPoolingFunction(Function):
@staticmethod
def forward(ctx,
data,
rois,
offset,
spatial_scale,
out_size,
out_channels,... | 2,370 | 32.871429 | 78 | py |
ROMP | ROMP-master/trace/lib/models/deform_conv/functions/deform_conv.py | import torch
from torch.autograd import Function
from torch.nn.modules.utils import _pair
from .. import deform_conv_cuda
class DeformConvFunction(Function):
@staticmethod
def forward(ctx,
input,
offset,
weight,
stride=1,
paddin... | 7,325 | 39.032787 | 79 | py |
ROMP | ROMP-master/trace/lib/models/deform_conv/modules/deform_pool.py | from torch import nn
from ..functions.deform_pool import deform_roi_pooling
class DeformRoIPooling(nn.Module):
def __init__(self,
spatial_scale,
out_size,
out_channels,
no_trans,
group_size=1,
part_size=None,
... | 7,058 | 39.803468 | 79 | py |
ROMP | ROMP-master/trace/lib/models/deform_conv/modules/deform_conv.py | import math
import torch
import torch.nn as nn
from torch.nn.modules.utils import _pair
from ..functions.deform_conv import deform_conv, modulated_deform_conv
class DeformConv(nn.Module):
def __init__(self,
in_channels,
out_channels,
kernel_size,
... | 5,198 | 31.905063 | 78 | py |
ROMP | ROMP-master/trace/lib/parallel/balanced_dataparallel.py | '''
borrowed from https://github.com/xingyizhou/CenterNet/blob/819e0d0dde02f7b8cb0644987a8d3a370aa8206a/src/lib/models/scatter_gather.py
'''
import torch
from torch.autograd import Variable
from torch.nn.parallel._functions import Scatter, Gather
from torch.nn.modules import Module
from torch.nn.parallel.scatter_gathe... | 6,785 | 42.5 | 132 | py |
ROMP | ROMP-master/trace/lib/parallel/criterion_parallel.py | ##+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
## Created by: Hang Zhang
## ECE Department, Rutgers University
## Email: zhang.hang@rutgers.edu
## Copyright (c) 2017
##
## This source code is licensed under the MIT-style license found in the
## LICENSE file in the root directory of this sou... | 7,126 | 37.317204 | 144 | py |
ROMP | ROMP-master/trace/lib/datasets/internet_video.py | import glob
import numpy as np
import random
import cv2
import torch
import shutil
import os
from datasets.image_base import *
import config
from config import args
from datasets.base import Base_Classes, Test_Funcs
default_mode = args().video_loading_mode if args().video else args().image_loading_mode
def InternetVi... | 5,037 | 42.808696 | 139 | py |
ROMP | ROMP-master/trace/lib/datasets/camera_parameters.py | from config import args
h36m_cameras_intrinsic_params = [
{
'id': '54138969',
'center': [512.54150390625, 515.4514770507812],
'focal_length': [1145.0494384765625, 1143.7811279296875],
'radial_distortion': [-0.20709891617298126, 0.24777518212795258, -0.0030751503072679043],
... | 14,996 | 38.779841 | 125 | py |
ROMP | ROMP-master/trace/lib/datasets/DynaCamRotation.py |
import sys, os
from collections import OrderedDict
from datasets.image_base import *
from datasets.base import Base_Classes, Test_Funcs
from utils.rotation_transform import angle_axis_to_rotation_matrix
default_mode = args().video_loading_mode if args().video else args().image_loading_mode
invalida_detection_seqs = ... | 18,814 | 53.066092 | 163 | py |
ROMP | ROMP-master/trace/lib/datasets/image_base_relative.py | from datasets.image_base import *
from maps_utils.centermap import _calc_radius_
from utils.rotation_transform import angle_axis_to_rotation_matrix, rotation_matrix_to_angle_axis
from utils.projection import perspective_projection_withfovs
class Image_base_relative(Image_base):
def __init__(self, train_flag=True, ... | 32,156 | 55.218531 | 218 | py |
ROMP | ROMP-master/trace/lib/datasets/image_base.py | import os
import numpy as np
import random
import cv2
import json
import torch
import shutil
import pickle
import copy
import logging
import scipy.io as scio
#import quaternion
from PIL import Image
import torchvision
from torch.utils.data import Dataset, DataLoader
from collections import OrderedDict
from config impo... | 42,364 | 50.917892 | 174 | py |
ROMP | ROMP-master/trace/lib/datasets/video_base_relative.py | from datasets.image_base_relative import *
from datasets.image_base import get_bounding_bbox
from random import sample
from utils.video_utils import convert_centers_to_trajectory
"""
python -m lib.datasets.pw3d --configs_yml='configs/video_v1.yml'
"""
class Video_base_relative(Image_base_relative):
def __init__(se... | 37,405 | 51.462833 | 197 | py |
ROMP | ROMP-master/trace/lib/visualization/visualization.py | import numpy as np
import torch
import cv2
import torch.nn.functional as F
import trimesh
import matplotlib
matplotlib.use('agg')
import matplotlib.pyplot as plt
import math
import copy
import os, sys
import pytorch3d.renderer as pyr
import constants
import config
from config import args
import utils.projection as pr... | 34,182 | 53.344992 | 195 | py |
ROMP | ROMP-master/trace/lib/visualization/world_vis.py | import numpy as np
import os, sys
import glob
import copy
import torch
import cv2
from torch import nn
import pytorch3d
import tqdm
import constants
from visualization.visualization import draw_skeleton
import quaternion
def _axis_angle_rotation(axis: str, angle: torch.Tensor) -> torch.Tensor:
"""
Return the r... | 18,318 | 53.520833 | 247 | py |
ROMP | ROMP-master/trace/lib/visualization/renderer_pt3d.py | # -*- coding: utf-8 -*-
# brought from https://github.com/mkocabas/VIBE/blob/master/lib/utils/renderer.py
import sys, os
import json
import torch
from torch import nn
import pickle
# Data structures and functions for rendering
from pytorch3d.structures import Meshes, join_meshes_as_scene, Pointclouds
from pytorch3d.ren... | 9,517 | 41.491071 | 151 | py |
ROMP | ROMP-master/trace/lib/epropnp/epropnp.py | """
Copyright (C) 2010-2022 Alibaba Group Holding Limited.
"""
import math
import torch
from abc import ABCMeta, abstractmethod
from functools import partial
from pyro.distributions import MultivariateStudentT
from .common import evaluate_pnp, pnp_normalize, pnp_denormalize
from .distributions import VonMisesUniform... | 16,389 | 46.784257 | 105 | py |
ROMP | ROMP-master/trace/lib/epropnp/distributions.py | """
Copyright (C) 2010-2022 Alibaba Group Holding Limited.
"""
import math
import numpy as np
import torch
from torch.distributions import VonMises
from torch.distributions.multivariate_normal import _batch_mahalanobis, _standard_normal, _batch_mv
from pyro.distributions import TorchDistribution, constraints
from pyr... | 3,459 | 42.25 | 107 | py |
ROMP | ROMP-master/trace/lib/epropnp/camera.py | """
Copyright (C) 2010-2022 Alibaba Group Holding Limited.
"""
import torch
from .common import yaw_to_rot_mat, quaternion_to_rot_mat, skew
def project_a(x3d, pose, cam_mats, z_min: float):
if pose.size(-1) == 4:
x3d_rot = x3d @ (yaw_to_rot_mat(pose[..., -1])).transpose(-1, -2)
else:
x3d_rot... | 7,629 | 37.535354 | 100 | py |
ROMP | ROMP-master/trace/lib/epropnp/levenberg_marquardt.py | """
Copyright (C) 2010-2022 Alibaba Group Holding Limited.
"""
import math
import torch
import torch.nn as nn
import torch.nn.functional as F
from functools import partial
from .common import evaluate_pnp, pnp_normalize, pnp_denormalize
def solve_wrapper(b, A):
if A.numel() > 0:
return torch.linalg.sol... | 16,326 | 45.121469 | 112 | py |
ROMP | ROMP-master/trace/lib/epropnp/common.py | """
Copyright (C) 2010-2022 Alibaba Group Holding Limited.
"""
import torch
def skew(x):
"""
Args:
x (torch.Tensor): shape (*, 3)
Returns:
torch.Tensor: (*, 3, 3), skew symmetric matrices
"""
mat = x.new_zeros(x.shape[:-1] + (3, 3))
mat[..., [2, 0, 1], [1, 2, 0]] = x
mat[... | 4,774 | 33.854015 | 98 | py |
ROMP | ROMP-master/trace/lib/epropnp/cost_fun.py | """
Copyright (C) 2010-2022 Alibaba Group Holding Limited.
"""
import torch
def huber_kernel(s_sqrt, delta):
half_rho = torch.where(s_sqrt <= delta,
0.5 * torch.square(s_sqrt),
delta * s_sqrt - 0.5 * torch.square(delta))
return half_rho
def huber_d_kern... | 4,519 | 32.984962 | 97 | py |
ROMP | ROMP-master/trace/lib/utils/smooth_filter.py | import numpy as np
import torch
def smooth_global_rot_matrix(pred_rots, OE_filter):
rot_mat = batch_rodrigues(pred_rots[None]).squeeze(0)
smoothed_rot_mat = OE_filter.process(rot_mat)
smoothed_rot = rotation_matrix_to_angle_axis(smoothed_rot_mat.reshape(1,3,3)).reshape(-1)
return smoothed_rot
dev... | 2,908 | 36.779221 | 179 | py |
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