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
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ConSERT | ConSERT-master/sentence_transformers/losses/TripletLoss.py | import torch
from torch import nn, Tensor
from typing import Union, Tuple, List, Iterable, Dict
import torch.nn.functional as F
from enum import Enum
from ..SentenceTransformer import SentenceTransformer
class TripletDistanceMetric(Enum):
"""
The metric for the triplet loss
"""
COSINE = lambda x, y: 1 ... | 2,728 | 45.254237 | 164 | py |
ConSERT | ConSERT-master/sentence_transformers/losses/BatchHardSoftMarginTripletLoss.py | import torch
from torch import nn, Tensor
from typing import Union, Tuple, List, Iterable, Dict
from .BatchHardTripletLoss import BatchHardTripletLoss, BatchHardTripletLossDistanceFunction
from sentence_transformers.SentenceTransformer import SentenceTransformer
class BatchHardSoftMarginTripletLoss(BatchHardTripletLos... | 4,942 | 54.539326 | 162 | py |
ConSERT | ConSERT-master/sentence_transformers/losses/AdvCLSoftmaxLoss.py | import torch
from torch import nn, Tensor
from typing import Union, Tuple, List, Iterable, Dict, Set
from ..SentenceTransformer import SentenceTransformer
import logging
LARGE_NUM = 1e9
def scheduler0(cur_step, global_step):
return 1.0, 1.0
def scheduler1(cur_step, global_step):
"""global_step=9814"""
if... | 43,055 | 52.752809 | 226 | py |
ConSERT | ConSERT-master/sentence_transformers/losses/MegaBatchMarginLoss.py | from .. import util
import torch
from torch import nn, Tensor
from typing import Iterable, Dict
import torch.nn.functional as F
class MegaBatchMarginLoss(nn.Module):
"""
Loss function inspired from ParaNMT paper:
https://www.aclweb.org/anthology/P18-1042/
Given a large batch (like 500 or more examples... | 5,229 | 51.828283 | 209 | py |
ConSERT | ConSERT-master/sentence_transformers/losses/BatchHardTripletLoss.py | import torch
from torch import nn, Tensor
from typing import Union, Tuple, List, Iterable, Dict
from sentence_transformers import util
from sentence_transformers.SentenceTransformer import SentenceTransformer
class BatchHardTripletLossDistanceFunction:
"""
This class defines distance functions, that can be us... | 9,398 | 45.300493 | 162 | py |
ConSERT | ConSERT-master/sentence_transformers/losses/MultipleNegativesRankingLoss.py | import torch
from torch import nn, Tensor
from typing import Iterable, Dict
from ..SentenceTransformer import SentenceTransformer
from .. import util
class MultipleNegativesRankingLoss(nn.Module):
"""
This loss expects as input a batch consisting of sentence pairs (a_1, p_1), (a_2, p_2)..., (a_n, p_n)
... | 3,613 | 47.837838 | 157 | py |
ConSERT | ConSERT-master/sentence_transformers/losses/SimCLRLoss.py | import torch
from torch import nn, Tensor
from typing import Union, Tuple, List, Iterable, Dict
from ..SentenceTransformer import SentenceTransformer
import logging
LARGE_NUM = 1e9
class MLP1(nn.Module):
def __init__(self, hidden_dim=2048, norm=None, activation="relu"): # bottleneck structure
super().__i... | 10,167 | 48.120773 | 155 | py |
ConSERT | ConSERT-master/sentence_transformers/losses/BatchAllTripletLoss.py | import torch
from torch import nn, Tensor
from typing import Union, Tuple, List, Iterable, Dict
from .BatchHardTripletLoss import BatchHardTripletLoss, BatchHardTripletLossDistanceFunction
from sentence_transformers.SentenceTransformer import SentenceTransformer
class BatchAllTripletLoss(nn.Module):
"""
Batch... | 4,700 | 50.659341 | 162 | py |
ConSERT | ConSERT-master/sentence_transformers/losses/BatchSemiHardTripletLoss.py | import torch
from torch import nn, Tensor
from typing import Union, Tuple, List, Iterable, Dict
from .BatchHardTripletLoss import BatchHardTripletLoss, BatchHardTripletLossDistanceFunction
from sentence_transformers.SentenceTransformer import SentenceTransformer
class BatchSemiHardTripletLoss(nn.Module):
"""
... | 5,586 | 48.442478 | 162 | py |
ConSERT | ConSERT-master/sentence_transformers/losses/AdvCLSoftmaxLoss_refactoring.py | import torch
from torch import nn, Tensor
from typing import Union, Tuple, List, Iterable, Dict
from ..SentenceTransformer import SentenceTransformer
import logging
LARGE_NUM = 1e9
class MLP(torch.nn.Module):
def __init__(self,
input_dim: int,
hidden_dim: int,
... | 21,694 | 51.026379 | 179 | py |
ConSERT | ConSERT-master/sentence_transformers/losses/OnlineContrastiveLoss.py | from typing import Iterable, Dict
import torch.nn.functional as F
from torch import nn, Tensor
from .ContrastiveLoss import SiameseDistanceMetric
from sentence_transformers.SentenceTransformer import SentenceTransformer
class OnlineContrastiveLoss(nn.Module):
"""
Online Contrastive loss. Similar to Constrativ... | 2,732 | 51.557692 | 162 | py |
ConSERT | ConSERT-master/sentence_transformers/losses/ContrastiveLoss.py | from enum import Enum
from typing import Iterable, Dict
import torch.nn.functional as F
from torch import nn, Tensor
from sentence_transformers.SentenceTransformer import SentenceTransformer
class SiameseDistanceMetric(Enum):
"""
The metric for the contrastive loss
"""
EUCLIDEAN = lambda x, y: F.pai... | 2,794 | 44.080645 | 162 | py |
ConSERT | ConSERT-master/sentence_transformers/losses/SoftmaxLoss.py | import torch
from torch import nn, Tensor
from typing import Union, Tuple, List, Iterable, Dict
from ..SentenceTransformer import SentenceTransformer
import logging
class SoftmaxLoss(nn.Module):
"""
This loss was used in our SBERT publication (https://arxiv.org/abs/1908.10084) to train the SentenceTransformer
... | 3,637 | 45.050633 | 152 | py |
ConSERT | ConSERT-master/sentence_transformers/losses/AdvSimSiamLoss.py | import torch
from torch import nn, Tensor
from typing import Union, Tuple, List, Iterable, Dict
from ..SentenceTransformer import SentenceTransformer
import logging
LARGE_NUM = 1e9
class MLP(torch.nn.Module):
def __init__(self,
input_dim: int,
hidden_dim: int,
... | 25,701 | 49.794466 | 179 | py |
pysm | pysm-master/docs/conf.py | # -*- coding: utf-8 -*-
# Licensed under a 3-clause BSD style license - see LICENSE.rst
#
# Astropy documentation build configuration file.
#
# This file is execfile()d with the current directory set to its containing dir.
#
# Note that not all possible configuration values are present in this file.
#
# All configurati... | 7,638 | 34.86385 | 88 | py |
ROMP | ROMP-master/simple_romp/setup_trace.py | import setuptools
from distutils.core import setup, Extension
from Cython.Build import cythonize
import numpy
with open("README.md", "r", encoding="utf-8") as fh:
long_description = fh.read()
requireds = ["opencv-python","torch",
'setuptools>=18.0.0',
'cython',
'numpy>=1.21.0',
'ty... | 3,860 | 32 | 86 | py |
ROMP | ROMP-master/simple_romp/setup.py | import setuptools
from distutils.core import setup, Extension
from Cython.Build import cythonize
import numpy
with open("README.md", "r", encoding="utf-8") as fh:
long_description = fh.read()
requireds = ["opencv-python","torch",
'setuptools>=18.0.0',
'cython',
'numpy>=1.21.0',
'ty... | 2,696 | 31.890244 | 86 | py |
ROMP | ROMP-master/simple_romp/tools/convert_checkpoints.py | from os import remove
from sklearn.model_selection import PredefinedSplit
import torch
import sys
def remove_prefix(state_dict, prefix='module.', remove_keys=['_result_parser', '_calc_loss']):
keys = list(state_dict.keys())
print('orginal keys:', keys)
for key in keys:
exist_flag = True
for... | 900 | 29.033333 | 94 | py |
ROMP | ROMP-master/simple_romp/evaluation/eval_AGORA.py | from bev import BEV
import argparse
import os, sys
import os.path as osp
import numpy as np
import cv2
import torch
import pickle
from romp import ResultSaver
from romp.utils import progress_bar
set_id = 0
set_name = ['validation', 'test'][set_id]
# preparing data:
# 1. Register and download 1280x720 images of test s... | 6,028 | 46.849206 | 244 | py |
ROMP | ROMP-master/simple_romp/evaluation/eval_cmu_panoptic.py | from unittest import result
from bev import BEV
import argparse
import os, sys
import os.path as osp
import numpy as np
import cv2
import torch
import pickle
from romp import ResultSaver
from romp.utils import progress_bar
from itertools import product
model_id = 2
model_dict = {
1: '/home/yusun/CenterMesh/trained... | 13,464 | 39.80303 | 150 | py |
ROMP | ROMP-master/simple_romp/evaluation/eval_Relative_Human.py |
import argparse
import os, sys
import os.path as osp
import numpy as np
import cv2
import torch
from romp import ResultSaver
from romp.utils import progress_bar
from RH_evaluation import RH_Evaluation
set_id = 1
set_name = ['val', 'test'][set_id]
method_id = 1
method_name = ['ROMP', 'BEV'][method_id]
model_path = ... | 6,799 | 44.637584 | 158 | py |
ROMP | ROMP-master/simple_romp/evaluation/RH_evaluation/evaluation.py | import os, sys
import os.path as osp
import numpy as np
import cv2
import torch
from .matching import match_2d_greedy
Relative_Human_dir = '/home/yusun/data_drive/dataset/Relative_human'
results_path = '/home/yusun/data_drive/evaluation_results/Relative_results/zip_files/CRMH_RH_results.npz'
relative_age_types = ['... | 13,517 | 48.881919 | 204 | py |
ROMP | ROMP-master/simple_romp/vis_human/main.py | import imp
import cv2
import torch
import numpy as np
from .vis_utils import mesh_color_left2right, mesh_color_trackID, rotate_view_perspective, rendering_mesh_rotating_view, \
rotate_view_weak_perspective, draw_skeleton_multiperson, Plotter3dPoses
import copy
import time
import os
def setup_renderer(name='sim3dr'... | 6,121 | 53.176991 | 156 | py |
ROMP | ROMP-master/simple_romp/vis_human/vis_utils.py | import cv2
import torch
import numpy as np
color_list = np.array([[.7, .7, .6],[.7, .5, .5],[.5, .5, .7], [.5, .55, .3],[.3, .5, .55], \
[1,0.855,0.725],[0.588,0.804,0.804],[1,0.757,0.757], [0.933,0.474,0.258],[0.847,191/255,0.847], [0.941,1,1]])
focal_length = 443.4
def get_rotate_x_mat(angle):
angle = ... | 15,199 | 43.970414 | 200 | py |
ROMP | ROMP-master/simple_romp/trace2/main.py | import cv2
import torch
from torch import nn
import numpy as np
np.set_printoptions(precision=3, suppress=True)
import time
from .track import collect_sequence_tracking_results
from .utils.infer_settings import update_seq_cfgs, get_seq_cfgs, trace_settings
from .models.raft.process import FlowExtract, show_seq_flow
f... | 8,264 | 56 | 187 | py |
ROMP | ROMP-master/simple_romp/trace2/track.py | import numpy as np
import torch
import glob
import os
import joblib
import lap
import cv2
from torch import nn
from trace2.tracker.tracker3D import Tracker
from trace2.evaluation.evaluate_tracking import evaluate_trackers
import pyrender, trimesh
def video2frame(video_name, frame_save_dir=None):
cap = OpenCVCaptur... | 31,441 | 50.292007 | 222 | py |
ROMP | ROMP-master/simple_romp/trace2/show.py | import os
import sys
import argparse
import numpy as np
from .utils.open3d_gui import visualize_world_annots
# pip install MarkupSafe==2.0.1 Werkzeug==2.0.3
import torch
import glob
from smplx import SMPL
smpl_model_folder = '/Users/mac/Desktop/Githubs/'
def show_settings(input_args=sys.argv[1:]):
parser = argpar... | 3,706 | 55.166667 | 169 | py |
ROMP | ROMP-master/simple_romp/trace2/evaluation/eval_3DPW.py | import argparse
import os, sys
import os.path as osp
import numpy as np
import cv2
import torch
from itertools import product
import joblib
from .smpl import SMPL, SMPLA_parser
import glob
import tqdm
from ..utils.utils import angle_axis_to_rotation_matrix, rotation_matrix_to_angle_axis
LSP_14 = {
'R_Ankle':0, 'R_... | 28,393 | 42.95356 | 176 | py |
ROMP | ROMP-master/simple_romp/trace2/evaluation/smpl.py | from __future__ import absolute_import
from __future__ import print_function
from __future__ import division
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_joints_idxs, J_regressor_extra9, J_regressor_h36m17, dt... | 16,373 | 40.877238 | 173 | py |
ROMP | ROMP-master/simple_romp/trace2/evaluation/dynacam_evaluation/utils.py | import numpy as np
import cv2
import os,sys
import torch
import quaternion
def joint_mapping(source_format, target_format):
mapping = np.ones(len(target_format),dtype=np.int32)*-1
for joint_name in target_format:
if joint_name in source_format:
mapping[target_format[joint_name]] = source_fo... | 2,754 | 41.384615 | 158 | py |
ROMP | ROMP-master/simple_romp/trace2/evaluation/dynacam_evaluation/loading_data.py | import numpy as np
np.set_printoptions(precision=3, suppress=True)
import os
import pickle
import torch
from .utils import glamr_mapping2D
def process_idx(reorganize_idx, vids=None):
result_size = reorganize_idx.shape[0]
reorganize_idx = reorganize_idx
used_org_inds = np.unique(reorganize_idx)
per_img_... | 6,320 | 45.138686 | 145 | py |
ROMP | ROMP-master/simple_romp/trace2/results_parser/smpl_wrapper_relative_temp.py | import torch
import torch.nn as nn
import numpy as np
import logging
from ..models.smpl import SMPL
from ..utils.utils import vertices_kp3d_projection, rot6D_to_angular, parse_age_cls_results
class SMPLWrapper(nn.Module):
def __init__(self, smpl_model_path):
super(SMPLWrapper, self).__init__()
log... | 4,503 | 51.372093 | 149 | py |
ROMP | ROMP-master/simple_romp/trace2/results_parser/centermap.py | import torch
import numpy as np
class CenterMap(object):
def __init__(self,centermap_conf_thresh=0.05,style='heatmap_adaptive_scale'):
self.style=style
self.size = 128
self.max_person = 64
self.shrink_scale = float(512//self.size)
self.kernel_size = 5
self.dims = 1
... | 20,333 | 43.69011 | 165 | py |
ROMP | ROMP-master/simple_romp/trace2/results_parser/temp_result_parser.py | import torch
import torch.nn as nn
import logging
import numpy as np
from .smpl_wrapper_relative_temp import SMPLWrapper
from .centermap import CenterMap
SMPL_24 = {'Pelvis_SMPL':0, 'L_Hip_SMPL':1, 'R_Hip_SMPL':2, 'Spine_SMPL': 3, 'L_Knee':4, 'R_Knee':5, 'Thorax_SMPL': 6, 'L_Ankle':7, 'R_Ankle':8,'Thorax_up_SMPL':9, \... | 5,643 | 47.239316 | 165 | py |
ROMP | ROMP-master/simple_romp/trace2/models/basic_modules.py | import torch
import torch.nn as nn
BN_MOMENTUM = 0.1
def conv3x3(in_planes, out_planes, stride=1):
"""3x3 convolution with padding"""
return nn.Conv2d(in_planes, out_planes, kernel_size=3, stride=stride,
padding=1, bias=False)
class BasicBlock(nn.Module):
expansion = 1
def __init... | 17,635 | 35.589212 | 135 | py |
ROMP | ROMP-master/simple_romp/trace2/models/model.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 .basic_modules import BasicBlock, BasicBlock_1D, BasicBlock_3D, ConvGRU, TemporalEncoder, get_coord_maps, get_3Dcoord_maps_zeroz
from .TempTracker imp... | 37,792 | 62.304858 | 198 | py |
ROMP | ROMP-master/simple_romp/trace2/models/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
def regress_joints_from_vertices(vertices, J_regressor):
if J_regre... | 15,230 | 39.400531 | 143 | py |
ROMP | ROMP-master/simple_romp/trace2/models/hrnet_32.py | from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import torch
import torch.nn as nn
from .basic_modules import BasicBlock,Bottleneck,HighResolutionModule
BN_MOMENTUM = 0.1
def BHWC_to_BCHW(x):
"""
:param x: torch tensor, B x H x W x C
:return: t... | 8,458 | 38.344186 | 90 | py |
ROMP | ROMP-master/simple_romp/trace2/models/TempTracker.py | import torch
import numpy as np
from ..tracker.tracker3D import Tracker
from ..utils.utils import denormalize_cam_params_to_trans, OneEuroFilter
def convert_traj2D2center_yxs(traj2D_gts, outmap_size, seq_mask):
"""
Flattening N valuable trajectories in 2D body centers, traj2D_gts,
from shape (batch, 64... | 27,567 | 55.607803 | 174 | py |
ROMP | ROMP-master/simple_romp/trace2/models/debug_utils.py | import torch
import glob
import numpy as np
import cv2
import os
def prepare_bare_temporal_inputs(clip_length=8, batch_size=2):
inputs = {'image':torch.rand(batch_size*clip_length,512,512,3).float().cuda(),
'sequence_mask':torch.ones(batch_size*clip_length).bool().cuda()}
return inputs
def pr... | 3,014 | 37.653846 | 106 | py |
ROMP | ROMP-master/simple_romp/trace2/models/raft/corr.py | import torch
import torch.nn.functional as F
from .utils.utils import bilinear_sampler, coords_grid
try:
import alt_cuda_corr
except:
print("alt_cuda_corr is not compiled")
pass
class CorrBlock:
def __init__(self, fmap1, fmap2, num_levels=4, radius=4):
self.num_levels = num_levels
sel... | 3,093 | 32.630435 | 74 | py |
ROMP | ROMP-master/simple_romp/trace2/models/raft/update.py | import torch
import torch.nn as nn
import torch.nn.functional as F
class FlowHead(nn.Module):
def __init__(self, input_dim=128, hidden_dim=256):
super(FlowHead, self).__init__()
self.conv1 = nn.Conv2d(input_dim, hidden_dim, 3, padding=1)
self.conv2 = nn.Conv2d(hidden_dim, 2, 3, padding=1)
... | 5,302 | 37.151079 | 87 | py |
ROMP | ROMP-master/simple_romp/trace2/models/raft/extractor.py | import torch
import torch.nn as nn
import torch.nn.functional as F
class ResidualBlock(nn.Module):
def __init__(self, in_planes, planes, norm_fn='group', stride=1):
super(ResidualBlock, self).__init__()
self.conv1 = nn.Conv2d(in_planes, planes, kernel_size=3, padding=1, stride=stride)
s... | 8,847 | 32.014925 | 93 | py |
ROMP | ROMP-master/simple_romp/trace2/models/raft/raft.py | import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
from ..raft.update import BasicUpdateBlock, SmallUpdateBlock
from ..raft.extractor import BasicEncoder, SmallEncoder
from ..raft.corr import CorrBlock, AlternateCorrBlock
from ..raft.utils.utils import bilinear_sampler, coords_grid, ... | 4,722 | 32.978417 | 102 | py |
ROMP | ROMP-master/simple_romp/trace2/models/raft/process.py |
import cv2
import numpy as np
import torch
import cv2
from torch import nn
import torch.nn.functional as F
from ..raft.raft import RAFT
from ..raft.utils import flow_viz
class FlowExtract(nn.Module):
def __init__(self, model_path, device='cuda'):
super(FlowExtract, self).__init__()
model = torch.... | 2,224 | 32.208955 | 172 | py |
ROMP | ROMP-master/simple_romp/trace2/models/raft/utils/utils.py | import torch
import torch.nn.functional as F
import numpy as np
from scipy import interpolate
class InputPadder:
""" Pads images such that dimensions are divisible by 8 """
def __init__(self, dims, mode='sintel'):
self.ht, self.wd = dims[-2:]
pad_ht = (((self.ht // 8) + 1) * 8 - self.ht) % 8
... | 2,489 | 29 | 93 | py |
ROMP | ROMP-master/simple_romp/trace2/models/raft/utils/augmentor.py | import numpy as np
import random
import math
from PIL import Image
import cv2
cv2.setNumThreads(0)
cv2.ocl.setUseOpenCL(False)
import torch
from torchvision.transforms import ColorJitter
import torch.nn.functional as F
class FlowAugmentor:
def __init__(self, crop_size, min_scale=-0.2, max_scale=0.5, do_flip=Tru... | 9,108 | 35.878543 | 97 | py |
ROMP | ROMP-master/simple_romp/trace2/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/simple_romp/trace2/models/deform_conv/functions/deform_pool.py | import torch
from torch.autograd import Function
import deform_pool_cuda
class DeformRoIPoolingFunction(Function):
@staticmethod
def forward(ctx,
data,
rois,
offset,
spatial_scale,
out_size,
out_channels,
... | 2,362 | 32.757143 | 78 | py |
ROMP | ROMP-master/simple_romp/trace2/models/deform_conv/functions/deform_conv.py | import torch
from torch.autograd import Function
from torch.nn.modules.utils import _pair
import deform_conv_cuda
class DeformConvFunction(Function):
@staticmethod
def forward(ctx,
input,
offset,
weight,
stride=1,
padding=0,
... | 7,317 | 38.989071 | 79 | py |
ROMP | ROMP-master/simple_romp/trace2/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/simple_romp/trace2/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/simple_romp/trace2/utils/infer_settings.py | import os, sys
import numpy as np
import argparse
import torch
import copy
from .utils import download_model
trace_model_dir = os.path.join(os.path.expanduser("~"),'.romp', 'TRACE_models')
def trace_settings(input_args=sys.argv[1:]):
parser = argparse.ArgumentParser(description='TRACE: 5D Temporal Regression of A... | 7,520 | 64.973684 | 190 | py |
ROMP | ROMP-master/simple_romp/trace2/utils/utils.py | import torch
from torch.nn import functional as F
import numpy as np
import cv2, os
#-----------------------------------------------------------------------------------------#
# save_paths #
#--------------------------------------------------... | 26,092 | 42.271973 | 172 | py |
ROMP | ROMP-master/simple_romp/trace2/utils/visualize_results.py | import cv2
import os
import numpy as np
import torch
import time
import copy
import math
from vis_human import setup_renderer
color_table_default = np.array([
[0.4, 0.6, 1], # blue
[0.8, 0.7, 1], # pink
[0.1, 0.9, 1], # cyan
[0.8, 0.9, 1], # gray
[1, 0.6, 0.4], # orange
[1, 0.7, 0.8], # rose
... | 19,766 | 50.746073 | 195 | py |
ROMP | ROMP-master/simple_romp/trace2/utils/infer_utils.py | import torch
import copy
import os
delete_output_keys = ['params_pred', 'verts', 'verts_camed_org', 'world_verts', 'world_j3d', 'world_verts_camed_org', 'detection_flag']
def remove_large_keys(outputs, del_keys=delete_output_keys):
save_outputs = copy.deepcopy(outputs)
for key in del_keys:
del save_ou... | 2,347 | 42.481481 | 192 | py |
ROMP | ROMP-master/simple_romp/trace2/utils/load_data.py | import glob
import random
import cv2
import torch
import os
import os.path as osp
import numpy as np
from torch.utils.data import Dataset, DataLoader
default_frame_dir = os.path.join(os.path.expanduser('~'), 'TRACE_input_frames')
def video2frame(video_path, frame_save_dir=None):
cap = cv2.VideoCapture(video_path)... | 8,257 | 45.920455 | 146 | py |
ROMP | ROMP-master/simple_romp/trace2/utils/open3d_gui.py | # This script is modified from https://github.com/isl-org/Open3D/blob/master/examples/python/gui/vis-gui.py
# This script also refers to https://github.com/mkocabas/body-model-visualizer
# ----------------------------------------------------------------------------
# - Open3D: www.open3d.org ... | 16,457 | 43.123324 | 130 | py |
ROMP | ROMP-master/simple_romp/romp/main.py | from .model import ROMPv1
import cv2
import numpy as np
import os, sys
import os.path as osp
import torch
from torch import nn
import argparse
from .post_parser import SMPL_parser, body_mesh_projection2image, parsing_outputs
from .utils import img_preprocess, create_OneEuroFilter, euclidean_distance, check_filter_stat... | 11,721 | 54.819048 | 182 | py |
ROMP | ROMP-master/simple_romp/romp/post_parser.py | import torch
from torch import nn
import sys,os
import numpy as np
from .smpl import SMPL
from .utils import rot6D_to_angular, batch_orth_proj, estimate_translation
class CenterMap(object):
def __init__(self, conf_thresh):
self.size = 64
self.max_person = 64
self.sigma = 1
self.conf... | 6,437 | 42.5 | 140 | py |
ROMP | ROMP-master/simple_romp/romp/utils.py | from __future__ import print_function
import torch
from torch.nn import functional as F
import numpy as np
import cv2, os, sys
import os.path as osp
from time import time
#from scipy.spatial.transform import Rotation as R
from threading import Thread
import re
#---------------------------------------------------------... | 32,059 | 38.629172 | 179 | py |
ROMP | ROMP-master/simple_romp/romp/model.py | from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import torch
import torch.nn as nn
def get_coord_maps(size=128):
xx_ones = torch.ones([1, size], dtype=torch.int32)
xx_ones = xx_ones.unsqueeze(-1)
xx_range = torch.arange(size, dtype=torch.int32)... | 20,057 | 36.421642 | 106 | py |
ROMP | ROMP-master/simple_romp/romp/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):
... | 15,346 | 39.600529 | 143 | py |
ROMP | ROMP-master/simple_romp/romp/pack_smpl_info.py | import pickle
import numpy as np
import os
import torch
import argparse
VERTEX_IDS = {
'smplh': {
'nose': 332,
'reye': 6260,
'leye': 2800,
'rear': 4071,
'lear': 583,
'rthumb': 6191,
... | 5,850 | 44.710938 | 207 | py |
ROMP | ROMP-master/simple_romp/bev/main.py |
import cv2
import numpy as np
import os, sys
import os.path as osp
import torch
from torch import nn
import argparse
import copy
from .model import BEVv1
from .post_parser import SMPLA_parser, body_mesh_projection2image, pack_params_dict,\
suppressing_redundant_prediction_via_projection, remove_outlier, denormali... | 18,658 | 56.767802 | 209 | py |
ROMP | ROMP-master/simple_romp/bev/post_parser.py | import torch
from torch import nn
import numpy as np
from romp.smpl import SMPL
from romp.utils import rot6D_to_angular
def get_3Dcoord_maps(size=128, z_base=None):
range_arr = torch.arange(size, dtype=torch.float32)
if z_base is None:
Z_map = range_arr.reshape(1,size,1,1,1).repeat(1,1,size,size,1) / s... | 12,442 | 43.758993 | 156 | py |
ROMP | ROMP-master/simple_romp/bev/model.py | import torch
import torch.nn as nn
import numpy as np
from romp.model import HigherResolutionNet, BasicBlock
from .post_parser import CenterMap3D
BN_MOMENTUM = 0.1
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_l... | 13,894 | 46.423208 | 170 | py |
ROMP | ROMP-master/simple_romp/bev/split2process.py | import numpy as np
import cv2
import torch
from .post_parser import remove_subjects
def padding_image_overlap(image, overlap_ratio=0.46):
h, w = image.shape[:2]
pad_length = int(h* overlap_ratio)
pad_w = w+2*pad_length
pad_image = np.zeros((h, pad_w, 3), dtype=np.uint8)
top, left = 0, pad_length
... | 2,634 | 34.133333 | 104 | py |
ROMP | ROMP-master/simple_romp/bev/pack_smil_info.py | import pickle
import numpy as np
import os
import torch
import argparse
VERTEX_IDS = {
'smplh': {
'nose': 332,
'reye': 6260,
'leye': 2800,
'rear': 4071,
'lear': 583,
'rthumb': 6191,
'rindex': 5782,
'... | 4,675 | 41.899083 | 207 | py |
ROMP | ROMP-master/romp/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
import config... | 11,054 | 56.279793 | 166 | py |
ROMP | ROMP-master/romp/pretrain.py | from base import *
from eval import val_result
from loss_funcs import Learnable_Loss
from maps_utils import HeatmapParser,CenterMap
from loss_funcs.maps_loss import focal_loss, Heatmap_AE_loss
from loss_funcs.keypoints_loss import batch_kp_2d_l2_loss
from visualization.visualization import draw_skeleton_multiperson, dr... | 10,468 | 49.090909 | 186 | py |
ROMP | ROMP-master/romp/test.py |
from .base import *
from loss_funcs import _calc_MPJAE, calc_mpjpe, calc_pampjpe, align_by_parts
from .eval import val_result,print_results
from visualization.visualization import draw_skeleton_multiperson
import pandas
import pickle
class Demo(Base):
def __init__(self):
super(Demo, self).__init__()
... | 9,897 | 46.816425 | 166 | py |
ROMP | ROMP-master/romp/eval.py |
from .base import *
from loss_funcs import _calc_MPJAE, calc_mpjpe, calc_pampjpe, calc_pck, align_by_parts
from evaluation import h36m_evaluation_act_wise, cmup_evaluation_act_wise
from evaluation.evaluation_matrix import _calc_relative_age_error_weak_, _calc_absolute_depth_error,\
... | 15,297 | 54.427536 | 179 | py |
ROMP | ROMP-master/romp/train.py | from .base import *
from .eval import val_result
from loss_funcs import Loss, Learnable_Loss
np.set_printoptions(precision=2, suppress=True)
class Trainer(Base):
def __init__(self):
super(Trainer, self).__init__()
self._build_model_()
self._build_optimizer()
self.set_up_val_loader(... | 8,171 | 48.228916 | 168 | py |
ROMP | ROMP-master/romp/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('romp', 'lib', 'config.py'), '')
source_dir... | 20,432 | 71.201413 | 206 | py |
ROMP | ROMP-master/romp/lib/evaluation/collect_VIBE_3DPW_results.py | import pickle
import pickle as pkl
import zipfile
import torch
import numpy as np
import os,sys,glob
import joblib
import time
from scipy.sparse import csr_matrix
sys.path.append(os.path.abspath(__file__).replace('core/collect_VIBE_3DPW_results.py',''))
from evaluation import compute_error_verts, compute_similarity_tra... | 26,957 | 49.960302 | 202 | py |
ROMP | ROMP-master/romp/lib/evaluation/collect_3DPW_results.py | import pickle
import zipfile
import sys, os
sys.path.append(os.path.abspath(__file__).replace('evaluation/collect_3DPW_results.py',''))
sys.path.append(os.path.abspath(__file__).replace('lib/evaluation/collect_3DPW_results.py',''))
from base import *
np.set_printoptions(precision=2, suppress=True)
class Submit(Base):
... | 10,270 | 48.379808 | 168 | py |
ROMP | ROMP-master/romp/lib/evaluation/eval_CRMH_results.py | import pickle
import pickle as pkl
import zipfile
import torch
import numpy as np
import os,sys,glob
import joblib
import time
import cv2
import json
from scipy.sparse import csr_matrix
sys.path.append(os.path.abspath(__file__).replace('evaluation/eval_CRMH_results.py',''))
from utils.util import transform_rot_represen... | 9,050 | 50.135593 | 186 | py |
ROMP | ROMP-master/romp/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/romp/lib/evaluation/collect_CRMH_3DPW_results.py | import pickle
import pickle as pkl
import zipfile
import torch
import numpy as np
import os,sys,glob
import joblib
import time
import cv2
from scipy.sparse import csr_matrix
sys.path.append(os.path.abspath(__file__).replace('evaluation/collect_CRMH_3DPW_results.py',''))
from utils.util import transform_rot_representati... | 22,192 | 47.245652 | 202 | py |
ROMP | ROMP-master/romp/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, \
... | 611 | 60.2 | 141 | py |
ROMP | ROMP-master/romp/lib/evaluation/evaluation_matrix.py | import os,sys
import torch
import numpy as np
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 _calc_matched_PCKh_(real, pred, kp2d_mask, error_thresh=0.143):
# error_thresh is set as the ratio betwe... | 17,750 | 34.933198 | 173 | py |
ROMP | ROMP-master/romp/lib/maps_utils/target_generators.py | #Brought from https://github.com/HRNet/HigherHRNet-Human-Pose-Estimation/blob/master/lib/dataset/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 __init... | 6,286 | 39.044586 | 135 | py |
ROMP | ROMP-master/romp/lib/maps_utils/kp_group.py | # ------------------------------------------------------------------------------
# Copyright (c) Microsoft
# Licensed under the MIT License.
# Some code is from https://github.com/princeton-vl/pose-ae-train/blob/454d4ba113bbb9775d4dc259ef5e6c07c2ceed54/utils/group.py
# Written by Bin Xiao (leoxiaobin@gmail.com)
# Modif... | 9,937 | 32.688136 | 145 | py |
ROMP | ROMP-master/romp/lib/maps_utils/centermap.py | import torch
import sys,os
import numpy as np
from config import args
from utils.cam_utils import convert_cam_params_to_centermap_coords, convert_scale_to_depth_level
class CenterMap(object):
def __init__(self,style='heatmap_adaptive_scale'):
self.style=style
self.size = args().centermap_size
... | 22,022 | 42.870518 | 165 | py |
ROMP | ROMP-master/romp/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... | 19,158 | 54.372832 | 214 | py |
ROMP | ROMP-master/romp/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):
return (item.shape, item.device)
elif isinstance(item, np.np.nd... | 617 | 23.72 | 57 | py |
ROMP | ROMP-master/romp/lib/dataset/camera_parameters.py | 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],
'tangential_distortion': [... | 14,171 | 38.921127 | 125 | py |
ROMP | ROMP-master/romp/lib/dataset/image_base_relative.py | from dataset.image_base import *
from maps_utils.centermap import _calc_radius_
class Image_base_relative(Image_base):
def __init__(self, train_flag=True, regress_smpl = False, **kwargs):
super(Image_base_relative,self).__init__(train_flag=train_flag, regress_smpl=regress_smpl)
self.depth_degree_th... | 15,632 | 55.233813 | 181 | py |
ROMP | ROMP-master/romp/lib/dataset/mixed_dataset.py | import torch
import numpy as np
import sys, os
from prettytable import PrettyTable
import config
from config import args
from torch.utils.data import Dataset
import logging
import random
from .h36m import H36M
from .cmu_panoptic_eval import CMU_Panoptic_eval
from .mpii import MPII
from .AICH import AICH
from .up impor... | 4,792 | 46.455446 | 169 | py |
ROMP | ROMP-master/romp/lib/dataset/image_base.py | import sys, os
import glob
import numpy as np
import random
import cv2
import json
import h5py
import torch
import shutil
import time
import pickle
import copy
import joblib
import logging
import scipy.io as scio
#import quaternion
from PIL import Image
import torchvision
from torch.utils.data import Dataset, DataLoade... | 36,575 | 50.155245 | 174 | py |
ROMP | ROMP-master/romp/lib/dataset/internet.py | import glob
import numpy as np
import random
import cv2
import torch
import shutil
import time
import copy
from PIL import Image
import torchvision
from torch.utils.data import Dataset, DataLoader
from dataset.image_base import *
from dataset.base import Base_Classes, Test_Funcs
import config
from config import args
im... | 3,438 | 32.067308 | 107 | py |
ROMP | ROMP-master/romp/lib/dataset/MuPoTS.py | from pycocotools.coco import COCO
from dataset.image_base import *
from dataset.base import Base_Classes, Test_Funcs
default_mode = args().image_loading_mode
def MuPoTS(base_class=default_mode):
class MuPoTS(Base_Classes[base_class]):
def __init__(self,train_flag=False, split='test', **kwargs):
... | 20,994 | 39.609284 | 178 | py |
ROMP | ROMP-master/romp/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 time
import pickle
import numpy as np
import config
import constants
from config import args
from utils import batch_rodrigues, rotation_matrix_to_angle_axis
def ba... | 3,904 | 42.388889 | 120 | py |
ROMP | ROMP-master/romp/lib/loss_funcs/learnable_loss.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 time
import pickle
import numpy as np
import math
from config import args
from loss_funcs.keypoints_loss import batch_kp_2d_l2_loss, ca... | 3,172 | 44.328571 | 153 | py |
ROMP | ROMP-master/romp/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 focal_lo... | 8,660 | 31.806818 | 118 | py |
ROMP | ROMP-master/romp/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 time
import pickle
import numpy as np
import torch.nn.functional as F
from evaluation import compute_error_verts, compute_similarity_tr... | 4,854 | 43.541284 | 114 | py |
ROMP | ROMP-master/romp/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... | 8,281 | 44.01087 | 145 | py |
ROMP | ROMP-master/romp/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
import time
import pickle
import numpy as np
from config import args
DEFAULT_DTYPE = torch.float32
class Interperlation_penalty(nn.Module):
def __i... | 15,092 | 38.823219 | 129 | py |
ROMP | ROMP-master/romp/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 time
import pickle
import numpy as np
import sys, os
import config
from config import args
import constants
from utils.center_utils import denormalize_center
from loss... | 9,411 | 54.692308 | 212 | py |
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