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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PoseTriplet | PoseTriplet-main/hallucinator/code_rib/train.py | import torch
import sys, os
sys.path.insert(0, os.path.dirname(__file__))
from LaFan import LaFan1
from torch.utils.data import Dataset, DataLoader
from model import StateEncoder, \
OffsetEncoder, \
TargetEncoder, \
LSTM, \
Decoder, \
... | 21,083 | 55.074468 | 159 | py |
PoseTriplet | PoseTriplet-main/hallucinator/code_rib/probe/plot_clusters.py | import torch
import os
import sys
import argparse
import importlib
import numpy as np
from os.path import join as pjoin
BASEPATH = os.path.dirname(__file__)
sys.path.insert(0, pjoin(BASEPATH))
sys.path.insert(0, pjoin(BASEPATH, '..'))
from data_loader import get_dataloader
from latent_plot_utils import get_all_plots,... | 3,359 | 31.307692 | 111 | py |
PoseTriplet | PoseTriplet-main/hallucinator/code_rib/probe/latent_plot_utils.py | import os
import sys
import numpy as np
import matplotlib.pyplot as plt
import torch
from sklearn.manifold import TSNE
from sklearn.decomposition import PCA
from matplotlib import cm
from matplotlib.legend_handler import HandlerLine2D, HandlerTuple
import tikzplotlib
from os.path import join as pjoin
BASEPATH = os.path... | 12,382 | 32.833333 | 128 | py |
PoseTriplet | PoseTriplet-main/hallucinator/code_rib/probe/anim_view.py | import os
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.animation as animation
from mpl_toolkits.mplot3d import Axes3D
import matplotlib.patheffects as pe
from matplotlib import cm
import torch
import argparse
import sys
from os.path import join as pjoin
BASEPATH = os.path.dirname(os.path.abspath... | 11,180 | 30.764205 | 133 | py |
PoseTriplet | PoseTriplet-main/estimator_inference/videopose-j16-wild-eval_run.py | import pickle
from common.arguments import parse_args
from common.camera import camera_to_world, normalize_screen_coordinates, image_coordinates
from common.generators import UnchunkedGenerator
from common.utils import evaluate, add_path
from tool.utils import *
import scipy.signal
import glob
add_path()
"""
inferen... | 12,683 | 43.041667 | 129 | py |
PoseTriplet | PoseTriplet-main/estimator_inference/joints_detectors/Alphapose/matching.py | # -----------------------------------------------------
# Copyright (c) Shanghai Jiao Tong University. All rights reserved.
# Written by Jiefeng Li (jeff.lee.sjtu@gmail.com)
# -----------------------------------------------------
import numpy as np
import torch
from scipy.optimize import linear_sum_assignment
sigmas ... | 7,289 | 30.695652 | 122 | py |
PoseTriplet | PoseTriplet-main/estimator_inference/joints_detectors/Alphapose/online_demo.py | import torch
from torch.autograd import Variable
import torch.nn.functional as F
import torchvision.transforms as transforms
import torch.nn as nn
import torch.utils.data
import numpy as np
from opt import opt
from dataloader import WebcamLoader, DataWriter, crop_from_dets, Mscoco
from yolo.darknet import Darknet
fro... | 5,619 | 35.732026 | 144 | py |
PoseTriplet | PoseTriplet-main/estimator_inference/joints_detectors/Alphapose/video_demo.py | import os
from SPPE.src.main_fast_inference import *
from dataloader import ImageLoader, DetectionLoader, DetectionProcessor, DataWriter, Mscoco
from fn import getTime
from opt import opt
from pPose_nms import write_json
from tqdm import tqdm
def main(args):
inputpath = args.inputpath
inputlist = args.inputl... | 4,055 | 32.520661 | 144 | py |
PoseTriplet | PoseTriplet-main/estimator_inference/joints_detectors/Alphapose/visualization_copy.py | # Copyright (c) 2018-present, Facebook, Inc.
# All rights reserved.
#
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
#
import time
import cv2
import matplotlib.pyplot as plt
import numpy as np
from matplotlib.animation import FuncAnimation, write... | 18,486 | 36.883197 | 147 | py |
PoseTriplet | PoseTriplet-main/estimator_inference/joints_detectors/Alphapose/fn.py | import collections
import math
import re
import time
import cv2
import numpy as np
import torch
from torch._six import string_classes, int_classes
RED = (0, 0, 255)
GREEN = (0, 255, 0)
BLUE = (255, 0, 0)
CYAN = (255, 255, 0)
YELLOW = (0, 255, 255)
ORANGE = (0, 165, 255)
PURPLE = (255, 0, 255)
numpy_type_map = {
... | 9,863 | 40.79661 | 122 | py |
PoseTriplet | PoseTriplet-main/estimator_inference/joints_detectors/Alphapose/dataloader.py | import os
import sys
import time
from multiprocessing import Queue as pQueue
from threading import Thread
import cv2
import numpy as np
import torch
import torch.multiprocessing as mp
import torch.utils.data as data
import torchvision.transforms as transforms
from PIL import Image
from torch.autograd import Variable
... | 28,847 | 35.842912 | 124 | py |
PoseTriplet | PoseTriplet-main/estimator_inference/joints_detectors/Alphapose/webcam_demo.py | from opt import opt
import os
import numpy as np
import cv2
from tqdm import tqdm
from SPPE.src.main_fast_inference import *
from dataloader_webcam import WebcamLoader, DetectionLoader, DetectionProcessor, DataWriter, Mscoco
from fn import getTime
from opt import opt
from pPose_nms import write_json
args = opt
args... | 3,903 | 32.655172 | 144 | py |
PoseTriplet | PoseTriplet-main/estimator_inference/joints_detectors/Alphapose/opt.py | import argparse
parser = argparse.ArgumentParser(description='PyTorch AlphaPose Training')
"----------------------------- General options -----------------------------"
parser.add_argument('--expID', default='default', type=str,
help='Experiment ID')
parser.add_argument('--dataset', default='coco'... | 7,755 | 51.761905 | 96 | py |
PoseTriplet | PoseTriplet-main/estimator_inference/joints_detectors/Alphapose/pPose_nms.py | # -*- coding: utf-8 -*-
import torch
import json
import os
import zipfile
import time
from multiprocessing.dummy import Pool as ThreadPool
import numpy as np
from opt import opt
''' Constant Configuration '''
delta1 = 1
mu = 1.7
delta2 = 2.65
gamma = 22.48
scoreThreds = 0.3
matchThreds = 5
areaThres = 0#40 * 40.5
alph... | 13,452 | 35.958791 | 126 | py |
PoseTriplet | PoseTriplet-main/estimator_inference/joints_detectors/Alphapose/detector_api.py | import ntpath
import os
import shutil
import numpy as np
import torch.utils.data
from tqdm import tqdm
from SPPE.src.main_fast_inference import *
from common.utils import calculate_area
from dataloader import DetectionLoader, DetectionProcessor, DataWriter, Mscoco, VideoLoader
from fn import getTime
from opt import o... | 8,005 | 34.114035 | 144 | py |
PoseTriplet | PoseTriplet-main/estimator_inference/joints_detectors/Alphapose/detector_api_realtime.py | import ntpath
import os
import shutil
import numpy as np
import torch.utils.data
from tqdm import tqdm
from SPPE.src.main_fast_inference import *
from common.utils import calculate_area
from dataloader import DetectionLoader, DetectionProcessor, DataWriter, Mscoco, VideoLoader
from fn import getTime
from opt import o... | 15,919 | 36.725118 | 146 | py |
PoseTriplet | PoseTriplet-main/estimator_inference/joints_detectors/Alphapose/demo.py | import os
os.environ["CUDA_DEVICE_ORDER"] = "PCI_BUS_ID"
os.environ["CUDA_VISIBLE_DEVICES"] = "1"
import numpy as np
from tqdm import tqdm
from SPPE.src.main_fast_inference import *
from dataloader import ImageLoader, DetectionLoader, DetectionProcessor, DataWriter, Mscoco
from fn import getTime
from opt import opt
... | 4,420 | 32.492424 | 144 | py |
PoseTriplet | PoseTriplet-main/estimator_inference/joints_detectors/Alphapose/gene_npz.py | import ntpath
import os
import shutil
import numpy as np
import torch.utils.data
from tqdm import tqdm
from SPPE.src.main_fast_inference import *
from common.utils import calculate_area
from dataloader import DetectionLoader, DetectionProcessor, DataWriter, Mscoco, VideoLoader
from fn import getTime
from opt import o... | 7,432 | 33.896714 | 144 | py |
PoseTriplet | PoseTriplet-main/estimator_inference/joints_detectors/Alphapose/dataloader_webcam.py | import os
import torch
from torch.autograd import Variable
import torch.utils.data as data
import torchvision.transforms as transforms
from PIL import Image, ImageDraw
from SPPE.src.utils.img import load_image, cropBox, im_to_torch
from opt import opt
from yolo.preprocess import prep_image, prep_frame, inp_to_image
fro... | 19,581 | 36.585413 | 115 | py |
PoseTriplet | PoseTriplet-main/estimator_inference/joints_detectors/Alphapose/train_sppe/src/evaluation.py | # -----------------------------------------------------
# Copyright (c) Shanghai Jiao Tong University. All rights reserved.
# Written by Jiefeng Li (jeff.lee.sjtu@gmail.com)
# -----------------------------------------------------
import torch
import torch.nn as nn
import torch.utils.data
from .predict.annot.coco_miniv... | 4,497 | 32.567164 | 78 | py |
PoseTriplet | PoseTriplet-main/estimator_inference/joints_detectors/Alphapose/train_sppe/src/train.py | # -----------------------------------------------------
# Copyright (c) Shanghai Jiao Tong University. All rights reserved.
# Written by Jiefeng Li (jeff.lee.sjtu@gmail.com)
# -----------------------------------------------------
import torch
import torch.utils.data
from .utils.dataset import coco
from opt import opt
... | 6,446 | 29.554502 | 106 | py |
PoseTriplet | PoseTriplet-main/estimator_inference/joints_detectors/Alphapose/train_sppe/src/models/FastPose.py | # -----------------------------------------------------
# Copyright (c) Shanghai Jiao Tong University. All rights reserved.
# Written by Jiefeng Li (jeff.lee.sjtu@gmail.com)
# -----------------------------------------------------
import torch.nn as nn
from .layers.DUC import DUC
from .layers.SE_Resnet import SEResnet... | 1,038 | 23.738095 | 76 | py |
PoseTriplet | PoseTriplet-main/estimator_inference/joints_detectors/Alphapose/train_sppe/src/models/layers/SE_module.py | # -----------------------------------------------------
# Copyright (c) Shanghai Jiao Tong University. All rights reserved.
# Written by Jiefeng Li (jeff.lee.sjtu@gmail.com)
# -----------------------------------------------------
from torch import nn
class SELayer(nn.Module):
def __init__(self, channel, reductio... | 783 | 30.36 | 67 | py |
PoseTriplet | PoseTriplet-main/estimator_inference/joints_detectors/Alphapose/train_sppe/src/models/layers/SE_Resnet.py | # -----------------------------------------------------
# Copyright (c) Shanghai Jiao Tong University. All rights reserved.
# Written by Jiefeng Li (jeff.lee.sjtu@gmail.com)
# -----------------------------------------------------
import torch.nn as nn
from models.layers.SE_module import SELayer
import torch.nn.functio... | 3,822 | 35.066038 | 85 | py |
PoseTriplet | PoseTriplet-main/estimator_inference/joints_detectors/Alphapose/train_sppe/src/models/layers/DUC.py | # -----------------------------------------------------
# Copyright (c) Shanghai Jiao Tong University. All rights reserved.
# Written by Jiefeng Li (jeff.lee.sjtu@gmail.com)
# -----------------------------------------------------
import torch.nn as nn
class DUC(nn.Module):
'''
Initialize: inplanes, planes, u... | 898 | 30 | 67 | py |
PoseTriplet | PoseTriplet-main/estimator_inference/joints_detectors/Alphapose/train_sppe/src/utils/img.py | # -----------------------------------------------------
# Copyright (c) Shanghai Jiao Tong University. All rights reserved.
# Written by Jiefeng Li (jeff.lee.sjtu@gmail.com)
# -----------------------------------------------------
import numpy as np
import torch
import scipy.misc
import torch.nn.functional as F
import ... | 9,817 | 30.773463 | 92 | py |
PoseTriplet | PoseTriplet-main/estimator_inference/joints_detectors/Alphapose/train_sppe/src/utils/pose.py | # -----------------------------------------------------
# Copyright (c) Shanghai Jiao Tong University. All rights reserved.
# Written by Jiefeng Li (jeff.lee.sjtu@gmail.com)
# -----------------------------------------------------
from utils.img import (load_image, drawGaussian, cropBox, transformBox, flip, shuffleLR, ... | 5,388 | 36.685315 | 115 | py |
PoseTriplet | PoseTriplet-main/estimator_inference/joints_detectors/Alphapose/train_sppe/src/utils/eval.py | # -----------------------------------------------------
# Copyright (c) Shanghai Jiao Tong University. All rights reserved.
# Written by Jiefeng Li (jeff.lee.sjtu@gmail.com)
# -----------------------------------------------------
from opt import opt
import sys
import numpy as np
import torch
from pycocotools.coco imp... | 6,340 | 29.339713 | 94 | py |
PoseTriplet | PoseTriplet-main/estimator_inference/joints_detectors/Alphapose/train_sppe/src/utils/dataset/coco.py | # -----------------------------------------------------
# Copyright (c) Shanghai Jiao Tong University. All rights reserved.
# Written by Jiefeng Li (jeff.lee.sjtu@gmail.com)
# -----------------------------------------------------
import os
import h5py
from functools import reduce
import torch.utils.data as data
from ... | 2,822 | 34.2875 | 80 | py |
PoseTriplet | PoseTriplet-main/estimator_inference/joints_detectors/Alphapose/train_sppe/src/predict/p_poseNMS.py | # -----------------------------------------------------
# Copyright (c) Shanghai Jiao Tong University. All rights reserved.
# Written by Jiefeng Li (jeff.lee.sjtu@gmail.com)
# -----------------------------------------------------
import torch
import json
import os
import numpy as np
''' Constant Configuration '''
del... | 9,924 | 30.60828 | 99 | py |
PoseTriplet | PoseTriplet-main/estimator_inference/joints_detectors/Alphapose/train_sppe/src/predict/annot/coco_minival.py | # -----------------------------------------------------
# Copyright (c) Shanghai Jiao Tong University. All rights reserved.
# Written by Jiefeng Li (jeff.lee.sjtu@gmail.com)
# -----------------------------------------------------
import os
import h5py
import torch
import torch.utils.data as data
from train_sppe.src.ut... | 3,055 | 32.955556 | 85 | py |
PoseTriplet | PoseTriplet-main/estimator_inference/joints_detectors/Alphapose/yolo/video_demo.py | from __future__ import division
import time
import torch
import torch.nn as nn
from torch.autograd import Variable
import numpy as np
import cv2
from .util import *
from .darknet import Darknet
from .preprocess import prep_image, inp_to_image, letterbox_image
import pandas as pd
import random
import pickle as pkl
im... | 5,807 | 30.058824 | 130 | py |
PoseTriplet | PoseTriplet-main/estimator_inference/joints_detectors/Alphapose/yolo/detect.py | from __future__ import division
import time
import torch
import torch.nn as nn
from torch.autograd import Variable
import numpy as np
import cv2
from .util import *
import argparse
import os
import os.path as osp
from .darknet import Darknet
from .preprocess import prep_image, inp_to_image
import pandas as pd
import... | 2,727 | 25.230769 | 101 | py |
PoseTriplet | PoseTriplet-main/estimator_inference/joints_detectors/Alphapose/yolo/cam_demo.py | from __future__ import division
import time
import torch
import torch.nn as nn
from torch.autograd import Variable
import numpy as np
import cv2
from .util import *
from .darknet import Darknet
from .preprocess import prep_image, inp_to_image
import pandas as pd
import random
import argparse
import pickle as pkl
de... | 4,682 | 26.710059 | 126 | py |
PoseTriplet | PoseTriplet-main/estimator_inference/joints_detectors/Alphapose/yolo/video_demo_half.py | from __future__ import division
import time
import torch
import torch.nn as nn
from torch.autograd import Variable
import numpy as np
import cv2
from .util import *
from .darknet import Darknet
from .preprocess import prep_image, inp_to_image, letterbox_image
import pandas as pd
import random
import pickle as pkl
im... | 5,937 | 30.252632 | 130 | py |
PoseTriplet | PoseTriplet-main/estimator_inference/joints_detectors/Alphapose/yolo/bbox.py | from __future__ import division
import torch
import random
import numpy as np
import cv2
def confidence_filter(result, confidence):
conf_mask = (result[:,:,4] > confidence).float().unsqueeze(2)
result = result*conf_mask
return result
def confidence_filter_cls(result, confidence):
max_score... | 3,130 | 26.464912 | 187 | py |
PoseTriplet | PoseTriplet-main/estimator_inference/joints_detectors/Alphapose/yolo/util.py |
from __future__ import division
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.autograd import Variable
import numpy as np
import cv2
import matplotlib.pyplot as plt
try:
from bbox import bbox_iou
except ImportError:
from yolo.bbox import bbox_iou
def count_parameters(model... | 13,284 | 33.239691 | 108 | py |
PoseTriplet | PoseTriplet-main/estimator_inference/joints_detectors/Alphapose/yolo/preprocess.py | from __future__ import division
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.autograd import Variable
import numpy as np
import cv2
import matplotlib.pyplot as plt
try:
from util import count_parameters as count
from util import convert2cpu as cpu
except ImportError:
from y... | 2,474 | 27.125 | 115 | py |
PoseTriplet | PoseTriplet-main/estimator_inference/joints_detectors/Alphapose/yolo/darknet.py | from __future__ import division
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.autograd import Variable
import numpy as np
import cv2
import matplotlib.pyplot as plt
try:
from util import count_parameters as count
from util import convert2cpu as cpu
from util import predic... | 18,505 | 32.708561 | 117 | py |
PoseTriplet | PoseTriplet-main/estimator_inference/joints_detectors/Alphapose/SPPE/src/opt.py | import argparse
import torch
parser = argparse.ArgumentParser(description='PyTorch AlphaPose Training')
"----------------------------- General options -----------------------------"
parser.add_argument('--expID', default='default', type=str,
help='Experiment ID')
parser.add_argument('--dataset', d... | 5,063 | 48.165049 | 86 | py |
PoseTriplet | PoseTriplet-main/estimator_inference/joints_detectors/Alphapose/SPPE/src/main_fast_inference.py | import sys
import torch
import torch._utils
import torch.nn as nn
import torch.utils.data
import torch.utils.data.distributed
from SPPE.src.models.FastPose import createModel
from SPPE.src.utils.img import flip, shuffleLR
try:
torch._utils._rebuild_tensor_v2
except AttributeError:
def _rebuild_tensor_v2(stor... | 1,979 | 28.117647 | 103 | py |
PoseTriplet | PoseTriplet-main/estimator_inference/joints_detectors/Alphapose/SPPE/src/models/hg-prm.py | import torch.nn as nn
from .layers.PRM import Residual as ResidualPyramid
from .layers.Residual import Residual as Residual
from torch.autograd import Variable
from opt import opt
from collections import defaultdict
class Hourglass(nn.Module):
def __init__(self, n, nFeats, nModules, inputResH, inputResW, net_type... | 4,899 | 37.582677 | 105 | py |
PoseTriplet | PoseTriplet-main/estimator_inference/joints_detectors/Alphapose/SPPE/src/models/FastPose.py | import torch.nn as nn
from torch.autograd import Variable
from .layers.SE_Resnet import SEResnet
from .layers.DUC import DUC
from opt import opt
def createModel():
return FastPose()
class FastPose(nn.Module):
DIM = 128
def __init__(self):
super(FastPose, self).__init__()
self.preact =... | 808 | 21.472222 | 71 | py |
PoseTriplet | PoseTriplet-main/estimator_inference/joints_detectors/Alphapose/SPPE/src/models/hgPRM.py | import torch.nn as nn
from .layers.PRM import Residual as ResidualPyramid
from .layers.Residual import Residual as Residual
from torch.autograd import Variable
import torch
from opt import opt
import math
class Hourglass(nn.Module):
def __init__(self, n, nFeats, nModules, inputResH, inputResW, net_type, B, C):
... | 8,687 | 35.658228 | 104 | py |
PoseTriplet | PoseTriplet-main/estimator_inference/joints_detectors/Alphapose/SPPE/src/models/layers/Resnet.py | import torch.nn as nn
import torch.nn.functional as F
class Bottleneck(nn.Module):
expansion = 4
def __init__(self, inplanes, planes, stride=1, downsample=None):
super(Bottleneck, self).__init__()
self.conv1 = nn.Conv2d(inplanes, planes, kernel_size=1, stride=1, bias=False)
self.bn1 =... | 2,957 | 34.638554 | 99 | py |
PoseTriplet | PoseTriplet-main/estimator_inference/joints_detectors/Alphapose/SPPE/src/models/layers/util_models.py | import torch
import torch.nn as nn
from torch.autograd import Variable
class ConcatTable(nn.Module):
def __init__(self, module_list=None):
super(ConcatTable, self).__init__()
self.modules_list = nn.ModuleList(module_list)
def forward(self, x: Variable):
y = []
for i in range(... | 920 | 23.236842 | 54 | py |
PoseTriplet | PoseTriplet-main/estimator_inference/joints_detectors/Alphapose/SPPE/src/models/layers/SE_module.py | from torch import nn
class SELayer(nn.Module):
def __init__(self, channel, reduction=1):
super(SELayer, self).__init__()
self.avg_pool = nn.AdaptiveAvgPool2d(1)
self.fc = nn.Sequential(
nn.Linear(channel, channel // reduction),
nn.ReLU(inplace=True),
nn.... | 552 | 26.65 | 53 | py |
PoseTriplet | PoseTriplet-main/estimator_inference/joints_detectors/Alphapose/SPPE/src/models/layers/Residual.py | import torch.nn as nn
import math
from .util_models import ConcatTable, CaddTable, Identity
from opt import opt
def Residual(numIn, numOut, *arg, stride=1, net_type='preact', useConv=False, **kw):
con = ConcatTable([convBlock(numIn, numOut, stride, net_type),
skipLayer(numIn, numOut, stride... | 1,684 | 29.636364 | 88 | py |
PoseTriplet | PoseTriplet-main/estimator_inference/joints_detectors/Alphapose/SPPE/src/models/layers/PRM.py | import torch.nn as nn
from .util_models import ConcatTable, CaddTable, Identity
import math
from opt import opt
class Residual(nn.Module):
def __init__(self, numIn, numOut, inputResH, inputResW, stride=1,
net_type='preact', useConv=False, baseWidth=9, cardinality=4):
super(Residual, self)... | 3,974 | 28.227941 | 95 | py |
PoseTriplet | PoseTriplet-main/estimator_inference/joints_detectors/Alphapose/SPPE/src/models/layers/SE_Resnet.py | import torch.nn as nn
from .SE_module import SELayer
import torch.nn.functional as F
class Bottleneck(nn.Module):
expansion = 4
def __init__(self, inplanes, planes, stride=1, downsample=None, reduction=False):
super(Bottleneck, self).__init__()
self.conv1 = nn.Conv2d(inplanes, planes, kernel_... | 3,491 | 33.92 | 91 | py |
PoseTriplet | PoseTriplet-main/estimator_inference/joints_detectors/Alphapose/SPPE/src/models/layers/DUC.py | import torch.nn as nn
import torch.nn.functional as F
class DUC(nn.Module):
'''
INPUT: inplanes, planes, upscale_factor
OUTPUT: (planes // 4)* ht * wd
'''
def __init__(self, inplanes, planes, upscale_factor=2):
super(DUC, self).__init__()
self.conv = nn.Conv2d(inplanes, planes, ker... | 639 | 25.666667 | 85 | py |
PoseTriplet | PoseTriplet-main/estimator_inference/joints_detectors/Alphapose/SPPE/src/utils/img.py | import numpy as np
import cv2
import torch
import scipy.misc
from torchvision import transforms
import torch.nn.functional as F
from scipy.ndimage import maximum_filter
from PIL import Image
from copy import deepcopy
import matplotlib
matplotlib.use('agg')
import matplotlib.pyplot as plt
def im_to_torch(img):
im... | 15,424 | 29.973896 | 93 | py |
PoseTriplet | PoseTriplet-main/estimator_inference/joints_detectors/Alphapose/SPPE/src/utils/pose.py | from .img import (load_image, drawGaussian, drawBigCircle, drawSmallCircle, cv_rotate,
cropBox, transformBox, transformBoxInvert, flip, shuffleLR, drawCOCO)
from .eval import getPrediction
import torch
import numpy as np
import random
from opt import opt
def rnd(x):
return max(-2 * x, min(2... | 8,728 | 50.347059 | 206 | py |
PoseTriplet | PoseTriplet-main/estimator_inference/joints_detectors/Alphapose/SPPE/src/utils/eval.py | from opt import opt
try:
from utils.img import transformBoxInvert, transformBoxInvert_batch, findPeak, processPeaks
except ImportError:
from SPPE.src.utils.img import transformBoxInvert, transformBoxInvert_batch, findPeak, processPeaks
import torch
class DataLogger(object):
def __init__(self):
sel... | 6,955 | 30.618182 | 103 | py |
PoseTriplet | PoseTriplet-main/estimator_inference/joints_detectors/Alphapose/SPPE/src/utils/dataset/mpii.py | import os
import h5py
from functools import reduce
import torch.utils.data as data
from ..pose import generateSampleBox
from opt import opt
class Mpii(data.Dataset):
def __init__(self, train=True, sigma=1,
scale_factor=0.25, rot_factor=30, label_type='Gaussian'):
self.img_folder = '../da... | 2,870 | 32.776471 | 86 | py |
PoseTriplet | PoseTriplet-main/estimator_inference/joints_detectors/Alphapose/SPPE/src/utils/dataset/fuse.py | import os
import h5py
from functools import reduce
import torch.utils.data as data
from ..pose import generateSampleBox
from opt import opt
class Mscoco(data.Dataset):
def __init__(self, train=True, sigma=1,
scale_factor=0.25, rot_factor=30, label_type='Gaussian'):
self.img_folder = '../... | 4,753 | 37.650407 | 93 | py |
PoseTriplet | PoseTriplet-main/estimator_inference/joints_detectors/Alphapose/SPPE/src/utils/dataset/coco.py | import os
import h5py
from functools import reduce
import torch.utils.data as data
from ..pose import generateSampleBox
from opt import opt
class Mscoco(data.Dataset):
def __init__(self, train=True, sigma=1,
scale_factor=(0.2, 0.3), rot_factor=40, label_type='Gaussian'):
self.img_folder ... | 2,988 | 33.755814 | 81 | py |
PoseTriplet | PoseTriplet-main/estimator_inference/common/camera.py | # Copyright (c) 2018-present, Facebook, Inc.
# All rights reserved.
#
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
#
import numpy as np
import torch
from common.quaternion import qrot, qinverse
from common.utils import wrap
def normalize_scr... | 3,217 | 28.796296 | 125 | py |
PoseTriplet | PoseTriplet-main/estimator_inference/common/loss.py | # Copyright (c) 2018-present, Facebook, Inc.
# All rights reserved.
#
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
#
import numpy as np
import torch
def mpjpe(predicted, target):
"""
Mean per-joint position error (i.e. mean Euclidean ... | 4,372 | 30.460432 | 106 | py |
PoseTriplet | PoseTriplet-main/estimator_inference/common/utils.py | # Copyright (c) 2018-present, Facebook, Inc.
# All rights reserved.
#
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
#
import hashlib
import os
import pathlib
import shutil
import sys
import time
import cv2
import numpy as np
import torch
def a... | 6,189 | 29.048544 | 140 | py |
PoseTriplet | PoseTriplet-main/estimator_inference/common/model.py | # Copyright (c) 2018-present, Facebook, Inc.
# All rights reserved.
#
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
#
import torch.nn as nn
class TemporalModelBase(nn.Module):
"""
Do not instantiate this class.
"""
def __init_... | 8,048 | 39.044776 | 130 | py |
PoseTriplet | PoseTriplet-main/estimator_inference/common/quaternion.py | # Copyright (c) 2018-present, Facebook, Inc.
# All rights reserved.
#
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
#
import torch
def qrot(q, v):
"""
Rotate vector(s) v about the rotation described by 四元数quaternion(s) q.
Expects a... | 1,004 | 26.162162 | 78 | py |
PoseTriplet | PoseTriplet-main/imitator/core/policy_disc.py | import torch.nn as nn
from utils.math import *
from core.distributions import Categorical
from core.policy import Policy
class PolicyDiscrete(Policy):
def __init__(self, net, action_num, net_out_dim=None):
super().__init__()
self.type = 'discrete'
if net_out_dim is None:
net_ou... | 828 | 28.607143 | 63 | py |
PoseTriplet | PoseTriplet-main/imitator/core/distributions.py | import torch
from torch.distributions import Normal
from torch.distributions import Categorical as TorchCategorical
class DiagGaussian(Normal):
def __init__(self, loc, scale):
super().__init__(loc, scale)
def kl(self):
loc1 = self.loc
scale1 = self.scale
log_scale1 = self.sca... | 1,379 | 27.75 | 107 | py |
PoseTriplet | PoseTriplet-main/imitator/core/policy_gaussian.py | import torch.nn as nn
from core.distributions import DiagGaussian
from core.policy import Policy
from utils.math import *
class PolicyGaussian(Policy):
def __init__(self, net, action_dim, net_out_dim=None, log_std=0, fix_std=False):
super().__init__()
self.type = 'gaussian'
self.net = net
... | 1,432 | 33.95122 | 106 | py |
PoseTriplet | PoseTriplet-main/imitator/core/policy.py | import torch.nn as nn
class Policy(nn.Module):
def __init__(self):
super().__init__()
def forward(self, x):
"""This function should return a distribution to sample action from"""
raise NotImplementedError
def select_action(self, x, mean_action=False):
dist = self.forward(... | 605 | 24.25 | 78 | py |
PoseTriplet | PoseTriplet-main/imitator/core/common.py | import torch
from utils import batch_to
def estimate_advantages(rewards, masks, values, gamma, tau):
device = rewards.device
rewards, masks, values = batch_to(torch.device('cpu'), rewards, masks, values)
tensor_type = type(rewards)
deltas = tensor_type(rewards.size(0), 1)
advantages = tensor_type(... | 858 | 32.038462 | 82 | py |
PoseTriplet | PoseTriplet-main/imitator/core/critic.py | import torch.nn as nn
import torch
class Value(nn.Module):
def __init__(self, net, net_out_dim=None):
super().__init__()
self.net = net
if net_out_dim is None:
net_out_dim = net.out_dim
self.value_head = nn.Linear(net_out_dim, 1)
self.value_head.weight.data.mul_... | 477 | 24.157895 | 51 | py |
PoseTriplet | PoseTriplet-main/imitator/pose_imitation/pose_mimic_eval.py | import argparse
import os
import sys
import pickle
import time
sys.path.append(os.getcwd())
import multiprocessing
from utils import *
from core.policy_gaussian import PolicyGaussian
from core.critic import Value
from models.mlp import MLP
from models.video_state_net import VideoStateNet
from models.video_reg_net impo... | 12,405 | 38.509554 | 148 | py |
PoseTriplet | PoseTriplet-main/imitator/pose_imitation/pose_mimic.py | import argparse
import os
import sys
import pickle
import time
sys.path.append(os.getcwd())
from utils import *
from core.policy_gaussian import PolicyGaussian
from core.critic import Value
from models.mlp import MLP
from models.video_state_net import VideoStateNet
from pose_imitation.envs.humanoid_v4 import HumanoidE... | 7,324 | 44.78125 | 138 | py |
PoseTriplet | PoseTriplet-main/imitator/pose_imitation/core/agent_mimic.py | import time
from utils.torch import *
from agents import AgentPPO
from core.common import *
from pose_imitation.core.trajbatch_mimic import TrajBatchEgo
class AgentEgo(AgentPPO):
def __init__(self, policy_vs_net=None, value_vs_net=None, **kwargs):
super().__init__(use_mini_batch=False, **kwargs)
... | 2,297 | 38.62069 | 95 | py |
PoseTriplet | PoseTriplet-main/imitator/pose_imitation/utils/poseaug_viz.py |
"""
Functions to visualize human poses
"""
import matplotlib.pyplot as plt
import numpy as np
import os
from mpl_toolkits.mplot3d import Axes3D
def show3DposePair(realt3d, faket3d, ax, lcolor="#3498db", rcolor="#e74c3c", add_labels=True,
gt=True, pred=False): # blue, orange
"""
Visualize a 3d ... | 9,657 | 30.459283 | 130 | py |
PoseTriplet | PoseTriplet-main/imitator/models/video_reg_net.py | from utils.torch import *
from torch import nn
from models.resnet import ResNet
from models.tcn import TemporalConvNet
from models.rnn import RNN
from models.mlp import MLP
from models.mobile_net import MobileNet
from models.linear_model import LinearModel
class VideoRegNet(nn.Module):
def __init__(self, out_dim... | 2,690 | 33.5 | 117 | py |
PoseTriplet | PoseTriplet-main/imitator/models/video_forecast_net.py | import torch.nn as nn
from models.tcn import TemporalConvNet
from models.rnn import RNN
from utils.torch import *
class VideoForecastNet(nn.Module):
def __init__(self, cnn_feat_dim, state_dim, v_hdim=128, v_margin=10, v_net_type='lstm', v_net_param=None,
s_hdim=None, s_net_type='id', dynamic_v=Fa... | 5,292 | 42.385246 | 119 | py |
PoseTriplet | PoseTriplet-main/imitator/models/resnet.py | from torch import nn
from torchvision import models
from utils.torch import *
class ResNet(nn.Module):
def __init__(self, out_dim, fix_params=False):
super().__init__()
self.out_dim = out_dim
self.resnet = models.resnet18(pretrained=True)
if fix_params:
for param in se... | 709 | 23.482759 | 71 | py |
PoseTriplet | PoseTriplet-main/imitator/models/video_state_net.py | import torch.nn as nn
from models.tcn import TemporalConvNet
from models.rnn import RNN
from models.empty import Empty
from utils.torch import *
class VideoStateNet(nn.Module):
def __init__(self, cnn_feat_dim, v_hdim=128, v_margin=10, v_net_type='lstm', v_net_param=None, causal=False):
super().__init__()
... | 3,688 | 43.445783 | 134 | py |
PoseTriplet | PoseTriplet-main/imitator/models/mlp.py | import torch.nn as nn
import torch
class MLP(nn.Module):
def __init__(self, input_dim, hidden_dims=(128, 128), activation='tanh', bn_flag=False):
super().__init__()
if activation == 'tanh':
self.activation = torch.tanh
elif activation == 'relu':
self.activation = to... | 860 | 29.75 | 92 | py |
PoseTriplet | PoseTriplet-main/imitator/models/empty.py | import torch.nn as nn
from utils.torch import *
class Empty(nn.Module):
def __init__(self, input_dim, out_dim, cell_type='lstm', bi_dir=False):
super().__init__()
self.input_dim = input_dim
self.out_dim = out_dim
self.cell_type = cell_type
self.bi_dir = bi_dir
self.... | 2,833 | 36.289474 | 90 | py |
PoseTriplet | PoseTriplet-main/imitator/models/rnn.py | import torch.nn as nn
from utils.torch import *
class RNN(nn.Module):
def __init__(self, input_dim, out_dim, cell_type='lstm', bi_dir=False):
super().__init__()
self.input_dim = input_dim
self.out_dim = out_dim
self.cell_type = cell_type
self.bi_dir = bi_dir
self.mo... | 2,422 | 33.614286 | 75 | py |
PoseTriplet | PoseTriplet-main/imitator/models/mobile_net.py | from torch import nn
from utils.torch import *
class MobileNet(nn.Module):
def __init__(self, out_dim):
super().__init__()
self.out_dim = out_dim
def conv_bn(inp, oup, stride):
return nn.Sequential(
nn.Conv2d(inp, oup, 3, stride, 1, bias=False),
... | 1,746 | 26.296875 | 74 | py |
PoseTriplet | PoseTriplet-main/imitator/models/tcn.py | import torch.nn as nn
from torch.nn.utils import weight_norm
from utils.torch import *
class Chomp1d(nn.Module):
def __init__(self, chomp_size):
super().__init__()
self.chomp_size = chomp_size
def forward(self, x):
return x[:, :, :-self.chomp_size].contiguous()
class TemporalBlock(n... | 2,770 | 33.6375 | 129 | py |
PoseTriplet | PoseTriplet-main/imitator/models/linear_model.py | from __future__ import absolute_import
import torch
import torch.nn as nn
def init_weights(m):
if isinstance(m, nn.Linear):
nn.init.kaiming_normal_(m.weight)
class Linear(nn.Module):
def __init__(self, linear_size, p_dropout=0.5):
super(Linear, self).__init__()
self.l_size = linear... | 2,492 | 25.242105 | 101 | py |
PoseTriplet | PoseTriplet-main/imitator/agents/agent.py | import multiprocessing
from core import LoggerRL, TrajBatch
from utils.memory import Memory
from utils.torch import *
import math
import time
class Agent:
def __init__(self, env, policy_net, value_net, dtype, device, custom_reward=None,
mean_action=False, render=False, running_state=None, num_th... | 4,626 | 36.617886 | 105 | py |
PoseTriplet | PoseTriplet-main/imitator/agents/agent_trpo.py | import scipy.optimize
from agents.agent_pg import AgentPG
from utils import *
def conjugate_gradients(Avp_f, b, nsteps, rdotr_tol=1e-10):
x = zeros(b.size())
if b.is_cuda:
x.to(b.get_device())
r = b.clone()
p = b.clone()
rdotr = torch.dot(r, r)
for i in range(nsteps):
Avp = Avp... | 5,491 | 38.797101 | 124 | py |
PoseTriplet | PoseTriplet-main/imitator/agents/agent_ppo.py | import math
from utils.torch import *
from agents.agent_pg import AgentPG
class AgentPPO(AgentPG):
def __init__(self, clip_epsilon=0.2, opt_batch_size=64, use_mini_batch=False,
policy_grad_clip=None, **kwargs):
super().__init__(**kwargs)
self.clip_epsilon = clip_epsilon
s... | 3,141 | 46.606061 | 115 | py |
PoseTriplet | PoseTriplet-main/imitator/agents/agent_pg.py | from core import estimate_advantages
from agents.agent import Agent
from utils.torch import *
import time
class AgentPG(Agent):
def __init__(self, gamma=0.99, tau=0.95, optimizer_policy=None, optimizer_value=None,
opt_num_epochs=1, value_opt_niter=1, **kwargs):
super().__init__(**kwargs)... | 2,377 | 40 | 98 | py |
PoseTriplet | PoseTriplet-main/imitator/utils/transformation.py | # -*- coding: utf-8 -*-
# transformations.py
# Copyright (c) 2006-2017, Christoph Gohlke
# Copyright (c) 2006-2017, The Regents of the University of California
# Produced at the Laboratory for Fluorescence Dynamics
# All rights reserved.
#
# Redistribution and use in source and binary forms, with or without
# modifica... | 66,941 | 33.311635 | 79 | py |
PoseTriplet | PoseTriplet-main/imitator/utils/torch.py | import torch
import numpy as np
tensor = torch.tensor
DoubleTensor = torch.DoubleTensor
FloatTensor = torch.FloatTensor
LongTensor = torch.LongTensor
ByteTensor = torch.ByteTensor
ones = torch.ones
zeros = torch.zeros
class to_cpu:
def __init__(self, *models):
self.models = list(filter(lambda x: x is no... | 4,140 | 25.044025 | 116 | py |
PoseTriplet | PoseTriplet-main/imitator/utils/math.py | import torch
import math
import numpy as np
from utils.transformation import quaternion_matrix, quaternion_about_axis,\
quaternion_inverse, quaternion_multiply, rotation_from_quaternion, rotation_from_matrix
def normal_entropy(std):
var = std.pow(2)
entropy = 0.5 + 0.5 * torch.log(2 * var * math.pi)
r... | 3,516 | 24.860294 | 101 | py |
PoseTriplet | PoseTriplet-main/imitator/utils/functions.py | import torch
import numpy as np
import torch.nn as nn
def mask_features(features, masked_freq):
"""
mask feature with give frequency
:param features: t x feature size
:return:
"""
mask = np.zeros_like(features)
mask[::masked_freq] = 1.
feature_out = features * mask
return feature_o... | 760 | 23.548387 | 53 | py |
PoseTriplet | PoseTriplet-main/imitator/utils/__init__.py | from utils.memory import *
from utils.zfilter import *
from utils.torch import *
from utils.math import *
from utils.tools import *
from utils.logger import *
from utils.tb_logger import *
from utils.functions import *
| 219 | 23.444444 | 29 | py |
PoseTriplet | PoseTriplet-main/imitator/utils/tb_logger.py | """
File: logger.py
Modified by: Senthil Purushwalkam
Code referenced from https://gist.github.com/gyglim/1f8dfb1b5c82627ae3efcfbbadb9f514
Email: spurushw<at>andrew<dot>cmu<dot>edu
Github: https://github.com/senthilps8
Description:
"""
import tensorflow as tf
from torch.autograd import Variable
import numpy as np
impo... | 3,687 | 30.521368 | 89 | py |
SFOM-DRO | SFOM-DRO-main/FairnessML/FML_FMD_Solver.py | import os
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import math
import time
import gurobipy as gp
from gurobipy import GRB
from statistics import mean
from tqdm import tqdm
from FML_UBRegret import R_dim_FMD
from FML_utils import *
from copy import deepcopy
import torch
def FMD_x(x, p,ste... | 25,115 | 38.24375 | 151 | py |
PRSummarizer | PRSummarizer-master/prsum/decode.py | # encoding=utf-8
import os
import sys
import time
import copy
import torch
from . import utils
from .pointer_model import PointerEncoderDecoder
from prsum.dataset import data
from prsum.dataset.data import Vocab
from prsum.dataset.batcher import Batcher
from prsum.dataset.train_util import get_input_from_batch
from ... | 10,179 | 38.305019 | 116 | py |
PRSummarizer | PRSummarizer-master/prsum/pointer_model.py | # encoding=utf-8
"""Attentional Encoder Decoder Model"""
import torch
from torch import nn
from torch.nn import functional as F
from torch.nn.utils.rnn import pack_padded_sequence, pad_packed_sequence
from .utils import try_load_state
class Encoder(nn.Module):
def __init__(self, hps, pad_id=1, batch_first=True... | 10,543 | 42.570248 | 115 | py |
PRSummarizer | PRSummarizer-master/prsum/utils.py | # encoding=utf-8
import re
import os
import sys
import csv
import json
import math
import time
import torch
import tempfile
from myrouge.rouge import Rouge
from nltk import sent_tokenize
import subprocess as sp
from typing import List
import logging
from pyrouge import Rouge155
from pyrouge.utils import log
import ten... | 7,582 | 27.294776 | 99 | py |
PRSummarizer | PRSummarizer-master/prsum/prsum.py | # -*- coding: utf-8 -*-
import re
import os
import csv
import sys
import time
import random
import torch
import numpy as np
from tqdm import tqdm
import tensorflow as tf
from torch.distributions.categorical import Categorical
from torch.optim import Adam
import torch.multiprocessing as mp
import fire
from . import u... | 24,173 | 41.634921 | 123 | py |
PRSummarizer | PRSummarizer-master/prsum/dataset/train_util.py | import numpy as np
import torch
def get_input_from_batch(params, batch, device):
device = torch.device(device)
batch_size = len(batch.enc_lens)
enc_batch = torch.from_numpy(batch.enc_batch).long().to(device)
enc_padding_mask = torch.from_numpy(batch.enc_padding_mask).float().to(device)
enc_lens =... | 1,522 | 36.146341 | 102 | py |
Smile-Pruning | Smile-Pruning-master/src/main.py | """This code is based on the official PyTorch ImageNet training example 'main.py'. Commit ID: 69d2798, 04/23/2020.
URL: https://github.com/pytorch/examples/tree/master/imagenet
"""
import argparse
import os
import random
import shutil
import time
import warnings
import torch
import torch.nn as nn
import torch.nn.p... | 11,592 | 41.156364 | 135 | py |
Smile-Pruning | Smile-Pruning-master/src/option.py | import torchvision.models as models
import configargparse
import sys
from utils import update_args
model_names = sorted(name for name in models.__dict__
if name.islower() and not name.startswith("__")
and callable(models.__dict__[name]))
parser = configargparse.ArgumentParser(description='Regularization-Prun... | 14,975 | 61.661088 | 150 | py |
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