nirshlezinger1

pjessesco

# Reference : https://stackoverflow.com/a/59539843

from PyQt5.QtCore import *
from PyQt5.QtGui import *
from PyQt5.QtWidgets import *
from PyQt5.QtCore import QObject, pyqtSignal
import sys
import time
import threading

arunavsk

import os.path as osp

import argparse
import torch
import torch.nn.functional as F
from torch_geometric.datasets import Planetoid
import torch_geometric.transforms as T
from torch_geometric.data import NeighborSampler
from torch_geometric.nn import GAE, SAGEConv
from torch_geometric.utils import train_test_split_edges

sgraaf

#!/usr/bin/env python
# -*- coding: utf-8 -*-
from argparse import ArgumentParser

import torch
import torch.distributed as dist
from torch.nn.parallel import DistributedDataParallel as DDP
from torch.utils.data import DataLoader, Dataset
from torch.utils.data.distributed import DistributedSampler
from transformers import BertForMaskedLM

rusty1s

import torch
import torch.nn.functional as F
from torchvision.datasets import MNIST
from torchvision.transforms import ToTensor
from torch.utils.data import DataLoader

from torch_geometric.utils import grid
from torch_geometric.nn import SplineConv

train_dataset = MNIST('/tmp/MNIST', train=True, transform=ToTensor())

anastasiia-kornilova

import static java.lang.Math.*;

public class GaussSmooth {

    private static double gauss(double sigma, double x) {
        double expVal = -0.5 * (pow(x, 2) / pow(sigma, 2));
        return exp(expVal);
    }

    private static double[] gaussKernel1d(double sigma, int lw) {

jin-zhe

Activity Recognition Datasets

An overview of recent action recognition datasets and their detection classes

Concepts & terminologies:

• Action: Atomic low-level movement such as standing up, sitting down, walking, talking etc.
• Activity/event: Higher level occurence then actions such as dining, playing, dancing
• Trimmed video: A short video clip containing event/action/activity of interest
• Untrimmed video: A video clip of arbitrary length potentially containing durations without activities of interest
• Localization: locating an instance of event/action/activity within a video at a spatial or temporal scale
• Spatial localization: Locating the region/area of an instance of action/activity within a video

bamos

#!/usr/bin/env python3

import numpy as np
import gym
from baselines.common.vec_env.subproc_vec_env import SubprocVecEnv

env_name = 'Pendulum-v0'
nproc = 8
T = 10

arose13

# Setup Ubuntu
sudo apt update --yes
sudo apt upgrade --yes

# Get Miniconda and make it the main Python interpreter
wget https://repo.continuum.io/miniconda/Miniconda3-latest-Linux-x86_64.sh -O ~/miniconda.sh
bash ~/miniconda.sh -b -p ~/miniconda 
rm ~/miniconda.sh

export PATH=~/miniconda/bin:$PATH

tehmas

import cv2
import cPickle
import socket
import struct

TCP_IP = '127.0.0.1'
TCP_PORT = 9501
server_address = (TCP_IP, TCP_PORT)
i = 0