woolpeeker

"""
V100-32G
==================================================
model_name: vit_base_patch16_224
iter_num: 1000
bs: 16
adamw avg time: 12.739 ms
fused adam avg time: 3.343 ms
"""
import torch

woolpeeker

# Usage: 
#    bash create_users.sh users.txt
# each line in users.txt is a username
# 
# Requires:
#   apt install pwgen 


user_exists(){ id $1 &>/dev/null; }

woolpeeker

while true
do
    m=`nvidia-smi --format=csv --query-gpu=memory.used 2>&1 | awk '{if(NR>1 && NR<=2)print $1}'`
    if [ $m -lt 2000 ]
    then
        echo executing
        exec python tools/train.py --gpus 0,1 configs/res18.yaml --name res18_base
    else
        echo memory $m
        sleep 1

woolpeeker

#include <iostream>
#include <chrono>
#include <ctime>
#include <cmath>

class Timer
{
public:
    void start()
    {

woolpeeker

# Non_maximum_suppression

import numpy as np

def nms(dets, scores, thresh):
    '''
    dets is a numpy array : num_dets, 4
    scores ia  nump array : num_dets,
    '''
    x1 = dets[:, 0]

woolpeeker

"""
Show three windows to visualize predictions, ground truth labels and real colors
press key t to synchronize the camera setting in win_follow
"""
import open3d as o3d
import numpy as np
import copy
import threading
import time
from easydict import EasyDict as edict

woolpeeker

"""
This file read a .abf file, find the peaks and troughs, and calculate delta values

Requirements:
pyabf
numpy
matplotlib
"""
import numpy as np
import sys

woolpeeker

"""
Usage:
    python gpu_analysis.py <gpu_load.txt> <gpu_num>
"""
import sys

gpu_num = int(sys.argv[2])
total = 0
utilized = 0
for line in open(sys.argv[1]).readlines():

woolpeeker

# drawMatches numpy version
def draw_matches(img1, kp1, img2, kp2, matches, color=None): 
    """Draws lines between matching keypoints of two images.  
    Keypoints not in a matching pair are not drawn.
    Places the images side by side in a new image and draws circles 
    around each keypoint, with line segments connecting matching pairs.
    You can tweak the r, thickness, and figsize values as needed.
    Args:
        img1: An openCV image ndarray in a grayscale or color format.
        kp1: ndarray [n1, 2]

woolpeeker

import os
import mxnet as mx
from mxnet import ndarray as nd
from easydict import EasyDict as edict
import json
import copy

SRC_SYM = './models/vgg-softmax-emore/model'
TGT_SYM = './models/vgg-softmax-emore-bnMerged/model'