ayush29feb

# ################################################################ #
# THE FOLLOWING IS AN EXPLANATION OF THE GRAMMAR THAT SKHD PARSES. #
# FOR SIMPLE EXAMPLE MAPPINGS LOOK FURTHER DOWN THIS FILE..        #
# ################################################################ #

# A list of all built-in modifier and literal keywords can
# be found at https://github.com/koekeishiya/skhd/issues/1
#
# A hotkey is written according to the following rules:
#

ayush29feb

Workflow Run ID,Trial Name,Run Status,View IDX,Scene,measures[lpips_original],Boosted NeRF,measures[ssim_new],measures[ssim_original],measures[lpips_new],NeRF
f309736745,blend.num_gt_views=5 | blend.view_idx=3 | data.ckpt_steps=300000 | data.scene=leaves | data=llff,SUCCEEDED,3,leaves,0.26212254166603,23.096223831177,0.78082925081253,0.68898797035217,0.15799787640572,22.124483108521
f309736677,blend.num_gt_views=5 | blend.view_idx=0 | data.ckpt_steps=300000 | data.scene=leaves | data=llff,SUCCEEDED,0,leaves,0.32919234037399,20.302335739136,0.66363716125488,0.59667128324509,0.22864061594009,19.740411758423
f309737621,blend.num_gt_views=5 | blend.view_idx=0 | data.ckpt_steps=300000 | data.scene=trex | data=llff,SUCCEEDED,0,trex,0.16852854192257,26.93808555603,0.9075939655304,0.88740074634552,0.13450619578362,26.506996154785
f309736680,blend.num_gt_views=5 | blend.view_idx=5 | data.ckpt_steps=300000 | data.scene=fortress | data=llff,SUCCEEDED,5,fortress,0.12893503904343,32.095138549805,0.9154891371727,0.89887768

ayush29feb

{
  "$schema": "https://vega.github.io/schema/vega-lite/v5.json",
  "description": "Google's stock price over time.",
  "data": {
    "url": "https://gist.githubusercontent.com/ayush29feb/d3d14a5b628d0e986706c0b1a52227d9/raw/7c0d806f567e789b4dae15d7b5c45e210a95856d/mlhub_x3d_hfnerf_eval_nerf_all.csv"
  },
  "transform": [{"fold": ["NeRF", "Boosted NeRF"]}],
  "hconcat": [{
    "mark": "line",
    "encoding": {

ayush29feb

,Workflow Run ID,Trial Name,Run Status,Scene,Steps,View IDX,lpips_original,ssim_new,ssim_original,lpips_new,NeRF,Boosted NeRF
0,f309439899,blend.num_gt_views=5 | blend.view_idx=6 | data.ckpt_steps=300000 | data.scene=trex | data=llff,SUCCEEDED,trex,300000,6,0.14237642288208,0.88777124881744,0.86636900901794,0.10582774877548,27.246841430664,27.702030181885
1,f309439249,blend.num_gt_views=5 | blend.view_idx=4 | data.ckpt_steps=30000 | data.scene=trex | data=llff,SUCCEEDED,trex,30000,4,0.29761147499084,0.84416288137436,0.77630245685577,0.17005723714828,23.335535049438,24.932920455933
2,f309439428,blend.num_gt_views=5 | blend.view_idx=6 | data.ckpt_steps=50000 | data.scene=trex | data=llff,SUCCEEDED,trex,50000,6,0.23266535997391,0.85452276468277,0.79912096261978,0.14091378450394,24.997121810913,26.421234130859
3,f309439372,blend.num_gt_views=5 | blend.view_idx=3 | data.ckpt_steps=50000 | data.scene=trex | data=llff,SUCCEEDED,trex,50000,3,0.29320457577705,0.81921368837357,0.75636798143387,0.17885021865368,23.10811

ayush29feb

Workflow Run ID,Trial Name,Run Status,Scene,Steps,View IDX,lpips_original,psnr_new,ssim_new,ssim_original,lpips_new,psnr_original
f309429308,blend.num_gt_views=5 | blend.view_idx=1 | data.ckpt_steps=10000 | data.scene=orchids | data=llff,SUCCEEDED,orchids,10000,1,0.41760471463203,21.295999526978,0.68568426370621,0.60498982667923,0.26054352521896,20.133796691895
f309429696,blend.num_gt_views=5 | blend.view_idx=0 | data.ckpt_steps=300000 | data.scene=orchids | data=llff,SUCCEEDED,orchids,300000,0,0.31438076496124,19.522722244263,0.65713888406754,0.63331681489944,0.24812999367714,19.498908996582
f309429373,blend.num_gt_views=5 | blend.view_idx=0 | data.ckpt_steps=30000 | data.scene=orchids | data=llff,SUCCEEDED,orchids,30000,0,0.39744836091995,19.716247558594,0.651231944561,0.60841828584671,0.27031049132347,19.375825881958
f309429561,blend.num_gt_views=5 | blend.view_idx=1 | data.ckpt_steps=100000 | data.scene=orchids | data=llff,SUCCEEDED,orchids,100000,1,0.26121062040329,22.320322036743,0.73363161087036,0.7011

ayush29feb

{
  "$schema": "https://vega.github.io/schema/vega-lite/v5.json",
  "data": { "url": "https://raw.githubusercontent.com/ayush29feb/data/master/2021-05-18-16-55-43/data.json"},
  "facet": {
    "column": {
      "field": "metric", 
      "type": "nominal"
    },
    "row": {
      "field": "dataset",

ayush29feb

!pip install Pillow==4.0.0
!pip install PIL
!pip install image

import matplotlib.pyplot as plt
import numpy as np
from torch.utils.data import Dataset, DataLoader
from torchvision import datasets, transforms

dataset = datasets.ImageFolder(

ayush29feb

import cv2
vidcap = cv2.VideoCapture('/gdrive/<filepath>')
success,image = vidcap.read()
count = 0
success = True
while success:
  success,image = vidcap.read()
  cv2.imwrite("/gdrive/<filepath>" % count, image)     # save frame as JPEG file
  if cv2.waitKey(10) == 27:                     # exit if Escape is hit
      break

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package simpledb;

import java.io.*;
import java.util.HashMap;
import java.util.HashSet;
import java.util.List;
import java.util.Map;
import java.util.Set;
import java.util.Stack;

ayush29feb

package simpledb;

import java.util.ArrayList;
import java.util.HashMap;
import java.util.HashSet;
import java.util.LinkedList;
import java.util.List;
import java.util.Map;
import java.util.Queue;
import java.util.Set;