leimao

#include <cassert>
#include <cstddef>
#include <cstdint>
#include <iomanip>
#include <iostream>
#include <random>
#include <stdexcept>
#include <vector>

#define BLOCK_DIM 32

leimao

# MIT License

# Copyright (c) 2021 Lei Mao <https://leimao.github.io/>

# Permission is hereby granted, free of charge, to any person obtaining a copy
# of this software and associated documentation files (the "Software"), to deal
# in the Software without restriction, including without limitation the rights
# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
# copies of the Software, and to permit persons to whom the Software is
# furnished to do so, subject to the following conditions:

leimao

#!/bin/bash
#
# script to extract ImageNet dataset
# ILSVRC2012_img_train.tar (about 138 GB)
# ILSVRC2012_img_val.tar (about 6.3 GB)
# make sure ILSVRC2012_img_train.tar & ILSVRC2012_img_val.tar in your current directory
#
#  https://github.com/facebook/fb.resnet.torch/blob/master/INSTALL.md
# 
#  train/

leimao

mkdir -p n01440764	
mkdir -p n01443537	
mkdir -p n01484850	
mkdir -p n01491361	
mkdir -p n01494475	
mkdir -p n01496331	
mkdir -p n01498041	
mkdir -p n01514668	
mkdir -p n01514859	
mkdir -p n01518878	

leimao

mkdir -p n01440764	
mkdir -p n01443537	
mkdir -p n01484850	
mkdir -p n01491361	
mkdir -p n01494475	
mkdir -p n01496331	
mkdir -p n01498041	
mkdir -p n01514668	
mkdir -p n01514859	
mkdir -p n01518878	

leimao

import argparse
import os
from filelock import FileLock

import torch.multiprocessing as mp
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
from torchvision import datasets, transforms
import torch.utils.data.distributed

leimao

import torch
from torch.utils.data.distributed import DistributedSampler
from torch.utils.data import DataLoader
import torch.nn as nn
import torch.optim as optim

import torchvision
import torchvision.transforms as transforms

import argparse

leimao

from typing import Iterable, List, Union, Set


class Graph:
    def __init__(self) -> None:

        # Adjacency set representation.
        # key: node name, value: adjacency node name set.
        self.edges = dict()

leimao

import torch
from torch.utils.data.distributed import DistributedSampler
from torch.utils.data import DataLoader
import torch.nn as nn
import torch.optim as optim

import torchvision
import torchvision.transforms as transforms

import argparse

leimao

mkdir coco
cd coco
mkdir images
cd images

wget http://images.cocodataset.org/zips/train2017.zip
wget http://images.cocodataset.org/zips/val2017.zip
wget http://images.cocodataset.org/zips/test2017.zip
wget http://images.cocodataset.org/zips/unlabeled2017.zip