mkolod

mkolod

mkolod

mkolod

class DisjointForest:
    
    class Subset:
        
        def __init__(self, elem, parent=None, rank=0):
            self.elem = elem
            self.parent = parent
            self.rank = rank
            
        def __repr__(self):

mkolod

#include <chrono>
#include <cmath>
#include <future>
#include <iostream>
#include <memory>
#include <mutex>
#include <thread>

template<typename Ret, typename Fun, typename Arg>
class ReusableWorkerThreadWithFuture {

mkolod

# NOTE: The network here is not means to make any sense. It's just for measuring perf impact.
import torch
import torch.nn.functional as F
from time import time

class Net(torch.nn.Module):
    def __init__(self):
        super(Net, self).__init__()
        fcs = [torch.nn.Linear(10, 100)] + [torch.nn.Linear(100, 100) for _ in range(20)]
        self.fcs = torch.nn.Sequential(*fcs)

mkolod

#include <stdio.h>
#include <thread>
#include <chrono>
#include <iostream>

const int N = 1 << 20;

__global__ void kernel(float *x, int n)
{
    int tid = threadIdx.x + blockIdx.x * blockDim.x;

mkolod

#include <chrono>
#include <iostream>
#include <vector>
#include <thread>

__global__ void do_nothing(int time_us, int clock_rate) {
  clock_t start = clock64();
  clock_t end;
  for (;;) {
    end = clock64();

mkolod

#include <csignal>
#include <cstdlib>
#include <fstream>
#include <iostream>
#include <unistd.h>
#include <limits.h>

#include <iostream>
#include <sstream>
#include <stdexcept>

mkolod

ImageNet validation set fix:

0. The training set is organized in directories, with each directory matching a class, e.g. "n01751748" matching "sea snake." However, the valset is a flat dir of JPEGs. The ImageNet labels provided in the devkit for the validation set (ILSVRC2012_validation_ground_truth.txt) are not consistent with the ordering used by PyTorch/TF/Keras/MXNet/Caffe, etc. for pre-trained models. For example, in the the above ground truth label file, "sea snake" is 490, but in PyTorch/TF, it's 65.
   Proof:
   • ImageNet validation labels: https://gist.githubusercontent.com/aaronpolhamus/964a4411c0906315deb9f4a3723aac57/raw/aa66dd9dbf6b56649fa3fab83659b2acbf3cbfd1/map_clsloc.txt
   • DL framework labels: https://gist.githubusercontent.com/aaronpolhamus/964a4411c0906315deb9f4a3723aac57/raw/aa66dd9dbf6b56649fa3fab83659b2acbf3cbfd1/map_clsloc.txt
1. Untar the valset file, you will get a flat dir of JPEGs.
2. Pull in the unflattening script into the directory where the val images were unpa