samson-wang’s gists

samson-wang / gpu_mem.json

Created June 29, 2017 03:27

gpu metrics for telegraf and chronograf

	{
	"id": "20850d4a-310a-4ab7-b33d-d33528b832af",
	"measurement": "gpu_mycollector",
	"app": "gpu_mem",
	"cells": [{
	"x": 0,
	"y": 0,
	"w": 4,
	"h": 4,
	"i": "da71e439-25ac-4bf7-9276-697ff69e1604",

samson-wang / fxxk_alimei.py

Last active October 24, 2017 10:28

阿里妹1024

	M_C = ['.-', '-...', '-.-.', '-..', '.', '..-.', '--.', '....', '..', '.---', '-.-', '.-..', '--', '-.', '---', '.--.', '--.-', '.-.', '...', '-', '..-', '...-', '.--', '-..-', '-.--', '--..']
	A_C = ['A', 'B', 'C', 'D', 'E', 'F', 'G', 'H', 'I', 'J', 'K', 'L', 'M', 'N', 'O', 'P', 'Q', 'R', 'S', 'T', 'U', 'V', 'W', 'X', 'Y', 'Z']

	M_C += ['.----', '..---', '...--', '....-', '.....', '-....', '--...', '---..', '----.', '-----']
	A_C += ['1', '2', '3', '4', '5', '6', '7', '8', '9', '10']

	BASE_DICT = dict(zip(M_C, A_C))

	TASH = 'ABCDEFGHIJKLMNOPQRSTUVWXYZ'

samson-wang / evalCOCO.m

Created February 11, 2018 09:28

A work-around for Realtime_Multi-Person_Pose_Estimation testing on coco dataset

	addpath('util/jsonlab/');
	addpath('src');
	addpath('util');
	addpath('util/ojwoodford-export_fig-5735e6d/');
	%addpath('/data/Repo/Realtime_Multi-Person_Pose_Estimation/training/dataset/COCO/coco/MatlabAPI');
	fid = fopen('../val2014_flist.2k.csv');
	data=textscan(fid,'%f %s','delimiter',',');
	fclose(fid);
	display(data);
	for i = 1:length(data{1})

samson-wang / video_cap.py

Last active March 23, 2018 11:30

capture from video

	import cv2
	import sys
	import numpy as np
	import imutils

	vids = [0] if len(sys.argv) < 2 else map(int, sys.argv[1].split(','))
	caps = [cv2.VideoCapture(_) for _ in vids]

	scale = .5

samson-wang / rfs.md

Last active March 3, 2024 12:15

ResNet Receptive Field Size

    

layer
resnet18
resnet34
resnet50
resnet101


conv1
7
7
7
7

maxpool
11
11
11
11

layer1
43
59
35
35

layer2
99
179
91
91

layer3
211
547
267
811

layer4
435
899
427
971



  

layer	resnet18	resnet34	resnet50	resnet101
conv1	7	7	7	7
maxpool	11	11	11	11
layer1	43	59	35	35
layer2	99	179	91	91
layer3	211	547	267	811
layer4	435	899	427	971

samson-wang / benchmark_pytorch.py

Created November 14, 2018 09:04

	import torch
	from torch.nn import Conv2d, Sequential
	import time
	from torchvision import models

	def score(arch, batch_size=32, num_batches=10):

	model = models.__dict__[arch]().cuda()

	data = torch.rand((batch_size, 3, 224, 224)).cuda()

samson-wang / result.log

Last active February 19, 2019 09:56

float multiplication test.

	Minimum float positive value: 1.175494E-38
	A: 0.403069, HEX: 0x3ece5f18
	A: 4.0306925774e-01, HEX: 0x3ece5f18
	A: 3.7674255964e-06, HEX: 0x367cd3e1
	A: 3.8132049561e+01, HEX: 0x42188738

	real 0m0.057s
	user 0m0.056s
	sys 0m0.000s
	Minimum float positive value: 1.175494E-38

samson-wang / test_mp.py

Created January 31, 2020 12:01

multi-processing

	import multiprocessing as mp
	import time
	import random

	todo = mp.Value('i', 0)
	done = mp.Value('i', 0)
	idx = mp.Value('i', 0)
	finish = mp.Value('i', 0)

	def worker(todo, done, idx, finish):

samson-wang / focal_loss.py

Last active April 2, 2024 11:56

A really simple pytorch implementation of focal loss for both sigmoid and softmax predictions.

	import torch
	from torch.nn.functional import log_softmax

	def sigmoid_focal_loss(logits, target, gamma=2., alpha=0.25):
	num_classes = logits.shape[1]
	dtype = target.dtype
	device = target.device
	class_range = torch.arange(0, num_classes, dtype=dtype, device=device).unsqueeze(0)

	t = target.unsqueeze(1)

samson-wang / benchmark_cpu.py

Created February 9, 2022 02:04

	import torch
	import torch.nn as nn
	import time

	class Net(nn.Module):
	def __init__(self, ch=3):
	super(Net, self).__init__()
	self.model = nn.Sequential(
	nn.Conv2d(ch, 8, 3, padding=1),
	nn.BatchNorm2d(8),

WANG Chenxi samson-wang