samson-wang

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),

samson-wang

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

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

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

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

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

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

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

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

{
	"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",