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Liangliang He llhe

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View GitHub Profile
View ssd-caffe
name: "MobileNet-SSD"
input: "data"
input_shape {
dim: 1
dim: 3
dim: 300
dim: 300
}
layer {
name: "conv0"
View photo-artifacts.md
  • Color banding
  • Purple fringing
  • Lens flare
  • Ringing artifacts
  • Posterization
  • Aliasing/Moiré Pattern
  • Chromatic aberration
  • Rolling shutter
  • Vignetting
  • Noise
@llhe
llhe / benchmark.txt
Last active Jun 29, 2018
benchmark.md
View benchmark.txt
model_name device_name soc abi runtime init warmup run_avg tuned
mobilenet_v2 polaris sdm845 armeabi-v7a GPU 42.868 11.087 9.908 True
mobilenet_v2 MI MAX msm8952 armeabi-v7a GPU 122.791 43.038 39.875 True
mobilenet_v2 BKL-AL00 kirin970 armeabi-v7a GPU 767.932 1226.373 47.597 True
mobilenet_v2 polaris sdm845 arm64-v8a GPU 42.3 10.737 10.004 True
mobilenet_v2 MI MAX msm8952 arm64-v8a GPU 129.123 42.584 39.552 True
mobilenet_v2 BKL-AL00 kirin970 arm64-v8a GPU 753.43 1170.291 48.016 True
mobilenet_v2 polaris sdm845 armeabi-v7a CPU 16.035 69.761 41.627 False
mobilenet_v2 MI MAX msm8952 armeabi-v7a CPU
View .vimrc
set nu
set ruler
set tabstop=2 shiftwidth=2 expandtab
set colorcolumn=80
" pathogen
execute pathogen#infect()
syntax on
filetype plugin indent on
@llhe
llhe / memtest.cc
Created Jun 21, 2017
memcpy benchmark
View memtest.cc
/*
* g++ -std=c++11 -pthread memtest.cc
*/
#include <cstring>
#include <chrono>
#include <condition_variable>
#include <functional>
#include <future>
@llhe
llhe / rdma_bench.py
Created May 4, 2017
Benchmark with RDMA
View rdma_bench.py
"""Benchmark tensorflow distributed by assigning a tensor between two workers.
Usage:
Start worker 1:
python rdma_bench.py --workers="hostname1:port,hostname2:port" --protocol=grpc+verbs --task 0
Start worker 2:
python rdma_bench.py --workers="hostname1:port,hostname2:port" --protocol=grpc+verbs --task 1
Run the tests:
View codedump.py
import argparse
import tensorflow as tf
from tensorflow.examples.tutorials.mnist import input_data
def train():
mnist = input_data.read_data_sets("/tmp/data",
one_hot=True,
fake_data=False)
sess = tf.InteractiveSession()
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