tnarihi

alias docker_build="docker build --build-arg http_proxy=${http_proxy} --build-arg https_proxy=${https_proxy}"
alias docker_run="nvidia-docker run -ehttp_proxy=${http_proxy} -ehttps_proxy=${https_proxy} -eftp_proxy=${ftp_proxy} --rm"
docker_run_user () {
    tempdir=$(mktemp -d)
    getent passwd > ${tempdir}/passwd
    getent group > ${tempdir}/group
    nvidia-docker run -ehttp_proxy=${http_proxy} -ehttps_proxy=${https_proxy} -eftp_proxy=${ftp_proxy} -v${HOME}:${HOME} -w$(pwd) --rm -u$(id -u):$(id -g) $(for i in $(id -G); do echo -n ' --group-add='$i; done)\
 -v ${tempdir}/passwd:/etc/passwd:ro -v ${tempdir}/group:/etc/group:ro "$@"
}

tnarihi

#! /usr/bin/env python
"""
<usage> ./dockerbuild.in <dockerfile.in> [dockerfile.in variables...] [docker build options...]

dockerfile.in variables can be passed in a format of "var:=value".

For example, if Dockerfile.in is the following:

```
FROM ubuntu:{ubuntu_version}

tnarihi

{
	"font_size": 10,
	"ignored_packages":
	[
		"Vintage"
	],
	"rulers":
	[
		80,
		100,

tnarihi

import numpy as np

def compute_hdr_whdr_core(pred, tar, w):
    """Compute HDR and WHDR of pairs in a single image
    
    Args:
        pred : 1d array, predicition
        tar : 1d array, target
        w : 1d array, weights (confidence)
        

tnarihi

from __future__ import print_function
import argparse
import random

parser = argparse.ArgumentParser()
parser.add_argument('text_input', help='Path to input file')
parser.add_argument('--seed', type=int, default=313,
                    help='Seed of random')
parser.add_argument('--stack', type=int, default=9,
                    help='Number of stack (default: 9)'),

tnarihi

Stack CaffeNet

Stacking caffenet N times in channels direction.

Example usage

This will generate 9 times stacked caffenet.

PYTHONPATH=$HOME/workspace/caffe-latest/python python stack_caffenet.py $HOME/caffe/models/bvlc_reference_caffenet/bvlc_reference_caffenet.caffemodel 9

Run python stack_caffenet.py -h for more details.

tnarihi

import numpy as np
from sklearn.metrics import confusion_matrix
preds = ()
labels = ()
n_samples = ... number of examples in test set ...
batch_size = ... batch size ...
net = ... caffe test net ...
n_batch = int(np.ceil(n_samples / batch_size))
for i in range(n_batch):
  net.forward()

tnarihi

tnarihi

tnarihi

Batch Python installation script for dependencies of tnarihi Caffe

This will install the Anaconda distribution of Python and enable opencv2 features and some CUDA wrappers. This script is tested on my Ubuntu 14.04.

This script is not well-designed and has no error handling, so I recommend you to run the commands in the script step-by-step.