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{"info": {"description": "COCO 2014 Dataset", "url": "http://cocodataset.org", "version": "1.0", "year": 2014, "contributor": "COCO Consortium", "date_created": "2017/09/01"}, "images": [{"license": 3, "file_name": "COCO_val2014_000000391895.jpg", "coco_url": "http://images.cocodataset.org/val2014/COCO_val2014_000000391895.jpg", "height": 360, "width": 640, "date_captured": "2013-11-14 11:18:45", "flickr_url": "http://farm9.staticflickr.com/8186/8119368305_4e622c8349_z.jpg", "id": 391895}, {"license": 4, "file_name": "COCO_val2014_000000060623.jpg", "coco_url": "http://images.cocodataset.org/val2014/COCO_val2014_000000060623.jpg", "height": 427, "width": 640, "date_captured": "2013-11-14 17:24:15", "flickr_url": "http://farm7.staticflickr.com/6080/6113512699_37b4c98473_z.jpg", "id": 60623}, {"license": 3, "file_name": "COCO_val2014_000000483108.jpg", "coco_url": "http://images.cocodataset.org/val2014/COCO_val2014_000000483108.jpg", "height": 640, "width": 428, "date_captured": "2013-11-14 18:27:53", "flickr_u

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import matplotlib as mpl
mpl.use('Agg')
import argparse
import pandas as pd
import matplotlib.pyplot as plt
import pandas as pd
import numpy as np

def plot_scaling_and_efficiency(df):
    """

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lr: 0.001
epochs: 10
noise_dim: 128
dim: 128
depth: 8
vq_image_size: 16
dropout: 0
cutn: 16
batch_size: 2
repeat: 8

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https://i.pinimg.com/736x/66/01/6c/66016c3ba27c0e04f39e2bd81a934e3e--anita-ekberg-bob-hope.jpg
http://www.standard.net/image/2015/02/04/800x_a16-9_b0_q81_p1/winter-fly-fishing.jpg
http://indianapolis-photos.funcityfinder.com/files/2009/12/Clearwater-Crossing-Shopping-Center-sign-Indianapolis-Indiana.jpg
http://www.abc.net.au/news/image/9066492-3x2-700x467.jpg
https://www.featurepics.com/StockImage/20090316/carrying-globe-stock-image-1115085.jpg
http://i.dailymail.co.uk/i/pix/2014/11/05/1415187324676_wps_31_Home_is_a_little_Deer_Ivy.jpg
http://www.waste360.com/sites/waste360.com/files/styles/article_featured_standard/public/Trista%2002%20007_0.jpg?itok=F1eJZsX3
https://media.gettyimages.com/photos/young-woman-seated-on-the-beach-picture-id97545987?s=612x612
https://worldjourneysdiscover.files.wordpress.com/2014/07/kyoto-07.jpg?w=860&h=645
http://piquemagazine.uk/wp-content/uploads/2017/10/LPO-24-Feb-Albrecht-Menzel-%C2%AE-Anne-Hornemann-300dpi.jpg

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import os
from imageio import imread
import pandas as pd
import lmdb
from caffe2.proto import caffe2_pb2

# Folder should contain  a set of images 
image_folder = "flickr30k_images"
# CSV should contain image filenames with corresponding captions
dataframe_path = "flickr30k_images/results.csv"

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#!/usr/bin/env bash
set -e

cd

case "$OSTYPE" in
  darwin*)  DOWNLOAD=https://repo.anaconda.com/miniconda/Miniconda3-latest-MacOSX-x86_64.sh ;;
  linux*)   DOWNLOAD=https://repo.anaconda.com/miniconda/Miniconda3-latest-Linux-x86_64.sh  ;;
  *)        echo "unknown: $OSTYPE" ;;
esac

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#Credits to https://stackoverflow.com/questions/49697634/tensorboard-export-csv-file-from-command-line
from collections import defaultdict
import argparse
import numpy as np
import tensorflow as tf
from clize import run
import pandas as pd

def save_tag_to_csv(fn):
    sess = tf.InteractiveSession()

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#!/bin/bash
# This file sets the environment variable CUDA_VISIBLE_DEVICES to the MPI local rank to enable multi-GPU usage of this benchmark. Note that this disables any GPU distribution handling by he batch scheduler.
# Background: Most/some batch schedulers set CUDA_VISIBLE_DEVICES to all available GPUs on a node. In that case, the Arbor benchmark would only use the first entry in the list, probably GPU#0. This script changes that.
# -Andreas Herten, Nov 2018

_verbose=1

localrank=$CUDA_VISIBLE_DEVICES

if [[ -n "$OMPI_COMM_WORLD_NODE_RANK" ]]; then

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# use ImageMagick convert
# the order is important. the density argument applies to input.pdf and resize and rotate to output.pdf
convert -density 90 input.pdf -rotate 0.5 -attenuate 0.2 +noise Multiplicative -colorspace Gray output.pdf

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"""
A bare bones examples of optimizing a black-box function (f) using
Natural Evolution Strategies (NES), where the parameter distribution is a 
gaussian of fixed standard deviation.
"""

import numpy as np
np.random.seed(0)

# the function we want to optimize