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#!/usr/bin/env python

# Implementation of "Deep Residual U-Net"
# arXiv-link: https://arxiv.org/abs/1711.10684

import chainer
import chainer.functions as F
import chainer.links as L
from chainer import initializers

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import chainer
from chainer import reporter as reporter_module
from chainer.training import extensions
from chainer import function
import numpy as np
import copy
from chainercv.evaluations import eval_semantic_segmentation

class IouEvaluator(extensions.Evaluator):

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#!/bin/bash

mkdir train 
mv ILSVRC2012_img_train.tar train/ 
cd train

tar -xvf ILSVRC2012_img_train.tar 
rm -f ILSVRC2012_img_train.tar

find . -name "*.tar" | while read NAME ; do mkdir -p "${NAME%.tar}"; tar -xvf "${NAME}" -C "${NAME%.tar}"; rm -f "${NAME}"; done

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import tensorflow as tf

def _get_values_from_tb(tb_file, key='val/aP50', values={}):
    sess = tf.InteractiveSession()
    with sess.as_default():
        for e in tf.train.summary_iterator(tb_file):
            for v in e.summary.value:
                if v.tag == key:
                    values[e.step] = v.simple_value
    return values

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