nn_breaking_detection/setup_cifar.py at master · jeffreywpli/nn_breaking_detection · GitHub

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## setup_cifar.py -- code to set up the CIFAR dataset
##
## Copyright (C) 2017, Nicholas Carlini <nicholas@carlini.com>.
##
## This program is licenced under the BSD 2-Clause licence,
## contained in the LICENCE file in this directory.

import tensorflow as tf
import numpy as np
import os
import pickle
import gzip
import pickle
import urllib.request

from resnet import ResnetBuilder
from keras.layers import Dropout

def load_batch(fpath, label_key="labels"):
    f = open(fpath, "rb")
    d = pickle.load(f, encoding="bytes")
    for k, v in d.items():
        del(d[k])
        d[k.decode("utf8")] = v
    f.close()
    data = d["data"]
    labels = d[label_key]

    data = data.reshape(data.shape[0], 3, 32, 32)
    final = np.zeros((data.shape[0], 32, 32, 3),dtype=np.float32)
    final[:,:,:,0] = data[:,0,:,:]
    final[:,:,:,1] = data[:,1,:,:]
    final[:,:,:,2] = data[:,2,:,:]

    final /= 255
    final -= .5
    labels2 = np.zeros((len(labels), 10))
    labels2[np.arange(len(labels2)), labels] = 1

    return final, labels

def load_batch(fpath):
    f = open(fpath,"rb").read()
    size = 32*32*3+1
    labels = []
    images = []
    for i in range(10000):
        arr = np.fromstring(f[i*size:(i+1)*size],dtype=np.uint8)
        lab = np.identity(10)[arr[0]]
        img = arr[1:].reshape((3,32,32)).transpose((1,2,0))
        labels.append(lab)
        images.append((img/255)-.5)
    return np.array(images),np.array(labels)


class CIFAR:
    def __init__(self):
        train_data = []
        train_labels = []

        if not os.path.exists("../Datasets/CIFAR-10"):
            os.mkdir("../Datasets/CIFAR-10")
            urllib.request.urlretrieve("https://www.cs.toronto.edu/~kriz/cifar-10-binary.tar.gz", "../Datasets/CIFAR-10/cifar-data.tar.gz")
            os.popen("tar -xzf ../Datasets/CIFAR-10/cifar-data.tar.gz -C ../Datasets/CIFAR-10").read()

        for i in range(5):
            r,s = load_batch("../Datasets/CIFAR-10/cifar-10-batches-bin/data_batch_"+str(i+1)+".bin")
            train_data.extend(r)
            train_labels.extend(s)

        train_data = np.array(train_data,dtype=np.float32)
        train_labels = np.array(train_labels)

        self.test_data, self.test_labels = load_batch("../Datasets/CIFAR-10/cifar-10-batches-bin/test_batch.bin")

        VALIDATION_SIZE = 5000

        self.validation_data = train_data[:VALIDATION_SIZE, :, :, :]
        self.validation_labels = train_labels[:VALIDATION_SIZE]
        self.train_data = train_data[VALIDATION_SIZE:, :, :, :]
        self.train_labels = train_labels[VALIDATION_SIZE:]

class CIFARModel:
    def __init__(self, restore=None, session=None, Dropout=Dropout, num_labels=10):
        self.num_channels = 3
        self.image_size = 32
        self.num_labels = num_labels

        model = ResnetBuilder.build_resnet_32((3, 32, 32), num_labels, activation=False, Dropout=Dropout)

        if restore != None:
            model.load_weights(restore)

        self.model = model

    def predict(self, data):
        return self.model(data)