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cinic10.py

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+import os
+import sys
+import numpy as np
+import matplotlib.pyplot as plt
+from scipy import misc
+from six.moves import urllib
+import tarfile
+
+
+def loadData(pathToDatasetFolder, oneHot=False):
+    """
+    pathToDatasetFolder: Parent folder of CINIC-10 dataset folder of CINIC-10.tar.gz file
+    oneHot: Label encoding (one hot encoding or not)
+
+    Return: Train, validation and test sets and label numpy arrays
+    """
+    sourceUrl = "https://datashare.is.ed.ac.uk/bitstream/handle/10283/3192/CINIC-10.tar.gz"
+    pathToFile = downloadDataset(pathToDatasetFolder, "CINIC-10.tar.gz", sourceUrl)
+
+    labelDict = {'airplane': 0, 'automobile': 1, 'bird': 2, 'cat': 3,
+                'deer': 4, 'dog': 5, 'frog': 6, 'horse': 7, 'ship': 8,
+                'truck': 9}
+
+    pathToTrain = os.path.join(pathToFile, "train")
+    pathToVal = os.path.join(pathToFile, "valid")
+    pathToTest = os.path.join(pathToFile, "test")
+
+    imgNamesTrain = [f for dp, dn, fn in os.walk(os.path.expanduser(pathToTrain)) for f in fn]
+    imgDirsTrain = [dp for dp, dn, fn in os.walk(os.path.expanduser(pathToTrain)) for f in fn]
+    imgNamesVal = [f for dp, dn, fn in os.walk(os.path.expanduser(pathToVal)) for f in fn]
+    imgDirsVal = [dp for dp, dn, fn in os.walk(os.path.expanduser(pathToVal)) for f in fn]
+    imgNamesTest = [f for dp, dn, fn in os.walk(os.path.expanduser(pathToTest)) for f in fn]
+    imgDirsTest = [dp for dp, dn, fn in os.walk(os.path.expanduser(pathToTest)) for f in fn]
+
+    XTrain = np.empty((len(imgNamesTrain), 32, 32, 3), dtype=np.float32)
+    YTrain = np.empty((len(imgNamesTrain)), dtype=np.int32)
+    XVal = np.empty((len(imgNamesVal), 32, 32, 3), dtype=np.float32)
+    YVal = np.empty((len(imgNamesVal)), dtype=np.int32)
+    XTest = np.empty((len(imgNamesTest), 32, 32, 3), dtype=np.float32)
+    YTest = np.empty((len(imgNamesTest)), dtype=np.int32)
+
+    print("Loading")
+
+    for i in range(len(imgNamesTrain)):
+        # img = plt.imread(os.path.join(imgDirsTrain[i], imgNamesTrain[i]))
+        img = misc.imread(os.path.join(imgDirsTrain[i], imgNamesTrain[i]))
+        if len(img.shape) == 2:
+            XTrain[i, :, :, 2] = XTrain[i, :, :, 1] = XTrain[i, :, :, 0] = img/255.
+        else:
+            XTrain[i] = img/255.
+        YTrain[i] = labelDict[os.path.basename(imgDirsTrain[i])]
+    for i in range(len(imgNamesVal)):
+        # img = plt.imread(os.path.join(imgDirsVal[i], imgNamesVal[i]))
+        img = misc.imread(os.path.join(imgDirsVal[i], imgNamesVal[i]))
+        if len(img.shape) == 2:
+            XVal[i, :, :, 2] = XVal[i, :, :, 1] = XVal[i, :, :, 0] = img/255.
+        else:
+            XVal[i] = img/255.
+        YVal[i] = labelDict[os.path.basename(imgDirsVal[i])]
+    for i in range(len(imgNamesTest)):
+        # img = plt.imread(os.path.join(imgDirsTest[i], imgNamesTest[i]))
+        img = misc.imread(os.path.join(imgDirsTest[i], imgNamesTest[i]))
+        if len(img.shape) == 2:
+            XTest[i, :, :, 2] = XTest[i, :, :, 1] = XTest[i, :, :, 0] = img/255.
+        else:
+            XTest[i] = img/255.
+        YTest[i] = labelDict[os.path.basename(imgDirsTest[i])]
+
+    if oneHot:
+        YTrain = toOneHot(YTrain, 10)
+        YVal = toOneHot(YVal, 10)
+        YTest = toOneHot(YTest, 10)
+
+    print("+ Dataset loaded")
+
+    return XTrain, YTrain, XVal, YVal, XTest, YTest
+
+
+def downloadDataset(dirName, fileName, sourceUrl):
+    """
+    https://github.com/tflearn/tflearn/blob/master/tflearn/datasets/cifar10.py
+    """
+    cinicDirName = os.path.join(dirName, "CINIC-10/")
+    if not os.path.exists(cinicDirName):
+        os.mkdir(cinicDirName)
+        pathToFile = os.path.join(dirName, fileName)
+        if not os.path.exists(pathToFile):
+            print("Downloading")
+            pathToFile, _ = urllib.request.urlretrieve(sourceUrl, pathToFile, reporthook)
+            print("+ Downloaded")
+        untar(pathToFile, cinicDirName)
+    else:
+        print("+ Dataset already downloaded")
+    return cinicDirName
+
+
+def reporthook(blocknum, blocksize, totalsize):
+    """
+    reporthook from stackoverflow #13881092
+    https://github.com/tflearn/tflearn/blob/master/tflearn/datasets/cifar10.py
+    """
+    readsofar = blocknum * blocksize
+    if totalsize > 0:
+        percent = readsofar * 1e2 / totalsize
+        s = "\r%5.1f%% %*d / %d" % (
+            percent, len(str(totalsize)), readsofar, totalsize)
+        sys.stderr.write(s)
+        if readsofar >= totalsize:  # near the end
+            sys.stderr.write("\n")
+    else:  # total size is unknown
+        sys.stderr.write("read %d\n" % (readsofar,))
+
+
+def untar(fname, path):
+    if (fname.endswith("tar.gz")):
+        print("Extracting tar file")
+        tar = tarfile.open(fname)
+        tar.extractall(path=path)
+        tar.close()
+        print("+ Extracted")
+    else:
+        print("Not a tar.gz file")
+
+
+def toOneHot(y, nb_classes=None):
+    """
+    https://github.com/tflearn/tflearn/blob/master/tflearn/data_utils.py#L36
+    """
+    if nb_classes:
+        # y = np.asarray(y, dtype='int32')
+        if len(y.shape) > 2:
+            print("Warning: data array ndim > 2")
+        if len(y.shape) > 1:
+            y = y.reshape(-1)
+        Y = np.zeros((len(y), nb_classes))
+        Y[np.arange(len(y)), y] = 1.
+        return Y
+    else:
+        y = np.array(y)
+        return (y[:, None] == np.unique(y)).astype(np.float32)

residual_network_cinic10.py

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+# -*- coding: utf-8 -*-
+
+""" Deep Residual Network.
+
+Applying a Deep Residual Network to CINIC-10 Dataset classification task.
+
+References:
+    - K. He, X. Zhang, S. Ren, and J. Sun. Deep Residual Learning for Image
+      Recognition, 2015.
+    - CINIC-10 is not ImageNet or CIFAR-10, Darlow et al., 2018.
+
+Links:
+    - [Deep Residual Network](http://arxiv.org/pdf/1512.03385.pdf)
+    - [CINIC-10 Dataset](https://github.com/BayesWatch/cinic-10)
+
+"""
+
+from __future__ import division, print_function, absolute_import
+
+import tflearn
+from tflearn.data_utils import shuffle
+
+# Residual blocks
+# 32 layers: n=5, 56 layers: n=9, 110 layers: n=18
+n = 18
+
+import cinic10
+X, Y, _, _, testX, testY = cinic10.loadData("/home/altinel/Downloads/datasets", oneHot=True)
+X, Y = shuffle(X, Y)
+
+# Real-time data preprocessing
+img_prep = tflearn.ImagePreprocessing()
+img_prep.add_featurewise_zero_center(per_channel=True)
+
+# Real-time data augmentation
+img_aug = tflearn.ImageAugmentation()
+img_aug.add_random_flip_leftright()
+img_aug.add_random_crop([32, 32], padding=4)
+
+# Building Residual Network
+net = tflearn.input_data(shape=[None, 32, 32, 3],
+                         data_preprocessing=img_prep,
+                         data_augmentation=img_aug)
+net = tflearn.conv_2d(net, 16, 3, regularizer='L2', weight_decay=0.0001)
+net = tflearn.residual_block(net, n, 16)
+net = tflearn.residual_block(net, 1, 32, downsample=True)
+net = tflearn.residual_block(net, n-1, 32)
+net = tflearn.residual_block(net, 1, 64, downsample=True)
+net = tflearn.residual_block(net, n-1, 64)
+net = tflearn.batch_normalization(net)
+net = tflearn.activation(net, 'relu')
+net = tflearn.global_avg_pool(net)
+# Regression
+net = tflearn.fully_connected(net, 10, activation='softmax')
+mom = tflearn.Momentum(0.1, lr_decay=0.1, decay_step=32000, staircase=True)
+net = tflearn.regression(net, optimizer=mom,
+                         loss='categorical_crossentropy')
+# Training
+model = tflearn.DNN(net, checkpoint_path='model_resnet_cinic10',
+                    max_checkpoints=10, tensorboard_verbose=0,
+                    clip_gradients=0.)
+
+model.fit(X, Y, n_epoch=200, validation_set=(testX, testY),
+          snapshot_epoch=False, snapshot_step=500,
+          show_metric=True, batch_size=128, shuffle=True,
+          run_id='resnet_cinic10')