Setting up port forwarding on Ubuntu 16.04

Here we demonstrate how to set up port forwarding on a VPS with Ubuntu 16.04, so that we can use this VPS as an Internet traffic forwarding service. This setup is useful when the route between the source and the destination IPs is bad, but the intermediate VPS has good connections to both the source and the destination.

First, set the net.ipv4.ip_forward=1 flag in the /etc/sysctl.conf file with vi, and use the following command to make it effective immediately:

sysctl -p

Next, say we want to use port 50000 to forward both TCP and UDP traffic to 168.10.0.1:40000. We use the following iptables commands:

iptables -t nat -A PREROUTING -p tcp -m tcp --dport 50000 -j DNAT --to-destination 168.10.0.1:40000
iptables -t nat -A PREROUTING -p udp -m udp --dport 50000 -j DNAT --to-destination 168.10.0.1:40000
iptables -t nat -A POSTROUTING -d 168.10.0.1/32 -p tcp -m tcp --dport 40000 -j SNAT --to-source 172.10.0.1
iptables -t nat -A POSTROUTING -d 168.10.0.1/32 -p udp -m udp --dport 40000 -j SNAT --to-source 172.10.0.1

In the command above, 172.10.0.1 is the private IP of the intermediate VPS. We can use the following command to check if we set up the NAT table correctly:

iptables -L -n -t nat

Finally, to make the iptables settings persistent, install:

apt-get install iptables-persistent

To save any additional changes to the NAT table, run:

netfilter-persistent save

Making predictions from images with models trained with the TensorFlow LeNet tutorial

Suppose we have trained a model with the TensorFlow LeNet tutorial, as outlined in this post. The following codes would allow you to read an image from disk, and make predictions with the trained LeNet model:

import tensorflow as tf
from tensorflow.contrib.layers import flatten
import cv2
import numpy as np

def LeNet(x):
    # Hyperparameters
    mu = 0
    sigma = 0.1

    # SOLUTION: Layer 1: Convolutional. Input = 32x32x1. Output = 28x28x6.
    conv1_W = tf.Variable(tf.truncated_normal(shape=(5, 5, 1, 6), mean=mu, stddev=sigma))
    conv1_b = tf.Variable(tf.zeros(6))
    conv1 = tf.nn.conv2d(x, conv1_W, strides=[1, 1, 1, 1], padding='VALID') + conv1_b

    # SOLUTION: Activation.
    conv1 = tf.nn.relu(conv1)

    # SOLUTION: Pooling. Input = 28x28x6. Output = 14x14x6.
    conv1 = tf.nn.max_pool(conv1, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='VALID')

    # SOLUTION: Layer 2: Convolutional. Output = 10x10x16.
    conv2_W = tf.Variable(tf.truncated_normal(shape=(5, 5, 6, 16), mean=mu, stddev=sigma))
    conv2_b = tf.Variable(tf.zeros(16))
    conv2 = tf.nn.conv2d(conv1, conv2_W, strides=[1, 1, 1, 1], padding='VALID') + conv2_b

    # SOLUTION: Activation.
    conv2 = tf.nn.relu(conv2)

    # SOLUTION: Pooling. Input = 10x10x16. Output = 5x5x16.
    conv2 = tf.nn.max_pool(conv2, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='VALID')

    # SOLUTION: Flatten. Input = 5x5x16. Output = 400.
    fc0 = flatten(conv2)

    # SOLUTION: Layer 3: Fully Connected. Input = 400. Output = 200.
    fc1_W = tf.Variable(tf.truncated_normal(shape=(400, 200), mean=mu, stddev=sigma))
    fc1_b = tf.Variable(tf.zeros(200))
    fc1 = tf.matmul(fc0, fc1_W) + fc1_b

    # SOLUTION: Activation.
    fc1 = tf.nn.relu(fc1)

    # SOLUTION: Layer 4: Fully Connected. Input = 200. Output = 200.
    fc2_W = tf.Variable(tf.truncated_normal(shape=(200, 200), mean=mu, stddev=sigma))
    fc2_b = tf.Variable(tf.zeros(200))
    fc2 = tf.matmul(fc1, fc2_W) + fc2_b

    # SOLUTION: Activation.
    fc2 = tf.nn.relu(fc2)

    # SOLUTION: Layer 5: Fully Connected. Input = 200. Output = 147.
    fc3_W = tf.Variable(tf.truncated_normal(shape=(200, 147), mean=mu, stddev=sigma))
    fc3_b = tf.Variable(tf.zeros(147))
    logits = tf.matmul(fc2, fc3_W) + fc3_b

    return logits

# Create placeholders for training data,
# x is a placeholder for a batch of input images. y is a placeholder for a batch of output labels.
x = tf.placeholder(tf.float32, (None, 32, 32, 1))
logits = LeNet(x)

saver = tf.train.Saver()

# load checkpoint and make prediction

with tf.Session() as sess:

    # read one input image from disk
    im = cv2.imread('test3.jpg')
    im = cv2.cvtColor(im, cv2.COLOR_RGB2GRAY)
    im = cv2.resize(im, (32, 32), interpolation=cv2.INTER_NEAREST)
    im = 255 - im  # for jpeg white is 255 black is 0, we revert so that white is 0 and black is 255
    im = np.divide(im.astype(np.float32), 255)

    # expand dims for input image
    im = np.expand_dims(im, axis=-1)
    im = np.expand_dims(im, axis=0)

    # restore training session and make prediction
    saver.restore(sess, tf.train.latest_checkpoint('.'))

    prediction = sess.run(logits, feed_dict={x: im})

    # sorted indices
    sorted_index = np.argsort(-prediction)
    print sorted_index

Prepare data in MNIST TensorFlow LeNet tutorial format

Let's say we have a custom dataset of 146 character classes, and we want to train a LeNet to recognize these characters. We put the image files in separate train/test directories, and for train and test we have subdirectories 0,1,2,3,...,145. Image filenames are in this format: ['train' or 'test']/[0-145]/any_name.jpg. First, create read_data.py as follows so that we can read the images and labels, and save them to npy files:

import os
import numpy as np
import cv2
import matplotlib.pyplot as plt

def read_data(path):
    im_list = []
    label_list = []
    for (dirpath, dirnames, filenames) in os.walk(path):
        print('Processing ' + dirpath + ' ...')
        for filename in filenames:
            if filename.endswith('.jpg'):
                fullfile = os.sep.join([dirpath, filename])
                im = cv2.imread(fullfile)
                if im is None:
                    print(' WARN: ' + filename + ' in ' + dirpath + ' is bad!')
                    continue
                im = cv2.cvtColor(im, cv2.COLOR_RGB2GRAY)
                im = cv2.resize(im, (32, 32), interpolation=cv2.INTER_NEAREST)
                # plt.imshow(im)
                im = 255 - im # for jpeg white is 255 black is 0, we revert so that white is 0 and black is 255
                im = np.divide(im.astype(np.float32), 255)
                im = np.expand_dims(im, 2) # add an additional dimension (i.e., from 28x28 to 28x28x1)
                im_list.append(im)
                label = int(os.path.split(dirpath)[-1])
                label_list.append(np.uint8(label)) # 0 to 145, so uint8 is fine (0-255)
    im_array = np.array(im_list)
    label_array = np.array(label_list)
    return (im_array, label_array)
if __name__ == "__main__":
    #base_dir = '/home/twang/data/hcr/single_char/train/'
    #(im_array, label_array) = read_data(base_dir)
    #np.save(base_dir + 'trainData', im_array)
    #np.save(base_dir + 'trainLabel', label_array)

    base_dir = '/home/twang/data/hcr/single_char/test/'
    (im_array, label_array) = read_data(base_dir)
    np.save(base_dir + 'testData', im_array)
    np.save(base_dir + 'testLabel', label_array)

The training and evaluation scripts are then given by (as per the TensorFlow tutorial and this project):

import tensorflow as tf
from tensorflow.contrib.layers import flatten
import numpy as np
from sklearn.utils import shuffle

EPOCHS = 10
BATCH_SIZE = 128


def LeNet(x):
    # Hyperparameters
    mu = 0
    sigma = 0.1

    # SOLUTION: Layer 1: Convolutional. Input = 32x32x1. Output = 28x28x6.
    conv1_W = tf.Variable(tf.truncated_normal(shape=(5, 5, 1, 6), mean=mu, stddev=sigma))
    conv1_b = tf.Variable(tf.zeros(6))
    conv1 = tf.nn.conv2d(x, conv1_W, strides=[1, 1, 1, 1], padding='VALID') + conv1_b

    # SOLUTION: Activation.
    conv1 = tf.nn.relu(conv1)

    # SOLUTION: Pooling. Input = 28x28x6. Output = 14x14x6.
    conv1 = tf.nn.max_pool(conv1, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='VALID')

    # SOLUTION: Layer 2: Convolutional. Output = 10x10x16.
    conv2_W = tf.Variable(tf.truncated_normal(shape=(5, 5, 6, 16), mean=mu, stddev=sigma))
    conv2_b = tf.Variable(tf.zeros(16))
    conv2 = tf.nn.conv2d(conv1, conv2_W, strides=[1, 1, 1, 1], padding='VALID') + conv2_b

    # SOLUTION: Activation.
    conv2 = tf.nn.relu(conv2)

    # SOLUTION: Pooling. Input = 10x10x16. Output = 5x5x16.
    conv2 = tf.nn.max_pool(conv2, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='VALID')

    # SOLUTION: Flatten. Input = 5x5x16. Output = 400.
    fc0 = flatten(conv2)

    # SOLUTION: Layer 3: Fully Connected. Input = 400. Output = 200.
    fc1_W = tf.Variable(tf.truncated_normal(shape=(400, 200), mean=mu, stddev=sigma))
    fc1_b = tf.Variable(tf.zeros(200))
    fc1 = tf.matmul(fc0, fc1_W) + fc1_b

    # SOLUTION: Activation.
    fc1 = tf.nn.relu(fc1)

    # SOLUTION: Layer 4: Fully Connected. Input = 200. Output = 200.
    fc2_W = tf.Variable(tf.truncated_normal(shape=(200, 200), mean=mu, stddev=sigma))
    fc2_b = tf.Variable(tf.zeros(200))
    fc2 = tf.matmul(fc1, fc2_W) + fc2_b

    # SOLUTION: Activation.
    fc2 = tf.nn.relu(fc2)

    # SOLUTION: Layer 5: Fully Connected. Input = 200. Output = 146.
    fc3_W = tf.Variable(tf.truncated_normal(shape=(200, 146), mean=mu, stddev=sigma))
    fc3_b = tf.Variable(tf.zeros(146))
    logits = tf.matmul(fc2, fc3_W) + fc3_b

    return logits

def evaluate(X_data, y_data):
    num_examples = len(X_data)
    total_accuracy = 0
    sess = tf.get_default_session()
    for offset in range(0, num_examples, BATCH_SIZE):
        batch_x, batch_y = X_data[offset:offset+BATCH_SIZE], y_data[offset:offset+BATCH_SIZE]
        accuracy = sess.run(accuracy_operation, feed_dict={x: batch_x, y: batch_y})
        total_accuracy += (accuracy * len(batch_x))
    return total_accuracy / num_examples

# mnist = input_data.read_data_sets("MNIST_data/", reshape=False)
# X_train, y_train           = mnist.train.images, mnist.train.labels
# X_validation, y_validation = mnist.validation.images, mnist.validation.labels
# X_test, y_test             = mnist.test.images, mnist.test.labels
X_train = np.load('/home/twang/data/hcr/single_char/train/trainData.npy')
y_train = np.load('/home/twang/data/hcr/single_char/train/trainLabel.npy')
X_test = np.load('/home/twang/data/hcr/single_char/test/testData.npy')
y_test = np.load('/home/twang/data/hcr/single_char/test/testLabel.npy')

assert(len(X_train) == len(y_train))
assert(len(X_test) == len(y_test))

print()
print("Image Shape: {}".format(X_train[0].shape))
print()
print("Training Set:   {} samples".format(len(X_train)))
print("Test Set:       {} samples".format(len(X_test)))

print("Image Shape: {}".format(X_train[0].shape))

# Shuffule training data
X_train, y_train = shuffle(X_train, y_train)

# Create placeholders for training data,
# x is a placeholder for a batch of input images. y is a placeholder for a batch of output labels.
x = tf.placeholder(tf.float32, (None, 32, 32, 1))
y = tf.placeholder(tf.int32, (None))
one_hot_y = tf.one_hot(y, 146)

# training pipeline
rate = 0.001

logits = LeNet(x)
cross_entropy = tf.nn.softmax_cross_entropy_with_logits(logits=logits, labels=one_hot_y)
loss_operation = tf.reduce_mean(cross_entropy)
optimizer = tf.train.AdamOptimizer(learning_rate = rate)
training_operation = optimizer.minimize(loss_operation)

# evaluation
correct_prediction = tf.equal(tf.argmax(logits, 1), tf.argmax(one_hot_y, 1))
accuracy_operation = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))
saver = tf.train.Saver()

# Training
with tf.Session() as sess:
    sess.run(tf.global_variables_initializer())
    num_examples = len(X_train)

    print("Training...")
    print()
    for i in range(EPOCHS):
        X_train, y_train = shuffle(X_train, y_train)
        for offset in range(0, num_examples, BATCH_SIZE):
            end = offset + BATCH_SIZE
            batch_x, batch_y = X_train[offset:end], y_train[offset:end]
            sess.run(training_operation, feed_dict={x: batch_x, y: batch_y})

        test_accuracy = evaluate(X_test, y_test)
        print("EPOCH {} ...".format(i + 1))
        print("Test Accuracy = {:.3f}".format(test_accuracy))
        print()

    saver.save(sess, 'lenet')
    print("Model saved")

with tf.Session() as sess:
    saver.restore(sess, tf.train.latest_checkpoint('.'))

    test_accuracy = evaluate(X_test, y_test)
    print("Test Accuracy = {:.3f}".format(test_accuracy))

Note that we have changed the dimensions of the last few layers (in the original LeNet, there are 10 classes, i.e., digits 0 to 9) to reflect the fact that we are working with a dataset of 146 classes.

Split contents of a file according to a list

The following script runs through a list in a first file, and check through a second file for lines that start with items in the list. It outputs the matched lines in the second file to a third file.

#!/bin/bash

while read p; do
  grep "^$p" $2 >> $3
done <$1

The script is useful if you have a dataset split file and wanted to extract relevant lines from another file (e.g., a detector's bounding box outputs) according to this split. For example, you may want to run

./split_result.sh train.txt output_trainval.txt output_train.txt
./split_result.sh val.txt output_trainval.txt output_val.txt

Note the script makes no assumption about the ordering of lines in the second file.

Reporting per-class accuracy with MatCaffe

With Caffe, the per-class accuracy output as specified in prototxt files seems to be buggy. Namely, if a particular class has no sample in a particular batch, the accuracy for that class will be set to zero for that particular batch. This is not a problem itself, but it seems these zero values will be counted towards calculating the average per-class accuracy, which is very misleading. See this PR for more information.

Use the following MATLAB code (you must compile MatCaffe first) for computing per-class accuracy on a validation or test set, instead.

% global parameters
DATA_ROOT = '/home/twang/data/flower/flower_531_crop_256/';
TEST_LIST_FILE = '/home/twang/data/flower/flower_531_meta/val.txt';
NUM_CLASSES = 531;

% caffe initialization
gpu_id = 0;
model = '/home/twang/caffe/models/resnet_flower531/deploy.prototxt';
weights = '/home/twang/caffe/models/resnet_flower531/resnet_flower531_iter_120000.caffemodel';

caffe.set_mode_gpu();
caffe.set_device(gpu_id);

net = caffe.Net(model, weights, 'test');

mean_data = caffe.io.read_mean('/home/twang/caffe/models/resnet_flower531/flower_mean.binaryproto');

% read files

[files,labels] = textread(TEST_LIST_FILE, '%s %d\n');
accuracy = zeros(2, NUM_CLASSES);

for ii = 0 : NUM_CLASSES-1
    class_files = files(labels == ii); % all files for current class
    accuracy(1, ii+1) = length(class_files);
    for jj = 1 : accuracy(1, ii+1)
        im_data = caffe.io.load_image([DATA_ROOT class_files{jj}]);
        input_data = {imresize(im_data - mean_data, [224 224])};
        scores = net.forward(input_data);
        [~, predict] = max(scores{1});
        if (predict == ii+1)
            accuracy(2, ii+1) = accuracy(2, ii+1) + 1;
        end
    end
    fprintf('Class #%03d accuracy = %.2f.\n', ii+1, accuracy(2, ii+1) / accuracy(1, ii+1));
end

caffe.reset_all();