main.py

import os.path
import tensorflow as tf
import helper
import warnings
from distutils.version import LooseVersion
import project_tests as tests


# Check TensorFlow Version
assert LooseVersion(tf.__version__) >= LooseVersion('1.0'), (
       'Please use TensorFlow version 1.0 or newer.  You are using {}'
       .format(tf.__version__))
print('TensorFlow Version: {}'.format(tf.__version__))

# Check for a GPU
if not tf.test.gpu_device_name():
    warnings.warn('No GPU found. Use a GPU to train your neural network.')
else:
    print('Default GPU Device: {}'.format(tf.test.gpu_device_name()))


def load_vgg(sess, vgg_path):
    """
    Load Pretrained VGG Model into TensorFlow.
    :param sess: TensorFlow Session
    :param vgg_path: Path to vgg folder, containing "variables/" and
                     "saved_model.pb"
    :return: Tuple of Tensors from VGG model
             (image_input, keep_prob, layer3_out, layer4_out, layer7_out)
    """
    # TODO: Implement function
    #   Use tf.saved_model.loader.load to load the model and weights
    vgg_tag = 'vgg16'
    vgg_input_tensor_name = 'image_input:0'
    vgg_keep_prob_tensor_name = 'keep_prob:0'
    vgg_layer3_out_tensor_name = 'layer3_out:0'
    vgg_layer4_out_tensor_name = 'layer4_out:0'
    vgg_layer7_out_tensor_name = 'layer7_out:0'

    tf.saved_model.loader.load(sess, [vgg_tag], vgg_path)
    graph = tf.get_default_graph()
    image_input = graph.get_tensor_by_name(vgg_input_tensor_name)
    keep_prob = graph.get_tensor_by_name(vgg_keep_prob_tensor_name)
    layer3_out = graph.get_tensor_by_name(vgg_layer3_out_tensor_name)
    layer4_out = graph.get_tensor_by_name(vgg_layer4_out_tensor_name)
    layer7_out = graph.get_tensor_by_name(vgg_layer7_out_tensor_name)

    return image_input, keep_prob, layer3_out, layer4_out, layer7_out

tests.test_load_vgg(load_vgg, tf)


def layers(vgg_layer3_out, vgg_layer4_out, vgg_layer7_out, num_classes):
    """
    Create the layers for a fully convolutional network.
    Build skip-layers using the vgg layers.
    :param vgg_layer7_out: TF Tensor for VGG Layer 7 output
    :param vgg_layer4_out: TF Tensor for VGG Layer 4 output
    :param vgg_layer3_out: TF Tensor for VGG Layer 3 output
    :param num_classes: Number of classes to classify
    :return: The Tensor for the last layer of output
    """
    # TODO: Implement function
    layer7_conv = tf.layers.conv2d(
        inputs=vgg_layer7_out,
        filters=num_classes,
        kernel_size=1,
        padding='same',
        kernel_initializer=tf.truncated_normal_initializer(stddev=0.01),
        kernel_regularizer=tf.contrib.layers.l2_regularizer(1e-3))

    layer7_up = tf.layers.conv2d_transpose(
        inputs=layer7_conv,
        filters=num_classes,
        kernel_size=4,
        strides=2,
        padding='same',
        kernel_initializer=tf.truncated_normal_initializer(stddev=0.01),
        kernel_regularizer=tf.contrib.layers.l2_regularizer(1e-3))

    layer4_conv = tf.layers.conv2d(
        inputs=vgg_layer4_out,
        filters=num_classes,
        kernel_size=1,
        padding='same',
        kernel_initializer=tf.truncated_normal_initializer(stddev=0.01),
        kernel_regularizer=tf.contrib.layers.l2_regularizer(1e-3))

    layer4_skip = tf.add(layer7_up, layer4_conv)

    layer4_up = tf.layers.conv2d_transpose(
        inputs=layer4_skip,
        filters=num_classes,
        kernel_size=4,
        strides=2,
        padding='same',
        kernel_initializer=tf.truncated_normal_initializer(stddev=0.01),
        kernel_regularizer=tf.contrib.layers.l2_regularizer(1e-3))

    layer3_conv = tf.layers.conv2d(
        inputs=vgg_layer3_out,
        filters=num_classes,
        kernel_size=1,
        padding='same',
        kernel_initializer=tf.truncated_normal_initializer(stddev=0.01),
        kernel_regularizer=tf.contrib.layers.l2_regularizer(1e-3))

    layer3_skip = tf.add(layer4_up, layer3_conv)

    layer3_up = tf.layers.conv2d_transpose(
        inputs=layer3_skip,
        filters=num_classes,
        kernel_size=16,
        strides=8,
        padding='same',
        kernel_initializer=tf.truncated_normal_initializer(stddev=0.01),
        kernel_regularizer=tf.contrib.layers.l2_regularizer(1e-3))

    return layer3_up

tests.test_layers(layers)


def optimize(nn_last_layer, correct_label, learning_rate, num_classes):
    """
    Build the TensorFLow loss and optimizer operations.
    :param nn_last_layer: TF Tensor of the last layer in the neural network
    :param correct_label: TF Placeholder for the correct label image
    :param learning_rate: TF Placeholder for the learning rate
    :param num_classes: Number of classes to classify
    :return: Tuple of (logits, train_op, cross_entropy_loss)
    """
    # TODO: Implement function
    logits = tf.reshape(nn_last_layer, (-1, num_classes))
    labels = tf.reshape(correct_label, (-1, num_classes))

    cross_entropy_loss = tf.reduce_mean(
        tf.nn.softmax_cross_entropy_with_logits(logits=logits,
                                                labels=labels))

    train_op = (tf.train
                .AdamOptimizer(learning_rate)
                .minimize(cross_entropy_loss))

    return logits, train_op, cross_entropy_loss

tests.test_optimize(optimize)


def train_nn(sess, epochs, batch_size, get_batches_fn,
             train_op, cross_entropy_loss, input_image,
             correct_label, keep_prob, learning_rate):
    """
    Train neural network and print out the loss during training.
    :param sess: TF Session
    :param epochs: Number of epochs
    :param batch_size: Batch size
    :param get_batches_fn: Function to get batches of training data.
                           Call using get_batches_fn(batch_size)
    :param train_op: TF Operation to train the neural network
    :param cross_entropy_loss: TF Tensor for the amount of loss
    :param input_image: TF Placeholder for input images
    :param correct_label: TF Placeholder for label images
    :param keep_prob: TF Placeholder for dropout keep probability
    :param learning_rate: TF Placeholder for learning rate
    """
    # TODO: Implement function

    for epoch in range(epochs):
        print('Running epoch %s/%s' % (epoch + 1, epochs))

        for images, labels in get_batches_fn(batch_size):

            _, loss = sess.run(
                [train_op, cross_entropy_loss],
                feed_dict={
                    input_image: images,
                    correct_label: labels,
                    keep_prob: 0.5,
                    learning_rate: 0.0005
                }
            )
            print('  - loss %8.4f (images: %d, labels: %d)' %
                  (loss, len(images), len(labels)))

tests.test_train_nn(train_nn)


def run():
    num_classes = 2
    image_shape = (160, 576)
    data_dir = './data'
    runs_dir = './runs'
    epochs = 30
    batch_size = 8
    tests.test_for_kitti_dataset(data_dir)

    # Download pretrained vgg model
    helper.maybe_download_pretrained_vgg(data_dir)

    # OPTIONAL: Train and Inference on the cityscapes dataset instead
    # of the Kitti dataset.
    # You'll need a GPU with at least 10 teraFLOPS to train on.
    #  https://www.cityscapes-dataset.com/

    with tf.Session() as sess:
        # Path to vgg model
        vgg_path = os.path.join(data_dir, 'vgg')
        # Create function to get batches
        training_path = os.path.join(data_dir, 'data_road/training')
        get_batches_fn = helper.gen_batch_function(training_path, image_shape)

        # OPTIONAL: Augment Images for better results
        #  https://datascience.stackexchange.com/questions/5224/how-to-prepare-augment-images-for-neural-network

        # TODO: Build NN using load_vgg, layers, and optimize function
        (input_image,
         keep_prob,
         vgg_layer3_out,
         vgg_layer4_out,
         vgg_layer7_out) = load_vgg(sess, vgg_path)

        output_layer = layers(vgg_layer3_out,
                              vgg_layer4_out,
                              vgg_layer7_out,
                              num_classes)

        learning_rate = tf.placeholder(dtype=tf.float32)
        correct_label = tf.placeholder(dtype=tf.float32)

        logits, train_op, cross_entropy_loss = optimize(output_layer,
                                                        correct_label,
                                                        learning_rate,
                                                        num_classes)

        # TODO: Train NN using the train_nn function
        sess.run(tf.global_variables_initializer())
        train_nn(sess, epochs, batch_size, get_batches_fn, train_op,
                 cross_entropy_loss, input_image, correct_label,
                 keep_prob, learning_rate)

        # TODO: Save inference data using helper.save_inference_samples
        # helper.save_inference_samples(runs_dir, data_dir, sess, image_shape,
        #                               logits, keep_prob, input_image)
        helper.save_inference_samples(runs_dir, data_dir, sess, image_shape,
                                      logits, keep_prob, input_image)

        # Save tf model
        saver = tf.train.Saver()
        saver.save(sess, 'model/model_01.ckpt')
        saver.export_meta_graph('model/model_01.meta')
        tf.train.write_graph(sess.graph_def, 'model/', 'model_01.pb', False)

        # OPTIONAL: Apply the trained model to a video


if __name__ == '__main__':
    run()