FCN main

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. Please 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)
    """
    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'

    # Loads model and weights
    tf.saved_model.loader.load(sess, [vgg_tag], vgg_path)
    graph = tf.get_default_graph()
    vgg_input_out = graph.get_tensor_by_name(vgg_input_tensor_name)
    keep_prob = graph.get_tensor_by_name(vgg_keep_prob_tensor_name)
    vgg_layer3_out = graph.get_tensor_by_name(vgg_layer3_out_tensor_name)
    vgg_layer4_out = graph.get_tensor_by_name(vgg_layer4_out_tensor_name)
    vgg_layer7_out = graph.get_tensor_by_name(vgg_layer7_out_tensor_name)

    return vgg_input_out, keep_prob, vgg_layer3_out, vgg_layer4_out, vgg_layer7_out


print('Test Load VGG')
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 3 output
    :param vgg_layer4_out: TF Tensor for VGG Layer 4 output
    :param vgg_layer3_out: TF Tensor for VGG Layer 7 output
    :param num_classes: Number of classes to classify
    :return: The Tensor for the last layer of output
    """
    # layer 7 1x1 convolution
    layer7_1x1 = tf.layers.conv2d(vgg_layer7_out, num_classes, kernel_size=(1, 1), padding='same',
                                  kernel_initializer=tf.random_normal_initializer(stddev=0.01),
                                  kernel_regularizer=tf.contrib.layers.l2_regularizer(1e-3), name='layer7_1x1')

    # upsample 1
    output_1 = tf.layers.conv2d_transpose(layer7_1x1, num_classes, kernel_size=(4, 4), strides=(2, 2), padding='same',
                                          kernel_initializer=tf.random_normal_initializer(stddev=0.01),
                                          kernel_regularizer=tf.contrib.layers.l2_regularizer(1e-3), name='output_1')

    # layer 4 1x1 convolution
    layer4_1x1 = tf.layers.conv2d(vgg_layer4_out, num_classes, kernel_size=(1, 1), padding='same',
                                  kernel_initializer=tf.random_normal_initializer(stddev=0.01),
                                  kernel_regularizer=tf.contrib.layers.l2_regularizer(1e-3), name='layer4_1x1')

    # Skip layer
    skip_layer_1 = tf.add(output_1, layer4_1x1, name='skip_layer_1')

    # upsample 2
    output_2 = tf.layers.conv2d_transpose(skip_layer_1, num_classes, kernel_size=(4, 4), strides=(2, 2), padding='same',
                                          kernel_initializer=tf.random_normal_initializer(stddev=0.01),
                                          kernel_regularizer=tf.contrib.layers.l2_regularizer(1e-3), name='output_2')

    # layer 3 1x1 convolution
    layer3_1x1 = tf.layers.conv2d(vgg_layer3_out, num_classes, kernel_size=(1, 1), padding='same',
                                  kernel_initializer=tf.random_normal_initializer(stddev=0.01),
                                  kernel_regularizer=tf.contrib.layers.l2_regularizer(1e-3), name='layer3_1x1')

    # Skip layer
    skip_layer_2 = tf.add(output_2, layer3_1x1, name='skip_layer_2')

    # upsample 3
    output_3 = tf.layers.conv2d_transpose(skip_layer_2, num_classes, kernel_size=(16, 16), strides=(8, 8),
                                          padding='same', kernel_initializer=tf.random_normal_initializer(stddev=0.01),
                                          kernel_regularizer=tf.contrib.layers.l2_regularizer(1e-3), name='output_3')

    return output_3


print('Test Layers')
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)
    """
    # Convert 4D output tensor from last layer to 2D logits
    logits = tf.reshape(nn_last_layer, (-1, num_classes))
    correct_label = tf.reshape(correct_label, (-1, num_classes))

    # Standard cross entropy for binary classification using logtis from above
    cross_entropy_loss = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits=logits, labels=correct_label))

    # Adam optimizer with custom learning rate minimizing the cross entropy loss function
    optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate)
    train_op = optimizer.minimize(cross_entropy_loss)

    return logits, train_op, cross_entropy_loss


print('Test Optimize')
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):
        for (image, label) in get_batches_fn(batch_size):
            # Training
            print('Epoch {}'.format(epoch))
            _, loss = sess.run([train_op, cross_entropy_loss],
                               feed_dict={input_image: image, correct_label: label, keep_prob: 0.5,
                                          learning_rate: 1e-4})


print('Test Train')
tests.test_train_nn(train_nn)


def run():
    num_classes = 2
    image_shape = (160, 576)
    data_dir = './data'
    runs_dir = './runs'
    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
        get_batches_fn = helper.gen_batch_function(os.path.join(data_dir, 'data_road/training'), image_shape)

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

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

        output_3 = layers(vgg_layer3_out, vgg_layer4_out, vgg_layer7_out, num_classes)

        correct_label = tf.placeholder(tf.float32, [None, None, None, num_classes], name='correct_label')
        learning_rate = tf.placeholder(tf.float32, name='learning_rate')

        logits, train_op, cross_entropy_loss = optimize(output_3, correct_label, learning_rate, num_classes)

        # Train NN using the train_nn function

        epochs = 2
        batch_size = 16

        train_nn(sess, epochs, batch_size, get_batches_fn, train_op, cross_entropy_loss, vgg_input_out,
                 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, vgg_input_out)

        # OPTIONAL: Apply the trained model to a video


if __name__ == '__main__':
    run()