main.py

import os.path
import tensorflow as tf
import helper
import warnings
from distutils.version import LooseVersion
import project_tests as tests
import scipy.misc
import numpy as np
from IPython.display import HTML

# 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()))

#settings
train_net = True  # train network
load_net = False  # load network
save_net = True  # save network

#global variables for process image function
image_shape = (1,1)
sess = tf.Session()
keep_prob = tf.placeholder(tf.float32)
loggits = tf.placeholder(tf.int32, [None, None, None, 2])
input_image = tf.placeholder(tf.int32, [None, None, 3])


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'

    tf.saved_model.loader.load(sess, [vgg_tag], vgg_path)
    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'

    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 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
    """
    # TODO: Implement function
    # 1x1 convolution of vgg layer 7
    init = tf.random_normal_initializer(stddev=0.01)
    reg  = tf.contrib.layers.l2_regularizer(1e-3)
    
    lyr_7 = tf.layers.conv2d(vgg_layer7_out, num_classes, 1, padding='same', 
                                kernel_initializer=init, kernel_regularizer=reg)
    
    # upsample
    dconv_7 = tf.layers.conv2d_transpose(lyr_7, num_classes, 4, strides=(2, 2), padding='same', 
                                kernel_initializer=init, kernel_regularizer=reg)
    
    # 1x1 convolution of vgg layer 4
    lyr_4 = tf.layers.conv2d(vgg_layer4_out, num_classes, 1, padding='same', 
                                kernel_initializer=init, kernel_regularizer=reg)
    
    # skip connection
    skip_lyr_4 = tf.add(dconv_7, lyr_4)
    
    # upsample
    dcnv_4 = tf.layers.conv2d_transpose(skip_lyr_4, num_classes, 4, strides=(2, 2), padding='same', 
                                kernel_initializer=init, kernel_regularizer=reg)
    
    # 1x1 convolution of vgg layer 3
    lyr_3 = tf.layers.conv2d(vgg_layer3_out, num_classes, 1, padding='same', 
                                kernel_initializer=init, kernel_regularizer=reg)
    
    # skip connection
    skip_lyr_3 = tf.add(dcnv_4, lyr_3)
    
    # upsample
    nn_llyr = tf.layers.conv2d_transpose(skip_lyr_3, num_classes, 16, strides=(8, 8), padding= 'same', 
                                kernel_initializer=init, 
                                kernel_regularizer= tf.contrib.layers.l2_regularizer(1e-3))
    
    return nn_llyr
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))
    correct_label = tf.reshape(correct_label, (-1, num_classes))

    cross_entropy_loss = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits=logits, labels=correct_label))
    optimizer = tf.train.AdamOptimizer(learning_rate).minimize(cross_entropy_loss)

    return logits, optimizer, 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):
        for batch, (image, label) in enumerate(get_batches_fn(batch_size)):
            feed_dict = {input_image: image, correct_label: label, keep_prob: 0.5, learning_rate: 1e-4}
            _, loss = sess.run([train_op, cross_entropy_loss], feed_dict=feed_dict)
            print('Epoch ', epoch, ' Batch ', batch, ' Loss ', loss, flush=True)

    #pass


tests.test_train_nn(train_nn)
   
def run():
    global image_shape, sess, logits, keep_prob, input_image
    num_classes = 2
    image_shape = (160, 576)
    data_dir = '/data'
    runs_dir = './runs'
    tests.test_for_kitti_dataset(data_dir)

    # Download pre-trained 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

        # TODO: Build NN using load_vgg, layers, and optimize function

        epochs = 30
        batch_size = 8
        learning_rate = tf.placeholder(tf.float32)
        correct_label = tf.placeholder(tf.int32, [None, None, None, num_classes])

        input_image, keep_prob, layer3_out, layer4_out, layer7_out = load_vgg(sess, vgg_path)
        layer_output = layers(layer3_out, layer4_out, layer7_out, num_classes)
        logits, optimizer, cross_entropy_loss = optimize(layer_output, correct_label, learning_rate, num_classes)

        # TODO: Train NN using the train_nn function
        saver = tf.train.Saver()
        sess.run(tf.global_variables_initializer())

        if(load_net):
            checkpoint = tf.train.get_checkpoint_state("save_network")
            if checkpoint and checkpoint.model_checkpoint_path:
                saver.restore(sess, checkpoint.model_checkpoint_path)
                print("Successfully loaded:", checkpoint.model_checkpoint_path)
            else:
                print("Could not find old network weights")

        if(train_net):
            train_nn(sess, epochs, batch_size, get_batches_fn, optimizer, cross_entropy_loss, input_image,
                     correct_label, keep_prob, learning_rate)

        # TODO: Save inference data using helper.save_inference_samples
#        saver.restore(sess, tf.train.latest_checkpoint("./save_network"))
        if(load_net):
          helper.save_inference_samples(runs_dir, data_dir, sess, image_shape, logits, keep_prob, input_image)

        if(save_net):
            save_path = saver.save(sess, "./save_network/model_60_8")

if __name__ == '__main__':
    run()