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()
w1 = graph.get_tensor_by_name(vgg_input_tensor_name)
keep = 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 w1, keep, 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_layer3_out: TF Tensor for VGG Layer 3 output
:param vgg_layer4_out: TF Tensor for VGG Layer 4 output
:param vgg_layer7_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
"""
# upsampling on layer7 by 2
input = tf.layers.conv2d(vgg_layer7_out, num_classes, 1, strides=(1,1), padding='same',
kernel_initializer=tf.truncated_normal_initializer(stddev=0.01),
kernel_regularizer=tf.contrib.layers.l2_regularizer(1e-3))
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()
w1 = graph.get_tensor_by_name(vgg_input_tensor_name)
keep = graph.get_tensor_by_name(vgg_keep_prob_tensor_name)
w3 = graph.get_tensor_by_name(vgg_layer3_out_tensor_name)
w4 = graph.get_tensor_by_name(vgg_layer4_out_tensor_name)
w7 = graph.get_tensor_by_name(vgg_layer7_out_tensor_name)
return w1, keep, w3, w4, w7
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_layer3_out: TF Tensor for VGG Layer 3 output
:param vgg_layer4_out: TF Tensor for VGG Layer 4 output
:param vgg_layer7_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
x = tf.layers.conv2d(
vgg_layer7_out,
def perform_tests():
tests.test_for_kitti_dataset(data_dir)
tests.test_load_vgg(load_vgg, tf)
tests.test_layers(layers)
tests.test_optimize(optimize)
tests.test_train_nn(train_nn)
def testRun():
tests.test_load_vgg(load_vgg, tf)
tests.test_layers(layers)
tests.test_optimize(optimize)
tests.test_train_nn(train_nn)
def run_tests(self):
tests.test_load_vgg(self.load_vgg, tf)
tests.test_layers(self.layers)
tests.test_optimize(self.optimize_cross_entropy)
tests.test_train_nn(self.train_nn)
def run():
num_classes = 2
image_shape = (160, 576)
data_dir = './data'
runs_dir = './runs'
# Hyperparameters
learning_rate = 0.0001
lr = tf.constant(learning_rate)
epochs = 100
batch_size = 10
# 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()))
# Perform unit tests
tests.test_for_kitti_dataset(data_dir)
tests.test_load_vgg(load_vgg, tf)
tests.test_layers(layers)
tests.test_optimize(optimize)
tests.test_train_nn(train_nn)
# 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
# TODO: Build NN using load_vgg, layers, and optimize function
correct_label = tf.placeholder(tf.float32,
[None, None, None, num_classes])
image_input, keep_prob, layer3_out, layer4_out, layer7_out = load_vgg(
sess, vgg_path)
output_layer = layers(layer3_out, layer4_out, layer7_out, num_classes)
logits, train_op, cross_entropy_loss = optimize(
output_layer, correct_label, lr, 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, image_input, correct_label, keep_prob, lr)
# TODO: Save inference data using helper.save_inference_samples
helper.save_inference_samples(runs_dir, data_dir, sess, image_shape,
logits, keep_prob, image_input)
def perform_tests():
tests.test_for_kitti_dataset(data_dir)
tests.test_load_vgg(load_vgg, tf)
tests.test_layers(layers)
tests.test_optimize(optimize)
tests.test_train_nn(train_nn)
def train(epochs: int = None, save_model_freq: int = None, batch_size: int = None, learning_rate: float = None,
keep_prob: float = None, dataset: str = None):
"""
Performs the FCN training from begining to end, that is, downloads required datasets and pretrained models,
constructs the FNC architecture, trains it, and saves the trained model.
:param epochs: number of epochs for training
:param save_model_freq: save model each save_model_freq epoch
:param batch_size: batch size for training
:param learning_rate: learning rate for training
:param keep_prob: keep probability for dropout layers for training
:param dataset: dataset name
"""
if None in [epochs, save_model_freq, batch_size, learning_rate, keep_prob, dataset]:
raise ValueError('some parameters were not specified for function "%s"' % train.__name__)
dataset = DATASETS[dataset]
if not os.path.exists(dataset.data_root_dir):
os.makedirs(dataset.data_root_dir)
# Download Kitti Road dataset
helper.maybe_download_dataset_from_yandex_disk(dataset)
# Download pretrained vgg model
helper.maybe_download_pretrained_vgg_from_yandex_disk(dataset.data_root_dir)
# Run tests to check that environment is ready to execute the semantic segmentation pipeline
if dataset.name == 'kitti_road':
tests.test_for_kitti_dataset(dataset.data_root_dir)
tests.test_load_vgg(load_vgg, tf)
tests.test_layers(layers)
tests.test_optimize(optimize)
tests.test_train_nn(train_nn, dataset)
# TODO: Train and Inference on the cityscapes dataset instead of the Kitti dataset.
# https://www.cityscapes-dataset.com/
with tf.Session(config=tf.ConfigProto(log_device_placement=True)) as sess:
# Path to vgg model
vgg_path = os.path.join(dataset.data_root_dir, 'vgg')
# Create function to get batches
get_batches_fn = helper.gen_batch_function(dataset.data_training_dir, dataset.image_shape)
# TODO: Augment Images for better results
# https://datascience.stackexchange.com/questions/5224/how-to-prepare-augment-images-for-neural-network
image_input_tensor, keep_prob_tensor, layer3_out_tensor, layer4_out_tensor, layer7_out_tensor = \
load_vgg(sess, vgg_path)
output_layer_tensor = layers(layer3_out_tensor, layer4_out_tensor, layer7_out_tensor, dataset.num_classes)
correct_label_tensor = tf.placeholder(tf.float32, (None, None, None, dataset.num_classes))
learning_rate_tensor = tf.placeholder(tf.float32)
logits_tensor, train_op_tensor, cross_entropy_loss_tensor, softmax_tensor = \
optimize(output_layer_tensor, correct_label_tensor, learning_rate_tensor, dataset.num_classes)
iou_tensor, iou_op_tensor = mean_iou(softmax_tensor, correct_label_tensor, dataset.num_classes)
train_nn(sess, dataset, epochs, save_model_freq, batch_size, learning_rate, keep_prob,
get_batches_fn, train_op_tensor, cross_entropy_loss_tensor, image_input_tensor, correct_label_tensor,
keep_prob_tensor, learning_rate_tensor, iou_tensor, iou_op_tensor)
save_model(sess, 'fcn8-final', dataset,
epochs=epochs, batch_size=batch_size, learning_rate=learning_rate, keep_prob=learning_rate)
