: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 i in range(epochs):
print("EPOCH {} ...".format(i+1))
for image, label in get_batches_fn(batch_size):
_, loss = sess.run([train_op, cross_entropy_loss],feed_dict={input_image: image, correct_label: label, keep_prob: 0.5, learning_rate: 0.001})
print("Loss: = {:.3f}".format(loss))
print()
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/
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)
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 {} Batch {} Loss {}'.format(epoch, batch, loss),
flush=True)
pass
tests.test_train_nn(train_nn)
def process_image(image):
# resize the image
orig_image_shape = image.shape[:2]
image = scipy.misc.imresize(image, image_shape)
net_output = sess.run([tf.nn.softmax(logits)], {
keep_prob: 1.0,
input_image: [image]
})
label_idx = np.argmax(net_output, axis=2)
value_fill = label_idx.copy()
def run_tests():
tests.test_layers(layers)
tests.test_optimize(optimize)
tests.test_for_kitti_dataset(DATA_DIRECTORY)
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)