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project_tests.py
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project_tests.py
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import sys
import os
from copy import deepcopy
from glob import glob
from unittest import mock
from sklearn.model_selection import train_test_split
import numpy as np
import tensorflow as tf
def test_safe(func):
"""
Isolate tests
"""
def func_wrapper(*args):
with tf.Graph().as_default():
result = func(*args)
print('Tests Passed')
return result
return func_wrapper
def _prevent_print(function, params):
sys.stdout = open(os.devnull, "w")
function(**params)
sys.stdout = sys.__stdout__
def _assert_tensor_shape(tensor, shape, display_name):
assert tf.assert_rank(tensor, len(shape), message='{} has wrong rank'.format(display_name))
tensor_shape = tensor.get_shape().as_list() if len(shape) else []
wrong_dimension = [ten_dim for ten_dim, cor_dim in zip(tensor_shape, shape)
if cor_dim is not None and ten_dim != cor_dim]
assert not wrong_dimension, \
'{} has wrong shape. Found {}'.format(display_name, tensor_shape)
class TmpMock(object):
"""
Mock a attribute. Restore attribute when exiting scope.
"""
def __init__(self, module, attrib_name):
self.original_attrib = deepcopy(getattr(module, attrib_name))
setattr(module, attrib_name, mock.MagicMock())
self.module = module
self.attrib_name = attrib_name
def __enter__(self):
return getattr(self.module, self.attrib_name)
def __exit__(self, type, value, traceback):
setattr(self.module, self.attrib_name, self.original_attrib)
@test_safe
def test_load_vgg(load_vgg, tf_module):
with TmpMock(tf_module.saved_model.loader, 'load') as mock_load_model:
vgg_path = ''
sess = tf.Session()
test_input_image = tf.placeholder(tf.float32, name='image_input')
test_keep_prob = tf.placeholder(tf.float32, name='keep_prob')
test_vgg_layer3_out = tf.placeholder(tf.float32, name='layer3_out')
test_vgg_layer4_out = tf.placeholder(tf.float32, name='layer4_out')
test_vgg_layer7_out = tf.placeholder(tf.float32, name='layer7_out')
input_image, keep_prob, vgg_layer3_out, vgg_layer4_out, vgg_layer7_out = load_vgg(sess, vgg_path)
assert mock_load_model.called, \
'tf.saved_model.loader.load() not called'
assert mock_load_model.call_args == mock.call(sess, ['vgg16'], vgg_path), \
'tf.saved_model.loader.load() called with wrong arguments.'
assert input_image == test_input_image, 'input_image is the wrong object'
assert keep_prob == test_keep_prob, 'keep_prob is the wrong object'
assert vgg_layer3_out == test_vgg_layer3_out, 'layer3_out is the wrong object'
assert vgg_layer4_out == test_vgg_layer4_out, 'layer4_out is the wrong object'
assert vgg_layer7_out == test_vgg_layer7_out, 'layer7_out is the wrong object'
@test_safe
def test_layers(layers):
num_classes = 2
vgg_layer3_out = tf.placeholder(tf.float32, [None, None, None, 256])
vgg_layer4_out = tf.placeholder(tf.float32, [None, None, None, 512])
vgg_layer7_out = tf.placeholder(tf.float32, [None, None, None, 4096])
is_training = tf.placeholder(tf.bool)
layers_output = layers(vgg_layer3_out, vgg_layer4_out, vgg_layer7_out, is_training, num_classes)
_assert_tensor_shape(layers_output, [None, None, None, num_classes], 'Layers Output')
@test_safe
def test_optimize(optimize):
num_classes = 2
shape = [2, 3, 4, num_classes]
layers_output = tf.Variable(tf.zeros(shape))
correct_label = tf.placeholder(tf.float32, [None, None, None, num_classes])
learning_rate = tf.placeholder(tf.float32)
logits, train_op, cross_entropy_loss, accuracy_op = optimize(layers_output, correct_label, learning_rate, num_classes)
_assert_tensor_shape(logits, [2, 3, 4, num_classes], 'Logits')
with tf.Session() as sess:
sess.run(tf.global_variables_initializer())
sess.run([train_op], {correct_label: np.arange(np.prod(shape)).reshape(shape), learning_rate: 10})
test, loss = sess.run([layers_output, cross_entropy_loss], {correct_label: np.arange(np.prod(shape)).reshape(shape)})
assert test.min() != 0 or test.max() != 0, 'Training operation not changing weights.'
@test_safe
def test_train_nn(train_nn):
epochs = 1
batch_size = 2
data_folder = "./data/data_road/training"
image_shape = (160, 576)
data_dir = './data'
data_folder = os.path.join(data_dir, 'data_road/training')
image_paths = glob(os.path.join(data_folder, 'image_2', '*.png'))
training_image_paths, validation_image_paths = train_test_split(image_paths, test_size=0.2)
def get_batches_fn(batach_size_parm):
shape = [batach_size_parm, 2, 3, 3]
return np.arange(np.prod(shape)).reshape(shape)
train_op = tf.constant(0)
cross_entropy_loss = tf.constant(10.11)
accuracy_op = tf.constant(0.11)
input_image = tf.placeholder(tf.float32, name='input_image')
correct_label = tf.placeholder(tf.float32, name='correct_label')
keep_prob = tf.placeholder(tf.float32, name='keep_prob')
learning_rate = tf.placeholder(tf.float32, name='learning_rate')
with tf.Session() as sess:
parameters = {
'sess': sess,
'epochs': epochs,
'data_folder': data_folder,
'batch_size': batch_size,
'image_shape': image_shape,
'training_image_paths': training_image_paths,
'validation_image_paths': validation_image_paths,
'train_op': train_op,
'cross_entropy_loss': cross_entropy_loss,
'accuracy_op': accuracy_op,
'input_image': input_image,
'correct_label': correct_label,
'keep_prob': keep_prob,
'learning_rate': learning_rate
}
_prevent_print(train_nn, parameters)
@test_safe
def test_for_kitti_dataset(data_dir):
kitti_dataset_path = os.path.join(data_dir, 'data_road')
training_labels_count = len(glob(os.path.join(kitti_dataset_path, 'training/gt_image_2/*_road_*.png')))
training_images_count = len(glob(os.path.join(kitti_dataset_path, 'training/image_2/*.png')))
testing_images_count = len(glob(os.path.join(kitti_dataset_path, 'testing/image_2/*.png')))
assert not (training_images_count == training_labels_count == testing_images_count == 0),\
'Kitti dataset not found. Extract Kitti dataset in {}'.format(kitti_dataset_path)
assert training_images_count == 289, 'Expected 289 training images, found {} images.'.format(training_images_count)
assert training_labels_count == 289, 'Expected 289 training labels, found {} labels.'.format(training_labels_count)
assert testing_images_count == 290, 'Expected 290 testing images, found {} images.'.format(testing_images_count)