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data_manager_test.py
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data_manager_test.py
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# -*- coding: utf-8 -*-
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import numpy as np
import unittest
from data_manager import DataManager
from data_manager import OP_AND, OP_IN_COMMON, OP_IGNORE
class DataManagerTest(unittest.TestCase):
def test_get_masked_image(self):
image = np.ones((80, 80, 3))
data_manager = DataManager()
masked_image = data_manager._get_masked_image(image)
# Shape check
self.assertTrue( masked_image.shape == (80,80,3) )
# Every element of masked image should be equal or smaller than original's
self.assertTrue( (masked_image <= image).all() )
def test_next_masked_batch(self):
data_manager = DataManager()
data_manager.prepare()
masked_xs, xs = data_manager.next_masked_batch(100)
self.assertTrue( len(xs) == 100 )
self.assertTrue( len(masked_xs) == 100 )
for i in range(100):
# Shape check
self.assertTrue( xs[i].shape == (80,80,3) )
self.assertTrue( masked_xs[i].shape == (80,80,3) )
# Every element of masked image should be equal or smaller than original's
self.assertTrue( (masked_xs[i] <= xs[i]).all() )
# Elements in image is 0.0 ~ 1.0
self.assertTrue( np.amax(xs[i]) <= 1.0 )
self.assertTrue( np.amin(xs[i]) >= 0.0 )
self.assertTrue( np.amax(masked_xs[i]) <= 1.0 )
self.assertTrue( np.amin(masked_xs[i]) >= 0.0 )
self.assertTrue( xs[i].dtype == np.float32 )
self.assertTrue( masked_xs[i].dtype == np.float32 )
def test_next_batch(self):
data_manager = DataManager()
data_manager.prepare()
xs, labels = data_manager.next_batch(100, use_labels=True)
self.assertTrue( len(xs) == 100 )
self.assertTrue( len(labels) == 100 )
for i in range(100):
# Shape check
self.assertTrue( xs[i].shape == (80,80,3) )
self.assertTrue( labels[i].shape == (51,) )
# Elements in image is 0.0 ~ 1.0
self.assertTrue( np.amax(xs[i]) <= 1.0 )
self.assertTrue( np.amin(xs[i]) >= 0.0 )
self.assertTrue( xs[i].dtype == np.float32 )
self.assertTrue( np.amax(labels[i]) <= 1.0 )
self.assertTrue( np.amin(labels[i]) >= 0.0 )
self.assertTrue( labels[i].dtype == np.float32 )
def test_index_to_labels(self):
data_manager = DataManager()
labels = data_manager._index_to_labels(0)
# Shape check
self.assertTrue( labels.shape == (51,) )
for i in range(51):
if i == 0 or i == 16 or i == 32 or i == 48:
labels[i] == 1.0
else:
labels[i] == 0.0
def test_choose_op_triplet(self):
data_manager = DataManager()
# Check AND operator
op = OP_AND
for _ in range(100):
param0, param1, param_out = data_manager._choose_op_triplet(op)
self.assertEqual( len(param0), 4 )
self.assertEqual( len(param1), 4 )
self.assertEqual( len(param_out), 4 )
for i in range(4):
p0 = param0[i]
p1 = param1[i]
p_out = param_out[i]
p = -1
if p0 != -1:
p = p0
if p1 != -1:
p = p1
self.assertEqual( p, p_out )
# Check IN_COMMON operator
op = OP_IN_COMMON
for _ in range(100):
param0, param1, param_out = data_manager._choose_op_triplet(op)
self.assertEqual( len(param0), 4 )
self.assertEqual( len(param1), 4 )
self.assertEqual( len(param_out), 4 )
for i in range(4):
p0 = param0[i]
p1 = param1[i]
p_out = param_out[i]
if p0 != -1:
if p0 == p1:
self.assertEqual( p0, p_out )
else:
self.assertNotEqual( p0, p_out )
# Check IGNORE_OP operator
op = OP_IGNORE
for _ in range(100):
param0, param1, param_out = data_manager._choose_op_triplet(op)
self.assertEqual( len(param0), 4 )
self.assertEqual( len(param1), 4 )
self.assertEqual( len(param_out), 4 )
for i in range(4):
p0 = param0[i]
p1 = param1[i]
p_out = param_out[i]
if p0 != -1:
if p1 != -1:
self.assertEqual( p_out, -1 )
else:
self.assertEqual( p_out, p0 )
else:
self.assertEqual( p_out, -1 )
if __name__ == '__main__':
unittest.main()