class TestBatchProcessor(unittest.TestCase):
def setUp(self):
self.train_batch = BatchProcessor(
X_dirpath='./tests/data/train/*',
y_dirpath='./tests/data/test/',
batchsize=100,
border=1,
limit=None,
rnd=rnd)
self.valid_batch = BatchProcessor(
X_dirpath='../tests/data/valid/*',
y_dirpath='./tests/data/test/',
batchsize=100,
border=1,
limit=None,
rnd=rnd)
def tearDown(self):
del self.train_batch, self.valid_batch
def test_iterating(self):
test1 = [(X,y) for X, y in self.train_batch]
test2 = [(X,y) for X, y in self.train_batch]
test3 = [(X,y) for X, y in self.train_batch]
test4 = [(X,y) for X, y in self.train_batch]
test5 = [(X,y) for X, y in self.train_batch]
@unittest.skipUnless(config.slow, 'slow test')
def test_bench(self):
bp = BatchProcessor(
X_dirpath='../data/train/*',
y_dirpath='../data/train_cleaned/',
batchsize=4000000,
border=2,
limit=None,
rnd=rnd)
start = timer()
bp.random = False
bp.slow = True
for X, y in bp: None
end = timer()
print "slow:\t\t %d" % (end - start)
start = timer()
bp.random = True
bp.slow = True
for X, y in bp: None
end = timer()
print "slow rand:\t\t %d" % (end - start)
start = timer()
bp.random = False
bp.slow = False
for X, y in bp: None
end = timer()
print "fast:\t\t %d" % (end - start)
start = timer()
bp.random = True
bp.slow = False
for X, y in bp: None
end = timer()
print "fast rand:\t\t %d" % (end - start)
start = timer()
bp.random = True
bp.random_mode = 'fully'
bp.slow = False
for X, y in bp: None
end = timer()
print "fully rand:\t\t %d" % (end - start)
def test_iterating_fully_random(self):
self.train_batch.random_mode = 'fully'
fullset = [i for i in self.train_batch]
fullset2 = [i for i in self.train_batch]
def test_len(self):
# Pixels manually calculated according test images
pixels = 50*34*3 / self.train_batch.batchsize
self.assertEqual(len(self.train_batch), pixels)
def test_consitency(self):
ds = self.train_batch.next()
self.train_batch.reset()
ds2 = self.train_batch.next()
center_index = (len(ds[0][0]) - 1) / 2
self.assertEqual(ds[0][0][center_index], ds2[0][0][center_index])
def test_randomness(self):
self.train_batch.random = True
X, y = self.train_batch.next()
self.train_batch.reset()
X2, y2 = self.train_batch.next()
center_index = (len(X[0]) - 1) / 2
self.assertNotEqual(X[0][center_index], X2[0][center_index])
self.assertNotEqual(X[-1][center_index], X2[-1][center_index])
X3, y3 = self.train_batch.next()
center_index = (len(X[0]) - 1) / 2
self.assertNotEqual(X[0][center_index], X3[0][center_index])
self.assertNotEqual(X[-1][center_index], X3[-1][center_index])
def test_batch_lengths(self):
validl = len(self.valid_batch)
validl2 = len([x for x in self.valid_batch])
self.assertEqual(validl, validl2)
trainl = len(self.train_batch)
trainl2 = len([x for x in self.train_batch])
self.assertEqual(trainl, trainl2)
def test_next_fully_random(self):
set = self.train_batch.next_fully_random()
set2 = self.train_batch.next()
self.assertEqual(type(set), type(set2))
self.assertEqual(set[0].shape, set2[0].shape)
self.assertEqual(set[1].shape, set2[1].shape)
def test_batch_sizes(self):
eq = True
for X, y in self.valid_batch:
eq = (X.shape[0] == self.valid_batch.batchsize)
if not eq: break
self.assertEqual(eq, True)
eq = True
for X, y in self.train_batch:
eq = (X.shape[0] == self.train_batch.batchsize)
if not eq: break
self.assertEqual(eq, True)