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from loss_functions import *
sess = tf.Session()
BATCH_SIZE = 2
NUM_SCALES = 5
MAX_P = 5
MAX_ALPHA = 1
# noinspection PyClassHasNoInit
class TestBCELoss:
def test_false_correct(self):
targets = tf.constant(np.zeros([5, 1]))
preds = 1e-7 * tf.constant(np.ones([5, 1]))
res = sess.run(bce_loss(preds, targets))
log_con = np.log10(1 - 1e-7)
res_tru = -1 * np.sum(np.array([log_con] * 5))
assert np.array_equal(np.around(res, 7), np.around(res_tru, 7))
def test_false_incorrect(self):
targets = tf.constant(np.zeros([5, 1]))
preds = tf.constant(np.ones([5, 1])) - 1e-7
res = sess.run(bce_loss(preds, targets))
log_con = np.log10(1e-7)
res_tru = -1 * np.sum(np.array([log_con] * 5))
assert np.array_equal(np.around(res, 7), np.around(res_tru, 7))
def test_false_half(self):
targets = tf.constant(np.zeros([5, 1]))
preds = 0.5 * tf.constant(np.ones([5, 1]))
res = sess.run(bce_loss(preds, targets))
log_con = np.log10(0.5)
res_tru = -1 * np.sum(np.array([log_con] * 5))
assert np.array_equal(np.around(res, 7), np.around(res_tru, 7))
def test_true_correct(self):
targets = tf.constant(np.ones([5, 1]))
preds = tf.constant(np.ones([5, 1])) - 1e-7
res = sess.run(bce_loss(preds, targets))
log = np.log10(1 - 1e-7)
res_tru = -1 * np.sum(np.array([log] * 5))
assert np.array_equal(np.around(res, 7), np.around(res_tru, 7))
def test_true_incorrect(self):
targets = tf.constant(np.ones([5, 1]))
preds = 1e-7 * tf.constant(np.ones([5, 1]))
res = sess.run(bce_loss(preds, targets))
log = np.log10(1e-7)
res_tru = -1 * np.sum(np.array([log] * 5))
assert np.array_equal(np.around(res, 7), np.around(res_tru, 7))
def test_true_half(self):
targets = tf.constant(np.ones([5, 1]))
preds = 0.5 * tf.constant(np.ones([5, 1]))
res = sess.run(bce_loss(preds, targets))
log = np.log10(0.5)
res_tru = -1 * np.sum(np.array([log] * 5))
assert np.array_equal(np.around(res, 7), np.around(res_tru, 7))
# noinspection PyClassHasNoInit
class TestLPLoss:
def test_same_images(self):
# generate scales
scale_preds = []
scale_truths = []
res_tru = 0
for i in xrange(1, NUM_SCALES + 1):
scale_preds.append(tf.constant(np.ones([BATCH_SIZE, 2**i, 2**i, 3])))
scale_truths.append(tf.constant(np.ones([BATCH_SIZE, 2**i, 2**i, 3])))
for p in xrange(1, MAX_P + 1):
res = sess.run(lp_loss(scale_preds, scale_truths, p))
assert res == res_tru, 'failed on p = %d' % p
def test_opposite_images(self):
# generate scales
scale_preds = []
scale_truths = []
res_tru = 0
for i in xrange(1, NUM_SCALES + 1):
scale_preds.append(tf.constant(np.zeros([BATCH_SIZE, 2**i, 2 ** i, 3])))
scale_truths.append(tf.constant(np.ones([BATCH_SIZE, 2**i, 2 ** i, 3])))
res_tru += BATCH_SIZE * 2**i * 2**i * 3
for p in xrange(1, MAX_P + 1):
res = sess.run(lp_loss(scale_preds, scale_truths, p))
assert res == res_tru, 'failed on p = %d' % p
def test_some_correct(self):
# generate scales
scale_preds = []
scale_truths = []
res_tru = 0
for i in xrange(1, NUM_SCALES + 1):
# generate batch of 3-deep identity matrices
preds = np.empty([BATCH_SIZE, 2**i, 2**i, 3])
imat = np.identity(2**i)
for elt in xrange(BATCH_SIZE):
preds[elt] = np.dstack([imat, imat, imat])
scale_preds.append(tf.constant(preds))
scale_truths.append(tf.constant(np.zeros([BATCH_SIZE, 2**i, 2**i, 3])))
res_tru += BATCH_SIZE * 2**i * 3
for p in xrange(1, MAX_P + 1):
res = sess.run(lp_loss(scale_preds, scale_truths, p))
assert res == res_tru, 'failed on p = %d' % p
def test_l_high(self):
# generate scales
scale_preds = []
scale_truths = []
res_tru = 0
for i in xrange(1, NUM_SCALES + 1):
# opposite images
preds = np.empty([BATCH_SIZE, 2**i, 2**i, 3])
preds.fill(3)
scale_preds.append(tf.constant(preds))
scale_truths.append(tf.constant(np.zeros([BATCH_SIZE, 2**i, 2**i, 3])))
res_tru += BATCH_SIZE * 2**i * 2**i * 3
for p in xrange(1, MAX_P + 1):
res = sess.run(lp_loss(scale_preds, scale_truths, p))
assert res == res_tru * (3**p), 'failed on p = %d' % p
# noinspection PyClassHasNoInit
class TestGDLLoss:
def test_same_uniform(self):
# generate scales
scale_preds = []
scale_truths = []
res_tru = 0
for i in xrange(1, NUM_SCALES + 1):
scale_preds.append(tf.ones([BATCH_SIZE, 2 ** i, 2 ** i, 3]))
scale_truths.append(tf.ones([BATCH_SIZE, 2 ** i, 2 ** i, 3]))
for a in xrange(1, MAX_ALPHA + 1):
res = sess.run(gdl_loss(scale_preds, scale_truths, a))
assert res == res_tru, 'failed on alpha = %d' % a
def test_same_nonuniform(self):
# generate scales
scale_preds = []
scale_truths = []
res_tru = 0
for i in xrange(1, NUM_SCALES + 1):
# generate batch of 3-deep identity matrices
arr = np.empty([BATCH_SIZE, 2 ** i, 2 ** i, 3])
imat = np.identity(2 ** i)
for elt in xrange(BATCH_SIZE):
arr[elt] = np.dstack([imat, imat, imat])
scale_preds.append(tf.constant(arr, dtype=tf.float32))
scale_truths.append(tf.constant(arr, dtype=tf.float32))
for a in xrange(1, MAX_ALPHA + 1):
res = sess.run(gdl_loss(scale_preds, scale_truths, a))
assert res == res_tru, 'failed on alpha = %d' % a
# TODO: Not 0 loss as expected because the 1s array is padded by 0s, so there is some gradient.
def test_diff_uniform(self):
# generate scales
scale_preds = []
scale_truths = []
res_tru = 0
for i in xrange(1, NUM_SCALES + 1):
scale_preds.append(tf.zeros([BATCH_SIZE, 2 ** i, 2 ** i, 3]))
scale_truths.append(tf.ones([BATCH_SIZE, 2 ** i, 2 ** i, 3]))
# every diff should have an abs value of 1, so no need for alpha handling
res_tru += BATCH_SIZE * 2 ** i * 2 * 3
for a in xrange(1, MAX_ALPHA + 1):
res = sess.run(gdl_loss(scale_preds, scale_truths, a))
assert res == res_tru, 'failed on alpha = %d' % a
def test_diff_one_uniform_one_not(self):
# generate scales
scale_preds = []
scale_truths = []
res_trus = np.zeros(MAX_ALPHA - 1)
for i in xrange(1, NUM_SCALES + 1):
# generate batch of 3-deep matrices with 3s on the diagonals
preds = np.empty([BATCH_SIZE, 2 ** i, 2 ** i, 3])
imat = np.identity(2 ** i) * 3
for elt in xrange(BATCH_SIZE):
preds[elt] = np.dstack([imat, imat, imat])
scale_preds.append(tf.constant(preds, dtype=tf.float32))
scale_truths.append(tf.zeros([BATCH_SIZE, 2 ** i, 2 ** i, 3]))
# every diff has an abs value of 3, so we can multiply that, raised to alpha
# for each alpha check, times the number of diffs in a batch:
# BATCH_SIZE * (diffs to left + down) * (diffs from up and right) * (# 3s in height) *
# (# channels)
num_diffs = BATCH_SIZE * 2 * 2 * 2**i * 3
for a in xrange(1, MAX_ALPHA):
res_trus[a] += num_diffs * 3**a
for a, res_tru in enumerate(res_trus):
res = sess.run(gdl_loss(scale_preds, scale_truths, a + 1))
assert res == res_tru, 'failed on alpha = %d' % (a + 1)
# noinspection PyClassHasNoInit
class TestAdvLoss:
def test_false_correct(self):
# generate scales
scale_preds = []
targets = tf.constant(np.zeros([5, 1]))
res_tru = 0
log_con = np.log10(1 - 1e-7)
for i in xrange(NUM_SCALES):
scale_preds.append(1e-7 * tf.constant(np.ones([5, 1])))
res_tru += -1 * np.sum(np.array([log_con] * 5))
res = sess.run(adv_loss(scale_preds, targets))
assert np.array_equal(np.around(res, 7), np.around(res_tru, 7))
def test_false_incorrect(self):
scale_preds = []
targets = tf.constant(np.zeros([5, 1]))
res_tru = 0
log_con = np.log10(1e-7)
for i in xrange(NUM_SCALES):
scale_preds.append(tf.constant(np.ones([5, 1])) - 1e-7)
res_tru += -1 * np.sum(np.array([log_con] * 5))
res = sess.run(adv_loss(scale_preds, targets))
assert np.array_equal(np.around(res, 7), np.around(res_tru, 7))
def test_false_half(self):
scale_preds = []
targets = tf.constant(np.zeros([5, 1]))
res_tru = 0
log_con = np.log10(0.5)
for i in xrange(NUM_SCALES):
scale_preds.append(0.5 * tf.constant(np.ones([5, 1])))
res_tru += -1 * np.sum(np.array([log_con] * 5))
res = sess.run(adv_loss(scale_preds, targets))
assert np.array_equal(np.around(res, 7), np.around(res_tru, 7))
def test_true_correct(self):
scale_preds = []
targets = tf.constant(np.ones([5, 1]))
res_tru = 0
log = np.log10(1 - 1e-7)
for i in xrange(NUM_SCALES):
scale_preds.append(tf.constant(np.ones([5, 1])) - 1e-7)
res_tru += -1 * np.sum(np.array([log] * 5))
res = sess.run(adv_loss(scale_preds, targets))
assert np.array_equal(np.around(res, 7), np.around(res_tru, 7))
def test_true_incorrect(self):
scale_preds = []
targets = tf.constant(np.ones([5, 1]))
res_tru = 0
log = np.log10(1e-7)
for i in xrange(NUM_SCALES):
scale_preds.append(1e-7 * tf.constant(np.ones([5, 1])))
res_tru += -1 * np.sum(np.array([log] * 5))
res = sess.run(adv_loss(scale_preds, targets))
assert np.array_equal(np.around(res, 7), np.around(res_tru, 7))
def test_true_half(self):
scale_preds = []
targets = tf.constant(np.ones([5, 1]))
res_tru = 0
log = np.log10(0.5)
for i in xrange(NUM_SCALES):
scale_preds.append(0.5 * tf.constant(np.ones([5, 1])))
res_tru += -1 * np.sum(np.array([log] * 5))
res = sess.run(adv_loss(scale_preds, targets))
assert np.array_equal(np.around(res, 7), np.around(res_tru, 7))
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