def test_invalid_input(self):
"""Test that functions properly fail on invalid input."""
with self.assertRaisesRegexp(ValueError, 'Shapes .* are incompatible'):
classifier_metrics.run_inception(array_ops.ones([7, 50, 50, 3]))
p = array_ops.zeros([8, 10])
p_logits = array_ops.zeros([8, 10])
q = array_ops.zeros([10])
with self.assertRaisesRegexp(ValueError, 'must be floating type'):
classifier_metrics._kl_divergence(
array_ops.zeros([8, 10], dtype=dtypes.int32), p_logits, q)
with self.assertRaisesRegexp(ValueError, 'must be floating type'):
classifier_metrics._kl_divergence(
p, array_ops.zeros([8, 10], dtype=dtypes.int32), q)
with self.assertRaisesRegexp(ValueError, 'must be floating type'):
classifier_metrics._kl_divergence(
p, p_logits, array_ops.zeros([10], dtype=dtypes.int32))
with self.assertRaisesRegexp(ValueError, 'must have rank 2'):
classifier_metrics._kl_divergence(array_ops.zeros([8]), p_logits,
q)
with self.assertRaisesRegexp(ValueError, 'must have rank 2'):
classifier_metrics._kl_divergence(p, array_ops.zeros([8]), q)
with self.assertRaisesRegexp(ValueError, 'must have rank 1'):
classifier_metrics._kl_divergence(p, p_logits,
array_ops.zeros([10, 8]))
def test_invalid_input(self):
"""Test that functions properly fail on invalid input."""
with self.assertRaisesRegexp(ValueError, 'Shapes .* are incompatible'):
classifier_metrics.run_inception(array_ops.ones([7, 50, 50, 3]))
p = array_ops.zeros([8, 10])
p_logits = array_ops.zeros([8, 10])
q = array_ops.zeros([10])
with self.assertRaisesRegexp(ValueError, 'must be floating type'):
classifier_metrics._kl_divergence(
array_ops.zeros([8, 10], dtype=dtypes.int32), p_logits, q)
with self.assertRaisesRegexp(ValueError, 'must be floating type'):
classifier_metrics._kl_divergence(p,
array_ops.zeros(
[8, 10], dtype=dtypes.int32), q)
with self.assertRaisesRegexp(ValueError, 'must be floating type'):
classifier_metrics._kl_divergence(p, p_logits,
array_ops.zeros(
[10], dtype=dtypes.int32))
with self.assertRaisesRegexp(ValueError, 'must have rank 2'):
classifier_metrics._kl_divergence(array_ops.zeros([8]), p_logits, q)
with self.assertRaisesRegexp(ValueError, 'must have rank 2'):
classifier_metrics._kl_divergence(p, array_ops.zeros([8]), q)
with self.assertRaisesRegexp(ValueError, 'must have rank 1'):
classifier_metrics._kl_divergence(p, p_logits, array_ops.zeros([10, 8]))
def test_run_inception_graph(self, use_default_graph_def):
"""Test `run_inception` graph construction."""
batch_size = 7
img = array_ops.ones([batch_size, 299, 299, 3])
if use_default_graph_def:
logits = _run_with_mock(classifier_metrics.run_inception, img)
else:
logits = classifier_metrics.run_inception(img, _get_dummy_graphdef())
self.assertIsInstance(logits, ops.Tensor)
logits.shape.assert_is_compatible_with([batch_size, 1001])
# Check that none of the model variables are trainable.
self.assertListEqual([], variables.trainable_variables())
def test_run_inception_graph(self, use_default_graph_def):
"""Test `run_inception` graph construction."""
batch_size = 7
img = array_ops.ones([batch_size, 299, 299, 3])
if use_default_graph_def:
logits = _run_with_mock(classifier_metrics.run_inception, img)
else:
logits = classifier_metrics.run_inception(img, _get_dummy_graphdef())
self.assertTrue(isinstance(logits, ops.Tensor))
logits.shape.assert_is_compatible_with([batch_size, 1001])
# Check that none of the model variables are trainable.
self.assertListEqual([], variables.trainable_variables())
def test_run_inception_graph_pool_output(self, use_default_graph_def):
"""Test `run_inception` graph construction with pool output."""
batch_size = 3
img = array_ops.ones([batch_size, 299, 299, 3])
if use_default_graph_def:
pool = _run_with_mock(
classifier_metrics.run_inception,
img,
output_tensor=classifier_metrics.INCEPTION_FINAL_POOL)
else:
pool = classifier_metrics.run_inception(
img, _get_dummy_graphdef(),
output_tensor=classifier_metrics.INCEPTION_FINAL_POOL)
self.assertIsInstance(pool, ops.Tensor)
pool.shape.assert_is_compatible_with([batch_size, 2048])
# Check that none of the model variables are trainable.
self.assertListEqual([], variables.trainable_variables())
def test_run_inception_graph_pool_output(self, use_default_graph_def):
"""Test `run_inception` graph construction with pool output."""
batch_size = 3
img = array_ops.ones([batch_size, 299, 299, 3])
if use_default_graph_def:
pool = _run_with_mock(
classifier_metrics.run_inception,
img,
output_tensor=classifier_metrics.INCEPTION_FINAL_POOL)
else:
pool = classifier_metrics.run_inception(
img, _get_dummy_graphdef(),
output_tensor=classifier_metrics.INCEPTION_FINAL_POOL)
self.assertTrue(isinstance(pool, ops.Tensor))
pool.shape.assert_is_compatible_with([batch_size, 2048])
# Check that none of the model variables are trainable.
self.assertListEqual([], variables.trainable_variables())
def main():
parser = argparse.ArgumentParser()
parser.add_argument('--real_dir', type=str, default='results/258')
parser.add_argument('--fake_dir', type=str, default='results/566')
parser.add_argument('--get_fid', action=store_true)
parser.add_argument('--get_sanity_check', action=store_true)
parser.add_argument('--get_embeddings', action=store_true)
args = parser.parse_args()
# CHANGE THESE PATHS TO DIRS
real_dir = Path(args.real_dir)
fake_dir = Path(args.fake_dir)
def load_image(image_path):
img = Image.open(image_path)
newImg = img.resize((299, 299), PIL.Image.BILINEAR).convert("RGB")
data = np.array(newImg.getdata())
return 2 * (data.reshape(
(newImg.size[0], newImg.size[1], 3)).astype(np.float32) / 255) - 1
real_imgs = []
for x in real_dir.iterdir():
if 'png' in str(x):
real_img = load_image(x)
real_imgs.append(real_img)
# print(real_imgs)
fake_imgs = []
for x in fake_dir.iterdir():
if 'png' in str(x):
fake_img = load_image(x)
fake_imgs.append(fake_img)
# print(fake_imgs)
real_imgs = np.array(real_imgs)
print(real_imgs.shape)
fake_imgs = np.array(fake_imgs)
print(fake_imgs.shape)
# KID
kid = classifier_metrics.kernel_inception_distance(
real_images=real_imgs, generated_images=fake_imgs)
with tf.Session() as sess:
print('KID:', sess.run(kid))
# Kernel Classifier Distance with Inception (technically KID)
if args.get_sanity_check:
kid_general = classifier_metrics.kernel_classifier_distance(
real_images=real_imgs,
generated_images=fake_imgs,
classifier_fn=classifier_metrics.run_inception)
with tf.Session() as sess:
print('KID sanity check:', sess.run(kid_general))
# FID
if args.get_fid:
fid = classifier_metrics.frechet_inception_distance(
real_images=real_imgs, generated_images=fake_imgs)
with tf.Session() as sess:
print('FID:', sess.run(fid))
# Get imagenet embeddings
if args.get_embeddings:
inception_embeddings_real = classifier_metrics.run_inception(real_imgs)
inception_embeddings_fake = classifier_metrics.run_inception(fake_imgs)
with tf.Session() as sess:
# TODO: save embeddings
print(sess.run(inception_embeddings_real))
print(sess.run(inception_embeddings_fake))
