def get_fid_tf(real_img, sample_img):
print('debug/real_img type')
print(real_img)
real_img = tf.convert_to_tensor(real_img)
real_img = preprocess_image(real_img)
print(real_img)
print('debug/sample_img type')
print(sample_img)
sample_img = tf.convert_to_tensor(sample_img)
sample_img = preprocess_image(sample_img)
print(sample_img)
real_single = (real_img.shape.ndims == 3)
sample_single = (sample_img.shape.ndims == 3)
if real_single and sample_single:
sample_img = tf.concat([sample_img, sample_img, sample_img], 3)
real_img = tf.concat([real_img, real_img, real_img], 3)
ll = real_img.shape[0]
print('debug/ll')
print(ll)
pbs = 4
while ll % pbs:
pbs += 1
FID = frechet_inception_distance(real_img,
sample_img,
num_batches=ll // pbs)
return FID
def test_preprocess_image_graph(self):
"""Test `preprocess_image` graph construction."""
incorrectly_sized_image = array_ops.zeros([520, 240, 3])
correct_image = classifier_metrics.preprocess_image(
images=incorrectly_sized_image)
_run_with_mock(classifier_metrics.run_inception,
array_ops.expand_dims(correct_image, 0))
def test_preprocess_image_graph(self):
"""Test `preprocess_image` graph construction."""
incorrectly_sized_image = array_ops.zeros([520, 240, 3])
correct_image = classifier_metrics.preprocess_image(
images=incorrectly_sized_image)
_run_with_mock(classifier_metrics.run_inception,
array_ops.expand_dims(correct_image, 0))
def get_inception_score_tf(sample_img):
print('debug/sample_img type')
print(sample_img.shape)
print(sample_img)
sample_img = tf.convert_to_tensor(sample_img)
sample_img = preprocess_image(sample_img)
print(sample_img.shape)
print(sample_img)
ll = sample_img.shape[0]
print('debug/ll')
print(ll)
print('debug/sample_img 2')
print(sample_img.shape)
pbs = 10
while True:
print('debug/pbs', pbs)
if ll % pbs == 0:
break
else:
pbs -= 1
print('debug/batch_size')
print(pbs)
IS = classifier_score(sample_img,
functools.partial(
run_image_classifier,
graph_def=graph_def,
input_tensor=INCEPTION_INPUT,
output_tensor=INCEPTION_OUTPUT,
),
num_batches=ll // 100)
# generated_images_list = array_ops.split(
# sample_img, num_or_size_splits=ll//pbs)
#
# # Compute the classifier splits using the memory-efficient `map_fn`.
# logits = functional_ops.map_fn(
# fn=functools.partial(
# run_image_classifier,
# graph_def=graph_def,
# input_tensor=INCEPTION_INPUT,
# output_tensor=INCEPTION_OUTPUT,),
# elems=array_ops.stack(generated_images_list),
# parallel_iterations=1,
# back_prop=False,
# swap_memory=True,
# name='RunClassifier')
# logits = array_ops.concat(array_ops.unstack(logits), 0)
# print('logits',logits)
# logits.shape.assert_has_rank(2)
#
# # Use maximum precision for best results.
# logits_dtype = logits.dtype
# if logits_dtype != dtypes.float64:
# logits = math_ops.to_double(logits)
#
# p = nn_ops.softmax(logits)
# q = math_ops.reduce_mean(p, axis=0)
# kl = _kl_divergence(p, logits, q)
# print('pqkl',p,q,kl)
# kl.shape.assert_has_rank(1)
#
# sess = tf.Session()
# sess.run(tf.Print(kl, [kl]))
#
# log_score = math_ops.reduce_mean(kl)
# print('log_score',log_score)
# final_score = math_ops.exp(log_score)
# print('final',final_score)
# if logits_dtype != dtypes.float64:
# final_score = math_ops.cast(final_score, logits_dtype)
# IS = final_score
print('debug/end IS')
IS = IS.shape
print('debug/end shape')
return IS
