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main.py
131 lines (96 loc) · 6.11 KB
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main.py
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from frechet_kernel_Inception_distance import *
from inception_score import *
from glob import glob
import os
def inception_score() :
filenames = glob(os.path.join('./fake', '*.*'))
images = [get_images(filename) for filename in filenames]
images = np.transpose(images, axes=[0, 3, 1, 2])
# A smaller BATCH_SIZE reduces GPU memory usage, but at the cost of a slight slowdown
BATCH_SIZE = 1
# Run images through Inception.
inception_images = tf.placeholder(tf.float32, [BATCH_SIZE, 3, None, None])
logits = inception_logits(inception_images)
IS = get_inception_score(BATCH_SIZE, images, inception_images, logits, splits=10)
print()
print("IS : ", IS)
def frechet_inception_distance() :
filenames = glob(os.path.join('./real_target', '*.*'))
real_images = [get_images(filename) for filename in filenames]
real_images = np.transpose(real_images, axes=[0, 3, 1, 2])
filenames = glob(os.path.join('./fake', '*.*'))
fake_images = [get_images(filename) for filename in filenames]
fake_images = np.transpose(fake_images, axes=[0, 3, 1, 2])
# A smaller BATCH_SIZE reduces GPU memory usage, but at the cost of a slight slowdown
BATCH_SIZE = 1
# Run images through Inception.
inception_images = tf.placeholder(tf.float32, [BATCH_SIZE, 3, None, None])
real_activation = tf.placeholder(tf.float32, [None, None], name='activations1')
fake_activation = tf.placeholder(tf.float32, [None, None], name='activations2')
fcd = frechet_classifier_distance_from_activations(real_activation, fake_activation)
activations = inception_activations(inception_images)
FID = get_fid(fcd, BATCH_SIZE, real_images, fake_images, inception_images, real_activation, fake_activation, activations)
print()
print("FID : ", FID / 100)
def kernel_inception_distance() :
filenames = glob(os.path.join('./real_target', '*.*'))
real_images = [get_images(filename) for filename in filenames]
real_images = np.transpose(real_images, axes=[0, 3, 1, 2])
filenames = glob(os.path.join('./fake', '*.*'))
fake_images = [get_images(filename) for filename in filenames]
fake_images = np.transpose(fake_images, axes=[0, 3, 1, 2])
# A smaller BATCH_SIZE reduces GPU memory usage, but at the cost of a slight slowdown
BATCH_SIZE = 1
# Run images through Inception.
inception_images = tf.placeholder(tf.float32, [BATCH_SIZE, 3, None, None])
real_activation = tf.placeholder(tf.float32, [None, None], name='activations1')
fake_activation = tf.placeholder(tf.float32, [None, None], name='activations2')
kcd_mean, kcd_stddev = kernel_classifier_distance_and_std_from_activations(real_activation, fake_activation, max_block_size=10)
activations = inception_activations(inception_images)
KID_mean = get_kid(kcd_mean, BATCH_SIZE, real_images, fake_images, inception_images, real_activation, fake_activation, activations)
KID_stddev = get_kid(kcd_stddev, BATCH_SIZE, real_images, fake_images, inception_images, real_activation, fake_activation, activations)
print()
print("KID_mean : ", KID_mean * 100)
print("KID_stddev : ", KID_stddev * 100)
def mean_kernel_inception_distance() :
source_alpha = 0.98
target_alpha = 1 - source_alpha
filenames = glob(os.path.join('./real_source', '*.*'))
real_source_images = [get_images(filename) for filename in filenames]
real_source_images = np.transpose(real_source_images, axes=[0, 3, 1, 2])
filenames = glob(os.path.join('./real_target', '*.*'))
real_target_images = [get_images(filename) for filename in filenames]
real_target_images = np.transpose(real_target_images, axes=[0, 3, 1, 2])
filenames = glob(os.path.join('./fake', '*.*'))
fake_images = [get_images(filename) for filename in filenames]
fake_images = np.transpose(fake_images, axes=[0, 3, 1, 2])
# A smaller BATCH_SIZE reduces GPU memory usage, but at the cost of a slight slowdown
BATCH_SIZE = 1
# Run images through Inception.
inception_images = tf.placeholder(tf.float32, [BATCH_SIZE, 3, None, None])
real_activation = tf.placeholder(tf.float32, [None, None], name='activations1')
fake_activation = tf.placeholder(tf.float32, [None, None], name='activations2')
fcd = frechet_classifier_distance_from_activations(real_activation, fake_activation)
kcd_mean, kcd_stddev = kernel_classifier_distance_and_std_from_activations(real_activation, fake_activation,
max_block_size=10)
activations = inception_activations(inception_images)
FID = get_fid(fcd, BATCH_SIZE, real_target_images, fake_images, inception_images, real_activation, fake_activation, activations)
KID_mean = get_kid(kcd_mean, BATCH_SIZE, real_target_images, fake_images, inception_images, real_activation, fake_activation, activations)
KID_stddev = get_kid(kcd_stddev, BATCH_SIZE, real_target_images, fake_images, inception_images, real_activation, fake_activation, activations)
mean_FID = get_fid(fcd, BATCH_SIZE, real_source_images, fake_images, inception_images, real_activation, fake_activation, activations)
mean_KID_mean = get_kid(kcd_mean, BATCH_SIZE, real_source_images, fake_images, inception_images, real_activation, fake_activation, activations)
mean_KID_stddev = get_kid(kcd_stddev, BATCH_SIZE, real_source_images, fake_images, inception_images, real_activation, fake_activation, activations)
mean_FID = (target_alpha * FID + source_alpha * mean_FID) / 2.0
mean_KID_mean = (target_alpha * KID_mean + source_alpha * mean_KID_mean) / 2.0
mean_KID_stddev = (target_alpha * KID_stddev + source_alpha * mean_KID_stddev) / 2.0
# mean_FID = (2 * FID * mean_FID) / (FID + mean_FID)
# mean_KID_mean = (2 * KID_mean * mean_KID_mean) / (KID_mean + mean_KID_mean)
# mean_KID_stddev = (2 * KID_stddev * mean_KID_stddev) / (KID_stddev + mean_KID_stddev)
print()
print("mean_FID : ", mean_FID / 100)
print("mean_KID_mean : ", mean_KID_mean * 100)
print("mean_KID_stddev : ", mean_KID_stddev * 100)
inception_score()
frechet_inception_distance()
kernel_inception_distance()
# mean_kernel_inception_distance()