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# Copyright (c) 2019, NVIDIA CORPORATION. All rights reserved.
#
# This work is licensed under the Creative Commons Attribution-NonCommercial
# 4.0 International License. To view a copy of this license, visit
# http://creativecommons.org/licenses/by-nc/4.0/ or send a letter to
# Creative Commons, PO Box 1866, Mountain View, CA 94042, USA.
"""Perceptual Path Length (PPL)."""
import numpy as np
import tensorflow as tf
import dnnlib.tflib as tflib
from metrics import metric_base
from training import misc
#----------------------------------------------------------------------------
# Normalize batch of vectors.
def normalize(v):
return v / tf.sqrt(tf.reduce_sum(tf.square(v), axis=-1, keepdims=True))
# Spherical interpolation of a batch of vectors.
def slerp(a, b, t):
a = normalize(a)
b = normalize(b)
d = tf.reduce_sum(a * b, axis=-1, keepdims=True)
p = t * tf.math.acos(d)
c = normalize(b - d * a)
d = a * tf.math.cos(p) + c * tf.math.sin(p)
return normalize(d)
#----------------------------------------------------------------------------
class PPL(metric_base.MetricBase):
def __init__(self, num_samples, epsilon, space, sampling, minibatch_per_gpu, **kwargs):
assert space in ['z', 'w']
assert sampling in ['full', 'end']
super().__init__(**kwargs)
self.num_samples = num_samples
self.epsilon = epsilon
self.space = space
self.sampling = sampling
self.minibatch_per_gpu = minibatch_per_gpu
def _evaluate(self, Gs, num_gpus):
minibatch_size = num_gpus * self.minibatch_per_gpu
# Construct TensorFlow graph.
distance_expr = []
for gpu_idx in range(num_gpus):
with tf.device('/gpu:%d' % gpu_idx):
Gs_clone = Gs.clone()
noise_vars = [var for name, var in Gs_clone.components.synthesis.vars.items() if name.startswith('noise')]
# Generate random latents and interpolation t-values.
lat_t01 = tf.random_normal([self.minibatch_per_gpu * 2] + Gs_clone.input_shape[1:])
lerp_t = tf.random_uniform([self.minibatch_per_gpu], 0.0, 1.0 if self.sampling == 'full' else 0.0)
# Interpolate in W or Z.
if self.space == 'w':
dlat_t01 = Gs_clone.components.mapping.get_output_for(lat_t01, None, is_validation=True)
dlat_t0, dlat_t1 = dlat_t01[0::2], dlat_t01[1::2]
dlat_e0 = tflib.lerp(dlat_t0, dlat_t1, lerp_t[:, np.newaxis, np.newaxis])
dlat_e1 = tflib.lerp(dlat_t0, dlat_t1, lerp_t[:, np.newaxis, np.newaxis] + self.epsilon)
dlat_e01 = tf.reshape(tf.stack([dlat_e0, dlat_e1], axis=1), dlat_t01.shape)
else: # space == 'z'
lat_t0, lat_t1 = lat_t01[0::2], lat_t01[1::2]
lat_e0 = slerp(lat_t0, lat_t1, lerp_t[:, np.newaxis])
lat_e1 = slerp(lat_t0, lat_t1, lerp_t[:, np.newaxis] + self.epsilon)
lat_e01 = tf.reshape(tf.stack([lat_e0, lat_e1], axis=1), lat_t01.shape)
dlat_e01 = Gs_clone.components.mapping.get_output_for(lat_e01, None, is_validation=True)
# Synthesize images.
with tf.control_dependencies([var.initializer for var in noise_vars]): # use same noise inputs for the entire minibatch
images = Gs_clone.components.synthesis.get_output_for(dlat_e01, is_validation=True, randomize_noise=False)
# Crop only the face region.
c = int(images.shape[2] // 8)
images = images[:, :, c*3 : c*7, c*2 : c*6]
# Downsample image to 256x256 if it's larger than that. VGG was built for 224x224 images.
if images.shape[2] > 256:
factor = images.shape[2] // 256
images = tf.reshape(images, [-1, images.shape[1], images.shape[2] // factor, factor, images.shape[3] // factor, factor])
images = tf.reduce_mean(images, axis=[3,5])
# Scale dynamic range from [-1,1] to [0,255] for VGG.
images = (images + 1) * (255 / 2)
# Evaluate perceptual distance.
img_e0, img_e1 = images[0::2], images[1::2]
distance_measure = misc.load_pkl('https://drive.google.com/uc?id=1N2-m9qszOeVC9Tq77WxsLnuWwOedQiD2') # vgg16_zhang_perceptual.pkl
distance_expr.append(distance_measure.get_output_for(img_e0, img_e1) * (1 / self.epsilon**2))
# Sampling loop.
all_distances = []
for _ in range(0, self.num_samples, minibatch_size):
all_distances += tflib.run(distance_expr)
all_distances = np.concatenate(all_distances, axis=0)
# Reject outliers.
lo = np.percentile(all_distances, 1, interpolation='lower')
hi = np.percentile(all_distances, 99, interpolation='higher')
filtered_distances = np.extract(np.logical_and(lo <= all_distances, all_distances <= hi), all_distances)
self._report_result(np.mean(filtered_distances))
#----------------------------------------------------------------------------
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