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7bef20f | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 | """Evaluator for reconstruction results."""
import warnings
from typing import Sequence, Optional, Mapping, Text
import numpy as np
from scipy import linalg
import torch
import torch.nn.functional as F
from .inception import get_inception_model
def get_covariance(sigma: torch.Tensor, total: torch.Tensor, num_examples: int) -> torch.Tensor:
"""Computes covariance of the input tensor.
Args:
sigma: A torch.Tensor, sum of outer products of input features.
total: A torch.Tensor, sum of all input features.
num_examples: An integer, number of examples in the input tensor.
Returns:
A torch.Tensor, covariance of the input tensor.
"""
if num_examples == 0:
return torch.zeros_like(sigma)
sub_matrix = torch.outer(total, total)
sub_matrix = sub_matrix / num_examples
return (sigma - sub_matrix) / (num_examples - 1)
class VQGANEvaluator:
def __init__(
self,
device,
enable_rfid: bool = True,
enable_inception_score: bool = True,
enable_codebook_usage_measure: bool = False,
enable_codebook_entropy_measure: bool = False,
num_codebook_entries: int = 1024
):
"""Initializes VQGAN Evaluator.
Args:
device: The device to use for evaluation.
enable_rfid: A boolean, whether enabling rFID score.
enable_inception_score: A boolean, whether enabling Inception Score.
enable_codebook_usage_measure: A boolean, whether enabling codebook usage measure.
enable_codebook_entropy_measure: A boolean, whether enabling codebook entropy measure.
num_codebook_entries: An integer, the number of codebook entries.
"""
self._device = device
self._enable_rfid = enable_rfid
self._enable_inception_score = enable_inception_score
self._enable_codebook_usage_measure = enable_codebook_usage_measure
self._enable_codebook_entropy_measure = enable_codebook_entropy_measure
self._num_codebook_entries = num_codebook_entries
# Variables related to Inception score and rFID.
self._inception_model = None
self._is_num_features = 0
self._rfid_num_features = 0
if self._enable_inception_score or self._enable_rfid:
self._rfid_num_features = 2048
self._is_num_features = 1008
self._inception_model = get_inception_model().to(self._device)
self._inception_model.eval()
self._is_eps = 1e-16
self._rfid_eps = 1e-6
self.reset_metrics()
def reset_metrics(self):
"""Resets all metrics."""
self._num_examples = 0
self._num_updates = 0
self._is_prob_total = torch.zeros(
self._is_num_features, dtype=torch.float64, device=self._device
)
self._is_total_kl_d = torch.zeros(
self._is_num_features, dtype=torch.float64, device=self._device
)
self._rfid_real_sigma = torch.zeros(
(self._rfid_num_features, self._rfid_num_features),
dtype=torch.float64, device=self._device
)
self._rfid_real_total = torch.zeros(
self._rfid_num_features, dtype=torch.float64, device=self._device
)
self._rfid_fake_sigma = torch.zeros(
(self._rfid_num_features, self._rfid_num_features),
dtype=torch.float64, device=self._device
)
self._rfid_fake_total = torch.zeros(
self._rfid_num_features, dtype=torch.float64, device=self._device
)
self._set_of_codebook_indices = set()
self._codebook_frequencies = torch.zeros((self._num_codebook_entries), dtype=torch.float64, device=self._device)
def update(
self,
real_images: torch.Tensor,
fake_images: torch.Tensor,
codebook_indices: Optional[torch.Tensor] = None
):
"""Updates the metrics with the given images.
Args:
real_images: A torch.Tensor, the real images.
fake_images: A torch.Tensor, the fake images.
codebook_indices: A torch.Tensor, the indices of the codebooks for each image.
Raises:
ValueError: If the fake images is not in RGB (3 channel).
ValueError: If the fake and real images have different shape.
"""
batch_size = real_images.shape[0]
dim = tuple(range(1, real_images.ndim))
self._num_examples += batch_size
self._num_updates += 1
if self._enable_inception_score or self._enable_rfid:
# Quantize to uint8 as a real image.
fake_inception_images = (fake_images * 255).to(torch.uint8)
features_fake = self._inception_model(fake_inception_images)
inception_logits_fake = features_fake["logits_unbiased"]
inception_probabilities_fake = F.softmax(inception_logits_fake, dim=-1)
if self._enable_inception_score:
probabiliies_sum = torch.sum(inception_probabilities_fake, 0, dtype=torch.float64)
log_prob = torch.log(inception_probabilities_fake + self._is_eps)
if log_prob.dtype != inception_probabilities_fake.dtype:
log_prob = log_prob.to(inception_probabilities_fake)
kl_sum = torch.sum(inception_probabilities_fake * log_prob, 0, dtype=torch.float64)
self._is_prob_total += probabiliies_sum
self._is_total_kl_d += kl_sum
if self._enable_rfid:
real_inception_images = (real_images * 255).to(torch.uint8)
features_real = self._inception_model(real_inception_images)
if (features_real['2048'].shape[0] != features_fake['2048'].shape[0] or
features_real['2048'].shape[1] != features_fake['2048'].shape[1]):
raise ValueError(f"Number of features should be equal for real and fake.")
for f_real, f_fake in zip(features_real['2048'], features_fake['2048']):
self._rfid_real_total += f_real
self._rfid_fake_total += f_fake
self._rfid_real_sigma += torch.outer(f_real, f_real)
self._rfid_fake_sigma += torch.outer(f_fake, f_fake)
if self._enable_codebook_usage_measure:
self._set_of_codebook_indices |= set(torch.unique(codebook_indices, sorted=False).tolist())
if self._enable_codebook_entropy_measure:
entries, counts = torch.unique(codebook_indices, sorted=False, return_counts=True)
self._codebook_frequencies.index_add_(0, entries.int(), counts.double())
def result(self) -> Mapping[Text, torch.Tensor]:
"""Returns the evaluation result."""
eval_score = {}
if self._num_examples < 1:
raise ValueError("No examples to evaluate.")
if self._enable_inception_score:
mean_probs = self._is_prob_total / self._num_examples
log_mean_probs = torch.log(mean_probs + self._is_eps)
if log_mean_probs.dtype != self._is_prob_total.dtype:
log_mean_probs = log_mean_probs.to(self._is_prob_total)
excess_entropy = self._is_prob_total * log_mean_probs
avg_kl_d = torch.sum(self._is_total_kl_d - excess_entropy) / self._num_examples
inception_score = torch.exp(avg_kl_d).item()
eval_score["InceptionScore"] = inception_score
if self._enable_rfid:
mu_real = self._rfid_real_total / self._num_examples
mu_fake = self._rfid_fake_total / self._num_examples
sigma_real = get_covariance(self._rfid_real_sigma, self._rfid_real_total, self._num_examples)
sigma_fake = get_covariance(self._rfid_fake_sigma, self._rfid_fake_total, self._num_examples)
mu_real, mu_fake = mu_real.cpu(), mu_fake.cpu()
sigma_real, sigma_fake = sigma_real.cpu(), sigma_fake.cpu()
diff = mu_real - mu_fake
# Product might be almost singular.
covmean, _ = linalg.sqrtm(sigma_real.mm(sigma_fake).numpy(), disp=False)
# Numerical error might give slight imaginary component.
if np.iscomplexobj(covmean):
if not np.allclose(np.diagonal(covmean).imag, 0, atol=1e-3):
m = np.max(np.abs(covmean.imag))
raise ValueError("Imaginary component {}".format(m))
covmean = covmean.real
tr_covmean = np.trace(covmean)
if not np.isfinite(covmean).all():
tr_covmean = np.sum(np.sqrt((
(np.diag(sigma_real) * self._rfid_eps) * (np.diag(sigma_fake) * self._rfid_eps))
/ (self._rfid_eps * self._rfid_eps)
))
rfid = float(diff.dot(diff).item() + torch.trace(sigma_real) + torch.trace(sigma_fake)
- 2 * tr_covmean
)
if torch.isnan(torch.tensor(rfid)) or torch.isinf(torch.tensor(rfid)):
warnings.warn("The product of covariance of train and test features is out of bounds.")
eval_score["rFID"] = rfid
if self._enable_codebook_usage_measure:
usage = float(len(self._set_of_codebook_indices)) / self._num_codebook_entries
eval_score["CodebookUsage"] = usage
if self._enable_codebook_entropy_measure:
probs = self._codebook_frequencies / self._codebook_frequencies.sum()
entropy = (-torch.log2(probs + 1e-8) * probs).sum()
eval_score["CodebookEntropy"] = entropy
return eval_score
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