Spaces:
Running
on
Zero
Running
on
Zero
File size: 12,396 Bytes
11e6f7b |
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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 |
# ---------------------------------------------------------------
# Copyright (c) 2021, NVIDIA CORPORATION. All rights reserved.
#
# This work is licensed under the NVIDIA Source Code License
# for LSGM. To view a copy of this license, see the LICENSE file.
# ---------------------------------------------------------------
import torch
import torch.nn.functional as F
from torch.distributions.bernoulli import Bernoulli as Bern
import numpy as np
from pdb import set_trace as st
# from util import utils
from .continuous_diffusion_utils import view4D
@torch.jit.script
def sample_normal_jit(mu, sigma):
rho = mu.mul(0).normal_()
z = rho.mul_(sigma).add_(mu)
return z, rho
@torch.jit.script
def log_p_standard_normal(samples):
log_p = - 0.5 * torch.square(samples) - 0.9189385332 # 0.5 * np.log(2 * np.pi)
return log_p
def log_p_var_normal(samples, var):
log_p = - 0.5 * torch.square(samples) / var - 0.5 * np.log(var) - 0.9189385332 # 0.5 * np.log(2 * np.pi)
return log_p
def one_hot(indices, depth, dim):
indices = indices.unsqueeze(dim)
size = list(indices.size())
size[dim] = depth
y_onehot = torch.zeros(size).cuda()
y_onehot.zero_()
y_onehot.scatter_(dim, indices, 1)
return y_onehot
# TODO: merge this with the next class
class PixelNormal(object):
def __init__(self, param, fixed_log_scales=None):
size = param.size()
C = size[1]
if fixed_log_scales is None:
self.num_c = C // 2
self.means = param[:, :self.num_c, :, :] # B, 1 or 3, H, W
self.log_scales = torch.clamp(param[:, self.num_c:, :, :], min=-7.0) # B, 1 or 3, H, W
raise NotImplementedError
else:
self.num_c = C
self.means = param # B, 1 or 3, H, W
self.log_scales = view4D(fixed_log_scales, size) # B, 1 or 3, H, W
def get_params(self):
return self.means, self.log_scales, self.num_c
def log_prob(self, samples):
B, C, H, W = samples.size()
assert C == self.num_c
log_probs = -0.5 * torch.square(self.means - samples) * torch.exp(-2.0 * self.log_scales) - self.log_scales - 0.9189385332 # -0.5*log(2*pi)
return log_probs
def sample(self, t=1.):
z, rho = sample_normal_jit(self.means, torch.exp(self.log_scales)*t) # B, 3, H, W
return z
def log_prob_discrete(self, samples):
"""
Calculates discrete pixel probabilities.
"""
# samples should be in [-1, 1] already
B, C, H, W = samples.size()
assert C == self.num_c
centered = samples - self.means
inv_stdv = torch.exp(- self.log_scales)
plus_in = inv_stdv * (centered + 1. / 255.)
cdf_plus = torch.distributions.Normal(0, 1).cdf(plus_in)
min_in = inv_stdv * (centered - 1. / 255.)
cdf_min = torch.distributions.Normal(0, 1).cdf(min_in)
log_cdf_plus = torch.log(torch.clamp(cdf_plus, min=1e-12))
log_one_minus_cdf_min = torch.log(torch.clamp(1. - cdf_min, min=1e-12))
cdf_delta = cdf_plus - cdf_min
log_probs = torch.where(samples < -0.999, log_cdf_plus, torch.where(samples > 0.999, log_one_minus_cdf_min,
torch.log(torch.clamp(cdf_delta, min=1e-12))))
assert log_probs.size() == samples.size()
return log_probs
def mean(self):
return self.means
class Normal:
def __init__(self, mu, log_sigma):
self.mu = mu
self.log_sigma = log_sigma
self.sigma = torch.exp(log_sigma)
def sample(self, t=1.):
return sample_normal_jit(self.mu, self.sigma * t)
def sample_given_rho(self, rho):
return rho * self.sigma + self.mu
def log_p(self, samples):
normalized_samples = (samples - self.mu) / self.sigma
log_p = - 0.5 * normalized_samples * normalized_samples - 0.5 * np.log(2 * np.pi) - self.log_sigma
return log_p
def kl(self, normal_dist):
term1 = (self.mu - normal_dist.mu) / normal_dist.sigma
term2 = self.sigma / normal_dist.sigma
return 0.5 * (term1 * term1 + term2 * term2) - 0.5 - torch.log(self.log_sigma) + normal_dist.log_sigma
def mean(self):
return self.mu
class Bernoulli:
def __init__(self, logits):
self.dist = Bern(logits=logits)
def log_p(self, samples):
# convert samples to {0, 1}
samples = (samples + 1.) / 2
return self.dist.log_prob(samples)
def mean(self):
# map the mean to [-1, 1]
return 2 * self.dist.mean - 1.
class DiscLogistic:
def __init__(self, param):
B, C, H, W = param.size()
self.num_c = C // 2
self.means = param[:, :self.num_c, :, :] # B, 3, H, W
self.log_scales = torch.clamp(param[:, self.num_c:, :, :], min=-7.0) # B, 3, H, W
def log_p(self, samples):
assert torch.max(samples) <= 1.0 and torch.min(samples) >= -1.0
B, C, H, W = samples.size()
assert C == self.num_c
centered = samples - self.means # B, 3, H, W
inv_stdv = torch.exp(- self.log_scales)
plus_in = inv_stdv * (centered + 1. / 255.)
cdf_plus = torch.sigmoid(plus_in)
min_in = inv_stdv * (centered - 1. / 255.)
cdf_min = torch.sigmoid(min_in)
log_cdf_plus = plus_in - F.softplus(plus_in)
log_one_minus_cdf_min = - F.softplus(min_in)
cdf_delta = cdf_plus - cdf_min
mid_in = inv_stdv * centered
log_pdf_mid = mid_in - self.log_scales - 2. * F.softplus(mid_in)
log_prob_mid_safe = torch.where(cdf_delta > 1e-5,
torch.log(torch.clamp(cdf_delta, min=1e-10)),
log_pdf_mid - np.log(127.5))
log_probs = torch.where(samples < -0.999, log_cdf_plus, torch.where(samples > 0.999, log_one_minus_cdf_min,
log_prob_mid_safe)) # B, 3, H, W
return log_probs
def sample(self):
u = torch.Tensor(self.means.size()).uniform_(1e-5, 1. - 1e-5).cuda() # B, 3, H, W
x = self.means + torch.exp(self.log_scales) * (torch.log(u) - torch.log(1. - u)) # B, 3, H, W
x = torch.clamp(x, -1, 1.)
return x
def mean(self):
return self.means
class DiscMixLogistic:
def __init__(self, param, num_mix=10, num_bits=8):
B, C, H, W = param.size()
self.num_mix = num_mix
self.logit_probs = param[:, :num_mix, :, :] # B, M, H, W
l = param[:, num_mix:, :, :].view(B, 3, 3 * num_mix, H, W) # B, 3, 3 * M, H, W
self.means = l[:, :, :num_mix, :, :] # B, 3, M, H, W
self.log_scales = torch.clamp(l[:, :, num_mix:2 * num_mix, :, :], min=-7.0) # B, 3, M, H, W
self.coeffs = torch.tanh(l[:, :, 2 * num_mix:3 * num_mix, :, :]) # B, 3, M, H, W
self.max_val = 2. ** num_bits - 1
def log_p(self, samples):
assert torch.max(samples) <= 1.0 and torch.min(samples) >= -1.0
B, C, H, W = samples.size()
assert C == 3, 'only RGB images are considered.'
samples = samples.unsqueeze(4) # B, 3, H , W
samples = samples.expand(-1, -1, -1, -1, self.num_mix).permute(0, 1, 4, 2, 3) # B, 3, M, H, W
mean1 = self.means[:, 0, :, :, :] # B, M, H, W
mean2 = self.means[:, 1, :, :, :] + \
self.coeffs[:, 0, :, :, :] * samples[:, 0, :, :, :] # B, M, H, W
mean3 = self.means[:, 2, :, :, :] + \
self.coeffs[:, 1, :, :, :] * samples[:, 0, :, :, :] + \
self.coeffs[:, 2, :, :, :] * samples[:, 1, :, :, :] # B, M, H, W
mean1 = mean1.unsqueeze(1) # B, 1, M, H, W
mean2 = mean2.unsqueeze(1) # B, 1, M, H, W
mean3 = mean3.unsqueeze(1) # B, 1, M, H, W
means = torch.cat([mean1, mean2, mean3], dim=1) # B, 3, M, H, W
centered = samples - means # B, 3, M, H, W
inv_stdv = torch.exp(- self.log_scales)
plus_in = inv_stdv * (centered + 1. / self.max_val)
cdf_plus = torch.sigmoid(plus_in)
min_in = inv_stdv * (centered - 1. / self.max_val)
cdf_min = torch.sigmoid(min_in)
log_cdf_plus = plus_in - F.softplus(plus_in)
log_one_minus_cdf_min = - F.softplus(min_in)
cdf_delta = cdf_plus - cdf_min
mid_in = inv_stdv * centered
log_pdf_mid = mid_in - self.log_scales - 2. * F.softplus(mid_in)
log_prob_mid_safe = torch.where(cdf_delta > 1e-5,
torch.log(torch.clamp(cdf_delta, min=1e-10)),
log_pdf_mid - np.log(self.max_val / 2))
log_probs = torch.where(samples < -0.999, log_cdf_plus, torch.where(samples > 0.999, log_one_minus_cdf_min,
log_prob_mid_safe)) # B, 3, M, H, W
log_probs = torch.sum(log_probs, 1) + F.log_softmax(self.logit_probs, dim=1) # B, M, H, W
return torch.logsumexp(log_probs, dim=1) # B, H, W
def sample(self, t=1.):
gumbel = -torch.log(- torch.log(torch.Tensor(self.logit_probs.size()).uniform_(1e-5, 1. - 1e-5).cuda())) # B, M, H, W
sel = one_hot(torch.argmax(self.logit_probs / t + gumbel, 1), self.num_mix, dim=1) # B, M, H, W
sel = sel.unsqueeze(1) # B, 1, M, H, W
# select logistic parameters
means = torch.sum(self.means * sel, dim=2) # B, 3, H, W
log_scales = torch.sum(self.log_scales * sel, dim=2) # B, 3, H, W
coeffs = torch.sum(self.coeffs * sel, dim=2) # B, 3, H, W
# cells from logistic & clip to interval
# we don't actually round to the nearest 8bit value when sampling
u = torch.Tensor(means.size()).uniform_(1e-5, 1. - 1e-5).cuda() # B, 3, H, W
x = means + torch.exp(log_scales) * t * (torch.log(u) - torch.log(1. - u)) # B, 3, H, W
x0 = torch.clamp(x[:, 0, :, :], -1, 1.) # B, H, W
x1 = torch.clamp(x[:, 1, :, :] + coeffs[:, 0, :, :] * x0, -1, 1) # B, H, W
x2 = torch.clamp(x[:, 2, :, :] + coeffs[:, 1, :, :] * x0 + coeffs[:, 2, :, :] * x1, -1, 1) # B, H, W
x0 = x0.unsqueeze(1)
x1 = x1.unsqueeze(1)
x2 = x2.unsqueeze(1)
x = torch.cat([x0, x1, x2], 1)
return x
def mean(self):
sel = torch.softmax(self.logit_probs, dim=1) # B, M, H, W
sel = sel.unsqueeze(1) # B, 1, M, H, W
# select logistic parameters
means = torch.sum(self.means * sel, dim=2) # B, 3, H, W
coeffs = torch.sum(self.coeffs * sel, dim=2) # B, 3, H, W
# we don't sample from logistic components, because of the linear dependencies, we use mean
x = means # B, 3, H, W
x0 = torch.clamp(x[:, 0, :, :], -1, 1.) # B, H, W
x1 = torch.clamp(x[:, 1, :, :] + coeffs[:, 0, :, :] * x0, -1, 1) # B, H, W
x2 = torch.clamp(x[:, 2, :, :] + coeffs[:, 1, :, :] * x0 + coeffs[:, 2, :, :] * x1, -1, 1) # B, H, W
x0 = x0.unsqueeze(1)
x1 = x1.unsqueeze(1)
x2 = x2.unsqueeze(1)
x = torch.cat([x0, x1, x2], 1)
return x
|