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utils.py
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utils.py
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import pdb
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.autograd import Variable
from torch.nn.utils import weight_norm as wn
import numpy as np
def concat_elu(x):
""" like concatenated ReLU (http://arxiv.org/abs/1603.05201), but then with ELU """
# Pytorch ordering
axis = len(x.size()) - 3
return F.elu(torch.cat([x, -x], dim=axis))
def log_sum_exp(x):
""" numerically stable log_sum_exp implementation that prevents overflow """
# TF ordering
axis = len(x.size()) - 1
m, _ = torch.max(x, dim=axis)
m2, _ = torch.max(x, dim=axis, keepdim=True)
return m + torch.log(torch.sum(torch.exp(x - m2), dim=axis))
def log_prob_from_logits(x):
""" numerically stable log_softmax implementation that prevents overflow """
# TF ordering
axis = len(x.size()) - 1
m, _ = torch.max(x, dim=axis, keepdim=True)
return x - m - torch.log(torch.sum(torch.exp(x - m), dim=axis, keepdim=True))
def discretized_mix_logistic_loss(x, l):
""" log-likelihood for mixture of discretized logistics, assumes the data has been rescaled to [-1,1] interval """
# Pytorch ordering
x = x.permute(0, 2, 3, 1)
l = l.permute(0, 2, 3, 1)
xs = [int(y) for y in x.size()]
ls = [int(y) for y in l.size()]
# here and below: unpacking the params of the mixture of logistics
nr_mix = int(ls[-1] / 10)
logit_probs = l[:, :, :, :nr_mix]
l = l[:, :, :, nr_mix:].contiguous().view(
xs + [nr_mix * 3]) # 3 for mean, scale, coef
means = l[:, :, :, :, :nr_mix]
# log_scales = torch.max(l[:, :, :, :, nr_mix:2 * nr_mix], -7.)
log_scales = torch.clamp(l[:, :, :, :, nr_mix:2 * nr_mix], min=-7.)
coeffs = F.tanh(l[:, :, :, :, 2 * nr_mix:3 * nr_mix])
# here and below: getting the means and adjusting them based on preceding
# sub-pixels
x = x.contiguous()
x = x.unsqueeze(-1) + Variable(torch.zeros(xs +
[nr_mix]).cuda(), requires_grad=False)
m2 = (means[:, :, :, 1, :] + coeffs[:, :, :, 0, :]
* x[:, :, :, 0, :]).view(xs[0], xs[1], xs[2], 1, nr_mix)
m3 = (means[:, :, :, 2, :] + coeffs[:, :, :, 1, :] * x[:, :, :, 0, :] +
coeffs[:, :, :, 2, :] * x[:, :, :, 1, :]).view(xs[0], xs[1], xs[2], 1, nr_mix)
means = torch.cat((means[:, :, :, 0, :].unsqueeze(3), m2, m3), dim=3)
centered_x = x - means
inv_stdv = torch.exp(-log_scales)
plus_in = inv_stdv * (centered_x + 1. / 255.)
cdf_plus = F.sigmoid(plus_in)
min_in = inv_stdv * (centered_x - 1. / 255.)
cdf_min = F.sigmoid(min_in)
# log probability for edge case of 0 (before scaling)
log_cdf_plus = plus_in - F.softplus(plus_in)
# log probability for edge case of 255 (before scaling)
log_one_minus_cdf_min = -F.softplus(min_in)
cdf_delta = cdf_plus - cdf_min # probability for all other cases
mid_in = inv_stdv * centered_x
# log probability in the center of the bin, to be used in extreme cases
# (not actually used in our code)
log_pdf_mid = mid_in - log_scales - 2. * F.softplus(mid_in)
# now select the right output: left edge case, right edge case, normal
# case, extremely low prob case (doesn't actually happen for us)
# this is what we are really doing, but using the robust version below for extreme cases in other applications and to avoid NaN issue with tf.select()
# log_probs = tf.select(x < -0.999, log_cdf_plus, tf.select(x > 0.999, log_one_minus_cdf_min, tf.log(cdf_delta)))
# robust version, that still works if probabilities are below 1e-5 (which never happens in our code)
# tensorflow backpropagates through tf.select() by multiplying with zero instead of selecting: this requires use to use some ugly tricks to avoid potential NaNs
# the 1e-12 in tf.maximum(cdf_delta, 1e-12) is never actually used as output, it's purely there to get around the tf.select() gradient issue
# if the probability on a sub-pixel is below 1e-5, we use an approximation
# based on the assumption that the log-density is constant in the bin of
# the observed sub-pixel value
inner_inner_cond = (cdf_delta > 1e-5).float()
inner_inner_out = inner_inner_cond * torch.log(torch.clamp(
cdf_delta, min=1e-12)) + (1. - inner_inner_cond) * (log_pdf_mid - np.log(127.5))
inner_cond = (x > 0.999).float()
inner_out = inner_cond * log_one_minus_cdf_min + \
(1. - inner_cond) * inner_inner_out
cond = (x < -0.999).float()
log_probs = cond * log_cdf_plus + (1. - cond) * inner_out
log_probs = torch.sum(log_probs, dim=3) + log_prob_from_logits(logit_probs)
return -torch.sum(log_sum_exp(log_probs))
def discretized_mix_logistic_loss_1d(x, l):
""" log-likelihood for mixture of discretized logistics, assumes the data has been rescaled to [-1,1] interval """
# Pytorch ordering
x = x.permute(0, 2, 3, 1)
l = l.permute(0, 2, 3, 1)
xs = [int(y) for y in x.size()]
ls = [int(y) for y in l.size()]
# here and below: unpacking the params of the mixture of logistics
nr_mix = int(ls[-1] / 3)
logit_probs = l[:, :, :, :nr_mix]
l = l[:, :, :, nr_mix:].contiguous().view(
xs + [nr_mix * 2]) # 2 for mean, scale
means = l[:, :, :, :, :nr_mix]
log_scales = torch.clamp(l[:, :, :, :, nr_mix:2 * nr_mix], min=-7.)
# here and below: getting the means and adjusting them based on preceding
# sub-pixels
x = x.contiguous()
x = x.unsqueeze(-1) + Variable(torch.zeros(xs +
[nr_mix]).cuda(), requires_grad=False)
# means = torch.cat((means[:, :, :, 0, :].unsqueeze(3), m2, m3), dim=3)
centered_x = x - means
inv_stdv = torch.exp(-log_scales)
plus_in = inv_stdv * (centered_x + 1. / 255.)
cdf_plus = F.sigmoid(plus_in)
min_in = inv_stdv * (centered_x - 1. / 255.)
cdf_min = F.sigmoid(min_in)
# log probability for edge case of 0 (before scaling)
log_cdf_plus = plus_in - F.softplus(plus_in)
# log probability for edge case of 255 (before scaling)
log_one_minus_cdf_min = -F.softplus(min_in)
cdf_delta = cdf_plus - cdf_min # probability for all other cases
mid_in = inv_stdv * centered_x
# log probability in the center of the bin, to be used in extreme cases
# (not actually used in our code)
log_pdf_mid = mid_in - log_scales - 2. * F.softplus(mid_in)
inner_inner_cond = (cdf_delta > 1e-5).float()
inner_inner_out = inner_inner_cond * torch.log(torch.clamp(
cdf_delta, min=1e-12)) + (1. - inner_inner_cond) * (log_pdf_mid - np.log(127.5))
inner_cond = (x > 0.999).float()
inner_out = inner_cond * log_one_minus_cdf_min + \
(1. - inner_cond) * inner_inner_out
cond = (x < -0.999).float()
log_probs = cond * log_cdf_plus + (1. - cond) * inner_out
log_probs = torch.sum(log_probs, dim=3) + log_prob_from_logits(logit_probs)
return -torch.sum(log_sum_exp(log_probs))
def to_one_hot(tensor, n, fill_with=1.):
# we perform one hot encore with respect to the last axis
one_hot = torch.FloatTensor(tensor.size() + (n,)).zero_()
if tensor.is_cuda:
one_hot = one_hot.cuda()
one_hot.scatter_(len(tensor.size()), tensor.unsqueeze(-1), fill_with)
return Variable(one_hot)
def sample_from_discretized_mix_logistic_1d(l, nr_mix):
# Pytorch ordering
l = l.permute(0, 2, 3, 1)
ls = [int(y) for y in l.size()]
xs = ls[:-1] + [1] # [3]
# unpack parameters
logit_probs = l[:, :, :, :nr_mix]
l = l[:, :, :, nr_mix:].contiguous().view(
xs + [nr_mix * 2]) # for mean, scale
# sample mixture indicator from softmax
temp = torch.FloatTensor(logit_probs.size())
if l.is_cuda:
temp = temp.cuda()
temp.uniform_(1e-5, 1. - 1e-5)
temp = logit_probs.data - torch.log(- torch.log(temp))
_, argmax = temp.max(dim=3)
one_hot = to_one_hot(argmax, nr_mix)
sel = one_hot.view(xs[:-1] + [1, nr_mix])
# select logistic parameters
means = torch.sum(l[:, :, :, :, :nr_mix] * sel, dim=4)
log_scales = torch.clamp(torch.sum(
l[:, :, :, :, nr_mix:2 * nr_mix] * sel, dim=4), min=-7.)
u = torch.FloatTensor(means.size())
if l.is_cuda:
u = u.cuda()
u.uniform_(1e-5, 1. - 1e-5)
u = Variable(u)
x = means + torch.exp(log_scales) * (torch.log(u) - torch.log(1. - u))
x0 = torch.clamp(torch.clamp(x[:, :, :, 0], min=-1.), max=1.)
out = x0.unsqueeze(1)
return out
def sample_from_discretized_mix_logistic(l, nr_mix):
# Pytorch ordering
l = l.permute(0, 2, 3, 1)
ls = [int(y) for y in l.size()]
xs = ls[:-1] + [3]
# unpack parameters
logit_probs = l[:, :, :, :nr_mix]
l = l[:, :, :, nr_mix:].contiguous().view(xs + [nr_mix * 3])
# sample mixture indicator from softmax
temp = torch.FloatTensor(logit_probs.size())
if l.is_cuda:
temp = temp.cuda()
temp.uniform_(1e-5, 1. - 1e-5)
temp = logit_probs.data - torch.log(- torch.log(temp))
_, argmax = temp.max(dim=3)
one_hot = to_one_hot(argmax, nr_mix)
sel = one_hot.view(xs[:-1] + [1, nr_mix])
# select logistic parameters
means = torch.sum(l[:, :, :, :, :nr_mix] * sel, dim=4)
log_scales = torch.clamp(torch.sum(
l[:, :, :, :, nr_mix:2 * nr_mix] * sel, dim=4), min=-7.)
coeffs = torch.sum(F.tanh(
l[:, :, :, :, 2 * nr_mix:3 * nr_mix]) * sel, dim=4)
# sample from logistic & clip to interval
# we don't actually round to the nearest 8bit value when sampling
u = torch.FloatTensor(means.size())
if l.is_cuda:
u = u.cuda()
u.uniform_(1e-5, 1. - 1e-5)
u = Variable(u)
x = means + torch.exp(log_scales) * (torch.log(u) - torch.log(1. - u))
x0 = torch.clamp(torch.clamp(x[:, :, :, 0], min=-1.), max=1.)
x1 = torch.clamp(torch.clamp(
x[:, :, :, 1] + coeffs[:, :, :, 0] * x0, min=-1.), max=1.)
x2 = torch.clamp(torch.clamp(
x[:, :, :, 2] + coeffs[:, :, :, 1] * x0 + coeffs[:, :, :, 2] * x1, min=-1.), max=1.)
out = torch.cat([x0.view(xs[:-1] + [1]),
x1.view(xs[:-1] + [1]), x2.view(xs[:-1] + [1])], dim=3)
# put back in Pytorch ordering
out = out.permute(0, 3, 1, 2)
return out
''' utilities for shifting the image around, efficient alternative to masking convolutions '''
def down_shift(x, pad=None):
# Pytorch ordering
xs = [int(y) for y in x.size()]
# when downshifting, the last row is removed
x = x[:, :, :xs[2] - 1, :]
# padding left, padding right, padding top, padding bottom
pad = nn.ZeroPad2d((0, 0, 1, 0)) if pad is None else pad
return pad(x)
def right_shift(x, pad=None):
# Pytorch ordering
xs = [int(y) for y in x.size()]
# when righshifting, the last column is removed
x = x[:, :, :, :xs[3] - 1]
# padding left, padding right, padding top, padding bottom
pad = nn.ZeroPad2d((1, 0, 0, 0)) if pad is None else pad
return pad(x)
def load_part_of_model(model, path):
params = torch.load(path)
added = 0
for name, param in params.items():
if name in model.state_dict().keys():
try:
model.state_dict()[name].copy_(param)
added += 1
except Exception as e:
print(e)
pass
print('added %s of params:' %
(added / float(len(model.state_dict().keys()))))