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import torch
from torch import nn
from torch.nn import functional as F
from vae_helpers import HModule, get_1x1, get_3x3, DmolNet, draw_gaussian_diag_samples, gaussian_analytical_kl
from collections import defaultdict
import numpy as np
import itertools
class Block(nn.Module):
def __init__(self, in_width, middle_width, out_width, down_rate=None, residual=False, use_3x3=True, zero_last=False):
super().__init__()
self.down_rate = down_rate
self.residual = residual
self.c1 = get_1x1(in_width, middle_width)
self.c2 = get_3x3(middle_width, middle_width) if use_3x3 else get_1x1(middle_width, middle_width)
self.c3 = get_3x3(middle_width, middle_width) if use_3x3 else get_1x1(middle_width, middle_width)
self.c4 = get_1x1(middle_width, out_width, zero_weights=zero_last)
def forward(self, x):
xhat = self.c1(F.gelu(x))
xhat = self.c2(F.gelu(xhat))
xhat = self.c3(F.gelu(xhat))
xhat = self.c4(F.gelu(xhat))
out = x + xhat if self.residual else xhat
if self.down_rate is not None:
out = F.avg_pool2d(out, kernel_size=self.down_rate, stride=self.down_rate)
return out
def parse_layer_string(s):
layers = []
for ss in s.split(','):
if 'x' in ss:
res, num = ss.split('x')
count = int(num)
layers += [(int(res), None) for _ in range(count)]
elif 'm' in ss:
res, mixin = [int(a) for a in ss.split('m')]
layers.append((res, mixin))
elif 'd' in ss:
res, down_rate = [int(a) for a in ss.split('d')]
layers.append((res, down_rate))
else:
res = int(ss)
layers.append((res, None))
return layers
def pad_channels(t, width):
d1, d2, d3, d4 = t.shape
empty = torch.zeros(d1, width, d3, d4, device=t.device)
empty[:, :d2, :, :] = t
return empty
def get_width_settings(width, s):
mapping = defaultdict(lambda: width)
if s:
s = s.split(',')
for ss in s:
k, v = ss.split(':')
mapping[int(k)] = int(v)
return mapping
class Encoder(HModule):
def build(self):
H = self.H
self.in_conv = get_3x3(H.image_channels, H.width)
self.widths = get_width_settings(H.width, H.custom_width_str)
enc_blocks = []
blockstr = parse_layer_string(H.enc_blocks)
for res, down_rate in blockstr:
use_3x3 = res > 2 # Don't use 3x3s for 1x1, 2x2 patches
enc_blocks.append(Block(self.widths[res], int(self.widths[res] * H.bottleneck_multiple), self.widths[res], down_rate=down_rate, residual=True, use_3x3=use_3x3))
n_blocks = len(blockstr)
for b in enc_blocks:
b.c4.weight.data *= np.sqrt(1 / n_blocks)
self.enc_blocks = nn.ModuleList(enc_blocks)
def forward(self, x):
x = x.permute(0, 3, 1, 2).contiguous()
x = self.in_conv(x)
activations = {}
activations[x.shape[2]] = x
for block in self.enc_blocks:
x = block(x)
res = x.shape[2]
x = x if x.shape[1] == self.widths[res] else pad_channels(x, self.widths[res])
activations[res] = x
return activations
class DecBlock(nn.Module):
def __init__(self, H, res, mixin, n_blocks):
super().__init__()
self.base = res
self.mixin = mixin
self.H = H
self.widths = get_width_settings(H.width, H.custom_width_str)
width = self.widths[res]
use_3x3 = res > 2
cond_width = int(width * H.bottleneck_multiple)
self.zdim = H.zdim
self.enc = Block(width * 2, cond_width, H.zdim * 2, residual=False, use_3x3=use_3x3)
self.prior = Block(width, cond_width, H.zdim * 2 + width, residual=False, use_3x3=use_3x3, zero_last=True)
self.z_proj = get_1x1(H.zdim, width)
self.z_proj.weight.data *= np.sqrt(1 / n_blocks)
self.resnet = Block(width, cond_width, width, residual=True, use_3x3=use_3x3)
self.resnet.c4.weight.data *= np.sqrt(1 / n_blocks)
self.z_fn = lambda x: self.z_proj(x)
def sample(self, x, acts):
qm, qv = self.enc(torch.cat([x, acts], dim=1)).chunk(2, dim=1)
feats = self.prior(x)
pm, pv, xpp = feats[:, :self.zdim, ...], feats[:, self.zdim:self.zdim * 2, ...], feats[:, self.zdim * 2:, ...]
x = x + xpp
z = draw_gaussian_diag_samples(qm, qv)
kl = gaussian_analytical_kl(qm, pm, qv, pv)
return z, x, kl
def sample_uncond(self, x, t=None, lvs=None):
n, c, h, w = x.shape
feats = self.prior(x)
pm, pv, xpp = feats[:, :self.zdim, ...], feats[:, self.zdim:self.zdim * 2, ...], feats[:, self.zdim * 2:, ...]
x = x + xpp
if lvs is not None:
z = lvs
else:
if t is not None:
pv = pv + torch.ones_like(pv) * np.log(t)
z = draw_gaussian_diag_samples(pm, pv)
return z, x
def get_inputs(self, xs, activations):
acts = activations[self.base]
try:
x = xs[self.base]
except KeyError:
x = torch.zeros_like(acts)
if acts.shape[0] != x.shape[0]:
x = x.repeat(acts.shape[0], 1, 1, 1)
return x, acts
def forward(self, xs, activations, get_latents=False):
x, acts = self.get_inputs(xs, activations)
if self.mixin is not None:
x = x + F.interpolate(xs[self.mixin][:, :x.shape[1], ...], scale_factor=self.base // self.mixin)
z, x, kl = self.sample(x, acts)
x = x + self.z_fn(z)
x = self.resnet(x)
xs[self.base] = x
if get_latents:
return xs, dict(z=z.detach(), kl=kl)
return xs, dict(kl=kl)
def forward_uncond(self, xs, t=None, lvs=None):
try:
x = xs[self.base]
except KeyError:
ref = xs[list(xs.keys())[0]]
x = torch.zeros(dtype=ref.dtype, size=(ref.shape[0], self.widths[self.base], self.base, self.base), device=ref.device)
if self.mixin is not None:
x = x + F.interpolate(xs[self.mixin][:, :x.shape[1], ...], scale_factor=self.base // self.mixin)
z, x = self.sample_uncond(x, t, lvs=lvs)
x = x + self.z_fn(z)
x = self.resnet(x)
xs[self.base] = x
return xs
class Decoder(HModule):
def build(self):
H = self.H
resos = set()
dec_blocks = []
self.widths = get_width_settings(H.width, H.custom_width_str)
blocks = parse_layer_string(H.dec_blocks)
for idx, (res, mixin) in enumerate(blocks):
dec_blocks.append(DecBlock(H, res, mixin, n_blocks=len(blocks)))
resos.add(res)
self.resolutions = sorted(resos)
self.dec_blocks = nn.ModuleList(dec_blocks)
self.bias_xs = nn.ParameterList([nn.Parameter(torch.zeros(1, self.widths[res], res, res)) for res in self.resolutions if res <= H.no_bias_above])
self.out_net = DmolNet(H)
self.gain = nn.Parameter(torch.ones(1, H.width, 1, 1))
self.bias = nn.Parameter(torch.zeros(1, H.width, 1, 1))
self.final_fn = lambda x: x * self.gain + self.bias
def forward(self, activations, get_latents=False):
stats = []
xs = {a.shape[2]: a for a in self.bias_xs}
for block in self.dec_blocks:
xs, block_stats = block(xs, activations, get_latents=get_latents)
stats.append(block_stats)
xs[self.H.image_size] = self.final_fn(xs[self.H.image_size])
return xs[self.H.image_size], stats
def forward_uncond(self, n, t=None, y=None):
xs = {}
for bias in self.bias_xs:
xs[bias.shape[2]] = bias.repeat(n, 1, 1, 1)
for idx, block in enumerate(self.dec_blocks):
try:
temp = t[idx]
except TypeError:
temp = t
xs = block.forward_uncond(xs, temp)
xs[self.H.image_size] = self.final_fn(xs[self.H.image_size])
return xs[self.H.image_size]
def forward_manual_latents(self, n, latents, t=None):
xs = {}
for bias in self.bias_xs:
xs[bias.shape[2]] = bias.repeat(n, 1, 1, 1)
for block, lvs in itertools.zip_longest(self.dec_blocks, latents):
xs = block.forward_uncond(xs, t, lvs=lvs)
xs[self.H.image_size] = self.final_fn(xs[self.H.image_size])
return xs[self.H.image_size]
class VAE(HModule):
def build(self):
self.encoder = Encoder(self.H)
self.decoder = Decoder(self.H)
def forward(self, x, x_target):
activations = self.encoder.forward(x)
px_z, stats = self.decoder.forward(activations)
distortion_per_pixel = self.decoder.out_net.nll(px_z, x_target)
rate_per_pixel = torch.zeros_like(distortion_per_pixel)
ndims = np.prod(x.shape[1:])
for statdict in stats:
rate_per_pixel += statdict['kl'].sum(dim=(1, 2, 3))
rate_per_pixel /= ndims
elbo = (distortion_per_pixel + rate_per_pixel).mean()
return dict(elbo=elbo, distortion=distortion_per_pixel.mean(), rate=rate_per_pixel.mean())
def forward_get_latents(self, x):
activations = self.encoder.forward(x)
_, stats = self.decoder.forward(activations, get_latents=True)
return stats
def forward_uncond_samples(self, n_batch, t=None):
px_z = self.decoder.forward_uncond(n_batch, t=t)
return self.decoder.out_net.sample(px_z)
def forward_samples_set_latents(self, n_batch, latents, t=None):
px_z = self.decoder.forward_manual_latents(n_batch, latents, t=t)
return self.decoder.out_net.sample(px_z)
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