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modules.py
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modules.py
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import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.distributions.normal import Normal
from torch.distributions import kl_divergence
from functions import vq, vq_st
def to_scalar(arr):
if type(arr) == list:
return [x.item() for x in arr]
else:
return arr.item()
def weights_init(m):
classname = m.__class__.__name__
if classname.find('Conv') != -1:
try:
nn.init.xavier_uniform_(m.weight.data)
m.bias.data.fill_(0)
except AttributeError:
print("Skipping initialization of ", classname)
class VAE(nn.Module):
def __init__(self, input_dim, dim, z_dim):
super().__init__()
self.encoder = nn.Sequential(
nn.Conv2d(input_dim, dim, 4, 2, 1),
nn.BatchNorm2d(dim),
nn.ReLU(True),
nn.Conv2d(dim, dim, 4, 2, 1),
nn.BatchNorm2d(dim),
nn.ReLU(True),
nn.Conv2d(dim, dim, 5, 1, 0),
nn.BatchNorm2d(dim),
nn.ReLU(True),
nn.Conv2d(dim, z_dim * 2, 3, 1, 0),
nn.BatchNorm2d(z_dim * 2)
)
self.decoder = nn.Sequential(
nn.ConvTranspose2d(z_dim, dim, 3, 1, 0),
nn.BatchNorm2d(dim),
nn.ReLU(True),
nn.ConvTranspose2d(dim, dim, 5, 1, 0),
nn.BatchNorm2d(dim),
nn.ReLU(True),
nn.ConvTranspose2d(dim, dim, 4, 2, 1),
nn.BatchNorm2d(dim),
nn.ReLU(True),
nn.ConvTranspose2d(dim, input_dim, 4, 2, 1),
nn.Tanh()
)
self.apply(weights_init)
def forward(self, x):
mu, logvar = self.encoder(x).chunk(2, dim=1)
q_z_x = Normal(mu, logvar.mul(.5).exp())
p_z = Normal(torch.zeros_like(mu), torch.ones_like(logvar))
kl_div = kl_divergence(q_z_x, p_z).sum(1).mean()
x_tilde = self.decoder(q_z_x.rsample())
return x_tilde, kl_div
class VQEmbedding(nn.Module):
def __init__(self, K, D):
super().__init__()
self.embedding = nn.Embedding(K, D)
self.embedding.weight.data.uniform_(-1./K, 1./K)
def forward(self, z_e_x):
z_e_x_ = z_e_x.permute(0, 2, 3, 1).contiguous()
latents = vq(z_e_x_, self.embedding.weight)
return latents
def straight_through(self, z_e_x):
z_e_x_ = z_e_x.permute(0, 2, 3, 1).contiguous()
z_q_x_, indices = vq_st(z_e_x_, self.embedding.weight.detach())
z_q_x = z_q_x_.permute(0, 3, 1, 2).contiguous()
z_q_x_bar_flatten = torch.index_select(self.embedding.weight,
dim=0, index=indices)
z_q_x_bar_ = z_q_x_bar_flatten.view_as(z_e_x_)
z_q_x_bar = z_q_x_bar_.permute(0, 3, 1, 2).contiguous()
return z_q_x, z_q_x_bar
class ResBlock(nn.Module):
def __init__(self, dim):
super().__init__()
self.block = nn.Sequential(
nn.ReLU(True),
nn.Conv2d(dim, dim, 3, 1, 1),
nn.BatchNorm2d(dim),
nn.ReLU(True),
nn.Conv2d(dim, dim, 1),
nn.BatchNorm2d(dim)
)
def forward(self, x):
return x + self.block(x)
class VectorQuantizedVAE(nn.Module):
def __init__(self, input_dim, dim, K=512):
super().__init__()
self.encoder = nn.Sequential(
nn.Conv2d(input_dim, dim, 4, 2, 1),
nn.BatchNorm2d(dim),
nn.ReLU(True),
nn.Conv2d(dim, dim, 4, 2, 1),
ResBlock(dim),
ResBlock(dim),
)
self.codebook = VQEmbedding(K, dim)
self.decoder = nn.Sequential(
ResBlock(dim),
ResBlock(dim),
nn.ReLU(True),
nn.ConvTranspose2d(dim, dim, 4, 2, 1),
nn.BatchNorm2d(dim),
nn.ReLU(True),
nn.ConvTranspose2d(dim, input_dim, 4, 2, 1),
nn.Tanh()
)
self.apply(weights_init)
def encode(self, x):
z_e_x = self.encoder(x)
latents = self.codebook(z_e_x)
return latents
def decode(self, latents):
z_q_x = self.codebook.embedding(latents).permute(0, 3, 1, 2) # (B, D, H, W)
x_tilde = self.decoder(z_q_x)
return x_tilde
def forward(self, x):
z_e_x = self.encoder(x)
z_q_x_st, z_q_x = self.codebook.straight_through(z_e_x)
x_tilde = self.decoder(z_q_x_st)
return x_tilde, z_e_x, z_q_x
class GatedActivation(nn.Module):
def __init__(self):
super().__init__()
def forward(self, x):
x, y = x.chunk(2, dim=1)
return F.tanh(x) * F.sigmoid(y)
class GatedMaskedConv2d(nn.Module):
def __init__(self, mask_type, dim, kernel, residual=True, n_classes=10):
super().__init__()
assert kernel % 2 == 1, print("Kernel size must be odd")
self.mask_type = mask_type
self.residual = residual
self.class_cond_embedding = nn.Embedding(
n_classes, 2 * dim
)
kernel_shp = (kernel // 2 + 1, kernel) # (ceil(n/2), n)
padding_shp = (kernel // 2, kernel // 2)
self.vert_stack = nn.Conv2d(
dim, dim * 2,
kernel_shp, 1, padding_shp
)
self.vert_to_horiz = nn.Conv2d(2 * dim, 2 * dim, 1)
kernel_shp = (1, kernel // 2 + 1)
padding_shp = (0, kernel // 2)
self.horiz_stack = nn.Conv2d(
dim, dim * 2,
kernel_shp, 1, padding_shp
)
self.horiz_resid = nn.Conv2d(dim, dim, 1)
self.gate = GatedActivation()
def make_causal(self):
self.vert_stack.weight.data[:, :, -1].zero_() # Mask final row
self.horiz_stack.weight.data[:, :, :, -1].zero_() # Mask final column
def forward(self, x_v, x_h, h):
if self.mask_type == 'A':
self.make_causal()
h = self.class_cond_embedding(h)
h_vert = self.vert_stack(x_v)
h_vert = h_vert[:, :, :x_v.size(-1), :]
out_v = self.gate(h_vert + h[:, :, None, None])
h_horiz = self.horiz_stack(x_h)
h_horiz = h_horiz[:, :, :, :x_h.size(-2)]
v2h = self.vert_to_horiz(h_vert)
out = self.gate(v2h + h_horiz + h[:, :, None, None])
if self.residual:
out_h = self.horiz_resid(out) + x_h
else:
out_h = self.horiz_resid(out)
return out_v, out_h
class GatedPixelCNN(nn.Module):
def __init__(self, input_dim=256, dim=64, n_layers=15, n_classes=10):
super().__init__()
self.dim = dim
# Create embedding layer to embed input
self.embedding = nn.Embedding(input_dim, dim)
# Building the PixelCNN layer by layer
self.layers = nn.ModuleList()
# Initial block with Mask-A convolution
# Rest with Mask-B convolutions
for i in range(n_layers):
mask_type = 'A' if i == 0 else 'B'
kernel = 7 if i == 0 else 3
residual = False if i == 0 else True
self.layers.append(
GatedMaskedConv2d(mask_type, dim, kernel, residual, n_classes)
)
# Add the output layer
self.output_conv = nn.Sequential(
nn.Conv2d(dim, 512, 1),
nn.ReLU(True),
nn.Conv2d(512, input_dim, 1)
)
self.apply(weights_init)
def forward(self, x, label):
shp = x.size() + (-1, )
x = self.embedding(x.view(-1)).view(shp) # (B, H, W, C)
x = x.permute(0, 3, 1, 2) # (B, C, W, W)
x_v, x_h = (x, x)
for i, layer in enumerate(self.layers):
x_v, x_h = layer(x_v, x_h, label)
return self.output_conv(x_h)
def generate(self, label, shape=(8, 8), batch_size=64):
param = next(self.parameters())
x = torch.zeros(
(batch_size, *shape),
dtype=torch.int64, device=param.device
)
for i in range(shape[0]):
for j in range(shape[1]):
logits = self.forward(x, label)
probs = F.softmax(logits[:, :, i, j], -1)
x.data[:, i, j].copy_(
probs.multinomial(1).squeeze().data
)
return x