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import torch
import torch.nn as nn
import torch.nn.functional as F
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 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 GatedMaskedConv1d(nn.Module):
def __init__(self, mask_type, dim, kernel, residual, 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) # (ceil(n/2), n)
padding_shp = (kernel // 2)
self.vert_stack = nn.Conv1d(
dim, dim * 2,
kernel_shp, 1, padding_shp
)
self.gate = GatedActivation()
if self.residual:
self.res = nn.Conv1d(dim, dim, 1)
def make_causal(self):
self.vert_stack.weight.data[:, :, -1].zero_() # Mask final row
def forward(self, x, h):
if self.mask_type == 'A':
self.make_causal()
h = self.class_cond_embedding(h)
h_vert = self.vert_stack(x)
h_vert = h_vert[:, :, :x.size(-2), :]
out = self.gate(h_vert + h[:, :, None, None])
if self.residual:
out = self.res(out) + x
return out
class GatedPixelCNN(nn.Module):
def __init__(self, input_dim=256, dim=64, n_layers=15, n_classes=10):
super().__init__()
self.dim = dim
self.embedding_aud_mo = nn.Conv1d(512, dim, 1, 1, padding=0)
self.fusion = nn.Conv1d(dim * 2, dim, 1, 1, padding=0)
# 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(
GatedMaskedConv1d(mask_type, dim, kernel, residual, n_classes)
)
# Add the output layer
self.output_conv = nn.Sequential(
nn.Conv1d(dim, 512, 1),
nn.ReLU(True),
nn.Conv1d(512, input_dim, 1)
)
self.apply(weights_init)
self.dp = nn.Dropout(0.1)
def forward(self, x, label, c):
x = x # (B, C, W)
for i, layer in enumerate(self.layers):
if i == 1:
c = self.embedding(c)
x = self.fusion(torch.cat([x, c], dim=1))
x = layer(x, label)
return self.output_conv(x)
def generate(self, label, shape=(8, 8), batch_size=64, aud_feat=None, pre_latents=None, pre_audio=None):
param = next(self.parameters())
x = torch.zeros(
(batch_size, *shape),
dtype=torch.int64, device=param.device
)
if pre_latents is not None:
x = torch.cat([pre_latents, x], dim=1)
aud_feat = torch.cat([pre_audio, aud_feat], dim=2)
h0 = pre_latents.shape[1]
h = h0 + shape[0]
else:
h0 = 0
h = shape[0]
for i in range(h0, h):
for j in range(shape[1]):
if self.audio:
logits = self.forward(x, label, aud_feat)
else:
logits = self.forward(x, label)
probs = F.softmax(logits[:, :, i, j], -1)
x.data[:, i, j].copy_(
probs.multinomial(1).squeeze().data
)
return x[:, h0:h]
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