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import torch
from torch import nn
import math
class LearnedUpsampling1d(nn.Module):
def __init__(self, in_channels, out_channels, kernel_size, bias=True):
super().__init__()
self.conv_t = nn.ConvTranspose1d(
in_channels=in_channels,
out_channels=out_channels,
kernel_size=kernel_size,
stride=kernel_size,
bias=False
)
if bias:
self.bias = nn.Parameter(
torch.FloatTensor(out_channels, kernel_size)
)
else:
self.register_parameter('bias', None)
self.reset_parameters()
def reset_parameters(self):
self.conv_t.reset_parameters()
nn.init.constant(self.bias, 0)
def forward(self, input):
(batch_size, _, length) = input.size()
(kernel_size,) = self.conv_t.kernel_size
bias = self.bias.unsqueeze(0).unsqueeze(2).expand(
batch_size, self.conv_t.out_channels,
length, kernel_size
).contiguous().view(
batch_size, self.conv_t.out_channels,
length * kernel_size
)
return self.conv_t(input) + bias
def lecun_uniform(tensor):
fan_in = nn.init._calculate_correct_fan(tensor, 'fan_in')
nn.init.uniform(tensor, -math.sqrt(3 / fan_in), math.sqrt(3 / fan_in))
def concat_init(tensor, inits):
try:
tensor = tensor.data
except AttributeError:
pass
(length, fan_out) = tensor.size()
fan_in = length // len(inits)
chunk = tensor.new(fan_in, fan_out)
for (i, init) in enumerate(inits):
init(chunk)
tensor[i * fan_in : (i + 1) * fan_in, :] = chunk
def sequence_nll_loss_bits(input, target, *args, **kwargs):
(_, _, n_classes) = input.size()
return nn.functional.nll_loss(
input.view(-1, n_classes), target.view(-1), *args, **kwargs
) * math.log(math.e, 2)
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