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vnet_modules.py
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vnet_modules.py
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import torch
import torch.nn as nn
import torch.nn.functional as F
def _normalize(tensor, dim):
denom = tensor.norm(p=2.0, dim=dim, keepdim=True).clamp_min(1e-12)
return tensor / denom
class vnetConv1d(nn.Conv1d):
def __init__(self,
in_channels,
out_channels,
kernel_size,
stride=1,
padding=0,
dilation=1,
bias=True,
padding_mode='zeros',
device=None,
dtype=None
):
super(vnetConv1d, self).__init__(
in_channels, out_channels, kernel_size, stride, padding=padding, dilation=dilation,
groups=1, bias=bias, padding_mode=padding_mode, device=device, dtype=dtype)
scale = self.weight.norm(p=2.0, dim=[1, 2], keepdim=True).clamp_min(1e-12)
self.weight = nn.parameter.Parameter(self.weight / scale.expand_as(self.weight))
self.scale = nn.parameter.Parameter(scale.view(out_channels))
if bias:
self.bias = nn.parameter.Parameter(torch.zeros(in_channels, device=device, dtype=dtype))
else:
self.register_parameter('bias', None)
def forward(self, input, flg_train=False):
if self.bias is not None:
input = input + self.bias.view(self.in_channels, 1)
normalized_weight = self._get_normalized_weight()
scale = self.scale.view(self.out_channels, 1)
if flg_train:
out_fun = F.conv1d(input, normalized_weight.detach(), None, self.stride,
self.padding, self.dilation, self.groups)
out_dir = F.conv1d(input.detach(), normalized_weight, None, self.stride,
self.padding, self.dilation, self.groups)
out = [out_fun * scale, out_dir * scale.detach()]
else:
out = F.conv1d(input, normalized_weight, None, self.stride,
self.padding, self.dilation, self.groups)
out = out * scale
return out
@torch.no_grad()
def normalize_weight(self):
self.weight.data = self._get_normalized_weight()
def _get_normalized_weight(self):
return _normalize(self.weight, dim=[1, 2])
class vnetConv2d(nn.Conv2d):
def __init__(self,
in_channels,
out_channels,
kernel_size,
stride=1,
padding=0,
dilation=1,
bias=True,
padding_mode='zeros',
device=None,
dtype=None
):
super(vnetConv2d, self).__init__(
in_channels, out_channels, kernel_size, stride, padding=padding, dilation=dilation,
groups=1, bias=bias, padding_mode=padding_mode, device=device, dtype=dtype)
scale = self.weight.norm(p=2.0, dim=[1, 2, 3], keepdim=True).clamp_min(1e-12)
self.weight = nn.parameter.Parameter(self.weight / scale.expand_as(self.weight))
self.scale = nn.parameter.Parameter(scale.view(out_channels))
if bias:
self.bias = nn.parameter.Parameter(torch.zeros(in_channels, device=device, dtype=dtype))
else:
self.register_parameter('bias', None)
def forward(self, input, flg_train=False):
if self.bias is not None:
input = input + self.bias.view(self.in_channels, 1, 1)
normalized_weight = self._get_normalized_weight()
scale = self.scale.view(self.out_channels, 1, 1)
if flg_train:
out_fun = F.conv2d(input, normalized_weight.detach(), None, self.stride,
self.padding, self.dilation, self.groups)
out_dir = F.conv2d(input.detach(), normalized_weight, None, self.stride,
self.padding, self.dilation, self.groups)
out = [out_fun * scale, out_dir * scale.detach()]
else:
out = F.conv2d(input, normalized_weight, None, self.stride,
self.padding, self.dilation, self.groups)
out = out * scale
return out
@torch.no_grad()
def normalize_weight(self):
self.weight.data = self._get_normalized_weight()
def _get_normalized_weight(self):
return _normalize(self.weight, dim=[1, 2, 3])
class vnetEmbedding(nn.Embedding):
def __init__(self, num_embeddings, embedding_dim,
scale_grad_by_freq=False,
sparse=False, _weight=None,
device=None, dtype=None):
super(vnetEmbedding, self).__init__(
num_embeddings, embedding_dim, padding_idx=None,
max_norm=None, norm_type=2., scale_grad_by_freq=scale_grad_by_freq,
sparse=sparse, _weight=_weight,
device=device, dtype=dtype)
scale = self.weight.norm(p=2.0, dim=1, keepdim=True).clamp_min(1e-12)
self.weight = nn.parameter.Parameter(self.weight / scale.expand_as(self.weight))
self.scale = nn.parameter.Parameter(scale)
def forward(self, input, flg_train=False):
out = F.embedding(
input, self.weight, self.padding_idx, self.max_norm,
self.norm_type, self.scale_grad_by_freq, self.sparse)
out = _normalize(out, dim=-1)
scale = F.embedding(
input, self.scale, self.padding_idx, self.max_norm,
self.norm_type, self.scale_grad_by_freq, self.sparse)
if flg_train:
out_fun = out.detach()
out_dir = out
out = [out_fun * scale, out_dir * scale.detach()]
else:
out = out * scale
return out
@torch.no_grad()
def normalize_weight(self):
self.weight.data = _normalize(self.weight, dim=1)