"""
.. module:: utils
:synopsis: utils
.. moduleauthor:: Liyuan Liu
"""
import numpy as np
import torch
import json
import torch
import torch.nn as nn
import torch.nn.init
from torch.autograd import Variable
[docs]def repackage_hidden(h):
"""
Wraps hidden states in new Variables, to detach them from their history
Parameters
----------
h : ``Tuple`` or ``Tensors``, required.
Tuple or Tensors, hidden states.
Returns
-------
hidden: ``Tuple`` or ``Tensors``.
detached hidden states
"""
if type(h) == torch.Tensor:
return h.detach()
else:
return tuple(repackage_hidden(v) for v in h)
[docs]def to_scalar(var):
"""
convert a tensor to a scalar number
"""
return var.view(-1).item()
[docs]def init_embedding(input_embedding):
"""
random initialize embedding
"""
bias = np.sqrt(3.0 / input_embedding.size(1))
nn.init.uniform_(input_embedding, -bias, bias)
[docs]def init_linear(input_linear):
"""
random initialize linear projection.
"""
bias = np.sqrt(6.0 / (input_linear.weight.size(0) + input_linear.weight.size(1)))
nn.init.uniform_(input_linear.weight, -bias, bias)
if input_linear.bias is not None:
input_linear.bias.data.zero_()
[docs]def adjust_learning_rate(optimizer, lr):
"""
adjust learning to the the new value.
Parameters
----------
optimizer : required.
pytorch optimizer.
float : ``float``, required.
the target learning rate.
"""
for param_group in optimizer.param_groups:
param_group['lr'] = lr
[docs]def init_lstm(input_lstm):
"""
random initialize lstms
"""
for ind in range(0, input_lstm.num_layers):
weight = eval('input_lstm.weight_ih_l'+str(ind))
bias = np.sqrt(6.0 / (weight.size(0)/4 + weight.size(1)))
nn.init.uniform_(weight, -bias, bias)
weight = eval('input_lstm.weight_hh_l'+str(ind))
bias = np.sqrt(6.0 / (weight.size(0)/4 + weight.size(1)))
nn.init.uniform_(weight, -bias, bias)
if input_lstm.bias:
for ind in range(0, input_lstm.num_layers):
weight = eval('input_lstm.bias_ih_l'+str(ind))
weight.data.zero_()
weight.data[input_lstm.hidden_size: 2 * input_lstm.hidden_size] = 1
weight = eval('input_lstm.bias_hh_l'+str(ind))
weight.data.zero_()
weight.data[input_lstm.hidden_size: 2 * input_lstm.hidden_size] = 1