# MIT License
#
# Copyright (c) 2021 Soohwan Kim and Sangchun Ha and Soyoung Cho
#
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# of this software and associated documentation files (the "Software"), to deal
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import torch
import torch.nn as nn
from typing import Union
[docs]class EnsembleSearch(nn.Module):
"""
Class for ensemble search.
Args:
models (tuple): list of ensemble model
Inputs:
- **inputs** (torch.FloatTensor): A input sequence passed to encoders. Typically for inputs this will be
a padded `FloatTensor` of size ``(batch, seq_length, dimension)``.
- **input_lengths** (torch.LongTensor): The length of input tensor. ``(batch)``
Returns:
* predictions (torch.LongTensor): prediction of ensemble models
"""
def __init__(self, models: Union[list, tuple]):
super(EnsembleSearch, self).__init__()
assert len(models) > 1, "Ensemble search should be multiple models."
self.models = models
def forward(self, inputs: torch.FloatTensor, input_lengths: torch.LongTensor):
logits = list()
for model in self.models:
output = model(inputs, input_lengths)
logits.append(output["logits"])
output = logits[0]
for logit in logits[1:]:
output += logit
return output.max(-1)[1]
[docs]class WeightedEnsembleSearch(nn.Module):
"""
Args:
models (tuple): list of ensemble model
weights (tuple: list of ensemble's weight
Inputs:
- **inputs** (torch.FloatTensor): A input sequence passed to encoders. Typically for inputs this will be
a padded `FloatTensor` of size ``(batch, seq_length, dimension)``.
- **input_lengths** (torch.LongTensor): The length of input tensor. ``(batch)``
Returns:
* predictions (torch.LongTensor): prediction of ensemble models
"""
def __init__(self, models: Union[list, tuple], weights: Union[list, tuple]):
super(WeightedEnsembleSearch, self).__init__()
assert len(models) > 1, "Ensemble search should be multiple models."
assert len(models) == len(weights), "len(models), len(weight) should be same."
self.models = models
self.weights = weights
def forward(self, inputs: torch.FloatTensor, input_lengths: torch.LongTensor):
logits = list()
for model in self.models:
output = model(inputs, input_lengths)
logits.append(output["logits"])
output = logits[0] * self.weights[0]
for idx, logit in enumerate(logits[1:]):
output += logit * self.weights[1]
return output.max(-1)[1]