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#!/usr/bin/env python
# -*- coding: utf-8 -*-
# Rhizome
# Version beta 0.0, August 2023
# Property of IBM Research, Accelerated Discovery
#
"""
PLEASE NOTE THIS IMPLEMENTATION INCLUDES THE ORIGINAL SOURCE CODE (AND SOME ADAPTATIONS)
OF THE MHG IMPLEMENTATION OF HIROSHI KAJINO AT IBM TRL ALREADY PUBLICLY AVAILABLE.
THIS MIGHT INFLUENCE THE DECISION OF THE FINAL LICENSE SO CAREFUL CHECK NEEDS BE DONE.
"""
""" Title """
__author__ = "Hiroshi Kajino <[email protected]>"
__copyright__ = "(c) Copyright IBM Corp. 2018"
__version__ = "0.1"
__date__ = "Aug 9 2018"
import abc
import numpy as np
import torch
import torch.nn.functional as F
from torch import nn
from typing import List
class EncoderBase(nn.Module):
def __init__(self):
super().__init__()
@abc.abstractmethod
def forward(self, in_seq):
''' forward model
Parameters
----------
in_seq_emb : Variable, shape (batch_size, max_len, input_dim)
Returns
-------
hidden_seq_emb : Tensor, shape (batch_size, max_len, 1 + bidirectional, hidden_dim)
'''
pass
@abc.abstractmethod
def init_hidden(self):
''' initialize the hidden states
'''
pass
class GRUEncoder(EncoderBase):
def __init__(self, input_dim: int, hidden_dim: int, num_layers: int,
bidirectional: bool, dropout: float, batch_size: int, use_gpu: bool,
no_dropout=False):
super().__init__()
self.input_dim = input_dim
self.hidden_dim = hidden_dim
self.num_layers = num_layers
self.bidirectional = bidirectional
self.dropout = dropout
self.batch_size = batch_size
self.use_gpu = use_gpu
self.model = nn.GRU(input_size=self.input_dim,
hidden_size=self.hidden_dim,
num_layers=self.num_layers,
batch_first=True,
bidirectional=self.bidirectional,
dropout=self.dropout if not no_dropout else 0)
if self.use_gpu:
self.model.cuda()
self.init_hidden()
def init_hidden(self):
self.h0 = torch.zeros(((self.bidirectional + 1) * self.num_layers,
self.batch_size,
self.hidden_dim),
requires_grad=False)
if self.use_gpu:
self.h0 = self.h0.cuda()
def forward(self, in_seq_emb):
''' forward model
Parameters
----------
in_seq_emb : Tensor, shape (batch_size, max_len, input_dim)
Returns
-------
hidden_seq_emb : Tensor, shape (batch_size, max_len, 1 + bidirectional, hidden_dim)
'''
max_len = in_seq_emb.size(1)
hidden_seq_emb, self.h0 = self.model(
in_seq_emb, self.h0)
hidden_seq_emb = hidden_seq_emb.view(self.batch_size,
max_len,
1 + self.bidirectional,
self.hidden_dim)
return hidden_seq_emb
class LSTMEncoder(EncoderBase):
def __init__(self, input_dim: int, hidden_dim: int, num_layers: int,
bidirectional: bool, dropout: float, batch_size: int, use_gpu: bool,
no_dropout=False):
super().__init__()
self.input_dim = input_dim
self.hidden_dim = hidden_dim
self.num_layers = num_layers
self.bidirectional = bidirectional
self.dropout = dropout
self.batch_size = batch_size
self.use_gpu = use_gpu
self.model = nn.LSTM(input_size=self.input_dim,
hidden_size=self.hidden_dim,
num_layers=self.num_layers,
batch_first=True,
bidirectional=self.bidirectional,
dropout=self.dropout if not no_dropout else 0)
if self.use_gpu:
self.model.cuda()
self.init_hidden()
def init_hidden(self):
self.h0 = torch.zeros(((self.bidirectional + 1) * self.num_layers,
self.batch_size,
self.hidden_dim),
requires_grad=False)
self.c0 = torch.zeros(((self.bidirectional + 1) * self.num_layers,
self.batch_size,
self.hidden_dim),
requires_grad=False)
if self.use_gpu:
self.h0 = self.h0.cuda()
self.c0 = self.c0.cuda()
def forward(self, in_seq_emb):
''' forward model
Parameters
----------
in_seq_emb : Tensor, shape (batch_size, max_len, input_dim)
Returns
-------
hidden_seq_emb : Tensor, shape (batch_size, max_len, 1 + bidirectional, hidden_dim)
'''
max_len = in_seq_emb.size(1)
hidden_seq_emb, (self.h0, self.c0) = self.model(
in_seq_emb, (self.h0, self.c0))
hidden_seq_emb = hidden_seq_emb.view(self.batch_size,
max_len,
1 + self.bidirectional,
self.hidden_dim)
return hidden_seq_emb
class FullConnectedEncoder(EncoderBase):
def __init__(self, input_dim: int, hidden_dim: int, max_len: int, hidden_dim_list: List[int],
batch_size: int, use_gpu: bool):
super().__init__()
self.input_dim = input_dim
self.hidden_dim = hidden_dim
self.max_len = max_len
self.hidden_dim_list = hidden_dim_list
self.use_gpu = use_gpu
in_out_dim_list = [input_dim * max_len] + list(hidden_dim_list) + [hidden_dim]
self.linear_list = nn.ModuleList(
[nn.Linear(in_out_dim_list[each_idx], in_out_dim_list[each_idx + 1])\
for each_idx in range(len(in_out_dim_list) - 1)])
def forward(self, in_seq_emb):
''' forward model
Parameters
----------
in_seq_emb : Tensor, shape (batch_size, max_len, input_dim)
Returns
-------
hidden_seq_emb : Tensor, shape (batch_size, max_len, 1 + bidirectional, hidden_dim)
'''
batch_size = in_seq_emb.size(0)
x = in_seq_emb.view(batch_size, -1)
for each_linear in self.linear_list:
x = F.relu(each_linear(x))
return x.view(batch_size, 1, -1)
def init_hidden(self):
pass
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