Add NLP task models

joint_paraphrase_model/libs/paraphrase_generation/__init__.py

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+from .main import *

joint_paraphrase_model/libs/paraphrase_generation/main.py

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+import json
+import math
+import os
+
+import random
+import time
+
+import matplotlib.pyplot as plt
+import matplotlib.ticker as ticker
+import numpy as np
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+import torch.optim as optim
+
+
+class EncoderRNN(nn.Module):
+    def __init__(self, input_size, hidden_size, embeddings):
+        super(EncoderRNN, self).__init__()
+        self.hidden_size = hidden_size
+
+        self.embedding = nn.Embedding.from_pretrained(embeddings)
+        self.gru = nn.GRU(input_size, hidden_size)
+
+    def forward(self, input, hidden):
+        embedded = self.embedding(input).view(1, 1, -1)
+        output = embedded
+        output, hidden = self.gru(output, hidden)
+        return output, hidden
+
+    def initHidden(self):
+        return torch.zeros(1, 1, self.hidden_size)
+
+class AttnDecoderRNN(nn.Module):
+    def __init__(
+        self,
+        input_size,
+        hidden_size,
+        output_size,
+        embeddings,
+        dropout_p,
+        max_length,
+    ):
+        super(AttnDecoderRNN, self).__init__()
+        self.input_size = input_size
+        self.hidden_size = hidden_size
+        self.output_size = output_size
+        self.dropout_p = dropout_p
+        self.max_length = max_length
+
+        self.embedding = nn.Embedding.from_pretrained(embeddings) #for paragen
+        #self.embedding = nn.Embedding(len(embeddings), 300) #for NMT with tamil, trying wiht senitment too
+        self.attn = nn.Linear(self.input_size + self.hidden_size, self.max_length)
+        self.attn_combine = nn.Linear(
+            self.input_size + self.hidden_size, self.hidden_size
+        )
+        self.dropout = nn.Dropout(self.dropout_p)
+        self.gru = nn.GRU(self.hidden_size, self.hidden_size)
+        self.out = nn.Linear(self.hidden_size, self.output_size)
+
+    def forward(self, input, hidden, encoder_outputs, fixations):
+        embedded = self.embedding(input).view(1, 1, -1)
+        embedded = self.dropout(embedded)
+
+        attn_weights = F.softmax(
+            self.attn(torch.cat((embedded[0], hidden[0]), 1)), dim=1
+        )
+
+        attn_weights = attn_weights * torch.nn.ConstantPad1d((0, attn_weights.shape[-1] - fixations.shape[-2]), 0)(fixations.squeeze().unsqueeze(0))
+
+        # attn_weights = torch.softmax(attn_weights * torch.nn.ConstantPad1d((0, attn_weights.shape[-1] - fixations.shape[-2]), 0)(fixations.squeeze().unsqueeze(0)), dim=1)
+        
+        attn_applied = torch.bmm(
+            attn_weights.unsqueeze(0), encoder_outputs.unsqueeze(0)
+        )
+
+        output = torch.cat((embedded[0], attn_applied[0]), 1)
+        output = self.attn_combine(output).unsqueeze(0)
+
+        output = F.relu(output)
+        output, hidden = self.gru(output, hidden)
+
+        # output = F.log_softmax(self.out(output[0]), dim=1)
+        output = self.out(output[0])
+        # output = F.log_softmax(output, dim=1)
+        return output, hidden, attn_weights