99 lines
3.6 KiB
Python
99 lines
3.6 KiB
Python
#!/usr/bin/env python3
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# -*- coding: utf-8 -*-
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"""
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CS224N 2018-19: Homework 5
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model_embeddings.py: Embeddings for the NMT model
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Pencheng Yin <pcyin@cs.cmu.edu>
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Sahil Chopra <schopra8@stanford.edu>
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Anand Dhoot <anandd@stanford.edu>
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Michael Hahn <mhahn2@stanford.edu>
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"""
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import torch.nn as nn
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# Do not change these imports; your module names should be
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# `CNN` in the file `cnn.py`
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# `Highway` in the file `highway.py`
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# Uncomment the following two imports once you're ready to run part 1(j)
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from cnn import CNN
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from highway import Highway
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# End "do not change"
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class ModelEmbeddings(nn.Module):
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"""
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Class that converts input words to their CNN-based embeddings.
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"""
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def __init__(self, embed_size, vocab):
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"""
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Init the Embedding layer for one language
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@param embed_size (int): Embedding size (dimensionality) for the output
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@param vocab (VocabEntry): VocabEntry object. See vocab.py for documentation.
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"""
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super(ModelEmbeddings, self).__init__()
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## A4 code
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# pad_token_idx = vocab.src['<pad>']
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# self.embeddings = nn.Embedding(len(vocab.src), embed_size, padding_idx=pad_token_idx)
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## End A4 code
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### YOUR CODE HERE for part 1j
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pad_token_idx = vocab.char2id['<pad>']
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e_char = 50
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self.embeddings = nn.Embedding(len(vocab.char2id),e_char,padding_idx=pad_token_idx)
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self.embed_size = embed_size
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self.cnn = CNN(in_ch=e_char, out_ch=embed_size,k=5)
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self.highway = Highway(D_in=embed_size, H=embed_size, D_out=embed_size,prob=0.3)
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self.dropout=nn.Dropout(0.3)
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### END YOUR CODE
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def forward(self, input):
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"""
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Looks up character-based CNN embeddings for the words in a batch of sentences.
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@param input: Tensor of integers of shape (sentence_length, batch_size, max_word_length) where
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each integer is an index into the character vocabulary
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@param output: Tensor of shape (sentence_length, batch_size, embed_size), containing the
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CNN-based embeddings for each word of the sentences in the batch
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"""
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## A4 code
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# output = self.embeddings(input)
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# return output
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## End A4 code
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### YOUR CODE HERE for part 1j
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#Map input (x_padded) to output (x_word_embed)
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#print('shape of input is',input.size())
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#print('*** input is',input)
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x_embed = self.embeddings(input)
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#x_embed = F.embedding(input,self.embeddings)
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#print('shape of x_embed is,',x_embed.size())
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#x_reshaped = x_embed.permute(1,0,3,2) #batch_size,sentence_length,max_word_len,chat_embed_size
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#print('shape of x_reshape is,',x_reshaped.size())
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x_reshaped_list = list(x_embed.size())
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#print(x_reshaped_list)
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x_reshaped_red = x_embed.reshape(-1,x_reshaped_list[3],x_reshaped_list[2])
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#print('shape of x_reshape_red is,',x_reshaped_red.size())
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x_cov_out = self.cnn(x_reshaped_red)
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#print('shape of x_cov_out is,',x_cov_out.size())
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#x_cov_out = x_cov_out.squeeze(2)
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#print('shape of x_cov_out is,',x_cov_out.size())
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x_cov_out = x_cov_out.reshape(x_reshaped_list[0],x_reshaped_list[1],-1)
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#print('to highway: shape of x_cov_out is,',x_cov_out.size())
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x_word_embed = self.highway(x_cov_out)
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#print('in:shape of x_word_embed is,',x_word_embed.size())
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x_word_embed = self.dropout(x_word_embed)
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#x_word_embed = x_word_embed.reshape(x_reshaped_list[1],x_reshaped_list[0],-1)
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#print('out:size of x_word_embed',x_word_embed.size())
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return x_word_embed
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### END YOUR CODE
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