Delete model.py

model.py

@@ -1,81 +0,0 @@
-import torch
-import torch.nn as nn
-from torch import Tensor
-from torch.nn import Transformer
-
-# helper Module that adds positional encoding to the token embedding to introduce a notion of word order.
-class PositionalEncoding(nn.Module):
-    def __init__(self,
-                 emb_size: int,
-                 dropout: float,
-                 maxlen: int = 5000):
-        super(PositionalEncoding, self).__init__()
-        den = torch.exp(- torch.arange(0, emb_size, 2)* torch.log(10000) / emb_size)
-        pos = torch.arange(0, maxlen).reshape(maxlen, 1)
-        pos_embedding = torch.zeros((maxlen, emb_size))
-        pos_embedding[:, 0::2] = torch.sin(pos * den)
-        pos_embedding[:, 1::2] = torch.cos(pos * den)
-        pos_embedding = pos_embedding.unsqueeze(-2)
-
-        self.dropout = nn.Dropout(dropout)
-        self.register_buffer('pos_embedding', pos_embedding)
-
-    def forward(self, token_embedding: Tensor):
-        return self.dropout(token_embedding + self.pos_embedding[:token_embedding.size(0), :])
-
-# helper Module to convert tensor of input indices into corresponding tensor of token embeddings
-class TokenEmbedding(nn.Module):
-    def __init__(self, vocab_size: int, emb_size):
-        super(TokenEmbedding, self).__init__()
-        self.embedding = nn.Embedding(vocab_size, emb_size)
-        self.emb_size = emb_size
-
-    def forward(self, tokens: Tensor):
-        return self.embedding(tokens.long()) * math.sqrt(self.emb_size)
-
-class Seq2SeqTransformer(nn.Module):
-    def __init__(self,
-                 num_encoder_layers: int,
-                 num_decoder_layers: int,
-                 emb_size: int,
-                 nhead: int,
-                 src_vocab_size: int,
-                 tgt_vocab_size: int,
-                 dim_feedforward: int = 512,
-                 dropout: float = 0.1):
-        super(Seq2SeqTransformer, self).__init__()
-        self.transformer = Transformer(d_model=emb_size,
-                                       nhead=nhead,
-                                       num_encoder_layers=num_encoder_layers,
-                                       num_decoder_layers=num_decoder_layers,
-                                       dim_feedforward=dim_feedforward,
-                                       dropout=dropout,
-                                       batch_first=True)
-        self.generator = nn.Linear(emb_size, tgt_vocab_size)
-        self.src_tok_emb = TokenEmbedding(src_vocab_size, emb_size)
-        self.tgt_tok_emb = TokenEmbedding(tgt_vocab_size, emb_size)
-        self.positional_encoding = PositionalEncoding(
-            emb_size, dropout=dropout)
-
-    def forward(self,
-                src: Tensor,
-                trg: Tensor,
-                src_mask: Tensor,
-                tgt_mask: Tensor,
-                src_padding_mask: Tensor,
-                tgt_padding_mask: Tensor,
-                memory_key_padding_mask: Tensor):
-        src_emb = self.positional_encoding(self.src_tok_emb(src))
-        tgt_emb = self.positional_encoding(self.tgt_tok_emb(trg))
-        outs = self.transformer(src_emb, tgt_emb, src_mask, tgt_mask, None,
-                                src_padding_mask, tgt_padding_mask, memory_key_padding_mask)
-        return self.generator(outs)
-
-    def encode(self, src: Tensor, src_mask: Tensor):
-        return self.transformer.encoder(self.positional_encoding(
-                            self.src_tok_emb(src)), src_mask)
-
-    def decode(self, tgt: Tensor, memory: Tensor, tgt_mask: Tensor):
-        return self.transformer.decoder(self.positional_encoding(
-                          self.tgt_tok_emb(tgt)), memory,
-                          tgt_mask)