cleanup

onmt/__init__.py

@@ -1,4 +1,6 @@
 """ Main entry point of the ONMT library """
+from __future__ import division, print_function
+
 import onmt.inputters
 import onmt.encoders
 import onmt.decoders

onmt/decoders/cnn_decoder.py

@@ -1,9 +1,9 @@
 """
-Implementation of the CNN Decoder part of "Convolutional Sequence to Sequence Learning"
+Implementation of the CNN Decoder part of
+    "Convolutional Sequence to Sequence Learning"
 """
 import torch
 import torch.nn as nn
-from torch.autograd import Variable
 
 import onmt.modules
 from onmt.decoders.decoder import DecoderState
@@ -92,8 +92,8 @@ def forward(self, tgt, memory_bank, state, memory_lengths=None):
         x = linear_out.view(tgt_emb.size(0), tgt_emb.size(1), -1)
         x = shape_transform(x)
 
-        pad = Variable(torch.zeros(x.size(0), x.size(1),
-                                   self.cnn_kernel_width - 1, 1))
+        pad = torch.zeros(x.size(0), x.size(1),
+                          self.cnn_kernel_width - 1, 1)
         pad = pad.type_as(x)
         base_target_emb = x
 
@@ -152,5 +152,5 @@ def update_state(self, new_input):
 
     def repeat_beam_size_times(self, beam_size):
         """ Repeat beam_size times along batch dimension. """
-        self.init_src = Variable(
-            self.init_src.data.repeat(1, beam_size, 1), volatile=True)
+        self.init_src = torch.tensor(
+            self.init_src.data.repeat(1, beam_size, 1), requires_grad=False)

onmt/decoders/decoder.py

@@ -163,8 +163,8 @@ def _fix_enc_hidden(hidden):
             # The encoder hidden is  (layers*directions) x batch x dim.
             # We need to convert it to layers x batch x (directions*dim).
             if self.bidirectional_encoder:
-                hidden = torch.cat(
-                    [hidden[0:hidden.size(0):2], hidden[1:hidden.size(0):2]], 2)
+                hidden = torch.cat([hidden[0:hidden.size(0):2],
+                                    hidden[1:hidden.size(0):2]], 2)
             return hidden
 
         if isinstance(encoder_final, tuple):  # LSTM

onmt/decoders/transformer.py

@@ -4,7 +4,6 @@
 
 import torch
 import torch.nn as nn
-from torch.autograd import Variable
 import numpy as np
 
 import onmt
@@ -57,8 +56,8 @@ def forward(self, inputs, memory_bank, src_pad_mask, tgt_pad_mask,
 
         src_batch, _, s_len = src_pad_mask.size()
         tgt_batch, _, _ = tgt_pad_mask.size()
-        #src_batch, t_len, s_len = src_pad_mask.size()
-        #tgt_batch, t_len_, t_len__ = tgt_pad_mask.size()
+        # src_batch, t_len, s_len = src_pad_mask.size()
+        # tgt_batch, t_len_, t_len__ = tgt_pad_mask.size()
         aeq(input_batch, contxt_batch, src_batch, tgt_batch)
         # aeq(t_len, t_len_, t_len__, input_len)
         aeq(s_len, contxt_len)

onmt/encoders/__init__.py

@@ -4,3 +4,6 @@
 from onmt.encoders.rnn_encoder import RNNEncoder
 from onmt.encoders.cnn_encoder import CNNEncoder
 from onmt.encoders.mean_encoder import MeanEncoder
+
+__all__ = ["EncoderBase", "TransformerEncoder", "RNNEncoder", "CNNEncoder",
+           "MeanEncoder"]

onmt/encoders/cnn_encoder.py

@@ -30,7 +30,7 @@ def forward(self, input, lengths=None, hidden=None):
         self._check_args(input, lengths, hidden)
 
         emb = self.embeddings(input)
-        #s_len, batch, emb_dim = emb.size()
+        # s_len, batch, emb_dim = emb.size()
 
         emb = emb.transpose(0, 1).contiguous()
         emb_reshape = emb.view(emb.size(0) * emb.size(1), -1)

onmt/encoders/rnn_encoder.py

@@ -55,7 +55,7 @@ def forward(self, src, lengths=None):
         self._check_args(src, lengths)
 
         emb = self.embeddings(src)
-        #s_len, batch, emb_dim = emb.size()
+        # s_len, batch, emb_dim = emb.size()
 
         packed_emb = emb
         if lengths is not None and not self.no_pack_padded_seq:

onmt/inputters/__init__.py

@@ -7,7 +7,7 @@
     collect_features, get_num_features, \
     load_fields_from_vocab, get_fields, \
     save_fields_to_vocab, build_dataset, \
-    build_vocab, merge_vocabs, OrderedIterator 
+    build_vocab, merge_vocabs, OrderedIterator
 from onmt.inputters.dataset_base import DatasetBase, PAD_WORD, BOS_WORD, \
     EOS_WORD, UNK
 from onmt.inputters.text_dataset import TextDataset, ShardedTextCorpusIterator

onmt/inputters/audio_dataset.py

@@ -1,13 +1,14 @@
-# -*- coding: utf-8 -*-
-""" Dataset for data_type=='audio'"""
-
+"""
+    AudioDataset
+"""
 import codecs
 import os
 
 import torch
 import torchtext
 
-from onmt.inputters.dataset_base import DatasetBase, PAD_WORD, BOS_WORD, EOS_WORD
+from onmt.inputters.dataset_base import DatasetBase, PAD_WORD, BOS_WORD, \
+    EOS_WORD
 
 
 class AudioDataset(DatasetBase):
@@ -140,7 +141,6 @@ def read_audio_file(path, src_dir, side, sample_rate, window_size,
         assert (src_dir is not None) and os.path.exists(src_dir),\
             "src_dir must be a valid directory if data_type is audio"
 
-        #global torchaudio, librosa, np
         import torchaudio
         import librosa
         import numpy as np

onmt/inputters/dataset_base.py

@@ -1,11 +1,11 @@
-# -*- coding: utf-8 -*-
-"""Define inputters DatasetBase Class."""
-
-
+"""
+    Base dataset class and constants
+"""
 from itertools import chain
-import torch
 import torchtext
 
+import onmt
+
 PAD_WORD = '<blank>'
 UNK_WORD = '<unk>'
 UNK = 0

onmt/inputters/image_dataset.py

@@ -1,13 +1,14 @@
-# -*- coding: utf-8 -*-
-""" Dataset for data_type=='img'"""
-
+"""
+    ImageDataset
+"""
 import codecs
 import os
 
 import torch
 import torchtext
 
-from onmt.inputters.dataset_base import DatasetBase, PAD_WORD, BOS_WORD, EOS_WORD
+from onmt.inputters.dataset_base import DatasetBase, PAD_WORD, BOS_WORD, \
+    EOS_WORD
 
 
 class ImageDataset(DatasetBase):
@@ -95,13 +96,13 @@ def make_image_examples_nfeats_tpl(img_iter, img_path, img_dir):
             else:
                 raise ValueError("""One of 'img_iter' and 'img_path'
                                     must be not None""")
-        examples_iter = ImageDataset.make_examples(data_iter, img_dir, 'src')
+        examples_iter = ImageDataset.make_examples(img_iter, img_dir, 'src')
         num_feats = 0  # Source side(img) has no features.
 
         return (examples_iter, num_feats)
 
     @staticmethod
-    def make_examples(data_iter, src_dir, side, truncate=None):
+    def make_examples(img_iter, src_dir, side, truncate=None):
         """
         Args:
             path (str): location of a src file containing image paths
@@ -115,10 +116,6 @@ def make_examples(data_iter, src_dir, side, truncate=None):
         assert (src_dir is not None) and os.path.exists(src_dir),\
             'src_dir must be a valid directory if data_type is img'
 
-        #global Image, transforms
-        from PIL import Image
-        from torchvision import transforms
-
         for index, (img, filename) in enumerate(img_iter):
             if truncate and truncate != (0, 0):
                 if not (img.size(1) <= truncate[0]
@@ -141,6 +138,9 @@ def make_img_iterator_from_file(path, src_dir):
             img: and image tensor
             filename(str): the image filename
         """
+        from PIL import Image
+        from torchvision import transforms
+
         with codecs.open(path, "r", "utf-8") as corpus_file:
             for line in corpus_file:
                 filename = line.strip()

onmt/inputters/inputter.py

@@ -1,7 +1,6 @@
-# -*- coding: utf-8 -*-
-"""Define generic inputters."""
-
-from __future__ import print_function
+"""
+    Defining general functions for inputters
+"""
 import glob
 import os
 from collections import Counter, defaultdict, OrderedDict
@@ -177,8 +176,8 @@ def collect_feature_vocabs(fields, side):
     return feature_vocabs
 
 
-def build_dataset(fields, data_type, src_data_iter=None, src_path=None, src_dir=None,
-                  tgt_data_iter=None, tgt_path=None,
+def build_dataset(fields, data_type, src_data_iter=None, src_path=None,
+                  src_dir=None, tgt_data_iter=None, tgt_path=None,
                   src_seq_length=0, tgt_seq_length=0,
                   src_seq_length_trunc=0, tgt_seq_length_trunc=0,
                   dynamic_dict=True, sample_rate=0,
@@ -522,7 +521,7 @@ def _lazy_dataset_loader(pt_file, corpus_type):
 
 def _load_fields(dataset, data_type, opt, checkpoint):
     if checkpoint is not None:
-        print('Loading vocab from checkpoint at %s.' % OPT.train_from)
+        print('Loading vocab from checkpoint at %s.' % opt.train_from)
         fields = load_fields_from_vocab(
             checkpoint['vocab'], data_type)
     else:

onmt/inputters/text_dataset.py

@@ -12,6 +12,7 @@
 
 from onmt.inputters.dataset_base import (DatasetBase, UNK_WORD,
                                          PAD_WORD, BOS_WORD, EOS_WORD)
+from onmt.utils import aeq
 
 
 class TextDataset(DatasetBase):

onmt/model_builder.py

@@ -14,9 +14,13 @@
 from onmt.encoders.transformer import TransformerEncoder
 from onmt.encoders.cnn_encoder import CNNEncoder
 from onmt.encoders.mean_encoder import MeanEncoder
+from onmt.encoders.audio_encoder import AudioEncoder
+from onmt.encoders.image_encoder import ImageEncoder
+
 from onmt.decoders.decoder import InputFeedRNNDecoder, StdRNNDecoder
 from onmt.decoders.transformer import TransformerDecoder
 from onmt.decoders.cnn_decoder import CNNDecoder
+
 from onmt.modules import Embeddings, CopyGenerator
 from onmt.utils.misc import use_gpu