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translate_mine.py

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+# Copyright (c) 2019-present, Facebook, Inc.
+# All rights reserved.
+#
+# This source code is licensed under the license found in the
+# LICENSE file in the root directory of this source tree.
+#
+# Translate sentences from the input stream.
+# The model will be faster is sentences are sorted by length.
+# Input sentences must have the same tokenization and BPE codes than the ones used in the model.
+#
+# Usage:
+#     cat source_sentences.bpe | \
+#     python translate.py --exp_name translate \
+#     --src_lang en --tgt_lang fr \
+#     --model_path trained_model.pth --output_path output
+#
+
+import os
+import io
+import sys
+import argparse
+import torch
+
+from src.utils import AttrDict
+from src.utils import bool_flag, initialize_exp
+from src.data.dictionary import Dictionary
+from src.model.transformer import TransformerModel
+
+
+def get_parser():
+    """
+    Generate a parameters parser.
+    """
+    # parse parameters
+    parser = argparse.ArgumentParser(description="Translate sentences")
+
+    # main parameters
+    parser.add_argument("--dump_path", type=str, default="./dumped/", help="Experiment dump path")
+    parser.add_argument("--exp_name", type=str, default="", help="Experiment name")
+    parser.add_argument("--exp_id", type=str, default="", help="Experiment ID")
+    parser.add_argument("--batch_size", type=int, default=32, help="Number of sentences per batch")
+
+    # model / output paths
+    parser.add_argument("--model_path", type=str, default="", help="Model path")
+    parser.add_argument("--output_path", type=str, default="", help="Output path")
+
+    # parser.add_argument("--max_vocab", type=int, default=-1, help="Maximum vocabulary size (-1 to disable)")
+    # parser.add_argument("--min_count", type=int, default=0, help="Minimum vocabulary count")
+
+    # source language / target language
+    parser.add_argument("--src_lang", type=str, default="", help="Source language")
+    parser.add_argument("--tgt_lang", type=str, default="", help="Target language")
+
+    return parser
+
+
+def main(params):
+    params.device = torch.device('cuda')
+    params.eval_only = True
+    params.log_file_prefix = False
+
+    # initialize the experiment
+    logger = initialize_exp(params)
+
+    # generate parser / parse parameters
+    parser = get_parser()
+    params = parser.parse_args()
+    reloaded = torch.load(params.model_path)
+    model_params = AttrDict(reloaded['params'])
+    logger.info("Supported languages: %s" % ", ".join(model_params.lang2id.keys()))
+
+    # update dictionary parameters
+    for name in ['n_words', 'bos_index', 'eos_index', 'pad_index', 'unk_index', 'mask_index']:
+        setattr(params, name, getattr(model_params, name))
+
+    # build dictionary / build encoder / build decoder / reload weights
+    dico = Dictionary(reloaded['dico_id2word'], reloaded['dico_word2id'], reloaded['dico_counts'])
+    encoder = TransformerModel(model_params, dico, is_encoder=True, with_output=True).cuda().eval()
+    decoder = TransformerModel(model_params, dico, is_encoder=False, with_output=True).cuda().eval()
+    encoder.load_state_dict(reloaded['encoder'])
+    decoder.load_state_dict(reloaded['decoder'])
+    params.src_id = model_params.lang2id[params.src_lang]
+    params.tgt_id = model_params.lang2id[params.tgt_lang]
+
+    # read sentences from stdin
+    src_sent = []
+    for line in sys.stdin.readlines():
+        assert len(line.strip().split()) > 0
+        src_sent.append(line)
+    logger.info("Read %i sentences from stdin. Translating ..." % len(src_sent))
+
+    f = io.open(params.output_path, 'w', encoding='utf-8')
+
+    for i in range(0, len(src_sent), params.batch_size):
+
+        # prepare batch
+        word_ids = [torch.LongTensor([dico.index(w) for w in s.strip().split()])
+                    for s in src_sent[i:i + params.batch_size]]
+        lengths = torch.LongTensor([len(s) + 2 for s in word_ids])
+        batch = torch.LongTensor(lengths.max().item(), lengths.size(0)).fill_(params.pad_index)
+        batch[0] = params.eos_index
+        for j, s in enumerate(word_ids):
+            if lengths[j] > 2:  # if sentence not empty
+                batch[1:lengths[j] - 1, j].copy_(s)
+            batch[lengths[j] - 1, j] = params.eos_index
+        langs = batch.clone().fill_(params.src_id)
+
+        # encode source batch and translate it
+        encoded = encoder('fwd', x=batch.cuda(), lengths=lengths.cuda(), langs=langs.cuda(), causal=False)
+        encoded = encoded.transpose(0, 1)
+        decoded, dec_lengths = decoder.generate(encoded, lengths.cuda(), params.tgt_id, max_len=int(1.5 * lengths.max().item() + 10))
+
+        # convert sentences to words
+        for j in range(decoded.size(1)):
+
+            # remove delimiters
+            sent = decoded[:, j]
+            delimiters = (sent == params.eos_index).nonzero().view(-1)
+            assert len(delimiters) >= 1 and delimiters[0].item() == 0
+            sent = sent[1:] if len(delimiters) == 1 else sent[1:delimiters[1]]
+
+            # output translation
+            source = src_sent[i + j].strip()
+            target = " ".join([dico[sent[k].item()] for k in range(len(sent))])
+            sys.stderr.write("%i / %i: %s -> %s\n" % (i + j, len(src_sent), source, target))
+            f.write(target + "\n")
+
+    f.close()
+
+
+if __name__ == '__main__':
+
+    # generate parser / parse parameters
+    parser = get_parser()
+    params = parser.parse_args()
+
+    # check parameters
+    assert os.path.isfile(params.model_path)
+    assert params.src_lang != '' and params.tgt_lang != '' and params.src_lang != params.tgt_lang
+    assert params.output_path and not os.path.isfile(params.output_path)
+
+    # translate
+    with torch.no_grad():
+        main(params)