├── data └── NOTE ├── charles.py ├── sylvia.py ├── config ├── sylvia.json └── charles.json ├── poemutils.py ├── environment.yml ├── README.md ├── verse_generator.py ├── model_builder_custom.py ├── poembase.py ├── prior_sampling.py └── LICENSE /data/NOTE: -------------------------------------------------------------------------------- 1 | Model files are not included due to their large file size. In order to 2 | obtain a copy of the models, send a mail to tim.vandecruys@irit.fr 3 | (don't forget to specify the model you're interested in). 4 | -------------------------------------------------------------------------------- /charles.py: -------------------------------------------------------------------------------- 1 | #!/usr/bin/env python 2 | 3 | from poembase import PoemBase 4 | 5 | class Poem(PoemBase): 6 | 7 | def __init__(self, form='short', config='config/charles.json'): 8 | super().__init__(form=form, config=config) 9 | 10 | -------------------------------------------------------------------------------- /sylvia.py: -------------------------------------------------------------------------------- 1 | #!/usr/bin/env python 2 | 3 | from poembase import PoemBase 4 | 5 | class Poem(PoemBase): 6 | 7 | def __init__(self, form='short', config='config/sylvia.json'): 8 | super().__init__(form=form, config=config) 9 | 10 | -------------------------------------------------------------------------------- /config/sylvia.json: -------------------------------------------------------------------------------- 1 | { 2 | "general": { 3 | "data_directory": "data", 4 | "language": "en", 5 | "name": "Sylvia" 6 | }, 7 | "nmf": { 8 | "matrix_file": "p_nmf_en_d100.npy", 9 | "description_file": "nmf_en_descriptions_d100.pickle" 10 | }, 11 | "rhyme" : { 12 | "freq_file": "rijmklanken_en.freq", 13 | "rhyme_dict_file": "rhymeDictionary_en.pickle", 14 | "rhyme_inv_dict_file": "rhymeDictionary_inv_en.pickle" 15 | }, 16 | "model" : { 17 | "parameter_file" : "combined2_l20_step_505000.pt", 18 | "ngram_file" : "all_3gram_pruned.binary" 19 | }, 20 | "poem" : { 21 | "length": 12, 22 | "entropy_threshold": 2.7 23 | } 24 | } 25 | -------------------------------------------------------------------------------- /config/charles.json: -------------------------------------------------------------------------------- 1 | { 2 | "general": { 3 | "data_directory": "data", 4 | "language": "fr", 5 | "name": "Charles" 6 | }, 7 | "nmf": { 8 | "matrix_file": "p_wd2_d100.npy", 9 | "description_file": "description_nmf_d100_it100.pickle" 10 | }, 11 | "rhyme" : { 12 | "freq_file": "rijm_fr_small.freq", 13 | "rhyme_dict_file": "rhymeDictionary_fr.pickle", 14 | "rhyme_inv_dict_file": "rhymeInv_fr_utf8.pickle" 15 | }, 16 | "model" : { 17 | "parameter_file" : "fr_full_rev-model_e512_d2048_general_acc_0.00_ppl_28.61_e25.pt", 18 | "ngram_file" : "corpus_pruned_3gram.binary" 19 | }, 20 | "poem" : { 21 | "length": 12, 22 | "entropy_threshold": 4.0 23 | } 24 | } 25 | -------------------------------------------------------------------------------- /poemutils.py: -------------------------------------------------------------------------------- 1 | # Count syllables in a word. 2 | # 3 | # Doesn't use any fancy knowledge, just a few super simple rules: 4 | # a vowel starts each syllable; 5 | # a doubled vowel doesn't add an extra syllable; 6 | # two or more different vowels together are a diphthong, 7 | # and probably don't start a new syllable but might; 8 | # y is considered a vowel when it follows a consonant. 9 | # 10 | # Even with these simple rules, it gets results far better 11 | # than python-hyphenate with the libreoffice hyphenation dictionary. 12 | # 13 | # Copyright 2013 by Akkana Peck http://shallowsky.com. 14 | # Share and enjoy under the terms of the GPLv2 or later. 15 | 16 | verbose = False 17 | 18 | def count_syllables(word): 19 | vowels = ['a', 'e', 'i', 'o', 'u'] 20 | 21 | on_vowel = False 22 | in_diphthong = False 23 | minsyl = 0 24 | maxsyl = 0 25 | lastchar = None 26 | 27 | word = word.lower() 28 | for c in word: 29 | is_vowel = c in vowels 30 | 31 | if on_vowel == None: 32 | on_vowel = is_vowel 33 | 34 | # y is a special case 35 | if c == 'y': 36 | is_vowel = not on_vowel 37 | 38 | if is_vowel: 39 | if verbose: print(c, "is a vowel") 40 | if not on_vowel: 41 | # We weren't on a vowel before. 42 | # Seeing a new vowel bumps the syllable count. 43 | if verbose: print("new syllable") 44 | minsyl += 1 45 | maxsyl += 1 46 | elif on_vowel and not in_diphthong and c != lastchar: 47 | # We were already in a vowel. 48 | # Don't increment anything except the max count, 49 | # and only do that once per diphthong. 50 | if verbose: print(c, "is a diphthong") 51 | in_diphthong = True 52 | maxsyl += 1 53 | elif verbose: print("[consonant]") 54 | 55 | on_vowel = is_vowel 56 | lastchar = c 57 | 58 | # Some special cases: 59 | if word[-1] == 'e': 60 | minsyl -= 1 61 | # if it ended with a consonant followed by y, count that as a syllable. 62 | if word[-1] == 'y' and not on_vowel: 63 | maxsyl += 1 64 | 65 | return minsyl, maxsyl 66 | 67 | def hmean(num): 68 | return len(num)/sum([1 / (n + 1e-20) for n in num]) 69 | 70 | -------------------------------------------------------------------------------- /environment.yml: -------------------------------------------------------------------------------- 1 | name: pytorch14 2 | channels: 3 | - pytorch 4 | - conda-forge 5 | - defaults 6 | dependencies: 7 | - _libgcc_mutex=0.1=conda_forge 8 | - _openmp_mutex=4.5=0_gnu 9 | - backcall=0.1.0=py_0 10 | - ca-certificates=2019.11.28=hecc5488_0 11 | - certifi=2019.11.28=py38_0 12 | - configargparse=1.0=py_0 13 | - cudatoolkit=10.1.243=h6bb024c_0 14 | - cython=0.29.15=py38he1b5a44_0 15 | - decorator=4.4.1=py_0 16 | - dill=0.3.1.1=py38_0 17 | - freetype=2.10.0=he983fc9_1 18 | - intel-openmp=2020.0=166 19 | - ipython=7.12.0=py38h5ca1d4c_0 20 | - ipython_genutils=0.2.0=py_1 21 | - jedi=0.16.0=py38_0 22 | - jpeg=9c=h14c3975_1001 23 | - ld_impl_linux-64=2.33.1=h53a641e_8 24 | - libblas=3.8.0=14_openblas 25 | - libcblas=3.8.0=14_openblas 26 | - libffi=3.2.1=he1b5a44_1006 27 | - libgcc-ng=9.2.0=h24d8f2e_2 28 | - libgfortran-ng=7.3.0=hdf63c60_5 29 | - libgomp=9.2.0=h24d8f2e_2 30 | - liblapack=3.8.0=14_openblas 31 | - libopenblas=0.3.7=h5ec1e0e_6 32 | - libpng=1.6.37=hed695b0_0 33 | - libstdcxx-ng=9.2.0=hdf63c60_2 34 | - libtiff=4.1.0=hc3755c2_3 35 | - lz4-c=1.8.3=he1b5a44_1001 36 | - mkl=2020.0=166 37 | - ncurses=6.1=hf484d3e_1002 38 | - ninja=1.10.0=hc9558a2_0 39 | - nltk=3.4.4=py_0 40 | - numpy=1.18.1=py38h95a1406_0 41 | - olefile=0.46=py_0 42 | - openssl=1.1.1d=h516909a_0 43 | - parso=0.6.1=py_0 44 | - pexpect=4.8.0=py38_0 45 | - pickleshare=0.7.5=py38_1000 46 | - pillow=7.0.0=py38hefe7db6_0 47 | - pip=20.0.2=py_2 48 | - prompt_toolkit=3.0.3=py_0 49 | - ptyprocess=0.6.0=py_1001 50 | - pygments=2.5.2=py_0 51 | - python=3.8.1=h357f687_2 52 | - pytorch=1.4.0=py3.8_cuda10.1.243_cudnn7.6.3_0 53 | - pyyaml=5.3=py38h516909a_0 54 | - readline=8.0=hf8c457e_0 55 | - scipy=1.4.1=py38h921218d_0 56 | - setuptools=45.2.0=py38_0 57 | - six=1.14.0=py38_0 58 | - sqlite=3.30.1=hcee41ef_0 59 | - tk=8.6.10=hed695b0_0 60 | - torchvision=0.5.0=py38_cu101 61 | - traitlets=4.3.3=py38_0 62 | - wcwidth=0.1.8=py_0 63 | - xz=5.2.4=h14c3975_1001 64 | - yaml=0.2.2=h516909a_1 65 | - zlib=1.2.11=h516909a_1006 66 | - zstd=1.4.4=h3b9ef0a_1 67 | - pip: 68 | - absl-py==0.9.0 69 | - cachetools==4.0.0 70 | - chardet==3.0.4 71 | - click==7.1.1 72 | - flask==1.1.1 73 | - future==0.18.2 74 | - google-auth==1.11.3 75 | - google-auth-oauthlib==0.4.1 76 | - grpcio==1.28.0rc3 77 | - idna==2.8 78 | - itsdangerous==1.1.0 79 | - jinja2==3.0.0a1 80 | - kenlm==0.0.0 81 | - markdown==3.2.1 82 | - markupsafe==1.1.1 83 | - oauthlib==3.1.0 84 | - opennmt-py==1.1.1 85 | - protobuf==3.11.3 86 | - pyasn1==0.4.8 87 | - pyasn1-modules==0.2.8 88 | - pyonmttok==1.18.3 89 | - requests==2.22.0 90 | - requests-oauthlib==1.3.0 91 | - rsa==4.0 92 | - sentencepiece==0.1.85 93 | - tensorboard==2.1.1 94 | - torchtext==0.4.0 95 | - tqdm==4.30.0 96 | - urllib3==1.25.8 97 | - waitress==1.4.3 98 | - werkzeug==1.0.0 99 | - wheel==0.34.2 100 | prefix: /home/cruys/anaconda3/envs/pytorch14 101 | 102 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # Automatic Poetry Generation from Prosaic Text 2 | 3 | v2.0 4 | 5 | Developed at KU Leuven, Belgium. 6 | 7 | tim.vandecruys@kuleuven.be 8 | 9 | www.timvandecruys.be 10 | 11 | 12 | ## Introduction 13 | 14 | Charles/Sylvia is a system for automatic poetry generation, developed 15 | at the Faculty of Arts, KU Leuven, Belgium. The system has been trained 16 | on billions of words extracted from generic web texts; its rhyming 17 | knowledge has been extracted from Wiktionary, and it automatically 18 | learned an elementary notion of sense by looking at the context of words. 19 | 20 | Sylvia writes in English, while Charles is French. 21 | 22 | A previous version of this system was developed at IRIT, Toulouse. 23 | 24 | ## Examples 25 | 26 | ~~~ 27 | 2020-07-05 23:42:52 nmfdim 1 (tendresse, joie, bonheur) 28 | 29 | je sentais les larmes sur son visage 30 | je le ressens au plus profond de mon cœur 31 | merci de ta tendresse , pour ce partage 32 | je t' aime d' amour , c' est un vrai bonheur 33 | 34 | la douceur de tes mots me rend malade 35 | tu es mon coeur , j' aime le silence 36 | tu es ma joie dans mes nuits froides 37 | tu me rappelle des souvenirs d' enfance 38 | 39 | - Charles 40 | ~~~ 41 | 42 | ~~~ 43 | 2020-07-05 23:44:53 nmfdim 13 (sorrow, longing, admiration) 44 | 45 | it seemed as though he 'd never had a heart attack 46 | after a moment , a sudden silence filled the room 47 | oh , dear , the man said , his voice almost black 48 | i smiled , admiring the sight of my hands in the bathroom 49 | 50 | for a moment , i felt a sense of great pride 51 | taking a deep breath , i roused myself to my feet 52 | i closed my eyes , turning my gaze to the far side 53 | i was restless , eager to see something to eat 54 | 55 | - Sylvia 56 | 57 | ~~~ 58 | 59 | ## Installation and execution 60 | 61 | 1) Clone the git repository: 62 | 63 | 64 | `git clone https://github.com/timvdc/poetry.git` 65 | 66 | 2) Create an anaconda (python3) environment with all the necessary 67 | dependencies; an environment description is included in the 68 | archive. The environment can be installed with the command: 69 | 70 | `conda env create -f environment.yml` 71 | 72 | 3) Put the required model files (not included) in directory `data` 73 | 74 | 4) Once installed and model files in place, activate the environment, 75 | and run python. A poem can then be written using the following 76 | commands (for French): 77 | 78 | ~~~ 79 | import charles 80 | p = charles.Poem() 81 | p.write() 82 | p.write(nmfDim=1) 83 | ~~~~ 84 | 85 | For English, replace `charles` with `sylvia`. 86 | 87 | NOTE: Currently hard-coded for execution on the GPU. 88 | 89 | ## Model files 90 | 91 | Model files (neural network parameters, rhyme dictionary, NMF model, 92 | n-gram model) are not included due to their large file size (2.6GB for 93 | French, 3.4GB for English). In order to obtain a copy, send a mail to 94 | tim.vandecruys@irit.fr 95 | 96 | ## Dependencies 97 | 98 | Pytorch is the most important one; all dependencies are stipulated in 99 | the file `environment.yml`, which can be used to create a suitable 100 | Anaconda environment. Note that the poetry generation system heavily 101 | relies on the Pytorch version of OpenNMT 102 | (https://github.com/OpenNMT/OpenNMT-py), which equally needs to be 103 | installed. 104 | 105 | ## Reference 106 | 107 | Tim Van de Cruys. 2020. [Automatic Poetry Generation from Prosaic 108 | Text](https://www.aclweb.org/anthology/2020.acl-main.223.pdf). In 109 | *Proceedings of the 58th Annual Meeting of the Association for 110 | Computational Linguistics (ACL)*, pp. 2471-2480. 111 | 112 | ~~~ 113 | @inproceedings{vandecruys2020automatic, 114 | title = "Automatic Poetry Generation from Prosaic Text", 115 | author = "Van de Cruys, Tim", 116 | booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics", 117 | year = "2020", 118 | publisher = "Association for Computational Linguistics", 119 | pages = "2471--2480", 120 | } 121 | ~~~ 122 | -------------------------------------------------------------------------------- /verse_generator.py: -------------------------------------------------------------------------------- 1 | #!/usr/bin/env python 2 | # -*- coding: utf-8 -*- 3 | 4 | from __future__ import unicode_literals 5 | from argparse import Namespace 6 | 7 | import onmt 8 | from prior_sampling import PriorSampling 9 | 10 | import onmt.opts as opts 11 | from onmt.utils.parse import ArgumentParser 12 | 13 | import pickle 14 | import torchtext 15 | import torch 16 | import codecs 17 | import random 18 | import numpy as np 19 | 20 | from model_builder_custom import load_test_model_with_projection_layer 21 | 22 | class VerseGenerator: 23 | def __init__(self, modelFile, entropy_threshold): 24 | 25 | 26 | opt = Namespace(models=[modelFile], data_type='text', 27 | fp32=False, batch_size=1) 28 | 29 | self.fields, self.model, self.model_opt = \ 30 | load_test_model_with_projection_layer(opt) 31 | 32 | self.vocab = self.fields["tgt"].base_field.vocab 33 | 34 | self.batch_size_encoder = opt.batch_size 35 | self.n_batches_decoder = 32 36 | self.batch_size_decoder = 32 37 | self.max_length = 30 38 | self.sampling_temp = 0.8 39 | self.entropy_threshold = entropy_threshold 40 | 41 | self.device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu") 42 | 43 | def generateCandidates(self, previous, rhymePrior, nmfPrior): 44 | if rhymePrior is not None: 45 | rhymePrior = torch.from_numpy(rhymePrior).float().to(self.device) 46 | 47 | if nmfPrior is not None: 48 | nmfPrior = torch.from_numpy(nmfPrior).float().to(self.device) 49 | 50 | 51 | if previous is None: 52 | # when no previous verse is defined (first verse of 53 | # the poem), encode the phrase "unk unk unk" - this 54 | # works better for initialization of the decoder than 55 | # an all-zero or random hidden encoder state 56 | src = torch.tensor([0, 0, 0]) 57 | else: 58 | src = torch.tensor([self.vocab.stoi[w] for w in previous]) 59 | 60 | src = src.view(-1,1,1).to(self.device) 61 | src_lengths = torch.tensor([src.size(0)]).to(self.device) 62 | 63 | #run encoder 64 | enc_states, memory_bank, src_lengths = self.model.encoder(src, src_lengths) 65 | 66 | results = { 67 | "predictions": [], 68 | "scores": [], 69 | } 70 | 71 | #variables to restart with each batch 72 | src_init = src 73 | src_lengths_init = src_lengths 74 | memory_bank_init = memory_bank 75 | enc_states_init = enc_states 76 | 77 | for n_batch in range(self.n_batches_decoder): 78 | #initialize decoder with encoder states 79 | self.model.decoder.init_state(src_init, memory_bank_init, enc_states_init) 80 | 81 | 82 | decode_strategy = PriorSampling( 83 | batch_size=self.batch_size_encoder, 84 | pad=self.vocab.stoi[self.fields["tgt"].base_field.pad_token], 85 | bos=self.vocab.stoi[self.fields["tgt"].base_field.init_token], 86 | eos=self.vocab.stoi[self.fields["tgt"].base_field.eos_token], 87 | sample_size=self.batch_size_decoder, 88 | min_length=0, 89 | max_length=self.max_length, 90 | return_attention=False, 91 | block_ngram_repeat=1, 92 | exclusion_tokens={}, 93 | sampling_temp=self.sampling_temp, 94 | keep_topk=-1, 95 | entropy_threshold=self.entropy_threshold, 96 | ) 97 | 98 | #initialize sampler 99 | src_map = None 100 | fn_map_state, memory_bank, memory_lengths, src_map = \ 101 | decode_strategy.initialize(memory_bank_init, src_lengths_init, src_map) 102 | if fn_map_state is not None: 103 | self.model.decoder.map_state(fn_map_state) 104 | 105 | #at beginning repeat both priors, size of decoder batch 106 | if rhymePrior is not None: 107 | rhymePrior_batch = rhymePrior.repeat(self.batch_size_decoder, 1) 108 | if nmfPrior is not None: 109 | nmfPrior_batch = nmfPrior.repeat(self.batch_size_decoder, 1) 110 | 111 | 112 | for step in range(self.max_length): 113 | decoder_input = decode_strategy.current_predictions.view(1, -1, 1) 114 | dec_out, dec_attn = self.model.decoder( 115 | decoder_input, memory_bank, memory_lengths=memory_lengths, step=step 116 | ) 117 | if "std" in dec_attn: 118 | attn = dec_attn["std"] 119 | else: 120 | attn = None 121 | log_probs = self.model.generator(dec_out.squeeze(0)) 122 | 123 | if step == 0 and rhymePrior is not None: 124 | decode_strategy.advance(log_probs, attn, prior=rhymePrior_batch) 125 | elif nmfPrior is not None: 126 | decode_strategy.advance(log_probs, attn, prior=nmfPrior_batch) 127 | else: 128 | decode_strategy.advance(log_probs, attn) 129 | 130 | any_finished = decode_strategy.is_finished.any() 131 | if any_finished: 132 | decode_strategy.update_finished() 133 | 134 | if decode_strategy.done: 135 | break 136 | 137 | select_indices = decode_strategy.select_indices 138 | 139 | if any_finished: 140 | if isinstance(memory_bank, tuple): 141 | memory_bank = tuple(x.index_select(1, select_indices) 142 | for x in memory_bank) 143 | else: 144 | memory_bank = memory_bank.index_select(1, select_indices) 145 | 146 | memory_lengths = memory_lengths.index_select(0, select_indices) 147 | 148 | 149 | #if any finished need to update nmfprior 150 | if nmfPrior is not None: 151 | nmfPrior_batch = nmfPrior.repeat(len(select_indices), 1) 152 | 153 | 154 | self.model.decoder.map_state( 155 | lambda state, dim: state.index_select(dim, select_indices)) 156 | 157 | results["scores"].extend(decode_strategy.scores[0]) 158 | results["predictions"].extend(decode_strategy.predictions[0]) 159 | 160 | 161 | allSents = [] 162 | for sent in results['predictions']: 163 | wsent = [self.vocab.itos[i] for i in sent[:-1]] 164 | wsent.reverse() 165 | allSents.append(wsent) 166 | allScores = list(results['scores']) 167 | return allSents, allScores 168 | -------------------------------------------------------------------------------- /model_builder_custom.py: -------------------------------------------------------------------------------- 1 | import re 2 | import torch 3 | import torch.nn as nn 4 | from torch.nn.init import xavier_uniform_ 5 | 6 | import onmt.inputters as inputters 7 | import onmt.modules 8 | from onmt.encoders import str2enc 9 | 10 | from onmt.decoders import str2dec 11 | 12 | from onmt.modules import Embeddings, VecEmbedding, CopyGenerator 13 | from onmt.modules.util_class import Cast 14 | from onmt.utils.misc import use_gpu 15 | from onmt.utils.logging import logger 16 | from onmt.utils.parse import ArgumentParser 17 | 18 | from onmt.model_builder import build_encoder, build_decoder, build_embeddings 19 | 20 | 21 | def load_test_model_with_projection_layer(opt, model_path=None): 22 | if model_path is None: 23 | model_path = opt.models[0] 24 | checkpoint = torch.load(model_path, 25 | map_location=lambda storage, loc: storage) 26 | 27 | model_opt = ArgumentParser.ckpt_model_opts(checkpoint['opt']) 28 | ArgumentParser.update_model_opts(model_opt) 29 | ArgumentParser.validate_model_opts(model_opt) 30 | vocab = checkpoint['vocab'] 31 | if inputters.old_style_vocab(vocab): 32 | fields = inputters.load_old_vocab( 33 | vocab, opt.data_type, dynamic_dict=model_opt.copy_attn 34 | ) 35 | else: 36 | fields = vocab 37 | 38 | model = build_base_model_with_projection_layer(model_opt, fields, checkpoint) 39 | if opt.fp32: 40 | model.float() 41 | model.eval() 42 | model.generator.eval() 43 | return fields, model, model_opt 44 | 45 | def build_base_model_with_projection_layer(model_opt, fields, checkpoint=None): 46 | """Build a model from opts. 47 | 48 | Args: 49 | model_opt: the option loaded from checkpoint. It's important that 50 | the opts have been updated and validated. See 51 | :class:`onmt.utils.parse.ArgumentParser`. 52 | fields (dict[str, torchtext.data.Field]): 53 | `Field` objects for the model. 54 | gpu (bool): whether to use gpu. 55 | checkpoint: the model gnerated by train phase, or a resumed snapshot 56 | model from a stopped training. 57 | gpu_id (int or NoneType): Which GPU to use. 58 | 59 | Returns: 60 | the NMTModel. 61 | """ 62 | 63 | # for back compat when attention_dropout was not defined 64 | try: 65 | model_opt.attention_dropout 66 | except AttributeError: 67 | model_opt.attention_dropout = model_opt.dropout 68 | 69 | # Build embeddings. 70 | if model_opt.model_type == "text" or model_opt.model_type == "vec": 71 | src_field = fields["src"] 72 | src_emb = build_embeddings(model_opt, src_field) 73 | else: 74 | src_emb = None 75 | 76 | # Build encoder. 77 | encoder = build_encoder(model_opt, src_emb) 78 | 79 | # Build decoder. 80 | tgt_field = fields["tgt"] 81 | tgt_emb = build_embeddings(model_opt, tgt_field, for_encoder=False) 82 | 83 | # Share the embedding matrix - preprocess with share_vocab required. 84 | if model_opt.share_embeddings: 85 | # src/tgt vocab should be the same if `-share_vocab` is specified. 86 | assert src_field.base_field.vocab == tgt_field.base_field.vocab, \ 87 | "preprocess with -share_vocab if you use share_embeddings" 88 | 89 | tgt_emb.word_lut.weight = src_emb.word_lut.weight 90 | 91 | decoder = build_decoder(model_opt, tgt_emb) 92 | 93 | # Build NMTModel(= encoder + decoder). 94 | device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu") 95 | model = onmt.models.NMTModel(encoder, decoder) 96 | 97 | # Build Generator. 98 | if not model_opt.copy_attn: 99 | if model_opt.generator_function == "sparsemax": 100 | gen_func = onmt.modules.sparse_activations.LogSparsemax(dim=-1) 101 | else: 102 | gen_func = nn.LogSoftmax(dim=-1) 103 | generator = nn.Sequential( 104 | ## ADDING PROJECTION LAYER 105 | ## Two commented lines changed 106 | #nn.Linear(model_opt.dec_rnn_size, 107 | # len(fields["tgt"].base_field.vocab)), 108 | nn.Linear(model_opt.rnn_size, model_opt.src_word_vec_size), 109 | nn.Linear(model_opt.src_word_vec_size, len(fields["tgt"].base_field.vocab)), 110 | #end change 111 | Cast(torch.float32), 112 | gen_func 113 | ) 114 | if model_opt.share_decoder_embeddings: 115 | ## AND CHANGED 0 TO 1 to correctly share embeddings 116 | generator[1].weight = decoder.embeddings.word_lut.weight 117 | else: 118 | tgt_base_field = fields["tgt"].base_field 119 | vocab_size = len(tgt_base_field.vocab) 120 | pad_idx = tgt_base_field.vocab.stoi[tgt_base_field.pad_token] 121 | generator = CopyGenerator(model_opt.dec_rnn_size, vocab_size, pad_idx) 122 | if model_opt.share_decoder_embeddings: 123 | generator.linear.weight = decoder.embeddings.word_lut.weight 124 | 125 | # Load the model states from checkpoint or initialize them. 126 | if checkpoint is not None: 127 | # This preserves backward-compat for models using customed layernorm 128 | def fix_key(s): 129 | s = re.sub(r'(.*)\.layer_norm((_\d+)?)\.b_2', 130 | r'\1.layer_norm\2.bias', s) 131 | s = re.sub(r'(.*)\.layer_norm((_\d+)?)\.a_2', 132 | r'\1.layer_norm\2.weight', s) 133 | return s 134 | 135 | checkpoint['model'] = {fix_key(k): v 136 | for k, v in checkpoint['model'].items()} 137 | # end of patch for backward compatibility 138 | 139 | model.load_state_dict(checkpoint['model'], strict=False) 140 | generator.load_state_dict(checkpoint['generator'], strict=False) 141 | else: 142 | if model_opt.param_init != 0.0: 143 | for p in model.parameters(): 144 | p.data.uniform_(-model_opt.param_init, model_opt.param_init) 145 | for p in generator.parameters(): 146 | p.data.uniform_(-model_opt.param_init, model_opt.param_init) 147 | if model_opt.param_init_glorot: 148 | for p in model.parameters(): 149 | if p.dim() > 1: 150 | xavier_uniform_(p) 151 | for p in generator.parameters(): 152 | if p.dim() > 1: 153 | xavier_uniform_(p) 154 | 155 | if hasattr(model.encoder, 'embeddings'): 156 | model.encoder.embeddings.load_pretrained_vectors( 157 | model_opt.pre_word_vecs_enc) 158 | if hasattr(model.decoder, 'embeddings'): 159 | model.decoder.embeddings.load_pretrained_vectors( 160 | model_opt.pre_word_vecs_dec) 161 | 162 | model.generator = generator 163 | model.to(device) 164 | if model_opt.model_dtype == 'fp16' and model_opt.optim == 'fusedadam': 165 | model.half() 166 | return model 167 | -------------------------------------------------------------------------------- /poembase.py: -------------------------------------------------------------------------------- 1 | #!/usr/bin/env python 2 | 3 | import sys 4 | import random 5 | #from countsyl import count_syllables 6 | import time 7 | import numpy as np 8 | import pickle 9 | import os 10 | import numpy as np 11 | import scipy.stats 12 | import kenlm 13 | from datetime import datetime 14 | import codecs 15 | import warnings 16 | from functools import reduce 17 | import copy 18 | from poemutils import count_syllables, hmean 19 | from pprint import pprint 20 | import onmt 21 | import argparse 22 | import torch 23 | import json 24 | import warnings 25 | 26 | from verse_generator import VerseGenerator 27 | 28 | warnings.filterwarnings("ignore") 29 | 30 | class PoemBase: 31 | 32 | def __init__(self, form, config): 33 | 34 | self.structureDict = {'sonnet': 35 | ('a','b','b','a', '', 36 | 'c','d','d','c', '', 37 | 'e','f','e', '', 38 | 'f', 'e', 'f'), 39 | 'short': 40 | ('a','b','a','b', '', 41 | 'c','d','c','d'), 42 | 'shorter': 43 | ('a','b','a','b'), 44 | 'pantoum': 45 | ('a', 'b', 'c', 'd', '', 46 | 'b','e','d','f','', 47 | 'e', 'g', 'f', 'h', '', 48 | 'g', 'a', 'h', 'c'), 49 | } 50 | 51 | self.form = form 52 | 53 | self.initializeConfig(config) 54 | self.loadRhymeDictionary() 55 | self.loadNMFData() 56 | 57 | self.generator = VerseGenerator(self.MODEL_FILE, self.entropy_threshold) 58 | 59 | self.loadVocabulary() 60 | 61 | self.ngramModel = kenlm.Model(self.NGRAM_FILE) 62 | 63 | if not os.path.exists('log'): 64 | os.makedirs('log') 65 | logfile = 'log/poem_' + datetime.now().strftime("%Y%m%d") 66 | self.log = open(logfile, 'a') 67 | 68 | 69 | def initializeConfig(self, config): 70 | 71 | with open(config) as json_config_file: 72 | configData = json.load(json_config_file) 73 | 74 | location = os.path.join( 75 | configData['general']['data_directory'], 76 | configData['general']['language'] 77 | ) 78 | 79 | self.NMF_FILE = os.path.join(location, configData['nmf']['matrix_file']) 80 | self.NMF_DESCRIPTION_FILE = os.path.join(location, configData['nmf']['description_file']) 81 | self.RHYME_FREQ_FILE = os.path.join(location, configData['rhyme']['freq_file']) 82 | self.RHYME_DICT_FILE = os.path.join(location, configData['rhyme']['rhyme_dict_file']) 83 | self.RHYME_INV_DICT_FILE = os.path.join(location, configData['rhyme']['rhyme_inv_dict_file']) 84 | self.MODEL_FILE = os.path.join(location, configData['model']['parameter_file']) 85 | self.NGRAM_FILE = os.path.join(location, configData['model']['ngram_file']) 86 | 87 | self.name = configData['general']['name'] 88 | self.length = configData['poem']['length'] 89 | self.entropy_threshold = configData['poem']['entropy_threshold'] 90 | 91 | def loadNMFData(self): 92 | self.W = np.load(self.NMF_FILE) 93 | with open(self.NMF_DESCRIPTION_FILE, 'rb') as f: 94 | self.nmf_descriptions = pickle.load(f, encoding='utf8') 95 | 96 | def loadRhymeDictionary(self): 97 | freqRhyme = {} 98 | with codecs.open(self.RHYME_FREQ_FILE, 'r', encoding='utf8') as f: 99 | for line in f: 100 | line = line.rstrip() 101 | rhyme, freq = line.split('\t') 102 | freqRhyme[rhyme] = int(freq) 103 | self.freqRhyme = freqRhyme 104 | self.rhymeDictionary = pickle.load(open(self.RHYME_DICT_FILE, 'rb')) 105 | self.rhymeInvDictionary = pickle.load(open(self.RHYME_INV_DICT_FILE, 'rb')) 106 | 107 | def loadVocabulary(self): 108 | self.i2w = self.generator.vocab.itos 109 | self.w2i = self.generator.vocab.stoi 110 | 111 | def write(self, constraints=('rhyme'), nmfDim=False): 112 | self.blacklist_words = set() 113 | self.blacklist = [] 114 | self.previous_sent = None 115 | if constraints == ('rhyme'): 116 | self.writeRhyme(nmfDim) 117 | 118 | def writeRhyme(self, nmfDim): 119 | rhymeStructure = self.getRhymeStructure() 120 | if nmfDim == 'random': 121 | nmfDim = random.randint(0,self.W.shape[1] - 1) 122 | elif type(nmfDim) == int: 123 | nmfDim = nmfDim 124 | else: 125 | nmfDim = None 126 | if not nmfDim == None: 127 | sys.stdout.write('\n' + datetime.now().strftime("%Y-%m-%d %H:%M:%S") +' nmfdim ' + str(nmfDim) + ' (' + ', '.join(self.nmf_descriptions[nmfDim]) + ')\n\n') 128 | self.log.write('\n' + datetime.now().strftime("%Y-%m-%d %H:%M:%S") + ' nmfdim ' + str(nmfDim) + ' (' + ', '.join(self.nmf_descriptions[nmfDim]) + ')\n\n') 129 | else: 130 | sys.stdout.write('\n' + datetime.now().strftime("%Y-%m-%d %H:%M:%S") + ' NO nmfdim' + '\n\n') 131 | self.log.write('\n' + datetime.now().strftime("%Y-%m-%d %H:%M:%S") + ' NO nmfdim' + '\n\n') 132 | for el in rhymeStructure: 133 | if el: 134 | try: 135 | words = self.getSentence(rhyme=el, syllables = True, nmf=nmfDim) 136 | except KeyError as e: 137 | print('err', e) 138 | continue 139 | else: 140 | sys.stdout.write(' '.join(words) + '\n') 141 | self.log.write(' '.join(words) + '\n') 142 | try: 143 | self.blacklist.append(self.rhymeDictionary[words[-1]]) 144 | self.blacklist_words = self.blacklist_words.union(words) 145 | except KeyError as e: 146 | #means verse does not follow rhyme, probably because of entropy computations 147 | #do not show error for presentation 148 | #print('err blacklist', e) 149 | pass 150 | except IndexError as e2: 151 | print('err blacklist index', e2) 152 | self.previous_sent = words 153 | else: 154 | sys.stdout.write('\n') 155 | self.log.write('\n') 156 | self.signature() 157 | self.log.write('\n\n') 158 | self.log.flush() 159 | 160 | def getSentence(self, rhyme, syllables, nmf): 161 | if self.previous_sent: 162 | previous = self.previous_sent 163 | else: 164 | previous = None 165 | if rhyme: 166 | rhymePrior = self.createRhymeProbVector(rhyme) 167 | else: 168 | rhymePrior = None 169 | if not nmf == None: 170 | nmfPrior = copy.deepcopy(self.W[:,nmf]) 171 | else: 172 | nmfPrior = None 173 | 174 | allCandidates = [] 175 | allProbScores = [] 176 | allEncDecScores = [] 177 | 178 | allCandidates, allProbScores = self.generator.generateCandidates(previous=previous,rhymePrior=rhymePrior, nmfPrior=nmfPrior) 179 | 180 | ngramScores = [] 181 | for ncand, candidate in enumerate(allCandidates): 182 | try: 183 | ngramScore = self.ngramModel.score(' '.join(candidate)) / len(candidate) 184 | except ZeroDivisionError: 185 | ngramScore = -100 186 | ngramScores.append(ngramScore) 187 | ngramScores = np.array(ngramScores) 188 | largest = ngramScores[np.argmax(ngramScores)] 189 | ngramNorm = np.exp(ngramScores - largest) 190 | 191 | allProbScores = np.array([i.cpu().detach().numpy() for i in allProbScores]) 192 | largest = allProbScores[np.argmax(allProbScores)] 193 | allProbNorm = np.exp(allProbScores - largest) 194 | 195 | scoreList = [] 196 | for ncand, candidate in enumerate(allCandidates): 197 | allScores = [allProbNorm[ncand], ngramNorm[ncand]] 198 | if syllables: 199 | syllablesScore = self.checkSyllablesScore(candidate, mean=self.length, std=2) 200 | allScores.append(syllablesScore) 201 | if nmf: 202 | NMFScore = self.checkNMF(candidate, [nmf]) 203 | allScores.append(NMFScore) 204 | allScore = hmean(allScores) 205 | scoreList.append((allScore, candidate, allScores)) 206 | 207 | scoreList.sort() 208 | scoreList.reverse() 209 | 210 | return scoreList[0][1] 211 | 212 | def getRhymeStructure(self, cutoff=10): 213 | chosenList = [] 214 | mapDict = {} 215 | structure = self.structureDict[self.form] 216 | for el in set(structure): 217 | freq = -1 218 | while True: 219 | rhymeForm = random.choice(list(self.freqRhyme.keys())) 220 | freq = self.freqRhyme[rhymeForm] 221 | if (freq >= cutoff) and not rhymeForm in chosenList: 222 | chosenList.append(rhymeForm) 223 | mapDict[el] = rhymeForm 224 | break 225 | rhymeStructure = [] 226 | for struct in structure: 227 | if struct: 228 | rhymeStructure.append(mapDict[struct]) 229 | else: 230 | rhymeStructure.append(struct) 231 | return rhymeStructure 232 | 233 | def createRhymeProbVector(self, rhyme): 234 | probVector = np.empty(len(self.i2w)) 235 | probVector.fill(1e-20) 236 | for w in self.rhymeInvDictionary[rhyme]: 237 | if not self.rhymeDictionary[w] in self.blacklist: 238 | probVector[self.w2i[w]] = 1 239 | return probVector / np.sum(probVector) 240 | 241 | def signature(self): 242 | sys.stdout.write('\n ') 243 | time.sleep(4) 244 | for el in '- ' + self.name: 245 | nap = random.uniform(0.1,0.6) 246 | sys.stdout.write(el) 247 | sys.stdout.flush() 248 | time.sleep(nap) 249 | sys.stdout.write('\n') 250 | 251 | def typeString(self, verse): 252 | for el in verse: 253 | nap = random.uniform(0.1,0.3) 254 | sys.stdout.write(el) 255 | sys.stdout.flush() 256 | time.sleep(nap) 257 | return None 258 | 259 | 260 | def checkSyllablesScore(self, words, mean, std): 261 | gaussian = scipy.stats.norm(mean,std) 262 | nSyllables = sum([count_syllables(w)[1] for w in words]) 263 | return gaussian.pdf(nSyllables) / 0.19 264 | 265 | def computeNMFScore(self,words,dimList): 266 | sm = 0 267 | sm = sum([max(self.W[self.w2i[w],dimList]) for w in words if w in self.w2i]) 268 | return sm 269 | 270 | def checkNMF(self, words, dimList): 271 | words = list(set([w for w in words if not w in self.blacklist_words])) 272 | NMFTop = np.max(np.max(self.W[:,dimList], axis=0)) 273 | NMFScore = self.computeNMFScore(words, dimList) 274 | return NMFScore / NMFTop 275 | -------------------------------------------------------------------------------- /prior_sampling.py: -------------------------------------------------------------------------------- 1 | import torch 2 | import torch.nn.functional as F 3 | 4 | from onmt.translate.decode_strategy import DecodeStrategy 5 | from onmt.utils.misc import tile 6 | 7 | 8 | # def sample_with_temperature(logits, sampling_temp, keep_topk): 9 | # """Select next tokens randomly from the top k possible next tokens. 10 | 11 | # Samples from a categorical distribution over the ``keep_topk`` words using 12 | # the category probabilities ``logits / sampling_temp``. 13 | 14 | # Args: 15 | # logits (FloatTensor): Shaped ``(batch_size, vocab_size)``. 16 | # These can be logits (``(-inf, inf)``) or log-probs (``(-inf, 0]``). 17 | # (The distribution actually uses the log-probabilities 18 | # ``logits - logits.logsumexp(-1)``, which equals the logits if 19 | # they are log-probabilities summing to 1.) 20 | # sampling_temp (float): Used to scale down logits. The higher the 21 | # value, the more likely it is that a non-max word will be 22 | # sampled. 23 | # keep_topk (int): This many words could potentially be chosen. The 24 | # other logits are set to have probability 0. 25 | 26 | # Returns: 27 | # (LongTensor, FloatTensor): 28 | 29 | # * topk_ids: Shaped ``(batch_size, 1)``. These are 30 | # the sampled word indices in the output vocab. 31 | # * topk_scores: Shaped ``(batch_size, 1)``. These 32 | # are essentially ``(logits / sampling_temp)[topk_ids]``. 33 | # """ 34 | 35 | # if sampling_temp == 0.0 or keep_topk == 1: 36 | # # For temp=0.0, take the argmax to avoid divide-by-zero errors. 37 | # # keep_topk=1 is also equivalent to argmax. 38 | # topk_scores, topk_ids = logits.topk(1, dim=-1) 39 | # if sampling_temp > 0: 40 | # topk_scores /= sampling_temp 41 | # else: 42 | # logits = torch.div(logits, sampling_temp) 43 | 44 | # if keep_topk > 0: 45 | # top_values, top_indices = torch.topk(logits, keep_topk, dim=1) 46 | # kth_best = top_values[:, -1].view([-1, 1]) 47 | # kth_best = kth_best.repeat([1, logits.shape[1]]).float() 48 | 49 | # # Set all logits that are not in the top-k to -10000. 50 | # # This puts the probabilities close to 0. 51 | # ignore = torch.lt(logits, kth_best) 52 | # logits = logits.masked_fill(ignore, -10000) 53 | 54 | # dist = torch.distributions.Multinomial( 55 | # logits=logits, total_count=1) 56 | # topk_ids = torch.argmax(dist.sample(), dim=1, keepdim=True) 57 | # topk_scores = logits.gather(dim=1, index=topk_ids) 58 | # return topk_ids, topk_scores 59 | 60 | def sample_with_temperature_default_logprob(logits, logits_default, sampling_temp, keep_topk): 61 | """Select next tokens randomly from the top k possible next tokens. 62 | 63 | TVDC NOTE: priors mess with log_probabilities; this function 64 | adapts sample function in order to take global score from decoder 65 | into account that uses logprobs without prior adaptation 66 | 67 | 68 | Samples from a categorical distribution over the ``keep_topk`` words using 69 | the category probabilities ``logits / sampling_temp``. 70 | 71 | Args: 72 | logits (FloatTensor): Shaped ``(batch_size, vocab_size)``. 73 | These can be logits (``(-inf, inf)``) or log-probs (``(-inf, 0]``). 74 | (The distribution actually uses the log-probabilities 75 | ``logits - logits.logsumexp(-1)``, which equals the logits if 76 | they are log-probabilities summing to 1.) 77 | sampling_temp (float): Used to scale down logits. The higher the 78 | value, the more likely it is that a non-max word will be 79 | sampled. 80 | keep_topk (int): This many words could potentially be chosen. The 81 | other logits are set to have probability 0. 82 | 83 | Returns: 84 | (LongTensor, FloatTensor): 85 | 86 | * topk_ids: Shaped ``(batch_size, 1)``. These are 87 | the sampled word indices in the output vocab. 88 | * topk_scores: Shaped ``(batch_size, 1)``. These 89 | are essentially ``(logits / sampling_temp)[topk_ids]``. 90 | 91 | """ 92 | 93 | if sampling_temp == 0.0 or keep_topk == 1: 94 | # For temp=0.0, take the argmax to avoid divide-by-zero errors. 95 | # keep_topk=1 is also equivalent to argmax. 96 | topk_scores, topk_ids = logits.topk(1, dim=-1) 97 | if sampling_temp > 0: 98 | topk_scores /= sampling_temp 99 | else: 100 | logits = torch.div(logits, sampling_temp) 101 | 102 | if keep_topk > 0: 103 | top_values, top_indices = torch.topk(logits, keep_topk, dim=1) 104 | kth_best = top_values[:, -1].view([-1, 1]) 105 | kth_best = kth_best.repeat([1, logits.shape[1]]).float() 106 | 107 | # Set all logits that are not in the top-k to -10000. 108 | # This puts the probabilities close to 0. 109 | ignore = torch.lt(logits, kth_best) 110 | logits = logits.masked_fill(ignore, -10000) 111 | 112 | dist = torch.distributions.Multinomial( 113 | logits=logits, total_count=1) 114 | topk_ids = torch.argmax(dist.sample(), dim=1, keepdim=True) 115 | topk_scores = logits_default.gather(dim=1, index=topk_ids) 116 | return topk_ids, topk_scores 117 | 118 | class PriorSampling(DecodeStrategy): 119 | """Select next tokens randomly from the top k possible next tokens. 120 | 121 | The ``scores`` attribute's lists are the score, after applying temperature, 122 | of the final prediction (either EOS or the final token in the event 123 | that ``max_length`` is reached) 124 | 125 | Args: 126 | pad (int): See base. 127 | bos (int): See base. 128 | eos (int): See base. 129 | batch_size (int): See base. 130 | min_length (int): See base. 131 | max_length (int): See base. 132 | block_ngram_repeat (int): See base. 133 | exclusion_tokens (set[int]): See base. 134 | return_attention (bool): See base. 135 | max_length (int): See base. 136 | sampling_temp (float): See 137 | :func:`~onmt.translate.greedy_search.sample_with_temperature()`. 138 | keep_topk (int): See 139 | :func:`~onmt.translate.greedy_search.sample_with_temperature()`. 140 | """ 141 | 142 | def __init__(self, pad, bos, eos, batch_size, sample_size, min_length, 143 | block_ngram_repeat, exclusion_tokens, return_attention, 144 | max_length, sampling_temp, keep_topk, entropy_threshold): 145 | #assert block_ngram_repeat == 0 146 | super(PriorSampling, self).__init__( 147 | pad, bos, eos, batch_size, sample_size, min_length, block_ngram_repeat, 148 | exclusion_tokens, return_attention, max_length) 149 | self.sampling_temp = sampling_temp 150 | self.keep_topk = keep_topk 151 | self.topk_scores = None 152 | self.sample_size = sample_size 153 | self.block_ngram_repeat = block_ngram_repeat 154 | self.entropy_threshold = entropy_threshold 155 | 156 | def initialize(self, memory_bank, src_lengths, src_map=None, device=None): 157 | """Initialize for decoding.""" 158 | #fn_map_state = None 159 | def fn_map_state(state, dim): 160 | return tile(state, self.sample_size, dim=dim) 161 | 162 | if isinstance(memory_bank, tuple): 163 | memory_bank = tuple(tile(x, self.sample_size, dim=1) 164 | for x in memory_bank) 165 | mb_device = memory_bank[0].device 166 | else: 167 | memory_bank = tile(memory_bank, self.sample_size, dim=1) 168 | mb_device = memory_bank.device 169 | if src_map is not None: 170 | src_map = tile(src_map, self.sample_size, dim=1) 171 | if device is None: 172 | device = mb_device 173 | 174 | self.memory_lengths = tile(src_lengths, self.sample_size) 175 | super(PriorSampling, self).initialize( 176 | memory_bank, self.memory_lengths, src_map, device) 177 | self.select_indices = torch.arange( 178 | self.batch_size * self.sample_size, dtype=torch.long, device=device) 179 | self.original_batch_idx = tile(torch.arange( 180 | self.batch_size, dtype=torch.long, device=device), self.sample_size) 181 | return fn_map_state, memory_bank, self.memory_lengths, src_map 182 | 183 | @property 184 | def current_predictions(self): 185 | return self.alive_seq[:, -1] 186 | 187 | @property 188 | def batch_offset(self): 189 | return self.select_indices 190 | 191 | def advance(self, log_probs, attn, prior=None): 192 | """Select next tokens randomly from the top k possible next tokens. 193 | 194 | Args: 195 | log_probs (FloatTensor): Shaped ``(batch_size, vocab_size)``. 196 | These can be logits (``(-inf, inf)``) or log-probs 197 | (``(-inf, 0]``). (The distribution actually uses the 198 | log-probabilities ``logits - logits.logsumexp(-1)``, 199 | which equals the logits if they are log-probabilities summing 200 | to 1.) 201 | attn (FloatTensor): Shaped ``(1, B, inp_seq_len)``. 202 | """ 203 | 204 | self.ensure_min_length(log_probs) 205 | if self.block_ngram_repeat: 206 | self.block_ngram_repeats(log_probs) 207 | ## don't want no unk generation 208 | log_probs[:,0] = -1e20 209 | default_log_probs = log_probs 210 | if prior is not None: 211 | prior_log_probs = log_probs + prior.log() 212 | default_probs = F.softmax(log_probs, dim=1) 213 | default_probs_log = default_probs.log() 214 | default_probs_log[default_probs_log == float('-inf')] = -1e20 215 | vector_entropies = -torch.sum(default_probs * default_probs_log, 1, keepdim=True) 216 | log_probs = torch.where(vector_entropies < self.entropy_threshold, default_log_probs, prior_log_probs) 217 | 218 | topk_ids, self.topk_scores = sample_with_temperature_default_logprob( 219 | log_probs, default_log_probs, self.sampling_temp, self.keep_topk) 220 | self.is_finished = topk_ids.eq(self.eos) 221 | self.alive_seq = torch.cat([self.alive_seq, topk_ids], -1) 222 | if self.return_attention: 223 | if self.alive_attn is None: 224 | self.alive_attn = attn 225 | else: 226 | self.alive_attn = torch.cat([self.alive_attn, attn], 0) 227 | self.ensure_max_length() 228 | 229 | def update_finished(self): 230 | """Finalize scores and predictions.""" 231 | # shape: (sum(~ self.is_finished), 1) 232 | finished_batches = self.is_finished.view(-1).nonzero() 233 | for b in finished_batches.view(-1): 234 | b_orig = self.original_batch_idx[b] 235 | self.scores[b_orig].append(self.topk_scores[b, 0]) 236 | self.predictions[b_orig].append(self.alive_seq[b, 1:]) 237 | self.attention[b_orig].append( 238 | self.alive_attn[:, b, :self.memory_lengths[b]] 239 | if self.alive_attn is not None else []) 240 | self.done = self.is_finished.all() 241 | if self.done: 242 | return 243 | is_alive = ~self.is_finished.view(-1) 244 | self.alive_seq = self.alive_seq[is_alive] 245 | if self.alive_attn is not None: 246 | self.alive_attn = self.alive_attn[:, is_alive] 247 | self.select_indices = is_alive.nonzero().view(-1) 248 | self.original_batch_idx = self.original_batch_idx[is_alive] 249 | -------------------------------------------------------------------------------- /LICENSE: -------------------------------------------------------------------------------- 1 | GNU GENERAL PUBLIC LICENSE 2 | Version 3, 29 June 2007 3 | 4 | Copyright (C) 2007 Free Software Foundation, Inc. 5 | Everyone is permitted to copy and distribute verbatim copies 6 | of this license document, but changing it is not allowed. 7 | 8 | Preamble 9 | 10 | The GNU General Public License is a free, copyleft license for 11 | software and other kinds of works. 12 | 13 | The licenses for most software and other practical works are designed 14 | to take away your freedom to share and change the works. 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Interpretation of Sections 15 and 16. 613 | 614 | If the disclaimer of warranty and limitation of liability provided 615 | above cannot be given local legal effect according to their terms, 616 | reviewing courts shall apply local law that most closely approximates 617 | an absolute waiver of all civil liability in connection with the 618 | Program, unless a warranty or assumption of liability accompanies a 619 | copy of the Program in return for a fee. 620 | 621 | END OF TERMS AND CONDITIONS 622 | 623 | How to Apply These Terms to Your New Programs 624 | 625 | If you develop a new program, and you want it to be of the greatest 626 | possible use to the public, the best way to achieve this is to make it 627 | free software which everyone can redistribute and change under these terms. 628 | 629 | To do so, attach the following notices to the program. It is safest 630 | to attach them to the start of each source file to most effectively 631 | state the exclusion of warranty; and each file should have at least 632 | the "copyright" line and a pointer to where the full notice is found. 633 | 634 | 635 | Copyright (C) 636 | 637 | This program is free software: you can redistribute it and/or modify 638 | it under the terms of the GNU General Public License as published by 639 | the Free Software Foundation, either version 3 of the License, or 640 | (at your option) any later version. 641 | 642 | This program is distributed in the hope that it will be useful, 643 | but WITHOUT ANY WARRANTY; without even the implied warranty of 644 | MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the 645 | GNU General Public License for more details. 646 | 647 | You should have received a copy of the GNU General Public License 648 | along with this program. If not, see . 649 | 650 | Also add information on how to contact you by electronic and paper mail. 651 | 652 | If the program does terminal interaction, make it output a short 653 | notice like this when it starts in an interactive mode: 654 | 655 | Copyright (C) 656 | This program comes with ABSOLUTELY NO WARRANTY; for details type `show w'. 657 | This is free software, and you are welcome to redistribute it 658 | under certain conditions; type `show c' for details. 659 | 660 | The hypothetical commands `show w' and `show c' should show the appropriate 661 | parts of the General Public License. Of course, your program's commands 662 | might be different; for a GUI interface, you would use an "about box". 663 | 664 | You should also get your employer (if you work as a programmer) or school, 665 | if any, to sign a "copyright disclaimer" for the program, if necessary. 666 | For more information on this, and how to apply and follow the GNU GPL, see 667 | . 668 | 669 | The GNU General Public License does not permit incorporating your program 670 | into proprietary programs. If your program is a subroutine library, you 671 | may consider it more useful to permit linking proprietary applications with 672 | the library. If this is what you want to do, use the GNU Lesser General 673 | Public License instead of this License. But first, please read 674 | . 675 | --------------------------------------------------------------------------------