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https://github.com/huggingface/transformers.git
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fa84ae26d6
This is the result of:
$ black --line-length 119 examples templates transformers utils hubconf.py setup.py
There's a lot of fairly long lines in the project. As a consequence, I'm
picking the longest widely accepted line length, 119 characters.
This is also Thomas' preference, because it allows for explicit variable
names, to make the code easier to understand.
322 lines
9.8 KiB
Python
322 lines
9.8 KiB
Python
#! /usr/bin/python3
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import argparse
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from collections import namedtuple
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import logging
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import os
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import sys
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import torch
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from torch.utils.data import DataLoader, SequentialSampler
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from tqdm import tqdm
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from transformers import BertTokenizer
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from modeling_bertabs import BertAbs, build_predictor
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from utils_summarization import (
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SummarizationDataset,
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encode_for_summarization,
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build_mask,
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fit_to_block_size,
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compute_token_type_ids,
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)
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logger = logging.getLogger(__name__)
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logging.basicConfig(stream=sys.stdout, level=logging.INFO)
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Batch = namedtuple("Batch", ["document_names", "batch_size", "src", "segs", "mask_src", "tgt_str"])
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def evaluate(args):
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tokenizer = BertTokenizer.from_pretrained("bert-base-uncased", do_lower_case=True)
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model = BertAbs.from_pretrained("bertabs-finetuned-cnndm")
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model.to(args.device)
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model.eval()
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symbols = {
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"BOS": tokenizer.vocab["[unused0]"],
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"EOS": tokenizer.vocab["[unused1]"],
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"PAD": tokenizer.vocab["[PAD]"],
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}
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if args.compute_rouge:
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reference_summaries = []
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generated_summaries = []
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import rouge
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import nltk
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nltk.download("punkt")
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rouge_evaluator = rouge.Rouge(
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metrics=["rouge-n", "rouge-l"],
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max_n=2,
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limit_length=True,
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length_limit=args.beam_size,
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length_limit_type="words",
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apply_avg=True,
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apply_best=False,
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alpha=0.5, # Default F1_score
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weight_factor=1.2,
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stemming=True,
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)
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# these (unused) arguments are defined to keep the compatibility
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# with the legacy code and will be deleted in a next iteration.
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args.result_path = ""
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args.temp_dir = ""
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data_iterator = build_data_iterator(args, tokenizer)
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predictor = build_predictor(args, tokenizer, symbols, model)
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logger.info("***** Running evaluation *****")
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logger.info(" Number examples = %d", len(data_iterator.dataset))
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logger.info(" Batch size = %d", args.batch_size)
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logger.info("")
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logger.info("***** Beam Search parameters *****")
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logger.info(" Beam size = %d", args.beam_size)
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logger.info(" Minimum length = %d", args.min_length)
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logger.info(" Maximum length = %d", args.max_length)
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logger.info(" Alpha (length penalty) = %.2f", args.alpha)
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logger.info(" Trigrams %s be blocked", ("will" if args.block_trigram else "will NOT"))
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for batch in tqdm(data_iterator):
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batch_data = predictor.translate_batch(batch)
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translations = predictor.from_batch(batch_data)
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summaries = [format_summary(t) for t in translations]
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save_summaries(summaries, args.summaries_output_dir, batch.document_names)
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if args.compute_rouge:
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reference_summaries += batch.tgt_str
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generated_summaries += summaries
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if args.compute_rouge:
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scores = rouge_evaluator.get_scores(generated_summaries, reference_summaries)
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str_scores = format_rouge_scores(scores)
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save_rouge_scores(str_scores)
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print(str_scores)
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def save_summaries(summaries, path, original_document_name):
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""" Write the summaries in fies that are prefixed by the original
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files' name with the `_summary` appended.
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Attributes:
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original_document_names: List[string]
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Name of the document that was summarized.
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path: string
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Path were the summaries will be written
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summaries: List[string]
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The summaries that we produced.
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"""
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for summary, document_name in zip(summaries, original_document_name):
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# Prepare the summary file's name
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if "." in document_name:
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bare_document_name = ".".join(document_name.split(".")[:-1])
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extension = document_name.split(".")[-1]
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name = bare_document_name + "_summary." + extension
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else:
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name = document_name + "_summary"
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file_path = os.path.join(path, name)
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with open(file_path, "w") as output:
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output.write(summary)
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def format_summary(translation):
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""" Transforms the output of the `from_batch` function
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into nicely formatted summaries.
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"""
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raw_summary, _, _ = translation
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summary = (
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raw_summary.replace("[unused0]", "")
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.replace("[unused3]", "")
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.replace("[PAD]", "")
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.replace("[unused1]", "")
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.replace(r" +", " ")
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.replace(" [unused2] ", ". ")
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.replace("[unused2]", "")
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.strip()
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)
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return summary
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def format_rouge_scores(scores):
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return """\n
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****** ROUGE SCORES ******
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** ROUGE 1
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F1 >> {:.3f}
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Precision >> {:.3f}
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Recall >> {:.3f}
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** ROUGE 2
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F1 >> {:.3f}
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Precision >> {:.3f}
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Recall >> {:.3f}
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** ROUGE L
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F1 >> {:.3f}
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Precision >> {:.3f}
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Recall >> {:.3f}""".format(
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scores["rouge-1"]["f"],
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scores["rouge-1"]["p"],
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scores["rouge-1"]["r"],
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scores["rouge-2"]["f"],
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scores["rouge-2"]["p"],
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scores["rouge-2"]["r"],
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scores["rouge-l"]["f"],
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scores["rouge-l"]["p"],
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scores["rouge-l"]["r"],
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)
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def save_rouge_scores(str_scores):
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with open("rouge_scores.txt", "w") as output:
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output.write(str_scores)
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#
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# LOAD the dataset
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#
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def build_data_iterator(args, tokenizer):
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dataset = load_and_cache_examples(args, tokenizer)
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sampler = SequentialSampler(dataset)
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collate_fn = lambda data: collate(data, tokenizer, block_size=512, device=args.device)
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iterator = DataLoader(dataset, sampler=sampler, batch_size=args.batch_size, collate_fn=collate_fn,)
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return iterator
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def load_and_cache_examples(args, tokenizer):
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dataset = SummarizationDataset(args.documents_dir)
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return dataset
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def collate(data, tokenizer, block_size, device):
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""" Collate formats the data passed to the data loader.
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In particular we tokenize the data batch after batch to avoid keeping them
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all in memory. We output the data as a namedtuple to fit the original BertAbs's
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API.
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"""
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data = [x for x in data if not len(x[1]) == 0] # remove empty_files
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names = [name for name, _, _ in data]
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summaries = [" ".join(summary_list) for _, _, summary_list in data]
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encoded_text = [encode_for_summarization(story, summary, tokenizer) for _, story, summary in data]
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encoded_stories = torch.tensor(
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[fit_to_block_size(story, block_size, tokenizer.pad_token_id) for story, _ in encoded_text]
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)
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encoder_token_type_ids = compute_token_type_ids(encoded_stories, tokenizer.cls_token_id)
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encoder_mask = build_mask(encoded_stories, tokenizer.pad_token_id)
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batch = Batch(
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document_names=names,
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batch_size=len(encoded_stories),
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src=encoded_stories.to(device),
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segs=encoder_token_type_ids.to(device),
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mask_src=encoder_mask.to(device),
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tgt_str=summaries,
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)
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return batch
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def decode_summary(summary_tokens, tokenizer):
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""" Decode the summary and return it in a format
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suitable for evaluation.
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"""
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summary_tokens = summary_tokens.to("cpu").numpy()
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summary = tokenizer.decode(summary_tokens)
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sentences = summary.split(".")
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sentences = [s + "." for s in sentences]
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return sentences
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def main():
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""" The main function defines the interface with the users.
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"""
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parser = argparse.ArgumentParser()
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parser.add_argument(
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"--documents_dir",
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default=None,
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type=str,
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required=True,
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help="The folder where the documents to summarize are located.",
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)
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parser.add_argument(
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"--summaries_output_dir",
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default=None,
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type=str,
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required=False,
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help="The folder in wich the summaries should be written. Defaults to the folder where the documents are",
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)
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parser.add_argument(
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"--compute_rouge",
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default=False,
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type=bool,
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required=False,
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help="Compute the ROUGE metrics during evaluation. Only available for the CNN/DailyMail dataset.",
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)
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# EVALUATION options
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parser.add_argument(
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"--no_cuda", default=False, type=bool, help="Whether to force the execution on CPU.",
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)
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parser.add_argument(
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"--batch_size", default=4, type=int, help="Batch size per GPU/CPU for training.",
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)
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# BEAM SEARCH arguments
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parser.add_argument(
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"--min_length", default=50, type=int, help="Minimum number of tokens for the summaries.",
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)
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parser.add_argument(
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"--max_length", default=200, type=int, help="Maixmum number of tokens for the summaries.",
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)
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parser.add_argument(
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"--beam_size", default=5, type=int, help="The number of beams to start with for each example.",
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)
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parser.add_argument(
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"--alpha", default=0.95, type=float, help="The value of alpha for the length penalty in the beam search.",
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)
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parser.add_argument(
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"--block_trigram",
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default=True,
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type=bool,
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help="Whether to block the existence of repeating trigrams in the text generated by beam search.",
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)
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args = parser.parse_args()
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# Select device (distibuted not available)
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args.device = torch.device("cuda" if torch.cuda.is_available() and not args.no_cuda else "cpu")
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# Check the existence of directories
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if not args.summaries_output_dir:
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args.summaries_output_dir = args.documents_dir
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if not documents_dir_is_valid(args.documents_dir):
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raise FileNotFoundError(
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"We could not find the directory you specified for the documents to summarize, or it was empty. Please specify a valid path."
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)
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os.makedirs(args.summaries_output_dir, exist_ok=True)
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evaluate(args)
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def documents_dir_is_valid(path):
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if not os.path.exists(path):
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return False
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file_list = os.listdir(path)
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if len(file_list) == 0:
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return False
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return True
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if __name__ == "__main__":
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main()
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