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pred_mod_8_no_merge.py
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pred_mod_8_no_merge.py
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# predict.py for a 4-way split of the doc along with merging
import argparse
import jsonlines
import torch
from tqdm import tqdm
from coref.coref_model2 import CorefModel
from coref.tokenizer_customization import *
from coref import bert, conll, utils
# usage : python pred_mod_8_no_merge.py roberta data_8_splits_jsonlines/english_test_head.jsonlines output.jsonlines
# output.jsonlines [output path] redundant right now
# pred.conll and gold.conll files written in the data/conll_logs dir, model wts loaded from data/
# the unsplitted doc .jsonlines should be in the data/ dir
def build_doc(doc: dict, model: CorefModel) -> dict:
filter_func = TOKENIZER_FILTERS.get(model.config.bert_model,
lambda _: True)
token_map = TOKENIZER_MAPS.get(model.config.bert_model, {})
word2subword = []
subwords = []
word_id = []
for i, word in enumerate(doc["cased_words"]):
tokenized_word = (token_map[word]
if word in token_map
else model.tokenizer.tokenize(word))
tokenized_word = list(filter(filter_func, tokenized_word))
word2subword.append((len(subwords), len(subwords) + len(tokenized_word)))
subwords.extend(tokenized_word)
word_id.extend([i] * len(tokenized_word))
doc["word2subword"] = word2subword
doc["subwords"] = subwords
doc["word_id"] = word_id
doc["head2span"] = []
if "speaker" not in doc:
doc["speaker"] = ["_" for _ in doc["cased_words"]]
doc["word_clusters"] = []
doc["span_clusters"] = []
doc['cluster_emb'] = []
doc["span_clusters_res"] = []
doc["words_emb"] = []
return doc
if __name__ == "__main__":
argparser = argparse.ArgumentParser()
argparser.add_argument("experiment")
argparser.add_argument("input_file")
argparser.add_argument("output_file")
argparser.add_argument("--config-file", default="config.toml")
argparser.add_argument("--batch-size", type=int,
help="Adjust to override the config value if you're"
" experiencing out-of-memory issues")
argparser.add_argument("--weights",
help="Path to file with weights to load."
" If not supplied, in the latest"
" weights of the experiment will be loaded;"
" if there aren't any, an error is raised.")
args = argparser.parse_args()
model = CorefModel(args.config_file, args.experiment)
if args.batch_size:
model.config.a_scoring_batch_size = args.batch_size
model.load_weights(path=args.weights, map_location="cpu",
ignore={"bert_optimizer", "general_optimizer",
"bert_scheduler", "general_scheduler"})
model.training = False
with jsonlines.open(args.input_file, mode="r") as input_data:
docs = [build_doc(doc, model) for doc in input_data]
# First run
with torch.no_grad():
for doc in tqdm(docs, unit="docs"):
result, word_emb = model.run(doc)
print(doc['document_id'])
doc["words_emb"] = word_emb
doc["span_clusters_res"] = result.span_clusters # predicted span clusters
doc["word_clusters"] = result.word_clusters
clusters = doc["span_clusters_res"]
for cluster in clusters:
cluster_i = []
for span in cluster:
span_embedding = None
start, end = span
for i in range(start, end):
if(span_embedding == None):
span_embedding = word_emb[i]
else:
span_embedding += word_emb[i]
span_embedding /= (end - start)
cluster_i.append(span_embedding)
cluster_i = torch.stack(cluster_i)
cluster_i = torch.mean(cluster_i, dim=0)
doc['cluster_emb'].append(cluster_i)
for key in ("word2subword", "subwords", "word_id", "head2span"):
del doc[key]
with torch.no_grad():
docs_new = {} #mapping for doc name to span clusters obtained after merging
for doc1, doc2, doc3, doc4, doc5, doc6, doc7, doc8 in zip(
docs[::8], docs[1::8], docs[2::8], docs[3::8],
docs[4::8], docs[5::8], docs[6::8], docs[7::8]
):
clusters1 = doc1["span_clusters_res"]
clusters2 = doc2["span_clusters_res"]
clusters3 = doc3["span_clusters_res"]
clusters4 = doc4["span_clusters_res"]
clusters5 = doc5["span_clusters_res"]
clusters6 = doc6["span_clusters_res"]
clusters7 = doc7["span_clusters_res"]
clusters8 = doc8["span_clusters_res"]
offset1 = 0
offset2 = len(doc1['cased_words'])
offset3 = offset2 + len(doc2['cased_words'])
offset4 = offset3 + len(doc3['cased_words'])
offset5 = offset4 + len(doc4["cased_words"])
offset6 = offset5 + len(doc5["cased_words"])
offset7 = offset6 + len(doc6["cased_words"])
offset8 = offset7 + len(doc7["cased_words"])
clusters2 = [[(start + offset2, end + offset2) for start, end in tuple_list] for tuple_list in clusters2]
clusters3 = [[(start + offset3, end + offset3) for start, end in tuple_list] for tuple_list in clusters3]
clusters4 = [[(start + offset4, end + offset4) for start, end in tuple_list] for tuple_list in clusters4]
clusters5 = [[(start + offset5, end + offset5) for start, end in tuple_list] for tuple_list in clusters5]
clusters6 = [[(start + offset6, end + offset6) for start, end in tuple_list] for tuple_list in clusters6]
clusters7 = [[(start + offset7, end + offset7) for start, end in tuple_list] for tuple_list in clusters7]
clusters8 = [[(start + offset8, end + offset8) for start, end in tuple_list] for tuple_list in clusters8]
combined_span_clusters = sorted(clusters1 + clusters2 + clusters3 + clusters4 + clusters5 + clusters6 + clusters7 + clusters8)
doc_id = doc1["document_id"][:-2]
docs_new[doc_id] = combined_span_clusters
data_split = 'test'
docs = model._get_docs(model.config.__dict__[f"{data_split}_data"])
with conll.open_(model.config, model.epochs_trained, data_split) \
as (gold_f, pred_f):
pbar = tqdm(docs, unit="docs", ncols=0)
for doc in pbar:
doc_id = doc['document_id']
pred_span_clusters = docs_new[doc_id]
conll.write_conll(doc, doc["span_clusters"], gold_f)
# remove singletons using ./coref-toolkit mod --strip-singletons data/conll_logs/roberta_test_e30.gold.conll > data/conll_logs/roberta_test_e30_x.gold.conll
# then rename it back
conll.write_conll(doc, pred_span_clusters, pred_f) # will be written in data/conll_logs/ dir
# to eval : python calculate_conll.py roberta test 30[no of epochs]