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pred_mod_after_training.py
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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_after_training.py roberta --weights 'data/roberta_(e30_2023.06.12_13.48).pt' litbank_splitted/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"] = []
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)
model2 = CorefModel(args.config_file, args.experiment)
if args.batch_size:
model.config.a_scoring_batch_size = args.batch_size
model2.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"})
# load the weights for the second model
model2.load_weights(path='data/roberta_(e40_2023.07.17_13.50).pt', map_location="cpu",
ignore={"bert_optimizer", "general_optimizer",
"bert_scheduler", "general_scheduler"})
model.training = False
model2.training = False
with jsonlines.open(args.input_file, mode="r") as input_data:
docs = [build_doc(doc, model) for doc in input_data]
# building the cluster embeddings
with torch.no_grad():
for doc in tqdm(docs, unit="docs"):
result, word_emb = model.run(doc)
# print(doc['document_id'])
doc["span_clusters_res"] = result.span_clusters
doc["word_clusters"] = result.word_clusters
clusters = doc["span_clusters_res"]
for cluster in clusters:
# you have to set a offset
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 in list(zip(docs,docs[1:]))[::2]:
span_clusters_mapping = {}
cluster_emb1 = doc1['cluster_emb']
clusters1 = doc1['span_clusters_res']
cluster_emb2 = doc2['cluster_emb']
clusters2 = doc2['span_clusters_res']
offset = len(doc1['cased_words'])
print(doc1['document_id'][:-2])
print(f'len of clusters1 {len(clusters1)}')
# print(clusters1)
clusters2 = [[(start + offset, end + offset) for start, end in tuple_list] for tuple_list in clusters2]
print(f'len of clusters2 {len(clusters2)}')
# print(clusters2)
cluster_emb_merged = torch.stack(cluster_emb1 + cluster_emb2)
cluster_emb_merged = cluster_emb_merged.to('cuda')
for i, cluster in enumerate(clusters1 + clusters2):
span_clusters_mapping[i] = cluster #List[Tuple[int, int]]
res = model2.run2(cluster_emb_merged)
# print(res.word_clusters)
combined_span_clusters = []
#mapping the indexes to the actual clusters of spans
for second_lvl_clusters in res.word_clusters:
combined_span_clusters_i = []
for x in second_lvl_clusters:
combined_span_clusters_i += span_clusters_mapping[x]
combined_span_clusters.append(sorted(combined_span_clusters_i))
# print("final res", combined_span_clusters)
doc_id = doc1["document_id"][:-2]
docs_new[doc_id] = combined_span_clusters
# with jsonlines.open(args.output_file, mode="w") as output_data:
# output_data.write_all(docs_new)
data_split = 'test'
docs = model._get_docs(model.config.__dict__[f"{data_split}_data"]) # from the head.jsonlines, because they contain 'span_clusters' not the other .jsonlines which contains the 'clusters'
# span clusters are formed after you run the convert_to_heads.py -- which are : clusters - some deleted clusters
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]
print(doc_id)
print(f'len of final clusters {len(pred_span_clusters)}')
# print(pred_span_clusters)
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]