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predict_openbookqa.py
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import torch
import csv
import argparse
from trainer import *
from tqdm import tqdm
from transformers import (
AdamW,
T5ForConditionalGeneration,
T5Tokenizer,
get_linear_schedule_with_warmup
)
from dataset import OBQAProcessor
import random
import numpy as np
import glob
import os
import re
def set_seed(seed):
random.seed(seed)
np.random.seed(seed)
torch.manual_seed(seed)
if torch.cuda.is_available():
torch.cuda.manual_seed_all(seed)
def extractValLoss(checkpoint_path):
"""Eg checkpoint path format: path_to_dir/checkpoint_epoch=4-val_loss=0.450662.ckpt"""
val_loss = float(re.search('val_loss=(.+?).ckpt', checkpoint_path).group(1))
return val_loss
def extractStepOREpochNum(checkpoint_path):
"""Eg checkpoint path format: path_to_dir/checkpoint_epoch=4.ckpt (or)
path_to_dir/checkpoint_epoch=4-step=50.ckpt (or)
"""
if "step" in checkpoint_path:
num = int(re.search('step=(.+?).ckpt', checkpoint_path).group(1))
else:
num = int(re.search('epoch=(.+?).ckpt', checkpoint_path).group(1))
return num
def getBestModelCheckpointPath(checkpoint_dir):
checkpoint_list = glob.glob(os.path.join(checkpoint_dir, "checkpoint_*.ckpt"))
try:
# Get the checkpoint with lowest validation loss
sorted_list = sorted(checkpoint_list, key=lambda x: extractValLoss(x.split("/")[-1]))
except:
# If validation loss is not present, get the checkpoint with highest step number or epoch number.
sorted_list = sorted(checkpoint_list, key=lambda x: extractStepOREpochNum(x.split("/")[-1]), reverse=True)
return sorted_list[0]
def run():
#torch.multiprocessing.freeze_support()
set_seed(42)
parser = argparse.ArgumentParser()
parser.add_argument('--data_dir', type=str, default="datasets/openbookqa",
help='Path for Data files')
parser.add_argument('--use_KB', type=lambda x: (str(x).lower() == 'true'), default="False",
help='Whether to use Knowledge Base while constructing the dataset (question/answer) pairs?')
parser.add_argument('--output_dir', type=str, default="outputs/openbookqa_prediction_outputs",
help='Path to save the checkpoints')
parser.add_argument('--checkpoint_dir', type=str, default="outputs/openbookqa_outputs",
help='Checkpoint directory')
parser.add_argument('--tokenizer_name_or_path', type=str, default="t5-base",
help='Tokenizer name or Path')
parser.add_argument('--max_seq_length', type=int, default=128,
help='Maximum Sequence Length')
parser.add_argument('--eval_batch_size', type=int, default=4,
help='Batch size for Evaluation')
args = parser.parse_known_args()[0]
print(args)
# Create a folder if output_dir doesn't exists:
if not os.path.exists(args.output_dir):
os.makedirs(args.output_dir)
print("Creating output directory")
best_checkpoint_path = getBestModelCheckpointPath(args.checkpoint_dir)
print("Using checkpoint = ", str(best_checkpoint_path))
t5model = T5FineTuner.load_from_checkpoint(best_checkpoint_path)
tokenizer = T5Tokenizer.from_pretrained(args.tokenizer_name_or_path)
dev_csvfile = open(os.path.join(args.output_dir, 'dev.csv'),'w')
dev_writier = csv.writer(dev_csvfile)
proc = OBQAProcessor(args.use_KB)
dev_examples = proc.get_dev_examples(args.data_dir)
def chunks(lst, n):
for i in range(0, len(lst), n):
yield lst[i : i + n]
device = "cuda" if torch.cuda.is_available() else "cpu"
print(device)
t5model.to(device)
for batch in tqdm(list(chunks(dev_examples, args.eval_batch_size))):
batch_question = [b.question for b in batch]
options = [['%s: %s' % (i, option) for i, option in zip('1234', b.answers)] for b in batch]
options = [" ".join(opts) for opts in options]
if args.use_KB:
articles = [b.article for b in batch]
else:
articles = None
inputs = []
if args.use_KB:
for article, question, option in zip(articles, batch_question, options):
inputs.append("article: %s context: %s options: %s </s>" % (article[0:300], question, option))
else:
for question, option in zip(batch_question, options):
inputs.append("context: %s options: %s </s>" % (question, option))
dct = tokenizer.batch_encode_plus(inputs, max_length=args.max_seq_length, return_tensors="pt", pad_to_max_length=True, truncation=True)
outs = t5model.model.generate(input_ids=dct['input_ids'].cuda(),
attention_mask=dct['attention_mask'].cuda(),
max_length=2)
LABELS = ['A', 'B', 'C', 'D']
dec = [LABELS[int(tokenizer.decode(ids))-1] for ids in outs]
for d in dec:
dev_writier.writerow([d])
test_csvfile = open(os.path.join(args.output_dir, 'test.csv'),'w')
test_writier = csv.writer(test_csvfile)
proc = OBQAProcessor(args.use_KB)
test_examples = proc.get_test_examples(args.data_dir)
for batch in tqdm(list(chunks(test_examples, args.eval_batch_size))):
batch_question = [b.question for b in batch]
options = [['%s: %s' % (i, option) for i, option in zip('1234', b.answers)] for b in batch]
options = [" ".join(opts) for opts in options]
if args.use_KB:
articles = [b.article for b in batch]
else:
articles = None
inputs = []
if args.use_KB:
for article, question, option in zip(articles, batch_question, options):
inputs.append("article: %s context: %s options: %s </s>" % (article[0:300], question, option))
else:
for question, option in zip(batch_question, options):
inputs.append("context: %s options: %s </s>" % (question, option))
dct = tokenizer.batch_encode_plus(inputs, max_length=args.max_seq_length, return_tensors="pt", pad_to_max_length=True, truncation=True)
outs = t5model.model.generate(input_ids=dct['input_ids'].cuda(),
attention_mask=dct['attention_mask'].cuda(),
max_length=2)
LABELS = ['A', 'B', 'C', 'D']
dec = [LABELS[int(tokenizer.decode(ids))-1] for ids in outs]
for d in dec:
test_writier.writerow([d])
if __name__ == '__main__':
run()