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Merge pull request #1553 from WilliamTambellini/timeSquadInference

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Add speed log to examples/run_squad.py
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Thomas Wolf
2019-11-05 11:13:12 +01:00
committed by GitHub
parent 237fad339c 0919389d9a
commit ba973342e3
1 changed files with 5 additions and 0 deletions
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examples/run_squad.py
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@@ -22,6 +22,7 @@ import logging
import os import os
import random import random
import glob import glob
import timeit
import numpy as np import numpy as np
import torch import torch
@@ -221,6 +222,7 @@ def evaluate(args, model, tokenizer, prefix=""):
logger.info(" Num examples = %d", len(dataset)) logger.info(" Num examples = %d", len(dataset))
logger.info(" Batch size = %d", args.eval_batch_size) logger.info(" Batch size = %d", args.eval_batch_size)
all_results = [] all_results = []
start_time = timeit.default_timer()
for batch in tqdm(eval_dataloader, desc="Evaluating"): for batch in tqdm(eval_dataloader, desc="Evaluating"):
model.eval() model.eval()
batch = tuple(t.to(args.device) for t in batch) batch = tuple(t.to(args.device) for t in batch)
@@ -253,6 +255,9 @@ def evaluate(args, model, tokenizer, prefix=""):
end_logits = to_list(outputs[1][i])) end_logits = to_list(outputs[1][i]))
all_results.append(result) all_results.append(result)
evalTime = timeit.default_timer() - start_time
logger.info(" Evaluation done in total %f secs (%f sec per example)", evalTime, evalTime / len(dataset))
# Compute predictions # Compute predictions
output_prediction_file = os.path.join(args.output_dir, "predictions_{}.json".format(prefix)) output_prediction_file = os.path.join(args.output_dir, "predictions_{}.json".format(prefix))
output_nbest_file = os.path.join(args.output_dir, "nbest_predictions_{}.json".format(prefix)) output_nbest_file = os.path.join(args.output_dir, "nbest_predictions_{}.json".format(prefix))