in src/datatuner/classification/run_classifier.py [0:0]
def evaluate(args, model, tokenizer, prefix=""):
# Loop to handle MNLI double evaluation (matched, mis-matched)
eval_task_names = ("mnli", "mnli-mm") if args.task_name == "mnli" else (args.task_name,)
# eval_outputs_dirs = (args.output_dir, args.output_dir + "-MM") if args.task_name == "mnli" else (args.output_dir,)
if args.stats_dir is None:
args.stats_dir = args.output_dir
eval_outputs_dirs = (args.stats_dir,) if args.task_name == "mnli" else (args.stats_dir,)
results = {}
for eval_task, eval_output_dir in zip(eval_task_names, eval_outputs_dirs):
eval_dataset, label_list = load_and_cache_examples(args, eval_task, tokenizer, evaluate=True)
if not os.path.exists(eval_output_dir) and args.local_rank in [-1, 0]:
os.makedirs(eval_output_dir)
args.eval_batch_size = args.per_gpu_eval_batch_size * max(1, args.n_gpu)
# Note that DistributedSampler samples randomly
eval_sampler = SequentialSampler(eval_dataset) if args.local_rank == -1 else DistributedSampler(eval_dataset)
eval_dataloader = DataLoader(eval_dataset, sampler=eval_sampler, batch_size=args.eval_batch_size)
# Eval!
logger.info("***** Running evaluation {} *****".format(prefix))
logger.info(" Num examples = %d", len(eval_dataset))
logger.info(" Batch size = %d", args.eval_batch_size)
eval_loss = 0.0
nb_eval_steps = 0
preds = None
out_label_ids = None
for batch in tqdm(eval_dataloader, desc="Evaluating"):
model.eval()
batch = tuple(t.to(args.device) for t in batch)
with torch.no_grad():
inputs = {"input_ids": batch[0], "attention_mask": batch[1], "labels": batch[3]}
if args.model_type != "distilbert":
inputs["token_type_ids"] = (
batch[2] if args.model_type in ["bert", "xlnet"] else None
) # XLM, DistilBERT and RoBERTa don't use segment_ids
outputs = model(**inputs)
tmp_eval_loss, logits = outputs[:2]
eval_loss += tmp_eval_loss.mean().item()
nb_eval_steps += 1
if preds is None:
preds = logits.detach().cpu().numpy()
out_label_ids = inputs["labels"].detach().cpu().numpy()
else:
preds = np.append(preds, logits.detach().cpu().numpy(), axis=0)
out_label_ids = np.append(out_label_ids, inputs["labels"].detach().cpu().numpy(), axis=0)
eval_loss = eval_loss / nb_eval_steps
if args.output_mode == "classification":
preds_prob = np.max(F.softmax(torch.tensor(preds), dim=-1).cpu().numpy(), axis=1)
preds = np.argmax(preds, axis=1)
elif args.output_mode == "regression":
preds = np.squeeze(preds)
results.update(
{"preds": list(label_list[int(x)] for x in preds), "preds_prob": list(float(x) for x in preds_prob)}
)
try:
if args.passed_examples:
return results
except:
pass
result = compute_metrics(eval_task, preds, out_label_ids)
results.update(result)
eval_output_dir = Path(eval_output_dir)
json.dump(results, open(eval_output_dir / "results.json", "w"), indent=2)
try:
c_report = classification_report(
out_label_ids, preds, target_names=label_list, labels=list(range(len(label_list)))
)
print(c_report)
cm = ConfusionMatrix([label_list[x] for x in out_label_ids], [label_list[x] for x in preds])
str_cm = str(cm)
print(str_cm)
str_stats = cm._str_stats()
print(str_stats)
cm.plot().get_figure().savefig(eval_output_dir / "output.png")
except:
c_report, str_cm, str_stats = "", "", ""
output_eval_file = os.path.join(eval_output_dir, prefix, "eval_results.txt")
with open(output_eval_file, "w") as writer:
logger.info("***** Eval results {} *****".format(prefix))
for key in sorted(result.keys()):
logger.info(" %s = %s", key, str(result[key]))
writer.write("%s = %s\n" % (key, str(result[key])))
writer.write("\n" + c_report + "\n")
writer.write("\n" + str_cm + "\n")
writer.write("\n" + str_stats + "\n")
return results