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"""
Copyright (c) 2022, salesforce.com, inc.
All rights reserved.
SPDX-License-Identifier: BSD-3-Clause
For full license text, see the LICENSE file in the repo root or https://opensource.org/licenses/BSD-3-Clause
"""
import json
import os
import logging
import numpy as np
import torch
from lavis.common.dist_utils import main_process
from lavis.common.registry import registry
from lavis.tasks.base_task import BaseTask
@registry.register_task("multimodal_classification")
class MultimodalClassificationTask(BaseTask):
def __init__(self):
super().__init__()
def valid_step(self, model, samples):
results = []
outputs = model.predict(samples)
predictions = outputs["predictions"]
targets = outputs["targets"]
predictions = predictions.max(1)[1].cpu().numpy()
targets = targets.cpu().numpy()
indices = samples[self.inst_id_key]
for pred, tgt, index in zip(predictions, targets, indices):
if isinstance(index, torch.Tensor):
index = index.item()
results.append(
{
self.inst_id_key: index,
"prediction": pred.item(),
"target": tgt.item(),
}
)
return results
def after_evaluation(self, val_result, split_name, epoch, **kwargs):
eval_result_file = self.save_result(
result=val_result,
result_dir=registry.get_path("result_dir"),
filename="{}_epoch{}".format(split_name, epoch),
remove_duplicate=self.inst_id_key,
)
metrics = self._report_metrics(
eval_result_file=eval_result_file, split_name=split_name
)
return metrics
@main_process
def _report_metrics(self, eval_result_file, split_name):
results = json.load(open(eval_result_file))
predictions = np.array([res["prediction"] for res in results])
targets = np.array([res["target"] for res in results])
accuracy = (targets == predictions).sum() / targets.shape[0]
metrics = {"agg_metrics": accuracy, "acc": accuracy}
log_stats = {split_name: {k: v for k, v in metrics.items()}}
with open(
os.path.join(registry.get_path("output_dir"), "evaluate.txt"), "a"
) as f:
f.write(json.dumps(log_stats) + "\n")
logging.info(metrics)
return metrics
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