mmdetection-2.26.0/tests/test_runtime/test_eval_hook.py

# Copyright (c) OpenMMLab. All rights reserved.
import os.path as osp
import tempfile
import unittest.mock as mock
from collections import OrderedDict
from unittest.mock import MagicMock, patch

import pytest
import torch
import torch.nn as nn
from mmcv.runner import EpochBasedRunner, build_optimizer
from mmcv.utils import get_logger
from torch.utils.data import DataLoader, Dataset

from mmdet.core import DistEvalHook, EvalHook


class ExampleDataset(Dataset):

    def __init__(self):
        self.index = 0
        self.eval_result = [0.1, 0.4, 0.3, 0.7, 0.2, 0.05, 0.4, 0.6]

    def __getitem__(self, idx):
        results = dict(imgs=torch.tensor([1]))
        return results

    def __len__(self):
        return 1

    @mock.create_autospec
    def evaluate(self, results, logger=None):
        pass


class EvalDataset(ExampleDataset):

    def evaluate(self, results, logger=None):
        mean_ap = self.eval_result[self.index]
        output = OrderedDict(mAP=mean_ap, index=self.index, score=mean_ap)
        self.index += 1
        return output


class ExampleModel(nn.Module):

    def __init__(self):
        super().__init__()
        self.conv = nn.Linear(1, 1)
        self.test_cfg = None

    def forward(self, imgs, rescale=False, return_loss=False):
        return imgs

    def train_step(self, data_batch, optimizer, **kwargs):
        outputs = {
            'loss': 0.5,
            'log_vars': {
                'accuracy': 0.98
            },
            'num_samples': 1
        }
        return outputs


@pytest.mark.skipif(
    not torch.cuda.is_available(), reason='requires CUDA support')
@patch('mmdet.apis.single_gpu_test', MagicMock)
@patch('mmdet.apis.multi_gpu_test', MagicMock)
@pytest.mark.parametrize('EvalHookCls', (EvalHook, DistEvalHook))
def test_eval_hook(EvalHookCls):
    with pytest.raises(TypeError):
        # dataloader must be a pytorch DataLoader
        test_dataset = ExampleDataset()
        data_loader = [
            DataLoader(
                test_dataset,
                batch_size=1,
                sampler=None,
                num_worker=0,
                shuffle=False)
        ]
        EvalHookCls(data_loader)

    with pytest.raises(KeyError):
        # rule must be in keys of rule_map
        test_dataset = ExampleDataset()
        data_loader = DataLoader(
            test_dataset,
            batch_size=1,
            sampler=None,
            num_workers=0,
            shuffle=False)
        EvalHookCls(data_loader, save_best='auto', rule='unsupport')

    with pytest.raises(ValueError):
        # key_indicator must be valid when rule_map is None
        test_dataset = ExampleDataset()
        data_loader = DataLoader(
            test_dataset,
            batch_size=1,
            sampler=None,
            num_workers=0,
            shuffle=False)
        EvalHookCls(data_loader, save_best='unsupport')

    optimizer_cfg = dict(
        type='SGD', lr=0.01, momentum=0.9, weight_decay=0.0001)

    test_dataset = ExampleDataset()
    loader = DataLoader(test_dataset, batch_size=1)
    model = ExampleModel()
    optimizer = build_optimizer(model, optimizer_cfg)

    data_loader = DataLoader(test_dataset, batch_size=1)
    eval_hook = EvalHookCls(data_loader, save_best=None)
    with tempfile.TemporaryDirectory() as tmpdir:
        logger = get_logger('test_eval')
        runner = EpochBasedRunner(
            model=model,
            batch_processor=None,
            optimizer=optimizer,
            work_dir=tmpdir,
            logger=logger)
        runner.register_hook(eval_hook)
        runner.run([loader], [('train', 1)], 1)
        assert runner.meta is None or 'best_score' not in runner.meta[
            'hook_msgs']
        assert runner.meta is None or 'best_ckpt' not in runner.meta[
            'hook_msgs']

    # when `save_best` is set to 'auto', first metric will be used.
    loader = DataLoader(EvalDataset(), batch_size=1)
    model = ExampleModel()
    data_loader = DataLoader(EvalDataset(), batch_size=1)
    eval_hook = EvalHookCls(data_loader, interval=1, save_best='auto')

    with tempfile.TemporaryDirectory() as tmpdir:
        logger = get_logger('test_eval')
        runner = EpochBasedRunner(
            model=model,
            batch_processor=None,
            optimizer=optimizer,
            work_dir=tmpdir,
            logger=logger)
        runner.register_checkpoint_hook(dict(interval=1))
        runner.register_hook(eval_hook)
        runner.run([loader], [('train', 1)], 8)

        real_path = osp.join(tmpdir, 'best_mAP_epoch_4.pth')

        assert runner.meta['hook_msgs']['best_ckpt'] == osp.realpath(real_path)
        assert runner.meta['hook_msgs']['best_score'] == 0.7

    loader = DataLoader(EvalDataset(), batch_size=1)
    model = ExampleModel()
    data_loader = DataLoader(EvalDataset(), batch_size=1)
    eval_hook = EvalHookCls(data_loader, interval=1, save_best='mAP')

    with tempfile.TemporaryDirectory() as tmpdir:
        logger = get_logger('test_eval')
        runner = EpochBasedRunner(
            model=model,
            batch_processor=None,
            optimizer=optimizer,
            work_dir=tmpdir,
            logger=logger)
        runner.register_checkpoint_hook(dict(interval=1))
        runner.register_hook(eval_hook)
        runner.run([loader], [('train', 1)], 8)

        real_path = osp.join(tmpdir, 'best_mAP_epoch_4.pth')

        assert runner.meta['hook_msgs']['best_ckpt'] == osp.realpath(real_path)
        assert runner.meta['hook_msgs']['best_score'] == 0.7

    data_loader = DataLoader(EvalDataset(), batch_size=1)
    eval_hook = EvalHookCls(
        data_loader, interval=1, save_best='score', rule='greater')
    with tempfile.TemporaryDirectory() as tmpdir:
        logger = get_logger('test_eval')
        runner = EpochBasedRunner(
            model=model,
            batch_processor=None,
            optimizer=optimizer,
            work_dir=tmpdir,
            logger=logger)
        runner.register_checkpoint_hook(dict(interval=1))
        runner.register_hook(eval_hook)
        runner.run([loader], [('train', 1)], 8)

        real_path = osp.join(tmpdir, 'best_score_epoch_4.pth')

        assert runner.meta['hook_msgs']['best_ckpt'] == osp.realpath(real_path)
        assert runner.meta['hook_msgs']['best_score'] == 0.7

    data_loader = DataLoader(EvalDataset(), batch_size=1)
    eval_hook = EvalHookCls(data_loader, save_best='mAP', rule='less')
    with tempfile.TemporaryDirectory() as tmpdir:
        logger = get_logger('test_eval')
        runner = EpochBasedRunner(
            model=model,
            batch_processor=None,
            optimizer=optimizer,
            work_dir=tmpdir,
            logger=logger)
        runner.register_checkpoint_hook(dict(interval=1))
        runner.register_hook(eval_hook)
        runner.run([loader], [('train', 1)], 8)

        real_path = osp.join(tmpdir, 'best_mAP_epoch_6.pth')

        assert runner.meta['hook_msgs']['best_ckpt'] == osp.realpath(real_path)
        assert runner.meta['hook_msgs']['best_score'] == 0.05

    data_loader = DataLoader(EvalDataset(), batch_size=1)
    eval_hook = EvalHookCls(data_loader, save_best='mAP')
    with tempfile.TemporaryDirectory() as tmpdir:
        logger = get_logger('test_eval')
        runner = EpochBasedRunner(
            model=model,
            batch_processor=None,
            optimizer=optimizer,
            work_dir=tmpdir,
            logger=logger)
        runner.register_checkpoint_hook(dict(interval=1))
        runner.register_hook(eval_hook)
        runner.run([loader], [('train', 1)], 2)

        real_path = osp.join(tmpdir, 'best_mAP_epoch_2.pth')

        assert runner.meta['hook_msgs']['best_ckpt'] == osp.realpath(real_path)
        assert runner.meta['hook_msgs']['best_score'] == 0.4

        resume_from = osp.join(tmpdir, 'latest.pth')
        loader = DataLoader(ExampleDataset(), batch_size=1)
        eval_hook = EvalHookCls(data_loader, save_best='mAP')
        runner = EpochBasedRunner(
            model=model,
            batch_processor=None,
            optimizer=optimizer,
            work_dir=tmpdir,
            logger=logger)
        runner.register_checkpoint_hook(dict(interval=1))
        runner.register_hook(eval_hook)
        runner.resume(resume_from)
        runner.run([loader], [('train', 1)], 8)

        real_path = osp.join(tmpdir, 'best_mAP_epoch_4.pth')

        assert runner.meta['hook_msgs']['best_ckpt'] == osp.realpath(real_path)
        assert runner.meta['hook_msgs']['best_score'] == 0.7