mirror of
https://github.com/open-mmlab/mmsegmentation.git
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e235c1a824
* Support progressive test with fewer memory cost. * Temp code * Using processor to refactor evaluation workflow. * refactor eval hook. * Fix process bar. * Fix middle save argument. * Modify some variable name of dataset evaluate api. * Modify some viriable name of eval hook. * Fix some priority bugs of eval hook. * Depreciated efficient_test. * Fix training progress blocked by eval hook. * Depreciated old test api. * Fix test api error. * Modify outer api. * Build a sampler test api. * TODO: Refactor format_results. * Modify variable names. * Fix num_classes bug. * Fix sampler index bug. * Fix grammaly bug. * Support batch sampler. * More readable test api. * Remove some command arg and fix eval hook bug. * Support format-only arg. * Modify format_results of datasets. * Modify tool which use test apis. * support cityscapes eval * fixed cityscapes * 1. Add comments for batch_sampler; 2. Keep eval hook api same and add deprecated warning; 3. Add doc string for dataset.pre_eval; * Add efficient_test doc string. * Modify test tool to compat old version. * Modify eval hook to compat with old version. * Modify test api to compat old version api. * Sampler explanation. * update warning * Modify deploy_test.py * compatible with old output, add efficient test back * clear logic of exclusive * Warning about efficient_test. * Modify format_results save folder. * Fix bugs of format_results. * Modify deploy_test.py. * Update doc * Fix deploy test bugs. * Fix custom dataset unit tests. * Fix dataset unit tests. * Fix eval hook unit tests. * Fix some imcompatible. * Add pre_eval argument for eval hooks. * Update eval hook doc string. * Make pre_eval false in default. * Add unit tests for dataset format_results. * Fix some comments and bc-breaking bug. * Fix pre_eval set cfg field. * Remove redundant codes. Co-authored-by: Jiarui XU <xvjiarui0826@gmail.com>
73 lines
1.8 KiB
Python
73 lines
1.8 KiB
Python
import shutil
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from unittest.mock import MagicMock
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import numpy as np
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import pytest
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import torch
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import torch.nn as nn
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from torch.utils.data import DataLoader, Dataset, dataloader
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from mmseg.apis import single_gpu_test
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class ExampleDataset(Dataset):
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def __getitem__(self, idx):
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results = dict(img=torch.tensor([1]), img_metas=dict())
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return results
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def __len__(self):
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return 1
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class ExampleModel(nn.Module):
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def __init__(self):
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super(ExampleModel, self).__init__()
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self.test_cfg = None
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self.conv = nn.Conv2d(3, 3, 3)
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def forward(self, img, img_metas, return_loss=False, **kwargs):
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return img
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def test_single_gpu():
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test_dataset = ExampleDataset()
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data_loader = DataLoader(
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test_dataset,
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batch_size=1,
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sampler=None,
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num_workers=0,
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shuffle=False,
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)
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model = ExampleModel()
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# Test efficient test compatibility (will be deprecated)
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results = single_gpu_test(model, data_loader, efficient_test=True)
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assert len(results) == 1
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pred = np.load(results[0])
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assert isinstance(pred, np.ndarray)
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assert pred.shape == (1, )
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assert pred[0] == 1
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shutil.rmtree('.efficient_test')
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# Test pre_eval
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test_dataset.pre_eval = MagicMock(return_value=['success'])
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results = single_gpu_test(model, data_loader, pre_eval=True)
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assert results == ['success']
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# Test format_only
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test_dataset.format_results = MagicMock(return_value=['success'])
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results = single_gpu_test(model, data_loader, format_only=True)
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assert results == ['success']
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# efficient_test, pre_eval and format_only are mutually exclusive
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with pytest.raises(AssertionError):
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single_gpu_test(
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model,
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dataloader,
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efficient_test=True,
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format_only=True,
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pre_eval=True)
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