| import logging |
| import tempfile |
| from unittest.mock import MagicMock, patch |
|
|
| import mmcv.runner |
| import pytest |
| import torch |
| import torch.nn as nn |
| from mmcv.runner import obj_from_dict |
| from torch.utils.data import DataLoader, Dataset |
|
|
| from mmseg.apis import single_gpu_test |
| from mmseg.core import DistEvalHook, EvalHook |
|
|
|
|
| class ExampleDataset(Dataset): |
|
|
| def __getitem__(self, idx): |
| results = dict(img=torch.tensor([1]), img_metas=dict()) |
| return results |
|
|
| def __len__(self): |
| return 1 |
|
|
|
|
| class ExampleModel(nn.Module): |
|
|
| def __init__(self): |
| super(ExampleModel, self).__init__() |
| self.test_cfg = None |
| self.conv = nn.Conv2d(3, 3, 3) |
|
|
| def forward(self, img, img_metas, test_mode=False, **kwargs): |
| return img |
|
|
| def train_step(self, data_batch, optimizer): |
| loss = self.forward(**data_batch) |
| return dict(loss=loss) |
|
|
|
|
| def test_iter_eval_hook(): |
| with pytest.raises(TypeError): |
| test_dataset = ExampleModel() |
| data_loader = [ |
| DataLoader( |
| test_dataset, |
| batch_size=1, |
| sampler=None, |
| num_worker=0, |
| shuffle=False) |
| ] |
| EvalHook(data_loader) |
|
|
| test_dataset = ExampleDataset() |
| test_dataset.evaluate = MagicMock(return_value=dict(test='success')) |
| loader = DataLoader(test_dataset, batch_size=1) |
| model = ExampleModel() |
| data_loader = DataLoader( |
| test_dataset, batch_size=1, sampler=None, num_workers=0, shuffle=False) |
| optim_cfg = dict(type='SGD', lr=0.01, momentum=0.9, weight_decay=0.0005) |
| optimizer = obj_from_dict(optim_cfg, torch.optim, |
| dict(params=model.parameters())) |
|
|
| |
| with tempfile.TemporaryDirectory() as tmpdir: |
| eval_hook = EvalHook(data_loader) |
| runner = mmcv.runner.IterBasedRunner( |
| model=model, |
| optimizer=optimizer, |
| work_dir=tmpdir, |
| logger=logging.getLogger()) |
| runner.register_hook(eval_hook) |
| runner.run([loader], [('train', 1)], 1) |
| test_dataset.evaluate.assert_called_with([torch.tensor([1])], |
| logger=runner.logger) |
|
|
|
|
| def test_epoch_eval_hook(): |
| with pytest.raises(TypeError): |
| test_dataset = ExampleModel() |
| data_loader = [ |
| DataLoader( |
| test_dataset, |
| batch_size=1, |
| sampler=None, |
| num_worker=0, |
| shuffle=False) |
| ] |
| EvalHook(data_loader, by_epoch=True) |
|
|
| test_dataset = ExampleDataset() |
| test_dataset.evaluate = MagicMock(return_value=dict(test='success')) |
| loader = DataLoader(test_dataset, batch_size=1) |
| model = ExampleModel() |
| data_loader = DataLoader( |
| test_dataset, batch_size=1, sampler=None, num_workers=0, shuffle=False) |
| optim_cfg = dict(type='SGD', lr=0.01, momentum=0.9, weight_decay=0.0005) |
| optimizer = obj_from_dict(optim_cfg, torch.optim, |
| dict(params=model.parameters())) |
|
|
| |
| with tempfile.TemporaryDirectory() as tmpdir: |
| eval_hook = EvalHook(data_loader, by_epoch=True, interval=2) |
| runner = mmcv.runner.EpochBasedRunner( |
| model=model, |
| optimizer=optimizer, |
| work_dir=tmpdir, |
| logger=logging.getLogger()) |
| runner.register_hook(eval_hook) |
| runner.run([loader], [('train', 1)], 2) |
| test_dataset.evaluate.assert_called_once_with([torch.tensor([1])], |
| logger=runner.logger) |
|
|
|
|
| def multi_gpu_test(model, data_loader, tmpdir=None, gpu_collect=False): |
| results = single_gpu_test(model, data_loader) |
| return results |
|
|
|
|
| @patch('mmseg.apis.multi_gpu_test', multi_gpu_test) |
| def test_dist_eval_hook(): |
| with pytest.raises(TypeError): |
| test_dataset = ExampleModel() |
| data_loader = [ |
| DataLoader( |
| test_dataset, |
| batch_size=1, |
| sampler=None, |
| num_worker=0, |
| shuffle=False) |
| ] |
| DistEvalHook(data_loader) |
|
|
| test_dataset = ExampleDataset() |
| test_dataset.evaluate = MagicMock(return_value=dict(test='success')) |
| loader = DataLoader(test_dataset, batch_size=1) |
| model = ExampleModel() |
| data_loader = DataLoader( |
| test_dataset, batch_size=1, sampler=None, num_workers=0, shuffle=False) |
| optim_cfg = dict(type='SGD', lr=0.01, momentum=0.9, weight_decay=0.0005) |
| optimizer = obj_from_dict(optim_cfg, torch.optim, |
| dict(params=model.parameters())) |
|
|
| |
| with tempfile.TemporaryDirectory() as tmpdir: |
| eval_hook = DistEvalHook(data_loader) |
| runner = mmcv.runner.IterBasedRunner( |
| model=model, |
| optimizer=optimizer, |
| work_dir=tmpdir, |
| logger=logging.getLogger()) |
| runner.register_hook(eval_hook) |
| runner.run([loader], [('train', 1)], 1) |
| test_dataset.evaluate.assert_called_with([torch.tensor([1])], |
| logger=runner.logger) |
|
|
|
|
| @patch('mmseg.apis.multi_gpu_test', multi_gpu_test) |
| def test_dist_eval_hook_epoch(): |
| with pytest.raises(TypeError): |
| test_dataset = ExampleModel() |
| data_loader = [ |
| DataLoader( |
| test_dataset, |
| batch_size=1, |
| sampler=None, |
| num_worker=0, |
| shuffle=False) |
| ] |
| DistEvalHook(data_loader) |
|
|
| test_dataset = ExampleDataset() |
| test_dataset.evaluate = MagicMock(return_value=dict(test='success')) |
| loader = DataLoader(test_dataset, batch_size=1) |
| model = ExampleModel() |
| data_loader = DataLoader( |
| test_dataset, batch_size=1, sampler=None, num_workers=0, shuffle=False) |
| optim_cfg = dict(type='SGD', lr=0.01, momentum=0.9, weight_decay=0.0005) |
| optimizer = obj_from_dict(optim_cfg, torch.optim, |
| dict(params=model.parameters())) |
|
|
| |
| with tempfile.TemporaryDirectory() as tmpdir: |
| eval_hook = DistEvalHook(data_loader, by_epoch=True, interval=2) |
| runner = mmcv.runner.EpochBasedRunner( |
| model=model, |
| optimizer=optimizer, |
| work_dir=tmpdir, |
| logger=logging.getLogger()) |
| runner.register_hook(eval_hook) |
| runner.run([loader], [('train', 1)], 2) |
| test_dataset.evaluate.assert_called_with([torch.tensor([1])], |
| logger=runner.logger) |
|
|
