|
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) |
|
|
