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@patch('torch.cuda.device_count', return_value=1)
@patch('torch.cuda.set_device')
@patch('torch.distributed.init_process_group')
@patch('subprocess.getoutput', return_value='127.0.0.1')
def test_init_dist(mock_getoutput, mock_dist_init, mock_set_device, mock_device_count):
with pytest.raises(ValueError):
... |
class ExampleDataset(Dataset):
def __init__(self):
self.index = 0
self.eval_result = [1, 4, 3, 7, 2, (- 3), 4, 6]
def __getitem__(self, idx):
results = dict(x=torch.tensor([1]))
return results
def __len__(self):
return 1
@mock.create_autospec
def evaluat... |
class EvalDataset(ExampleDataset):
def evaluate(self, results, logger=None):
acc = self.eval_result[self.index]
output = OrderedDict(acc=acc, index=self.index, score=acc, loss_top=acc)
self.index += 1
return output
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class Model(nn.Module):
def __init__(self):
super().__init__()
self.param = nn.Parameter(torch.tensor([1.0]))
def forward(self, x, **kwargs):
return (self.param * x)
def train_step(self, data_batch, optimizer, **kwargs):
return {'loss': torch.sum(self(data_batch['x']))}
... |
def _build_epoch_runner():
model = Model()
tmp_dir = tempfile.mkdtemp()
runner = EpochBasedRunner(model=model, work_dir=tmp_dir, logger=get_logger('demo'))
return runner
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def _build_iter_runner():
model = Model()
tmp_dir = tempfile.mkdtemp()
runner = IterBasedRunner(model=model, work_dir=tmp_dir, logger=get_logger('demo'))
return runner
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class EvalHook(BaseEvalHook):
_default_greater_keys = ['acc', 'top']
_default_less_keys = ['loss', 'loss_top']
def __init__(self, *args, **kwargs):
super().__init__(*args, **kwargs)
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class DistEvalHook(BaseDistEvalHook):
greater_keys = ['acc', 'top']
less_keys = ['loss', 'loss_top']
def __init__(self, *args, **kwargs):
super().__init__(*args, **kwargs)
|
def test_eval_hook():
with pytest.raises(AssertionError):
test_dataset = Model()
data_loader = DataLoader(test_dataset)
EvalHook(data_loader, save_best=True)
with pytest.raises(TypeError):
test_dataset = Model()
data_loader = [DataLoader(test_dataset)]
EvalHook(... |
@patch('mmcv.engine.single_gpu_test', MagicMock)
@patch('mmcv.engine.multi_gpu_test', MagicMock)
@pytest.mark.parametrize('EvalHookParam', [EvalHook, DistEvalHook])
@pytest.mark.parametrize('_build_demo_runner,by_epoch', [(_build_epoch_runner, True), (_build_iter_runner, False)])
def test_start_param(EvalHookParam, _... |
@pytest.mark.parametrize('runner,by_epoch,eval_hook_priority', [(EpochBasedRunner, True, 'NORMAL'), (EpochBasedRunner, True, 'LOW'), (IterBasedRunner, False, 'LOW')])
def test_logger(runner, by_epoch, eval_hook_priority):
loader = DataLoader(EvalDataset())
model = Model()
data_loader = DataLoader(EvalData... |
def test_cast_tensor_type():
inputs = torch.FloatTensor([5.0])
src_type = torch.float32
dst_type = torch.int32
outputs = cast_tensor_type(inputs, src_type, dst_type)
assert isinstance(outputs, torch.Tensor)
assert (outputs.dtype == dst_type)
inputs = torch.FloatTensor([5.0])
src_type =... |
def test_auto_fp16():
with pytest.raises(TypeError):
class ExampleObject(object):
@auto_fp16()
def __call__(self, x):
return x
model = ExampleObject()
input_x = torch.ones(1, dtype=torch.float32)
model(input_x)
class ExampleModule(nn.M... |
def test_force_fp32():
with pytest.raises(TypeError):
class ExampleObject(object):
@force_fp32()
def __call__(self, x):
return x
model = ExampleObject()
input_x = torch.ones(1, dtype=torch.float32)
model(input_x)
class ExampleModule(nn... |
def test_optimizerhook():
class Model(nn.Module):
def __init__(self):
super().__init__()
self.conv1 = nn.Conv2d(in_channels=1, out_channels=2, kernel_size=3, stride=1, padding=1, dilation=1)
self.conv2 = nn.Conv2d(in_channels=2, out_channels=2, kernel_size=3, stride=1... |
def test_checkpoint_hook(tmp_path):
'xdoctest -m tests/test_runner/test_hooks.py test_checkpoint_hook.'
loader = DataLoader(torch.ones((5, 2)))
runner = _build_demo_runner('EpochBasedRunner', max_epochs=1)
runner.meta = dict()
checkpointhook = CheckpointHook(interval=1, by_epoch=True)
runner.r... |
def test_ema_hook():
'xdoctest -m tests/test_hooks.py test_ema_hook.'
class DemoModel(nn.Module):
def __init__(self):
super().__init__()
self.conv = nn.Conv2d(in_channels=1, out_channels=2, kernel_size=1, padding=1, bias=True)
self._init_weight()
def _ini... |
def test_custom_hook():
@HOOKS.register_module()
class ToyHook(Hook):
def __init__(self, info, *args, **kwargs):
super().__init__()
self.info = info
runner = _build_demo_runner_without_hook('EpochBasedRunner', max_epochs=1)
runner.register_custom_hooks(None)
asser... |
def test_pavi_hook():
sys.modules['pavi'] = MagicMock()
loader = DataLoader(torch.ones((5, 2)))
runner = _build_demo_runner()
runner.meta = dict(config_dict=dict(lr=0.02, gpu_ids=range(1)))
hook = PaviLoggerHook(add_graph=False, add_last_ckpt=True)
runner.register_hook(hook)
runner.run([lo... |
def test_sync_buffers_hook():
loader = DataLoader(torch.ones((5, 2)))
runner = _build_demo_runner()
runner.register_hook_from_cfg(dict(type='SyncBuffersHook'))
runner.run([loader, loader], [('train', 1), ('val', 1)])
shutil.rmtree(runner.work_dir)
|
@pytest.mark.parametrize('multi_optimizers, max_iters, gamma, cyclic_times', [(True, 8, 1, 1), (False, 8, 0.5, 2)])
def test_momentum_runner_hook(multi_optimizers, max_iters, gamma, cyclic_times):
'xdoctest -m tests/test_hooks.py test_momentum_runner_hook.'
sys.modules['pavi'] = MagicMock()
loader = DataL... |
@pytest.mark.parametrize('multi_optimizers', (True, False))
def test_cosine_runner_hook(multi_optimizers):
'xdoctest -m tests/test_hooks.py test_cosine_runner_hook.'
sys.modules['pavi'] = MagicMock()
loader = DataLoader(torch.ones((10, 2)))
runner = _build_demo_runner(multi_optimizers=multi_optimizers... |
@pytest.mark.parametrize('multi_optimizers, by_epoch', [(False, False), (True, False), (False, True), (True, True)])
def test_flat_cosine_runner_hook(multi_optimizers, by_epoch):
'xdoctest -m tests/test_hooks.py test_flat_cosine_runner_hook.'
sys.modules['pavi'] = MagicMock()
loader = DataLoader(torch.one... |
@pytest.mark.parametrize('multi_optimizers, max_iters', [(True, 10), (True, 2), (False, 10), (False, 2)])
def test_one_cycle_runner_hook(multi_optimizers, max_iters):
'Test OneCycleLrUpdaterHook and OneCycleMomentumUpdaterHook.'
with pytest.raises(AssertionError):
OneCycleLrUpdaterHook(max_lr=0.1, by_... |
@pytest.mark.parametrize('multi_optimizers', (True, False))
def test_cosine_restart_lr_update_hook(multi_optimizers):
'Test CosineRestartLrUpdaterHook.'
with pytest.raises(AssertionError):
CosineRestartLrUpdaterHook(by_epoch=False, periods=[2, 10], restart_weights=[0.5, 0.5], min_lr=0.1, min_lr_ratio=... |
@pytest.mark.parametrize('multi_optimizers', (True, False))
def test_step_runner_hook(multi_optimizers):
'Test StepLrUpdaterHook.'
with pytest.raises(TypeError):
StepLrUpdaterHook()
with pytest.raises(AssertionError):
StepLrUpdaterHook((- 10))
with pytest.raises(AssertionError):
... |
@pytest.mark.parametrize('multi_optimizers, max_iters, gamma, cyclic_times', [(True, 8, 1, 1), (False, 8, 0.5, 2)])
def test_cyclic_lr_update_hook(multi_optimizers, max_iters, gamma, cyclic_times):
'Test CyclicLrUpdateHook.'
with pytest.raises(AssertionError):
CyclicLrUpdaterHook(by_epoch=True)
wi... |
@pytest.mark.parametrize('log_model', (True, False))
def test_mlflow_hook(log_model):
sys.modules['mlflow'] = MagicMock()
sys.modules['mlflow.pytorch'] = MagicMock()
runner = _build_demo_runner()
loader = DataLoader(torch.ones((5, 2)))
hook = MlflowLoggerHook(exp_name='test', log_model=log_model)
... |
def test_segmind_hook():
sys.modules['segmind'] = MagicMock()
runner = _build_demo_runner()
hook = SegmindLoggerHook()
loader = DataLoader(torch.ones((5, 2)))
runner.register_hook(hook)
runner.run([loader, loader], [('train', 1), ('val', 1)])
shutil.rmtree(runner.work_dir)
hook.mlflow_... |
def test_wandb_hook():
sys.modules['wandb'] = MagicMock()
runner = _build_demo_runner()
hook = WandbLoggerHook(log_artifact=True)
loader = DataLoader(torch.ones((5, 2)))
runner.register_hook(hook)
runner.run([loader, loader], [('train', 1), ('val', 1)])
shutil.rmtree(runner.work_dir)
h... |
def test_neptune_hook():
sys.modules['neptune'] = MagicMock()
sys.modules['neptune.new'] = MagicMock()
runner = _build_demo_runner()
hook = NeptuneLoggerHook()
loader = DataLoader(torch.ones((5, 2)))
runner.register_hook(hook)
runner.run([loader, loader], [('train', 1), ('val', 1)])
sh... |
def test_dvclive_hook():
sys.modules['dvclive'] = MagicMock()
runner = _build_demo_runner()
hook = DvcliveLoggerHook()
dvclive_mock = hook.dvclive
loader = DataLoader(torch.ones((5, 2)))
runner.register_hook(hook)
runner.run([loader, loader], [('train', 1), ('val', 1)])
shutil.rmtree(r... |
def test_dvclive_hook_model_file(tmp_path):
sys.modules['dvclive'] = MagicMock()
runner = _build_demo_runner()
hook = DvcliveLoggerHook(model_file=osp.join(runner.work_dir, 'model.pth'))
runner.register_hook(hook)
loader = torch.utils.data.DataLoader(torch.ones((5, 2)))
loader = DataLoader(tor... |
def _build_demo_runner_without_hook(runner_type='EpochBasedRunner', max_epochs=1, max_iters=None, multi_optimizers=False):
class Model(nn.Module):
def __init__(self):
super().__init__()
self.linear = nn.Linear(2, 1)
self.conv = nn.Conv2d(3, 3, 3)
def forward(... |
def _build_demo_runner(runner_type='EpochBasedRunner', max_epochs=1, max_iters=None, multi_optimizers=False):
log_config = dict(interval=1, hooks=[dict(type='TextLoggerHook')])
runner = _build_demo_runner_without_hook(runner_type, max_epochs, max_iters, multi_optimizers)
runner.register_checkpoint_hook(di... |
def test_runner_with_revise_keys():
import os
class Model(nn.Module):
def __init__(self):
super().__init__()
self.conv = nn.Conv2d(3, 3, 1)
class PrefixModel(nn.Module):
def __init__(self):
super().__init__()
self.backbone = Model()
p... |
def test_get_triggered_stages():
class ToyHook(Hook):
def before_run():
pass
def after_epoch():
pass
hook = ToyHook()
expected_stages = ['before_run', 'after_train_epoch', 'after_val_epoch']
assert (hook.get_triggered_stages() == expected_stages)
|
def test_gradient_cumulative_optimizer_hook():
class ToyModel(nn.Module):
def __init__(self, with_norm=False):
super().__init__()
self.fp16_enabled = False
self.fc = nn.Linear(3, 2)
nn.init.constant_(self.fc.weight, 1.0)
nn.init.constant_(self.... |
@pytest.mark.skipif((not torch.cuda.is_available()), reason='requires CUDA support')
def test_gradient_cumulative_fp16_optimizer_hook():
class ToyModel(nn.Module):
def __init__(self):
super().__init__()
self.fp16_enabled = False
self.fc = nn.Linear(3, 2)
n... |
class SubModel(nn.Module):
def __init__(self):
super().__init__()
self.conv1 = nn.Conv2d(2, 2, kernel_size=1, groups=2)
self.gn = nn.GroupNorm(2, 2)
self.param1 = nn.Parameter(torch.ones(1))
def forward(self, x):
return x
|
class ExampleModel(nn.Module):
def __init__(self):
super().__init__()
self.param1 = nn.Parameter(torch.ones(1))
self.conv1 = nn.Conv2d(3, 4, kernel_size=1, bias=False)
self.conv2 = nn.Conv2d(4, 2, kernel_size=1)
self.bn = nn.BatchNorm2d(2)
self.sub = SubModel()
... |
class ExampleDuplicateModel(nn.Module):
def __init__(self):
super().__init__()
self.param1 = nn.Parameter(torch.ones(1))
self.conv1 = nn.Sequential(nn.Conv2d(3, 4, kernel_size=1, bias=False))
self.conv2 = nn.Sequential(nn.Conv2d(4, 2, kernel_size=1))
self.bn = nn.BatchNorm... |
class PseudoDataParallel(nn.Module):
def __init__(self):
super().__init__()
self.module = ExampleModel()
def forward(self, x):
return x
|
def check_default_optimizer(optimizer, model, prefix=''):
assert isinstance(optimizer, torch.optim.SGD)
assert (optimizer.defaults['lr'] == base_lr)
assert (optimizer.defaults['momentum'] == momentum)
assert (optimizer.defaults['weight_decay'] == base_wd)
param_groups = optimizer.param_groups[0]
... |
def check_sgd_optimizer(optimizer, model, prefix='', bias_lr_mult=1, bias_decay_mult=1, norm_decay_mult=1, dwconv_decay_mult=1, dcn_offset_lr_mult=1, bypass_duplicate=False):
param_groups = optimizer.param_groups
assert isinstance(optimizer, torch.optim.SGD)
assert (optimizer.defaults['lr'] == base_lr)
... |
def test_default_optimizer_constructor():
model = ExampleModel()
with pytest.raises(TypeError):
optimizer_cfg = []
optim_constructor = DefaultOptimizerConstructor(optimizer_cfg)
optim_constructor(model)
with pytest.raises(TypeError):
optimizer_cfg = dict(lr=0.0001)
... |
def test_torch_optimizers():
torch_optimizers = ['ASGD', 'Adadelta', 'Adagrad', 'Adam', 'AdamW', 'Adamax', 'LBFGS', 'Optimizer', 'RMSprop', 'Rprop', 'SGD', 'SparseAdam']
assert set(torch_optimizers).issubset(set(TORCH_OPTIMIZERS))
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def test_build_optimizer_constructor():
model = ExampleModel()
optimizer_cfg = dict(type='SGD', lr=base_lr, weight_decay=base_wd, momentum=momentum)
paramwise_cfg = dict(bias_lr_mult=2, bias_decay_mult=0.5, norm_decay_mult=0, dwconv_decay_mult=0.1, dcn_offset_lr_mult=0.1)
optim_constructor_cfg = dict(... |
def test_build_optimizer():
model = ExampleModel()
optimizer_cfg = dict(type='SGD', lr=base_lr, weight_decay=base_wd, momentum=momentum)
optimizer = build_optimizer(model, optimizer_cfg)
check_default_optimizer(optimizer, model)
model = ExampleModel()
optimizer_cfg = dict(type='SGD', lr=base_l... |
class OldStyleModel(nn.Module):
def __init__(self):
super().__init__()
self.conv = nn.Conv2d(3, 3, 1)
|
class Model(OldStyleModel):
def train_step(self):
pass
def val_step(self):
pass
|
def test_build_runner():
temp_root = tempfile.gettempdir()
dir_name = ''.join([random.choice(string.ascii_letters) for _ in range(10)])
default_args = dict(model=Model(), work_dir=osp.join(temp_root, dir_name), logger=logging.getLogger())
cfg = dict(type='EpochBasedRunner', max_epochs=1)
runner = ... |
@pytest.mark.parametrize('runner_class', RUNNERS.module_dict.values())
def test_epoch_based_runner(runner_class):
with pytest.warns(DeprecationWarning):
model = OldStyleModel()
def batch_processor():
pass
_ = runner_class(model, batch_processor, logger=logging.getLogger())
... |
@pytest.mark.parametrize('runner_class', RUNNERS.module_dict.values())
def test_runner_with_parallel(runner_class):
def batch_processor():
pass
model = MMDataParallel(OldStyleModel())
_ = runner_class(model, batch_processor, logger=logging.getLogger())
model = MMDataParallel(Model())
_ = ... |
@pytest.mark.parametrize('runner_class', RUNNERS.module_dict.values())
def test_save_checkpoint(runner_class):
model = Model()
runner = runner_class(model=model, logger=logging.getLogger())
with pytest.raises(TypeError):
runner.save_checkpoint('.', meta=list())
with tempfile.TemporaryDirectory... |
@pytest.mark.parametrize('runner_class', RUNNERS.module_dict.values())
def test_build_lr_momentum_hook(runner_class):
model = Model()
runner = runner_class(model=model, logger=logging.getLogger())
lr_config = dict(policy='CosineAnnealing', by_epoch=False, min_lr_ratio=0, warmup_iters=2, warmup_ratio=0.9)
... |
@pytest.mark.parametrize('runner_class', RUNNERS.module_dict.values())
def test_register_timer_hook(runner_class):
model = Model()
runner = runner_class(model=model, logger=logging.getLogger())
timer_config = None
runner.register_timer_hook(timer_config)
assert (len(runner.hooks) == 0)
timer_c... |
def test_set_random_seed():
set_random_seed(0)
a_random = random.randint(0, 10)
a_np_random = np.random.rand(2, 2)
a_torch_random = torch.rand(2, 2)
assert (torch.backends.cudnn.deterministic is False)
assert (torch.backends.cudnn.benchmark is False)
assert (os.environ['PYTHONHASHSEED'] ==... |
def test_construct():
cfg = Config()
assert (cfg.filename is None)
assert (cfg.text == '')
assert (len(cfg) == 0)
assert (cfg._cfg_dict == {})
with pytest.raises(TypeError):
Config([0, 1])
cfg_dict = dict(item1=[1, 2], item2=dict(a=0), item3=True, item4='test')
cfg_file = osp.j... |
def test_fromfile():
for filename in ['a.py', 'a.b.py', 'b.json', 'c.yaml']:
cfg_file = osp.join(data_path, 'config', filename)
cfg = Config.fromfile(cfg_file)
assert isinstance(cfg, Config)
assert (cfg.filename == cfg_file)
assert (cfg.text == ((osp.abspath(osp.expanduser(... |
def test_fromstring():
for filename in ['a.py', 'a.b.py', 'b.json', 'c.yaml']:
cfg_file = osp.join(data_path, 'config', filename)
file_format = osp.splitext(filename)[(- 1)]
in_cfg = Config.fromfile(cfg_file)
out_cfg = Config.fromstring(in_cfg.pretty_text, '.py')
assert (in... |
def test_merge_from_base():
cfg_file = osp.join(data_path, 'config/d.py')
cfg = Config.fromfile(cfg_file)
assert isinstance(cfg, Config)
assert (cfg.filename == cfg_file)
base_cfg_file = osp.join(data_path, 'config/base.py')
merge_text = ((osp.abspath(osp.expanduser(base_cfg_file)) + '\n') + o... |
def test_merge_from_multiple_bases():
cfg_file = osp.join(data_path, 'config/l.py')
cfg = Config.fromfile(cfg_file)
assert isinstance(cfg, Config)
assert (cfg.filename == cfg_file)
assert (cfg.item1 == [1, 2])
assert (cfg.item2.a == 0)
assert (cfg.item3 is False)
assert (cfg.item4 == '... |
def test_base_variables():
for file in ['t.py', 't.json', 't.yaml']:
cfg_file = osp.join(data_path, f'config/{file}')
cfg = Config.fromfile(cfg_file)
assert isinstance(cfg, Config)
assert (cfg.filename == cfg_file)
assert (cfg.item1 == [1, 2])
assert (cfg.item2.a ==... |
def test_merge_recursive_bases():
cfg_file = osp.join(data_path, 'config/f.py')
cfg = Config.fromfile(cfg_file)
assert isinstance(cfg, Config)
assert (cfg.filename == cfg_file)
assert (cfg.item1 == [2, 3])
assert (cfg.item2.a == 1)
assert (cfg.item3 is False)
assert (cfg.item4 == 'test... |
def test_merge_from_dict():
cfg_file = osp.join(data_path, 'config/a.py')
cfg = Config.fromfile(cfg_file)
input_options = {'item2.a': 1, 'item2.b': 0.1, 'item3': False}
cfg.merge_from_dict(input_options)
assert (cfg.item2 == dict(a=1, b=0.1))
assert (cfg.item3 is False)
cfg_file = osp.join... |
def test_merge_delete():
cfg_file = osp.join(data_path, 'config/delete.py')
cfg = Config.fromfile(cfg_file)
assert (cfg.item1 == dict(a=0))
assert (cfg.item2 == dict(a=0, b=0))
assert (cfg.item3 is True)
assert (cfg.item4 == 'test')
assert ('_delete_' not in cfg.item2)
assert (type(cfg... |
def test_merge_intermediate_variable():
cfg_file = osp.join(data_path, 'config/i_child.py')
cfg = Config.fromfile(cfg_file)
assert (cfg.item1 == [1, 2])
assert (cfg.item2 == dict(a=0))
assert (cfg.item3 is True)
assert (cfg.item4 == 'test')
assert (cfg.item_cfg == dict(b=2))
assert (cf... |
def test_fromfile_in_config():
cfg_file = osp.join(data_path, 'config/code.py')
cfg = Config.fromfile(cfg_file)
assert (cfg.cfg.item1 == [1, 2])
assert (cfg.cfg.item2 == dict(a=0))
assert (cfg.cfg.item3 is True)
assert (cfg.cfg.item4 == 'test')
assert (cfg.item5 == 1)
|
def test_dict():
cfg_dict = dict(item1=[1, 2], item2=dict(a=0), item3=True, item4='test')
for filename in ['a.py', 'b.json', 'c.yaml']:
cfg_file = osp.join(data_path, 'config', filename)
cfg = Config.fromfile(cfg_file)
assert (len(cfg) == 4)
assert (set(cfg.keys()) == set(cfg_d... |
def test_setattr():
cfg = Config()
cfg.item1 = [1, 2]
cfg.item2 = {'a': 0}
cfg['item5'] = {'a': {'b': None}}
assert (cfg._cfg_dict['item1'] == [1, 2])
assert (cfg.item1 == [1, 2])
assert (cfg._cfg_dict['item2'] == {'a': 0})
assert (cfg.item2.a == 0)
assert (cfg._cfg_dict['item5'] =... |
def test_pretty_text():
cfg_file = osp.join(data_path, 'config/l.py')
cfg = Config.fromfile(cfg_file)
with tempfile.TemporaryDirectory() as temp_config_dir:
text_cfg_filename = osp.join(temp_config_dir, '_text_config.py')
with open(text_cfg_filename, 'w') as f:
f.write(cfg.pret... |
def test_dict_action():
parser = argparse.ArgumentParser(description='Train a detector')
parser.add_argument('--options', nargs='+', action=DictAction, help='custom options')
args = parser.parse_args(['--options', 'item2.a=a,b', 'item2.b=[(a,b), [1,2], false]'])
out_dict = {'item2.a': ['a', 'b'], 'ite... |
def test_dump_mapping():
cfg_file = osp.join(data_path, 'config/n.py')
cfg = Config.fromfile(cfg_file)
with tempfile.TemporaryDirectory() as temp_config_dir:
text_cfg_filename = osp.join(temp_config_dir, '_text_config.py')
cfg.dump(text_cfg_filename)
text_cfg = Config.fromfile(text... |
def test_reserved_key():
cfg_file = osp.join(data_path, 'config/g.py')
with pytest.raises(KeyError):
Config.fromfile(cfg_file)
|
def test_syntax_error():
temp_cfg_file = tempfile.NamedTemporaryFile(suffix='.py', delete=False)
temp_cfg_path = temp_cfg_file.name
with open(temp_cfg_path, 'w') as f:
f.write('a=0b=dict(c=1)')
with pytest.raises(SyntaxError, match='There are syntax errors in config file'):
Config.from... |
def test_pickle_support():
cfg_file = osp.join(data_path, 'config/n.py')
cfg = Config.fromfile(cfg_file)
with tempfile.TemporaryDirectory() as temp_config_dir:
pkl_cfg_filename = osp.join(temp_config_dir, '_pickle.pkl')
dump(cfg, pkl_cfg_filename)
pkl_cfg = load(pkl_cfg_filename)
... |
def test_deprecation():
deprecated_cfg_files = [osp.join(data_path, 'config/deprecated.py'), osp.join(data_path, 'config/deprecated_as_base.py')]
for cfg_file in deprecated_cfg_files:
with pytest.warns(DeprecationWarning):
cfg = Config.fromfile(cfg_file)
assert (cfg.item1 == 'expec... |
def test_deepcopy():
cfg_file = osp.join(data_path, 'config/n.py')
cfg = Config.fromfile(cfg_file)
new_cfg = copy.deepcopy(cfg)
assert isinstance(new_cfg, Config)
assert (new_cfg._cfg_dict == cfg._cfg_dict)
assert (new_cfg._cfg_dict is not cfg._cfg_dict)
assert (new_cfg._filename == cfg._f... |
def test_copy():
cfg_file = osp.join(data_path, 'config/n.py')
cfg = Config.fromfile(cfg_file)
new_cfg = copy.copy(cfg)
assert isinstance(new_cfg, Config)
assert (new_cfg is not cfg)
assert (new_cfg._cfg_dict is cfg._cfg_dict)
assert (new_cfg._filename == cfg._filename)
assert (new_cfg... |
def test_collect_env():
try:
import torch
except ModuleNotFoundError:
pytest.skip('skipping tests that require PyTorch')
from mmcv.utils import collect_env
env_info = collect_env()
expected_keys = ['sys.platform', 'Python', 'CUDA available', 'PyTorch', 'PyTorch compiling details', ... |
def test_load_url():
url1 = 'https://download.openmmlab.com/mmcv/test_data/saved_in_pt1.5.pth'
url2 = 'https://download.openmmlab.com/mmcv/test_data/saved_in_pt1.6.pth'
if (digit_version(TORCH_VERSION) < digit_version('1.7.0')):
model_zoo.load_url(url1)
with pytest.raises(RuntimeError):
... |
@patch('torch.distributed.get_rank', (lambda : 0))
@patch('torch.distributed.is_initialized', (lambda : True))
@patch('torch.distributed.is_available', (lambda : True))
def test_get_logger_rank0():
logger = get_logger('rank0.pkg1')
assert isinstance(logger, logging.Logger)
assert (len(logger.handlers) == ... |
@patch('torch.distributed.get_rank', (lambda : 1))
@patch('torch.distributed.is_initialized', (lambda : True))
@patch('torch.distributed.is_available', (lambda : True))
def test_get_logger_rank1():
logger = get_logger('rank1.pkg1')
assert isinstance(logger, logging.Logger)
assert (len(logger.handlers) == ... |
def test_print_log_print(capsys):
print_log('welcome', logger=None)
(out, _) = capsys.readouterr()
assert (out == 'welcome\n')
|
def test_print_log_silent(capsys, caplog):
print_log('welcome', logger='silent')
(out, _) = capsys.readouterr()
assert (out == '')
assert (len(caplog.records) == 0)
|
def test_print_log_logger(caplog):
print_log('welcome', logger='mmcv')
assert (caplog.record_tuples[(- 1)] == ('mmcv', logging.INFO, 'welcome'))
print_log('welcome', logger='mmcv', level=logging.ERROR)
assert (caplog.record_tuples[(- 1)] == ('mmcv', logging.ERROR, 'welcome'))
with tempfile.NamedTe... |
def test_print_log_exception():
with pytest.raises(TypeError):
print_log('welcome', logger=0)
|
def test_to_ntuple():
single_number = 2
assert (mmcv.utils.to_1tuple(single_number) == (single_number,))
assert (mmcv.utils.to_2tuple(single_number) == (single_number, single_number))
assert (mmcv.utils.to_3tuple(single_number) == (single_number, single_number, single_number))
assert (mmcv.utils.t... |
def test_iter_cast():
assert (mmcv.list_cast([1, 2, 3], int) == [1, 2, 3])
assert (mmcv.list_cast(['1.1', 2, '3'], float) == [1.1, 2.0, 3.0])
assert (mmcv.list_cast([1, 2, 3], str) == ['1', '2', '3'])
assert (mmcv.tuple_cast((1, 2, 3), str) == ('1', '2', '3'))
assert (next(mmcv.iter_cast([1, 2, 3]... |
def test_is_seq_of():
assert mmcv.is_seq_of([1.0, 2.0, 3.0], float)
assert mmcv.is_seq_of([(1,), (2,), (3,)], tuple)
assert mmcv.is_seq_of((1.0, 2.0, 3.0), float)
assert mmcv.is_list_of([1.0, 2.0, 3.0], float)
assert (not mmcv.is_seq_of((1.0, 2.0, 3.0), float, seq_type=list))
assert (not mmcv.... |
def test_slice_list():
in_list = [1, 2, 3, 4, 5, 6]
assert (mmcv.slice_list(in_list, [1, 2, 3]) == [[1], [2, 3], [4, 5, 6]])
assert (mmcv.slice_list(in_list, [len(in_list)]) == [in_list])
with pytest.raises(TypeError):
mmcv.slice_list(in_list, 2.0)
with pytest.raises(ValueError):
m... |
def test_concat_list():
assert (mmcv.concat_list([[1, 2]]) == [1, 2])
assert (mmcv.concat_list([[1, 2], [3, 4, 5], [6]]) == [1, 2, 3, 4, 5, 6])
|
def test_requires_package(capsys):
@mmcv.requires_package('nnn')
def func_a():
pass
@mmcv.requires_package(['numpy', 'n1', 'n2'])
def func_b():
pass
@mmcv.requires_package('numpy')
def func_c():
return 1
with pytest.raises(RuntimeError):
func_a()
(out... |
def test_requires_executable(capsys):
@mmcv.requires_executable('nnn')
def func_a():
pass
@mmcv.requires_executable(['ls', 'n1', 'n2'])
def func_b():
pass
@mmcv.requires_executable('mv')
def func_c():
return 1
with pytest.raises(RuntimeError):
func_a()
... |
def test_import_modules_from_strings():
import os.path as osp_
import sys as sys_
(osp, sys) = mmcv.import_modules_from_strings(['os.path', 'sys'])
assert (osp == osp_)
assert (sys == sys_)
osp = mmcv.import_modules_from_strings('os.path')
assert (osp == osp_)
assert (mmcv.import_modul... |
def test_is_method_overridden():
class Base():
def foo1():
pass
def foo2():
pass
class Sub(Base):
def foo1():
pass
assert mmcv.is_method_overridden('foo1', Base, Sub)
assert (not mmcv.is_method_overridden('foo2', Base, Sub))
sub_inst... |
def test_has_method():
class Foo():
def __init__(self, name):
self.name = name
def print_name(self):
print(self.name)
foo = Foo('foo')
assert (not has_method(foo, 'name'))
assert has_method(foo, 'print_name')
|
def test_deprecated_api_warning():
@deprecated_api_warning(name_dict=dict(old_key='new_key'))
def dummy_func(new_key=1):
return new_key
assert (dummy_func(old_key=2) == 2)
with pytest.raises(AssertionError):
dummy_func(old_key=1, new_key=2)
|
class TestJit(object):
def test_add_dict(self):
@mmcv.jit
def add_dict(oper):
rets = (oper['x'] + oper['y'])
return {'result': rets}
def add_dict_pyfunc(oper):
rets = (oper['x'] + oper['y'])
return {'result': rets}
a = torch.rand((... |
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