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Unverified Commit 26c095dc authored by Hakjin Lee's avatar Hakjin Lee Committed by GitHub
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[Fix] Fix the undefined name error (#1695)

parent 1231ef1c
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Showing with 34 additions and 34 deletions
tests/test_runner/test_hooks.py
View file @ 26c095dc
......@@ -361,7 +361,7 @@ def test_momentum_runner_hook(multi_optimizers, max_iters, gamma,
"""xdoctest -m tests/test_hooks.py test_momentum_runner_hook."""
sys.modules['pavi'] = MagicMock()
loader = DataLoader(torch.ones((10, 2)))
runner = _build_demo_runner(multi_optimziers=multi_optimizers)
runner = _build_demo_runner(multi_optimizers=multi_optimizers)
# add momentum scheduler
hook_cfg = dict(
......@@ -438,7 +438,7 @@ def test_momentum_runner_hook(multi_optimizers, max_iters, gamma,
# test constant momentum warmup
sys.modules['pavi'] = MagicMock()
runner = _build_demo_runner(multi_optimziers=multi_optimziers)
runner = _build_demo_runner(multi_optimizers=multi_optimizers)
# add momentum scheduler
hook_cfg = dict(
......@@ -458,7 +458,7 @@ def test_momentum_runner_hook(multi_optimizers, max_iters, gamma,
shutil.rmtree(runner.work_dir)
assert hasattr(hook, 'writer')
if multi_optimziers:
if multi_optimizers:
calls = [
call(
'train', {
......@@ -502,7 +502,7 @@ def test_momentum_runner_hook(multi_optimizers, max_iters, gamma,
# test linear momentum warmup
sys.modules['pavi'] = MagicMock()
runner = _build_demo_runner(multi_optimziers=multi_optimziers)
runner = _build_demo_runner(multi_optimizers=multi_optimizers)
# add momentum scheduler
hook_cfg = dict(
......@@ -522,7 +522,7 @@ def test_momentum_runner_hook(multi_optimizers, max_iters, gamma,
shutil.rmtree(runner.work_dir)
assert hasattr(hook, 'writer')
if multi_optimziers:
if multi_optimizers:
calls = [
call(
'train', {
......@@ -566,7 +566,7 @@ def test_momentum_runner_hook(multi_optimizers, max_iters, gamma,
# test exponentially momentum warmup
sys.modules['pavi'] = MagicMock()
runner = _build_demo_runner(multi_optimziers=multi_optimziers)
runner = _build_demo_runner(multi_optimizers=multi_optimizers)
# add momentum scheduler
hook_cfg = dict(
......@@ -586,7 +586,7 @@ def test_momentum_runner_hook(multi_optimizers, max_iters, gamma,
shutil.rmtree(runner.work_dir)
assert hasattr(hook, 'writer')
if multi_optimziers:
if multi_optimizers:
calls = [
call(
'train', {
......@@ -629,12 +629,12 @@ def test_momentum_runner_hook(multi_optimizers, max_iters, gamma,
hook.writer.add_scalars.assert_has_calls(calls, any_order=True)
@pytest.mark.parametrize('multi_optimziers', (True, False))
def test_cosine_runner_hook(multi_optimziers):
@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_optimziers=multi_optimziers)
runner = _build_demo_runner(multi_optimizers=multi_optimizers)
# add momentum scheduler
hook_cfg = dict(
......@@ -663,7 +663,7 @@ def test_cosine_runner_hook(multi_optimziers):
# TODO: use a more elegant way to check values
assert hasattr(hook, 'writer')
if multi_optimziers:
if multi_optimizers:
calls = [
call(
'train', {
......@@ -706,17 +706,17 @@ def test_cosine_runner_hook(multi_optimziers):
hook.writer.add_scalars.assert_has_calls(calls, any_order=True)
@pytest.mark.parametrize('multi_optimziers, by_epoch', [(False, False),
@pytest.mark.parametrize('multi_optimizers, by_epoch', [(False, False),
(True, False),
(False, True),
(True, True)])
def test_flat_cosine_runner_hook(multi_optimziers, by_epoch):
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.ones((10, 2)))
max_epochs = 10 if by_epoch else 1
runner = _build_demo_runner(
multi_optimziers=multi_optimziers, max_epochs=max_epochs)
multi_optimizers=multi_optimizers, max_epochs=max_epochs)
with pytest.raises(ValueError):
# start_percent: expected float between 0 and 1
......@@ -742,7 +742,7 @@ def test_flat_cosine_runner_hook(multi_optimziers, by_epoch):
# TODO: use a more elegant way to check values
assert hasattr(hook, 'writer')
if multi_optimziers:
if multi_optimizers:
if by_epoch:
calls = [
call(
......@@ -847,10 +847,10 @@ def test_flat_cosine_runner_hook(multi_optimziers, by_epoch):
hook.writer.add_scalars.assert_has_calls(calls, any_order=True)
@pytest.mark.parametrize('multi_optimziers, max_iters', [(True, 10), (True, 2),
@pytest.mark.parametrize('multi_optimizers, max_iters', [(True, 10), (True, 2),
(False, 10),
(False, 2)])
def test_one_cycle_runner_hook(multi_optimziers, max_iters):
def test_one_cycle_runner_hook(multi_optimizers, max_iters):
"""Test OneCycleLrUpdaterHook and OneCycleMomentumUpdaterHook."""
with pytest.raises(AssertionError):
# by_epoch should be False
......@@ -866,7 +866,7 @@ def test_one_cycle_runner_hook(multi_optimziers, max_iters):
sys.modules['pavi'] = MagicMock()
loader = DataLoader(torch.ones((10, 2)))
runner = _build_demo_runner(multi_optimziers=multi_optimziers)
runner = _build_demo_runner(multi_optimizers=multi_optimizers)
# add momentum scheduler
hook_cfg = dict(
......@@ -898,7 +898,7 @@ def test_one_cycle_runner_hook(multi_optimziers, max_iters):
# TODO: use a more elegant way to check values
assert hasattr(hook, 'writer')
if multi_optimziers:
if multi_optimizers:
calls = [
call(
'train', {
......@@ -975,8 +975,8 @@ def test_one_cycle_runner_hook(multi_optimziers, max_iters):
assert lr_target[-1] == lr_last[0]
@pytest.mark.parametrize('multi_optimziers', (True, False))
def test_cosine_restart_lr_update_hook(multi_optimziers):
@pytest.mark.parametrize('multi_optimizers', (True, False))
def test_cosine_restart_lr_update_hook(multi_optimizers):
"""Test CosineRestartLrUpdaterHook."""
with pytest.raises(AssertionError):
# either `min_lr` or `min_lr_ratio` should be specified
......@@ -1018,7 +1018,7 @@ def test_cosine_restart_lr_update_hook(multi_optimziers):
sys.modules['pavi'] = MagicMock()
loader = DataLoader(torch.ones((10, 2)))
runner = _build_demo_runner(multi_optimziers=multi_optimziers)
runner = _build_demo_runner(multi_optimizers=multi_optimizers)
# add cosine restart LR scheduler
hook = CosineRestartLrUpdaterHook(
......@@ -1037,7 +1037,7 @@ def test_cosine_restart_lr_update_hook(multi_optimziers):
# TODO: use a more elegant way to check values
assert hasattr(hook, 'writer')
if multi_optimziers:
if multi_optimizers:
calls = [
call(
'train', {
......@@ -1079,8 +1079,8 @@ def test_cosine_restart_lr_update_hook(multi_optimziers):
hook.writer.add_scalars.assert_has_calls(calls, any_order=True)
@pytest.mark.parametrize('multi_optimziers', (True, False))
def test_step_runner_hook(multi_optimziers):
@pytest.mark.parametrize('multi_optimizers', (True, False))
def test_step_runner_hook(multi_optimizers):
"""Test StepLrUpdaterHook."""
with pytest.raises(TypeError):
# `step` should be specified
......@@ -1095,7 +1095,7 @@ def test_step_runner_hook(multi_optimziers):
# test StepLrUpdaterHook with int `step` value
sys.modules['pavi'] = MagicMock()
loader = DataLoader(torch.ones((30, 2)))
runner = _build_demo_runner(multi_optimziers=multi_optimziers)
runner = _build_demo_runner(multi_optimizers=multi_optimizers)
# add momentum scheduler
hook_cfg = dict(
......@@ -1119,7 +1119,7 @@ def test_step_runner_hook(multi_optimziers):
# TODO: use a more elegant way to check values
assert hasattr(hook, 'writer')
if multi_optimziers:
if multi_optimizers:
calls = [
call(
'train', {
......@@ -1196,7 +1196,7 @@ def test_step_runner_hook(multi_optimziers):
# test StepLrUpdaterHook with list[int] `step` value
sys.modules['pavi'] = MagicMock()
loader = DataLoader(torch.ones((10, 2)))
runner = _build_demo_runner(multi_optimziers=multi_optimziers)
runner = _build_demo_runner(multi_optimizers=multi_optimizers)
# add momentum scheduler
hook_cfg = dict(
......@@ -1219,7 +1219,7 @@ def test_step_runner_hook(multi_optimziers):
# TODO: use a more elegant way to check values
assert hasattr(hook, 'writer')
if multi_optimziers:
if multi_optimizers:
calls = [
call(
'train', {
......@@ -1305,7 +1305,7 @@ def test_cyclic_lr_update_hook(multi_optimizers, max_iters, gamma,
runner_type='IterBasedRunner',
max_epochs=None,
max_iters=max_iters,
multi_optimziers=multi_optimizers)
multi_optimizers=multi_optimizers)
# add cyclic LR scheduler
schedule_hook = CyclicLrUpdaterHook(
......@@ -1472,7 +1472,7 @@ def test_dvclive_hook_model_file(tmp_path):
def _build_demo_runner_without_hook(runner_type='EpochBasedRunner',
max_epochs=1,
max_iters=None,
multi_optimziers=False):
multi_optimizers=False):
class Model(nn.Module):
......@@ -1492,7 +1492,7 @@ def _build_demo_runner_without_hook(runner_type='EpochBasedRunner',
model = Model()
if multi_optimziers:
if multi_optimizers:
optimizer = {
'model1':
torch.optim.SGD(model.linear.parameters(), lr=0.02, momentum=0.95),
......@@ -1518,14 +1518,14 @@ def _build_demo_runner_without_hook(runner_type='EpochBasedRunner',
def _build_demo_runner(runner_type='EpochBasedRunner',
max_epochs=1,
max_iters=None,
multi_optimziers=False):
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_optimziers)
max_iters, multi_optimizers)
runner.register_checkpoint_hook(dict(interval=1))
runner.register_logger_hooks(log_config)
......
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