def test_checkpoint_hook():
"""xdoctest -m tests/test_runner/test_hooks.py test_checkpoint_hook."""
# test epoch based runner
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.register_hook(checkpointhook)
runner.run([loader], [('train', 1)])
assert runner.meta['hook_msgs']['last_ckpt'] == osp.join(
runner.work_dir, 'epoch_1.pth')
shutil.rmtree(runner.work_dir)
# test iter based runner
runner = _build_demo_runner('IterBasedRunner',
max_iters=1,
max_epochs=None)
runner.meta = dict()
checkpointhook = CheckpointHook(interval=1, by_epoch=False)
runner.register_hook(checkpointhook)
runner.run([loader], [('train', 1)])
assert runner.meta['hook_msgs']['last_ckpt'] == osp.join(
runner.work_dir, 'iter_1.pth')
shutil.rmtree(runner.work_dir)
def register_qat_hooks(self,
loss,
metrics,
lr_policies,
qat_policies,
ckpt_interval=None,
runtime_hook=None):
assert isinstance(loss, dict)
assert isinstance(metrics, (tuple, list))
assert isinstance(lr_policies, (tuple, list))
assert isinstance(qat_policies, (tuple, list))
loss = training.build_loss(loss)
metrics = training.build_metrics(*metrics)
lr_policies = training.build_lr_policies(*lr_policies)
qat_policies = training.build_qat_policies(*qat_policies)
# make sure loss firstly getting ready after `batch_processor`
self.register_hook(loss, priority="HIGH")
self.register_hook(IterTimerHook())
if ckpt_interval:
self.register_hook(CheckpointHook(interval=ckpt_interval))
for hook in chain(metrics, qat_policies, lr_policies):
if isinstance(hook, HijackModuleOutput):
priority = "LOW"
else:
priority = "NORMAL"
self.register_hook(hook, priority)
if runtime_hook is not None:
interval = runtime_hook["interval"]
hooks = runtime_hook["hooks"]
post_process = runtime_hook.get("post_process")
self.inject_runtime_hooks(interval, hooks, post_process)
else:
self.inject_runtime_hooks(-1, [], None)
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 _init_weight(self):
constant_(self.conv.weight, 0)
constant_(self.conv.bias, 0)
def forward(self, x):
return self.conv(x).sum()
def train_step(self, x, optimizer, **kwargs):
return dict(loss=self(x))
def val_step(self, x, optimizer, **kwargs):
return dict(loss=self(x))
loader = DataLoader(torch.ones((1, 1, 1, 1)))
runner = _build_demo_runner()
demo_model = DemoModel()
runner.model = demo_model
emahook = EMAHook(momentum=0.1, interval=2, warm_up=100, resume_from=None)
checkpointhook = CheckpointHook(interval=1, by_epoch=True)
runner.register_hook(emahook, priority='HIGHEST')
runner.register_hook(checkpointhook)
runner.run([loader, loader], [('train', 1), ('val', 1)])
checkpoint = torch.load(f'{runner.work_dir}/epoch_1.pth')
contain_ema_buffer = False
for name, value in checkpoint['state_dict'].items():
if 'ema' in name:
contain_ema_buffer = True
assert value.sum() == 0
value.fill_(1)
else:
assert value.sum() == 0
assert contain_ema_buffer
torch.save(checkpoint, f'{runner.work_dir}/epoch_1.pth')
work_dir = runner.work_dir
resume_ema_hook = EMAHook(momentum=0.5,
warm_up=0,
resume_from=f'{work_dir}/epoch_1.pth')
runner = _build_demo_runner(max_epochs=2)
runner.model = demo_model
runner.register_hook(resume_ema_hook, priority='HIGHEST')
checkpointhook = CheckpointHook(interval=1, by_epoch=True)
runner.register_hook(checkpointhook)
runner.run([loader, loader], [('train', 1), ('val', 1)])
checkpoint = torch.load(f'{runner.work_dir}/epoch_2.pth')
contain_ema_buffer = False
for name, value in checkpoint['state_dict'].items():
if 'ema' in name:
contain_ema_buffer = True
assert value.sum() == 2
else:
assert value.sum() == 1
assert contain_ema_buffer
shutil.rmtree(runner.work_dir)
shutil.rmtree(work_dir)
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.bn = nn.BatchNorm2d(2)
self._init_weight()
def _init_weight(self):
constant_(self.conv.weight, 0)
constant_(self.conv.bias, 0)
constant_(self.bn.weight, 0)
constant_(self.bn.bias, 0)
def forward(self, x):
return self.bn(self.conv(x)).sum()
def train_step(self, x, optimizer, **kwargs):
return dict(loss=self(x))
def val_step(self, x, optimizer, **kwargs):
return dict(loss=self(x))
loader = DataLoader(torch.ones((1, 1, 1, 1)))
runner = _build_demo_runner()
demo_model = DemoModel()
runner.model = demo_model
ema_hook = ExpMomentumEMAHook(momentum=0.0002,
total_iter=1,
skip_buffers=True,
interval=2,
resume_from=None)
checkpointhook = CheckpointHook(interval=1, by_epoch=True)
runner.register_hook(ema_hook, priority='HIGHEST')
runner.register_hook(checkpointhook)
runner.run([loader, loader], [('train', 1), ('val', 1)])
checkpoint = torch.load(f'{runner.work_dir}/epoch_1.pth')
num_eam_params = 0
for name, value in checkpoint['state_dict'].items():
if 'ema' in name:
num_eam_params += 1
value.fill_(1)
assert num_eam_params == 4
torch.save(checkpoint, f'{runner.work_dir}/epoch_1.pth')
work_dir = runner.work_dir
resume_ema_hook = ExpMomentumEMAHook(momentum=0.5,
total_iter=10,
skip_buffers=True,
interval=1,
resume_from=f'{work_dir}/epoch_1.pth')
runner = _build_demo_runner(max_epochs=2)
runner.model = demo_model
runner.register_hook(resume_ema_hook, priority='HIGHEST')
checkpointhook = CheckpointHook(interval=1, by_epoch=True)
runner.register_hook(checkpointhook)
runner.run([loader, loader], [('train', 1), ('val', 1)])
checkpoint = torch.load(f'{runner.work_dir}/epoch_2.pth')
num_eam_params = 0
desired_output = [0.9094, 0.9094]
for name, value in checkpoint['state_dict'].items():
if 'ema' in name:
num_eam_params += 1
assert value.sum() == 2
else:
if ('weight' in name) or ('bias' in name):
np.allclose(value.data.cpu().numpy().reshape(-1),
desired_output, 1e-4)
assert num_eam_params == 4
shutil.rmtree(runner.work_dir)
shutil.rmtree(work_dir)
