def save_chkpt(_):
if debug:
print(trainer.state.iteration, "save_chkpt")
fp = dirname / "test.pt"
from ignite.engine.deterministic import _repr_rng_state
tsd = trainer.state_dict()
if debug:
print("->", _repr_rng_state(tsd["rng_states"]))
torch.save([model.state_dict(), opt.state_dict(), tsd], fp)
chkpt.append(fp)
def proc_fn(e, b):
from ignite.engine.deterministic import _get_rng_states, _repr_rng_state
s = _repr_rng_state(_get_rng_states())
model.train()
opt.zero_grad()
y = model(b.to(device))
y.sum().backward()
opt.step()
if debug:
print(trainer.state.iteration, trainer.state.epoch,
"proc_fn - b.shape", b.shape,
torch.norm(y).item(), s)
def _train(save_iter=None, save_epoch=None, sd=None):
w_norms = []
grad_norms = []
data = []
chkpt = []
manual_seed(12)
arch = [
nn.Conv2d(3, 10, 3),
nn.ReLU(),
nn.Conv2d(10, 10, 3),
nn.ReLU(),
nn.AdaptiveAvgPool2d(1),
nn.Flatten(),
nn.Linear(10, 5),
nn.ReLU(),
nn.Linear(5, 2),
]
if with_dropout:
arch.insert(2, nn.Dropout2d())
arch.insert(-2, nn.Dropout())
model = nn.Sequential(*arch).to(device)
opt = SGD(model.parameters(), lr=0.001)
def proc_fn(e, b):
from ignite.engine.deterministic import _get_rng_states, _repr_rng_state
s = _repr_rng_state(_get_rng_states())
model.train()
opt.zero_grad()
y = model(b.to(device))
y.sum().backward()
opt.step()
if debug:
print(trainer.state.iteration, trainer.state.epoch,
"proc_fn - b.shape", b.shape,
torch.norm(y).item(), s)
trainer = DeterministicEngine(proc_fn)
if save_iter is not None:
ev = Events.ITERATION_COMPLETED(once=save_iter)
elif save_epoch is not None:
ev = Events.EPOCH_COMPLETED(once=save_epoch)
save_iter = save_epoch * (data_size // batch_size)
@trainer.on(ev)
def save_chkpt(_):
if debug:
print(trainer.state.iteration, "save_chkpt")
fp = dirname / "test.pt"
from ignite.engine.deterministic import _repr_rng_state
tsd = trainer.state_dict()
if debug:
print("->", _repr_rng_state(tsd["rng_states"]))
torch.save([model.state_dict(), opt.state_dict(), tsd], fp)
chkpt.append(fp)
def log_event_filter(_, event):
if (event // save_iter == 1) and 1 <= (event % save_iter) <= 5:
return True
return False
@trainer.on(Events.ITERATION_COMPLETED(event_filter=log_event_filter))
def write_data_grads_weights(e):
x = e.state.batch
i = e.state.iteration
data.append([i, x.mean().item(), x.std().item()])
total = [0.0, 0.0]
out1 = []
out2 = []
for p in model.parameters():
n1 = torch.norm(p).item()
n2 = torch.norm(p.grad).item()
out1.append(n1)
out2.append(n2)
total[0] += n1
total[1] += n2
w_norms.append([i, total[0]] + out1)
grad_norms.append([i, total[1]] + out2)
if sd is not None:
sd = torch.load(sd)
model.load_state_dict(sd[0])
opt.load_state_dict(sd[1])
from ignite.engine.deterministic import _repr_rng_state
if debug:
print("-->", _repr_rng_state(sd[2]["rng_states"]))
trainer.load_state_dict(sd[2])
manual_seed(32)
trainer.run(random_train_data_loader(size=data_size), max_epochs=5)
return {
"sd": chkpt,
"data": data,
"grads": grad_norms,
"weights": w_norms
}
