def __init__(self, trainer, res_dir='results', **kwargs):
self.trainer = trainer
self.start_datetime = datetime.now().strftime('%Y-%m-%d %H:%M:%S')
self.res_dir = Path(res_dir) / self.start_datetime
self.res_dir.mkdir(parents=True)
metric_loss = Average()
metric_loss.attach(self.trainer, 'loss')
def attach_pbar_and_metrics(trainer, evaluator):
loss_metric = Average(output_transform=lambda output: output["loss"])
accuracy_metric = Accuracy(
output_transform=lambda output: (output["logit"], output["label"]))
pbar = ProgressBar()
loss_metric.attach(trainer, "loss")
accuracy_metric.attach(trainer, "accuracy")
accuracy_metric.attach(evaluator, "accuracy")
pbar.attach(trainer)
def create_classification_trainer(
model,
optimizer,
loss_fn,
device=None,
non_blocking=False,
prepare_batch=_prepare_batch,
output_transform=lambda x, y, y_pred, loss: loss.item()): # noqa
"""
Factory function for creating a trainer for supervised models.
Args:
model (`torch.nn.Module`): the model to train.
optimizer (`torch.optim.Optimizer`): the optimizer to use.
loss_fn (torch.nn loss function): the loss function to use.
device (str, optional): device type specification (default: None).
Applies to both model and batches.
non_blocking (bool, optional): if True and this copy is between CPU and GPU, the copy may occur asynchronously
with respect to the host. For other cases, this argument has no effect.
prepare_batch (callable, optional): function that receives `batch`, `device`, `non_blocking` and outputs
tuple of tensors `(batch_x, batch_y)`.
output_transform (callable, optional): function that receives 'x', 'y', 'y_pred', 'loss' and returns value
to be assigned to engine's state.output after each iteration. Default is returning `loss.item()`.
Note: `engine.state.output` for this engine is defind by `output_transform` parameter and is the loss
of the processed batch by default.
Returns:
Engine: a trainer engine with supervised update function.
"""
if device:
model.to(device)
def _update(engine, batch):
model.train()
optimizer.zero_grad()
x, y = prepare_batch(batch, device=device, non_blocking=non_blocking)
y_pred = model(x)
loss = loss_fn(y_pred, y)
loss.backward()
optimizer.step()
return output_transform(x, y, y_pred, loss)
engine = Engine(_update)
metric_loss = Average()
metric_loss.attach(engine, 'loss')
return engine
def main(ctx, config_file, dataset_root, res_root_dir, debug, device,
num_workers, **kwargs):
with open(config_file) as stream:
config = yaml.safe_load(stream)
train_transforms = get_transforms(config['train_augment'])
val_transforms = get_transforms(config['val_augment'])
train_loader, val_loader = get_loaders(train_transforms=train_transforms,
val_transforms=val_transforms,
dataset_root=dataset_root,
num_workers=num_workers,
**config['dataset'])
label_names = get_labels(train_loader)
net, criterion = get_model(n_class=len(label_names), **config['model'])
optimizer = get_optimizer(net, **config['optimizer'])
trainer = create_supervised_trainer(net, optimizer, criterion, device,
prepare_batch=prepare_batch)
metric_loss = Average()
metric_loss.attach(trainer, 'loss')
metrics = get_metrics(label_names, config['evaluate'])
metric_names = list(metrics.keys())
evaluator = create_supervised_evaluator(net, metrics, device,
prepare_batch=prepare_batch)
@trainer.on(Events.EPOCH_COMPLETED)
def compute_metrics(engine):
evaluator.run(val_loader)
res_dir = Path(res_root_dir) / config['dataset']['dataset_name']
train_extend = TrainExtension(trainer, evaluator, res_dir)
train_extend.print_metrics(metric_names)
train_extend.set_progressbar()
train_extend.schedule_lr(optimizer, **config['lr_schedule'])
if not debug:
train_extend.copy_configs(config_file)
train_extend.set_tensorboard(metric_names)
train_extend.save_model(net, **config['model_checkpoint'])
train_extend.show_config_on_tensorboard(config)
trainer.run(train_loader, max_epochs=config['epochs'])
def create_supervised_trainer2(model,
optimizer,
loss_fn,
replay_buffer,
device=None,
non_blocking=False,
prepare_batch=_prepare_batch):
"使用 ignite 包，不用 ignite 时，需要修改该函数"
from ignite.engine.engine import Engine
from ignite.metrics import Average
if device:
model.to(device)
def _update(engine, batch):
model.train()
LogSumExpf = lambda x: LogSumExp(model(x))
optimizer.zero_grad()
x, y = prepare_batch(batch, device=device, non_blocking=non_blocking)
x = x.detach()
y_pred = model(x)
loss_elf = loss_fn(y_pred, y)
x_sample = Sample(LogSumExpf, x.shape[0], x.shape[1], replay_buffer,
device)
replay_buffer.add(x_sample.cpu())
loss_gen = -(LogSumExpf(x) - LogSumExpf(x_sample)).mean()
loss = loss_elf + loss_gen
loss.backward()
optimizer.step()
return {
'loss': loss.item(),
'loss_elf': loss_elf.item(),
'loss_gen': loss_gen.item()
}
engine = Engine(_update)
metric_loss = Average(output_transform=lambda output: output['loss'])
metric_loss_elf = Average(
output_transform=lambda output: output['loss_elf'])
metric_loss_gen = Average(
output_transform=lambda output: output['loss_gen'])
metric_loss.attach(engine, "loss")
metric_loss_elf.attach(engine, "loss_elf")
metric_loss_gen.attach(engine, "loss_gen")
return engine
def main(hparams):
results_dir = get_results_directory(hparams.output_dir)
writer = SummaryWriter(log_dir=str(results_dir))
ds = get_dataset(hparams.dataset, root=hparams.data_root)
input_size, num_classes, train_dataset, test_dataset = ds
hparams.seed = set_seed(hparams.seed)
if hparams.n_inducing_points is None:
hparams.n_inducing_points = num_classes
print(f"Training with {hparams}")
hparams.save(results_dir / "hparams.json")
if hparams.ard:
# Hardcoded to WRN output size
ard = 640
else:
ard = None
feature_extractor = WideResNet(
spectral_normalization=hparams.spectral_normalization,
dropout_rate=hparams.dropout_rate,
coeff=hparams.coeff,
n_power_iterations=hparams.n_power_iterations,
batchnorm_momentum=hparams.batchnorm_momentum,
)
initial_inducing_points, initial_lengthscale = initial_values_for_GP(
train_dataset, feature_extractor, hparams.n_inducing_points
)
gp = GP(
num_outputs=num_classes,
initial_lengthscale=initial_lengthscale,
initial_inducing_points=initial_inducing_points,
separate_inducing_points=hparams.separate_inducing_points,
kernel=hparams.kernel,
ard=ard,
lengthscale_prior=hparams.lengthscale_prior,
)
model = DKL_GP(feature_extractor, gp)
model = model.cuda()
likelihood = SoftmaxLikelihood(num_classes=num_classes, mixing_weights=False)
likelihood = likelihood.cuda()
elbo_fn = VariationalELBO(likelihood, gp, num_data=len(train_dataset))
parameters = [
{"params": feature_extractor.parameters(), "lr": hparams.learning_rate},
{"params": gp.parameters(), "lr": hparams.learning_rate},
{"params": likelihood.parameters(), "lr": hparams.learning_rate},
]
optimizer = torch.optim.SGD(
parameters, momentum=0.9, weight_decay=hparams.weight_decay
)
milestones = [60, 120, 160]
scheduler = torch.optim.lr_scheduler.MultiStepLR(
optimizer, milestones=milestones, gamma=0.2
)
def step(engine, batch):
model.train()
likelihood.train()
optimizer.zero_grad()
x, y = batch
x, y = x.cuda(), y.cuda()
y_pred = model(x)
elbo = -elbo_fn(y_pred, y)
elbo.backward()
optimizer.step()
return elbo.item()
def eval_step(engine, batch):
model.eval()
likelihood.eval()
x, y = batch
x, y = x.cuda(), y.cuda()
with torch.no_grad():
y_pred = model(x)
return y_pred, y
trainer = Engine(step)
evaluator = Engine(eval_step)
metric = Average()
metric.attach(trainer, "elbo")
def output_transform(output):
y_pred, y = output
# Sample softmax values independently for classification at test time
y_pred = y_pred.to_data_independent_dist()
# The mean here is over likelihood samples
y_pred = likelihood(y_pred).probs.mean(0)
return y_pred, y
metric = Accuracy(output_transform=output_transform)
metric.attach(evaluator, "accuracy")
metric = Loss(lambda y_pred, y: -elbo_fn(y_pred, y))
metric.attach(evaluator, "elbo")
kwargs = {"num_workers": 4, "pin_memory": True}
train_loader = torch.utils.data.DataLoader(
train_dataset,
batch_size=hparams.batch_size,
shuffle=True,
drop_last=True,
**kwargs,
)
test_loader = torch.utils.data.DataLoader(
test_dataset, batch_size=512, shuffle=False, **kwargs
)
@trainer.on(Events.EPOCH_COMPLETED)
def log_results(trainer):
metrics = trainer.state.metrics
elbo = metrics["elbo"]
print(f"Train - Epoch: {trainer.state.epoch} ELBO: {elbo:.2f} ")
writer.add_scalar("Likelihood/train", elbo, trainer.state.epoch)
if hparams.spectral_normalization:
for name, layer in model.feature_extractor.named_modules():
if isinstance(layer, torch.nn.Conv2d):
writer.add_scalar(
f"sigma/{name}", layer.weight_sigma, trainer.state.epoch
)
if not hparams.ard:
# Otherwise it's too much to submit to tensorboard
length_scales = model.gp.covar_module.base_kernel.lengthscale.squeeze()
for i in range(length_scales.shape[0]):
writer.add_scalar(
f"length_scale/{i}", length_scales[i], trainer.state.epoch
)
if trainer.state.epoch > 150 and trainer.state.epoch % 5 == 0:
_, auroc, aupr = get_ood_metrics(
hparams.dataset, "SVHN", model, likelihood, hparams.data_root
)
print(f"OoD Metrics - AUROC: {auroc}, AUPR: {aupr}")
writer.add_scalar("OoD/auroc", auroc, trainer.state.epoch)
writer.add_scalar("OoD/auprc", aupr, trainer.state.epoch)
evaluator.run(test_loader)
metrics = evaluator.state.metrics
acc = metrics["accuracy"]
elbo = metrics["elbo"]
print(
f"Test - Epoch: {trainer.state.epoch} "
f"Acc: {acc:.4f} "
f"ELBO: {elbo:.2f} "
)
writer.add_scalar("Likelihood/test", elbo, trainer.state.epoch)
writer.add_scalar("Accuracy/test", acc, trainer.state.epoch)
scheduler.step()
pbar = ProgressBar(dynamic_ncols=True)
pbar.attach(trainer)
trainer.run(train_loader, max_epochs=200)
# Done training - time to evaluate
results = {}
evaluator.run(train_loader)
train_acc = evaluator.state.metrics["accuracy"]
train_elbo = evaluator.state.metrics["elbo"]
results["train_accuracy"] = train_acc
results["train_elbo"] = train_elbo
evaluator.run(test_loader)
test_acc = evaluator.state.metrics["accuracy"]
test_elbo = evaluator.state.metrics["elbo"]
results["test_accuracy"] = test_acc
results["test_elbo"] = test_elbo
_, auroc, aupr = get_ood_metrics(
hparams.dataset, "SVHN", model, likelihood, hparams.data_root
)
results["auroc_ood_svhn"] = auroc
results["aupr_ood_svhn"] = aupr
print(f"Test - Accuracy {results['test_accuracy']:.4f}")
results_json = json.dumps(results, indent=4, sort_keys=True)
(results_dir / "results.json").write_text(results_json)
torch.save(model.state_dict(), results_dir / "model.pt")
torch.save(likelihood.state_dict(), results_dir / "likelihood.pt")
writer.close()
def get_engine():
engine = Engine(sum_data)
average = Average()
average.attach(engine, "average")
return engine
def train(model,
train_loader,
eval_loaders,
optimizer,
loss_fn,
n_it_max,
patience,
split_names,
select_metric='Val accuracy_0',
select_mode='max',
viz=None,
device='cpu',
lr_scheduler=None,
name=None,
log_steps=None,
log_epoch=False,
_run=None,
prepare_batch=_prepare_batch,
single_pass=False,
n_ep_max=None):
# print(model)
if not log_steps and not log_epoch:
logger.warning('/!\\ No logging during training /!\\')
if log_steps is None:
log_steps = []
epoch_steps = len(train_loader)
if log_epoch:
log_steps.append(epoch_steps)
if single_pass:
max_epoch = 1
elif n_ep_max is None:
assert n_it_max is not None
max_epoch = int(n_it_max / epoch_steps) + 1
else:
assert n_it_max is None
max_epoch = n_ep_max
all_metrics = defaultdict(dict)
trainer = create_supervised_trainer(model,
optimizer,
loss_fn,
device=device,
prepare_batch=prepare_batch)
if hasattr(model, 'new_epoch_hook'):
trainer.add_event_handler(Events.EPOCH_STARTED, model.new_epoch_hook)
if hasattr(model, 'new_iter_hook'):
trainer.add_event_handler(Events.ITERATION_STARTED,
model.new_iter_hook)
trainer._logger.setLevel(logging.WARNING)
# trainer output is in the format (x, y, y_pred, loss, optionals)
train_loss = RunningAverage(output_transform=lambda out: out[3].item(),
epoch_bound=True)
train_loss.attach(trainer, 'Trainer loss')
if hasattr(model, 's'):
met = Average(output_transform=lambda _: float('nan')
if model.s is None else model.s)
met.attach(trainer, 'cur_s')
trainer.add_event_handler(Events.ITERATION_COMPLETED, met.completed,
'cur_s')
if hasattr(model, 'arch_sampler') and model.arch_sampler.distrib_dim > 0:
met = Average(output_transform=lambda _: float('nan')
if model.cur_split is None else model.cur_split)
met.attach(trainer, 'Trainer split')
trainer.add_event_handler(Events.ITERATION_COMPLETED, met.completed,
'Trainer split')
# trainer.add_event_handler(Events.EPOCH_STARTED, met.started)
all_ent = Average(
output_transform=lambda out: out[-1]['arch_entropy_avg'].item())
all_ent.attach(trainer, 'Trainer all entropy')
trainer.add_event_handler(Events.ITERATION_COMPLETED,
all_ent.completed, 'Trainer all entropy')
train_ent = Average(
output_transform=lambda out: out[-1]['arch_entropy_sample'].item())
train_ent.attach(trainer, 'Trainer sampling entropy')
trainer.add_event_handler(Events.ITERATION_COMPLETED,
train_ent.completed,
'Trainer sampling entropy')
trainer.add_event_handler(
Events.EPOCH_COMPLETED, lambda engine: model.check_arch_freezing(
ent=train_ent.compute(),
epoch=engine.state.iteration / (epoch_steps * max_epoch)))
def log_always(engine, name):
val = engine.state.output[-1][name]
all_metrics[name][engine.state.iteration /
epoch_steps] = val.mean().item()
def log_always_dict(engine, name):
for node, val in engine.state.output[-1][name].items():
all_metrics['node {} {}'.format(
node, name)][engine.state.iteration /
epoch_steps] = val.mean().item()
trainer.add_event_handler(Events.ITERATION_COMPLETED,
log_always_dict,
name='arch_grads')
trainer.add_event_handler(Events.ITERATION_COMPLETED,
log_always_dict,
name='arch_probas')
trainer.add_event_handler(Events.ITERATION_COMPLETED,
log_always_dict,
name='node_grads')
trainer.add_event_handler(Events.ITERATION_COMPLETED,
log_always,
name='task all_loss')
trainer.add_event_handler(Events.ITERATION_COMPLETED,
log_always,
name='arch all_loss')
trainer.add_event_handler(Events.ITERATION_COMPLETED,
log_always,
name='entropy all_loss')
if n_it_max is not None:
StopAfterIterations([n_it_max]).attach(trainer)
# epoch_pbar = ProgressBar(bar_format='{l_bar}{bar}{r_bar}', desc=name,
# persist=True, disable=not (_run or viz))
# epoch_pbar.attach(trainer, metric_names=['Train loss'])
#
# training_pbar = ProgressBar(bar_format='{l_bar}{bar}{r_bar}', desc=name,
# persist=True, disable=not (_run or viz))
# training_pbar.attach(trainer, event_name=Events.EPOCH_COMPLETED,
# closing_event_name=Events.COMPLETED)
total_time = Timer(average=False)
eval_time = Timer(average=False)
eval_time.pause()
data_time = Timer(average=False)
forward_time = Timer(average=False)
forward_time.attach(trainer,
start=Events.EPOCH_STARTED,
pause=Events.ITERATION_COMPLETED,
resume=Events.ITERATION_STARTED,
step=Events.ITERATION_COMPLETED)
epoch_time = Timer(average=False)
epoch_time.attach(trainer,
start=Events.EPOCH_STARTED,
pause=Events.EPOCH_COMPLETED,
resume=Events.EPOCH_STARTED,
step=Events.EPOCH_COMPLETED)
def get_loss(y_pred, y):
l = loss_fn(y_pred, y)
if not torch.is_tensor(l):
l, *l_details = l
return l.mean()
def get_member(x, n=0):
if isinstance(x, (list, tuple)):
return x[n]
return x
eval_metrics = {'loss': Loss(get_loss)}
for i in range(model.n_out):
out_trans = get_attr_transform(i)
def extract_ys(out):
x, y, y_pred, loss, _ = out
return out_trans((y_pred, y))
train_acc = Accuracy(extract_ys)
train_acc.attach(trainer, 'Trainer accuracy_{}'.format(i))
trainer.add_event_handler(Events.ITERATION_COMPLETED,
train_acc.completed,
'Trainer accuracy_{}'.format(i))
eval_metrics['accuracy_{}'.format(i)] = \
Accuracy(output_transform=out_trans)
# if isinstance(model, SSNWrapper):
# model.arch_sampler.entropy().mean()
evaluator = create_supervised_evaluator(model,
metrics=eval_metrics,
device=device,
prepare_batch=prepare_batch)
last_iteration = 0
patience_counter = 0
best = {
'value': float('inf') * 1 if select_mode == 'min' else -1,
'iter': -1,
'state_dict': None
}
def is_better(new, old):
if select_mode == 'min':
return new < old
else:
return new > old
def log_results(evaluator, data_loader, iteration, split_name):
evaluator.run(data_loader)
metrics = evaluator.state.metrics
log_metrics = {}
for metric_name, metric_val in metrics.items():
log_name = '{} {}'.format(split_name, metric_name)
if viz:
first = iteration == 0 and split_name == split_names[0]
viz.line(
[metric_val],
X=[iteration],
win=metric_name,
name=log_name,
update=None if first else 'append',
opts={
'title': metric_name,
'showlegend': True,
'width': 500,
'xlabel': 'iterations'
})
viz.line(
[metric_val],
X=[iteration / epoch_steps],
win='{}epoch'.format(metric_name),
name=log_name,
update=None if first else 'append',
opts={
'title': metric_name,
'showlegend': True,
'width': 500,
'xlabel': 'epoch'
})
if _run:
_run.log_scalar(log_name, metric_val, iteration)
log_metrics[log_name] = metric_val
all_metrics[log_name][iteration] = metric_val
return log_metrics
if lr_scheduler is not None:
@trainer.on(Events.EPOCH_COMPLETED)
def step(_):
lr_scheduler.step()
# logger.warning('current lr {:.5e}'.format(
# optimizer.param_groups[0]['lr']))
@trainer.on(Events.ITERATION_COMPLETED)
def log_event(trainer):
iteration = trainer.state.iteration if trainer.state else 0
nonlocal last_iteration, patience_counter, best
if not log_steps or not \
(iteration in log_steps or iteration % log_steps[-1] == 0):
return
epoch_time.pause()
eval_time.resume()
all_metrics['training_epoch'][iteration] = iteration / epoch_steps
all_metrics['training_iteration'][iteration] = iteration
if hasattr(model, 'arch_sampler'):
all_metrics['training_archs'][iteration] = \
model.arch_sampler().squeeze().detach()
# if hasattr(model, 'distrib_gen'):
# entropy = model.distrib_gen.entropy()
# all_metrics['entropy'][iteration] = entropy.mean().item()
# if trainer.state and len(trainer.state.metrics) > 1:
# raise ValueError(trainer.state.metrics)
all_metrics['data time'][iteration] = data_time.value()
all_metrics['data time_ps'][iteration] = data_time.value() / max(
data_time.step_count, 1.)
all_metrics['forward time'][iteration] = forward_time.value()
all_metrics['forward time_ps'][iteration] = forward_time.value() / max(
forward_time.step_count, 1.)
all_metrics['epoch time'][iteration] = epoch_time.value()
all_metrics['epoch time_ps'][iteration] = epoch_time.value() / max(
epoch_time.step_count, 1.)
if trainer.state:
# logger.warning(trainer.state.metrics)
for metric, value in trainer.state.metrics.items():
all_metrics[metric][iteration] = value
if viz:
viz.line(
[value],
X=[iteration],
win=metric.split()[-1],
name=metric,
update=None if iteration == 0 else 'append',
opts={
'title': metric,
'showlegend': True,
'width': 500,
'xlabel': 'iterations'
})
iter_this_step = iteration - last_iteration
for d_loader, name in zip(eval_loaders, split_names):
if name == 'Train':
if iteration == 0:
all_metrics['Trainer loss'][iteration] = float('nan')
all_metrics['Trainer accuracy_0'][iteration] = float('nan')
if hasattr(model, 'arch_sampler'):
all_metrics['Trainer all entropy'][iteration] = float(
'nan')
all_metrics['Trainer sampling entropy'][
iteration] = float('nan')
# if hasattr(model, 'cur_split'):
all_metrics['Trainer split'][iteration] = float('nan')
continue
split_metrics = log_results(evaluator, d_loader, iteration, name)
if select_metric not in split_metrics:
continue
if is_better(split_metrics[select_metric], best['value']):
best['value'] = split_metrics[select_metric]
best['iter'] = iteration
best['state_dict'] = copy.deepcopy(model.state_dict())
if patience > 0:
patience_counter = 0
elif patience > 0:
patience_counter += iter_this_step
if patience_counter >= patience:
logger.info('#####')
logger.info('# Early stopping Run')
logger.info('#####')
trainer.terminate()
last_iteration = iteration
eval_time.pause()
eval_time.step()
all_metrics['eval time'][iteration] = eval_time.value()
all_metrics['eval time_ps'][iteration] = eval_time.value(
) / eval_time.step_count
all_metrics['total time'][iteration] = total_time.value()
epoch_time.resume()
log_event(trainer)
#
# @trainer.on(Events.EPOCH_COMPLETED)
# def log_epoch(trainer):
# iteration = trainer.state.iteration if trainer.state else 0
# epoch = iteration/epoch_steps
# fw_t = forward_time.value()
# fw_t_ps = fw_t / forward_time.step_count
# d_t = data_time.value()
# d_t_ps = d_t / data_time.step_count
# e_t = epoch_time.value()
# e_t_ps = e_t / epoch_time.step_count
# ev_t = eval_time.value()
# ev_t_ps = ev_t / eval_time.step_count
# logger.warning('<{}> Epoch {}/{} finished (Forward: {:.3f}s({:.3f}), '
# 'data: {:.3f}s({:.3f}), epoch: {:.3f}s({:.3f}),'
# ' Eval: {:.3f}s({:.3f}), Total: '
# '{:.3f}s)'.format(type(model).__name__, epoch,
# max_epoch, fw_t, fw_t_ps, d_t, d_t_ps,
# e_t, e_t_ps, ev_t, ev_t_ps,
# total_time.value()))
data_time.attach(trainer,
start=Events.STARTED,
pause=Events.ITERATION_STARTED,
resume=Events.ITERATION_COMPLETED,
step=Events.ITERATION_STARTED)
if hasattr(model, 'iter_per_epoch'):
model.iter_per_epoch = len(train_loader)
trainer.run(train_loader, max_epochs=max_epoch)
return trainer.state.iteration, all_metrics, best
def run_training(self):
########## init wandb ###########
print(GREEN + "*************** START TRAINING *******************")
# fixme check if this works
for (key, val) in self.config.items():
print(GREEN + f"{key}: {val}") # print to console
wandb.config.update({key: val}) # update wandb config
print(GREEN + "**************************************************" +
ENDC)
########## checkpoints ##########
if self.config["general"]["restart"]:
mod_ckpt, op_ckpt = self._load_ckpt("reg_ckpt")
# flow_ckpt, flow_op_ckpt = self._load_ckpt("flow_ckpt")
else:
mod_ckpt = op_ckpt = None
dataset, image_transforms = get_dataset(self.config["data"])
transforms = tt.Compose([tt.ToTensor()])
train_dataset = dataset(transforms,
data_keys=self.data_keys,
mode="train",
label_transfer=True,
debug=self.config["general"]["debug"],
crop_app=True,
**self.config["data"])
# if seq_length is pruned, use min seq_length, such that the seq_length of test_dataset lower or equal than that of the train dataset
# collect_len = train_dataset.seq_length
# self.collect_recon_loss_seq = {
# k: np.zeros(shape=[k])
# for k in range(collect_len[0], collect_len[-1])
# }
# self.collect_count_seq_lens = np.zeros(shape=[collect_len[-1]])
# # adapt sequence_length
# self.config["data"]["seq_length"] = (
# min(self.config["data"]["seq_length"][0], train_dataset.seq_length[0]),
# min(self.config["data"]["seq_length"][1], train_dataset.seq_length[1]),
# )
train_sampler = RandomSampler(data_source=train_dataset)
seq_sampler_train = SequenceSampler(
train_dataset,
sampler=train_sampler,
batch_size=self.config["training"]["batch_size"],
drop_last=True,
)
train_loader = DataLoader(
train_dataset,
num_workers=0 if self.config["general"]["debug"] else
self.config["data"]["n_data_workers"],
batch_sampler=seq_sampler_train,
)
# test data
t_datakeys = [key for key in self.data_keys] + [
"action",
"sample_ids",
"intrinsics",
"intrinsics_paired",
"extrinsics",
"extrinsics_paired",
]
test_dataset = dataset(image_transforms,
data_keys=t_datakeys,
mode="test",
debug=self.config["general"]["debug"],
label_transfer=True,
**self.config["data"])
assert (test_dataset.action_id_to_action is not None)
rand_sampler_test = RandomSampler(data_source=test_dataset)
seq_sampler_test = SequenceSampler(
test_dataset,
rand_sampler_test,
batch_size=self.config["training"]["batch_size"],
drop_last=True,
)
test_loader = DataLoader(
test_dataset,
num_workers=0 if self.config["general"]["debug"] else
self.config["data"]["n_data_workers"],
batch_sampler=seq_sampler_test,
)
#
rand_sampler_transfer = RandomSampler(data_source=test_dataset)
seq_sampler_transfer = SequenceSampler(
test_dataset,
rand_sampler_transfer,
batch_size=1,
drop_last=True,
)
transfer_loader = DataLoader(
test_dataset,
batch_sampler=seq_sampler_transfer,
num_workers=0 if self.config["general"]["debug"] else
self.config["data"]["n_data_workers"],
)
#
# compare_dataset = dataset(
# transforms,
# data_keys=t_datakeys,
# mode="train",
# label_transfer=True,
# debug=self.config["general"]["debug"],
# crop_app=True,
# **self.config["data"]
# )
## Classifier action
# n_actions = len(train_dataset.action_id_to_action)
# classifier_action = Classifier_action(len(train_dataset.dim_to_use), n_actions, dropout=0, dim=512).to(self.device)
# optimizer_classifier = Adam(classifier_action.parameters(), lr=0.0001, weight_decay=1e-4)
# print("Number of parameters in classifier action", sum(p.numel() for p in classifier_action.parameters()))
#
n_actions = len(train_dataset.action_id_to_action)
# # classifier_action2 = Classifier_action(len(train_dataset.dim_to_use), n_actions, dropout=0, dim=512).to(self.device)
# classifier_action2 = Sequence_disc_michael([2, 1, 1, 1], len(train_dataset.dim_to_use), out_dim=n_actions).to(self.device)
# optimizer_classifier2 = Adam(classifier_action2.parameters(), lr=0.0001, weight_decay=1e-5)
# print("Number of parameters in classifier action", sum(p.numel() for p in classifier_action2.parameters()))
# Classifier beta
classifier_beta = Classifier_action_beta(512,
n_actions).to(self.device)
optimizer_classifier_beta = Adam(classifier_beta.parameters(),
lr=0.001)
print("Number of parameters in classifier on beta",
sum(p.numel() for p in classifier_beta.parameters()))
# # Regressor
# regressor = Regressor_fly(self.config["architecture"]["dim_hidden_b"], len(train_dataset.dim_to_use)).to(self.device)
# optimizer_regressor = Adam(regressor.parameters(), lr=0.0001)
# print("Number of parameters in regressor", sum(p.numel() for p in regressor.parameters()))
########## load network and optimizer ##########
net = MTVAE(self.config["architecture"], len(train_dataset.dim_to_use),
self.device)
print(
"Number of parameters in VAE model",
sum(p.numel() for p in net.parameters()),
)
if self.config["general"]["restart"]:
if mod_ckpt is not None:
print(BLUE + f"***** Initializing VAE from checkpoint! *****" +
ENDC)
net.load_state_dict(mod_ckpt)
net.to(self.device)
optimizer = Adam(net.parameters(),
lr=self.config["training"]["lr_init"],
weight_decay=self.config["training"]["weight_decay"])
wandb.watch(net, log="all", log_freq=len(train_loader))
if self.config["general"]["restart"]:
if op_ckpt is not None:
optimizer.load_state_dict(op_ckpt)
# scheduler = torch.optim.lr_scheduler.MultiStepLR(
# optimizer, milestones=self.config["training"]["tau"], gamma=self.config["training"]["gamma"]
# )
# rec_loss = nn.MSELoss(reduction="none")
############## DISCRIMINATOR ##############################
n_kps = len(train_dataset.dim_to_use)
# make gan loss weights
print(
f"len of train_dataset: {len(train_dataset)}, len of train_loader: {len(train_loader)}"
)
# 10 epochs of fine tuning
total_steps = (self.config["training"]["n_epochs"] -
10) * len(train_loader)
get_kl_weight = partial(
linear_var,
start_it=0,
end_it=total_steps,
start_val=1e-5,
end_val=1,
clip_min=0,
clip_max=1,
)
def train_fn(engine, batch):
net.train()
# reference keypoints with label #1
kps = batch["keypoints"].to(torch.float).to(self.device)
# keypoints for cross label transfer, label #2
kps_cross = batch["paired_keypoints"].to(torch.float).to(
self.device)
p_id = batch["paired_sample_ids"].to(torch.int)
# reconstruct second sequence with inferred b
labels = batch['action'][:, 0] - 2
out_seq, mu, logstd, out_cycle = net(kps, kps_cross)
ps = torch.randn_like(out_cycle, requires_grad=False)
cycle_loss = torch.mean(torch.abs(out_cycle - ps))
kps_loss = torch.mean(torch.abs(out_seq - kps[:, net.div:]))
l_kl = kl_loss(mu, logstd)
k_vel = self.config["training"]["k_vel"]
vel_tgt = kps[:, net.div:net.div +
k_vel] - kps[:, net.div - 1:net.div + k_vel - 1]
vel_pred = out_seq[:, :k_vel] - torch.cat(
[kps[:, net.div - 1].unsqueeze(1), out_seq[:, :k_vel - 1]],
dim=1)
motion_loss = torch.mean(torch.abs(vel_tgt - vel_pred))
kl_weight = get_kl_weight(engine.state.iteration)
loss = kps_loss + kl_weight * l_kl + self.config["training"]["weight_motion"] * motion_loss \
+ self.config["training"]["weight_cycle"] * cycle_loss
#
#
if engine.state.epoch < self.config["training"]["n_epochs"] - 10:
optimizer.zero_grad()
loss.backward()
optimizer.step()
out_dict = {
"loss": loss.detach().item(),
"motion_loss": motion_loss.detach().item(),
"rec_loss": kps_loss.detach().item(),
"cycle_loss": cycle_loss.detach().item(),
"kl_loss": l_kl.detach().item(),
"kl_weight": kl_weight
}
#
#
# ## Train classifier on action
# predict = classifier_action(seq_b)[0]
# loss_classifier_action = nn.CrossEntropyLoss()(predict, labels.to(self.device))
# optimizer_classifier.zero_grad()
# loss_classifier_action.backward()
# optimizer_classifier.step()
# _, labels_pred = torch.max(nn.Sigmoid()(predict), dim=1)
# acc_action = torch.sum(labels_pred.cpu() == labels).float() / labels_pred.shape[0]
#
# predict = classifier_action2((seq_b[:, 1:] - seq_b[:, :-1]).transpose(1, 2))[0]
# loss_classifier_action2 = nn.CrossEntropyLoss()(predict, labels.to(self.device))
# optimizer_classifier2.zero_grad()
# loss_classifier_action2.backward()
# optimizer_classifier2.step()
# _, labels_pred = torch.max(nn.Sigmoid()(predict), dim=1)
# acc_action2 = torch.sum(labels_pred.cpu() == labels).float() / labels_pred.shape[0]
#
# ## Train classifier on beta
# if engine.state.epoch >= self.config["training"]["n_epochs"] - 10:
net.eval()
with torch.no_grad():
_, mu, *_ = net(kps, kps_cross)
predict = classifier_beta(mu)
loss_classifier_action_beta = nn.CrossEntropyLoss()(
predict, labels.to(self.device))
optimizer_classifier_beta.zero_grad()
loss_classifier_action_beta.backward()
optimizer_classifier_beta.step()
_, labels_pred = torch.max(nn.Sigmoid()(predict), dim=1)
acc_action_beta = torch.sum(
labels_pred.cpu() == labels).float() / labels_pred.shape[0]
#
# out_dict = {}
# # this is only run if flow training is enable
#
# # add info to out_dict
# out_dict['loss_classifier_action'] = loss_classifier_action.detach().item()
# out_dict['acc_classifier_action'] = acc_action.item()
# out_dict['loss_classifier_action2'] = loss_classifier_action2.detach().item()
# out_dict['acc_classifier_action2'] = acc_action2.item()
#
# # if engine.state.epoch >= self.config["training"]["n_epochs"] - 10:
out_dict[
'loss_classifier_action_beta'] = loss_classifier_action_beta.detach(
).item()
out_dict['acc_action_beta'] = acc_action_beta.item()
# out_dict["loss"] = loss.detach().item()
# out_dict["kl_loss"] = kl_loss_avg.detach().item()
#
# out_dict["mu_s"] = torch.mean(mu_s).item()
# out_dict["logstd_s"] = torch.mean(logstd_s).item()
# # if self.config["training"]["use_regressor"]:
# # out_dict["loss_regressor"] = torch.mean(loss_regressor).item()
# out_dict["loss_recon"] = recon_loss.detach().item()
# out_dict["loss_per_seq_recon"] = (
# recon_loss_per_seq.detach().cpu().numpy()
# )
# out_dict["seq_len"] = seq_len
#
return out_dict
##### CREATE TRAINING RUN #####
trainer = Engine(train_fn)
pbar = ProgressBar()
pbar.attach(
trainer,
output_transform=lambda x:
{key: x[key]
for key in x if "per_seq" not in key},
)
# compute averages for all outputs of train function which are specified in the list
# fixme this can be used to log as soon as losses for mtvae are defined and named
loss_avg = Average(output_transform=lambda x: x["loss"])
loss_avg.attach(trainer, "loss")
recon_loss_avg = Average(output_transform=lambda x: x["rec_loss"])
recon_loss_avg.attach(trainer, "rec_loss")
kl_loss_avg = Average(output_transform=lambda x: x["kl_loss"])
kl_loss_avg.attach(trainer, "kl_loss")
kl_loss_avg = Average(output_transform=lambda x: x["motion_loss"])
kl_loss_avg.attach(trainer, "motion_loss")
kl_loss_avg = Average(output_transform=lambda x: x["cycle_loss"])
kl_loss_avg.attach(trainer, "cycle_loss")
# mu_s_avg = Average(output_transform=lambda x: x["mu_s"])
# mu_s_avg.attach(trainer, "mu_s")
# logstd_s_avg = Average(output_transform=lambda x: x["logstd_s"])
# logstd_s_avg.attach(trainer, "logstd_s")
#
# loss_classifier = Average(output_transform=lambda x: x["loss_classifier_action"] if "loss_classifier_action" in x else 0)
# loss_classifier.attach(trainer, "loss_classifier_action")
# acc_classifier = Average(output_transform=lambda x: x["acc_classifier_action"] if "acc_classifier_action" in x else 0)
# acc_classifier.attach(trainer, "acc_classifier_action")
#
# loss_classifier_action2 = Average(output_transform=lambda x: x["loss_classifier_action2"] if "loss_classifier_action2" in x else 0)
# loss_classifier_action2.attach(trainer, "loss_classifier_action2")
# acc_classifier_action2 = Average(output_transform=lambda x: x["acc_classifier_action2"] if "acc_classifier_action2" in x else 0)
# acc_classifier_action2.attach(trainer, "acc_classifier_action2")
#
loss_classifier_action_beta = Average(
output_transform=lambda x: x["loss_classifier_action_beta"]
if "loss_classifier_action_beta" in x else 0)
loss_classifier_action_beta.attach(trainer,
"loss_classifier_action_beta")
acc_action_beta = Average(output_transform=lambda x: x[
"acc_action_beta"] if "acc_action_beta" in x else 0)
acc_action_beta.attach(trainer, "acc_action_beta")
# loss_avg = Average(output_transform=lambda x: x["loss"])
# loss_avg.attach(trainer, "loss")
##### TRAINING HOOKS ######
# @trainer.on(Events.ITERATION_COMPLETED)
# def collect_training_info(engine):
# it = engine.state.iteration
#
# self.collect_recon_loss_seq[seq_len] += engine.state.output[
# "loss_per_seq_recon"
# ]
# self.collect_count_seq_lens[seq_len] += self.config["training"]["batch_size"]
# @trainer.on(Events.EPOCH_COMPLETED)
# def update_optimizer_params(engine):
# scheduler.step()
def log_wandb(engine):
wandb.log({
"epoch": engine.state.epoch,
"iteration": engine.state.iteration,
})
print(
f"Logging metrics: Currently, the following metrics are tracked: {list(engine.state.metrics.keys())}"
)
for key in engine.state.metrics:
val = engine.state.metrics[key]
wandb.log({key + "-epoch-avg": val})
print(ENDC + f" [metrics] {key}:{val}")
# reset
# self.collect_recon_loss_seq = {
# k: np.zeros(shape=[k])
# for k in range(collect_len[0], collect_len[-1])
# }
# self.collect_count_seq_lens = np.zeros(shape=[collect_len[-1]])
loss_avg = engine.state.metrics["loss"]
print(GREEN + f"Epoch {engine.state.epoch} summary:")
print(ENDC + f" [losses] loss overall:{loss_avg}")
def eval_model(engine):
eval_nets(net,
test_loader,
self.device,
engine.state.epoch,
cf_action_beta=classifier_beta,
debug=self.config["general"]["debug"])
#
#
def transfer_behavior_test(engine):
visualize_transfer3d(
net,
transfer_loader,
self.device,
name="Test-Set: ",
dirs=self.dirs,
revert_coord_space=False,
epoch=engine.state.epoch,
n_vid_to_generate=self.config["logging"]["n_vid_to_generate"])
# # compare predictions on train and test set
# def eval_grid(engine):
# if self.config["data"]["dataset"] != "HumanEva":
# make_eval_grid(
# net,
# transfer_loader,
# self.device,
# dirs=self.dirs,
# revert_coord_space=False,
# epoch=engine.state.epoch,
# synth_ckpt=self.synth_ckpt,
# synth_params=self.synth_params,
# )
# def latent_interpolations(engine):
# latent_interpolate(
# net,
# transfer_loader,
# self.device,
# dirs=self.dirs,
# epoch=engine.state.epoch,
# synth_params=self.synth_params,
# synth_ckpt=self.synth_ckpt,
# n_vid_to_generate=self.config["logging"]["n_vid_to_generate"]
# )
ckpt_handler_reg = ModelCheckpoint(self.dirs["ckpt"],
"reg_ckpt",
n_saved=100,
require_empty=False)
save_dict = {"model": net, "optimizer": optimizer}
trainer.add_event_handler(Events.EPOCH_COMPLETED(every=1),
ckpt_handler_reg, save_dict)
trainer.add_event_handler(Events.EPOCH_COMPLETED, log_wandb)
def log_outputs(engine):
for key in engine.state.output:
val = engine.state.output[key]
wandb.log({key + "-epoch-step": val})
trainer.add_event_handler(
Events.ITERATION_COMPLETED(
every=10 if self.config["general"]["debug"] else 1000),
log_outputs)
trainer.add_event_handler(Events.EPOCH_COMPLETED, eval_model)
trainer.add_event_handler(
Events.EPOCH_COMPLETED(
every=1 if self.config["general"]["debug"] else 3),
transfer_behavior_test,
)
# trainer.add_event_handler(
# Events.EPOCH_COMPLETED(
# every=10
# ),
# latent_interpolations,
# )
# trainer.add_event_handler(
# Events.EPOCH_COMPLETED(
# every=3
# ),
# eval_grid,
# )
####### RUN TRAINING ##############
print(BLUE + "*************** Train VAE *******************" + ENDC)
trainer.run(
train_loader,
max_epochs=self.config["training"]["n_epochs"],
epoch_length=10
if self.config["general"]["debug"] else len(train_loader),
)
print(BLUE + "*************** VAE training ends *******************" +
ENDC)
def main(
batch_size,
epochs,
length_scale,
centroid_size,
model_output_size,
learning_rate,
l_gradient_penalty,
gamma,
weight_decay,
final_model,
):
name = f"DUQ_{length_scale}__{l_gradient_penalty}_{gamma}_{centroid_size}"
writer = SummaryWriter(comment=name)
ds = all_datasets["CIFAR10"]()
input_size, num_classes, dataset, test_dataset = ds
# Split up training set
idx = list(range(len(dataset)))
random.shuffle(idx)
if final_model:
train_dataset = dataset
val_dataset = test_dataset
else:
val_size = int(len(dataset) * 0.8)
train_dataset = torch.utils.data.Subset(dataset, idx[:val_size])
val_dataset = torch.utils.data.Subset(dataset, idx[val_size:])
val_dataset.transform = (test_dataset.transform
) # Test time preprocessing for validation
model = ResNet_DUQ(
input_size,
num_classes,
centroid_size,
model_output_size,
length_scale,
gamma,
)
model = model.cuda()
optimizer = torch.optim.SGD(model.parameters(),
lr=learning_rate,
momentum=0.9,
weight_decay=weight_decay)
scheduler = torch.optim.lr_scheduler.MultiStepLR(optimizer,
milestones=[25, 50, 75],
gamma=0.2)
def bce_loss_fn(y_pred, y):
bce = F.binary_cross_entropy(y_pred, y, reduction="sum").div(
num_classes * y_pred.shape[0])
return bce
def output_transform_bce(output):
y_pred, y, x = output
y = F.one_hot(y, num_classes).float()
return y_pred, y
def output_transform_acc(output):
y_pred, y, x = output
return y_pred, y
def output_transform_gp(output):
y_pred, y, x = output
return x, y_pred
def calc_gradients_input(x, y_pred):
gradients = torch.autograd.grad(
outputs=y_pred,
inputs=x,
grad_outputs=torch.ones_like(y_pred),
create_graph=True,
)[0]
gradients = gradients.flatten(start_dim=1)
return gradients
def calc_gradient_penalty(x, y_pred):
gradients = calc_gradients_input(x, y_pred)
# L2 norm
grad_norm = gradients.norm(2, dim=1)
# Two sided penalty
gradient_penalty = ((grad_norm - 1)**2).mean()
return gradient_penalty
def step(engine, batch):
model.train()
optimizer.zero_grad()
x, y = batch
x, y = x.cuda(), y.cuda()
if l_gradient_penalty > 0:
x.requires_grad_(True)
z, y_pred = model(x)
y = F.one_hot(y, num_classes).float()
loss = bce_loss_fn(y_pred, y)
if l_gradient_penalty > 0:
loss += l_gradient_penalty * calc_gradient_penalty(x, y_pred)
loss.backward()
optimizer.step()
x.requires_grad_(False)
with torch.no_grad():
model.eval()
model.update_embeddings(x, y)
return loss.item()
def eval_step(engine, batch):
model.eval()
x, y = batch
x, y = x.cuda(), y.cuda()
x.requires_grad_(True)
z, y_pred = model(x)
return y_pred, y, x
trainer = Engine(step)
evaluator = Engine(eval_step)
metric = Average()
metric.attach(trainer, "loss")
metric = Accuracy(output_transform=output_transform_acc)
metric.attach(evaluator, "accuracy")
metric = Loss(F.binary_cross_entropy,
output_transform=output_transform_bce)
metric.attach(evaluator, "bce")
metric = Loss(calc_gradient_penalty, output_transform=output_transform_gp)
metric.attach(evaluator, "gradient_penalty")
kwargs = {"num_workers": 4, "pin_memory": True}
train_loader = torch.utils.data.DataLoader(train_dataset,
batch_size=batch_size,
shuffle=True,
drop_last=True,
**kwargs)
val_loader = torch.utils.data.DataLoader(val_dataset,
batch_size=1000,
shuffle=False,
**kwargs)
test_loader = torch.utils.data.DataLoader(test_dataset,
batch_size=1000,
shuffle=False,
**kwargs)
@trainer.on(Events.EPOCH_COMPLETED)
def log_results(trainer):
metrics = trainer.state.metrics
loss = metrics["loss"]
print(f"Train - Epoch: {trainer.state.epoch} Loss: {loss:.2f} ")
writer.add_scalar("Loss/train", loss, trainer.state.epoch)
if trainer.state.epoch % 5 == 0 or trainer.state.epoch > 65:
accuracy, auroc = get_cifar_svhn_ood(model)
print(f"Test Accuracy: {accuracy}, AUROC: {auroc}")
writer.add_scalar("OoD/test_accuracy", accuracy,
trainer.state.epoch)
writer.add_scalar("OoD/roc_auc", auroc, trainer.state.epoch)
accuracy, auroc = get_auroc_classification(val_dataset, model)
print(f"AUROC - uncertainty: {auroc}")
writer.add_scalar("OoD/val_accuracy", accuracy,
trainer.state.epoch)
writer.add_scalar("OoD/roc_auc_classification", auroc,
trainer.state.epoch)
evaluator.run(val_loader)
metrics = evaluator.state.metrics
acc = metrics["accuracy"]
bce = metrics["bce"]
GP = metrics["gradient_penalty"]
loss = bce + l_gradient_penalty * GP
print((f"Valid - Epoch: {trainer.state.epoch} "
f"Acc: {acc:.4f} "
f"Loss: {loss:.2f} "
f"BCE: {bce:.2f} "
f"GP: {GP:.2f} "))
writer.add_scalar("Loss/valid", loss, trainer.state.epoch)
writer.add_scalar("BCE/valid", bce, trainer.state.epoch)
writer.add_scalar("GP/valid", GP, trainer.state.epoch)
writer.add_scalar("Accuracy/valid", acc, trainer.state.epoch)
print(f"Centroid norm: {torch.norm(model.m / model.N, dim=0)}")
scheduler.step()
if trainer.state.epoch > 65:
torch.save(model.state_dict(),
f"saved_models/{name}_{trainer.state.epoch}.pt")
pbar = ProgressBar(dynamic_ncols=True)
pbar.attach(trainer)
trainer.run(train_loader, max_epochs=epochs)
evaluator.run(test_loader)
acc = evaluator.state.metrics["accuracy"]
print(f"Test - Accuracy {acc:.4f}")
writer.close()
def main(
architecture,
batch_size,
length_scale,
centroid_size,
learning_rate,
l_gradient_penalty,
gamma,
weight_decay,
final_model,
output_dir,
):
writer = SummaryWriter(log_dir=f"runs/{output_dir}")
ds = all_datasets["CIFAR10"]()
input_size, num_classes, dataset, test_dataset = ds
# Split up training set
idx = list(range(len(dataset)))
random.shuffle(idx)
if final_model:
train_dataset = dataset
val_dataset = test_dataset
else:
val_size = int(len(dataset) * 0.8)
train_dataset = torch.utils.data.Subset(dataset, idx[:val_size])
val_dataset = torch.utils.data.Subset(dataset, idx[val_size:])
val_dataset.transform = (test_dataset.transform
) # Test time preprocessing for validation
if architecture == "WRN":
model_output_size = 640
epochs = 200
milestones = [60, 120, 160]
feature_extractor = WideResNet()
elif architecture == "ResNet18":
model_output_size = 512
epochs = 200
milestones = [60, 120, 160]
feature_extractor = resnet18()
elif architecture == "ResNet50":
model_output_size = 2048
epochs = 200
milestones = [60, 120, 160]
feature_extractor = resnet50()
elif architecture == "ResNet110":
model_output_size = 2048
epochs = 200
milestones = [60, 120, 160]
feature_extractor = resnet110()
elif architecture == "DenseNet121":
model_output_size = 1024
epochs = 200
milestones = [60, 120, 160]
feature_extractor = densenet121()
# Adapted resnet from:
# https://github.com/kuangliu/pytorch-cifar/blob/master/models/resnet.py
feature_extractor.conv1 = torch.nn.Conv2d(3,
64,
kernel_size=3,
stride=1,
padding=1,
bias=False)
feature_extractor.maxpool = torch.nn.Identity()
feature_extractor.fc = torch.nn.Identity()
if centroid_size is None:
centroid_size = model_output_size
model = ResNet_DUQ(
feature_extractor,
num_classes,
centroid_size,
model_output_size,
length_scale,
gamma,
)
model = model.cuda()
optimizer = torch.optim.SGD(model.parameters(),
lr=learning_rate,
momentum=0.9,
weight_decay=weight_decay)
scheduler = torch.optim.lr_scheduler.MultiStepLR(optimizer,
milestones=milestones,
gamma=0.2)
def calc_gradients_input(x, y_pred):
gradients = torch.autograd.grad(
outputs=y_pred,
inputs=x,
grad_outputs=torch.ones_like(y_pred),
create_graph=True,
)[0]
gradients = gradients.flatten(start_dim=1)
return gradients
def calc_gradient_penalty(x, y_pred):
gradients = calc_gradients_input(x, y_pred)
# L2 norm
grad_norm = gradients.norm(2, dim=1)
# Two sided penalty
gradient_penalty = ((grad_norm - 1)**2).mean()
return gradient_penalty
def step(engine, batch):
model.train()
optimizer.zero_grad()
x, y = batch
x, y = x.cuda(), y.cuda()
x.requires_grad_(True)
y_pred = model(x)
y = F.one_hot(y, num_classes).float()
loss = F.binary_cross_entropy(y_pred, y, reduction="mean")
if l_gradient_penalty > 0:
gp = calc_gradient_penalty(x, y_pred)
loss += l_gradient_penalty * gp
loss.backward()
optimizer.step()
x.requires_grad_(False)
with torch.no_grad():
model.eval()
model.update_embeddings(x, y)
return loss.item()
def eval_step(engine, batch):
model.eval()
x, y = batch
x, y = x.cuda(), y.cuda()
x.requires_grad_(True)
y_pred = model(x)
return {"x": x, "y": y, "y_pred": y_pred}
trainer = Engine(step)
evaluator = Engine(eval_step)
metric = Average()
metric.attach(trainer, "loss")
metric = Accuracy(output_transform=lambda out: (out["y_pred"], out["y"]))
metric.attach(evaluator, "accuracy")
def bce_output_transform(out):
return (out["y_pred"], F.one_hot(out["y"], num_classes).float())
metric = Loss(F.binary_cross_entropy,
output_transform=bce_output_transform)
metric.attach(evaluator, "bce")
metric = Loss(calc_gradient_penalty,
output_transform=lambda out: (out["x"], out["y_pred"]))
metric.attach(evaluator, "gradient_penalty")
pbar = ProgressBar(dynamic_ncols=True)
pbar.attach(trainer)
kwargs = {"num_workers": 4, "pin_memory": True}
train_loader = torch.utils.data.DataLoader(train_dataset,
batch_size=batch_size,
shuffle=True,
drop_last=True,
**kwargs)
val_loader = torch.utils.data.DataLoader(val_dataset,
batch_size=batch_size,
shuffle=False,
**kwargs)
test_loader = torch.utils.data.DataLoader(test_dataset,
batch_size=batch_size,
shuffle=False,
**kwargs)
@trainer.on(Events.EPOCH_COMPLETED)
def log_results(trainer):
metrics = trainer.state.metrics
loss = metrics["loss"]
print(f"Train - Epoch: {trainer.state.epoch} Loss: {loss:.2f}")
writer.add_scalar("Loss/train", loss, trainer.state.epoch)
if trainer.state.epoch > (epochs - 5):
accuracy, auroc = get_cifar_svhn_ood(model)
print(f"Test Accuracy: {accuracy}, AUROC: {auroc}")
writer.add_scalar("OoD/test_accuracy", accuracy,
trainer.state.epoch)
writer.add_scalar("OoD/roc_auc", auroc, trainer.state.epoch)
accuracy, auroc = get_auroc_classification(val_dataset, model)
print(f"AUROC - uncertainty: {auroc}")
writer.add_scalar("OoD/val_accuracy", accuracy,
trainer.state.epoch)
writer.add_scalar("OoD/roc_auc_classification", auroc,
trainer.state.epoch)
evaluator.run(val_loader)
metrics = evaluator.state.metrics
acc = metrics["accuracy"]
bce = metrics["bce"]
GP = metrics["gradient_penalty"]
loss = bce + l_gradient_penalty * GP
print((f"Valid - Epoch: {trainer.state.epoch} "
f"Acc: {acc:.4f} "
f"Loss: {loss:.2f} "
f"BCE: {bce:.2f} "
f"GP: {GP:.2f} "))
writer.add_scalar("Loss/valid", loss, trainer.state.epoch)
writer.add_scalar("BCE/valid", bce, trainer.state.epoch)
writer.add_scalar("GP/valid", GP, trainer.state.epoch)
writer.add_scalar("Accuracy/valid", acc, trainer.state.epoch)
scheduler.step()
trainer.run(train_loader, max_epochs=epochs)
evaluator.run(test_loader)
acc = evaluator.state.metrics["accuracy"]
print(f"Test - Accuracy {acc:.4f}")
torch.save(model.state_dict(), f"runs/{output_dir}/model.pt")
writer.close()
def get_train_mean_std(train_dataset, unique_id="", cache_dir="/tmp/unosat/"):
# # Ensure that only process 0 in distributed performs the computation, and the others will use the cache
# if dist.get_rank() > 0:
# torch.distributed.barrier() # synchronization point for all processes > 0
cache_dir = Path(cache_dir)
if not cache_dir.exists():
cache_dir.mkdir(parents=True)
if len(unique_id) > 0:
unique_id += "_"
fp = cache_dir / "train_mean_std_{}{}.pth".format(len(train_dataset),
unique_id)
if fp.exists():
mean_std = torch.load(fp.as_posix())
else:
if dist.is_available() and dist.is_initialized():
raise RuntimeError(
"Current implementation of Mean/Std computation is not working in distrib config"
)
from ignite.engine import Engine
from ignite.metrics import Average
from ignite.contrib.handlers import ProgressBar
from albumentations.pytorch import ToTensorV2
train_dataset = TransformedDataset(train_dataset,
transform_fn=ToTensorV2())
train_loader = DataLoader(train_dataset,
shuffle=False,
drop_last=False,
batch_size=16,
num_workers=10,
pin_memory=False)
def compute_mean_std(engine, batch):
b, c, *_ = batch['image'].shape
data = batch['image'].reshape(b, c, -1).to(dtype=torch.float64)
mean = torch.mean(data, dim=-1)
mean2 = torch.mean(data**2, dim=-1)
return {
"mean": mean,
"mean^2": mean2,
}
compute_engine = Engine(compute_mean_std)
ProgressBar(desc="Compute Mean/Std").attach(compute_engine)
img_mean = Average(output_transform=lambda output: output['mean'])
img_mean2 = Average(output_transform=lambda output: output['mean^2'])
img_mean.attach(compute_engine, 'mean')
img_mean2.attach(compute_engine, 'mean2')
state = compute_engine.run(train_loader)
state.metrics['std'] = torch.sqrt(state.metrics['mean2'] -
state.metrics['mean']**2)
mean_std = {'mean': state.metrics['mean'], 'std': state.metrics['std']}
# if dist.get_rank() < 1:
torch.save(mean_std, fp.as_posix())
# if dist.get_rank() < 1:
# torch.distributed.barrier() # synchronization point for process 0
return mean_std['mean'].tolist(), mean_std['std'].tolist()
