def main(config, output_dir):
seed = config.get('seed', 0)
rng = jax.random.PRNGKey(seed)
tf.random.set_seed(seed)
if config.get('data_dir'):
logging.info('data_dir=%s', config.data_dir)
logging.info('Output dir: %s', output_dir)
save_checkpoint_path = None
if config.get('checkpoint_steps'):
gfile.makedirs(output_dir)
save_checkpoint_path = os.path.join(output_dir, 'checkpoint.npz')
# Create an asynchronous multi-metric writer.
writer = metric_writers.create_default_writer(
output_dir, just_logging=jax.process_index() > 0)
# The pool is used to perform misc operations such as logging in async way.
pool = multiprocessing.pool.ThreadPool()
def write_note(note):
if jax.process_index() == 0:
logging.info('NOTE: %s', note)
write_note('Initializing...')
# Verify settings to make sure no checkpoints are accidentally missed.
if config.get('keep_checkpoint_steps'):
assert config.get('checkpoint_steps'), 'Specify `checkpoint_steps`.'
assert config.keep_checkpoint_steps % config.checkpoint_steps == 0, (
f'`keep_checkpoint_steps` ({config.checkpoint_steps}) should be'
f'divisible by `checkpoint_steps ({config.checkpoint_steps}).`')
batch_size = config.batch_size
batch_size_eval = config.get('batch_size_eval', batch_size)
if (batch_size % jax.device_count() != 0
or batch_size_eval % jax.device_count() != 0):
raise ValueError(
f'Batch sizes ({batch_size} and {batch_size_eval}) must '
f'be divisible by device number ({jax.device_count()})')
local_batch_size = batch_size // jax.process_count()
local_batch_size_eval = batch_size_eval // jax.process_count()
logging.info(
'Global batch size %d on %d hosts results in %d local batch size. '
'With %d devices per host (%d devices total), that\'s a %d per-device '
'batch size.', batch_size, jax.process_count(), local_batch_size,
jax.local_device_count(), jax.device_count(),
local_batch_size // jax.local_device_count())
write_note('Initializing train dataset...')
rng, train_ds_rng = jax.random.split(rng)
train_ds_rng = jax.random.fold_in(train_ds_rng, jax.process_index())
train_ds = input_utils.get_data(
dataset=config.dataset,
split=config.train_split,
rng=train_ds_rng,
process_batch_size=local_batch_size,
preprocess_fn=preprocess_spec.parse(
spec=config.pp_train, available_ops=preprocess_utils.all_ops()),
shuffle_buffer_size=config.shuffle_buffer_size,
prefetch_size=config.get('prefetch_to_host', 2),
data_dir=config.get('data_dir'))
# Start prefetching already.
train_iter = input_utils.start_input_pipeline(
train_ds, config.get('prefetch_to_device', 1))
write_note('Initializing val dataset(s)...')
def _get_val_split(dataset, split, pp_eval, data_dir=None):
# We do ceil rounding such that we include the last incomplete batch.
nval_img = input_utils.get_num_examples(
dataset,
split=split,
process_batch_size=local_batch_size_eval,
drop_remainder=False,
data_dir=data_dir)
val_steps = int(np.ceil(nval_img / batch_size_eval))
logging.info('Running validation for %d steps for %s, %s', val_steps,
dataset, split)
if isinstance(pp_eval, str):
pp_eval = preprocess_spec.parse(
spec=pp_eval, available_ops=preprocess_utils.all_ops())
val_ds = input_utils.get_data(dataset=dataset,
split=split,
rng=None,
process_batch_size=local_batch_size_eval,
preprocess_fn=pp_eval,
cache=config.get('val_cache', 'batched'),
repeat_after_batching=True,
shuffle=False,
prefetch_size=config.get(
'prefetch_to_host', 2),
drop_remainder=False,
data_dir=data_dir)
val_iter = input_utils.start_input_pipeline(
val_ds, config.get('prefetch_to_device', 1))
return (val_iter, val_steps)
val_iter_splits = {
'val':
_get_val_split(config.dataset,
split=config.val_split,
pp_eval=config.pp_eval,
data_dir=config.get('data_dir'))
}
if config.get('eval_on_cifar_10h'):
cifar10_to_cifar10h_fn = data_uncertainty_utils.create_cifar10_to_cifar10h_fn(
config.get('data_dir', None))
preprocess_fn = preprocess_spec.parse(
spec=config.pp_eval_cifar_10h,
available_ops=preprocess_utils.all_ops())
pp_eval = lambda ex: preprocess_fn(cifar10_to_cifar10h_fn(ex))
val_iter_splits['cifar_10h'] = _get_val_split(
'cifar10',
split=config.get('cifar_10h_split') or 'test',
pp_eval=pp_eval,
data_dir=config.get('data_dir'))
elif config.get('eval_on_imagenet_real'):
imagenet_to_real_fn = data_uncertainty_utils.create_imagenet_to_real_fn(
)
preprocess_fn = preprocess_spec.parse(
spec=config.pp_eval_imagenet_real,
available_ops=preprocess_utils.all_ops())
pp_eval = lambda ex: preprocess_fn(imagenet_to_real_fn(ex))
val_iter_imagenet_real, val_steps = _get_val_split(
'imagenet2012_real',
split=config.get('imagenet_real_split') or 'validation',
pp_eval=pp_eval,
data_dir=config.get('data_dir'))
val_iter_splits['imagenet_real'] = (val_iter_imagenet_real, val_steps)
ood_ds = {}
if config.get('ood_datasets') and config.get('ood_methods'):
if config.get(
'ood_methods'): # config.ood_methods is not a empty list
logging.info('loading OOD dataset = %s', config.get('ood_dataset'))
ood_ds, ood_ds_names = ood_utils.load_ood_datasets(
config.dataset,
config.ood_datasets,
config.ood_split,
config.pp_eval,
config.pp_eval_ood,
config.ood_methods,
config.train_split,
config.get('data_dir'),
_get_val_split,
)
ntrain_img = input_utils.get_num_examples(
config.dataset,
split=config.train_split,
process_batch_size=local_batch_size,
data_dir=config.get('data_dir'))
steps_per_epoch = int(ntrain_img / batch_size)
if config.get('num_epochs'):
total_steps = int(config.num_epochs * steps_per_epoch)
assert not config.get(
'total_steps'), 'Set either num_epochs or total_steps'
else:
total_steps = config.total_steps
logging.info('Total train data points: %d', ntrain_img)
logging.info(
'Running for %d steps, that means %f epochs and %d steps per epoch',
total_steps, total_steps * batch_size / ntrain_img, steps_per_epoch)
write_note('Initializing model...')
logging.info('config.model = %s', config.get('model'))
model = ub.models.vision_transformer(num_classes=config.num_classes,
**config.get('model', {}))
# We want all parameters to be created in host RAM, not on any device, they'll
# be sent there later as needed, otherwise we already encountered two
# situations where we allocate them twice.
@partial(jax.jit, backend='cpu')
def init(rng):
image_size = tuple(train_ds.element_spec['image'].shape[2:])
logging.info('image_size = %s', image_size)
dummy_input = jnp.zeros((local_batch_size, ) + image_size, jnp.float32)
params = flax.core.unfreeze(model.init(rng, dummy_input,
train=False))['params']
# Set bias in the head to a low value, such that loss is small initially.
params['head']['bias'] = jnp.full_like(params['head']['bias'],
config.get('init_head_bias', 0))
# init head kernel to all zeros for fine-tuning
if config.get('model_init'):
params['head']['kernel'] = jnp.full_like(params['head']['kernel'],
0)
return params
rng, rng_init = jax.random.split(rng)
params_cpu = init(rng_init)
if jax.process_index() == 0:
num_params = sum(p.size for p in jax.tree_flatten(params_cpu)[0])
parameter_overview.log_parameter_overview(params_cpu)
writer.write_scalars(step=0, scalars={'num_params': num_params})
@partial(jax.pmap, axis_name='batch')
def evaluation_fn(params, images, labels, mask):
# Ignore the entries with all zero labels for evaluation.
mask *= labels.max(axis=1)
logits, out = model.apply({'params': flax.core.freeze(params)},
images,
train=False)
label_indices = config.get('label_indices')
logging.info('!!! mask %s, label_indices %s', mask, label_indices)
if label_indices:
logits = logits[:, label_indices]
# Note that logits and labels are usually of the shape [batch,num_classes].
# But for OOD data, when num_classes_ood > num_classes_ind, we need to
# adjust labels to labels[:, :config.num_classes] to match the shape of
# logits. That is just to avoid shape mismatch. The output losses does not
# have any meaning for OOD data, because OOD not belong to any IND class.
losses = getattr(train_utils, config.get('loss', 'sigmoid_xent'))(
logits=logits,
labels=labels[:, :(
len(label_indices) if label_indices else config.num_classes)],
reduction=False)
loss = jax.lax.psum(losses * mask, axis_name='batch')
top1_idx = jnp.argmax(logits, axis=1)
# Extracts the label at the highest logit index for each image.
top1_correct = jnp.take_along_axis(labels, top1_idx[:, None],
axis=1)[:, 0]
ncorrect = jax.lax.psum(top1_correct * mask, axis_name='batch')
n = jax.lax.psum(mask, axis_name='batch')
metric_args = jax.lax.all_gather(
[logits, labels, out['pre_logits'], mask], axis_name='batch')
return ncorrect, loss, n, metric_args
@partial(jax.pmap, axis_name='batch')
def cifar_10h_evaluation_fn(params, images, labels, mask):
logits, out = model.apply({'params': flax.core.freeze(params)},
images,
train=False)
label_indices = config.get('label_indices')
if label_indices:
logits = logits[:, label_indices]
losses = getattr(train_utils,
config.get('loss', 'softmax_xent'))(logits=logits,
labels=labels,
reduction=False)
loss = jax.lax.psum(losses, axis_name='batch')
top1_idx = jnp.argmax(logits, axis=1)
# Extracts the label at the highest logit index for each image.
one_hot_labels = jnp.eye(10)[jnp.argmax(labels, axis=1)]
top1_correct = jnp.take_along_axis(one_hot_labels,
top1_idx[:, None],
axis=1)[:, 0]
ncorrect = jax.lax.psum(top1_correct, axis_name='batch')
n = jax.lax.psum(one_hot_labels, axis_name='batch')
metric_args = jax.lax.all_gather(
[logits, labels, out['pre_logits'], mask], axis_name='batch')
return ncorrect, loss, n, metric_args
# Setup function for computing representation.
@partial(jax.pmap, axis_name='batch')
def representation_fn(params, images, labels, mask):
_, outputs = model.apply({'params': flax.core.freeze(params)},
images,
train=False)
representation = outputs[config.fewshot.representation_layer]
representation = jax.lax.all_gather(representation, 'batch')
labels = jax.lax.all_gather(labels, 'batch')
mask = jax.lax.all_gather(mask, 'batch')
return representation, labels, mask
# Load the optimizer from flax.
opt_name = config.get('optim_name')
write_note(f'Initializing {opt_name} optimizer...')
opt_def = getattr(flax.optim, opt_name)(**config.get('optim', {}))
# We jit this, such that the arrays that are created are created on the same
# device as the input is, in this case the CPU. Else they'd be on device[0].
opt_cpu = jax.jit(opt_def.create)(params_cpu)
weight_decay_rules = config.get('weight_decay', []) or []
rescale_value = config.lr.base if config.get(
'weight_decay_decouple') else 1.
weight_decay_fn = train_utils.get_weight_decay_fn(
weight_decay_rules=weight_decay_rules, rescale_value=rescale_value)
@partial(jax.pmap, axis_name='batch', donate_argnums=(0, ))
def update_fn(opt, lr, images, labels, rng):
"""Update step."""
measurements = {}
# Get device-specific loss rng.
rng, rng_model = jax.random.split(rng, 2)
rng_model_local = jax.random.fold_in(rng_model,
jax.lax.axis_index('batch'))
def loss_fn(params, images, labels):
logits, _ = model.apply({'params': flax.core.freeze(params)},
images,
train=True,
rngs={'dropout': rng_model_local})
label_indices = config.get('label_indices')
if label_indices:
logits = logits[:, label_indices]
return getattr(train_utils,
config.get('loss', 'sigmoid_xent'))(logits=logits,
labels=labels)
# Implementation considerations compared and summarized at
# https://docs.google.com/document/d/1g3kMEvqu1DOawaflKNyUsIoQ4yIVEoyE5ZlIPkIl4Lc/edit?hl=en#
l, g = train_utils.accumulate_gradient(jax.value_and_grad(loss_fn),
opt.target, images, labels,
config.get('grad_accum_steps'))
l, g = jax.lax.pmean((l, g), axis_name='batch')
# Log the gradient norm only if we need to compute it anyways (clipping)
# or if we don't use grad_accum_steps, as they interact badly.
if config.get('grad_accum_steps',
1) == 1 or config.get('grad_clip_norm'):
grads, _ = jax.tree_flatten(g)
l2_g = jnp.sqrt(sum([jnp.vdot(p, p) for p in grads]))
measurements['l2_grads'] = l2_g
# Optionally resize the global gradient to a maximum norm. We found this
# useful in some cases across optimizers, hence it's in the main loop.
if config.get('grad_clip_norm'):
g_factor = jnp.minimum(1.0, config.grad_clip_norm / l2_g)
g = jax.tree_util.tree_map(lambda p: g_factor * p, g)
opt = opt.apply_gradient(g, learning_rate=lr)
opt = opt.replace(target=weight_decay_fn(opt.target, lr))
params, _ = jax.tree_flatten(opt.target)
measurements['l2_params'] = jnp.sqrt(
sum([jnp.vdot(p, p) for p in params]))
return opt, l, rng, measurements
rng, train_loop_rngs = jax.random.split(rng)
reint_params = ('head/kernel', 'head/bias')
if config.get('only_eval', False) or not config.get('reint_head', True):
reint_params = []
checkpoint_data = checkpoint_utils.maybe_load_checkpoint(
train_loop_rngs=train_loop_rngs,
save_checkpoint_path=save_checkpoint_path,
init_optimizer=opt_cpu,
init_params=params_cpu,
init_fixed_model_states=None,
default_reinit_params=reint_params,
config=config,
)
train_loop_rngs = checkpoint_data.train_loop_rngs
opt_cpu = checkpoint_data.optimizer
accumulated_train_time = checkpoint_data.accumulated_train_time
write_note('Adapting the checkpoint model...')
adapted_params = checkpoint_utils.adapt_upstream_architecture(
init_params=params_cpu, loaded_params=opt_cpu.target)
opt_cpu = opt_cpu.replace(target=adapted_params)
write_note('Kicking off misc stuff...')
first_step = int(opt_cpu.state.step) # Might be a DeviceArray type.
if first_step == 0 and jax.process_index() == 0:
writer.write_hparams(dict(config))
chrono = train_utils.Chrono(first_step, total_steps, batch_size,
accumulated_train_time)
# Note: switch to ProfileAllHosts() if you need to profile all hosts.
# (Xprof data become much larger and take longer to load for analysis)
profiler = periodic_actions.Profile(
# Create profile after every restart to analyze pre-emption related
# problems and assure we get similar performance in every run.
logdir=output_dir,
first_profile=first_step + 10)
# Prepare the learning-rate and pre-fetch it to device to avoid delays.
lr_fn = train_utils.create_learning_rate_schedule(total_steps,
**config.get('lr', {}))
# TODO(dusenberrymw): According to flax docs, prefetching shouldn't be
# necessary for TPUs.
lr_iter = train_utils.prefetch_scalar(map(lr_fn, range(total_steps)),
config.get('prefetch_to_device', 1))
write_note(f'Replicating...\n{chrono.note}')
opt_repl = flax_utils.replicate(opt_cpu)
write_note(f'Initializing few-shotters...\n{chrono.note}')
fewshotter = None
if 'fewshot' in config and fewshot is not None:
fewshotter = fewshot.FewShotEvaluator(
representation_fn, config.fewshot,
config.fewshot.get('batch_size') or batch_size_eval)
checkpoint_writer = None
# Note: we return the train loss, val loss, and fewshot best l2s for use in
# reproducibility unit tests.
train_loss = -jnp.inf
val_loss = {val_name: -jnp.inf for val_name, _ in val_iter_splits.items()}
fewshot_results = {'dummy': {(0, 1): -jnp.inf}}
write_note(f'First step compilations...\n{chrono.note}')
logging.info('first_step = %s', first_step)
# Advance the iterators if we are restarting from an earlier checkpoint.
# TODO(dusenberrymw): Look into checkpointing dataset state instead.
if first_step > 0:
write_note('Advancing iterators after resuming from a checkpoint...')
lr_iter = itertools.islice(lr_iter, first_step, None)
train_iter = itertools.islice(train_iter, first_step, None)
# NOTE: Validation eval is only run on certain steps, so determine how many
# times it was run previously.
num_val_runs = sum(
map(
lambda i: train_utils.itstime(i, config.log_eval_steps,
total_steps),
range(1, first_step + 1)))
for val_name, (val_iter, val_steps) in val_iter_splits.items():
val_iter = itertools.islice(val_iter, num_val_runs * val_steps,
None)
val_iter_splits[val_name] = (val_iter, val_steps)
# Using a python integer for step here, because opt.state.step is allocated
# on TPU during replication.
for step, train_batch, lr_repl in zip(
range(first_step + 1, total_steps + 1), train_iter, lr_iter):
with jax.profiler.TraceAnnotation('train_step', step_num=step, _r=1):
if not config.get('only_eval', False):
opt_repl, loss_value, train_loop_rngs, extra_measurements = update_fn(
opt_repl,
lr_repl,
train_batch['image'],
train_batch['labels'],
rng=train_loop_rngs)
if jax.process_index() == 0:
profiler(step)
# Checkpoint saving
if not config.get('only_eval', False) and train_utils.itstime(
step, config.get('checkpoint_steps'), total_steps, process=0):
write_note('Checkpointing...')
chrono.pause()
train_utils.checkpointing_timeout(
checkpoint_writer, config.get('checkpoint_timeout', 1))
accumulated_train_time = chrono.accum_train_time
# We need to transfer the weights over now or else we risk keeping them
# alive while they'll be updated in a future step, creating hard to debug
# memory errors (see b/160593526). Also, takes device 0's params only.
opt_cpu = jax.tree_util.tree_map(lambda x: np.array(x[0]),
opt_repl)
# Check whether we want to keep a copy of the current checkpoint.
copy_step = None
if train_utils.itstime(step, config.get('keep_checkpoint_steps'),
total_steps):
write_note('Keeping a checkpoint copy...')
copy_step = step
# Checkpoint should be a nested dictionary or FLAX datataclasses from
# `flax.struct`. Both can be present in a checkpoint.
checkpoint_data = checkpoint_utils.CheckpointData(
train_loop_rngs=train_loop_rngs,
optimizer=opt_cpu,
accumulated_train_time=accumulated_train_time)
checkpoint_writer = pool.apply_async(
checkpoint_utils.checkpoint_trained_model,
(checkpoint_data, save_checkpoint_path, copy_step))
chrono.resume()
# Report training progress
if not config.get('only_eval', False) and train_utils.itstime(
step, config.log_training_steps, total_steps, process=0):
write_note('Reporting training progress...')
train_loss = loss_value[
0] # Keep to return for reproducibility tests.
timing_measurements, note = chrono.tick(step)
write_note(note)
train_measurements = {}
train_measurements.update({
'learning_rate': lr_repl[0],
'training_loss': train_loss,
})
train_measurements.update(
flax.jax_utils.unreplicate(extra_measurements))
train_measurements.update(timing_measurements)
writer.write_scalars(step, train_measurements)
# Report validation performance
if train_utils.itstime(step, config.log_eval_steps, total_steps):
write_note('Evaluating on the validation set...')
chrono.pause()
for val_name, (val_iter, val_steps) in val_iter_splits.items():
# Sets up evaluation metrics.
ece_num_bins = config.get('ece_num_bins', 15)
auc_num_bins = config.get('auc_num_bins', 1000)
ece = rm.metrics.ExpectedCalibrationError(
num_bins=ece_num_bins)
calib_auc = rm.metrics.CalibrationAUC(
correct_pred_as_pos_label=False)
oc_auc_0_5 = rm.metrics.OracleCollaborativeAUC(
oracle_fraction=0.005, num_bins=auc_num_bins)
oc_auc_1 = rm.metrics.OracleCollaborativeAUC(
oracle_fraction=0.01, num_bins=auc_num_bins)
oc_auc_2 = rm.metrics.OracleCollaborativeAUC(
oracle_fraction=0.02, num_bins=auc_num_bins)
oc_auc_5 = rm.metrics.OracleCollaborativeAUC(
oracle_fraction=0.05, num_bins=auc_num_bins)
label_diversity = tf.keras.metrics.Mean()
sample_diversity = tf.keras.metrics.Mean()
ged = tf.keras.metrics.Mean()
# Runs evaluation loop.
ncorrect, loss, nseen = 0, 0, 0
for _, batch in zip(range(val_steps), val_iter):
if val_name == 'cifar_10h':
batch_ncorrect, batch_losses, batch_n, batch_metric_args = (
cifar_10h_evaluation_fn(opt_repl.target,
batch['image'],
batch['labels'],
batch['mask']))
else:
batch_ncorrect, batch_losses, batch_n, batch_metric_args = (
evaluation_fn(opt_repl.target, batch['image'],
batch['labels'], batch['mask']))
# All results are a replicated array shaped as follows:
# (local_devices, per_device_batch_size, elem_shape...)
# with each local device's entry being identical as they got psum'd.
# So let's just take the first one to the host as numpy.
ncorrect += np.sum(np.array(batch_ncorrect[0]))
loss += np.sum(np.array(batch_losses[0]))
nseen += np.sum(np.array(batch_n[0]))
if config.get('loss', 'sigmoid_xent') != 'sigmoid_xent':
# Here we parse batch_metric_args to compute uncertainty metrics.
# (e.g., ECE or Calibration AUC).
logits, labels, _, masks = batch_metric_args
masks = np.array(masks[0], dtype=np.bool)
logits = np.array(logits[0])
probs = jax.nn.softmax(logits)
# From one-hot to integer labels, as required by ECE.
int_labels = np.argmax(np.array(labels[0]), axis=-1)
int_preds = np.argmax(logits, axis=-1)
confidence = np.max(probs, axis=-1)
for p, c, l, d, m, label in zip(
probs, confidence, int_labels, int_preds,
masks, labels[0]):
ece.add_batch(p[m, :], label=l[m])
calib_auc.add_batch(d[m],
label=l[m],
confidence=c[m])
# TODO(jereliu): Extend to support soft multi-class probabilities.
oc_auc_0_5.add_batch(d[m],
label=l[m],
custom_binning_score=c[m])
oc_auc_1.add_batch(d[m],
label=l[m],
custom_binning_score=c[m])
oc_auc_2.add_batch(d[m],
label=l[m],
custom_binning_score=c[m])
oc_auc_5.add_batch(d[m],
label=l[m],
custom_binning_score=c[m])
if val_name == 'cifar_10h' or val_name == 'imagenet_real':
batch_label_diversity, batch_sample_diversity, batch_ged = data_uncertainty_utils.generalized_energy_distance(
label[m], p[m, :], config.num_classes)
label_diversity.update_state(
batch_label_diversity)
sample_diversity.update_state(
batch_sample_diversity)
ged.update_state(batch_ged)
val_loss[
val_name] = loss / nseen # Keep for reproducibility tests.
val_measurements = {
f'{val_name}_prec@1': ncorrect / nseen,
f'{val_name}_loss': val_loss[val_name],
}
if config.get('loss', 'sigmoid_xent') != 'sigmoid_xent':
val_measurements[f'{val_name}_ece'] = ece.result()['ece']
val_measurements[
f'{val_name}_calib_auc'] = calib_auc.result(
)['calibration_auc']
val_measurements[
f'{val_name}_oc_auc_0.5%'] = oc_auc_0_5.result(
)['collaborative_auc']
val_measurements[
f'{val_name}_oc_auc_1%'] = oc_auc_1.result(
)['collaborative_auc']
val_measurements[
f'{val_name}_oc_auc_2%'] = oc_auc_2.result(
)['collaborative_auc']
val_measurements[
f'{val_name}_oc_auc_5%'] = oc_auc_5.result(
)['collaborative_auc']
writer.write_scalars(step, val_measurements)
if val_name == 'cifar_10h' or val_name == 'imagenet_real':
cifar_10h_measurements = {
f'{val_name}_label_diversity':
label_diversity.result(),
f'{val_name}_sample_diversity':
sample_diversity.result(),
f'{val_name}_ged': ged.result(),
}
writer.write_scalars(step, cifar_10h_measurements)
# OOD eval
# Entries in the ood_ds dict include:
# (ind_dataset, ood_dataset1, ood_dataset2, ...).
# OOD metrics are computed using ind_dataset paired with each of the
# ood_dataset. When Mahalanobis distance method is applied, train_ind_ds
# is also included in the ood_ds.
if ood_ds and config.ood_methods:
ood_measurements = ood_utils.eval_ood_metrics(
ood_ds, ood_ds_names, config.ood_methods, evaluation_fn,
opt_repl)
writer.write_scalars(step, ood_measurements)
chrono.resume()
if 'fewshot' in config and fewshotter is not None:
# Compute few-shot on-the-fly evaluation.
if train_utils.itstime(step, config.fewshot.log_steps,
total_steps):
chrono.pause()
write_note(f'Few-shot evaluation...\n{chrono.note}')
# Keep `results` to return for reproducibility tests.
fewshot_results, best_l2 = fewshotter.run_all(
opt_repl.target, config.fewshot.datasets)
# TODO(dusenberrymw): Remove this once fewshot.py is updated.
def make_writer_measure_fn(step):
def writer_measure(name, value):
writer.write_scalars(step, {name: value})
return writer_measure
fewshotter.walk_results(make_writer_measure_fn(step),
fewshot_results, best_l2)
chrono.resume()
# End of step.
if config.get('testing_failure_step'):
# Break early to simulate infra failures in test cases.
if config.testing_failure_step == step:
break
write_note(f'Done!\n{chrono.note}')
pool.close()
pool.join()
writer.close()
# Return final training loss, validation loss, and fewshot results for
# reproducibility test cases.
return train_loss, val_loss, fewshot_results
def main(config, output_dir):
seed = config.get('seed', 0)
tf.random.set_seed(seed)
if config.get('data_dir'):
logging.info('data_dir=%s', config.data_dir)
logging.info('Output dir: %s', output_dir)
tf.io.gfile.makedirs(output_dir)
# Create an asynchronous multi-metric writer.
writer = metric_writers.create_default_writer(
output_dir, just_logging=jax.process_index() > 0)
# The pool is used to perform misc operations such as logging in async way.
pool = multiprocessing.pool.ThreadPool()
def write_note(note):
if jax.process_index() == 0:
logging.info('NOTE: %s', note)
write_note('Initializing...')
batch_size = config.batch_size
batch_size_eval = config.get('batch_size_eval', batch_size)
if (batch_size % jax.device_count() != 0
or batch_size_eval % jax.device_count() != 0):
raise ValueError(
f'Batch sizes ({batch_size} and {batch_size_eval}) must '
f'be divisible by device number ({jax.device_count()})')
local_batch_size = batch_size // jax.process_count()
local_batch_size_eval = batch_size_eval // jax.process_count()
logging.info(
'Global batch size %d on %d hosts results in %d local batch size. '
'With %d devices per host (%d devices total), that\'s a %d per-device '
'batch size.', batch_size, jax.process_count(), local_batch_size,
jax.local_device_count(), jax.device_count(),
local_batch_size // jax.local_device_count())
write_note('Initializing val dataset(s)...')
def _get_val_split(dataset, split, pp_eval, data_dir=None):
# We do ceil rounding such that we include the last incomplete batch.
nval_img = input_utils.get_num_examples(
dataset,
split=split,
process_batch_size=local_batch_size_eval,
drop_remainder=False,
data_dir=data_dir)
val_steps = int(np.ceil(nval_img / batch_size_eval))
logging.info('Running validation for %d steps for %s, %s', val_steps,
dataset, split)
if isinstance(pp_eval, str):
pp_eval = preprocess_spec.parse(
spec=pp_eval, available_ops=preprocess_utils.all_ops())
val_ds = input_utils.get_data(dataset=dataset,
split=split,
rng=None,
process_batch_size=local_batch_size_eval,
preprocess_fn=pp_eval,
cache=config.get('val_cache', 'batched'),
num_epochs=1,
repeat_after_batching=True,
shuffle=False,
prefetch_size=config.get(
'prefetch_to_host', 2),
drop_remainder=False,
data_dir=data_dir)
return val_ds
val_ds_splits = {
'val':
_get_val_split(config.dataset,
split=config.val_split,
pp_eval=config.pp_eval,
data_dir=config.get('data_dir'))
}
if config.get('test_split'):
val_ds_splits.update({
'test':
_get_val_split(config.dataset,
split=config.test_split,
pp_eval=config.pp_eval,
data_dir=config.get('data_dir'))
})
if config.get('eval_on_cifar_10h'):
cifar10_to_cifar10h_fn = data_uncertainty_utils.create_cifar10_to_cifar10h_fn(
config.get('data_dir', None))
preprocess_fn = preprocess_spec.parse(
spec=config.pp_eval_cifar_10h,
available_ops=preprocess_utils.all_ops())
pp_eval = lambda ex: preprocess_fn(cifar10_to_cifar10h_fn(ex))
val_ds_splits['cifar_10h'] = _get_val_split(
'cifar10',
split=config.get('cifar_10h_split') or 'test',
pp_eval=pp_eval,
data_dir=config.get('data_dir'))
elif config.get('eval_on_imagenet_real'):
imagenet_to_real_fn = data_uncertainty_utils.create_imagenet_to_real_fn(
)
preprocess_fn = preprocess_spec.parse(
spec=config.pp_eval_imagenet_real,
available_ops=preprocess_utils.all_ops())
pp_eval = lambda ex: preprocess_fn(imagenet_to_real_fn(ex))
val_ds_splits['imagenet_real'] = _get_val_split(
'imagenet2012_real',
split=config.get('imagenet_real_split') or 'validation',
pp_eval=pp_eval,
data_dir=config.get('data_dir'))
ood_ds = {}
if config.get('ood_datasets') and config.get('ood_methods'):
if config.get(
'ood_methods'): # config.ood_methods is not a empty list
logging.info('loading OOD dataset = %s',
config.get('ood_datasets'))
ood_ds, ood_ds_names = ood_utils.load_ood_datasets(
config.dataset,
config.ood_datasets,
config.ood_split,
config.pp_eval,
config.pp_eval_ood,
config.ood_methods,
config.train_split,
config.get('data_dir'),
_get_val_split,
)
write_note('Initializing model...')
logging.info('config.model = %s', config.model)
model = ub.models.vision_transformer(num_classes=config.num_classes,
**config.model)
ensemble_pred_fn = functools.partial(ensemble_prediction_fn, model.apply)
@functools.partial(jax.pmap, axis_name='batch')
def evaluation_fn(params, images, labels, mask):
# params is a dict of the form:
# {'model_1': params_model_1, 'model_2': params_model_2, ...}
# Ignore the entries with all zero labels for evaluation.
mask *= labels.max(axis=1)
loss_as_str = config.get('loss', 'sigmoid_xent')
ens_logits, ens_prelogits = ensemble_pred_fn(params, images,
loss_as_str)
label_indices = config.get('label_indices')
logging.info('!!! mask %s, label_indices %s', mask, label_indices)
if label_indices:
ens_logits = ens_logits[:, label_indices]
# Note that logits and labels are usually of the shape [batch,num_classes].
# But for OOD data, when num_classes_ood > num_classes_ind, we need to
# adjust labels to labels[:, :config.num_classes] to match the shape of
# logits. That is just to avoid shape mismatch. The output losses does not
# have any meaning for OOD data, because OOD not belong to any IND class.
losses = getattr(train_utils, loss_as_str)(
logits=ens_logits,
labels=labels[:, :(
len(label_indices) if label_indices else config.num_classes)],
reduction=False)
loss = jax.lax.psum(losses * mask, axis_name='batch')
top1_idx = jnp.argmax(ens_logits, axis=1)
# Extracts the label at the highest logit index for each image.
top1_correct = jnp.take_along_axis(labels, top1_idx[:, None],
axis=1)[:, 0]
ncorrect = jax.lax.psum(top1_correct * mask, axis_name='batch')
n = jax.lax.psum(mask, axis_name='batch')
metric_args = jax.lax.all_gather(
[ens_logits, labels, ens_prelogits, mask], axis_name='batch')
return ncorrect, loss, n, metric_args
@functools.partial(jax.pmap, axis_name='batch')
def cifar_10h_evaluation_fn(params, images, labels, mask):
loss_as_str = config.get('loss', 'softmax_xent')
ens_logits, ens_prelogits = ensemble_pred_fn(params, images,
loss_as_str)
label_indices = config.get('label_indices')
if label_indices:
ens_logits = ens_logits[:, label_indices]
losses = getattr(train_utils, loss_as_str)(logits=ens_logits,
labels=labels,
reduction=False)
loss = jax.lax.psum(losses, axis_name='batch')
top1_idx = jnp.argmax(ens_logits, axis=1)
# Extracts the label at the highest logit index for each image.
one_hot_labels = jnp.eye(10)[jnp.argmax(labels, axis=1)]
top1_correct = jnp.take_along_axis(one_hot_labels,
top1_idx[:, None],
axis=1)[:, 0]
ncorrect = jax.lax.psum(top1_correct, axis_name='batch')
n = jax.lax.psum(one_hot_labels, axis_name='batch')
metric_args = jax.lax.all_gather(
[ens_logits, labels, ens_prelogits, mask], axis_name='batch')
return ncorrect, loss, n, metric_args
# Setup function for computing representation.
@functools.partial(jax.pmap, axis_name='batch')
def representation_fn(params, images, labels, mask):
# Return shape [batch_size, representation_size * ensemble_size]. During
# few-shot eval, a single linear regressor is applied over all dimensions.
representation = []
for p in params.values():
_, outputs = model.apply({'params': flax.core.freeze(p)},
images,
train=False)
representation += [outputs[config.fewshot.representation_layer]]
representation = jnp.concatenate(representation, axis=1)
representation = jax.lax.all_gather(representation, 'batch')
labels = jax.lax.all_gather(labels, 'batch')
mask = jax.lax.all_gather(mask, 'batch')
return representation, labels, mask
write_note('Load checkpoints...')
ensemble_params = load_checkpoints(config)
write_note('Replicating...')
ensemble_params = flax.jax_utils.replicate(ensemble_params)
if jax.process_index() == 0:
writer.write_hparams(dict(config))
write_note('Initializing few-shotters...')
fewshotter = None
if 'fewshot' in config and fewshot is not None:
fewshotter = fewshot.FewShotEvaluator(
representation_fn, config.fewshot,
config.fewshot.get('batch_size') or batch_size_eval)
# Note: we return the train loss, val loss, and fewshot best l2s for use in
# reproducibility unit tests.
val_loss = {val_name: -jnp.inf for val_name, _ in val_ds_splits.items()}
fewshot_results = {'dummy': {(0, 1): -jnp.inf}}
step = 1
# Report validation performance.
write_note('Evaluating on the validation set...')
for val_name, val_ds in val_ds_splits.items():
# Sets up evaluation metrics.
ece_num_bins = config.get('ece_num_bins', 15)
auc_num_bins = config.get('auc_num_bins', 1000)
ece = rm.metrics.ExpectedCalibrationError(num_bins=ece_num_bins)
calib_auc = rm.metrics.CalibrationAUC(correct_pred_as_pos_label=False)
oc_auc_0_5 = rm.metrics.OracleCollaborativeAUC(oracle_fraction=0.005,
num_bins=auc_num_bins)
oc_auc_1 = rm.metrics.OracleCollaborativeAUC(oracle_fraction=0.01,
num_bins=auc_num_bins)
oc_auc_2 = rm.metrics.OracleCollaborativeAUC(oracle_fraction=0.02,
num_bins=auc_num_bins)
oc_auc_5 = rm.metrics.OracleCollaborativeAUC(oracle_fraction=0.05,
num_bins=auc_num_bins)
label_diversity = tf.keras.metrics.Mean()
sample_diversity = tf.keras.metrics.Mean()
ged = tf.keras.metrics.Mean()
# Runs evaluation loop.
val_iter = input_utils.start_input_pipeline(
val_ds, config.get('prefetch_to_device', 1))
ncorrect, loss, nseen = 0, 0, 0
for batch in val_iter:
if val_name == 'cifar_10h':
batch_ncorrect, batch_losses, batch_n, batch_metric_args = (
cifar_10h_evaluation_fn(ensemble_params, batch['image'],
batch['labels'], batch['mask']))
else:
batch_ncorrect, batch_losses, batch_n, batch_metric_args = (
evaluation_fn(ensemble_params, batch['image'],
batch['labels'], batch['mask']))
# All results are a replicated array shaped as follows:
# (local_devices, per_device_batch_size, elem_shape...)
# with each local device's entry being identical as they got psum'd.
# So let's just take the first one to the host as numpy.
ncorrect += np.sum(np.array(batch_ncorrect[0]))
loss += np.sum(np.array(batch_losses[0]))
nseen += np.sum(np.array(batch_n[0]))
if config.get('loss', 'sigmoid_xent') != 'sigmoid_xent':
# Here we parse batch_metric_args to compute uncertainty metrics.
# (e.g., ECE or Calibration AUC).
logits, labels, _, masks = batch_metric_args
masks = np.array(masks[0], dtype=np.bool)
logits = np.array(logits[0])
probs = jax.nn.softmax(logits)
# From one-hot to integer labels, as required by ECE.
int_labels = np.argmax(np.array(labels[0]), axis=-1)
int_preds = np.argmax(logits, axis=-1)
confidence = np.max(probs, axis=-1)
for p, c, l, d, m, label in zip(probs, confidence, int_labels,
int_preds, masks, labels[0]):
ece.add_batch(p[m, :], label=l[m])
calib_auc.add_batch(d[m], label=l[m], confidence=c[m])
# TODO(jereliu): Extend to support soft multi-class probabilities.
oc_auc_0_5.add_batch(d[m],
label=l[m],
custom_binning_score=c[m])
oc_auc_1.add_batch(d[m],
label=l[m],
custom_binning_score=c[m])
oc_auc_2.add_batch(d[m],
label=l[m],
custom_binning_score=c[m])
oc_auc_5.add_batch(d[m],
label=l[m],
custom_binning_score=c[m])
if val_name == 'cifar_10h' or val_name == 'imagenet_real':
batch_label_diversity, batch_sample_diversity, batch_ged = data_uncertainty_utils.generalized_energy_distance(
label[m], p[m, :], config.num_classes)
label_diversity.update_state(batch_label_diversity)
sample_diversity.update_state(batch_sample_diversity)
ged.update_state(batch_ged)
val_loss[val_name] = loss / nseen # Keep for reproducibility tests.
val_measurements = {
f'{val_name}_prec@1': ncorrect / nseen,
f'{val_name}_loss': val_loss[val_name],
}
if config.get('loss', 'sigmoid_xent') != 'sigmoid_xent':
val_measurements[f'{val_name}_ece'] = ece.result()['ece']
val_measurements[f'{val_name}_calib_auc'] = calib_auc.result(
)['calibration_auc']
val_measurements[f'{val_name}_oc_auc_0.5%'] = oc_auc_0_5.result(
)['collaborative_auc']
val_measurements[f'{val_name}_oc_auc_1%'] = oc_auc_1.result(
)['collaborative_auc']
val_measurements[f'{val_name}_oc_auc_2%'] = oc_auc_2.result(
)['collaborative_auc']
val_measurements[f'{val_name}_oc_auc_5%'] = oc_auc_5.result(
)['collaborative_auc']
writer.write_scalars(step, val_measurements)
if val_name == 'cifar_10h' or val_name == 'imagenet_real':
cifar_10h_measurements = {
f'{val_name}_label_diversity': label_diversity.result(),
f'{val_name}_sample_diversity': sample_diversity.result(),
f'{val_name}_ged': ged.result(),
}
writer.write_scalars(step, cifar_10h_measurements)
# OOD eval
# Entries in the ood_ds dict include:
# (ind_dataset, ood_dataset1, ood_dataset2, ...).
# OOD metrics are computed using ind_dataset paired with each of the
# ood_dataset. When Mahalanobis distance method is applied, train_ind_ds
# is also included in the ood_ds.
if ood_ds and config.ood_methods:
ood_measurements = ood_utils.eval_ood_metrics(ood_ds,
ood_ds_names,
config.ood_methods,
evaluation_fn,
ensemble_params,
n_prefetch=config.get(
'prefetch_to_device',
1))
writer.write_scalars(step, ood_measurements)
if 'fewshot' in config and fewshotter is not None:
# Compute few-shot on-the-fly evaluation.
write_note('Few-shot evaluation...')
# Keep `results` to return for reproducibility tests.
fewshot_results, best_l2 = fewshotter.run_all(ensemble_params,
config.fewshot.datasets)
# TODO(dusenberrymw): Remove this once fewshot.py is updated.
def make_writer_measure_fn(step):
def writer_measure(name, value):
writer.write_scalars(step, {name: value})
return writer_measure
fewshotter.walk_results(make_writer_measure_fn(step), fewshot_results,
best_l2)
write_note('Done!')
pool.close()
pool.join()
writer.close()
# Return final training loss, validation loss, and fewshot results for
# reproducibility test cases.
return val_loss, fewshot_results
