def __init__(self, optimizer, config, lightweight_output=False):
"""
Initializes the trainer.
Args:
optimizer: A NASLib optimizer
config (AttrDict): The configuration loaded from a yaml file, e.g
via `utils.get_config_from_args()`
"""
self.optimizer = optimizer
self.config = config
self.epochs = self.config.search.epochs
self.lightweight_output = lightweight_output
# preparations
self.device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
# measuring stuff
self.train_top1 = utils.AverageMeter()
self.train_top5 = utils.AverageMeter()
self.train_loss = utils.AverageMeter()
self.val_top1 = utils.AverageMeter()
self.val_top5 = utils.AverageMeter()
self.val_loss = utils.AverageMeter()
n_parameters = optimizer.get_model_size()
logger.info("param size = %fMB", n_parameters)
self.errors_dict = utils.AttrDict(
{'train_acc': [],
'train_loss': [],
'valid_acc': [],
'valid_loss': [],
'test_acc': [],
'test_loss': [],
'runtime': [],
'train_time': [],
'arch_eval': [],
'params': n_parameters}
)
def evaluate(
self,
retrain=True,
search_model="",
resume_from="",
best_arch=None,
):
"""
Evaluate the final architecture as given from the optimizer.
If the search space has an interface to a benchmark then query that.
Otherwise train as defined in the config.
Args:
retrain (bool): Reset the weights from the architecure search
search_model (str): Path to checkpoint file that was created during
search. If not provided, then try to load 'model_final.pth' from search
resume_from (str): Resume retraining from the given checkpoint file.
best_arch: Parsed model you want to directly evaluate and ignore the final model
from the optimizer.
"""
logger.info("Start evaluation")
if not best_arch:
if not search_model:
search_model = os.path.join(self.config.save, "search",
"model_final.pth")
self._setup_checkpointers(
search_model) # required to load the architecture
best_arch = self.optimizer.get_final_architecture()
logger.info("Final architecture:\n" + best_arch.modules_str())
if best_arch.QUERYABLE:
metric = Metric.TEST_ACCURACY
result = best_arch.query(metric=metric,
dataset=self.config.dataset)
logger.info("Queried results ({}): {}".format(metric, result))
else:
best_arch.to(self.device)
if retrain:
logger.info("Starting retraining from scratch")
best_arch.reset_weights(inplace=True)
self.train_queue, self.valid_queue, self.test_queue = self.build_eval_dataloaders(
self.config)
optim = self.build_eval_optimizer(best_arch.parameters(),
self.config)
scheduler = self.build_eval_scheduler(optim, self.config)
start_epoch = self._setup_checkpointers(
resume_from,
search=False,
period=self.config.evaluation.checkpoint_freq,
model=best_arch, # checkpointables start here
optim=optim,
scheduler=scheduler)
grad_clip = self.config.evaluation.grad_clip
loss = torch.nn.CrossEntropyLoss()
best_arch.train()
self.train_top1.reset()
self.train_top5.reset()
self.val_top1.reset()
self.val_top5.reset()
# Enable drop path
best_arch.update_edges(update_func=lambda edge: edge.data.set(
'op', DropPathWrapper(edge.data.op)),
scope=best_arch.OPTIMIZER_SCOPE,
private_edge_data=True)
# train from scratch
epochs = self.config.evaluation.epochs
for e in range(start_epoch, epochs):
if torch.cuda.is_available():
log_first_n(logging.INFO,
"cuda consumption\n {}".format(
torch.cuda.memory_summary()),
n=20)
# update drop path probability
drop_path_prob = self.config.evaluation.drop_path_prob * e / epochs
best_arch.update_edges(
update_func=lambda edge: edge.data.set(
'drop_path_prob', drop_path_prob),
scope=best_arch.OPTIMIZER_SCOPE,
private_edge_data=True)
# Train queue
for i, (input_train,
target_train) in enumerate(self.train_queue):
input_train = input_train.to(self.device)
target_train = target_train.to(self.device,
non_blocking=True)
optim.zero_grad()
logits_train = best_arch(input_train)
train_loss = loss(logits_train, target_train)
if hasattr(best_arch,
'auxilary_logits'): # darts specific stuff
log_first_n(logging.INFO,
"Auxiliary is used",
n=10)
auxiliary_loss = loss(best_arch.auxilary_logits(),
target_train)
train_loss += self.config.evaluation.auxiliary_weight * auxiliary_loss
train_loss.backward()
if grad_clip:
torch.nn.utils.clip_grad_norm_(
best_arch.parameters(), grad_clip)
optim.step()
self._store_accuracies(logits_train, target_train,
'train')
log_every_n_seconds(
logging.INFO,
"Epoch {}-{}, Train loss: {:.5}, learning rate: {}"
.format(e, i, train_loss, scheduler.get_last_lr()),
n=5)
# Validation queue
if self.valid_queue:
for i, (input_valid,
target_valid) in enumerate(self.valid_queue):
input_valid = input_valid.cuda().float()
target_valid = target_valid.cuda().float()
# just log the validation accuracy
with torch.no_grad():
logits_valid = best_arch(input_valid)
self._store_accuracies(logits_valid,
target_valid, 'val')
scheduler.step()
self.periodic_checkpointer.step(e)
self._log_and_reset_accuracies(e)
# Disable drop path
best_arch.update_edges(update_func=lambda edge: edge.data.set(
'op', edge.data.op.get_embedded_ops()),
scope=best_arch.OPTIMIZER_SCOPE,
private_edge_data=True)
# measure final test accuracy
top1 = utils.AverageMeter()
top5 = utils.AverageMeter()
best_arch.eval()
for i, data_test in enumerate(self.test_queue):
input_test, target_test = data_test
input_test = input_test.to(self.device)
target_test = target_test.to(self.device, non_blocking=True)
n = input_test.size(0)
with torch.no_grad():
logits = best_arch(input_test)
prec1, prec5 = utils.accuracy(logits,
target_test,
topk=(1, 5))
top1.update(prec1.data.item(), n)
top5.update(prec5.data.item(), n)
log_every_n_seconds(logging.INFO,
"Inference batch {} of {}.".format(
i, len(self.test_queue)),
n=5)
logger.info(
"Evaluation finished. Test accuracies: top-1 = {:.5}, top-5 = {:.5}"
.format(top1.avg, top5.avg))
def evaluate(
self,
retrain=True,
search_model="",
resume_from="",
best_arch=None,
):
"""
Evaluate the final architecture as given from the optimizer.
If the search space has an interface to a benchmark then query that.
Otherwise train as defined in the config.
Args:
retrain (bool): Reset the weights from the architecure search
search_model (str): Path to checkpoint file that was created during
search. If not provided, then try to load 'model_final.pth' from search
resume_from (str): Resume retraining from the given checkpoint file.
multi_gpu (bool): Distribute training on multiple gpus.
best_arch: Parsed model you want to directly evaluate and ignore the final model
from the optimizer.
"""
#best_arch.to(self.device)
self.config.evaluation.resume_from = resume_from
if retrain:
if self.config.gpu is not None:
logger.warning(
'You have chosen a specific GPU. This will completely \
disable data parallelism.'
)
if self.config.evaluation.dist_url == "env://" and self.config.evaluation.world_size == -1:
self.config.evaluation.world_size = int(os.environ["WORLD_SIZE"])
self.config.evaluation.distributed = \
self.config.evaluation.world_size > 1 or self.config.evaluation.multiprocessing_distributed
ngpus_per_node = torch.cuda.device_count()
if self.config.evaluation.multiprocessing_distributed:
# Since we have ngpus_per_node processes per node, the
# total world_size needs to be adjusted
self.config.evaluation.world_size = ngpus_per_node * self.config.evaluation.world_size
# Use torch.multiprocessing.spawn to launch distributed
# processes: the main_worker process function
mp.spawn(self.main_worker, nprocs=ngpus_per_node,
args=(ngpus_per_node, self.config.evaluation,
search_model, best_arch))
else:
# Simply call main_worker function
self.main_worker(self.config.gpu, ngpus_per_node,
self.config.evaluation,
search_model, best_arch)
if not self.QUERYABLE:
# Disable drop path
best_arch.update_edges(
update_func=lambda edge: edge.data.set('op', edge.data.op.get_embedded_ops()),
scope=best_arch.OPTIMIZER_SCOPE,
private_edge_data=True
)
# measure final test accuracy
top1 = utils.AverageMeter()
top5 = utils.AverageMeter()
best_arch.eval()
for i, data_test in enumerate(self.test_queue):
input_test, target_test = data_test
input_test = input_test.to(self.device)
target_test = target_test.to(self.device, non_blocking=True)
n = input_test.size(0)
with torch.no_grad():
logits = best_arch(input_test)
prec1, prec5 = utils.accuracy(logits, target_test, topk=(1, 5))
top1.update(prec1.data.item(), n)
top5.update(prec5.data.item(), n)
log_every_n_seconds(
logging.INFO,
"Inference batch {} of {}.".format(
i, len(self.test_queue)
), n=5
)
logger.info("Evaluation finished. Test accuracies: top-1 = {:.5}, \
top-5 = {:.5}".format(top1.avg, top5.avg))