def __init__(self,
cfg: RetrievalConfig,
model_mgr: model_retrieval.RetrievalModelManager,
exp_group: str,
exp_name: str,
run_name: str,
train_loader_length: int,
*,
log_dir: str = "experiments",
log_level: Optional[int] = None,
logger: Optional[logging.Logger] = None,
print_graph: bool = False,
reset: bool = False,
load_best: bool = False,
load_epoch: Optional[int] = None,
load_model: Optional[str] = None,
inference_only: bool = False):
super().__init__(cfg,
model_mgr,
exp_group,
exp_name,
run_name,
train_loader_length,
ExperimentTypesConst.RETRIEVAL,
log_dir=log_dir,
log_level=log_level,
logger=logger,
print_graph=print_graph,
reset=reset,
load_best=load_best,
load_epoch=load_epoch,
load_model=load_model,
is_test=inference_only)
# ---------- setup ----------
# update type hints from base classes to inherited classes
self.cfg: RetrievalConfig = self.cfg
self.model_mgr: model_retrieval.RetrievalModelManager = self.model_mgr
# overwrite default state with inherited trainer state in case we need additional state fields
self.state = RetrievalTrainerState()
# ---------- loss ----------
# contrastive loss
assert self.cfg.train.loss_func == loss_fn.LossesConst.CONTRASTIVE
self.loss_contr = ContrastiveLoss(
self.cfg.train.contrastive_loss_config.margin,
use_cuda=self.cfg.use_cuda)
# cycle consistency
if self.cfg.train.loss_cycle_cons != 0:
self.loss_cycle_cons = CycleConsistencyLoss(
use_cuda=self.cfg.use_cuda)
# ---------- additional metrics ----------
# loss proportions
self.metrics.add_meter(CMeters.VAL_LOSS_CC, use_avg=False)
self.metrics.add_meter(CMeters.VAL_LOSS_CONTRASTIVE, use_avg=False)
self.metrics.add_meter(CMeters.TRAIN_LOSS_CC,
per_step=True,
use_avg=False)
self.metrics.add_meter(CMeters.TRAIN_LOSS_CONTRASTIVE,
per_step=True,
use_avg=False)
# retrieval validation metrics must be constructed as product of two lists
for modality in CMeters.RET_MODALITIES:
# modality: retrieval from where to where
for metric in CMeters.RET_METRICS:
# metric: retrieval@1, mean, ...
if metric == "r1":
# log r1 metric to the overview class
metric_class = "val_base"
else:
# log all other metrics to the detail class
metric_class = "val_ret"
self.metrics.add_meter(f"{metric_class}/{modality}-{metric}",
use_avg=False)
# ---------- optimization ----------
self.optimizer = None
self.lr_scheduler = None
# skip optimizer if not training
if not self.is_test:
# create optimizer
params, _param_names, _params_flat = self.model_mgr.get_all_params(
)
self.optimizer = optimization.make_optimizer(
self.cfg.optimizer, params)
# create lr scheduler
self.lr_scheduler = lr_scheduler.make_lr_scheduler(
self.optimizer,
self.cfg.lr_scheduler,
self.cfg.optimizer.lr,
self.cfg.train.num_epochs,
self.train_loader_length,
logger=self.logger)
# post init hook for checkpoint loading
self.hook_post_init()
def __init__(self,
cfg: MLPMNISTExperimentConfig,
model_mgr: MLPModelManager,
exp_dir: str,
exp_name: str,
run_name: str,
train_loader_length: int,
*,
log_dir: str = Paths.DIR_EXPERIMENTS,
log_level: Optional[int] = None,
logger: Optional[logging.Logger] = None,
print_graph: bool = False,
reset: bool = False,
load_best: bool = False,
load_epoch: Optional[int] = None,
inference_only: bool = False):
super().__init__(cfg,
model_mgr,
exp_dir,
exp_name,
run_name,
train_loader_length,
"mlpmnist",
log_dir=log_dir,
log_level=log_level,
logger=logger,
print_graph=print_graph,
reset=reset,
load_best=load_best,
load_epoch=load_epoch,
is_test=inference_only)
# ---------- setup ----------
# update type hints from base classes to inherited classes
self.cfg: MLPMNISTExperimentConfig = self.cfg
self.model_mgr: MLPModelManager = self.model_mgr
# update trainer state if loading is requested
if self.load:
self.state.current_epoch = self.load_ep
# ---------- loss ----------
# contrastive
assert self.cfg.train.loss_func == "crossentropy"
self.loss_ce = nn.CrossEntropyLoss()
# ---------- additional metrics ----------
# metrics logged once per epoch, log only value
for field in ("val_base/accuracy", ):
self.metrics.add_meter(field, use_avg=False)
# create optimizer
params, _param_names, _params_flat = self.model_mgr.get_all_params()
self.optimizer = optimization.make_optimizer(self.cfg.optimizer,
params)
# create lr scheduler
self.lr_scheduler = lr_scheduler.make_lr_scheduler(
self.optimizer,
self.cfg.lr_scheduler,
self.cfg.optimizer.lr,
self.cfg.train.num_epochs,
self.train_loader_length,
logger=self.logger)
self.hook_post_init()