def train_manifold_flow_sequential(args, dataset, model, simulator):
""" MFMF-A training """
assert not args.specified
trainer = ManifoldFlowTrainer(model) if simulator.parameter_dim(
) is None else ConditionalManifoldFlowTrainer(model)
common_kwargs = {
"dataset": dataset,
"batch_size": args.batchsize,
"initial_lr": args.lr,
"scheduler": optim.lr_scheduler.CosineAnnealingLR,
"clip_gradient": args.clip,
"validation_split": args.validationsplit,
}
if args.weightdecay is not None:
common_kwargs["optimizer_kwargs"] = {
"weight_decay": float(args.weightdecay)
}
logger.info("Starting training MF, phase 1: manifold training")
learning_curves = trainer.train(
loss_functions=[losses.mse],
loss_labels=["MSE"],
loss_weights=[args.msefactor],
epochs=args.epochs // 2,
parameters=list(model.outer_transform.parameters()) +
list(model.encoder.parameters())
if args.algorithm == "emf" else model.outer_transform.parameters(),
callbacks=[
callbacks.save_model_after_every_epoch(
create_filename("checkpoint", None, args)[:-3] +
"_epoch_A{}.pt")
],
forward_kwargs={"mode": "projection"},
**common_kwargs,
)
learning_curves = np.vstack(learning_curves).T
logger.info("Starting training MF, phase 2: density training")
learning_curves_ = trainer.train(
loss_functions=[losses.nll],
loss_labels=["NLL"],
loss_weights=[args.nllfactor],
epochs=args.epochs - (args.epochs // 2),
parameters=model.inner_transform.parameters(),
callbacks=[
callbacks.save_model_after_every_epoch(
create_filename("checkpoint", None, args)[:-3] +
"_epoch_B{}.pt")
],
forward_kwargs={"mode": "mf-fixed-manifold"},
**common_kwargs,
)
learning_curves_ = np.vstack(learning_curves_).T
learning_curves = learning_curves_ if learning_curves is None else np.vstack(
(learning_curves, learning_curves_))
return learning_curves
def train_pie(args, dataset, model, simulator):
""" PIE training """
trainer = ForwardTrainer(model) if simulator.parameter_dim(
) is None else ConditionalForwardTrainer(
model) if args.scandal is None else SCANDALForwardTrainer(model)
common_kwargs, scandal_loss, scandal_label, scandal_weight = make_training_kwargs(
args, dataset)
callbacks_ = [
callbacks.save_model_after_every_epoch(
create_filename("checkpoint", None, args))
]
if simulator.is_image():
callbacks_.append(
callbacks.plot_sample_images(
create_filename("training_plot", None, args),
context=None if simulator.parameter_dim() is None else
torch.zeros(30, simulator.parameter_dim())))
callbacks_.append(
callbacks.plot_reco_images(
create_filename("training_plot", "reco_epoch", args)))
logger.info("Starting training PIE on NLL")
learning_curves = trainer.train(
loss_functions=[losses.nll] + scandal_loss,
loss_labels=["NLL"] + scandal_label,
loss_weights=[args.nllfactor * nat_to_bit_per_dim(args.datadim)] +
scandal_weight,
epochs=args.epochs,
callbacks=callbacks_,
forward_kwargs={"mode": "pie"},
initial_epoch=args.startepoch,
**common_kwargs,
)
learning_curves = np.vstack(learning_curves).T
return learning_curves
def train_generative_adversarial_manifold_flow(args, dataset, model,
simulator):
""" MFMF-OT training """
gen_trainer = AdversarialTrainer(model) if simulator.parameter_dim(
) is None else ConditionalAdversarialTrainer(model)
common_kwargs, scandal_loss, scandal_label, scandal_weight = make_training_kwargs(
args, dataset)
common_kwargs["batch_size"] = args.genbatchsize
logger.info("Starting training GAMF: Sinkhorn-GAN")
callbacks_ = [
callbacks.save_model_after_every_epoch(
create_filename("checkpoint", None, args))
]
if args.debug:
callbacks_.append(callbacks.print_mf_weight_statistics())
learning_curves_ = gen_trainer.train(
loss_functions=[losses.make_sinkhorn_divergence()],
loss_labels=["GED"],
loss_weights=[args.sinkhornfactor],
epochs=args.epochs,
callbacks=callbacks_,
compute_loss_variance=True,
initial_epoch=args.startepoch,
**common_kwargs,
)
learning_curves = np.vstack(learning_curves_).T
return learning_curves
def train_specified_manifold_flow(args, dataset, model, simulator):
""" FOM training """
trainer = ForwardTrainer(model) if simulator.parameter_dim(
) is None else ConditionalForwardTrainer(
model) if args.scandal is None else SCANDALForwardTrainer(model)
common_kwargs, scandal_loss, scandal_label, scandal_weight = make_training_kwargs(
args, dataset)
logger.info("Starting training MF with specified manifold on NLL")
learning_curves = trainer.train(
loss_functions=[losses.mse, losses.nll] + scandal_loss,
loss_labels=["MSE", "NLL"] + scandal_label,
loss_weights=[
0.0, args.nllfactor * nat_to_bit_per_dim(args.modellatentdim)
] + scandal_weight,
epochs=args.epochs,
callbacks=[
callbacks.save_model_after_every_epoch(
create_filename("checkpoint", None, args))
],
forward_kwargs={"mode": "mf"},
initial_epoch=args.startepoch,
**common_kwargs,
)
learning_curves = np.vstack(learning_curves).T
return learning_curves
def train_pie(args, dataset, model, simulator):
""" PIE training """
trainer = ManifoldFlowTrainer(model) if simulator.parameter_dim(
) is None else ConditionalManifoldFlowTrainer(model)
logger.info("Starting training PIE on NLL")
common_kwargs = {
"dataset": dataset,
"batch_size": args.batchsize,
"initial_lr": args.lr,
"scheduler": optim.lr_scheduler.CosineAnnealingLR,
"clip_gradient": args.clip,
"validation_split": args.validationsplit,
}
if args.weightdecay is not None:
common_kwargs["optimizer_kwargs"] = {
"weight_decay": float(args.weightdecay)
}
learning_curves = trainer.train(
loss_functions=[losses.nll],
loss_labels=["NLL"],
loss_weights=[args.nllfactor],
epochs=args.epochs,
callbacks=[
callbacks.save_model_after_every_epoch(
create_filename("checkpoint", None, args)[:-3] +
"_epoch_{}.pt")
],
forward_kwargs={"mode": "pie"},
**common_kwargs,
)
learning_curves = np.vstack(learning_curves).T
return learning_curves
def train_generative_adversarial_manifold_flow_alternating(
args, dataset, model, simulator):
""" MFMF-OTA training """
assert not args.specified
gen_trainer = GenerativeTrainer(model) if simulator.parameter_dim(
) is None else ConditionalGenerativeTrainer(model)
likelihood_trainer = ManifoldFlowTrainer(model) if simulator.parameter_dim(
) is None else ConditionalManifoldFlowTrainer(model)
metatrainer = AlternatingTrainer(model, gen_trainer, likelihood_trainer)
meta_kwargs = {
"dataset": dataset,
"initial_lr": args.lr,
"scheduler": optim.lr_scheduler.CosineAnnealingLR,
"validation_split": args.validationsplit
}
if args.weightdecay is not None:
meta_kwargs["optimizer_kwargs"] = {
"weight_decay": float(args.weightdecay)
}
phase1_kwargs = {"clip_gradient": args.clip}
phase2_kwargs = {
"forward_kwargs": {
"mode": "mf-fixed-manifold"
},
"clip_gradient": args.clip
}
phase1_parameters = model.parameters()
phase2_parameters = model.inner_transform.parameters()
logger.info(
"Starting training GAMF, alternating between Sinkhorn divergence and log likelihood"
)
learning_curves_ = metatrainer.train(
loss_functions=[losses.make_sinkhorn_divergence(), losses.nll],
loss_function_trainers=[0, 1],
loss_labels=["GED", "NLL"],
loss_weights=[args.sinkhornfactor, args.nllfactor],
batch_sizes=[args.genbatchsize, args.batchsize],
epochs=args.epochs // 2,
parameters=[phase1_parameters, phase2_parameters],
callbacks=[
callbacks.save_model_after_every_epoch(
create_filename("checkpoint", None, args)[:-3] +
"_epoch_{}.pt")
],
trainer_kwargs=[phase1_kwargs, phase2_kwargs],
subsets=args.subsets,
subset_callbacks=[callbacks.print_mf_weight_statistics()]
if args.debug else None,
**meta_kwargs,
)
learning_curves = np.vstack(learning_curves_).T
return learning_curves
def train_generative_adversarial_manifold_flow(args, dataset, model,
simulator):
""" MFMF-OT training """
gen_trainer = GenerativeTrainer(model) if simulator.parameter_dim(
) is None else ConditionalGenerativeTrainer(model)
common_kwargs = {
"dataset": dataset,
"initial_lr": args.lr,
"scheduler": optim.lr_scheduler.CosineAnnealingLR,
"clip_gradient": args.clip,
"validation_split": args.validationsplit,
}
if args.weightdecay is not None:
common_kwargs["optimizer_kwargs"] = {
"weight_decay": float(args.weightdecay)
}
logger.info("Starting training GAMF: Sinkhorn-GAN")
callbacks_ = [
callbacks.save_model_after_every_epoch(
create_filename("checkpoint", None, args)[:-3] + "_epoch_{}.pt")
]
if args.debug:
callbacks_.append(callbacks.print_mf_weight_statistics())
learning_curves_ = gen_trainer.train(
loss_functions=[losses.make_sinkhorn_divergence()],
loss_labels=["GED"],
loss_weights=[args.sinkhornfactor],
epochs=args.epochs,
callbacks=callbacks_,
batch_size=args.genbatchsize,
compute_loss_variance=True,
**common_kwargs,
)
learning_curves = np.vstack(learning_curves_).T
return learning_curves
def train_dough(args, dataset, model, simulator):
""" PIE with variable epsilons training """
trainer = VariableDimensionManifoldFlowTrainer(
model) if simulator.parameter_dim(
) is None else ConditionalVariableDimensionManifoldFlowTrainer(model)
common_kwargs = {
"dataset": dataset,
"batch_size": args.batchsize,
"initial_lr": args.lr,
"scheduler": optim.lr_scheduler.CosineAnnealingLR,
"clip_gradient": args.clip,
"validation_split": args.validationsplit,
}
if args.weightdecay is not None:
common_kwargs["optimizer_kwargs"] = {
"weight_decay": float(args.weightdecay)
}
logger.info(
"Starting training dough, phase 1: NLL without latent regularization")
learning_curves = trainer.train(
loss_functions=[losses.nll],
loss_labels=["NLL"],
loss_weights=[args.nllfactor],
epochs=args.epochs,
callbacks=[
callbacks.save_model_after_every_epoch(
create_filename("checkpoint", None, args)[:-3] +
"_epoch_{}.pt")
],
l1=args.doughl1reg,
**common_kwargs,
)
learning_curves = np.vstack(learning_curves).T
return learning_curves
def train_manifold_flow_sequential(args, dataset, model, simulator):
""" Sequential MFMF-M/D training """
assert not args.specified
if simulator.parameter_dim() is None:
trainer1 = ForwardTrainer(model)
trainer2 = ForwardTrainer(model)
else:
trainer1 = ConditionalForwardTrainer(model)
if args.scandal is None:
trainer2 = ConditionalForwardTrainer(model)
else:
trainer2 = SCANDALForwardTrainer(model)
common_kwargs, scandal_loss, scandal_label, scandal_weight = make_training_kwargs(
args, dataset)
callbacks1 = [
callbacks.save_model_after_every_epoch(
create_filename("checkpoint", "A", args)),
callbacks.print_mf_latent_statistics(),
callbacks.print_mf_weight_statistics()
]
callbacks2 = [
callbacks.save_model_after_every_epoch(
create_filename("checkpoint", "B", args)),
callbacks.print_mf_latent_statistics(),
callbacks.print_mf_weight_statistics()
]
if simulator.is_image():
callbacks1.append(
callbacks.plot_sample_images(
create_filename("training_plot", "sample_epoch_A", args),
context=None if simulator.parameter_dim() is None else
torch.zeros(30, simulator.parameter_dim()),
))
callbacks2.append(
callbacks.plot_sample_images(
create_filename("training_plot", "sample_epoch_B", args),
context=None if simulator.parameter_dim() is None else
torch.zeros(30, simulator.parameter_dim()),
))
callbacks1.append(
callbacks.plot_reco_images(
create_filename("training_plot", "reco_epoch_A", args)))
callbacks2.append(
callbacks.plot_reco_images(
create_filename("training_plot", "reco_epoch_B", args)))
logger.info("Starting training MF, phase 1: manifold training")
learning_curves = trainer1.train(
loss_functions=[losses.smooth_l1_loss if args.l1 else losses.mse] +
([] if args.uvl2reg is None else [losses.hiddenl2reg]),
loss_labels=["L1" if args.l1 else "MSE"] +
([] if args.uvl2reg is None else ["L2_lat"]),
loss_weights=[args.msefactor] +
([] if args.uvl2reg is None else [args.uvl2reg]),
epochs=args.epochs // 2,
parameters=list(model.outer_transform.parameters()) +
list(model.encoder.parameters()) if args.algorithm == "emf" else list(
model.outer_transform.parameters()),
callbacks=callbacks1,
forward_kwargs={
"mode": "projection",
"return_hidden": args.uvl2reg is not None
},
initial_epoch=args.startepoch,
**common_kwargs,
)
learning_curves = np.vstack(learning_curves).T
logger.info("Starting training MF, phase 2: density training")
learning_curves_ = trainer2.train(
loss_functions=[losses.nll] + scandal_loss,
loss_labels=["NLL"] + scandal_label,
loss_weights=[
args.nllfactor * nat_to_bit_per_dim(args.modellatentdim)
] + scandal_weight,
epochs=args.epochs - (args.epochs // 2),
parameters=list(model.inner_transform.parameters()),
callbacks=callbacks2,
forward_kwargs={"mode": "mf-fixed-manifold"},
initial_epoch=args.startepoch - args.epochs // 2,
**common_kwargs,
)
learning_curves = np.vstack(
(learning_curves, np.vstack(learning_curves_).T))
return learning_curves
def train_manifold_flow_alternating(args, dataset, model, simulator):
""" MFMF-A training """
assert not args.specified
trainer1 = ForwardTrainer(model) if simulator.parameter_dim(
) is None else ConditionalForwardTrainer(model)
trainer2 = ForwardTrainer(model) if simulator.parameter_dim(
) is None else ConditionalForwardTrainer(
model) if args.scandal is None else SCANDALForwardTrainer(model)
metatrainer = AlternatingTrainer(model, trainer1, trainer2)
meta_kwargs = {
"dataset": dataset,
"initial_lr": args.lr,
"scheduler": optim.lr_scheduler.CosineAnnealingLR,
"validation_split": args.validationsplit
}
if args.weightdecay is not None:
meta_kwargs["optimizer_kwargs"] = {
"weight_decay": float(args.weightdecay)
}
_, scandal_loss, scandal_label, scandal_weight = make_training_kwargs(
args, dataset)
phase1_kwargs = {
"forward_kwargs": {
"mode": "projection"
},
"clip_gradient": args.clip
}
phase2_kwargs = {
"forward_kwargs": {
"mode": "mf-fixed-manifold"
},
"clip_gradient": args.clip
}
phase1_parameters = list(model.outer_transform.parameters()) + list(
model.encoder.parameters(
)) if args.algorithm == "emf" else model.outer_transform.parameters()
phase2_parameters = list(model.inner_transform.parameters())
logger.info(
"Starting training MF, alternating between reconstruction error and log likelihood"
)
learning_curves_ = metatrainer.train(
loss_functions=[
losses.smooth_l1_loss if args.l1 else losses.mse, losses.nll
] + scandal_loss,
loss_function_trainers=[0, 1] +
[1] if args.scandal is not None else [],
loss_labels=["L1" if args.l1 else "MSE", "NLL"] + scandal_label,
loss_weights=[
args.msefactor,
args.nllfactor * nat_to_bit_per_dim(args.modellatentdim)
] + scandal_weight,
epochs=args.epochs // 2,
subsets=args.subsets,
batch_sizes=[args.batchsize, args.batchsize],
parameters=[phase1_parameters, phase2_parameters],
callbacks=[
callbacks.save_model_after_every_epoch(
create_filename("checkpoint", None, args))
],
trainer_kwargs=[phase1_kwargs, phase2_kwargs],
**meta_kwargs,
)
learning_curves = np.vstack(learning_curves_).T
return learning_curves
def train_manifold_flow(args, dataset, model, simulator):
""" MFMF-S training """
assert not args.specified
trainer = ForwardTrainer(model) if simulator.parameter_dim(
) is None else ConditionalForwardTrainer(
model) if args.scandal is None else SCANDALForwardTrainer(model)
common_kwargs, scandal_loss, scandal_label, scandal_weight = make_training_kwargs(
args, dataset)
logger.info(
"Starting training MF, phase 1: pretraining on reconstruction error")
learning_curves = trainer.train(
loss_functions=[losses.mse],
loss_labels=["MSE"],
loss_weights=[args.msefactor],
epochs=args.epochs // 3,
callbacks=[
callbacks.save_model_after_every_epoch(
create_filename("checkpoint", "A", args))
],
forward_kwargs={"mode": "projection"},
initial_epoch=args.startepoch,
**common_kwargs,
)
learning_curves = np.vstack(learning_curves).T
logger.info("Starting training MF, phase 2: mixed training")
learning_curves_ = trainer.train(
loss_functions=[losses.mse, losses.nll] + scandal_loss,
loss_labels=["MSE", "NLL"] + scandal_label,
loss_weights=[
args.msefactor,
args.addnllfactor * nat_to_bit_per_dim(args.modellatentdim)
] + scandal_weight,
epochs=args.epochs - 2 * (args.epochs // 3),
parameters=list(model.parameters()),
callbacks=[
callbacks.save_model_after_every_epoch(
create_filename("checkpoint", "B", args))
],
forward_kwargs={"mode": "mf"},
initial_epoch=args.startepoch - (args.epochs // 3),
**common_kwargs,
)
learning_curves_ = np.vstack(learning_curves_).T
learning_curves = learning_curves_ if learning_curves is None else np.vstack(
(learning_curves, learning_curves_))
logger.info(
"Starting training MF, phase 3: training only inner flow on NLL")
learning_curves_ = trainer.train(
loss_functions=[losses.mse, losses.nll] + scandal_loss,
loss_labels=["MSE", "NLL"] + scandal_label,
loss_weights=[
0.0, args.nllfactor * nat_to_bit_per_dim(args.modellatentdim)
] + scandal_weight,
epochs=args.epochs // 3,
parameters=list(model.inner_transform.parameters()),
callbacks=[
callbacks.save_model_after_every_epoch(
create_filename("checkpoint", "C", args))
],
forward_kwargs={"mode": "mf-fixed-manifold"},
initial_epoch=args.startepoch - (args.epochs - (args.epochs // 3)),
**common_kwargs,
)
learning_curves_ = np.vstack(learning_curves_).T
learning_curves = np.vstack(
(learning_curves, np.vstack(learning_curves_).T))
return learning_curves
def train_slice_of_pie(args, dataset, model, simulator):
""" SLICE training """
trainer = ManifoldFlowTrainer(model) if simulator.parameter_dim(
) is None else ConditionalManifoldFlowTrainer(model)
common_kwargs = {
"dataset": dataset,
"batch_size": args.batchsize,
"initial_lr": args.lr,
"scheduler": optim.lr_scheduler.CosineAnnealingLR,
"clip_gradient": args.clip,
"validation_split": args.validationsplit,
}
if args.weightdecay is not None:
common_kwargs["optimizer_kwargs"] = {
"weight_decay": float(args.weightdecay)
}
if args.nopretraining or args.epochs // 3 < 1:
logger.info("Skipping pretraining phase")
learning_curves = np.zeros((0, 2))
else:
logger.info(
"Starting training slice of PIE, phase 1: pretraining on reconstruction error"
)
learning_curves = trainer.train(
loss_functions=[losses.mse],
loss_labels=["MSE"],
loss_weights=[args.initialmsefactor],
epochs=args.epochs // 3,
callbacks=[
callbacks.save_model_after_every_epoch(
create_filename("checkpoint", None, args)[:-3] +
"_epoch_A{}.pt")
],
forward_kwargs={"mode": "projection"},
**common_kwargs,
)
learning_curves = np.vstack(learning_curves).T
logger.info("Starting training slice of PIE, phase 2: mixed training")
learning_curves_ = trainer.train(
loss_functions=[losses.mse, losses.nll],
loss_labels=["MSE", "NLL"],
loss_weights=[args.initialmsefactor, args.initialnllfactor],
epochs=args.epochs - (1 if args.nopretraining else 2) *
(args.epochs // 3),
parameters=model.inner_transform.parameters(),
callbacks=[
callbacks.save_model_after_every_epoch(
create_filename("checkpoint", None, args)[:-3] +
"_epoch_B{}.pt")
],
forward_kwargs={"mode": "slice"},
**common_kwargs,
)
learning_curves_ = np.vstack(learning_curves_).T
learning_curves = np.vstack((learning_curves, learning_curves_))
logger.info(
"Starting training slice of PIE, phase 3: training only inner flow on NLL"
)
learning_curves_ = trainer.train(
loss_functions=[losses.mse, losses.nll],
loss_labels=["MSE", "NLL"],
loss_weights=[args.msefactor, args.nllfactor],
epochs=args.epochs // 3,
parameters=model.inner_transform.parameters(),
callbacks=[
callbacks.save_model_after_every_epoch(
create_filename("checkpoint", None, args)[:-3] +
"_epoch_C{}.pt")
],
forward_kwargs={"mode": "slice"},
**common_kwargs,
)
learning_curves_ = np.vstack(learning_curves_).T
learning_curves = np.vstack((learning_curves, learning_curves_))
return learning_curves
def train_manifold_flow_alternating(args, dataset, model, simulator):
""" MFMF-A training """
assert not args.specified
trainer = ManifoldFlowTrainer(model) if simulator.parameter_dim(
) is None else ConditionalManifoldFlowTrainer(model)
metatrainer = AlternatingTrainer(model, trainer, trainer)
meta_kwargs = {
"dataset": dataset,
"initial_lr": args.lr,
"scheduler": optim.lr_scheduler.CosineAnnealingLR
}
if args.weightdecay is not None:
meta_kwargs["optimizer_kwargs"] = {
"weight_decay": float(args.weightdecay)
}
phase1_kwargs = {
"forward_kwargs": {
"mode": "projection"
},
"clip_gradient": args.clip,
"validation_split": args.validationsplit
}
phase2_kwargs = {
"forward_kwargs": {
"mode": "mf-fixed-manifold"
},
"clip_gradient": args.clip,
"validation_split": args.validationsplit
}
phase1_parameters = (list(model.outer_transform.parameters()) +
list(model.encoder.parameters()) if args.algorithm
== "emf" else model.outer_transform.parameters())
phase2_parameters = model.inner_transform.parameters()
logger.info(
"Starting training MF, alternating between reconstruction error and log likelihood"
)
learning_curves_ = metatrainer.train(
loss_functions=[losses.mse, losses.nll],
loss_function_trainers=[0, 1],
loss_labels=["MSE", "NLL"],
loss_weights=[args.msefactor, args.nllfactor],
epochs=args.epochs // 2,
subsets=args.subsets,
batch_sizes=[args.batchsize, args.batchsize],
parameters=[phase1_parameters, phase2_parameters],
callbacks=[
callbacks.save_model_after_every_epoch(
create_filename("checkpoint", None, args)[:-3] +
"_epoch_{}.pt")
],
trainer_kwargs=[phase1_kwargs, phase2_kwargs],
**meta_kwargs,
)
learning_curves = np.vstack(learning_curves_).T
return learning_curves
def train_manifold_flow(args, dataset, model, simulator):
""" MFMF-S training """
trainer = ManifoldFlowTrainer(model) if simulator.parameter_dim(
) is None else ConditionalManifoldFlowTrainer(model)
common_kwargs = {
"dataset": dataset,
"batch_size": args.batchsize,
"initial_lr": args.lr,
"scheduler": optim.lr_scheduler.CosineAnnealingLR,
"clip_gradient": args.clip,
"validation_split": args.validationsplit,
}
if args.weightdecay is not None:
common_kwargs["optimizer_kwargs"] = {
"weight_decay": float(args.weightdecay)
}
if args.specified:
logger.info("Starting training MF with specified manifold on NLL")
learning_curves = trainer.train(
loss_functions=[losses.mse, losses.nll],
loss_labels=["MSE", "NLL"],
loss_weights=[0.0, args.nllfactor],
epochs=args.epochs,
callbacks=[
callbacks.save_model_after_every_epoch(
create_filename("checkpoint", None, args)[:-3] +
"_epoch_{}.pt")
],
forward_kwargs={"mode": "mf"},
**common_kwargs,
)
learning_curves = np.vstack(learning_curves).T
else:
if args.nopretraining or args.epochs // args.prepostfraction < 1:
logger.info("Skipping pretraining phase")
learning_curves = None
elif args.prepie:
logger.info(
"Starting training MF, phase 1: pretraining on PIE likelihood")
learning_curves = trainer.train(
loss_functions=[losses.nll],
loss_labels=["NLL"],
loss_weights=[args.nllfactor],
epochs=args.epochs // args.prepostfraction,
callbacks=[
callbacks.save_model_after_every_epoch(
create_filename("checkpoint", None, args)[:-3] +
"_epoch_A{}.pt")
],
forward_kwargs={"mode": "pie"},
**common_kwargs,
)
learning_curves = np.vstack(learning_curves).T
else:
logger.info(
"Starting training MF, phase 1: pretraining on reconstruction error"
)
learning_curves = trainer.train(
loss_functions=[losses.mse],
loss_labels=["MSE"],
loss_weights=[args.msefactor],
epochs=args.epochs // args.prepostfraction,
callbacks=[
callbacks.save_model_after_every_epoch(
create_filename("checkpoint", None, args)[:-3] +
"_epoch_A{}.pt")
],
forward_kwargs={"mode": "projection"},
**common_kwargs,
)
learning_curves = np.vstack(learning_curves).T
logger.info("Starting training MF, phase 2: mixed training")
learning_curves_ = trainer.train(
loss_functions=[losses.mse, losses.nll],
loss_labels=["MSE", "NLL"],
loss_weights=[args.msefactor, args.addnllfactor],
epochs=args.epochs -
(2 - int(args.nopretraining) - int(args.noposttraining)) *
(args.epochs // args.prepostfraction),
parameters=model.parameters(),
callbacks=[
callbacks.save_model_after_every_epoch(
create_filename("checkpoint", None, args)[:-3] +
"_epoch_B{}.pt")
],
forward_kwargs={"mode": "mf"},
**common_kwargs,
)
learning_curves_ = np.vstack(learning_curves_).T
learning_curves = learning_curves_ if learning_curves is None else np.vstack(
(learning_curves, learning_curves_))
if args.nopretraining or args.epochs // args.prepostfraction < 1:
logger.info("Skipping inner flow phase")
else:
logger.info(
"Starting training MF, phase 3: training only inner flow on NLL"
)
learning_curves_ = trainer.train(
loss_functions=[losses.mse, losses.nll],
loss_labels=["MSE", "NLL"],
loss_weights=[0.0, args.nllfactor],
epochs=args.epochs // args.prepostfraction,
parameters=model.inner_transform.parameters(),
callbacks=[
callbacks.save_model_after_every_epoch(
create_filename("checkpoint", None, args)[:-3] +
"_epoch_C{}.pt")
],
forward_kwargs={"mode": "mf-fixed-manifold"},
**common_kwargs,
)
learning_curves_ = np.vstack(learning_curves_).T
learning_curves = np.vstack((learning_curves, learning_curves_))
return learning_curves
