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Add args and always save the model at the end of training #388

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Add args and always save the model at the end of training #388

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0abeaab
Add argument for low-res datasets
vturrisi Jun 15, 2024
3d58244
Always save model at the end of training
vturrisi Jun 15, 2024
ccdba0b
Update checkpointer.py
vturrisi Jun 18, 2024
6985b84
Update checkpointer.py
vturrisi Jun 18, 2024
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8 changes: 4 additions & 4 deletions solo/methods/base.py
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Original file line number Diff line number Diff line change
Expand Up @@ -195,8 +195,8 @@ def __init__(self, cfg: omegaconf.DictConfig):
self.features_dim: int = self.backbone.inplanes
# remove fc layer
self.backbone.fc = nn.Identity()
cifar = cfg.data.dataset in ["cifar10", "cifar100"]
if cifar:
low_res = cfg.data.dataset in ["cifar10", "cifar100"] or cfg.method_kwargs.get("low_res", False)
if low_res:
self.backbone.conv1 = nn.Conv2d(
3, 64, kernel_size=3, stride=1, padding=2, bias=False
)
Expand Down Expand Up @@ -644,8 +644,8 @@ def __init__(
if self.backbone_name.startswith("resnet"):
# remove fc layer
self.momentum_backbone.fc = nn.Identity()
cifar = cfg.data.dataset in ["cifar10", "cifar100"]
if cifar:
low_res = cfg.data.dataset in ["cifar10", "cifar100"] or cfg.method_kwargs.get("low_res", False)
if low_res:
self.momentum_backbone.conv1 = nn.Conv2d(
3, 64, kernel_size=3, stride=1, padding=2, bias=False
)
Expand Down
11 changes: 10 additions & 1 deletion solo/utils/checkpointer.py
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Original file line number Diff line number Diff line change
Expand Up @@ -164,7 +164,7 @@ def on_train_start(self, trainer: pl.Trainer, _):
self.save_args(trainer)

def on_train_epoch_end(self, trainer: pl.Trainer, _):
"""Tries to save current checkpoint at the end of each train epoch.
"""Tries to save the current checkpoint at the end of each train epoch.

Args:
trainer (pl.Trainer): pytorch lightning trainer object.
Expand All @@ -173,3 +173,12 @@ def on_train_epoch_end(self, trainer: pl.Trainer, _):
epoch = trainer.current_epoch # type: ignore
if epoch % self.frequency == 0:
self.save(trainer)

def on_train_end(self, trainer: pl.Trainer, _):
"""Saves model at the end of training.

Args:
trainer (pl.Trainer): pytorch lightning trainer object.
"""

self.save(trainer)