bob.ip.binseg.engine.trainer¶
Functions
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Fits an FCN model using supervised learning and save it to disk. |
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Context manager to turn ON/OFF model evaluation |
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bob.ip.binseg.engine.trainer.torch_evaluation(model)[source]¶ Context manager to turn ON/OFF model evaluation
This context manager will turn evaluation mode ON on entry and turn it OFF when exiting the
withstatement block.- Parameters
model (
torch.nn.Module) – Network (e.g. driu, hed, unet)- Yields
model (
torch.nn.Module) – Network (e.g. driu, hed, unet)
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bob.ip.binseg.engine.trainer.run(model, data_loader, valid_loader, optimizer, criterion, scheduler, checkpointer, checkpoint_period, device, arguments, output_folder)[source]¶ Fits an FCN model using supervised learning and save it to disk.
This method supports periodic checkpointing and the output of a CSV-formatted log with the evolution of some figures during training.
- Parameters
model (
torch.nn.Module) – Network (e.g. driu, hed, unet)data_loader (
torch.utils.data.DataLoader) – To be used to train the modelvalid_loader (
torch.utils.data.DataLoader) – To be used to validate the model and enable automatic checkpointing. If set toNone, then do not validate it.optimizer (
torch.optim) –criterion (
torch.nn.modules.loss._Loss) – loss functionscheduler (
torch.optim) – learning rate schedulercheckpointer (
bob.ip.binseg.utils.checkpointer.Checkpointer) – checkpointer implementationcheckpoint_period (int) – save a checkpoint every
nepochs. If set to0(zero), then do not save intermediary checkpointsdevice (
torch.device) – device to usearguments (dict) – start and end epochs
output_folder (str) – output path