gantrainer
Module
GanTrainer
class makes use of hooks. Hooks are a collection of methods which provide
quick access to exact entry in loop. In this way, we can override these methods with custom functionality
in either training, evaluation or test loops.
Non-lifecycle hooks
Methods to initalize class attributes
Docs
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class
farabio.core.gantrainer.
GanTrainer
(config)[source]
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__init__
(config)[source]
Initializes trainer object
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default_attr
(*args)[source]
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init_attr
(*args)[source]
Abstract method that initializes object attributes
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define_data_attr
(*args)[source]
Define data related attributes here
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define_model_attr
(*args)[source]
Define model related attributes here
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define_train_attr
(*args)[source]
Define training related attributes here
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define_test_attr
(*args)[source]
Define training related attributes here
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define_log_attr
(*args)[source]
Define log related attributes here
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define_compute_attr
(*args)[source]
Define compute related attributes here
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define_misc_attr
(*args)[source]
Define miscellaneous attributes here
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build_model
(*args)[source]
Abstract method that builds model
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get_trainloader
(*args)[source]
Hook: Retreives training set of torch.utils.data.DataLoader class
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get_testloader
(*args)[source]
Hook: Retreives test set of torch.utils.data.DataLoader class
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train
()[source]
Training loop with hooks
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train_loop
()[source]
Hook: training loop
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train_epoch
()[source]
Hook: epoch of training loop
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train_batch
(args)[source]
Hook: batch of training loop
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on_train_start
()[source]
Hook: On start of training loop
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start_logger
(*args)[source]
Hook: Starts logger
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on_train_epoch_start
()[source]
Hook: On epoch start
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on_start_training_batch
(*args)[source]
Hook: On training batch start
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on_end_training_batch
(*args)[source]
Hook: On end of training batch
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on_train_epoch_end
(*args)[source]
Hook: On end of training epoch
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on_train_end
()[source]
Hook: On end of training
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stop_train
(*args)[source]
On end of training
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evaluate_epoch
()[source]
Hook: epoch of evaluation loop
- Parameters
- epochint
Current epoch
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evaluate_batch
(*args)[source]
Hook: batch of evaluation loop
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on_evaluate_start
(*args)[source]
Hook: on evaluation end
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on_evaluate_epoch_start
()[source]
Hook: on evaluation start
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on_evaluate_batch_start
()[source]
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on_evaluate_batch_end
()[source]
Hook: On evaluate batch end
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on_evaluate_epoch_end
(*args)[source]
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on_evaluate_end
(*args)[source]
Hook: on evaluation end
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on_epoch_end
(*args)[source]
Hook: on epoch end
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test
()[source]
Hook: Test lifecycle
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test_loop
()[source]
Hook: test loop
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get_dataloader
()[source]
Hook: Retreives torch.utils.data.DataLoader object
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on_test_start
(*args)[source]
Hook: on test start
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on_start_test_batch
(*args)[source]
Hook: on test batch start
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test_step
(*args)[source]
Test action (Put test here)
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on_end_test_batch
(*args)[source]
Hook: on end of batch test
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on_test_end
(*args)[source]
Hook: on end test
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load_model
(*args)[source]
Hook: load model
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save_model
(*args)[source]
Hook: saves model
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discriminator_zero_grad
()[source]
Hook: Zero gradients of discriminator
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discriminator_loss
(*args)[source]
Hook: Training action (Put training here)
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discriminator_backward
()[source]
Hook: Discriminator back-propagation
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discriminator_optim_step
()[source]
Discriminator optimizer step
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generator_zero_grad
()[source]
Hook: Zero gradients of generator
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generator_loss
(*args)[source]
Hook: Training action (Put training here)
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generator_backward
()[source]
Hook: sends backward
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generator_optim_step
()[source]
Discriminator optimizer step