srgan_trainer
Module
SrganTrainer
class uses Super-Resolution Using a Generative Adversarial Network model, which is originally proposed in this arXiv .
Implemented trainer module uses this Git code as reference work.
References
-
class
farabio.models.superres.srgan.srgan_trainer.
SrganTrainer
(config)[source]
SrganTrainer trainer class. Override with custom methods here.
- Parameters
- GanTrainerparent object
Parent object of SrganTrainer
-
define_data_attr
()[source]
Define data related attributes here
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define_model_attr
()[source]
Define model related attributes here
-
define_train_attr
()[source]
Define training related attributes here
-
define_log_attr
()[source]
Define log related attributes here
-
define_compute_attr
()[source]
Define compute related attributes here
-
define_misc_attr
()[source]
Define miscellaneous attributes here
-
get_trainloader
()[source]
Hook: Retreives training set of torch.utils.data.DataLoader class
-
get_testloader
()[source]
Hook: Retreives test set of torch.utils.data.DataLoader class
-
build_model
()[source]
Build model
- Parameters
- epochint
current epoch
-
start_logger
()[source]
Hook: Starts logger
-
on_train_epoch_start
()[source]
Hook: On epoch start
-
train_batch
(args)[source]
Hook: batch of training loop
-
on_start_training_batch
(args)[source]
Hook: On training batch start
-
discriminator_zero_grad
()[source]
Hook: Zero gradients of discriminator
-
discriminator_loss
()[source]
Hook: Training action (Put training here)
-
discriminator_optim_step
()[source]
Discriminator optimizer step
-
generator_zero_grad
()[source]
Zero grad
-
generator_loss
()[source]
Hook: Training action (Put training here)
-
generator_backward
()[source]
Hook: sends backward
-
generator_optim_step
()[source]
Discriminator optimizer step
-
optimizer_zero_grad
()[source]
Zero grad
-
discriminator_backward
()[source]
Hook: Discriminator back-propagation
-
on_end_training_batch
()[source]
Hook: On end of training batch
-
on_epoch_end
()[source]
Hook: on epoch end
-
on_evaluate_epoch_start
()[source]
Hook: on evaluation start
-
evaluate_batch
(args)[source]
Hook: batch of evaluation loop
-
on_evaluate_batch_end
()[source]
Hook: On evaluate batch end
-
on_evaluate_epoch_end
()[source]
-
save_model
()[source]
Save model
- Parameters
- epochint
current epoch
-
save_csv
()[source]
-
load_model
()[source]
Hook: load model
-
test_batch
(model_name)[source]
-
on_test_start
()[source]
Hook: on test start
-
test_step
(test_arg)[source]
Test action (Put test here)
-
on_test_end
()[source]
Hook: on end test