toad.nn.module module

class toad.nn.module.Module

Bases: torch.nn.modules.module.Module

base module for every model

__init__()

define model struct

device

device of model

fit(loader, trainer=None, optimizer=None, early_stopping=None, **kwargs)

train model

Parameters:

• loader (DataLoader) – loader for training model
• trainer (Trainer) – trainer for training model
• optimizer (torch.Optimier) – the default optimizer is Adam(lr = 1e-3)
• early_stopping (earlystopping) – the default value is loss_earlystopping, you can set it to False to disable early stopping
• epoch (int) – number of epoch for training loop
• callback (callable) – callable function will be called every epoch

evaluate(loader, trainer=None)

evaluate model :param loader: loader for evaluate model :type loader: DataLoader :param trainer: trainer for evaluate model :type trainer: Trainer

fit_step(batch, *args, **kwargs)

step for fitting :param batch: batch data from dataloader :type batch: Any

Returns:loss of this step Return type:Tensor

save(path)

save model

load(path)

load model

log(key, value)

log values to history

Parameters:

• key (str) – name of message
• value (Tensor) – tensor of values

distributed(backend=None, **kwargs)

get distributed model

class toad.nn.module.DistModule(module, device_ids=None, output_device=None, dim=0, broadcast_buffers=True, process_group=None, bucket_cap_mb=25, find_unused_parameters=False, check_reduction=False, gradient_as_bucket_view=False)

Bases: torch.nn.parallel.distributed.DistributedDataParallel

distributed module class