dlk.core.imodels package
Submodules
dlk.core.imodels.basic module
- class dlk.core.imodels.basic.BasicIModel(config: dlk.core.imodels.basic.BasicIModelConfig, checkpoint=False)[source]
Bases:
pytorch_lightning.core.lightning.LightningModule,dlk.core.imodels.GatherOutputMixin- property epoch_training_steps: int
every epoch training steps inferred from datamodule and devices.
- forward(inputs: Dict[str, torch.Tensor]) Dict[str, torch.Tensor][source]
do forward on a mini batch
- Parameters
batch – a mini batch inputs
- Returns
the outputs
- get_progress_bar_dict()[source]
rewrite the prograss_bar_dict, remove the ‘v_num’ which we don’t need
- Returns
progress_bar dict
- property num_training_epochs: int
Total training epochs inferred from datamodule and devices.
- property num_training_steps: int
Total training steps inferred from datamodule and devices.
- predict_step(batch: Dict, batch_idx: int) Dict[source]
do predict on a mini batch
- Parameters
batch – a mini batch inputs
batch_idx – the index(dataloader) of the mini batch
- Returns
the outputs
- test_epoch_end(outputs: List[Dict]) List[Dict][source]
Gather the outputs of all node and do postprocess on it.
- Parameters
outputs – current node returnd output list
- Returns
all node outputs
- test_step(batch: Dict[str, torch.Tensor], batch_idx: int) Dict[source]
do test on a mini batch
The outputs only gather the keys in self.gather_data.keys for postprocess :param batch: a mini batch inputs :param batch_idx: the index(dataloader) of the mini batch
- Returns
the outputs
- training: bool
- training_step(batch: Dict[str, torch.Tensor], batch_idx: int)[source]
do training_step on a mini batch
- Parameters
batch – a mini batch inputs
batch_idx – the index(dataloader) of the mini batch
- Returns
the outputs
- validation_epoch_end(outputs: List[Dict]) List[Dict][source]
Gather the outputs of all node and do postprocess on it.
The outputs only gather the keys in self.gather_data.keys for postprocess :param outputs: current node returnd output list
- Returns
all node outputs
- validation_step(batch: Dict[str, torch.Tensor], batch_idx: int) Dict[str, torch.Tensor][source]
do validation on a mini batch
The outputs only gather the keys in self.gather_data.keys for postprocess :param batch: a mini batch inputs :param batch_idx: the index(dataloader) of the mini batch
- Returns
the outputs
- class dlk.core.imodels.basic.BasicIModelConfig(config: Dict)[source]
Bases:
dlk.utils.config.BaseConfigbasic imodel config will provide all the config for model/optimizer/loss/scheduler/postprocess
- get_loss(config: Dict)[source]
Use config to init the loss
- Parameters
config – loss config
- Returns
Loss, LossConfig
- get_model(config: Dict)[source]
Use config to init the model
- Parameters
config – model config
- Returns
Model, ModelConfig
- get_optimizer(config: Dict)[source]
Use config to init the optimizer
- Parameters
config – optimizer config
- Returns
Optimizer, OptimizerConfig
dlk.core.imodels.distill module
Module contents
imodels
- class dlk.core.imodels.GatherOutputMixin[source]
Bases:
objectgather all the small batches output to a big batch
- concat_list_of_dict_outputs(outputs: List[Dict]) Dict[source]
only support all the outputs has the same dim, now is deprecated.
- Parameters
outputs – multi node returned output (list of dict)
- Returns
Concat all list by name
- gather_outputs(outputs: List[Dict])[source]
gather the dist outputs
- Parameters
outputs – one node outputs
- Returns
all outputs
- static proc_dist_outputs(dist_outputs: List[Dict]) List[Dict][source]
gather all distributed outputs to outputs which is like in a single worker.
- Parameters
dist_outputs – the inputs of pytorch_lightning train/test/.._epoch_end when using ddp
- Returns
the inputs of pytorch_lightning train/test/.._epoch_end when only run on one worker.