"""
The main module for run inference
"""
from abc import ABCMeta
from torch.nn import Module
from tqdm import tqdm
import torch
from piepline.utils.checkpoints_manager import CheckpointsManager
from piepline.data_producer.data_producer import DataProducer
from piepline.utils.fsm import FileStructManager
from piepline.data_processor.data_processor import DataProcessor
__all__ = ['Predictor', 'DataProducerPredictor']
class BasePredictor(metaclass=ABCMeta):
def __init__(self, model: Module, checkpoints_manager: CheckpointsManager):
self._data_processor = DataProcessor(model)
checkpoints_manager.unpack()
checkpoints_manager.load_model_weights(model)
checkpoints_manager.pack()
[docs]class Predictor(BasePredictor):
"""
Predictor run inference by training parameters
:param model: model object, used for predict
:param fsm: :class:`FileStructManager` object
"""
def __init__(self, model: Module, checkpoints_manager: CheckpointsManager):
super().__init__(model, checkpoints_manager)
[docs] def predict(self, data: torch.Tensor or dict):
"""
Predict ine data
:param data: data as :class:`torch.Tensor` or dict with key ``data``
:return: processed output
:rtype: model output type
"""
return self._data_processor.predict(data)
class DataProducerPredictor(BasePredictor):
def __init__(self, model: Module, checkpoints_manager: CheckpointsManager):
super().__init__(model, checkpoints_manager)
def predict(self, data_producer: DataProducer, callback: callable) -> None:
"""
Run prediction iterates by ``data_producer``
:param data_producer: :class:`DataProducer` object
:param callback: callback, that call for every data prediction and get it's result as parameter
"""
loader = data_producer.get_loader()
for img in tqdm(loader):
callback(self._data_processor.predict(img))
del img