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"""plm.py.
File for providing the Plm model implementation.
"""
import logging
import torch
from transformers import AutoModelForImageTextToText, AutoProcessor
from transformers.feature_extraction_utils import BatchFeature
from src.models.base import ModelBase
from src.models.config import Config
class PlmModel(ModelBase):
"""PLM model implementation."""
def __init__(self, config: Config) -> None:
"""Initialization of the PLM model.
Args:
config (Config): Parsed config
"""
# initialize the parent class
super().__init__(config)
def _load_specific_model(self) -> None:
"""Overridden function to populate self.model."""
self.model = AutoModelForImageTextToText.from_pretrained(
self.model_path, **self.config.model
) if hasattr(self.config, 'model') else (
AutoModelForImageTextToText.from_pretrained(
self.model_path
)
)
self.model.to(self.config.device)
def _init_processor(self) -> None:
"""Initialize the self.processor by loading from the path."""
self.processor = AutoProcessor.from_pretrained(self.model_path, use_fast=True)
def _forward(self, data: BatchFeature) -> None:
"""Given some input data, performs a single forward pass.
This function itself can be overriden, while _hook_and_eval
should be left in tact.
Args:
data (BatchFeature): The given data tensor.
"""
data.to(self.config.device)
with torch.no_grad():
_ = self.model.generate(**data, **self.config.forward)
logging.debug('Completed forward pass...')
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