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import torch
from abc import ABC, abstractmethod
from utils.common.file import ensure_dir
from utils.common.log import logger
import time
from typing import List
class BaseModel(ABC):
def __init__(self,
name: str,
models_dict_path: str,
device: str):
self.name = name
self.models_dict_path = models_dict_path
self.models_dict = torch.load(models_dict_path, map_location=device)
self.device = device
assert set(self.get_required_model_components()) <= set(list(self.models_dict.keys()))
self.to(device)
logger.info(f'[model] init model: {dict(name=name, components=self.get_required_model_components())}')
logger.debug(self.models_dict)
@abstractmethod
def get_required_model_components(self) -> List[str]:
pass
@abstractmethod
def get_accuracy(self, test_loader, *args, **kwargs):
pass
@abstractmethod
def infer(self, x, *args, **kwargs):
pass
def save_model(self, p: str):
logger.debug(f'[model] save model: {self.name}')
ensure_dir(p)
torch.save(self.models_dict, p)
def load_model(self, p: str):
logger.debug(f'[model] load model: {self.name}, from {p}')
self.models_dict = torch.load(p, map_location=self.device)
def to(self, device):
logger.debug(f'[model] to device: {device}')
for k, v in self.models_dict.items():
try:
self.models_dict[k] = v.to(device)
except Exception as e:
pass
def to_eval_mode(self, verbose=False):
if verbose:
logger.info(f'[model] to eval mode')
for k, v in self.models_dict.items():
try:
self.models_dict[k].eval()
except Exception as e:
pass
def to_train_mode(self, verbose=False):
if verbose:
logger.info(f'[model] to train mode')
for k, v in self.models_dict.items():
try:
self.models_dict[k].train()
except Exception as e:
pass
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