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from pathlib import Path
from typing import Dict
from ..callbacks.metrics import accuracy_score
from ..processing.seq_tokenizer import SeqTokenizer
from ..score.score import infer_from_model, infer_testset
from ..utils.file import load, save
from ..utils.utils import *
def infer_benchmark(cfg:Dict= None,path:str = None):
if cfg['tensorboard']:
from ..callbacks.tbWriter import writer
model = cfg["model_name"]+'_'+cfg['task']
#set seed
set_seed_and_device(cfg["seed"],cfg["device_number"])
#get data
ad = load(cfg["train_config"].dataset_path_train)
#instantiate dataset class
dataset_class = SeqTokenizer(ad.var,cfg)
test_data = load(cfg["train_config"].dataset_path_test)
#prepare data for training and inference
all_data = prepare_data_benchmark(dataset_class,test_data,cfg)
#sync skorch config with params in train and model config
sync_skorch_with_config(cfg["model"]["skorch_model"],cfg)
# instantiate skorch model
net = instantiate_predictor(cfg["model"]["skorch_model"], cfg,path)
net.initialize()
net.load_params(f_params=f'{cfg["inference_settings"]["model_path"]}')
#perform inference on task specific testset
if cfg["inference_settings"]["infere_original_testset"]:
infer_testset(net,cfg,all_data,accuracy_score)
#inference on custom data
predicted_labels,logits,_,_ = infer_from_model(net,all_data["infere_data"])
prepare_inference_results_benchmarck(net,cfg,predicted_labels,logits,all_data)
save(path=Path(__file__).parent.parent.absolute() / f'inference_results_{model}',data=all_data["infere_rna_seq"])
if cfg['tensorboard']:
writer.close() |