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from module import config, transformers_utility as tr, utils, metrics, dataio
from prettytable import PrettyTable
import numpy as np
table = PrettyTable()
table.field_names = config.tissues
TOKENIZER_DIR = config.models / "byte-level-bpe-tokenizer"
PRETRAINED_MODEL = config.models / "transformer" / "prediction-model" / "saved_model.pth"
DATA_DIR = config.data
def load_model(args, settings):
return tr.load_model(
args.model_name,
args.tokenizer_dir,
pretrained_model=args.pretrained_model,
log_offset=args.log_offset,
**settings,
)
def main(TEST_DATA):
args = utils.get_args(
data_dir=DATA_DIR,
train_data=TEST_DATA,
test_data=TEST_DATA,
pretrained_model=PRETRAINED_MODEL,
tokenizer_dir=TOKENIZER_DIR,
model_name="roberta-pred-mean-pool",
)
settings = utils.get_model_settings(config.settings, args)
if args.output_mode:
settings["output_mode"] = args.output_mode
if args.tissue_subset is not None:
settings["num_labels"] = len(args.tissue_subset)
print("Loading model...")
config_obj, tokenizer, model = load_model(args, settings)
print("Loading data...")
datasets = dataio.load_datasets(
tokenizer,
args.train_data,
eval_data=args.eval_data,
test_data=args.test_data,
seq_key="text",
file_type="text",
filter_empty=args.filter_empty,
shuffle=False,
)
dataset_test = datasets["train"]
print("Getting predictions:")
preds = np.exp(np.array(metrics.get_predictions(model, dataset_test))) - 1
for e in preds:
table.add_row(e)
print(table)
if __name__ == "__main__":
main("test.txt") |