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| from .config_manager import ConfigManager | |
| import os | |
| from typing import Dict | |
| from torch import nn | |
| from tqdm import tqdm | |
| from tqdm import trange | |
| from dataset import load_iterators | |
| from trainer import GeneralTrainer | |
| class DiacritizationTester(GeneralTrainer): | |
| def __init__(self, config_path: str, model_kind: str) -> None: | |
| self.config_path = config_path | |
| self.model_kind = model_kind | |
| self.config_manager = ConfigManager( | |
| config_path=config_path, model_kind=model_kind | |
| ) | |
| self.config = self.config_manager.config | |
| self.pad_idx = 0 | |
| self.criterion = nn.CrossEntropyLoss(ignore_index=self.pad_idx) | |
| self.set_device() | |
| self.text_encoder = self.config_manager.text_encoder | |
| self.start_symbol_id = self.text_encoder.start_symbol_id | |
| self.model = self.config_manager.get_model() | |
| self.model = self.model.to(self.device) | |
| self.load_model(model_path=self.config["test_model_path"], load_optimizer=False) | |
| self.load_diacritizer() | |
| self.diacritizer.set_model(self.model) | |
| self.initialize_model() | |
| self.print_config() | |
| def run(self): | |
| self.config_manager.config["load_training_data"] = False | |
| self.config_manager.config["load_validation_data"] = False | |
| self.config_manager.config["load_test_data"] = True | |
| _, test_iterator, _ = load_iterators(self.config_manager) | |
| tqdm_eval = trange(0, len(test_iterator), leave=True) | |
| tqdm_error_rates = trange(0, len(test_iterator), leave=True) | |
| loss, acc = self.evaluate(test_iterator, tqdm_eval, log = False) | |
| error_rates, _ = self.evaluate_with_error_rates(test_iterator, tqdm_error_rates, log = False) | |
| tqdm_eval.close() | |
| tqdm_error_rates.close() | |
| WER = error_rates["WER"] | |
| DER = error_rates["DER"] | |
| DER1 = error_rates["DER*"] | |
| WER1 = error_rates["WER*"] | |
| error_rates = f"DER: {DER}, WER: {WER}, DER*: {DER1}, WER*: {WER1}" | |
| print(f"global step : {self.global_step}") | |
| print(f"Evaluate {self.global_step}: accuracy, {acc}, loss: {loss}") | |
| print(f"WER/DER {self.global_step}: {error_rates}") | |