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| import comet.src.train.batch as batch | |
| import comet.src.evaluate.evaluate as base_evaluate | |
| import numpy as np | |
| def make_evaluator(opt, *args): | |
| if opt.exp == "generation": | |
| return AtomicGenerationEvaluator(opt, *args) | |
| else: | |
| return AtomicClassificationEvaluator(opt, *args) | |
| class AtomicGenerationEvaluator(base_evaluate.Evaluator): | |
| def __init__(self, opt, model, data_loader): | |
| super(AtomicGenerationEvaluator, self).__init__( | |
| opt, model, data_loader) | |
| self.batch = batch.batch_atomic_generate | |
| def initialize_losses(self): | |
| average_loss = {"total_micro": 0, "total_macro": 0} | |
| nums = {"total_micro": 0, "total_macro": 0} | |
| return average_loss, nums | |
| def compute_final_scores(self, average_loss, nums): | |
| average_loss["total_macro"] /= nums["total_macro"] | |
| average_loss["total_micro"] /= nums["total_micro"] | |
| average_loss["ppl_macro"] = np.exp(average_loss["total_macro"]) | |
| average_loss["ppl_micro"] = np.exp(average_loss["total_micro"]) | |
| return average_loss | |
| def counter(self, nums): | |
| return nums["total_macro"] | |
| def print_result(self, split, epoch_losses): | |
| print("{} Loss: \t {}".format( | |
| split, epoch_losses["total_micro"])) | |
| print("{} Perplexity: \t {}".format( | |
| split, epoch_losses["ppl_micro"])) | |