133 lines
5.3 KiB
Python
133 lines
5.3 KiB
Python
import os
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import json
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import torch
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from tqdm import tqdm
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from PytorchBoot.config import ConfigManager
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import PytorchBoot.namespace as namespace
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import PytorchBoot.stereotype as stereotype
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from PytorchBoot.factory import ComponentFactory
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from PytorchBoot.dataset import BaseDataset
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from PytorchBoot.runners.runner import Runner
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from PytorchBoot.utils import Log
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@stereotype.runner("default_evaluator")
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class DefaultEvaluator(Runner):
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def __init__(self, config_path):
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super().__init__(config_path)
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''' Pipeline '''
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self.pipeline_name = self.config[namespace.Stereotype.PIPELINE]
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self.pipeline = ComponentFactory.create(namespace.Stereotype.PIPELINE, self.pipeline_name)
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self.pipeline:torch.nn.Module = self.pipeline.to(self.device)
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''' Experiment '''
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self.model_path = self.config["experiment"]["model_path"]
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self.load_experiment("default_evaluator")
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''' Test '''
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self.test_config = ConfigManager.get(namespace.Stereotype.RUNNER, namespace.Mode.TEST)
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self.test_dataset_name_list = self.test_config["dataset_list"]
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self.test_set_list = []
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self.test_writer_list = []
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seen_name = set()
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for test_dataset_name in self.test_dataset_name_list:
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if test_dataset_name not in seen_name:
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seen_name.add(test_dataset_name)
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else:
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raise ValueError("Duplicate test dataset name: {}".format(test_dataset_name))
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test_set: BaseDataset = ComponentFactory.create(namespace.Stereotype.DATASET, test_dataset_name)
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self.test_set_list.append(test_set)
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self.print_info()
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def run(self):
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eval_result = self.test()
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self.save_eval_result(eval_result)
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def test(self):
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self.pipeline.eval()
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eval_result = {}
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with torch.no_grad():
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test_set: BaseDataset
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for dataset_idx, test_set in enumerate(self.test_set_list):
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test_set_config = test_set.get_config()
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eval_list = test_set_config["eval_list"]
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ratio = test_set_config["ratio"]
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test_set_name = test_set.get_name()
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output_list = []
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data_list = []
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test_loader = test_set.get_loader()
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loop = tqdm(enumerate(test_loader), total=int(len(test_loader)))
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for _, data in loop:
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test_set.process_batch(data, self.device)
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data["mode"] = namespace.Mode.TEST
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output = self.pipeline(data)
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output_list.append(output)
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data_list.append(data)
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loop.set_description(
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f'Evaluating [{dataset_idx+1}/{len(self.test_set_list)}] (Test: {test_set_name}, ratio={ratio})')
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result_dict = self.eval_fn(output_list, data_list, eval_list)
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eval_result[test_set_name] = result_dict
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return eval_result
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def save_eval_result(self, eval_result):
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result_dir = os.path.join(str(self.experiment_path), namespace.Direcotry.RESULT_DIR_NAME)
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eval_result_path = os.path.join(result_dir, self.file_name + "_eval_result.json")
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with open(eval_result_path, "w") as f:
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json.dump(eval_result, f, indent=4)
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Log.success(f"Saved evaluation result to {eval_result_path}")
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@staticmethod
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def eval_fn(output_list, data_list, eval_list):
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collected_result = {}
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for eval_method_name in eval_list:
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eval_method = ComponentFactory.create(namespace.Stereotype.EVALUATION_METHOD, eval_method_name)
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eval_results:dict = eval_method.evaluate(output_list, data_list)
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for data_type, eval_result in eval_results.items():
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if data_type not in collected_result:
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collected_result[data_type] = {}
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for name, value in eval_result.items():
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collected_result[data_type][name] = value
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return collected_result
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def load_checkpoint(self):
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self.load(self.model_path)
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Log.success(f"Loaded checkpoint from {self.model_path}")
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def load_experiment(self, backup_name=None):
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super().load_experiment(backup_name)
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self.load_checkpoint()
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def create_experiment(self, backup_name=None):
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super().create_experiment(backup_name)
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result_dir = os.path.join(str(self.experiment_path), namespace.Direcotry.RESULT_DIR_NAME)
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os.makedirs(result_dir)
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def load(self, path):
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state_dict = torch.load(path)
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self.pipeline.load_state_dict(state_dict)
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def print_info(self):
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def print_dataset(dataset: BaseDataset):
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config = dataset.get_config()
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name = dataset.get_name()
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Log.blue(f"Dataset: {name}")
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for k,v in config.items():
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Log.blue(f"\t{k}: {v}")
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super().print_info()
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table_size = 70
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Log.blue(f"{'+' + '-' * (table_size // 2)} Pipeline {'-' * (table_size // 2)}" + '+')
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Log.blue(self.pipeline)
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Log.blue(f"{'+' + '-' * (table_size // 2)} Datasets {'-' * (table_size // 2)}" + '+')
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for i, test_set in enumerate(self.test_set_list):
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Log.blue(f"test dataset {i}: ")
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print_dataset(test_set)
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Log.blue(f"{'+' + '-' * (table_size // 2)}----------{'-' * (table_size // 2)}" + '+')
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