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127 行
4.1 KiB
127 行
4.1 KiB
import os
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import unittest
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import json
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from enum import Enum
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import time
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from mlagents.trainers.training_status import (
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StatusType,
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StatusMetaData,
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GlobalTrainingStatus,
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)
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from mlagents.trainers.policy.checkpoint_manager import (
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ModelCheckpointManager,
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ModelCheckpoint,
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)
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def test_globaltrainingstatus(tmpdir):
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path_dir = os.path.join(tmpdir, "test.json")
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GlobalTrainingStatus.set_parameter_state("Category1", StatusType.LESSON_NUM, 3)
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GlobalTrainingStatus.save_state(path_dir)
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with open(path_dir) as fp:
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test_json = json.load(fp)
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assert "Category1" in test_json
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assert StatusType.LESSON_NUM.value in test_json["Category1"]
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assert test_json["Category1"][StatusType.LESSON_NUM.value] == 3
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assert "metadata" in test_json
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GlobalTrainingStatus.load_state(path_dir)
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restored_val = GlobalTrainingStatus.get_parameter_state(
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"Category1", StatusType.LESSON_NUM
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)
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assert restored_val == 3
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# Test unknown categories and status types (keys)
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unknown_category = GlobalTrainingStatus.get_parameter_state(
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"Category3", StatusType.LESSON_NUM
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)
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class FakeStatusType(Enum):
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NOTAREALKEY = "notarealkey"
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unknown_key = GlobalTrainingStatus.get_parameter_state(
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"Category1", FakeStatusType.NOTAREALKEY
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)
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assert unknown_category is None
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assert unknown_key is None
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def test_model_management(tmpdir):
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results_path = os.path.join(tmpdir, "results")
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brain_name = "Mock_brain"
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final_model_path = os.path.join(results_path, brain_name)
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test_checkpoint_list = [
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{
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"steps": 1,
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"file_path": os.path.join(final_model_path, f"{brain_name}-1.nn"),
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"reward": 1.312,
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"creation_time": time.time(),
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"auxillary_file_paths": [],
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},
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{
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"steps": 2,
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"file_path": os.path.join(final_model_path, f"{brain_name}-2.nn"),
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"reward": 1.912,
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"creation_time": time.time(),
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"auxillary_file_paths": [],
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},
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{
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"steps": 3,
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"file_path": os.path.join(final_model_path, f"{brain_name}-3.nn"),
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"reward": 2.312,
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"creation_time": time.time(),
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"auxillary_file_paths": [],
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},
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]
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GlobalTrainingStatus.set_parameter_state(
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brain_name, StatusType.CHECKPOINTS, test_checkpoint_list
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)
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new_checkpoint_4 = ModelCheckpoint(
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4, os.path.join(final_model_path, f"{brain_name}-4.nn"), 2.678, time.time()
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)
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ModelCheckpointManager.add_checkpoint(brain_name, new_checkpoint_4, 4)
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assert len(ModelCheckpointManager.get_checkpoints(brain_name)) == 4
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new_checkpoint_5 = ModelCheckpoint(
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5, os.path.join(final_model_path, f"{brain_name}-5.nn"), 3.122, time.time()
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)
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ModelCheckpointManager.add_checkpoint(brain_name, new_checkpoint_5, 4)
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assert len(ModelCheckpointManager.get_checkpoints(brain_name)) == 4
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final_model_path = f"{final_model_path}.nn"
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final_model_time = time.time()
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current_step = 6
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final_model = ModelCheckpoint(
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current_step, final_model_path, 3.294, final_model_time
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)
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ModelCheckpointManager.track_final_checkpoint(brain_name, final_model)
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assert len(ModelCheckpointManager.get_checkpoints(brain_name)) == 4
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check_checkpoints = GlobalTrainingStatus.saved_state[brain_name][
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StatusType.CHECKPOINTS.value
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]
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assert check_checkpoints is not None
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final_model = GlobalTrainingStatus.saved_state[StatusType.FINAL_CHECKPOINT.value]
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assert final_model is not None
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class StatsMetaDataTest(unittest.TestCase):
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def test_metadata_compare(self):
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# Test write_stats
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with self.assertLogs("mlagents.trainers", level="WARNING") as cm:
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default_metadata = StatusMetaData()
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version_statsmetadata = StatusMetaData(mlagents_version="test")
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default_metadata.check_compatibility(version_statsmetadata)
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torch_version_statsmetadata = StatusMetaData(torch_version="test")
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default_metadata.check_compatibility(torch_version_statsmetadata)
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# Assert that 2 warnings have been thrown
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assert len(cm.output) == 2
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