Unity 机器学习代理工具包 (ML-Agents) 是一个开源项目,它使游戏和模拟能够作为训练智能代理的环境。
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from unittest import mock
import os
import pytest
import tempfile
import unittest
import csv
from mlagents.trainers.stats import (
StatsReporter,
TensorboardWriter,
CSVWriter,
StatsSummary,
GaugeWriter,
ConsoleWriter,
StatsPropertyType,
)
def test_stat_reporter_add_summary_write():
# Test add_writer
StatsReporter.writers.clear()
mock_writer1 = mock.Mock()
mock_writer2 = mock.Mock()
StatsReporter.add_writer(mock_writer1)
StatsReporter.add_writer(mock_writer2)
assert len(StatsReporter.writers) == 2
# Test add_stats and summaries
statsreporter1 = StatsReporter("category1")
statsreporter2 = StatsReporter("category2")
for i in range(10):
statsreporter1.add_stat("key1", float(i))
statsreporter2.add_stat("key2", float(i))
statssummary1 = statsreporter1.get_stats_summaries("key1")
statssummary2 = statsreporter2.get_stats_summaries("key2")
assert statssummary1.num == 10
assert statssummary2.num == 10
assert statssummary1.mean == 4.5
assert statssummary2.mean == 4.5
assert statssummary1.std == pytest.approx(2.9, abs=0.1)
assert statssummary2.std == pytest.approx(2.9, abs=0.1)
# Test write_stats
step = 10
statsreporter1.write_stats(step)
mock_writer1.write_stats.assert_called_once_with(
"category1", {"key1": statssummary1}, step
)
mock_writer2.write_stats.assert_called_once_with(
"category1", {"key1": statssummary1}, step
)
def test_stat_reporter_property():
# Test add_writer
mock_writer = mock.Mock()
StatsReporter.writers.clear()
StatsReporter.add_writer(mock_writer)
assert len(StatsReporter.writers) == 1
statsreporter1 = StatsReporter("category1")
# Test add_property
statsreporter1.add_property("key", "this is a text")
mock_writer.add_property.assert_called_once_with(
"category1", "key", "this is a text"
)
@mock.patch("mlagents.tf_utils.tf.Summary")
@mock.patch("mlagents.tf_utils.tf.summary.FileWriter")
def test_tensorboard_writer(mock_filewriter, mock_summary):
# Test write_stats
category = "category1"
with tempfile.TemporaryDirectory(prefix="unittest-") as base_dir:
tb_writer = TensorboardWriter(base_dir)
statssummary1 = StatsSummary(mean=1.0, std=1.0, num=1)
tb_writer.write_stats("category1", {"key1": statssummary1}, 10)
# Test that the filewriter has been created and the directory has been created.
filewriter_dir = "{basedir}/{category}".format(
basedir=base_dir, category=category
)
assert os.path.exists(filewriter_dir)
mock_filewriter.assert_called_once_with(filewriter_dir)
# Test that the filewriter was written to and the summary was added.
mock_summary.return_value.value.add.assert_called_once_with(
tag="key1", simple_value=1.0
)
mock_filewriter.return_value.add_summary.assert_called_once_with(
mock_summary.return_value, 10
)
mock_filewriter.return_value.flush.assert_called_once()
# Test hyperparameter writing - no good way to parse the TB string though.
tb_writer.add_property(
"category1", StatsPropertyType.HYPERPARAMETERS, {"example": 1.0}
)
assert mock_filewriter.return_value.add_summary.call_count > 1
def test_csv_writer():
# Test write_stats
category = "category1"
with tempfile.TemporaryDirectory(prefix="unittest-") as base_dir:
csv_writer = CSVWriter(base_dir, required_fields=["key1", "key2"])
statssummary1 = StatsSummary(mean=1.0, std=1.0, num=1)
csv_writer.write_stats("category1", {"key1": statssummary1}, 10)
# Test that the filewriter has been created and the directory has been created.
filewriter_dir = "{basedir}/{category}.csv".format(
basedir=base_dir, category=category
)
# The required keys weren't in the stats
assert not os.path.exists(filewriter_dir)
csv_writer.write_stats(
"category1", {"key1": statssummary1, "key2": statssummary1}, 10
)
csv_writer.write_stats(
"category1", {"key1": statssummary1, "key2": statssummary1}, 20
)
# The required keys were in the stats
assert os.path.exists(filewriter_dir)
with open(filewriter_dir) as csv_file:
csv_reader = csv.reader(csv_file, delimiter=",")
line_count = 0
for row in csv_reader:
if line_count == 0:
assert "key1" in row
assert "key2" in row
assert "Steps" in row
line_count += 1
else:
assert len(row) == 3
line_count += 1
assert line_count == 3
def test_gauge_stat_writer_sanitize():
assert GaugeWriter.sanitize_string("Policy/Learning Rate") == "Policy.LearningRate"
assert (
GaugeWriter.sanitize_string("Very/Very/Very Nested Stat")
== "Very.Very.VeryNestedStat"
)
class ConsoleWriterTest(unittest.TestCase):
def test_console_writer(self):
# Test write_stats
with self.assertLogs("mlagents.trainers", level="INFO") as cm:
category = "category1"
console_writer = ConsoleWriter()
statssummary1 = StatsSummary(mean=1.0, std=1.0, num=1)
console_writer.write_stats(
category,
{
"Environment/Cumulative Reward": statssummary1,
"Is Training": statssummary1,
},
10,
)
statssummary2 = StatsSummary(mean=0.0, std=0.0, num=1)
console_writer.write_stats(
category,
{
"Environment/Cumulative Reward": statssummary1,
"Is Training": statssummary2,
},
10,
)
# Test hyperparameter writing - no good way to parse the TB string though.
console_writer.add_property(
"category1", StatsPropertyType.HYPERPARAMETERS, {"example": 1.0}
)
self.assertIn(
"Mean Reward: 1.000. Std of Reward: 1.000. Training.", cm.output[0]
)
self.assertIn("Not Training.", cm.output[1])
self.assertIn("Hyperparameters for behavior name", cm.output[2])
self.assertIn("example:\t1.0", cm.output[2])
def test_selfplay_console_writer(self):
with self.assertLogs("mlagents.trainers", level="INFO") as cm:
category = "category1"
console_writer = ConsoleWriter()
console_writer.add_property(category, StatsPropertyType.SELF_PLAY, True)
statssummary1 = StatsSummary(mean=1.0, std=1.0, num=1)
console_writer.write_stats(
category,
{
"Environment/Cumulative Reward": statssummary1,
"Is Training": statssummary1,
"Self-play/ELO": statssummary1,
},
10,
)
self.assertIn(
"Mean Reward: 1.000. Std of Reward: 1.000. Training.", cm.output[0]
)