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 mlagents.trainers.optimizer.torch_optimizer import TorchOptimizer
import pytest
from unittest import mock
import os
import numpy as np
from mlagents.torch_utils import torch, default_device
from mlagents.trainers.policy.torch_policy import TorchPolicy
from mlagents.trainers.ppo.optimizer_torch import TorchPPOOptimizer
from mlagents.trainers.sac.optimizer_torch import TorchSACOptimizer
from mlagents.trainers.poca.optimizer_torch import TorchPOCAOptimizer
from mlagents.trainers.model_saver.torch_model_saver import TorchModelSaver
from mlagents.trainers.settings import (
TrainerSettings,
NetworkSettings,
EncoderType,
PPOSettings,
SACSettings,
POCASettings,
)
from mlagents.trainers.tests import mock_brain as mb
from mlagents.trainers.tests.torch.test_policy import create_policy_mock
from mlagents.trainers.torch.utils import ModelUtils
def test_register(tmp_path):
trainer_params = TrainerSettings()
model_saver = TorchModelSaver(trainer_params, tmp_path)
opt = mock.Mock(spec=TorchPPOOptimizer)
opt.get_modules = mock.Mock(return_value={})
model_saver.register(opt)
assert model_saver.policy is None
trainer_params = TrainerSettings()
policy = create_policy_mock(trainer_params)
opt.get_modules = mock.Mock(return_value={})
model_saver.register(policy)
assert model_saver.policy is not None
def test_load_save_policy(tmp_path):
path1 = os.path.join(tmp_path, "runid1")
path2 = os.path.join(tmp_path, "runid2")
trainer_params = TrainerSettings()
policy = create_policy_mock(trainer_params)
model_saver = TorchModelSaver(trainer_params, path1)
model_saver.register(policy)
model_saver.initialize_or_load(policy)
policy.set_step(2000)
mock_brain_name = "MockBrain"
model_saver.save_checkpoint(mock_brain_name, 2000)
assert len(os.listdir(tmp_path)) > 0
# Try load from this path
model_saver2 = TorchModelSaver(trainer_params, path1, load=True)
policy2 = create_policy_mock(trainer_params)
model_saver2.register(policy2)
model_saver2.initialize_or_load(policy2)
_compare_two_policies(policy, policy2)
assert policy2.get_current_step() == 2000
# Try initialize from path 1
trainer_params.init_path = path1
model_saver3 = TorchModelSaver(trainer_params, path2)
policy3 = create_policy_mock(trainer_params)
model_saver3.register(policy3)
model_saver3.initialize_or_load(policy3)
_compare_two_policies(policy2, policy3)
# Assert that the steps are 0.
assert policy3.get_current_step() == 0
@pytest.mark.parametrize("vis_encode_type", ["resnet", "nature_cnn", "match3"])
def test_load_policy_different_hidden_units(tmp_path, vis_encode_type):
path1 = os.path.join(tmp_path, "runid1")
trainer_params = TrainerSettings()
trainer_params.network_settings = NetworkSettings(
hidden_units=12, vis_encode_type=EncoderType(vis_encode_type)
)
policy = create_policy_mock(trainer_params, use_visual=True)
conv_params = [mod for mod in policy.actor.parameters() if len(mod.shape) > 2]
model_saver = TorchModelSaver(trainer_params, path1)
model_saver.register(policy)
model_saver.initialize_or_load(policy)
policy.set_step(2000)
mock_brain_name = "MockBrain"
model_saver.save_checkpoint(mock_brain_name, 2000)
# Try load from this path
trainer_params2 = TrainerSettings()
trainer_params2.network_settings = NetworkSettings(
hidden_units=10, vis_encode_type=EncoderType(vis_encode_type)
)
model_saver2 = TorchModelSaver(trainer_params2, path1, load=True)
policy2 = create_policy_mock(trainer_params2, use_visual=True)
conv_params2 = [mod for mod in policy2.actor.parameters() if len(mod.shape) > 2]
# asserts convolutions have different parameters before load
for conv1, conv2 in zip(conv_params, conv_params2):
assert not torch.equal(conv1, conv2)
# asserts layers still have different dimensions
for mod1, mod2 in zip(policy.actor.parameters(), policy2.actor.parameters()):
if mod1.shape[0] == 12:
assert mod2.shape[0] == 10
model_saver2.register(policy2)
model_saver2.initialize_or_load(policy2)
# asserts convolutions have same parameters after load
for conv1, conv2 in zip(conv_params, conv_params2):
assert torch.equal(conv1, conv2)
# asserts layers still have different dimensions
for mod1, mod2 in zip(policy.actor.parameters(), policy2.actor.parameters()):
if mod1.shape[0] == 12:
assert mod2.shape[0] == 10
@pytest.mark.parametrize(
"optimizer",
[
(TorchPPOOptimizer, PPOSettings),
(TorchSACOptimizer, SACSettings),
(TorchPOCAOptimizer, POCASettings),
],
ids=["ppo", "sac", "poca"],
)
def test_load_save_optimizer(tmp_path, optimizer):
OptimizerClass, HyperparametersClass = optimizer
trainer_settings = TrainerSettings()
trainer_settings.hyperparameters = HyperparametersClass()
policy = create_policy_mock(trainer_settings, use_discrete=False)
optimizer = OptimizerClass(policy, trainer_settings)
# save at path 1
path1 = os.path.join(tmp_path, "runid1")
model_saver = TorchModelSaver(trainer_settings, path1)
model_saver.register(policy)
model_saver.register(optimizer)
model_saver.initialize_or_load()
policy.set_step(2000)
model_saver.save_checkpoint("MockBrain", 2000)
# create a new optimizer and policy
policy2 = create_policy_mock(trainer_settings, use_discrete=False)
optimizer2 = OptimizerClass(policy2, trainer_settings)
# load weights
model_saver2 = TorchModelSaver(trainer_settings, path1, load=True)
model_saver2.register(policy2)
model_saver2.register(optimizer2)
model_saver2.initialize_or_load() # This is to load the optimizers
# Compare the two optimizers
_compare_two_optimizers(optimizer, optimizer2)
# TorchPolicy.evalute() returns log_probs instead of all_log_probs like tf does.
# resulting in indeterministic results for testing.
# So here use sample_actions instead.
def _compare_two_policies(policy1: TorchPolicy, policy2: TorchPolicy) -> None:
"""
Make sure two policies have the same output for the same input.
"""
policy1.actor = policy1.actor.to(default_device())
policy2.actor = policy2.actor.to(default_device())
decision_step, _ = mb.create_steps_from_behavior_spec(
policy1.behavior_spec, num_agents=1
)
np_obs = decision_step.obs
masks = policy1._extract_masks(decision_step)
memories = torch.as_tensor(
policy1.retrieve_memories(list(decision_step.agent_id))
).unsqueeze(0)
tensor_obs = [ModelUtils.list_to_tensor(obs) for obs in np_obs]
with torch.no_grad():
_, log_probs1, _, _ = policy1.sample_actions(
tensor_obs, masks=masks, memories=memories
)
_, log_probs2, _, _ = policy2.sample_actions(
tensor_obs, masks=masks, memories=memories
)
np.testing.assert_array_equal(
ModelUtils.to_numpy(log_probs1.all_discrete_tensor),
ModelUtils.to_numpy(log_probs2.all_discrete_tensor),
)
def _compare_two_optimizers(opt1: TorchOptimizer, opt2: TorchOptimizer) -> None:
trajectory = mb.make_fake_trajectory(
length=10,
observation_specs=opt1.policy.behavior_spec.observation_specs,
action_spec=opt1.policy.behavior_spec.action_spec,
max_step_complete=True,
)
with torch.no_grad():
_, opt1_val_out, _ = opt1.get_trajectory_value_estimates(
trajectory.to_agentbuffer(), trajectory.next_obs, done=False
)
_, opt2_val_out, _ = opt2.get_trajectory_value_estimates(
trajectory.to_agentbuffer(), trajectory.next_obs, done=False
)
for opt1_val, opt2_val in zip(opt1_val_out.values(), opt2_val_out.values()):
np.testing.assert_array_equal(opt1_val, opt2_val)
@pytest.mark.parametrize("discrete", [True, False], ids=["discrete", "continuous"])
@pytest.mark.parametrize("visual", [True, False], ids=["visual", "vector"])
@pytest.mark.parametrize("rnn", [True, False], ids=["rnn", "no_rnn"])
def test_checkpoint_conversion(tmpdir, rnn, visual, discrete):
dummy_config = TrainerSettings()
model_path = os.path.join(tmpdir, "Mock_Brain")
policy = create_policy_mock(
dummy_config, use_rnn=rnn, use_discrete=discrete, use_visual=visual
)
trainer_params = TrainerSettings()
model_saver = TorchModelSaver(trainer_params, model_path)
model_saver.register(policy)
model_saver.save_checkpoint("Mock_Brain", 100)
assert os.path.isfile(model_path + "/Mock_Brain-100.onnx")