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105 行
4.0 KiB
105 行
4.0 KiB
import pytest
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from unittest import mock
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import os
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import numpy as np
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from mlagents.torch_utils import torch
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from mlagents.trainers.policy.torch_policy import TorchPolicy
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from mlagents.trainers.ppo.optimizer_torch import TorchPPOOptimizer
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from mlagents.trainers.model_saver.torch_model_saver import TorchModelSaver
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from mlagents.trainers.settings import TrainerSettings
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from mlagents.trainers.tests import mock_brain as mb
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from mlagents.trainers.tests.torch.test_policy import create_policy_mock
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def test_register(tmp_path):
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trainer_params = TrainerSettings()
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model_saver = TorchModelSaver(trainer_params, tmp_path)
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opt = mock.Mock(spec=TorchPPOOptimizer)
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opt.get_modules = mock.Mock(return_value={})
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model_saver.register(opt)
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assert model_saver.policy is None
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trainer_params = TrainerSettings()
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policy = create_policy_mock(trainer_params)
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opt.get_modules = mock.Mock(return_value={})
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model_saver.register(policy)
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assert model_saver.policy is not None
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def test_load_save(tmp_path):
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path1 = os.path.join(tmp_path, "runid1")
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path2 = os.path.join(tmp_path, "runid2")
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trainer_params = TrainerSettings()
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policy = create_policy_mock(trainer_params)
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model_saver = TorchModelSaver(trainer_params, path1)
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model_saver.register(policy)
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model_saver.initialize_or_load(policy)
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policy.set_step(2000)
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mock_brain_name = "MockBrain"
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model_saver.save_checkpoint(mock_brain_name, 2000)
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assert len(os.listdir(tmp_path)) > 0
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# Try load from this path
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model_saver2 = TorchModelSaver(trainer_params, path1, load=True)
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policy2 = create_policy_mock(trainer_params)
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model_saver2.register(policy2)
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model_saver2.initialize_or_load(policy2)
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_compare_two_policies(policy, policy2)
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assert policy2.get_current_step() == 2000
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# Try initialize from path 1
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trainer_params.init_path = path1
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model_saver3 = TorchModelSaver(trainer_params, path2)
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policy3 = create_policy_mock(trainer_params)
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model_saver3.register(policy3)
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model_saver3.initialize_or_load(policy3)
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_compare_two_policies(policy2, policy3)
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# Assert that the steps are 0.
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assert policy3.get_current_step() == 0
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# TorchPolicy.evalute() returns log_probs instead of all_log_probs like tf does.
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# resulting in indeterministic results for testing.
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# So here use sample_actions instead.
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def _compare_two_policies(policy1: TorchPolicy, policy2: TorchPolicy) -> None:
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"""
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Make sure two policies have the same output for the same input.
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"""
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decision_step, _ = mb.create_steps_from_behavior_spec(
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policy1.behavior_spec, num_agents=1
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)
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vec_vis_obs, masks = policy1._split_decision_step(decision_step)
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vec_obs = [torch.as_tensor(vec_vis_obs.vector_observations)]
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vis_obs = [torch.as_tensor(vis_ob) for vis_ob in vec_vis_obs.visual_observations]
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memories = torch.as_tensor(
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policy1.retrieve_memories(list(decision_step.agent_id))
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).unsqueeze(0)
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with torch.no_grad():
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_, log_probs1, _, _ = policy1.sample_actions(
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vec_obs, vis_obs, masks=masks, memories=memories, all_log_probs=True
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)
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_, log_probs2, _, _ = policy2.sample_actions(
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vec_obs, vis_obs, masks=masks, memories=memories, all_log_probs=True
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)
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np.testing.assert_array_equal(log_probs1, log_probs2)
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@pytest.mark.parametrize("discrete", [True, False], ids=["discrete", "continuous"])
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@pytest.mark.parametrize("visual", [True, False], ids=["visual", "vector"])
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@pytest.mark.parametrize("rnn", [True, False], ids=["rnn", "no_rnn"])
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def test_checkpoint_conversion(tmpdir, rnn, visual, discrete):
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dummy_config = TrainerSettings()
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model_path = os.path.join(tmpdir, "Mock_Brain")
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policy = create_policy_mock(
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dummy_config, use_rnn=rnn, use_discrete=discrete, use_visual=visual
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)
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trainer_params = TrainerSettings()
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model_saver = TorchModelSaver(trainer_params, model_path)
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model_saver.register(policy)
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model_saver.save_checkpoint("Mock_Brain", 100)
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assert os.path.isfile(model_path + "/Mock_Brain-100.onnx")
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