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123 行
3.7 KiB
123 行
3.7 KiB
import unittest.mock as mock
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import pytest
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from mlagents.tf_utils import tf
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import yaml
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from mlagents.trainers.ppo.multi_gpu_policy import MultiGpuPPOPolicy
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from mlagents.trainers.tests.mock_brain import create_mock_brainparams
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@pytest.fixture
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def dummy_config():
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return yaml.safe_load(
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"""
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trainer: ppo
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batch_size: 32
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beta: 5.0e-3
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buffer_size: 512
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epsilon: 0.2
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hidden_units: 128
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lambd: 0.95
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learning_rate: 3.0e-4
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max_steps: 5.0e4
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normalize: true
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num_epoch: 5
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num_layers: 2
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time_horizon: 64
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sequence_length: 64
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summary_freq: 1000
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use_recurrent: false
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memory_size: 8
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curiosity_strength: 0.0
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curiosity_enc_size: 1
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reward_signals:
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extrinsic:
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strength: 1.0
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gamma: 0.99
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"""
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)
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@mock.patch("mlagents.trainers.ppo.multi_gpu_policy.get_devices")
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def test_create_model(mock_get_devices, dummy_config):
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tf.reset_default_graph()
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mock_get_devices.return_value = [
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"/device:GPU:0",
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"/device:GPU:1",
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"/device:GPU:2",
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"/device:GPU:3",
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]
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trainer_parameters = dummy_config
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trainer_parameters["model_path"] = ""
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trainer_parameters["keep_checkpoints"] = 3
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brain = create_mock_brainparams()
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policy = MultiGpuPPOPolicy(0, brain, trainer_parameters, False, False)
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assert len(policy.towers) == len(mock_get_devices.return_value)
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@mock.patch("mlagents.trainers.ppo.multi_gpu_policy.get_devices")
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def test_average_gradients(mock_get_devices, dummy_config):
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tf.reset_default_graph()
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mock_get_devices.return_value = [
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"/device:GPU:0",
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"/device:GPU:1",
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"/device:GPU:2",
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"/device:GPU:3",
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]
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trainer_parameters = dummy_config
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trainer_parameters["model_path"] = ""
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trainer_parameters["keep_checkpoints"] = 3
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brain = create_mock_brainparams()
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with tf.Session() as sess:
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policy = MultiGpuPPOPolicy(0, brain, trainer_parameters, False, False)
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var = tf.Variable(0)
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tower_grads = [
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[(tf.constant(0.1), var)],
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[(tf.constant(0.2), var)],
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[(tf.constant(0.3), var)],
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[(tf.constant(0.4), var)],
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]
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avg_grads = policy.average_gradients(tower_grads)
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init = tf.global_variables_initializer()
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sess.run(init)
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run_out = sess.run(avg_grads)
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assert run_out == [(0.25, 0)]
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@mock.patch("mlagents.trainers.tf_policy.TFPolicy._execute_model")
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@mock.patch("mlagents.trainers.ppo.policy.PPOPolicy.construct_feed_dict")
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@mock.patch("mlagents.trainers.ppo.multi_gpu_policy.get_devices")
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def test_update(
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mock_get_devices, mock_construct_feed_dict, mock_execute_model, dummy_config
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):
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tf.reset_default_graph()
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mock_get_devices.return_value = ["/device:GPU:0", "/device:GPU:1"]
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mock_construct_feed_dict.return_value = {}
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mock_execute_model.return_value = {
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"value_loss": 0.1,
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"policy_loss": 0.3,
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"update_batch": None,
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}
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trainer_parameters = dummy_config
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trainer_parameters["model_path"] = ""
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trainer_parameters["keep_checkpoints"] = 3
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brain = create_mock_brainparams()
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policy = MultiGpuPPOPolicy(0, brain, trainer_parameters, False, False)
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mock_mini_batch = mock.Mock()
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mock_mini_batch.items.return_value = [("action", [1, 2]), ("value", [3, 4])]
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run_out = policy.update(mock_mini_batch, 1)
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assert mock_mini_batch.items.call_count == len(mock_get_devices.return_value)
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assert mock_construct_feed_dict.call_count == len(mock_get_devices.return_value)
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assert run_out["Losses/Value Loss"] == 0.1
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assert run_out["Losses/Policy Loss"] == 0.3
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if __name__ == "__main__":
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pytest.main()
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