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How to use test_env_spec method in tox

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test_gaussian_cnn_baseline.py

Source:test_gaussian_cnn_baseline.py

1import pickle2from unittest import mock3import akro4import numpy as np5import pytest6import tensorflow as tf7from garage.envs.env_spec import EnvSpec8from garage.tf.baselines import GaussianCNNBaseline9from garage.tf.optimizers import LbfgsOptimizer10from tests.fixtures import TfGraphTestCase11def get_train_test_data():12    matrices = [13        np.linspace(i - 0.5, i + 0.5, 300).reshape((10, 10, 3))14        for i in range(110)15    ]16    data = [np.sin(matrices[i]) for i in range(100)]17    obs = [{'observations': [x], 'returns': [np.mean(x)]} for x in data]18    observations = np.concatenate([p['observations'] for p in obs])19    returns = np.concatenate([p['returns'] for p in obs])20    returns = returns.reshape((-1, 1))21    paths = {'observations': [np.sin(matrices[i]) for i in range(100, 110)]}22    expected = [[np.mean(x)] for x in paths['observations']]23    return (obs, observations, returns), (paths, expected)24test_env_spec = EnvSpec(observation_space=akro.Box(low=-1,25                                                   high=1,26                                                   shape=(10, 10, 3)),27                        action_space=None)28class TestGaussianCNNBaseline(TfGraphTestCase):29    @pytest.mark.large30    def test_fit_normalized(self):31        gcr = GaussianCNNBaseline(env_spec=test_env_spec,32                                  filters=((3, (3, 3)), (6, (3, 3))),33                                  strides=(1, 1),34                                  padding='SAME',35                                  hidden_sizes=(32, ),36                                  adaptive_std=False,37                                  use_trust_region=True)38        train_data, test_data = get_train_test_data()39        train_paths, observations, returns = train_data40        for _ in range(20):41            gcr.fit(train_paths)42        test_paths, expected = test_data43        prediction = gcr.predict(test_paths)44        average_error = 0.045        for i, exp in enumerate(expected):46            average_error += np.abs(exp - prediction[i])47        average_error /= len(expected)48        assert average_error <= 0.149        x_mean = self.sess.run(gcr._networks['default'].x_mean)50        x_mean_expected = np.mean(observations, axis=0, keepdims=True)51        x_std = self.sess.run(gcr._networks['default'].x_std)52        x_std_expected = np.std(observations, axis=0, keepdims=True)53        assert np.allclose(x_mean, x_mean_expected)54        assert np.allclose(x_std, x_std_expected)55        y_mean = self.sess.run(gcr._networks['default'].y_mean)56        y_mean_expected = np.mean(returns, axis=0, keepdims=True)57        y_std = self.sess.run(gcr._networks['default'].y_std)58        y_std_expected = np.std(returns, axis=0, keepdims=True)59        assert np.allclose(y_mean, y_mean_expected)60        assert np.allclose(y_std, y_std_expected)61    @pytest.mark.large62    def test_fit_unnormalized(self):63        gcr = GaussianCNNBaseline(env_spec=test_env_spec,64                                  filters=((3, (3, 3)), (6, (3, 3))),65                                  strides=(1, 1),66                                  padding='SAME',67                                  hidden_sizes=(32, ),68                                  adaptive_std=True,69                                  normalize_inputs=False,70                                  normalize_outputs=False)71        train_data, test_data = get_train_test_data()72        train_paths, _, _ = train_data73        for _ in range(20):74            gcr.fit(train_paths)75        test_paths, expected = test_data76        prediction = gcr.predict(test_paths)77        average_error = 0.078        for i, exp in enumerate(expected):79            average_error += np.abs(exp - prediction[i])80        average_error /= len(expected)81        assert average_error <= 0.182        x_mean = self.sess.run(gcr._networks['default'].x_mean)83        x_mean_expected = np.zeros_like(x_mean)84        x_std = self.sess.run(gcr._networks['default'].x_std)85        x_std_expected = np.ones_like(x_std)86        assert np.array_equal(x_mean, x_mean_expected)87        assert np.array_equal(x_std, x_std_expected)88        y_mean = self.sess.run(gcr._networks['default'].y_mean)89        y_mean_expected = np.zeros_like(y_mean)90        y_std = self.sess.run(gcr._networks['default'].y_std)91        y_std_expected = np.ones_like(y_std)92        assert np.allclose(y_mean, y_mean_expected)93        assert np.allclose(y_std, y_std_expected)94    @pytest.mark.large95    def test_fit_smaller_subsample_factor(self):96        gcr = GaussianCNNBaseline(env_spec=test_env_spec,97                                  filters=((3, (3, 3)), (6, (3, 3))),98                                  strides=(1, 1),99                                  padding='SAME',100                                  hidden_sizes=(32, ),101                                  subsample_factor=0.9,102                                  adaptive_std=False)103        train_data, test_data = get_train_test_data()104        train_paths, _, _ = train_data105        for _ in range(20):106            gcr.fit(train_paths)107        test_paths, expected = test_data108        prediction = gcr.predict(test_paths)109        average_error = 0.0110        for i, exp in enumerate(expected):111            average_error += np.abs(exp - prediction[i])112        average_error /= len(expected)113        assert average_error <= 0.1114    @pytest.mark.large115    def test_fit_without_trusted_region(self):116        gcr = GaussianCNNBaseline(env_spec=test_env_spec,117                                  filters=((3, (3, 3)), (6, (3, 3))),118                                  strides=(1, 1),119                                  padding='SAME',120                                  hidden_sizes=(32, ),121                                  adaptive_std=False,122                                  use_trust_region=False)123        train_data, test_data = get_train_test_data()124        train_paths, _, _ = train_data125        for _ in range(20):126            gcr.fit(train_paths)127        test_paths, expected = test_data128        prediction = gcr.predict(test_paths)129        average_error = 0.0130        for i, exp in enumerate(expected):131            average_error += np.abs(exp - prediction[i])132        average_error /= len(expected)133        assert average_error <= 0.1134    @mock.patch('tests.garage.tf.baselines.'135                'test_gaussian_cnn_baseline.'136                'LbfgsOptimizer')137    def test_optimizer_args(self, mock_lbfgs):138        lbfgs_args = dict(max_opt_itr=25)139        gcr = GaussianCNNBaseline(env_spec=test_env_spec,140                                  filters=((3, (3, 3)), (6, (3, 3))),141                                  strides=(1, 1),142                                  padding='SAME',143                                  hidden_sizes=(32, ),144                                  optimizer=LbfgsOptimizer,145                                  optimizer_args=lbfgs_args,146                                  use_trust_region=True)147        assert mock_lbfgs.return_value is gcr._optimizer148        mock_lbfgs.assert_called_with(max_opt_itr=25)149    def test_is_pickleable(self):150        gcr = GaussianCNNBaseline(env_spec=test_env_spec,151                                  filters=((3, (3, 3)), (6, (3, 3))),152                                  strides=(1, 1),153                                  padding='SAME',154                                  hidden_sizes=(32, ),155                                  adaptive_std=False,156                                  use_trust_region=False)157        with tf.compat.v1.variable_scope('GaussianCNNBaseline', reuse=True):158            bias = tf.compat.v1.get_variable(159                'dist_params/mean_network/hidden_0/bias')160        bias.load(tf.ones_like(bias).eval())161        _, test_data = get_train_test_data()162        test_paths, _ = test_data163        result1 = gcr.predict(test_paths)164        h = pickle.dumps(gcr)165        with tf.compat.v1.Session(graph=tf.Graph()):166            gcr_pickled = pickle.loads(h)167            result2 = gcr_pickled.predict(test_paths)168            assert np.array_equal(result1, result2)169    def test_param_values(self):170        gcb = GaussianCNNBaseline(env_spec=test_env_spec,171                                  filters=((3, (3, 3)), (6, (3, 3))),172                                  strides=(1, 1),173                                  padding='SAME',174                                  hidden_sizes=(32, ),175                                  adaptive_std=False,176                                  use_trust_region=False)177        new_gcb = GaussianCNNBaseline(env_spec=test_env_spec,178                                      filters=((3, (3, 3)), (6, (3, 3))),179                                      strides=(1, 1),180                                      padding='SAME',181                                      hidden_sizes=(32, ),182                                      adaptive_std=False,183                                      use_trust_region=False,184                                      name='GaussianCNNBaseline2')185        # Manual change the parameter of GaussianCNNBaseline186        with tf.compat.v1.variable_scope('GaussianCNNBaseline', reuse=True):187            bias_var = tf.compat.v1.get_variable(188                'dist_params/mean_network/hidden_0/bias')189        bias_var.load(tf.ones_like(bias_var).eval())190        old_param_values = gcb.get_param_values()191        new_param_values = new_gcb.get_param_values()192        assert not np.array_equal(old_param_values, new_param_values)193        new_gcb.set_param_values(old_param_values)194        new_param_values = new_gcb.get_param_values()195        assert np.array_equal(old_param_values, new_param_values)196    def test_get_params(self):197        gcb = GaussianCNNBaseline(env_spec=test_env_spec,198                                  filters=((3, (3, 3)), (6, (3, 3))),199                                  strides=(1, 1),200                                  padding='SAME',201                                  hidden_sizes=(32, ),202                                  adaptive_std=False,203                                  use_trust_region=False)204        params_interal = gcb.get_params()205        trainable_params = tf.compat.v1.trainable_variables(206            scope='GaussianCNNBaseline')...

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