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* Begin enabling pre-commit hooks Signed-off-by: droctothorpe <[email protected]> * Address PR feedback Signed-off-by: droctothorpe <[email protected]> --------- Signed-off-by: droctothorpe <[email protected]>
208 lines
8.5 KiB
Python
208 lines
8.5 KiB
Python
# Copyright 2022 The Kubeflow Authors.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import os
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import shutil
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import unittest
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import grpc
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import grpc_testing
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import pytest
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from pkg.apis.manager.v1beta1.python import api_pb2
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from pkg.suggestion.v1beta1.nas.enas.service import EnasService
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class TestEnas(unittest.TestCase):
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def setUp(self):
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services = {
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api_pb2.DESCRIPTOR.services_by_name['Suggestion']: EnasService(
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)
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}
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self.test_server = grpc_testing.server_from_dictionary(
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services, grpc_testing.strict_real_time())
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def test_get_suggestion(self):
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trials = [
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api_pb2.Trial(
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name="first-trial",
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spec=api_pb2.TrialSpec(
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objective=api_pb2.ObjectiveSpec(
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type=api_pb2.MAXIMIZE,
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objective_metric_name="Validation-Accuracy",
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goal=0.99
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),
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parameter_assignments=api_pb2.TrialSpec.ParameterAssignments(
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assignments=[
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api_pb2.ParameterAssignment(
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name="architecture",
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value="[[3], [0, 1], [0, 0, 1], [2, 1, 0, 0]]",
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),
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api_pb2.ParameterAssignment(
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name="nn_config",
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value="{'num_layers': 4}",
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),
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]
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)
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),
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status=api_pb2.TrialStatus(
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observation=api_pb2.Observation(
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metrics=[
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api_pb2.Metric(
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name="Validation-Accuracy",
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value="0.88"
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),
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]
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),
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condition=api_pb2.TrialStatus.TrialConditionType.SUCCEEDED,
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)
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),
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api_pb2.Trial(
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name="second-trial",
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spec=api_pb2.TrialSpec(
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objective=api_pb2.ObjectiveSpec(
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type=api_pb2.MAXIMIZE,
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objective_metric_name="Validation-Accuracy",
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goal=0.99
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),
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parameter_assignments=api_pb2.TrialSpec.ParameterAssignments(
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assignments=[
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api_pb2.ParameterAssignment(
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name="architecture",
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value="[[1], [0, 1], [2, 1, 1], [2, 1, 1, 0]]",
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),
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api_pb2.ParameterAssignment(
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name="nn_config",
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value="{'num_layers': 4}",
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),
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],
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)
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),
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status=api_pb2.TrialStatus(
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observation=api_pb2.Observation(
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metrics=[
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api_pb2.Metric(
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name="Validation-Accuracy",
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value="0.84"
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),
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]
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),
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condition=api_pb2.TrialStatus.TrialConditionType.SUCCEEDED,
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)
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)
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]
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experiment = api_pb2.Experiment(
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name="enas-experiment",
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spec=api_pb2.ExperimentSpec(
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algorithm=api_pb2.AlgorithmSpec(
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algorithm_name="enas",
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),
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objective=api_pb2.ObjectiveSpec(
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type=api_pb2.MAXIMIZE,
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goal=0.9,
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objective_metric_name="Validation-Accuracy"
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),
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parallel_trial_count=2,
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max_trial_count=10,
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nas_config=api_pb2.NasConfig(
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graph_config=api_pb2.GraphConfig(
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num_layers=4,
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input_sizes=[32, 32, 8],
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output_sizes=[10]
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),
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operations=api_pb2.NasConfig.Operations(
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operation=[
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api_pb2.Operation(
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operation_type="convolution",
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parameter_specs=api_pb2.Operation.ParameterSpecs(
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parameters=[
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api_pb2.ParameterSpec(
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name="filter_size",
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parameter_type=api_pb2.CATEGORICAL,
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feasible_space=api_pb2.FeasibleSpace(
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max=None, min=None, list=["5"])
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),
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api_pb2.ParameterSpec(
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name="num_filter",
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parameter_type=api_pb2.CATEGORICAL,
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feasible_space=api_pb2.FeasibleSpace(
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max=None, min=None, list=["128"])
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),
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api_pb2.ParameterSpec(
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name="stride",
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parameter_type=api_pb2.CATEGORICAL,
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feasible_space=api_pb2.FeasibleSpace(
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max=None, min=None, list=["1", "2"])
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),
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]
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)
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),
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api_pb2.Operation(
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operation_type="reduction",
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parameter_specs=api_pb2.Operation.ParameterSpecs(
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parameters=[
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api_pb2.ParameterSpec(
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name="reduction_type",
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parameter_type=api_pb2.CATEGORICAL,
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feasible_space=api_pb2.FeasibleSpace(
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max=None, min=None, list=["max_pooling"])
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),
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api_pb2.ParameterSpec(
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name="pool_size",
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parameter_type=api_pb2.INT,
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feasible_space=api_pb2.FeasibleSpace(
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min="2", max="3", step="1", list=[])
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),
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]
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)
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),
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],
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)
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)
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)
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)
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request = api_pb2.GetSuggestionsRequest(
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experiment=experiment,
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trials=trials,
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current_request_number=2,
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)
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get_suggestion = self.test_server.invoke_unary_unary(
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method_descriptor=(api_pb2.DESCRIPTOR
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.services_by_name['Suggestion']
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.methods_by_name['GetSuggestions']),
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invocation_metadata={},
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request=request, timeout=100)
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response, metadata, code, details = get_suggestion.termination()
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print(response.parameter_assignments)
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self.assertEqual(code, grpc.StatusCode.OK)
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self.assertEqual(2, len(response.parameter_assignments))
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@pytest.fixture(scope='function', autouse=True)
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def tear_down():
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yield
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working_dir = os.getcwd()
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target_path = os.path.join(working_dir, "ctrl_cache")
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if os.path.isdir(target_path):
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shutil.rmtree(target_path)
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if __name__ == '__main__':
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unittest.main()
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