yeandy commented on code in PR #21738: URL: https://github.com/apache/beam/pull/21738#discussion_r955315368
########## sdks/python/apache_beam/testing/benchmarks/inference/pytorch_image_classification_benchmarks.py: ########## @@ -0,0 +1,63 @@ +# +# Licensed to the Apache Software Foundation (ASF) under one or more +# contributor license agreements. See the NOTICE file distributed with +# this work for additional information regarding copyright ownership. +# The ASF licenses this file to You under the Apache License, Version 2.0 +# (the "License"); you may not use this file except in compliance with +# the License. You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# +# pytype: skip-file + +import logging + +from apache_beam.examples.inference import pytorch_image_classification +from apache_beam.testing.load_tests.load_test import LoadTest +from torchvision import models + +_PERF_TEST_MODELS = ['resnet50', 'resnet101', 'resnet152'] +_PRETRAINED_MODEL_MODULE = 'torchvision.models' + + +class PytorchVisionBenchmarkTest(LoadTest): + def __init__(self): + # TODO (anandinguva): make get_namespace() method in RunInference static + self.metrics_namespace = 'RunInferencePytorch' + super().__init__(metrics_namespace=self.metrics_namespace) + + def test(self): + pretrained_model_name = self.pipeline.get_option('pretrained_model_name') + if not pretrained_model_name: + raise RuntimeError( + 'Please provide a pretrained torch model name.' + ' Model name must be from the module torchvision.models') + if pretrained_model_name == _PERF_TEST_MODELS[0]: + model_class = models.resnet50 + elif pretrained_model_name == _PERF_TEST_MODELS[1]: + model_class = models.resnet101 + elif pretrained_model_name == _PERF_TEST_MODELS[2]: + model_class = models.resnet152 + else: + raise NotImplementedError + + # model_params are same for all the models. But this may change if we add + # different models. + model_params = {'num_classes': 1000, 'pretrained': False} Review Comment: The same `model_params` is being used for all model types. I was thinking we could attach a separate `model_params` to each model type, but that would be repeating a bunch of the same logic. I think what we have is fine. ########## sdks/python/apache_beam/testing/benchmarks/inference/pytorch_image_classification_benchmarks.py: ########## @@ -0,0 +1,63 @@ +# +# Licensed to the Apache Software Foundation (ASF) under one or more +# contributor license agreements. See the NOTICE file distributed with +# this work for additional information regarding copyright ownership. +# The ASF licenses this file to You under the Apache License, Version 2.0 +# (the "License"); you may not use this file except in compliance with +# the License. You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# +# pytype: skip-file + +import logging + +from apache_beam.examples.inference import pytorch_image_classification +from apache_beam.testing.load_tests.load_test import LoadTest +from torchvision import models + +_PERF_TEST_MODELS = ['resnet50', 'resnet101', 'resnet152'] +_PRETRAINED_MODEL_MODULE = 'torchvision.models' + + +class PytorchVisionBenchmarkTest(LoadTest): Review Comment: Got it, thanks. -- This is an automated message from the Apache Git Service. To respond to the message, please log on to GitHub and use the URL above to go to the specific comment. To unsubscribe, e-mail: [email protected] For queries about this service, please contact Infrastructure at: [email protected]
