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8c0844f497
Summary: Executor test that checks on different models that model params are the same when using a given executor and simple net Reviewed By: akyrola Differential Revision: D5908769 fbshipit-source-id: b6f5a2cf89c5c67b68e8b9be3264f38d5740d897
67 lines
2 KiB
Python
67 lines
2 KiB
Python
from __future__ import absolute_import
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from __future__ import division
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from __future__ import print_function
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from caffe2.python import workspace
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from caffe2.python.executor_test_util import (
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conv_model_generators,
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build_conv_model,
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build_resnet50_dataparallel_model,
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run_resnet50_epoch,
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ExecutorTestBase)
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from hypothesis import given
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import hypothesis.strategies as st
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import unittest
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EXECUTORS = ["dag", "async_dag"]
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ITERATIONS = 2
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class ExecutorCPUConvNetTest(ExecutorTestBase):
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@given(executor=st.sampled_from(EXECUTORS),
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model_name=st.sampled_from(conv_model_generators().keys()),
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batch_size=st.sampled_from([8]),
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num_workers=st.sampled_from([8]))
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def test_executor(self, executor, model_name, batch_size, num_workers):
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model = build_conv_model(model_name, batch_size)
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model.Proto().num_workers = num_workers
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def run_model():
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iterations = ITERATIONS
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if model_name == "MLP":
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iterations = 1 # avoid numeric instability with MLP gradients
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workspace.RunNet(model.net, iterations)
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self.compare_executors(
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model,
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ref_executor="simple",
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test_executor=executor,
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model_run_func=run_model,
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)
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@unittest.skipIf(not workspace.has_gpu_support, "no gpu")
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class ExecutorGPUResNetTest(ExecutorTestBase):
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@given(executor=st.sampled_from(EXECUTORS),
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num_workers=st.sampled_from([8]))
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def test_executor(self, executor, num_workers):
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model = build_resnet50_dataparallel_model(
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num_gpus=workspace.NumCudaDevices(), batch_size=32, epoch_size=32)
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model.Proto().num_workers = num_workers
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def run_model():
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run_resnet50_epoch(model, batch_size=32, epoch_size=32)
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self.compare_executors(
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model,
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ref_executor="simple",
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test_executor=executor,
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model_run_func=run_model,
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)
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if __name__ == '__main__':
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unittest.main()
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