FM / test

/usr/local/lib64/python3.6/site-packages/caffe2/python/test/

/usr/local/lib64/python3.6/site-packages/caffe2/python/test

Name	Size	Mode	Actions
__pycache__/	-	0755	rm
blob_deallocation_test.py	632	0644	edit dl rm
do_op_test.py	2055	0644	edit dl rm
executor_test.py	3039	0644	edit dl rm
executor_test_util.py	8058	0644	edit dl rm
fakefp16_transform_test.py	723	0644	edit dl rm
gpu_context_test.py	1121	0644	edit dl rm
inference_lstm_op_test.py	2190	0644	edit dl rm
python_protobuf_test.py	624	0644	edit dl rm
__init__.py	0	0644	edit dl rm

Edit: /usr/local/lib64/python3.6/site-packages/caffe2/python/test/executor_test.py (3039B)

from caffe2.python.test_util import TestCase

from hypothesis import given
import hypothesis.strategies as st

import unittest

EXECUTORS = ["parallel", "async_scheduling"]
ITERATIONS = 1

class ExecutorCPUConvNetTest(ExecutorTestBase):
    @given(executor=st.sampled_from(EXECUTORS),
           model_name=st.sampled_from(executor_test_model_names()),
           batch_size=st.sampled_from([1]),
           num_workers=st.sampled_from([8]))
    @executor_test_settings
    def test_executor(self, executor, model_name, batch_size, num_workers):
        model = build_conv_model(model_name, batch_size)
        model.Proto().num_workers = num_workers

def run_model():
            iterations = ITERATIONS
            if model_name == "MLP":
                iterations = 1  # avoid numeric instability with MLP gradients
            workspace.RunNet(model.net, iterations)

self.compare_executors(
            model,
            ref_executor="simple",
            test_executor=executor,
            model_run_func=run_model,
        )

@unittest.skipIf(not workspace.has_gpu_support, "no gpu")
class ExecutorGPUResNetTest(ExecutorTestBase):
    @given(executor=st.sampled_from(EXECUTORS),
           num_workers=st.sampled_from([8]))
    @executor_test_settings
    def test_executor(self, executor, num_workers):
        model = build_resnet50_dataparallel_model(
            num_gpus=workspace.NumGpuDevices(), batch_size=8, epoch_size=8)
        model.Proto().num_workers = num_workers

def run_model():
            run_resnet50_epoch(model, batch_size=8, epoch_size=8)

self.compare_executors(
            model,
            ref_executor="simple",
            test_executor=executor,
            model_run_func=run_model,
        )

class ExecutorFailingOpTest(TestCase):
    def test_failing_op(self):
        def create_failing_net(throw_exception):
            net = core.Net("failing_net")
            if throw_exception:
                net.ThrowException([], [])
            else:
                net.Fail([], [])
            net.Proto().type = "async_scheduling"
            return net

workspace.ResetWorkspace()
        net = create_failing_net(throw_exception=True)
        workspace.CreateNet(net)
        with self.assertRaises(RuntimeError):
            workspace.RunNet(net)

with self.assertRaises(RuntimeError):
            workspace.RunNet(net, allow_fail=True)

workspace.ResetWorkspace()
        net = create_failing_net(throw_exception=False)
        workspace.CreateNet(net)

with self.assertRaises(RuntimeError):
            workspace.RunNet(net)

res = workspace.RunNet(net, allow_fail=True)
        self.assertFalse(res)

if __name__ == '__main__':
    unittest.main()

from caffe2.python import core, workspace from caffe2.python.test.executor_test_util import ( build_conv_model, build_resnet50_dataparallel_model, run_resnet50_epoch, ExecutorTestBase, executor_test_settings, executor_test_model_names) from caffe2.python.test_util import TestCase from hypothesis import given import hypothesis.strategies as st import unittest EXECUTORS = ["parallel", "async_scheduling"] ITERATIONS = 1 class ExecutorCPUConvNetTest(ExecutorTestBase): @given(executor=st.sampled_from(EXECUTORS), model_name=st.sampled_from(executor_test_model_names()), batch_size=st.sampled_from([1]), num_workers=st.sampled_from([8])) @executor_test_settings def test_executor(self, executor, model_name, batch_size, num_workers): model = build_conv_model(model_name, batch_size) model.Proto().num_workers = num_workers def run_model(): iterations = ITERATIONS if model_name == "MLP": iterations = 1 # avoid numeric instability with MLP gradients workspace.RunNet(model.net, iterations) self.compare_executors( model, ref_executor="simple", test_executor=executor, model_run_func=run_model, ) @unittest.skipIf(not workspace.has_gpu_support, "no gpu") class ExecutorGPUResNetTest(ExecutorTestBase): @given(executor=st.sampled_from(EXECUTORS), num_workers=st.sampled_from([8])) @executor_test_settings def test_executor(self, executor, num_workers): model = build_resnet50_dataparallel_model( num_gpus=workspace.NumGpuDevices(), batch_size=8, epoch_size=8) model.Proto().num_workers = num_workers def run_model(): run_resnet50_epoch(model, batch_size=8, epoch_size=8) self.compare_executors( model, ref_executor="simple", test_executor=executor, model_run_func=run_model, ) class ExecutorFailingOpTest(TestCase): def test_failing_op(self): def create_failing_net(throw_exception): net = core.Net("failing_net") if throw_exception: net.ThrowException([], []) else: net.Fail([], []) net.Proto().type = "async_scheduling" return net workspace.ResetWorkspace() net = create_failing_net(throw_exception=True) workspace.CreateNet(net) with self.assertRaises(RuntimeError): workspace.RunNet(net) with self.assertRaises(RuntimeError): workspace.RunNet(net, allow_fail=True) workspace.ResetWorkspace() net = create_failing_net(throw_exception=False) workspace.CreateNet(net) with self.assertRaises(RuntimeError): workspace.RunNet(net) res = workspace.RunNet(net, allow_fail=True) self.assertFalse(res) if __name__ == '__main__': unittest.main()