def test_mobile_exporter_datatypes(self):
model = ModelHelper(name="mobile_exporter_test_model")
model.Copy("data_int", "out")
model.params.append("data_int")
# Create our mobile exportable networks
workspace.RunNetOnce(model.param_init_net)
np_data_int = np.random.randint(100, size=(1, 1, 28, 28), dtype=np.int32)
workspace.FeedBlob("data_int", np_data_int)
init_net, predict_net = mobile_exporter.Export(
workspace, model.net, model.params
)
workspace.CreateNet(model.net)
workspace.RunNet(model.net)
ref_out = workspace.FetchBlob("out")
# Clear the workspace
workspace.ResetWorkspace()
# Populate the workspace with data
workspace.RunNetOnce(init_net)
# Overwrite the old net
workspace.CreateNet(predict_net, True)
workspace.RunNet(predict_net.name)
manual_run_out = workspace.FetchBlob("out")
np.testing.assert_allclose(
ref_out, manual_run_out, atol=1e-10, rtol=1e-10
)
# Clear the workspace
workspace.ResetWorkspace()
# Predictor interface test (simulates writing to disk)
predictor = workspace.Predictor(
init_net.SerializeToString(), predict_net.SerializeToString()
)
# Output is a vector of outputs but we only care about the first and only result
predictor_out = predictor.run([])
assert len(predictor_out) == 1
predictor_out = predictor_out[0]
np.testing.assert_allclose(
ref_out, predictor_out, atol=1e-10, rtol=1e-10
)
def test_mobile_exporter_datatypes(self):
model = ModelHelper(name="mobile_exporter_test_model")
model.Copy("data_int", "out")
model.params.append("data_int")
model.Copy("data_obj", "out_obj")
model.params.append("data_obj")
# Create our mobile exportable networks
workspace.RunNetOnce(model.param_init_net)
np_data_int = np.random.randint(100,
size=(1, 1, 28, 28),
dtype=np.int32)
workspace.FeedBlob("data_int", np_data_int)
np_data_obj = np.array(['aa', 'bb']).astype(np.dtype('O'))
workspace.FeedBlob("data_obj", np_data_obj)
init_net, predict_net = mobile_exporter.Export(workspace, model.net,
model.params)
workspace.CreateNet(model.net)
workspace.RunNet(model.net)
ref_out = workspace.FetchBlob("out")
ref_out_obj = workspace.FetchBlob("out_obj")
# Clear the workspace
workspace.ResetWorkspace()
# Populate the workspace with data
workspace.RunNetOnce(init_net)
# Overwrite the old net
workspace.CreateNet(predict_net, True)
workspace.RunNet(predict_net.name)
manual_run_out = workspace.FetchBlob("out")
manual_run_out_obj = workspace.FetchBlob("out_obj")
np.testing.assert_allclose(ref_out,
manual_run_out,
atol=1e-10,
rtol=1e-10)
np.testing.assert_equal(ref_out_obj, manual_run_out_obj)
# Clear the workspace
workspace.ResetWorkspace()
# Predictor interface test (simulates writing to disk)
predictor = workspace.Predictor(init_net.SerializeToString(),
predict_net.SerializeToString())
# Output is a vector of outputs.
predictor_out = predictor.run([])
assert len(predictor_out) == 2
predictor_out_int = predictor_out[1]
predictor_out_obj = predictor_out[0]
# The order in predictor_out is non-deterministic. Use type of the entry
# to figure out what to compare it to.
if isinstance(predictor_out[1][0], bytes):
predictor_out_int = predictor_out[0]
predictor_out_obj = predictor_out[1]
np.testing.assert_allclose(ref_out,
predictor_out_int,
atol=1e-10,
rtol=1e-10)
np.testing.assert_equal(ref_out_obj, predictor_out_obj)
