def test_infer_dynamic_network_with_set_blob_twice():
from conftest import create_encoder
import ngraph as ng
shape, p_shape = [1, 4, 20, 20], [(0, 5), 4, 20, 20]
ref_shape1, ref_shape2 = [2, 4, 20, 20], [3, 4, 20, 20]
function = create_encoder(shape)
net = ng.function_to_cnn(function)
net.reshape({"data": p_shape})
ie_core = ie.IECore()
ie_core.register_plugin("templatePlugin", "TEMPLATE")
exec_net = ie_core.load_network(net, "TEMPLATE")
request = exec_net.requests[0]
td = request.input_blobs['data'].tensor_desc
td.dims = ref_shape1
blob = ie.Blob(td)
request.set_blob("data", blob)
request.infer({"data": np.ones(ref_shape1)})
assert exec_net.requests[0].input_blobs[
"data"].tensor_desc.dims == ref_shape1
assert request.output_blobs['out'].tensor_desc.dims == ref_shape1
td = request.input_blobs['data'].tensor_desc
td.dims = ref_shape2
blob = ie.Blob(td)
request.set_blob("data", blob)
request.infer({"data": np.ones(ref_shape2)})
assert exec_net.requests[0].input_blobs[
"data"].tensor_desc.dims == ref_shape2
assert request.output_blobs['out'].tensor_desc.dims == ref_shape2
def test_set_blob_with_incorrect_name():
function = create_encoder([4, 4, 20, 20])
net = ng.function_to_cnn(function)
ie_core = ie.IECore()
ie_core.register_plugin("ov_template_plugin", "TEMPLATE")
exec_net = ie_core.load_network(net, "TEMPLATE")
tensor_desc = exec_net.requests[0].input_blobs["data"].tensor_desc
tensor_desc.dims = [4, 4, 20, 20]
blob = ie.Blob(tensor_desc)
with pytest.raises(RuntimeError) as e:
exec_net.requests[0].set_blob("incorrect_name", blob)
assert f"Failed to find input or output with name: 'incorrect_name'" in str(
e.value)
def test_async_infer_dynamic_network_3_requests(shapes):
function = create_encoder([3, 4, 20, 20])
net = ng.function_to_cnn(function)
net.reshape({"data": [3, 4, (20, 50), (20, 50)]})
ie_core = ie.IECore()
ie_core.register_plugin("ov_template_plugin", "TEMPLATE")
exec_net = ie_core.load_network(net, "TEMPLATE", num_requests=3)
for i, request in enumerate(exec_net.requests):
request.async_infer({"data": np.ones(shapes[i])})
for i, request in enumerate(exec_net.requests):
status = request.wait(ie.WaitMode.RESULT_READY)
assert status == ie.StatusCode.OK
assert request.output_blobs['out'].tensor_desc.dims == shapes[i]
def test_blob_set_shape_after_async_infer():
function = create_encoder([1, 4, 20, 20])
net = ng.function_to_cnn(function)
net.reshape({"data": [(1, 5), 4, 20, 20]})
ie_core = IECore()
ie_core.register_plugin("templatePlugin", "TEMPLATE")
exec_net = ie_core.load_network(net, "TEMPLATE")
request = exec_net.requests[0]
request.async_infer({"data": np.ones([4, 4, 20, 20])})
with pytest.raises(RuntimeError) as e:
request.input_blobs['data'].set_shape([3, 4, 20, 20])
assert "REQUEST_BUSY" in str(e.value)
request.wait()
def test_infer_dynamic_network_without_set_shape(shape, p_shape, ref_shape):
function = create_encoder(shape)
net = ng.function_to_cnn(function)
net.reshape({"data": p_shape})
ie_core = ie.IECore()
ie_core.register_plugin("ov_template_plugin", "TEMPLATE")
exec_net = ie_core.load_network(net, "TEMPLATE")
exec_net.infer({"data": np.ones(ref_shape)})
assert exec_net.requests[0].input_blobs[
"data"].tensor_desc.dims == ref_shape
request = exec_net.requests[0]
request.async_infer({"data": np.ones(ref_shape)})
status = request.wait(ie.WaitMode.RESULT_READY)
assert status == ie.StatusCode.OK
assert request.output_blobs['out'].tensor_desc.dims == ref_shape
def test_set_blob_after_async_infer():
function = create_encoder([1, 4, 20, 20])
net = ng.function_to_cnn(function)
net.reshape({"data": [(0, 5), 4, 20, 20]})
ie_core = ie.IECore()
ie_core.register_plugin("ov_template_plugin", "TEMPLATE")
exec_net = ie_core.load_network(net, "TEMPLATE")
request = exec_net.requests[0]
tensor_desc = request.input_blobs['data'].tensor_desc
tensor_desc.dims = [2, 4, 20, 20]
blob = ie.Blob(tensor_desc)
request.async_infer({"data": np.ones([4, 4, 20, 20])})
with pytest.raises(RuntimeError) as e:
request.set_blob("data", blob)
assert "REQUEST_BUSY" in str(e.value)
request.wait()
def test_set_blob_with_incorrect_size():
function = create_encoder([4, 4, 20, 20])
net = ng.function_to_cnn(function)
ie_core = ie.IECore()
ie_core.register_plugin("ov_template_plugin", "TEMPLATE")
exec_net = ie_core.load_network(net, "TEMPLATE")
tensor_desc = exec_net.requests[0].input_blobs["data"].tensor_desc
tensor_desc.dims = [tensor_desc.dims[0] * 2, 4, 20, 20]
blob = ie.Blob(tensor_desc)
print(exec_net.requests[0].output_blobs)
with pytest.raises(RuntimeError) as e:
exec_net.requests[0].set_blob("data", blob)
assert f"Input blob size is not equal network input size" in str(e.value)
with pytest.raises(RuntimeError) as e:
exec_net.requests[0].set_blob("out", blob)
assert f"Output blob size is not equal network output size" in str(e.value)
def test_infer_dynamic_network_twice():
shape, p_shape = [1, 4, 20, 20], [(0, 5), 4, 20, 20]
ref_shape1, ref_shape2 = [2, 4, 20, 20], [3, 4, 20, 20]
function = create_encoder(shape)
net = ng.function_to_cnn(function)
net.reshape({"data": p_shape})
ie_core = ie.IECore()
ie_core.register_plugin("ov_template_plugin", "TEMPLATE")
exec_net = ie_core.load_network(net, "TEMPLATE")
request = exec_net.requests[0]
request.infer({"data": np.ones(ref_shape1)})
assert exec_net.requests[0].input_blobs[
"data"].tensor_desc.dims == ref_shape1
assert request.output_blobs['out'].tensor_desc.dims == ref_shape1
request.infer({"data": np.ones(ref_shape2)})
assert exec_net.requests[0].input_blobs[
"data"].tensor_desc.dims == ref_shape2
assert request.output_blobs['out'].tensor_desc.dims == ref_shape2
def test_infer_dynamic_network_with_set_blob(shape, p_shape, ref_shape):
from conftest import create_encoder
import ngraph as ng
function = create_encoder(shape)
net = ng.function_to_cnn(function)
net.reshape({"data": p_shape})
ie_core = ie.IECore()
ie_core.register_plugin("templatePlugin", "TEMPLATE")
exec_net = ie_core.load_network(net, "TEMPLATE")
tensor_desc = exec_net.requests[0].input_blobs["data"].tensor_desc
tensor_desc.dims = ref_shape
blob = ie.Blob(tensor_desc)
exec_net.requests[0].set_blob("data", blob)
assert exec_net.requests[0].input_blobs[
"data"].tensor_desc.dims == ref_shape
request = exec_net.requests[0]
request.infer({"data": np.ones(ref_shape)})
request.async_infer({"data": np.ones(ref_shape)})
status = request.wait(ie.WaitMode.RESULT_READY)
assert status == ie.StatusCode.OK
assert request.output_blobs["out"].tensor_desc.dims == ref_shape
