def moc_emit_ir(ngraph_function: Function, argv: argparse.Namespace):
output_dir = argv.output_dir if argv.output_dir != '.' else os.getcwd()
network = function_to_cnn(ngraph_function)
from mo.back.offline_transformations import apply_user_transformations, apply_moc_transformations
apply_user_transformations(network, parse_transform(argv.transform))
apply_moc_transformations(network)
orig_model_name = os.path.normpath(
os.path.join(output_dir, argv.model_name))
network.serialize(orig_model_name + ".xml", orig_model_name + ".bin")
del argv.feManager
# add meta information to IR
append_ir_info(file=orig_model_name,
meta_info=get_meta_info(argv),
mean_data=None,
input_names=None)
print('[ SUCCESS ] Generated IR version {} model.'.format(
get_ir_version(argv)))
print('[ SUCCESS ] XML file: {}.xml'.format(orig_model_name))
print('[ SUCCESS ] BIN file: {}.bin'.format(orig_model_name))
return 0
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_incorrect_reshape():
function = create_relu([1, 3, 22, 22])
net = ng.function_to_cnn(function)
with pytest.raises(ValueError) as e:
net.reshape({"data": [(2, 4, 6), 3, 22, 22]})
assert "Incorrect PartialShape dimension definition '(2, 4, 6)' " \
"in shape '[(2, 4, 6), 3, 22, 22]', expected one or two values for a dimension! " in str(e.value)
def ngraph_embedding(ids, vocab_embeddings, vocab_size, embedding_dim,
padding_idx, sparse):
"""
decomposing embedding with ngraph ops.
"""
import ngraph as ng
from ngraph import opset8 as opset
from openvino.inference_engine import IECore
if vocab_embeddings is None:
#
vocab_embeddings = np.zeros(
(vocab_size, embedding_dim)).astype("float32")
node_ids = ng.parameter(shape=ids.shape, name='ids', dtype=ids.dtype)
node_w = ng.parameter(shape=vocab_embeddings.shape,
name='w',
dtype=vocab_embeddings.dtype)
if padding_idx == -1:
padding_idx += vocab_size
if padding_idx is not None:
'''
mask W
'''
masked_embeddings = np.ones(vocab_embeddings.shape, dtype='int64')
masked_embeddings[padding_idx, :] = 0 # mask
node_mask = ng.constant(masked_embeddings,
name='mask',
dtype=vocab_embeddings.dtype)
node_masked_w = ng.multiply(node_w, node_mask)
node_axis = ng.constant([0], name='const0', dtype=np.int64)
node_gather = opset.gather(data=node_masked_w if padding_idx else node_w,
indices=node_ids,
axis=node_axis,
batch_dims=0)
graph = ng.result(node_gather, name='y')
parameters = [node_ids, node_w]
inputs_dict = {'ids': ids, "w": vocab_embeddings}
#
function = ng.Function(graph, parameters, "embedding")
ie_network = ng.function_to_cnn(function)
ie = IECore()
executable_network = ie.load_network(ie_network, 'CPU')
output = executable_network.infer(inputs_dict)
return output
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_is_dynamic():
function = create_relu([-1, 3, 20, 20])
net = ng.function_to_cnn(function)
ie = IECore()
ie.register_plugin("openvino_template_plugin", "TEMPLATE")
exec_net = ie.load_network(net, "TEMPLATE")
assert exec_net.outputs["out"].is_dynamic
p_shape = ng.partial_shape_from_data(exec_net.outputs["out"])
assert isinstance(p_shape, ng.impl.PartialShape)
with pytest.raises(RuntimeError) as e:
exec_net.outputs["out"].shape
assert "Cannot return dims for Data with dynamic shapes!" in str(e.value)
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_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_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_create_two_exec_net():
function = create_relu([
ng.Dimension(0, 5),
ng.Dimension(4),
ng.Dimension(20),
ng.Dimension(20)
])
net = ng.function_to_cnn(function)
ie_core = IECore()
ie_core.register_plugin("ov_template_plugin", "TEMPLATE")
exec_net1 = ie_core.load_network(net, "TEMPLATE", num_requests=2)
assert ng.function_from_cnn(net) != None
exec_net2 = ie_core.load_network(net, "TEMPLATE", num_requests=2)
assert ng.function_from_cnn(net) != None
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_set_blob_after_async_infer():
from conftest import create_ngraph_function
import ngraph as ng
function = create_ngraph_function([ng.Dimension(0,5), ng.Dimension(4), ng.Dimension(20), ng.Dimension(20)])
net = ng.function_to_cnn(function)
ie_core = ie.IECore()
ie_core.register_plugin("templatePlugin", "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)
def test_reshape_with_partial_shape(device, shape, p_shape):
function = create_relu(shape)
net = ng.function_to_cnn(function)
net.reshape({"data": p_shape})
changedFunction = ng.function_from_cnn(net)
p_shape = ng.impl.PartialShape(p_shape)
assert changedFunction.get_parameters()[0].get_partial_shape().is_dynamic
assert changedFunction.get_results()[0].get_output_partial_shape(
0).is_dynamic
assert function.get_parameters()[0].get_partial_shape().is_dynamic
assert function.get_results()[0].get_output_partial_shape(0).is_dynamic
assert changedFunction.get_parameters()[0].get_partial_shape() == p_shape
assert changedFunction.get_results()[0].get_output_partial_shape(
0) == p_shape
assert function.get_parameters()[0].get_partial_shape() == p_shape
assert function.get_results()[0].get_output_partial_shape(0) == p_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_with_set_blob(shape, p_shape, ref_shape):
from conftest import create_ngraph_function
import ngraph as ng
function = create_ngraph_function(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
def test_is_dynamic():
from conftest import create_ngraph_function
import ngraph as ng
function = create_ngraph_function([-1, 3, 20, 20])
net = ng.function_to_cnn(function)
assert net.input_info["data"].input_data.is_dynamic
assert net.outputs["out"].is_dynamic
p_shape = ng.partial_shape_from_data(net.input_info["data"].input_data)
assert isinstance(p_shape, ng.impl.PartialShape)
p_shape = ng.partial_shape_from_data(net.outputs["out"])
assert isinstance(p_shape, ng.impl.PartialShape)
with pytest.raises(RuntimeError) as e:
net.input_info["data"].input_data.shape
assert "Cannot return dims for Data with dynamic shapes!" in str(e.value)
ie = IECore()
ie.register_plugin("templatePlugin", "TEMPLATE")
exec_net = ie.load_network(net, "TEMPLATE")
assert exec_net.input_info["data"].input_data.is_dynamic
p_shape = ng.partial_shape_from_data(
exec_net.input_info["data"].input_data)
assert isinstance(p_shape, ng.impl.PartialShape)