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_resize_algorithm_work(device):
ie_core = ie.IECore()
net = ie_core.read_network(test_net_xml, test_net_bin)
exec_net_1 = ie_core.load_network(network=net, device_name=device, num_requests=1)
img = read_image()
res_1 = np.sort(exec_net_1.infer({"data": img})['fc_out'])
net.input_info['data'].preprocess_info.resize_algorithm = ie.ResizeAlgorithm.RESIZE_BILINEAR
exec_net_2 = ie_core.load_network(net, device)
import cv2
image = cv2.imread(path_to_img)
if image is None:
raise FileNotFoundError("Input image not found")
image = image / 255
image = image.transpose((2, 0, 1)).astype(np.float32)
image = np.expand_dims(image, 0)
tensor_desc = ie.TensorDesc("FP32", [1, 3, image.shape[2], image.shape[3]], "NCHW")
img_blob = ie.Blob(tensor_desc, image)
request = exec_net_2.requests[0]
assert request.preprocess_info["data"].resize_algorithm == ie.ResizeAlgorithm.RESIZE_BILINEAR
request.set_blob('data', img_blob)
request.infer()
res_2 = np.sort(request.output_blobs['fc_out'].buffer)
assert np.allclose(res_1, res_2, atol=1e-2, rtol=1e-2)
def test_blob_setter(device):
ie_core = ie.IECore()
if device == "CPU":
if ie_core.get_metric(device,
"FULL_DEVICE_NAME") == "arm_compute::NEON":
pytest.skip("Can't run on ARM plugin")
net = ie_core.read_network(test_net_xml, test_net_bin)
exec_net_1 = ie_core.load_network(network=net,
device_name=device,
num_requests=1)
net.input_info['data'].layout = "NHWC"
exec_net_2 = ie_core.load_network(network=net,
device_name=device,
num_requests=1)
img = read_image()
res_1 = np.sort(exec_net_1.infer({"data": img})['fc_out'])
img = np.transpose(img, axes=(0, 2, 3, 1)).astype(np.float32)
tensor_desc = ie.TensorDesc("FP32", [1, 3, 32, 32], "NHWC")
img_blob = ie.Blob(tensor_desc, img)
request = exec_net_2.requests[0]
request.set_blob('data', img_blob)
request.infer()
res_2 = np.sort(request.output_blobs['fc_out'].buffer)
assert np.allclose(res_1, res_2, atol=1e-2, rtol=1e-2)
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_blob_setter_with_preprocess(device):
ie_core = ie.IECore()
net = ie_core.read_network(test_net_xml, test_net_bin)
exec_net = ie_core.load_network(network=net, device_name=device, num_requests=1)
img = read_image()
tensor_desc = ie.TensorDesc("FP32", [1, 3, 32, 32], "NCHW")
img_blob = ie.Blob(tensor_desc, img)
preprocess_info = ie.PreProcessInfo()
preprocess_info.mean_variant = ie.MeanVariant.MEAN_IMAGE
request = exec_net.requests[0]
request.set_blob('data', img_blob, preprocess_info)
pp = request.preprocess_info["data"]
assert pp.mean_variant == ie.MeanVariant.MEAN_IMAGE
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_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_infer_dynamic_network_with_set_blob(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")
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_blob_setter(device):
ie_core = ie.IECore()
net = ie_core.read_network(test_net_xml, test_net_bin)
exec_net_1 = ie_core.load_network(network=net, device_name=device, num_requests=1)
net.input_info['data'].layout = "NHWC"
exec_net_2 = ie_core.load_network(network=net, device_name=device, num_requests=1)
img = read_image()
res_1 = np.sort(exec_net_1.infer({"data": img})['fc_out'])
img = np.transpose(img, axes=(0, 2, 3, 1)).astype(np.float32)
tensor_desc = ie.TensorDesc("FP32", [1, 3, 32, 32], "NHWC")
img_blob = ie.Blob(tensor_desc, img)
request = exec_net_2.requests[0]
request.set_blob('data', img_blob)
request.infer()
res_2 = np.sort(request.output_blobs['fc_out'].buffer)
assert np.allclose(res_1, res_2, atol=1e-2, rtol=1e-2)
