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_output_blobs(device):
ie_core = ie.IECore()
net = ie_core.read_network(test_net_xml, test_net_bin)
executable_network = ie_core.load_network(net, device, num_requests=2)
td = ie.TensorDesc("FP32", (1, 10), "NC")
assert executable_network.requests[0].output_blobs[
'fc_out'].tensor_desc == td
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_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_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)
def test_input_blobs(device):
ie_core = ie.IECore()
net = ie_core.read_network(test_net_xml, test_net_bin)
executable_network = ie_core.load_network(net, device, num_requests=2)
td = ie.TensorDesc("FP32", (1, 3, 32, 32), "NCHW")
assert executable_network.requests[0].input_blobs['data'].tensor_desc == td
