Update openvino to 2024 ver

face_detection_and_extraction/modules/openvino/model.py

@@ -1,13 +1,13 @@
 import numpy as np
-from openvino.inference_engine import IECore
+import openvino as ov
 
 from modules.models.base import Model
 from modules.utils.image import pad_resize_image
 
 
 class OVModel(Model):
 
-    __slots__ = ["OVExec", "in_layer", "out_layer", "in_shape", "out_shape"]
+    __slots__ = ["compiled_model", "out_layer_ir", "in_shape", "out_shape"]
 
     def __init__(
             self,
@@ -17,26 +17,24 @@ def __init__(
             bbox_area_thres: float,
             device: str = "CPU",
             verbose: bool = False):
-        OVIE = IECore()
-        OVNet = OVIE.read_network(model=xml_path, weights=bin_path)
-
-        self.OVExec = OVIE.load_network(network=OVNet, device_name=device)
+        core = ov.Core()
+        model = core.read_model(model=xml_path, weights=bin_path)
+        self.compiled_model = core.compile_model(model=model, device_name=device)
 
         self.det_thres = det_thres
         self.bbox_area_thres = bbox_area_thres
         # get input/output layer information
-        self.in_layer = next(iter(OVNet.input_info))
-        self.out_layer = next(iter(OVNet.outputs))
-        self.in_shape = OVNet.input_info[self.in_layer].input_data.shape
-        self.out_shape = OVNet.outputs[self.out_layer].shape
+        self.out_layer_ir = self.compiled_model.output(model.outputs[0].names.pop())
+        self.in_shape = model.inputs[0].shape
+        self.out_shape = model.outputs[0].shape
         Model.__init__(self, self.in_shape[2:][::-1],
                        det_thres, bbox_area_thres)
 
         if verbose:
             # print model input/output info and shapes
-            print("Available Devices: ", OVIE.available_devices)
-            print("Input Layer: ", self.in_layer)
-            print("Output Layer: ", self.out_layer)
+            print("Available Devices: ", core.available_devices)
+            print("Input Layer: ", model.inputs)
+            print("Output Layer: ", model.outputs)
             print("Input Shape: ", self.in_shape)
             print("Output Shape: ", self.out_shape)
 
@@ -49,41 +47,39 @@ def __call__(
         resized = resized.transpose((2, 0, 1))  # HWC to CHW
         in_image = resized.reshape((N, C, H, W))
         # openVINO expects BGR format
-        detections = self.OVExec.infer(inputs={self.in_layer: in_image})
-        detections = detections[self.out_layer][0][0]
+        detections = self.compiled_model([in_image])
+        detections = detections[self.out_layer_ir][0][0]
         # reorder dets to have [xmin, ymin, xmax, ymax, conf] format
         # from a [_, _, conf, xmin, ymin, xmax, ymax] fmt
         return detections[:, [3, 4, 5, 6, 2]]
 
 
 class OVFeatModel(Model):
 
-    __slots__ = ["OVExec", "in_layer", "out_layer", "in_shape", "out_shape"]
+    __slots__ = ["compiled_model", "out_layer_ir", "in_shape", "out_shape"]
 
     def __init__(
             self,
             xml_path: str,
             bin_path: str,
             device: str = "CPU",
             verbose: bool = False):
-        OVIE = IECore()
-        OVNet = OVIE.read_network(model=xml_path, weights=bin_path)
-
-        self.OVExec = OVIE.load_network(network=OVNet, device_name=device)
+        core = ov.Core()
+        model = core.read_model(model=xml_path, weights=bin_path)
+        self.compiled_model = core.compile_model(model=model, device_name=device)
 
         # get input/output layer information
-        self.in_layer = next(iter(OVNet.input_info))
-        self.out_layer = next(iter(OVNet.outputs))
-        self.in_shape = OVNet.input_info[self.in_layer].input_data.shape
-        self.out_shape = OVNet.outputs[self.out_layer].shape
+        self.out_layer_ir = self.compiled_model.output(model.outputs[0].names.pop())
+        self.in_shape =  model.inputs[0].shape
+        self.out_shape =  model.outputs[0].shape
         Model.__init__(self, self.in_shape[2:][::-1],
                        det_thres=None, bbox_area_thres=None)
 
         if verbose:
             # print model input/output info and shapes
-            print("Available Devices: ", OVIE.available_devices)
-            print("Input Layer: ", self.in_layer)
-            print("Output Layer: ", self.out_layer)
+            print("Available Devices: ", core.available_devices)
+            print("Input Layer: ", model.inputs)
+            print("Output Layer: ", model.outputs)
             print("Input Shape: ", self.in_shape)
             print("Output Shape: ", self.out_shape)
 
@@ -96,6 +92,6 @@ def __call__(
         resized = resized.transpose((2, 0, 1))  # HWC to CHW
         in_image = resized.reshape((N, C, H, W))
         # openVINO expects BGR format
-        detections = self.OVExec.infer(inputs={self.in_layer: in_image})
-        detections = detections[self.out_layer].squeeze()
+        detections = self.compiled_model([in_image])
+        detections = detections[self.out_layer_ir].squeeze()
         return detections

face_detection_and_extraction/tests/image_tests/test_openvino.py

@@ -36,11 +36,11 @@ def test_3_faces_jpg(mock_openvino_model, mock_3_faces_image):
         dets, (w, h), (iw, ih), model.det_thres, model.bbox_area_thres)
 
     gt_areas = np.array([
-        0.0377225852, 0.0099964741, 0.0082628834
+        0.0099964741, 0.0377225852, 0.0082628834
     ])
     gt_boxes = np.array([
-        [513, 203, 634, 365],
         [408, 213, 469, 299],
+        [513, 203, 634, 365],
         [285, 231, 342, 307],
     ], dtype=np.float32)