Move to newer OMZ (#163)

* Move to newer OMZ

After this is merged It will be required to release model_api 0.2.0 only after openvino 2024.0 because model_api is going to import OMZ using a new namespace

* Replace ssd300

* Remove mobilenet-v3-large-1.0-224-tf

* Test mobilenet-v3-large-1.0-224-tf

* Replace ssd300

* black

* Update ref

* Update ref

* Align README.md

Author: Wovchena

.github/workflows/test_precommit.yml

@@ -149,10 +149,8 @@ jobs:
       fail-fast: false
       matrix:
         os: [ubuntu-20.04, ubuntu-22.04, macos-12]
-        python-version: [3.7, 3.8, 3.9, '3.10', '3.11']
+        python-version: [3.8, 3.9, '3.10', '3.11']
         exclude:
-          - os: macos-12
-            python-version: 3.7
           - os: macos-12
             python-version: 3.9
           - os: macos-12

README.md

@@ -47,8 +47,8 @@ Model API searches for additional information required for model inference, data
 from openvino.model_api.models import DetectionModel
 
 # Create a model (downloaded and cached automatically for OpenVINO Model Zoo models)
-# Use URL to work with served model, e.g. "localhost:9000/models/ssd300"
-ssd = DetectionModel.create_model("ssd300")
+# Use URL to work with served model, e.g. "localhost:9000/models/ssdlite_mobilenet_v2"
+ssd = DetectionModel.create_model("ssdlite_mobilenet_v2")
 
 # Run synchronous inference locally
 detections = ssd(image)  # image is numpy.ndarray
@@ -63,7 +63,7 @@ print(f"Detection results: {detections}")
 #include <models/results.h>
 
 // Load the model fetched using Python API
-auto model = DetectionModel::create_model("~/.cache/omz/public/ssd300/FP16/ssd300.xml");
+auto model = DetectionModel::create_model("~/.cache/omz/public/ssdlite_mobilenet_v2/FP16/ssdlite_mobilenet_v2.xml");
 
 // Run synchronous inference locally
 auto result = model->infer(image); // image is cv::Mat
@@ -81,13 +81,13 @@ Model's static method `create_model()` has two overloads. One constructs the mod
 There are usecases when it is not possible to modify an internal `ov::Model` and it is hidden behind `InferenceAdapter`. For example the model can be served using [OVMS](https://github.com/openvinotoolkit/model_server). `create_model()` can construct a model from a given `InferenceAdapter`. That approach assumes that the model in `InferenceAdapter` was already configured by `create_model()` called with a string (a path or a model name). It is possible to prepare such model using C++ or Python:
 C++
 ```Cpp
-auto model = DetectionModel::create_model("~/.cache/omz/public/ssd300/FP16/ssd300.xml");
+auto model = DetectionModel::create_model("~/.cache/omz/public/ssdlite_mobilenet_v2/FP16/ssdlite_mobilenet_v2.xml");
 const std::shared_ptr<ov::Model>& ov_model = model->getModel();
 ov::serialize(ov_model, "serialized.xml");
 ```
 Python
 ```python
-model = DetectionModel.create_model("~/.cache/omz/public/ssd300/FP16/ssd300.xml")
+model = DetectionModel.create_model("~/.cache/omz/public/ssdlite_mobilenet_v2/FP16/ssdlite_mobilenet_v2.xml")
 model.save("serialized.xml")
 ```
 After that the model can be constructed from `InferenceAdapter`:
@@ -107,7 +107,7 @@ For more details please refer to the [examples](https://github.com/openvinotoolk
   - [OpenVINO Model Zoo models](https://github.com/openvinotoolkit/open_model_zoo/blob/master/models/public/index.md#classification-models)
 - Object Detection:
   - [OpenVINO Model Zoo models](https://github.com/openvinotoolkit/open_model_zoo/blob/master/models/public/index.md#object-detection-models):
-    - SSD-based models (e.g. "ssd300", "ssdlite_mobilenet_v2", etc.)
+    - SSD-based models (e.g. "ssdlite_mobilenet_v2", etc.)
     - YOLO-based models (e.g. "yolov3", "yolov4", etc.)
     - CTPN: "ctpn"
     - DETR: "detr-resnet50"
@@ -127,7 +127,7 @@ For more details please refer to the [examples](https://github.com/openvinotoolk
 - Image Classification:
   - [OpenVINO Model Zoo models](https://github.com/openvinotoolkit/open_model_zoo/blob/master/models/public/index.md#classification-models)
 - Object Detection:
-  - SSD-based models (e.g. "ssd300", "ssdlite_mobilenet_v2", etc.)
+  - SSD-based models (e.g. "ssdlite_mobilenet_v2", etc.)
     - YOLO-based models (e.g. "yolov3", "yolov4", etc.)
     - CenterNet: "ctdet_coco_dlav0_512"
     - FaceBoxes: "faceboxes-pytorch"

examples/python/synchronous_api/run.py

@@ -18,7 +18,6 @@
 import sys
 
 import cv2
-import openvino.runtime as ov
 from openvino.model_api.models import (
     ClassificationModel,
     DetectionModel,

model_api/python/openvino/model_api/adapters/openvino_adapter.py

@@ -185,7 +185,7 @@ def __init__(
             self.model = core.read_model(self.model_path, weights_path)
             return
         if isinstance(model, str):
-            from openvino.model_zoo.models import OMZModel, list_models
+            from omz_tools.models import OMZModel, list_models
 
             if model in list_models():
                 omz_model = OMZModel.download(

model_api/python/requirements.txt

@@ -1,5 +1,7 @@
 numpy>=1.16.6
 opencv-python
 scipy>=1.5.4
-openvino>=2023.0.0
+# TODO: set 2024.0 after the release
+openvino>=2023.3
 openvino-dev>=2023.0.0
+omz_tools @ git+https://github.com/openvinotoolkit/open_model_zoo.git@master#egg=omz_tools&subdirectory=tools/model_tools

tests/cpp/precommit/public_scope.json

@@ -1,8 +1,4 @@
 [
-    {
-        "name": "ssd300",
-        "type": "DetectionModel"
-    },
     {
         "name": "ssd_mobilenet_v1_fpn_coco",
         "type": "DetectionModel"

tests/python/accuracy/public_scope.json

@@ -70,16 +70,6 @@
             }
         ]
     },
-    {
-        "name": "ssd300",
-        "type": "DetectionModel",
-        "test_data": [
-            {
-                "image": "coco128/images/train2017/000000000074.jpg",
-                "reference": ["0, 13, 169, 338, 2 (bicycle): 0.870; 58, 275, 360, 384, 12 (dog): 0.998; 324, 98, 344, 148, 15 (person): 0.652; [0]; [0]"]
-            }
-        ]
-    },
     {
         "name": "ssd_mobilenet_v1_fpn_coco",
         "type": "DetectionModel",
@@ -142,12 +132,12 @@
         ]
     },
     {
-        "name": "se-resnext-50",
+        "name": "mobilenet-v3-large-1.0-224-tf",
         "type": "ClassificationModel",
         "test_data": [
             {
                 "image": "coco128/images/train2017/000000000074.jpg",
-                "reference": ["175 (Labrador_retriever): 0.616, [0], [0], [0]"]
+                "reference": ["208 (Labrador_retriever): 0.166, [0], [0], [0]"]
             }
         ]
     },

tests/python/precommit/test_save.py

@@ -3,7 +3,8 @@
 
 def test_detector_save(tmp_path):
     downloaded = Model.create_model(
-        "ssd300", configuration={"mean_values": [0, 0, 0], "confidence_threshold": 0.6}
+        "ssd_mobilenet_v1_fpn_coco",
+        configuration={"mean_values": [0, 0, 0], "confidence_threshold": 0.6},
     )
     assert True == downloaded.get_model().get_rt_info(
         ["model_info", "embedded_processing"]