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deployment/Detection task/README.md

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+# Exportable code
+
+Exportable code is a .zip archive that contains simple demo to get and visualize result of model inference.
+
+## Structure of generated zip
+
+- model
+  - `model.xml`
+  - `model.bin`
+  - `config.json`
+- python
+  - model_wrappers (Optional)
+    - `__init__.py`
+    - model_wrappers required to run demo
+  - `README.md`
+  - `LICENSE`
+  - `demo.py`
+  - `requirements.txt`
+
+> **NOTE**: Zip archive contains model_wrappers when [ModelAPI](https://github.com/openvinotoolkit/open_model_zoo/tree/master/demos/common/python/openvino/model_zoo/model_api) has no appropriate standard model wrapper for the model.
+
+## Prerequisites
+
+- [Python 3.8](https://www.python.org/downloads/)
+- [Git](https://git-scm.com/)
+
+## Install requirements to run demo
+
+1. Install [prerequisites](#prerequisites). You may also need to [install pip](https://pip.pypa.io/en/stable/installation/). For example, on Ubuntu execute the following command to get pip installed:
+
+   ```bash
+   sudo apt install python3-pip
+   ```
+
+1. Create clean virtual environment:
+
+   One of the possible ways for creating a virtual environment is to use `virtualenv`:
+
+   ```bash
+   python -m pip install virtualenv
+   python -m virtualenv <directory_for_environment>
+   ```
+
+   Before starting to work inside virtual environment, it should be activated:
+
+   On Linux and macOS:
+
+   ```bash
+   source <directory_for_environment>/bin/activate
+   ```
+
+   On Windows:
+
+   ```bash
+   .\<directory_for_environment>\Scripts\activate
+   ```
+
+   Please make sure that the environment contains [wheel](https://pypi.org/project/wheel/) by calling the following command:
+
+   ```bash
+   python -m pip install wheel
+   ```
+
+   > **NOTE**: On Linux and macOS, you may need to type `python3` instead of `python`.
+
+1. Install requirements in the environment:
+
+   ```bash
+   python -m pip install -r requirements.txt
+   ```
+
+1. Add `model_wrappers` package to PYTHONPATH:
+
+   On Linux and macOS:
+
+   ```bash
+   export PYTHONPATH=$PYTHONPATH:/path/to/model_wrappers
+   ```
+
+   On Windows:
+
+   ```bash
+   set PYTHONPATH=%PYTHONPATH%;/path/to/model_wrappers
+   ```
+
+## Usecase
+
+1. Running the `demo.py` application with the `-h` option yields the following usage message:
+
+   ```bash
+   usage: demo.py [-h] -i INPUT -m MODELS [MODELS ...] [-it {sync,async}] [-l] [--no_show] [-d {CPU,GPU}] [--output OUTPUT]
+
+   Options:
+   -h, --help            Show this help message and exit.
+   -i INPUT, --input INPUT
+                           Required. An input to process. The input must be a single image, a folder of images, video file or camera id.
+   -m MODELS [MODELS ...], --models MODELS [MODELS ...]
+                           Required. Path to directory with trained model and configuration file. If you provide several models you will start the task chain pipeline with the provided models in the order in
+                           which they were specified.
+   -it {sync,async}, --inference_type {sync,async}
+                           Optional. Type of inference for single model.
+   -l, --loop            Optional. Enable reading the input in a loop.
+   --no_show             Optional. Disables showing inference results on UI.
+   -d {CPU,GPU}, --device {CPU,GPU}
+                           Optional. Device to infer the model.
+   --output OUTPUT       Optional. Output path to save input data with predictions.
+   ```
+
+2. As a `model`, you can use path to model directory from generated zip. You can pass as `input` a single image, a folder of images, a video file, or a web camera id. So you can use the following command to do inference with a pre-trained model:
+
+   ```bash
+   python3 demo.py \
+   -i <path_to_video>/inputVideo.mp4 \
+   -m <path_to_model_directory>
+   ```
+
+   You can press `Q` to stop inference during demo running.
+
+   > **NOTE**: If you provide a single image as input, the demo processes and renders it quickly, then exits. To continuously
+   > visualize inference results on the screen, apply the `--loop` option, which enforces processing a single image in a loop.
+   > In this case, you can stop the demo by pressing `Q` button or killing the process in the terminal (`Ctrl+C` for Linux).
+   >
+   > **NOTE**: Default configuration contains info about pre- and post processing for inference and is guaranteed to be correct.
+   > Also you can change `config.json` that specifies the confidence threshold and color for each class visualization, but any
+   > changes should be made with caution.
+
+3. To save inferenced results with predictions on it, you can specify the folder path, using `--output`.
+   It works for images, videos, image folders and web cameras. To prevent issues, do not specify it together with a `--loop` parameter.
+
+   ```bash
+   python3 demo.py \
+      --input <path_to_image>/inputImage.jpg \
+      --models ../model \
+      --output resulted_images
+   ```
+
+4. To run a demo on a web camera, you need to know its ID.
+   You can check a list of camera devices by running this command line on Linux system:
+
+   ```bash
+   sudo apt-get install v4l-utils
+   v4l2-ctl --list-devices
+   ```
+
+   The output will look like this:
+
+   ```bash
+   Integrated Camera (usb-0000:00:1a.0-1.6):
+      /dev/video0
+   ```
+
+   After that, you can use this `/dev/video0` as a camera ID for `--input`.
+
+## Troubleshooting
+
+1. If you have access to the Internet through the proxy server only, please use pip with proxy call as demonstrated by command below:
+
+   ```bash
+   python -m pip install --proxy http://<usr_name>:<password>@<proxyserver_name>:<port#> <pkg_name>
+   ```
+
+1. If you use Anaconda environment, you should consider that OpenVINO has limited [Conda support](https://docs.openvino.ai/2021.4/openvino_docs_install_guides_installing_openvino_conda.html) for Python 3.6 and 3.7 versions only. But the demo package requires python 3.8. So please use other tools to create the environment (like `venv` or `virtualenv`) and use `pip` as a package manager.
+
+1. If you have problems when you try to use `pip install` command, please update pip version by following command:
+
+   ```bash
+   python -m pip install --upgrade pip
+   ```

deployment/Detection task/model.json

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+{
+   "id": "6483aa7459c02bd70e92382b",
+   "name": "YOLOX OpenVINO INT8",
+   "version": 1,
+   "creation_date": "2023-06-09T22:40:52.451000+00:00",
+   "model_format": "OpenVINO",
+   "precision": [
+      "INT8"
+   ],
+   "has_xai_head": false,
+   "target_device": "CPU",
+   "target_device_type": null,
+   "performance": {
+      "score": 0.9440353460972017
+   },
+   "size": 5854179,
+   "latency": 0,
+   "fps_throughput": 0,
+   "optimization_type": "POT",
+   "optimization_objectives": {},
+   "model_status": "SUCCESS",
+   "configurations": [
+      {
+         "name": "sample_size",
+         "value": 300
+      }
+   ],
+   "previous_revision_id": "6483aa7459c02bd70e92382a",
+   "previous_trained_revision_id": "64837b2359c02bd70e910cd2",
+   "optimization_methods": [
+      "QUANTIZATION"
+   ]
+}

deployment/Detection task/model/config.json

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+{
+    "type_of_model": "OTX_SSD",
+    "converter_type": "DETECTION",
+    "model_parameters": {
+        "result_based_confidence_threshold": true,
+        "confidence_threshold": 0.675000011920929,
+        "use_ellipse_shapes": false,
+        "labels": {
+            "label_tree": {
+                "type": "tree",
+                "directed": true,
+                "nodes": [],
+                "edges": []
+            },
+            "label_groups": [
+                {
+                    "_id": "6483613d18fb8c1c529cb064",
+                    "name": "Default group",
+                    "label_ids": [
+                        "6483613d18fb8c1c529cb061"
+                    ],
+                    "relation_type": "EXCLUSIVE"
+                },
+                {
+                    "_id": "6483613d18fb8c1c529cb066",
+                    "name": "No Object",
+                    "label_ids": [
+                        "6483613d18fb8c1c529cb065"
+                    ],
+                    "relation_type": "EMPTY_LABEL"
+                }
+            ],
+            "all_labels": {
+                "6483613d18fb8c1c529cb061": {
+                    "_id": "6483613d18fb8c1c529cb061",
+                    "name": "bird",
+                    "color": {
+                        "red": 255,
+                        "green": 0,
+                        "blue": 0,
+                        "alpha": 255
+                    },
+                    "hotkey": "",
+                    "domain": "DETECTION",
+                    "creation_date": "2023-06-09T17:28:29.943000",
+                    "is_empty": false,
+                    "is_anomalous": false
+                },
+                "6483613d18fb8c1c529cb065": {
+                    "_id": "6483613d18fb8c1c529cb065",
+                    "name": "No Object",
+                    "color": {
+                        "red": 0,
+                        "green": 0,
+                        "blue": 0,
+                        "alpha": 255
+                    },
+                    "hotkey": "",
+                    "domain": "DETECTION",
+                    "creation_date": "2023-06-09T17:28:29.945000",
+                    "is_empty": true,
+                    "is_anomalous": false
+                }
+            }
+        }
+    },
+    "tiling_parameters": {
+        "visible_in_ui": true,
+        "type": "PARAMETER_GROUP",
+        "enable_tiling": false,
+        "enable_tile_classifier": false,
+        "enable_adaptive_params": true,
+        "tile_size": 400,
+        "tile_overlap": 0.2,
+        "tile_max_number": 1500,
+        "tile_ir_scale_factor": 2.0,
+        "tile_sampling_ratio": 1.0,
+        "object_tile_ratio": 0.03,
+        "header": "Tiling Parameters",
+        "description": "Tiling Parameters",
+        "_ParameterGroup__metadata_overrides": {},
+        "groups": [],
+        "parameters": [
+            "enable_adaptive_params",
+            "enable_tile_classifier",
+            "enable_tiling",
+            "object_tile_ratio",
+            "tile_ir_scale_factor",
+            "tile_max_number",
+            "tile_overlap",
+            "tile_sampling_ratio",
+            "tile_size"
+        ]
+    }
+}

deployment/Detection task/model/model.bin

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+version https://git-lfs.github.com/spec/v1
+oid sha256:3416c16f40670b254987c2ce1588ac7e99592907addbc4c0824f7b5638bedf45
+size 5086055

deployment/Detection task/python/LICENSE

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deployment/Detection task/python/demo.py

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+"""Demo based on ModelAPI."""
+# Copyright (C) 2021-2022 Intel Corporation
+# SPDX-License-Identifier: Apache-2.0
+#
+
+import os
+import sys
+from argparse import SUPPRESS, ArgumentParser
+from pathlib import Path
+
+os.environ["FEATURE_FLAGS_OTX_ACTION_TASKS"] = "1"
+
+# pylint: disable=no-name-in-module, import-error
+from otx.api.usecases.exportable_code.demo.demo_package import (
+    AsyncExecutor,
+    ChainExecutor,
+    ModelContainer,
+    SyncExecutor,
+    create_visualizer,
+)
+
+
+def build_argparser():
+    """Parses command line arguments."""
+    parser = ArgumentParser(add_help=False)
+    args = parser.add_argument_group("Options")
+    args.add_argument(
+        "-h",
+        "--help",
+        action="help",
+        default=SUPPRESS,
+        help="Show this help message and exit.",
+    )
+    args.add_argument(
+        "-i",
+        "--input",
+        required=True,
+        help="Required. An input to process. The input must be a single image, "
+        "a folder of images, video file or camera id.",
+    )
+    args.add_argument(
+        "-m",
+        "--models",
+        help="Required. Path to directory with trained model and configuration file. "
+        "If you provide several models you will start the task chain pipeline with "
+        "the provided models in the order in which they were specified.",
+        nargs="+",
+        required=True,
+        type=Path,
+    )
+    args.add_argument(
+        "-it",
+        "--inference_type",
+        help="Optional. Type of inference for single model.",
+        choices=["sync", "async"],
+        default="sync",
+        type=str,
+    )
+    args.add_argument(
+        "-l",
+        "--loop",
+        help="Optional. Enable reading the input in a loop.",
+        default=False,
+        action="store_true",
+    )
+    args.add_argument(
+        "--no_show",
+        help="Optional. Disables showing inference results on UI.",
+        default=False,
+        action="store_true",
+    )
+    args.add_argument(
+        "-d",
+        "--device",
+        help="Optional. Device to infer the model.",
+        choices=["CPU", "GPU"],
+        default="CPU",
+        type=str,
+    )
+    args.add_argument(
+        "--output",
+        default=None,
+        type=str,
+        help="Optional. Output path to save input data with predictions.",
+    )
+
+    return parser
+
+
+EXECUTORS = {
+    "sync": SyncExecutor,
+    "async": AsyncExecutor,
+    "chain": ChainExecutor,
+}
+
+
+def get_inferencer_class(type_inference, models):
+    """Return class for inference of models."""
+    if len(models) > 1:
+        type_inference = "chain"
+        print("You started the task chain pipeline with the provided models in the order in which they were specified")
+    return EXECUTORS[type_inference]
+
+
+def main():
+    """Main function that is used to run demo."""
+    args = build_argparser().parse_args()
+
+    if args.loop and args.output:
+        raise ValueError("--loop and --output cannot be both specified")
+
+    # create models
+    models = []
+    for model_dir in args.models:
+        model = ModelContainer(model_dir, device=args.device)
+        models.append(model)
+
+    inferencer = get_inferencer_class(args.inference_type, models)
+
+    # create visualizer
+    visualizer = create_visualizer(models[-1].task_type, no_show=args.no_show, output=args.output)
+
+    if len(models) == 1:
+        models = models[0]
+
+    # create inferencer and run
+    demo = inferencer(models, visualizer)
+    demo.run(args.input, args.loop and not args.no_show)
+
+
+if __name__ == "__main__":
+    sys.exit(main() or 0)

deployment/Detection task/python/model_wrappers/__init__.py

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+"""Model Wrapper Initialization of OTX Detection."""
+
+# Copyright (C) 2021 Intel Corporation
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing,
+# software distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions
+# and limitations under the License.
+
+from .openvino_models import OTXMaskRCNNModel, OTXSSDModel
+
+__all__ = ["OTXMaskRCNNModel", "OTXSSDModel"]

deployment/Detection task/python/model_wrappers/openvino_models.py

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+"""OTXMaskRCNNModel & OTXSSDModel of OTX Detection."""
+
+# Copyright (C) 2022 Intel Corporation
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing,
+# software distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions
+# and limitations under the License.
+
+from typing import Dict
+
+import numpy as np
+
+try:
+    from openvino.model_zoo.model_api.models.instance_segmentation import MaskRCNNModel
+    from openvino.model_zoo.model_api.models.ssd import SSD, find_layer_by_name
+    from openvino.model_zoo.model_api.models.utils import Detection
+except ImportError as e:
+    import warnings
+
+    warnings.warn(f"{e}: ModelAPI was not found.")
+
+
+class OTXMaskRCNNModel(MaskRCNNModel):
+    """OpenVINO model wrapper for OTX MaskRCNN model."""
+
+    __model__ = "OTX_MaskRCNN"
+
+    def __init__(self, model_adapter, configuration, preload=False):
+        super().__init__(model_adapter, configuration, preload)
+        self.resize_mask = True
+
+    def _check_io_number(self, number_of_inputs, number_of_outputs):
+        """Checks whether the number of model inputs/outputs is supported.
+
+        Args:
+            number_of_inputs (int, Tuple(int)): number of inputs supported by wrapper.
+              Use -1 to omit the check
+            number_of_outputs (int, Tuple(int)): number of outputs supported by wrapper.
+              Use -1 to omit the check
+
+        Raises:
+            WrapperError: if the model has unsupported number of inputs/outputs
+        """
+        super()._check_io_number(number_of_inputs, -1)
+
+    def _get_outputs(self):
+        output_match_dict = {}
+        output_names = ["boxes", "labels", "masks", "feature_vector", "saliency_map"]
+        for output_name in output_names:
+            for node_name, node_meta in self.outputs.items():
+                if output_name in node_meta.names:
+                    output_match_dict[output_name] = node_name
+                    break
+        return output_match_dict
+
+    def postprocess(self, outputs, meta):
+        """Post process function for OTX MaskRCNN model."""
+
+        # pylint: disable-msg=too-many-locals
+        # FIXME: here, batch dim of IR must be 1
+        boxes = outputs[self.output_blob_name["boxes"]]
+        if boxes.shape[0] == 1:
+            boxes = boxes.squeeze(0)
+        assert boxes.ndim == 2
+        masks = outputs[self.output_blob_name["masks"]]
+        if masks.shape[0] == 1:
+            masks = masks.squeeze(0)
+        assert masks.ndim == 3
+        classes = outputs[self.output_blob_name["labels"]].astype(np.uint32)
+        if classes.shape[0] == 1:
+            classes = classes.squeeze(0)
+        assert classes.ndim == 1
+        if self.is_segmentoly:
+            scores = outputs[self.output_blob_name["scores"]]
+        else:
+            scores = boxes[:, 4]
+            boxes = boxes[:, :4]
+            classes += 1
+
+        # Filter out detections with low confidence.
+        detections_filter = scores > self.confidence_threshold  # pylint: disable=no-member
+        scores = scores[detections_filter]
+        boxes = boxes[detections_filter]
+        masks = masks[detections_filter]
+        classes = classes[detections_filter]
+
+        scale_x = meta["resized_shape"][1] / meta["original_shape"][1]
+        scale_y = meta["resized_shape"][0] / meta["original_shape"][0]
+        boxes[:, 0::2] /= scale_x
+        boxes[:, 1::2] /= scale_y
+
+        resized_masks = []
+        for box, cls, raw_mask in zip(boxes, classes, masks):
+            raw_cls_mask = raw_mask[cls, ...] if self.is_segmentoly else raw_mask
+            if self.resize_mask:
+                resized_masks.append(self._segm_postprocess(box, raw_cls_mask, *meta["original_shape"][:-1]))
+            else:
+                resized_masks.append(raw_cls_mask)
+
+        return scores, classes, boxes, resized_masks
+
+    def segm_postprocess(self, *args, **kwargs):
+        """Post-process for segmentation masks."""
+        return self._segm_postprocess(*args, **kwargs)
+
+    def disable_mask_resizing(self):
+        """Disable mask resizing.
+
+        There is no need to resize mask in tile as it will be processed at the end.
+        """
+        self.resize_mask = False
+
+
+class OTXSSDModel(SSD):
+    """OpenVINO model wrapper for OTX SSD model."""
+
+    __model__ = "OTX_SSD"
+
+    def __init__(self, model_adapter, configuration=None, preload=False):
+        # pylint: disable-next=bad-super-call
+        super(SSD, self).__init__(model_adapter, configuration, preload)
+        self.image_info_blob_name = self.image_info_blob_names[0] if len(self.image_info_blob_names) == 1 else None
+        self.output_parser = BatchBoxesLabelsParser(
+            self.outputs,
+            self.inputs[self.image_blob_name].shape[2:][::-1],
+        )
+
+    def _get_outputs(self) -> Dict:
+        """Match the output names with graph node index."""
+        output_match_dict = {}
+        output_names = ["boxes", "labels", "feature_vector", "saliency_map"]
+        for output_name in output_names:
+            for node_name, node_meta in self.outputs.items():
+                if output_name in node_meta.names:
+                    output_match_dict[output_name] = node_name
+                    break
+        return output_match_dict
+
+
+class BatchBoxesLabelsParser:
+    """Batched output parser."""
+
+    def __init__(self, layers, input_size, labels_layer="labels", default_label=0):
+        try:
+            self.labels_layer = find_layer_by_name(labels_layer, layers)
+        except ValueError:
+            self.labels_layer = None
+            self.default_label = default_label
+
+        try:
+            self.bboxes_layer = self.find_layer_bboxes_output(layers)
+        except ValueError:
+            self.bboxes_layer = find_layer_by_name("boxes", layers)
+
+        self.input_size = input_size
+
+    @staticmethod
+    def find_layer_bboxes_output(layers):
+        """find_layer_bboxes_output."""
+        filter_outputs = [name for name, data in layers.items() if len(data.shape) == 3 and data.shape[-1] == 5]
+        if not filter_outputs:
+            raise ValueError("Suitable output with bounding boxes is not found")
+        if len(filter_outputs) > 1:
+            raise ValueError("More than 1 candidate for output with bounding boxes.")
+        return filter_outputs[0]
+
+    def __call__(self, outputs):
+        """Parse bboxes."""
+        # FIXME: here, batch dim of IR must be 1
+        bboxes = outputs[self.bboxes_layer]
+        if bboxes.shape[0] == 1:
+            bboxes = bboxes.squeeze(0)
+        assert bboxes.ndim == 2
+        scores = bboxes[:, 4]
+        bboxes = bboxes[:, :4]
+        bboxes[:, 0::2] /= self.input_size[0]
+        bboxes[:, 1::2] /= self.input_size[1]
+        if self.labels_layer:
+            labels = outputs[self.labels_layer]
+        else:
+            labels = np.full(len(bboxes), self.default_label, dtype=bboxes.dtype)
+        if labels.shape[0] == 1:
+            labels = labels.squeeze(0)
+
+        detections = [Detection(*bbox, score, label) for label, score, bbox in zip(labels, scores, bboxes)]
+        return detections

deployment/Detection task/python/requirements.txt

@@ -0,0 +1,4 @@
+openvino==2022.3.0
+openmodelzoo-modelapi==2022.3.0
+otx=1.2.3.3
+numpy>=1.21.0,<=1.23.5  # np.bool was removed in 1.24.0 which was used in openvino runtime

deployment/project.json

@@ -0,0 +1,67 @@
+{
+   "id": "6483613d18fb8c1c529cb05a",
+   "name": "birds",
+   "creation_time": "2023-06-09T17:28:29.944000+00:00",
+   "creator_id": "[email protected]",
+   "pipeline": {
+      "tasks": [
+         {
+            "id": "6483613d18fb8c1c529cb05b",
+            "title": "Dataset",
+            "task_type": "dataset"
+         },
+         {
+            "id": "6483613d18fb8c1c529cb05e",
+            "title": "Detection task",
+            "task_type": "detection",
+            "labels": [
+               {
+                  "id": "6483613d18fb8c1c529cb061",
+                  "name": "bird",
+                  "is_anomalous": false,
+                  "color": "#ff0000ff",
+                  "hotkey": "",
+                  "is_empty": false,
+                  "group": "Default group",
+                  "parent_id": null
+               },
+               {
+                  "id": "6483613d18fb8c1c529cb065",
+                  "name": "No Object",
+                  "is_anomalous": false,
+                  "color": "#000000ff",
+                  "hotkey": "",
+                  "is_empty": true,
+                  "group": "No Object",
+                  "parent_id": null
+               }
+            ],
+            "label_schema_id": "6483613d18fb8c1c529cb067"
+         }
+      ],
+      "connections": [
+         {
+            "from": "6483613d18fb8c1c529cb05b",
+            "to": "6483613d18fb8c1c529cb05e"
+         }
+      ]
+   },
+   "datasets": [
+      {
+         "id": "6483613d18fb8c1c529cb062",
+         "name": "Dataset",
+         "use_for_training": true,
+         "creation_time": "2023-06-09T17:28:29.944000+00:00"
+      },
+      {
+         "id": "6483613e18fb8c1c529cb068",
+         "name": "Testing set 1",
+         "use_for_training": false,
+         "creation_time": "2023-06-09T17:28:30.055000+00:00"
+      }
+   ],
+   "thumbnail": "/api/v1/workspaces/6487656fb7efbf83c9b9ec35/projects/6483613d18fb8c1c529cb05a/thumbnail",
+   "performance": {
+      "score": 0.939078751857355
+   }
+}