ultralytics 8.2.25 latest TensorFlow 2.16 support (#13176)

Signed-off-by: Glenn Jocher <glenn.jocher@ultralytics.com>
Co-authored-by: UltralyticsAssistant <web@ultralytics.com>
Co-authored-by: lakshanthad <lakshanthad@yahoo.com>

.github/workflows/ci.yaml

@@ -206,11 +206,16 @@ jobs:
         run: |
           # CoreML must be installed before export due to protobuf error from AutoInstall
           python -m pip install --upgrade pip wheel
+          slow=""
           torch=""
           if [ "${{ matrix.torch }}" == "1.8.0" ]; then
               torch="torch==1.8.0 torchvision==0.9.0"
           fi
-          pip install -e . $torch pytest-cov "coremltools>=7.0; platform_system != 'Windows' and python_version <= '3.11'" --extra-index-url https://download.pytorch.org/whl/cpu
+          if [[ "${{ github.event_name }}" =~ ^(schedule|workflow_dispatch)$ ]]; then
+              slow="pycocotools mlflow ray[tune]"
+          fi
+          slow="pycocotools mlflow ray[tune]"
+          pip install -e ".[export]" $torch $slow pytest-cov --extra-index-url https://download.pytorch.org/whl/cpu
       - name: Check environment
         run: |
           yolo checks
@@ -220,7 +225,6 @@ jobs:
         run: |
           slow=""
           if [[ "${{ github.event_name }}" =~ ^(schedule|workflow_dispatch)$ ]]; then
-              pip install pycocotools mlflow "ray[tune]"
               slow="--slow"
           fi
           pytest $slow --cov=ultralytics/ --cov-report xml tests/
@@ -272,7 +276,7 @@ jobs:
       - name: Install requirements
         run: |
           python -m pip install --upgrade pip wheel
-          pip install --no-cache-dir -e ".[export]" pytest mlflow pycocotools "ray[tune]"
+          pip install -e ".[export]" pytest mlflow pycocotools "ray[tune]"
       - name: Check environment
         run: |
           yolo checks

docs/en/guides/analytics.md

@@ -16,7 +16,7 @@ This guide provides a comprehensive overview of three fundamental types of data
 |:------------------------------------------------------------------------------------------------------------------:|:----------------------------------------------------------------------------------------------------------------:|:-----------------------------------------------------------------------------------------------------------------:|
 | ![Line Graph](https://github.com/RizwanMunawar/RizwanMunawar/assets/62513924/eeabd90c-04fd-4e5b-aac9-c7777f892200) | ![Bar Plot](https://github.com/RizwanMunawar/RizwanMunawar/assets/62513924/c1da2d6a-99ff-43a8-b5dc-ca93127917f8) | ![Pie Chart](https://github.com/RizwanMunawar/RizwanMunawar/assets/62513924/9d8acce6-d9e4-4685-949d-cd4851483187) |
 
-###  Why Graphs are Important
+### Why Graphs are Important
 
 - Line graphs are ideal for tracking changes over short and long periods and for comparing changes for multiple groups over the same period.
 - Bar plots, on the other hand, are suitable for comparing quantities across different categories and showing relationships between a category and its numerical value.
@@ -174,21 +174,21 @@ This guide provides a comprehensive overview of three fundamental types of data
 
 Here's a table with the `Analytics` arguments:
 
-| Name         | Type              | Default       | Description                                 |
+| Name         | Type              | Default       | Description                         |
-|--------------|-------------------|---------------|---------------------------------------------|
+|--------------|-------------------|---------------|-------------------------------------|
-| `type`       | `str`             | `None`        | Type of data or object.                     |
+| `type`       | `str`             | `None`        | Type of data or object.             |
-| `im0_shape`  | `tuple`           | `None`        | Shape of the initial image.                 |
+| `im0_shape`  | `tuple`           | `None`        | Shape of the initial image.         |
-| `writer`     | `cv2.VideoWriter` | `None`        | Object for writing video files.             |
+| `writer`     | `cv2.VideoWriter` | `None`        | Object for writing video files.     |
-| `title`      | `str`             | `ultralytics` | Title for the visualization.                |
+| `title`      | `str`             | `ultralytics` | Title for the visualization.        |
-| `x_label`    | `str`             | `x`           | Label for the x-axis.                       |
+| `x_label`    | `str`             | `x`           | Label for the x-axis.               |
-| `y_label`    | `str`             | `y`           | Label for the y-axis.                       |
+| `y_label`    | `str`             | `y`           | Label for the y-axis.               |
-| `bg_color`   | `str`             | `white`       | Background color.                           |
+| `bg_color`   | `str`             | `white`       | Background color.                   |
-| `fg_color`   | `str`             | `black`       | Foreground color.                           |
+| `fg_color`   | `str`             | `black`       | Foreground color.                   |
-| `line_color` | `str`             | `yellow`      | Color of the lines.                         |
+| `line_color` | `str`             | `yellow`      | Color of the lines.                 |
-| `line_width` | `int`             | `2`           | Width of the lines.                         |
+| `line_width` | `int`             | `2`           | Width of the lines.                 |
-| `fontsize`   | `int`             | `13`          | Font size for text.                         |
+| `fontsize`   | `int`             | `13`          | Font size for text.                 |
-| `view_img`   | `bool`            | `False`       | Flag to display the image or video.         |
+| `view_img`   | `bool`            | `False`       | Flag to display the image or video. |
-| `save_img`   | `bool`            | `True`        | Flag to save the image or video.            |
+| `save_img`   | `bool`            | `True`        | Flag to save the image or video.    |
 
 ### Arguments `model.track`
 

docs/en/guides/defining-project-goals.md

@@ -41,8 +41,8 @@ Setting measurable objectives is key to the success of a computer vision project
 
 For example, if you are developing a system to estimate vehicle speeds on a highway. You could consider the following measurable objectives:
 
--  To achieve at least 95% accuracy in speed detection within six months, using a dataset of 10,000 vehicle images.
+- To achieve at least 95% accuracy in speed detection within six months, using a dataset of 10,000 vehicle images.
--  The system should be able to process real-time video feeds at 30 frames per second with minimal delay. 
+- The system should be able to process real-time video feeds at 30 frames per second with minimal delay.
 
 By setting specific and quantifiable goals, you can effectively track progress, identify areas for improvement, and ensure the project stays on course.
 

docs/en/guides/steps-of-a-cv-project.md

@@ -16,7 +16,7 @@ Computer vision techniques like [object detection](../tasks/detect.md), [image c
   <img width="100%" src="https://media.licdn.com/dms/image/D4D12AQGf61lmNOm3xA/article-cover_image-shrink_720_1280/0/1656513646049?e=1722470400&v=beta&t=23Rqohhxfie38U5syPeL2XepV2QZe6_HSSC-4rAAvt4" alt="Overview of computer vision techniques">
 </p>
 
-Working on your own computer vision projects is a great way to understand and learn more about computer vision. However, a computer vision project can consist of many steps, and it might seem confusing at first. By the end of this guide, you’ll be familiar with the steps involved in a computer vision project.  We’ll walk through everything from the beginning to the end of a project, explaining why each part is important. Let’s get started and make your computer vision project a success!
+Working on your own computer vision projects is a great way to understand and learn more about computer vision. However, a computer vision project can consist of many steps, and it might seem confusing at first. By the end of this guide, you’ll be familiar with the steps involved in a computer vision project. We’ll walk through everything from the beginning to the end of a project, explaining why each part is important. Let’s get started and make your computer vision project a success!
 
 ## An Overview of a Computer Vision Project
 

pyproject.toml

@@ -100,8 +100,9 @@ export = [
     "onnx>=1.12.0", # ONNX export
     "coremltools>=7.0; platform_system != 'Windows' and python_version <= '3.11'", # CoreML supported on macOS and Linux
     "openvino>=2024.0.0", # OpenVINO export
-    "tensorflow<=2.13.1; python_version <= '3.11'", # TF bug https://github.com/ultralytics/ultralytics/issues/5161
+    "tensorflow>=2.0.0", # TF bug https://github.com/ultralytics/ultralytics/issues/5161
-    "tensorflowjs>=3.9.0; python_version <= '3.11'", # TF.js export, automatically installs tensorflow
+    "tensorflowjs>=3.9.0", # TF.js export, automatically installs tensorflow
+    "keras",  # not installed auotomatically by tensorflow>=2.16
     "flatbuffers>=23.5.26,<100; platform_machine == 'aarch64'", # update old 'flatbuffers' included inside tensorflow package
     "numpy==1.23.5; platform_machine == 'aarch64'", # fix error: `np.bool` was a deprecated alias for the builtin `bool` when using TensorRT models on NVIDIA Jetson
     "h5py!=3.11.0; platform_machine == 'aarch64'", # fix h5py build issues due to missing aarch64 wheels in 3.11 release
@@ -111,7 +112,6 @@ explorer = [
     "duckdb<=0.9.2", # SQL queries, duckdb==0.10.0 bug https://github.com/ultralytics/ultralytics/pull/8181
     "streamlit", # visualizing with GUI
 ]
-# tensorflow>=2.4.1,<=2.13.1  # TF exports (-cpu, -aarch64, -macos)
 # tflite-support  # for TFLite model metadata
 # nvidia-pyindex  # TensorRT export
 # nvidia-tensorrt  # TensorRT export

tests/test_exports.py

@@ -129,6 +129,31 @@ def test_export_coreml_matrix(task, dynamic, int8, half, batch):
     shutil.rmtree(file)  # cleanup
 
 
+@pytest.mark.slow
+@pytest.mark.skipif(not checks.IS_PYTHON_MINIMUM_3_10, reason="TFLite export requires Python>=3.10")
+@pytest.mark.skipif(not LINUX, reason="Test disabled as TF suffers from install conflicts on Windows and macOS")
+@pytest.mark.parametrize(
+    "task, dynamic, int8, half, batch",
+    [  # generate all combinations but exclude those where both int8 and half are True
+        (task, dynamic, int8, half, batch)
+        for task, dynamic, int8, half, batch in product(TASKS, [False], [True, False], [True, False], [1])
+        if not (int8 and half)  # exclude cases where both int8 and half are True
+    ],
+)
+def test_export_tflite_matrix(task, dynamic, int8, half, batch):
+    """Test YOLO exports to TFLite format."""
+    file = YOLO(TASK2MODEL[task]).export(
+        format="tflite",
+        imgsz=32,
+        dynamic=dynamic,
+        int8=int8,
+        half=half,
+        batch=batch,
+    )
+    YOLO(file)([SOURCE] * batch, imgsz=32)  # exported model inference at batch=3
+    Path(file).unlink()  # cleanup
+
+
 @pytest.mark.skipif(not TORCH_1_9, reason="CoreML>=7.2 not supported with PyTorch<=1.8")
 @pytest.mark.skipif(WINDOWS, reason="CoreML not supported on Windows")  # RuntimeError: BlobWriter not loaded
 @pytest.mark.skipif(IS_RASPBERRYPI, reason="CoreML not supported on Raspberry Pi")
@@ -142,6 +167,7 @@ def test_export_coreml():
         YOLO(MODEL).export(format="coreml", nms=True, imgsz=32)
 
 
+@pytest.mark.skipif(not checks.IS_PYTHON_MINIMUM_3_10, reason="TFLite export requires Python>=3.10")
 @pytest.mark.skipif(not LINUX, reason="Test disabled as TF suffers from install conflicts on Windows and macOS")
 def test_export_tflite():
     """

ultralytics/__init__.py

@@ -1,6 +1,6 @@
 # Ultralytics YOLO 🚀, AGPL-3.0 license
 
-__version__ = "8.2.24"
+__version__ = "8.2.25"
 
 import os
 

ultralytics/engine/exporter.py

@@ -83,7 +83,6 @@ from ultralytics.utils import (
     WINDOWS,
     __version__,
     callbacks,
-    checks,
     colorstr,
     get_default_args,
     yaml_save,
@@ -813,15 +812,16 @@ class Exporter:
             import tensorflow as tf  # noqa
         except ImportError:
             suffix = "-macos" if MACOS else "-aarch64" if ARM64 else "" if cuda else "-cpu"
-            version = "" if ARM64 else "<=2.13.1"
+            version = ">=2.0.0"
-            check_requirements((f"tensorflow{suffix}{version}", "keras"))
+            check_requirements(f"tensorflow{suffix}{version}")
             import tensorflow as tf  # noqa
         if ARM64:
             check_requirements("cmake")  # 'cmake' is needed to build onnxsim on aarch64
         check_requirements(
             (
+                "keras",
                 "onnx>=1.12.0",
-                "onnx2tf>=1.15.4,<=1.17.5",
+                "onnx2tf>1.17.5,<=1.22.3",
                 "sng4onnx>=1.0.1",
                 "onnxsim>=0.4.33",
                 "onnx_graphsurgeon>=0.3.26",
@@ -835,7 +835,7 @@ class Exporter:
         LOGGER.info(f"\n{prefix} starting export with tensorflow {tf.__version__}...")
         check_version(
             tf.__version__,
-            "<=2.13.1",
+            ">=2.0.0",
             name="tensorflow",
             verbose=True,
             msg="https://github.com/ultralytics/ultralytics/issues/5161",

ultralytics/utils/benchmarks.py

@@ -88,14 +88,14 @@ def benchmark(
         emoji, filename = "❌", None  # export defaults
         try:
             # Checks
-            if i == 5:  # CoreML
+            if i == 7:  # TF GraphDef
-                assert not (IS_RASPBERRYPI or IS_JETSON), "CoreML export not supported on Raspberry Pi or NVIDIA Jetson"
-            if i == 9:  # Edge TPU
-                assert LINUX and not ARM64, "Edge TPU export only supported on non-aarch64 Linux"
-            elif i == 7:  # TF GraphDef
                 assert model.task != "obb", "TensorFlow GraphDef not supported for OBB task"
+            elif i == 9:  # Edge TPU
+                assert LINUX and not ARM64, "Edge TPU export only supported on non-aarch64 Linux"
             elif i in {5, 10}:  # CoreML and TF.js
-                assert MACOS or LINUX, "export only supported on macOS and Linux"
+                assert MACOS or LINUX, "CoreML and TF.js export only supported on macOS and Linux"
+                assert not IS_RASPBERRYPI, "CoreML and TF.js export not supported on Raspberry Pi"
+                assert not IS_JETSON, "CoreML and TF.js export not supported on NVIDIA Jetson"
             if i in {3, 5}:  # CoreML and OpenVINO
                 assert not IS_PYTHON_3_12, "CoreML and OpenVINO not supported on Python 3.12"
             if i in {6, 7, 8, 9, 10}:  # All TF formats