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>
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Glenn Jocher
GitHub
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9 changed files with 79 additions and 49 deletions
10
.github/workflows/ci.yaml
vendored
10
.github/workflows/ci.yaml
vendored
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@ -206,11 +206,16 @@ jobs:
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run: |
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# CoreML must be installed before export due to protobuf error from AutoInstall
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python -m pip install --upgrade pip wheel
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slow=""
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torch=""
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if [ "${{ matrix.torch }}" == "1.8.0" ]; then
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torch="torch==1.8.0 torchvision==0.9.0"
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fi
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pip install -e . $torch pytest-cov "coremltools>=7.0; platform_system != 'Windows' and python_version <= '3.11'" --extra-index-url https://download.pytorch.org/whl/cpu
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if [[ "${{ github.event_name }}" =~ ^(schedule|workflow_dispatch)$ ]]; then
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slow="pycocotools mlflow ray[tune]"
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fi
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slow="pycocotools mlflow ray[tune]"
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pip install -e ".[export]" $torch $slow pytest-cov --extra-index-url https://download.pytorch.org/whl/cpu
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- name: Check environment
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run: |
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yolo checks
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@ -220,7 +225,6 @@ jobs:
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run: |
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slow=""
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if [[ "${{ github.event_name }}" =~ ^(schedule|workflow_dispatch)$ ]]; then
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pip install pycocotools mlflow "ray[tune]"
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slow="--slow"
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fi
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pytest $slow --cov=ultralytics/ --cov-report xml tests/
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@ -272,7 +276,7 @@ jobs:
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- name: Install requirements
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run: |
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python -m pip install --upgrade pip wheel
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pip install --no-cache-dir -e ".[export]" pytest mlflow pycocotools "ray[tune]"
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pip install -e ".[export]" pytest mlflow pycocotools "ray[tune]"
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- name: Check environment
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run: |
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yolo checks
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@ -16,7 +16,7 @@ This guide provides a comprehensive overview of three fundamental types of data
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|:------------------------------------------------------------------------------------------------------------------:|:----------------------------------------------------------------------------------------------------------------:|:-----------------------------------------------------------------------------------------------------------------:|
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|  |  |  |
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### Why Graphs are Important
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### Why Graphs are Important
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- Line graphs are ideal for tracking changes over short and long periods and for comparing changes for multiple groups over the same period.
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- Bar plots, on the other hand, are suitable for comparing quantities across different categories and showing relationships between a category and its numerical value.
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@ -174,21 +174,21 @@ This guide provides a comprehensive overview of three fundamental types of data
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Here's a table with the `Analytics` arguments:
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| Name | Type | Default | Description |
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|--------------|-------------------|---------------|---------------------------------------------|
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| `type` | `str` | `None` | Type of data or object. |
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| `im0_shape` | `tuple` | `None` | Shape of the initial image. |
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| `writer` | `cv2.VideoWriter` | `None` | Object for writing video files. |
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| `title` | `str` | `ultralytics` | Title for the visualization. |
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| `x_label` | `str` | `x` | Label for the x-axis. |
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| `y_label` | `str` | `y` | Label for the y-axis. |
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| `bg_color` | `str` | `white` | Background color. |
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| `fg_color` | `str` | `black` | Foreground color. |
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| `line_color` | `str` | `yellow` | Color of the lines. |
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| `line_width` | `int` | `2` | Width of the lines. |
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| `fontsize` | `int` | `13` | Font size for text. |
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| `view_img` | `bool` | `False` | Flag to display the image or video. |
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| `save_img` | `bool` | `True` | Flag to save the image or video. |
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| Name | Type | Default | Description |
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|--------------|-------------------|---------------|-------------------------------------|
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| `type` | `str` | `None` | Type of data or object. |
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| `im0_shape` | `tuple` | `None` | Shape of the initial image. |
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| `writer` | `cv2.VideoWriter` | `None` | Object for writing video files. |
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| `title` | `str` | `ultralytics` | Title for the visualization. |
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| `x_label` | `str` | `x` | Label for the x-axis. |
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| `y_label` | `str` | `y` | Label for the y-axis. |
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| `bg_color` | `str` | `white` | Background color. |
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| `fg_color` | `str` | `black` | Foreground color. |
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| `line_color` | `str` | `yellow` | Color of the lines. |
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| `line_width` | `int` | `2` | Width of the lines. |
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| `fontsize` | `int` | `13` | Font size for text. |
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| `view_img` | `bool` | `False` | Flag to display the image or video. |
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| `save_img` | `bool` | `True` | Flag to save the image or video. |
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### Arguments `model.track`
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@ -41,8 +41,8 @@ Setting measurable objectives is key to the success of a computer vision project
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For example, if you are developing a system to estimate vehicle speeds on a highway. You could consider the following measurable objectives:
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- To achieve at least 95% accuracy in speed detection within six months, using a dataset of 10,000 vehicle images.
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- The system should be able to process real-time video feeds at 30 frames per second with minimal delay.
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- To achieve at least 95% accuracy in speed detection within six months, using a dataset of 10,000 vehicle images.
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- The system should be able to process real-time video feeds at 30 frames per second with minimal delay.
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By setting specific and quantifiable goals, you can effectively track progress, identify areas for improvement, and ensure the project stays on course.
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@ -16,7 +16,7 @@ Computer vision techniques like [object detection](../tasks/detect.md), [image c
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<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">
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</p>
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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!
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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!
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## An Overview of a Computer Vision Project
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@ -100,8 +100,9 @@ export = [
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"onnx>=1.12.0", # ONNX export
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"coremltools>=7.0; platform_system != 'Windows' and python_version <= '3.11'", # CoreML supported on macOS and Linux
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"openvino>=2024.0.0", # OpenVINO export
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"tensorflow<=2.13.1; python_version <= '3.11'", # TF bug https://github.com/ultralytics/ultralytics/issues/5161
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"tensorflowjs>=3.9.0; python_version <= '3.11'", # TF.js export, automatically installs tensorflow
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"tensorflow>=2.0.0", # TF bug https://github.com/ultralytics/ultralytics/issues/5161
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"tensorflowjs>=3.9.0", # TF.js export, automatically installs tensorflow
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"keras", # not installed auotomatically by tensorflow>=2.16
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"flatbuffers>=23.5.26,<100; platform_machine == 'aarch64'", # update old 'flatbuffers' included inside tensorflow package
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"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
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"h5py!=3.11.0; platform_machine == 'aarch64'", # fix h5py build issues due to missing aarch64 wheels in 3.11 release
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@ -111,7 +112,6 @@ explorer = [
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"duckdb<=0.9.2", # SQL queries, duckdb==0.10.0 bug https://github.com/ultralytics/ultralytics/pull/8181
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"streamlit", # visualizing with GUI
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]
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# tensorflow>=2.4.1,<=2.13.1 # TF exports (-cpu, -aarch64, -macos)
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# tflite-support # for TFLite model metadata
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# nvidia-pyindex # TensorRT export
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# nvidia-tensorrt # TensorRT export
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@ -129,6 +129,31 @@ def test_export_coreml_matrix(task, dynamic, int8, half, batch):
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shutil.rmtree(file) # cleanup
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@pytest.mark.slow
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@pytest.mark.skipif(not checks.IS_PYTHON_MINIMUM_3_10, reason="TFLite export requires Python>=3.10")
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@pytest.mark.skipif(not LINUX, reason="Test disabled as TF suffers from install conflicts on Windows and macOS")
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@pytest.mark.parametrize(
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"task, dynamic, int8, half, batch",
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[ # generate all combinations but exclude those where both int8 and half are True
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(task, dynamic, int8, half, batch)
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for task, dynamic, int8, half, batch in product(TASKS, [False], [True, False], [True, False], [1])
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if not (int8 and half) # exclude cases where both int8 and half are True
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],
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)
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def test_export_tflite_matrix(task, dynamic, int8, half, batch):
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"""Test YOLO exports to TFLite format."""
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file = YOLO(TASK2MODEL[task]).export(
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format="tflite",
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imgsz=32,
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dynamic=dynamic,
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int8=int8,
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half=half,
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batch=batch,
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)
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YOLO(file)([SOURCE] * batch, imgsz=32) # exported model inference at batch=3
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Path(file).unlink() # cleanup
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@pytest.mark.skipif(not TORCH_1_9, reason="CoreML>=7.2 not supported with PyTorch<=1.8")
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@pytest.mark.skipif(WINDOWS, reason="CoreML not supported on Windows") # RuntimeError: BlobWriter not loaded
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@pytest.mark.skipif(IS_RASPBERRYPI, reason="CoreML not supported on Raspberry Pi")
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@ -142,6 +167,7 @@ def test_export_coreml():
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YOLO(MODEL).export(format="coreml", nms=True, imgsz=32)
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@pytest.mark.skipif(not checks.IS_PYTHON_MINIMUM_3_10, reason="TFLite export requires Python>=3.10")
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@pytest.mark.skipif(not LINUX, reason="Test disabled as TF suffers from install conflicts on Windows and macOS")
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def test_export_tflite():
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"""
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@ -1,6 +1,6 @@
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# Ultralytics YOLO 🚀, AGPL-3.0 license
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__version__ = "8.2.24"
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__version__ = "8.2.25"
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import os
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@ -83,7 +83,6 @@ from ultralytics.utils import (
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WINDOWS,
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__version__,
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callbacks,
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checks,
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colorstr,
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get_default_args,
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yaml_save,
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@ -813,15 +812,16 @@ class Exporter:
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import tensorflow as tf # noqa
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except ImportError:
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suffix = "-macos" if MACOS else "-aarch64" if ARM64 else "" if cuda else "-cpu"
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version = "" if ARM64 else "<=2.13.1"
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check_requirements((f"tensorflow{suffix}{version}", "keras"))
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version = ">=2.0.0"
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check_requirements(f"tensorflow{suffix}{version}")
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import tensorflow as tf # noqa
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if ARM64:
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check_requirements("cmake") # 'cmake' is needed to build onnxsim on aarch64
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check_requirements(
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(
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"keras",
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"onnx>=1.12.0",
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"onnx2tf>=1.15.4,<=1.17.5",
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"onnx2tf>1.17.5,<=1.22.3",
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"sng4onnx>=1.0.1",
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"onnxsim>=0.4.33",
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"onnx_graphsurgeon>=0.3.26",
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@ -835,7 +835,7 @@ class Exporter:
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LOGGER.info(f"\n{prefix} starting export with tensorflow {tf.__version__}...")
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check_version(
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tf.__version__,
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"<=2.13.1",
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">=2.0.0",
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name="tensorflow",
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verbose=True,
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msg="https://github.com/ultralytics/ultralytics/issues/5161",
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@ -88,14 +88,14 @@ def benchmark(
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emoji, filename = "❌", None # export defaults
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try:
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# Checks
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if i == 5: # CoreML
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assert not (IS_RASPBERRYPI or IS_JETSON), "CoreML export not supported on Raspberry Pi or NVIDIA Jetson"
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if i == 9: # Edge TPU
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assert LINUX and not ARM64, "Edge TPU export only supported on non-aarch64 Linux"
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elif i == 7: # TF GraphDef
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if i == 7: # TF GraphDef
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assert model.task != "obb", "TensorFlow GraphDef not supported for OBB task"
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elif i == 9: # Edge TPU
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assert LINUX and not ARM64, "Edge TPU export only supported on non-aarch64 Linux"
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elif i in {5, 10}: # CoreML and TF.js
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assert MACOS or LINUX, "export only supported on macOS and Linux"
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assert MACOS or LINUX, "CoreML and TF.js export only supported on macOS and Linux"
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assert not IS_RASPBERRYPI, "CoreML and TF.js export not supported on Raspberry Pi"
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assert not IS_JETSON, "CoreML and TF.js export not supported on NVIDIA Jetson"
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if i in {3, 5}: # CoreML and OpenVINO
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assert not IS_PYTHON_3_12, "CoreML and OpenVINO not supported on Python 3.12"
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if i in {6, 7, 8, 9, 10}: # All TF formats
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