ultralytics 8.2.27 replace onnxsim with onnxslim (#12989)

Co-authored-by: inisis <desmond.yao@buaa.edu.cn>
Co-authored-by: Glenn Jocher <glenn.jocher@ultralytics.com>
Co-authored-by: inisis <46103969+inisis@users.noreply.github.com>
Co-authored-by: UltralyticsAssistant <web@ultralytics.com>

docker/Dockerfile-arm64

@@ -16,10 +16,9 @@ ADD https://github.com/ultralytics/assets/releases/download/v0.0.0/Arial.ttf \
 
 # Install linux packages
 # g++ required to build 'tflite_support' and 'lap' packages, libusb-1.0-0 required for 'tflite_support' package
-# cmake and build-essential are needed to build 'onnxsim' when exporting to TFLite
 # pkg-config and libhdf5-dev (not included) are needed to build 'h5py==3.11.0' aarch64 wheel required by 'tensorflow'
 RUN apt update \
-    && apt install --no-install-recommends -y python3-pip git zip curl htop gcc libgl1 libglib2.0-0 libpython3-dev gnupg g++ libusb-1.0-0 build-essential
+    && apt install --no-install-recommends -y python3-pip git zip curl htop gcc libgl1 libglib2.0-0 libpython3-dev gnupg g++ libusb-1.0-0
 
 # Create working directory
 WORKDIR $APP_HOME

docs/en/modes/export.md

@@ -83,7 +83,7 @@ This table details the configurations and options available for exporting YOLO m
 | `half`      | `bool`           | `False`         | Enables FP16 (half-precision) quantization, reducing model size and potentially speeding up inference on supported hardware.                                     |
 | `int8`      | `bool`           | `False`         | Activates INT8 quantization, further compressing the model and speeding up inference with minimal accuracy loss, primarily for edge devices.                     |
 | `dynamic`   | `bool`           | `False`         | Allows dynamic input sizes for ONNX and TensorRT exports, enhancing flexibility in handling varying image dimensions.                                            |
-| `simplify`  | `bool`           | `False`         | Simplifies the model graph for ONNX exports, potentially improving performance and compatibility.                                                                |
+| `simplify`  | `bool`           | `False`         | Simplifies the model graph for ONNX exports with `onnxsim`, potentially improving performance and compatibility.                                                 |
 | `opset`     | `int`            | `None`          | Specifies the ONNX opset version for compatibility with different ONNX parsers and runtimes. If not set, uses the latest supported version.                      |
 | `workspace` | `float`          | `4.0`           | Sets the maximum workspace size in GiB for TensorRT optimizations, balancing memory usage and performance.                                                       |
 | `nms`       | `bool`           | `False`         | Adds Non-Maximum Suppression (NMS) to the CoreML export, essential for accurate and efficient detection post-processing.                                         |

tests/test_cuda.py

@@ -41,6 +41,7 @@ def test_export_engine_matrix(task, dynamic, int8, half, batch):
         batch=batch,
         data=TASK2DATA[task],
         workspace=1,  # reduce workspace GB for less resource utilization during testing
+        simplify=True,  # use 'onnxslim'
     )
     YOLO(file)([SOURCE] * batch, imgsz=64 if dynamic else 32)  # exported model inference
     Path(file).unlink()  # cleanup

tests/test_exports.py

@@ -72,8 +72,10 @@ def test_export_openvino_matrix(task, dynamic, int8, half, batch):
 
 
 @pytest.mark.slow
-@pytest.mark.parametrize("task, dynamic, int8, half, batch", product(TASKS, [True, False], [False], [False], [1, 2]))
-def test_export_onnx_matrix(task, dynamic, int8, half, batch):
+@pytest.mark.parametrize(
+    "task, dynamic, int8, half, batch, simplify", product(TASKS, [True, False], [False], [False], [1, 2], [True, False])
+)
+def test_export_onnx_matrix(task, dynamic, int8, half, batch, simplify):
     """Test YOLO exports to ONNX format."""
     file = YOLO(TASK2MODEL[task]).export(
         format="onnx",
@@ -82,6 +84,7 @@ def test_export_onnx_matrix(task, dynamic, int8, half, batch):
         int8=int8,
         half=half,
         batch=batch,
+        simplify=simplify,
     )
     YOLO(file)([SOURCE] * batch, imgsz=64 if dynamic else 32)  # exported model inference
     Path(file).unlink()  # cleanup

ultralytics/__init__.py

@@ -1,6 +1,6 @@
 # Ultralytics YOLO 🚀, AGPL-3.0 license
 
-__version__ = "8.2.26"
+__version__ = "8.2.27"
 
 import os
 

ultralytics/cfg/default.yaml

@@ -81,7 +81,7 @@ keras: False # (bool) use Kera=s
 optimize: False # (bool) TorchScript: optimize for mobile
 int8: False # (bool) CoreML/TF INT8 quantization
 dynamic: False # (bool) ONNX/TF/TensorRT: dynamic axes
-simplify: False # (bool) ONNX: simplify model
+simplify: False # (bool) ONNX: simplify model using `onnxslim`
 opset: # (int, optional) ONNX: opset version
 workspace: 4 # (int) TensorRT: workspace size (GB)
 nms: False # (bool) CoreML: add NMS

ultralytics/engine/exporter.py

@@ -384,7 +384,7 @@ class Exporter:
         """YOLOv8 ONNX export."""
         requirements = ["onnx>=1.12.0"]
         if self.args.simplify:
-            requirements += ["cmake", "onnxsim>=0.4.33", "onnxruntime" + ("-gpu" if torch.cuda.is_available() else "")]
+            requirements += ["onnxslim==0.1.28", "onnxruntime" + ("-gpu" if torch.cuda.is_available() else "")]
         check_requirements(requirements)
         import onnx  # noqa
 
@@ -421,14 +421,17 @@ class Exporter:
         # Simplify
         if self.args.simplify:
             try:
-                import onnxsim
+                import onnxslim
 
-                LOGGER.info(f"{prefix} simplifying with onnxsim {onnxsim.__version__}...")
-                # subprocess.run(f'onnxsim "{f}" "{f}"', shell=True)
-                model_onnx, check = onnxsim.simplify(model_onnx)
-                assert check, "Simplified ONNX model could not be validated"
+                LOGGER.info(f"{prefix} slimming with onnxslim {onnxslim.__version__}...")
+                model_onnx = onnxslim.slim(model_onnx)
+
+                # ONNX Simplifier (deprecated as must be compiled with 'cmake' in aarch64 and Conda CI environments)
+                # import onnxsim
+                # model_onnx, check = onnxsim.simplify(model_onnx)
+                # assert check, "Simplified ONNX model could not be validated"
             except Exception as e:
-                LOGGER.info(f"{prefix} simplifier failure: {e}")
+                LOGGER.warning(f"{prefix} simplifier failure: {e}")
 
         # Metadata
         for k, v in self.metadata.items():
@@ -672,8 +675,8 @@ class Exporter:
     def export_engine(self, prefix=colorstr("TensorRT:")):
         """YOLOv8 TensorRT export https://developer.nvidia.com/tensorrt."""
         assert self.im.device.type != "cpu", "export running on CPU but must be on GPU, i.e. use 'device=0'"
-        self.args.simplify = True
-        f_onnx, _ = self.export_onnx()  # run before trt import https://github.com/ultralytics/ultralytics/issues/7016
+        # self.args.simplify = True
+        f_onnx, _ = self.export_onnx()  # run before TRT import https://github.com/ultralytics/ultralytics/issues/7016
 
         try:
             import tensorrt as trt  # noqa
@@ -815,13 +818,12 @@ class Exporter:
             import tensorflow as tf  # noqa
         check_requirements(
             (
-                "cmake",  # 'cmake' is needed to build onnxsim on aarch64 and Conda runners
                 "keras",  # required by onnx2tf package
                 "tf_keras",  # required by onnx2tf package
                 "onnx>=1.12.0",
                 "onnx2tf>1.17.5,<=1.22.3",
                 "sng4onnx>=1.0.1",
-                "onnxsim>=0.4.33",
+                "onnxslim==0.1.28",
                 "onnx_graphsurgeon>=0.3.26",
                 "tflite_support<=0.4.3" if IS_JETSON else "tflite_support",  # fix ImportError 'GLIBCXX_3.4.29'
                 "flatbuffers>=23.5.26,<100",  # update old 'flatbuffers' included inside tensorflow package

ultralytics/nn/modules/__init__.py

@@ -13,7 +13,7 @@ Example:
     m = Conv(128, 128)
     f = f'{m._get_name()}.onnx'
     torch.onnx.export(m, x, f)
-    os.system(f'onnxsim {f} {f} && open {f}')
+    os.system(f'onnxslim {f} {f} && open {f}')  # pip install onnxslim
     ```
 """
 

ultralytics/utils/benchmarks.py

@@ -457,6 +457,8 @@ class ProfileModels:
 
         input_tensor = sess.get_inputs()[0]
         input_type = input_tensor.type
+        dynamic = not all(isinstance(dim, int) and dim >= 0 for dim in input_tensor.shape)  # dynamic input shape
+        input_shape = (1, 3, self.imgsz, self.imgsz) if dynamic else input_tensor.shape
 
         # Mapping ONNX datatype to numpy datatype
         if "float16" in input_type:
@@ -472,7 +474,7 @@ class ProfileModels:
         else:
             raise ValueError(f"Unsupported ONNX datatype {input_type}")
 
-        input_data = np.random.rand(*input_tensor.shape).astype(input_dtype)
+        input_data = np.random.rand(*input_shape).astype(input_dtype)
         input_name = input_tensor.name
         output_name = sess.get_outputs()[0].name