ultralytics 8.0.40 TensorRT metadata and Results visualizer (#1014)
Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com> Co-authored-by: Laughing <61612323+Laughing-q@users.noreply.github.com> Co-authored-by: Bogdan Gheorghe <112427971+bogdan-galileo@users.noreply.github.com> Co-authored-by: Ayush Chaurasia <ayush.chaurarsia@gmail.com> Co-authored-by: Jaap van de Loosdrecht <jaap@vdlmv.nl> Co-authored-by: Noobtoss <96134731+Noobtoss@users.noreply.github.com> Co-authored-by: nerdyespresso <106761627+nerdyespresso@users.noreply.github.com>
This commit is contained in:
Glenn Jocher
GitHub
parent
e799592718
commit
9047d737f4
40 changed files with 576 additions and 280 deletions
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@ -18,8 +18,8 @@ TensorFlow.js | `tfjs` | yolov8n_web_model/
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PaddlePaddle | `paddle` | yolov8n_paddle_model/
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Requirements:
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$ pip install -r requirements.txt coremltools onnx onnx-simplifier onnxruntime openvino-dev tensorflow-cpu # CPU
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$ pip install -r requirements.txt coremltools onnx onnx-simplifier onnxruntime-gpu openvino-dev tensorflow # GPU
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$ pip install -r requirements.txt coremltools onnx onnxsim onnxruntime openvino-dev tensorflow-cpu # CPU
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$ pip install -r requirements.txt coremltools onnx onnxsim onnxruntime-gpu openvino-dev tensorflow # GPU
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Python:
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from ultralytics import YOLO
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@ -69,13 +69,14 @@ from ultralytics.nn.tasks import DetectionModel, SegmentationModel
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from ultralytics.yolo.cfg import get_cfg
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from ultralytics.yolo.data.dataloaders.stream_loaders import LoadImages
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from ultralytics.yolo.data.utils import IMAGENET_MEAN, IMAGENET_STD, check_det_dataset
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from ultralytics.yolo.utils import DEFAULT_CFG, LOGGER, __version__, callbacks, colorstr, get_default_args, yaml_save
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from ultralytics.yolo.utils import (DEFAULT_CFG, LINUX, LOGGER, MACOS, WINDOWS, __version__, callbacks, colorstr,
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get_default_args, yaml_save)
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from ultralytics.yolo.utils.checks import check_imgsz, check_requirements, check_version, check_yaml
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from ultralytics.yolo.utils.files import file_size
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from ultralytics.yolo.utils.ops import Profile
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from ultralytics.yolo.utils.torch_utils import get_latest_opset, select_device, smart_inference_mode
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MACOS = platform.system() == 'Darwin' # macOS environment
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CUDA = torch.cuda.is_available()
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def export_formats():
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@ -229,27 +230,24 @@ class Exporter:
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if coreml: # CoreML
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f[4], _ = self._export_coreml()
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if any((saved_model, pb, tflite, edgetpu, tfjs)): # TensorFlow formats
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LOGGER.warning('WARNING ⚠️ YOLOv8 TensorFlow export support is still under development. '
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LOGGER.warning('WARNING ⚠️ YOLOv8 TensorFlow export is still under development. '
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'Please consider contributing to the effort if you have TF expertise. Thank you!')
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nms = False
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f[5], s_model = self._export_saved_model(nms=nms or self.args.agnostic_nms or tfjs,
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agnostic_nms=self.args.agnostic_nms or tfjs)
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debug = False
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if debug:
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if pb or tfjs: # pb prerequisite to tfjs
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f[6], _ = self._export_pb(s_model)
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if tflite or edgetpu:
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f[7], _ = self._export_tflite(s_model,
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int8=self.args.int8 or edgetpu,
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data=self.args.data,
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nms=nms,
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agnostic_nms=self.args.agnostic_nms)
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if edgetpu:
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f[8], _ = self._export_edgetpu()
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self._add_tflite_metadata(f[8] or f[7])
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if tfjs:
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f[9], _ = self._export_tfjs()
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if pb or tfjs: # pb prerequisite to tfjs
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f[6], _ = self._export_pb(s_model)
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if tflite or edgetpu:
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f[7] = str(Path(f[5]) / (self.file.stem + '_float16.tflite'))
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# f[7], _ = self._export_tflite(s_model,
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# int8=self.args.int8 or edgetpu,
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# data=self.args.data,
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# nms=nms,
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# agnostic_nms=self.args.agnostic_nms)
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if edgetpu:
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f[8], _ = self._export_edgetpu(tflite_model=f[7])
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if tfjs:
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f[9], _ = self._export_tfjs()
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if paddle: # PaddlePaddle
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f[10], _ = self._export_paddle()
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@ -258,13 +256,14 @@ class Exporter:
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if any(f):
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f = str(Path(f[-1]))
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square = self.imgsz[0] == self.imgsz[1]
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s = f"WARNING ⚠️ non-PyTorch val requires square images, 'imgsz={self.imgsz}' will not work. Use " \
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f"export 'imgsz={max(self.imgsz)}' if val is required." if not square else ''
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s = '' if square else f"WARNING ⚠️ non-PyTorch val requires square images, 'imgsz={self.imgsz}' will not " \
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f"work. Use export 'imgsz={max(self.imgsz)}' if val is required."
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imgsz = self.imgsz[0] if square else str(self.imgsz)[1:-1].replace(' ', '')
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data = f"data={self.args.data}" if model.task == 'segment' and format == 'pb' else ''
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LOGGER.info(
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f'\nExport complete ({time.time() - t:.1f}s)'
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f"\nResults saved to {colorstr('bold', file.parent.resolve())}"
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f"\nPredict: yolo task={model.task} mode=predict model={f} imgsz={imgsz}"
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f"\nPredict: yolo task={model.task} mode=predict model={f} imgsz={imgsz} {data}"
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f"\nValidate: yolo task={model.task} mode=val model={f} imgsz={imgsz} data={self.args.data} {s}"
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f"\nVisualize: https://netron.app")
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@ -335,7 +334,7 @@ class Exporter:
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check_requirements('onnxsim')
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import onnxsim
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LOGGER.info(f'{prefix} simplifying with onnx-simplifier {onnxsim.__version__}...')
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LOGGER.info(f'{prefix} simplifying with onnxsim {onnxsim.__version__}...')
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subprocess.run(f'onnxsim {f} {f}', shell=True)
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except Exception as e:
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LOGGER.info(f'{prefix} simplifier failure: {e}')
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@ -358,7 +357,7 @@ class Exporter:
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framework="onnx",
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compress_to_fp16=self.args.half) # export
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ov.serialize(ov_model, f_ov) # save
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yaml_save(Path(f) / self.file.with_suffix('.yaml').name, self.metadata) # add metadata.yaml
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yaml_save(Path(f) / 'metadata.yaml', self.metadata) # add metadata.yaml
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return f, None
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@try_export
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@ -372,7 +371,7 @@ class Exporter:
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f = str(self.file).replace(self.file.suffix, f'_paddle_model{os.sep}')
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pytorch2paddle(module=self.model, save_dir=f, jit_type='trace', input_examples=[self.im]) # export
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yaml_save(Path(f) / self.file.with_suffix('.yaml').name, self.metadata) # add metadata.yaml
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yaml_save(Path(f) / 'metadata.yaml', self.metadata) # add metadata.yaml
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return f, None
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@try_export
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@ -436,7 +435,7 @@ class Exporter:
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try:
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import tensorrt as trt # noqa
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except ImportError:
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if platform.system() == 'Linux':
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if LINUX:
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check_requirements('nvidia-tensorrt', cmds='-U --index-url https://pypi.ngc.nvidia.com')
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import tensorrt as trt # noqa
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@ -482,8 +481,16 @@ class Exporter:
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f'{prefix} building FP{16 if builder.platform_has_fast_fp16 and self.args.half else 32} engine as {f}')
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if builder.platform_has_fast_fp16 and self.args.half:
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config.set_flag(trt.BuilderFlag.FP16)
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# Write file
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with builder.build_engine(network, config) as engine, open(f, 'wb') as t:
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# Metadata
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meta = json.dumps(self.metadata)
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t.write(len(meta).to_bytes(4, byteorder='little', signed=True))
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t.write(meta.encode())
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# Model
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t.write(engine.serialize())
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return f, None
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@try_export
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@ -500,10 +507,10 @@ class Exporter:
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try:
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import tensorflow as tf # noqa
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except ImportError:
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check_requirements(f"tensorflow{'' if torch.cuda.is_available() else '-macos' if MACOS else '-cpu'}")
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check_requirements(f"tensorflow{'' if CUDA else '-macos' if MACOS else '-cpu' if LINUX else ''}")
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import tensorflow as tf # noqa
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check_requirements(("onnx", "onnx2tf", "sng4onnx", "onnxsim", "onnx_graphsurgeon", "tflite_support"),
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cmds="--extra-index-url https://pypi.ngc.nvidia.com ")
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cmds="--extra-index-url https://pypi.ngc.nvidia.com")
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LOGGER.info(f'\n{prefix} starting export with tensorflow {tf.__version__}...')
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f = str(self.file).replace(self.file.suffix, '_saved_model')
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@ -514,10 +521,11 @@ class Exporter:
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# Export to TF SavedModel
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subprocess.run(f'onnx2tf -i {onnx} -o {f} --non_verbose', shell=True)
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yaml_save(Path(f) / 'metadata.yaml', self.metadata) # add metadata.yaml
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# Add TFLite metadata
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for tflite_file in Path(f).rglob('*.tflite'):
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self._add_tflite_metadata(tflite_file)
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for file in Path(f).rglob('*.tflite'):
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self._add_tflite_metadata(file)
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# Load saved_model
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keras_model = tf.saved_model.load(f, tags=None, options=None)
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@ -537,7 +545,7 @@ class Exporter:
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try:
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import tensorflow as tf # noqa
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except ImportError:
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check_requirements(f"tensorflow{'' if torch.cuda.is_available() else '-macos' if MACOS else '-cpu'}")
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check_requirements(f"tensorflow{'' if CUDA else '-macos' if MACOS else '-cpu' if LINUX else ''}")
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import tensorflow as tf # noqa
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# from models.tf import TFModel
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from tensorflow.python.framework.convert_to_constants import convert_variables_to_constants_v2 # noqa
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@ -628,11 +636,11 @@ class Exporter:
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return f, None
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@try_export
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def _export_edgetpu(self, prefix=colorstr('Edge TPU:')):
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def _export_edgetpu(self, tflite_model='', prefix=colorstr('Edge TPU:')):
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# YOLOv8 Edge TPU export https://coral.ai/docs/edgetpu/models-intro/
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cmd = 'edgetpu_compiler --version'
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help_url = 'https://coral.ai/docs/edgetpu/compiler/'
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assert platform.system() == 'Linux', f'export only supported on Linux. See {help_url}'
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assert LINUX, f'export only supported on Linux. See {help_url}'
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if subprocess.run(f'{cmd} >/dev/null', shell=True).returncode != 0:
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LOGGER.info(f'\n{prefix} export requires Edge TPU compiler. Attempting install from {help_url}')
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sudo = subprocess.run('sudo --version >/dev/null', shell=True).returncode == 0 # sudo installed on system
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@ -646,11 +654,11 @@ class Exporter:
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ver = subprocess.run(cmd, shell=True, capture_output=True, check=True).stdout.decode().split()[-1]
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LOGGER.info(f'\n{prefix} starting export with Edge TPU compiler {ver}...')
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f = str(self.file).replace(self.file.suffix, '-int8_edgetpu.tflite') # Edge TPU model
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f_tfl = str(self.file).replace(self.file.suffix, '-int8.tflite') # TFLite model
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f = str(tflite_model).replace('.tflite', '_edgetpu.tflite') # Edge TPU model
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cmd = f"edgetpu_compiler -s -d -k 10 --out_dir {self.file.parent} {f_tfl}"
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cmd = f"edgetpu_compiler -s -d -k 10 --out_dir {self.file.parent} {tflite_model}"
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subprocess.run(cmd.split(), check=True)
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self._add_tflite_metadata(f)
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return f, None
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@try_export
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@ -681,6 +689,7 @@ class Exporter:
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f_json.read_text(),
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)
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j.write(subst)
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yaml_save(Path(f) / 'metadata.yaml', self.metadata) # add metadata.yaml
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return f, None
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def _add_tflite_metadata(self, file):
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@ -736,14 +745,6 @@ class Exporter:
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populator.populate()
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tmp_file.unlink()
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# TODO Rename this here and in `_add_tflite_metadata`
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def _extracted_from__add_tflite_metadata_15(self, _metadata_fb, arg1, arg2):
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# Creates input info.
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result = _metadata_fb.TensorMetadataT()
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result.name = arg1
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result.description = arg2
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return result
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def _pipeline_coreml(self, model, prefix=colorstr('CoreML Pipeline:')):
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# YOLOv8 CoreML pipeline
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import coremltools as ct # noqa
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