Update .pre-commit-config.yaml (#1026)
This commit is contained in:
Glenn Jocher
GitHub
parent
9047d737f4
commit
edd3ff1669
76 changed files with 928 additions and 935 deletions
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@ -144,7 +144,7 @@ class Exporter:
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@smart_inference_mode()
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def __call__(self, model=None):
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self.run_callbacks("on_export_start")
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self.run_callbacks('on_export_start')
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t = time.time()
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format = self.args.format.lower() # to lowercase
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if format in {'tensorrt', 'trt'}: # engine aliases
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@ -207,7 +207,7 @@ class Exporter:
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self.output_shape = tuple(y.shape) if isinstance(y, torch.Tensor) else tuple(tuple(x.shape) for x in y)
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self.pretty_name = self.file.stem.replace('yolo', 'YOLO')
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self.metadata = {
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'description': f"Ultralytics {self.pretty_name} model trained on {self.args.data}",
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'description': f'Ultralytics {self.pretty_name} model trained on {self.args.data}',
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'author': 'Ultralytics',
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'license': 'GPL-3.0 https://ultralytics.com/license',
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'version': __version__,
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@ -215,7 +215,7 @@ class Exporter:
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'names': model.names} # model metadata
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LOGGER.info(f"\n{colorstr('PyTorch:')} starting from {file} with input shape {tuple(im.shape)} BCHW and "
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f"output shape(s) {self.output_shape} ({file_size(file):.1f} MB)")
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f'output shape(s) {self.output_shape} ({file_size(file):.1f} MB)')
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# Exports
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f = [''] * len(fmts) # exported filenames
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@ -259,15 +259,15 @@ class Exporter:
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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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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} {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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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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self.run_callbacks("on_export_end")
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self.run_callbacks('on_export_end')
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return f # return list of exported files/dirs
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@try_export
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@ -277,7 +277,7 @@ class Exporter:
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f = self.file.with_suffix('.torchscript')
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ts = torch.jit.trace(self.model, self.im, strict=False)
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d = {"shape": self.im.shape, "stride": int(max(self.model.stride)), "names": self.model.names}
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d = {'shape': self.im.shape, 'stride': int(max(self.model.stride)), 'names': self.model.names}
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extra_files = {'config.txt': json.dumps(d)} # torch._C.ExtraFilesMap()
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if self.args.optimize: # https://pytorch.org/tutorials/recipes/mobile_interpreter.html
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LOGGER.info(f'{prefix} optimizing for mobile...')
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@ -354,7 +354,7 @@ class Exporter:
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ov_model = mo.convert_model(f_onnx,
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model_name=self.pretty_name,
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framework="onnx",
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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) / 'metadata.yaml', self.metadata) # add metadata.yaml
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@ -471,7 +471,7 @@ class Exporter:
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if self.args.dynamic:
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shape = self.im.shape
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if shape[0] <= 1:
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LOGGER.warning(f"{prefix} WARNING ⚠️ --dynamic model requires maximum --batch-size argument")
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LOGGER.warning(f'{prefix} WARNING ⚠️ --dynamic model requires maximum --batch-size argument')
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profile = builder.create_optimization_profile()
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for inp in inputs:
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profile.set_shape(inp.name, (1, *shape[1:]), (max(1, shape[0] // 2), *shape[1:]), shape)
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@ -509,8 +509,8 @@ class Exporter:
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except ImportError:
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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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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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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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@ -632,7 +632,7 @@ class Exporter:
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converter.target_spec.supported_ops.append(tf.lite.OpsSet.SELECT_TF_OPS)
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tflite_model = converter.convert()
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open(f, "wb").write(tflite_model)
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open(f, 'wb').write(tflite_model)
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return f, None
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@try_export
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@ -656,7 +656,7 @@ class Exporter:
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LOGGER.info(f'\n{prefix} starting export with Edge TPU compiler {ver}...')
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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} {tflite_model}"
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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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@ -707,8 +707,8 @@ class Exporter:
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# Creates input info.
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input_meta = _metadata_fb.TensorMetadataT()
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input_meta.name = "image"
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input_meta.description = "Input image to be detected."
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input_meta.name = 'image'
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input_meta.description = 'Input image to be detected.'
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input_meta.content = _metadata_fb.ContentT()
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input_meta.content.contentProperties = _metadata_fb.ImagePropertiesT()
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input_meta.content.contentProperties.colorSpace = _metadata_fb.ColorSpaceType.RGB
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@ -716,8 +716,8 @@ class Exporter:
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# Creates output info.
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output_meta = _metadata_fb.TensorMetadataT()
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output_meta.name = "output"
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output_meta.description = "Coordinates of detected objects, class labels, and confidence score."
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output_meta.name = 'output'
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output_meta.description = 'Coordinates of detected objects, class labels, and confidence score.'
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# Label file
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tmp_file = Path('/tmp/meta.txt')
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@ -868,8 +868,8 @@ class Exporter:
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def export(cfg=DEFAULT_CFG):
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cfg.model = cfg.model or "yolov8n.yaml"
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cfg.format = cfg.format or "torchscript"
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cfg.model = cfg.model or 'yolov8n.yaml'
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cfg.format = cfg.format or 'torchscript'
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# exporter = Exporter(cfg)
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#
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@ -888,7 +888,7 @@ def export(cfg=DEFAULT_CFG):
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model.export(**vars(cfg))
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if __name__ == "__main__":
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if __name__ == '__main__':
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"""
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CLI:
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yolo mode=export model=yolov8n.yaml format=onnx
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