New `@try_export` decorator (#9096)
* New export decorator * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * New export decorator * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * Cleanup * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * rename fcn to func * rename to @try_export Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>pull/9105/head
Glenn Jocher
GitHub
parent
eab35f66f9
commit
d0fa0042bd
569
export.py
569
export.py
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@ -67,8 +67,8 @@ if platform.system() != 'Windows':
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from models.experimental import attempt_load
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from models.yolo import Detect
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from utils.dataloaders import LoadImages
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from utils.general import (LOGGER, check_dataset, check_img_size, check_requirements, check_version, check_yaml,
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colorstr, file_size, print_args, url2file)
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from utils.general import (LOGGER, Profile, check_dataset, check_img_size, check_requirements, check_version,
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check_yaml, colorstr, file_size, get_default_args, print_args, url2file)
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from utils.torch_utils import select_device, smart_inference_mode
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@ -89,200 +89,199 @@ def export_formats():
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return pd.DataFrame(x, columns=['Format', 'Argument', 'Suffix', 'CPU', 'GPU'])
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def try_export(inner_func):
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# YOLOv5 export decorator, i..e @try_export
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inner_args = get_default_args(inner_func)
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def outer_func(*args, **kwargs):
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prefix = inner_args['prefix']
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try:
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with Profile() as dt:
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f, model = inner_func(*args, **kwargs)
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LOGGER.info(f'{prefix} export success ✅ {dt.t:.1f}s, saved as {f} ({file_size(f):.1f} MB)')
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return f, model
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except Exception as e:
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LOGGER.info(f'{prefix} export failure ❌ {dt.t:.1f}s: {e}')
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return None, None
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return outer_func
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@try_export
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def export_torchscript(model, im, file, optimize, prefix=colorstr('TorchScript:')):
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# YOLOv5 TorchScript model export
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try:
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LOGGER.info(f'\n{prefix} starting export with torch {torch.__version__}...')
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f = file.with_suffix('.torchscript')
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LOGGER.info(f'\n{prefix} starting export with torch {torch.__version__}...')
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f = file.with_suffix('.torchscript')
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ts = torch.jit.trace(model, im, strict=False)
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d = {"shape": im.shape, "stride": int(max(model.stride)), "names": model.names}
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extra_files = {'config.txt': json.dumps(d)} # torch._C.ExtraFilesMap()
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if optimize: # https://pytorch.org/tutorials/recipes/mobile_interpreter.html
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optimize_for_mobile(ts)._save_for_lite_interpreter(str(f), _extra_files=extra_files)
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else:
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ts.save(str(f), _extra_files=extra_files)
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LOGGER.info(f'{prefix} export success, saved as {f} ({file_size(f):.1f} MB)')
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return f
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except Exception as e:
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LOGGER.info(f'{prefix} export failure: {e}')
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ts = torch.jit.trace(model, im, strict=False)
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d = {"shape": im.shape, "stride": int(max(model.stride)), "names": model.names}
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extra_files = {'config.txt': json.dumps(d)} # torch._C.ExtraFilesMap()
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if optimize: # https://pytorch.org/tutorials/recipes/mobile_interpreter.html
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optimize_for_mobile(ts)._save_for_lite_interpreter(str(f), _extra_files=extra_files)
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else:
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ts.save(str(f), _extra_files=extra_files)
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return f, None
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@try_export
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def export_onnx(model, im, file, opset, train, dynamic, simplify, prefix=colorstr('ONNX:')):
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# YOLOv5 ONNX export
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try:
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check_requirements(('onnx',))
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import onnx
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check_requirements(('onnx',))
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import onnx
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LOGGER.info(f'\n{prefix} starting export with onnx {onnx.__version__}...')
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f = file.with_suffix('.onnx')
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LOGGER.info(f'\n{prefix} starting export with onnx {onnx.__version__}...')
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f = file.with_suffix('.onnx')
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torch.onnx.export(
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model.cpu() if dynamic else model, # --dynamic only compatible with cpu
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im.cpu() if dynamic else im,
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f,
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verbose=False,
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opset_version=opset,
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training=torch.onnx.TrainingMode.TRAINING if train else torch.onnx.TrainingMode.EVAL,
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do_constant_folding=not train,
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input_names=['images'],
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output_names=['output'],
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dynamic_axes={
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'images': {
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0: 'batch',
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2: 'height',
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3: 'width'}, # shape(1,3,640,640)
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'output': {
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0: 'batch',
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1: 'anchors'} # shape(1,25200,85)
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} if dynamic else None)
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torch.onnx.export(
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model.cpu() if dynamic else model, # --dynamic only compatible with cpu
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im.cpu() if dynamic else im,
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f,
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verbose=False,
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opset_version=opset,
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training=torch.onnx.TrainingMode.TRAINING if train else torch.onnx.TrainingMode.EVAL,
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do_constant_folding=not train,
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input_names=['images'],
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output_names=['output'],
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dynamic_axes={
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'images': {
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0: 'batch',
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2: 'height',
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3: 'width'}, # shape(1,3,640,640)
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'output': {
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0: 'batch',
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1: 'anchors'} # shape(1,25200,85)
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} if dynamic else None)
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# Checks
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model_onnx = onnx.load(f) # load onnx model
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onnx.checker.check_model(model_onnx) # check onnx model
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# Checks
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model_onnx = onnx.load(f) # load onnx model
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onnx.checker.check_model(model_onnx) # check onnx model
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# Metadata
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d = {'stride': int(max(model.stride)), 'names': model.names}
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for k, v in d.items():
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meta = model_onnx.metadata_props.add()
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meta.key, meta.value = k, str(v)
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onnx.save(model_onnx, f)
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# Metadata
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d = {'stride': int(max(model.stride)), 'names': model.names}
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for k, v in d.items():
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meta = model_onnx.metadata_props.add()
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meta.key, meta.value = k, str(v)
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onnx.save(model_onnx, f)
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# Simplify
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if simplify:
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try:
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cuda = torch.cuda.is_available()
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check_requirements(('onnxruntime-gpu' if cuda else 'onnxruntime', 'onnx-simplifier>=0.4.1'))
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import onnxsim
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# Simplify
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if simplify:
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try:
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cuda = torch.cuda.is_available()
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check_requirements(('onnxruntime-gpu' if cuda else 'onnxruntime', 'onnx-simplifier>=0.4.1'))
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import onnxsim
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LOGGER.info(f'{prefix} simplifying with onnx-simplifier {onnxsim.__version__}...')
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model_onnx, check = onnxsim.simplify(model_onnx)
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assert check, 'assert check failed'
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onnx.save(model_onnx, f)
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except Exception as e:
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LOGGER.info(f'{prefix} simplifier failure: {e}')
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LOGGER.info(f'{prefix} export success, saved as {f} ({file_size(f):.1f} MB)')
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return f
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except Exception as e:
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LOGGER.info(f'{prefix} export failure: {e}')
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LOGGER.info(f'{prefix} simplifying with onnx-simplifier {onnxsim.__version__}...')
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model_onnx, check = onnxsim.simplify(model_onnx)
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assert check, 'assert check failed'
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onnx.save(model_onnx, f)
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except Exception as e:
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LOGGER.info(f'{prefix} simplifier failure: {e}')
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return f, model_onnx
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@try_export
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def export_openvino(model, file, half, prefix=colorstr('OpenVINO:')):
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# YOLOv5 OpenVINO export
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try:
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check_requirements(('openvino-dev',)) # requires openvino-dev: https://pypi.org/project/openvino-dev/
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import openvino.inference_engine as ie
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check_requirements(('openvino-dev',)) # requires openvino-dev: https://pypi.org/project/openvino-dev/
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import openvino.inference_engine as ie
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LOGGER.info(f'\n{prefix} starting export with openvino {ie.__version__}...')
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f = str(file).replace('.pt', f'_openvino_model{os.sep}')
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LOGGER.info(f'\n{prefix} starting export with openvino {ie.__version__}...')
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f = str(file).replace('.pt', f'_openvino_model{os.sep}')
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cmd = f"mo --input_model {file.with_suffix('.onnx')} --output_dir {f} --data_type {'FP16' if half else 'FP32'}"
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subprocess.check_output(cmd.split()) # export
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with open(Path(f) / file.with_suffix('.yaml').name, 'w') as g:
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yaml.dump({'stride': int(max(model.stride)), 'names': model.names}, g) # add metadata.yaml
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LOGGER.info(f'{prefix} export success, saved as {f} ({file_size(f):.1f} MB)')
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return f
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except Exception as e:
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LOGGER.info(f'\n{prefix} export failure: {e}')
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cmd = f"mo --input_model {file.with_suffix('.onnx')} --output_dir {f} --data_type {'FP16' if half else 'FP32'}"
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subprocess.check_output(cmd.split()) # export
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with open(Path(f) / file.with_suffix('.yaml').name, 'w') as g:
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yaml.dump({'stride': int(max(model.stride)), 'names': model.names}, g) # add metadata.yaml
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return f, None
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@try_export
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def export_coreml(model, im, file, int8, half, prefix=colorstr('CoreML:')):
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# YOLOv5 CoreML export
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try:
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check_requirements(('coremltools',))
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import coremltools as ct
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check_requirements(('coremltools',))
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import coremltools as ct
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LOGGER.info(f'\n{prefix} starting export with coremltools {ct.__version__}...')
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f = file.with_suffix('.mlmodel')
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LOGGER.info(f'\n{prefix} starting export with coremltools {ct.__version__}...')
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f = file.with_suffix('.mlmodel')
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ts = torch.jit.trace(model, im, strict=False) # TorchScript model
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ct_model = ct.convert(ts, inputs=[ct.ImageType('image', shape=im.shape, scale=1 / 255, bias=[0, 0, 0])])
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bits, mode = (8, 'kmeans_lut') if int8 else (16, 'linear') if half else (32, None)
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if bits < 32:
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if platform.system() == 'Darwin': # quantization only supported on macOS
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with warnings.catch_warnings():
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warnings.filterwarnings("ignore", category=DeprecationWarning) # suppress numpy==1.20 float warning
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ct_model = ct.models.neural_network.quantization_utils.quantize_weights(ct_model, bits, mode)
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else:
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print(f'{prefix} quantization only supported on macOS, skipping...')
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ct_model.save(f)
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LOGGER.info(f'{prefix} export success, saved as {f} ({file_size(f):.1f} MB)')
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return ct_model, f
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except Exception as e:
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LOGGER.info(f'\n{prefix} export failure: {e}')
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return None, None
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ts = torch.jit.trace(model, im, strict=False) # TorchScript model
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ct_model = ct.convert(ts, inputs=[ct.ImageType('image', shape=im.shape, scale=1 / 255, bias=[0, 0, 0])])
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bits, mode = (8, 'kmeans_lut') if int8 else (16, 'linear') if half else (32, None)
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if bits < 32:
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if platform.system() == 'Darwin': # quantization only supported on macOS
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with warnings.catch_warnings():
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warnings.filterwarnings("ignore", category=DeprecationWarning) # suppress numpy==1.20 float warning
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ct_model = ct.models.neural_network.quantization_utils.quantize_weights(ct_model, bits, mode)
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else:
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print(f'{prefix} quantization only supported on macOS, skipping...')
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ct_model.save(f)
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return f, ct_model
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def export_engine(model, im, file, train, half, dynamic, simplify, workspace=4, verbose=False):
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@try_export
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def export_engine(model, im, file, half, dynamic, simplify, workspace=4, verbose=False, prefix=colorstr('TensorRT:')):
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# YOLOv5 TensorRT export https://developer.nvidia.com/tensorrt
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prefix = colorstr('TensorRT:')
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assert im.device.type != 'cpu', 'export running on CPU but must be on GPU, i.e. `python export.py --device 0`'
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try:
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assert im.device.type != 'cpu', 'export running on CPU but must be on GPU, i.e. `python export.py --device 0`'
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try:
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import tensorrt as trt
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except Exception:
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if platform.system() == '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
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import tensorrt as trt
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except Exception:
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if platform.system() == '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
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if trt.__version__[0] == '7': # TensorRT 7 handling https://github.com/ultralytics/yolov5/issues/6012
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grid = model.model[-1].anchor_grid
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model.model[-1].anchor_grid = [a[..., :1, :1, :] for a in grid]
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export_onnx(model, im, file, 12, train, dynamic, simplify) # opset 12
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model.model[-1].anchor_grid = grid
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else: # TensorRT >= 8
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check_version(trt.__version__, '8.0.0', hard=True) # require tensorrt>=8.0.0
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export_onnx(model, im, file, 13, train, dynamic, simplify) # opset 13
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onnx = file.with_suffix('.onnx')
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if trt.__version__[0] == '7': # TensorRT 7 handling https://github.com/ultralytics/yolov5/issues/6012
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grid = model.model[-1].anchor_grid
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model.model[-1].anchor_grid = [a[..., :1, :1, :] for a in grid]
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export_onnx(model, im, file, 12, False, dynamic, simplify) # opset 12
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model.model[-1].anchor_grid = grid
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else: # TensorRT >= 8
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check_version(trt.__version__, '8.0.0', hard=True) # require tensorrt>=8.0.0
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export_onnx(model, im, file, 13, False, dynamic, simplify) # opset 13
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onnx = file.with_suffix('.onnx')
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LOGGER.info(f'\n{prefix} starting export with TensorRT {trt.__version__}...')
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assert onnx.exists(), f'failed to export ONNX file: {onnx}'
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f = file.with_suffix('.engine') # TensorRT engine file
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logger = trt.Logger(trt.Logger.INFO)
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if verbose:
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logger.min_severity = trt.Logger.Severity.VERBOSE
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LOGGER.info(f'\n{prefix} starting export with TensorRT {trt.__version__}...')
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assert onnx.exists(), f'failed to export ONNX file: {onnx}'
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f = file.with_suffix('.engine') # TensorRT engine file
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logger = trt.Logger(trt.Logger.INFO)
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if verbose:
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logger.min_severity = trt.Logger.Severity.VERBOSE
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builder = trt.Builder(logger)
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config = builder.create_builder_config()
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config.max_workspace_size = workspace * 1 << 30
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# config.set_memory_pool_limit(trt.MemoryPoolType.WORKSPACE, workspace << 30) # fix TRT 8.4 deprecation notice
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builder = trt.Builder(logger)
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config = builder.create_builder_config()
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config.max_workspace_size = workspace * 1 << 30
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# config.set_memory_pool_limit(trt.MemoryPoolType.WORKSPACE, workspace << 30) # fix TRT 8.4 deprecation notice
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flag = (1 << int(trt.NetworkDefinitionCreationFlag.EXPLICIT_BATCH))
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network = builder.create_network(flag)
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parser = trt.OnnxParser(network, logger)
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if not parser.parse_from_file(str(onnx)):
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raise RuntimeError(f'failed to load ONNX file: {onnx}')
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flag = (1 << int(trt.NetworkDefinitionCreationFlag.EXPLICIT_BATCH))
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network = builder.create_network(flag)
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parser = trt.OnnxParser(network, logger)
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if not parser.parse_from_file(str(onnx)):
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raise RuntimeError(f'failed to load ONNX file: {onnx}')
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inputs = [network.get_input(i) for i in range(network.num_inputs)]
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outputs = [network.get_output(i) for i in range(network.num_outputs)]
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LOGGER.info(f'{prefix} Network Description:')
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inputs = [network.get_input(i) for i in range(network.num_inputs)]
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outputs = [network.get_output(i) for i in range(network.num_outputs)]
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LOGGER.info(f'{prefix} Network Description:')
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for inp in inputs:
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LOGGER.info(f'{prefix}\tinput "{inp.name}" with shape {inp.shape} and dtype {inp.dtype}')
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for out in outputs:
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LOGGER.info(f'{prefix}\toutput "{out.name}" with shape {out.shape} and dtype {out.dtype}')
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if dynamic:
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if im.shape[0] <= 1:
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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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LOGGER.info(f'{prefix}\tinput "{inp.name}" with shape {inp.shape} and dtype {inp.dtype}')
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for out in outputs:
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LOGGER.info(f'{prefix}\toutput "{out.name}" with shape {out.shape} and dtype {out.dtype}')
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profile.set_shape(inp.name, (1, *im.shape[1:]), (max(1, im.shape[0] // 2), *im.shape[1:]), im.shape)
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config.add_optimization_profile(profile)
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if dynamic:
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if im.shape[0] <= 1:
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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, *im.shape[1:]), (max(1, im.shape[0] // 2), *im.shape[1:]), im.shape)
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config.add_optimization_profile(profile)
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LOGGER.info(f'{prefix} building FP{16 if builder.platform_has_fast_fp16 and half else 32} engine in {f}')
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if builder.platform_has_fast_fp16 and half:
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config.set_flag(trt.BuilderFlag.FP16)
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with builder.build_engine(network, config) as engine, open(f, 'wb') as t:
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t.write(engine.serialize())
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LOGGER.info(f'{prefix} export success, saved as {f} ({file_size(f):.1f} MB)')
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return f
|
||||
except Exception as e:
|
||||
LOGGER.info(f'\n{prefix} export failure: {e}')
|
||||
LOGGER.info(f'{prefix} building FP{16 if builder.platform_has_fast_fp16 and half else 32} engine in {f}')
|
||||
if builder.platform_has_fast_fp16 and half:
|
||||
config.set_flag(trt.BuilderFlag.FP16)
|
||||
with builder.build_engine(network, config) as engine, open(f, 'wb') as t:
|
||||
t.write(engine.serialize())
|
||||
return f, None
|
||||
|
||||
|
||||
@try_export
|
||||
def export_saved_model(model,
|
||||
im,
|
||||
file,
|
||||
|
|
@ -296,162 +295,142 @@ def export_saved_model(model,
|
|||
keras=False,
|
||||
prefix=colorstr('TensorFlow SavedModel:')):
|
||||
# YOLOv5 TensorFlow SavedModel export
|
||||
try:
|
||||
import tensorflow as tf
|
||||
from tensorflow.python.framework.convert_to_constants import convert_variables_to_constants_v2
|
||||
import tensorflow as tf
|
||||
from tensorflow.python.framework.convert_to_constants import convert_variables_to_constants_v2
|
||||
|
||||
from models.tf import TFDetect, TFModel
|
||||
from models.tf import TFModel
|
||||
|
||||
LOGGER.info(f'\n{prefix} starting export with tensorflow {tf.__version__}...')
|
||||
f = str(file).replace('.pt', '_saved_model')
|
||||
batch_size, ch, *imgsz = list(im.shape) # BCHW
|
||||
LOGGER.info(f'\n{prefix} starting export with tensorflow {tf.__version__}...')
|
||||
f = str(file).replace('.pt', '_saved_model')
|
||||
batch_size, ch, *imgsz = list(im.shape) # BCHW
|
||||
|
||||
tf_model = TFModel(cfg=model.yaml, model=model, nc=model.nc, imgsz=imgsz)
|
||||
im = tf.zeros((batch_size, *imgsz, ch)) # BHWC order for TensorFlow
|
||||
_ = tf_model.predict(im, tf_nms, agnostic_nms, topk_per_class, topk_all, iou_thres, conf_thres)
|
||||
inputs = tf.keras.Input(shape=(*imgsz, ch), batch_size=None if dynamic else batch_size)
|
||||
outputs = tf_model.predict(inputs, tf_nms, agnostic_nms, topk_per_class, topk_all, iou_thres, conf_thres)
|
||||
keras_model = tf.keras.Model(inputs=inputs, outputs=outputs)
|
||||
keras_model.trainable = False
|
||||
keras_model.summary()
|
||||
if keras:
|
||||
keras_model.save(f, save_format='tf')
|
||||
else:
|
||||
spec = tf.TensorSpec(keras_model.inputs[0].shape, keras_model.inputs[0].dtype)
|
||||
m = tf.function(lambda x: keras_model(x)) # full model
|
||||
m = m.get_concrete_function(spec)
|
||||
frozen_func = convert_variables_to_constants_v2(m)
|
||||
tfm = tf.Module()
|
||||
tfm.__call__ = tf.function(lambda x: frozen_func(x)[:4] if tf_nms else frozen_func(x)[0], [spec])
|
||||
tfm.__call__(im)
|
||||
tf.saved_model.save(tfm,
|
||||
f,
|
||||
options=tf.saved_model.SaveOptions(experimental_custom_gradients=False)
|
||||
if check_version(tf.__version__, '2.6') else tf.saved_model.SaveOptions())
|
||||
LOGGER.info(f'{prefix} export success, saved as {f} ({file_size(f):.1f} MB)')
|
||||
return keras_model, f
|
||||
except Exception as e:
|
||||
LOGGER.info(f'\n{prefix} export failure: {e}')
|
||||
return None, None
|
||||
tf_model = TFModel(cfg=model.yaml, model=model, nc=model.nc, imgsz=imgsz)
|
||||
im = tf.zeros((batch_size, *imgsz, ch)) # BHWC order for TensorFlow
|
||||
_ = tf_model.predict(im, tf_nms, agnostic_nms, topk_per_class, topk_all, iou_thres, conf_thres)
|
||||
inputs = tf.keras.Input(shape=(*imgsz, ch), batch_size=None if dynamic else batch_size)
|
||||
outputs = tf_model.predict(inputs, tf_nms, agnostic_nms, topk_per_class, topk_all, iou_thres, conf_thres)
|
||||
keras_model = tf.keras.Model(inputs=inputs, outputs=outputs)
|
||||
keras_model.trainable = False
|
||||
keras_model.summary()
|
||||
if keras:
|
||||
keras_model.save(f, save_format='tf')
|
||||
else:
|
||||
spec = tf.TensorSpec(keras_model.inputs[0].shape, keras_model.inputs[0].dtype)
|
||||
m = tf.function(lambda x: keras_model(x)) # full model
|
||||
m = m.get_concrete_function(spec)
|
||||
frozen_func = convert_variables_to_constants_v2(m)
|
||||
tfm = tf.Module()
|
||||
tfm.__call__ = tf.function(lambda x: frozen_func(x)[:4] if tf_nms else frozen_func(x)[0], [spec])
|
||||
tfm.__call__(im)
|
||||
tf.saved_model.save(tfm,
|
||||
f,
|
||||
options=tf.saved_model.SaveOptions(experimental_custom_gradients=False) if check_version(
|
||||
tf.__version__, '2.6') else tf.saved_model.SaveOptions())
|
||||
return f, keras_model
|
||||
|
||||
|
||||
@try_export
|
||||
def export_pb(keras_model, file, prefix=colorstr('TensorFlow GraphDef:')):
|
||||
# YOLOv5 TensorFlow GraphDef *.pb export https://github.com/leimao/Frozen_Graph_TensorFlow
|
||||
try:
|
||||
import tensorflow as tf
|
||||
from tensorflow.python.framework.convert_to_constants import convert_variables_to_constants_v2
|
||||
import tensorflow as tf
|
||||
from tensorflow.python.framework.convert_to_constants import convert_variables_to_constants_v2
|
||||
|
||||
LOGGER.info(f'\n{prefix} starting export with tensorflow {tf.__version__}...')
|
||||
f = file.with_suffix('.pb')
|
||||
LOGGER.info(f'\n{prefix} starting export with tensorflow {tf.__version__}...')
|
||||
f = file.with_suffix('.pb')
|
||||
|
||||
m = tf.function(lambda x: keras_model(x)) # full model
|
||||
m = m.get_concrete_function(tf.TensorSpec(keras_model.inputs[0].shape, keras_model.inputs[0].dtype))
|
||||
frozen_func = convert_variables_to_constants_v2(m)
|
||||
frozen_func.graph.as_graph_def()
|
||||
tf.io.write_graph(graph_or_graph_def=frozen_func.graph, logdir=str(f.parent), name=f.name, as_text=False)
|
||||
|
||||
LOGGER.info(f'{prefix} export success, saved as {f} ({file_size(f):.1f} MB)')
|
||||
return f
|
||||
except Exception as e:
|
||||
LOGGER.info(f'\n{prefix} export failure: {e}')
|
||||
m = tf.function(lambda x: keras_model(x)) # full model
|
||||
m = m.get_concrete_function(tf.TensorSpec(keras_model.inputs[0].shape, keras_model.inputs[0].dtype))
|
||||
frozen_func = convert_variables_to_constants_v2(m)
|
||||
frozen_func.graph.as_graph_def()
|
||||
tf.io.write_graph(graph_or_graph_def=frozen_func.graph, logdir=str(f.parent), name=f.name, as_text=False)
|
||||
return f, None
|
||||
|
||||
|
||||
@try_export
|
||||
def export_tflite(keras_model, im, file, int8, data, nms, agnostic_nms, prefix=colorstr('TensorFlow Lite:')):
|
||||
# YOLOv5 TensorFlow Lite export
|
||||
try:
|
||||
import tensorflow as tf
|
||||
import tensorflow as tf
|
||||
|
||||
LOGGER.info(f'\n{prefix} starting export with tensorflow {tf.__version__}...')
|
||||
batch_size, ch, *imgsz = list(im.shape) # BCHW
|
||||
f = str(file).replace('.pt', '-fp16.tflite')
|
||||
LOGGER.info(f'\n{prefix} starting export with tensorflow {tf.__version__}...')
|
||||
batch_size, ch, *imgsz = list(im.shape) # BCHW
|
||||
f = str(file).replace('.pt', '-fp16.tflite')
|
||||
|
||||
converter = tf.lite.TFLiteConverter.from_keras_model(keras_model)
|
||||
converter.target_spec.supported_ops = [tf.lite.OpsSet.TFLITE_BUILTINS]
|
||||
converter.target_spec.supported_types = [tf.float16]
|
||||
converter.optimizations = [tf.lite.Optimize.DEFAULT]
|
||||
if int8:
|
||||
from models.tf import representative_dataset_gen
|
||||
dataset = LoadImages(check_dataset(check_yaml(data))['train'], img_size=imgsz, auto=False)
|
||||
converter.representative_dataset = lambda: representative_dataset_gen(dataset, ncalib=100)
|
||||
converter.target_spec.supported_ops = [tf.lite.OpsSet.TFLITE_BUILTINS_INT8]
|
||||
converter.target_spec.supported_types = []
|
||||
converter.inference_input_type = tf.uint8 # or tf.int8
|
||||
converter.inference_output_type = tf.uint8 # or tf.int8
|
||||
converter.experimental_new_quantizer = True
|
||||
f = str(file).replace('.pt', '-int8.tflite')
|
||||
if nms or agnostic_nms:
|
||||
converter.target_spec.supported_ops.append(tf.lite.OpsSet.SELECT_TF_OPS)
|
||||
converter = tf.lite.TFLiteConverter.from_keras_model(keras_model)
|
||||
converter.target_spec.supported_ops = [tf.lite.OpsSet.TFLITE_BUILTINS]
|
||||
converter.target_spec.supported_types = [tf.float16]
|
||||
converter.optimizations = [tf.lite.Optimize.DEFAULT]
|
||||
if int8:
|
||||
from models.tf import representative_dataset_gen
|
||||
dataset = LoadImages(check_dataset(check_yaml(data))['train'], img_size=imgsz, auto=False)
|
||||
converter.representative_dataset = lambda: representative_dataset_gen(dataset, ncalib=100)
|
||||
converter.target_spec.supported_ops = [tf.lite.OpsSet.TFLITE_BUILTINS_INT8]
|
||||
converter.target_spec.supported_types = []
|
||||
converter.inference_input_type = tf.uint8 # or tf.int8
|
||||
converter.inference_output_type = tf.uint8 # or tf.int8
|
||||
converter.experimental_new_quantizer = True
|
||||
f = str(file).replace('.pt', '-int8.tflite')
|
||||
if nms or agnostic_nms:
|
||||
converter.target_spec.supported_ops.append(tf.lite.OpsSet.SELECT_TF_OPS)
|
||||
|
||||
tflite_model = converter.convert()
|
||||
open(f, "wb").write(tflite_model)
|
||||
LOGGER.info(f'{prefix} export success, saved as {f} ({file_size(f):.1f} MB)')
|
||||
return f
|
||||
except Exception as e:
|
||||
LOGGER.info(f'\n{prefix} export failure: {e}')
|
||||
tflite_model = converter.convert()
|
||||
open(f, "wb").write(tflite_model)
|
||||
return f, None
|
||||
|
||||
|
||||
@try_export
|
||||
def export_edgetpu(file, prefix=colorstr('Edge TPU:')):
|
||||
# YOLOv5 Edge TPU export https://coral.ai/docs/edgetpu/models-intro/
|
||||
try:
|
||||
cmd = 'edgetpu_compiler --version'
|
||||
help_url = 'https://coral.ai/docs/edgetpu/compiler/'
|
||||
assert platform.system() == 'Linux', f'export only supported on Linux. See {help_url}'
|
||||
if subprocess.run(f'{cmd} >/dev/null', shell=True).returncode != 0:
|
||||
LOGGER.info(f'\n{prefix} export requires Edge TPU compiler. Attempting install from {help_url}')
|
||||
sudo = subprocess.run('sudo --version >/dev/null', shell=True).returncode == 0 # sudo installed on system
|
||||
for c in (
|
||||
'curl https://packages.cloud.google.com/apt/doc/apt-key.gpg | sudo apt-key add -',
|
||||
'echo "deb https://packages.cloud.google.com/apt coral-edgetpu-stable main" | sudo tee /etc/apt/sources.list.d/coral-edgetpu.list',
|
||||
'sudo apt-get update', 'sudo apt-get install edgetpu-compiler'):
|
||||
subprocess.run(c if sudo else c.replace('sudo ', ''), shell=True, check=True)
|
||||
ver = subprocess.run(cmd, shell=True, capture_output=True, check=True).stdout.decode().split()[-1]
|
||||
cmd = 'edgetpu_compiler --version'
|
||||
help_url = 'https://coral.ai/docs/edgetpu/compiler/'
|
||||
assert platform.system() == 'Linux', f'export only supported on Linux. See {help_url}'
|
||||
if subprocess.run(f'{cmd} >/dev/null', shell=True).returncode != 0:
|
||||
LOGGER.info(f'\n{prefix} export requires Edge TPU compiler. Attempting install from {help_url}')
|
||||
sudo = subprocess.run('sudo --version >/dev/null', shell=True).returncode == 0 # sudo installed on system
|
||||
for c in (
|
||||
'curl https://packages.cloud.google.com/apt/doc/apt-key.gpg | sudo apt-key add -',
|
||||
'echo "deb https://packages.cloud.google.com/apt coral-edgetpu-stable main" | sudo tee /etc/apt/sources.list.d/coral-edgetpu.list',
|
||||
'sudo apt-get update', 'sudo apt-get install edgetpu-compiler'):
|
||||
subprocess.run(c if sudo else c.replace('sudo ', ''), shell=True, check=True)
|
||||
ver = subprocess.run(cmd, shell=True, capture_output=True, check=True).stdout.decode().split()[-1]
|
||||
|
||||
LOGGER.info(f'\n{prefix} starting export with Edge TPU compiler {ver}...')
|
||||
f = str(file).replace('.pt', '-int8_edgetpu.tflite') # Edge TPU model
|
||||
f_tfl = str(file).replace('.pt', '-int8.tflite') # TFLite model
|
||||
LOGGER.info(f'\n{prefix} starting export with Edge TPU compiler {ver}...')
|
||||
f = str(file).replace('.pt', '-int8_edgetpu.tflite') # Edge TPU model
|
||||
f_tfl = str(file).replace('.pt', '-int8.tflite') # TFLite model
|
||||
|
||||
cmd = f"edgetpu_compiler -s -d -k 10 --out_dir {file.parent} {f_tfl}"
|
||||
subprocess.run(cmd.split(), check=True)
|
||||
|
||||
LOGGER.info(f'{prefix} export success, saved as {f} ({file_size(f):.1f} MB)')
|
||||
return f
|
||||
except Exception as e:
|
||||
LOGGER.info(f'\n{prefix} export failure: {e}')
|
||||
cmd = f"edgetpu_compiler -s -d -k 10 --out_dir {file.parent} {f_tfl}"
|
||||
subprocess.run(cmd.split(), check=True)
|
||||
return f, None
|
||||
|
||||
|
||||
@try_export
|
||||
def export_tfjs(file, prefix=colorstr('TensorFlow.js:')):
|
||||
# YOLOv5 TensorFlow.js export
|
||||
try:
|
||||
check_requirements(('tensorflowjs',))
|
||||
import re
|
||||
check_requirements(('tensorflowjs',))
|
||||
import re
|
||||
|
||||
import tensorflowjs as tfjs
|
||||
import tensorflowjs as tfjs
|
||||
|
||||
LOGGER.info(f'\n{prefix} starting export with tensorflowjs {tfjs.__version__}...')
|
||||
f = str(file).replace('.pt', '_web_model') # js dir
|
||||
f_pb = file.with_suffix('.pb') # *.pb path
|
||||
f_json = f'{f}/model.json' # *.json path
|
||||
LOGGER.info(f'\n{prefix} starting export with tensorflowjs {tfjs.__version__}...')
|
||||
f = str(file).replace('.pt', '_web_model') # js dir
|
||||
f_pb = file.with_suffix('.pb') # *.pb path
|
||||
f_json = f'{f}/model.json' # *.json path
|
||||
|
||||
cmd = f'tensorflowjs_converter --input_format=tf_frozen_model ' \
|
||||
f'--output_node_names=Identity,Identity_1,Identity_2,Identity_3 {f_pb} {f}'
|
||||
subprocess.run(cmd.split())
|
||||
cmd = f'tensorflowjs_converter --input_format=tf_frozen_model ' \
|
||||
f'--output_node_names=Identity,Identity_1,Identity_2,Identity_3 {f_pb} {f}'
|
||||
subprocess.run(cmd.split())
|
||||
|
||||
json = Path(f_json).read_text()
|
||||
with open(f_json, 'w') as j: # sort JSON Identity_* in ascending order
|
||||
subst = re.sub(
|
||||
r'{"outputs": {"Identity.?.?": {"name": "Identity.?.?"}, '
|
||||
r'"Identity.?.?": {"name": "Identity.?.?"}, '
|
||||
r'"Identity.?.?": {"name": "Identity.?.?"}, '
|
||||
r'"Identity.?.?": {"name": "Identity.?.?"}}}', r'{"outputs": {"Identity": {"name": "Identity"}, '
|
||||
r'"Identity_1": {"name": "Identity_1"}, '
|
||||
r'"Identity_2": {"name": "Identity_2"}, '
|
||||
r'"Identity_3": {"name": "Identity_3"}}}', json)
|
||||
j.write(subst)
|
||||
|
||||
LOGGER.info(f'{prefix} export success, saved as {f} ({file_size(f):.1f} MB)')
|
||||
return f
|
||||
except Exception as e:
|
||||
LOGGER.info(f'\n{prefix} export failure: {e}')
|
||||
json = Path(f_json).read_text()
|
||||
with open(f_json, 'w') as j: # sort JSON Identity_* in ascending order
|
||||
subst = re.sub(
|
||||
r'{"outputs": {"Identity.?.?": {"name": "Identity.?.?"}, '
|
||||
r'"Identity.?.?": {"name": "Identity.?.?"}, '
|
||||
r'"Identity.?.?": {"name": "Identity.?.?"}, '
|
||||
r'"Identity.?.?": {"name": "Identity.?.?"}}}', r'{"outputs": {"Identity": {"name": "Identity"}, '
|
||||
r'"Identity_1": {"name": "Identity_1"}, '
|
||||
r'"Identity_2": {"name": "Identity_2"}, '
|
||||
r'"Identity_3": {"name": "Identity_3"}}}', json)
|
||||
j.write(subst)
|
||||
return f, None
|
||||
|
||||
|
||||
@smart_inference_mode()
|
||||
|
|
@ -524,22 +503,22 @@ def run(
|
|||
f = [''] * 10 # exported filenames
|
||||
warnings.filterwarnings(action='ignore', category=torch.jit.TracerWarning) # suppress TracerWarning
|
||||
if jit:
|
||||
f[0] = export_torchscript(model, im, file, optimize)
|
||||
f[0], _ = export_torchscript(model, im, file, optimize)
|
||||
if engine: # TensorRT required before ONNX
|
||||
f[1] = export_engine(model, im, file, train, half, dynamic, simplify, workspace, verbose)
|
||||
f[1], _ = export_engine(model, im, file, half, dynamic, simplify, workspace, verbose)
|
||||
if onnx or xml: # OpenVINO requires ONNX
|
||||
f[2] = export_onnx(model, im, file, opset, train, dynamic, simplify)
|
||||
f[2], _ = export_onnx(model, im, file, opset, train, dynamic, simplify)
|
||||
if xml: # OpenVINO
|
||||
f[3] = export_openvino(model, file, half)
|
||||
f[3], _ = export_openvino(model, file, half)
|
||||
if coreml:
|
||||
_, f[4] = export_coreml(model, im, file, int8, half)
|
||||
f[4], _ = export_coreml(model, im, file, int8, half)
|
||||
|
||||
# TensorFlow Exports
|
||||
if any((saved_model, pb, tflite, edgetpu, tfjs)):
|
||||
if int8 or edgetpu: # TFLite --int8 bug https://github.com/ultralytics/yolov5/issues/5707
|
||||
check_requirements(('flatbuffers==1.12',)) # required before `import tensorflow`
|
||||
assert not tflite or not tfjs, 'TFLite and TF.js models must be exported separately, please pass only one type.'
|
||||
model, f[5] = export_saved_model(model.cpu(),
|
||||
f[5], model = export_saved_model(model.cpu(),
|
||||
im,
|
||||
file,
|
||||
dynamic,
|
||||
|
|
@ -551,19 +530,19 @@ def run(
|
|||
conf_thres=conf_thres,
|
||||
keras=keras)
|
||||
if pb or tfjs: # pb prerequisite to tfjs
|
||||
f[6] = export_pb(model, file)
|
||||
f[6], _ = export_pb(model, file)
|
||||
if tflite or edgetpu:
|
||||
f[7] = export_tflite(model, im, file, int8=int8 or edgetpu, data=data, nms=nms, agnostic_nms=agnostic_nms)
|
||||
f[7], _ = export_tflite(model, im, file, int8 or edgetpu, data=data, nms=nms, agnostic_nms=agnostic_nms)
|
||||
if edgetpu:
|
||||
f[8] = export_edgetpu(file)
|
||||
f[8], _ = export_edgetpu(file)
|
||||
if tfjs:
|
||||
f[9] = export_tfjs(file)
|
||||
f[9], _ = export_tfjs(file)
|
||||
|
||||
# Finish
|
||||
f = [str(x) for x in f if x] # filter out '' and None
|
||||
if any(f):
|
||||
h = '--half' if half else '' # --half FP16 inference arg
|
||||
LOGGER.info(f'\nExport complete ({time.time() - t:.2f}s)'
|
||||
LOGGER.info(f'\nExport complete ({time.time() - t:.1f}s)'
|
||||
f"\nResults saved to {colorstr('bold', file.parent.resolve())}"
|
||||
f"\nDetect: python detect.py --weights {f[-1]} {h}"
|
||||
f"\nValidate: python val.py --weights {f[-1]} {h}"
|
||||
|
|
|
|||
|
|
@ -148,6 +148,7 @@ class Profile(contextlib.ContextDecorator):
|
|||
|
||||
def __enter__(self):
|
||||
self.start = self.time()
|
||||
return self
|
||||
|
||||
def __exit__(self, type, value, traceback):
|
||||
self.dt = self.time() - self.start # delta-time
|
||||
|
|
@ -220,10 +221,10 @@ def methods(instance):
|
|||
return [f for f in dir(instance) if callable(getattr(instance, f)) and not f.startswith("__")]
|
||||
|
||||
|
||||
def print_args(args: Optional[dict] = None, show_file=True, show_fcn=False):
|
||||
def print_args(args: Optional[dict] = None, show_file=True, show_func=False):
|
||||
# Print function arguments (optional args dict)
|
||||
x = inspect.currentframe().f_back # previous frame
|
||||
file, _, fcn, _, _ = inspect.getframeinfo(x)
|
||||
file, _, func, _, _ = inspect.getframeinfo(x)
|
||||
if args is None: # get args automatically
|
||||
args, _, _, frm = inspect.getargvalues(x)
|
||||
args = {k: v for k, v in frm.items() if k in args}
|
||||
|
|
@ -231,7 +232,7 @@ def print_args(args: Optional[dict] = None, show_file=True, show_fcn=False):
|
|||
file = Path(file).resolve().relative_to(ROOT).with_suffix('')
|
||||
except ValueError:
|
||||
file = Path(file).stem
|
||||
s = (f'{file}: ' if show_file else '') + (f'{fcn}: ' if show_fcn else '')
|
||||
s = (f'{file}: ' if show_file else '') + (f'{func}: ' if show_func else '')
|
||||
LOGGER.info(colorstr(s) + ', '.join(f'{k}={v}' for k, v in args.items()))
|
||||
|
||||
|
||||
|
|
@ -255,7 +256,13 @@ def init_seeds(seed=0, deterministic=False):
|
|||
|
||||
def intersect_dicts(da, db, exclude=()):
|
||||
# Dictionary intersection of matching keys and shapes, omitting 'exclude' keys, using da values
|
||||
return {k: v for k, v in da.items() if k in db and not any(x in k for x in exclude) and v.shape == db[k].shape}
|
||||
return {k: v for k, v in da.items() if k in db and all(x not in k for x in exclude) and v.shape == db[k].shape}
|
||||
|
||||
|
||||
def get_default_args(func):
|
||||
# Get func() default arguments
|
||||
signature = inspect.signature(func)
|
||||
return {k: v.default for k, v in signature.parameters.items() if v.default is not inspect.Parameter.empty}
|
||||
|
||||
|
||||
def get_latest_run(search_dir='.'):
|
||||
|
|
|
|||
Loading…
Reference in New Issue