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ultralytics 8.0.150 new Apple *.mlpackage export format (#4043)

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Co-authored-by: MLBoy_DaisukeMajima <rockyshikoku@gmail.com>
Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Co-authored-by: Louis Lac <lac.louis5@gmail.com>
This commit is contained in:
Glenn Jocher
2023-08-08 17:00:42 +02:00
committed by GitHub
parent f5fc807fa7
commit 7dfdb63cde
22 changed files with 95 additions and 59 deletions
Download Patch File Download Diff File Expand all files Collapse all files

2
ultralytics/__init__.py
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@ -1,6 +1,6 @@
# Ultralytics YOLO 🚀, AGPL-3.0 license
__version__ = '8.0.149'
__version__ = '8.0.150'
from ultralytics.hub import start
from ultralytics.models import RTDETR, SAM, YOLO

2
ultralytics/cfg/datasets/VOC.yaml
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@ -72,7 +72,7 @@ download: |
xmlbox = obj.find('bndbox')
bb = convert_box((w, h), [float(xmlbox.find(x).text) for x in ('xmin', 'xmax', 'ymin', 'ymax')])
cls_id = names.index(cls) # class id
out_file.write(" ".join([str(a) for a in (cls_id, *bb)]) + '\n')
out_file.write(" ".join(str(a) for a in (cls_id, *bb)) + '\n')
# Download

65
ultralytics/engine/exporter.py
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@ -9,7 +9,7 @@ TorchScript | `torchscript` | yolov8n.torchscript
ONNX | `onnx` | yolov8n.onnx
OpenVINO | `openvino` | yolov8n_openvino_model/
TensorRT | `engine` | yolov8n.engine
CoreML | `coreml` | yolov8n.mlmodel
CoreML | `coreml` | yolov8n.mlpackage
TensorFlow SavedModel | `saved_model` | yolov8n_saved_model/
TensorFlow GraphDef | `pb` | yolov8n.pb
TensorFlow Lite | `tflite` | yolov8n.tflite
@ -35,7 +35,7 @@ Inference:
yolov8n.onnx # ONNX Runtime or OpenCV DNN with dnn=True
yolov8n_openvino_model # OpenVINO
yolov8n.engine # TensorRT
yolov8n.mlmodel # CoreML (macOS-only)
yolov8n.mlpackage # CoreML (macOS-only)
yolov8n_saved_model # TensorFlow SavedModel
yolov8n.pb # TensorFlow GraphDef
yolov8n.tflite # TensorFlow Lite
@ -82,7 +82,7 @@ def export_formats():
['ONNX', 'onnx', '.onnx', True, True],
['OpenVINO', 'openvino', '_openvino_model', True, False],
['TensorRT', 'engine', '.engine', False, True],
['CoreML', 'coreml', '.mlmodel', True, False],
['CoreML', 'coreml', '.mlpackage', True, False],
['TensorFlow SavedModel', 'saved_model', '_saved_model', True, True],
['TensorFlow GraphDef', 'pb', '.pb', True, True],
['TensorFlow Lite', 'tflite', '.tflite', True, False],
@ -149,8 +149,10 @@ class Exporter:
self.run_callbacks('on_export_start')
t = time.time()
format = self.args.format.lower() # to lowercase
if format in ('tensorrt', 'trt'): # engine aliases
if format in ('tensorrt', 'trt'): # 'engine' aliases
format = 'engine'
if format in ('mlmodel', 'mlpackage', 'mlprogram', 'apple', 'ios'): # 'coreml' aliases
format = 'coreml'
fmts = tuple(export_formats()['Argument'][1:]) # available export formats
flags = [x == format for x in fmts]
if sum(flags) != 1:
@ -319,7 +321,7 @@ class Exporter:
dynamic['output0'] = {0: 'batch', 2: 'anchors'} # shape(1, 84, 8400)
torch.onnx.export(
self.model.cpu() if dynamic else self.model, # --dynamic only compatible with cpu
self.model.cpu() if dynamic else self.model, # dynamic=True only compatible with cpu
self.im.cpu() if dynamic else self.im,
f,
verbose=False,
@ -461,14 +463,16 @@ class Exporter:
yaml_save(f / 'metadata.yaml', self.metadata) # add metadata.yaml
return str(f), None
@try_export
def export_coreml(self, prefix=colorstr('CoreML:')):
"""YOLOv8 CoreML export."""
check_requirements('coremltools>=6.0,<=6.2')
mlmodel = self.args.format.lower() == 'mlmodel' # legacy *.mlmodel export format requested
check_requirements('coremltools>=6.0,<=6.2' if mlmodel else 'coremltools>=7.0.b1')
import coremltools as ct # noqa
LOGGER.info(f'\n{prefix} starting export with coremltools {ct.__version__}...')
f = self.file.with_suffix('.mlmodel')
f = self.file.with_suffix('.mlmodel' if mlmodel else '.mlpackage')
if f.is_dir():
shutil.rmtree(f)
bias = [0.0, 0.0, 0.0]
scale = 1 / 255
@ -479,20 +483,38 @@ class Exporter:
elif self.model.task == 'detect':
model = iOSDetectModel(self.model, self.im) if self.args.nms else self.model
else:
# TODO CoreML Segment and Pose model pipelining
if self.args.nms:
LOGGER.warning(f"{prefix} WARNING ⚠️ 'nms=True' is only available for Detect models like 'yolov8n.pt'.")
# TODO CoreML Segment and Pose model pipelining
model = self.model
ts = torch.jit.trace(model.eval(), self.im, strict=False) # TorchScript model
ct_model = ct.convert(ts,
inputs=[ct.ImageType('image', shape=self.im.shape, scale=scale, bias=bias)],
classifier_config=classifier_config)
bits, mode = (8, 'kmeans_lut') if self.args.int8 else (16, 'linear') if self.args.half else (32, None)
classifier_config=classifier_config,
convert_to='neuralnetwork' if mlmodel else 'mlprogram')
bits, mode = (8, 'kmeans') if self.args.int8 else (16, 'linear') if self.args.half else (32, None)
if bits < 32:
if 'kmeans' in mode:
check_requirements('scikit-learn') # scikit-learn package required for k-means quantization
ct_model = ct.models.neural_network.quantization_utils.quantize_weights(ct_model, bits, mode)
if mlmodel:
ct_model = ct.models.neural_network.quantization_utils.quantize_weights(ct_model, bits, mode)
else:
import coremltools.optimize.coreml as cto
op_config = cto.OpPalettizerConfig(mode=mode, nbits=bits, weight_threshold=512)
config = cto.OptimizationConfig(global_config=op_config)
ct_model = cto.palettize_weights(ct_model, config=config)
if self.args.nms and self.model.task == 'detect':
ct_model = self._pipeline_coreml(ct_model)
if mlmodel:
import platform
# coremltools<=6.2 NMS export requires Python<3.11
check_version(platform.python_version(), '<3.11', name='Python ', hard=True)
weights_dir = None
else:
ct_model.save(str(f)) # save otherwise weights_dir does not exist
weights_dir = str(f / 'Data/com.apple.CoreML/weights')
ct_model = self._pipeline_coreml(ct_model, weights_dir=weights_dir)
m = self.metadata # metadata dict
ct_model.short_description = m.pop('description')
@ -500,7 +522,14 @@ class Exporter:
ct_model.license = m.pop('license')
ct_model.version = m.pop('version')
ct_model.user_defined_metadata.update({k: str(v) for k, v in m.items()})
ct_model.save(str(f))
try:
ct_model.save(str(f)) # save *.mlpackage
except Exception as e:
LOGGER.warning(
f'{prefix} WARNING ⚠️ CoreML export to *.mlpackage failed ({e}), reverting to *.mlmodel export. '
f'Known coremltools Python 3.11 and Windows bugs https://github.com/apple/coremltools/issues/1928.')
f = f.with_suffix('.mlmodel')
ct_model.save(str(f))
return f, ct_model
@try_export
@ -546,7 +575,7 @@ class Exporter:
if self.args.dynamic:
shape = self.im.shape
if shape[0] <= 1:
LOGGER.warning(f'{prefix} WARNING ⚠️ --dynamic model requires maximum --batch-size argument')
LOGGER.warning(f"{prefix} WARNING ⚠️ 'dynamic=True' model requires max batch size, i.e. 'batch=16'")
profile = builder.create_optimization_profile()
for inp in inputs:
profile.set_shape(inp.name, (1, *shape[1:]), (max(1, shape[0] // 2), *shape[1:]), shape)
@ -805,7 +834,7 @@ class Exporter:
populator.populate()
tmp_file.unlink()
def _pipeline_coreml(self, model, prefix=colorstr('CoreML Pipeline:')):
def _pipeline_coreml(self, model, weights_dir=None, prefix=colorstr('CoreML Pipeline:')):
"""YOLOv8 CoreML pipeline."""
import coremltools as ct # noqa
@ -853,7 +882,7 @@ class Exporter:
# print(spec.description)
# Model from spec
model = ct.models.MLModel(spec)
model = ct.models.MLModel(spec, weights_dir=weights_dir)
# 3. Create NMS protobuf
nms_spec = ct.proto.Model_pb2.Model()
@ -912,7 +941,7 @@ class Exporter:
'Confidence threshold': str(nms.confidenceThreshold)})
# Save the model
model = ct.models.MLModel(pipeline.spec)
model = ct.models.MLModel(pipeline.spec, weights_dir=weights_dir)
model.input_description['image'] = 'Input image'
model.input_description['iouThreshold'] = f'(optional) IOU threshold override (default: {nms.iouThreshold})'
model.input_description['confidenceThreshold'] = \

2
ultralytics/engine/model.py
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@ -320,7 +320,7 @@ class Model:
half=overrides['half'],
int8=overrides['int8'],
device=overrides['device'],
verbose=overrides['verbose'])
verbose=kwargs.get('verbose'))
def export(self, **kwargs):
"""

2
ultralytics/engine/predictor.py
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@ -20,7 +20,7 @@ Usage - formats:
yolov8n.onnx # ONNX Runtime or OpenCV DNN with dnn=True
yolov8n_openvino_model # OpenVINO
yolov8n.engine # TensorRT
yolov8n.mlmodel # CoreML (macOS-only)
yolov8n.mlpackage # CoreML (macOS-only)
yolov8n_saved_model # TensorFlow SavedModel
yolov8n.pb # TensorFlow GraphDef
yolov8n.tflite # TensorFlow Lite

2
ultralytics/engine/validator.py
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@ -11,7 +11,7 @@ Usage - formats:
yolov8n.onnx # ONNX Runtime or OpenCV DNN with dnn=True
yolov8n_openvino_model # OpenVINO
yolov8n.engine # TensorRT
yolov8n.mlmodel # CoreML (macOS-only)
yolov8n.mlpackage # CoreML (macOS-only)
yolov8n_saved_model # TensorFlow SavedModel
yolov8n.pb # TensorFlow GraphDef
yolov8n.tflite # TensorFlow Lite

13
ultralytics/nn/autobackend.py
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@ -68,7 +68,7 @@ class AutoBackend(nn.Module):
| ONNX Runtime | *.onnx |
| ONNX OpenCV DNN | *.onnx dnn=True |
| OpenVINO | *.xml |
| CoreML | *.mlmodel |
| CoreML | *.mlpackage |
| TensorRT | *.engine |
| TensorFlow SavedModel | *_saved_model |
| TensorFlow GraphDef | *.pb |
@ -485,8 +485,13 @@ class AutoBackend(nn.Module):
sf = list(export_formats().Suffix) # export suffixes
if not is_url(p, check=False) and not isinstance(p, str):
check_suffix(p, sf) # checks
url = urlparse(p) # if url may be Triton inference server
types = [s in Path(p).name for s in sf]
name = Path(p).name
types = [s in name for s in sf]
types[5] |= name.endswith('.mlmodel') # retain support for older Apple CoreML *.mlmodel formats
types[8] &= not types[9] # tflite &= not edgetpu
triton = not any(types) and all([any(s in url.scheme for s in ['http', 'grpc']), url.netloc])
if any(types):
triton = False
else:
url = urlparse(p) # if url may be Triton inference server
triton = all([any(s in url.scheme for s in ['http', 'grpc']), url.netloc])
return types + [triton]

2
ultralytics/utils/benchmarks.py
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@ -14,7 +14,7 @@ TorchScript | `torchscript` | yolov8n.torchscript
ONNX | `onnx` | yolov8n.onnx
OpenVINO | `openvino` | yolov8n_openvino_model/
TensorRT | `engine` | yolov8n.engine
CoreML | `coreml` | yolov8n.mlmodel
CoreML | `coreml` | yolov8n.mlpackage
TensorFlow SavedModel | `saved_model` | yolov8n_saved_model/
TensorFlow GraphDef | `pb` | yolov8n.pb
TensorFlow Lite | `tflite` | yolov8n.tflite

9
ultralytics/utils/checks.py
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@ -265,20 +265,21 @@ def check_requirements(requirements=ROOT.parent / 'requirements.txt', exclude=()
elif isinstance(requirements, str):
requirements = [requirements]
s = '' # console string
pkgs = []
for r in requirements:
r_stripped = r.split('/')[-1].replace('.git', '') # replace git+https://org/repo.git -> 'repo'
try:
pkg.require(r_stripped)
except (pkg.VersionConflict, pkg.DistributionNotFound): # exception if requirements not met
pkg.require(r_stripped) # exception if requirements not met
except pkg.DistributionNotFound:
try: # attempt to import (slower but more accurate)
import importlib
importlib.import_module(next(pkg.parse_requirements(r_stripped)).name)
except ImportError:
s += f'"{r}" '
pkgs.append(r)
except pkg.VersionConflict:
pkgs.append(r)
s = ' '.join(f'"{x}"' for x in pkgs) # console string
if s:
if install and AUTOINSTALL: # check environment variable
n = len(pkgs) # number of packages updates