class Exporter:
"""
Exporter
A class for exporting a model.
Attributes:
args (OmegaConf): Configuration for the exporter.
save_dir (Path): Directory to save results.
"""
def __init__(self, config=DEFAULT_CONFIG, overrides=None):
"""
Initializes the Exporter class.
Args:
config (str, optional): Path to a configuration file. Defaults to DEFAULT_CONFIG.
overrides (dict, optional): Configuration overrides. Defaults to None.
"""
if overrides is None:
overrides = {}
self.args = get_config(config, overrides)
self.callbacks = defaultdict(list, {k: [v] for k, v in callbacks.default_callbacks.items()}) # add callbacks
callbacks.add_integration_callbacks(self)
@smart_inference_mode()
def __call__(self, model=None):
self.run_callbacks("on_export_start")
t = time.time()
format = self.args.format.lower() # to lowercase
fmts = tuple(export_formats()['Argument'][1:]) # available export formats
flags = [x == format for x in fmts]
assert sum(flags), f'ERROR: Invalid format={format}, valid formats are {fmts}'
jit, onnx, xml, engine, coreml, saved_model, pb, tflite, edgetpu, tfjs, paddle = flags # export booleans
# Load PyTorch model
self.device = select_device('cpu' if self.args.device is None else self.args.device)
if self.args.half:
if self.device.type == 'cpu' and not coreml:
LOGGER.info('half=True only compatible with GPU or CoreML export, i.e. use device=0 or format=coreml')
self.args.half = False
assert not self.args.dynamic, '--half not compatible with --dynamic, i.e. use either --half or --dynamic'
# Checks
# if self.args.batch == model.args['batch_size']: # user has not modified training batch_size
self.args.batch = 1
self.imgsz = check_imgsz(self.args.imgsz, stride=model.stride, min_dim=2) # check image size
if self.args.optimize:
assert self.device.type == 'cpu', '--optimize not compatible with cuda devices, i.e. use --device cpu'
# Input
im = torch.zeros(self.args.batch, 3, *self.imgsz).to(self.device)
file = Path(getattr(model, 'pt_path', None) or model.yaml['yaml_file'])
if file.suffix == '.yaml':
file = Path(file.name)
# Update model
model = deepcopy(model).to(self.device)
for p in model.parameters():
p.requires_grad = False
model.eval()
model = model.fuse()
for k, m in model.named_modules():
if isinstance(m, (Detect, Segment)):
m.dynamic = self.args.dynamic
m.export = True
y = None
for _ in range(2):
y = model(im) # dry runs
if self.args.half and not coreml:
im, model = im.half(), model.half() # to FP16
shape = tuple((y[0] if isinstance(y, tuple) else y).shape) # model output shape
LOGGER.info(
f"\n{colorstr('PyTorch:')} starting from {file} with output shape {shape} ({file_size(file):.1f} MB)")
# Warnings
warnings.filterwarnings('ignore', category=torch.jit.TracerWarning) # suppress TracerWarning
warnings.filterwarnings('ignore', category=UserWarning) # suppress shape prim::Constant missing ONNX warning
warnings.filterwarnings('ignore', category=DeprecationWarning) # suppress CoreML np.bool deprecation warning
# Assign
self.im = im
self.model = model
self.file = file
self.output_shape = tuple(y.shape) if isinstance(y, torch.Tensor) else (x.shape for x in y)
self.metadata = {'stride': int(max(model.stride)), 'names': model.names} # model metadata
self.pretty_name = self.file.stem.replace('yolo', 'YOLO')
# Exports
f = [''] * len(fmts) # exported filenames
if jit: # TorchScript
f[0], _ = self._export_torchscript()
if engine: # TensorRT required before ONNX
f[1], _ = self._export_engine()
if onnx or xml: # OpenVINO requires ONNX
f[2], _ = self._export_onnx()
if xml: # OpenVINO
f[3], _ = self._export_openvino()
if coreml: # CoreML
f[4], _ = self._export_coreml()
if any((saved_model, pb, tflite, edgetpu, tfjs)): # TensorFlow formats
raise NotImplementedError('YOLOv8 TensorFlow export support is still under development. '
'Please consider contributing to the effort if you have TF expertise. Thank you!')
assert not isinstance(model, ClassificationModel), 'ClassificationModel TF exports not yet supported.'
nms = False
f[5], s_model = self._export_saved_model(nms=nms or self.args.agnostic_nms or tfjs,
agnostic_nms=self.args.agnostic_nms or tfjs)
if pb or tfjs: # pb prerequisite to tfjs
f[6], _ = self._export_pb(s_model)
if tflite or edgetpu:
f[7], _ = self._export_tflite(s_model,
int8=self.args.int8 or edgetpu,
data=self.args.data,
nms=nms,
agnostic_nms=self.args.agnostic_nms)
if edgetpu:
f[8], _ = self._export_edgetpu()
self._add_tflite_metadata(f[8] or f[7], num_outputs=len(s_model.outputs))
if tfjs:
f[9], _ = self._export_tfjs()
if paddle: # PaddlePaddle
f[10], _ = self._export_paddle()
# Finish
f = [str(x) for x in f if x] # filter out '' and None
if any(f):
task = guess_task_from_head(model.yaml["head"][-1][-2])
s = "-WARNING ⚠️ not yet supported for YOLOv8 exported models"
LOGGER.info(f'\nExport complete ({time.time() - t:.1f}s)'
f"\nResults saved to {colorstr('bold', file.parent.resolve())}"
f"\nPredict: yolo task={task} mode=predict model={f[-1]} {s}"
f"\nValidate: yolo task={task} mode=val model={f[-1]} {s}"
f"\nVisualize: https://netron.app")
self.run_callbacks("on_export_end")
return f # return list of exported files/dirs
@try_export
def _export_torchscript(self, prefix=colorstr('TorchScript:')):
# YOLOv5 TorchScript model export
LOGGER.info(f'\n{prefix} starting export with torch {torch.__version__}...')
f = self.file.with_suffix('.torchscript')
ts = torch.jit.trace(self.model, self.im, strict=False)
d = {"shape": self.im.shape, "stride": int(max(self.model.stride)), "names": self.model.names}
extra_files = {'config.txt': json.dumps(d)} # torch._C.ExtraFilesMap()
if self.args.optimize: # https://pytorch.org/tutorials/recipes/mobile_interpreter.html
LOGGER.info(f'{prefix} optimizing for mobile...')
from torch.utils.mobile_optimizer import optimize_for_mobile
optimize_for_mobile(ts)._save_for_lite_interpreter(str(f), _extra_files=extra_files)
else:
ts.save(str(f), _extra_files=extra_files)
return f, None
@try_export
def _export_onnx(self, prefix=colorstr('ONNX:')):
# YOLOv5 ONNX export
check_requirements('onnx>=1.12.0')
import onnx # noqa
LOGGER.info(f'\n{prefix} starting export with onnx {onnx.__version__}...')
f = str(self.file.with_suffix('.onnx'))
output_names = ['output0', 'output1'] if isinstance(self.model, SegmentationModel) else ['output0']
dynamic = self.args.dynamic
if dynamic:
dynamic = {'images': {0: 'batch', 2: 'height', 3: 'width'}} # shape(1,3,640,640)
if isinstance(self.model, SegmentationModel):
dynamic['output0'] = {0: 'batch', 1: 'anchors'} # shape(1,25200,85)
dynamic['output1'] = {0: 'batch', 2: 'mask_height', 3: 'mask_width'} # shape(1,32,160,160)
elif isinstance(self.model, DetectionModel):
dynamic['output0'] = {0: 'batch', 1: 'anchors'} # shape(1,25200,85)
torch.onnx.export(
self.model.cpu() if dynamic else self.model, # --dynamic only compatible with cpu
self.im.cpu() if dynamic else self.im,
f,
verbose=False,
opset_version=self.args.opset,
do_constant_folding=True, # WARNING: DNN inference with torch>=1.12 may require do_constant_folding=False
input_names=['images'],
output_names=output_names,
dynamic_axes=dynamic or None)
# Checks
model_onnx = onnx.load(f) # load onnx model
onnx.checker.check_model(model_onnx) # check onnx model
# Metadata
d = {'stride': int(max(self.model.stride)), 'names': self.model.names}
for k, v in d.items():
meta = model_onnx.metadata_props.add()
meta.key, meta.value = k, str(v)
onnx.save(model_onnx, f)
# Simplify
if self.args.simplify:
try:
check_requirements('onnxsim')
import onnxsim
LOGGER.info(f'{prefix} simplifying with onnx-simplifier {onnxsim.__version__}...')
subprocess.run(f'onnxsim {f} {f}', shell=True)
except Exception as e:
LOGGER.info(f'{prefix} simplifier failure: {e}')
return f, model_onnx
@try_export
def _export_openvino(self, prefix=colorstr('OpenVINO:')):
# YOLOv5 OpenVINO export
check_requirements('openvino-dev') # requires openvino-dev: https://pypi.org/project/openvino-dev/
import openvino.inference_engine as ie # noqa
LOGGER.info(f'\n{prefix} starting export with openvino {ie.__version__}...')
f = str(self.file).replace(self.file.suffix, f'_openvino_model{os.sep}')
f_onnx = self.file.with_suffix('.onnx')
cmd = f"mo --input_model {f_onnx} --output_dir {f} --data_type {'FP16' if self.args.half else 'FP32'}"
subprocess.run(cmd.split(), check=True, env=os.environ) # export
yaml_save(Path(f) / self.file.with_suffix('.yaml').name, self.metadata) # add metadata.yaml
return f, None
@try_export
def _export_paddle(self, prefix=colorstr('PaddlePaddle:')):
# YOLOv5 Paddle export
check_requirements(('paddlepaddle', 'x2paddle'))
import x2paddle # noqa
from x2paddle.convert import pytorch2paddle # noqa
LOGGER.info(f'\n{prefix} starting export with X2Paddle {x2paddle.__version__}...')
f = str(self.file).replace(self.file.suffix, f'_paddle_model{os.sep}')
pytorch2paddle(module=self.model, save_dir=f, jit_type='trace', input_examples=[self.im]) # export
yaml_save(Path(f) / self.file.with_suffix('.yaml').name, self.metadata) # add metadata.yaml
return f, None
@try_export
def _export_coreml(self, prefix=colorstr('CoreML:')):
# YOLOv5 CoreML export
check_requirements('coremltools>=6.0')
import coremltools as ct # noqa
class iOSModel(torch.nn.Module):
# Wrap an Ultralytics YOLO model for iOS export
def __init__(self, model, im):
super().__init__()
b, c, h, w = im.shape # batch, channel, height, width
self.model = model
self.nc = len(model.names) # number of classes
if w == h:
self.normalize = 1.0 / w # scalar
else:
self.normalize = torch.tensor([1.0 / w, 1.0 / h, 1.0 / w, 1.0 / h]) # broadcast (slower, smaller)
def forward(self, x):
xywh, cls = self.model(x)[0].transpose(0, 1).split((4, self.nc), 1)
return cls, xywh * self.normalize # confidence (3780, 80), coordinates (3780, 4)
LOGGER.info(f'\n{prefix} starting export with coremltools {ct.__version__}...')
f = self.file.with_suffix('.mlmodel')
model = iOSModel(self.model, self.im) if self.args.nms else self.model
ts = torch.jit.trace(model, self.im, strict=False) # TorchScript model
ct_model = ct.convert(ts, inputs=[ct.ImageType('image', shape=self.im.shape, scale=1 / 255, bias=[0, 0, 0])])
bits, mode = (8, 'kmeans_lut') if self.args.int8 else (16, 'linear') if self.args.half else (32, None)
if bits < 32:
if MACOS: # quantization only supported on macOS
ct_model = ct.models.neural_network.quantization_utils.quantize_weights(ct_model, bits, mode)
else:
LOGGER.info(f'{prefix} quantization only supported on macOS, skipping...')
if self.args.nms:
ct_model = self._pipeline_coreml(ct_model)
ct_model.save(str(f))
return f, ct_model
@try_export
def _export_engine(self, workspace=4, verbose=False, prefix=colorstr('TensorRT:')):
# YOLOv5 TensorRT export https://developer.nvidia.com/tensorrt
assert self.im.device.type != 'cpu', 'export running on CPU but must be on GPU, i.e. `device==0`'
try:
import tensorrt as trt # noqa
except ImportError:
if platform.system() == 'Linux':
check_requirements('nvidia-tensorrt', cmds='-U --index-url https://pypi.ngc.nvidia.com')
import tensorrt as trt # noqa
check_version(trt.__version__, '7.0.0', hard=True) # require tensorrt>=8.0.0
self._export_onnx()
onnx = self.file.with_suffix('.onnx')
LOGGER.info(f'\n{prefix} starting export with TensorRT {trt.__version__}...')
assert onnx.exists(), f'failed to export ONNX file: {onnx}'
f = self.file.with_suffix('.engine') # TensorRT engine file
logger = trt.Logger(trt.Logger.INFO)
if verbose:
logger.min_severity = trt.Logger.Severity.VERBOSE
builder = trt.Builder(logger)
config = builder.create_builder_config()
config.max_workspace_size = workspace * 1 << 30
# config.set_memory_pool_limit(trt.MemoryPoolType.WORKSPACE, workspace << 30) # fix TRT 8.4 deprecation notice
flag = (1 << int(trt.NetworkDefinitionCreationFlag.EXPLICIT_BATCH))
network = builder.create_network(flag)
parser = trt.OnnxParser(network, logger)
if not parser.parse_from_file(str(onnx)):
raise RuntimeError(f'failed to load ONNX file: {onnx}')
inputs = [network.get_input(i) for i in range(network.num_inputs)]
outputs = [network.get_output(i) for i in range(network.num_outputs)]
for inp in inputs:
LOGGER.info(f'{prefix} input "{inp.name}" with shape{inp.shape} {inp.dtype}')
for out in outputs:
LOGGER.info(f'{prefix} output "{out.name}" with shape{out.shape} {out.dtype}')
if self.args.dynamic:
shape = self.im.shape
if shape[0] <= 1:
LOGGER.warning(f"{prefix} WARNING ⚠️ --dynamic model requires maximum --batch-size argument")
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)
config.add_optimization_profile(profile)
LOGGER.info(
f'{prefix} building FP{16 if builder.platform_has_fast_fp16 and self.args.half else 32} engine as {f}')
if builder.platform_has_fast_fp16 and self.args.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(self,
nms=False,
agnostic_nms=False,
topk_per_class=100,
topk_all=100,
iou_thres=0.45,
conf_thres=0.25,
prefix=colorstr('TensorFlow SavedModel:')):
# YOLOv5 TensorFlow SavedModel export
try:
import tensorflow as tf # noqa
except ImportError:
check_requirements(f"tensorflow{'' if torch.cuda.is_available() else '-macos' if MACOS else '-cpu'}")
import tensorflow as tf # noqa
check_requirements(("onnx", "onnx2tf", "sng4onnx", "onnxsim", "onnx_graphsurgeon"),
cmds="--extra-index-url https://pypi.ngc.nvidia.com ")
LOGGER.info(f'\n{prefix} starting export with tensorflow {tf.__version__}...')
f = str(self.file).replace(self.file.suffix, '_saved_model')
# Export to ONNX
self._export_onnx()
onnx = self.file.with_suffix('.onnx')
# Export to TF SavedModel
subprocess.run(f'onnx2tf -i {onnx} --output_signaturedefs -o {f}', shell=True)
# Load saved_model
keras_model = tf.saved_model.load(f, tags=None, options=None)
return f, keras_model
@try_export
def _export_saved_model_OLD(self,
nms=False,
agnostic_nms=False,
topk_per_class=100,
topk_all=100,
iou_thres=0.45,
conf_thres=0.25,
prefix=colorstr('TensorFlow SavedModel:')):
# YOLOv5 TensorFlow SavedModel export
try:
import tensorflow as tf # noqa
except ImportError:
check_requirements(f"tensorflow{'' if torch.cuda.is_available() else '-macos' if MACOS else '-cpu'}")
import tensorflow as tf # noqa
# from models.tf import TFModel
from tensorflow.python.framework.convert_to_constants import convert_variables_to_constants_v2 # noqa
LOGGER.info(f'\n{prefix} starting export with tensorflow {tf.__version__}...')
f = str(self.file).replace(self.file.suffix, '_saved_model')
batch_size, ch, *imgsz = list(self.im.shape) # BCHW
tf_models = None # TODO: no TF modules available
tf_model = tf_models.TFModel(cfg=self.model.yaml, model=self.model.cpu(), nc=self.model.nc, imgsz=imgsz)
im = tf.zeros((batch_size, *imgsz, ch)) # BHWC order for TensorFlow
_ = tf_model.predict(im, nms, agnostic_nms, topk_per_class, topk_all, iou_thres, conf_thres)
inputs = tf.keras.Input(shape=(*imgsz, ch), batch_size=None if self.args.dynamic else batch_size)
outputs = tf_model.predict(inputs, 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 self.args.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 nms else frozen_func(x), [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(self, keras_model, file, prefix=colorstr('TensorFlow GraphDef:')):
# YOLOv5 TensorFlow GraphDef *.pb export https://github.com/leimao/Frozen_Graph_TensorFlow
import tensorflow as tf # noqa
from tensorflow.python.framework.convert_to_constants import convert_variables_to_constants_v2 # noqa
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)
return f, None
@try_export
def _export_tflite(self, keras_model, int8, data, nms, agnostic_nms, prefix=colorstr('TensorFlow Lite:')):
# YOLOv5 TensorFlow Lite export
import tensorflow as tf # noqa
LOGGER.info(f'\n{prefix} starting export with tensorflow {tf.__version__}...')
batch_size, ch, *imgsz = list(self.im.shape) # BCHW
f = str(self.file).replace(self.file.suffix, '-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:
def representative_dataset_gen(dataset, n_images=100):
# Dataset generator for use with converter.representative_dataset, returns a generator of np arrays
for n, (path, img, im0s, vid_cap, string) in enumerate(dataset):
im = np.transpose(img, [1, 2, 0])
im = np.expand_dims(im, axis=0).astype(np.float32)
im /= 255
yield [im]
if n >= n_images:
break
dataset = LoadImages(check_dataset(check_yaml(data))['train'], imgsz=imgsz, auto=False)
converter.representative_dataset = lambda: representative_dataset_gen(dataset, n_images=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(self.file).replace(self.file.suffix, '-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)
return f, None
@try_export
def _export_edgetpu(self, prefix=colorstr('Edge TPU:')):
# YOLOv5 Edge TPU export https://coral.ai/docs/edgetpu/models-intro/
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" | ' # no comma
'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(self.file).replace(self.file.suffix, '-int8_edgetpu.tflite') # Edge TPU model
f_tfl = str(self.file).replace(self.file.suffix, '-int8.tflite') # TFLite model
cmd = f"edgetpu_compiler -s -d -k 10 --out_dir {self.file.parent} {f_tfl}"
subprocess.run(cmd.split(), check=True)
return f, None
@try_export
def _export_tfjs(self, prefix=colorstr('TensorFlow.js:')):
# YOLOv5 TensorFlow.js export
check_requirements('tensorflowjs')
import tensorflowjs as tfjs # noqa
LOGGER.info(f'\n{prefix} starting export with tensorflowjs {tfjs.__version__}...')
f = str(self.file).replace(self.file.suffix, '_web_model') # js dir
f_pb = self.file.with_suffix('.pb') # *.pb path
f_json = Path(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())
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"}}}', f_json.read_text())
j.write(subst)
return f, None
def _add_tflite_metadata(self, file, num_outputs):
# Add metadata to *.tflite models per https://www.tensorflow.org/lite/models/convert/metadata
with contextlib.suppress(ImportError):
# check_requirements('tflite_support')
from tflite_support import flatbuffers # noqa
from tflite_support import metadata as _metadata # noqa
from tflite_support import metadata_schema_py_generated as _metadata_fb # noqa
tmp_file = Path('/tmp/meta.txt')
with open(tmp_file, 'w') as meta_f:
meta_f.write(str(self.metadata))
model_meta = _metadata_fb.ModelMetadataT()
label_file = _metadata_fb.AssociatedFileT()
label_file.name = tmp_file.name
model_meta.associatedFiles = [label_file]
subgraph = _metadata_fb.SubGraphMetadataT()
subgraph.inputTensorMetadata = [_metadata_fb.TensorMetadataT()]
subgraph.outputTensorMetadata = [_metadata_fb.TensorMetadataT()] * num_outputs
model_meta.subgraphMetadata = [subgraph]
b = flatbuffers.Builder(0)
b.Finish(model_meta.Pack(b), _metadata.MetadataPopulator.METADATA_FILE_IDENTIFIER)
metadata_buf = b.Output()
populator = _metadata.MetadataPopulator.with_model_file(file)
populator.load_metadata_buffer(metadata_buf)
populator.load_associated_files([str(tmp_file)])
populator.populate()
tmp_file.unlink()
def _pipeline_coreml(self, model, prefix=colorstr('CoreML Pipeline:')):
# YOLOv5 CoreML pipeline
import coremltools as ct # noqa
LOGGER.info(f'{prefix} starting pipeline with coremltools {ct.__version__}...')
batch_size, ch, h, w = list(self.im.shape) # BCHW
# Output shapes
spec = model.get_spec()
out0, out1 = iter(spec.description.output)
if MACOS:
from PIL import Image
img = Image.new('RGB', (w, h)) # img(192 width, 320 height)
# img = torch.zeros((*opt.img_size, 3)).numpy() # img size(320,192,3) iDetection
out = model.predict({'image': img})
out0_shape = out[out0.name].shape
out1_shape = out[out1.name].shape
else: # linux and windows can not run model.predict(), get sizes from pytorch output y
out0_shape = self.output_shape[1], self.output_shape[2] - 5 # (3780, 80)
out1_shape = self.output_shape[1], 4 # (3780, 4)
# Checks
names = self.metadata['names']
nx, ny = spec.description.input[0].type.imageType.width, spec.description.input[0].type.imageType.height
na, nc = out0_shape
# na, nc = out0.type.multiArrayType.shape # number anchors, classes
assert len(names) == nc, f'{len(names)} names found for nc={nc}' # check
# Define output shapes (missing)
out0.type.multiArrayType.shape[:] = out0_shape # (3780, 80)
out1.type.multiArrayType.shape[:] = out1_shape # (3780, 4)
# spec.neuralNetwork.preprocessing[0].featureName = '0'
# Flexible input shapes
# from coremltools.models.neural_network import flexible_shape_utils
# s = [] # shapes
# s.append(flexible_shape_utils.NeuralNetworkImageSize(320, 192))
# s.append(flexible_shape_utils.NeuralNetworkImageSize(640, 384)) # (height, width)
# flexible_shape_utils.add_enumerated_image_sizes(spec, feature_name='image', sizes=s)
# r = flexible_shape_utils.NeuralNetworkImageSizeRange() # shape ranges
# r.add_height_range((192, 640))
# r.add_width_range((192, 640))
# flexible_shape_utils.update_image_size_range(spec, feature_name='image', size_range=r)
# Print
print(spec.description)
# Model from spec
model = ct.models.MLModel(spec)
# 3. Create NMS protobuf
nms_spec = ct.proto.Model_pb2.Model()
nms_spec.specificationVersion = 5
for i in range(2):
decoder_output = model._spec.description.output[i].SerializeToString()
nms_spec.description.input.add()
nms_spec.description.input[i].ParseFromString(decoder_output)
nms_spec.description.output.add()
nms_spec.description.output[i].ParseFromString(decoder_output)
nms_spec.description.output[0].name = 'confidence'
nms_spec.description.output[1].name = 'coordinates'
output_sizes = [nc, 4]
for i in range(2):
ma_type = nms_spec.description.output[i].type.multiArrayType
ma_type.shapeRange.sizeRanges.add()
ma_type.shapeRange.sizeRanges[0].lowerBound = 0
ma_type.shapeRange.sizeRanges[0].upperBound = -1
ma_type.shapeRange.sizeRanges.add()
ma_type.shapeRange.sizeRanges[1].lowerBound = output_sizes[i]
ma_type.shapeRange.sizeRanges[1].upperBound = output_sizes[i]
del ma_type.shape[:]
nms = nms_spec.nonMaximumSuppression
nms.confidenceInputFeatureName = out0.name # 1x507x80
nms.coordinatesInputFeatureName = out1.name # 1x507x4
nms.confidenceOutputFeatureName = 'confidence'
nms.coordinatesOutputFeatureName = 'coordinates'
nms.iouThresholdInputFeatureName = 'iouThreshold'
nms.confidenceThresholdInputFeatureName = 'confidenceThreshold'
nms.iouThreshold = 0.45
nms.confidenceThreshold = 0.25
nms.pickTop.perClass = True
nms.stringClassLabels.vector.extend(names.values())
nms_model = ct.models.MLModel(nms_spec)
# 4. Pipeline models together
pipeline = ct.models.pipeline.Pipeline(input_features=[('image', ct.models.datatypes.Array(3, ny, nx)),
('iouThreshold', ct.models.datatypes.Double()),
('confidenceThreshold', ct.models.datatypes.Double())],
output_features=['confidence', 'coordinates'])
pipeline.add_model(model)
pipeline.add_model(nms_model)
# Correct datatypes
pipeline.spec.description.input[0].ParseFromString(model._spec.description.input[0].SerializeToString())
pipeline.spec.description.output[0].ParseFromString(nms_model._spec.description.output[0].SerializeToString())
pipeline.spec.description.output[1].ParseFromString(nms_model._spec.description.output[1].SerializeToString())
# Update metadata
pipeline.spec.specificationVersion = 5
pipeline.spec.description.metadata.versionString = f'Ultralytics YOLOv{ultralytics.__version__}'
pipeline.spec.description.metadata.shortDescription = f'Ultralytics {self.pretty_name} CoreML model'
pipeline.spec.description.metadata.author = 'Ultralytics (https://ultralytics.com)'
pipeline.spec.description.metadata.license = 'GPL-3.0 license (https://ultralytics.com/license)'
pipeline.spec.description.metadata.userDefined.update({
'IoU threshold': str(nms.iouThreshold),
'Confidence threshold': str(nms.confidenceThreshold)})
# Save the model
model = ct.models.MLModel(pipeline.spec)
model.input_description['image'] = 'Input image'
model.input_description['iouThreshold'] = f'(optional) IOU threshold override (default: {nms.iouThreshold})'
model.input_description['confidenceThreshold'] = \
f'(optional) Confidence threshold override (default: {nms.confidenceThreshold})'
model.output_description['confidence'] = 'Boxes × Class confidence (see user-defined metadata "classes")'
model.output_description['coordinates'] = 'Boxes × [x, y, width, height] (relative to image size)'
LOGGER.info(f'{prefix} pipeline success')
return model
def run_callbacks(self, event: str):
for callback in self.callbacks.get(event, []):
callback(self)