@ -18,8 +18,8 @@ TensorFlow.js | `tfjs` | yolov8n_web_model/
PaddlePaddle | ` paddle ` | yolov8n_paddle_model /
Requirements :
$ pip install - r requirements . txt coremltools onnx onnx - plifier onnxruntime openvino - dev tensorflow - cpu # CPU
$ pip install - r requirements . txt coremltools onnx onnx - plifier onnxruntime - gpu openvino - dev tensorflow # GPU
$ pip install - r requirements . txt coremltools onnx onnx sim onnxruntime openvino - dev tensorflow - cpu # CPU
$ pip install - r requirements . txt coremltools onnx onnx sim onnxruntime - gpu openvino - dev tensorflow # GPU
Python :
from ultralytics import YOLO
@ -69,13 +69,14 @@ from ultralytics.nn.tasks import DetectionModel, SegmentationModel
from ultralytics . yolo . cfg import get_cfg
from ultralytics . yolo . data . dataloaders . stream_loaders import LoadImages
from ultralytics . yolo . data . utils import IMAGENET_MEAN , IMAGENET_STD , check_det_dataset
from ultralytics . yolo . utils import DEFAULT_CFG , LOGGER , __version__ , callbacks , colorstr , get_default_args , yaml_save
from ultralytics . yolo . utils import ( DEFAULT_CFG , LINUX , LOGGER , MACOS , WINDOWS , __version__ , callbacks , colorstr ,
get_default_args , yaml_save )
from ultralytics . yolo . utils . checks import check_imgsz , check_requirements , check_version , check_yaml
from ultralytics . yolo . utils . files import file_size
from ultralytics . yolo . utils . ops import Profile
from ultralytics . yolo . utils . torch_utils import get_latest_opset , select_device , smart_inference_mode
MACOS = platform . system ( ) == ' Darwin ' # macOS environment
CUDA = torch . cuda . is_available ( )
def export_formats ( ) :
@ -229,25 +230,22 @@ class Exporter:
if coreml : # CoreML
f [ 4 ] , _ = self . _export_coreml ( )
if any ( ( saved_model , pb , tflite , edgetpu , tfjs ) ) : # TensorFlow formats
LOGGER . warning ( ' WARNING ⚠️ YOLOv8 TensorFlow export support is still under development. '
LOGGER . warning ( ' WARNING ⚠️ YOLOv8 TensorFlow export is still under development. '
' Please consider contributing to the effort if you have TF expertise. Thank you! ' )
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 )
debug = False
if debug :
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 )
f [ 7 ] = str ( Path ( f [ 5 ] ) / ( self . file . stem + ' _float16.tflite ' ) )
# 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 ] )
f [ 8 ] , _ = self . _export_edgetpu ( tflite_model = f [ 7 ] )
if tfjs :
f [ 9 ] , _ = self . _export_tfjs ( )
if paddle : # PaddlePaddle
@ -258,13 +256,14 @@ class Exporter:
if any ( f ) :
f = str ( Path ( f [ - 1 ] ) )
square = self . imgsz [ 0 ] == self . imgsz [ 1 ]
s = " WARNING ⚠️ non-PyTorch val requires square images, ' imgsz= { self . imgsz } ' will not work. Use " \
f " ' imgsz= { max ( self . imgsz ) } ' if val is required. " if not square else ' '
s = ' ' if square else " WARNING ⚠️ non-PyTorch val requires square images, ' imgsz= { self . imgsz } ' will not " \
f " work. Use export ' imgsz= { max ( self . imgsz ) } ' if val is required. "
imgsz = self . imgsz [ 0 ] if square else str ( self . imgsz ) [ 1 : - 1 ] . replace ( ' ' , ' ' )
data = f " data= { self . args . data } " if model . task == ' segment ' and format == ' pb ' else ' '
LOGGER . info (
f ' \n Export complete ( { time . time ( ) - t : .1f } s) '
f " \n Results saved to { colorstr ( ' bold ' , file . parent . resolve ( ) ) } "
f " \n Predict: yolo task= { model . task } mode=predict model= { f } imgsz= { imgsz }
f " \n Predict: yolo task= { model . task } mode=predict model= { f } imgsz= { imgsz } { data } "
f " \n Validate: yolo task= { model . task } mode=val model= { f } imgsz= { imgsz } data= { self . args . data } { s } "
f " \n Visualize: https://netron.app " )
@ -335,7 +334,7 @@ class Exporter:
check_requirements ( ' onnxsim ' )
import onnxsim
LOGGER . info ( f ' { prefix } simplifying with onnx - simplifier { onnxsim . __version__ } ... ' )
LOGGER . info ( f ' { prefix } simplifying with onnx sim { onnxsim . __version__ } ... ' )
subprocess . run ( f ' onnxsim { f } { f } ' , shell = True )
except Exception as e :
LOGGER . info ( f ' { prefix } simplifier failure: { e } ' )
@ -358,7 +357,7 @@ class Exporter:
framework = " onnx " ,
compress_to_fp16 = self . args . half ) # export
ov . serialize ( ov_model , f_ov ) # save
yaml_save ( Path ( f ) / self . file . with_suffix ( ' ' ) . name , self . metadata ) # add metadata.yaml
yaml_save ( Path ( f ) / ' metadata .yaml' , self . metadata ) # add metadata.yaml
return f , None
@try_export
@ -372,7 +371,7 @@ class Exporter:
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 ( ' ' ) . name , self . metadata ) # add metadata.yaml
yaml_save ( Path ( f ) / ' metadata .yaml' , self . metadata ) # add metadata.yaml
return f , None
@try_export
@ -436,7 +435,7 @@ class Exporter:
try :
import tensorrt as trt # noqa
except ImportError :
if platform. system ( ) == ' Linux ' :
if LINUX :
check_requirements ( ' nvidia-tensorrt ' , cmds = ' -U --index-url https://pypi.ngc.nvidia.com ' )
import tensorrt as trt # noqa
@ -482,8 +481,16 @@ class Exporter:
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 )
# Write file
with builder . build_engine ( network , config ) as engine , open ( f , ' wb ' ) as t :
# Metadata
meta = json . dumps ( self . metadata )
t . write ( len ( meta ) . to_bytes ( 4 , byteorder = ' little ' , signed = True ) )
t . write ( meta . encode ( ) )
# Model
t . write ( engine . serialize ( ) )
return f , None
@try_export
@ -500,10 +507,10 @@ class Exporter:
try :
import tensorflow as tf # noqa
except ImportError :
check_requirements ( f " tensorflow { ' ' if torch. cuda . is_available ( ) else ' -macos ' if MACOS else ' -cpu ' } " )
check_requirements ( f " tensorflow { ' ' if CUDA else ' -macos ' if MACOS else ' -cpu ' if LINUX else ' ' } " )
import tensorflow as tf # noqa
check_requirements ( ( " onnx " , " onnx2tf " , " sng4onnx " , " onnxsim " , " onnx_graphsurgeon " , " tflite_support " ) ,
cmds = " --extra-index-url https://pypi.ngc.nvidia.com " )
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 ' )
@ -514,10 +521,11 @@ class Exporter:
# Export to TF SavedModel
subprocess . run ( f ' onnx2tf -i { onnx } -o { f } --non_verbose ' , shell = True )
yaml_save ( Path ( f ) / ' metadata.yaml ' , self . metadata ) # add metadata.yaml
# Add TFLite metadata
for tflite_ file in Path ( f ) . rglob ( ' *.tflite ' ) :
self . _add_tflite_metadata ( tflite_ file)
for in Path ( f ) . rglob ( ' *.tflite ' ) :
self . _add_tflite_metadata ( )
# Load saved_model
keras_model = tf . saved_model . load ( f , tags = None , options = None )
@ -537,7 +545,7 @@ class Exporter:
try :
import tensorflow as tf # noqa
except ImportError :
check_requirements ( f " tensorflow { ' ' if torch. cuda . is_available ( ) else ' -macos ' if MACOS else ' -cpu ' } " )
check_requirements ( f " tensorflow { ' ' if CUDA else ' -macos ' if MACOS else ' -cpu ' if LINUX else ' ' } " )
import tensorflow as tf # noqa
# from models.tf import TFModel
from tensorflow . python . framework . convert_to_constants import convert_variables_to_constants_v2 # noqa
@ -628,11 +636,11 @@ class Exporter:
return f , None
@try_export
def _export_edgetpu ( self , = colorstr ( ' Edge TPU: ' ) ) :
def _export_edgetpu ( self , tflite_model= ' ' , prefix= colorstr ( ' Edge TPU: ' ) ) :
# YOLOv8 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 } '
assert 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
@ -646,11 +654,11 @@ class Exporter:
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
f = str ( tflite_model ) . replace ( ' .tflite ' , ' _edgetpu.tflite ' ) # Edge TPU model
cmd = f " edgetpu_compiler -s -d -k 10 --out_dir { self . file . parent } { f_tf l} "
cmd = f " edgetpu_compiler -s -d -k 10 --out_dir { self . file . parent } { tflite_mode l} "
subprocess . run ( cmd . split ( ) , check = True )
self . _add_tflite_metadata ( f )
return f , None
@try_export
@ -681,6 +689,7 @@ class Exporter:
f_json . read_text ( ) ,
)
j . write ( subst )
yaml_save ( Path ( f ) / ' metadata.yaml ' , self . metadata ) # add metadata.yaml
return f , None
def _add_tflite_metadata ( self , file ) :
@ -736,14 +745,6 @@ class Exporter:
populator . populate ( )
tmp_file . unlink ( )
# TODO Rename this here and in `_add_tflite_metadata`
def _extracted_from__add_tflite_metadata_15 ( self , _metadata_fb , arg1 , arg2 ) :
# Creates input info.
result = _metadata_fb . TensorMetadataT ( )
result . name = arg1
result . description = arg2
return result
def _pipeline_coreml ( self , model , prefix = colorstr ( ' CoreML Pipeline: ' ) ) :
# YOLOv8 CoreML pipeline
import coremltools as ct # noqa
Glenn Jocher
2 years ago
committed by
GitHub