Datasets
Models
Downloads: 1
283 lines (278 with data), 31.4 kB
B
¢eª¤ ã @ sø d Z ddlZddlZddlZddlZddlZddlZddlZddlZddl Z ddl
Z
ddl m Z ddl
ZddlZddlmZ e e ¡ Zejd Zeeejkr°ej ee¡ e ¡ dkrÒe ej ee ¡ ¡Zddl m Z ddl m Z m!Z!m"Z"m#Z# ddl$m%Z% dd l&m'Z'm(Z(m)Z)m*Z*m+Z+m,Z,m-Z-m.Z.m/Z/m0Z0m1Z1m2Z2m3Z3 dd
l4m5Z5m6Z6 e ¡ d kZ7G d d
d
ej8j9Z:dd Z;dd Z<e<e.dfddZ=e<e.dfddZ>e<e.dfddZ?e<e.dfd d Z@e<e.d fd d ZAe<d!d"e.d#fd$d%ZBe<d"d"d&d&d'd(d"e.d)fd*d+ZCe<e.d,fd-d.ZDe<e.d/fd0d1ZEe<e.d2fd3d4ZFe<e.d5fd6d7ZGd8d9 ZHe.d:fd;d<ZIe6 ed= ed> d?d@dAdBd"d"d"d"d"d"d"d"dCd"d!d"d"d&d&d'd(fdDdEZJdKdFdGZKdHdI ZLeMdJkrôeK ZNeLeN dS )Lag
Export a YOLOv5 PyTorch model to other formats. TensorFlow exports authored by https://github.com/zldrobit
Format | `export.py --include` | Model
--- | --- | ---
PyTorch | - | yolov5s.pt
TorchScript | `torchscript` | yolov5s.torchscript
ONNX | `onnx` | yolov5s.onnx
OpenVINO | `openvino` | yolov5s_openvino_model/
TensorRT | `engine` | yolov5s.engine
CoreML | `coreml` | yolov5s.mlmodel
TensorFlow SavedModel | `saved_model` | yolov5s_saved_model/
TensorFlow GraphDef | `pb` | yolov5s.pb
TensorFlow Lite | `tflite` | yolov5s.tflite
TensorFlow Edge TPU | `edgetpu` | yolov5s_edgetpu.tflite
TensorFlow.js | `tfjs` | yolov5s_web_model/
PaddlePaddle | `paddle` | yolov5s_paddle_model/
Requirements:
$ pip install -r requirements.txt coremltools onnx onnx-simplifier onnxruntime openvino-dev tensorflow-cpu # CPU
$ pip install -r requirements.txt coremltools onnx onnx-simplifier onnxruntime-gpu openvino-dev tensorflow # GPU
Usage:
$ python export.py --weights yolov5s.pt --include torchscript onnx openvino engine coreml tflite ...
Inference:
$ python detect.py --weights yolov5s.pt # PyTorch
yolov5s.torchscript # TorchScript
yolov5s.onnx # ONNX Runtime or OpenCV DNN with --dnn
yolov5s_openvino_model # OpenVINO
yolov5s.engine # TensorRT
yolov5s.mlmodel # CoreML (macOS-only)
yolov5s_saved_model # TensorFlow SavedModel
yolov5s.pb # TensorFlow GraphDef
yolov5s.tflite # TensorFlow Lite
yolov5s_edgetpu.tflite # TensorFlow Edge TPU
yolov5s_paddle_model # PaddlePaddle
TensorFlow.js:
$ cd .. && git clone https://github.com/zldrobit/tfjs-yolov5-example.git && cd tfjs-yolov5-example
$ npm install
$ ln -s ../../yolov5/yolov5s_web_model public/yolov5s_web_model
$ npm start
é N)ÚPath)Úoptimize_for_mobileÚWindows)Ú attempt_load)ÚClassificationModelÚDetectÚDetectionModelÚSegmentationModel)Ú
LoadImages)
ÚLOGGERÚProfileÚ
check_datasetÚcheck_img_sizeÚcheck_requirementsÚ
check_versionÚ
check_yamlÚcolorstrÚ file_sizeÚget_default_argsÚ
print_argsÚurl2fileÚ yaml_save)Ú
select_deviceÚsmart_inference_modeÚDarwinc s$ e Zd Z fddZdd Z ZS )ÚiOSModelc sb t ¡ |j\}}}}|| _|j| _||kr:d| | _n$t d| d| d| d| g¡| _d S )Ng ð?)ÚsuperÚ__init__ÚshapeÚmodelÚncÚ normalizeÚtorchÚtensor)Úselfr ÚimÚbÚcÚhÚw)Ú __class__© ú5/home/iml/Desktop/Talha/YOLOV5_Model/yolov5/export.pyr R s
ziOSModel.__init__c C s: | |¡d ¡ dd| jfd¡\}}}|| || j fS )Nr é é )r ÚsqueezeÚsplitr r! )r$ ÚxÚxywhÚconfÚclsr+ r+ r, Úforward^ s (ziOSModel.forward)Ú__name__Ú
__module__Ú __qualname__r r5 Ú
__classcell__r+ r+ )r* r, r P s r c C s¬ dddddgdddddgdd d
ddgd d d
ddgdddddgdddddgdddddgdddddgdd d ddgd d d ddgd!d"d#ddgd$d%d&ddgg } t j| d'd(d)d*d+gd,S )-NÚPyTorchÚ-z.ptTÚ TorchScriptÚ torchscriptz .torchscriptÚONNXÚonnxz.onnxÚOpenVINOÚopenvinoÚ_openvino_modelFÚTensorRTÚenginez.engineÚCoreMLÚcoremlz.mlmodelzTensorFlow SavedModelÚ saved_modelÚ _saved_modelzTensorFlow GraphDefÚpbz.pbzTensorFlow LiteÚtflitez.tflitezTensorFlow Edge TPUÚedgetpuz_edgetpu.tflitez
TensorFlow.jsÚtfjsÚ
_web_modelÚ PaddlePaddleÚpaddleÚ
_paddle_modelÚFormatÚArgumentÚSuffixÚCPUÚGPU)Úcolumns)ÚpdÚ DataFrame)r1 r+ r+ r, Úexport_formatsc s rY c s t fdd}|S )Nc
s¢ d }yTt }| |\}}W d Q R X t | d|jdd| dt|dd¡ ||fS tk
r } z"t | d|jdd| ¡ d S d }~X Y nX d S )
NÚprefixu export success â
z.1fz s, saved as z (z MB)u export failure â zs: )NN)r r ÚinfoÚtr Ú Exception)ÚargsÚkwargsrZ ÚdtÚfr Úe)Ú
inner_argsÚ
inner_funcr+ r, Ú
outer_funcy s , z try_export.<locals>.outer_func)r )rd re r+ )rc rd r, Ú
try_exportu s rf z TorchScript:c C s t d| dtj d¡ | d¡}tjj| |dd}|jtt | j
| j d}dt
|¡i}|rzt|jt||d n|jt||d |d fS )
NÚ
z starting export with torch z...z .torchscriptF)Ústrict)r ÚstrideÚnamesz
config.txt)Ú _extra_files)r r[ r" Ú __version__Ú with_suffixÚjitÚtracer ÚintÚmaxri rj ÚjsonÚdumpsr Ú_save_for_lite_interpreterÚstrÚsave) r r% ÚfileÚoptimizerZ ra ÚtsÚdÚ extra_filesr+ r+ r, Úexport_torchscript s
r| zONNX:c C s t d dd l}t d| d|j d¡ t| d¡}t| trJddgndg} |r¦d d
d d d
i}t| trd
dd|d< d
ddd
|d< nt| t r¦d
dd|d< t
jj |r¸| ¡ n| |rÆ| ¡ n||d|dd g| |pÜd d |
|¡}
|j |
¡ tt| j| jd} x2| ¡ D ]&\} }
|
j ¡ }| t|
|_|_qW | |
|¡ |rúylt
j ¡ }t |rldnddf dd l }t | d|j d¡ | |
¡\}
}|s²t d| |
|¡ W n8 t k
rø } zt | d| ¡ W d d }~X Y nX ||
fS ) Nz onnx>=1.12.0r rg z starting export with onnx z...z.onnxÚoutput0Úoutput1ÚimagesÚbatchÚheightÚwidth)r é é Úanchors)r r. Ú mask_heightÚ
mask_widthFT)ÚverboseÚ
opset_versionÚdo_constant_foldingÚ input_namesÚ output_namesÚ dynamic_axes)ri rj zonnxruntime-gpuÚ onnxruntimezonnx-simplifier>=0.4.1z" simplifying with onnx-simplifier zassert check failedz simplifier failure: ) r r? r r[ rl ru rm Ú
isinstancer r r" ÚexportÚcpuÚloadÚcheckerZ check_modelrp rq ri rj ÚitemsÚmetadata_propsÚaddÚkeyÚvaluerv ÚcudaÚ is_availableÚonnxsimÚsimplifyÚAssertionErrorr] )r r% rw ÚopsetÚdynamicr rZ r? ra r Ú
model_onnxrz ÚkÚvÚmetar r Úcheckrb r+ r+ r, Ú export_onnx sR
&r¥ z OpenVINO:c sV t d dd lm} ddlm} t d| d|j d¡ t| | j
dt j ¡}|
d¡} tt||
d ¡j }
|rt d
dd l} dd ldd lm} dd lm | }
|
| ¡}jd
fdd d fdd }fdd}||}| ||¡}| j||| jjd}n|j| | j d|d}| ||
¡ t t||
d¡j | |d fS ) Nzopenvino-dev>=2023.0r )Úmorg z starting export with openvino z...rB z.onnxz.xmlz nncf>=2.4.0)ÚCore)Úcreate_dataloader)Úimagec s. | j¡}|d }|jdkr* |d¡}|S )Ng ào@r r )ÚastypeÚfloat32ÚndimÚ expand_dims)r© Ú input_tensor)Únpr+ r, Úprepare_input_tensorä s
z-export_openvino.<locals>.prepare_input_tensorÚtrainé r- c
s4 t | }t|} || |ddddd|dd }|S )Nr. é g à?F)ÚimgszÚ
batch_sizeri ÚpadÚ
single_clsÚrectÚworkersr )r r
)Ú yaml_pathÚtaskr´ r¹ Z data_yamlÚdataÚ
dataloader)r¨ r+ r, Úgen_dataloaderì s z'export_openvino.<locals>.gen_dataloaderc s | d ¡ } |}|S )aB
Quantization transform function. Extracts and preprocess input data from dataloader item for quantization.
Parameters:
data_item: Tuple with data item produced by DataLoader during iteration
Returns:
input_tensor: Input data for quantization
r )Únumpy)Z data_itemÚimgr® )r° r+ r, Ú transform_fnû s z%export_openvino.<locals>.transform_fn)Úpresetr? )Ú
model_nameÚ frameworkÚcompress_to_fp16z.yaml)r± r² r- ) r Úopenvino.runtimeÚruntimeÚopenvino.toolsr¦ r r[ rl ru ÚreplaceÚsuffixÚosÚseprm r ÚnameÚnncfr¿ r§ Úutils.dataloadersr¨ Ú
read_modelÚndarrayÚDatasetÚquantizeZQuantizationPresetZMIXEDÚ
convert_modelÚstemÚ serializer )rw ÚmetadataÚhalfÚint8r¼ rZ Úovr¦ ra Úf_onnxÚf_ovrÎ r§ ÚcoreZ
onnx_modelr¾ rÁ ÚdsZquantization_datasetÚov_modelr+ )r¨ r¯ r° r, Úexport_openvinoÏ s2
rà z
PaddlePaddle:c C s t d dd l}ddlm} t d| d|j d¡ t| ddt j
¡}|| |d |gd
t t ||
d ¡j | |d fS ) N)Ú paddlepaddleÚx2paddler )Úpytorch2paddlerg z starting export with X2Paddle z...z.ptrP ro )ÚmoduleÚsave_dirÚjit_typeÚinput_examplesz.yaml)r râ Úx2paddle.convertrã r r[ rl ru rÉ rË rÌ r r rm rÍ )r r% rw r× rZ râ rã ra r+ r+ r, Ú
export_paddle s ré zCoreML:c
C sö t d dd l}t d| d|j d¡ | d¡}|rBt| |} tjj | |dd} |j
| |j d |j d
dddgd gd }
|rd
n
|rdnd\} } | dk rät
rÖt ¡ ( tjdtd |jjj |
| | ¡}
W d Q R X nt| d |
|¡ ||
fS )NÚ coremltoolsr rg z" starting export with coremltools z...z.mlmodelF)rh r© gp?)r ÚscaleÚbias)Úinputs)é Ú
kmeans_lut)é Úlinear)r³ Nr³ Úignore)Úcategoryz2 quantization only supported on macOS, skipping...)r rê r r[ rl rm r r" rn ro ÚconvertÚ ImageTyper ÚMACOSÚwarningsÚcatch_warningsÚfilterwarningsÚDeprecationWarningÚmodelsÚneural_networkÚquantization_utilsÚquantize_weightsÚprintrv )
r r% rw rÙ rØ ÚnmsrZ Úctra ry Úct_modelÚbitsÚmoder+ r+ r, Ú
export_coreml! s"
&
r r- Fz TensorRT:c
sD |j jdkstdy dd l} W n4 tk
rT t ¡ dkrHtddd dd l} Y nX | jd dkr¦| j d j
}
d
d |
D | j d _
t | ||d || |
| j d _
n"t | jd
dd t | ||d || |
d¡} t d| d| j d¡ | ¡ std| |
d¡} | | jj¡}
|r.| jjj|
_| |
¡}| ¡ }|d d> |_dt| jj> }| |¡ | |
¡}| t | ¡st d| fdd t! j"D } fdd t! j#D }x4|D ],}t | d|j$ d |j% d |j& ¡ qÈW x4|D ],}t | d |j$ d |j% d |j& ¡ qþW |r¸|j%d dkrTt '| d ¡ | (¡ }xP|D ]H}| )|j$d&|j%dd
t*d|j%d d f|j%dd
|j%¡ qbW | +|¡ t | d!|j,rÔ|rÔd"nd# d$| ¡ |j,r |r | -| j.j/¡ | 0 |¡*}t1| d%}| 2| 3¡ ¡ W d Q R X W d Q R X | d fS )'Nr zLexport running on CPU but must be on GPU, i.e. `python export.py --device 0`r ÚLinuxznvidia-tensorrtz*-U --index-url https://pypi.ngc.nvidia.com)ÚcmdsÚ7éÿÿÿÿc S s( g | ] }|d dd
dd
dd
f qS ).Nr. r+ )Ú.0Úar+ r+ r, ú
<listcomp>G s z!export_engine.<locals>.<listcomp>é z8.0.0T)Úhardz.onnxrg z starting export with TensorRT z...z failed to export ONNX file: z.enginer. é zfailed to load ONNX file: c s g | ]} |¡qS r+ )Ú get_input)r
Úi)Únetworkr+ r, r a s c s g | ]} |¡qS r+ )Ú
get_output)r
r )r r+ r, r b s z input "z " with shapeÚ z output "uF WARNING â ï¸ --dynamic model requires maximum --batch-size argumentr z building FPrð r³ z engine as Úwb)r. )4ÚdeviceÚtyper Útensorrtr] ÚplatformÚsystemr rl r Ú anchor_gridr¥ r rm r r[ ÚexistsÚLoggerÚINFOÚSeverityÚVERBOSEÚ min_severityÚBuilderÚcreate_builder_configÚmax_workspace_sizerp Ú NetworkDefinitionCreationFlagÚEXPLICIT_BATCHÚcreate_networkÚ
OnnxParserÚparse_from_fileru Ú RuntimeErrorÚrangeÚ
num_inputsÚ num_outputsrÍ r ÚdtypeÚwarningÚcreate_optimization_profileÚ set_shaperq Úadd_optimization_profileÚplatform_has_fast_fp16Úset_flagÚ BuilderFlagÚFP16Ú build_engineÚopenÚwriterÖ )r r% rw rØ r r Ú workspacer rZ ÚtrtÚgridr? ra ÚloggerÚbuilderÚconfigÚflagÚparserrí ÚoutputsÚinpÚoutÚprofilerD r\ r+ )r r, Ú
export_engine: s`
,
,
H
,"rF éd gÍÌÌÌÌÌÜ?g Ð?zTensorFlow SavedModel:c s y dd l } W n@ tk
rL tdtj ¡ r.dn
tr6dnd dd l } Y nX ddlm}
ddl m
} t d| d | j
d
¡ | j
d kr¦d }t d
| j
d| ¡ t| dd¡}t|j^}}}|| j| | j|d}| |f||f¡}| |||||| ¡}| jj||f|r d n|d}| |||||| ¡}| jj||dd_ ¡ |
rnj|dd n | j d jj d j ¡}| fdd¡}| |¡}|
| | !¡ }| fdd|g¡|_"| "|¡ | j#j||t$| j
dr | j#j%ddn| j# %¡ d |fS ) Nr Ú
tensorflowÚ z-macosz-cpu)Ú!convert_variables_to_constants_v2)ÚTFModelrg z! starting export with tensorflow z...z2.13.1z2https://github.com/ultralytics/yolov5/issues/12489u WARNING â ï¸ using Tensorflow z? > 2.13.1 might cause issue when exporting the model to tflite z.ptrH )Úcfgr r r´ )r rµ )rí rB FÚtf)Z save_formatc s | S )Nr+ )r1 )Ú keras_modelr+ r, Ú<lambda>¤ ó z$export_saved_model.<locals>.<lambda>c s r | d d
S | S )Nr- r+ )r1 )Ú frozen_funcÚtf_nmsr+ r, rO ¨ rP z2.6)Z experimental_custom_gradients)Úoptions)&rH r] r r" r r rö Ú0tensorflow.python.framework.convert_to_constantsrJ Ú models.tfrK r r[ rl ru rÉ Úlistr Úyamlr ÚzerosÚpredictÚkerasÚInputÚModelZ trainableÚsummaryrv Ú
TensorSpecrí r. ÚfunctionÚget_concrete_functionÚModuleÚ__call__rG r Z SaveOptions)r r% rw r rR Ú agnostic_nmsÚtopk_per_classÚtopk_allÚ iou_thresÚ
conf_thresrZ rZ rM rJ rK Z
helper_urlra rµ Úchr´ Ztf_modelÚ_rí rB ÚspecÚmÚtfmr+ )rQ rN rR r, Úexport_saved_modelx sH $
(rm zTensorFlow GraphDef:c s¤ dd l }ddlm} t d| d|j d¡ | d¡}| fdd¡}| | j
d j j
d j ¡¡}||}|j
¡ |jj|j
t|j|jd d
|d fS ) Nr )rJ rg z! starting export with tensorflow z...z.pbc s | S )Nr+ )r1 )rN r+ r, rO º rP zexport_pb.<locals>.<lambda>F)Úgraph_or_graph_defÚlogdirrÍ Úas_text)rH rT rJ r r[ rl rm r_ r` r^ rí r r. ÚgraphÚ as_graph_defÚioÚ write_graphru ÚparentrÍ )rN rw rZ rM rJ ra rk rQ r+ )rN r, Ú export_pb± s
"
rv zTensorFlow Lite:c s< dd l } t d| d| j d¡ t|j^}
} } t| dd¡}
| jj
| ¡}| jj j g|j
_| jg|j
_| jjjg|_|rüddlm ttt|d | d d
fd d |_| jj jg|j
_g |j
_| j|_ | j|_ d
|_ |rìd
|_ t| dd¡}
|s|r |j
j | jj j!¡ | "¡ }t#|
d $|¡ |
d fS )Nr rg z! starting export with tensorflow z...z.ptz -fp16.tflite)Úrepresentative_dataset_genr± F)Úimg_sizeÚautoc s ddS )NrG )Zncalibr+ r+ )Údatasetrw r+ r, rO Ó rP z export_tflite.<locals>.<lambda>Tz -int8.tfliter )%rH r r[ rl rV r ru rÉ ÚliteZTFLiteConverterZfrom_keras_modelZOpsSetZTFLITE_BUILTINSZ target_specZ
supported_opsÚfloat16Zsupported_typesZOptimizeÚDEFAULTZ
optimizationsrU rw r
r
r Zrepresentative_datasetZTFLITE_BUILTINS_INT8Úuint8Zinference_input_typeZinference_output_typeZexperimental_new_quantizerZ!_experimental_disable_per_channelÚappendZ
SELECT_TF_OPSrô r8 r9 )rN r% rw rÙ Ú
per_tensorr¼ r rc rZ rM rµ rh r´ ra Ú converterÚ tflite_modelr+ )rz rw r, Ú
export_tflite s2 r z Edge TPU:c C s d}d}t ¡ dks"td| tj| dddjdkrt d | d
| ¡ tjd ddjdk}x,d D ]$}tj|r||n
| d
d¡ddd qlW tj|ddddj
¡ ¡ d }t d | d| d¡ t | dd¡}t | dd¡}tjdddddd t | j
|gdd |d fS ) Nzedgetpu_compiler --versionz'https://coral.ai/docs/edgetpu/compiler/r z$export only supported on Linux. See z > /dev/null 2>&1T)Úshellr rg z< export requires Edge TPU compiler. Attempting install from zsudo --version >/dev/null)zOcurl https://packages.cloud.google.com/apt/doc/apt-key.gpg | sudo apt-key add -zecho "deb https://packages.cloud.google.com/apt coral-edgetpu-stable main" | sudo tee /etc/apt/sources.list.d/coral-edgetpu.listzsudo apt-get updatez%sudo apt-get install edgetpu-compilerzsudo rI )r r¤ )r Úcapture_outputr¤ r z( starting export with Edge TPU compiler z...z.ptz-int8_edgetpu.tflitez -int8.tfliteZedgetpu_compilerz-sz-dz-kÚ10z --out_dir)r¤ )r r r Ú
subprocessÚrunÚ
returncoder r[ rÉ ÚstdoutÚdecoder0 ru ru ) rw rZ ÚcmdÚhelp_urlÚsudor' Úverra Zf_tflr+ r+ r, Úexport_edgetpuä s. $ r zTensorFlow.js:c C sÈ t d dd l}t d| d|j d¡ t| dd¡}| d¡}| d }d
d |rZd nd
dt|t|g}tj dd |D dd t
| ¡ }t |d } t
dd|¡}
| |
¡ W d Q R X |d fS )NÚ tensorflowjsr rg z# starting export with tensorflowjs z...z.ptrM z.pbz /model.jsonZtensorflowjs_converterz --input_format=tf_frozen_modelz--quantize_uint8rI z=--output_node_names=Identity,Identity_1,Identity_2,Identity_3c S s g | ] }|r|qS r+ r+ )r
Úargr+ r+ r, r s z export_tfjs.<locals>.<listcomp>T)r¤ r) zµ{"outputs": {"Identity.?.?": {"name": "Identity.?.?"}, "Identity.?.?": {"name": "Identity.?.?"}, "Identity.?.?": {"name": "Identity.?.?"}, "Identity.?.?": {"name": "Identity.?.?"}}}z¡{"outputs": {"Identity": {"name": "Identity"}, "Identity_1": {"name": "Identity_1"}, "Identity_2": {"name": "Identity_2"}, "Identity_3": {"name": "Identity_3"}}})r r r r[ rl ru rÉ rm r r r Ú read_textr8 ÚreÚsubr9 ) rw rÙ rZ rL ra Úf_pbZf_jsonr^ rr ÚjZsubstr+ r+ r, Ú export_tfjs s*
r c
C s t t¡ ddlm} ddlm} ddlm} td}t|d}| t
|¡ W d Q R X | ¡ }| ¡ } |j
| _
| g|_| ¡ }
| ¡ g|
_| ¡ g| |
_|
g|_| d¡} | | | ¡|jj¡ | ¡ } |j | ¡}
|
| ¡ |
t
|g¡ |
¡ | ¡ W d Q R X d S )Nr )Ú flatbuffers)r× )Ú metadata_schema_py_generatedz
/tmp/meta.txtr) ) Ú
contextlibÚsuppressÚ ImportErrorÚtflite_supportr r× r r r8 r9 ru ÚModelMetadataTÚAssociatedFileTrÍ ÚassociatedFilesÚSubGraphMetadataTÚTensorMetadataTÚinputTensorMetadataÚoutputTensorMetadataÚsubgraphMetadatar" ÚFinishÚPackÚMetadataPopulatorÚMETADATA_FILE_IDENTIFIERÚOutputÚwith_model_fileÚload_metadata_bufferÚload_associated_filesÚpopulateÚunlink)rw r× r- r Ú _metadataÚ _metadata_fbÚtmp_fileZmeta_fÚ
model_metaÚ
label_fileÚsubgraphr& Ú metadata_bufÚ populatorr+ r+ r, Úadd_tflite_metadata& s.
r¹ zCoreML Pipeline:c " C s¢ dd l }ddlm} t| d|j d t|j\}} }
} t ¡ } | ¡ }
t |
j
j \}}t
¡ dkr¢| d| |
f¡}| d|i¡}||j j||j j }}n0t|d j}|d |d d
f|d d f }}|
j
jd jjj|
j
jd jjj }}|\}}t||ks$tt| d | ||jjjd d
< ||jjjd d
< t|
j
|j |
¡} |j j ¡ }d
|_ x`t d D ]T}| j!j
j | "¡ }|j
j #¡ |j
j| $|¡ |j
j #¡ |j
j | $|¡ qzW d
|j
j d _d|j
j d _|d g} xt d D ]z}|j
j | jj} | j%j& #¡ d| j%j&d _'d| j%j&d _(| j%j& #¡ | | | j%j&d _'| | | j%j&d _(| jd d
= q W |j)} |j| _*|j| _+d
| _,d| _-d| _.d| _/d| _0d| _1d| j2_3| j4j5 6| 7¡ ¡ |j |¡} |jj8j9d|jj: ;d||¡fd|jj: <¡ fd|jj: <¡ fgd
dgd} | =| ¡ | =| ¡ | j>j
jd $| j!j
jd "¡ ¡ | j>j
j d $| j!j
j d "¡ ¡ | j>j
j d $| j!j
j d "¡ ¡ d
| j>_ d| j>j
j?_@d| j>j
j?_Ad| j>j
j?_Bd| j>j
j?_C| j>j
j?jD Ed F| 7¡ ¡tG| j0tG| j1d¡ | Hd ¡}!|j | j>¡} d | jId< d | j0 d | jId< d | j1 d | jId< d!| jJd
< d"| jJd< | K|!¡ t| d#t ¡ | d$d%|! d&tL|!d'd( d S ))Nr )ÚImagez$ starting pipeline with coremltools z...r ÚRGBr© r. r é r- z names found for nc=Ú
confidenceÚ coordinatesr Ú iouThresholdÚconfidenceThresholdgÍÌÌÌÌÌÜ?g Ð?Tr )Úinput_featuresÚoutput_featuresz%https://github.com/ultralytics/yolov5z glenn.jocher@ultralytics.comz9https://github.com/ultralytics/yolov5/blob/master/LICENSEú,)ÚclassesÚ
iou_thresholdZconfidence_thresholdz.mlmodelz Input imagez,(optional) IOU Threshold override (default: ú)z3(optional) Confidence Threshold override (default: u? Boxes à Class confidence (see user-defined metadata "classes")u7 Boxes à [x, y, width, height] (relative to image size)z pipeline success (z.2fz
s), saved as z (z.1fz MB))Mrê ÚPILrº rÿ rl rV r ÚtimeÚget_specÚiterÚ descriptionÚoutputr r ÚnewrY rÍ ÚtupleÚinputr Ú imageTyper r Úlenr ÚmultiArrayTyperû ÚMLModelÚprotoÚ Model_pb2r\ ÚspecificationVersionr+ Ú_specÚSerializeToStringr ÚParseFromStringÚ
shapeRangeÚ
sizeRangesÚ
lowerBoundÚ
upperBoundÚnonMaximumSuppressionÚconfidenceInputFeatureNameÚcoordinatesInputFeatureNameÚconfidenceOutputFeatureNameÚ coordinatesOutputFeatureNameÚ iouThresholdInputFeatureNameÚ#confidenceThresholdInputFeatureNamer¿ rÀ ÚpickTopÚperClassÚstringClassLabelsÚvectorÚextendÚvaluesÚpipelineÚPipelineÚ datatypesÚArrayÚDoubleÚ add_modelrj r× Z
versionStringÚshortDescriptionÚauthorÚlicenseÚ userDefinedÚupdateÚjoinru rm Úinput_descriptionÚoutput_descriptionrv r )"r r% rw rj ÚyrZ r rº rµ rh r( r) r\ rj Úout0Úout1rÀ rD Ú
out0_shapeÚ
out1_shapeÚsÚnxÚnyÚnar Únms_specr Údecoder_outputÚ output_sizesÚma_typer Ú nms_modelrë ra r+ r+ r, Úpipeline_coremlG s "&$
"""
r zdata/coco128.yamlz
yolov5s.pt)i i r. r )r= r? r
c 6 s. t ¡ }dd D tt d dd
}fdd|D }t|tksbtd d| |\ }}} } } } } }!}"}#}$tt| d¡rt |n|}%t
|}|rÈ|j d ks¼| s¼td
| rÈtd t ||d d d
|t|dkrêdnd9 }| r
|j d ks
tdt
tj fdd|D }tj|df| |¡}& ¡ x4 ¡ D ](\}'}(t|(trT||(_| |(_d |(_qTW xtdD ]})|&}*qW |rº| sº|& ¡ ¡ }&tt|*trÐ|*d n|*j }+t
tjj d},t dt d d|% d|+ dt!|%dd ¡ dgt| }-t"j#d tj$j%d |rXt&|&|%| \|-d< })| r|t'|&|%|| |
||\|-d< })|s| r¢t(|&|%|| |
\|-d< })| rÀt)|%|,||
| \|-d< })| røt*|&|%|
||\|-d < }.|røt+|.|&|%j |* t,| | |!|"|#fr|!r |#r td tt-r4td t. /¡ |&|%| |pP|pP|#|pX|#|||||d! \|-d"< }/| s||#rt0|/|%\|-d#< })|!s|"røt1|/|&|%|
pª|"| | ||d$\|-d%< })|"rÖt2|%\|-d&< })t3|-d& pè|-d% |,t|/j4d' |#rt5|%|
\|-d(< })|$r,t6|&|%|,\|-d)< })d*d |-D }-t,|-r*fd+d,t-t7t8fD \}0}1}2|1|2 M }1t|2rxd-n |0rd.nd}3|rd/nd}4|0r d0n |2rªd1nd}5t d2t ¡ | dd3t d4|%j9 :¡ d5|3|1râd6nd7 d8|-d9 d:|4 d;|3d< d8|-d9 d:|4 d=|-d9 d>|5 d?¡ |-S )@Nc S s g | ] }| ¡ qS r+ )Úlower)r
r1 r+ r+ r, r Ý s zrun.<locals>.<listcomp>rR r. c s g | ] }| kqS r+ r+ )r
r1 )Úincluder+ r, r ß s zERROR: Invalid --include z , valid --include arguments are )zhttp:/zhttps:/r z;--half only compatible with GPU export, i.e. use --device 0zV--half not compatible with --dynamic, i.e. use either --half or --dynamic but not bothT)r ÚinplaceÚfuser zB--optimize not compatible with cuda devices, i.e. use --device cpuc s g | ]}t | qS r+ )r )r
r1 )Úgsr+ r, r ò s r r )ri rj rg zPyTorch:z starting from z with output shape z (z.1fz MB)rI rò )Úactionró r- zOTFLite and TF.js models must be exported separately, please pass only one type.z;ClassificationModel export to TF formats not yet supported.)rR rc rd re rf rg rZ r¼ é )r¼ r rc é rî )r- é é
c S s g | ]}|rt |qS r+ )ru )r
r1 r+ r+ r, r 6 s c 3 s | ]}t |V qd S )N)r )r
r1 )r r+ r, ú <genexpr>8 s zrun.<locals>.<genexpr>ÚsegmentÚclassifyz--halfuZ # WARNING â ï¸ ClassificationModel not yet supported for PyTorch Hub AutoShape inferenceuX # WARNING â ï¸ SegmentationModel not yet supported for PyTorch Hub AutoShape inferencez
Export complete (zs)
Results saved to Úboldz
Detect: python z detect.pyz
predict.pyz --weights r r z
Validate: python zval.pyzJ
PyTorch Hub: model = torch.hub.load('ultralytics/yolov5', 'custom', 'z') z$
Visualize: https://netron.app);rÈ rÎ rY ÚsumrÑ r r ru Ú
startswithr r r r rp rq ri r" rX ÚtoÚevalÚ
named_modulesr r r
r r r+ rØ r rj r r[ r r r÷ rù rn Ú
TracerWarningr| rF r¥ rà r r Úanyr rm r rv r r r¹ rB r ré r r ru Úresolve)6r¼ Úweightsr´ rµ r r rØ r
rZ rx rÙ r r r r r r: r rc rd re rf rg r\ ÚfmtsÚflagsrn r? ÚxmlrD rF rG rI rJ rK rL rO rw r% r¡ rk ri rù r r× ra r Ús_modelr4 ÚdetÚsegÚdirr( rþ r+ )r r r r, r  s® $ . "
|r c C sÎ t ¡ }|jdttd dd |jddttd dd |jd
d d dtd
d
gdd |jdtddd |jdddd |jdddd |jdddd |jd dd d |jd dd d |jd dd!d |jd"dd#d |jd$dd%d |jd&dd'd |jd(td)d*d |jd+dd,d |jd-td.d/d |jd0dd1d |jd2dd3d |jd4td5d6d |jd7td5d8d |jd9td:d;d |jd<td=d>d |jd?dd@gdAdB | r¶| ¡ dC n| ¡ }t t
| |S )DNz--datazdata/coco128.yamlzdataset.yaml path)r ÚdefaultÚhelpz --weightsú+z
yolov5s.ptzmodel.pt path(s))Únargsr r& r' z--imgszz--imgz
--img-sizei z image (h, w)z --batch-sizer. z
batch sizez--devicer z%cuda device, i.e. 0 or 0,1,2,3 or cpu)r& r' z--halfÚ
store_truezFP16 half-precision export)r
r' z --inplacez set YOLOv5 Detect() inplace=Truez--kerasz
TF: use Kerasz
--optimizez TorchScript: optimize for mobilez--int8z$CoreML/TF/OpenVINO INT8 quantizationz --per-tensorzTF per-tensor quantizationz --dynamicz ONNX/TF/TensorRT: dynamic axesz
--simplifyzONNX: simplify modelz--opseté zONNX: opset versionz --verbosezTensorRT: verbose logz --workspacer- z TensorRT: workspace size (GB)z--nmszTF: add NMS to modelz--agnostic-nmsz TF: add agnostic NMS to modelz--topk-per-classrG z!TF.js NMS: topk per class to keepz
--topk-allz'TF.js NMS: topk for all classes to keepz --iou-thresgÍÌÌÌÌÌÜ?zTF.js NMS: IoU thresholdz --conf-thresg Ð?z TF.js NMS: confidence thresholdz --includer= z[torchscript, onnx, openvino, engine, coreml, saved_model, pb, tflite, edgetpu, tfjs, paddle)r) r& r' r ) ÚargparseÚArgumentParserÚ add_argumentru ÚROOTrp ÚfloatÚparse_known_argsÚ
parse_argsr Úvars)ZknownrA Úoptr+ r+ r, Ú parse_optG s> r5 c C s8 x2t | jtr| jn| jgD ]| _tf t| q W d S )N)r r rV r r3 )r4 r+ r+ r, Úmaini s "r6 Ú__main__)F)OÚ__doc__r, r rr rË r r r ÚsysrÈ r÷ Úpathlibr ÚpandasrW r" Ú torch.utils.mobile_optimizerr Ú__file__r ÚFILEÚparentsr/ ru Úpathr r ÚrelpathÚcwdÚmodels.experimentalr Ú models.yolor r r r rÏ r
Ú
utils.generalr r r
r r r r r r r r r r Úutils.torch_utilsr r rö Únnra r rY rf r| r¥ rà ré r rF rm rv r r r r¹ r r r5 r6 r6 r4 r+ r+ r+ r, Ú<module>- s²
< 7B=- !!{m
"