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# YOLOv5 🚀 by Ultralytics, GPL-3.0 license | |
""" | |
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/ | |
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 path/to/export.py --weights yolov5s.pt --include torchscript onnx openvino engine coreml tflite ... | |
Inference: | |
$ python path/to/detect.py --weights yolov5s.pt # PyTorch | |
yolov5s.torchscript # TorchScript | |
yolov5s.onnx # ONNX Runtime or OpenCV DNN with --dnn | |
yolov5s.xml # 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 | |
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 | |
""" | |
import argparse | |
import json | |
import os | |
import platform | |
import subprocess | |
import sys | |
import time | |
import warnings | |
from pathlib import Path | |
import pandas as pd | |
import torch | |
import torch.nn as nn | |
from torch.utils.mobile_optimizer import optimize_for_mobile | |
FILE = Path(__file__).resolve() | |
ROOT = FILE.parents[0] # YOLOv5 root directory | |
if str(ROOT) not in sys.path: | |
sys.path.append(str(ROOT)) # add ROOT to PATH | |
ROOT = Path(os.path.relpath(ROOT, Path.cwd())) # relative | |
from models.common import Conv | |
from models.experimental import attempt_load | |
from models.yolo import Detect | |
from utils.activations import SiLU | |
from utils.datasets import LoadImages | |
from utils.general import (LOGGER, check_dataset, check_img_size, check_requirements, check_version, colorstr, | |
file_size, print_args, url2file) | |
from utils.torch_utils import select_device | |
def export_formats(): | |
# YOLOv5 export formats | |
x = [['PyTorch', '-', '.pt', True], | |
['TorchScript', 'torchscript', '.torchscript', True], | |
['ONNX', 'onnx', '.onnx', True], | |
['OpenVINO', 'openvino', '_openvino_model', False], | |
['TensorRT', 'engine', '.engine', True], | |
['CoreML', 'coreml', '.mlmodel', False], | |
['TensorFlow SavedModel', 'saved_model', '_saved_model', True], | |
['TensorFlow GraphDef', 'pb', '.pb', True], | |
['TensorFlow Lite', 'tflite', '.tflite', False], | |
['TensorFlow Edge TPU', 'edgetpu', '_edgetpu.tflite', False], | |
['TensorFlow.js', 'tfjs', '_web_model', False]] | |
return pd.DataFrame(x, columns=['Format', 'Argument', 'Suffix', 'GPU']) | |
def export_torchscript(model, im, file, optimize, prefix=colorstr('TorchScript:')): | |
# YOLOv5 TorchScript model export | |
try: | |
LOGGER.info(f'\n{prefix} starting export with torch {torch.__version__}...') | |
f = file.with_suffix('.torchscript') | |
ts = torch.jit.trace(model, im, strict=False) | |
d = {"shape": im.shape, "stride": int(max(model.stride)), "names": model.names} | |
extra_files = {'config.txt': json.dumps(d)} # torch._C.ExtraFilesMap() | |
if optimize: # https://pytorch.org/tutorials/recipes/mobile_interpreter.html | |
optimize_for_mobile(ts)._save_for_lite_interpreter(str(f), _extra_files=extra_files) | |
else: | |
ts.save(str(f), _extra_files=extra_files) | |
LOGGER.info(f'{prefix} export success, saved as {f} ({file_size(f):.1f} MB)') | |
return f | |
except Exception as e: | |
LOGGER.info(f'{prefix} export failure: {e}') | |
def export_onnx(model, im, file, opset, train, dynamic, simplify, prefix=colorstr('ONNX:')): | |
# YOLOv5 ONNX export | |
try: | |
check_requirements(('onnx',)) | |
import onnx | |
LOGGER.info(f'\n{prefix} starting export with onnx {onnx.__version__}...') | |
f = file.with_suffix('.onnx') | |
torch.onnx.export(model, im, f, verbose=False, opset_version=opset, | |
training=torch.onnx.TrainingMode.TRAINING if train else torch.onnx.TrainingMode.EVAL, | |
do_constant_folding=not train, | |
input_names=['images'], | |
output_names=['output'], | |
dynamic_axes={'images': {0: 'batch', 2: 'height', 3: 'width'}, # shape(1,3,640,640) | |
'output': {0: 'batch', 1: 'anchors'} # shape(1,25200,85) | |
} if dynamic else None) | |
# Checks | |
model_onnx = onnx.load(f) # load onnx model | |
onnx.checker.check_model(model_onnx) # check onnx model | |
# LOGGER.info(onnx.helper.printable_graph(model_onnx.graph)) # print | |
# Simplify | |
if simplify: | |
try: | |
check_requirements(('onnx-simplifier',)) | |
import onnxsim | |
LOGGER.info(f'{prefix} simplifying with onnx-simplifier {onnxsim.__version__}...') | |
model_onnx, check = onnxsim.simplify( | |
model_onnx, | |
dynamic_input_shape=dynamic, | |
input_shapes={'images': list(im.shape)} if dynamic else None) | |
assert check, 'assert check failed' | |
onnx.save(model_onnx, f) | |
except Exception as e: | |
LOGGER.info(f'{prefix} simplifier failure: {e}') | |
LOGGER.info(f'{prefix} export success, saved as {f} ({file_size(f):.1f} MB)') | |
return f | |
except Exception as e: | |
LOGGER.info(f'{prefix} export failure: {e}') | |
def export_openvino(model, im, file, prefix=colorstr('OpenVINO:')): | |
# YOLOv5 OpenVINO export | |
try: | |
check_requirements(('openvino-dev',)) # requires openvino-dev: https://pypi.org/project/openvino-dev/ | |
import openvino.inference_engine as ie | |
LOGGER.info(f'\n{prefix} starting export with openvino {ie.__version__}...') | |
f = str(file).replace('.pt', '_openvino_model' + os.sep) | |
cmd = f"mo --input_model {file.with_suffix('.onnx')} --output_dir {f}" | |
subprocess.check_output(cmd, shell=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}') | |
def export_coreml(model, im, file, prefix=colorstr('CoreML:')): | |
# YOLOv5 CoreML export | |
try: | |
check_requirements(('coremltools',)) | |
import coremltools as ct | |
LOGGER.info(f'\n{prefix} starting export with coremltools {ct.__version__}...') | |
f = file.with_suffix('.mlmodel') | |
ts = torch.jit.trace(model, im, strict=False) # TorchScript model | |
ct_model = ct.convert(ts, inputs=[ct.ImageType('image', shape=im.shape, scale=1 / 255, bias=[0, 0, 0])]) | |
ct_model.save(f) | |
LOGGER.info(f'{prefix} export success, saved as {f} ({file_size(f):.1f} MB)') | |
return ct_model, f | |
except Exception as e: | |
LOGGER.info(f'\n{prefix} export failure: {e}') | |
return None, None | |
def export_engine(model, im, file, train, half, simplify, workspace=4, verbose=False, prefix=colorstr('TensorRT:')): | |
# YOLOv5 TensorRT export https://developer.nvidia.com/tensorrt | |
try: | |
check_requirements(('tensorrt',)) | |
import tensorrt as trt | |
if trt.__version__[0] == '7': # TensorRT 7 handling https://github.com/ultralytics/yolov5/issues/6012 | |
grid = model.model[-1].anchor_grid | |
model.model[-1].anchor_grid = [a[..., :1, :1, :] for a in grid] | |
export_onnx(model, im, file, 12, train, False, simplify) # opset 12 | |
model.model[-1].anchor_grid = grid | |
else: # TensorRT >= 8 | |
check_version(trt.__version__, '8.0.0', hard=True) # require tensorrt>=8.0.0 | |
export_onnx(model, im, file, 13, train, False, simplify) # opset 13 | |
onnx = file.with_suffix('.onnx') | |
LOGGER.info(f'\n{prefix} starting export with TensorRT {trt.__version__}...') | |
assert im.device.type != 'cpu', 'export running on CPU but must be on GPU, i.e. `python export.py --device 0`' | |
assert onnx.exists(), f'failed to export ONNX file: {onnx}' | |
f = 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)] | |
LOGGER.info(f'{prefix} Network Description:') | |
for inp in inputs: | |
LOGGER.info(f'{prefix}\tinput "{inp.name}" with shape {inp.shape} and dtype {inp.dtype}') | |
for out in outputs: | |
LOGGER.info(f'{prefix}\toutput "{out.name}" with shape {out.shape} and dtype {out.dtype}') | |
LOGGER.info(f'{prefix} building FP{16 if builder.platform_has_fast_fp16 else 32} engine in {f}') | |
if builder.platform_has_fast_fp16: | |
config.set_flag(trt.BuilderFlag.FP16) | |
with builder.build_engine(network, config) as engine, open(f, 'wb') as t: | |
t.write(engine.serialize()) | |
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}') | |
def export_saved_model(model, im, file, dynamic, | |
tf_nms=False, agnostic_nms=False, topk_per_class=100, topk_all=100, iou_thres=0.45, | |
conf_thres=0.25, 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 | |
from models.tf import TFDetect, 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 | |
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: | |
m = tf.function(lambda x: keras_model(x)) # full model | |
spec = tf.TensorSpec(keras_model.inputs[0].shape, keras_model.inputs[0].dtype) | |
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)[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 | |
def export_pb(keras_model, im, 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 | |
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}') | |
def export_tflite(keras_model, im, file, int8, data, ncalib, prefix=colorstr('TensorFlow Lite:')): | |
# YOLOv5 TensorFlow Lite export | |
try: | |
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') | |
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(data)['train'], img_size=imgsz, auto=False) # representative data | |
converter.representative_dataset = lambda: representative_dataset_gen(dataset, ncalib) | |
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') | |
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}') | |
def export_edgetpu(keras_model, im, 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(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 | |
cmd = f"edgetpu_compiler -s {f_tfl}" | |
subprocess.run(cmd, shell=True, 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}') | |
def export_tfjs(keras_model, im, file, prefix=colorstr('TensorFlow.js:')): | |
# YOLOv5 TensorFlow.js export | |
try: | |
check_requirements(('tensorflowjs',)) | |
import re | |
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 + '/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, shell=True) | |
json = open(f_json).read() | |
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}') | |
def run(data=ROOT / 'data/coco128.yaml', # 'dataset.yaml path' | |
weights=ROOT / 'yolov5s.pt', # weights path | |
imgsz=(640, 640), # image (height, width) | |
batch_size=1, # batch size | |
device='cpu', # cuda device, i.e. 0 or 0,1,2,3 or cpu | |
include=('torchscript', 'onnx'), # include formats | |
half=False, # FP16 half-precision export | |
inplace=False, # set YOLOv5 Detect() inplace=True | |
train=False, # model.train() mode | |
optimize=False, # TorchScript: optimize for mobile | |
int8=False, # CoreML/TF INT8 quantization | |
dynamic=False, # ONNX/TF: dynamic axes | |
simplify=False, # ONNX: simplify model | |
opset=12, # ONNX: opset version | |
verbose=False, # TensorRT: verbose log | |
workspace=4, # TensorRT: workspace size (GB) | |
nms=False, # TF: add NMS to model | |
agnostic_nms=False, # TF: add agnostic NMS to model | |
topk_per_class=100, # TF.js NMS: topk per class to keep | |
topk_all=100, # TF.js NMS: topk for all classes to keep | |
iou_thres=0.45, # TF.js NMS: IoU threshold | |
conf_thres=0.25 # TF.js NMS: confidence threshold | |
): | |
t = time.time() | |
include = [x.lower() for x in include] # to lowercase | |
formats = tuple(export_formats()['Argument'][1:]) # --include arguments | |
flags = [x in include for x in formats] | |
assert sum(flags) == len(include), f'ERROR: Invalid --include {include}, valid --include arguments are {formats}' | |
jit, onnx, xml, engine, coreml, saved_model, pb, tflite, edgetpu, tfjs = flags # export booleans | |
file = Path(url2file(weights) if str(weights).startswith(('http:/', 'https:/')) else weights) # PyTorch weights | |
# Load PyTorch model | |
device = select_device(device) | |
assert not (device.type == 'cpu' and half), '--half only compatible with GPU export, i.e. use --device 0' | |
model = attempt_load(weights, map_location=device, inplace=True, fuse=True) # load FP32 model | |
nc, names = model.nc, model.names # number of classes, class names | |
# Checks | |
imgsz *= 2 if len(imgsz) == 1 else 1 # expand | |
opset = 12 if ('openvino' in include) else opset # OpenVINO requires opset <= 12 | |
assert nc == len(names), f'Model class count {nc} != len(names) {len(names)}' | |
# Input | |
gs = int(max(model.stride)) # grid size (max stride) | |
imgsz = [check_img_size(x, gs) for x in imgsz] # verify img_size are gs-multiples | |
im = torch.zeros(batch_size, 3, *imgsz).to(device) # image size(1,3,320,192) BCHW iDetection | |
# Update model | |
if half: | |
im, model = im.half(), model.half() # to FP16 | |
model.train() if train else model.eval() # training mode = no Detect() layer grid construction | |
for k, m in model.named_modules(): | |
if isinstance(m, Conv): # assign export-friendly activations | |
if isinstance(m.act, nn.SiLU): | |
m.act = SiLU() | |
elif isinstance(m, Detect): | |
m.inplace = inplace | |
m.onnx_dynamic = dynamic | |
if hasattr(m, 'forward_export'): | |
m.forward = m.forward_export # assign custom forward (optional) | |
for _ in range(2): | |
y = model(im) # dry runs | |
shape = tuple(y[0].shape) # model output shape | |
LOGGER.info(f"\n{colorstr('PyTorch:')} starting from {file} with output shape {shape} ({file_size(file):.1f} MB)") | |
# Exports | |
f = [''] * 10 # exported filenames | |
warnings.filterwarnings(action='ignore', category=torch.jit.TracerWarning) # suppress TracerWarning | |
if jit: | |
f[0] = export_torchscript(model, im, file, optimize) | |
if engine: # TensorRT required before ONNX | |
f[1] = export_engine(model, im, file, train, half, simplify, workspace, verbose) | |
if onnx or xml: # OpenVINO requires ONNX | |
f[2] = export_onnx(model, im, file, opset, train, dynamic, simplify) | |
if xml: # OpenVINO | |
f[3] = export_openvino(model, im, file) | |
if coreml: | |
_, f[4] = export_coreml(model, im, file) | |
# 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 and tfjs), 'TFLite and TF.js models must be exported separately, please pass only one type.' | |
model, f[5] = export_saved_model(model.cpu(), im, file, dynamic, tf_nms=nms or agnostic_nms or tfjs, | |
agnostic_nms=agnostic_nms or tfjs, topk_per_class=topk_per_class, | |
topk_all=topk_all, conf_thres=conf_thres, iou_thres=iou_thres) # keras model | |
if pb or tfjs: # pb prerequisite to tfjs | |
f[6] = export_pb(model, im, file) | |
if tflite or edgetpu: | |
f[7] = export_tflite(model, im, file, int8=int8 or edgetpu, data=data, ncalib=100) | |
if edgetpu: | |
f[8] = export_edgetpu(model, im, file) | |
if tfjs: | |
f[9] = export_tfjs(model, im, file) | |
# Finish | |
f = [str(x) for x in f if x] # filter out '' and None | |
if any(f): | |
LOGGER.info(f'\nExport complete ({time.time() - t:.2f}s)' | |
f"\nResults saved to {colorstr('bold', file.parent.resolve())}" | |
f"\nDetect: python detect.py --weights {f[-1]}" | |
f"\nPyTorch Hub: model = torch.hub.load('ultralytics/yolov5', 'custom', '{f[-1]}')" | |
f"\nValidate: python val.py --weights {f[-1]}" | |
f"\nVisualize: https://netron.app") | |
return f # return list of exported files/dirs | |
def parse_opt(): | |
parser = argparse.ArgumentParser() | |
parser.add_argument('--data', type=str, default=ROOT / 'data/coco128.yaml', help='dataset.yaml path') | |
parser.add_argument('--weights', nargs='+', type=str, default=ROOT / 'yolov5s.pt', help='model.pt path(s)') | |
parser.add_argument('--imgsz', '--img', '--img-size', nargs='+', type=int, default=[640, 640], help='image (h, w)') | |
parser.add_argument('--batch-size', type=int, default=1, help='batch size') | |
parser.add_argument('--device', default='cpu', help='cuda device, i.e. 0 or 0,1,2,3 or cpu') | |
parser.add_argument('--half', action='store_true', help='FP16 half-precision export') | |
parser.add_argument('--inplace', action='store_true', help='set YOLOv5 Detect() inplace=True') | |
parser.add_argument('--train', action='store_true', help='model.train() mode') | |
parser.add_argument('--optimize', action='store_true', help='TorchScript: optimize for mobile') | |
parser.add_argument('--int8', action='store_true', help='CoreML/TF INT8 quantization') | |
parser.add_argument('--dynamic', action='store_true', help='ONNX/TF: dynamic axes') | |
parser.add_argument('--simplify', action='store_true', help='ONNX: simplify model') | |
parser.add_argument('--opset', type=int, default=12, help='ONNX: opset version') | |
parser.add_argument('--verbose', action='store_true', help='TensorRT: verbose log') | |
parser.add_argument('--workspace', type=int, default=4, help='TensorRT: workspace size (GB)') | |
parser.add_argument('--nms', action='store_true', help='TF: add NMS to model') | |
parser.add_argument('--agnostic-nms', action='store_true', help='TF: add agnostic NMS to model') | |
parser.add_argument('--topk-per-class', type=int, default=100, help='TF.js NMS: topk per class to keep') | |
parser.add_argument('--topk-all', type=int, default=100, help='TF.js NMS: topk for all classes to keep') | |
parser.add_argument('--iou-thres', type=float, default=0.45, help='TF.js NMS: IoU threshold') | |
parser.add_argument('--conf-thres', type=float, default=0.25, help='TF.js NMS: confidence threshold') | |
parser.add_argument('--include', nargs='+', | |
default=['torchscript', 'onnx'], | |
help='torchscript, onnx, openvino, engine, coreml, saved_model, pb, tflite, edgetpu, tfjs') | |
opt = parser.parse_args() | |
print_args(FILE.stem, opt) | |
return opt | |
def main(opt): | |
for opt.weights in (opt.weights if isinstance(opt.weights, list) else [opt.weights]): | |
run(**vars(opt)) | |
if __name__ == "__main__": | |
opt = parse_opt() | |
main(opt) | |