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]
tf_exports = list(x in include
for x in ('saved_model', 'pb', 'tflite', 'edgetpu',
'tfjs')) # TensorFlow exports
file = Path(
url2file(weights) if str(weights).startswith(('http:/',
'https:/')) else 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 'torchscript' in include:
f[0] = export_torchscript(model, im, file, optimize)
if 'engine' in include: # TensorRT required before ONNX
f[1] = export_engine(model, im, file, train, half, simplify, workspace,
verbose)
if ('onnx' in include) or ('openvino'
in include): # OpenVINO requires ONNX
f[2] = export_onnx(model, im, file, opset, train, dynamic, simplify)
if 'openvino' in include:
f[3] = export_openvino(model, im, file)
if 'coreml' in include:
_, f[4] = export_coreml(model, im, file)
# TensorFlow Exports
if any(tf_exports):
pb, tflite, edgetpu, tfjs = tf_exports[1:]
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,
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
opt.img_size = [check_img_size(x, gs)
for x in opt.img_size] # verify img_size are gs-multiples
# Input
img = torch.zeros(opt.batch_size, 3, *opt.img_size).to(
device) # image size(1,3,320,192) iDetection
# Update model
for k, m in model.named_modules():
m._non_persistent_buffers_set = set() # pytorch 1.6.0 compatibility
if isinstance(
m, models.common.Conv): # assign export-friendly activations
if isinstance(m.act, nn.Hardswish):
m.act = Hardswish()
elif isinstance(m.act, nn.SiLU):
m.act = SiLU()
# elif isinstance(m, models.yolo.Detect):
# m.forward = m.forward_export # assign forward (optional)
model.model[-1].export = not opt.grid # set Detect() layer grid export
y = model(img) # dry run
# TorchScript export
try:
print('\nStarting TorchScript export with torch %s...' %
torch.__version__)
f = opt.weights.replace('.pt', '.torchscript.pt') # filename
ts = torch.jit.trace(model, img, strict=False)
ts.save(f)
print('TorchScript export success, saved as %s' % f)
except Exception as e:
print('TorchScript export failure: %s' % e)
def main():
parser = argparse.ArgumentParser()
parser.add_argument('--weights', type=str, default='./yolov5s.pt', help='weights path')
parser.add_argument('--img-size', nargs='+', type=int, default=[640, 640], help='image size') # height, width
parser.add_argument('--batch-size', type=int, default=1, help='batch size')
parser.add_argument('--grid', action='store_true', help='export Detect() layer grid')
parser.add_argument('--device', default='cpu', help='cuda device, i.e. 0 or 0,1,2,3 or cpu')
parser.add_argument('--dynamic', action='store_true', help='dynamic ONNX axes') # ONNX-only
parser.add_argument('--simplify', action='store_true', help='simplify ONNX model') # ONNX-only
opt = parser.parse_args()
opt.img_size *= 2 if len(opt.img_size) == 1 else 1 # expand
print(opt)
set_logging()
t = time.time()
# Load PyTorch model
device = select_device(opt.device)
# add yolov5 folder to system path
here = Path(__file__).parents[1].absolute()
yolov5_folder_dir = str(here)
sys.path.insert(0, yolov5_folder_dir)
model = attempt_load(opt.weights, map_location=device) # load FP32 model
labels = model.names
# Checks
gs = int(max(model.stride)) # grid size (max stride)
opt.img_size = [check_img_size(x, gs) for x in opt.img_size] # verify img_size are gs-multiples
# Input
img = torch.zeros(opt.batch_size, 3, *opt.img_size).to(device) # image size(1,3,320,192) iDetection
# Update model
for k, m in model.named_modules():
m._non_persistent_buffers_set = set() # pytorch 1.6.0 compatibility
if isinstance(m, models.common.Conv): # assign export-friendly activations
if isinstance(m.act, nn.Hardswish):
m.act = Hardswish()
elif isinstance(m.act, nn.SiLU):
m.act = SiLU()
# elif isinstance(m, models.yolo.Detect):
# m.forward = m.forward_export # assign forward (optional)
model.model[-1].export = not opt.grid # set Detect() layer grid export
for _ in range(2):
y = model(img) # dry runs
print(f"\n{colorstr('PyTorch:')} starting from {opt.weights} ({file_size(opt.weights):.1f} MB)")
# remove yolov5 folder from system path
sys.path.remove(yolov5_folder_dir)
# TorchScript export -----------------------------------------------------------------------------------------------
prefix = colorstr('TorchScript:')
try:
print(f'\n{prefix} starting export with torch {torch.__version__}...')
f = opt.weights.replace('.pt', '.torchscript.pt') # filename
ts = torch.jit.trace(model, img, strict=False)
ts = optimize_for_mobile(ts) # https://pytorch.org/tutorials/recipes/script_optimized.html
ts.save(f)
print(f'{prefix} export success, saved as {f} ({file_size(f):.1f} MB)')
except Exception as e:
print(f'{prefix} export failure: {e}')
# ONNX export ------------------------------------------------------------------------------------------------------
prefix = colorstr('ONNX:')
try:
import onnx
print(f'{prefix} starting export with onnx {onnx.__version__}...')
f = opt.weights.replace('.pt', '.onnx') # filename
torch.onnx.export(model, img, f, verbose=False, opset_version=12, input_names=['images'],
dynamic_axes={'images': {0: 'batch', 2: 'height', 3: 'width'}, # size(1,3,640,640)
'output': {0: 'batch', 2: 'y', 3: 'x'}} if opt.dynamic else None)
# Checks
model_onnx = onnx.load(f) # load onnx model
onnx.checker.check_model(model_onnx) # check onnx model
# print(onnx.helper.printable_graph(model_onnx.graph)) # print
# Simplify
if opt.simplify:
try:
check_requirements(['onnx-simplifier'])
import onnxsim
print(f'{prefix} simplifying with onnx-simplifier {onnxsim.__version__}...')
model_onnx, check = onnxsim.simplify(model_onnx,
dynamic_input_shape=opt.dynamic,
input_shapes={'images': list(img.shape)} if opt.dynamic else None)
assert check, 'assert check failed'
onnx.save(model_onnx, f)
except Exception as e:
print(f'{prefix} simplifier failure: {e}')
print(f'{prefix} export success, saved as {f} ({file_size(f):.1f} MB)')
except Exception as e:
print(f'{prefix} export failure: {e}')
# CoreML export ----------------------------------------------------------------------------------------------------
prefix = colorstr('CoreML:')
try:
import coremltools as ct
print(f'{prefix} starting export with coremltools {ct.__version__}...')
# convert model from torchscript and apply pixel scaling as per detect.py
model = ct.convert(ts, inputs=[ct.ImageType(name='image', shape=img.shape, scale=1 / 255.0, bias=[0, 0, 0])])
f = opt.weights.replace('.pt', '.mlmodel') # filename
model.save(f)
print(f'{prefix} export success, saved as {f} ({file_size(f):.1f} MB)')
except Exception as e:
print(f'{prefix} export failure: {e}')
# Finish
print(f'\nExport complete ({time.time() - t:.2f}s). Visualize with https://github.com/lutzroeder/netron.')