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Add --include torchscript onnx coreml argument (#3137)
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* Allow users to skip exporting in formats that they don't care about

* Correct comments

* Update export.py

renamed --skip-format to --exclude

* Switched format from exclude to include (as instructed by @glenn-jocher)

* cleanup

Co-authored-by: Glenn Jocher <glenn.jocher@ultralytics.com>
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@CristiFati @glenn-jocher
CristiFati and glenn-jocher committed May 12, 2021
1 parent bd6f6a7 commit d9b4e6b
Showing 1 changed file with 61 additions and 55 deletions.
116 changes: 61 additions & 55 deletions models/export.py
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Original file line number Diff line number Diff line change
@@ -1,7 +1,7 @@
"""Exports a YOLOv5 *.pt model to ONNX and TorchScript formats
"""Exports a YOLOv5 *.pt model to TorchScript, ONNX, CoreML formats
Usage:
$ export PYTHONPATH="$PWD" && python models/export.py --weights yolov5s.pt --img 640 --batch 1
$ python path/to/models/export.py --weights yolov5s.pt --img 640 --batch 1
"""

import argparse
Expand All @@ -27,6 +27,7 @@
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('--device', default='cpu', help='cuda device, i.e. 0 or 0,1,2,3 or cpu')
parser.add_argument('--include', nargs='+', default=['torchscript', 'onnx', 'coreml'], help='include formats')
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')
Expand All @@ -35,6 +36,7 @@
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
opt.include = [x.lower() for x in opt.include]
print(opt)
set_logging()
t = time.time()
Expand All @@ -47,7 +49,7 @@
# 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
assert not (opt.device.lower() == "cpu" and opt.half), '--half only compatible with GPU export, i.e. use --device 0'
assert not (opt.device.lower() == 'cpu' and opt.half), '--half only compatible with GPU export, i.e. use --device 0'

# Input
img = torch.zeros(opt.batch_size, 3, *opt.img_size).to(device) # image size(1,3,320,192) iDetection
Expand All @@ -74,62 +76,66 @@
print(f"\n{colorstr('PyTorch:')} starting from {opt.weights} ({file_size(opt.weights):.1f} MB)")

# 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)
(optimize_for_mobile(ts) if opt.optimize else 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}')
if 'torchscript' in opt.include or 'coreml' in opt.include:
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)
(optimize_for_mobile(ts) if opt.optimize else 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}')
if 'onnx' in opt.include:
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__}...')
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}')
if 'coreml' in opt.include:
prefix = colorstr('CoreML:')
try:
import coremltools as ct

print(f'{prefix} starting export with coremltools {ct.__version__}...')
model = ct.convert(ts, inputs=[ct.ImageType('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.')

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