From 442a7abdf263bb24c51e494b4fd41d81cb097943 Mon Sep 17 00:00:00 2001
From: Glenn Jocher <glenn.jocher@ultralytics.com>
Date: Tue, 20 Jul 2021 13:21:52 +0200
Subject: [PATCH] Refactor `export.py` (#4080)
* Refactor `export.py`
* cleanup
* Update check_requirements()
* Update export.py
---
export.py | 148 +++++++++++++++++++++++++++++-------------------------
1 file changed, 80 insertions(+), 68 deletions(-)
diff --git a/export.py b/export.py
index b7ff0748b..34cd21449 100644
--- a/export.py
+++ b/export.py
@@ -24,6 +24,78 @@ from utils.general import colorstr, check_img_size, check_requirements, file_siz
from utils.torch_utils import select_device
+def export_torchscript(model, img, file, optimize):
+ # TorchScript model export
+ prefix = colorstr('TorchScript:')
+ try:
+ print(f'\n{prefix} starting export with torch {torch.__version__}...')
+ f = file.with_suffix('.torchscript.pt')
+ ts = torch.jit.trace(model, img, strict=False)
+ (optimize_for_mobile(ts) if optimize else ts).save(f)
+ print(f'{prefix} export success, saved as {f} ({file_size(f):.1f} MB)')
+ return ts
+ except Exception as e:
+ print(f'{prefix} export failure: {e}')
+
+
+def export_onnx(model, img, file, opset_version, train, dynamic, simplify):
+ # ONNX model export
+ prefix = colorstr('ONNX:')
+ try:
+ check_requirements(('onnx', 'onnx-simplifier'))
+ import onnx
+
+ print(f'{prefix} starting export with onnx {onnx.__version__}...')
+ f = file.with_suffix('.onnx')
+ torch.onnx.export(model, img, f, verbose=False, opset_version=opset_version,
+ 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
+ # print(onnx.helper.printable_graph(model_onnx.graph)) # print
+
+ # Simplify
+ if simplify:
+ try:
+ import onnxsim
+
+ print(f'{prefix} simplifying with onnx-simplifier {onnxsim.__version__}...')
+ model_onnx, check = onnxsim.simplify(
+ model_onnx,
+ dynamic_input_shape=dynamic,
+ input_shapes={'images': list(img.shape)} if 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}')
+
+
+def export_coreml(ts_model, img, file, train):
+ # CoreML model export
+ prefix = colorstr('CoreML:')
+ try:
+ import coremltools as ct
+
+ print(f'{prefix} starting export with coremltools {ct.__version__}...')
+ f = file.with_suffix('.mlmodel')
+ assert train, 'CoreML exports should be placed in model.train() mode with `python export.py --train`'
+ model = ct.convert(ts_model, inputs=[ct.ImageType('image', shape=img.shape, scale=1 / 255.0, bias=[0, 0, 0])])
+ 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}')
+
+
def run(weights='./yolov5s.pt', # weights path
img_size=(640, 640), # image (height, width)
batch_size=1, # batch size
@@ -40,12 +112,13 @@ def run(weights='./yolov5s.pt', # weights path
t = time.time()
include = [x.lower() for x in include]
img_size *= 2 if len(img_size) == 1 else 1 # expand
+ file = Path(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) # load FP32 model
- labels = model.names
+ names = model.names
# Input
gs = int(max(model.stride)) # grid size (max stride)
@@ -57,7 +130,6 @@ def run(weights='./yolov5s.pt', # weights path
img, model = img.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():
- m._non_persistent_buffers_set = set() # pytorch 1.6.0 compatibility
if isinstance(m, Conv): # assign export-friendly activations
if isinstance(m.act, nn.Hardswish):
m.act = Hardswish()
@@ -72,73 +144,13 @@ def run(weights='./yolov5s.pt', # weights path
y = model(img) # dry runs
print(f"\n{colorstr('PyTorch:')} starting from {weights} ({file_size(weights):.1f} MB)")
- # TorchScript export -----------------------------------------------------------------------------------------------
- if 'torchscript' in include or 'coreml' in include:
- prefix = colorstr('TorchScript:')
- try:
- print(f'\n{prefix} starting export with torch {torch.__version__}...')
- f = weights.replace('.pt', '.torchscript.pt') # filename
- ts = torch.jit.trace(model, img, strict=False)
- (optimize_for_mobile(ts) if 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 ------------------------------------------------------------------------------------------------------
+ # Exports
if 'onnx' in include:
- prefix = colorstr('ONNX:')
- try:
- import onnx
-
- print(f'{prefix} starting export with onnx {onnx.__version__}...')
- f = weights.replace('.pt', '.onnx') # filename
- torch.onnx.export(model, img, f, verbose=False, opset_version=opset_version,
- 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
- # print(onnx.helper.printable_graph(model_onnx.graph)) # print
-
- # Simplify
- if 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=dynamic,
- input_shapes={'images': list(img.shape)} if 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 ----------------------------------------------------------------------------------------------------
- if 'coreml' in include:
- prefix = colorstr('CoreML:')
- try:
- import coremltools as ct
-
- print(f'{prefix} starting export with coremltools {ct.__version__}...')
- assert train, 'CoreML exports should be placed in model.train() mode with `python export.py --train`'
- model = ct.convert(ts, inputs=[ct.ImageType('image', shape=img.shape, scale=1 / 255.0, bias=[0, 0, 0])])
- f = 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}')
+ export_onnx(model, img, file, opset_version, train, dynamic, simplify)
+ if 'torchscript' in include or 'coreml' in include:
+ ts = export_torchscript(model, img, file, optimize)
+ if 'coreml' in include:
+ export_coreml(ts, img, file, train)
# Finish
print(f'\nExport complete ({time.time() - t:.2f}s). Visualize with https://github.com/lutzroeder/netron.')