ONNX Simplifier (#2815)
* ONNX Simplifier Add ONNX Simplifier to ONNX export pipeline in export.py. Will auto-install onnx-simplifier if onnx is installed but onnx-simplifier is not. * Update general.py
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@@ -1,7 +1,7 @@
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"""Exports a YOLOv5 *.pt model to ONNX and TorchScript formats
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"""Exports a YOLOv5 *.pt model to ONNX and TorchScript formats
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Usage:
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Usage:
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$ export PYTHONPATH="$PWD" && python models/export.py --weights ./weights/yolov5s.pt --img 640 --batch 1
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$ export PYTHONPATH="$PWD" && python models/export.py --weights yolov5s.pt --img 640 --batch 1
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"""
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"""
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import argparse
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import argparse
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@@ -16,7 +16,7 @@ import torch.nn as nn
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import models
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import models
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from models.experimental import attempt_load
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from models.experimental import attempt_load
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from utils.activations import Hardswish, SiLU
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from utils.activations import Hardswish, SiLU
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from utils.general import set_logging, check_img_size
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from utils.general import colorstr, check_img_size, check_requirements, set_logging
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from utils.torch_utils import select_device
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from utils.torch_utils import select_device
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if __name__ == '__main__':
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if __name__ == '__main__':
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@@ -59,20 +59,22 @@ if __name__ == '__main__':
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y = model(img) # dry run
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y = model(img) # dry run
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# TorchScript export
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# TorchScript export
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prefix = colorstr('TorchScript:')
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try:
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try:
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print('\nStarting TorchScript export with torch %s...' % torch.__version__)
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print(f'\n{prefix} starting export with torch {torch.__version__}...')
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f = opt.weights.replace('.pt', '.torchscript.pt') # filename
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f = opt.weights.replace('.pt', '.torchscript.pt') # filename
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ts = torch.jit.trace(model, img, strict=False)
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ts = torch.jit.trace(model, img, strict=False)
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ts.save(f)
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ts.save(f)
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print('TorchScript export success, saved as %s' % f)
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print(f'{prefix} export success, saved as {f}')
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except Exception as e:
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except Exception as e:
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print('TorchScript export failure: %s' % e)
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print(f'{prefix} export failure: {e}')
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# ONNX export
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# ONNX export
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prefix = colorstr('ONNX:')
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try:
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try:
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import onnx
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import onnx
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print('\nStarting ONNX export with onnx %s...' % onnx.__version__)
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print(f'{prefix} starting export with onnx {onnx.__version__}...')
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f = opt.weights.replace('.pt', '.onnx') # filename
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f = opt.weights.replace('.pt', '.onnx') # filename
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torch.onnx.export(model, img, f, verbose=False, opset_version=12, input_names=['images'],
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torch.onnx.export(model, img, f, verbose=False, opset_version=12, input_names=['images'],
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output_names=['classes', 'boxes'] if y is None else ['output'],
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output_names=['classes', 'boxes'] if y is None else ['output'],
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@@ -80,25 +82,38 @@ if __name__ == '__main__':
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'output': {0: 'batch', 2: 'y', 3: 'x'}} if opt.dynamic else None)
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'output': {0: 'batch', 2: 'y', 3: 'x'}} if opt.dynamic else None)
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# Checks
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# Checks
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onnx_model = onnx.load(f) # load onnx model
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model_onnx = onnx.load(f) # load onnx model
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onnx.checker.check_model(onnx_model) # check onnx model
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onnx.checker.check_model(model_onnx) # check onnx model
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# print(onnx.helper.printable_graph(onnx_model.graph)) # print a human readable model
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# print(onnx.helper.printable_graph(model_onnx.graph)) # print
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print('ONNX export success, saved as %s' % f)
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# Simplify
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try:
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check_requirements(['onnx-simplifier'])
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import onnxsim
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print(f'{prefix} simplifying with onnx-simplifier {onnxsim.__version__}...')
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model_onnx, check = onnxsim.simplify(model_onnx)
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assert check, 'assert check failed'
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onnx.save(model_onnx, f)
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except Exception as e:
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print(f'{prefix} simplifier failure: {e}')
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print(f'{prefix} export success, saved as {f}')
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except Exception as e:
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except Exception as e:
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print('ONNX export failure: %s' % e)
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print(f'{prefix} export failure: {e}')
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# CoreML export
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# CoreML export
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prefix = colorstr('CoreML:')
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try:
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try:
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import coremltools as ct
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import coremltools as ct
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print('\nStarting CoreML export with coremltools %s...' % ct.__version__)
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print(f'{prefix} starting export with coremltools {onnx.__version__}...')
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# convert model from torchscript and apply pixel scaling as per detect.py
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# convert model from torchscript and apply pixel scaling as per detect.py
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model = ct.convert(ts, inputs=[ct.ImageType(name='image', shape=img.shape, scale=1 / 255.0, bias=[0, 0, 0])])
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model = ct.convert(ts, inputs=[ct.ImageType(name='image', shape=img.shape, scale=1 / 255.0, bias=[0, 0, 0])])
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f = opt.weights.replace('.pt', '.mlmodel') # filename
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f = opt.weights.replace('.pt', '.mlmodel') # filename
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model.save(f)
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model.save(f)
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print('CoreML export success, saved as %s' % f)
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print(f'{prefix} export success, saved as {f}')
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except Exception as e:
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except Exception as e:
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print('CoreML export failure: %s' % e)
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print(f'{prefix} export failure: {e}')
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# Finish
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# Finish
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print('\nExport complete (%.2fs). Visualize with https://github.com/lutzroeder/netron.' % (time.time() - t))
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print(f'\nExport complete ({time.time() - t:.2f}s). Visualize with https://github.com/lutzroeder/netron.')
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+1
-1
@@ -111,7 +111,7 @@ def check_requirements(requirements='requirements.txt', exclude=()):
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except Exception as e: # DistributionNotFound or VersionConflict if requirements not met
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except Exception as e: # DistributionNotFound or VersionConflict if requirements not met
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n += 1
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n += 1
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print(f"{prefix} {e.req} not found and is required by YOLOv5, attempting auto-update...")
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print(f"{prefix} {e.req} not found and is required by YOLOv5, attempting auto-update...")
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print(subprocess.check_output(f"pip install '{e.req}'", shell=True).decode())
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print(subprocess.check_output(f"pip install {e.req}", shell=True).decode())
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if n: # if packages updated
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if n: # if packages updated
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source = file.resolve() if 'file' in locals() else requirements
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source = file.resolve() if 'file' in locals() else requirements
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