mirror of https://github.com/WongKinYiu/yolov7.git
End2end (#61)
* export end2end onnx model * fixbug * add web demo (#58) * Update README.md * main code update yolov7-tiny deploy cfg * main code update yolov7-tiny training cfg * main code @liguagua752109150 https://github.com/WongKinYiu/yolov7/issues/33#issuecomment-1178669212 * main code @albertfaromatics https://github.com/WongKinYiu/yolov7/issues/35#issuecomment-1178800685 * main code update link * main code add custom hyp * main code update default activation function * main code update path * main figure add more tasks * main code update readme * main code update reparameterization * Update README.md * main code update readme * main code update aux training * main code update aux training * main code update aux training * main figure update yolov7 prediction * main code update readme * main code rename * main code rename * main code rename * main code rename * main code update readme * main code update visualization * main code fix gain for train_aux * main code update loss * main code update instance segmentation demo * main code update keypoint detection demo * main code update pose demo * main code update pose * main code update pose * main code update pose * main code update pose * main code update trace * Update README.md * main code fix ciou * main code fix nan of aux training https://github.com/WongKinYiu/yolov7/issues/250#issue-1312356380 @hudingding * support onnx to tensorrt convert (#114) * fuse IDetect (#148) * Fixes #199 (#203) * minor fix * resolve conflict * resolve conflict * resolve conflict * resolve conflict * resolve conflict * resolve * resolve * resolve * resolve Co-authored-by: AK391 <81195143+AK391@users.noreply.github.com> Co-authored-by: Alexey <AlexeyAB@users.noreply.github.com> Co-authored-by: Kin-Yiu, Wong <102582011@cc.ncu.edu.tw> Co-authored-by: linghu8812 <36389436+linghu8812@users.noreply.github.com> Co-authored-by: Alexander <84590713+SashaAlderson@users.noreply.github.com> Co-authored-by: Ben Raymond <ben@theraymonds.org> Co-authored-by: AlexeyAB84 <alexeyab84@gmail.com>pull/280/head
Linaom1214
GitHub
parent
2596994f39
commit
afdc86f519
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@ -0,0 +1,3 @@
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# 默认忽略的文件
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/shelf/
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/workspace.xml
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@ -0,0 +1,46 @@
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<component name="InspectionProjectProfileManager">
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<profile version="1.0">
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<option name="myName" value="Project Default" />
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<inspection_tool class="PyPackageRequirementsInspection" enabled="true" level="WARNING" enabled_by_default="true">
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<option name="ignoredPackages">
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<value>
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<list size="18">
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<item index="0" class="java.lang.String" itemvalue="onnxruntime" />
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<item index="1" class="java.lang.String" itemvalue="onnx-simplifier" />
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<item index="2" class="java.lang.String" itemvalue="scipy" />
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<item index="3" class="java.lang.String" itemvalue="thop" />
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<item index="4" class="java.lang.String" itemvalue="opencv-python" />
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<item index="5" class="java.lang.String" itemvalue="torch" />
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<item index="6" class="java.lang.String" itemvalue="numpy" />
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<item index="7" class="java.lang.String" itemvalue="torchvision" />
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<item index="8" class="java.lang.String" itemvalue="tqdm" />
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<item index="9" class="java.lang.String" itemvalue="pandas" />
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<item index="10" class="java.lang.String" itemvalue="tensorboard" />
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<item index="11" class="java.lang.String" itemvalue="seaborn" />
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<item index="12" class="java.lang.String" itemvalue="matplotlib" />
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<item index="13" class="java.lang.String" itemvalue="Cython" />
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<item index="14" class="java.lang.String" itemvalue="pycocotools" />
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<item index="15" class="java.lang.String" itemvalue="h5py" />
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<item index="16" class="java.lang.String" itemvalue="opencv_python" />
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<item index="17" class="java.lang.String" itemvalue="Pillow" />
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</list>
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</value>
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</option>
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</inspection_tool>
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<inspection_tool class="PyPep8NamingInspection" enabled="true" level="WEAK WARNING" enabled_by_default="true">
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<option name="ignoredErrors">
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<list>
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<option value="N806" />
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<option value="N801" />
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</list>
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</option>
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</inspection_tool>
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<inspection_tool class="PyUnresolvedReferencesInspection" enabled="true" level="WARNING" enabled_by_default="true">
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<option name="ignoredIdentifiers">
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<list>
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<option value="tkinter.*" />
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</list>
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</option>
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</inspection_tool>
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</profile>
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</component>
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@ -0,0 +1,6 @@
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<component name="InspectionProjectProfileManager">
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<settings>
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<option name="USE_PROJECT_PROFILE" value="false" />
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<version value="1.0" />
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</settings>
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</component>
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@ -0,0 +1,4 @@
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<?xml version="1.0" encoding="UTF-8"?>
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<project version="4">
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<component name="ProjectRootManager" version="2" project-jdk-name="Python 3.8" project-jdk-type="Python SDK" />
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</project>
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<?xml version="1.0" encoding="UTF-8"?>
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<project version="4">
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<component name="ProjectModuleManager">
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<modules>
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<module fileurl="file://$PROJECT_DIR$/.idea/yolov7.iml" filepath="$PROJECT_DIR$/.idea/yolov7.iml" />
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</modules>
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</component>
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</project>
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@ -0,0 +1,6 @@
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<?xml version="1.0" encoding="UTF-8"?>
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<project version="4">
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<component name="VcsDirectoryMappings">
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<mapping directory="$PROJECT_DIR$" vcs="Git" />
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</component>
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</project>
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<?xml version="1.0" encoding="UTF-8"?>
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<module type="PYTHON_MODULE" version="4">
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<component name="NewModuleRootManager">
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<content url="file://$MODULE_DIR$" />
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<orderEntry type="inheritedJdk" />
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<orderEntry type="sourceFolder" forTests="false" />
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</component>
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<component name="PyDocumentationSettings">
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<option name="format" value="PLAIN" />
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<option name="myDocStringFormat" value="Plain" />
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</component>
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</module>
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@ -151,6 +151,13 @@ python detect.py --weights yolov7.pt --conf 0.25 --img-size 640 --source inferen
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</a>
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</div>
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## Export
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Use the args `--include-nms` can to export end to end onnx model which include the `EfficientNMS`.
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```shell
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python models/export.py --weights yolov7.pt --grid --include-nms
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```
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## Citation
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```
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15
export.py
15
export.py
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@ -12,6 +12,7 @@ from models.experimental import attempt_load
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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.torch_utils import select_device
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from utils.add_nms import RegisterNMS
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if __name__ == '__main__':
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parser = argparse.ArgumentParser()
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@ -22,6 +23,7 @@ if __name__ == '__main__':
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parser.add_argument('--grid', action='store_true', help='export Detect() layer grid')
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parser.add_argument('--device', default='cpu', help='cuda device, i.e. 0 or 0,1,2,3 or cpu')
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parser.add_argument('--simplify', action='store_true', help='simplify onnx model')
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parser.add_argument('--include-nms', action='store_true', help='export end2end onnx')
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opt = parser.parse_args()
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opt.img_size *= 2 if len(opt.img_size) == 1 else 1 # expand
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print(opt)
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@ -52,7 +54,9 @@ if __name__ == '__main__':
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# m.forward = m.forward_export # assign forward (optional)
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model.model[-1].export = not opt.grid # set Detect() layer grid export
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y = model(img) # dry run
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if opt.include_nms:
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model.model[-1].include_nms = True
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y = None
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# TorchScript export
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try:
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print('\nStarting TorchScript export with torch %s...' % torch.__version__)
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@ -75,9 +79,16 @@ if __name__ == '__main__':
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dynamic_axes={'images': {0: 'batch', 2: 'height', 3: 'width'}, # size(1,3,640,640)
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'output': {0: 'batch', 2: 'y', 3: 'x'}} if opt.dynamic else None)
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if opt.include_nms:
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print('Registering NMS plugin...')
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mo = RegisterNMS(f)
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mo.register_nms()
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mo.save(f)
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else:
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# Checks
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onnx_model = onnx.load(f) # load onnx model
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onnx.checker.check_model(onnx_model) # check onnx model
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# print(onnx.helper.printable_graph(onnx_model.graph)) # print a human readable model
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# # Metadata
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# d = {'stride': int(max(model.stride))}
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@ -95,11 +106,9 @@ if __name__ == '__main__':
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assert check, 'assert check failed'
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except Exception as e:
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print(f'Simplifier failure: {e}')
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# print(onnx.helper.printable_graph(onnx_model.graph)) # print a human readable model
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print('ONNX export success, saved as %s' % f)
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except Exception as e:
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print('ONNX export failure: %s' % e)
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# CoreML export
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try:
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import coremltools as ct
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@ -236,7 +236,7 @@ class Res(nn.Module):
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class ResX(Res):
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# ResNet bottleneck
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def __init__(self, c1, c2, shortcut=True, g=32, e=0.5): # ch_in, ch_out, shortcut, groups, expansion
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super().__init__(c1, c2, shortcu, g, e)
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super().__init__(c1, c2, shortcut, g, e)
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c_ = int(c2 * e) # hidden channels
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@ -5,7 +5,7 @@ from copy import deepcopy
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sys.path.append('./') # to run '$ python *.py' files in subdirectories
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logger = logging.getLogger(__name__)
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import torch
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from models.common import *
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from models.experimental import *
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from utils.autoanchor import check_anchor_order
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@ -23,7 +23,7 @@ except ImportError:
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class Detect(nn.Module):
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stride = None # strides computed during build
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export = False # onnx export
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include_nms = False
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def __init__(self, nc=80, anchors=(), ch=()): # detection layer
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super(Detect, self).__init__()
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self.nc = nc # number of classes
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@ -48,7 +48,6 @@ class Detect(nn.Module):
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if not self.training: # inference
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if self.grid[i].shape[2:4] != x[i].shape[2:4]:
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self.grid[i] = self._make_grid(nx, ny).to(x[i].device)
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y = x[i].sigmoid()
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if not torch.onnx.is_in_onnx_export():
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y[..., 0:2] = (y[..., 0:2] * 2. - 0.5 + self.grid[i]) * self.stride[i] # xy
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@ -59,13 +58,28 @@ class Detect(nn.Module):
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y = torch.cat((xy, wh, y[..., 4:]), -1)
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z.append(y.view(bs, -1, self.no))
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return x if self.training else (torch.cat(z, 1), x)
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if self.include_nms:
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z = self.convert(z)
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return x if self.training else (z, ) if self.include_nms else (torch.cat(z, 1), x)
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@staticmethod
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def _make_grid(nx=20, ny=20):
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yv, xv = torch.meshgrid([torch.arange(ny), torch.arange(nx)])
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return torch.stack((xv, yv), 2).view((1, 1, ny, nx, 2)).float()
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def convert(self, z):
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z = torch.cat(z, 1)
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box = z[:, :, :4]
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conf = z[:, :, 4:5]
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score = z[:, :, 5:]
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score *= conf
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convert_matrix = torch.tensor([[1, 0, 1, 0], [0, 1, 0, 1], [-0.5, 0, 0.5, 0], [0, -0.5, 0, 0.5]],
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dtype=torch.float32,
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device=z.device)
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box @= convert_matrix
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return (box, score)
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class IDetect(nn.Module):
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stride = None # strides computed during build
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import numpy as np
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import onnx
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from onnx import shape_inference
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import onnx_graphsurgeon as gs
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import logging
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LOGGER = logging.getLogger(__name__)
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class RegisterNMS(object):
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def __init__(
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self,
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onnx_model_path: str,
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precision: str = "fp32",
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):
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self.graph = gs.import_onnx(onnx.load(onnx_model_path))
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assert self.graph
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LOGGER.info("ONNX graph created successfully")
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# Fold constants via ONNX-GS that PyTorch2ONNX may have missed
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self.graph.fold_constants()
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self.precision = precision
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self.batch_size = 1
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def infer(self):
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"""
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Sanitize the graph by cleaning any unconnected nodes, do a topological resort,
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and fold constant inputs values. When possible, run shape inference on the
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ONNX graph to determine tensor shapes.
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"""
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for _ in range(3):
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count_before = len(self.graph.nodes)
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self.graph.cleanup().toposort()
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try:
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for node in self.graph.nodes:
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for o in node.outputs:
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o.shape = None
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model = gs.export_onnx(self.graph)
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model = shape_inference.infer_shapes(model)
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self.graph = gs.import_onnx(model)
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except Exception as e:
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LOGGER.info(f"Shape inference could not be performed at this time:\n{e}")
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try:
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self.graph.fold_constants(fold_shapes=True)
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except TypeError as e:
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LOGGER.error(
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"This version of ONNX GraphSurgeon does not support folding shapes, "
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f"please upgrade your onnx_graphsurgeon module. Error:\n{e}"
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)
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raise
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count_after = len(self.graph.nodes)
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if count_before == count_after:
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# No new folding occurred in this iteration, so we can stop for now.
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break
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def save(self, output_path):
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"""
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Save the ONNX model to the given location.
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Args:
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output_path: Path pointing to the location where to write
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out the updated ONNX model.
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"""
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self.graph.cleanup().toposort()
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model = gs.export_onnx(self.graph)
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onnx.save(model, output_path)
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LOGGER.info(f"Saved ONNX model to {output_path}")
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def register_nms(
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self,
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*,
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score_thresh: float = 0.25,
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nms_thresh: float = 0.45,
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detections_per_img: int = 100,
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):
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"""
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Register the ``EfficientNMS_TRT`` plugin node.
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NMS expects these shapes for its input tensors:
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- box_net: [batch_size, number_boxes, 4]
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- class_net: [batch_size, number_boxes, number_labels]
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Args:
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score_thresh (float): The scalar threshold for score (low scoring boxes are removed).
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nms_thresh (float): The scalar threshold for IOU (new boxes that have high IOU
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overlap with previously selected boxes are removed).
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detections_per_img (int): Number of best detections to keep after NMS.
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"""
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self.infer()
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# Find the concat node at the end of the network
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op_inputs = self.graph.outputs
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op = "EfficientNMS_TRT"
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attrs = {
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"plugin_version": "1",
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"background_class": -1, # no background class
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"max_output_boxes": detections_per_img,
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"score_threshold": score_thresh,
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"iou_threshold": nms_thresh,
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"score_activation": False,
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"box_coding": 0,
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}
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|
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if self.precision == "fp32":
|
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dtype_output = np.float32
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elif self.precision == "fp16":
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dtype_output = np.float16
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else:
|
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raise NotImplementedError(f"Currently not supports precision: {self.precision}")
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|
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# NMS Outputs
|
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output_num_detections = gs.Variable(
|
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name="num_detections",
|
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dtype=np.int32,
|
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shape=[self.batch_size, 1],
|
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) # A scalar indicating the number of valid detections per batch image.
|
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output_boxes = gs.Variable(
|
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name="detection_boxes",
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dtype=dtype_output,
|
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shape=[self.batch_size, detections_per_img, 4],
|
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)
|
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output_scores = gs.Variable(
|
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name="detection_scores",
|
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dtype=dtype_output,
|
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shape=[self.batch_size, detections_per_img],
|
||||
)
|
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output_labels = gs.Variable(
|
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name="detection_classes",
|
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dtype=np.int32,
|
||||
shape=[self.batch_size, detections_per_img],
|
||||
)
|
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|
||||
op_outputs = [output_num_detections, output_boxes, output_scores, output_labels]
|
||||
|
||||
# Create the NMS Plugin node with the selected inputs. The outputs of the node will also
|
||||
# become the final outputs of the graph.
|
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self.graph.layer(op=op, name="batched_nms", inputs=op_inputs, outputs=op_outputs, attrs=attrs)
|
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LOGGER.info(f"Created NMS plugin '{op}' with attributes: {attrs}")
|
||||
|
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self.graph.outputs = op_outputs
|
||||
|
||||
self.infer()
|
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|
||||
def save(self, output_path):
|
||||
"""
|
||||
Save the ONNX model to the given location.
|
||||
Args:
|
||||
output_path: Path pointing to the location where to write
|
||||
out the updated ONNX model.
|
||||
"""
|
||||
self.graph.cleanup().toposort()
|
||||
model = gs.export_onnx(self.graph)
|
||||
onnx.save(model, output_path)
|
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LOGGER.info(f"Saved ONNX model to {output_path}")
|
||||
Loading…
Reference in New Issue