mirror of
https://gitee.com/mymagicpower/AIAS.git
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139 lines
5.2 KiB
Markdown
139 lines
5.2 KiB
Markdown
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### Download the model, place it in the models directory, and unzip
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- Link: https://github.com/mymagicpower/AIAS/releases/download/apps/smart_construction_models.zip
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### Smart Construction Detection SDK
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Supported categories:
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- person (human body)
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- head (without safety helmet)
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- helmet (with safety helmet)
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### SDK Functionality
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Construction safety detection, providing detection boxes and confidence levels.
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- Provides three models:
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- Small model (yolov5s 29.7M)
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- Medium model (yolov5m 86.8M)
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- Large model (yolov5l 190.8M)
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-
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### Run Small Model Example- Yolov5sExample
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- Test image effect (only display safety helmet detection, filter out other categories for display, see code for details)
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### Run Medium Model Example- Yolov5mExample
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- Test image effect
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### Run Large Model Example- Yolov5lExample
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- Test image effect
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After a successful run, the command line should display the following information:
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```text
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[INFO ] - [
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class: "helmet", probability: 0.89502, bounds: [x=0.956, y=0.525, width=0.044, height=0.067]
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class: "helmet", probability: 0.85951, bounds: [x=0.237, y=0.439, width=0.036, height=0.046]
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class: "helmet", probability: 0.81705, bounds: [x=0.901, y=0.378, width=0.036, height=0.052]
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class: "helmet", probability: 0.80817, bounds: [x=0.250, y=0.399, width=0.029, height=0.040]
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class: "helmet", probability: 0.80528, bounds: [x=0.771, y=0.336, width=0.029, height=0.043]
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]
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```
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### Open source algorithm
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#### 1. Open source algorithm used by the SDK
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- [Smart_Construction](https://github.com/PeterH0323/Smart_Construction)
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#### 2. How to export the model?
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- [how_to_convert_your_model_to_torchscript](http://docs.djl.ai/docs/pytorch/how_to_convert_your_model_to_torchscript.html)
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- Export model (pytorch model is special, CPU&GPU models are not universal. So CPU and GPU need to be exported separately)
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- torch.device('cpu')
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- torch.device('gpu')
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```text
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"""Exports a YOLOv5 *.pt model to ONNX and TorchScript formats
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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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"""
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import argparse
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import torch
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from utils.google_utils import attempt_download
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if __name__ == '__main__':
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parser = argparse.ArgumentParser()
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parser.add_argument('--weights', type=str, default='./helmet_head_person_s.pt', help='weights path')
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parser.add_argument('--img-size', nargs='+', type=int, default=[640, 640], help='image size')
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parser.add_argument('--batch-size', type=int, default=1, help='batch size')
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parser.add_argument('--augment', action='store_true', help='augmented inference')
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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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# Input
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img = torch.zeros((opt.batch_size, 3, *opt.img_size)) # image size(1,3,320,192) iDetection
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# Load PyTorch model
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attempt_download(opt.weights)
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model = torch.load(opt.weights, map_location=torch.device('cpu'))['model'].float()
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model.eval()
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model.model[-1].export = False # set Detect() layer export=True
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if img.ndimension() == 3:
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img = img.unsqueeze(0)
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y = model(img) # dry run
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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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f = opt.weights.replace('.pt', '.torchscript.pt') # filename
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ts = torch.jit.trace(model, img)
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ts.save(f)
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print('TorchScript export success, saved as %s' % f)
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except Exception as e:
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print('TorchScript export failure: %s' % e)
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# # ONNX export
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# try:
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# import onnx
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#
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# print('\nStarting ONNX export with onnx %s...' % onnx.__version__)
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# f = opt.weights.replace('.pt', '.onnx') # filename
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# model.fuse() # only for ONNX
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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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#
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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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# 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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#
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# # CoreML export
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# try:
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# import coremltools as ct
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#
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# print('\nStarting CoreML export with coremltools %s...' % ct.__version__)
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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='images', 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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# model.save(f)
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# print('CoreML export success, saved as %s' % f)
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# except Exception as e:
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# print('CoreML export failure: %s' % e)
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# Finish
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print('\nExport complete. Visualize with https://github.com/lutzroeder/netron.')
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```
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