102 lines
3.4 KiB
Python
102 lines
3.4 KiB
Python
import os
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import onnx
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import torch
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import torch.nn as nn
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from super_gradients.training import models
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class DeepStreamOutput(nn.Module):
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def __init__(self):
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super().__init__()
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def forward(self, x):
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boxes = x[0]
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scores, labels = torch.max(x[1], dim=-1, keepdim=True)
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return torch.cat([boxes, scores, labels.to(boxes.dtype)], dim=-1)
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def suppress_warnings():
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import warnings
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warnings.filterwarnings('ignore', category=torch.jit.TracerWarning)
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warnings.filterwarnings('ignore', category=UserWarning)
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warnings.filterwarnings('ignore', category=DeprecationWarning)
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warnings.filterwarnings('ignore', category=FutureWarning)
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warnings.filterwarnings('ignore', category=ResourceWarning)
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def yolonas_export(model_name, weights, num_classes, size):
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img_size = size * 2 if len(size) == 1 else size
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model = models.get(model_name, num_classes=num_classes, checkpoint_path=weights)
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model.eval()
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model.prep_model_for_conversion(input_size=[1, 3, *img_size])
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return model
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def main(args):
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suppress_warnings()
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print(f'\nStarting: {args.weights}')
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print('Opening YOLO-NAS model')
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device = torch.device('cpu')
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model = yolonas_export(args.model, args.weights, args.classes, args.size)
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model = nn.Sequential(model, DeepStreamOutput())
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img_size = args.size * 2 if len(args.size) == 1 else args.size
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onnx_input_im = torch.zeros(args.batch, 3, *img_size).to(device)
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onnx_output_file = f'{args.weights}.onnx'
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dynamic_axes = {
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'input': {
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0: 'batch'
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},
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'output': {
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0: 'batch'
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}
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}
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print('Exporting the model to ONNX')
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torch.onnx.export(
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model, onnx_input_im, onnx_output_file, verbose=False, opset_version=args.opset, do_constant_folding=True,
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input_names=['input'], output_names=['output'], dynamic_axes=dynamic_axes if args.dynamic else None
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)
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if args.simplify:
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print('Simplifying the ONNX model')
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import onnxslim
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model_onnx = onnx.load(onnx_output_file)
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model_onnx = onnxslim.slim(model_onnx)
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onnx.save(model_onnx, onnx_output_file)
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print(f'Done: {onnx_output_file}\n')
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def parse_args():
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import argparse
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parser = argparse.ArgumentParser(description='DeepStream YOLO-NAS conversion')
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parser.add_argument('-m', '--model', required=True, help='Model name (required)')
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parser.add_argument('-w', '--weights', required=True, help='Input weights (.pth) file path (required)')
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parser.add_argument('-n', '--classes', type=int, default=80, help='Number of trained classes (default 80)')
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parser.add_argument('-s', '--size', nargs='+', type=int, default=[640], help='Inference size [H,W] (default [640])')
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parser.add_argument('--opset', type=int, default=14, help='ONNX opset version')
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parser.add_argument('--simplify', action='store_true', help='ONNX simplify model')
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parser.add_argument('--dynamic', action='store_true', help='Dynamic batch-size')
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parser.add_argument('--batch', type=int, default=1, help='Static batch-size')
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args = parser.parse_args()
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if args.model == '':
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raise SystemExit('Invalid model name')
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if not os.path.isfile(args.weights):
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raise SystemExit('Invalid weights file')
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if args.dynamic and args.batch > 1:
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raise SystemExit('Cannot set dynamic batch-size and static batch-size at same time')
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return args
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if __name__ == '__main__':
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args = parse_args()
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main(args)
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