106 lines
3.5 KiB
Python
106 lines
3.5 KiB
Python
import os
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import sys
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import argparse
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import warnings
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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, classes = torch.max(x[1], 2, keepdim=True)
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classes = classes.float()
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return boxes, scores, classes
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def suppress_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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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('\nStarting: %s' % args.weights)
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print('Opening YOLO-NAS model\n')
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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 = os.path.basename(args.weights).split('.pt')[0] + '.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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'boxes': {
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0: 'batch'
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},
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'scores': {
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0: 'batch'
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},
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'classes': {
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0: 'batch'
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}
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}
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print('\nExporting the model to ONNX')
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torch.onnx.export(model, onnx_input_im, onnx_output_file, verbose=False, opset_version=args.opset,
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do_constant_folding=True, input_names=['input'], output_names=['boxes', 'scores', 'classes'],
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dynamic_axes=dynamic_axes if args.dynamic else None)
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if args.simplify:
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print('Simplifying the ONNX model')
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import onnxsim
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model_onnx = onnx.load(onnx_output_file)
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model_onnx, _ = onnxsim.simplify(model_onnx)
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onnx.save(model_onnx, onnx_output_file)
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print('Done: %s\n' % onnx_output_file)
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def parse_args():
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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='Implicit 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 implicit 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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sys.exit(main(args))
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