113 lines
4.0 KiB
Python
Executable File
113 lines
4.0 KiB
Python
Executable File
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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import torch.nn.functional as F
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from src.core import YAMLConfig
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class DeepStreamOutput(nn.Module):
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def __init__(self, img_size, use_focal_loss):
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super().__init__()
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self.img_size = img_size
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self.use_focal_loss = use_focal_loss
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def forward(self, x):
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boxes = x['pred_boxes']
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convert_matrix = torch.tensor(
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[[1, 0, 1, 0], [0, 1, 0, 1], [-0.5, 0, 0.5, 0], [0, -0.5, 0, 0.5]], dtype=boxes.dtype, device=boxes.device
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)
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boxes @= convert_matrix
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boxes *= torch.as_tensor([[*self.img_size]]).flip(1).tile([1, 2]).unsqueeze(1)
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scores = F.sigmoid(x['pred_logits']) if self.use_focal_loss else F.softmax(x['pred_logits'])[:, :, :-1]
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scores, labels = torch.max(scores, dim=-1, keepdim=True)
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return torch.cat([boxes, scores, labels.to(boxes.dtype)], dim=-1)
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def rtdetr_pytorch_export(weights, cfg_file, device):
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cfg = YAMLConfig(cfg_file, resume=weights)
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checkpoint = torch.load(weights, map_location=device)
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if 'ema' in checkpoint:
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state = checkpoint['ema']['module']
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else:
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state = checkpoint['model']
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cfg.model.load_state_dict(state)
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return cfg.model.deploy(), cfg.postprocessor.use_focal_loss
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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 main(args):
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suppress_warnings()
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print(f'\nStarting: {args.weights}')
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print('Opening RT-DETR PyTorch model')
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device = torch.device('cpu')
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model, use_focal_loss = rtdetr_pytorch_export(args.weights, args.config, device)
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img_size = args.size * 2 if len(args.size) == 1 else args.size
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model = nn.Sequential(model, DeepStreamOutput(img_size, use_focal_loss))
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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 RT-DETR PyTorch conversion')
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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('-c', '--config', required=True, help='Input YAML (.yml) file path (required)')
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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=16, 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 not os.path.isfile(args.weights):
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raise SystemExit('Invalid weights file')
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if not os.path.isfile(args.config):
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raise SystemExit('Invalid config 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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